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While the topic of artificial intelligence (AI) in multinational enterprises has been receiving attention for some time, small and medium enterprises (SMEs) have recently begun to recognize the potential of this new technology. However, the focus of previous research and AI applications has therefore mostly been on large enterprises. This poses a particular issue, as the vastly different starting conditions of various company sizes, such as data availability, play a central role in the context of AI. For this reason, our systematic literature review, based on the PRISMA protocol, consolidates the state of the art of AI with an explicit focus on SMEs and highlights the perceived challenges regarding implementation in this company size. This allowed us to identify various business activities that have been scarcely considered. Simultaneously, it led to the discovery of a total of 27 different challenges perceived by SMEs in the adoption of AI. This enables SMEs to apply the identified challenges to their own AI projects in advance, preventing the oversight of any potential obstacles or risks. The lack of knowledge, costs, and inadequate infrastructure are perceived as the most common barriers to implementation, addressing social, economic, and technological aspects in particular. This illustrates the need for a wide range of support for SMEs regarding an AI introduction, which covers various subject areas, like funding and advice, and differentiates between company sizes.
While recent studies have demonstrated that events are fundamentally climate sensitive, this seems to not be fully considered in event research or corporate event practice. Thus, this study aims to identify the influencing factors that affect the acceptance of climate adaptation measures among decision-makers in the event industry. The analysis was divided into three main parts. First, the existing literature related to climate change in an events context was reviewed. Using 15 semi structured interviews, the findings from this review were then critically discussed with stakeholders in Germany involved in event planning. Finally, explicit climate adaptation measures were proposed and discussed. Based on all findings, there appears to be a low level of awareness of and interest in climate adaptation amongst German event industry players. There is an imminent need for further research on climate adaptation and for decision-makers to better prepare for climate change in order to counteract resulting negative impacts.
The 3GPP release 16 integrates TSN functionality into 5G and standardizes various options for TSN time synchronization over 5G such as transparent mode and bridge mode. The time domains for the TSN network and the 5G network are kept separate with an option to synchronize either of the networks to the other. The TSN time synchronization over 5G is possible either by using the IEEE 1588 generalized Precision Time Protocol (gPTP) based on UDP/IP multicast or via IEEE 802.1AS based on Ethernet PDUs. The INET and Simu5G simulation frameworks, which are both based on the OMNeT++ discrete event simulator, are widely used for simulating TSN and 5G networks. The INET framework comprises the 802.1AS based time synchronization mechanism, and Simu5G provides the 5G user plane carrying IP PDUs. We modified the 802.1AS-based synchronization model of INET so that it works over UDP/IP. With that, it is possible to synchronize TSN slaves (connected to 5G UEs), across a 5G network, with a TSN master clock, present within a TSN network, that is connected to the 5G core network. Our simulation results show that 500 microseconds of synchronization accuracy can be achieved with the corrected asymmetric propagation delay of uplink and downlink between the gNodeB (gNB) and the User Equipment (UE). Furthermore, the synchronization accuracy can be improved if the delay difference between uplink and downlink is known.
Recent real-time networking developments have enabled ultra reliability, very low latency and high data rates in wired networks. Wireless networking developments have also shown that they can achieve very high data rates with consistency, but they still lack in providing ultra reliability and extremely low latency. Time Sensitive Networking (TSN) developments have brought these capabilities in Industry automation and Automotive industry too. Although TSN is standardized for wired networks for a long time, for wireless networks it will be standardized within the IEEE 802.11be standard for Wi-Fi and 3GPP Release 17 for 5G in the near future. This paper provides an overview of TSN in wired and wireless networks with the aim of comparing different simulators and presenting their offered functionality and shortcomings. These tools can be used to make oneself familiar with TSN algorithms, standards, and for the development and testing of time sensitive networks. Afterwards, the paper discusses open research questions for using TSN over wireless networks.
