Search Results (6,131)

As Industry 5.0 emerges as a human-centric evolution of industrial systems, this study investigates the effectiveness of training interventions in companies aimed at supporting the transition to Industry 5.0, emphasizing human-centric and resilient skill development. Drawing from multiple case studies involving engineers and operators, the research applies both meta-analysis and meta-regression to assess the added value of experiential learning approaches such as Teaching and Learning Factories. In addition, a novel methodology combining quantitative analyses with qualitative interpretation of emerging competences is presented. Principal Component Analysis and classification frameworks are employed to identify and organize key competence clusters along technological, organizational, and social dimensions. Special attention is given to the emergence of human-centered competences such as decision empowerment, which are shown to complement traditional operational capabilities. The findings confirm that experiential training interventions enhance both self-efficacy and adaptive operational readiness, while the use of fusion techniques enables the generalization of results across heterogeneous corporate settings. This work contributes to ongoing discourse on Industry 5.0 readiness by linking training design to strategic company incentives and highlights the role of structured evaluation in informing future policy and implementation pathways. Full article

The rapid evolution of unmanned aerial vehicle (UAV) technology and low-altitude economic development have propelled drone applications in critical infrastructure monitoring, particularly in intelligent transportation systems where real-time aerial image processing has emerged as a pressing requirement. We address the pivotal challenge of highway lane extraction from low-altitude UAV perspectives by applying a novel three-stage framework. This framework consists of (1) a deep learning-based semantic segmentation module that employs an enhanced STDC network with boundary-aware loss for precise detection of roads and lane markings; (2) an optimized polynomial fitting algorithm incorporating iterative classification and adaptive error thresholds to achieve robust lane marking consolidation; and (3) a global optimization module designed for context-aware lane generation. Our methodology demonstrates superior performance with 94.11% F1-score and 93.84% IoU, effectively bridging the technical gap in UAV-based lane extraction while establishing a reliable foundation for advanced traffic monitoring applications. Full article

To address the challenge of postharvest spoilage in flat peaches and white apricots, we developed fermented peach–apricot mixed juice (PAMJ) using these fruits as raw materials through multi-strain synergistic fermentation. Its fermentation processes were optimised through uniform design and single-factor experiments. The flavour characteristics of PAMJ were analysed using an electronic nose, an electronic tongue, gas chromatography–mass spectrometry (GC-MS) and sensory evaluation indices. PAMJ demonstrated optimal performance in terms of peach–apricot flavour profile, sweetness-sourness balance, and overall acceptability, achieving the highest sensory scores. Additionally, GC-MS analysis identified 116 volatile organic compounds, with PAMJ exhibiting the highest contents of terpenes and ketones. PAMJ was identified as the optimal fermentation matrix. Subsequently, response surface methodology was used to optimise its fermentation parameters. PAMJ represented a post-mixing fermentation system wherein peaches and apricots were initially mixed and subsequently fermented with a bacterial consortium comprising Limosilactobacillus fermentum (15%), Lactobacillus acidophilus (10%), Levilactobacillus brevis (34%), Lacticaseibacillus paracasei subsp. Tolerans (13%), Lactiplantibacillus plantarum subsp. plantarum (13%) and Limosilactobacillus reuteri (15%). After fermentation with an initial inoculum concentration of 5.2 × 10⁶ CFU/mL at 37 °C for 20 h, the initial soluble solid content of PAMJ increased from 16 to 16.5 °Brix, superoxide dismutase (SOD) activity increased from 250 to 295 U/mL and the number of volatile compounds (NVC) increased from 60 to 66. Furthermore, the storage stability of pasteurised PAMJ was evaluated by monitoring SOD and NVC at 5-day intervals. The data were analysed using kinetic and Arrhenius equations. The shelf life of PAMJ at 4 °C, 25 °C and 37 °C was 69, 48 and 39 days when NVC was used as the index and 99, 63 and 49 days when SOD activity was used as the index, respectively. These findings indicate that fermentation with lactic acid bacteria exerts positive effects on the quality of mixed juices, providing a novel strategy for processing speciality fruits in Xinjiang. Full article

