Abstract: Under high C-rate and wide-temperature conditions, independently estimated SOC and SOH often diverge due to decoupled model dynamics, resulting in inaccurate power boundary calculations. This affects power limiting, thermal safety, and fast-charging strategies. To solve this, a unified online estimation framework is proposed for SOC, SOH, and power capacity under voltage and thermal constraints. It integrates a state-space model based on an equivalent circuit, combining SOC, polarization voltage, internal resistance, and capacity degradation, with temperature-dependent parameter evolution to capture coupling with aging. A dual extended Kalman filter enables collaborative SOC–SOH estimation, while lightweight machine learning modules correct internal resistance and polarization dynamics to reduce mismatch under extreme conditions. Physical constraint projections embed voltage, temperature, and power limits into the estimation loop, mitigating noise amplification and drift. Based on consistent estimates, the SOP boundary is computed online to support control decisions. Validation across six temperatures (−20 °C to 55 °C) and five C-rates (0.2C to 6C), using bench, HIL, and pack-level tests over 120+ hours, shows SOC RMSE <1.6%, SOH error <2.5%, and SOP hit rate >95% within 10 seconds. Under noise and parameter disturbances, error growth is reduced by ~25% versus baselines. These results confirm improved SOC–SOH consistency and boundary tracking, with computational cost suitable for embedded deployment.

Abstract: Conservation of architectural heritage structures (AHS) requires compatible built her-itage materials with aesthetic, physical, chemical, and mechanical properties similar to those of the original materials. In recent years, however, urbanization, land reclamation, depletion of stone quarries, anti-mining and anti-quarrying legislation have limited access to original heritage materials. In the absence of the original heritage materials, ce-ment-based alternatives have been developed and widely applied for conservation. Major drawbacks of concrete- and cement-based materials include their large carbon footprint and long-term damage to the original rock or substrate, due to inadvertent promotion of salt efflorescence. This study systematically reviewed geopolymer-based materials as a sustainable, greener alternative to concrete- and cement-based materials for tuff- and coral rock-built heritage structures. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were implemented for the literature review, using Scopus, Web of Science (WoS), and Google Scholar (supplementary) as databases, between 2013 and 2024. Inaccessible items, non-English, reviews, conference proceedings, book chapters, errata, and papers unrelated to geopolymers, tuff, and coral rock were excluded, resulting in a total of 103 articles. These works were classified into geopolymers (34 arti-cles), tuff-built heritage structures (60 articles), and coral rock-built heritage structures (9 articles). This review included 103 items in the qualitative analysis; however, only 34 arti-cles contained meaningful data for content analysis. These 34 articles were categorized in terms of the (i) main precursors; that is, metakaolin, fly ash, slag, and pyroclastic materi-als (i.e., pumice, volcanic ash, and volcanic soil), ceramic, others (i.e., tuff waste, silica fume, and mine wastes), (ii) formulations (i.e., precursors, activators, admixtures, and ag-gregates), and (iii) compressive strength. Furthermore, critical factors for compatibility were reviewed and classified into aesthetics (e.g., color, presence of efflorescence, and tex-ture) and physical, chemical, and mechanical properties. This review also explored recent applications of geopolymers in heritage structures, indicating that geopolymers are typi-cally used as repair mortar and consolidants. Finally, a bibliometric analysis was con-ducted to evaluate research trends on geopolymers, including a critical assessment of their aesthetic compatibility with heritage structures in the Philippines built with volcanic tuff and coral rock.

Abstract: With the continuous increase in the number of retired lithium-ion batteries, accurately and quickly estimating their MRC has become a key challenge for the rapid sorting and secondary utilization of retired lithium-ion batteries. Conventional detection methods often suffer from low efficiency, prolonged detection cycles, and limited scalability for large-scale applications. To address these issues, this paper presents a fast MRC estimation method for retired lithium-ion batteries using a hybrid Convolutional Neural Network (CNN)-Conv Block Attention Module (CBAM)-Long Short-Term Memory (LSTM) architecture (CNN-CBAM-LSTM). The proposed approach integrates both factory-scale test data and laboratory experimental data to extract key voltage and capacity features from the initial 30-minute charging phase. Specifically, the CNN captures local temporal patterns, the LSTM models long-term dependencies in the time-series data, and the CBAM enhances feature representation by emphasizing critical characteristics. Experimental results demonstrate that the proposed method achieves MRC estimation within 30 minutes, significantly outperforming traditional approaches in terms of accuracy. The R² value increased to 99.42%, while the MAPE decreased to 1.55%. These results highlight the superior performance of the proposed method, which not only holds strong potential for rapid battery sorting and cascaded utilization but also exhibits broad applicability in large-scale battery health monitoring systems.