Water retention properties of wood fiber based growing media and their impact on irrigation strategy
(2024)
Distribution of water and air in growing media during ebb-and-flow irrigation depends on water storage properties (water retention curve) and water transport properties (hydraulic conductivity) of the materials. Growing media with their high number of coarse pores are known to exhibit strong hysteresis, i.e., differences in the water retention properties during drying and wetting cycles. To account for potential ecological disadvantages of peat, wood fibers are commonly used as substitutes for peat in growing media. However, the wood fibers generally have higher air capacities and hydraulic conductivities and lower water capacities compared to peat which may results in necessary adaptions of the irrigation strategy. Tools to optimize irrigation systems are physically based water transport models, such as HYDRUS-1D, which is commonly used to describe water transport in soils, but not often for growing media. In this study, white peat and pure wood fibers were used to describe differences in their water retention behavior. Water retention curves (drying cycles) and hydraulic conductivities were measured with standard analytical procedures. Hysteresis of the water retention curves was analytically determined based on their capillary rise properties. The results were used with a modified HYDRUS-1D model to test model quality against measured water contents during ebb-and-flow irrigation cycles and to optimize the irrigation strategy for the different materials. The results showed that the model quality was sufficiently good only if the strong hysteresis of the water retention curves was considered during the simulation process. Different strategies were tested to modify ebb-and-flow irrigation (irrigation frequency, irrigation duration and irrigation height) in that way that the water suction in the root zone was similar to that of the peat material. Simulation results showed that significant improvements could only be reached by increasing the flooding depth in ebb-and-flow systems to ensure an optimum water supply of plants in the wood fiber based growing media.
Wood fibers can contribute to replacing peat in growing media and thus help to protect peatlands. As domestic, renewable raw materials, they represent a sustainable option for this purpose. To date, however, wood fibers are usually used as a peat substitute at a maxi-mum of 30% (v/v). A main reason for this limitation is the insufficient microbial stability of wood fibers, which favors nitrogen immobilization and can thus impair nitrogen supply of plants. To address this drawback, in this study wood fibers were subjected to different thermal or thermal-hydrolytic treatments. Seedling tests with napa cabbage were conducted to determine whether treated wood fibers were free of phytotoxic substances. Mixtures with 50% (v/v) wood fiber and white peat each were used. In addition, three wood fiber varieties were evaluated in the cultivation of petunia. Two wood fiber proportions (30 and 60% v/v) and two nitrogen fertilization rates (common and increased supply) were included in each case. In the seedling trial with napa cabbage, no phytotoxic effects were detectable in any of the wood fiber variants investigated. However, when cultivating petunias, both shoot mass growth and number of flowers decreased with increasing wood fiber content. In substrates with a wood fiber content of 60% (v/v), plant development was inhibited so severely that the petunias no longer achieved marketable quality. Increased nitrogen fertilization was able to compensate for this negative effect only in few cases. This suggests that other factors than nitrogen limited plant growth in wood fiber-rich substrates. Among others, physical proper-ties such as the lower water capacity of wood fibers may be a cause. More in-depth investigations are still required in this regard.
Enhancing the nutritional value of pears through agronomic biofortification with iodine (Abstract)
(2024)
The development of base metal electrodes that can act as active and stable oxygen generating electrodes in water electrolysis systems, especially at low pH levels, remains a challenge. The use of suspensions as electrolytes for water splitting has until recently been limited to photoelectrocatalytic approaches. A high current density (j=30 mA/cm2) for water electrolysis has been achieved at a very low oxygen evolution reaction (OER) potential (E=1.36 V vs. RHE) using a SnO2/H2SO4 suspension-based electrolyte in combination with a steel anode. More importantly, the high charge-to-oxygen conversion rate (Faraday efficiency of 88% for OER at j=10 mA/cm2 current density). Since cyclic voltammetry (CV) experiments show that oxygen evolution starts at a low, but not exceptionally low, potential, the reason for the low potential in chronoamperometry (CP) tests is an increase in the active electrode area, which has been confirmed by various experiments. For the first time, the addition of a relatively small amount of solids to a clear electrolyte has been shown to significantly reduce the overpotential of the OER in water electrolysis down to the 100 mV region, resulting in a remarkable reduction in anode wear while maintaining a high current density.
