Abstract: Currently, research aimed at optimizing the power rating and energy capacity of electrical energy storage (EES) systems while accounting for multiple sources of uncertainty remains underrepresented in the scientific literature, due to the complexity of solving multidimensional uncertainty problems in microgrids. Regarding the comprehensive assessment of EES parameters considering the influence of various factors, despite numerous studies dedicated to the evaluation and rational selection of EES parameters, this task remains largely unresolved. This paper proposes a methodology for selecting EES parameters that accounts for the uncertainty of wind power plant (WPP) generation and electric vehicle charging station (EVCS) load, EES performance degradation, as well as the reliability and cost of microgrid implementation to ensure uninterrupted operation of EV supply equipment within a distribution network with limited available power capacity. The developed method and EVCS load profile model enable the generation of a time-based power profile under input data uncertainty. The work presents a mathematical model of microgrid operation that considers the integrated performance of EES, WPP, and EVCS. The EES parameter selection methodology is demonstrated using examples of various system configuration scenarios.

Abstract: Dynamic fire-safety systems are no longer futuristic; they are a viable alternative to rigid, prescriptive approaches to occupant protection and evacuation. This paper analyses three case studies where Building Information Modelling (BIM), a Digital Twin (DT), Internet-of-Things (IoT) sensor networks, Artificial Intelligence (AI) control algorithms, and dynamic evacuation signage were integrated to support a Dynamic Fire-Safety System (DFS). Secondary research covered a university building in Lille, the Beijing Capital Airport Emergency Center, and a shopping mall in the Taipei 101 high-rise complex. All facilities meet formal requirements, yet a BIM/DT/IoT/AI layer suggests better performance under fire conditions. Methods included a structured literature review, BIM-based fire modelling in Fire Dynamics Simulator (FDS), evacuation simulations, and comparison of static versus dynamic paradigms. The workflow reconstructs fire–evacuation scenarios to assess time-dependent tenability, exit viability, and congestion-driven bottlenecks. In Lille, DFS serves as a computational laboratory for design decisions; in Beijing, as a decision-support core controlling signage in near real time; and in Taipei 101, as an optimisation-driven strategy for multi-storey occupant populations. Across the cases, DFS-oriented solutions are reported to shorten evacuation time and/or increase the probability of successful evacuation relative to static arrangements. Reported benefits depend on clear cue visibility and timely actuation of guidance signals. Implications for Poland are discussed: prescriptive rules should remain a baseline, while complex facilities may adopt performance-based solutions grounded in BIM/DT/IoT/AI, provided equivalence to conventional protection is demonstrated.

Abstract: To address the issues of insufficient restoration of texture details in deblurred images and inadequate learning of frequency domain features, an image deblurring algorithm based on frequency domain feature enhancement and convolutional neural networks is proposed. First, a Fourier residual module with a parallel structure is constructed to achieve collaborative learning and modeling of spatial and frequency domain features. By introducing the Fourier transform, the frequency domain feature learning is enhanced to improve the restoration of texture details. Second, after a gated feed-forward unit is applied to Fourier residual module, its nonlinear representation capability is further improved. In addition, a supervised attention module is introduced at the decoder stage to promote more effective extraction of key features essential for image reconstruction. Experimental results have demonstrated that the proposed algorithm effectively removes blur while better preserving image details.

Abstract: In the Industry 4.0 era, the AECO sector faces a strategic challenge in integrating MEP systems during early design stages, where a lack of "Design for Maintainability" contributes to building defect rates of up to 28%. This study evaluates a digital innovation framework synthesizing Serious Games and Cooperative Problem-Based Learning (CPBL) via Minecraft to foster systemic thinking and spatial reservation logic at LOD 100–150. Using a mixed-methods design (n=25), the curriculum employed a "Mirror Mapping" mechanism, translating game physics (e.g., Redstone and water mechanics) into real-world electrical and plumbing logic. While students achieved 93% in management competency and nearly 100% accuracy in pipeline logic among upper-level cohorts, a significant "Symbolic Transformation Gap" persisted, with performance in system analogy (80%) lagging behind procedural mastery. The findings validate the framework as a potent tool for spatial externalization, yet emphasize the necessity of "bridging activities" and Digital Twin linkages to ensure effective knowledge transfer from simulated environments to professional AECO practice.

