Abstract: This study investigates the differences in flexural behavior of ultra-high-performance concrete (UHPC) arising from variations in test methods and key experimental parameters. Flexural tensile tests were conducted on 51 specimens representing 17 combinations of test variables, including steel fiber length (13 mm and 19.5 mm), specimen cross-sectional dimensions (75×75 mm, 100×100 mm, and 150×150 mm), presence or absence of a notch, and loading configuration (three-point and four-point loading). The tests were performed in accordance with ASTM C1609 and EN 14651, and both deflection and crack mouth opening displacement (CMOD) were normalized by the span length to compare the influence of each parameter. The notched specimens demonstrated significantly improved reliability, exhibiting up to an 8.4-fold reduction in standard deviation due to the consistent initiation of cracking. Regarding size effects, the 75×75 mm specimens showed an overestimation of flexural performance due to the wall effect of fiber distribution, whereas the 100×100 mm and 150×150 mm specimens exhibited similar flexural responses. The comparison of loading configurations revealed that three-point loading produced up to 11.7% higher flexural tensile strength than four-point loading, attributable to concentrated moment–shear interaction and the combined effects of fiber bridging and shear resistance mechanisms. In addition, specimens with longer steel fibers (19.5 mm) exhibited 5.2–9.7% higher flexural performance than those with shorter fibers (13 mm), which is attributed to enhanced interfacial bonding and improved crack dispersion capacity.

Abstract: This study investigates the influence of array height, irradiance, and wind speed on temperature difference and thermal gradients in photovoltaic (PV) arrays operating in hot, arid conditions. A field experiment was conducted in Mesa, Arizona (latitude 33° N), using two fixed-tilt PV module arrays installed at different elevations—one at 1 m and the other at 2 m above ground level. Each array comprised seven monocrystalline PV modules arranged in a single row with an 18° tilt angle optimized for summer performance. Data were collected between June and September 2025 and the analysis was restricted to 10:00–13:00 h to avoid shading and ensure uniform irradiance exposure on both arrays. Measurements included module backsheet temperatures at the center and edge modules, ambient temperature, plane-of-array (POA) irradiance, and wind speed. By maintaining identical orientation, tilt, and exposure conditions, the evaluation isolated the effect of height on module operating temperature and intra-array thermal gradients. Results indicate that the 2 m array consistently operated 1–3°C cooler than the 1 m array, confirming the positive impact of elevation on convective cooling. This reduction corresponds to a 0.4–0.9 % improvement in module efficiency or power based on standard temperature coefficients of crystalline silicon modules. The 1 m array exhibited a mean edge–center temperature gradience of −1.54°C, while the 2 m array showed −2.47°C, indicating stronger edge cooling in the elevated configuration. The 1 m array displayed a broader temperature range (−7 °C to +3°C) compared to the 2 m array (−5°C to +2°C), reflecting greater variability and weaker convective uniformity near ground level. The temperature gradience became more negative as irradiance increased, signifying intensified edge cooling under higher solar loading. Conversely, wind speed inversely affected ΔT, mitigating thermal gradients at higher airflow velocities. Overall, elevating PV arrays enhances convective heat transfer, reduces module temperature, and improves reliability and power output. These findings highlight the importance of array height, array length, irradiance, and wind conditions in optimizing PV system thermal and electrical performance.

Abstract: Against the backdrop of the global energy transition, the efficient exploitation of marine renewable energy has become a key pathway toward carbon neutrality. Wind–wave hybrid systems (WWHSs) have attracted increasing attention due to their resource complementarity, efficient spatial utilization, and shared infrastructure. However, most existing studies focus on single components or local optimization. A systematic integration of the full technology chain remains limited, hindering the transition from demonstration projects to commercial deployment. This review provides a comprehensive overview of the technological evolution and key characteristics of offshore wind turbine (OWT) foundations and wave energy converters (WECs). Fixed-bottom foundations remain the mainstream solution for near-shore development. Floating offshore wind turbines (FOWTs) represent the core direction for deep-sea deployment. Among WEC technologies, oscillating buoy (OB) WECs are the dominant research pathway. Yet high costs and poor performance under extreme sea states remain major barriers to commercialization. On this basis, the paper summarizes three major integration modes of WWHSs. Among them, hybrid configurations have become the research focus due to their structural sharing, hydrodynamic coupling, and significant cost and energy synergies. Furthermore, the review synthesizes optimization strategies for both technology design and spatial layout, aiming to enhance energy capture, structural stability, and overall economic performance. Finally, the paper critically identifies current research gaps and bottlenecks, and outlines key technological pathways required for future commercial viability. These include the development of high-performance adaptive power take-off (PTO) systems, deeper understanding of multi-physics coupling mechanisms, intelligent operation and maintenance enabled by digital twins, and comprehensive life-cycle techno-economic and environmental assessments. This review aims to provide a systematic reference for the advancement of multi-energy offshore systems and to support future integrated energy development in deep-sea environments.