Artificial intelligence (AI) and human-machine interaction (HMI) are two keywords that usually do not fit embedded applications. Within the steps needed before applying AI to solve a specific task, HMI is usually missing during the AI architecture design and the training of an AI model. The human-in-the-loop concept is prevalent in all other steps of developing AI, from data analysis via data selection and cleaning to performance evaluation. During AI architecture design, HMI can immediately highlight unproductive layers of the architecture so that lightweight network architecture for embedded applications can be created easily. We show that by using this HMI, users can instantly distinguish which AI architecture should be trained and evaluated first since a high accuracy on the task could be expected. This approach reduces the resources needed for AI development by avoiding training and evaluating AI architectures with unproductive layers and leads to lightweight AI architectures. These resulting lightweight AI architectures will enable HMI while running the AI on an edge device. By enabling HMI during an AI uses inference, we will introduce the AI-in-the-loop concept that combines AI's and humans' strengths. In our AI-in-the-loop approach, the AI remains the working horse and primarily solves the task. If the AI is unsure whether its inference solves the task correctly, it asks the user to use an appropriate HMI. Consequently, AI will become available in many applications soon since HMI will make AI more reliable and explainable.
Nostalgia is a construct that, even when rooted in lived experiences, serves the ultimate purpose of creating a desired sense of world. Fundamental cognitive competencies, including memory and imagination, are utilized by the nostalgic subject to fulfill a need for narrative coherence. A temporal or spatial distance is necessary for the occurrence of a nostalgic episode, which can be conceptualized as a “had been” state of being, as direct access to the experience is often impossible. Nostalgia may thus be viewed as a tool for sense-making rather than solely as a yearning for the past. The nostalgic narrative form is a construct that permits human subjects to comprehend their existence in the world while drawing upon their roots. These tools for sense-making serve as bridges between past experiences and current conditions. Ultimately, nostalgic identity is not just about longing for the past but also about utilizing the past as a resource for navigating the labyrinths of the present. Analyses are conducted to examine the medium of music video at three levels - auditory, visual, and linguistic - in order to investigate the strategies and techniques employed by the Iranian diaspora to create nostalgic narratives. Samples of original pieces and renditions are contrasted in order to identify elements of nostalgic narrativity. Drawing on empirical research, it is argued that the unity of a music video arises from the integration of separate layers of sensory and conceptual inputs that have been composed towards an affective resonance and narrative coherence.
Methods: Systematic review of randomized controlled trials (RCT). Searches were conducted in five electronic databases. Studies were selected if they included patients with NP over 18 years old treated with aerobic exercise (AE) (e.g., cycling, running, hiking, and walking). The main outcome of interest was pain intensity. Qualitative and quantitative data were extracted. The risk of bias (RoB) was determined using the Cochrane RoB Tool-2 and the overall certainty of the evidence with the GRADE recommendations.
Results: Out of 21,585 initial records screened, a total of six individual studies published in ten manuscripts were included. There was a great heterogeneity between protocols, comparisons, and studies’ results (different magnitudes and directions). When looking at the effect of aerobic exercise versus control groups or other interventions on pain intensity measured with the VAS, not statistically (nor clinical) significant differences between aerobic exercise and control groups (MD [95%CI] 5.16 mm [-6.38, 16.70]) were identified. The combined effect of AE plus other interventions seems to be effective. Strength exercise obtained better effects than aerobic exercises (MD [95%CI]: -11.34 mm [-21.6, -1.09]).
Conclusions: Aerobic exercise presented positive results to reduce pain intensity, and improving disability, and physical and emotional functioning. However, the evidence is restricted, low quality, and heterogeneous.
Methods: The searches were conducted on five electronic databases. RCTs or CTs with patients over 18 years old of both sexes with OFP diagnoses were targeted. The intervention of interest was AE (i.e., walking, cycling, and running), compared to any other conservative and non-conservative therapy. The primary outcome was pain intensity. Risk of bias (RoB) was done with the Cochrane RoB tool (RoB 2). The overall certainty of the evidence was evaluated with GRADE.
