Search Results (303)

As industries deploy more connected devices in factories, warehouses, and smart facilities, the need for artificial intelligence (AI) systems that can operate securely in distributed, data-intensive environments is growing. Traditional centralized learning and online education platforms struggle when students and systems have to process real-time streams (sensors, video, text) with strict latency and privacy requirements. To address this challenge, a blockchain-secured, edge-enabled multimodal federated learning framework tailored for Industrial IoT (IIoT) environments is proposed. The model integrates four key layers: (i) a blockchain layer that provides credentialing, transparency, and token-based incentives; (ii) a multimodal community layer that supports group formation, peer consensus, and cross-modal learning across text, images, audio, and sensor data; (iii) an edge computing layer that enables low-latency task offloading and secure training within Intel SGX enclaves; and (iv) a data layer that applies pre-processing, differential privacy, and synthetic augmentation to safeguard sensitive information. Experiments on industrial multimodal datasets demonstrate 42% faster model aggregation, 78.9% multimodal accuracy, and 1.9% accuracy loss under ε = 1.0 differential privacy. This shows a scalable and practical path for decentralized AI training in next-generation IIoT systems, confirming the possibility of technical support for educational processes. However, the conducted research requires a validation of pedagogical effectiveness. Full article

This study investigates Downlink/Uplink Decoupling (DUDe) in 5G networks, a framework that allows user equipment to select its uplink serving cell independently of the downlink anchor. This approach is designed to alleviate the “macro bias” and pathloss issues that typically degrade performance for Internet of Things (IoT) traffic. We propose a framework managed by Mobile Edge Computing (MEC) that operates on a per-Transmission Time Interval (TTI) basis, incorporating stability mechanisms such as hysteresis and Time to Trigger to prevent frequent, unnecessary handovers. The performance is evaluated using a system-level simulator across two scenarios: a high-density urban IoT deployment and an Industry 4.0 smart factory environment. Our results demonstrate that the proposed framework significantly improves uplink throughput and reduces tail latency compared to traditional coupled association methods. Furthermore, an ablation study confirms that these performance gains are derived from the structural decoupling of links, providing a scalable path for improving connectivity in 5G and beyond. Full article

Understanding individuals’ connection to nature is crucial for promoting sustainable attitudes and behaviors. The environmental identity (EID) scale, widely used to assess this connection, plays a key role in environmental research; however, its cross-cultural application requires rigorous psychometric validation. Although the revised 14-item EID scale has demonstrated good reliability, questions remain regarding its dimensionality and the potential influence of acquiescence due to exclusively positive worded items. This study examined both issues in Portuguese samples. In Study 1, exploratory and confirmatory factor analyses were conducted to test the factorial structure. Results supported a two-factor model with correlated dimensions: Restorative Connection to Nature (RCN) and Ecological Identity (EI), rather than a strictly unidimensional solution. In Study 2 acquiescence was assessed by comparing the original version with a balanced version that included partially reverse-worded items. Item distributions, factor loadings, and reliability were analyzed. The balanced version did not improve control of acquiescence; instead, reversed-worded items showed weaker loadings, lower explanation variance, and method effects, suggesting increased measurement bias. Overall, the findings support the robustness of the revised 14-item EID scale in Portugal while indicating that environmental identity is better conceptualized as a bidimensional construct portraying both reflective connection and identity-based engagement with nature. The results also highlight the limitations of reverse-worded items as a strategy for reducing response bias in value-laden constructs. Full article