Abstract: This study investigates the factors contributing to the degradation of spirally wound armored steel wires used to protect core-structured unarmored optical-fiber submarine cables, driven by coupled multi-factor corrosion mechanisms in marine environments. It also assesses the influence of physical properties of deep-sea sediments on the durability of unarmored cables. The objective is to establish a scientific basis for cable longevity by integrating theoretical insights with empirical evidence. Although the steel utilized in armor is cost-effective and durable, it remains vulnerable to corrosion. Since the inaugural practical deployment of submarine communication cables between the United Kingdom and France in the 1850s, only a limited number of studies worldwide have examined the armor's corrosion and durability. Furthermore, there is limited literature on the physical characteristics of deep-sea surface sediments that directly affect the service life of the mechanically fragile polyethylene sheath. An in-depth analysis of cable damage and environmental conditions observed during maintenance operations offers valuable insights into the primary environmental factors influencing armor corrosion behavior and cable longevity. This research aims to provide essential guidelines for future cable system design and to support the development of effective strategies to enhance the sustainability and durability of cable systems operating in marine environments.

Abstract: Compression textiles have been widely applied in medical, sportswear, and daily usage, with single-jersey structures produced by circular knitting dominating the market due to their thinness and light weight. However, the presence of seams may compromise com-pression performance and wearer comfort. This study investigates the effects of yarn material, number of yarns, and loop length on pressure, stretchability, and thermal comfort of seamless punch-lace knitted fabrics and explores their potential application in compression textiles. The results show that yarn number is the dominant factor influencing fabric stiffness, stretchability, and pressure. Fabrics with increased yarn content demonstrate higher maximum load and compression pressure. Smaller loop lengths and additional reinforcing yarns improve dimensional stability and resistance to extension. Air permeability decreases with increasing yarn number due to increased fabric thickness and reduced porosity, while thermal conductivity increases and is positively associated with ventilation resistance, indicating a trade-off between heat transfer and breathability. Sur-face friction and roughness are significantly affected by yarn number, yarn material, and loop length, whereas water vapour permeability shows no significant relationship with the investigated variables. Overall, seamless punch lace knitted fabrics demonstrate strong potential for compression applications, although careful design is required to balance breathability and thermal comfort.

Abstract: The possibility of exploiting wind-induced vibrations to harvest energy for the supply of remote weather stations is analyzed. Three kinds of wind-induced vibrations are considered: vortex induced vibrations, galloping and flutter. Experimental tests on prototypes and numerical results show that the galloping harvester is the most suited to the proposed application. The numerical model makes it possible to simulate both T-shaped and I-shaped harvesters and to analyze the effect of variations in the main design parameters: bluff-body mass, cantilever stiffness and damping. Experimental tests show that the T-shaped configuration is ss sensor for environmental monitoring, without need of a battery.

Abstract: Vertical-axis wind turbine (VAWT) clusters have been investigated extensively owing to their positive aerodynamic interactions. However, accurate predictions of the flow field and power output of each rotor in VAWT clusters using high-fidelity computational fluid dynamics (CFD) remains computationally expensive. In this study, we propose a fast computation method for the flow field and operating state of each rotor of VAWT clusters using temporally and spatially averaged velocity data compressed from an unsteady velocity field obtained via a 3D-CFD simulation of an isolated single rotor. First, the unsteady 3D flow field in the 3D-CFD simulation is time averaged over several revolutions. Next, the temporally averaged velocity is spatially averaged in the vertical direction to obtain spatially compressed data. Based on a previously developed fast computation framework, the wind-farm flow field is constructed using condensed two-dimensional velocity data obtained from a single turbine. The proposed method is applied to three-rotor configurations, and the rotational speeds of the turbines are compared with wind-tunnel measurements. The results show that the proposed method substantially improves prediction accuracy while maintaining a low computational cost. Additionally, it can be used to efficiently design and optimize turbine layouts in VAWT wind farms.