Abstract: Unexploded ordnance (UXO) poses a significant hazard in military training areas. This paper assesses the effectiveness of aerial drone-mounted magnetometry for detecting buried UXO located outside the designated impact zones of the National Training Center (CENAD) of San Gregorio (Zaragoza, Spain), considered the largest maneuver area in Europe. To this end, a high-resolution aeromagnetic survey was conducted using a GEM GSMP-35U proton magnetometer mounted on a hexacopter drone. Data were collected at flight altitudes of 7 m and 2 m above ground level along a grid with 1-m line spacing. For its validation, eleven UXOs were deliberately buried at known coordinates to evaluate the system’s sensitivity and spatial resolution under operational conditions. The results demonstrate the capability of aerial drone-based magnetometry to detect small magnetic anomalies (with amplitudes between 2 and 18 nT) associated with buried UXO in complex environments characterised by high ferromagnetic noise, achieving signal-to-noise ratios greater than 5 (SNR > 5) at 2-m altitude and a geolocation accuracy of approximately 0.5 m. These findings support the use of unmanned aerial magnetometry as a viable tool for identifying hazardous remnants in military training ranges and operational scenarios, enabling coverage of 0.53 ha in less than one hour of effective flight time.

Abstract: This study develops and validates a simulation driven, human centric lighting framework for UK residential buildings that integrates circadian and biophilic design, daylight harvesting, and dynamic smart controls using DIALux Evo. A comparative quantitative design was adopted to evaluate traditional manual calculation versus simulation based optimisation across twenty lighting scenes in one bedroom flats, under identical spatial and environmental conditions and in compliance with EN 12464 1 and CIBSE LG standards. Performance was assessed using electrical energy consumption (kWh), average illuminance (lux), and luminous efficacy (lm/W), with statistical validation via paired t tests. The optimised design reduced mean energy consumption from 10.25 kWh to 8.68 kWh (t = 5.12, p = 1.2×10⁻⁵), increased mean illuminance from 94.36 lux to 116.93 lux (t = 7.095, p = 1.0×10⁻⁶), and improved luminous efficacy from 57.2–65.65 lm/W to 98.25–105.35 lm/W across living, kitchen, bedroom, and bathroom areas. Although a minority of scenes showed neutral or adverse energy outcomes, the dominant trend evidences statistically significant reductions in demand and enhanced lighting quality. The contribution is a reproducible and standards aligned methodology that advances best practice for low carbon residential lighting, with actionable guidance for architects, engineers, and policymakers pursuing Net Zero targets and occupant well being.

Abstract: Transmission error (TE) is one of the most important sources of gear noise and vibration. Manufacturing tolerances and assembly shifts introduce deviations in tooth geometry that produce periodic mesh disturbances; these disturbances excite the drivetrain and radiate as airborne sound. While many studies have modelled individual tolerance effects in deterministic simulations, few have evaluated the combined influence of realistic tolerance distributions using open Monte–Carlo data. This work analyses the public Gear Statistical tolerance analysis dataset (≈40 k samples) to answer the following questions: (Q1) How accurately can the kinematic transmission error be predicted from measured tolerance and process‑shift data? (Q2) Which individual tolerances and their interactions have the greatest impact on TE? (Q3) Can a TE‑derived proxy quantify noise‑critical excitation without an acoustic model? (Q4) What tolerance combinations minimise TE and noise while respecting manufacturing cost? (Q5) Is there a quantifiable trade‑off between tolerance tightening cost and noise reduction? To address these questions we formulate hypotheses: H1 Non‑linear machine‑learning models achieve high predictive accuracy for TE (R² > 0.85) compared with linear baselines. H2 A small subset of tolerances—profile and lead errors—dominate the TE variance and interact non‑linearly. H3 A relative noise proxy (RNP) derived from TE faithfully ranks noise‑critical excitation across tolerance combinations. H4 A Pareto front exists in the cost–noise plane, enabling cost‑effective tolerance optimisation. H5 Targeted adjustment of the top two tolerances yields larger noise‑reduction per cost than uniform tightening. The following sections describe the data, the modelling framework and the results that support these hypotheses.

