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Dynamic Programming
(2024)
Queueing Theory
(2024)
The Decision Tree Procedure
(2024)
Heuristic Methods
(2024)
Network Analysis Method
(2024)
Farming communities confronted with climate change adopt formal and informal adaptation strategies to mitigate the effects of climate change. While the environmental and social effects of climate change are well documented, there is still a dearth of literature on girl-child marriage (formal marriage or informal union between a child under the age of 18 and an adult or another child) as a response to the effects of climate change. In this research, we ask if girl-child marriage is promoted as a social protection mechanism first, rather than as simply a response to climate-induced poverty. We use qualitative semi-structured interviews and focus group discussions to explore this question in a rural farming community in Northern Ghana. Our findings reveal that climate change shocks result in poverty and compel farmers to marry off their young daughters. The unmarried girl-child is perceived as an ‘extra mouth to feed’, a liability whose marriage becomes a strategy for protecting the family, the family’s reputation, and the girl child. The emphasis in girl-child marriage is not on the girl-child as an individual but on the family as a group. Hence, what is good for the family is assumed to be in the best interest of the girl-child. We place our analysis at the intersection of climate change, social protection, and the incidence of girl-child marriages. We argue that understanding this link is crucial and can contribute significantly to our knowledge of girl-child marriage as well as our ability to address this in Sub-Saharan Africa.
Integrating physical simulation data into data ecosystems challenges the compatibility and interoperability of data management tools. Semantic web technologies and relational databases mostly use other data types, such as measurement or manufacturing design data. Standardizing simulation data storage and harmonizing the data structures with other domains is still a challenge, as current standards such as the ISO standard STEP (ISO 10303 ”Standard for the Exchange of Product model data”) fail to bridge the gap between design and simulation data. This challenge requires new methods, such as ontologies, to rethink simulation results integration. This research describes a new software architecture and application methodology based on the industrial standard ”Virtual Material Modelling in Manufacturing” (VMAP). The architecture integrates large quantities of structured simulation data and their analyses into a semantic data structure. It is capable of providing data permeability from the global digital twin level to the detailed numerical values of data entries and even new key indicators in a three-step approach: It represents a file as an instance in a knowledge graph, queries the file’s metadata, and finds a semantically represented process that enables new metadata to be created and instantiated.
The Information and Communication Technology (ICT) sector is a significant global industry, and addressing climate change is of critical importance. This paper aims to assess the resources utilized by the ICT sector, the associated negative environmental impacts, and potential mitigation measures. In order to understand these aspects, this study attempts to categorize the resources used by ICT, analyze the amount consumed and the resulting negative impacts, and determine what measures exist to mitigate them. An economic and empirical evaluation shows a negative trend in ICT’s resource consumption, mainly due to increased energy consumption and rising carbon emissions from devices such as smartphones and data centers. The investigated countermeasures focus on Green IT strategies that encompass energy efficiency, carbon awareness, and hardware efficiency principles as outlined by the Green Software Foundation. Special attention is given to reducing the environmental footprint of data center operations and smartphones. This paper concludes that Green IT strategies, although promising in theory, are often not implemented at an industry level.
Accurate global horizontal irradiance (GHI) forecasting is critical for integrating solar energy into the power grid and operating solar power plants. The Weather Research and Forecasting model with its solar radiation extension (WRF-Solar) has been used to forecast solar irradiance in different regions around the world. However, the application of the WRF-Solar model to the prediction of GHI in West Africa, particularly Ghana, has not yet been investigated. The aim of this study is to evaluate the performance of the WRF-Solar model for predicting GHI in Ghana, focusing on three automatic weather stations (Akwatia, Kumasi and Kologo) for the year 2021. We used two one-way nested domains (D1 = 15 km and D2 = 3 km) to investigate the ability of the fully coupled WRF-Solar model to forecast GHI up to 72-hour ahead under different atmospheric conditions. The initial and lateral boundary conditions were taken from the ECMWF high-resolution operational forecasts. Our findings reveal that the WRF-Solar model performs better under clear skies than cloudy skies. Under clear skies, Kologo performed best in predicting 72-hour GHI, with a first day nRMSE of 9.62 %. However, forecasting GHI under cloudy skies at all three sites had significant uncertainties. Additionally, WRF-Solar model is able to reproduce the observed GHI diurnal cycle under high AOD conditions in most of the selected days. This study enhances the understanding of the WRF-Solar model’s capabilities and limitations for GHI forecasting in West Africa, particularly in Ghana. The findings provide valuable information for stakeholders involved in solar energy generation and grid integration towards optimized management in the region.
The Peren-Clement Index
(2024)
Sequencing Problems
(2024)
Linear Optimization
(2024)
The Peren Theorem
(2024)
This article deals with the under-researched phenomenon of rural health entrepreneurship and its major characteristics. The purpose of this study is to explicate the process of providing health services in rural areas of a developing country and their relation to SDGs. The paper is based on six semi-structured interviews conducted with Serbian health entrepreneurs in rural areas (two private practices, two policlinics, and two dental practices), a review of laws and strategies relevant to the field, and three sessions of discussions with eight experts (four authors and four additional experts). The research methodology follows an empirical, mixed-method case study research procedure. The results are presented in relation to the aspects of frugality, family orientation, and sustainability-oriented innovation. The timeline of the six case studies demonstrates the increasing importance of health entrepreneurs in rural areas due to the aging population and, therefore, increased needs for quality healthcare in these areas. The financing instruments have also become more formal and substantial in recent years, enabling the growth of healthcare businesses in rural areas. However, a major obstacle to further sustainable development remains the non-refundability of services before the state-owned, obligatory health fund, creating major social inequalities, especially in rural areas.
Pyrolysis–Gas Chromatography
(2024)
The methodology of analytical pyrolysis-GC/MS has been known for several years, but is seldom used in research laboratories and process control in the chemical industry. This is due to the relative difficulty of interpreting the identified pyrolysis products as well as the variety of them. This book contains full identification of several classes of polymers/copolymers and biopolymers that can be very helpful to the user. In addition, the practical applications can encourage analytical chemists and engineers to use the techniques explored in this volume.
While humans can effortlessly pick a view from multiple streams, automatically choosing the best view is a challenge. Choosing the best view from multi-camera streams poses a problem regarding which objective metrics should be considered. Existing works on view selection lack consensus about which metrics should be considered to select the best view. The literature on view selection describes diverse possible metrics. And strategies such as information-theoretic, instructional design, or aesthetics-motivated fail to incorporate all approaches. In this work, we postulate a strategy incorporating information-theoretic and instructional design-based objective metrics to select the best view from a set of views. Traditionally, information-theoretic measures have been used to find the goodness of a view, such as in 3D rendering. We adapted a similar measure known as the viewpoint entropy for real-world 2D images. Additionally, we incorporated similarity penalization to get a more accurate measure of the entropy of a view, which is one of the metrics for the best view selection. Since the choice of the best view is domain-dependent, we chose demonstration-based training scenarios as our use case. The limitation of our chosen scenarios is that they do not include collaborative training and solely feature a single trainer. To incorporate instructional design considerations, we included the trainer’s body pose, face, face when instructing, and hands visibility as metrics. To incorporate domain knowledge we included predetermined regions’ visibility as another metric. All of those metrics are taken into account to produce a parameterized view recommendation approach for demonstration-based training. An online study using recorded multi-camera video streams from a simulation environment was used to validate those metrics. Furthermore, the responses from the online study were used to optimize the view recommendation performance with a normalized discounted cumulative gain (NDCG) value of 0.912, which shows good performance with respect to matching user choices.
Social policy research on the ageing workforce from the perspective of employees and employers
(2024)
The lattice Boltzmann method (LBM) stands apart from conventional macroscopic approaches due to its low numerical dissipation and reduced computational cost, attributed to a simple streaming and local collision step. While this property makes the method particularly attractive for applications such as direct noise computation, it also renders the method highly susceptible to instabilities. A vast body of literature exists on stability-enhancing techniques, which can be categorized into selective filtering, regularized LBM, and multi-relaxation time (MRT) models. Although each technique bolsters stability by adding numerical dissipation, they act on different modes. Consequently, there is not a universal scheme optimally suited for a wide range of different flows. The reason for this lies in the static nature of these methods; they cannot adapt to local or global flow features. Still, adaptive filtering using a shear sensor constitutes an exception to this. For this reason, we developed a novel collision operator that uses space- and time-variant collision rates associated with the bulk viscosity. These rates are optimized by a physically informed neural net. In this study, the training data consists of a time series of different instances of a 2D barotropic vortex solution, obtained from a high-order Navier–Stokes solver that embodies desirable numerical features. For this specific text case our results demonstrate that the relaxation times adapt to the local flow and show a dependence on the velocity field. Furthermore, the novel collision operator demonstrates a better stability-to-precision ratio and outperforms conventional techniques that use an empirical constant for the bulk viscosity.
