Search Results (16,408)

Green hydrogen production via water electrolysis is a cornerstone of the sustainable energy transition. However, the oxygen evolution reaction (OER) remains the kinetic bottleneck, limiting overall efficiency. Nickel–iron (NiFe)-based catalysts are among the most promising nonprecious materials for the OER in alkaline media, offering high activity and low cost. Nevertheless, their practical application at industrially relevant current densities (>100 mA cm⁻²) is hindered by several challenges: structural degradation, uncontrolled surface reconstruction, metal dissolution (corrosion), particularly Fe leaching, and the ambiguous role of the fundamental mechanisms. This review critically discusses the current understanding of these degradation pathways, the influence of preparation methods, the interplay between Ni and Fe redox chemistry, and strategies for enhancing long-term stability. Future directions for designing durable NiFe OER electrocatalysts are also outlined. The paper also considers a strategy for investigating new catalysts using electrochemical and non-electrochemical techniques, devoted to young scientists interested in this field. In the Outlook and Perspective section, the key drawback is presented, and a possible strategy for improvement is discussed. Full article

This study presents a systematic review of the literature on business localness, a concept which, despite increasing interest, remains theoretically fragmented. This fragmentation limits a comprehensive understanding of how firms are embedded in local economies and how localness can be systematically measured. Using the PRISMA methodology, 54 articles were analyzed from an initial set of 500 publications in the Scopus database. Combining bibliometric clustering and qualitative synthesis, the study identifies seven major research clusters and organizes them into broader research streams. The findings suggest that localness is primarily examined in terms of its relational, spatial and economic dimensions. Mechanisms such as knowledge diffusion, trust and access to resources are emphasized, as are outcomes relating to innovation, business performance, sustainability and regional development. However, the economic dimension remains fragmented and is rarely conceptualized as a distinct and measurable component of business localness. Combining findings from previously fragmented research streams, this study develops an integrative framework for business localness that incorporates spatial, relational, and economic dimensions of embeddedness and links them to the mechanisms and outcomes through which firms contribute to local economies. The study is limited by its reliance on English-language journal articles indexed in Scopus and by the conceptual nature of the proposed framework, which requires further empirical validation across different contexts and industries. By explicitly introducing economic embeddedness as a distinct analytical dimension, the framework extends existing embeddedness theory and provides a foundation for future empirical research on how firms contribute to local economic development and sustainability. Full article

Construction cost estimation often relies on subjective expert judgment, which introduces variability and inconsistency. Standardizing data and procedures can improve reliability and enable repeatable workflows. This research investigates how price lists used for public construction can be semantically linked to Building Information Modeling (BIM) via the Industry Foundation Classes (IFC) standard to support objective, repeatable, semi-automated model-to-cost estimation. By an inductive case-based design, the work uses Veneto Region price list and maps selected cost items to IFC properties. Six representative price list items (slabs, partition walls, plasterboards, plasters, doors, and windows) are examined to identify discriminating parameters (e.g., material, thickness, dimensions, fire rating) that are mappable to IFC entities and property sets. The methodology distinguishes primary charges from surcharges, then assesses the model-ability of parameters and their semantic coherence within BIM’s object-based paradigm. Findings show that through formalization and standardization of cost item characteristics via IFC properties, the approach reduces subjectivity, enabling structured and objective matching and laying the groundwork for future automated workflows. Limitations are discussed, including incomplete representation of some cost-driving attributes, reliance on naming conventions, and opportunities associated with Digital Product Passport implementation (DPP). Full article

With the increasing importance of green design in the business landscape, designers are compelled to shift towards eco-design practices. However, existing methodologies face challenges related to resource requirements, abstract concepts, and industry specificity. To address these challenges and stimulate innovation, this study proposes a green design methodology that integrates TRIZ concepts and is anchored in TRIZ evolutionary trends. The methodology includes function and attribute analysis, the introduction of green features, the identification of TRIZ trends through a two-stage process, and the use of a developed system to improve calculation efficiency. Detailed design solutions are generated by combining green features, TRIZ trends, and inventive principles. A case study validates the methodology, showcasing its value in promoting sustainable development. By leveraging the evolutionary potential of products and incorporating TRIZ, the methodology offers a promising approach to address sustainability challenges and drive innovation. This research serves as a starting point for a practical and efficient design methodology that utilizes TRIZ concepts and a computer-aided application tool. Future steps involve stress-testing the methodology and exploring its application in different domains. Full article

