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.

Abstract: The constant growth of IP data traffic, driven by sustained annual increases surpassing 26\%, is pushing current optical transport infrastructures towards their capacity limits. Since the deployment of new fiber cables is economically demanding, ultra-wideband transmission is emerging as a promising costly-effective solution, enabled by multi-band amplifiers and transceivers spanning the entire low-loss window of standard single-mode fibers. In this scenario, an accurate modeling of the frequency-dependent fiber parameters is essential to reliably model optical signal propagation. In particular, the combined impact of attenuation variations with frequency and inter-channel stimulated Raman scattering (SRS) fundamentally shapes the power evolution of wide wavelength division multiplexing (WDM) combs and directly affects nonlinear interference (NLI) generation, as well as the amount of ASE noise. In this work, we review a set of analytical approximations, based on phenomenological approaches, for frequency-dependent attenuation and Raman scattering gain, and analyze their impact on achieving an effective balance between computational efficiency and physical fidelity. Through extensive analyses performed with the open-source software GNPy on an optical line system exploring multi-band scenarios spanning C+L+S, C+L+E, and U-to-E transmission, we demonstrate that the proposed approximations reproduce the reference SRS power evolution and NLI profiles with root mean square errors (RMSEs) consistently below 0.03 dB, and down to the 10⁻³–10⁻² dB range for the most accurate configurations. Although the current implementation does not yet provide a direct reduction in computational time, the proposed framework lays the groundwork for future developments toward closed-form or semi-analytical solutions, enabling more efficient modeling and optimization of ultra-wideband optical transmission.

Abstract: People life remains always one of the impressive conditions to be respected essentially if it is connected to very high places. Indeed, for scaffolding lifespan of each part used must be highlighted to respect the 'SAFETY' condition. There are various accidents, even fatal, which have been seen during scaffolding and examples are very numerous following fatigue problem fatigue of uncontrolled parts. This paper focuses on fatigue problem of a part called 'stirrup' commonly used in construction scaffolding. An expertise of existing state part considered took place through mechanical characterization, microhardness as well as finite element modeling. Johnson-Cook model was used to compare the hysteresis curves of two methods namely experimental and that of finite elements (FEM). The numerical technique XFEM was established to model crack growth into Stirrup. Result shows that hardening phenomenon and fatigue (cracks) are the main causes of stirrup premature damage. Crack growth stages were depicted to show fatigue zones as well as crack path and stirrup bending.

Abstract: Rural Bangladesh still contains hard‑to‑reach islands, chars and riverine communities where grid extension is expensive and vulnerable to storms and flooding. This paper presents the design and performance evaluation of a cost‑constrained, PV‑battery‑diesel hybrid microgrid sized for about 1,000 connections in a coastal char context. The design is grounded in Bangladesh’s measured solar resource (Global Solar Atlas) and policy/market conditions (IDCOL financing, tariffs, net‑metering guidelines). Using current component cost benchmarks (IRENA, BloombergNEF) and fuel price data, we model levelized cost of electricity (LCOE), energy delivery and backup performance over a 20‑year horizon. The reference plant is a 330 kWp PV array, 1.2 MWh LiFePO₄ storage, and a 150 kVA diesel genset feeding a three‑phase low‑voltage distribution network. Unsubsidized LCOE is estimated at 0.41 USD/kWh; with a representative IDCOL‑style capital grant the effective LCOE falls to 0.27 USD/kWh, broadly consistent with reported mini‑grid tariffs of about BDT 30-32 per kWh. The system delivers about 442 MWh/year with an 8–12% diesel share under conservative assumptions, and maintains service during multi‑day low‑irradiance events via battery plus right‑sized genset. Sensitivity analysis shows the LCOE is most affected by distribution network CAPEX, battery replacement pricing, and demand realization. We discuss implementation risks, grid‑arrival strategies, and productive‑use enablement. The results indicate that, in Bangladesh’s remaining off‑grid pockets, carefully engineered PV‑battery microgrids can meet 24/7 demand at a cost in line with observed tariffs, while cutting local air pollution and diesel exposure. Key data are provided to support replication and peer review.

Abstract: With the deployment and application of the fifth-generation communication technology as well as the research on the sixth-generation communication technology, the space-air-ground-sea integrated network has emerged as a key vision for future communications. Unmanned aerial vehicles (UAVs), serving as aerial nodes, can be utilized in emergency rescue and disaster relief, mapping, environmental monitoring, and enhancement of communication coverage, among other areas. In terms of enhancing communication coverage, the integrated space-ground network, with UAVs as an important component, can provide seamless communication coverage to remote areas, deserts, oceans, and other all-domain three-dimensional spaces. UAVs have become important research objects due to their low cost and high flexibility, and the enhancement of communication coverage in the form of base station-relay UAV-slave UAV based on one-hop relaying has become a significant direction. However, the high mobility and extensive coverage of UAVs also give rise to synchronization challenges. In this work, to tackle the challenges of round-trip delay (RTD) from long-distance transmission and Doppler frequency offset in uplink synchronization between ground base stations and relay UAVs, a long-range random access preamble design is proposed. An enhanced two-step detection framework is introduced, where two distinct root sequence preambles are utilized for RTD estimation and random access respectively, and Doppler frequency offset is mitigated via pre-compensation. For the uplink synchronization in the sidelink between slave UAVs and relay UAVs, to address Doppler frequency offset, improve access efficiency, reduce resource consumption, and simultaneously account for the asynchrony among different users, an asynchronous non-orthogonal multiple access (A-NOMA)-based two-step random access scheme is developed. The scheme leverages existing physical random access channel (PRACH) preamble sequences with paired indexing for Doppler frequency offset estimation; on this basis, a successive interference cancellation algorithm based on Doppler frequency offset and phase compensation is designed to demodulate user data. For downlink synchronization between slave UAVs and relay UAVs, improvements to frequency offset estimation are achieved through redesigned sidelink synchronization signal block (S-SSB) resource allocation. Alongside this, a down-sampling-based detection scheme is designed to reduce UAV power consumption given energy constraints, with a comprehensive link algorithm developed to support implementation.

Abstract: This study focuses on the aerodynamic performance optimisation of the SG6043 airfoil for application in small horizontal axis wind turbines (HAWTs) operating under low Reynolds number conditions. Recognizing the critical role of lift-to-drag ratio (CL/CD ) in maximizing turbine power output. The research investigates the performance of SG6043 through design modifications and computational analysis. Initially, the baseline airfoil's aerodynamic characteristics were verified using simulation tools like QBlade software, confirming its previously reported performance. Subsequently, the airfoil was systematically modified by varying key parameters including thickness-to-camber ratio, angle of attack (AOA), operating at different Reynolds numbers. Among the modified versions, SG6043M5-7, SG6043M5-8, and SG6043M5-9 showed significant aerodynamic performance improvement, with SG6042M5-9 achieving the highest CL/CD ratio of 193.44 at Re = 6×10⁵ and AOA = 3.5°. The results demonstrated that a reduced thickness (5%) combined with moderate to high camber (7–9%) enhances the aerodynamic performance.

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