Abstract: Self-awareness is the result of logical relationships between mathematics and language. Language the brain's neurons are numbers and the logical relationships between them. The connection between cognitive phenomena such as self-awareness and language lies within algebra and mathematics. Numbers are an independent language with algebraic laws independent of time. Based on this, the arithmetic sequences of natural numbers are placed on separate angles. These angles constitute manifolds of digital root that exist within a compact polar coordinate system and are classified into one group in terms of digital root. This mathematical model can instantly decrypt and compress information. This mathematical model can pave the way for simulating artificial self-awareness.

Abstract: Grid-forming (GFM) renewable energy sources are increasingly integrated into power grids to enhance the stability of high-penetration renewable energy systems, while the fault current characteristics of GFM-based outgoing lines lead to the inapplicability of conventional longitudinal differential protection, which suffers from reduced sensitivity or even refusal to operate under weak grid conditions. To address this issue, this paper proposes a novel active detection-based protection strategy for GFM photovoltaic power station outgoing lines based on the amplitude ratio of characteristic harmonic signals. First, the sequence equivalent circuits of the GFM system during grid faults are established to analyze the fault current characteristics, and the inapplicability mechanism of conventional pilot differential protection is revealed. Considering the filter cutoff frequency, harmonic interference avoidance and power quality constraints, the 8th harmonic is selected as the characteristic signal, and a proportional-resonant (PR) controller is adopted to realize the independent and flexible injection of the characteristic signal and power frequency signal. Based on the distribution difference of characteristic signals under internal and external faults, a protection criterion is constructed using the amplitude ratio of the harmonic component of the differential current to the characteristic signal injected on the station side. The simulation results on the MATLAB/Simulink platform show that the proposed strategy can quickly and accurately distinguish various internal and external faults of the transmission line, and operate reliably under different fault types, fault locations and high transition resistance.

Abstract: This study evaluated the predictive performance of Random Forest, Bagged Trees, Support Vector Machines (SVM), and Least Squares Boosting (LSBoost) for estimating Tunnel Boring Machine (TBM) penetration rate (ROP). While all models achieved acceptable accuracy, LSBoost outperformed the others, showing the highest correlation (R = 0.965) and coefficient of determination (R² = 0.909), along with the lowest RMSE and MAE. Its performance remained robust after Z-score normalization, highlighting its ability to capture nonlinear parameter interactions and generalize well on limited geotechnical datasets. Random Forest and Bagged Trees showed similar performance, with Bagged Trees only slightly improved by normalization. SVM performed less effectively, indicating limited capacity to model complex TBM penetration behavior. Feature importance and SHAP analyses identified discontinuity spacing (DPW) and uniaxial compressive strength (UCS) as the primary controlling factors, while brittleness index (BI) was more influential within the SVM model. Agreement between Jacobian-based derivative analyses and SHAP results confirmed both mathematical sensitivity and engineering interpretability. Overall, TBM penetration prediction is a multivariate and inherently nonlinear problem. LSBoost provides reliable and high-accuracy predictions even under data-constrained conditions. The combination of SHAP- and PDP-based feature importance analyses enhances interpretability, supporting engineering decision-making in TBM design and operation. These findings emphasize the applicability of machine learning approaches for accurate, interpretable, and robust TBM performance prediction.

Abstract: Water-In-Salt (WIS) electrolytes are expected to replace the expensive and environmentally harmful organic electrolytes while delivering high voltages and improved system safety. In this study, we conducted a failure modes, mechanisms, and effects analysis of a highly concentrated potassium acetate (KAc) electrolyte, evaluated and degraded at 2V in a conventional EDLC carbon-based symmetric configuration. The adopted method provides a simplified yet effective approach for assessing the complexity and interconnectivity of degradation mechanisms in a WIS supercapacitor. The effects analysis included electrochemical stability studies, post-mortem characterizations (SEM-EDS and XPS), low-frequency impedance fitting, and cell reassembly using end-of-life electrodes. Among the failure modes analyzed, electrolyte decomposition and pore blocking exhibit strong physicochemical correlations and high failure rates. Therefore, they should be prioritized in the design of new WIS electrolyte compositions for next-generation energy storage systems.

