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Fundamentals of Energy Meteorology - Influence of atmospheric parameters on solar energy production
(2015)
Mesenchymal stem cells (MSCs) are an attractive cell source for Regenerative Dentistry in particular due to their ability to differentiate towards osteoblasts, among other lineages. Tooth and jaw bone loss are frequent sequelae of traumatic and pathological conditions in both the young and the elderly and must be met by appropriate prosthetic replacements. For successful osseointegration of the dental implant a sufficient bone level is necessary. Besides the utilization of bone autografts or synthetic biomaterials, medical research is more and more focused on the utilization of MSCs. Compared to cells obtained from liposuction material, ectomesenchymal stem cells derived from the head area e.g. out of dental follicles or particulate, non-vascularized bone chips show a higher differentiation potential towards osteoblasts.
It is know that mesenchymal stem cells (MSCs) actively secretemultiple biologically-active factors during their process of differentiation which gives rise to a variey of cytotypes including bone and fatcells. It is also acknowledged that the chemokines secreted throughoutMSC differentiation may play an important role in the development and growth of tumor cells, although literature data appear somewhat indeterminate due to the contradictory evidence often found.
Background: Falls and fall-related injuries are a serious public health issue. Exercise programs can effectively reduce fall risk in older people. The iStoppFalls project developed an Information and Communication Technology-based system to deliver an unsupervised exercise program in older people’s homes. The primary aims of the iStoppFalls randomized controlled trial were to assess the feasibility (exercise adherence, acceptability and safety) of the intervention program and its effectiveness on common fall risk factors.
Methods: A total of 153 community-dwelling people aged 65+ years took part in this international, multicentre, randomized controlled trial. Intervention group participants conducted the exercise program for 16 weeks, with a recommended duration of 120 min/week for balance exergames and 60 min/week for strength exercises. All intervention and control participants received educational material including advice on a healthy lifestyle and fall prevention. Assessments included physical and cognitive tests, and questionnaires for health, fear of falling, number of falls, quality of life and psychosocial outcomes.
Results: The median total exercise duration was 11.7 h (IQR = 22.0) over the 16-week intervention period. There were no adverse events. Physiological fall risk (Physiological Profile Assessment, PPA) reduced significantly more in the intervention group compared to the control group (F1,127 = 4.54, p = 0.035). There was a significant three-way interaction for fall risk assessed by the PPA between the high-adherence (>90 min/week; n = 18, 25.4 %), low-adherence (<90 min/week; n = 53, 74.6 %) and control group (F2,125 = 3.12, n = 75, p = 0.044). Post hoc analysis revealed a significantly larger effect in favour of the high-adherence group compared to the control group for fall risk (p = 0.031), postural sway (p = 0.046), stepping reaction time (p = 0.041), executive functioning (p = 0.044), and quality of life (p for trend = 0.052).
Conclusions: The iStoppFalls exercise program reduced physiological fall risk in the study sample. Additional subgroup analyses revealed that intervention participants with better adherence also improved in postural sway, stepping reaction, and executive function.
The latest advances in the field of smart card technologies allow modern cards to be more than just simple security tokens. Recent developments facilitate the use of interactive components like buttons, displays or even touch-sensors within the card's body thus conquering whole new areas of application. With interactive functionalities the usability aspect becomes the most important one for designing secure and popularly accepted products. Unfortunately, the usability can only be tested fully with completely integrated hence expensive smart card prototypes. This restricts severely application specific research, case studies of new smart card user interfaces and the optimization of design aspects, as well as hardware requirements by making usability and acceptance tests in smart card development very costly and time-consuming. Rapid development and simulation of smart card interfaces and applications can help to avoid this restriction. This paper presents a rapid development process for new smart card interfaces and applications based on common smartphone technology using a tool called SCUID^Sim. We will demonstrate the variety of usability aspects that can be analyzed with such a simulator by discussing some selected example projects.
