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Economic expansion is commonly accompanied by accelerated growth in electricity consumption. In this context, distribution networks play a fundamental role in delivering electricity to end users; however, they remain subject to technical losses. Among system components, power transformers are particularly relevant because, despite their high efficiency, they still generate significant losses due to continuous operation and their widespread presence throughout the electrical system. The literature addresses the relocation of distribution transformers as a strategy to minimize technical losses. However, the application of this approach to equipment installed in electrical substations remains less explored. Therefore, this study proposes an optimization model for reallocating a pool of existing power transformers among electrical substations, matching transformer rated capacities to substation demands to reduce technical losses and enhance energy efficiency. The research methodology involved the mathematical formulation of the problem, adopting the minimization of accumulated total cost as the objective function, which incorporates both technical losses and relocation costs. The model was applied to a case study using technical and operational data from power transformers commonly installed in HV/MV substations. The results demonstrate that the optimal relocation reduced transformer energy losses by approximately 5% over the 10-year planning horizon, generating net economic savings that exceeded the equipment relocation costs. The optimization model suggests adjustments in transformer allocation, aligning loading levels with more appropriate operating ranges and thereby increasing transformer efficiency. Consequently, the proposed approach provides an engineering design-oriented decision-support method for optimizing transformer allocation, contributing to more energy-efficient and economically effective substation planning. Full article

Hydraulic transients induced by rapid changes in operating conditions represent a major challenge in the design and safe operation of offshore Firewater systems. During emergency events, such as fire pump start-up or rapid valve operations, pressure waves may propagate throughout the distribution network, generating water hammer effects capable of compromising the integrity and reliability of critical safety equipment. This study investigates the transient hydraulic response of an offshore Firewater ring-main system installed on a Floating Production Storage and Offloading (FPSO) unit using a detailed numerical model developed in PIPENET Transient. Three representative emergency operating scenarios were analysed, including fire pump start-up, deluge valve closure, and monitor valve closure. For each scenario, the hydraulic response of the original system configuration was compared with a modified pressure-protection arrangement involving relocation of the check valve immediately downstream of the fire-pump discharge flange and reduction of the pressure safety valve (PSV) set pressure from 17.5 barg to 16.5 barg. The simulations enabled the identification of critical pressure locations, evaluation of transient pressure propagation, and assessment of the effectiveness of the proposed mitigation strategy. The results demonstrate that the modified pressure-protection arrangement reduces the governing system-level pressure peaks and attenuates transient pressure oscillations under the investigated operating conditions. The proposed engineering methodology provides practical support for the design verification and optimization of offshore Firewater systems and contributes to improving the operational safety and reliability of safety-critical piping networks. Full article

Accurate estimation of SOC for lithium-ion batteries is a very important job in battery management systems, but under complex dynamic operating conditions, model misalignment often happens, and filtering algorithms usually do not make enough use of historical data, so the estimation accuracy is lowered. This paper puts forward a lithium-ion battery SOC estimation method that is based on weighted multi-innovation unscented Kalman filtering (WMIUKF); a hybrid parameter identification strategy that combines FFRLS and PSO is introduced to supply initial values for the global optimization of the model and to track dynamic drifts. To deal with the problems that the unscented Kalman Filter (UKF) does not make effective use of historical information and lacks an adaptive correction mechanism, multi-innovation theory and exponentially decaying weighting factors are incorporated into it; then, by fusing current and historical multi-step prediction residuals, a weighted freshness matrix can be constructed, and through this the method, we can improve the utilization efficiency of historical data and the system’s ability to resist interference. The performance of the proposed algorithm was validated through comparative experiments under various typical dynamic operating conditions, as well as at different temperatures (0 °C–45 °C) and discharge rates (0.5 C–2 C). The results indicate that the PSO-FFRLS hybrid parameter identification effectively improves model accuracy; compared to the UKF, MIUKF, and PSO-MIUKF algorithms, the WMIUKF achieved optimal SOC tracking under all types of dynamic operating conditions, with a root mean square error (RMSE) of no more than 0.58%. Even under extreme temperatures and high-rate discharge conditions, the error remained stable at a low level, demonstrating good environmental adaptability and robustness. Full article