Abstract: The rapid mobilization of sediment stored behind dams, in amounts that are large relative to mean annual sediment loads can jump start river restoration but can also adversely impact habitat, infrastructure, land, and water use upstream of, within, and downstream of the former impoundment. A wide range of geomorphic and engineering assessment tools were applied to help manage sediment-related risks associated with the removal of two dams from the Elwha River in Washington State and the release of roughly 21 million m3 of sediment. Each of these tools had their strengths and weaknesses, which are explored here. The processes of sediment erosion, transport and deposition were complex. No one model was able to fully simulate all these with the accuracy necessary for predicting the magnitude and timing of coarse and fine sediment release from the reservoir. Collectively, however, the model outputs provided enough information to guide the adaptive sediment management process during dam removal. When the complexity of the morphodynamic responses to dam removal and the associated risks exceeded the capacity of any one tool to adequately assess, synoptic forecasting proved useful. The lessons learned on the Elwha have provided insights into how to use a variety of modeling techniques to address sediment management issues such as dam removal scale, complexity and risk increase.

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The increasing demand for sustainable materials in electrical engineering has encouraged the substitution of conventional fillers in epoxy insulation with recycled industrial by-products. This study investigates the potential use of waste tire rubber particles and zinc oxide recovered from electric arc furnace dust as eco-friendly fillers for epoxy resins in high-voltage insulation applications. Four material variants were fabricated: pure epoxy, epoxy with 10 wt% ZnO, epoxy with 10 wt% tire rubber, and epoxy with 20 wt% tire rubber. The breakdown voltage of each composite was measured under AC voltage. Results indicate that the incorporation of recycled fillers influences the breakdown voltage depending on both the type and concentration of filler. The 10 wt% ZnO-filled epoxy exhibited a moderate enhancement in breakdown voltage compared to pure epoxy, attributable to interfacial polarization and charge trapping at the epoxy-ZnO interface. Conversely, tire rubber fillers introduced localized field distortion and interfacial voids, resulting in a gradual reduction of breakdown voltage with increasing filler content. The results show that ZnO from metallurgical waste can function as an effective additive to improve dielectric performance. This approach supports circular-economy principles and offers a sustainable option for future high-voltage insulation materials.

Abstract: An extended operational space kinematics and dynamics formulation is presented for control of redundant non-serial compound robotic manipulators. A broad spectrum of high load capacity non-serial manipulators used in earth moving, material handling, and construction applications is addressed. Departing from conventional approaches that rely on Jacobian pseudoinverses and local null-space projections, a globally valid, differential geometry-based, multi-valued inverse kinematic mapping is defined at the configuration level, with explicit self-motion parameterization of manipulator redundancy. The formulation yields coupled second-order ordinary differential equations of manipulator dynamics on the product space of task variables and self-motion coordinates. This enables direct integration of system dynamics with control strategies, such as model predictive control or feedback design, while maintaining task constraint compliance. The methods presented are validated through simulation and control of a multi-degree of redundancy non-serial compound material loader manipulator, demonstrating advantages in generality, numerical accuracy, and trajectory smoothness.