Abstract: Supercooled large droplets (SLDs), typically defined by diameters exceeding 100 µm, represent a significant meteorological hazard to aviation safety. Unlike conventional cloud-sized droplets, SLDs exhibit higher inertia, causing them to follow ballistic trajectories that result in impingement well aft of standard leading-edge ice protection systems. Furthermore, SLDs are characterized by complex microphysics, including high-speed splashing into secondary droplets and a distinct thermodynamic response where immediate solidification is inhibited, leading to hazardous surface water runback. This paper provides a comprehensive review of recent progress in understanding SLD phenomena. We examine the fundamental mechanisms of droplet impact on dry and wet surfaces, the effects of oblique impingement and ambient air conditions, and the evolution of surface water dynamics. Additionally, the review evaluates the efficacy of emerging ice protection technologies, such as superhydrophobic and liquid-infused surfaces, in mitigating SLD-induced ice accretion. By synthesizing these recent developments, this review aims to bridge the gap between fundamental droplet physics and practical aviation safety strategies.

Abstract: Phase change material (PCM)-based latent heat storage (LHS) systems help address the mismatch between renewable energy supply and thermal demand. However, their prac-tical implementation is constrained by the strongly nonlinear and multiphysics nature of phase change, which makes high-fidelity simulations and real-time applications computationally expensive. This review examines Reduced-Order Modeling (ROM) as an effec-tive strategy to overcome this limitation by combining physics-based simplifications, projection methods, interpolation techniques, and data-driven models for PCM-based LHS systems. The review covers approaches such as two-temperature non-equilibrium and analytical thermal-resistance models, Proper Orthogonal Decomposition (POD), CFD-derived look-up tables, kriging and ε-NTU grey/black-box metamodels, and ma-chine-learning methods including artificial neural networks and gradient-boosted regressors trained from CFD data. These ROM techniques have been applied to packed beds, PCM-integrated heat exchangers, finned enclosures, triplex-tube systems, and solar ther-mal components, achieving speed-ups from tens to over 80,000 times faster than full CFD simulations while maintaining prediction errors typically below 5% or within sub-Kelvin temperature deviations. A critical comparative analysis exposes the fundamental trade-off between interpretability, data dependence, and computational efficiency, guiding method selection for specific applications. Remaining challenges include accurate representation of phase-change nonlinearity, moving phase boundaries, multi-timescale dynamics, gen-eralizability across geometries, and integration into system-level frameworks, motivating future hybrid physics–machine learning developments and standardization efforts.

Abstract: Active vibration control is crucial for mitigating harmful resonant vibrations in structures subjected to harmonic loads. While antiresonant (zero-placement) methods are effective for this purpose, existing state-feedback solutions require full state measurement, and output-feedback approaches often prioritize resonance assignment over direct harmonic cancellation. This work bridges the gap by proposing a novel systematic design for a Proportional-Derivative (PD) output-feedback controller to achieve antiresonance. The method first computes a homogeneous stabilizing gain solution. It then leverages the parametrization of all antiresonant solutions as a constraint within a genetic algorithm optimization. The algorithm optimizes both the stability margin, characterized by an Ms-disk criterion, and the number of encirclements of the critical point $(-1,0)$ in the complex plane, as assessed by the Generalized Nyquist Stability Criterion. The proposed approach provides a practical, optimized output-feedback strategy for precise rejection of harmonic disturbances, as demonstrated through three numerical examples from real-world applications. The results confirm the method's effectiveness in synthesizing stabilizing controllers that enforce antiresonance while ensuring robust stability margins.