Results: Out of 21,585 initial records found in the initial database search, only one study (reported on three manuscripts) was included. The diagnosis of interest was headache plus temporomandibular disorders (TMD). Three treatment groups (strengthening (Str) exercise + manual therapy (MT) (G1); AE + MT + Str exercises (G2); AE (G3)) were compared. The main outcome was pain; the secondary outcomes included disability, strength, anxiety, and quality of life. The combined treatment (AE+MT+Str exercises) had the strongest effect to decrease pain and headache intensity in patients with OFP (SMD: 9.99 [95%CI: 7.19, 12.80].
Conclusions: a multimodal treatment strategy achieved the greatest positive effects on pain and other outcomes in the short/medium term. AE seems to be an important component of this strategy. However, the scientific evidence supporting AE’s isolated effect is limited, indicating a research gap in this scientific field.
The excitement sparked by the emergence of AI open platforms has encountered significant scrutiny from educators and educational planners, who have raised valid concerns about issues such as plagiarism, testing protocols, and the authenticity of content submitted by students. While these concerns are timely and crucial, it's essential not to overlook other pressing issues that often go unnoticed in the lived educational experience of learners, particularly within the field of social sciences. This paper aims to advocate for a humanistic approach with a focus on education in the generative AI Era.
Recording of Low-Oxygen Stress Response Using Chlorophyll Fluorescence Kinetics in Apple Fruit
(2023)
Long-term storage of apples (Malus x domestica, Borkh.) is increasingly taking place under Dynamic Controlled Atmosphere (DCA). The oxygen level is lowered to ≤ 1 kPa O2 and the apples are stored just above the Lower Oxygen Limit (LOL). Low oxygen stress during controlled atmosphere storage can lead to fermentation in apples if oxygen levels are too low. Chlorophyll fluorescence can be used to detect low-oxygen stress at an early stage during storage. The currently available non-imaging fluorescence systems often use the minimal fluorescence (Fo) parameter. In contrast, the use of chlorophyll fluorescence kinetics is insufficiently described. Therefore, this study aimed to gain more knowledge about the response of chlorophyll fluorescence kinetics to low oxygen stress in apples using a fluorescence imaging system. The results show that the kinetic fluorescence curves differ under aerobic and fermentation conditions. The fermentative conditions initiated a decrease in fluorescence intensity upon application of the saturation pulses during exposure to actinic light. This result was made at 18 °C and 2 °C ambient temperatures. Interestingly, the kinetic curve changed at 2 °C before fermentation products accumulated in the apples. Non-photochemical quenching (NPQ) decreased under fermentation conditions in the dark phase after relaxation. Upon entering the dark relaxation phase after Kautsky induction, ɸPSII began to increase. Under atmospheric oxygen conditions, ɸPSII reached values of 0.81 to 0.76, while under fermentation, ɸPSII values ranged from 0.57 to 0.44.
Hyperhydricity (HH) is one of the most important physiological disorders that negatively affects various plant tissue culture techniques. The objective of this study was to characterize optical features to allow an automated detection of HH. For this purpose, HH was induced in two plant species, apple and Arabidopsis thaliana, and the severity was quantified based on visual scoring and determination of apoplastic liquid volume. The comparison between the HH score and the apoplastic liquid volume revealed a significant correlation, but different response dynamics. Corresponding leaf reflectance spectra were collected and different approaches of spectral analyses were evaluated for their ability to identify HH-specific wavelengths. Statistical analysis of raw spectra showed significantly lower reflection of hyperhydric leaves in the VIS, NIR and SWIR region. Application of the continuum removal hull method to raw spectra identified HH-specific absorption features over time and major absorption peaks at 980 nm, 1150 nm, 1400 nm, 1520 nm, 1780 nm and 1930 nm for the various conducted experiments. Machine learning (ML) model spot checking specified the support vector machine to be most suited for classification of hyperhydric explants, with a test accuracy of 85% outperforming traditional classification via vegetation index with 63% test accuracy and the other ML models tested. Investigations on the predictor importance revealed 1950 nm, 1445 nm in SWIR region and 415 nm in the VIS region to be most important for classification. The validity of the developed spectral classifier was tested on an available hyperspectral image acquisition in the SWIR-region.
Background
The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable.
Results
An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed.
Conclusion
The technical realization of “Phenomenon” allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time.