Abstract: The article deals with the issue of designing reverse curves in a railway track, i.e. a geometric system consisting of two circular arcs (usually with different radii), directed in opposite directions and directly connected to each other. It is also about being able to recreate (i.e. model) the existing geometric system with reverse arcs, so that it is then possible to correct the horizontal ordinates in the area where the circular arcs connect. An analytical method of designing track geometric systems was used, in which individual elements of these systems are described using mathematical equations. The design itself is carried out in the appropriate local Cartesian coordinate system, which is based on symmetrically arranged adjacent main directions of the route. The origin of this system is located at the point of intersection of adjacent main directions, whose coordinates in the global system are known. In the case of reverse curves, a third main direction appears, which significantly complicates the design procedure. The initial values ​​of the radii of the reverse arcs must correspond to the existing system of main directions. The introduction of transition curves causes these radii to decrease; their values ​​are determined iteratively. A set of formulas for creating a geometric system of reverse curves is presented. These formulas were used in the calculation example. A graph of the horizontal curvature of the track axis and a method for determining the possible train speed without the use of cant on an arc and with the use of cant are shown. The presented procedure is universal and can be applied to other geometric situations involving the design of reverse curves.

Abstract: Self-driving laboratories are redefining autonomous experimentation by integrating robotic manipulation, computer vision, and intelligent planning to accelerate scientific discovery. This work presents a vision-guided motion planning framework for robotic manipulators operating in dynamic laboratory environments, with a focus on evaluating motion smoothness and control stability. The framework enables autonomous detection, tracking, and interaction with textured objects through a hybrid scheme that couples advanced motion planning algorithms with real-time visual feedback. Kinematic modeling of the manipulator is carried out using the screw theory formulations, which provides a rigorous foundation for deriving forward kinematics and the space Jacobian. These formulations are further employed to compute inverse kinematic solutions via the Damped Least Squares (DLS) method, ensuring stable and continuous joint trajectories even in the presence of redundancy and singularities. Motion trajectories toward target objects are generated using the RRT* algorithm, offering optimal path planning under dynamic constraints. Object pose estimation is achieved through a vision pipeline that integrates feature-based detection with homography-driven depth analysis, enabling adaptive tracking and dynamic grasping of textured objects. The manipulator’s performance is quantitatively evaluated using smoothness metrics, RMSE pose errors, and joint motion profiles including velocity continuity, acceleration, jerk, and snap. Simulation studies demonstrate the robustness and adaptability of the proposed framework in autonomous experimentation workflows, highlighting its potential to enhance precision, scalability, and efficiency in next-generation self-driving laboratories.

Abstract: Synthetic jets, generated through the periodic suction and ejection of fluid without net mass addition, offer distinct benefits, such as compactness, ease of integration, and independence from external fluid sources. These characteristics make them well-suited for flow control and convective heat transfer applications. However, conventional sin-gle-actuator configurations are constrained by limited jet formation, narrow surface coverage, and diminished effectiveness in the far field. This review critically evaluates the key limitations and explores four advanced configurations developed to mitigate them: dual-cavity synthetic jets, single-actuator multi-orifice jets, coaxial synthetic jets, and synthetic jet arrays. Dual-cavity synthetic jets enhance volume flow rate and surface coverage by generating multiple vortices and enabling jet vectoring, though they remain constrained by downstream vortex diffusion. Single-actuator multi-orifice designs en-hance near-field heat transfer through multiple interacting vortices, yet far-field per-formance remains an issue. Coaxial synthetic jets improve vortex dynamics and overall performance but face challenges at high Reynolds numbers. Synthetic jet arrays with independently controlled actuators offer the greatest potential, enabling jet vectoring and focusing to enhance entrainment, expand spanwise coverage, and improve far-field performance. By examining key limitations and technological advances, this review lays the foundation for expanded use of synthetic jets in practical engineering applications.

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The scientific interest in Triply Periodic Minimal Surface (TPMS) lattices for thermal applications has grown exponentially in recent years, largely driven by the advances in additive manufacturing. However, the lack of a transparent and reproducible selection methodology in previously published reviews hinders the clarity and comparability of findings. This paper adopts and customizes the APISSER framework, a structured and repeatable method that guides literature reviews through five steps: defining research questions, identifying sources, screening studies, extracting data, and reporting results. This approach is applied to investigate the use of TPMS structures in heat transfer applications, including heat sinks and heat exchangers. The study covers peer-reviewed journal articles from 2000 to 2024, analyzing key aspects such as application domain, topology, working fluid, flow regime, additive manufacturing method, and numerical modeling details. Results show a predominant use of numerical studies, with Gyroid and Diamond topologies being the most investigated. These structures are frequently modeled as porous media, especially for estimating pressure drops, although detailed thermal analysis often relies on full-resolution geometries. Water and air are the most common working fluids, while turbulence modeling remains limited to RANS approaches. The structured methodology adopted ensures high reproducibility and offers a quantitative foundation for the identified knowledge gaps to guide future experimental and computational research.