Secure vehicular communication has been discussed over a long period of time. Now,- this technology is implemented in different Intelligent Transportation System (ITS) projects in europe. In most of these projects a suitable Public Key Infrastructure (PKI) for a secure communication between involved entities in a Vehicular Ad hoc Network (VANET) is needed. A first proposal for a PKI architecture for Intelligent Vehicular Systems (IVS PKI) is given by the car2car communication consortium. This architecture however mainly deals with inter vehicular communication and is less focused on the needs of Road Side Units. Here, we propose a multi-domain PKI architecture for Intelligent Transportation Systems, which considers the necessities of road infrastructure authorities and vehicle manufacturers, today. The PKI domains are cryptographically linked based on local trust lists. In addition, a crypto agility concept is suggested, which takes adaptation of key length and cryptographic algorithms during PKI operation into account.
Introduction: After cellulose, lignin represents the most abundant biopolymer on earth that accounts for up to 18-35 % by weight of lignocellulose biomass. Today, it is a by-product of the paper and pulping industry. Although lignin is available in huge amounts, mainly in form of so called black liquor produced via Kraft-pulping, processes for the valorization of lignin are still limited [1]. Due to its hyperbranched polyphenol-like structure, lignin gained increasing interest as biobased building block for polymer synthesis [2]. The present work is focused on extraction and purification of lignin from industrial black liquor and synthesis of lignin-based polyurethanes.
Semantic Image Segmentation Combining Visible and Near-Infrared Channels with Depth Information
(2015)
Image understanding is a vital task in computer vision that has many applications in areas such as robotics, surveillance and the automobile industry. An important precondition for image understanding is semantic image segmentation, i.e. the correct labeling of every image pixel with its corresponding object name or class. This thesis proposes a machine learning approach for semantic image segmentation that uses images from a multi-modal camera rig. It demonstrates that semantic segmentation can be improved by combining different image types as inputs to a convolutional neural network (CNN), when compared to a single-image approach. In this work a multi-channel near-infrared (NIR) image, an RGB image and a depth map are used. The detection of people is further improved by using a skin image that indicates the presence of human skin in the scene and is computed based on NIR information. It is also shown that segmentation accuracy can be enhanced by using a class voting method based on a superpixel pre-segmentation. Models are trained for 10-class, 3-class and binary classification tasks using an original dataset. Compared to the NIR-only approach, average class accuracy is increased by 7% for 10-class, and by 22% for 3-class classification, reaching a total of 48% and 70% accuracy, respectively. The binary classification task, which focuses on the detection of people, achieves a classification accuracy of 95% and true positive rate of 66%. The report at hand describes the proposed approach and the encountered challenges and shows that a CNN can successfully learn and combine features from multi-modal image sets and use them to predict scene labeling.
TinyECC 2.0 is an open source library for Elliptic Curve Cryptography (ECC) in wireless sensor networks. This paper analyzes the side channel susceptibility of TinyECC 2.0 on a LOTUS sensor node platform. In our work we measured the electromagnetic (EM) emanation during computation of the scalar multiplication using 56 different configurations of TinyECC 2.0. All of them were found to be vulnerable, but to a different degree. The different degrees of leakage include adversary success using (i) Simple EM Analysis (SEMA) with a single measurement, (ii) SEMA using averaging, and (iii) Multiple-Exponent Single-Data (MESD) with a single measurement of the secret scalar. It is extremely critical that in 30 TinyECC 2.0 configurations a single EM measurement of an ECC private key operation is sufficient to simply read out the secret scalar. MESD requires additional adversary capabilities and it affects all TinyECC 2.0 configurations, again with only a single measurement of the ECC private key operation. These findings give evidence that in security applications a configuration of TinyECC 2.0 should be chosen that withstands SEMA with a single measurement and, beyond that, an addition of appropriate randomizing countermeasures is necessary.
This paper proposes an Artificial Plasmodium Algorithm (APA) mimicked a contraction wave of a plasmodium of physarum polucephalum. Plasmodia can live using the contracion wave in their body to communicate to others and transport a nutriments. In the APA, each plasmodium has two information as the wave information: the direction and food index. We apply APA to a maze solving and route planning of road map.
In this paper, a set of micro-benchmarks is proposed to determine basic performance parameters of single-node mainstream hardware architectures for High Performance Computing. Performance parameters of recent processors, including those of accelerators, are determined. The investigated systems are Intel server processor architectures as well as the two accelerator lines Intel Xeon Phi and Nvidia graphic processors. Results show similarities for some parameters between all architectures, but significant differences for others.
