Search Results (2,351)

Modern data centers are becoming increasingly energy-intensive as AI workloads, hyperscale architectures, and high-power processors push power demand to unprecedented levels. This work examines the sources of rising energy consumption, including evolving IT load dynamics, variability, and the limitations of legacy AC-based power-delivery architectures. These challenges amplify the environmental impact of data centers and highlight their growing influence on global electricity systems. The paper analyzes why conventional grid-tied designs are insufficient for meeting future efficiency, flexibility, and sustainability requirements and surveys emerging solutions centered on DC microgrids, high-voltage DC distribution, and advanced wide-bandgap power electronics. The review further discusses the technical enablers that allow data centers to integrate renewable energy and energy storage more effectively, including simplified conversion chains, coordinated control hierarchies, and demand-aware workload management. Through documented strategies such as on-site renewable deployment, off-site procurement, hybrid energy systems, and flexible demand shaping, the study shows how data centers are increasingly positioned not only as major energy consumers but also as key catalysts for accelerating renewable-energy adoption. Overall, the findings illustrate how the evolving power architectures of large-scale data centers can drive innovation and growth across the renewable energy sector. Full article

We present an inverse designed metalens for long-wave infrared (LWIR) imaging tailored to consumer and Internet of Things (IoT) platforms. Conventional LWIR optics either rely on costly specialty materials or suffer from low efficiency and narrow fields of view (FoV), limiting scalability. Our approach integrates adjoint-based inverse design with fabrication-aware constraints and a cone-shaped source model that efficiently captures oblique incidence during optimization. The resulting multi-level metalens achieves a wide FoV up to ${100}^{\circ }$ while maintaining robust focusing efficiency and stable angle-to-position mapping on low-power $4\times 4$ microbolometer arrays representative of edge devices. We further demonstrate application-level imaging on $4\times 4$ microbolometer arrays, showing that the proposed metalens delivers a substantially wider FoV than a commercial narrow FoV lens while meeting low-resolution, low-cost, and low-power constraints for edge LWIR modules. By eliminating bulky multi-element stacks and reducing cost and form factor, the proposed design provides a practical pathway to compact, energy-efficient LWIR modules for consumer applications such as occupancy analytics, smart-building automation, mobile sensing, and outdoor fire surveillance. Full article

The agroecological (AE) transition of agri-food systems can help address climate change impacts in Tunisia, including reduced local food production and high import dependency, but it requires understanding consumer behavior toward eco-friendly food products. Thus, a survey of 521 Tunisian consumers was conducted to assess environmental awareness and willingness to pay (WTP) for food produced under AE practices. Principal Component Analysis (PCA) indicated that sustainable consumption is mainly influenced by knowledge of AE practices, which is stronger among consumers with higher education and income. However, WTP for sustainable products remains low, making it essential to develop marketing strategies that target distinct demographic groups, improve product labeling, and enhance environmental education. Full article

Peanut, as an important economic crop, is widely cultivated and rich in nutrients. Classifying peanuts based on the number of seeds helps assess yield and economic value, providing a basis for selection and breeding. However, traditional peanut grading relies on manual labor, which is inefficient and time-consuming. To improve detection efficiency and accuracy, this study proposes an improved BTM-YOLOv8 model and tests it on an independently designed pod detection device. In the backbone network, the BiFormer module is introduced, employing a dual-route attention mechanism with dynamic, content-aware, and query-adaptive sparse attention to extract features from densely packed peanuts. In addition, the Triple Attention mechanism is incorporated to strengthen the model’s multidimensional interaction and feature responsiveness. Finally, the original CIoU loss function is replaced with MPDIoU loss, simplifying distance metric computation and enabling more scale-focused optimization in bounding box regression. The results show that BTM-YOLOv8 has stronger detection performance for ‘Quan Hua 557’ peanut pods, with precision, recall, mAP50, and F1 score reaching 98.40%, 96.20%, 99.00%, and 97.29%, respectively. Compared to the original YOLOv8, these values improved by 3.9%, 2.4%, 1.2%, and 3.14%, respectively. Ablation experiments further validate the effectiveness of the introduced modules, showing reduced attention to irrelevant information, enhanced target feature capture, and lower false detection rates. Through comparisons with various mainstream deep learning models, it was further demonstrated that BTM-YOLOv8 performs well in detecting ‘Quan Hua 557’ peanut pods. When comparing the device’s detection results with manual counts, the R2 value was 0.999, and the RMSE value was 12.69, indicating high accuracy. This study improves the efficiency of ‘Quan Hua 557’ peanut pod detection, reduces labor costs, and provides quantifiable data support for breeding, offering a new technical reference for the detection of other crops. Full article

