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Found 6278 publications
Psychological foundations of ambiguity in the hybrid workplace: The role of managerial risk-taking and AI-induced job insecurity
Publication Name: Acta Psychologica
Publication Date: 2026-02-01
Volume: 262
Issue: Unknown
Page Range: Unknown
Description:
In the modern ever-changing organizational environment, where hybrid workplace arrangement is becoming increasingly common, and artificial intelligence (AI) technology has been used widely, employees tend to face a situation characterized by ambiguity of work and it is difficult to perceive an understanding of role, expectations, and employment. The paper explores the interrelationship between task ambiguity, risk-taking by managers, AI-induced job insecurity, and employee outcomes in a hybrid working environment. This is based on Social Information Processing Theory where we advance a theoretical model that explores how workforce members actively learn and process information in their social context to get through ambiguity and foster resilience. The evidence of the proposed relationships is substantiated by three studies. Study 1 focuses on the way task ambiguity influences active lurking and also job engagement. Study 2 explores with the moderating factor on the relationship among the variables of task ambiguity, active lurking, and job engagement on managerial risk-taking. Study 3 examines how AI-induced job insecurity can moderate the link between task ambiguity and active lurking and job engagement. The results emphasize the need to ensure clear task specification, active lurking, management risk-taking and proactive efforts to reduce the issue of AI-induced job insecurity as factors that enhance employee engagement. The implications of the study are given and recommendations to conduct further research are outlined.
Open Access: Yes
A Fuzzy Bayesian-Based Integrated Framework for Risk Analysis of a Dual-Cycle Liquefied Natural Gas Cold Energy Power Generation System
Publication Name: Energies
Publication Date: 2026-02-01
Volume: 19
Issue: 3
Page Range: Unknown
Description:
LNG serves as a pivotal element within integrated energy systems, especially in coastal regions where the implementation of a stable and reliable LNG cold energy power generation system significantly elevates energy efficiency. This system can effectively meet concurrent demands for cold energy utilization and electricity supply while contributing to the mitigation of carbon emissions. However, the inherent complexity of the system coupled with the scarcity of historical operational data for the novel dual-Rankine cycle process renders conventional reliability assessment methodologies inadequate. This study proposes an integrated framework utilizing fuzzy Bayesian methods to address data scarcity during the early stages of equipment deployment. A hierarchical risk factor model, incorporating process decomposition, expert evaluations, and triangular fuzzy numbers, is developed to quantify uncertainties in failure probabilities. The Bayesian network models the causal relationships among equipment failure factors, allowing for the inference of overall system reliability from individual equipment performance. Through a case study of a LNG terminal in Zhoushan, this approach integrates sensitivity analysis with forward-backward reasoning methodologies to rigorously evaluate and quantify system reliability under operational conditions. The results show that under high load conditions within the 1000 h prior to overhaul, following long-term accumulated operation, the probability of complete system shutdown in the power generation system is 3.30%, while the probability of the LNG cold energy power generation system failing to operate fully due to aging-related faults is 8.24%, demonstrating the system’s strong reliability under extreme conditions. Critical risks identified through backward inference include the seawater pump SWP1, with a posterior failure probability of 59.92% during complete shutdown, and the propane-side pump SWP3, with a posterior failure probability of 32.29% when the cold energy power generation system can only operate in a single-cycle mode. This study provides an advanced methodological framework for risk management in newly constructed LNG cold energy power generation systems, playing a crucial role in promoting sustainable, low-carbon technologies in the energy sector.
Open Access: Yes
DOI: 10.3390/en19030688
Uncovering the energy infrastructure in Europe: Data-driven digital twin for policy analysis and interpretation via multi-way analysis
Publication Name: Energy
Publication Date: 2026-02-01
Volume: 344
Issue: Unknown
Page Range: Unknown
Description:
With the adoption of the European Green Deal, the target to reduce net greenhouse gas emissions by 55 % by 2030, compared to 1990 levels, requires a higher renewable energy fraction and better energy efficiency. This requires a comprehensive re-evaluation of the power infrastructure within the European Union (EU). To achieve this, a EU-focused digital twin has been constructed, focusing on the European region and neighboring countries. The twin uses annual and 30-min resolution data from 113 main stations representing 40 countries, with a focus on EU member states. Multi-way analysis (PARAFAC2) is used to align interpretation for both data and high-resolution data, prioritizing regional energy infrastructure features. An automated graph-theory (P-graph) approach is used to construct a large-scale multi-time-sliced energy-balanced model as a digital twin model. This novel integration of macro-level trend analysis (via PARAFAC2), time-resolved optimization, and equity-based constraints enables a data-driven exploration of diverse policy scenarios. This study shows that effective EU energy policy should balance renewable diversification, equity in energy access, and regional cooperation, as policy shifts significantly affect energy flows and trade dynamics. While resilient infrastructure may require high investment, trade-off analysis reveals cost-effective, balanced pathways that optimize both sustainability and security objectives. The work demonstrates the potential for data-driven policy making for regional or international infrastructure, focusing on optimization of energy transfer activities, promotion of renewable sources, and systematic planning.