Persons entering the working range of industrial robots are exposed to a high risk of collision with moving parts of the system, potentially causing severe injuries. Conventional systems, which restrict the access to this area, range from walls and fences to light barriers and other vision based protective devices (VBPD). None of these systems allow to distinguish between humans and workpieces in a safe and reliable manner. In this work, a new approach is investigated, which uses an active near-infrared (NIR) camera system with advanced capabilities of skin detection to distinguish humans from workpieces based on characteristic spectral signatures. This approach allows to implement more intelligent muting processes and at the same time increases the safety of persons working close to the robots. The conceptual integration of such a camera system into a VBPD and the enhancement of person detection methods through skin detection are described and evaluated in this paper. Based upon this work, next steps could be the development of multimodal sensor systems to safeguard working ranges of collaborating robots using the described camera system.
Manufacturers of machinery are increasingly using application programming of safety controls in order to implement safety functions. The EN ISO 13849-1 and EN 62061 standards define requirements concerning the development of software employed for safety functions. The IFA began addressing the subject of safety-related application software many years ago. Between 2011 and 2013, Project FF-FP0319 concerning standardscompliant development and documentation of safetyrelated user software in machine construction was successfully completed at the Bonn-Rhein-Sieg University of Applied Sciences in conjunction with numerous partner bodies from the machine construction sector and with funding from the DGUV. For this purpose, a procedure – the IFA matrix method – was developed, and evaluated and documented with reference to examples from industry, for implementation of the requirements concerning the development of software for machine safety functions. This paper provides insights into both the IFA matrix method and the new IFA report on the subject, and with information on what further tools are planned.
The proper use of protective hoods on panel saws should reliably prevent severe injuries from (hand) contact with the blade or material kickbacks. It also should minimize long-term lung damages from fine-particle pollution. To achieve both purposes the hood must be adjusted properly by the operator for each workpiece to fit its height. After a work process is finished, the hood must be lowered down completely to the bench. Unfortunately, in practice the protective hood is fixed at a high position for most of the work time and herein loses its safety features. A system for an automatic height adjustment of the hood would increase comfort and safety. If the system can distinguish between workpieces and skin reliably, it furthermore will reduce occupational hazards for panel saw users. A functional demonstrator of such a system has been designed and implemented to show the feasibility of this approach. A specific optical sensor system is used to observe a point on the extended cut axis in front of the blade. The sensor determines the surface material reliably and measures the distance to the workpiece surface simultaneously. If the distance changes because of a workpiece fed to the machine, the control unit will set the motor-adjusted hood to the correct height. If the sensor detects skin, the hood will not be moved. In addition a camera observes the area under the hood. If there are no workpieces or offcuts left under the hood, it will be lowered back to the default position.
Over the last 50 years, the controlled motion of robots has become a very mature domain of expertise. It can deal with all sorts of topologies and types of joints and actuators, with kinematic as well as dynamic models of devices, and with one or several tools or sensors attached to the mechanical structure. Nevertheless, the domain has not succeeded in standardizing the modelling of robot devices (including such fundamental entities as “reference frames”!), let alone the semantics of their motion specification and control. This thesis aims to solve this long-standing problem, from three different sides: semantic models for robot kinematics and dynamics, semantic models of all possible motion specification and control problems, and software that can support the latter while being configured by a systematic use of the former.
Polyether and polyether/ester based TPU (thermoplastic polyurethanes) were investigated with wide-angle XRD (X-ray diffraction) and SAXS (small angle X-ray scattering). Furthermore, SAXS measurements were performed in the temperature range of 30 °C to 130 °C. Polyether based polymers exhibit only one broad diffraction signal in a region of 2 θ 15° to 25°. In case of polyurethanes with ether/ester modification, the broad diffraction signal arises with small sharp diffraction signals. SAXS measurements of polymers reveal the size and shape of the crystalline zones of the polymer. Between 30 °C and 130 °C the size of the crystalline zone changes significantly. The size decreases in most of investigated TPU. In the case of Desmopan 9365D an increase of the particle size was observed.
