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Publications - 6374
Potentials of blockchain technologies for supply chain collaboration: a conceptual framework
Publication Name: International Journal of Logistics Management
Publication Date: 2021-01-01
Volume: 32
Issue: 3
Page Range: 973-994
Description:
Purpose: The purpose of this study is to investigate the potentials of blockchain technologies (BC) for supply chain collaboration (SCC). Design/methodology/approach: Building on a narrative literature review and analysis of seminal SCC research, BC characteristics are integrated into a conceptual framework consisting of seven key dimensions: information sharing, resource sharing, decision synchronization, goal congruence, incentive alignment, collaborative communication and joint knowledge creation. The relevance of each category is briefly assessed. Findings: BC technologies can impact collaboration between transaction partners in modern supply chains (SCs) by streamlining information sharing processes, by supporting decision and reward models and by strengthening communicative relationships with SC partners. BC promises important future capabilities in SCs by facilitating auditability, improving accountability, enhancing data and information transparency and improving trust in B2B relationships. The technology also promises to strengthen collaboration and to overcome vulnerabilities related to moral hazard and shortcomings found in legacy technologies. Research limitations/implications: The paper is mainly focused on the potentials of BC technologies on SCC as envisioned in the current academic literature. Hence, there is a need to validate the theoretical inferences with other approaches such as expert interviews and empirical tests. This study is of use to practitioners and decision-makers seeking to engage in BC-collaborative SC models. Originality/value: The value of this paper lies in its call for an increased focus on the possibilities of BC technologies to support SCC. This study also contributes to the literature by filling the knowledge gap of how BC potentially impacts SC management.
Open Access: Yes
Two sides of one medal: Arable weed vegetation of Europe in phytosociological data compared to agronomical weed surveys
Alicia Cirujeda
Valerijus Rašomavičius
Michael Glemnitz
Jana Bürger
Guillaume Fried
Jevgenija Ņečajeva
Lena Ulber
Silvia Fogliatto
Michaela Kolářová
Eva Hernández Plaza
Matthias Schumacher
Bärbel Gerowitt
G. Pinke
Sandrine Petit
Christoph Redwitz
Helen Metcalfe
Denise Fu Dostatny
Filip Küzmič
José Luis González-Andújar
Zdeňka Lososová
Francesco Vidotto
Erwin Bergmeier
Milan Chytrý
Jordi Izquierdo
Florian Jansen
Urban Šilc
Publication Name: Applied Vegetation Science
Publication Date: 2022-01-01
Volume: 25
Issue: 1
Page Range: Unknown
Description:
Questions: Two scientific disciplines, vegetation science and weed science, study arable weed vegetation, which has seen a strong diversity decrease in Europe over the last decades. We compared two collections of plot-based vegetation records originating from these two disciplines. The aim was to check the suitability of the collections for joint analysis and for addressing research questions from the opposing domains. We asked: are these collections complementary? If so, how can they be used for joint analysis?. Location: Europe. Methods: We compared 13 311 phytosociological relevés and 13 328 records from weed science, concerning both data collection properties and the recorded species richness. To deal with bias in the data, we also analysed different subsets (i.e., crops, geographical regions, organic vs conventional fields, center vs edge plots). Results: Records from vegetation science have an average species number of 19.0 ± 10.4. Metadata on survey methodology or agronomic practices are rare in this collection. Records from weed science have an average species number of 8.5 ± 6.4. They are accompanied by extensive methodological information. Vegetation science records and the weed science records taken at field edges or from organic fields have similar species numbers. The collections cover different parts of Europe but the results are consistent in six geographical subsets and the overall data set. The difference in species numbers may be caused by differences in methodology between the disciplines, i.e., plot positioning within fields, plot sizes, or survey timing. Conclusion: This comparison of arable weed data that were originally sampled with a different purpose represents a new effort in connecting research between vegetation scientists and weed scientists. Both collections show different aspects of weed vegetation, which means the joint use of the data is valuable as it can contribute to a more complete picture of weed species diversity in European arable landscapes.
