Search Results (3,144)

The integration of machine learning (ML) into industrial automation is fundamentally reshaping how manufacturing systems are monitored, inspected, and optimized. By applying machine learning to real-time sensor data and operational histories, advanced models enable proactive fault prediction, intelligent inspection, and dynamic process control—directly enhancing system reliability, product quality, and efficiency. This review explores the transformative role of ML across three key domains: Predictive Maintenance (PdM), Quality Control (QC), and Process Optimization (PO). It also analyzes how Digital Twin (DT) and Edge AI technologies are expanding the practical impact of ML in these areas. Our analysis reveals a marked rise in deep learning, especially convolutional and recurrent architectures, with a growing shift toward real-time, edge-based deployment. The paper also catalogs the datasets used, the tools and sensors employed for data collection, and the industrial software platforms supporting ML deployment in practice. This review not only maps the current research terrain but also highlights emerging opportunities in self-learning systems, federated architectures, explainable AI, and themes such as self-adaptive control, collaborative intelligence, and autonomous defect diagnosis—indicating that ML is poised to become deeply embedded across the full spectrum of industrial operations in the coming years. Full article

Collagen implants in neurosurgery are widely used due to their biocompatibility, biodegradability, and ability to support tissue regeneration, but their mechanical properties, such as low tensile strength and susceptibility to enzymatic degradation, remain challenging. Current technologies are improving these implants through cross-linking, synthetic reinforcements, and advanced manufacturing techniques such as 3D bioprinting to improve durability and predictability. Industry 4.0 is contributing to this by automating production, using data analytics and machine learning to optimize implant properties and ensure quality control. In Industry 5.0, the focus is shifting to personalization, enabling the creation of patient-specific implants through human–machine collaboration and advanced biofabrication. eHealth integrates digital monitoring systems, enabling real-time tracking of implant healing and performance to inform personalized care. Despite progress, challenges such as cost, material property variability, and scalability for mass production remain. The future lies in smart biomaterials, AI-driven design, and precision biofabrication, which could mean the possibility of creating more effective, accessible, and patient-specific collagen implants. The aim of this article is to examine the current state and determine the prospects for the development of mechanical properties of collagen implant used in neurosurgery towards Industry 4.0/5.0, including ML model. Full article

Flammulina filiformis, one of the most delicious and commercially important mushrooms, demonstrates remarkable adaptability to diverse agricultural wastes. However, it is unclear how different substrates affect the degradation of lignocellulosic biomass and the production of lignocellulolytic enzymes in F. filiformis. In this study, label-free comparative proteomic analysis of F. filiformis cultivated on sugarcane bagasse, cotton seed shells, corn cobs, and glucose substrates was conducted to identify degradation mechanism across various substrates. Label-free quantitative proteomics identified 1104 proteins. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of protein expression differences were predominantly enriched in energy metabolism and carbohydrate metabolic pathways. Detailed characterization of carbohydrate-active enzymes among the identified proteins revealed glucanase (GH7, A0A067NSK0) as the key enzyme. F. filiformis secreted higher levels of cellulases and hemicellulases on sugarcane bagasse substrate. In the cotton seed shells substrate, multiple cellulases functioned collaboratively, while in the corn cobs substrate, glucanase predominated among the cellulases. These findings reveal the enzymatic strategies and metabolic flexibility of F. filiformis in lignocellulose utilization, providing novel insights for metabolic engineering applications in biotechnology. The study establishes a theoretical foundation for optimizing biomass conversion and developing innovative substrates using targeted enzyme systems. Full article

Against the backdrop of multifaceted strategies to combat climate change, understanding soil erosion’s role in carbon cycling is critical due to terrestrial carbon pool vulnerability. This study integrates bibliometric methods with visualization tools (CiteSpace, VOSviewer) to analyze 3880 Web of Science core publications (1994–2024, inclusive), constructing knowledge graphs and forecasting trends. The results show exponential publication growth, shifting from slow development (1994–2011) to rapid expansion (2012–2024), aligning with international climate policy milestones. The Chinese Academy of Sciences led productivity (519 articles), while the US demonstrated major influence (H-index 117; 52,297 citations), creating a China–US bipolar research pattern. It was also found that Dutch journals dominate this research field. A keyword analysis revealed a shift from erosion-driven carbon transport to ecosystem service assessments. Emerging hotspots include microbial community regulation, climate–erosion feedback, and model–policy integration, though developing country collaboration remains limited. Future research should prioritize isotope tracing, multiscale modeling, and studies in ecologically vulnerable regions to enhance global soil carbon management. This study provides a novel analytical framework and forward-looking perspective for the soil erosion research on soil carbon cycling, serving as an extension of climate change mitigation strategies. Full article