Abstract: This paper presents a practical industrial hybrid control architecture that augments the widely deployed 49-rule Mamdani fuzzy supervisory PID controller with a lightweight online meta-tuner based on Soft Actor-Critic (SAC) reinforcement learning. While the inner 1 kHz fuzzy-PID loop remains fully deterministic and identical to the industrial baseline, a separate 10 Hz SAC agent autonomously adapts the three output scaling factors (α_Kp, α_Ki, α_Kd ∈ [0.5, 2.5]) of the fuzzy layer using an ONNX Runtime inference engine. The complete controller is implemented and experimentally validated on a real Siemens S7-1214C PLC (6ES7214-1AG40-0XB0) in a hardware-in-the-loop setup with a high-fidelity 5-DoF manipulator model incorporating measured friction, backlash, sensor noise, and payload variation (0–2.5 kg). Across four demanding scenarios (sinusoidal tracking, sudden payload jumps, sustained disturbances up to 0.76 Nm, and high-speed motions), the proposed method consistently achieves 46–52 % lower RMSE and 28–30 % reduced control energy compared to the fixed-scaling industrial baseline, while preserving strict real-time constraints (inner loop cycle time 0.68–0.89 ms, SAC inference < 0.6 ms). The full PLC program (SCL/FBD), HIL environment, and trained policies will be released open-source upon acceptance (DOI to be provided during revision).The full PLC program, HIL environment, and trained SAC policies will be released open-source as a preprint supplement.

Abstract: This critical review examines the evolution of mathematical modeling approaches for aerobic digestion processes in food industry waste management, highlighting their role in operational optimization and dynamic prediction. Starting from mass conservation principles, simple kinetic models such as first-order and Monod models are analyzed. These models assume homogeneity and perfect mixing but fail to capture the heterogeneity of effluents rich in variable carbohydrates, proteins, and lipids. Structural limitations, such as numerical rigidity, parametric non-identifiability, and idealized assumptions that underestimate spatial gradients and stochastic fluctuations, are discussed. In continuous systems, coupled substrate-biomass-oxygen dynamics, washout phenomena, and extensions to partial differential equations for real heterogeneity are explored. Structured models such as ASM incorporate multicomponent fractions but face parameterization crises exacerbated by data scarcity in industrial settings, where less than 25% of plants use formal modeling. Emerging paradigms include hybrid mechanistic-machine learning approaches for prediction under perturbations, multiscale modeling , and spatially explicit modeling. A table distributes approaches by food matrix, revealing the dominance of simple kinetics in composting and ASM in activated sludge. Finally, a progressive selection framework based on operational objectives is proposed, balancing complexity with predictive robustness and experimental validation, emphasizing that sophistication must be justified to overcome barriers such as sensor costs and stochastic variability, thus promoting sustainable industrial adoption.

Abstract: The emergence of chiplet-based architectures represents a paradigm shift in post-Moore’s Law computing systems, offering substantial cost and yield advantages through functional disaggregation. However, the heterogeneity of inter-chiplet communication introduces unique performance challenges that conventional partitioning strategies fail to address. This work presents a comprehensive characterization of how poor workload partitioning degrades communication performance in chiplet-based systems. We demonstrate, through detailed experimental analysis, that suboptimal workload partitioning can increase inter-chiplet communication latency by up to 10×, and can inflate network congestion beyond sustainable levels as systems scale. Our findings show that optimized partitioning strategies can achieve 87.4% reduction in inter-chiplet traffic, improve system throughput by 8.75×, and enhance energy efficiency by 10.3× compared to naive partitioning approaches. We further characterize how these effects compound with system scalability, revealing that communication overhead can consume 85% of execution time in poorly partitioned 16-chiplet systems, versus only 35% in well partitioned configurations. This work provides essential insights into the communication-aware design space of chiplet systems and validates the critical importance of sophisticated workload partitioning algorithms.

Abstract: The purpose of this study is to evaluate the impact of AI-based route optimization on carbon emission reduction in urban construction logistics, with a particular focus on the mediating role of vehicle load optimization.The study adopts a cross-sectional quantitative research approach, targeting three urban locations in Ghana. The stratified random sampling was adopted to select 405 participants who answered a 5-point likert scale self-administered structured questionnaire. The data was entered and cleaned into SPSS. The descriptives, reliability and correlations were analyzed. The study further used the linear regression and mediation analysis to investigate the relationship between the study variables.The study findings showed that AI-based route optimization had a positive significant effect on carbon emission reduction and vehicle load optimization. Vehicle load optimization had a positive significant effect on carbon emissions reduction. Furthermore, the findings pointed to a partial mediation of AI-based route optimization on carbon emission reduction through vehicle load optimization.Ghanaian policymakers should invest in AI technologies and endorse vehicle load optimization solutions to meet carbon reduction targets, enhancing urban logistical efficiency. Future research should use longitudinal design, and focusing on rural construction logistics.