Open Access: Yes
Therapeutic role of physical activity in relationship between chronic pain and sleep quality in musculoskeletal disorders
Publication Name: Orvosi Hetilap
Publication Date: 2026-02-01
Volume: 167
Issue: 8
Page Range: 300-308
Description:
Musculoskeletal diseases represent a major public health problem worldwide, as they are associated with pain, reduced musculoskeletal function, and diminished quality of life. They affect more than 1.7 billion people globally, and their prevalence continues to rise due to ageing, obesity, sedentary lifestyle, and psychosocial factors. Chronic pain is the most common complaint, which may appear as an independent condition or as a consequence of other diseases such as osteoarthritis, spondylosis, or rheumatoid arthritis. Pain and sleep disturbances are closely and mutually related: inadequate sleep increases pain sensitivity, reduces the efficiency of pain processing, and contributes to the persistence of chronic pain. The aim of this review study is to examine the relationship between pain, the most common symptom of degenerative joint and spinal diseases and sleep quality. Sleep disorders are highly prevalent in certain musculoskeletal diseases and predict a subsequent decline in functional status. Therapeutic options include balneotherapy and complex physiotherapy, both of which have been shown to reduce pain and improve musculoskeletal function. Several studies have reported long-term benefits of balneotherapy, mud therapy, and aquatic physiotherapy. In addition, regular moderate-intensity physical activity plays a key role in reducing pain and improving functional status and sleep quality. Monitoring physical activity using questionnaire-based methods and objective wearable devices enables a more accurate assessment, safe planning, and continuous tracking of physical activity levels. Overall, a multimodal, individualized therapeutic approach is the most effective in the management of musculoskeletal diseases, targeting the simultaneous improvement of pain, sleep, and lifestyle-related factors. Orv Hetil. 2026; 167(8): 300–308.
Open Access: Yes
Machining of Fe-Based Amorphous Alloy Ribbons with Sub-50 Femtosecond Laser Pulses
Publication Name: Micromachines
Publication Date: 2026-02-01
Volume: 17
Issue: 2
Page Range: Unknown
Description:
Fe-based metallic glasses are ideal candidates to be utilized in transformer cores owing to their outstanding soft magnetic properties. However, they are difficult to machine properly by conventional means due to their mechanical brittleness and poor thermal conductivity. Here, the cutting of Fe91 –Si4.5 –C4.0 –Al0.5 amorphous alloy ribbons is reported with a sub-50 fs laser pulses. A systematic study is performed on local morphological and chemical composition changes to the machined edge in comparison to crystalline metals. It is shown that only the innermost 80 (Formula presented.) m wide region of the cut edge shows any detectable modifications, which is much less than for continuous laser machining. Therefore, the proposed method is indeed a valuable approach to overcome the fine machining difficulties of metallic glasses.
Open Access: Yes
DOI: 10.3390/mi17020214
Asbestos Poverty as a New Paradigm for Multidimensional Urban Sustainability
Publication Name: Journal of Urban Health
Publication Date: 2026-02-01
Volume: 103
Issue: 1
Page Range: 214-228
Description:
The popularity of asbestos-containing products stemmed from their fire resistance, thermal insulation properties, and mechanical strength. However, their well-documented adverse health effects led to the prohibition of their use in many countries. This research aims to conduct a comprehensive examination of the often-overlooked social dimensions associated with asbestos, with a specific focus on the affected population’s circumstances and the potential solutions accessible to them. Its analysis encompasses legal regulations concerning asbestos, societal awareness, and the economic implications of asbestos removal from the perspective of those impacted. The findings highlight that the remediation of asbestos-containing products is often contingent on the financial and social conditions of the affected population, posing significant challenges for the economic sector and environmental protection efforts. This research contributes to the development of integrated approaches that address social, economic, and environmental dimensions in tandem. Its originality lies in situating the concepts of social sustainability and socially oriented environmental development within the context of asbestos-related policies. The findings suggest that achieving asbestos-free environments is feasible only through the integration of social dimensions, taking into account the economic and social conditions of the affected communities.
Open Access: Yes
What diseases and risks cause health losses in Hungary?