Environmental, social, and governance (ESG) initiatives have gained increasing attention in the hotel industry, yet the consumer-level psychological processes through which such activities relate to booking intentions remain incompletely understood. This study aims to examine how hotel ESG activities are associated with consumers’ booking intentions by focusing on the mediating roles of corporate image and consumer trust, as well as the moderating role of environmental awareness. Survey data were collected from consumers with recent hotel stay experiences in China and analyzed using a dual-method approach, combining partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The results show that social and governance activities are positively associated with corporate image, whereas environmental and social activities are positively associated with consumer trust. Corporate image and consumer trust are, in turn, associated with higher booking intentions, while environmental awareness strengthens only the relationship between environmental activities and corporate image. In addition, the fsQCA results reveal multiple configurational pathways through which different combinations of ESG activities and consumer psychological responses are associated with high booking intention. Overall, the findings suggest that hotel ESG initiatives relate to booking intentions through differentiated psychological mechanisms and multiple pathways. Full article

Financial institutions face data quality (DQ) challenges in regulatory reporting due to complex architectures where data flows through multiple systems. Data consumers struggle to assess quality because traditional DQ tools operate on data snapshots without capturing temporal event sequences and business contexts that determine whether anomalies represent genuine issues or valid behavior. Existing approaches address either semantic representation (ontologies for static knowledge) or temporal pattern detection (event processing without semantics), but not their integration. This paper presents CEDAR (Contextual Events and Domain-driven Associative Representation), integrating financial ontologies with event-driven processing for context-aware DQ assessment. Novel contributions include (1) ontology-driven rule derivation that automatically translates OWL business constraints into executable detection logic; (2) temporal ontological reasoning extending static quality assessment with event stream processing; (3) explainable assessment tracing anomalies through causal chains to violated constraints; and (4) standards-based design using W3C technologies with FIBO extensions. Following the Design Science Research Methodology, we document the first, early-stage iteration focused on design novelty and technical feasibility. We present conceptual models, a working prototype, controlled validation with synthetic equity derivative data, and comparative analysis against existing approaches. The prototype successfully detects context-dependent quality issues and enables ontological root cause exploration. Contributions: A novel integration of ontologies and event processing for financial DQ management with validated technical feasibility, demonstrating how semantic web technologies address operational challenges in event-driven architectures. Full article

The contribution of members of the agri-food system to achieving the Sustainable Development Goals is a key element in the global transition to sustainable development. The use of sustainable management systems supports the development of an integrated approach with a spirit of continuous improvement. Such organization is based on risk-management tools that are applied to multiple stakeholders, e.g., those responsible for product quality, occupational health and safety, and environmental impact, thus enabling better global performance. In this review, the term “ethical food systems” is used in our discussion of the concrete methods that can be used to endorse fairness and concern across the food chain. This comprises safeguarding equitable access to nutritious foods, defending animal welfare, assisting ecologically accountable production, and addressing social and labor justice within supply chains. Ethical factors also include transparency, cultural respect, and intergenerational responsibility. Consequently, the objective of this review is to address how these ethical values can be implemented within a One Health framework, predominantly by assimilating plant-based diets, developing governance tools, and resolving nutritional insecurity. Within the One Health framework, decoding ethical principles into practice necessitates a set of concrete interventions: (i) raising awareness of animal rights; (ii) distributing nutritional and environmental knowledge; (iii) endorsing plant-based food research, commercialization, and consumption; (iv) development of social inclusion and positive recognition of vegan/vegetarian identity. At the same time, it should be noted that this perspective represents only one side of the coin, as many populations continue to consume meat and rely on animal proteins for their nutritional value; thus, the role and benefits of meat and other animal-derived foods must also be recognized and discussed. This operational definition provides a foundation for asking how ethical perspectives can be applied. A case study from Africa shows the implementation of a sustainable and healthy future through the One Health approach. Full article

