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.

Abstract: Rising global food demand, increasing labor costs, and persistent labor shortages have created significant challenges for specialty crop production, particularly in la-bor-intensive tasks such as fruit harvesting. Robotic harvesting offers a promising long-term solution, yet its adoption in orchard environments remains limited due to unstructured conditions, variable lighting, and difficulties in fruit recognition and ma-nipulation. This study presents an improved robotic fruit harvesting system, Orchard roBot (OrBot), developed by the Robotics Vision Lab at Northwest Nazarene University, with the goal of advancing autonomous apple harvesting toward greater practicality and economic viability. The updated OrBot platform integrates a dual-camera vision system consisting of an eye-to-hand stereo camera with a wide field of view for fruit target detection and an eye-in-hand RGB-D camera for precise manipulation. The con-trol architecture was redesigned using Robot Operating System 2 (ROS2) and Python, enabling modular subsystem development and improved system coordination. Fruit detection was performed using a YOLOv5 deep learning model, and visual servoing was employed to guide the robotic manipulator toward the target fruit. System performance was evaluated through laboratory experiments using artificial trees and field tests conducted in a commercial apple orchard in Idaho. OrBot achieved a 100% harvesting success rate in indoor tests and a 75–80% success rate in outdoor orchard conditions, with improved performance observed following orchard pruning. Experimental results demonstrate that the dual-camera approach significantly enhances fruit search effi-ciency and harvesting reliability. Identified limitations include sensitivity to lighting conditions, end effector performance with varying fruit sizes, and depth estimation errors. Overall, the results indicate that OrBot represents a meaningful step toward ef-fective robotic fruit harvesting and highlights key areas for future improvement in vi-sion, manipulation, and system robustness.

Abstract: The use of flotation reagents in the form of microemulsions significantly enhances the recovery of noble metals during the flotation of gold-bearing ore and technogenic materials by improving the hydrophobicity of finely dispersed sulfides. This study in-vestigates the effect of a microemulsified dibutyldithiophosphate (DBDTP) on the flo-tation performance of gold-bearing ore and technogenic materials. The research objects were gold-bearing ore and aged flotation tailings from a Kazakhstani deposit contain-ing 3.43 g/t and 0.62 g/t of gold, respectively. Flotation beneficiation was conducted using a microemulsion of DBDTP generated in WAMG. Flotation kinetics demonstrat-ed that the application of the DBDTP microemulsion accelerates the flotation process, increasing gold recovery by 4.65% and reducing gold content in flotation tailings by 0.17 g/t. Under the baseline regime, 37.51% of gold is distributed into the −0.025+0 mm size fraction of tailings with a gold grade of 0.98 g/t. When the microemulsion reagent produced by the WAMG is applied, gold distribution in the −0.025+0 mm size fraction decreases to 28.29% (9.22% lower than the baseline), with a gold grade of 0.62 g/t. In the flotation of aged tailings, the microemulsion application increases gold recovery in the concentrate by 5.88% while maintaining concentrate quality.

Abstract: Achieving high density in complex powder metallurgy components like spur gears is often hindered by friction-induced density gradients and ejection defects. This study investigates a novel elastic die system designed to mitigate these issues through controlled radial deformation. Spur gears were compacted using Ancorsteel 2000 powder under pressures of 400–700 MPa, utilizing a tapered elastic sleeve to apply radial compression. Green and sintered densities were measured, while porosity distribution was quantified via image analysis. Additionally, a 3D finite element simulation using FORGE software was conducted to model the thermo-mechanical behavior and stress distribution during the process. Experimental trials demonstrated that the elastic relaxation of the sleeve enabled free ejection of the compacts without requiring extraction force. Image analysis confirmed a homogenous porosity distribution across the gear teeth, and higher die pre-stressing strokes were found to correlate with increased sintered density. Finite element modeling accurately predicted critical stress concentrations of 700 MPa at the die-sleeve interface and validated the strain distribution. The results confirm that elastic die technology effectively eliminates ejection friction and improves density uniformity in complex gears, offering a viable solution for reducing tool wear and manufacturing defects in high-precision powder metallurgy.

Abstract: Permanent deformation, manifested as rutting, remains one of the most critical threats to the structural integrity and functional performance of flexible pavements. The Mechanistic-Empirical Pavement Design Guide (MEPDG) includes rutting models that are highly sensitive to the dynamic modulus (E*) of asphalt mixtures – a parameter that can be determined experimentally or predicted by analytical models. In this study, the influence of E* prediction error on rutting estimation is systematically evaluated by comparing laboratory-measured E* values with those predicted by two models: NCHRP 1-37A and a locally calibrated model. The dynamic pavement behavior and rut depth predictions were determined using the finite layer program 3D-Move under standard traffic loads. Comparative analysis revealed that the NCHRP 1-37A model tends to underestimate E*, leading to significant overestimation of vertical strains and accumulated permanent deformation. In contrast, the locally calibrated model provided predictions that closely matched the laboratory measurements, resulting in minimal deviation in rut depth estimates. The results highlight the importance of local calibration and model selection to improve the reliability of mechanistic-empirical pavement predictions, enabling smarter pavement performance evaluation and supporting more sustainable pavement management practices, especially when laboratory testing is not feasible.