Abstract: This study investigates the behaviour of Compressed Earth Cylinders (CECs) and Compressed Earth Blocks (CEBs) during direct compression tests and examines the influence of aspect ratio and the effects of platen restraint. The experimental investigation utilises two soil types and examines the impact of jute fibre reinforcement on the failure mechanism of CECs with aspect ratios ranging from 0.50 to 2.00. Through experimental analysis and numerical modelling, the effects of platen restraint are examined, and a novel hypothesis of intersecting cones is presented. The results show that specimens with a lower aspect ratio exhibited higher compressive strength due to confinement caused by platen restraint. Moreover, this research has derived new aspect ratio correction factors which enable conversion from Apparent Compressive Strength (ACS) to Unconfined Compressive Strength (UCS) of unstabilised and fibre-reinforced CECs. A theoretical relationship between CECs and CEBs was also determined, with an accuracy of 2.7 %. The outcome of this research recommends a standard approach to the application of aspect ratio correction factors when interpreting and reporting the compressive strength of CECs and CEBs.

Abstract: In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation information obtained from such experiments may not be applicable to the entire lifecycle. To address this, we develop a stage-wise state-of-health (SOH) prediction approach that combines offline training with online updating. During the offline training phase, multiple single-cell experiments were conducted under various combinations of depth of discharge (DOD) and C-_rate. Multi-dimensional health features (HFs) were extracted and an accelerated aging probability p_AA was defined. Based on the correlation statistics between HF, k_HF, SOH, and p_AA, all cells in the dataset were divided into general early, middle, and late aging stages. For each stage, cells were further classified by their longevity (long, medium, short), and multiple models were trained offline for each category. The results show that models trained on cells following similar aging paths achieve significantly better performance than a model trained on all data combined. Meanwhile, HF optimization was performed via a three-step process: an initial screening based on expert knowledge, a second screening using Spearman correlation coefficients, and an automatic feature importance ranking using a random forest regression (RFR) model. The proposed method offers the following innovations: (1) The stagewise multi-model strategy significantly improves SOH prediction accuracy across the entire lifecycle, maintaining the mean absolute percentage error (MAPE) within 1%. (2) The improved model provides uncertainty quantification, issuing a warning signal at least 50 cycles before the onset of accelerated aging, thereby enabling early detection of accelerating degradation. (3) Analysis of feature importance from the model outputs allows indirect identification of the primary aging mechanisms at different stages. (4) The model is robust against missing or low-quality HFs—if certain features cannot be obtained or are of poor quality, the prediction process does not fail.

Abstract: This paper assesses the steady-state voltage impact of ultra-fast electric vehicle (EV) charging on the IEEE 33-bus radial distribution feeder. Four practical scenarios are examined by combining two penetration levels (6 and 12 charging points, representing approximately 20% and 40% of PQ buses) with two charger ratings (1 MW and 350 kW per point). Candidate buses for EV station integration are selected through a nodal voltage–reactive sensitivity ranking (∂V/∂Q), prioritizing electrically robust locations. To capture realistic operating uncertainty, a 24-hour quasi-static time-series power-flow study is performed using Monte Carlo sampling, which jointly models residential-demand variability and stochastic EV charging activation. Whenever the expected minimum-hourly voltage violates the 0.95 p.u. threshold, a closed-form sensitivity-guided reactive compensation is computed and injected at the critical bus, and the power flow is re-solved. Results show that ultra-fast charging can produce sustained under-voltage even under robust siting, particularly at high penetration and 1 MW ratings; however, the proposed compensation consistently raises the minimum-voltage trajectory by about 0.03–0.12 p.u., substantially reducing the depth and duration of violations. The cross-case comparison confirms that lowering unit charger power mitigates voltage degradation and reactive-support requirements, while charger clustering accelerates stability-margin depletion. Overall, the Monte Carlo V–Q sensitivity framework provides a lightweight and reproducible tool for probabilistic voltage-stability assessment and targeted mitigation in EV-rich distribution networks.

