Search Results (9,462)

This paper presents WaveCORAL-DCCA, an unsupervised domain adaptation (UDA) framework specifically developed to address data distribution shifts and operational variabilities (OVs) in rotor fault diagnosis. The framework introduces the novel integration of discrete wavelet transformation for robust time–frequency feature extraction and an enhanced deep canonical correlation analysis (DCCA) network with correlation alignment (CORAL) loss for superior domain-invariant representation learning. This combination enables more effective alignment of source and target feature distributions without requiring any labelled data from the target domain. Comprehensive validation on both experimental and numerically simulated rotor datasets across three health conditions—i.e., normal, unbalanced, and misaligned—demonstrates that WaveCORAL-DCCA achieves an average diagnostic accuracy of 95%. Notably, it outperforms established UDA benchmarks by at least 5–17% in cross-domain scenarios. These results confirm that WaveCORAL-DCCA provides robust generalisation across machines, fault severities, and operational conditions, even with scarce target domain samples, offering a scalable and practical solution for industrial rotor fault diagnosis. Full article

The integration of virtual and augmented reality is transforming processes in the field of product design. This study evaluates the usability of immersive digital tools applied to industrial design through a combined market research and empirical case study, using the software ‘Gravity Sketch’ and the immersive headset ‘Meta Quest 3’. An embedded single case study was conducted based on the international standard ISO 9241-11, considering the dimensions of effectiveness, efficiency, and satisfaction, analysed through nine indicators: tasks completed, time to complete tasks, dimensional accuracy, interoperability, interactivity, fatigue, human error, learning curve, and perceived creativity. The results show a progressive improvement in user–system interaction across the seven Design Units, as users become more familiar with immersive technologies. Effectiveness improves as users gain experience, though it remains sensitive to design complexity. Efficiency shows favourable values even in early stages, reflecting operational fluency despite learning demands. Satisfaction records the greatest improvement, driven by smoother interaction and greater creative freedom. These findings highlight the potential of immersive tools to support design processes while also underlining the need for future research on sustained usability, interface ergonomics, and collaborative workflows in extended reality environments. Full article

Widespread antibiotic residues are accumulating in the environment, potentially causing adverse effects for humans, animals, and the ecosystem, including an increase in antibiotic-resistant bacteria, resulting in worldwide concern. There are various commonly used physical, chemical, and biological treatments for the degradation of antibiotics. However, the elimination of toxic end products generated by physicochemical methods and the need for industrial applications pose significant challenges. Hence, environmentally sustainable, green, and readily available approaches for the transformation and degradation of these antibiotic compounds are being sought. Herein, we review the impact of sustainable fungal laccase-based bioremediation strategies. Fungal laccase enzyme is considered one of the most active enzymes for biotransformation and biodegradation of antibiotic residue in vitro. For industrial applications, the low laccase yields in natural and genetically modified hosts may constitute a bottleneck. Methods to screen for high-laccase-producing sources, optimizing cultivation conditions, and identifying key genes and metabolites involved in extracellular laccase activity are reviewed. These include advanced transcriptomics, proteomics, and metagenomics technologies, as well as diverse laccase-immobilization technologies with different inert carrier/support materials improving enzyme performance whilst shifting from experimental assays to in situ monitoring of residual toxicity. Still, more basic and applied research on laccase-mediated bioremediation of pharmaceuticals, especially antibiotics that are recalcitrant and prevalent, is needed. Full article

Efficient and clean utilization remains a pivotal development focus within the coal industry. Nevertheless, the application of weakly caking coal results in energy loss due to the caking property, thereby leading to a waste of resources. This paper, therefore, concentrates on the caking property, offering insights into the relevant caking mechanism, evaluation indexes, and regulation technologies associated with it. The caking mechanism delineates the transformation process of coal into coke. During pyrolysis, the active component generates the plastic mass in which gas, liquid, and solid phases coexist. With an increase in temperature, the liquid phase is diminished gradually, causing the inert components to bond. Based on the caking mechanism, evaluation indexes such as that characteristic of char residue, the caking index, and the maximal thickness of the plastic layer are proposed. These indexes are used to distinguish the strength of the caking property. However, they frequently exhibit a poor differentiation ability and high subjectivity. Additionally, some technologies have been demonstrated to regulate the caking property. Technologies such as rapid heating treatment and hydrogenation modification increase the amount of plastic mass generated, thereby improving the caking property. Meanwhile, technologies such as mechanical breaking and pre-oxidation reduce the caking property by destroying agglomerates or consuming plastic mass. Full article