Publication Name: Orvosi Hetilap
Publication Date: 2026-02-01
Volume: 167
Issue: 6
Page Range: 232-242
Description:
Introduction: Using Global Burden of Disease 2023 data, this study examines the structure of health losses in Hungary, focusing on diseases, risk factors, and international comparisons. Objective: To identify which diseases and risk factors contribute most to Hungary’s health burden, how these relate to disability and premature mortality, and how patterns differ by gender and in comparison, with Central European countries. Method: Age-standardized values per 100,000 inhabitants, broken down by gender and disease/risk category, were analyzed for Hungary and compared with Austria, the Czech Republic, Poland, and Slovakia. Results: Cardiovascular diseases, cancers, and musculoskeletal disorders caused the largest losses. High blood pressure was the leading risk factor. Premature mortality was substantially higher in Hungary; men showed especially elevated levels due to smoking, diet, and hypertension. Morbidity-related losses were dominated by musculoskeletal and mental disorders. Discussion: Hungary’s burden stems not only from mortality but also from chronic disabling conditions. The mortality component is particularly unfavourable in international comparison. Conclusion: Improving treatment quality, timely care, and early diagnosis is essential, while reducing morbidity requires stronger long-term care and rehabilitation. Effective policy should complement lifestyle-focused prevention with better access to high-quality curative care and gender-responsive interventions. Consistent use of objective burden-of-disease data can support decision-making. A systemic approach – combining prevention, supportive environments, and a strengthened healthcare system – is needed to reduce health losses in Hungary. Orv Hetil. 2026; 167(6): 232–242.
Open Access: Yes
Introducing LEAF: LLM Edge Assessment Framework for Generative AI on the Edge
Publication Name: Machine Learning and Knowledge Extraction
Publication Date: 2026-02-01
Volume: 8
Issue: 2
Page Range: Unknown
Description:
The transition of Large Language Models (LLMs) from centralized clouds to edge environments is critical for addressing privacy concerns, latency bottlenecks, and operational costs. However, existing edge benchmarking frameworks remain tailored to discriminative Deep Learning tasks (e.g., object detection), failing to capture the multidimensional challenges of generative AI, specifically the trade-offs between token generation speed, semantic accuracy, and hardware sustainability. To address this gap, we introduce LEAF (LLM Edge Assessment Framework), a novel evaluation methodology that integrates Circular Economy principles directly into performance metrics. LEAF assesses edge deployments across five synergistic pillars: Circular Economy Score, Energy Efficiency (Joules/Token), Performance Speed (Tokens/Second), semantic accuracy (BERTScore), and End-to-End Latency. We validate LEAF through an extensive experimental analysis of five distinct hardware classes, ranging from embedded IoT devices (Raspberry Pi 4 and 5, NVIDIA Jetson Nano) to professional edge servers (NVIDIA T400) and repurposed legacy workstations (NVIDIA GTX 1050 Ti). Utilizing 4-bit quantized models via the Ollama runtime, our results reveal a counterintuitive insight: repurposed consumer hardware significantly outperforms modern purpose-built edge SoCs. The legacy GTX 1050 Ti achieved a 20× speedup over the Raspberry Pi 4 and maintained superior energy-per-task efficiency compared to low-power ARM architectures by minimizing active runtime. These findings challenge the prevailing narrative that newer silicon is essential for Edge AI, demonstrating that sustainable, high-performance inference can be achieved by extending the lifecycle of existing hardware. LEAF thus provides a blueprint for a “Green Edge” ecosystem that balances computational capability with environmental responsibility.
Open Access: Yes
DOI: 10.3390/make8020048
From waste to wealth: innovations in the recovery of bioactive compounds from agro-food by-products
Publication Name: European Food Research and Technology
Publication Date: 2026-02-01
Volume: 252
Issue: 2
Page Range: Unknown
Description:
The valorization of food waste biomass for the extraction of bioactive compounds presents a sustainable solution to global food waste challenges while offering significant economic and environmental benefits. This review comprehensively examines advanced green extraction technologies such as ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), supercritical fluid extraction (SFE), and enzyme-assisted extraction (EAE) for recovering polyphenols, carotenoids, flavonoids, and other high-value compounds from fruit, vegetable, cereal, and animal-derived waste. Highlighting optimized extraction parameters showed that modern techniques outperform conventional methods in yield, efficiency, and cost-effectiveness, with UAE reducing manufacturing costs by up to 84% compared to Soxhlet extraction. The review also addresses pretreatment strategies, purification innovations (e.g., flash chromatography), and emerging solvents like natural deep eutectic solvents (NADES) with lower environmental impact, higher efficiency, recyclability and biodegradability. Challenges such as contamination risks, temperature sensitivity, and scalability are critically analyzed, alongside future directions integrating nanotechnology, artificial intelligence, and hybrid extraction systems. By bridging gaps in waste classification, compound identification, and process optimization, this study underscores the potential of food waste as a resource for pharmaceuticals, nutraceuticals, and functional foods, aligning with circular economy goals and sustainable development.
Open Access: Yes