To address the data acquisition limitations of traditional flow balance methods that stem from insufficient flow rate measurements, this study establishes a pipeline flow calculation model based on the pressure data and proposes a pipeline leak identification approach for product oil pipelines. Firstly, field leak tests are designed and conducted on a product oil pipeline in East China by discharging oil in a valve chamber to simulate the leak process. Subsequently, combining the Bernoulli equation with the Leapienzon formula, a calculation model is established for flow rate prediction using the pressure data monitored at the stations and valve chambers along the pipeline. By analyzing the instantaneous flow rate changes at each pipeline section and pressure drops at each station and valve chamber, a dual-parameter collaborative threshold is set based on the flow balance principle, and leaks are identified when both parameters exceed the threshold simultaneously. Finally, the proposed flow rate calculation model and leak identification method are validated with respect to the field test data. The results show that the flow rate model yields a relative error as low as 0.48%, and the leak identification method accurately captured all six leak events in the field test, indicating very good stability and accuracy, with great potential for leak identification and alarm systems for product oil pipelines in engineering applications. Full article

The sharing economy is considered to be a potentially efficacious approach for promoting sustainable production and consumption (SPC). This study utilizes dockless bike sharing (DBS) in Beijing as a case study to examine how sharing economy policies and business practices advance SPC. It also dynamically quantifies the environmental and economic performance of DBS practices from a life cycle perspective. The findings indicate that effective SPC practices can be achieved through the collaborative efforts of multiple stakeholders, including the government, operators, manufacturers, consumers, recycling agencies, and other business partners, supported by regulatory systems and advanced technologies. The SPC practices markedly improved the sustainability of DBS promotion in Beijing. This is evidenced by the increase in greenhouse gas (GHG) emission reduction benefits, which have risen from approximately 35.81 g CO₂-eq to 124.40 g CO₂-eq per kilometer of DBS travel. Considering changes in private bicycle ownership, this value could reach approximately 150.60 g CO₂-eq. Although the economic performance of DBS operators has also improved, it remains challenging to achieve profitability, even when considering the economic value of the emission reduction benefits. In certain scenarios, DBS can maximize profits by optimizing fleet size and efficiency, without compromising the benefits of emission reductions. The framework of stakeholder interaction proposed in this study and the results of empirical analysis not only assist regulators, businesses, and the public in better understanding and promoting sustainable production and consumption practices in the sharing economy but also provide valuable insights for achieving a win-win situation of platform profitability and environmental benefits in the SPC practice process. Full article

Nigerian poverty research is often fragmented and focuses on samples with minimal actionable strategies. This study aims to identify essential poverty alleviation and climate change strategies by synthesizing existing research, extracting the most critical poverty alleviation and climate change factors, and assessing strategies to combat poverty and climate change in Nigeria. We obtained, utilizing the centrality measures of social network analysis and the visualization tools of bibliometric analysis, the research hotspots extracted from 119 articles from the SCOPUS database for the period 1994–2023, compared outcomes with other countries, and analyzed their implications for eradicating poverty in Nigeria. We find that low agricultural productivity and food insecurity are some of the essential poverty-engendering factors in Nigeria, which are being intensified by climate change irregularities. Also, researchers demonstrate weak collaboration and synergy, as only 0.02% of researchers collaborated. Our findings highlight the need to direct poverty alleviation efforts to the key areas identified in this study and increase cooperation between poverty alleviation and climate researchers. Full article

In today’s volatile supply chain environment, organizations require flexible and collaborative procurement strategies. Swap contracts, originally developed as financial instruments, have recently been adopted to address inventory imbalances—such as the 2021 COVID-19 vaccine swap between South Korea and Israel. Despite its increasing adoption in the real world, theoretical studies on swap-based procurement remain limited. This study proposes an integrated model that combines buyer-to-buyer swap agreements with long-term wholesale contracts under demand uncertainty. The model quantifies the expected swap quantity between parties and embeds it into the profit function to derive optimal order quantities. Numerical experiments are conducted to compare the performance of the proposed strategy with that of a baseline wholesale contract. Sensitivity analyses are performed on key parameters, including demand asymmetry and swap prices. The numerical analysis indicates that the swap-integrated procurement strategy consistently outperforms procurement based on long-term wholesale contracts. Moreover, the results reveal that under the swap-integrated strategy, the optimal order quantity must be adjusted—either increased or decreased—depending on the demand scale of the counterpart and the specified swap price, deviating from the optimal quantity under traditional long-term contracts. These findings highlight the potential of swap-integrated procurement strategies as practical coordination mechanisms across both private and public sectors, offering strategic value in contexts such as vaccine distribution, fresh produce, and other critical products. Full article