This study aimed to evaluate solid-state fermentation (SSF) as a sustainable approach for the simultaneous detoxification of olive pomace (OP) and the production of industrially relevant enzymes. OP, a semisolid byproduct of olive oil extraction, is rich in lignocellulose and phenolic compounds, which limit its direct reuse due to phytotoxicity. A native strain of Aspergillus sp., isolated from OP, was employed as the biological agent, while grape pomace (GP) was added as a co-substrate to enhance substrate structure. Fermentations were conducted at two scales, Petri dishes (20 g) and a fixed-bed bioreactor (FBR, 2 kg), under controlled conditions (25 °C, 7 days). Key parameters monitored included dry and wet weight loss, pH, color, phenolic content, and enzymatic activity. Significant reductions in color and polyphenol content were achieved, reaching 68% in Petri dishes and 88.1% in the FBR, respectively. In the FBR, simultaneous monitoring of dry and wet weight loss enabled the estimation of fungal biotransformation, revealing a hysteresis phenomenon not previously reported in SSF studies. Enzymes such as xylanase, endopolygalacturonase, cellulase, and tannase exhibited peak activities between 150 and 180 h, with maximum values of 424.6 U·g⁻¹, 153.6 U·g⁻¹, 67.43 U·g⁻¹, and 6.72 U·g⁻¹, respectively. The experimental data for weight loss, enzyme production, and phenolic reduction were accurately described by logistic and first-order models. These findings demonstrate the high metabolic efficiency of the fungal isolate under SSF conditions and support the feasibility of scaling up this process. The proposed strategy offers a low-cost and sustainable solution for OP valorization, aligning with circular economy principles by transforming agro-industrial residues into valuable bioproducts. Full article

Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article

China has identified the high-quality development of its green manufacturing transition as the top priority for upgrading their industrial structure system which will lead to the sustainable development of an innovation ecosystem. To assess their progress in this area, this study selects the panel data of 31 provinces in China from 2011 to 2021 and constructs an evaluation index system for the green transformation of the manufacturing industry from four dimensions: environment, resources, economy, and industrial structure. This not only comprehensively and systematically reflects the dynamic changes in the green transformation of the manufacturing industry but also addresses the limitations of currently used indices. The entropy value method is used to calculate the comprehensive score of the green transformation of the manufacturing industry, while the key factors influencing the convergence of the green transformation of the manufacturing industry are further explored. The results show that first, the overall level of the green transformation of the manufacturing industry has significantly improved as evidenced by an approximate 32% increase. Second, regional differences are significant with the eastern region experiencing significantly higher levels of transformation compared to the central and western regions, along with a decreasing trend from the east to the central and western regions. From a policy perspective, the findings suggest that tailored production methods for each region should be adopted with a greater emphasis on knowledge exchanges to promote green transition in less developed regions. In addition, further regulations are required which, in part, focus on increasing the degree of openness to the outside world to promote the level of green manufacturing transition. Full article

Integrating BIM curriculum into traditional construction management (CM) programs is essential to meet the increasing industry demand for BIM-ready graduates. However, academia struggles with BIM curriculum integration due to unfamiliar emerging BIM technologies, and the increased workload associated with curriculum transformation. Disciplines including nursing, health science, and medical overcame the same challenges using the ability-desire-knowledge-ability-reinforcement (ADKAR) change management model, while CM programs have not explored this model for BIM curriculum development. Thus, this research introduces the ADKAR change management lens to BIM curriculum development by proposing a practically modified and replicable ADKAR model for CM programs. Focus group interviews with 14 academics from the UK, USA, Korea, and Australia, revealed establishing a sense of urgency by appointing a BIM champion is the most critical step before the BIM curriculum development. Instant advice demystifying uncertain BIM concepts is recognised the most effective motivation among academia. Well-balanced BIM concept integrations is ‘sine qua non’ since excessively saturating BIM aspects across the program can dilute students’ essential domain knowledge. Students’ evaluation over the BIM curriculum were collected through a six-year longitudinal focus group interviews, revealing that progressive BIM learnings scaffolded from foundational concepts to advanced applications throughout their coursework is the most valuable. Full article