Abstract: Shared electric scooters (e-scooters) are increasingly promoted as low-carbon urban mobility solutions due to their energy efficiency and zero tailpipe emissions. However, recent life cycle assessment (LCA) studies indicate that the environmental performance of shared e-scooter systems is highly sensitive to factors such as manufacturing processes, charging practices, fleet rebalancing operations, and limited vehicle lifetimes. Most existing assessments rely on static, average-based LCA models that fail to capture the influence of operational decisions and temporal variability. This study proposes a carbon-aware operational framework that reframes sustainability assessment in shared e-scooter systems as an operational decision-making problem. The framework integrates ride-level vehicle telemetry, temporally varying electricity grid carbon intensity, and dynamically allocated lifecycle impacts to estimate greenhouse gas emissions on a per-ride basis. These metrics are embedded into operational control processes to enable carbon-aware charging and rebalancing strategies. To support early validation, we simulate 1,000 urban e-scooter rides under both conventional and carbon-aware scenarios. Results show that ride-level GHG emissions can be reduced by 24.5% solely through improved charging schedules and optimized logistics—without changes in hardware or fleet size. This work offers a data-driven and algorithm-agnostic decision-support architecture that advances LCA from retrospective reporting to real-time environmental management in micro-mobility systems.

Abstract: This study presents the design, implementation, and validation of a thermoelectric energy harvesting system that exploits waste heat from an industrial electric motor to power an autonomous wireless sensor device. The proposed prototype integrates a single thermoelectric generator directly onto the motor housing and leverages the built-in cooling fan to maintain a stable thermal gradient of approximately 4–5 C. Under real factory conditions, the system harvested 6.17 J of energy over 9612 s, sustaining continuous operation and 41 successful Long Range (LoRa) data transmissions with a positive energy balance. Compared with related works, the prototype achieved competitive or superior performance while operating at a lower motor rating of 0.25 kW, highlighting its efficiency relative to system scale. Key innovations include a hybrid DC/DC conversion chain bridging ultra-low input voltages to modern microcontrollers, and an adaptive transmission strategy that ensures predictable energy management and reliable wireless communication. These results demonstrate the feasibility of battery-free sensing in industrial environments and underline the potential of thermoelectric harvesting as a cost-effective, maintenance-free, and environmentally responsible solution for predictive maintenance and Industry 4.0 applications.

Abstract: Aiming at the problem of accurate estimation of coherent parameters for the distributed coherent jamming system (DCJS), this paper first establishes a transmit-receive signal model of the DCJS in the presence of coherent parameter estimation errors. Then, it analyzes and verifies that the generalized cross-correlation function weighting method causes a decrease in the estimation accuracy of coherent parameters due to whitening processing, which in turn impairs the synthesis efficiency of the DCJS. Finally, a coherent parameter estimation method based on frequency-domain feature matching is proposed. The weighting method based on frequency-domain feature matching can effectively preserve the intra-pulse features of signals, thereby improving the estimation accuracy of coherent parameters. Simulation results show that, compared with the existing algorithms, the proposed method improves the time delay estimation accuracy by 30% and the phase difference estimation accuracy by 7.7%.

Abstract: The autonomous grasping of flexible slings is a pivotal challenge for unmanned crane systems, primarily stemming from the slings' geometric indeterminacy, material compliance under load,and stochastic initial pose relative to the hook.To address this challenge,we propose an intelligent hook system featuring a novel compound mechanical architecture.This architecture integrates a horizontal slewing mechanism for in-plane alignment with a self-locking worm-gear drive for secure grasping.A coordinated control strategy,employing a Fuzzy PID algorithm,ensures robust dynamic performance under variable loading conditions.Finite element analysis confirms structural integrity under a rated load of 500 kg,with a maximum stress of 344.34 MPa.Experimental results demonstrate that the hook completes a full pick-and-release cycle in approximately 2 seconds for parallel slings, with a success rate exceeding 95%.This represents an approximately 60% improvement in operational efficiency over manual operation.This work provides a practical and efficient solution for automating flexible sling handling.

Abstract: 5G Networks deployment is an important milestone in wireless communication. This research looks into the Indian scientific view on 5G, concentrating on link performance estimation and module-operation optimization. Indian scientists have made contributions towards aspects such as energy-efficient network architectures, Cloud Radio Access Network (CRAN) optimization, and broadband coverage modeling. Nonetheless, additional India-focused research will be required to solve deployment-related challenges unique to India. Performance estimation is based on system-level simulations, measurement-based methods, and analytical models, each with their own limitations like scalability issues and model accuracy dependencies. A hybrid method combining these methods can enhance accuracy. Module-operation optimization increases network efficiency by optimizing dynamic resource allocation, energy-efficient transmission, interference management, and load balancing via network slicing and Multi-access Edge Computing (MEC). Real-world implementation intricacies continue to exist. India's heterogeneity of infrastructure, expensive spectrum, and differentiated service expectations demand customized solutions. Research in the future needs to concentrate on AI-optimized optimization, real-time adaptive algorithms, and cyber security boosters. There needs to be a multidisciplinary strategy incorporating engineering, economics, and policy to enable sustainable deployment of 5G. Future research must investigate quantum computing and block chain for secure, optimized networks.

