Abstract: This paper presents a parametric modeling and aerodynamic optimization methodology for the second-stage stator of a multi-stage centrifugal compressor. Based on the geometric configuration of the two-stage components, a flexible parametric template is established for the second-stage stator. Numerical simulations are conducted to analyze the internal flow field and evaluate the performance of the initial design of this compressor, revealing performance deficits such as significant vortex-induced losses and a large outlet circumferential flow angle (-12.138°). To this end, an aerodynamic optimization framework integrating a Kriging surrogate model and a Genetic Algorithm (GA) is applied to the second-stage stator, targeting at the aerodynamic matching optimization under multiple operating conditions. The optimization objectives include maximizing the overall polytropic efficiency of compressor and static pressure ratio of second-stage stator, as well as minimizing the total pressure loss coefficient and the outlet circumferential flow angle of second-stage stator. The results demonstrate that the optimized design achieves a 2.17% improvement in the overall polytropic efficiency and a 12.01% improvement in the static pressure recovery coefficient at the design condition, along with a notable reduction in the outlet circumferential flow angle to 0.663°. Under multi-condition operation, the optimized stator exhibits enhanced the performance stability. The overall polytropic efficiency is improved by 2.06% and the static pressure recovery coefficient is improved by 23.31% at the low-flow condition, confirming the effectiveness of the proposed parametric modeling and sequential optimization approach.

Abstract: Evacuation planning is increasingly challenged by compound hazards in which interacting threats degrade infrastructure, influence human behavior, and destabilize transportation systems. Although agent-based models and dynamic traffic simulations have advanced substantially, much of the evacuation literature remains hazard-specific, case-bound, or difficult to transfer across regions. In parallel, transportation resilience research shows that multi-hazard effects are often non-additive and that cascading infrastructure failures can amplify disruption beyond directly affected areas. These realities motivate the development of evacuation modeling frameworks that are modular, adaptable, and able to represent co-evolving behavioral and network processes under compound conditions. This review synthesizes advances in evacuation agent-based modeling, dynamic traffic assignment, hazard-induced network degradation, and compound disaster research to propose an adaptable compound-hazard evacuation framework integrating three interdependent layers: hazard processes, transportation network dynamics, and agent decision-making. The proposed framework is organized around four principles: (1) modular hazard representation, (2) decoupling behavioral decision logic from hazard physics, (3) dynamic network state evolution, and (4) neighborhood-scale performance metrics. The framework prioritizes planning-relevant, spatially resolved outputs, including neighborhood clearance time, isolation probability, and shelter demand imbalance. By prioritizing modularity, configurability, and policy-aligned metrics, this review bridges the gap between methodological advances in evacuation modeling and the operational needs of local multi-hazard planning.

Abstract: Indonesia's E10 blending mandate presents a strategic opportunity for decarbonization and inclusive rural development, contingent on a robust supply chain integrating smallholder farmers. This study developed a novel supply-chain framework for corn products in Sumenep to facilitate sustainable ethanol production. Methods involved comprehensive data collection, mathematical modeling using the p-median method, and farmer clustering techniques. Findings reveal that Sumenep Regency's substantial corn harvest of 8,475,914.5 tons, yielding 1,271,387.175 tons of kernels, can produce 381,416.1525 liters of bioethanol. By applying clustering supply chain model, the farmers' group profit is Rp 205,693,725,826, while Rp 177,394,823,353 profit for non-clustering model. It increasing profit 16% compared to the model without clustering. This localized production, enabled by a simplified, decentralized supply-chain architecture, significantly enhances national energy security, reduces greenhouse gas emissions, and improves the economic stability of smallholder farmers through equitable value capture and minimized logistical costs. The framework offers a practical, implementable strategy for Indonesia's energy transition, fostering environmental sustainability and inclusive socio-economic development.

