Abstract: Vehicle–infrastructure cooperative perception (VICP) overcomes the sensing limitations and field-of-view constraints of single-vehicle intelligence by integrating multi-source information from onboard and roadside sensors. However, in complex urban environments, system robustness—particularly regarding blind-spot coverage and feature representation—is severely compromised by occlusion (static and dynamic) and distance-induced point cloud sparsity. To address these challenges, this paper proposes a 3D object detection framework incorporating point cloud feature enhancement and spatial adaptive fusion. First, to mitigate feature degradation under sparse and occluded conditions, a Redefined-SENet (R-SENet) attention module is embedded into the feature encoding stage. This module employs a dual-dimensional squeeze-and-excitation mechanism—across pillars and intra-pillar points—to adaptively recalibrate key geometric features. Concurrently, a Feature Pyramid Backbone Network (FPB-Net) is constructed to enhance unified target modeling across varying distances via multi-scale extraction and cross-layer aggregation. Second, a Spatial Adaptive Feature Fusion (SAFF) module is introduced to resolve feature heterogeneity and spatial misalignment. By explicitly encoding feature origins and leveraging spatial attention, SAFF enables dynamic weighting and complementary fusion of fine-grained vehicle-side features and global roadside semantics. Experiments on the DAIR-V2X benchmark and a custom dataset demonstrate that the proposed method outperforms state-of-the-art approaches, achieving Average Precision (AP) scores of 0.762 and 0.694 at IoU 0.5, and 0.617 and 0.563 at IoU 0.7, respectively. Furthermore, the inference speed satisfies real-time requirements, validating the method’s effectiveness and potential for engineering deployment.

Abstract: This study presents a comprehensive numerical and experimental investigation into the influence of punch shaft geometry on punching force and tool durability in the cold forming of S500MC steel sheets using an AISI D2 punch. Finite element analyses were conducted to evaluate the effects of varying punch shaft diameters on stress distribution, deformation behavior, and resultant punching forces. Experimental validation was performed through controlled punching tests, measuring force responses and assessing tool wear. The results demonstrate that optimizing the punch shaft diameter reduces the maximum punching force and minimizes stress concentrations, thereby enhancing tool life. Specifically, larger punch shaft diameters contribute to more uniform stress distribution and decreased risk of premature tool failure. These findings provide valuable insights for tooling design in high-strength steel sheet forming processes, enabling improved efficiency and cost-effectiveness in manufacturing operations.

Abstract: In recent years, artificial intelligence has become embedded in environmental decision-making, shaping how climate risk is zoned, how land use is planned and managed, and how regulatory oversight and energy-related decisions are carried out. Despite this expansion, discussions surrounding the use of AI in decisions related to sustainability often focus on performance measures, with limited attention given to broader institutional and environmental implications. Such accounts frequently sidestep questions of governance legitimacy while underestimating the environmental burdens associated with computational processes and the infrastructure that supports them. This paper develops algorithmic sustainability as a governance framework oriented toward public policy in contexts where artificial intelligence informs environmental decision-making. The concept is defined through the simultaneous alignment of three conditions. These include ecological effectiveness assessed across the full lifecycle of AI systems, institutional accountability anchored in oversight that can be enforced in practice, and ethical legitimacy grounded in freedom, justice, and the possibility to contest decisions. Rather than treating these dimensions as separable, the framework assumes that sustainability claims weaken when any one condition is absent. The research methodology adopts a framework-development approach supported by a qualitative comparative review. The review integrates scholarship on climate impact pathways with ethical and political analyses of algorithmic authority, while also drawing on governance instruments found in global normative frameworks, regional regulatory models, and organizational practice. Through this synthesis, the paper produces two outcomes. One is a four-domain ethical risk register that consolidates epistemic and technical concerns, risks tied to justice and political economy, issues of accountability and legitimacy, and impacts associated with the environmental footprint of AI systems over time. The second outcome is a governance toolkit that translates algorithmic sustainability into practice through proportional risk tiering based on decision criticality, requirements for documentation and auditability, a tiered Environmental AI Impact Assessment, standardized disclosure of environmental footprints, procurement-based leverage, and enforceable mechanisms that allow contestation and remedy. The analysis shows that environmental AI governance remains institutionally fragile when sustainability evaluation is disconnected from transparency obligations, challenge pathways, and distributive accountability as they operate in practice.