Open Access: Yes
DOI: 10.1111/avsc.12460
Virtual reality headsets for employee training in enterprises: fuzzy SRP data-driven framework for a comprehensive evaluation
Publication Name: Virtual Reality
Publication Date: 2026-03-01
Volume: 30
Issue: 1
Page Range: Unknown
Description:
Virtual reality (VR) is progressively transforming employee training in companies by offering immersive and engaging learning experiences. Nevertheless, the selection of an appropriate VR headset is vital for optimizing training effectiveness. This paper addresses this issue by proposing a novel hybrid fuzzy multi-criteria decision-making model that integrates the improved fuzzy stepwise weight assessment ratio analysis (IF-SWARA) with the fuzzy simple ranking process (F-SRP). The IF-SWARA methodology is employed to compute the relative weights of the selection criteria for VR headsets utilized in employee training, whereas the newly developed F-SRP is implemented to rank the various VR headsets. By employing the IF-SWARA method, the model offers a more nuanced understanding of criteria weights, thereby reflecting the differing significance of various headset features. The research’s novelties and contributions are as follows: (1) This study is the first to select VR headsets by applying multi-criteria methods. (2) The F-SRP model is developed for the first time in the literature. (3) The introduced F-SRP methodology allows for a comprehensive ranking of the available VR headsets, facilitating informed decision-making. The paramount indicators for selecting VR headset options for training in enterprises consist of technical specifications, comfort and ergonomics, and screen specifications. The results obtained from the fuzzy SRP indicate that the Apple Vision Pro surpasses the other alternatives. Finally, the robustness and applicability of the proposed model are evaluated through an exhaustive sensitivity analysis. This research possesses broader implications for VR training in enterprises by providing a robust and reliable framework, ultimately contributing to the development of more effective and impactful VR training programs.
Open Access: Yes
Thermal and Sliding Wear Properties of Wood Waste-Filled Poly(Lactic Acid) Biocomposites
Publication Name: Polymers
Publication Date: 2022-06-01
Volume: 14
Issue: 11
Page Range: Unknown
Description:
In our study, the effects of wood waste content (0, 2.5, 5, 7.5, and 10 wt.%) on thermal and dry sliding wear properties of poly(lactic acid) (PLA) biocomposites were investigated. The wear of developed composites was examined under dry contact conditions at different operating parameters, such as sliding velocity (1 m/s, 2 m/s, and 3 m/s) and normal load (10 N, 20 N, and 30 N) at a fixed sliding distance of 2000 m. Thermogravimetric analysis demonstrated that the inclusion of wood waste decreased the thermal stability of PLA biocomposites. The experimental results indicate that wear of biocomposites increased with a rise in load and sliding velocity. There was a 26–38% reduction in wear compared with pure PLA when 2.5 wt.% wood waste was added to composites. The Taguchi method with L25 orthogonal array was used to analyze the sliding wear behavior of the developed biocomposites. The results indicate that the wood waste content with 46.82% contribution emerged as the most crucial parameter affecting the wear of PLA biocomposites. The worn surfaces of the biocomposites were examined by scanning electron microscopy to study possible wear mechanisms and correlate them with the obtained wear results.
Open Access: Yes
Predicting somatic cell count in milk samples using machine learning∗
Publication Name: Annales Mathematicae Et Informaticae
Publication Date: 2024-01-01
Volume: 60
Issue: Unknown
Page Range: 159-168
Description:
Milk quality is an important factor both for the farmers to be able to sell their products and for the milk industry to be able to plan its production based on quantity and quality. Milk quality has a direct link with cow health, more specifically with utter health. One of the most common utter diseases is mastitis. It always captures a lot of interest based on its frequency and cost as a dairy disease which eventually leads to an involuntary and premature culling of milking cows and decreased milk yield. The genetic evaluation of mastitis is very difficult as it is a low heritable trait and categorical in nature [2]. That is why it is necessary to find markers that could predict the occurrence of mastitis. One of the widely used such markers is the somatic cell count (SCC) [9] which is considered to be the most suitable indicator trait for mastitis resistance given its medium to high genetic correlation with mastitis and its greater heritability than mastitis. The SCC is also easy to record in the practice. The selection for lower SCC in milk has a positive effect on the incidence of mastitis. The selection against high SCC also does not deteriorate the immune system of cattle and decreases the risk of infection at the same time. The genetic evaluation [1] of this trait is mostly based on somatic cell score (SCS), a logarithmic transformation of SCC to achieve normality of distribution. In our study, we used the milk database of Holstein cows from 3 different farms. From each farm, we had altogether 8000 samples tested. The samples were analyzed using chemical methods every month for a year. 11 different types of data were recorded from each sample. Our aim was to find the best mixture of recorded data that would predict the value of linearized somatic cell count. After the logarithmic linearization the SCC results were divided into 3 main groups (based on the probability of mastitis). Thus our prediction problem turned into a classification problem. We used machine learning to train our algorithm. We experimented with different types of classification methods and found good results for the prediction of SCC in milk samples. We changed the input variables as not all the 9 measured input variables will be necessary for good prediction results. Our preliminary results show that using machine learning it is possible to build a model that can be used to predict mastitis in dairy cows based on variables generally analyzed during milk quality checking tests.
