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.

Abstract: Improving indoor thermal comfort in high-altitude rural housing remains a persistent challenge for low-income communities in the Peruvian Andes. This study evaluates the thermal performance of a standardized Sumaq Wasi modular dwelling in Langui (Cusco, Peru, 3969 m.a.s.l.) and proposes passive envelope modifications that enhance comfort while preserving economic feasibility. A multi-objective optimization approach combining EnergyPlus simulations with the NSGA-II algorithm was applied to minimize total thermal discomfort (TDI_total), bedroom underheating (TDIU_bedrooms), and 10-year life cycle costs (LCC). The calibrated model incorporated field measurements of indoor air temperatures. Global sensitivity analysis using Morris and Sobol methods identified ceiling thermal transmittance as the dominant contributor for TDI_total, and exterior wall solar absorptance as the driver of TDIU_bedrooms. Optimization reduced TDI_total and TDIU_bedrooms to 22% and 8% of the base case, requiring additional investments of USD 2,347 and USD 1,959, above the base case cost (USD 8,100), respectively. Cost-neutral strategies, raising exterior wall solar absorptance to 0.9 and increasing the skylight to roof ratio (13.1%), reduced bedroom underheating to 30% of the base case and outperformed a scenario with two 400W electric heaters. These results demonstrate that context-appropriate passive design can substantially improve comfort under severe climatic and financial constraints.

Abstract: This study presents the development and experimental evaluation of HygroCatch, a portable hybrid fog water harvesting prototype that integrates active and passive col-lection mechanisms. The device operates by combining fog droplet ionization in a high-voltage direct-current (HV DC) electrostatic field, thermoelectric cooling based on the Peltier effect, and mechanical deposition of droplets on electrode grids. This hybrid approach enables adaptive operation across a wide range of fog liquid water content (LWC) conditions. The work establishes operating parameters for stable electrostatic ionization and evaluates the contribution of thermoelectric cooling to additional water collection. The results indicate that an operating voltage of 13–14 kV provides a stable ionization over a broad LWC range. The average fog water harvesting rate reached 3.15 kg/m²/h, with a maximum observed value of 4.44 kg/m²/h. On average, 56% of the collected water was obtained through HV DC ionization, 25% through Peltier-based thermoelectric cooling, and 19% through mechanical deposition on electrode grids under high LWC conditions. The total electrical power consumption of the device did not exceed 38.3 Wh/kg. The results demonstrate that a hybrid fog water harvesting strategy enables stable and efficient water collection under environmental conditions in which individual passive or active methods become ineffective.

Abstract: The rapid electrification of road vehicles has fundamentally reshaped the priorities of noise, vibration, and harshness (NVH) engineering. In the absence of combustion-related broadband masking, tonal and order-related phenomena originating from the electric machine, inverter switching, and high-speed reduction gearing have become clearly per-ceptible and, in many cases, acoustically dominant. Consequently, drivetrain noise in electric vehicles can no longer be assessed at component level alone; it must be understood as a coupled system response shaped by excitation mechanisms, structural dynamics, transfer paths, radiation efficiency, and ultimately human perception. This review adopts a source-to-perception perspective and consolidates the principal physical mechanisms governing vibro-acoustic behaviour in integrated electric drive units. Electromagnetic force harmonics and torque ripple are discussed alongside transmission-error-driven gear mesh excitation, while bearing and shaft nonlinearities are examined in the context of high-speed operation. In addition, ancillary thermoacoustic and aerodynamic contribu-tions are considered, reflecting the increasingly integrated packaging of modern e-axle architectures. On this mechanism-oriented basis, dominant excitation types are linked to frequency-appropriate modelling strategies, spanning electromagnetic force extraction, multibody drivetrain simulation, structural finite element analysis, transfer path analysis, and acoustic radiation prediction. Particular attention is given to workflow integration across domains. Finally, the paper identifies research challenges that predominantly arise at system level, including multi-source interaction effects, installation-dependent trans-fer-path variability, emergent resonances in assembled structures, manufacturing-induced tonal artefacts, and the still limited correlation between predicted vibration fields and perceived sound quality.