Abstract: Manual harvesting of white button mushrooms involves coordinated bending and twisting motions to detach the fruiting body while minimizing surface damage; however, replicating these actions in automated systems remains challenging. In this study, a vacuum-based end-effector that mimics manual twist–bend detachment using a single-point contact was designed and evaluated to reduce mechanical damage. Key detachment parameters, including the friction coefficient (mean 0.62), bending angle (average 5.72°), and twisting torque (average 2.56 N·m), were experimentally analyzed to determine the minimum vacuum pressures required for effective bending and twisting, which were −8.64 ± 2.21 kPa and −8.91 ± 2.45 kPa, respectively, with no significant difference observed between the two motions (p = 0.51). A customized vision-based image processing algorithm was developed to quantify postharvest surface damage using a whiteness index (WI). An optimal vacuum pressure of −17.17 kPa was identified, together with a bending angle of 10° and a twisting angle of 90°, balancing high harvesting success with preservation of mushroom quality. The results highlight the influence of end-effector design parameters, including vacuum cup material, contact area, bending direction, and vacuum application duration, on harvesting performance and product marketability, supporting the development of robotic systems for fresh mushroom harvesting.

Abstract: This research explores the cause of uncertainty of rainfed and irrigated systems’ crop yield in the Philippines, and compares the impacts of nitrogen, phosphorus, and magnesium fertilizers on crop yield. To analyze these relationships, Spearman’s rank correlation coefficient is used to assess the associations among soil fertility, nutrient status, and crop yield. Our results show that an adequate water supply will enable effective nutrient use. Conversely, rainfed systems exhibit a strong negative relationship with nitrogen (r = –0.562, p < 0.001) and phosphorus (r = –0.565, p < 0.001) use, suggesting water-stress limitations. In contrast, irrigation reveals a high positive correlation with nitrogen application (r = 0.773, p < 0.001) and magnesium application (r = 0.346, p = 0.001), among other nutrients. To examine predictive potential, we applied several machine learning algorithms, including Decision Tree, Random Forest, Support Vector Regression (SVR), and K-Nearest Neighbors (KNN). When comparing model performance, the Random Forest model showed high robustness and consistency across both irrigated and rainfed regions, with only a minor increase in MAE (0.3107 to 0.3607), MSE (0.1790 to 0.2391), and RMSE (0.4230 to 0.4890), and still maintaining a high R² (from 0.8661 to 0.8095). These findings point towards the necessity for specific agriculture practices, with a focus on coordinated application management of water and fertilizers in irrigation fields and water conservation in rainfed fields, to improve rice roductivity and food security.

Abstract: This study investigates the effects of two process parameters (dispense delay and recoat speed) on green part density and powder bed density in binder jetting additive manufacturing using silicon carbide powder. These two process parameters control the amount of powder dispensed on the powder bed for each powder layer. Experiments were conducted at three levels of dispense delay (0.2, 1, and 5 s) and three levels of recoat speeds (5, 10, and 20 mm/s). The one-way ANOVA results reveal that both dispense delay and recoat speed have statistically significant effects on green part density and powder bed density. Experimental results show that increasing dispense delay or decreasing recoat speed leads to higher green part density and powder bed density. These findings provide useful insights into optimizing binder jetting additive manufacturing process parameters to achieve desired green part density without employing powder bed compaction.

Abstract: In a bid to investigate the optimum transportation method for the offshore wind produced hydrogen (H₂) and assess the feasibility of repurposing the existing oil and gas infrastructure for H2 transmission; this paper assesses the existing H2 transportation methods with a comprehensive review of the H2 impact on the existing natural gas pipelines infrastructure. To establish the possibility of repurposing the existing natural gas (NG) pipelines for H2 gas transport, this paper reviews the influential technical measures; composition, pressure, temperature, volumetric energy density, density, and pressure drop to assess whether the characteristics of hydrogen gas are compatible with the natural gas pipeline infrastructure. Based on these reviews, it was found that the current NG pipelines pressure exacerbates the H2 embrittlement and for the existing NG pipelines to be repurposed, the operating pressure should be reduced, and the pipeline material should be revised. It was found that higher strength steels can be re-used with major modifications, or the pipeline should be constructed from X52 material grade or below. Nevertheless, the fitness of the existing NG pipelines for H2 transmission should be assessed on a case-by-case basis and other factors such as erosion, leakage, monitoring and rigorous assessment of welds and joints should also be considered.