Background/Objectives: The optimal surgical approach for Percutaneous KyphoPlasty (PKP) in patients with recent single-level Osteoporotic Vertebral Compression Fractures (OVCFs) remains controversial. Most available previous meta-analyses included studies with variable heterogeneity, often mixing unilateral and bilateral MIS approaches, differing surgical techniques, and various fracture patterns, which limited the reliability of their conclusions. This meta-analysis aimed to compare the efficacy and safety of unilateral versus bilateral PKP exclusively in patients with recent single-level OVCFs only. Methods: A systematic review was conducted according to the PRISMA 2020 guidelines. PubMed, Scopus, Cochrane Library, and ScienceDirect were searched for comparative studies published between 2000 and 2025. Randomized Controlled Trials (RCTs), prospective, and retrospective comparative studies comparing unilateral and bilateral PKP for recent single-level OVCFs were included. Clinical and radiological outcomes as well as perioperative complications and safety outcomes were analyzed using random-effects meta-analysis. Predefined subgroup analyses according to study design and sensitivity analyses were performed. Results: Eleven studies involving 1374 patients (705 unilateral and 669 bilateral PKP) met the inclusion criteria. No significant differences were observed between the two surgical approaches regarding short- or long-term pain relief, cement leakage, number of adjacent vertebral fractures, or overall clinical outcomes. Bilateral PKP demonstrated statistically significant, but clinically negligible, advantages in anterior vertebral body height restoration and kyphosis correction. Unilateral PKP required an insignificantly lower cement volume. For operative time, the overall pooled estimate favored unilateral PKP by approximately 10 min but showed extreme heterogeneity (I² = 98.5%). Importantly, the two RCTs showed no statistically significant between-group difference (MD = +1.2 min, 95% CI −4.5 to +6.8), indicating that the apparent overall effect was largely driven by observational evidence. Similar discrepancies between randomized and retrospective studies were observed for several other outcomes, underscoring the importance of considering study design when interpreting the results. Conclusions: Current evidence does not demonstrate clinically meaningful superiority of either unilateral or bilateral PKP for the treatment of recent single-level OVCFs. Bilateral PKP may provide small advantages in selected radiographic outcomes, whereas unilateral PKP uses modestly less bone cement; however, the relevance of these differences remains clinically uncertain. Surgical approach selection may therefore be individualized according to vertebral morphology, pedicle anatomy, fracture characteristics, surgeon experience, and technical feasibility rather than expectations of superior clinical outcomes. Further adequately powered randomized trials with standardized outcome reporting and long-term follow-up are warranted. Full article

High-temperature proton-exchange membrane fuel cells (HT-PEMFCs) generate useful thermal energy that can be recovered for additional power production. This study investigates an integrated HT-PEMFC–organic Rankine cycle (ORC) system by combining response surface methodology (RSM) with thermodynamic energy analysis. A 17-run response-surface design was used to quantify the effects of pressure, temperature, and current density on polarization voltage. Power density was derived directly from the RSM-predicted voltage using P_d = iE to preserve physical consistency. The electrochemical model was benchmarked against published phosphoric-acid-doped polybenzimidazole HT-PEMFC polarization data under comparable conditions. The constrained optimization identified an operating condition of 400 kPa, 443 K, and approximately 1.198 A cm⁻², giving a predicted voltage of 0.5395 V and a power density of approximately 0.6462 W cm⁻². This represents a 12.9% increase in power density relative to the adopted reference condition. Separately, the reference thermodynamic case produced 13.08 kW of gross HT-PEMFC stack electrical power and 15.45 kW of thermal output assumed available to the ORC. The available legacy R409A reference case was evaluated at an evaporator pressure of 2 MPa, yielding approximately 1.24 kW of ORC net power and a net thermal efficiency of about 8.02%. The resulting combined modeled electrical output was approximately 14.32 kW before unmodeled balance-of-plant auxiliary power consumption, with the ORC contribution corresponding to about 9.5% of the gross HT-PEMFC stack output. The results demonstrate the complementary potential of physically consistent HT-PEMFC operating-condition optimization and waste-heat recovery, while the ORC results remain specific to the retained R409A reference dataset. Full article

Despite the robust mechanical and chemical stability that make hollow flat-sheet ceramic membranes highly attractive for membrane bioreactors (MBRs), the fundamental relationship between their structural design, specifically pore size and structural symmetry, and biological fouling behavior remains elusive. To decouple the effects of membrane architecture on fouling mechanisms, a series of symmetric and asymmetric hollow flat-sheet alumina membranes were systematically engineered. Symmetric architectures with tunable pore sizes were fabricated by controlling aggregate particle sizes, whereas asymmetric counterparts featuring distinct separation layer thicknesses were developed via a tailored dip-coating process. Long-term operational evaluations treating municipal wastewater uncovered a counterintuitive phenomenon. Asymmetric membranes, despite yielding superior retention, experienced markedly accelerated transmembrane pressure evolution and severe cake layer fouling compared to the symmetric supports. Resistance-in-series analysis coupled with classical filtration models demonstrated that thicker separation layers and larger pore sizes were associated with shifts in the dominant fouling mechanism toward rapid and dense cake layer formation, which significantly exacerbated irreversible biological fouling. Furthermore, advanced spectroscopic and high-throughput sequencing techniques revealed that structurally complex asymmetric layers were associated with shifts in extracellular polymeric substances and specific fouling-associated bacterial phyla at the membrane interface. Ultimately, these findings underscore the necessity of architectural optimization to mitigate biofouling and prolong the operational lifespan of ceramic membranes, highlighting the sustainable advantages of symmetric structures. Full article