Facing the severe environmental challenge of massive red mud (RM) stockpiles, iron extraction research is accelerating from traditional pyrometallurgy and other conventional processes toward green low-carbon, multi-source synergistic, and total-component high-value utilization approaches—a transition that continues to evolve. This review systematically examines three frontiers: green reduction technologies, synergistic valorization via waste-treating-waste, and integrated cascading strategies for total-component high-value utilization. Evaluation focuses on the principles, advantages, and challenges of biomass reduction, hydrogen metallurgy, selective flocculation, advanced heating techniques, co-processing with other solid wastes, and multi-metal cascading extraction. Evidence suggests that future RM iron extraction technology lies in establishing cross-industry circular economy networks, transforming RM from a singular waste into a resource hub linking aluminum, steel, and construction industries to maximize environmental and economic benefits. Full article

With the continuous advancement of ocean exploration and development, energy supply for underwater electronic equipment has become a key bottleneck restricting long-term operation. Traditional wired power supply and battery-powered operation suffer from corrosion, high maintenance costs, limited endurance, and replacement difficulties. Underwater ultrasonic wireless power transfer (UUWPT) achieves contactless electric–acoustic–electric conversion via piezoelectric transducers. It offers unique advantages, including insensitivity to electromagnetic interference, metal-penetration capability, excellent directivity, and medium-to-long-distance transmission. This paper systematically reviews the technical principles and development history of UUWPT. We trace its evolution from early feasibility verification, through theoretical improvements, to current system engineering and industrialization. Key frontier research directions are highlighted, such as MIMO/MISO arrays, simultaneous wireless power and data transfer (SWPDT), adaptive tuning, and novel transducer structures. Application prospects in marine monitoring, AUV endurance replenishment, marine energy development, and the Internet of Underwater Things are also analyzed. Finally, we discuss remaining challenges, including the trade-off between transmission efficiency and distance, insufficient adaptability to complex marine environments, and the lack of standardized system frameworks. Future research should prioritize high-efficiency long-distance power transfer, system reliability, and engineering applications. Full article

This paper combines the application of AI technology in the field of architectural design to construct a “three-stage model” (imitation, exploration, and creation) centered on cultivating students’ creative thinking and innovative ability, with the goals of AI literacy cultivation, digital twin practice, and interdisciplinary collaboration. By integrating the theoretical model with the latest practical cases, the effectiveness of the new generation of AI-driven innovative teaching modes is verified. Taking library architectural design and old building renovation teaching as examples, the teaching process and evaluation system with real-time feedback, intelligent assessment, and full-process traceability are designed to achieve the dual improvement of teaching efficiency and students’ practical innovation ability. The research shows that the characteristics of artificial intelligence, including multimodal generation, immersive interaction, and full-cycle simulation, are reconstructing the core logic of architectural design education, promoting the in-depth transformation of the teaching mode from “imitation” to “creation”, building a talent cultivation system adapted to the future development of the construction industry, and providing a feasible reference path for the innovation of education modes. Full article

The increasing interdependence between environmental degradation, geopolitical instability, and public-health pressures requires structured information-system architectures capable of integrating heterogeneous data and transforming them into decision-support knowledge. In this context, this study develops a data-driven information system architecture for the exploratory analysis of environmental, geopolitical, and health-related risks in the EU-27 during 2013–2023. The proposed system is structured as a multi-layered analytical pipeline designed to process country-year panel data and generate interpretable outputs. The methodological framework integrates Principal Component Analysis (PCA) for exploratory dimensionality reduction, K-Means clustering for structural pattern identification, a RandomTree classification model for translating cluster membership into decision rules, and a Two-Part Fixed Effects Model. Experimental results indicate an optimal and interpretable clustering configuration at k = 3, revealing three broad structural profiles among EU Member States. A moderate positive relationship is identified between greenhouse gas emissions per capita (GHGE) and health expenditure (SHA) (r = 0.34), while geopolitical risk (GPR) exhibits weak and statistically insignificant associations. This association is interpreted cautiously, as it may reflect the combined effect of industrial activity, environmental exposure, economic development, and the higher financial capacity of some Member States to allocate resources to healthcare systems. The results indicate the dominant contribution of GHGE and SHA in differentiating the identified profiles, while GPR shows limited explanatory power within the analyzed context. The RandomTree model achieved an accuracy of 93.58% in reproducing the cluster labels; however, it is used as an interpretability layer rather than as an independent validation of clustering. The system supports the identification of vulnerability-related structural patterns and provides an exploratory basis for future data-driven monitoring and early-warning applications. Full article