Design and characterization of geopolymer foams reinforced with Miscanthus x giganteus fibers
(2024)
This paper presents the effects of different amounts of fibers and foaming agent, as well as different fiber sizes, on the mechanical and thermal properties of fly ash-based geopolymer foams reinforced with Miscanthus x giganteus fibers. The mechanical properties of the geopolymer foams were measured through compressive strength, and their thermal properties were characterized by thermal conductivity and X-ray micro-computed tomography. Furthermore, design of experiment (DoE) were used to optimize the thermal conductivity and compressive strength of Miscanthus x giganteus reinforced geopolymer foams. In addition, the microstructure was studied using X-ray diffraction (XRD), Field emission scanning electron microscopy (SEM) and Fourier-Transform Infrared Spectroscopy (FTIR). Mixtures with a low thermal conductivity of 0.056 W (m K)−1 and a porosity of 79 vol% achieved a compressive strength of only 0.02 MPa. In comparison, mixtures with a thermal conductivity of 0.087 W (m K)−1 and a porosity of 58 vol% achieved a compressive strength of 0.45 MPa.
Sustainable urban soil management is becoming increasingly crucial due to its vital role in climate and water regulation and its significant potential for storing soil organic carbon (SOC). This significance is emphasized considering the ongoing urbanization and climate change issues. Although SOC is influenced by many factors, such as soil type and climate fluctuations (temperature, precipitation patterns), on a regional scale, land use and management practices (e.g., fertilization, irrigation) can have a more significant impact on SOC storage and the balance of soil-atmosphere carbon fluxes. However, there is still a limited understanding of the amount of humus content in urban soils and the effects of urban development and management practices on soil health and carbon storage. We investigated how management practices in urban green spaces influence soil carbon storage as the primary indicator of soil health.
The present study was carried out in the Bonn-Rhein-Sieg area, as the region is vital in terms of sustainable urban and regional development with a high population density (Rhein-Sieg district: 338.4, Bonn: 520.9 inhabitants/km2) in Germany. A survey was conducted with owners and managers of urban private (e.g., allotment and backyard garden) and public green spaces on the practices for the most common vegetation types (e.g., lawn, vegetable, ornamental). In the autumn and winter of 2022, 248 soil samples (0–20 cm depth) were collected from 95 private and public green spaces in the study area and analyzed for physiochemical and biological properties. Multivariate Analysis of Variance (MANOVA) was performed to assess the effects of different management practices on soil properties.
Our results indicate that the average SOC stock in public green areas (94.67 Mg ha-1) is substantially higher than in private ones (house garden 67.72 Mg ha-1, allotment garden 73.15 Mg ha-1). Moreover, urban green spaces with vegetables (91.66 Mg ha-1) and ornamentals (85.05 mg ha-1) show greater SOC stock levels when comparing vegetation types (lawn 62.48 Mg ha-1). Significant differences in SOC are also found for various management practices. Specifically, the monthly fertilization schedule resulted in higher SOC levels (127.37 Mg ha⁻¹) compared to the yearly fertilization schedule (76.88 Mg ha⁻¹). Additionally, the use of organic fertilizers contributed to increased SOC levels (84.40 Mg ha⁻¹) in contrast to mineral fertilizer applications (65.31 Mg ha⁻¹). The average SOC stock in all the studied urban green spaces (85 mg ha-1) was higher than the average SOC stock in arable soils in Germany (47.30 Mg ha-1). The higher SOC in the region could be due to vegetation types and fertilization frequencies, which show statistically significant effects (p-value <0.001). Other management practices (e.g., irrigation type and frequency) did not show a significant effect. Our findings highlight the significance of soil management practices, particularly in selecting vegetation types and determining fertilization frequency, as essential factors influencing urban SOC.
Protocol for conducting advanced cyclic tests in lithium-ion batteries to estimate capacity fade
(2024)
Using advanced cyclic testing techniques improves accuracy in estimating capacity fade and incorporates real-world scenarios in battery cycle aging assessment. Here, we present a protocol for conducting cyclic tests in lithium-ion batteries to estimate capacity fade. We describe steps for implementing strategies for accounting for variations in rest periods, charge-discharge rates, and temperatures. We also detail procedures for validating tests experimentally within a climate-controlled chamber and for developing an empirical model to estimate capacity fading under various testing objectives. For complete details on the use and execution of this protocol, please refer to Mulpuri et al.1.
Process-induced changes in thermo-mechanical viscoelastic properties and the corresponding morphology of biodegradable polybutylene adipate terephthalate (PBAT) and polylactic acid (PLA) blown film blends modified with four multifunctional chain-extending cross-linkers (CECL) were investigated. The introduction of CECL modified the properties of the reference PBAT/PLA blend significantly. The thermal analysis showed that the chemical reactions were incomplete after compounding, and that film blowing extended them. SEM investigations of the fracture surfaces of blown extrusion films reveal the significant effect of CECL on the morphology formed during the processing. The anisotropic morphology introduced during film blowing proved to affect the degradation processes as well. Furthermore, the reactions of CECL with PBAT/PLA induced by the processing depend on the deformation directions. The blow-up ratio parameter was altered to investigate further process-induced changes proving synergy with mechanical and morphological features. Using blown film extrusion, the elongational behavior represents a very important characteristic. However, its evaluation may be quite often problematic, but with the SER Universal Testing Platform it was possible to determine changes in the duration of time intervals corresponding to the rupture of elongated samples.
Traditional and newly developed testing methods were used for extensive application-related characterization of transdermal therapeutic systems (TTS) and pressure sensitive adhesives (PSA). Large amplitude oscillatory shear tests of PSAs were correlated to the material behavior during the patient’s motion and showed that all PSAs were located close to the gel point. Furthermore, an increasing strain amplitude results in stretching and yielding of the PSA´s microstructure causing a consolidation of the network and a release with increasing strain amplitude. RheoTack approach was developed to allow for an advanced tack characterization of TTS with visual inspection. The results showed a clear resin content and rod geometry dependent behavior, and displays the PSA´s viscoelasticity resulting in either high tack and long stretched fibrils or non-adhesion and brittle behavior. Moreover, diffusion of water / sweat during TTS´s application might influence its performance. Therefore, a dielectric analysis based evaluation method displayed occurring water diffusion into the PSA from which the diffusion coefficient can be determined, and showed clear material and resin content dependent behavior. All methods allow for an advanced product-oriented material testing that can be utilized within further TTS development.
A firm link between endoplasmic reticulum (ER) stress and tumors has been wildly reported. Endoplasmic reticulum oxidoreductase 1 alpha (ERO1α), an ER-resident thiol oxidoreductase, is confirmed to be highly upregulated in various cancer types and associated with a significantly worse prognosis. Of importance, under ER stress, the functional interplay of ERO1α/PDI axis plays a pivotal role to orchestrate proper protein folding and other key processes. Multiple lines of evidence propose ERO1α as an attractive potential target for cancer treatment. However, the unavailability of specific inhibitor for ERO1α, its molecular inter-relatedness with closely related paralog ERO1β and the tightly regulated processes with other members of flavoenzyme family of enzymes, raises several concerns about its clinical translation. Herein, we have provided a detailed description of ERO1α in human cancers and its vulnerability towards the aforementioned concerns. Besides, we have discussed a few key considerations that may improve our understanding about ERO1α in tumors.