The steadily decreasing prices of display technologies and computer graphics hardware contribute to the increasing popularity of multiple-display environments, like large, high-resolution displays. It is therefore necessary that educational organizations give the new generation of computer scientists an opportunity to become familiar with this kind of technology. However, there is a lack of tools that allow for getting started easily. Existing frameworks and libraries that provide support for multi-display rendering are often complex in understanding, configuration and extension. This is critical especially in educational context where the time that students have for their projects is limited and quite short. These tools are also rather known and used in research communities only, thus providing less benefit for future non-scientists. In this work we present an extension for the Unity game engine. The extension allows – with a small overhead – for implementation of applications that are apt to run on both single-display and multi-display systems. It takes care of the most common issues in the context of distributed and multi-display rendering like frame, camera and animation synchronization, thus reducing and simplifying the first steps into the topic. In conjunction with Unity, which significantly simplifies the creation of different kinds of virtual environments, the extension affords students to build mock-up virtual reality applications for large, high-resolution displays, and to implement and evaluate new interaction techniques and metaphors and visualization concepts. Unity itself, in our experience, is very popular among computer graphics students and therefore familiar to most of them. It is also often employed in projects of both research institutions and commercial organizations; so learning it will provide students with qualification in high demand.
This paper proposes a new artificial neural network-based maximum power point tracker for photovoltaic application. This tracker significantly improves efficiency of the photovoltaic system with series-connection of photovoltaic modules in non-uniform irradiance on photovoltaic array surfaces. The artificial neural network uses irradiance and temperature sensors to generate the maximum power point reference voltage and employ a classical perturb and observe searching algorithm. The structure of the artificial neural network was obtained by numerical modelling using Matlab/Simulink. The artificial neural network was trained using Bayesian regularisation back-propagation algorithms and demonstrated a good prediction of the maximum power point. Relative number of Vmpp prediction errors in range of ±0.2V is 0.05% based on validation data.
The paper presents a new control strategy of management of transport companies operating in completive transport environment. It is aimed to optimise the headway of transport companies to provide the balance between costs and benefits of operation under competition. The model of transport system build using AnyLogic comprises agent-based and discrete-event techniques. The model combined two transport companies was investigated under condition of the competition between them. It was demonstrated that the control strategy can ensure the balance of interests of transport companies trying to find compromise between cost of operation and quality of service.
This book chapter describes application examples of gas chromatography/mass spectrometry and pyrolysis – gas chromatography/mass spectrometry in failure analysis for the identification of chemical materials like mineral oils and nitrile rubber gaskets. Furthermore, failure cases demanding identification of polymers/copolymers in fouling on the compressor wall of a car air conditioner and identification of fouling on the surface of a bearing race from the automotive industry are demonstrated. The obtained analytical results were then used for troubleshooting and remedial action of the technological process.
In the fermentation process sugars are transformed into lactic acid. pH meters have traditionally been used for fermentation process monitoring based on acidity. More recently, near infrared (NIR) spectroscopy has proven to provide an accurate and non-invasive method to detect when the transformation of sugars into lactic acid is finished. The fermentation process when sugars are transformed into lactic acid. This research proposes the use of simplified NIR spectroscopy using multispectral optical sensors as a simpler and less expensive measure to end the fermentation process. The NIR spectrum of milk and yogurt is compared to find and extract features that can be used to design a simple sensor to monitor the yogurt fermentation process. Multispectral images in four selected wavebands within the NIR spectrum are captured and show different spectral remission characteristics for milk, yogurt and water, which support the selection of these wavebands for milk and yogurt classification.
This paper proposes an Artificial Plasmodium Algorithm (APA) mimicked a contraction wave of a plasmodium of physarum polucephalum. Plasmodia can live using the contracion wave in their body to communicate to others and transport a nutriments. In the APA, each plasmodium has two information as the wave information: the direction and food index. We apply the APA to 4 types of mazes and confirm that the APA can solve the mazes.
Binary relations with certain properties such as biorders, equivalences or difunctional relations can be represented as particular matrices. In order for these properties to be identified usually a rearrangement of rows and columns is required in order to reshape it into a recognisable normal form. Most algorithms performing these transformations are working on binary matrix representations of the underlying relations. This paper presents an approach to use the RLE-compressed matrix representation as a data structure for storing relations to test whether they are biorders in a hopefully more efficient way.
