Abstract: The chemical agents, the injection modes and displacement characteristics of chemical compound flooding, consisting of plugging agent, oil displacement agent, and viscosity reducer, were investigated by laboratory experiments for the target heavy oil reservoirs after multiple cycles of huff and puff. The performance of oil displacement agent, viscosity reducer and plugging agent were evaluated and the formulation and concentration were optimized. The oil displacement effects and displacement characteristics of different injection modes were studied by two-pipe models. The experiment results showed that the alternating injection of oil displacement agent and viscosity reducer yielded better results than their mixed injection, and small segments alternating injection achieved the highest recovery, which playing a role in gradual adjustment of the profile and its seepage resistance was greater. The dosage of the plugging agent should be no less than 0.5 pore volume (0.5 PV). There was a balance between the viscosity increase of polymer and the reduction of interfacial tension of viscosity reducer. The larger the dosage of the oil displacement agent, the higher the capacity to expand the swept volume and to adjust the profile enhanced, the larger the maximum liquid production ratio between high and low permeability layer, but the shorter of the liquid production reverse duration. The larger the dosage of the viscosity reducer, the greater the water cut decrease, but the smaller of maximum liquid production ratio. For chemical compound flooding in the Zhong'er block in Gudao oilfield, the recommended injection mode was 0.1 PV plugging agent + 2000mg/L oil displacement agent + 0.5wt% viscosity reducer, with small segments of oil displacement agent followed by viscosity reducer at an injection slug ratio of 6:4, which providing an efficient and economical chemical compound flooding technology solution for field application.

Abstract: Fused Deposition Modeling (FDM) components require accurate identification of printing parameters to support reliable quality assessment and scalable reverse‑engineering workflows. This study evaluates whether mechanical response curves can be used to infer critical manufacturing parameters—specifically build direction, layer thickness, and infill density. Force–displacement and stress–strain data obtained from tensile tests were converted into image‑based representations and classified using individual and ensemble machine learning models. The influence of applying a moving‑average filter to smooth the curve‑derived images was also examined. Ensemble approaches, particularly AdaBoost, achieved higher accuracy and robustness across the evaluated variables, with the best results obtained from unfiltered stress–strain images. Under limited‑data conditions, ensemble models generally outperformed individual classifiers, while Multilayer Perceptron and Support Vector Machine models showed more stable but less accurate behavior. Overall, the findings demonstrate the feasibility of predicting FDM printing parameters directly from mechanical‑curve‑derived images, enabling a non‑destructive approach suitable for scalable reverse‑engineering and improved traceability within additive manufacturing processes.

Abstract: Denoising-based CT reconstruction methods can suppress high-frequency textures that are relevant for subtle lesion visibility. Motivated by hybrid convolution–attention designs such as CTLformer, this paper proposes a frequency-constrained denoising framework that preserves diagnostically relevant textures while reducing noise. The method introduces a dual-domain loss combining spatial fidelity with frequency-band constraints computed using discrete cosine transform representations. Evaluations on 52,000 paired slices from two low-dose CT datasets show that, relative to CNN-only and attention-only baselines, the proposed approach increases PSNR by 0.7–1.1 dB while maintaining higher high-frequency energy consistency. Reader-oriented texture metrics also improve by 8%–14% in regions with fine structural patterns.

Abstract: The modern prosthetic foot market is characterized by a pronounced polarization between affordable but low-function devices and high-performance yet costly composite prosthe-ses. The aim of this study was to develop and comprehensively evaluate cost-effective, functional prosthetic feet manufactured by fused deposition modeling (FDM). An iterative design methodology was employed, combining finite element analysis to optimize the biomechanical response of the device, incorporation of user-specific requirements and ex-perimental validation. Two TPU 95A-based 3D-printed prosthetic foot designs were de-signed and developed, and their strength and functional characteristics were assessed numerically under the ISO 22675:2024 normative loading cycle. Bench-top mechanical tests were conducted on the fabricated prototypes. Functional performance was evaluated by a transtibial amputee using an inertial motion capture system to analyze gait kinemat-ics. The results demonstrated that both designs operate predominantly within the elastic range with an adequate safety margin. The pilot gait assessment indicated biomechani-cally acceptable walking kinematics for both prototypes, with a subjective preference for the smoother rollover provided by Model 2.