In the context of the global response to climate change and the active promotion of energy transformation, a number of low-carbon policies coupled with the development of synergies to help power system transformation is an important initiative. However, the insufficient articulation of the green power market, tradable green certificate (TGC) market, and carbon emission trading (CET) mechanism, and the ambiguous policy boundaries affect the trading decisions made by its market participants. Therefore, this paper systematically analyses the composition of the main players in the electricity-CET-TGC markets and their relationship with each other, and designs the synergistic mechanism of the electricity-CET-TGC markets, based on which, it constructs the optimal profit model of the thermal power plant operators, renewable energy manufacturers, power grid enterprises, power users and load aggregators under the electricity-CET-TGC markets synergy, and analyses the behavioural decision-making of the main players in the electricity-CET-TGC markets as well as the electric power system to optimise the trading strategy of each player. The results of the study show that: (1) The synergistic mechanism of electricity-CET-TGC markets can increase the proportion of green power grid-connected in the new type of power system. (2) In the selection of different environmental rights and benefits products, the direct participation of green power in the market-oriented trading is the main way, followed by applying for conversion of green power into China certified emission reduction (CCER). (3) The development of independent energy storage technology can produce greater economic and environmental benefits. This study provides policy support to promote the synergistic development of the electricity-CET-TGC markets and assist the low-carbon transformation of the power industry. Full article

This paper addressed the problem of oil diagnostics lubricants applied to the predictive maintenance of industrial gearboxes, proposing the integration of an artificial intelligence (AI) system into the process analysis. The main objective was to overcome the critical issues of the traditional method, characterized by long analysis times and a marked dependence on the subjective interpretation of operators. The method includes a detailed statistical analysis of the common ways to assess the condition of lubricants, such as optical emission spectroscopy, particle counting, measuring viscosity and density, and Fourier-transform infrared spectroscopy (FT-IR). These methods are then combined with an artificial intelligence model. Tested on commercial gearbox data, the proposed approach demonstrates agreement between IA and expert evaluation. The application has shown that it can effectively support diagnoses, reduce processing time by 60%, and minimize human errors. It also improves knowledge sharing through an increase in the stability and repetitiveness of diagnoses and promotes consistency and clarity in reporting. Full article

This study investigates the hot deformation behaviour and flow stress prediction of metastable β-Ti-15Mo alloy, a promising material for biomedical applications requiring strength–modulus optimisation and thermomechanical tunability. Isothermal compression tests were performed within the temperature range of 923–1173 K and at strain rates of 0.17, 1.72, and 17.2 ${\mathrm{s}}^{-1}$ to assess the material’s response under industrially relevant hot working conditions. The alloy showed significant sensitivity to temperature and strain rate, with dynamic recovery (DRV) and dynamic recrystallisation (DRX) dominating the softening behaviour depending on the conditions. A strain-compensated Arrhenius-type constitutive model was developed and validated, resulting in an apparent activation energy of approximately 234 kJ/mol. Zener–Hollomon parameter analysis confirmed a transition in deformation mechanisms. Although microstructural and diffraction data suggest possible contributions from nanoscale phase transformations, including ω-phase dissolution at high temperatures, these aspects remain to be fully elucidated. The model offers reliable predictions of flow behaviour and supports optimisation of thermomechanical processing routes for biomedical β-Ti alloys. Full article