This review provides a critical and technically grounded assessment of continuous electrocoagulation processes (CEPs) for the treatment of industrial inorganic pollutants, emphasizing recent innovations, methodological developments, and practical outcomes. A comprehensive literature survey indicates that 53 studies published over the past 25 years have investigated CEPs for inorganic contaminant removal, with 36 focusing on standalone electrocoagulation systems and 17 exploring integrated CEPs approaches. Recent advancements in reactor design, such as enhanced internal mixing, optimized electrode geometry, and modular configurations, have significantly improved treatment efficiency, scalability, and operational stability. Evidence indicates that CEPs can achieve high removal efficiencies for a wide range of inorganic contaminants, including fluoride, arsenic, heavy metals (e.g., chromium, lead, nickel, iron), nitrates, and phosphates, particularly under optimized operating conditions. Compared to conventional treatment methods, CEPs offer several advantages, such as simplified operation, reduced chemical consumption, lower sludge generation, and compatibility with renewable energy sources and complementary processes like membrane filtration, flotation, and advanced oxidation. Despite these promising outcomes, industrial-scale implementation remains constrained by non-standardized reactor designs, variable operational parameters, electrode passivation, high energy requirements, and limited long-term field data. Furthermore, few studies have addressed the modeling and optimization of integrated CEPs systems, highlighting critical research gaps for process enhancement and reliable scale-up. In conclusion, CEPs emerge as a novel, adaptable, and potentially sustainable approach to industrial inorganic wastewater treatment. Its future deployment will rely on continued technological refinement, standardization, validation under real-world conditions, and alignment with regulatory and economic frameworks. Full article

This paper presents the development and implementation of a shopfloor visualization-oriented digitalization framework for heterogeneous industrial equipment, aimed to enhance sustainable performance in manufacturing environments. The proposed solution addresses a critical challenge in modern industry: the integration of legacy and modern equipment into a unified, real-time monitoring and control system. In this paper, a modular and scalable architecture that enables data acquisition from equipment with varying communication protocols and technological maturity was designed and implemented, utilizing Industrial Internet of Things (IIoT) gateways, protocol converters, and Open Platform Communications Unified Architecture (OPC UA). A key contribution of this work is the integration of various data sources into a centralized visualization platform that supports real-time monitoring, anomaly detection, and performance analytics. By visualizing operational parameters—including energy consumption, machine efficiency, and environmental indicators—the system facilitates data-driven decision-making and supports predictive maintenance strategies. The implementation was validated in a real industrial setting, where the solution significantly improved transparency, reduced downtime, and contributed to measurable energy efficiency gains. This research demonstrates that visualization-oriented digitalization not only enables interoperability among heterogeneous assets, but also acts as a catalyst for achieving sustainability goals. The developed methodology and tools provide a replicable model for manufacturing organizations seeking to transition toward Industry 4.0 in a resource-efficient and future-proof manner. Full article

To address the high fossil energy dependency and the low-value utilization of stillage (WDGS) in conventional cassava-based ethanol production—factors that increase greenhouse gas emissions and limit overall sustainability—this study develops an integrated ethanol–biogas–CHP system that valorizes stillage and enhances energy recovery. Three process scenarios were designed and evaluated through life cycle assessment (LCA) and techno-economic analysis: Case-I (WDGS dried and sold as animal feed), Case-II (stillage anaerobically digested for biogas used for heat), and Case-III (biogas further utilized in a combined heat and power system). Process simulation was conducted in Aspen Plus V11, while environmental impacts were quantified with the CML 2001 methodology under a cradle-to-gate boundary across six categories, including global warming potential (GWP) and abiotic depletion potential (ADP). Results show that Case-III achieves the highest environmental and economic performance, with a net GWP of −1515.05 kg CO₂-eq/ton ethanol and the greatest profit of 396.80 USD/ton of ethanol, attributed to internal energy self-sufficiency and surplus electricity generation. Sensitivity analysis further confirms Case-III’s robustness under variations in transportation distance and electricity demand. Overall, valorizing cassava stillage through biogas–CHP integration significantly improves the sustainability of ethanol production, offering a practical pathway toward low-carbon bioenergy with potential for negative emissions. This study fills a gap in previous life cycle research by jointly assessing WDGS utilization pathways with techno-economic evaluation, providing actionable insights for carbon-neutral bioenergy policies in cassava-producing regions. Certain limitations, such as software version and data accessibility, remain to be addressed in future work. Full article