Abstract: A control algorithm based on artificial neural networks was developed to regulate the hot-air drying temperature for carrot dehydration within an IoT-enabled cyber-physical system. The experimental setup employs an Arduino Mega 2560 equipped with AM2302, MLX90614, and SHT35 sensors, an HX711 load cell, and a WS68 anemometer, with cloud communication provided by an ESP8266 module for remote monitoring via Wi-Fi under an IoT framework. The neural controller, implemented using the Arduino Neurona li-brary, adjusts the dryer temperature in real time, ensuring thermal stability throughout operation. Three initial loads (2, 4, and 6 kg) were analyzed to obtain the drying kinetics and determine the thermal efficiency. In the dehydration experiments, the 2 kg load reached a final moisture content of 10% in 4.4 h, consuming 1,390 kJ with a thermal effi-ciency of 83%. The 4 kg load exhibited the best time–energy balance (6.6 h, 1,850.0 kJ, 88%), while the 6 kg load achieved the highest efficiency (8.1 h, 2,250.0 kJ, 91%). These results demonstrate the effectiveness of neural-network-based control implemented on low-cost microcontrollers to enhance thermal efficiency in food dehydration processes.

Abstract: Rural architectural heritage sites in Korea, including rice mills, breweries, and granaries, face an increasing risk of neglect, damage, and demolition. Because most of these structures lack recognition in formal heritage designation systems, their conservation and management are challenging. This study proposes a comprehensive evaluation framework for rural architectural heritage. Based on a literature review and expert consultations, we derived 18 evaluation indicators grouped into six value criteria: historical, architectural/artistic, social/cultural, landscape, economic, and utilitarian values. The Analytic Hierarchy Process (AHP) was employed to determine the relative importance and priority of these indicators. The results indicate that historical value had the highest weight among the six criteria, followed by architectural/artistic and social/cultural values. Among the 18 indicators, “representativeness of the period” ranked highest, followed by “rarity,” “historicity,” and "architectural excellence." However, the indicators associated with economic and utilitarian values received relatively low weights. The framework validated by applying it to 17 rural heritage sites in Buyeo County, a representative rural region in Korea. This study presents a systematic and value-based evaluation framework that reflects the regional and industrial characteristics of rural architectural heritage and provides theoretical and practical implications for its conservation and adaptive reuse.

Abstract: This article compares selected fire-safety regulatory systems in Japan, China, the United States, and the EU/UK, interpreted through the lens of responsive architecture and the implementation of digital technologies—Building Information Modeling (BIM), Digital Twins (DT), Artificial Intelligence (AI), and the Internet of Things (IoT). The study adopts a qualitative approach based on a structured review of legal acts, technical standards, public-sector reports, and scientific and professional literature, organised using a common analytical framework. First, the analysis identifies shared foundations across regimes: the primacy of life safety, mandatory detection and alarm functions, fire compartmentation, requirements for protected means of egress, and the increasing importance of documenting the operational status of protection measures [1,6]. It then contrasts key differences, including the permissibility of performance-based design (PBD), the extent to which digital documentation is formally recognised, organisational enforcement models, and approaches to cybersecurity for integrated Fire Alarm/Voice Alarm/Building Management/IoT ecosystems. Japan and selected Chinese cities combine stringent requirements with openness to dynamic solutions and urban-scale data platforms [2]. The USA relies on a decentralised, code-based ecosystem with a strong role for professional and industry bodies, while the EU/UK continue to strengthen harmonised standards and digital building registers, reinforced by lessons following the Grenfell Tower fire [3,4]. Against this background, Poland is discussed as broadly aligned in goals and baseline technical requirements, yet lagging in implementing PBD pathways, digital registers, formal BIM/DT integration, and minimum cybersecurity requirements. The proposed directions for change aim to create a more predictable regulatory and technical framework for the development of responsive architecture and dynamic fire-safety systems in Poland.