Accurate cross-dataset state-of-health prediction for lithium-ion batteries remains challenging due to distribution shifts arising from diverse cathode chemistries, operating temperatures, and charge–discharge protocols across heterogeneous battery fleets. Drawing upon established machine learning paradigms, this study tailors a Domain-Adaptive Mixture-of-Experts (DA-MoE) framework to the battery prognostic context, automatically selecting the optimal domain adaptation strategy for each target domain through a physics-aware, lightweight linear gating network comprising merely 32 learnable parameters. The framework integrates a shared Transformer-based backbone with four adaptation strategies spanning the full spectrum of target-domain information utilization, namely zero-shot transfer, Test-Time Adaptation, Fine-Tuning, and Model-Agnostic Meta-Learning. A comprehensive evaluation on 564 battery cells from seven publicly available datasets under Leave-One-Domain-Out Cross-Validation protocol demonstrates that the proposed framework achieves an average coefficient of determination of 0.864 with perfect oracle strategy alignment under full domain training and maintains competitive generalization at an average R² of 0.795 when each target domain is held out during gating network training. Hard argmax selection consistently outperforms weighted fusion across all seven domains with an average margin of +0.027 in R², confirming that the four adaptation strategies compete rather than cooperate in this application context. A feature ablation analysis identifies sample count as the dominant determinant of strategy selection with performance degradation of ΔR² = −0.182 upon removal, followed by the early-cycle degradation slope and early-cycle nonlinearity index as secondary signals, all of which are computable at deployment time without future ground-truth SOH information. The proposed framework provides a practically deployable solution for battery management systems operating across heterogeneous fleets with minimal computational overhead and strong cross-dataset generalization capability. Full article

Digital twins (DTs) based on agent-based artificial intelligence (Agentic AI) provide a transformative framework for streamlining nondestructive testing (NDT) of 3D-printed rehabilitation equipment. This study applies a conceptual research methodology based on the integration and analysis of recent advances in Agentic AI, digital twin architectures, additive manufacturing, NDT technologies, and intelligent rehabilitation systems to establish a framework for autonomous quality monitoring and lifecycle management of 3D-printed medical devices. By creating intelligent virtual replicas of physical devices, these systems enable continuous monitoring of structural integrity, functional performance, and degradation mechanisms throughout the product lifecycle. Unlike conventional AI-based DTs, Agentic AI-driven DTs can autonomously perceive, reason, plan, and execute corrective actions based on real-time sensor data, NDT results, manufacturing information, and historical knowledge. The main conclusion of this work is that Agentic AI-enhanced DTs have the potential to transform NDT from a passive inspection approach into an intelligent, predictive, and autonomous decision-support system for rehabilitation equipment. Advanced machine learning and autonomous decision-making algorithms enable the identification of early signs of material degradation, manufacturing defects, fatigue accumulation, and performance anomalies, supporting predictive maintenance and proactive quality assurance. Integrating Agentic AI DTs with additive manufacturing processes enables real-time optimization of printing parameters, adaptive process control, and continuous refinement of inspection strategies without production interruption or destructive sampling, thereby supporting Industry 4.0 and smart manufacturing principles. The main innovation of this research lies in proposing an autonomous closed-loop framework that combines Agentic AI, DTs, additive manufacturing, and NDT into a unified system capable of continuous learning, reasoning, and operational optimization. Compared with existing studies that mainly focus on AI-assisted defect detection or static digital twin models, this approach introduces autonomous agents capable of coordinating sensing, simulation, diagnosis, prediction, and corrective actions across the entire lifecycle of 3D-printed rehabilitation devices. The proposed concept extends current digital twin applications by incorporating virtual stress testing, autonomous simulation, patient-specific customization, and adaptive device management, reducing dependence on physical prototypes, minimizing material waste, and accelerating design validation. By combining autonomous reasoning with predictive analytics, Agentic AI-based DTs represent a next-generation solution for intelligent, adaptive, and sustainable nondestructive testing, advancing both additive manufacturing technologies and personalized rehabilitation engineering. Full article