(1) Background: This study investigates the impact of digital device usage on the visual system, with a focus on binocular vision. It also highlights the importance of objective assessment in accurately diagnosing and guiding therapeutic approaches for Digital Eye Strain Syndrome (DESS). (2) Methods: A comprehensive narrative review was conducted to synthesize existing evidence. The methodological quality of observational and case–control studies was assessed using the Newcastle–Ottawa scale, while randomized controlled trials (RCTs) were evaluated using the Cochrane risk-of-bias (RoB 2) tool. (3) Results: Fifteen articles were included in this review, with a predominant focus on binocular vision anomalies, particularly accommodative and vergence dysfunctions, as well as ocular surface anomalies related to DESS. Clinical assessments relied primarily on symptom-based questionnaires, which represent a significant limitation. The included studies were largely observational, with a lack of longitudinal and RCTs. In contrast, research in dry eye disease has been more comprehensive, with multiple RCTs already conducted. (4) Therefore, it is essential to develop validated objective metrics that support accurate clinical diagnosis and guide evidence-based interventions. Conclusions: It remains unclear whether changes in binocular vision are a cause or consequence of DESS. However, prolonged screen time can exacerbate pre-existing binocular vision anomalies due to continuous strain on convergence and accommodation, leading to symptoms. Future research should prioritize prospective longitudinal studies and well-designed RCTs that integrate objective clinical measures to elucidate causal relationships and improve diagnostic and therapeutic frameworks. Full article

This study considers a robust design methodology to reduce cogging torque in the EPS (Electric Power Steering) of an automotive system. Cogging torque reduction is the key design factor to improve steering feeling and drive stability in an EPS system. For this reason, an SPMSM (Surface Permanent Magnet Synchronous Motor) has been widely applied to drive a motor in an EPS system. Furthermore, two design methods, which are a magnet shape and step-skew design for rotor assembly, have been mainly used to reduce cogging torque in an SPMSM. In this paper, an SPMSM is selected as the drive motor and a robust design methodology is proposed to reduce cogging torque in an EPS system. Firstly, a cycloid curve is used for the magnet shape to reduce cogging torque. An evaluation index δq is also used to compare this with a conventional magnet shape design. Secondly, based on the results of the magnet shape design with the cycloid curve, a step-skew design for rotor assembly is also applied to reduce cogging torque. In order to validate the effectiveness of the robust design for the cycloid curve and conventional magnet shape with rotor step-skew, the results from FEM (Finite Element Method) analysis and prototype tests are compared. The cycloid curve magnet shape model with rotor step-skew was verified to reduce the cogging torque and enhance the robustness for cogging torque variation through the analysis and protype test results. The verified results for the proposed model will be extended to meet the required cogging torque variation for the various applications driven by SPMSM with the robust design model. Full article

Smart mobility has emerged as a transformative enabler for achieving the United Nations Sustainable Development Goals (SDGs), offering technological and systemic solutions to pressing urban challenges such as congestion, environmental degradation, accessibility, and economic inclusion. Realizing this potential, however, depends not only on technological maturity but also on robust education and capacity-building frameworks. This paper addresses two critical gaps: the absence of a systematic review of structured academic curricula, vocational training programs, and professional development pathways dedicated to smart mobility, and the lack of a formal approach to demonstrate how structured, research-oriented education can effectively bridge theory and practice. The review examines a wide spectrum of initiatives, including academic programs, industry training, challenge-based competitions, and community-driven platforms. The analysis shows significant progress in Europe and North America but also reveals important gaps, particularly the limited availability of structured initiatives in the Global South, the underrepresentation of accessibility and inclusivity, and the insufficient integration of governance, ethical AI, policy, and cybersecurity. A case study of the AI for Smart Mobility course, developed using a design science methodology, illustrates how research-oriented education can be operationalized in practice. Since 2020, the course has engaged hundreds of students and professionals, with project dissemination through the AI4SM Medium hub attracting more than 20,000 views and 11,000 reads worldwide. The findings highlight both the progress made and the persistent gaps in smart mobility education, underscoring the need for wider geographic reach, stronger emphasis on inclusivity and governance, and structured approaches that effectively link theory with practice. Full article