Abstract: Water pollution due to insufficient wastewater treatment is a global concern. In this paper coagulation and flocculation as a tertiary unit process was investigated to find the solution for a non-complying wastewater treatment facility. The Palapye Pond Enhanced Treatment and Operation (PETRO) system has not been compliant for a long time with effluent characterised by high turbidity, Biological Oxygen Demand/Chemical Oxygen Demand (BOD/COD), Total Suspended Solids (TSS), Nitrates (NO3), and Phosphates (PO4.) The effluent from the plant is released into the stream that drains into the nearby Lotsane dam, posing a lot of danger to the water quality of the dam. The main objective of the project was to investigate the effect of coagulation and flocculation processes at the secondary stage of the wastewater treatment. Response Surface Methodology (RSM), Central Composite Design (CCD) and Multi Response Surface (MRS) were used to optimize the coagulation process and generate regression models to predict the coagulation and flocculation. The performance was evaluated using turbidity, Colour, COD and TSS as response variables. Response surface analysis indicated that the experimental data could be adequately Fitted to quadratic polynomial models. Under optimum conditions the removal efficiency for Al2(SO4)3·18H2O: 91.1% (turbidity), 88.2% (colour), 58.9% (COD), 83.0% (TSS); for FeCl3·6H2O: 93.2%, 88.7%, 63.8%, 91.3%; for Moringa: 91.8%, 85.4%, 56.6%, 83.7%. The optimal removals based on MRS for Al2(SO4)3.18H2O, FeCl3.6H2O and Moringa were 90.7%, 89.7%, 59.9% and 88.5%; 94.7%, 90.8%, 58.1% and 93.8%; 94.0%, 87.2%, 60.1% and 82.1% for turbidity, colour, COD and TSS respectively. This research has demonstrated that the coagulation/flocculation process can be incorporated into the secondary stage of the wastewater treatment facility and the treatment process optimized using RSM, CCD and MRS. The study introduces comparative evaluation of three coagulants within a single RSM-CCD optimization framework, employing desirability functions for multi-response optimization.

Abstract: Earth-centric satellite systems are increasingly constrained by orbital congestion, collision exposure that scales nonlinearly with constellation size, and geometry-driven power intermittency. This paper proposes Heliocentric Artificial Planets (HAPs): modular, actively controlled heliocentric hubs that deliver persistent solar power, autonomous coordination, and data aggregation for distributed satellite networks. We provide quantified scaling laws, explicit numerical evaluations, and a system-of-systems architecture that together demonstrate physical feasibility within known laws of orbital mechanics and electromagnetic transmission. The concept reframes future space systems from spacecraft-centric to infrastructure-centric design and positions heliocentric placement as a structural solution to Earth-orbit scalability limits.

Abstract: Artisanal and illegal gold extraction in ecologically sensitive tropical landscapes can generate persistent environmental damage and public fiscal liabilities that accumulate even under formal mining prohibitions. Here we develop a decision-grade pipeline that converts observable environmental signals into (i) spatial prioritisation surfaces, (ii) phase-timed remediation portfolios, and (iii) present-value (PV) comparisons of legislative policy pathways under uncertainty, demonstrated for the Crucitas mining landscape (Cutris, northern Costa Rica). Five linked models are implemented. Remote-sensing change proxies are derived using consistent baseline (January 2019–December 2020) and recent (February 2024–January 2025) windows; multi-criteria indices then produce a 0–100 grid-cell prioritisation surface integrating land, water, and hydrologic dimensions. This prioritisation output is translated into a phased remediation portfolio across 1,324 costed grid cells, yielding a gross liability of US$548.0 million (10-year PV; 5% discount rate). PSA-related credits total US$167.3 million PV; enforcing a cell-level non-negativity floor yields a baseline net PV of US$408.0 million (simple gross-minus-credits would be US$380.8 million). Deterministic policy overlays produce policy-adjusted net PV of US$336.1 million under Exp. 24.717 (Δ = −US$71.9 million vs baseline; modeled royalty PV = US$93.8 million), US$418.6 million under Exp. 24.675 (Δ = +US$10.6 million), and US$421.7 million under Law No. 8904 (Δ = +US$13.7 million). Monte Carlo propagation yields a right-tailed baseline distribution (P10–P90 = US$385.4–519.1 million; P50 = US$450.1 million), with exceedance probabilities P(>US$400 million) = 0.8357 and P(>US$500 million) = 0.1786. Policy-adjusted uncertainty bounds indicate substantially reduced exceedance risk under Exp. 24.717 (P(>US$400 million) = 0.3833; P(>US$500 million) = 0.0179) and increased exceedance risk under non-mining pathways. Collectively, the results enable PV-consistent, uncertainty-aware ranking of contested pathways, with outcomes conditional on enforceable offsets, credible enforcement effectiveness, and residual-risk provisioning. The framework is transferable to other contested mining landscapes where phased interventions and policy alternatives require fiscally comparable evaluation.