Is It Really You Who Forgot the Password? When Account Recovery Meets Risk-Based Authentication
(2024)
Improved Thermal Comfort Model Leveraging Conditional Tabular GAN Focusing on Feature Selection
(2024)
The indoor thermal comfort in both homes and workplaces significantly influences the health and productivity of inhabitants. The heating system, controlled by Artificial Intelligence (AI), can automatically calibrate the indoor thermal condition by analyzing various physiological and environmental variables. To ensure a comfortable indoor environment, smart home systems can adjust parameters related to thermal comfort based on accurate predictions of inhabitants’ preferences. Modeling personal thermal comfort preferences poses two significant challenges: the inadequacy of data and its high dimensionality. An adequate amount of data is a prerequisite for training efficient machine learning (ML) models. Additionally, high-dimensional data tends to contain multiple irrelevant and noisy features, which might hinder ML models’ performance. To address these challenges, we propose a framework for predicting personal thermal comfort preferences, combining the conditional tabular generative adversarial network (CTGAN) with multiple feature selection techniques. We first address the data inadequacy challenge by applying CTGAN to generate synthetic data samples, incorporating challenges associated with multimodal distributions and categorical features. Then, multiple feature selection techniques are employed to identify the best possible sets of features. Experimental results based on a wide range of settings on a standard dataset demonstrated state-of-the-art performance in predicting personal thermal comfort preferences. The results also indicated that ML models trained on synthetic data achieved significantly better performance than models trained on real data. Overall, our method, combining CTGAN and feature selection techniques, outperformed existing known related work in thermal comfort prediction in terms of multiple evaluation metrics, including area under the curve (AUC), Cohen’s Kappa, and accuracy. Additionally, we presented a global, model-agnostic explanation of the thermal preference prediction system, providing an avenue for thermal comfort experiment designers to consciously select the data to be collected.
Introduction: A multitude of findings from cell cultures and animal studies are available to support the anti-cancer properties of cannabidiol (CBD). Since CBD acts on multiple molecular targets, its clinical adaptation, especially in combination with cancer immunotherapy regimen remains a serious concern.
Methods: Considering this, we extensively studied the effect of CBD on the cytokine-induced killer (CIK) cell immunotherapy approach using multiple non-small cell lung cancer (NSCLC) cells harboring diverse genotypes.
Results: Our analysis showed that, a) The Transient Receptor Potential Cation Channel Subfamily V Member 2 (TRPV2) channel was intracellularly expressed both in NSCLC cells and CIK cells. b) A synergistic effect of CIK combined with CBD, resulted in a significant increase in tumor lysis and Interferon gamma (IFN-g) production. c) CBD had a preference to elevate the CD25+CD69+ population and the CD62L_CD45RA+terminal effector memory (EMRA) population in NKT-CIK cells, suggesting early-stage activation and effector memory differentiation in CD3+CD56+ CIK cells. Of interest, we observed that CBD enhanced the calcium influx, which was mediated by the TRPV2 channel and elevated phosphor-Extracellular signal-Regulated Kinase (p-ERK) expression directly in CIK cells, whereas ERK selective inhibitor FR180204 inhibited the increasing cytotoxic CIK ability induced by CBD. Further examinations revealed that CBD induced DNA double-strand breaks via upregulation of histone H2AX phosphorylation in NSCLC cells and the migration and invasion ability of NSCLC cells suppressed by CBD were rescued using the TRPV2 antagonist (Tranilast) in the absence of CIK cells. We further investigated the epigenetic effects of this synergy and found that adding CBD to CIK cells decreased the Long Interspersed Nuclear Element-1 (LINE-1) mRNA expression and the global DNA methylation level in NSCLC cells carrying KRAS mutation. We further investigated the epigenetic effects of this synergy and found that adding CBD to CIK cells decreased the Long Interspersed Nuclear Element-1 (LINE-1) mRNA expression and the global DNA methylation level in NSCLC cells carrying KRAS mutation.
Conclusions: Taken together, CBD holds a great potential for treating NSCLC with CIK cell immunotherapy. In addition, we utilized NSCLC with different driver mutations to investigate the efficacy of CBD. Our findings might provide evidence for CBD-personized treatment with NSCLC patients.
Green infrastructure has been widely recognized for the benefits to human health and biodiversity conservation. However, knowledge of the qualities and requirements of such spaces and structures for the effective delivery of the range of ecosystem services expected is still limited, as well as the identification of trade-offs between services. In this study, we apply the One Health approach in the context of green spaces to investigate how urban park characteristics affect human mental health and wildlife support outcomes and identify synergies and trade-offs between these dimensions. Here we show that perceived restorativeness of park users varies significantly across sites and is mainly affected by safety and naturalness perceptions. In turn, these perceptions are driven by objective indicators of quality, such as maintenance of facilities and vegetation structure, and subjective estimations of biodiversity levels. The presence of water bodies benefited both mental health and wildlife. However, high tree canopy coverage provided greater restoration potential whereas a certain level of habitat heterogeneity was important to support a wider range of bird species requirements. To reconcile human and wildlife needs in green spaces, cities should strategically implement a heterogeneous green infrastructure network that considers trade-offs and maximizes synergies between these dimensions.
Trade of wild-caught animals is illegal for many taxa and in many countries. Common regulatory procedures involve documentation and marking techniques. However, these procedures are subject to fraud and thus should be complemented by routine genetic testing in order to authenticate the captive-bred origin of animals intended for trade. A suitable class of genetic markers are SNPSTRs that combine a short tandem repeat (STR) and single nucleotide polymorphisms (SNPs) within one amplicon. This combined marker type can be used for genetic identification and for parentage analyses and in addition, provides insight into haplotype history. As a proof of principle, this study establishes a set of 20 SNPSTR markers for Athene noctua, one of the most trafficked owls in CITES Appendix II. These markers can be coamplified in a single multiplex reaction. Based on population data, the percentage of observed and expected heterozygosities of the markers ranged from 0.400 to 1.000 and 0.545 to 0.850, respectively. A combined probability of identity of 5.3*10-23 was achieved with the whole set, and combined parentage exclusion probabilities reached over 99.99%, even if the genotype of one parent was missing. A direct comparison of an owl family and an unrelated owl demonstrated the applicability of the SNPSTR set in parentage testing. The established SNPSTR set thus proved to be highly useful for identifying individuals and analysing parentage to determine wild or captive origin. We propose to implement SNPSTR-based routine certification in wildlife trade as a way to reveal animal laundering and misdeclaration of wild-caught animals.
Force field (FF) based molecular modeling is an often used method to investigate and study structural and dynamic properties of (bio-)chemical substances and systems. When such a system is modeled or refined, the force field parameters need to be adjusted. This force field parameter optimization can be a tedious task and is always a trade-off in terms of errors regarding the targeted properties. To better control the balance of various properties’ errors, in this study we introduce weighting factors for the optimization objectives. Different weighting strategies are compared to fine-tune the balance between bulk-phase density and relative conformational energies (RCE), using n-octane as a representative system. Additionally, a non-linear projection of the individual property-specific parts of the optimized loss function is deployed to further improve the balance between them. The results show that the overall error is reduced. One interesting outcome is a large variety in the resulting optimized force field parameters (FFParams) and corresponding errors, suggesting that the optimization landscape is multi-modal and very dependent on the weighting factor setup. We conclude that adjusting the weighting factors can be a very important feature to lower the overall error in the FF optimization procedure, giving researchers the possibility to fine-tune their FFs.
This work proposes a novel approach for probabilistic end-to-end all-sky imager-based nowcasting with horizons of up to 30 min using an ImageNet pre-trained deep neural network. The method involves a two-stage approach. First, a backbone model is trained to estimate the irradiance from all-sky imager (ASI) images. The model is then extended and retrained on image and parameter sequences for forecasting. An open access data set is used for training and evaluation. We investigated the impact of simultaneously considering global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI) on training time and forecast performance as well as the effect of adding parameters describing the irradiance variability proposed in the literature. The backbone model estimates current GHI with an RMSE and MAE of 58.06 and 29.33 W m−2, respectively. When extended for forecasting, the model achieves an overall positive skill score reaching 18.6 % compared to a smart persistence forecast. Minor modifications to the deterministic backbone and forecasting models enables the architecture to output an asymmetrical probability distribution and reduces training time while leading to similar errors for the backbone models. Investigating the impact of variability parameters shows that they reduce training time but have no significant impact on the GHI forecasting performance for both deterministic and probabilistic forecasting while simultaneously forecasting GHI, DNI, and DHI reduces the forecast performance.
In vision tasks, a larger effective receptive field (ERF) is associated with better performance. While attention natively supports global context, convolution requires multiple stacked layers and a hierarchical structure for large context. In this work, we extend Hyena, a convolution-based attention replacement, from causal sequences to the non-causal two-dimensional image space. We scale the Hyena convolution kernels beyond the feature map size up to 191$\times$191 to maximize the ERF while maintaining sub-quadratic complexity in the number of pixels. We integrate our two-dimensional Hyena, HyenaPixel, and bidirectional Hyena into the MetaFormer framework. For image categorization, HyenaPixel and bidirectional Hyena achieve a competitive ImageNet-1k top-1 accuracy of 83.0% and 83.5%, respectively, while outperforming other large-kernel networks. Combining HyenaPixel with attention further increases accuracy to 83.6%. We attribute the success of attention to the lack of spatial bias in later stages and support this finding with bidirectional Hyena.