This paper investigates lightweight structural systems based on pultruded GFRP girders for the replacement of deteriorated concrete bridge decks on existing piers and abutments. The study is motivated by the need to rehabilitate short- and medium-span bridges affected by aging deterioration such as reinforcement corrosion. The approach preserves existing piers and foundations and, when required, enables rapid deployment for temporary or emergency applications. The proposed GFRP deck–girder solutions significantly reduce structural mass compared to conventional concrete systems. This reduction leads to lower seismic demand and smaller horizontal forces transmitted to the substructures. The research assesses the structural performance and feasibility of these systems, with particular attention to strength and serviceability behavior. The objective is to identify solutions that can be replicated across different bridge configurations, while also outlining efficient strategies for onsite assembly. After a reasoned review of the solutions available in the literature and of the limitations related to deformability, strength, and instability for a preliminary analytical design approach, three-dimensional numerical simulations of GFRP bridge deck systems are performed to evaluate global behavior and load-transfer mechanisms. The latest design codes and guidelines for GFRP bridges are reviewed and applied. Based on the results, recommendations are provided regarding cross-sectional proportions and member slenderness. The numerical results are compared with the analytical design approach, showing that, under characteristic load combinations, maximum deflections can be limited to approximately L/300–L/400 when the beam depth-to-span ratio range is between 1/10 and 1/6. Within these relationships, spans between 10 m and 25 m are found to be efficient. Additional guidance is proposed for modular construction strategies based on standardized pultruded elements and factory-controlled bonded connections. Full article

The rapid industrialization envisioned in Saudi Arabia’s Vision 2030 requires that new small and medium-sized enterprise (SME) factories be digitally capable from inception. This article proposes a policy framework that establishes a minimum Industry 4.0 maturity threshold as a condition for licensing new SME manufacturing facilities. Building upon international best practices and Saudi Arabia’s specific context, the article specifies minimum technical and organizational requirements across three dimensions: process, technology, and organization. Core elements include basic automation, industrial connectivity, vertical and horizontal integration, data-driven decision-making, workforce digital literacy, and leadership commitment to transformation. The rationale centers on cost efficiency, global competitiveness, and alignment with national goals. Recognizing common barriers such as limited skills, high upfront costs, and cybersecurity risks, the article outlines mitigation strategies including government incentives, public–private partnerships, and regulatory support. The framework is conceptual in nature and intended to inform pilot implementation and subsequent empirical evaluation. Establishing clear digital standards at the factory design stage can support more sustainable and scalable growth for Saudi SMEs while strengthening their readiness for participation in global Industry 4.0 ecosystems. Full article

Earthen materials are attractive sustainable building solutions due to their low embodied energy and ecological benefits. However, their inherent weaknesses, such as low strength and poor durability, severely restrict modern engineering applications. Traditional physical or chemical modification methods struggle to balance significant improvement in mechanical performance with the preservation of their core sustainable attributes. To overcome this long-standing challenge, this study proposes a paradigm-shifting solution: a prefabricated monolithic lattice–earth composite wall structure. This system abandons the single-material-centered modification approach. Instead, through macroscopic system-level composite design, reinforced concrete lattices and earthen blocks are prefabricated into integral wall panels in a factory. These panels then work collaboratively with the peripheral frame through reliable integral connections. Via quasi-static tests and theoretical analysis on four scaled wall specimens with different design parameters, this study systematically reveals the working mechanism and performance regulation principles of this composite system. The core findings indicate: (1) The system achieves multiple seismic defense lines and a controllable energy dissipation path through a sequential damage mechanism: “earthen material cracking and friction → lattice yielding and energy dissipation → final defense by the frame.” (2) The ratio of the equivalent lateral stiffness of the prefabricated wall panel to the stiffness of the outer frame is a key dimensionless design parameter controlling the failure mode (ductile shear or brittle bending), and the lattice configuration is an effective means to adjust this parameter. (3) Based on tests and an equivalent stiffness model, quantitative design guidelines are proposed, focusing on optimizing lattice density (recommended: 3–4 lattice columns), limiting the aspect ratio (preferably ≤1.5), and ensuring “strong connections.” This study demonstrates that the system, without sacrificing the intrinsic sustainable advantages of earthen materials, successfully endows them with high performance, meeting modern seismic code requirements and potential for prefabricated construction through system integration innovation. It provides a new path with theoretical foundation and practical feasibility to resolve the core contradiction in the modernization of traditional earthen buildings—the incompatibility between ecological attributes and engineering performance. This lays an important foundation for developing next-generation high-performance green building structural systems. Full article