Abstract: The increasing penetration of converter-interfaced renewable energy sources has led to a reduction in system inertia and has intensified frequency stability challenges in modern power systems. Battery energy storage systems (BESSs) can provide fast active power support. However, their effectiveness depends on installation location, power rating and network operating conditions. This paper proposes a power flow informed sensitivity based method for the placement and sizing of distributed BESSs to improve frequency nadir performance in low-inertia power systems. The proposed method combines marginal frequency sensitivity obtained from time domain screening simulations with network coupling information derived from power flow. These components are integrated into an optimization formulation subject to practical installation constraints and solved using particle swarm optimization. The method is evaluated using time domain simulations on the IEEE 39-bus New England test system under multiple generator outage contingencies. The results show that BESS locations exhibit non-uniform and nonlinear contributions to frequency nadir and rate of change of frequency improvement. The proposed optimal placement and sizing method distributes BESS capacity across multiple buses based on frequency impact and network coupling. Compared with the baseline case and a benchmark metaheuristic optimal placement and sizing method, the proposed method achieves higher frequency nadirs and lower RoCoF values across all evaluated contingencies. The performance is maintained under load variation scenarios and reduced system inertia due to renewable energy integration. The proposed method provides a physically meaningful and computationally efficient approach for allocating distributed BESSs to support frequency stability in low-inertia power systems.

Abstract: Maritime UAV perception must reliably detect and track tiny vessels under harsh specular glare. In practice, detection failures are dominated by two coupled factors: (i) vessels often occupy only a few pixels, causing small-object recall collapse, and (ii) sun glint and sea-surface reflections generate over-exposed regions that trigger false positives and unstable associations. This paper presents Resi-YOLO, a system-level pipeline that improves tiny-vessel sensitivity while preserving embedded throughput on a Jetson Orin Nano. At the model level, Resi-YOLO combines a P2-enhanced feature path with an attention-based glare suppression module to strengthen high-resolution semantics and suppress glare-induced artifacts; optional SAHI-style slicing is supported for ultra-high-resolution scenes. At the system level, we adopt a heterogeneous dual-brain deployment, where the Orin Nano performs primary inference and an MCU-based safety-island tracker mitigates delay/jitter via time-stamped measurement replay and IMM-UKF updates. We further define a Glare Severity Score (GSS) to stratify evaluation by illumination intensity for transparent robustness reporting beyond average mAP. Experiments on maritime detection and tracking sequences demonstrate consistent improvements over YOLO baselines in tiny-object regimes and high-glare conditions, while sustaining real-time operation with approximately 100 ms end-to-end latency on the Orin Nano under TensorRT FP16 deployment.

Abstract: Embedded GPUs with unified memory (UMA) often suffer from the memory wall: modern restoration/segmentation pipelines trigger heavy DRAM traffic and incur long-tail latency jitter. We present MW-DSNet (Memory-Wall-aware Dual-Stream Network), a latency-deterministic hardware–software co-design that combines roofline-based diagnosis, DRAM traffic accounting, and activation-bounding deployment rules with a static-shape TensorRT pipeline and a lightweight Sigmoid-based Inverted-Parabola Attention Module (IP-SIAM). On Jetson Orin Nano (15 W), MW-DSNet sustains 720p@30 FPS with P95 latency 35.1 ms, and reduces DRAM traffic per frame by 3–9× versus transformer/diffusion baselines under fixed power/clock settings. Here, the reported 30 FPS / 35.1 ms (p95) is measured on the visual restoration engine (restoration stage) only; the downstream segmentation head is evaluated separately to isolate restoration-induced robustness gains. The resulting Design Rules provide practical guidance for deterministic real-time perception on memory-wall-bounded edge GPUs.

Abstract: In multibody systems (MBS), such as robot structures, classical modeling is often based on simplified assumptions concerning mass geometry. This paper introduces a formal theoretical model to overcome these limitations by introducing the concept of mass dis-tribution, which describes the continuous nature of mass properties within kinetic as-semblies. Furthermore, the research integrates higher-order acceleration energies into the dynamic formulation – a topic less explored in conventional approaches. By applying the principles of analytical dynamics, particularly a generalized form of D'Alem-bert-Lagrange principle, a comprehensive model based on higher-order acceleration en-ergies is developed. Matrix exponentials and higher-order differential operators are ap-plied to determine the dynamic equations. Generalized forces are also analyzed as es-sential dynamical parameters, directly related to generalized variables and characterized by mass properties, including mass centers, inertial tensors, and pseudo-inertial tensors. The dynamic behavior of the system is described by using matrix-based expressions for defining kinetic and acceleration energies, and their time derivatives. The paper proposes a unified, matrix-based theoretical framework for modeling advanced dynamics in MBS, emphasizing the role of mass distribution and higher-order acceleration energies. This formulation facilitates a deeper understanding of inertial properties and dynamic inter-actions in complex mechanical systems such as robots.

Abstract: Artificial intelligence is increasingly embedded in public environmental decision-making, shaping how risks are classified, resources allocated, and regulatory authority exercised. While policy attention often focuses on predictive performance and ethical principles, less scrutiny is directed toward the institutional conditions under which algorithmic outputs acquire decision relevance. This policy review addresses that gap by framing environmental artificial intelligence as decision-making infrastructure rather than as neutral analytical software. It introduces the concept of algorithmic sustainability, defined not as a technical property of algorithms but as a governance condition that aligns lifecycle environmental impacts, enforceable accountability, and procedural legitimacy. Drawing on international policy frameworks and regulatory developments, the review shows how current governance instruments insufficiently integrate lifecycle environmental footprints into decision justification. To operationalize algorithmic sustainability, the paper proposes Environmental Algorithmic Impact Assessment as a gatekeeping and renewal mechanism for artificial intelligence used in environmental governance. The review concludes that aligning algorithmic deployment with sustainability and the rule of law depends on institutional design choices made before and during system use, rather than on technical optimization alone.