Abstract: Soil suction is a crucial factor affecting the hydraulic and mechanical property of unsaturated soils, playing an important role in geotechnical, geoenvironmental, and hydrological engineering applications such as slope stability, foundation design and irrigation planning. Conventionally, measuring and modeling soil suction and its associated curves like Soil Water Characteristic Curve (SWCC) and Soil Water Retention Curve (SWRC) require extensive, time-consuming tests in the laboratory. Recent progress in Machine Learning (ML) offers powerful as well as data-driven and reliable alternatives ways that can enhance the efficiency and accuracy of suction-related predictions across a wide range of soil conditions. This study aims to cover the current state of the art research on the integration of ML techniques into the prediction and analysis of soil suction behavior. Studies utilized various algorithms including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Multi-Expression Programming (MEP), K-Nearest Neighbors (KNN), and AdaBoost (AB) to predict soil suction. These models demonstrated high predictive performance (R² > 0.90 in majority cases) based on soil parameters which can be easily evaluated like soil texture, bulk density, climate parameters, and remotely sensed data. Overall, this study covers the understanding of the current research gap related to SWCC and SWRC using different data driven and ML techniques.

Abstract: Proton exchange membrane fuel cells (PEMFCs) are promising energy conversion devices owing to high efficiency and zero local emissions. Accurate PEMFC performance assessment and control require well-posed models, whose predictive accuracy is largely determined by the correct calibration of key parameters. Metaheuristic algorithms (MHAs) have therefore been widely applied to PEMFC stack parameter estimation, but their rapid proliferation calls for a more systematic and fine-grained synthesis. This review refines the taxonomy of PEMFC mathematical modeling approaches and summarizes Zero-Dimensional PEMFC modeling methods, key parameters, and representative improvement directions aimed at reducing identification difficulty while retaining physical meaning. Newly developed MHAs and enhanced variants of existing methods are then surveyed, and over 40 distinctive optimization approaches are selected for systematic comparison. Key fuel-cell parameters, evaluation criteria, and representative commercial PEMFC types are summarized. In addition, 26 representative algorithms and their variants are compiled and benchmarked across the five most widely used commercial PEMFC models to enable cross-model comparison.

Abstract: High-performance difference patterns (DPs) are critical for compact and inte-grated microwave array systems, particularly in monopulse tracking and beam-scanning applications. However, the design of monopulse phased arrays with steep slopes, high directivity, low sidelobes, and symmetric main lobes remains challenging due to con-straints imposed by the array aperture and radome structure. In this paper, a novel design method is proposed to maximize the DP directivities for monopulse linear and planar phased arrays composed of microstrip patch antennas. The DP synthesis problem is first formulated as a nonconvex optimization model for directivity maximization. By fixing the reference phase of the DP slope and applying a first-order Taylor expansion of the quad-ratic function, the original problem is decomposed into a sequence of convex subproblems that can be solved efficiently. The proposed method fully exploits the flexibility of the phased array feed network, enabling directivity enhancement without altering the geo-metric configuration of the monopulse array. Finally, two numerical examples employing a radome-enclosed linear phased array and a uniform planar phased array are presented to demonstrate effectiveness of the proposed method in achieving the monopluse array DP synthesis with high directivity and symmetric main-lobes.

Abstract: The primary focus of enhancing the efficiency of operations in the Industry 4.0 setting is Predictive and Preventive Maintenance (PPM). The paper introduces a predictive-maintenance system based on the Unified Namespace (UNS), which involves real-time sensor measurements, photogrammetry, and modeling of a digital twin to improve fault prediction and responsiveness to maintenance. This experiment was conducted over six months in a medium-sized discrete electromechanical production plant equipped with motors, Variable Speed Drives (VSDs), robot/cobots, precision grip systems, pipework systems, Magnemotion/linear motor drives, and a CNC machine. The continuous data, such as high-frequency vibration, temperature, current, and pressure, were monitored and analysed with machine-learning models, including support-vector machines, gradient-boosting, long-short-term memory, and Random Forest, through which temporal degradation can be predicted. UNS architecture integrated all sensor and imaging data into a vendor-neutral data model through OPC UA to help ensure that all experiments could be integrated consistently and be updated in real time to real digital twins. The suggested system correctly identified mechanical and electrical failures and predicted failures before they really took place. Consequently, machine downtime was reduced by 42.25, and Mean Time to Repair (MTTR) by 36, which was mainly caused by a previous anomaly detection and pre-inspection supported by a digital-twin. Altogether, the paper proves that the integration of UNS with multi-modal sensing and digital-twin technologies will greatly enhance predictive maintenance. The framework provides a data-driven, scalable solution to organisations that aim to modernise their maintenance processes, attain greater reliability and better equipment utilisation, as well as enhanced Industry 4.0 preparedness.

