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Background:
Access to electricity is one of the enabling factors for healthcare service provision. From the sustainable development perspective, an essential requirement for improving health and caring for our environment is to assure that health facilities have sufficient and reliable access to the supply of clean and sustainable energy. The objective of this work is to investigate the users’ perceptions of electricity needs and electricity sources and the way those influence different attributes and their relevance for the diffusion of renewable electricity systems in healthcare facilities.
Methods:
To identify preferences and choices, Stated Choice modelling was applied as the use of solar PV systems in health facilities is not widespread in Ghana. This method allows to present the respondents with hypothetical options, which have attributes close to the real world. Four attributes were considered, namely electricity system configuration, initial investment cost, monthly costs, and improvements to the reliability of the electricity supply.
Results:
The largest share of the 200 health facilities interviewed reported services provision as outpatient treatment, provision of maternity services and family planning, which are relatively low electricity-intensive services. However, there was a general perception that increased reliability on the electricity supply can improve the health service provision and operation of the facilities. Moreover, despite that preferences towards the solar systems, the initial investment costs of the solar systems is still perceived as preventing the adoption of this technology
Conclusion:
From this study we can conclude that health facilities in Ghana rely greatly on the national supply which has issues with reliability, compromising the delivery of healthcare services. However, the adoption of alternative electricity technologies based on renewable sources is not likely to occur at the facility level without the engagement of other actors that can help bridging the barriers for adoption, as initial investment costs.
Question Answering (QA) has gained significant attention in recent years, with transformer-based models improving natural language processing. However, issues of explainability remain, as it is difficult to determine whether an answer is based on a true fact or a hallucination. Knowledge-based question answering (KBQA) methods can address this problem by retrieving answers from a knowledge graph. This paper proposes a hybrid approach to KBQA called FRED, which combines pattern-based entity retrieval with a transformer-based question encoder. The method uses an evolutionary approach to learn SPARQL patterns, which retrieve candidate entities from a knowledge base. The transformer-based regressor is then trained to estimate each pattern’s expected F1 score for answering the question, resulting in a ranking ofcandidate entities. Unlike other approaches, FRED can attribute results to learned SPARQL patterns, making them more interpretable. The method is evaluated on two datasets and yields MAP scores of up to 73 percent, with the transformer-based interpretation falling only 4 pp short of an oracle run. Additionally, the learned patterns successfully complement manually generated ones and generalize well to novel questions.
This paper addresses the classification of Arabic text data in the field of Natural Language Processing (NLP), with a particular focus on Natural Language Inference (NLI) and Contradiction Detection (CD). Arabic is considered a resource-poor language, meaning that there are few data sets available, which leads to limited availability of NLP methods. To overcome this limitation, we create a dedicated data set from publicly available resources. Subsequently, transformer-based machine learning models are being trained and evaluated. We find that a language-specific model (AraBERT) performs competitively with state-of-the-art multilingual approaches, when we apply linguistically informed pre-training methods such as Named Entity Recognition (NER). To our knowledge, this is the first large-scale evaluation for this task in Arabic, as well as the first application of multi-task pre-training in this context.
Business Process Management
(2023)
Airborne and spaceborne platforms are the primary data sources for large-scale forest mapping, but visual interpretation for individual species determination is labor-intensive. Hence, various studies focusing on forests have investigated the benefits of multiple sensors for automated tree species classification. However, transferable deep learning approaches for large-scale applications are still lacking. This gap motivated us to create a novel dataset for tree species classification in central Europe based on multi-sensor data from aerial, Sentinel-1 and Sentinel-2 imagery. In this paper, we introduce the TreeSatAI Benchmark Archive, which contains labels of 20 European tree species (i.e., 15 tree genera) derived from forest administration data of the federal state of Lower Saxony, Germany. We propose models and guidelines for the application of the latest machine learning techniques for the task of tree species classification with multi-label data. Finally, we provide various benchmark experiments showcasing the information which can be derived from the different sensors including artificial neural networks and tree-based machine learning methods. We found that residual neural networks (ResNet) perform sufficiently well with weighted precision scores up to 79 % only by using the RGB bands of aerial imagery. This result indicates that the spatial content present within the 0.2 m resolution data is very informative for tree species classification. With the incorporation of Sentinel-1 and Sentinel-2 imagery, performance improved marginally. However, the sole use of Sentinel-2 still allows for weighted precision scores of up to 74 % using either multi-layer perceptron (MLP) or Light Gradient Boosting Machine (LightGBM) models. Since the dataset is derived from real-world reference data, it contains high class imbalances. We found that this dataset attribute negatively affects the models' performances for many of the underrepresented classes (i.e., scarce tree species). However, the class-wise precision of the best-performing late fusion model still reached values ranging from 54 % (Acer) to 88 % (Pinus). Based on our results, we conclude that deep learning techniques using aerial imagery could considerably support forestry administration in the provision of large-scale tree species maps at a very high resolution to plan for challenges driven by global environmental change. The original dataset used in this paper is shared via Zenodo (https://doi.org/10.5281/zenodo.6598390, Schulz et al., 2022). For citation of the dataset, we refer to this article.
KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents
(2023)
Dried serum spots that are well prepared can be attractive alternatives to frozen serum samples for shelving specimens in a medical or research center's biobank and mailing freshly prepared serum to specialized laboratories. During the pre-analytical phase, complications can arise which are often challenging to identify or are entirely overlooked. These complications can lead to reproducibility issues, which can be avoided in serum protein analysis by implementing optimized storage and transfer procedures. With a method that ensures accurate loading of filter paper discs with donor or patient serum, a gap in dried serum spot preparation and subsequent serum analysis shall be filled. Pre-punched filter paper discs with a 3 mm diameter are loaded within seconds in a highly reproducible fashion (approximately 10% standard deviation) when fully submerged in 10 μl of serum, named the "Submerge and Dry" protocol. Such prepared dried serum spots can store several hundred micrograms of proteins and other serum components. Serum-borne antigens and antibodies are reproducibly released in 20 μl elution buffer in high yields (approximately 90%). Dried serum spot-stored and eluted antigens kept their epitopes and antibodies their antigen binding abilities as was assessed by SDS-PAGE, 2D gel electrophoresis-based proteomics, and Western blot analysis, suggesting pre-punched filter paper discs as handy solution for serological tests.
IT Controlling
(2023)
IT controlling is established as a tool for controlling information technology. The job description of the IT controller has changed only moderately over a long period of time. It was mainly associated with IT budgeting, IT portfolio management, IT cost planning, accounting and controlling. However, digitalization has brought movement in goals, contents and methods. New topics such as digital strategy management, cloud controlling, data science, etc. are being discussed. The task profile is changing away from pure IT cost analysis to the management of the digitization strategy with a focus on strategic IT portfolio management. Some voices are already talking about "smart controlling" or "digital controlling".
This book presents an IT controlling concept for the digital age and explains the relevant methods in a practical way.
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
Recent findings in South Africa have once again underlined the fact that the oldest people in the world obviously came from Africa. Thus, historically, this continent has a very special significance. However, its history in more recent times, especially from the mid-19th century onwards, was strongly influenced by colonisation by European states. Many deep wounds from that time still have an impact on society as a whole today. However, the continent is currently also confronted with a greater number of challenges of a different nature.
On the one hand, Africa is trying to strengthen internal cohesion by means of a number of regional organisations and the African Union as a globally active institution; on the other hand, the continent has been marked by political and military conflicts between neighbouring states over the past decades until the recent present. In addition, there are regular internal social upheavals in individual countries due to violent or manipulated political change.
Yet the continent could well be on a good development path, since it has a large number of important raw materials - also in comparison to other continents. However, the individual African states - and especially their citizens - often do not benefit from this to an adequate extent. This results in a social imbalance in large parts of the continent (data collection until the end of June 2023), which leads to considerable internal tensions. To make matters worse, Africa is the continent most affected by climate change.
A closer look at the partly very different economic, political and social situations of the large continent leads to an overall predominantly critical assessment of Africa's further development, which is explained in more detail in the final chapter with regard to the foreseeable consequences for the continent.
Vehicle emissions have been identified as a cause of air pollution and one of the major reasons why air quality in many large German cities such as Berlin, Bonn, Hamburg, Cologne or Munich does not meet EU-wide limits. As a result, in the recent past, judicial driving bans on diesel vehicles have been imposed in many places since those vehicles emit critical pollutant groups. For the increasing urban population, the challenge is whether and how a change of the modal split in favor of the more environmentally and climate-friendly public transport can be achieved.
This paper presents the case of the Federal City of Bonn, one of five model cities sponsored by the German federal government that are testing measures to reduce traffic-related pollutant emissions by expanding the range of public transport services on offer. We present the results of a quantitative survey (N = 14,296) performed in the Bonn/Rhein-Sieg area and the neighboring municipalities as well as the ensuing logistic regressions confirming that a change in individual mobility behavior in favor of public transport is possible through expanding services. Our results show that individual traffic could be reduced, especially on the city's main traffic axes. To sustainably improve air quality, such services must be made permanently available.
Trends of environmental awareness, combined with a focus on personal fitness and health, motivate many people to switch from cars and public transport to micromobility solutions, namely bicycles, electric bicycles, cargo bikes, or scooters. To accommodate urban planning for these changes, cities and communities need to know how many micromobility vehicles are on the road. In a previous work, we proposed a concept for a compact, mobile, and energy-efficient system to classify and count micromobility vehicles utilizing uncooled long-wave infrared (LWIR) image sensors and a neuromorphic co-processor. In this work, we elaborate on this concept by focusing on the feature extraction process with the goal to increase the classification accuracy. We demonstrate that even with a reduced feature list compared with our early concept, we manage to increase the detection precision to more than 90%. This is achieved by reducing the images of 160 × 120 pixels to only 12 × 18 pixels and combining them with contour moments to a feature vector of only 247 bytes.