This paper presents a novel edge-computing-based architecture for optimal inverse time overcurrent relays installed to protect mesh microgrids (MGs) with distributed generation. The procedure employs graph theory to automate the detection of network changes, fault locations, and relay pairs in an MG. In addition, an automated process obtains the initial protection settings based on the operating conditions of the MG. Furthermore, the Continuous Genetic Algorithm (CGA), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) were implemented to determine the optimal protection settings to obtain better coordination between primary and backup protection relays. These processes were implemented using PowerFactory 2024 Service Pack 5A and Python 3.13.1. The proposal was validated in 68 operating scenarios that considered the islanded and connected operation modes of the MG, charging and discharging cycles of electric vehicle stations, and the presence or absence of photovoltaic generation. The overcurrent protection relays were organized into 100 primary–backup relay pairs to ensure proper coordination and selectivity. The total miscoordination time (TMT) index was used to measure when all pairs of relays were coordinated, with a minimum time close to zero. The results of the graph theory show that all the meshes, fault locations, and relay pairs were identified in the MG. The approach successfully coordinated 100 relay pairs across 68 scenarios, demonstrating its scalability in complex real-world MGs. The automation process obtained an average TMT of 12.2%, while the optimization obtained a TMS of 91.6% with the CGA, and a TMT of 99% was obtained with the SSA and PSO, demonstrating the effectiveness of the optimization process in ensuring selectivity and appropriate fault clearing times. Full article

Ultra-low latency communication is fundamentally required to reduce end-to-end (E2E) latency related to the transportation of time-critical or time-sensitive traffic in 5G networks. Time-sensitive networking has significant prospects in factory automation and Industrial Internet of Things (IIoT) as a key technology that can provide low-latency, highly reliable, and deterministic communications over Ethernet, whereas IETF deterministic networking (DetNet) seeks to provide a complementary network layer to support ultra-low latency communications. DetNet, as standardized in the IETF, provides time-sensitive characteristics that assure extremely low packet loss and latency for ultra-reliable low-latency communications. This study develops a novel framework to enable 3GPP support for DetNet functionalities. First, the proposed framework seeks to support IP-based DetNet traffic and urgent data transmission in the network overload conditions of 3GPP 5G systems. Additionally, the proposed design supports DetNet service connectivity between non-DetNet and DetNet service areas. Based on simulation results, the proposed framework can guarantee deterministic latency requirements and critical data transmission for DetNet compared with conventional approaches. The proposed scheme can achieve more effective performance for moving DetNet devices. Full article

Mock theta functions, introduced by Ramanujan in his last letter to Hardy, play a significant role in q-series theory and have natural connections to fractional q-calculus. In this paper, we study bilateral hypergeometric series of the form ${}_2\psi _2$= ${\sum }_{n=-\infty }^{\infty }{\displaystyle \frac{{(a,b;q)}_n}{{(c,d;q)}_n}}{z}^n,$ where ${(a;q)}_n$ denotes the q-shifted factorial. Using Slater’s three-term transformation formula for bilateral ${}_2\psi _2$ series, we derive new identities for Ramanujan’s mock theta functions of orders 2, 3, 6, and 8. These transformations reveal previously unknown relationships between different q-series representations and extend the classical theory of mock theta functions within the framework of q-special functions. Full article

Lean implementations often deploy tools in isolation, leaving gaps in how abnormalities are exposed, resolved at the root cause, escalated when needed, and converted into organisational learning. This study proposes a five-stage closed-loop routine for daily factory management that integrates problem visibility, standardised shop-floor cadence, disciplined problem-solving, and tiered escalation within a single operating logic. The novelty lies not in the individual Lean tools, but in the specification of cadence, triggers, accountable roles, and verification steps that connect them into a replicable end-to-end routine. The model was evaluated through a 19-month longitudinal, single-site field intervention (quasi-experimental before–and–after) on the bottleneck production line of a pharmaceutical plant in Hengoed, Wales (UK). Line OEE increased by over 50% in relative terms. At factory level, total output increased by 20% year-on-year in 2024 (context indicator), alongside qualitative field observations of shorter time-to-resolution and improved cross-functional coordination. As a single-site study, external validity is context-dependent; nevertheless, the paper provides a specified closed-loop routine and field evidence on the operational effects of embedding an integrated Lean cycle into daily management. Practically, the study provides a specified routine that practitioners can replicate and adapt; academically, it contributes to Lean implementation research by showing how tool bundles can be operationalised as an end-to-end daily management routine with observable performance effects. Full article