Abstract: Utilizing depleted gas reservoirs for geological carbon sequestration offers a robust, technically viable pathway for large-scale greenhouse gas mitigation. This research utilizes ECLIPSE 300 to execute a rigorous compositional reservoir simulation, advancing a preliminary undergraduate investigation into a high-fidelity assessment of subsurface CO2 behavior. Central to this study is a sophisticated equation-of-state (EOS) model designed to track the long-term lifecycle of injected CO2, specifically focusing on plume migration patterns, pressure stabilization, and the multifaceted trapping mechanisms that ensure storage integrity. By systematically varying parameters such as permeability anisotropy, thermal regimes, and geological heterogeneity, the analysis identifies vertical permeability and injection velocity as the primary determinants of containment efficiency. The findings demonstrate that while depleted fields possess the capacity for significant CO2 volumes, the accuracy of storage projections depends heavily on compositional modeling rather than simplified black-oil approaches. These insights provide a refined framework for the technical screening and operational design of industrial-scale sequestration initiatives.

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

Abstract: In the era of renewable dominated grids and integration of dynamic load such as EV charging stations has increased the operational challenges in multifolds particularly in DC microgrids (DC MG). Traditional battery dominated grid’s energy management strategies (EMS) are often not capable of handling fast transients due to limitations of battery electrochemistry. To overcome this limitation, an hierarchical hybrid energy management strategy is proposed that uses the combination of data driven and metaheuristic algorithms. The designed optimization framework consists of particle swarm optimization (PSO) and neural network (NN) implemented in central controller of 4 bus ringmain DC MG. A efficient decoupling of fast and slow storage dynamics is performed, where supercapacitor (SC) is optimized using NN and battery is optimized using PSO. This selective optimization reduces the computational overhead on the PSO making it more feasible for realtime implementation. The designed hybrid PSO-Neural EMS framework is initially designed on MATLAB and further validated on realtime hardware setup. Robustness of the control scheme is verified with various case studies such as, renewable intermittency, dynamic loading and partial shading scenarios. An effective optimization of SC in both transient and heavy load scenarios is observed. LabVIEW interfacing is used for MODBUS based interaction with PV emulators and DC-DC converters.

Abstract: Operable windows are critical for indoor environmental quality (IEQ) and occupant agency, yet their usability is increasingly compromised by the conflict between regulatory compliance and building performance. This study investigates the gap between geometrically compliant provisions and genuinely operable windows. By conducting a comparative policy analysis of mandatory codes (Level 1), green rating systems (Level 2), and regenerative frameworks (Level 3), this research identifies a critical discrepancy termed the Geometric Trap. Results reveal that in the US, South Korea, Japan, and the UK, mechanical ventilation legally substitutes for natural access. While the US, South Korea, and Japan employ explicit 'OR' waivers, the UK enforces substitution through conditional constraints like noise. Consequently, windows are rendered sealed despite geometric compliance, unlike in Germany where operability remains mandatory. Furthermore, while evolving green rating systems (Level 2) prioritize resilience, they still treat operability as an optional trade-off. In contrast, regenerative frameworks (Level 3) mandate it as a non-negotiable prerequisite for occupant health. Finally, the study argues for a regulatory shift toward the Effective Opening Area framework to resolve this discrepancy. By redefining operability through the lens of accessibility and agency, this research contributes to a paradigm shift from static geometric compliance to dynamic, occupant-centric performance.

Abstract: This study presents the development and evaluation of surface functionalized solidly mounted resonators (SMRs), including custom UWAR devices and commercial Sorex sensors, for the detection and classification of plant-emitted volatile organic compounds (VOCs). The sensors were tested against linalool, trans-2-hexenal (T2H), and D-limonene at different concentrations under both dry and humid conditions (up to 33% RH). A Python-based signal-processing workflow was established to filter frequency responses and extract key features, such as baseline, saturation point, and frequency shift (Δf). Adsorption behaviour was modelled using the Freundlich isotherm, showing good agreement with experimental data and suggesting heterogeneous, multilayer adsorption on CH₃-terminated EC surfaces. A 2D polar classification framework combining vector-normalized Δf values from UWAR and Sorex sensors enabled clear separation of the VOCs. The results highlight the complementary performance of the two types of SMR sensors and demonstrate that feature-engineered resonant devices, combined with computational classification, offer strong potential for future use in plant health monitoring systems.