Abstract: Speed management plays a critical role in road safety; however, conventional speed limits are determined based on geometry and traffic characteristics, with limited consideration of pavement structural condition and surface distress. This study proposes an integrated mechanistic–quantitative framework that links pavement distress and road safety indicators to the selection of speed limits. A flexible pavement section on Highway No. 80 in Iraq is analyzed as a case study. Mechanistic pavement analysis using KENPAVE is employed to estimate critical strains based on field traffic data and Equivalent Single Axle Loads (ESAL). The rate of failure is estimated by comparing the ESAL and the allowable load repetitions. Safety-related constraints are then derived to quantify hydroplaning risk, braking performance through stopping sight distance, and the vertical shock criterion. The results indicate that the existing pavement structure is marginal, with a high probability of fatigue failure and sensitivity to rutting under traffic growth. The integrated safety analysis yields a critical wet-weather speed of approximately 67–70 km/h, while localized settlements exceeding 10 mm require speed reductions to 50–60 km/h to maintain vehicle stability. The proposed framework demonstrates that pavement condition directly influences safe speed and provides a rational basis for safety-oriented speed management.

Abstract: Extensive research has been conducted to develop technologies that enable paratransit systems to operate autonomously, including advanced sensing technologies and associated software. However, there remains a significant gap in research addressing the development of adaptive operational algorithms for such systems in urban environments. Autonomous Shuttles (AS) represent an emerging technology that has gained attention from industry, government, and academia as a novel public transit solution. AS hold the potential to enable Ride-shared Autonomous Mobility on Demand (RAMoD), which can improve accessibility and service equity in transportation-disadvantaged populations across urban and surrounding regions. To address this gap, this study applies an imitation-learning-assisted Deep Reinforcement Learning (DRL) approach to develop a routing method for AS under stochastic and dynamic passenger demand conditions. The proposed framework integrates Generative Adversarial Imitation Learning with Proximal Policy Optimization to enable real-time pickup and drop-off decision-making without centralized re-optimization. The DRL agent was trained over approximately 1.5 million training steps and evaluated across twenty episodes with stochastic passenger generation. Its performance was benchmarked against a deterministic Dial-a-Ride Problem (DARP) solver implemented using Google’s OR-Tools, which employs a Cheapest Insertion heuristic with Local Search refinement. Comparative analysis showed median percentage differences of 37%, –6%, 20%, and 44% in passenger wait time, in-vehicle time, total service time, and episode completion time relative to the DARP baseline. The OR-Tools implementation was selected as a benchmark due to the lack of established step-wise evaluation methods for dynamic routing optimization in simulation environments. These findings demonstrate the potential of learning-based routing policies to support scalable, demand-responsive autonomous mobility services and future smart urban transportation systems.

Abstract: Heating gases to high temperatures is essential for supplying energy to thermal and thermochemical processes. This study presents the optical–thermal design of a mini heliostat field coupled with a tubular solar receiver equipped with second optics, aiming to heat nitrogen to approximately 850 K. The secondary optical system redistributed up to 40% of the incident solar flux from the front to the rear surface of the receiver, improving radial temperature uniformity and significantly reducing thermal gradients along the tube wall. An overall optical efficiency of 65.25% was achieved, accounting for atmospheric attenuation, shading, blocking, and the cosine effect. A coupled computational model was developed by solving the conservation equations of mass, momentum, and energy, with the spatially resolved solar flux distribution obtained via ray tracing used as a thermal boundary condition. The simulation results, validated with an empirical correlation, include solar flux contours, nitrogen temperature distributions, surface temperatures, and heat transfer coefficients. The configuration with a 12 mm vertex spacing between secondary reflectors demonstrated the best thermal performance, reducing the maximum tube surface temperature by 11% and improving radial symmetry, while maintaining nitrogen outlet temperatures near the design target of 850 K. These results confirm the suitability of the system for high-temperature applications such as solar pyrolysis using nitrogen as the heat transfer fluid to deliver the required thermal energy.