Abstract: Static linear lumped circuits (conductances, independent sources, and linear dependent sources, with no storage) can be studied through their boundary behavior: the set of boundary voltage–current pairs consistent with internal circuit laws. Fixing a set of accessible boundary nodes B of size n, and assuming standard well-posedness conditions for modified nodal analysis (MNA), we show that the boundary current injection vector iB depends affinely on the boundary voltage vector vB on an admissible affine set: iB = Yeq vB + i0 for all vB ∈ VB . We then provide a canonical boundary normal form that realizes this law using only indepen- dent current sources and voltage-controlled current sources (VCCS) connected directly to the boundary nodes. The construction is deterministic and idempotent, and it yields a complete classification: two circuits are behaviorally equivalent on the same boundary if and only if their normal-form parameters agree (modulo boundary constraints). A worked example (including a dependent source), an explicit VCCS synthesis list, and an exact numerical spot-check are included.

Abstract: In this paper, we propose an enhanced preamble scheme for the physical random access channel (PRACH) applied to low-altitude integrated sensing and communication (ISAC) systems, aiming to expand the sensing capability of traditional mobile networks with PRACH frames based on ZC sequences. To enable the network to possess target sensing capability before successful terminal access, we transform PRACH from a mere initial access channel into an ISAC system capable of supporting high-speed terminal access and user equipment sensing by introducing a time-frequency orthogonal block structure and orthogonal cover codes (OCCs). Specifically, we first derive the Cramér-Rao lower bound (CRLB) for estimating the distance and velocity of user equipment using OCC-ZC sequences, and establish the evaluation metric for communications named detection probabilities. Then, the ISAC problem is formulated as a multi-objective optimization function. Since the multi-objective optimization problem is non-convex, we propose the NSAG-II algorithm to solve it, simultaneously improving the estimation accuracy of distance and velocity in the sensing aspect and the detection probability in the communication aspect.

Abstract: Estimating Remaining Useful Life (RUL) and predicting bearing faults based on data-driven models have become central components of modern Prognostics and Health Management (PHM) systems. Although deep learning models have demonstrated strong performance under controlled and stationary operating conditions, their reliability in real-world industrial and marine environments is limited. In practice, operating conditions, sensor properties, and degradation mechanisms evolve continuously over time, leading to non-stationary and shifting data distributions that violate the assumptions of conventional static learning approaches. To address these challenges, two research areas have gained increasing attention: Domain Adaptation (DA), which aims to mitigate distribution discrepancies across operating conditions or machines, and Continual Learning (CL), which enables models to learn sequentially while mitigating catastrophic forgetting. However, existing studies often examine these paradigms in isolation, limiting their effectiveness in long-term deployments, where domain shifts and temporal evolution coexist. This paper presents a comprehensive and systematic review of data-driven bearing fault prognosis and RUL prediction under evolving data distributions, adopting the framework of Domain-Adaptive Continual Learning (DACL). By jointly examining the DA and CL methods, this review analyzes how these approaches have been individually and implicitly combined to cope with nonstationarity, knowledge retention, and limited label availability in practical PHM scenarios. We categorised existing methods, highlighted their underlying assumptions and limitations, and critically assessed their applicability to long-term, real-world monitoring systems. Furthermore, key open challenges, including scalability, robustness under sequential domain shifts, uncertainty handling, and plasticity–stability trade-offs, are identified, and research directions are outlined based on the identified limitations and practical deployment requirements of the proposed method. This review aims to establish a structured and critical reference framework for understanding the role of domain-adaptive CL in data-driven prognostics, clarifying current research trends, limitations, and open challenges in evolving data distributions.

Abstract: Modern requirements for critical threads, such as drilling lock threads or running trapezoidal threads of heavy machine tools dictate the need for very durable and at the same time very accurate thread cutters. Conventional thread cutters supplied to the world market have the same profile as the thread for which they are intended. However, for durability and productivity, such tools should have effective geometric parameters of the cutting part, namely: the rake angle and the angle of inclination of the cutting edge. However, there are no known algorithms for profiling such cutters in order to ensure their maximum possible accuracy. This analytical study is specifically designed to identify an algorithm that makes it possible to make highly productive and at the same time highly accurate thread cutters with straight sides of the profile for the manufacture of threads with trapezoidal, triangular and buttress profiles, including for parts made of difficult-to-machine materials.