Abstract: The growing importance of integrating renewable energy sources (RES) into mainline railway traction networks stems from the sector's substantial electricity demands, traditionally met by carbon-intensive thermal generation. This paper addresses the potential of wind and solar power to enhance energy efficiency and reduce emissions in rail transport. It details the devel-opment of digital models for simulating DC traction power systems (TPS) coupled with RES, specifically wind turbines. Given the complexity of TPS, effective integration requires digital modeling that accounts for their unique properties. The proposed methodology, based on phase coordinates algorithms, offers a universal and comprehensive framework. It enables the identi-fication of various operational modes (normal, emergency, special) for diverse network com-ponents, including traction networks, transmission lines, and transformers. These models were used to simulate real-world train operations, generating data on electrical parameter dynamics and transformer thermal conditions. The results confirm that wind integration can improve energy efficiency, validating the methodology's practical applicability for RES projects in DC traction networks, including advanced high-voltage systems.

Abstract: Buildings located in highly urbanized areas have not been considered for photovoltaic (PV) deployment on building walls due to limitation of ground and rooftops space. As the need for increasing energy demand due to population growth in cities, and the advancements in the efficiency of semi-transparent (ST-PV) solar cell technology, the integration of ST-PV modules into building windows, become feasible. The present article proposes a novel methodology for calculating the incident solar energy on PV vertical modules deployed on building walls and windows facing the southern direction and obscured by a nearby building in front. The present work analyses analytically, for the first time, the incident energy and its distribution on PV vertical modules along a wall height. Monthly and annually direct beam, diffuse and global energies are calculated for different wall height, building separation and orientation. The results shows, for example, that both the front and rear building walls receive the same amount of annual direct beam energy 913 kWh/m² for a distance 25 m between the buildings. Decreasing the distance from 25 m to 10 m, decreases the annual incident global energy on the rear-building wall by 15 %.

Abstract: In order to respond to the national " double carbon " strategic goal, promote the green and low-carbon transformation of the building materials industry, and develop low-carbon and environmentally friendly grouting materials, an AACGMs was prepared in this study. The effects of CG content, alkali activator modulus and alkali activator content on material fluidity, setting time, compressive strength and impermeability were systematically studied by orthogonal test. The optimal mix ratio was determined, and its internal mechanism was revealed by microscopic analysis. The results show that the comprehensive performance is the best when the content of CG is 50%, the modulus of alkali activator is 1.6 and the content of alkali activator is 14%. The primary and secondary order of the influence of various factors on the performance is : CG content > alkali activator content > alkali activator modulus. Microscopic analysis reveals that the hydrolysis polymerization products of the material are mainly C-S-H, C- (N) -A-S-H gel and zeolite-like phase, forming a dense three-dimensional network structure, which is the internal mechanism of its good mechanical and impermeability properties. This study provides a new idea for the utilization of CG, and the prepared materials are of great significance in the field of grouting reinforcement in underground engineering.

Abstract: This review examines implementation dimensions of integrated lemon biorefinery systems, including cascade valorisation design, circular-economy integration, life-cycle assessment, techno-economic feasibility, and regulatory frameworks. Bibliometric analysis of Web of Science data (2015–2025) reveals exponential growth in citrus-biorefinery research, with lemon representing a burgeoning subset. Techno-economic assessments indicate that cascade biorefineries recovering essential oils, pectin, polyphenols, nanocellulose, and bioenergy can achieve cumulative revenues of USD 400–650 per tonne of dry peel. Whilst small-scale units (<500 tonnes/year) struggle to achieve viability, industrial simulations demonstrate Internal Rates of Return exceeding 18% at processing scales above 100,000 tonnes annually (2025 basis). Life-cycle assessments confirm environmental benefits, with greenhouse gas reductions of 60–85% relative to conventional disposal. Critical success factors include adopting green extraction technologies to preserve bioactive integrity and mitigating D-limonene inhibition in downstream anaerobic digestion. These findings establish lemon biorefineries as technically mature, economically viable pathways for circular bioeconomy transitions, provided regulatory hurdles—Novel Foods authorisation (EU) and GRAS determination (US)—are effectively navigated.