Hybrid Renewable Energy Systems are pivotal for the sustainable energy transition, yet their design and operation present complex optimization challenges due to diverse components, stochastic resources, and multifaceted objectives. This systematic review formalizes the HRES optimization problem space and identifies critical research gaps. Employing the PRISMA 2020 guidelines, it comprehensively analyzes the literature (2015–2025) from Scopus, IEEE Xplore, and Web of Science, focusing on architectures, mathematical formulations, objectives, and solution methodologies. The results reveal a decisive shift from single-objective to multi-objective optimization (MOO), increasingly incorporating environmental and emerging social criteria alongside traditional economic and technical goals. Metaheuristic algorithms (e.g., NSGA-II, MOPSO) and AI techniques dominate solution strategies, though challenges persist in scalability, uncertainty management, and real-time control. The integration of hydrogen storage, vehicle-to-grid (V2G) technology, and multi-vector energy systems expands system boundaries. Key gaps include the lack of holistic frameworks co-optimizing techno-economic, environmental, social, and resilience objectives; disconnect between long-term planning and short-term operation; computational limitations for large-scale or real-time applications; explainability of AI-based controllers; high-fidelity degradation modeling for emerging technologies; and bridging the “valley of death” between simulation and bankable deployment. Future research must prioritize interdisciplinary collaboration, standardized social/resilience metrics, scalable and trustworthy AI, and validation frameworks to unlock HRESs’ potential. Full article

3D bioprinting has emerged as a key tool in tissue engineering by facilitating the creation of customized scaffolds with properties tailored to specific needs. Among the design parameters, porosity stands out as a determining factor, as it directly influences critical mechanical and biological properties such as nutrient diffusion, cell adhesion and structural integrity. This review comprehensively analyses the state of the art in scaffold design, emphasizing how porosity-related parameters such as pore size, geometry, distribution and interconnectivity affect cellular behavior and mechanical performance. It also addresses advances in manufacturing methods, such as additive manufacturing and computer-aided design (CAD), which allow the development of scaffolds with hierarchical structures and controlled porosity. In addition, the use of computational modelling, in particular finite element analysis (FEA), as an essential predictive tool to optimize the design of scaffolds under physiological conditions is highlighted. This narrative review analyzed 112 core articles retrieved primarily from Scopus (2014–2025) to provide a comprehensive and up-to-date synthesis. Despite recent progress, significant challenges persist, including the lack of standardized methodologies for characterizing and comparing porosity parameters across different studies. This review identifies these gaps and suggests future research directions, such as the development of unified characterization and classification systems and the enhancement of nanoscale resolution in bioprinting technologies. By integrating structural design with biological functionality, this review underscores the transformative potential of porosity research applied to 3D bioprinting, positioning it as a key strategy to meet current clinical needs in tissue engineering. Full article

This study provides an in-depth structural analysis of UNESCO World Heritage Apulian trulli, considering the three-layer dry-stone structure of their characteristic conical roofs. An integrated approach involving laser scanning, ground-penetrating radar, endoscopic investigation, and laboratory materials testing is used to identify and characterize the multi-wythe masonry system. A detailed finite element model is created in ANSYS to analyze seismic performance on Italian building codes. The model is validated through ambient vibration testing using accelerometric measurements. The diagnostic survey identified a three-layer system including exterior stone wythe, interior wythe, and rubble core, with compressive strength of stone averaging 2.5 MPa and mortar strength of 0.8 MPa. The seismic assessment will allow the examination of displacement patterns and stress distribution under design load conditions (ag = 0.15 g). The structural analysis demonstrates adequate performance under design loading conditions, with maximum stress levels remaining within acceptable limits for historic masonry construction. The experimental validation confirmed the finite element model predictions, with good correlation between numerical and experimental frequencies. The improvement of the overall seismic performance with the multi-wythe configuration and the role of wall thickness and geometric proportions will be taken into account. The methodology aims to provide technical evidence supporting the continued use of vernacular buildings while contributing to scientifically informed conservation practices throughout the region. Full article