Iron deficiency is a global issue and can lead to a variety of clinical pictures. The biofor-tification of vegetables with iron could complement the existing portfolio of iron-rich products, thus improving iron supply in the long term. In order to determine whether the iron-biofortified vegetables could meet this demand and would address appropriate target groups, a quantitative online survey was conducted in Germany. Based on 1000 consumer responses, a cluster analysis was performed. The results showed a four-cluster solution. The first cluster was holistically engaged, the second was fitness-affine but health unconcerned, the third cluster consists frugal eaters with a focus on medical prevention, and the fourth cluster are hedonists. No cluster focused its consumption on iron-enriched products, but instead all developed an individual mix of the three product groups.
The mineralization of soil organic nitrogen (N) and crop residues can significantly contribute to the N supply of vegetable crops. However, short-term mineralization dynamics are difficult to predict. On the other hand, fast-growing crops like spinach are highly sensitive to N shortage. Therefore, in situ soil columns have been tested to estimate the actual N supply via mineralization in field-grown spinach. In ten fertilization trials covered soil columns (20 cm in diameter) were driven into the soil to a depth of 30 cm at the start of the cultivation. Eight columns were repeated in three blocks within a total trial area of 0.10 to 0.25 ha. Net N mineralization was derived by subtracting the soil mineral N concentration (Nmin) in the upper 30 cm before installation from the concentration inside the columns at harvest. For comparison, a balance sheet was calculated for spinach plots receiving no N fertilization (zero plots) as well as fertilized plots and used as a proxy for net N mineralization. In this approach the initial Nmin concentration in the upper 30 cm of the soil, the N supply via irrigation, and fertilization as well as the total aboveground N uptake by spinach and the Nmin residue were considered. By using soil columns, N mineralization was determined with a mean coefficient of variation of 18%. A higher spatial variability of up to 43% was observed when spinach was grown as a second crop. The average net N mineralization rate ranged between 2 kg ha‑1 week‑1 (0-30 cm) in winter-grown spinach and 3-7 kg ha‑1 week‑1 (0-30 cm) in the other seasons. Nitrogen mineralization measured by the soil columns was qualitatively confirmed with the data obtained by the balance sheet. Soil columns enable repeated samplings during the spinach cultivation. In this way, top dressing rates can be adjusted to the actual N supply.
Spinach is a nitrogen (N)-demanding crop characterized by a shallow root architecture. Especially in the first weeks after sowing, significant N uptake is limited to the uppermost few centimetres of the soil. However, base fertilization is usually based on the soil mineral N (Nmin) concentration in the upper 30 cm. Therefore, the objective of this study was to examine whether the soil sample depth for calculating the base N fertilization can be reduced to the 0-15 cm layer. In seven field trials, conducted during spring, summer and autumn seasons, either a low or high base fertilization dose was applied at sowing. Until top dressing, soil samples were frequently taken in the upper 0-15 and 15-30 cm layers to determine the average Nmin concentration in each layer. Top dressing was applied when the first true leaves had unfurled. With this fertilizer application, the total N supply was aligned between both treatments based on the Nmin concentration in the upper 30 cm of the soil. Aboveground fresh and dry masses were determined after reaching a fresh mass yield of 15-20 t ha‑1 and related to the mean Nmin concentration in the first 3 to 4 weeks of cultivation between sowing and top dressing. It was shown that the Nmin concentration in the upper 0-15 cm of the soil highly reflects the base fertilization rate. By contrast, the Nmin concentration in the 15-30 cm layer remained unaffected. However, the Nmin concentration of both top soil layers can affect fresh and dry mass yield at harvest. Therefore, the entire 0-30 cm soil layer should be considered when calculating the base N fertilization rate in field-grown spinach. Measurements revealed that spinach fresh and dry masses were increased until the N availability of between 54 and 59 kg ha‑1 (0-30 cm) was reached at the seedlings stage, respectively.