The transport sector is a major source of air pollution and thus a major contributor to the changing climate. As a result, in the recent past, driving bans have been imposed on cars with critical pollutant groups. As an international UN campus and self-proclaimed climate capital, the Federal City of Bonn declared a climate emergency in 2019 and participated in a federally funded “Lead City” project to optimise air quality. A key goal of the project is to reduce private motorised transport and strengthen public transport. Among the implemented measures, a “climate ticket” was introduced in 2019 whereby consumers could purchase an annual 365 € ticket for all local public transport. This paper reports on an analysis of that ticket’s changes in travel behavior.
A quantitative survey (n = 1,315) of the climate ticket users as well as the multiple regressions confirm that the climate ticket attracted more customers to the buses and trams and that a modal shift for the period of the measure was recognisable. The multiple regressions showed that the ticket was perceived significantly more positively by full-time employed users than by unemployed people. The results also show that, in addition to the price, it is essential that travel time and reliability are ensured. Furthermore, the eligible groups of people, the area of coverage, and good connecting services should be extended. To sustainably improve air quality, this type of mobility service must be optimised and introduced on a permanent basis.
Pipeline transport is an efficient method for transporting fluids in energy supply and other technical applications. While natural gas is the classical example, the transport of hydrogen is becoming more and more important; both are transmitted under high pressure in a gaseous state. Also relevant is the transport of carbon dioxide, captured in the places of formation, transferred under high pressure in a liquid or supercritical state and pumped into underground reservoirs for storage. The transport of other fluids is also required in technical applications. Meanwhile, the transport equations for different fluids are essentially the same, and the simulation can be performed using the same methods. In this paper, the effect of control elements such as compressors, regulators and flaptraps on the stability of fluid transport simulations is studied. It is shown that modeling of these elements can lead to instabilities, both in stationary and dynamic simulations. Special regularization methods were developed to overcome these problems. Their functionality also for dynamic simulations is demonstrated for a number of numerical experiments.
In addition to the long-term goal of mitigating climate change, the current geopolitical upheavals heighten the urgency to transform Europe's energy system. This involves expanding renewable energies while managing intermittent electricity generation. Hydrogen is a promising solution to balance generation and demand, simultaneously decarbonizing complex applications. To model the energy system's transformation, the project TransHyDE-Sys, funded by the German Federal Ministry of Education and Research, takes an integrated approach beyond traditional energy system analysis, incorporating a diverse range of more detailed methods and tools. Herein, TransHyDE-Sys is situated within the recent policy discussion. It addresses the requirements for energy system modeling to gain insights into transforming the European hydrogen and energy infrastructure. It identifies knowledge gaps in the existing literature on hydrogen infrastructure-oriented energy system modeling and presents the research approach of TransHyDE-Sys. TransHyDE-Sys analyzes the development of hydrogen and energy infrastructures from “the system” and “the stakeholder” perspectives. The integrated modeling landscape captures temporal and spatial interactions among hydrogen, electricity, and natural gas infrastructure, providing comprehensive insights for systemic infrastructure planning. This allows a more accurate representation of the energy system's dynamics and aids in decision-making for achieving sustainable and efficient hydrogen network development integration.
Self-motion perception is a multi-sensory process that involves visual, vestibular, and other cues. When perception of self-motion is induced using only visual motion, vestibular cues indicate that the body remains stationary, which may bias an observer’s perception. When lowering the precision of the vestibular cue by for example, lying down or by adapting to microgravity, these biases may decrease, accompanied by a decrease in precision. To test this hypothesis, we used a move-to-target task in virtual reality. Astronauts and Earth-based controls were shown a target at a range of simulated distances. After the target disappeared, forward self-motion was induced by optic flow. Participants indicated when they thought they had arrived at the target’s previously seen location. Astronauts completed the task on Earth (supine and sitting upright) prior to space travel, early and late in space, and early and late after landing. Controls completed the experiment on Earth using a similar regime with a supine posture used to simulate being in space. While variability was similar across all conditions, the supine posture led to significantly higher gains (target distance/perceived travel distance) than the sitting posture for the astronauts pre-flight and early post-flight but not late post-flight. No difference was detected between the astronauts’ performance on Earth and onboard the ISS, indicating that judgments of traveled distance were largely unaffected by long-term exposure to microgravity. Overall, this constitutes mixed evidence as to whether non-visual cues to travel distance are integrated with relevant visual cues when self-motion is simulated using optic flow alone.
This study addresses the common occurrence of cell-to-cell variations arising from manufacturing tolerances and their implications during battery production. The focus is on assessing the impact of these inherent differences in cells and exploring diverse cell and module connection methods on battery pack performance and their subsequent influence on the driving range of electric vehicles (EVs). The analysis spans three battery pack sizes, encompassing various constant discharge rates and nine distinct drive cycles representative of driving behaviours across different regions of India. Two interconnection topologies, categorised as “string” and “cross”, are examined. The findings reveal that cross-connected packs exhibit reduced energy output compared to string-connected configurations, which is reflected in the driving range outcomes observed during drive cycle simulations. Additionally, the study investigates the effects of standard deviation in cell parameters, concluding that an increased standard deviation (SD) leads to decreased energy output from the packs. Notably, string-connected packs demonstrate superior performance in terms of extractable energy under such conditions.
In this work, the surface reactions of the homemade explosive triacetone triperoxide on tungsten oxide (WO3) sensor surfaces are studied to obtain detailed information about the chemical reactions taking place. Semiconductor gas sensors based on WO3 nanopowders are therefore produced and characterized by scanning electron microscopy, X-ray diffraction, and Raman spectroscopy. To analyze the reaction mechanisms at the sensor surface, the sensor is monitored online under operation conditions using Raman spectroscopy, which allows to identify the temperature-dependent sensor reactions. By combining information from the Raman spectra with data on the changing resistivity of the underlying semiconductor, it is possible to establish a correlation between the adsorbed gas species and the physical properties of the WO3 layer. In the results, it is indicated that a Lewis acid–base reaction is the most likely mechanism for the increase in resistance observed at temperatures below 150 °C. In the results, at higher temperatures, the assumption of a radical mechanism that causes a decrease in resistance is supported.
DT-13 attenuates inflammation by inhibiting NLRP3-inflammasome related genes in RAW264.7 macrophages
(2024)
Plant derived saponins or other glycosides are widely used for their anti-inflammatory, antioxidant, and anti-viral properties in therapeutic medicine. In this study, we focus on understanding the function of the less known steroidal saponin from the roots of Liriope muscari L. H. Bailey – saponin C (also known as DT-13) in lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages in comparison to the well-known saponin ginsenoside Rk1 and anti-inflammatory drug dexamethasone. We proved that DT-13 reduces LPS-induced inflammation by inhibiting nitric oxide (NO) production, interleukin-6 (IL-6) release, cycloxygenase-2 (COX-2), tumour necrosis factor-alpha (TNF-α) gene expression, and nuclear factor kappa-B (NFκB) translocation into the nucleus. It also inhibits the inflammasome component NOD-like receptor family pyrin domain containing protein 3 (NLRP3) regulating the inflammasome activation. This was supported by the significant inhibition of caspase-1 and interleukin-1 beta (IL-1β) expression and release. This study demonstrates the anti-inflammatory effect of saponins on LPS-stimulated macrophages. For the first time, an in vitro study shows the attenuating effect of DT-13 on NLRP3-inflammasome activation. In comparison to the existing anti-inflammatory drug, dexamethasone, and triterpenoid saponin Rk1, DT-13 more efficiently inhibits inflammation in the applied cell culture model. Therefore, DT-13 may serve as a lead compound for the development of new more effective anti-inflammatory drugs with minimised side effects.
In recent years, eXtended Reality (XR) technology like Augmented Reality and Virtual Reality became both technically feasible as well as affordable which lead to a drastic demand of professionally designed and developed applications. However, this demand combined with a rapid pace of innovation revealed a lack of design tool support for professional interaction designers as well as a knowledge gap regarding their approaches and needs. To address this gap, this thesis engages with the work of professional XR interaction designers in a qualitative research into XR interaction design approach. Therefore, this thesis applies two complementary lenses stemming from scientific design and social practice theory discourses to observe, describe, analyze, and understand professional XR interaction designers' challenges and approaches with a focus on application prototyping.