Open Access: Yes
Error handling techniques of genetic algorithms in parallel computing environment
Publication Name: Pollack Periodica
Publication Date: 2008-08-01
Volume: 3
Issue: 2
Page Range: 3-14
Description:
It is easy to create parallel genetic algorithm software with master-slave type paralelization on a cluster of workstations. In a real situation the probability of errors in communication or in some of the slave processes during a long calculation is significant. In this paper we deal with different error handling strategies in master-slave type paralelization of standard GA algorithms and show results of test calculations. Our simulations are close to real applications in the sense that we examine the best achieved objective function value at a fixed wall clock time with different error handling strategies depending on the probability of errors and number of processors. Using these results we make suggestions on the selection of a good error handling method in different optimization problems. © 2008 Akadémiai Kiadó.
Open Access: Yes
Human–GenAI-based agent collaboration: How employee perceptions shape knowledge sharing, thriving, and well-being
Publication Name: Acta Psychologica
Publication Date: 2026-03-01
Volume: 263
Issue: Unknown
Page Range: Unknown
Description:
The growing pace of the introduction of generative artificial intelligence (GenAI) into organizational processes is changing the way workers cooperate with technology. Based on Social Exchange Theory, we propose that the perception of employees regarding the value of GenAI-based agents, their vulnerability and privacy, and their self-concern would determine the collaboration with GenAI agents, which subsequently would predict the knowledge sharing, job thriving, and well-being. The findings show that perceived GenAI-based value has a strong positive impact on human-GenAI-based agent collaboration, but data vulnerability and privacy concerns have no significance. Interestingly, self-concern has a positive effect, which implies that identity-based fears can be used to drive active use of GenAI-based agents. Knowledge sharing, job thriving, and well-being are highly predicted by human-GenAI-based agent collaboration, and organizational exploratory innovation moderates these correlations. These results extrapolate the Social Exchange Theory to human-AI situations, dispel the assumptions of the consistently negative impact of risk perception, and emphasize the relevance of organizational climate to the implementation of the advantages of AI cooperation. The paper provides both theoretical and practical understanding of the way in which employees interact with GenAI-based agents to ensure that organizational learning and psychological outcomes of employees are achieved.
Open Access: Yes
Classification of Time Series Using Singular Values and Wavelet Subband Analysis with ANN and SVM Classifiers
Publication Name: Journal of Advanced Computational Intelligence and Intelligent Informatics
Publication Date: 2006-07-01
Volume: 10
Issue: 4
Page Range: 498-503
Description:
Oscillation of cerebral blood flow (CBF) in physiological or pathophysiological brain states is common, therefore it is promising to identify cerebral circulation disorders based on CBF signal classification. To characterize temporal blood flow patterns, we applied two feature extractions, spectral matrix and wavelet subband analysis. To distinguish between different physiological states, two different classifications have been developed – the radial basis function-based neural network and a support vector classifier with a Gaussian kernel. Feature extraction and classification are evaluated and their efficiency compared. Calculation was done using Mathematica 5.1 and its Wavelet Application.
Open Access: Yes
Anisotropic vector hysteresis model applying Everett function and neural network
Publication Name: Physica B Condensed Matter
Publication Date: 2006-02-01
Volume: 372
Issue: 1-2
Page Range: 138-142
Description:
This paper deals with a simulation technique based on neural networks and an identification method to approximate the behavior of vector hysteresis characteristics of ferromagnetic materials. The identification procedure is based on theoretical measured vector Everett functions using Fourier expansion to deal with angle dependence of the measured scalar Everett functions and of the vector Everett functions in the 2D or in the 3D space. Computing afterwards the theoretical measured vector Everett functions for some given directions, the corresponding hysteresis models are approximated by neural networks and are used to build up the vectorial hysteresis model both in isotropic and anisotropic case. The properties of the anisotropic model has been analyzed and shown in figures. For some examples, the first order reversal curves determined from the vectorial model are compared with the corresponding measured curves that have been used to compute the measured scalar Everett functions being the input for the identification procedure. © 2005 Elsevier B.V. All rights reserved.
Open Access: Yes