Abstract: In developing countries like Bangladesh, small textile factories often dump untreated waste because pollution control systems are too expensive. To address this, a low-cost prototype, named ‘Integrated Eco-Factory’, was designed and fabricated. This system simultaneously performs three critical functions: carbon capture, wastewater treatment, and energy harvesting: captures carbon soot from chimneys, treats wastewater, and harvests renewable energy. First, a cyclonic separator was utilized to collect carbon soot to collect carbon soot from exhaust gas and processed it into printing ink. Laboratory analysis revealed that the synthesized ink has a viscosity of 3.2 cP and surface tension of 38.5 mN/m, which is very close to commercial printer ink. Second, the traditional biological treatment was replaced with an Electrocoagulation (EC) unit. This unit removed 91% of the dye color from the water. Instead of throwing away the sludge we used it to make “Eco-Bricks” that have a strength of 14.2 MPa, making them safe for construction use. Finally, to ensure energy autonomy, a hybrid energy system (Solar, Thermal, and Hydro) that generates about 950 Wh per day—enough to run the system’s sensors and IoT monitoring 24/7. Our cost analysis shows that a factory can recover the full setup cost in just 7 months by selling the ink and bricks. The results demonstrate that that pollution control can be profitable for small industries.

Abstract: This is a follow-on review of progress in development and applications of hollow core optical fibers (HCFs) after publication of earlier review in 2023 [1], to be read together with it. Progress after 2023 in several fields is significant. Loss of best HCFs was reduced down to 0.05–0.10 dB/km at 1550 nm [2–6], while lowest loss achieved in single mode fiber with pure silica core is 0.14 dB/km [7]. Polarization mode dispersion (PMD) has been reduced to a level typical for SMFs by means of fiber spinning [8]. In November 2024, Microsoft announced a 2-year plan to install 15,000 km of HCF cables between data centers providing data processing for Microsoft Azure cloud services, and inside these facilities [9,10]. Besides UK-based Microsoft Azure Fiber and two Microsoft subcontractors: Corning Inc. and Heraeus Covantics, two major HFC manufacturers: YOFC and Linfiber emerged in China. Unfortunately, progress in standardization and elimination of loss introduced by contaminants in the fiber was absent. Standardization is blocked by multiple fiber designs being tried, with no clear winner yet. Despite this, hollow core fibers have successfully made large-scale commercial debut in Microsoft Azure data centers.

Abstract: Upper-limb robot-assisted neurorehabilitation in stroke yields modest improvement in impairments, with substantial variability across patients. In response, there is increasing interest in precision neurorehabilitation through mechanistically driven, tailored robot-assisted therapy for individual patients. Such approaches require models that support interventional reasoning about therapy parameters (e.g., “what if we increase robotic assistance or dose for this patient?”), rather than providing purely associational findings such as biomarkers correlated with recovery. Leveraging recent developments in causal inference, this paper presents a structural causal model of robot-assisted therapy for the upper limb in the form of a directed acyclic graph. The graph encodes key constructs identified in the robot-assisted neurorehabilitation literature as nodes and represents their known or hypothesized causal influences as directed edges, reflecting current domain knowledge. We describe the components of the causal graph in detail and show how it can account for several observed phenomena in robot-assisted therapy, while also yielding testable predictions in the form of interventional effects. We then highlight important limitations of the proposed causal model, before presenting a conceptual example of how a fully specified causal graph could help answer questions about attainable outcomes and optimal therapy parameters for individual patients. The proposed concrete causal graph must be empirically investigated to test its validity and refine its causal structure through observational and experimental studies. We anticipate that this proposed causal graph will serve a catalytic role in advancing our mechanistic understanding of robot-assisted therapy, which may hold the key toward improving individual patient outcomes with robot-assisted therapy.