Robot mobile fulfillment systems (RMFSs), as human–robot collaborative smart warehouses, transform the traditional person-to-goods order picking mode into a goods-to-person mode. Order picking optimization is a core decision-making challenge in RMFSs to improve the efficiency of the system, which needs to jointly optimize pod selection, robot scheduling, station assignment, and manual picking. Although recent studies have widely investigated integrated operational optimization in RMFSs, most of them rely on deterministic transportation and processing time and ignore uncertainties in practical human–robot collaborative operations. It remains challenging to jointly optimize these coupled decisions under uncertain operation times. To address this challenge, we model the concerned problem with the objective of minimizing fuzzy makespan and design an adaptive large-neighborhood-based variable neighborhood descent algorithm to efficiently solve it. The algorithm adopts three-dimensional coupling encoding and multi-stage heuristic decoding mechanisms. It further integrates a learning-based adaptive destroy operator selection method and a variable neighborhood descent search strategy to enhance its exploration and exploitation abilities. In a large number of systematic experiments, ALVND achieved great performance in solving the concerned problem. The objective function value obtained by it was 5.6–25.1% lower than its competitors, demonstrating its effectiveness in uncertain human–robot collaborative warehouse scenarios. Full article

Capacitive angle sensors are crucial components in many industrial applications due to their ability to provide precise angle measurement at low cost and low power consumption. Unfortunately, their frequency-domain characteristics have yet to be thoroughly investigated. In this paper, we investigate the frequency response curves of capacitive angle sensors and find that these sensors have an optimal operating frequency. We also show that operating frequency for capacitive angle sensors does not impact systematic error, but it does impact random error. Based on our findings, we demonstrate the optimization of a candidate sensor’s operating frequency and show a 11.5% ± 1.5% (p = 95% and k = 2.14) reduction in the random error compared to the sensor operating at the typical 20 kHz frequency. The frequency-domain characteristics presented in this paper refine our understanding of capacitive angle sensors and provide guidance for sensor design and optimization. Full article

Modern power systems are evolving rapidly with growing distributed generation and high penetration of renewables and electric vehicles. Renewable sources, being inherently intermittent and weather dependent, may lead to rapid power fluctuations that challenge grid stability and power quality. Multilevel inverters (MLIs) have become a key enabling technology in modern power grids due to the escalating need for high-power, high-voltage, and high-quality energy conversion. Research on the control of MLIs in modern grid scenarios has significant scope due to the rising penetration of renewables, distributed generation and smart grid technologies. Hence sophisticated control strategies and topology selection of MLIs are required to enhance efficiency and to ensure optimal performance. Emerging areas like AI-based adaptive control find relevance in enabling stable, flexible and sustainable future power systems. In this comprehensive review, the state-of-the-art MLI classification, AI-driven control techniques, and their emerging technological applications are investigated. There is limited exploration of AI-based adaptive control for self-tuning operation and coordinated control of multiple MLIs in microgrids, active filtering and harmonic compensation in MLI-based modern power systems, and vehicle-to-grid (V2G) and bidirectional battery inverter control optimization integrated with MLI control. Full article

In the complex problem of air-to-ground mission planning, multi-UAV systems face significant challenges such as system complexity and heterogeneity, insufficient target observability, and difficulties in collaborating information sharing. To address these issues, this paper proposes a novel learnable sparse attention graph architecture (SAGA). This architecture deeply integrates graph reasoning and policy optimization within the MAPPO framework and includes three innovative mechanisms: (i) a GATv2-based graph neural network encoder that performs multi-round distributed consensus on the communication graph among UAVs via a multi-head attention mechanism, enabling selective aggregation of tactical information; (ii) an edge predictor that learns to prune low-value communication links, generating a sparse and mission-adaptive communication topology; and (iii) an L1 sparsity penalty term that further enhances communication efficiency. In a self-developed simulation environment for heterogeneous multi-UAV mission planning, comprehensive comparative experiments were conducted against the following baseline reinforcement learning algorithms: MADDPG, MATD3, QMIX, MAPPO, TarMAC, DGN, and G2ANet. The experimental results show that SAGA achieves reward values of 390 and 1100 in small-scale and large-scale scenarios, and outperforms the best-performing baseline algorithm by more than 20% across all operational performance metrics. Generalization experiments validate the model’s robust transfer capability under unknown defense deployment modes. Ablation experiments further confirmed the individual contributions of the three components. This study provides an innovative and effective method for mission planning of heterogeneous multi-UAV systems in partially observable adversarial environments. Full article