Abstract: This paper presents the design, modeling, and numerical validation of a compact artificial magnetic conductor (AMC)–backed flexible UHF RFID tag antenna intended for on-body biomedical and wearable sensing applications. Human tissue proximity typically causes severe detuning, radiation efficiency degradation, and increased specific absorption rate (SAR) for conventional RFID tag antennas. To address these limitations, a miniaturized AMC metasurface based on a modified Jerusalem-cross geometry with meandered and interdigitated features is developed on a high-permittivity biocompatible substrate using CST Studio Software. Full-wave simulations demonstrate that the proposed design, with an ultra-compact footprint of 0.0246 λ2 (32.12 mm × 64.24 mm), functions as an effective shielding element, significantly enhancing the tag antenna gain and reading range by an order of magnitude compared to conventional on-body tags, while simultaneously reducing backward radiation and SAR. The antenna demonstrates robust platform tolerance and excellent isolation from the human body, ensuring high reliability. Fabricated on a thin, flexible, biocompatible, silicon-doped dielectric substrate, this device also functions as an epidermal antenna for on-skin health parameter sampling. This research paves the way for advanced, non-invasive wearable medical devices with superior performance.

Abstract: This paper investigates the feasibility of interior permanent magnet (IPM) rotor for a 1MW-class high-speed permanent magnet synchronous machines (PMSMs) in hybrid propulsion system of electrified aviation. A double-layer IPM machine and a surface-mounted PM (SPM) benchmark machine with Halbach-array PMs, which are typical employed in the aviation applications, are designed using the same design specifications, the same stator, double-three-phase winding layout, physical air-gap length, outer and inner diameters of rotor, and the same materials. The rotor robustness of IPM machine using high-strength iron material has been verified through mechanical strength analysis with outstanding safety factor margin. The electromagnetic performances of IPM and SPM benchmark machines are compared. It is found that the IPM design can achieve similar high torque/power density and high efficiency to the SPM benchmark machine, using 48% less rare-earth PM materials and simpler rotor structure without carbon-fiber sleeve for easy manufacturing. The investigation confirms the feasibility of IPM topology for MW-class high speed aviation propulsion machines for lower cost and more sustainable purposes.

Abstract: This study presents a novel data driven framework for developing airport-specific landing policies and procedures from historical successful landing data. The proposed process, termed the Airport Dependent Landing Procedure (ADLP), is motivated by the fact that airports rely on uniquely tailored approach charts reflecting local operational constraints and environmental conditions. While existing approach charts and landing procedures are primarily designed based on expert knowledge, safety margins, and regulatory conventions, the authors argue that data science and data mining techniques offer a complementary and empirically grounded methodology for extracting operationally meaningful structures directly from historical landing data. In this work, we construct a probabilistic three-dimensional environment from real-world aircraft approach trajectories, capturing spatiotemporal relationships under varying atmospheric conditions during approach. The proposed methodology integrates Adversarial Inverse Reinforcement Learning (AIRL) with Recurrent Proximal Policy Optimization (R-PPO) to establish a foundation for automated landing without pilot intervention. AIRL infers reward functions that are consistent with behaviors exhibited in prior successful landings. Subsequently, R-PPO is employed to learn control policies that satisfy safety constraints related to airspeed, sink rate, and runway alignment. Application of the proposed framework to real approach trajectories at Guam International Airport demonstrates the efficiency and effectiveness of the methodology.

Abstract: Polymer–metal hybrid composites (PMHCs) represent an emerging class of materials that combine the lightweight processability of polymers with the structural and functional advantages of metals. Recent advances in material design and manufacturing have shifted attention from traditional particulate or fibrous reinforcement toward metallic architectures—continuous, architected, or topologically optimized metallic networks intentionally embedded within polymer matrices. These metallic architectures play a key role in defining the composite’s global performance, influencing stiffness, energy absorption, failure mechanisms, and multifunctional properties such as electrical or thermal conductivity. This review examines how the geometry, connectivity, and topology of metallic reinforcements govern mechanical behaviour and functional responses in PMHCs. Emphasis is placed on the interplay between architecture and interface design, including surface modification strategies and mechanical interlocking phenomena. Furthermore, the paper discusses the contribution of additive manufacturing technologies in enabling complex metallic architectures and hybrid processing routes. By integrating structural, interfacial, and manufacturing perspectives, this review aims to establish a comprehensive understanding of the role of metallic architecture in advancing polymer–metal hybrid composites toward multifunctional and design-driven engineering applications.

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.