Abstract: Nonlocal elasticity theory plays a significant role in the analysis of small-scale effects in micro- and nanostructures. In continuum mechanics, Eringen’s integral constitutive relation is often considered more general than its differential counterpart. However, the governing equations are complex integro-differential equations, which complicate numerical solution and can limit their use in nonlocal analyses of micro- and nanostructures. To address this challenge, this paper proposes a numerical solution method based on a symplectic system for the study and resolution of the free vibration problem of small-scale Kirchhoff plates. By integrating an element that accounts for long-range interaction forces, this method effectively discretizes the nonlocal integral operator. The model is used to systematically investigate the effects of nonlocal parameters, mixture parameters, mode numbers, kernel function types, and geometric parameters on the natural frequencies of nonlocal Kirchhoff plates. The numerical results indicate that nonlocal effects soften structural stiffness and that higher-order modes are more sensitive to nonlocal parameters. The convergence and accuracy of the proposed algorithm are verified by comparison with existing differential nonlocal solution schemes.

Abstract: In unmanned aerial vehicle (UAV) applications, the performance of simultaneous localization and mapping (SLAM) systems often degrades under high-frequency vibrations induced by airflow and wind disturbances, which can corrupt LiDAR measurements and lead to pose estimation drift or even complete system failure. To address this challenge, this paper proposes a graph optimizationbased multi-keyframe SLAM method designed to enhance robustness and accuracy under strong vibration conditions. Unlike conventional approaches that align the current frame only with the most recent keyframe, the proposed method aligns each LiDAR frame with multiple historical keyframes to construct a factor graph. Each alignment is modeled as a relative pose constraint between nodes, while an adaptive weighting strategy based on spatial distance and temporal interval dynamically balances the contributions of different keyframes. Global pose optimization is then performed within a non-linear least squares framework using the Gauss–Newton method. Experimental results on the NTU VIRAL dataset demonstrate that the proposed method significantly reduces both trajectory and rotational errors compared with LeGO-LOAM, achieving improvements of up to 71% and 84% in position and orientation accuracy, respectively. Furthermore, real-world UAV experiments validate the effectiveness and reliability of the proposed approach, showing stable and accurate mapping performance even under rapid aerial motion and external disturbances.

Abstract: Metal packaging materials remain fundamental across food, beverage, pharmaceutical, cosmetic, and technical sectors owing to their combination of mechanical robustness, total light and gas barrier performance, thermal resistance, and established recyclability. Aluminum alloys, tinplate, tin-free steel (TFS/ECCS), stainless steels, metal–matrix composites (MMCs), and metal–polymer or metal–paper laminates define distinct metal-based packaging architectures whose metallurgical and interfacial design governs forming behaviour, corrosion and migration pathways, coating integrity, and mechanical reliability. In this review, these architectures are examined from a materials- and systems-oriented perspective, linking composition, microstructure, processing routes, and surface engineering to functional performance across rigid, semi-rigid, and flexible formats. The analysis also considers the ongoing transition from bisphenol A (BPA)-based epoxy linings to BPA-free and hybrid coating chemistries, the use of nano-structured metallic and metal-oxide surfaces, and the role of composite laminates in which thin metallic foils are combined with polymeric or paper-based structural layers. These material and architectural aspects are discussed together with safety, regulatory, and circularity considerations that increasingly influence the design and selection of metal-based packaging. Ion migration, coating degradation, and corrosion under realistic storage environments are considered in relation to EU, FDA, ISO, and sector-specific requirements, while attention is also paid to the contrast between well-established closed-loop recycling infrastructures for aluminum and steel and the more complex end-of-life management of coated metals and multilayer laminates. The review provides a unified framework connecting materials selection, metallurgical design, processing, performance, regulatory compliance, and sustainability in metal-based packaging systems. Applications spanning consumer goods, pharmaceuticals, cosmetics, and advanced electronics are integrated to support an overall understanding of how metallic and hybrid metal-based architectures underpin functional reliability and life-cycle sustainability.