A Universal Numerical Integrator (UNI) is a computational framework that combines a classical numerical integration method, such as Euler, Runge–Kutta, or Adams–Bashforth, with a universal approximator of functions, such as a feed-forward neural network (including MLP, SVM, RBF, among others) or a fuzzy inference system. The Euler-Type Universal Numerical Integrator (E–TUNI) is a particular case of UNI based on the first-order Euler integrator and is designed to model non-linear dynamic systems observed in real-world scenarios accurately. The UNI framework can be organized into three primary methodologies: the NARMAX model (Non-linear AutoRegressive Moving Average with eXogenous input), the mean derivatives approach (which characterizes E–TUNI), and the instantaneous derivatives approach. The E–TUNI methodology relies exclusively on mean derivative functions, distinguishing it from techniques that employ instantaneous derivatives. Although it is based on a first-order scheme, the E–TUNI achieves an accuracy level comparable to that of higher-order integrators. This performance is made possible by the incorporation of a neural network acting as a universal approximator, which significantly reduces the approximation error. This article provides a comprehensive overview of the E–TUNI methodology, focusing on its application to the modeling of non-linear autonomous dynamic systems and its use in predictive control. Several computational experiments are presented to illustrate and validate the effectiveness of the proposed method. Full article

This study presents a rigorous experimental and numerical investigation of the synergistic effect of metakaolin (MK) and waste glass (WG) on the structural performance of reinforced concrete (RC) beams without stirrups. A two-phase methodology was adopted: (i) optimization of MK and WG replacement levels through concrete-equivalent mortar mixtures and (ii) evaluation of the fresh and hardened properties of concrete, including compressive and tensile strengths, elastic modulus, sorptivity, and beam shear capacity. Five beam groups incorporating up to 30% MK, 15% WG, and 1% steel fiber were tested under four-point bending. The results demonstrated that MK enhanced compressive strength (up to 22%), WG improved workability but reduced ductility, and the combined system achieved a 13% increase in shear strength relative to the control. Steel fibers further restored ductility, increasing the ductility index from 1.338 for WG-only beams to 2.489. Finite Element Modeling (FEM) using ABAQUS with the Concrete Damage Plasticity (CDP) model reproduced experimental (EXP) load–deflection responses, peak loads, and crack evolution with high fidelity. This confirmed the predictive capability of the numerical framework. By integrating material-level optimization, structural-scale testing, and validated FEM simulations, this study provides robust evidence that MK–WG concrete, especially when fiber-reinforced, delivers mechanical, durability, and structural performance improvements. These findings establish a reliable pathway for incorporating sustainable cementitious blends into design-oriented applications, with direct implications for the advancement of performance-based structural codes. Full article

In the context of Industry 4.0 and the proliferation of smart buildings, elevators represent critical assets whose performance is often inadequately measured by traditional indicators that overlook energy consumption. This study addresses the need for a more holistic Key Performance Indicator (KPI) by developing the Overall Equipment Effectiveness for Elevators (OEEE), an index designed to integrate operational effectiveness with energy efficiency. The methodology involves adapting the classical OEE framework through a comprehensive literature review and an analysis of elevator energy standards. This leads to a novel structure that incorporates a dedicated energy efficiency dimension alongside the traditional pillars of availability, performance, and quality. The framework further refines the performance and energy efficiency dimensions, resulting in six distinct sub-indicators that specifically measure operational uptime, speed adherence, electromechanical conversion, fault-free cycles (as a proxy for operational quality), and energy use during both movement and standby modes. The primary result is the complete mathematical formulation of the OEEE, a single, integrated KPI derived from these six metrics and designed for implementation using data from modern IoT-enabled elevators. The study concludes that the OEEE provides a more accurate and comprehensive tool for asset management, enabling data-driven decisions to enhance reliability, optimise energy consumption, and reduce operational costs in smart vertical transportation systems. Full article