This article systematically reviews the structural characteristics of layered double hydroxides and biochar (LDHs/biochar) composites, summarizes the features and optimization strategies of preparation methods such as coprecipitation, hydrothermal synthesis, ball milling, and calcination–reconstruction, analyzes their adsorption performance and mechanisms in controlling various water pollutants including organic contaminants, heavy metals, and nutrients, and provides insights into future research trends and practical applications, aiming to offer references for improving material performance and promoting practical use. The existing research results show that LDHs/biochar composites exhibit good application potential for various pollutants, such as dyes, antibiotics, heavy metal ions, and phosphates. The coprecipitation method is simple and easy to operate, and the LDHs/biochar composites prepared by this method exhibit favorable adsorption performance, with potential for industrial-scale production. The mechanisms of pollutant removal by LDHs/biochar composites primarily include electrostatic attraction, ion exchange, hydrogen bonding, complexation, and π–π electron interactions. Both the biomass type and the LDH type influence the adsorption performance of the composites. Therefore, designing LDHs/biochar composites based on pollutant characteristics and adsorption mechanisms is key to achieving effective pollution control. Currently, research on target pollutant-oriented material design and material regeneration remains underdeveloped and requires further breakthroughs. Full article

This review evaluates multi-omics applications in dairy research across nutrition, safety, and health. Through multi-omics integration, we reveal nutrient differences driven by species, rearing practices, and processing techniques, identify protein patterns and allergen profiles, and construct adulteration detection fingerprints and species-specific peptide markers, thereby improving the timeliness and accuracy of safety assessment. The coupling of metagenomics and metabolomics effectively predicts spoilage-related microbial risks, enabling better risk control. Furthermore, multi-omics approaches systematically elucidate the functional mechanisms of bioactive peptides (e.g., ACE-inhibitory peptides), clarify the prebiotic effects of functional oligosaccharides, and build interaction networks between dairy components and gut microbiota. The introduction of machine learning enables origin and shelf-life prediction, as well as the discovery of novel biomarkers, promoting personalized nutrition and precision fermentation strategies. However, the field is currently constrained by severe reproducibility issues arising from the absence of standardized operating procedures, excessive optimism regarding machine learning models that rarely generalize across laboratories or product matrices, and a persistent disconnect between laboratory-scale biomarker discovery and industrial implementation. Without rigorous cross-platform validation and openly shared multi-omics reference datasets, most published markers remain unfit for regulatory or industrial application. Future efforts should establish standardized workflows and expand the evidence base to drive the dairy industry toward safer, healthier, and more traceable directions. Full article

The global cattle industry is experiencing significant growth, requiring advanced methods for monitoring animal health and welfare to ensure productivity and sustainability. Traditional manual monitoring techniques are labor-intensive and often impractical for large-scale operations. This review provides a comprehensive analysis of existing and emerging computer vision tools applied to the monitoring of cattle health and welfare. By systematically examining studies across major databases, this paper addresses six key research questions focusing on (1) the issues addressed by computer vision technologies, (2) data acquisition systems, (3) implemented techniques and algorithms, (4) performance outcomes, (5) challenges faced, and (6) potential applications for underexplored health and welfare aspects in cattle farming. The findings show that computer vision technologies have significantly progressed in areas such as body condition score detection, lameness detection, weight estimation, estrus detection, monitoring of feeding and drinking behavior, breathing detection, and recognition of general behaviors. Despite the progress, challenges such as variability in environmental conditions, the need for large annotated datasets, and the high cost of advanced imaging equipment persist. The review emphasizes future research opportunities to address these challenges by focusing on disease-specific monitoring. This review aims to provide veterinarians, farmers, and animal health professionals with greater insight into computer vision technologies and to promote their adoption by discussing their practical applications. Full article