Abstract: Real-time monitoring of key parameters (e.g., substrate) is crucial for the precise control of biological fermentation processes. To address the technical bottlenecks of significant lag in offline analysis and the limitations of traditional online sensors, this study de-signed and implemented a universal AI-enabled electronic nose system. The system features a modular hardware architecture integrating a high-sensitivity MOS gas sensor array, a precision constant-temperature chamber, and low-noise signal acquisition circuits to ensure signal stability. On the software side, a software architecture was designed based on the RUP 4+1 view model, employing multi-threaded technology for parallel data processing. An innovative five-stage sampling period was designed to match the dynamic response of MOS sensors, facilitating reliable data acquisition. Combined with a truncated average filtering strategy and peak response feature ex-traction, a lightweight single-hidden-layer neural network model was constructed for real-time prediction. Taking the real-time prediction of methanol concentration during glucoamylase fermentation by Pichia pastoris as a case study, the system demonstrated outstanding performance: R² reached 0.9998, RMSE was 13.5326 ppm, and the prediction delay was less than 1 second. The proposed system provides a robust, efficient, and universally applicable hardware-software solution, demonstrating significant potential for intelligent biomanufacturing.

Abstract: Unmanned aircraft system traffic management (UTM) frameworks from multiple international initiatives define essential services but remain deliberately non-prescriptive about how architectures should be implemented, how multiple providers should be federated, and how performance should be validated under high-density operations. This research investigated the operational performance of single-provider versus federated multi-provider UTM configurations through controlled simulation experiments. We designed and implemented a modular five-layer UTM experimental framework that includes strategic planning, tactical deconfliction, safety monitoring, identity and security, and federation interfaces, aligned with current UTM concepts and service definitions. A discrete-event simulation instantiated this framework to evaluate system performance across different traffic demand levels (20, 60, and 100 flights per hour) and communication delay conditions (0.2, 1.0, and 5.0 seconds) under both federated and non-federated configurations. Experimental results showed that federation performance depends on the operating regime. At high demand with moderate delay (100 flights per hour and 1.0 second delay), federation reduced mean conflicts by 34% (from 44.0±2.9 to 29.0±11.0) and improved safety scores from 0.637 to 0.818 through coordinated intent planning. In contrast, at low demand with minimal delay (20 flights per hour and 0.2 second delay), federation overhead increased conflicts by roughly a factor of four (from 0.33±0.47 to 1.33±1.89), while safety scores remained high (above 0.96). These findings quantify the operational envelope in which multi-provider coordination provides a net benefit and establish empirical baselines for deployment and policy decisions in federated UTM ecosystems.

Abstract: To meet the requirements for high-precision acquisition of multi-channel weak current signals in magnetic field tomography diagnostics for the CN-H1 star-simulator, this paper presents a 64-channel microcurrent data acquisition system based on the ZYNQ-7000 SoC. The system employs independent high-gain transimpedance amplification and filtering circuits for front-end conditioning. Utilising FPGA control over four AD4115 analogue-to-digital converters, it achieves parallel high-precision acquisition of 64 signals. Experiments demonstrate that within the ±2 μA input range, the system's equivalent input noise remains below 0.2 nA (RMS). The average effective number of bits (ENOB) per channel reaches 13.28 bits, with high inter-channel consistency (standard deviation of 0.04 bits). This system combines high channel density with outstanding single-channel measurement performance, making it suitable for precision measurement scenarios requiring parallel, high-precision acquisition of minute currents.

Abstract: The construction industry in Ghana plays a critical role in national development but continues to face significant challenges related to material waste, operational inconsistencies, and uncontrolled project costs. In response to growing sustainability needs, Artificial Intelligence (AI) has emerged as a viable tool for improving resource utilization and reducing waste across construction processes. This study examines the relationship between AI-driven resource optimization and waste reduction in Ghana’s construction industry, with particular emphasis on the mediating role of process efficiency.Adopting a quantitative cross-sectional survey design, data were collected from 450 construction professionals across six construction subsectors in Ghana. The data were analysed using SPSS, incorporating descriptive statistics, correlation analysis, and mediation analysis through Hayes’ Process Macro Model 4. The results indicate that AI resource optimization exerts a positive and statistically significant effect on waste reduction. Additionally, process efficiency partially mediates this relationship, strengthening the influence of AI-driven resource optimization on waste reduction outcomes.The study provides empirical evidence from a developing economy context, demonstrating that the effectiveness of AI adoption in construction is enhanced when supported by efficient operational processes. The findings offer practical insights for construction firms, industry stakeholders, and policymakers seeking to advance sustainable construction practices, minimize material waste, and support national development objectives aligned with the United Nations Sustainable Development Goals.