Abstract: Aiming at the problems of difficult hard constraint enforcement, weak environmental generalization ability in the safe trajectory planning of manipulators in complex environments, a Policy-Guided Model Predictive Path Integral (PG-MPPI) planning framework is proposed. This framework integrates the advantages of reinforcement learning and model predictive control to construct a global prior guidance, local real-time optimization and hard constraint safety assurance: a Constraint-Discounted Soft Actor-Critic (CD-SAC) offline learning policy is designed, which incorporates the configuration-space distance field as a safety guidance term to realize the learning of obstacle avoidance behavior; the offline policy is used to guide the online sampling and optimization of MPPI, improving sampling efficiency and planning quality; a Control Barrier Function (CBF) safety filter is introduced to revise control commands in real time, ensuring the strict satisfaction of constraints. Taking the SIASUN T12B manipulator as the research object, simulation comparison experiments are carried out in multi-obstacle scenarios. The results show that the PG-MPPI algorithm outperforms the comparison algorithms in the success rate of collision-free target reaching, ensure the smoothness and feasibility of the trajectory, and has a good adaptive capacity to dynamic environments, thus providing an efficient solution for the autonomous and safe operation of manipulators.

Abstract: In the state estimation problem for nonlinear systems, the Unscented Kalman Filter (UKF) has gained attention as an algorithm capable of accurate state estimation based on high-fidelity discretization for strongly nonlinear systems. Furthermore, for applying the UKF to continuous-time state-space models, a method employing the Runge-Kutta method in the time-update equation for sigma points has already been proposed to achieve high-precision state estimation. While this method uses high-order numerical approximations, the associated decrease in computational efficiency due to processing time becomes problematic. It is thus unsuitable for state estimation of relatively fast-moving objects, such as autonomous vehicles and drones, which require high sampling frequencies. In this study, to reduce computational load while achieving relatively high estimation accuracy, we newly apply the Adams-Bashforth method to the UKF algorithm. The effectiveness of the proposed method is demonstrated by first explaining a low-dimensional model’s state estimation problem, followed by a comparison of estimation accuracy and computation time in a state estimation simulation for a UAV model of a tandem-rotor drone.

Abstract: The performance of ballasted railway tracks under cyclic loading is a critical issue in urban railway systems, where high traffic frequency and geometric constraints accelerate track degradation, which, in turn, leads to accumulation of plastic deformations that potentially reduce operation efficiency. This study presents a numerical framework for rail track performance assessment based on two complementary modeling approaches: a fully continuous Finite Difference Method (FDM) model, and a hybrid Discrete Element Method–Finite Difference Method (DEM–FDM) model. The continuous FDM simulations are employed to evaluate the global mechanical response of the track support system and to compute conventional stability indicators, including the factor of safety (FS). In parallel, the hybrid DEM–FDM simulations explicitly represent the ballast layer using DEM to capture inter-particle interactions, accumulation of permanent deformation, and particle fragmentation under cyclic loading, while rails, sleepers, sub-ballast, and subgrade are modeled using FDM to describe system-level load transfer. Ballast performance is assessed by linking safety factors obtained from the continuous models with mechanically derived permanent deformation and stress measures extracted from the hybrid simulations. This dual-modeling framework enables a systematic investigation of the influence of ballast layer thickness and material type on deformation accumulation, stress transmission, and granular degradation mechanisms. The results reveal distinct behavioral trends among different ballast materials, showing that increased ballast thickness generally improves track performance, while material-specific degradation mechanisms govern the evolution of permanent deformation under repeated loading. The proposed approach establishes a quantitative bridge between traditional stability-based design metrics and deformation-based performance indicators, providing a rational basis for performance-based evaluation, comparison, and optimization of ballast configurations, through a set of robust numerically derived relationships for railway track design abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

Abstract: A key technological challenge for automotive manufacturers is producing multiple vehicle variants on a single production line. At the body-in-white shop of Magna’s complete vehicle plant in Graz, this is addressed through transportable positioning devices that serve as part carriers and adapters between different products, while ensuring consistent geometric alignment throughout the process. Geometrical deviations in these devices can adversely impact product quality along the entire vehicle assembly chain. This paper presents the development and implementation of two patented use cases: a cyber-physical inspection system, fully operational in serial production and a cyber-physical assembly system, tested successfully in the prototype phase. The first actively mitigates the effects of device deviations in real time, while the second enables on-demand configuration of flexible, advanced positioning devices via precision part matching, effectively preventing systematic deviations. Challenges and insights from both systems are discussed. Four previously introduced building blocks for automating quality control processes are validated and generalized for broad applicability across manufacturing processes and project phases via cross-system comparative analysis: integrated capture of process and product data, automated data analytics, automated decision-making, and autonomous process intervention. This work proposes a validated, scalable framework integrating design and implementation of cyber-physical systems to support zero-defect manufacturing.