Background: The global livestock industry faces pressure to reduce antimicrobial usage while maintaining animal health and productivity. Non-pharmaceutical interventions (NPIs) including probiotics, prebiotics, phytogenics, essential oils, organic acids, and enzymes have emerged as alternatives to antibiotic growth promoters. Commercial success depends on consumer acceptance and willingness to pay (WTP) for products from animals raised using these approaches. Objective: This systematic review synthesized peer-reviewed literature examining consumer knowledge, attitudes, perceptions, and WTP toward animal products produced using NPIs or marketed as antibiotic-free (ABF) to identify a critical gap in existing research. Methods: Following PRISMA 2020 guidelines, four databases (PubMed, Web of Science, Scopus, and Google Scholar) were searched for peer-reviewed studies published from January 2020 to December 2024. Inclusion criteria encompassed original research examining consumer perspectives toward NPIs or antibiotic-free (ABF) animal products. Narrative synthesis was employed due to study heterogeneity. Results: From 847 records, 15 studies met inclusion criteria. A critical finding was that virtually no peer-reviewed research directly examines consumer perceptions of specific NPIs such as probiotics, prebiotics, phytogenics, organic acids, or enzymes as feed additives. The included studies predominantly examined ABF production generally (60%) without specifying alternatives employed. Europe accounted for 80% of studies, while Asia accounted for 20%. Consumer awareness of agricultural antibiotic use was consistently low across contexts. Attitudes toward ABF products were favorable with one study reporting WTP premiums of 18–20%. Health consciousness was the strongest predictor of acceptance. Conclusions: The review highlights that while substantial literature exists on ABF products, no studies examine consumer perceptions of specific non-pharmaceutical interventions. Future research should investigate consumer responses to intervention specific labeling and communication strategies. Full article

Increasing consumption of plant-based alternatives is promoted to reduce the environmental impact of food systems, yet adoption remains limited. The aim of this study was to identify distinct consumer segments and examine differences in their perceptions, consumption habits, and trial intentions concerning plant-based dairy alternatives (PBDAs). Conceptually, it advances PBDAs segmentation by jointly incorporating pro-dairy justifications, avoidance of animal-origin considerations, and self-reported PBDAs familiarity, capturing psychological defence mechanisms alongside knowledge-related influences on adoption. Data were collected in a nationwide cross-sectional CAWI survey of 1220 Polish adults responsible for household food purchasing, stratified and quota-matched by gender, age, region, and settlement size. Factor analysis of the segmenting variables was conducted using principal component analysis with varimax rotation, followed by two-step cluster analysis. Alternative cluster solutions were compared using the Bayesian Information Criterion based on the log-likelihood (BIC-LL). The selected five-cluster solution showed acceptable to good clustering quality, as indicated by silhouette-based measures of cohesion and separation. Given the cross-sectional CAWI design and reliance on self-reported measures, the findings do not allow causal inference and should be interpreted as context-specific to the Polish, dairy-centric food culture. Cluster analysis identified five segments that differed in PBDA-related beliefs, product image evaluations, consumption patterns, and trial intentions. PBDA-oriented segments, comprising a dairy-critical segment and a dual-consumption segment, exhibited higher perceived familiarity and stronger ethical and environmental concerns and showed greater PBDA use and willingness to try new products. The dual-consumption segment reported the highest use and trial readiness. In contrast, resistant segments showed stronger dairy attachment, lower perceived familiarity, and more sceptical evaluations of PBDAs’ healthfulness, naturalness, and sensory appeal, and rarely consumed plant-based alternatives. The findings highlight substantial heterogeneity in how Polish dairy consumers perceive PBDAs, emphasising the importance of segment-specific approaches for communication and product development. Tailored strategies can help address the diverse motivations and barriers of consumers, supporting a dietary shift toward more plant-based options. Full article