The adaptive reuse of industrial heritage is increasingly recognized as an effective low-carbon strategy that reduces resource consumption, lowers embodied carbon emissions, and supports sustainable urban transitions. Developing appropriate reuse strategies, however, requires a robust understanding of heritage value. As material evidence of China’s modern industrialization, railway-associated industrial heritage possesses the characteristics of linear cultural heritage. Yet systematic and multi-scalar value assessments from a linear heritage perspective remain limited. Focusing on the Guanzhong Section of the Longhai Railway—one of the most representative industrial development axes in Northwest China—this study establishes a two-level value assessment framework and conducts a comprehensive evaluation of fourteen textile industrial heritage units. At the individual level, five dimensions—historical significance, architectural features, structural integrity, authenticity, and rarity—were assessed through field investigation, and type-specific weights were introduced to correct structural imbalances between quantity and value across building categories. At the unit level, the Analytic Hierarchy Process (AHP) was employed to determine the weights of spatial–functional integrity, process completeness, railway connectivity, industrial landscape characteristics, and the integrated individual-level value. The results show that factory workshops and warehouses consistently exhibit the highest value, whereas structures and residential buildings, despite their numerical dominance, contribute relatively little. Spatially, a clear west–east gradient emerges: high-value units cluster in Baoji and Xi’an, medium-value units in Xianyang, and low-value units mainly in Weinan and surrounding counties. The findings indicate that textile industrial heritage along the Guanzhong Section forms a railway-linked linear cultural heritage system rather than isolated sites. The proposed evaluation framework not only supports heritage identification and conservation planning but also provides a theoretical basis for promoting low-carbon adaptive reuse of existing industrial buildings. Full article

This review examines the impact of data analytics powered by the Internet of Things (IoT), edge computing, and artificial intelligence (AI) on improving energy efficiency in smart environments, with a focus on smart factories, smart cities, and smart territories. Advanced AI, machine learning (ML), and deep learning (DL) techniques enable real-time energy optimization and intelligent decision-making in complex, data-intensive systems. Integrating edge computing reduces latency and improves responsiveness in IoT and Industrial Internet of Things (IIoT) networks, enabling local energy management and reducing grid load. Federated learning further enhances data privacy and efficiency by enabling decentralized model training across distributed smart nodes without exposing sensitive information or personal data. Emerging 5G and 6G technologies provide the necessary bandwidth and speed for seamless data exchange and control across energy-intensive, connected infrastructures. Blockchain increases transparency, security, and trust in energy transactions and decentralized energy trading in smart grids. Together, these technologies support dynamic demand response mechanisms, predictive maintenance, and self-regulating systems, leading to significant improvements in energy sustainability. Case studies of smart cities and industrial ecosystems within Industry 4.0/5.0/6.0 demonstrate measurable reductions in energy consumption and carbon emissions through these synergistic approaches. Despite significant progress, challenges remain in interoperability, scalability, and regulatory frameworks. This review demonstrates that AI-based edge computing, supported by robust connectivity and secure IoT and IIoT architectures, has a transformative potential for creating energy-efficient and sustainable smart environments. Full article