Abstract: Hemorrhagic shock remains one of the leading causes of preventable death for both civilian and military trauma. Fluid resuscitation is the primary treatment but requires constant monitoring, particularly for volume non-responsive patients susceptible to fluid overload, pulmonary edema, and other life-threatening conditions. To overcome fluid non-responsiveness, vasoactive drugs or vasopressors can be necessary adjuvants to fluid therapy but require tedious titrations that can be difficult to manage during mass casualty situations. This study developed and evaluated automated closed-loop vasopressor controllers for hemorrhage scenarios. Ten physiological closed-loop controller (PCLC) configurations with different underlying functionalities were tuned to be either more aggressive or conservative to reach target mean arterial pressure. A hardware-in-loop test platform with fluid-pressure responsiveness derived from animal data tested each controller across three different starting pressure scenarios. The platform successfully differentiated controller designs based on performance metrics. While some configurations overshot target and others could not reach target pressure, strong-performing PCLCs consistently reached and maintained target quickly. Three candidate PCLCs outperformed the rest and will be evaluated across wider scenarios to develop a robust controller design. This work accelerates PCLC-driven vasopressor administration development, providing a necessary fluid resuscitation adjuvant for precise hemodynamic management in hemorrhagic trauma.

Abstract: This study presents the Structural–Typological–Value Sensitivity Model (STVSM), a multidimensional framework for evaluating vulnerability in historic buildings where physical fragility cannot be adequately captured through structural indicators alone. While existing approaches primarily prioritize load-bearing behaviour, they often overlook typological discontinuity, spatial fragmentation, and the erosion of architectural and cultural value. STVSM addresses this limitation through three weighted sub-indices: structural vulnerability (SV), typological degradation (TV), and heritage value (HV), each calibrated using expert-derived micro- and macro-level weighting coefficients. Field-based deterioration scores (0–1) are combined with these weights to generate SV, TV, and HV values, which are then integrated into a Conservation Priority Index (CPI). Although conceptually informed by building-scale seismic vulnerability literature, the model does not aim to simulate earthquake performance or replace numerical structural analysis. Instead, it operates as a comparative decision-support framework that incorporates seismic-informed deterioration patterns within a broader, conservation-oriented logic. The model is applied to twenty-five historic buildings across three heritage contexts: traditional houses in Cumalikizik, vernacular dwellings in Balıkesir–Karesi, and nineteenth-century Greek Orthodox churches in Bursa. The results demonstrate that integrating structural condition, typological integrity, and heritage value provides a transparent, repeatable, and scalable basis for conservation prioritization across diverse historic building stocks.

Abstract: The globalization of high-technology supply chains has concentrated design and tech-nological control in advanced economies, limiting the industrial upgrading potential of emerging regions. At the same time, increasing sustainability pressures demand the integration of circular economy principles into production systems. However, existing research rarely integrates supply chain localization strategies, circular value creation mechanisms, and regional capability development within a unified explanatory framework. This study develops a conceptual circular supply chain framework for the sustainable localization of high-technology unmanned aerial vehicle (UAV) systems in emerging economies. Drawing on localization theory, circular supply chain design, and capability accumulation literature, the framework conceptualizes localization as a systemic config-uration composed of three interdependent structural dimensions: (1) core technological supply chain processes, (2) transversal circular value creation mechanisms, and (3) re-gional capability accumulation pathways. Unlike linear acquisition models, the proposed framework embeds modularity, re-pairability, remanufacturing, and lifecycle management within the supply chain's op-erational architecture. This integration enables simultaneous outcomes in environmental sustainability, economic resilience, and social upgrading. The framework further iden-tifies boundary conditions and aligns structurally with Sustainable Development Goals related to responsible production, industrial innovation, and climate action.