During robot-assisted therapy, a robot typically needs to be partially or fully controlled by therapists, for instance using a Wizard-of-Oz protocol; this makes therapeutic sessions tedious to conduct, as therapists cannot fully focus on the interaction with the person under therapy. In this work, we develop a learning-based behaviour model that can be used to increase the autonomy of a robot’s decision-making process. We investigate reinforcement learning as a model training technique and compare different reward functions that consider a user’s engagement and activity performance. We also analyse various strategies that aim to make the learning process more tractable, namely i) behaviour model training with a learned user model, ii) policy transfer between user groups, and iii) policy learning from expert feedback. We demonstrate that policy transfer can significantly speed up the policy learning process, although the reward function has an important effect on the actions that a robot can choose. Although the main focus of this paper is the personalisation pipeline itself, we further evaluate the learned behaviour models in a small-scale real-world feasibility study in which six users participated in a sequence learning game with an assistive robot. The results of this study seem to suggest that learning from guidance may result in the most adequate policies in terms of increasing the engagement and game performance of users, but a large-scale user study is needed to verify the validity of that observation.
Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device’s modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements.
Selection Performance and Reliability of Eye and Head Gaze Tracking Under Varying Light Conditions
(2024)
Pollution with anthropogenic waste, particularly persistent plastic, has now reached every remote corner of the world. The French Atlantic coast, given its extensive coastline, is particularly affected. To gain an overview of current plastic pollution, this study examined a stretch of 250 km along the Silver Coast of France. Sampling was conducted at a total of 14 beach sections, each with five sampling sites in a transect. At each collection site, a square of 0.25 m2 was marked. The top 5 cm of beach sediment was collected and sieved on-site using an analysis sieve (mesh size 1 mm), resulting in a total of approximately 0.8 m3 of sediment, corresponding to a total weight of 1300 kg of examined beach sediment. A total of 1972 plastic particles were extracted and analysed using infrared spectroscopy, corresponding to 1.5 particles kg−1 of beach sediment. Pellets (885 particles), polyethylene as the polymer type (1349 particles), and particles in the size range of microplastics (943 particles) were most frequently found. The significant pollution by pellets suggests that the spread of plastic waste is not primarily attributable to tourism (in February/March 2023). The substantial accumulation of meso- and macro-waste (with 863 and 166 particles) also indicates that research focusing on microplastics should be expanded to include these size categories, as microplastics can develop from them over time.
The digitization of financial activities in consumers' lives is increasing, and the digitalization of invoicing processes is expected to play a significant role, although this area is not well understood regarding the private sector. Human-Computer Interaction (HCI) and Computer Supported Cooperative Work (CSCW) research have a long history of analyzing the socio-material and temporal aspects of work practices that are relevant for the domestic domain. The socio-material structuring of invoicing work and the working styles of consumers must be considered when designing effective consumer support systems. In this ethnomethodologically-informed, design-oriented interview study, we followed 17 consumers in their daily practices of dealing with invoices to make the invisible administrative work involved in this process visible. We identified and described the meaningful artifacts that were used in a spatial-temporal process within various storage locations such as input, reminding, intermediate (for postponing cases) buffers, and archive systems. Furthermore, we identified three different working styles that consumers exhibited: direct completion, at the next opportunity, and postpone as far as possible. This study contributes to our understanding of household economics and domestic workplace studies in the tradition of CSCW and has implications for the design of electronic invoicing systems.
Statins are a group of hypolipidemic drugs that act by competitive inhibition of the HMGR enzyme. They are generally considered effective and safe but claimed to have side effects on skeletal muscles. A molecular side effect of statins is the block of terpene biosynthesis and hence of dolichol involved in N-glycosylation and O-mannosylation of proteins. Defects in O-mannosylation lead to α-dystroglycan (α-DG) hypoglycosylation and a series of hereditary dystroglycanopathies. The current project aims to get insight into molecular pathomechanisms induced by statins in mammalian muscle cells and to unravel a potential link between these effects and statin-induced decreases of α-DG O-mannosylation. The study was based on mass spectrometric proteomics supported by western blot analysis to reveal Rosuvastatin effects on cellular pathways under high (micromolar) or low (nanomolar) conditions. Differential proteomics revealed higher statin effects on muscle cell function in micromolar than nanomolar concentration, which is reached in the patient’s plasma. We demonstrated distinct and partially overlapping patterns of fold-changed proteins under high and low statin conditions. Gene ontology term enrichment (GOTE) analyses of fold-changed proteins revealed cellular pathways related to muscle function and development are affected, even under low statin conditions, typically reached in the patient’s plasma during prophylactic medication.
Introduction: Antimicrobial resistance (AMR) has emerged as one of the leading threats to public health. AMR possesses a multidimensional challenge that has social, economic, and environmental dimensions that encompass the food production system, influencing human and animal health. The One Health approach highlights the inextricable linkage and interdependence between the health of people, animals, agriculture, and the environment. Antibiotic use in any of these One Health areas can potentially impact the health of other areas. There is a dearth of evidence on AMR from the natural environment, such as the plant-based agriculture sector. Antibiotics, antibiotic-resistant bacteria (ARB), and related AMR genes (ARGs) are assumed to present in the natural environment and disseminate resistance to fresh produce/vegetables and thus to human health upon consumption. Therefore, this study aims to investigate the role of vegetables in the spread of AMR through an agroecosystem exploration from a One Health perspective in Ahmedabad, India.
Protocol: The present study will be executed in Ahmedabad, located in Gujarat state in the Western part of India, by adopting a mixed-method approach. First, a systematic review will be conducted to document the prevalence of ARB and ARGs on fresh produce in South Asia. Second, agriculture farmland surveys will be used to collect the general farming practices and the data on common vegetables consumed raw by the households in Ahmedabad. Third, vegetable and soil samples will be collected from the selected agriculture farms and analyzed for the presence or absence of ARB and ARGs using standard microbiological and molecular methods.
Discussion: The analysis will help to understand the spread of ARB/ARGs through the agroecosystem. This is anticipated to provide an insight into the current state of ARB/ARGs contamination of fresh produce/vegetables and will assist in identifying the relevant strategies for effectively controlling and preventing the spread of AMR.
RELA haploinsufficiency is a recently described autoinflammatory condition presenting with intermittent fevers and mucocutaneous ulcerations. The RELA gene encodes the p65 protein, one of five NF-κB family transcription factors. As RELA is an essential regulator of mucosal homeostasis, haploinsufficiency leads to decreased NF-κB signaling which promotes TNF-driven mucosal apoptosis with impaired epithelial recovery. Thus far, only eight cases have been reported in the literature. Here, we report four families with three novel and one previously described pathogenic variant in RELA. These four families included 23 affected individuals for which genetic testing was available in 16. Almost half of these patients had been previously diagnosed with more common rheumatologic entities (such as Behcet's Disease; BD) prior to the discovery of their pathogenic RELA variants. The most common clinical features were orogenital ulcers, rash, joint inflammation, and fever. The least common were conjunctivitis and recurrent infections. Clinical variability was remarkable even among familial cases, and incomplete penetrance was observed. Patients in our series were treated with a variety of medications, and benefit was observed with glucocorticoids, colchicine, and TNF inhibitors. Altogether, our work adds to the current literature and doubles the number of reported cases with RELA-Associated Inflammatory Disease (RAID). It reaffirms the central importance of the NF-κB pathway in immunity and inflammation, as well as the important regulatory role of RELA in mucosal homeostasis. RELA associated inflammatory disease should be considered in all patients with BD, particularly those with early onset and/or with a strong family history.
Although climate-induced liquidity risks can cause significant disruptions and instabilities in the financial sector, they are frequently overlooked in current debates and policy discussions. This paper proposes a macro-financial agent-based integrated assessment model to investigate the transmission channels of climate risks to financial instability and study the emergence of liquidity crises through interbank market dynamics. Our simulations show that the financial system could experience serious funding and market liquidity shortages due to climate-induced liquidity crises. Our investigation contributes to our understanding of the impact - and possible solutions - to climate-induced liquidity crises, besides the issue of asset stranding related to transition risks usually considered in the existing studies.