Background/Objectives: Collaboration in mental health care is essential for implementing a model oriented towards the psychosocial rehabilitation of people based on multifaceted interventions involving different actors and sectors of society to respond to demands. Despite the benefits presented by the scientific evidence, there are still many barriers to collaborative care, and professionals continue to struggle in reorienting their conduct. The current situation demands organization and the framing of well-founded action plans to overcome challenges, which in turn requires a detailed understanding of collaborative practices in mental health care and their conceptual boundaries. A concept analysis was undertaken to propose a working definition of collaborative practices in mental health care (CPMHC). Methods: This paper used the Walker and Avant concept analysis method. This includes identifying the defining concept attributes, antecedents, consequences, and empirical referents. A literature search was carried out from November 2024 to February 2025 in three databases (Medline, CINAHL, and LILACS), considering studies published between 2010 and 2024. Results: The final sample of literature investigated consisted of 30 studies. The key attributes were effective communication, building bonds, co-responsibility for care, hierarchical flexibility, articulation between services, providers and community, monitoring and evaluating of care processes, and attention to the plurality of sociocultural contexts. Conclusions: This comprehensive analysis contributes to guiding future research and policy development of collaborative practices in mental health, considering the individual, relational, institutional, and social levels. Further research is possible to deepen the understanding of the production of collaborative practices in mental health in the face of the complexity of social relations and structural inequities. Full article

The construction industry has been troubled by a shortage of skilled labor and safety accidents in recent years. Therefore, more and more robots are introduced to undertake dangerous and repetitive jobs, so that human workers can concentrate on higher-value and creative problem-solving tasks. Nevertheless, although human–robot collaboration (HRC) shows great potential, most existing evaluation methods still focus on the single performance of either the human or robot, and systematic indicators for a whole HRC team remain insufficient. To fill this research gap, the present study constructs a comprehensive evaluation framework for HRC team performance in construction projects. Firstly, a detailed literature review is carried out, and three theories are integrated to build 33 indicators preliminarily. Afterwards, an expert questionnaire survey (N = 15) is adopted to revise and verify the model empirically. The survey yielded a Cronbach’s alpha of 0.916, indicating excellent internal consistency. The indicators rated highest in importance were task completion time (µ = 4.53) and dynamic separation distance (µ = 4.47) on a 5-point scale. Eight indicators were excluded due to mean importance ratings falling below the 3.0 threshold. The framework is formed with five main dimensions and 25 concrete indicators. Finally, an AHP-TOPSIS method is used to evaluate the HRC team performance. The AHP analysis reveals that Safety (weight = 0.2708) is prioritized over Productivity (weight = 0.2327) by experts, establishing a safety-first principle for successful HRC deployment. The framework is demonstrated through a case study of a human–robot plastering team, whose team performance scored as fair. This shows that the framework can help practitioners find out the advantages and disadvantages of HRC team performance and provide targeted improvement strategies. Furthermore, the framework offers construction managers a scientific basis for deciding robot deployment and team assignment, thus promoting safer, more efficient, and more creative HRC in construction projects. Full article

The European Union promotes decarbonization in energy-intensive industries like glass manufacturing. Collaboration between industry and researchers focuses on reducing CO₂ emissions through hydrogen (H₂) integration as a natural gas substitute. However, to the best of the authors’ knowledge, no updated real-world case studies are available in the literature that consider the on-site implementation of an electrolyzer for autonomous hydrogen production capable of meeting the needs of a glass manufacturing plant within current technological constraints. This study examines a representative hollow glass plant and develops various decarbonization scenarios through detailed process simulations in Aspen Plus. The models provide consistent mass and energy balances, enabling the quantification of energy demand and key cost drivers associated with H₂ integration. These results form the basis for a scenario-specific techno-economic assessment, including both on-grid and off-grid configurations. Subsequently, the analysis estimates the levelized costs of hydrogen (LCOH) for each scenario and compares them to current and projected benchmarks. The study also highlights ongoing research projects and technological advancements in the transition from natural gas to H₂ in the glass sector. Finally, potential barriers to large-scale implementation are discussed, along with policy and infrastructure recommendations to foster industrial adoption. These findings suggest that hybrid configurations represent the most promising path toward industrial H₂ adoption in glass manufacturing. Full article

Marine biomass, particularly from waste streams, by-products, underutilized, invasive, or potential cultivable marine species, offers a sustainable source of high-value biopolymers such as collagen and chitin. These macromolecules have gained significant attention due to their biocompatibility, biodegradability, functional versatility, and broad applicability across health, food, wellness, and environmental fields. This review highlights recent advances in the uses of marine-derived collagen and chitin/chitosan. In alignment with the United Nations Sustainable Development Goals (SDGs), we analyze how these applications contribute to sustainability, particularly in SDGs related to responsible consumption and production, good health and well-being, and life below water. Furthermore, we contextualize the advancement of product development using marine collagen and chitin/chitosan within the European Union’s Blue bioeconomy strategies, highlighting trends in scientific research and technological innovation through bibliometric and patent data. Finally, the review addresses challenges facing the development of robust value chains for these marine biopolymers, including collaboration, regulatory hurdles, supply-chain constraints, policy and financial support, education and training, and the need for integrated marine resource management. The paper concludes with recommendations for fostering innovation and sustainability in the valorization of these marine resources. Full article