Mobile robots used in edge-assisted Industrial Internet-of-Things (IIoT) settings generate coupled motion, LiDAR, edge-compute, and network telemetry. Public datasets that place these streams in one tabular format, with scenario labels suitable for machine-learning experiments, are still limited. This data descriptor presents a pilot-real-calibrated indoor robotic IoT benchmark dataset with 120,000 records sampled at 2 Hz across nominal navigation and nine anomaly scenarios. The benchmark rows are generated from physically constrained simulation rules and are explicitly labeled as synthetic benchmark data. Real pilot evidence is included separately: ROS Noetic runs on a TurtleBot3 Burger, successful LD08 LiDAR bringup after resolving a driver mismatch, and NVIDIA Jetson Nano tegrastats logs under normal-navigation workloads. The calibrated file aligns normal-navigation LiDAR and edge-compute distributions with these pilot measurements while keeping the multi-scenario structure needed for controlled anomaly-detection experiments. The package includes CSV files, metadata, a data dictionary, validation reports, baseline scripts, ROS collection utilities, and a plan for future fully physical data collection. The complete dataset is openly available on Zenodo. Full article

As industrial robots become increasingly essential to modern manufacturing and automation systems, ensuring their durability and operational integrity has emerged as a key concern. Traditional defect detection methods typically depend on labeled datasets and supervised learning techniques, which can be difficult and impractical to implement in real-world industries. In contrast, unsupervised learning presents a compelling alternative by facilitating anomaly detection and fault diagnosis without the need for labeled data. This article offers a thorough analysis of unsupervised learning techniques used in the health monitoring of industrial robots. We explore significant trends and key algorithms, such as clustering, autoencoders, and generative models, assessing their effectiveness in identifying faults and performance degradation. The research addresses the unique challenges associated with high-dimensional sensor data, variable operating conditions, and the lack of ground truth labels. Additionally, we highlight unresolved research questions and potential future directions, emphasizing the need for scalable, interpretable, and real-time solutions. This survey serves as a foundational reference for researchers and practitioners aiming to develop resilient and autonomous health monitoring systems for industrial robots. Full article

This review shows that lignocellulosic biomass is not merely an abundant feedstock for nanocellulose production but a strategic platform for building the next generation of sustainable, high-performance materials, integrating feedstock diversity, processing logic, characterization, market direction, and translational applications into a single narrative. Comparing woody and non-woody biomass through the lens of processability, recalcitrance, and value creation while showing why agricultural residues are increasingly central to low-cost, circular nanocellulose production beyond the usual acid-hydrolysis-centered discussion by emphasizing enzymatic hydrolysis as a lower-energy, lower-toxicity alternative while still acknowledging the persistent industrial advantages and environmental costs of chemical and mechanical routes. A further strength of this review is its effort to bridge structure and function: it links extraction strategy to morphology, crystallinity, thermal stability, and surface chemistry, then connects these properties to real applications in packaging, drug delivery, electronics, filtration, energy storage, and biomedical systems. Its distinctive contribution lies in showing that the future of nanocellulose depends not only on how it is extracted but also on how intelligently the biomass source, processing route, material performance, and market need are aligned. Full article

Outdoor industrial sites, including logistics terminals, construction yards, and civil infrastructure worksites, increasingly require supervisory systems for monitoring autonomous mobile units under variable wireless and operational conditions. This study presents an edge-cloud telemetry platform that connects V2X on-board and roadside units to a normalized data pipeline and an operator dashboard. The architecture assigns frame reception and data validation to the edge layer, while cloud services perform stream ingestion, storage, querying, and visualization using a Kafka-Elasticsearch-Grafana stack. A fixed supervisory schema was defined for position, heading, speed, mission state, battery level, and error flags so that virtual fields used in early validation can later be replaced by measured signals without changing downstream interfaces. Physical field validation was conducted using a single test vehicle in a construction-site emulation environment to evaluate communication continuity and dashboard refresh behavior. Multi-unit applicability was examined at the architecture and schema levels, and a preliminary payload-level capacity estimate was derived using the telemetry frequency and payload-length assumptions. Under the tested site conditions, the system maintained continuous reception and visualization over an approximately 700 m distance from the RSU-side reference location. The measured end-to-end display delay averaged 0.78 s, with a standard deviation of 0.059 s and a maximum of 0.96 s. Under a 10 Hz status-message condition, the estimated pure-payload traffic was approximately 23 kbps per mobile unit. These results indicate that V2X-based edge-cloud telemetry can provide a practical baseline for supervisory monitoring in outdoor industrial sites, while simultaneous multi-vehicle validation, detailed network-load evaluation, and long-term field testing remain necessary future work. Full article