Abstract: A vertiport is a key supporting infrastructure for providing takeoff and landing facilities for aircraft within the rapidly growing Advanced Air Mobility (AAM) ecosystem, particularly in urban areas or Urban Air Mobility (UAM) which faces significant challenges such as dense urban obstacles and limited available space, urging improvement in the vertiport space usage. The configuration variation on a two-pad vertiport is modeled and simulated using AnyLogic Discrete Event Simulation (DES), varying number of stands, operational concepts, and aircraft turnaround times, while considering Linear and Satellite topology to analyze their influence on the vertiport output capacity. The results indicate that each configuration responds differently with turnaround time variation, showing delays and capacity reduction for shorter time. Increasing number of stands provides a significant capacity benefit for longer turnaround times across all simulated configurations, with capacity gains ranging from 100–120%. Overall, the linear–independent vertiport achieves the highest total capacity of 163 and 229 aircraft for the 4 and 6 stand variants, respectively, while the satellite vertiport attains the highest total capacity of 266 and 299 aircraft for the 8 and 10 stand variants. However, considering urban space usage, the satellite vertiport benefits in lower stands number while linear vertiport benefits in higher stands number.

Abstract: The rapid expansion of Unmanned Aircraft Systems (UAS) operations has created an urgent need for scalable strategic conflict resolution methods within the U-space framework. When requested 4D flight plans overlap with previously authorised ones, the Flight Authorisation Service (FAS) denies the request and can provide the UAS operator with an alternative, conflict-free route. While traditional pathfinding algorithms ensure optimal routes, their computational cost creates a critical bottleneck during the flight activation phase or emergency missions, which demand near-instantaneous responses. To address this, we propose a three-stage framework. First, an Octree spatial partitioning discretises the airspace to identify occupied cells. Second, the A* algorithm is implemented to establish an optimal reference route. Finally, a standard Deep Reinforcement Learning (DRL) model, trained on realistic PX4 Simulator trajectories and using a well-adjusted reward function, generates alternative paths that optimise distance and energy. Our results demonstrate that this DRL architecture achieves near-optimal routing behaviour. Crucially, it reduces computation time by several orders of magnitude compared to A*, solving complex conflicts in milliseconds rather than seconds. We conclude that a simple, well-tuned DRL architecture overcomes latency limitations of classical pathfinding while achieving optimal results, ensuring rapid, safe, and efficient conflict resolution for high-density U-space.

Abstract: To address the challenges of unstable target localization and poor multi-module coordination in automated green pepper harvesting—caused by occlusions from branches and leaves, as well as varying lighting conditions—this paper presents the design and implementation of a modular robotic picking system. At the perception level, the system integrates a YOLOv8 detector with a RealSense D435i camera to identify and locate the calyx–ectocarp junctions of green peppers. A multi-target tracking with filtering algorithm is proposed, combining IoU-based association, Mahalanobis-distance-based matching, the Hungarian algorithm, Kalman filtering, and single exponential smoothing. This algorithm suppresses depth noise and trajectory jitter, thereby enhancing the stability and accuracy of 3D localization. At the control and execution level, a depth-first picking sequence strategy with ID freeze-state management is implemented within a multithreaded software–hardware co-design architecture. This approach avoids task conflicts and duplicate operations while supporting continuous multi-fruit harvesting. Field experiments under natural outdoor lighting and varying occlusion levels demon-strate that the proposed system achieves recognition rates of 91.57% and 80.29%, and harvesting success rates of 82.85% and 77.68% for non-occluded and lightly occluded fruits, respectively. The average picking cycle per pepper fruit is 9.8 s. This system provides an effective technical solution for addressing stability control challenges in the automated harvesting process of green peppers.