Annual Report 2013 - 2014
(2015)
Ultra-fast photopolymerization of experimental composites: DEA and FT-NIRS measurement comparison
(2015)
Simultaneous multifrequency radio observations of the Galactic Centre magnetar SGR J1745-2900
(2015)
Reducing energy consumption is one of the most pursued economic and ecologic challenges concerning societies as a whole, individuals and organizations alike. While politics start taking measures for energy turnaround and smart home energy monitors are becoming popular, few studies have touched on sustainability in office environments so far, though they account for almost every second workplace in modern economics. In this paper, we present findings of two parallel studies in an organizational context using behavioral change oriented strategies to raise energy awareness. Next to demonstrating potentials, it shows that energy feedback needs must fit to the local organizational context to succeed and should consider typical work patterns to foster accountability of consumption.
Reducing energy consumption is one of the most pursued economic and ecologic challenges concerning societies as a whole, individuals and organizations alike. While politics start taking measures for energy turnaround and smart home energy monitors are becoming popular, few studies have touched on sustainability in office environments so far, though they account for almost every second workplace in modern economics. In this paper, we present findings of two parallel studies in an organizational context using behavioral change oriented strategies to raise energy awareness. Next to demonstrating potentials, it shows that energy feedback needs must fit to the local organizational context to succeed and should consider typical work patterns to foster accountability of consumption.
So far, sustainable HCI has mainly focused on the domestic context, but there is a growing body of work looking at the organizational context. As in the domestic context, these works still rest on psychological theories for behaviour change used for the domestic context. We supplement this view with an organizational theory-informed approach that adopts organizational roles as a key element. We will show how a role-based analysis could be applied to uncover information needs and to give em-ployee’s eco-feedback, which is linked to their tasks at hand. We illustrate the approach on a qualitative case study that was part of a broader, ongoing action research conducted in a German production company.
Competitions for Benchmarking: Task and Functionality Scoring Complete Performance Assessment
(2015)
The central theme of the 2014 Annual Report is human thinking.
In an interview, University President Hartmut Ihne and 3Sat moderator Gert Scobel discuss the concept of thought: "Should we be allowed to give up our autonomy voluntarily?"
Our university’s Language Centre Director James Chamberlain examines to what extent thinking varies in different languages.
Professor Paul Plöger from the Department of Computer Science explains why robots have tremendous problems understanding complex relationships in open environments.
Rather than focusing solely on our university’s future, the Annual Report links the fascinating theme to the enormous variety of life, research and tuition offered by H-BRS.
The study of locomotion in virtual environments is a diverse and rewarding research area. Yet, creating effective and intuitive locomotion techniques is challenging, especially when users cannot move around freely. While using handheld input devices for navigation may often be good enough, it does not match our natural experience of motion in the real world. Frequently, there are strong arguments for supporting body-centered self-motion cues as they may improve orientation and spatial judgments, and reduce motion sickness. Yet, how these cues can be introduced while the user is not moving around physically is not well understood. Actuated solutions such as motion platforms can be an option, but they are expensive and difficult to maintain. Alternatively, within this article we focus on the effect of upper-body tilt while users are seated, as previous work has indicated positive effects on self-motion perception. We report on two studies that investigated the effects of static and dynamic upper body leaning on perceived distances traveled and self-motion perception (vection). Static leaning (i.e., keeping a constant forward torso inclination) had a positive effect on self-motion, while dynamic torso leaning showed mixed results. We discuss these results and identify further steps necessary to design improved embodied locomotion control techniques that do not require actuated motion platforms.
Since being introduced in the sixties and seventies, semi-implicit RosenbrockWanner (ROW) methods have become an important tool for the timeintegration of ODE and DAE problems. Over the years, these methods have been further developed in order to save computational effort by regarding approximations with respect to the given Jacobian [5], reduce effects of order reduction by introducing additional conditions [2, 4] or use advantages of partial explicit integration by considering underlying Runge-Kutta formulations [1]. As a consequence, there is a large number of different ROW-type schemes with characteristic properties for solving various problem formulations given in literature today.
We propose a high-performance GPU implementation of Ray Histogram Fusion (RHF), a denoising method for stochastic global illumination rendering. Based on the CPU implementation of the original algorithm, we present a naive GPU implementation and the necessary optimization steps. Eventually, we show that our optimizations increase the performance of RHF by two orders of magnitude when compared to the original CPU implementation and one order of magnitude compared to the naive GPU implementation. We show how the quality for identical rendering times relates to unfiltered path tracing and how much time is needed to achieve identical quality when compared to an unfiltered path traced result. Finally, we summarize our work and describe possible future applications and research based on this.