A PM2.5 concentration prediction framework with vehicle tracking system: From cause to effect
(2023)
Representing 3D surfaces as level sets of continuous functions over R3 is the common denominator of neural implicit representations, which recently enabled remarkable progress in geometric deep learning and computer vision tasks. In order to represent 3D motion within this framework, it is often assumed (either explicitly or implicitly) that the transformations which a surface may undergo are homeomorphic: this is not necessarily true, for instance, in the case of fluid dynamics. In order to represent more general classes of deformations, we propose to apply this theoretical framework as regularizers for the optimization of simple 4D implicit functions (such as signed distance fields). We show that our representation is capable of capturing both homeomorphic and topology-changing deformations, while also defining correspondences over the continuously-reconstructed surfaces.
Analytical Chemistry I
(2023)
This workbook takes you through the successful work Harris, Textbook of Quantitative Analysis and is designed primarily for self-study. In five parts, the lecture content of analytical chemistry is summarized and explained using selected examples. Basic concepts of analytical chemistry are presented as well as the principle and various techniques of dimensional analysis and chromatography. UV/VIS, infrared and Raman spectroscopy are used to explain the investigation of molecularly present compounds, and selected techniques of atomic spectroscopy conclude the introduction to the fundamentals of analysis. The textbook's essential sections and illustrations are repeatedly referred to, which facilitates independent learning of the fundamentals of analytical chemistry.
Spektroskopische Qualifizierung und Quantifizierung von Hyaluronsäure in Nahrungsergänzungsmitteln
(2023)
The paper investigates the nature of Kenya's entrepreneurship education ecosystem (EEE) through a comparative analysis of three entrepreneurship education programs and an examination of how the institutions foster a favourable entrepreneurial environment. This study looks at the entrepreneurship education ecosystem through the lens of universities, NGO's and private institutes in Kenya.
A systemic analysis of EEE is provided by utilizing the Actiotope Model as a conceptual framework. The exploratory research adopts a pragmatic mixed-method methodological approach best suited to understand the research problem.
The results reveal that entrepreneurship education at higher education institutions was primarily theoretical and relied on traditional forms of entrepreneurship education. Recurring rigid patterns show minimal personalization of content and learning styles within the University, with more personalization reported in the Mully Model of education and the more specialized entrepreneurship program of the Identity Projects.
The adaptation of the Actiotope Model provided a new and unique approach to analyzing entrepreneurship ecosystems. The person-centred approach of the model provides valuable insights to learners and to entrepreneurship education institutions and researchers.
Enhanced collaboration between the different entrepreneurial education stakeholders could be a more effective short to medium-term solution to addressing the gaps in entrepreneurial education at tertiary institutions.
In the long term, the study recommends adopting practical-based and goal-oriented entrepreneurship teaching models.
Social businesses have a great positive impact on communities and are a sustainable way to do business today and in the future. This impact can be amplified through the means of digitalization. In the past, traditional for-profit business models have been used to understand the structures of business operations. However, the underlying business model of digital social businesses has not yet been explored. This study presents a building block analysis of business models and a subsequent typology. Digital and social business models are identified via a literature review. The building block analysis encompasses an assessment of the individual business activities contained in the business models. The typology is developed from existing literature utilizing a matrix for the evaluation of digital social businesses. Additionally, five semi-structured expert interviews are conducted to inform, extend, or content the findings of this study. To this end, an inductive coding procedure is applied to the transcribed interviews for the detection of themes within the text. This study contributes to social business model research by providing a first insight into the unique building blocks of digital social business models. It also creates a typology tool based on two parameters, which enables the comparison of digital social businesses.
Mobile technologies have evolved into the means of gaining access to information for learning. Its application in higher education is still a novel concept, particularly in underdeveloped countries. This study is aimed at exploring the views of doctoral students regarding their learning experiences with mobile technologies. Student focus group interviews of 24 doctoral students from 3 different academic institutions were interviewed. The participants’ responses were recorded, transcribed, and analyzed to make conclusions. According to the findings of this study, mobile devices play an important part in the learning experiences of doctoral students. The participating students engaged in collaborative learning using mobile technologies. Given the benefits of adopting mobile technologies for learning activities, academic institutions should focus on teaching faculty members to use this to involve students in their learning process. The implications of this study call for the continued advancement of mobile technologies to facilitate effective learning experience for the multitude of mobile learners in developing countries. Another implication is that academic institutions with collaboration with libraries should see the need to develop user friendly mobile app that is linked to the library management system. Such an application would allow the students to optimally use their smartphones and tablets to search the library’s resources from their mobile devices. Training should be offered to the teaching faculty members to come to terms with the benefits of mobile technologies for learning activities.
The UN Declaration on the Right to Development (UNDRTD) adopted in 1986 and the 2030 Agenda for Sustainable Development adopted in 2015 share a universal concept of development that refers both to individual and collective dimensions of prosperity and thus includes the rights of future generations.2 They thus offer a definition of the relationship between development and human rights that is very relevant for the 21st century. The core norm of the UNDRTD has been defined later as “the right of peoples and individuals to the constant improvement of their wellbeing and to a national and global enabling environment conducive to just, equitable, participatory and human-centred development respectful of all human rights”3.
The continuously increasing number of biomedical scholarly publications makes it challenging to construct document recommendation algorithms that can efficiently navigate through literature. Such algorithms would help researchers in finding similar, relevant, and related publications that align with their research interests. Natural Language Processing offers various alternatives to compare publications, ranging from entity recognition to document embeddings. In this paper, we present the results of a comparative analysis of vector-based approaches to assess document similarity in the RELISH corpus. We aim to determine the best approach that resembles relevance without the need for further training. Specifically, we employ five different techniques to generate vectors representing the text in the documents. These techniques employ a combination of various Natural Language Processing frameworks such as Word2Vec, Doc2Vec, dictionary-based Named Entity Recognition, and state-of-the-art models based on BERT. To evaluate the document similarity obtained by these approaches, we utilize different evaluation metrics that account for relevance judgment, relevance search, and re-ranking of the relevance search. Our results demonstrate that the most promising approach is an in-house version of document embeddings, starting with word embeddings and using centroids to aggregate them by document.
The continuous increase of biomedical scholarly publications makes it challenging to construct document recommendation algorithms to navigate through literature, an important feature for researchers to keep up with relevant publications. Understanding semantic relatedness and similarity between two documents could improve document recommendations. The objective of this study is performing a comparative analysis of vector-based approaches to assess document similarity in the RELISH corpus. Here we present our approach to compare five different techniques to generate vectors representing the text in the documents. These techniques employ a combination of various Natural Language Processing frameworks such as Word2Vec, Doc2Vec, dictionary-based Named Entity Recognition as well as state-of-the-art models based on BERT.
Tourism in Rwanda is challenging. Since the country is small and hilly, it is difficult to tap the potential. As the country is blessed with diverse nature, the Rwandan government decided to combine ecotourism with high-end tourism, to exploit the full potential. This study aims to assess the extent to which these two types of tourism fit together, as well as if sustainability is a decisive argument in this upscale segment. In this context, ecotourism is characterized by its 3 core criteria: education, nature and sustainability. To evaluate the main question: to what extent can ecotourism projects help to promote the perception of Rwanda as a high-end tourist destination on the German market? As well as if sustainability is a decisive argument, interviews with stakeholder from the Rwandan tourism industry as well as German tour operators were conducted, to gain an understanding of both sites and then evaluate them according to the 3 ecotourism core criteria and the demands of high-end tourists. The results showed that there is a difference in the perception of the needs of high-end tourists. While the 3 core criteria seem to be too relevant while they are in booking decision with the tour operator. The high-end lodges in Rwanda state an interest in these three criteria. It is evident from the results that there is a limited active demand for sustainable tourist products, while nature and education are more relevant, but not yet fully exploited. However, all interviewees indicated that ecotourism, and in particular sustainability, is experiencing an increase in demand and will continue to grow in importance in the future. Accordingly, the results suggest the driving markets approach is relevant to further drive demand in that segment.