Employee turnover remains a critical challenge for organizations, prompting an examination of how leadership approaches influence employees’ intentions to leave. This study investigates the impact of transformational leadership on turnover intention, focusing on emotional exhaustion and creativity as potential mediators. The study employs a quantitative design grounded in leadership and organizational psychology theory and surveys 182 professionals working in the information technology sector across Bosnia and Herzegovina, Croatia, Serbia, and Montenegro. Structural equation modeling reveals that transformational leadership reduces turnover intention by alleviating emotional exhaustion, highlighting the importance of psychological well-being in employee retention. While transformational leadership enhances employee creativity, creativity did not significantly mediate turnover intention in this context. These findings suggest that strategies that foster engagement and reduce burnout in knowledge-intensive industries can strengthen organizational commitment and improve retention. This study contributes to the understanding of behavioral mechanisms linking leadership to employee outcomes and offers actionable insights for modern organizations aiming to address turnover through supportive, empowering leadership practices. Additional mediators and contextual variables should be explored in further research. Full article

Amidst global climate change, carbon emissions across the edible vegetable oil supply chain are critical for sustainable development. This paper systematically reviews the existing literature, employing life cycle assessment (LCA) to analyze key factors influencing carbon footprints at stages including cultivation, processing, and transportation. It reveals the differential impacts of fertilizer application, energy structures, and regional policies. Unlike previous reviews that focus on single crops or regions, this study uniquely integrates global data across major edible oils, identifying three critical gaps: methodological inconsistency (60% of studies deviate from the requirements and guidelines for LCA); data imbalance (80% concentrated on soybean/rapeseed); weak policy-technical linkage. Key findings: fertilizer emissions dominate cultivation (40–60% of total footprint), while renewable energy substitution in processing reduces emissions by 35%. Future efforts should prioritize multidisciplinary integration, enhanced data infrastructure, and policy scenario analysis to provide scientific insights for the low-carbon transformation of the global edible oil industry. Full article

The traditional manufacturing industry is facing the challenge of digital transformation, which involves the enhancement of intelligence and production efficiency. Many robotic applications have been discussed to enable collaborative robots to perform operations smartly rather than just automatically. This article tackles the issues of intelligent robots with cognitive and coordination capability by introducing cyber-physical integration technology. The authors propose a system architecture with open-source software and low-cost hardware based on the 5C hierarchy and then conduct experiments to verify the proposed framework. These experiments involve the collection of real-time data using a depth camera, object detection to recognize obstacles, simulation of collision avoidance for a robotic arm, and cyber-physical integration to perform a robotic task. The proposed framework realizes the scheme of the 5C architecture of Industry 4.0 and establishes a digital twin in cyberspace. By utilizing connection, conversion, calculation, simulation, verification, and operation, the robotic arm is capable of making independent judgments and appropriate decisions to successfully complete the assigned task, thereby verifying the proposed framework. Such a cyber-physical integration system is characterized by low cost but good effectiveness. Full article

With the advancement of low-carbon and sustainable development initiatives, electric scooters, recognized as essential transportation tools and leisure products, have gained significant popularity, particularly among young people. However, the current electric scooter market is plagued by severe product similarity. Once the initial novelty fades for users, the usage frequency declines, resulting in considerable resource wastage. This research collected user needs via surveys and employed the KJ method (affinity diagram) to synthesize fragmented insights into cohesive thematic clusters. Subsequently, a hierarchical needs model for electric scooters was constructed using analytical hierarchy process (AHP) principles, enabling systematic prioritization of user requirements through multi-criteria evaluation. By establishing a house of quality (HoQ), user needs were transformed into technical characteristics of electric scooter products, and the corresponding weights were calculated. After analyzing the positive and negative correlation degrees of the technical characteristic indicators, it was found that there are technical contradictions between functional zoning and compact size, lightweight design and material structure, and smart interaction and usability. Then, based on the theory of inventive problem solving (TRIZ), the contradictions were classified, and corresponding problem-solving principles were identified to achieve a multi-functional innovative design for electric scooters. This research, leveraging a systematic industrial design analysis framework, identified critical pain points among electric scooter users, established hierarchical user needs through priority ranking, and improved product lifecycle sustainability. It offers novel methodologies and perspectives for advancing theoretical research and design practices in the electric scooter domain. Full article