Indoor air quality (IAQ) in high-rise residential buildings is an increasing concern, especially in hot and humid climates where prolonged indoor exposure elevates health risks. This study evaluates the performance of Fresh Air Handling Units (FAHUs) using two complementary approaches: (1) real-time sensor data to quantify IAQ conditions and (2) occupant survey responses to capture perceived comfort and pollution indicators. The results show that floor level did not predict satisfaction, even though AQI data revealed clear differences between flats, suggesting perceptions are driven more by sensory cues than by actual pollutant levels. Longer weekday exposure emerged as a stronger predictor of dissatisfaction. These gaps between perceived and measured IAQ highlight the need for improved ventilation scheduling and greater occupant awareness. FAHUs were found to be inefficient, consuming 21–26% of total building energy while lacking pollutant-specific monitoring capabilities. To address these issues, the study recommends the integration of IoT-enabled sensors for real-time pollutant detection, enhanced facade sealing to minimize external infiltration, and the upgrade of filtration systems with HEPA filters and UV purification. Additionally, AI-driven predictive maintenance and automated ventilation optimization through Building Management Systems (BMS) are suggested. These findings offer valuable insights for improving IAQ management in high-rise buildings, with future research focusing on AI-based predictive modeling for dynamic air quality control. Full article

Achieving climate neutrality in construction requires more than available low-carbon technologies; it also depends on informed demand and consumers’ willingness to adopt sustainable materials. This paper examines ecological awareness, attitudes, and behavioral intentions toward eco-friendly building materials in Poland, using four independent waves of a nationwide online survey (CAWI) conducted in 2023 and 2025 (N ≈ 1000 per wave; adults aged 18–80). The questionnaires measured environmental awareness; willingness to pay a price premium (WTP) for properties built with eco-materials; actual purchasing behavior during renovations; support for regulations mandating developers’ use of ecological materials; and key socio-demographic factors. While the results confirm a pronounced attitude–behavior gap, the article details the research design and analytical approach, reports awareness, attitudes, and WTP across waves and subgroups, and discusses implications for “soft” interventions (e.g., norms, information, defaults) that can complement regulatory frameworks and financial incentives. It concludes with limitations and practical recommendations for policymakers, industry, and civil society to accelerate the adoption of low-emission materials. Full article

Background/Objectives: Craft non-alcoholic and low-alcohol beer (NABLAB) is attracting increasing attention as potential functional beverages due to their content of bioactive compounds such as polyphenols, vitamins, and minerals, and their suitability for health-oriented lifestyles. This study investigated Italian consumers’ perceptions of craft NABLAB and explored possible generational differences in their acceptance. Methods: A descriptive cross-sectional online survey was conducted between March 2024 and March 2025 among adults living in Italy. The questionnaire, composed entirely of closed-ended questions, investigated familiarity with craft NABLAB, attitudes toward their potential health-related properties, and willingness to recommend them. Results: A total of 527 valid responses were analyzed descriptively and grouped by generation (Generation Z, Millennials, Generation X, and Baby Boomers). Results showed that 68.3% of participants would recommend craft NABLAB to others interested in their functional properties, while 55.0% reported higher motivation to purchase when informed about their potential health benefits. Familiarity with these products remained limited (34.7% had tried them, and only 22.2% considered them easy to find). Baby Boomers and Millennials were more receptive, possibly due to greater health awareness and openness to innovation, whereas Generation Z displayed curiosity despite lower consumption experience. Conclusions: Overall, Italian consumers show a growing interest in craft NABLAB, especially when linked to wellness and active lifestyle benefits. Enhancing product availability and communication focused on health and functionality could promote more moderate and conscious drinking habits, contributing to a gradual cultural shift toward reduced alcohol consumption. Full article