DIGI4Teach - Handbook
(2023)
One of the important outputs of our DIGI4Teach consortium is this Handbook, which consists of two parts. Part A contains an analysis of the most important descriptive research results conducted within the DIGI4Teach Erasmus+ project regarding the use of digital technology in teaching economic disciplines in partner countries. Part B contains twelve case studies from different areas of economics and business (accounting, finance, marketing, tourism and trade) that were prepared using various digital tools and they can be freely used in classes or other forms of education.
Semi-natural grasslands (SNGs) are an essential part of European cultural landscapes. They are an important habitat for many animal and plant species and offer a variety of ecological functions. Diverse plant communities have evolved over time depending on environmental and management factors in grasslands. These different plant communities offer multiple ecosystem services and also have an effect on the forage value of fodder for domestic livestock. However, with increasing intensification in agriculture and the loss of SNGs, the biodiversity of grasslands continues to decline. In this paper, we present a method to spatially classify plant communities in grasslands in order to identify and map plant communities and weed species that occur in a semi-natural meadow. For this, high-resolution multispectral remote sensing data were captured by an unmanned aerial vehicle (UAV) in regular intervals and classified by a convolutional neural network (CNN). As the study area, a heterogeneous semi-natural hay meadow with first- and second-growth vegetation was chosen. Botanical relevés of fixed plots were used as ground truth and independent test data. Accuracies up to 88% on these independent test data were achieved, showing the great potential of the usage of CNNs for plant community mapping in high-resolution UAV data for ecological and agricultural applications.
Purpose
The purpose of this paper is to distinguish different types of sustainable digital entrepreneurs (SDEs) and explore their approaches toward enhancing organizational resilience.
Design/methodology/approach
Investigation of entrepreneur characteristics using Grounded Theory methodology; 12 semi-structured telephone interviews with (owner-)managers of digital-resilient small and medium-sized enterprises (SMEs) and start-ups in Germany; adaptation of a sustainability-digitalization-matrix for initial clustering; investigation of reoccurring patterns (within and between clusters) through variable-oriented content analysis; application of the capability-based conceptualization of organizational resilience for synthesis and extension.
Findings
First, the authors present a new typology of SDEs, including descriptions of the four main types (Process-Oriented System Thinker, Unconventional Strategist, Dynamic Visionary and Success-Oriented Opportunist). Second, the authors propose a conceptual framework with six success factors of organizational resilience. The framework accentuates the influence of SDEs on organizational culture and the macro-environment.
Practical implications
Digital sustainability and resilience are emerging management principles. The insights gained will allow (future) entrepreneurs to perform a self-assessment and replicate approaches toward enhancing SME resilience; for example, governing the co-creation of an organizational culture with a strong integrative view on sustainability and digitalization.
Originality/value
SMEs are characterized by high vulnerability and a reactive response to the disruptions caused by sustainability crises and digitalization. Blending sustainable and digital entrepreneurship at a micro-level, the authors identified the success factors underpinning organizational resilience that are associated with the characteristics of four types of SDEs.
In recent years, the issue of land consumption or land use has become increasingly important in many areas of our society. Logistics processes in particular take up a lot of space and have a significant impact on the environment. The question is how this use of land can be optimised. Based on a systematic literature review and interviews with experts in the period between May 2021 and July 2021, this paper presents indicators that constitute or influence space-efficient logistics in the context of cooperation. The results show that in addition to the established cooperation characteristics, there are other indicators that are directly related to land use. In the logistics sector, there is strong competitive pressure and, as a result, little trust between companies. It has been shown that with the help of a neutral moderator, the gap between trusting, land-efficient cooperation and one’s own entrepreneurial interests can be narrowed, and cooperation can be profitable for all participants. In addition, digitisation actually does not seem to be sufficient to meet the information needs of a cooperation. The exchange of information not only serves to automate processes, but also makes cooperation more transparent. It shows that legal and municipal requirements need to be developed. It also becomes clear that the indicators have a mutual influence on each other and cannot be considered in isolation when it comes to the actual implementation of a cooperation. By increasing the efficiency of cooperative processes and value creation, it offers the opportunity to make land use more sustainable.