With the rapid development of new energy, high-proportion new energy power systems have significantly reduced inertia and voltage support capacity, facing severe stability challenges. Virtual Synchronous Generator (VSG) control, which simulates the inertia and voltage source characteristics of traditional synchronous generators, enables friendly grid connection of new energy converters and has become a key technology for large-scale new energy applications. This paper addresses two key issues in low-voltage ride through (LVRT) of grid-forming converters under VSG control: (1) converter overcurrent suppression during LVRT; (2) reduced reactive power support due to retaining voltage-reactive power droop control during faults. It proposes an adaptive virtual impedance-based overcurrent suppression method and a frozen reactive power–voltage droop-based reactive support method. Based on the converter’s mathematical model, a DIgSILENT/PowerFactory simulation model is built. Time-domain simulations verify the converter’s operating characteristics and the improved LVRT strategy’s effect, providing theoretical and technical support for large-scale applications of grid-forming converters. Full article

The ongoing decarbonisation of power systems is displacing synchronous generators (SGs) with converter-based plants, requiring a consistent assessment of grid-following inverters (GFLIs) and grid-forming inverters (GFMIs). Using an openly available four-bus root-mean-square (RMS) benchmark modelled in DIgSILENT PowerFactory, this work compares three generation configurations: (i) a single local SG connected at the point of common coupling; (ii) the same generator combined with a GFLI; and (iii) the generator combined with a GFMI. These configurations are evaluated under three disturbance scenarios: (1) a balanced load step, (2) an unbalanced double line-to-ground fault at low short-circuit ratio (SCR) with temporary islanding and single-shot auto-reclose, and (3) full islanding with under-frequency load shedding (UFLS), partial resynchronisation, and staged restoration. For the tested tuning ranges and within this RMS benchmark, the grid-forming configuration behaves as a low-impedance source at the point of common coupling in the phasor sense, yielding higher frequency nadirs during active-power disturbances and faster positive-sequence voltage recovery under weak and unbalanced conditions than the SG-only and SG+GFLI cases. During islanding, it supports selective UFLS, secure resynchronisation, and orderly load restoration. Rather than introducing new control theory, this work contributes a reproducible RMS benchmarking framework that integrates low-SCR operation, unbalance, and restoration sequences with a documented cross-technology tuning procedure. The findings indicate system-level improvements in frequency resilience and voltage recovery for the tested benchmark relative to the alternative configurations, while recognising that instantaneous device-level effects and broader generality will require electromagnetic-transient (EMT) or hybrid EMT/RMS validation in future work. Full article

This study investigated the effects of Bacillus subtilis (B. subtilis) supplementation on microbiota and metabolites in the feces of Leizhou goats. Eight Leizhou goats were used in a replicated 4 × 4 Latin square design according to their gender (nanny goats and billy goats) with a 4 × 2 factorial arrangement of treatments that included four B. subtilis additive doses (control [0 g/d; NC, BC], low [2.5 g/d, NL, BL], medium [5 g/d, NM, BM], and high [7.5 g/d, NH, BH]) and 28 d periods (n = 4 per group), each consisting of 27 d adaption and 1 d sample collection. After collecting 32 fecal samples, 16S rRNA gene sequencing and LC-MS were performed to analyze microbial composition and metabolites, respectively. At the genus level, the relative abundance of Rikenellaceae_RC9_gut_group was significantly higher (p < 0.05) in the NM group than in the NC group. The relative abundance of Treponema sp. was significantly lower (p < 0.05) in the NM group than in the NC group. In billy goats, the relative abundances of UCG-005 and Rikenellaceae_RC9_gut_group were significantly higher (p < 0.05) in the BH group than in the BC group. The relative abundance of Treponema sp. was significantly lower (p < 0.05) in the BL, BM, and BH groups than in the BC group. Furthermore, metabolomic analysis revealed that B. subtilis significantly altered the concentrations of glucose metabolism modulators (1-deoxynojirimycin, 1-DNJ) and certain bioactive peptides. Many amino acid metabolic pathways were also enriched. Correlation analysis demonstrated close connections between differential metabolites and the top 10 bacterial genera in fecal samples. These results provide new insights into the impact of B. subtilis on the microbial community and metabolic profile of the feces of Leizhou goats. In this experiment, the appropriate doses of B. subtilis for nanny goats and billy goats were 5 g/d and 7.5 g/d, respectively, but the optimal doses still need to be verified based on performance-based feeding tests in the next study. Full article