Tin-based halide perovskites have emerged as one of the most promising classes of lead-free materials for next-generation photovoltaic technologies. Their favorable optoelectronic properties, narrow band gaps, and structural compatibility with the ABX₃ perovskite framework position them as viable alternatives to conventional lead-based absorbers. This review summarizes the fundamental structural principles governing Sn-based perovskites, including the role of A-site cations, halide composition, tolerance factor, and octahedral distortion in determining phase stability and electronic structure. Recent advances in material engineering—such as additive-assisted crystallization, interface modification, precursor chemistry optimization, and two-dimensional/three-dimensional heterostructure formation—have significantly improved film quality, reduced defect densities, and enhanced device performance. Despite these achievements, challenges remain, particularly the spontaneous oxidation of Sn²⁺ to Sn⁴⁺, uncontrolled crystallization, and limited operational stability. Strategies aimed at stabilizing the Sn²⁺ oxidation state, suppressing self-doping, and improving charge transport are discussed in detail. The review also highlights the strategic importance of tin as a sustainable element for renewable energy technologies and provides an overview of global tin resources relevant to future photovoltaic deployment. Overall, tin-based perovskites represent a compelling pathway toward environmentally responsible and high-efficiency solar cells, with continued research expected to accelerate their transition from laboratory materials to commercially viable technologies. Full article

Tower-crane layout planning becomes challenging when deployment decisions are coupled across construction stages. This study develops a dynamic multi-stage mixed-integer linear programming (MILP) model that jointly determines crane type, number, and location while considering lifting demand, operational constraints, and inter-stage relocation penalties. BIM is used as a data-extraction and visualization platform to provide stage-dependent model inputs, screen feasible candidate locations, and support spatial verification of the optimized layouts. The model was evaluated using a reconstructed published benchmark and a three-stage convention-center project with irregular geometry. The benchmark demonstrated the transferability of the proposed model to an existing multi-stage problem and revealed rapid nonlinear growth in computational effort: a 3.79-fold increase in the combined number of candidate crane and demand locations led to a 318.39-fold increase in solution time, although all tested instances reached a 0% MIP gap. For the convention-center case, the model contained 365,897 variables and 890,873 constraints and was solved by Gurobi in 1415 s with a 0% MIP gap. The dynamic layout reduced the total cost from $931,020 to $621,880, corresponding to a 33.20% saving relative to the contractor’s plan. It also saved $41,400, or 6.24% compared with the static stage-wise layout by avoiding unnecessary relocation. These results demonstrate solver-certified optimality for medium-scale multi-stage planning within the predefined candidate-location set and quantify the economic effect of inter-stage deployment decisions. Full article

This study proposes Uncertainty-Aware Staged Trust-Adaptive Residual Physics-Informed Machine Learning (U-STAR-PIML), a recursive one-step dynamics model for fixed-wing unmanned aerial vehicles, evaluated with the JSBSim C172x as a surrogate simulation benchmark. It combines a learnable compact six-degree-of-freedom prior, history-dependent temporal residual correction, trust-aware state-dependent residual gating, hard kinematics, a heteroscedastic one-step uncertainty head, and staged optimization. The protocol separates one-step accuracy, recursive rollout, predictive-interval behavior, and physically distinct distribution shifts. Across five training seeds, the full U-STAR-PIML model (E5) achieves a mean one-step root-mean-square error (RMSE) of $0.005515\pm 0.000009$ and the lowest mean rollout-position RMSE of $18.61\pm 2.15$ m; the data-driven baseline has the lowest 20 s all-state RMSE of $2.923\pm 0.623$. Recursive rankings remain seed-sensitive, without a universal winner. For the representative E5 cross-condition evaluation, the exact wind vector used in the JSBSim simulation is supplied to the model at every prediction step, i.e., perfect wind information is assumed. Under this assumption, wind out-of-distribution (OOD) conditions cause the largest degradation, with rollout-position RMSE reaching 72.76 m; wind-estimation error is not evaluated. An external zero-shot evaluation on 10 independent IDF-DS Ranger 2400 real-flight logs reduces pooled one-step all-state RMSE from 0.12374 for Persistence to 0.03379, although improvements are not uniform across dynamic state groups. The uncertainty head yields 95% empirical coverage of 96.68–100%, with conservative over-coverage under most conditions. These results support simulation-based prediction and an initial cross-airframe transfer diagnostic but do not establish same-airframe sim-to-real transfer, recursive real-flight stability, or operational validity. Full article