With the accelerating globalization and rapid development of science and technology, scientific collaboration has become a key driver of knowledge production, yet its patterns vary significantly across disciplines. This study aims to explore the disciplinary differences in scholars’ scientific collaboration patterns and their underlying mechanisms. Data were collected from the China National Knowledge Infrastructure (CNKI) database, covering papers from four disciplines: mathematics, mechanical engineering, philosophy, and sociology. Using social network analysis, we examined core network metrics (degree centrality, neighbor connectivity, clustering coefficient) in collaboration networks, analyzed collaboration patterns across scholars of different academic ages, and compared the academic age distribution of collaborators and network characteristics across career stages. Key findings include the following. (1) Mechanical engineering exhibits the highest and most stable clustering coefficient (mean 0.62) across all academic ages, reflecting tight team collaboration, with degree centrality increasing fastest with academic age (3.2 times higher for senior vs. beginner scholars), driven by its reliance on experimental resources and skill division. (2) Philosophy shows high degree centrality in early career stages (mean 0.38 for beginners) but a sharp decline in clustering coefficient in senior stages (from 0.42 to 0.17), indicating broad early collaboration but loose later ties due to individualized knowledge production. (3) Mathematics scholars prefer collaborating with high-centrality peers (higher neighbor connectivity, mean 0.51), while sociology shows more inclusive collaboration with dispersed partner centrality. Full article

Flocculants are indispensable in water and wastewater treatment, enabling the aggregation and removal of suspended particles, colloids, and emulsions. However, the conventional development and application of flocculants rely heavily on empirical methods, which are time-consuming, resource-intensive, and environmentally problematic due to issues such as sludge production and chemical residues. Recent advances in machine learning (ML) have opened transformative avenues for the design, optimization, and intelligent application of flocculants. This review systematically examines the integration of ML into flocculant research, covering algorithmic approaches, data-driven structure–property modeling, high-throughput formulation screening, and smart process control. ML models—including random forests, neural networks, and Gaussian processes—have successfully predicted flocculation performance, guided synthesis optimization, and enabled real-time dosing control. Applications extend to both synthetic and bioflocculants, with ML facilitating strain engineering, fermentation yield prediction, and polymer degradability assessments. Furthermore, the convergence of ML with IoT, digital twins, and life cycle assessment tools has accelerated the transition toward sustainable, adaptive, and low-impact treatment technologies. Despite its potential, challenges remain in data standardization, model interpretability, and real-world implementation. This review concludes by outlining strategic pathways for future research, including the development of open datasets, hybrid physics–ML frameworks, and interdisciplinary collaborations. By leveraging ML, the next generation of flocculant systems can be more effective, environmentally benign, and intelligently controlled, contributing to global water sustainability goals. Full article

This study analyzes the relationship between ergonomic and psychosocial risk factors and labor productivity using a bibliometric approach through a general analysis and one that includes inclusion criteria such as English language, open access, and primary research publications to identify only those articles that explicitly address the relationship between ergonomic and psychosocial risk factors and labor productivity. It is recognized that both physical and psychosocial conditions of the work environment directly influence workers’ health and organizational performance. For this purpose, a bibliometric review was conducted in academic databases, including Scopus, Web of Science, ScienceDirect, and Taylor & Francis, resulting in the selection of 4794 relevant articles for general analysis. Additionally, 116 relevant articles were selected based on the inclusion criteria. Tools and methodologies, such as Rayyan, Excel, VOSviewer 1.6.20, and PRISMA, were used to classify the studies and identify trends, collaboration networks, and geographical distribution. The results reveal a sustained growth in scientific production, with clusters on occupational safety and health, work environment factors, and the characteristics of the population, approach, and methodologies used in the studies. Likewise, Procedia Manufacturing, International Journal of Occupational Safety and Ergonomics, and Ergonomics stand out as the main sources of publication, while countries such as Sweden, Poland, and the United States lead the scientific production in this field. In addition, the network of co-occurrence of keywords evidences a comprehensive approach that articulates physical or ergonomic and psychosocial risk factors with organizational performance, while the network of authors shows consolidated collaborations and studies focused on analyzing the relationship between physical demands and musculoskeletal disorders from advanced ergonomic approaches. Full article