As a developing economy, Rwanda has been exploring transitioning to being a technologically driven and sustainable economy. Moreover, research on economic growth have focused on the need to improve human capacity potential within increasing demands of climate change activists but there remains a theoretic and practical lacuna in including renewable energy resources in economic growth and expansion of electricity access. Therefore, it is necessary to study the impact of competent skill acquisition and graduate employment market on the interaction mix between economic growth and the expansion of energy access in Rwanda, particularly finding out the problems advancing the non-inclusiveness of engineering graduates, which result to high rate of unemployment and diversions, especially for the graduates specializing in energy fields. As a result, the following open questions were raised with variations 1; how did employees penetrate energy-sector labour market opportunity in Rwanda? 2; what influenced employee’s decision in pursuing a career in Rwanda’s labour market, 3; what were the specific employee competent skills that enabled smooth transition in energy-sector employment after graduation and the ones required to maintain their current positions? 4; what specific competent skills are required for inclusivity of today's engineering graduates in energy sector employment market? The study is qualitative and it uses the exploratory research design. It is based on the growth pole theory employing snowball/chain purposeful sampling technique, whereby key informants in Rwanda energy sector were located. Data was specifically collected from these primary sources through semi-structured interviews and documentary method. Interview data and text from documents were inductively analysed. The study generally recommended institution or program for connecting learning institutions, industry and employment market in the distributed and renewable energy resources to promote competent skills acquisition, competition and improve graduates’ inclusiveness in the expansion of electricity access, thereby leading to economic growth in Rwanda.
Rapid and sustained innovation in developed markets triggers the generation of innovative start-ups, some with disruptive innovations. However, when their offering faces a saturated market with satisfactory and widely available established traditional solutions, many innovative start-ups from these markets may fail. The literature on some start-ups that successfully brought their innovation to emerging markets shows how using leapfrogging traditional solutions to innovative solutions can offer survival and growth opportunities to these start-ups. However, a wide exploitation of leapfrogging processes in emerging markets for survival or business growth of innovative start-ups from developed markets is not yet theorized. To contribute to closing this gap, we propose a conceptual framework to assess the readiness of an emerging market to leapfrog to innovative solutions.
The design of the conceptual framework uses a scenario-planning like approach with two key factors, namely Context Readiness and Value Network Integration. To test and refine the proposed framework and show its relevance for coming to an informed expansion decision making, we used PAR (Participatory Action Research). For the illustration of the application of the proposed conceptual framework, the case of telehealth in Morocco is used.
While 14 % of the world's working-age population currently lives in sub-Saharan Africa (SSA), this figure will predictably be higher than the rest of the world combined by 2036. If this demographic group finds meaningful employment, Africa experiences an economic and social upswing. To tap this potential, the paper intends to answer the research question, "What are the prerequisites and how are they defined for the successful implementation of sustainable business model ideas in SSA?", by developing a top ten ranking consisting of previously identified sustainable business model ideas best suited for productive use. This achieves a novel approach to implementing future-oriented business models and contributes to current research on sustainable models. Since the geographical scope of SSA is pervasive, this paper focuses on Namibia, Rwanda, Senegal, and Uganda. An extensive literature review on these countries was conducted to gain a broader understanding of the situation in SSA. Additionally, research was carried out on the agricultural, energy, and information and communications technology (ICT) sectors to identify the most promising ideas. To contribute to current knowledge, experts were interviewed, and panel discussions were analyzed. Furthermore, the Business Model Canvas (BMC) was combined with the circular economy concept, which served as a framework for the business model ideas. Experts evaluated these ideas, which were subsequently ranked using fuzzy logic with artificial intelligence, based on the system for exploring country risks (CRISK-Explorer). The paper shows that skipping individual development processes opens up promising opportunities, such as the ICT-based business model e-crowd logistics or the renewable energy-based model e-Boda-Boda. Seven prerequisites for the successful implementation of these ideas were identified and defined: value delivery, promising customers, sufficient capital, presence of key resources, possibility to perform the key activities, sustainability, and profitability. The paper concludes by identifying limitations and suggesting avenues for future research.
Channels of distribution are important factors in the connection between goods and services produced for the final consumer and, therefore, determine the effectiveness with which they are delivered and ultimately availed to the final consumers. Globally, studies show that channels of distribution and sales play an essential role in building bonds between manufacturers, retailers, wholesalers and their consumers. The main purpose of this study is to examine the influence of distribution channels and networks on customer choice of fast-moving consumer goods (FCMG) in the Upper East Region of Ghana. The study adopted a quantitative approach and questionnaires were used to collect primary data from 110 customers of Unilever Ghana Limited in the Upper East Region of Ghana. The findings reveal that product-related factors, such as the price of products, perishability of products, size and weight of products, promote the effective distribution of Unilever goods and services, whilst consumer-related factors, such as the number of customers and increased consumer base, promote effective distribution channels. The study also established a positive influence of factors, such as incentives, receiving feedback and sales performance, on customer choice of fast-moving consumer goods (FMCG). Managers and producers in the FMCGs industry should implement reward and incentive programmes and policies to boost the sale and distribution of fast-moving consumer goods and services in the retail industry in Ghana.
Entrepreneurship is labelled as the panacea for graduate unemployment in Ghana. In the training process, students are mandatorily required to read a course in entrepreneurship, so as to be able to start their own businesses in the face of job adversities caused by the inadequacy of job opportunities created by government and lack of government drive to diversify the economy for more jobs to be created. This study, therefore aimed at investigating the critical precursors of entrepreneurial intentions among higher education students in Ghana. Using the analytical cross-sectional survey design, 250 respondents were recruited from public universities using probability sampling techniques (stratified-disproportionate and simple random) to participate in the survey. Respondents were required to respond to three constructs (entrepreneurial scaffolding, psychological capital, and entrepreneurial intentions). The data analyses were performed using multivariate regression. The study findings showed that entrepreneurial scaffolding and psychological capital were significant predictors of entrepreneurial intentions. The researchers concluded that students' convictions in succeeding or otherwise and planning to engage in entrepreneurial behaviours depended on proper entrepreneurial guidance and a positive mind-set. Therefore, it was recommended that higher education institutions in Ghana strengthened and included practical guides to entrepreneurial training. This will encourage higher education students to consider entrepreneurship, hence, reducing graduate unemployment in Ghana.
The dawn of the 21st Century has witnessed a tremendous increase in trade pacts among nations, resulting in renewed hopes for sustainable enterprise development in emerging economies worldwide. Ghana and other sub- Saharan African (SSA) countries have signed onto several North-South and South-South free trade agreements with the hope of strengthening their presence in the international trade arena, and to promote economic growth in SSA. For over two decades, however, very little has changed, and many have dashed their high hopes as enterprises continue to struggle in SSA. Not even the African Continental Free Trade Agreement (AfCFTA) could renew the hopes of sceptics. Several studies opined that enterprises in SSA could improve their domestic and international competitiveness by establishing mutually beneficial partnerships with their counterparts from the Global North and South. This study delved into the issues that affect North-South and South-South business collaborations and recommends key success factors that could help promote mutually beneficial cross-border business partnerships. The research includes both literature and empirical information on the key success factors of business partnerships between African enterprises as well as between African enterprises and firms from the Global North. We approached the study qualitatively using a phenomenological research design. Research participants included important stakeholders in Africa and Europe's international trade and sustainable enterprise development ecosystem. The study identified several challenges with the current business collaborations and recommended new ways of making such partnerships more beneficial.
The differentiation of the higher education sector in Ethiopia has created a new sector of Higher Education Institutions: Universities of Applied Sciences (UAS). Its focus is on educating academically trained experts for regional industries. Close cooperation between industries and UAS is set as a key requirement. However, Ethiopian industries in many regions are not developed enough that those could be considered as active partners for UASs and able to accommodate interns or to provide expert teachers to UAS classes. European UAS structures serve as benchmarks for the Ethiopian Ministry of Education (MoE). Therefore, UAS curricula of study programmes in building-construction, electro-engineering and economic/business/tourism from different European countries build a common ground for Ethiopian UASs. But, due to the lack of industries in the regions, Ethiopian UAS are not able to mirror the European counterparts, where study programmes at bachelor level comprise 70 credits out or 210 credits as practical works, internships and bachelor thesis. - The question is, how can Ethiopian UASs in the absence of companies offer practice-oriented education in their study programmes? This paper refers to the ongoing research, on how to integrate UAS (academic and non-academic) departments at UAS campuses to create internship placements for students in the absence of internship placements in the private sector. Kotebe University of Education (KUE) - as one of the newly founded UAS in Ethiopia - has agreed to act as subject of this try-out.
Here we present a doc-2-doc relevance assessment performed on a subset of the TREC Genomics Track 2005 collection. Our approach includes an experimental set up to manually assess doc-2-doc relevance and the corresponding analysis done on the results obtained from this experiment. The experiment takes one document as a reference and assesses a second document regarding its relevance to the reference one. The consistency of the assessments done by 4 domain experts was evaluated. The lack of agreement between annotators may be due to: i) The abstract lacks key information and/or ii) Lack of experience of the annotators in the evaluation of some topics.