Abstract: Improving the ride comfort of commercial vehicles is crucial for driver health and operational safety.This study focuses on optimizing the parameters of a cab suspension system to improve its vibration isolation performance. Initially, nonlinear fitting was applied to experimental data characterizing air spring stiffness and damping, which informed the development of a multi-body rigid-flexible coupled dynamic model of the suspension system; its dynamic characteristics were subsequently validated through modal analysis. Road excitation data, filtered through the chassis suspension, were collected during vehicle testing, and displacement excitations for ride comfort simulation were reconstructed using virtual iteration technology. Thereafter, an integrated ISIGHT platform, combining ADAMS and MATLAB, was employed to systematically optimize suspension parameters and key bushing stiffness via a multi-island genetic algorithm. The optimization results demonstrated significant performance improvements: on General roads, the overall weighted root-mean-square acceleration was markedly reduced with enhanced isolation efficiency; on Belgian pave roads, resonance in the cab's X-axis direction was effectively suppressed; and on Cobblestone roads, the pitch angle was successfully constrained within the design limit. This research provides an effective parameter matching methodology for performance optimization of cab suspension systems.

Abstract: Efficient last-mile delivery remains a critical challenge for rural agricultural logistics, globally, particularly in cold-climate regions with dispersed agricultural operations. This review evaluates the potential of GIS-enabled truck–drone hybrid systems to overcome infrastructural, environmental, and operational barriers in such settings. This study uses North Dakota, USA as a representative case alongside insights from similar rural regions worldwide. The study conducts a systematic review of 82 high-quality publications. It identifies seven interconnected research domains: GIS analytics, truck–drone coordination, smart agriculture integration, rural implementation, sustainability assessment, strategic design, and data security. The findings stipulate that GIS enhances hybrid logistics through route optimization, launch site planning, and real-time monitoring. Additionally, this study emphasizes the rural, low-density context and identifies specific gaps related to cold-weather performance, restrictions to line-of-sight operations, and economic feasibility in ultra-low-density delivery networks. The study concludes with a roadmap for research and policy development to enable practical deployment in cold-climate agricultural regions.

Abstract: This paper presents a resilient, multi-layer architecture designed to ensure reliable autonomous operation of single and multiple quadcopters. The architecture leverages the resilient spacecraft executive to hierarchically organize trajectory-planning and flight-control functions, and integrates Simplex architectures at each level to provide safety assurance. A compound subsystem expands robustness by employing multiple candidate algorithms for planning and control, while a supervisory program adapts Simplex behavior based on system states and environmental conditions to enable high-level mission management. The architecture is evaluated in simulations involving environmental uncertainties, including varying wind and obstacles, within a bridge-inspection mission using both single- and multi-quadcopter configurations. Results show that the system maintains safe and effective operation across a wide range of conditions, demonstrating scalability for cooperative multi-agent tasks.

Abstract: Residential retrofitting is a cornerstone of national strategies for achieving net-zero emissions by 2050. Yet, despite decades of policy incentives, adoption remains limited, delivery performance is uneven, and realised carbon reductions often fall short of predictions. This paper conceptualises housing retrofit projects as complex micro-projects—small in scale but marked by technical, organisational, and social inter-dependencies that challenge conventional project delivery. Drawing on an integrative review of 56 studies, we synthesise evidence across the Owner, Supplier, and Delivery domains to identify the managerial, coordination, and communication barriers that shape whole-life CO₂ outcomes. By framing these challenges through concepts of static and dynamic complexity, the study demonstrates that performance shortfalls stem less from technological gaps than from fragmented project organisation and weak cross-domain coordination. The review contributes an evidence-based understanding of how complexity affects retrofit delivery, outlines implications for policy and practice, and proposes a research agenda for improving assurance, learning, and verification in housing retrofit management.