In the race against climate change, small and medium-sized enterprises (SMEs) play a fundamental role. To clarify the contribution of corporate culture to SMEs' emission reduction, three perspectives can be useful: corporate culture as driver and barrier, current and planned corporate culture development actions, and the corporate culture profile as an outcome. As the first application of the extended Belief-Action-Outcome framework, this single case study exemplifies the role of corporate culture in an SME from the steel construction and manufacturing sector in Germany. The investigated SME has achieved emission reduction while increasing its revenue and is an early adopter of sustainable and digital development. The rich insights from an employee survey, semi-structured interviews, observation, and document analysis allowed us to outline an informed approach toward corporate culture development that emphasizes vision development of the desired corporate culture and the role of information systems for promoting emission reduction.
In view of the rapid depletion of natural resources and the associated overloading of the biological ecosystem, the concept of circular business models (CBMs) is increasingly discussed in the literature as well as in business practice. CBMs have the potential to significantly reduce the demand for natural resources. Despite their increasing relevance, the diffusion of CBMs in business practice is largely unexplored. Consequently, this article investigates the extent to which CBMs have already been adopted by large German companies. To answer this question, the annual and sustainability reports of the members of the DAX40 are analyzed for the presence of five specific types of CBMs. Data was gathered for the years 2015 and 2020 in order to describe the development over time. The results show an increasing prevalence of CBMs in the DAX companies. In addition, it is noticeable that CBM types that serve to close material cycles are implemented more frequently than those that decelerate material cycles. In particular Sharing Platforms and Product as a Service stand out due to comparatively low adoption. Potential reasons for these findings are discussed and managerial as well as policy implications suggested.
Knowledge of the maximum friction coefficient µmax between tire and road is necessary for implementing autonomous driving. As this coefficient cannot be measured via existing serial vehicle sensors, µmax estimation is a challenging field in modern automotive research. In particular, model-based approaches are applied, which are limited in the estimation accuracy by the physical vehicle model. Therefore, this paper presents a data-based µmax estimation using serial vehicle sensors. For this purpose, recurrent artificial neural networks are trained, validated, and tested based on driving maneuvers carried out with a test vehicle showing improved results compared to the model-based algorithm from previous works.
Within the consortium “Experimentation Field Agro-Nordwest”, a practical concept for knowledge and technology transfer of digital competence in agriculture was created. For this purpose, the web-based e-learning system “SensX” was set up, consisting of videos, presentations and instructions. In addition, the classical e-learning concept was extended by data sets, student experiments and sensor data of plants acquired by a remote phenotyping robot. This resulted in a massive open online course (MOOC), which was tested with agricultural and biotechnology students in higher education at the University of Applied Sciences Osnabrück over two years. The evaluation process of “SensX” included an empirical survey, qualitative interviews of the participating students by an external institution and an evaluation of the concept by the lecturers.
Computer-image processing becomes more and more important in the analysis of data in biological and agricultural research and practice. However, robust image processing is highly de pendent on the histogram analysis algorithms used and the quality of the data being processed. The algorithm presented here aims to improve the accuracy of the classification of image data generated under complex boundary situations and inconsistent lighting conditions. Using the example of the determination of nitrogen content of tomato leaves and the qualitative determination of starch con tent of apples on the basis of color image processing, we showed that the developed algorithm is able to perform a robust classification and represents an improvement to simple histogram analysis.
This textbook provides a comprehensive foundation of food physics by addressing the physical properties of food, food ingredients, and their measurements. Physical properties of food play a key role in all fields where modern technological processes are applied for the generation of food raw materials and the production of food. The determination of the physical properties of food and related products is a pre-requisite for product and process development, production engineering and automation in today’s food, pharmaceutical and cosmetics industries, as well as related quality control activities.
Following the success of its first edition published in 2007, the book has been updated to reflect recent industrial applications of novel physical food processing technologies. Each chapter begins with basic principles and progresses to a comprehensive coverage of the topic. The authors enriched this second edition with several didactic elements, including definition boxes, examples, and chapter-end summaries.
This textbook helps readers to build up their knowledge of the important aspects surrounding the physical properties of foods and food ingredients. It is also an essential resource for students of food science and technology to complement textbooks in food chemistry and food microbiology, as well as for food and chemical engineers, technologists, and technicians in the food industry.