This paper presents the preliminary results of the Socialist Republic of Vietnam country case study conducted as part of the research project Sustainable Labour Migration implemented by the University of Applied Science Bonn-Rhein-Sieg. The project focuses on stakeholder perspectives on countries of origin benefits and the sustainability of different transnational skill partnership schemes. Existing and ongoing small-scale initiatives indicate that opportunities exist for all three types of labour mobility pathways, from recruiting youth for apprenticeships and subsequent skilled work to recruitment and recognition of skilled 'professionals' certificates for direct work contracts to initial vocational education and training programs in a dual-track approach. While the latter has the highest potential to be more beneficial than other approaches, pursuing and supporting the scaling up of all three pathways in parallel will have additional, mutually reinforcing and supporting effects. The potential for benefits over and above those already realised by existing skill partnerships appears high, especially considering the favourable framework conditions specific to the long-standing German-Vietnamese relationship. If the potential of well-managed skill partnerships was realised, such sustainable models of skilled labour migration could serve as a unique selling point in the international competition for skilled labour.
What does ‘desirable’ or ‘sustainable’ mean in the context of labour migration? And what should programmes geared towards making migration more compatible with development look like? These questions provided the starting point for the ‘Sustainable Labour Migration’ research project implemented by Hochschule Bonn-Rhein-Sieg University of Applied Sciences between December 2020 and August 2022. The project looked at how sustainability in different transnational skills partnership schemes was perceived by different stakeholders in three countries chosen as case studies: Georgia, Kosovo and Vietnam. Embracing the notion of a ‘triple win’, many transnational skills partnership schemes aim to deliver benefits for their main stakeholder groups. As well as reflecting critically on this triple-win narrative, this paper also argues for a more nuanced approach in order to grasp the complexity of skilled labour migration. The paper introduces one such approach, namely the sustainable labour migration framework, and highlights the key elements of the research project. It details the methodology used in the study (systematic literature review – employer survey – semi-structured, in-depth interviews – focus group interviews) and explores the perception of sustainability in skilled labour migration with a focus on the cost/benefit ratio, the relationship between vocational education and training and labour migration, and the various arrangements for the partnerships employed in the schemes. In introducing the key findings from the three countries selected for the case studies, the paper highlights that the perception of sustainability is not underpinned by a comprehensive understanding of the term amongst most stakeholders. Within all the schemes, however, some stakeholders identified elements which make a positive contribution to development in the respective countries of origin and thus identify elements of sustainable labour migration with room for improvement in multiple areas. The paper concludes with a presentation of overall policy recommendations: The ongoing reform of Germany’s labour migration policy should be accompanied by more development-oriented activities. As part of this, the German Federal Ministry for Economic Cooperation and Development (BMZ) should stand up more forcefully for the needs of migrants and potential partner countries in order to reduce existing inequalities
Neuromorphic computing aims to mimic the computational principles of the brain in silico and has motivated research into event-based vision and spiking neural networks (SNNs). Event cameras (ECs) capture local, independent changes in brightness, and offer superior power consumption, response latencies, and dynamic ranges compared to frame-based cameras. SNNs replicate neuronal dynamics observed in biological neurons and propagate information in sparse sequences of ”spikes”. Apart from biological fidelity, SNNs have demonstrated potential as an alternative to conventional artificial neural networks (ANNs), such as in reducing energy expenditure and inference time in visual classification. Although potentially beneficial for robotics, the novel event-driven and spike-based paradigms remain scarcely explored outside the domain of aerial robots.
To investigate the utility of brain-inspired sensing and data processing in a robotics application, we developed a neuromorphic approach to real-time, online obstacle avoidance on a manipulator with an onboard camera. Our approach adapts high-level trajectory plans with reactive maneuvers by processing emulated event data in a convolutional SNN, decoding neural activations into avoidance motions, and adjusting plans in a dynamic motion primitive formulation. We conducted simulated and real experiments with a Kinova Gen3 arm performing simple reaching tasks involving static and dynamic obstacles. Our implementation was systematically tuned, validated, and tested in sets of distinct task scenarios, and compared to a non-adaptive baseline through formalized quantitative metrics and qualitative criteria.
The neuromorphic implementation facilitated reliable avoidance of imminent collisions in most scenarios, with 84% and 92% median success rates in simulated and real experiments, where the baseline consistently failed. Adapted trajectories were qualitatively similar to baseline trajectories, indicating low impacts on safety, predictability and smoothness criteria. Among notable properties of the SNN were the correlation of processing time with the magnitude of perceived motions (captured in events) and robustness to different event emulation methods. Preliminary tests with a DAVIS346 EC showed similar performance, validating our experimental event emulation method. These results motivate future efforts to incorporate SNN learning, utilize neuromorphic processors, and target other robot tasks to further explore this approach.
Trust-Building in Peer-to-Peer Carsharing: Design Case Study for Algorithm-Based Reputation Systems
(2023)
Peer-to-peer sharing platforms become increasingly important in the platform economy. From an HCI-perspective, this development is of high interest, as those platforms mediate between different users. Such mediation entails dealing with various social issues, e.g., building trust between peers online without any physical presence. Peer ratings have proven to be an important mechanism in this regard. At the same time, scoring via car telematics become more common for risk assessment by car insurances. Since user ratings face crucial problems such as fake or biased ratings, we conducted a design case study to determine whether algorithm-based scoring has the potential to improve trust-building in P2P-carsharing. We started with 16 problem-centered interviews to examine how people understand algorithm-based scoring, we co-designed an app with scored profiles, and finally evaluated it with 12 participants. Our findings show that scoring systems can support trust-building in P2P-carsharing and give insights how they should be designed.
Trueness and precision of milled and 3D printed root-analogue implants: A comparative in vitro study
(2023)
This thesis investigates the benefit of rubrics for grading short answers using an active learning mechanism. Automating short answer grading using Natural Language Processing (NLP) is one of the active research areas in the education domain. This could save time for the evaluator and invest more time in preparing for the lecture. Most of the research on short answer grading was treated as a similarity task between reference and student answers. However, grading based on reference answers does not account for partial grades and does not provide feedback. Also, the grading is automatic that tries to replace the evaluator. Hence, using rubrics for short answer grading with active learning eliminates the drawbacks mentioned earlier.
Initially, the proposed approach is evaluated on the Mohler dataset, popularly used to benchmark the methodology. This phase is used to determine the parameters for the proposed approach. Therefore, the approach with the selected parameter exceeds the performance of current State-Of-The-Art (SOTA) methods resulting in the Pearson correlation value of 0.63 and Root Mean Square Error (RMSE) of 0.85. The proposed approach has surpassed the SOTA methods by almost 4%.
Finally, the benchmarked approach is used to grade the short answer based on rubrics instead of reference answers. The proposed approach evaluates short answers from Autonomous Mobile Robot (AMR) dataset to provide scores and feedback (formative assessment) based on the rubrics. The average performance of the dataset results in the Pearson correlation value of 0.61 and RMSE of 0.83. Thus, this research has proven that rubrics-based grading achieves formative assessment without compromising performance. In addition, the rubrics have the advantage of generalizability to all answers.
Hydrogen as a versatile, greenhouse gas-free energy carrier will play an important role in our future economy. Yet sustainable, competitive production and distribution of hydrogen remains a challenge. Highly integrated solar water splitting systems aim to combine solar energy harvesting and electrolysis in a single device, similar to a photovoltaic module.[1] Such a system can produce hydrogen locally without the requirement to be connected to the electricity grid. Unlike large electrolysis that draws power from the grid, the power density of such a device is reduced so far that it does not require active cooling, but its operating temperature will closely follow outdoor conditions.
Here, we present our work on high-efficiency integrated solar water splitting devices based on multi-junction solar absorbers. The light-absorbing component is sensitive to the shape of the solar spectrum and generally becomes more efficient at lower temperatures. Catalysis, on the other hand, benefits from higher temperatures. These conflicting trends wih respect to the temperature impact the design of the solar hydrogen production system. We analyse how the local climate affects production efficiency[2] and show in a lab study that adequate system design allows efficient operation at temperatures as low as -20°C.[3] These insights can help to design small-scale distributed solar hydrogen production for both temperate regions, but also more extreme climatic conditions.