This presentation gives an overview of current research in the area of high quality rendering and visualization at the Institute of Visual Computing (IVC). Our research facility has some unique software and hardware installations of which we will describe a large, ultra- high resolution (72 megapixel) video wall in this presentation.
We present a system that combines voxel and polygonal representations into a single octree acceleration structure that can be used for ray tracing. Voxels are well-suited to create good level-of-detail for high-frequency models where polygonal simplifications usually fail due to the complex structure of the model. However, polygonal descriptions provide the higher visual fidelity. In addition, voxel representations often oversample the geometric domain especially for large triangles, whereas a few polygons can be tested for intersection more quickly.
A recent trend in interactive environments are large, ultra high resolution displays (LUHRDs). Compared to other large interactive installations, like the CAVE tm , LUHRDs are usually flat or (slightly) curved and have a significantly higher resolution, offering new research and application opportunities.
This tutorial provides information for researchers and engineers who plan to install and use a large ultra-high resolution display. We will give detailed information on the hardware and software of recently created and established installations and will show the variety of possible approaches. Also, we will talk about rendering software, rendering techniques and interaction for LUHRDs, as well as applications.
With the increasing average age of the population in many developed countries, afflictions like cardiovascular diseases have also increased. Exercising has a proven therapeutic effect on the cardiovascular system and can counteract this development. To avoid overstrain, determining an optimal training dose is crucial. In previous research, heart rate has been shown to be a good measure for cardiovascular behavior. Hence, prediction of the heart rate from work load information is an essential part in models used for training control. Most heart-rate-based models are described in the context of specific scenarios, and have been evaluated on unique datasets only. In this paper, we conduct a joint evaluation of existing approaches to model the cardiovascular system under a certain strain, and compare their predictive performance. For this purpose, we investigated some analytical models as well as some machine learning approaches in two scenarios: prediction over a certain time horizon into the future, and estimation of the relation between work load and heart rate over a whole training session.
Although much effort is made to prevent risks arising from food, food-borne diseases are an ever-present threat to the consumers’ health. The consumption of fresh food that is contaminated with pathogens like fungi, viruses or bacteria can cause food poisoning that leads to severe health damages or even death. The outbreak of Shiga Toxin-producing enterohemorrhagic E. coli (EHEC) in Germany and neighbouring countries in 2011 has shown this dramatically. Nearly 4.000 people were reported of being affected and more than 50 people died during the so called EHEC-crisis. As a result the consumers’ trust in the safety of fruits and vegetables decreased sharply.
Although much effort is made to prevent risks arising from food, food-borne diseases are an ever present-threat to the consumers’ health. The consumption of fresh food that is contaminated with pathogens like fungi, viruses or bacteria can cause food poisoning that leads to severe health damages or even death. The outbreak of Shiga Toxin-producing enterohemorrhagic E. coli (EHEC) in Germany and neighbouring countries in 2011 has shown this dramatically. Nearly 4.000 people were reported of being affected and more than 50 people died during the so called EHEC-crisis. As a result the consumers’ trust in the safety of fruits and vegetables decreased sharply.
The phenomenon of the deviation between purchase attitudes and actual buying behaviour of responsible consumers is called the attitude-behaviour gap. It is influenced by individual, social and situational factors. The main purchasing barriers for sustainable (organic) food are price, lack of immediate availability, sensory criteria, lack or overload of information as well as the low-involvement feature of food products in conjunction with well-established consumption routines, lack of transparency and trust towards labels and certifications. The last three barriers are mainly of a psychological nature. Especially the low-involvement feature of food products due to daily purchase routines and relatively low prices tends to result in fast, automatic and subconscious decisions based on a so-called human mental system 1, derived from Daniel Kahneman’s (Nobel-Prize laureate in Behavioural Economics) model in behavioural psychology. In contrast, the human mental system 2 is especially important for the transformations of individual behaviour towards a more sustainable consumption. Decisions based on the human mental system 2 are slow, logical, rational, conscious and arduous. This so-called dual action model also influences the reliability of responses in consumer surveys. It seems that the consumer behaviour is the most unstable and unpredictable part of the entire supply chain and requires special attention. Concrete measures to influence consumer behaviour towards sustainable consumption are highly complex. Reviews of interdisciplinary research literature on behavioural psychology, behavioural economics and consumer behaviour and an empirical analysis of selected countries worldwide with a view to sustainable food are presented. The example of Denmark serves as a ‘best practice’ case study to illustrate how sustainable food consumption can be encouraged. It demonstrates that common efforts and a shared responsibility of consumers, business, interdisciplinary researchers, mass media and policy are needed. It takes pioneers of change who succeed in assembling a ‘critical mass’ willing to increase its ‘sustainable’ behaviour. Considering the strong psychological barriers of consumers and the continuing low market share of organic food, proactive policy measures would be conducive to foster the personal responsibility of the consumers and offer incentives towards a sustainable production. Also, further self-obligations of companies (Corporate Social Responsibility – CSR) as well as more transparency and simplification of reliable labels and certifications are needed to encourage the process towards a sustainable development.