To explore the mechanism of consumers’ battery electric vehicle (BEV) purchase behavior in depth and address research gaps related to insufficient consideration of psychological latent variables and neglect of consumer heterogeneity in existing studies, this study constructs a latent class model (LCM) that integrates personal attributes, vehicle attributes, and six psychological latent variables: perceived usefulness, perceived ease of use, perceived risk, environmental awareness, purchase attitude, and purchase intention. Based on 1044 valid questionnaires collected from Urumqi, latent profile analysis (LPA) is used to classify consumers. The results indicate that BEV consumers can be divided into five distinct latent profiles with significant differences in purchase preferences: the risk-avoidance type, the moderate–low intention wait-and-see type, the utility-oriented and low environmental concern type, the high utility cognition and low-risk proactive type, and the all-dimensional high-intention core type. Socioeconomic and vehicle-related factors exert heterogeneous impacts on the psychological variables and purchase decisions of each profile. This study clarifies the intrinsic psychological mechanism of BEV purchase behavior, providing a theoretical basis and targeted strategy references for the government and enterprises to promote BEV adoption and advance sustainable transportation development. Full article

Building high-quality multimodal instruction datasets is often time-consuming and costly. Recent studies have shown that a small amount of carefully selected high-quality data can be more effective for improving LVLM performance than large volumes of low-quality data. Based on these observations, we propose an error-guided multimodal sample selection framework with hallucination suppression for LVLM fine-tuning. First, semantic embeddings of queries are clustered to form balanced subsets that preserve task diversity. A visual contrastive decoding module is then used to reduce hallucinations and expose genuinely difficult examples. For closed-ended tasks, such as object detection, we estimate sample value using prediction accuracy; for open-ended question answering, we use the perplexity of generated responses as a difficulty signal. Within each cluster, high-error or high-perplexity samples are preferentially selected to construct a compact yet informative training set. Experiments on the InsPLAD detection benchmark and the PowerQA visual question answering dataset show that our method consistently outperforms random sampling under the same data budget, achieving higher F1, cosine similarity, BLEU (Bilingual Evaluation Understudy), and GPT-4o-based evaluation scores. This demonstrates that hallucination-aware, uncertainty-driven data selection can improve LVLM robustness and data efficiency. Full article

Transistor-level Integrated Circuit (IC) design is fundamental to modern electronics, yet it remains one of the most expertise-intensive and time-consuming stages of chip development. As circuit complexity continues to rise, the need to automate this low-level design process has become critical to sustaining innovation and productivity across the semiconductor industry. This study presents a fully automated methodology for transistor-level IC design using a novel framework that integrates Grammatical Evolution (GE) with Cadence SKILL code. Beyond automation, the framework explicitly examines how symmetry and asymmetry shape the evolutionary search space and resulting circuit structures. To address the time-consuming and expertise-intensive nature of conventional integrated circuit design, the framework automates the synthesis of both digital and analogue circuits without requiring prior domain knowledge. A specialised attribute grammar (AG) evolves circuit topology and sizing, with performance assessed by a multi-objective fitness function. Symmetry is analysed at three levels: (i) domain-level structural dualities (e.g., NAND/NOR mirror topologies and PMOS/NMOS exchanges), (ii) objective-level symmetries created by logic threshold settings, and (iii) representational symmetries managed through grammatical constraints that preserve valid connectivity while avoiding redundant isomorphs. Validation was carried out on universal logic gates (NAND and NOR) at multiple logic thresholds, as well as on a temperature sensor. Under stricter thresholds, the evolved logic gates display emergent duality, converging to mirror-image transistor configurations; relaxed thresholds increase symmetric plateaus and slow convergence. The evolved logic gates achieve superior performance over conventional Complementary Metal–Oxide–Semiconductor (CMOS), Transmission Gate Logic (TGL), and Gate Diffusion Input (GDI) implementations, demonstrating lower power consumption, a reduced Power–Delay Product (PDP), and fewer transistors. Similarly, the evolved temperature sensor exhibits improved sensitivity, reduced power, and Integral Nonlinearity (INL), and a smaller area compared to the conventional Proportional to Absolute Temperature (PTAT) or “gold” circuit, without requiring resistors. The analogue design further demonstrates beneficial asymmetry in device roles, breaking canonical structures to achieve higher performance. Across all case studies, the evolved designs matched or outperformed their manually designed counterparts, demonstrating that this GE-based approach provides a scalable and effective path toward fully automated, symmetry-aware integrated circuit synthesis. Full article