A Fourier scatterometry setup is evaluated to recover the key parameters of optical phase gratings. Based on these parameters, systematic errors in the printing process of two-photon polymerization (TPP) gray-scale lithography three-dimensional printers can be compensated, namely tilt and curvature deviations. The proposed setup is significantly cheaper than a confocal microscope, which is usually used to determine calibration parameters for compensation of the TPP printing process. The grating parameters recovered this way are compared to those obtained with a confocal microscope. A clear correlation between confocal and scatterometric measurements is first shown for structures containing either tilt or curvature. The correlation is also shown for structures containing a mixture of tilt and curvature errors (squared Pearson coefficient r2 = 0.92). This compensation method is demonstrated on a TPP printer: a diffractive optical element printed with correction parameters obtained from Fourier scatterometry shows a significant reduction in noise as compared to the uncompensated system. This verifies the successful reduction of tilt and curvature errors. Further improvements of the method are proposed, which may enable the measurements to become more precise than confocal measurements in the future, since scatterometry is not affected by the diffraction limit.
LiDAR-based Indoor Localization with Optimal Particle Filters using Surface Normal Constraints
(2023)
In intensively used agricultural landscapes, path margins are one of the few refuges and nurseries for wildlife. They provide e. g. food sources and overwintering opportunities for many insects, serve as migration corridors for animals, protect soil from erosion, increase its water-holding capacity, and increase soil organic carbon, contributing thus directly to biodiversity conservation and climate change mitigation. Path margins are often municipally owned but used and managed by agriculture. For a path margin to be functional, certain conditions must be fulfilled, such as the width, the botanical composition, and how it is managed through the seasons. Therefore, it must be managed under specific requirements. A multifunctional path margin can be achieved only through the commitment of all stakeholders (e.g., farmers, municipalities, conservationists, and civil society).
A Fourier scatterometry setup is evaluated to recover the key parameters of optical phase gratings. Based on these parameters, systematic errors in the printing process of two photon polymerization (TPP) gray-scale lithography 3d printers can be compensated, namely tilt and curvature deviations. The proposed setup is significantly cheaper than a confocal microscope, which is usually used to determine calibrations parameters for compensation of the TPP printing process. The grating parameters recovered this way are compared to those obtained with a confocal microscope. A clear correlation between confocal and scatterometric measurements is first shown for structures containing either tilt or curvature. The correlation is also shown for structures containing a mixture of tilt and curvature errors (squared Pearson coefficient $r^2$ = 0.92). This new compensation method is demonstrated on a TPP printer: A diffractive optical element (DOE) printed with correction parameters obtained from Fourier scatterometry shows a significant reduction in noise as compared to the uncompensated system. This verifies the successful reduction of tilt and curvature errors. Further improvements of the method are proposed, which may enable the measurements to become more precise than confocal measurements in the future, since scatterometry is not affected by the diffraction limit.
Dynamic Programming
(2024)
Queueing Theory
(2024)
The Decision Tree Procedure
(2024)
Heuristic Methods
(2024)
Network Analysis Method
(2024)
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.
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.
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.
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.
Selection Performance and Reliability of Eye and Head Gaze Tracking Under Varying Light Conditions
(2024)
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.
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.
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.
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.
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.
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.
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.
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.
The overall goal of this paper is to contribute to the research on customer satisfaction at airports. Existing studies have focussed on airport service experience in America, Europe and Asia. Specifically it contributes to the development of the knowledge of service quality expectations at a major airport hub in Africa.
The exploratory study integrated elements of the 22 item SERVQUAL scale developed by Parasuraman et al., (1988). A quantitative research was conducted and responses from 280 departing international travellers at the Jomo Kenyatta International Airport was used to test five hypotheses. An independent samples t-test was utilised to assess whether the means of two groups are statistically significant from one another. The variables to be tested were service performance against the respective service expectation. The findings indicate atmosphere related aspects of the airport experience showed a significant influence on the respondents’ customer satisfaction. The feeling of being safe in the airport, ease of way finding, facilities for people with reduced mobility and the availability of leisure rooms were the most significant elements in the traveller’s positive experience while at the airport. The study was not without limitations. In utilising the gap analysis model, this study focused on understanding what the customers want. Other elements of the gap analysis model require further illumination. The findings of this study will help contribute to the development of a conceptual model for a much more exhaustive study on airport passenger satisfaction at other Kenyan airports and internationally.