Sustainable development needs sustainable production and sustainable consumption. During the last decades the encouragement of sustainable production has been the focus of research and policy makers under the implicit assumption that the observable increasing ‘green’ values of consumers would also entail a growing sustainable consumption. However, it has been found that the actual purchasing behaviour often deviates from ‘green’ attitudes. This phenomenon is called the attitude-behaviour gap. It is influenced by individual, social and situational factors. The main purchasing barriers for sustainable (organic) food are price, lack of immediate availability, sensory criteria, lack or overload of information as well as the low-involvement feature of food products in conjunction with well-established consumption routines, lack of transparency and trust towards labels and certifications.
Solar energy is one option to serve the rising global energy demand with low environmental impact.1 Building an energy system with a considerable share of solar power requires long-term investment and a careful investigation of potential sites. Therefore, understanding the impacts from varying regionally and locally determined meteorological conditions on solar energy production will influence energy yield projections. Clouds are moving on a short term timescale and have a high influence on the available solar radiation, as they absorb, reflect and scatter parts of the incoming light.2 However, the impact of cloudiness on photovoltaic power yields (PV) and cloud induced deviations from average yields might vary depending on the technology, location and time scale under consideration.
Solar energy is one option to serve the rising global energy demand with low environmental Impact [1]. Building an energy system with a considerable share of solar power requires long-term investment and a careful investigation of potential sites. Therefore, understanding the impacts from varying regionally and locally determined meteorological conditions on solar energy production will influence energy yield projections. Clouds are moving on a short term timescale and have a high influence on the available solar radiation, as they absorb, reflect and scatter parts of the incoming light [2]. However, modeling photovoltaic (PV) power yields with a spectral resolution and local cloud information gives new insights on the atmospheric impact on solar energy.
Hand speed is particularly important in boxing both for protection against incoming blows and delivering blows. Sixteen amateur boxers (10 male, 6 female) with varying levels of experience from a boxing gym performed 20 jabs and 20 cross punches in air. The movement was recorded with a small wrist mounted accelerometer under the glove. The maximum velocity of each punch was determined from the RMS acceleration profile. The mean values of the jab maximal velocity was higher than the cross maximal velocity for 9 participants. The cross showed some dependence on reach (Spearman's correlation coefficient r = 0.57) and the jab dependence on experience (Spearman's correlation coefficient r = 0.56). The accelerometer technique has some promise for routine assessment of fist speed.
Virtual reality environments are increasingly being used to encourage individuals to exercise more regularly, including as part of treatment in those with mental health or neurological disorders. The success of virtual environments likely depends on whether a sense of presence can be established, where participants become fully immersed in the virtual environment. Exposure to virtual environments is associated with physiological responses, including cortical activation changes. Whether the addition of a real exercise within a virtual environment alters sense of presence perception, or the accompanying physiological changes, is not known. In a randomized and controlled study design, trials of moderate-intensity exercise (i.e. self-paced cycling) and no-exercise (i.e. automatic propulsion) were performed within three levels of virtual environment exposure. Each trial was 5-min in duration and was followed by post-trial assessments of heart rate, perceived sense of presence, EEG, and mental state. Changes in psychological strain and physical state were generally mirrored by neural activation patterns. Furthermore these change indicated that exercise augments the demands of virtual environment exposures and this likely contributed to an enhanced sense of presence.