Abstract: Freight transportation is a significant contributor to greenhouse gas (GHG) emissions in the US. As an emerging technology, truck platooning leverages vehicle-to-vehicle communications to enable trucks to travel in convoys with close proximity, which reduces air drag and consequently truck fuel use and GHGemissions. However, uncertainties remain about how this emerging technology may be adopted and its climate impacts. To this end, this paper investigates the role of truck platooning adoption in mitigating the climate impact of trucking from a system perspective. Considering the dynamic nature of truck platooning adoption, System Dynamics (SD) models based on stock and flow diagrams are developed to estimate the potential reduction of fuel use and CO₂ emissions in the US trucking sector when truck platooning technology becomes available. The results show that adopting platooning could save 292 Million Metric Tons of CO₂ emissions in 180 months after the initial introduction of the technology in the US truck sector.

Abstract: Several finite element numerical simulations were conducted in this study to investigate the laterally loaded response of pile foundations under general scour conditions in clay, the finite element model was verified by centrifuge tests, and the “model change” method was used to simulate the formation process of general scour and its impact on the lateral bearing response of pile foundations. The effects of overall scour and progressive scour on the load-displacement relationship, pile-soil deformation and failure mode, bending moment, displacement of circular and square pile foundations with equal cross-sectional areas under the same scour depth were analyzed. The results show that under no scour and two general scour conditions, the lateral bearing capacity of square-section pile foundations is higher than that of circular pile foundations with equal cross-sections; the general scour changes the pile-soil deformation and failure mode of laterally loaded pile foundations and reduces the wedge-shaped failure zone of soil around the pile; the wedge-shaped failure area of the soil around laterally loaded square cross-section piles is larger than that of circular cross-section piles of equal cross-sectional area; when the scour depth is the same, one overall scour and progressive scour have less impact on the lateral bearing capacity of the pile foundation; under the same scour depth conditions, one overall scour and distribution scour have less impact on the lateral bearing capacity of the pile foundation.

Abstract: Energy hydrogen is emerging as a key driver for the deep decarbonization of energy systems in the Americas, particularly in sectors that are difficult to electrify, such as heavy industry, long-distance transportation, and seasonal energy storage. This article presents a comprehensive review of current prospects and long-term planning for hydrogen in North America, Central America, and South America, analyzing its role within energy transition strategies to long term. It examines techno-logical advancements in green hydrogen production from renewable energy sources, projected costs, required infrastructure, and potential integration schemes with existing electricity systems. Furthermore, it assesses emerging regulatory frameworks, public policies, and national and regional initiatives that seek to position hydrogen as a pillar of energy security, economic competitiveness, and emissions reduction. The study identifies differentiated opportunities based on the availability of renewable resources, industrial capacities, and socioeconomic contexts, as well as common challenges related to investment, standardization, and social acceptance. Finally, implications for long-term energy planning are discussed, highlighting the potential of hydrogen to strengthen the resilience and sustainability of the energy system in the Americas.

Abstract: This paper proposes an adaptive photovoltaic (PV) power forecasting approach integrating Double Q-Learning and Stacking ensemble with XGBoost meta-learner to address the poor adaptability of conventional methods in real-time grid-connected PV systems. Unlike single models with limited generalization or fixed-weight ensembles that merely imitate experience superficially, the proposed approach adapts dynamically to time-varying meteorological and operational conditions. It pre-trains three complementary base models, namely RF, SVR and LightGBM, constructs a Stacking framework with XGBoost as the secondary-learner to generate high-precision baseline predictions via out-of-fold validation, and embeds a Double Q-Learning agent to output adaptive weights by capturing meteorological-temporal features and real-time prediction errors. The final prediction is obtained by fusing the Stacking output and Double Q-Learning adjusted base model outputs. Tests on a 50MW PV station dataset show it outperforms four single models and traditional ensembles in MAE, MSE, RMSE, and R², enabling reliable, generalized and adaptive real-time predictions.