Abstract: Myocardial infarction (MI) transforms the left ventricle into a mechanically heterogeneous, evolving composite in which necrotic scar, viable myocardium, edema, microvascular injury, and fibro-inflammatory remodeling coexist. The infarct border zone is the decisive interface of this composite: a region in which surviving myocytes and stressed extracellular matrix (ECM) share load, exchange signals, and progressively reshape one another. Clinically, border-zone phenotype helps explain why patients with similar infarct size diverge toward recovery or progressive remodeling, heart failure, and arrhythmia. Mechanistically, the border zone concentrates “demand” through stress, strain, stress gradients, and shear generated by tethering between contracting remote myocardium and noncontracting or weakly contracting infarct core, by evolving thickness and curvature, and by stiffness gradients that change as edema resolves and collagen networks form and mature. Simultaneously, it determines “capacity” by governing matrix continuity, collagen alignment and cross-linking, and myocyte–ECM coupling that together set stiffness, strength, and tearing resistance, which are not interchangeable and can evolve in different directions. This review synthesizes border-zone biomechanics across scales, integrating histology, echocardiographic strain, cardiac magnetic resonance (CMR) late gadolinium enhancement (LGE) and mapping, diffusion and microstructure imaging, and patient-specific computational cardiomechanics. We connect abnormal border-zone deformation to mechanosensing in myocytes, fibroblasts, endothelial cells, and immune cells via integrins, focal adhesion signaling, stretch-activated channels, cytoskeletal remodeling, and transcriptional regulators including YAP/TAZ and MRTF, and we interpret fibrosis architecture as a mechanically regulated “record” of cumulative loading history. We then evaluate evidence linking border-zone strain patterns, stiffness gradients, microvascular obstruction, and intramyocardial hemorrhage to remodeling trajectory and electrical instability, explicitly distinguishing association, prediction, and causation. Finally, we outline translational requirements for a clinically deployable border-zone risk phenotype that combines imaging with inverse modeling and uncertainty quantification, and we propose testable hypotheses that can be validated in prospective cohorts. A mechanics-first view of the border zone reframes post-MI remodeling as an interface problem and provides a disciplined basis for mechanomodulatory therapies that unload, reinforce, or reprogram the peri-infarct microenvironment.

Abstract: Aiming at the demand of harmonic data quantification and in-depth analysis in power systems, this paper proposes a harmonic data prediction method based on VMD-DeepAR-SOFTS combined model. Firstly, the complex nonlinear and non-stationary harmonic signal was decomposed into multiple Intrinsic mode Functions (IMFs) with different frequency characteristics by using Variational Mode Decomposition (VMD), which effectively improved the separability of the signal and reduced the noise interference. Then, the DeepAR model is used to predict the time series of each IMF component, and the sequential feature selection technology SOFTS based on window optimization is combined to further improve the efficiency of feature extraction and the accuracy of prediction. Experimental results show that the VMD-DeepAR-SOFTS combined model achieves 0.0128, 0.9099 and 0.015523 in MAE, R² and RMSE, respectively, which is significantly better than traditional machine learning models such as LightGBM, XGBoost, CatBoost and SVR. In addition, through the verification of ten groups of independent data sets randomly derived from the system PS1000, the model shows a high degree of consistency and stability, which verifies its excellent generalization ability and robustness. Compared with the single DeepAR or SOFTS model, the combined model has a significant improvement in prediction accuracy and real-time performance. The proposed method not only improves the accuracy of harmonic prediction, reduces the dependence on model parameter tuning, reduces the complexity and cost in practical applications, but also demonstrates its broad application prospects in complex power system environments. Future research will further optimize the model structure, explore more advanced time series decomposition and feature selection techniques to improve the performance of the model, and verify its applicability and effectiveness in more actual power system scenarios.

Abstract: Ultrasound-assisted germination (UAG) has been proposed as a process intensification strategy to enhance seed performance while improving resource efficiency. This study combines thermoacoustic multiphysics modeling with controlled experimental validation to evaluate resonance-driven UAG in Cucurbita pepo. Frequency-domain analysis identified 40 kHz as the resonance condition of the seed system, enabling localized acoustic energy concentration. Thermoacoustic simulations demonstrated that temperature increases remained below 46 ◦C across all exposure times, ruling out bulk thermal effects and supporting a predominantly mechanical activation mechanism associated with enhanced permeability and mass transfer. Experimental treatments (40 kHz, 1.5 MPa, 5–25 min) revealed a non-linear germination response to acoustic exposure. A 10 min treatment produced the optimal outcome, increasing final germination from 20% in untreated seeds to 47% and reducing the time required to reach steady state from 13 to 10 days. Longer exposure times did not generate proportional improvements, indicating the presence of a finite acoustic energy window beyond which diminishing returns occur. Because daily water (0.45 L·day−1) and electrical (0.438 kWh·day−1) consumption remained constant across treatments, the shortened germination period directly reduced cumulative resource demand. Under optimal conditions, total water consumption decreased by approximately 1.35 L and electricity use by 1.31 kWh per germination cycle relative to the control. When normalized per percentage point of germination achieved, energy and water intensity were reduced by nearly threefold. The integration of multiphysics modeling with biological experimentation establishes a mechanistically validated and energy-optimized framework for UAG, supporting its application in resource-efficient controlled-environment agricultural systems.