Abstract: Laser polishing (LP) is widely used to improve the surface quality of additively manufactured (AM) metals; however, its behaviour within deep or narrow internal geometries remains insufficiently understood. Many high-performance AM components including biomedical implants, turbine cooling channels, and metal microfluidic devices contain confined internal features where heat-transfer conditions differ substantially from those at open surfaces. In this study, LPBF-fabricated 316L stainless steel specimens containing ~10 mm deep slots with widths ranging from 1 to 5 mm were laser polished to examine how internal geometry influences microstructural evolution and mechanical response. A clear depth-dependent microhardness gradient was observed along the slot wall, with hardness decreasing from approximately 270 HV in the lower region to about 210 HV toward the slot opening. The gradient was more pronounced in narrower slots. Microstructural characterization revealed finer grains near the slot base and progressively coarser grains toward the upper regions. These variations are consistent with differences in conductive coupling to the surrounding bulk substrate along the slot depth, which influence local cooling conditions during solidification. The results provide quantitative evidence that internal geometric boundary conditions can affect microstructure and hardness development during laser polishing, even when nominal processing parameters are held constant. This work highlights the importance of considering feature geometry in the post-processing of AM components containing confined internal structures and offers guidance for achieving more predictable local mechanical performance.

Abstract: Under high C-rate and wide-temperature conditions, independently estimated SOC and SOH often diverge due to decoupled model dynamics, resulting in inaccurate power boundary calculations. This affects power limiting, thermal safety, and fast-charging strategies. To solve this, a unified online estimation framework is proposed for SOC, SOH, and power capacity under voltage and thermal constraints. It integrates a state-space model based on an equivalent circuit, combining SOC, polarization voltage, internal resistance, and capacity degradation, with temperature-dependent parameter evolution to capture coupling with aging. A dual extended Kalman filter enables collaborative SOC–SOH estimation, while lightweight machine learning modules correct internal resistance and polarization dynamics to reduce mismatch under extreme conditions. Physical constraint projections embed voltage, temperature, and power limits into the estimation loop, mitigating noise amplification and drift. Based on consistent estimates, the SOP boundary is computed online to support control decisions. Validation across six temperatures (−20 °C to 55 °C) and five C-rates (0.2C to 6C), using bench, HIL, and pack-level tests over 120+ hours, shows SOC RMSE <1.6%, SOH error <2.5%, and SOP hit rate >95% within 10 seconds. Under noise and parameter disturbances, error growth is reduced by ~25% versus baselines. These results confirm improved SOC–SOH consistency and boundary tracking, with computational cost suitable for embedded deployment.

Abstract: Conservation of architectural heritage structures (AHS) requires compatible built her-itage materials with aesthetic, physical, chemical, and mechanical properties similar to those of the original materials. In recent years, however, urbanization, land reclamation, depletion of stone quarries, anti-mining and anti-quarrying legislation have limited access to original heritage materials. In the absence of the original heritage materials, ce-ment-based alternatives have been developed and widely applied for conservation. Major drawbacks of concrete- and cement-based materials include their large carbon footprint and long-term damage to the original rock or substrate, due to inadvertent promotion of salt efflorescence. This study systematically reviewed geopolymer-based materials as a sustainable, greener alternative to concrete- and cement-based materials for tuff- and coral rock-built heritage structures. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were implemented for the literature review, using Scopus, Web of Science (WoS), and Google Scholar (supplementary) as databases, between 2013 and 2024. Inaccessible items, non-English, reviews, conference proceedings, book chapters, errata, and papers unrelated to geopolymers, tuff, and coral rock were excluded, resulting in a total of 103 articles. These works were classified into geopolymers (34 arti-cles), tuff-built heritage structures (60 articles), and coral rock-built heritage structures (9 articles). This review included 103 items in the qualitative analysis; however, only 34 arti-cles contained meaningful data for content analysis. These 34 articles were categorized in terms of the (i) main precursors; that is, metakaolin, fly ash, slag, and pyroclastic materi-als (i.e., pumice, volcanic ash, and volcanic soil), ceramic, others (i.e., tuff waste, silica fume, and mine wastes), (ii) formulations (i.e., precursors, activators, admixtures, and ag-gregates), and (iii) compressive strength. Furthermore, critical factors for compatibility were reviewed and classified into aesthetics (e.g., color, presence of efflorescence, and tex-ture) and physical, chemical, and mechanical properties. This review also explored recent applications of geopolymers in heritage structures, indicating that geopolymers are typi-cally used as repair mortar and consolidants. Finally, a bibliometric analysis was con-ducted to evaluate research trends on geopolymers, including a critical assessment of their aesthetic compatibility with heritage structures in the Philippines built with volcanic tuff and coral rock.