Abstract: Documentation of immovable cultural heritage is a fundamental prerequisite for its con-servation, restoration, and sustainable management. Recent advances in geospatial tech-nologies have significantly improved the accuracy, efficiency, and completeness of spatial data acquisition for historic structures. This study evaluates the contribution of terrestrial laser scanning (TLS) and close-range photogrammetry based on unmanned aerial vehi-cles (UAVs) to the engineering and architectural documentation of immovable cultural heritage. The Church of St. Petka (Sitovo village, Bulgaria), a 19th-century stone masonry monument, is used as a case study. High-density point clouds were generated using TLS and UAV-based photogrammetry and were georeferenced through classical surveying methods. The resulting datasets were assessed in terms of geometric accuracy, level of de-tail, and applicability for architectural documentation and conservation tasks. Accuracy evaluation based on measured control distances indicates a mean squared error below 1 cm for both methods. The results demonstrate that TLS provides superior precision and reliability for interior documentation, while UAV-based photogrammetry is particularly effective for capturing roof structures and inaccessible exterior elements. The integration of both technologies enables the creation of accurate 3D models and GIS-ready spatial prod-ucts, supporting informed decision-making in cultural heritage conservation.

Abstract: To analyse impact of levee axis adjustment on flow variation in the Xinsha Island which is located in the middle segment of the Fuchun river waterway in Fuyang, Hangzhou, a two-dimensional river flow model was constructed. In the model steady flow with different return periods and unsteady flow in 20-year period were simulated. Consistent outcomes were obtained under steady and unsteady flow. Results indicated that after the levee axis is adjusted, the longer the return periods, the higher the water level in the southern waterway, with a maximum increase of 0.183 m. Conversely, the northern waterway exhibits a more pronounced water level decrease, with a maxi-mum reduction of 0.128 m. The flow velocity of the southern waterway slows down, and the flow velocity of the northern waterway increases. After the levee axis is ad-justed, the flow diversion capacity of the north waterway is effectively enhanced, thereby benefiting flood regulation. These findings provide a sound theoretical basis and well-founded recommendations for adjusting levee axis position and enhancing flood resilience in the Xinsha Island area of the Fuchun River.

Abstract: The strategy developed to carry out a scaled test program aimed at reproducing the behavior of skin heat exchangers to alleviate the heat dissipation requirements in future hybrid electric propulsion regional aircraft is presented. The test program is intended to reproduce, as best possible, the conditions faced by the skin heat exchanger on a predefined nominal cruise flight operation, while conducting the tests in a wind tunnel operating at low velocities and near standard atmospheric conditions. For that purpose, dimensional analysis is used to establish the best geometrical scale and approach flow conditions in the wind tunnel test program. The validation of the strategy is achieved by comparing dimensionless parameters characterizing the turbulent heat transfer process taking place at the skin heat exchanger/airflow interface surface in the flight and wind tunnel environments, by using CFD analysis based on RANS turbulence modeling. The comparison reveals that the adopted wind tunnel strategy is indeed capable of reproducing the heat transfer process taking place in the flight environment, thus paving the way to achieve mid TLR validation of the skin heat exchanger technology.

Abstract: In public DC fast-charging scenarios, protocol inconsistencies, current-limiting variations, and communication anomalies often lead to handshake failures, current oscillations, voltage overshoot, and delayed fault recovery. Under high-power conditions, mishandling these issues can cause prolonged high-temperature, high-stress battery operation, elevating safety risks. To address this, a fast-charging safety framework is proposed, integrating hierarchical control, fault diagnostics, and staged recovery for high-voltage battery systems. A charging state machine is designed to cover phases such as handshake, pre-charge, CC/CV transition, derating, disconnection, and recovery. Transition nodes include consistency checks to handle packet loss, timing errors, and abnormal responses. Charging current is generated through a constrained optimization model incorporating cell voltage, temperature rise, predicted power limits, protection boundaries, equipment constraints, and diagnostics-based disconnection triggers. The system enables smooth, recoverable current control and active fault response. Tests across 3,000 sessions show a 38% drop in interruption rate, recovery time cut from 6.5 s to 2.1 s, voltage overshoot reduced by 45%, and peak temperature rise lowered by 0.8–1.3 °C. This validates the framework’s effectiveness for safe, stable fast charging in complex, interoperable networks.

Abstract: This study presents a techno-economic analysis of deploying distributed energy resources (DERs), specifically photovoltaic (PV), battery energy storage systems (BESS) and electric vehicles (EVs), in apartment buildings configured as Virtual Power Plants (VPPs). Utiliz-ing cooperative game theory, the research models strategic collaboration between apart-ment residents (demand side) and utility operators (plant side) to maximize energy effi-ciency and economic returns. The VPP structure is analysed over a 15-year life cycle, in-corporating net present value (NPV), payback period (PBP), and government subsidy im-pacts. A cooperative game framework is applied using the Shapley value to ensure fair profit allocation based on each party’s contribution. Results indicate improved self-sufficiency, peak load reduction, and mutual financial benefits. Scenario analyses show that government subsidies to the plant side significantly increase the likelihood of successful cooperation, while declining DER costs enhance the VPP’s economic viability. The findings demonstrate that apartments configured as VPPs achieve strong economic viability (39% ROI, 10.5-year payback) and operational performance (70% self-sufficiency, 40% peak reduction) when grid arbitrage is enabled and moderate government subsidies (35% PV, 45% BESS) are provided. This research provides a replicable model for urban en-ergy planning and policy development, promoting sustainable energy transitions through shared DER infrastructure and cooperative stakeholder engagement.

Abstract: The global urgency to mitigate climate change has intensified the development of Carbon Capture, Utilization and Storage (CCUS) technologies. A critical step in CCUS is the safe and efficient pipeline transport of supercritical CO₂ (sCO₂), where flow dynamics are strongly influenced by phase change phenomena under transient heat transfer or depressurization conditions. Indeed, pressure disturbances, such as leaks or rapid decompression events, can induce vaporization and condensation, processes further complicated by the inevitable presence of impurities (e.g., N₂,CH₄,Ar) originating from different conditions at sources. These impurities not only shift thermodynamic boundaries but also alter the kinetics of phase transitions, directly impacting pipeline safety and design. In this study, we investigate the effect of impurities on leakage mass flow rate, and decompression waves in sCO₂ pipeline transport through computational fluid dynamics (CFD) simulations, benchmarked against experimental data. A real-fluid model (RFM) implemented in the CONVERGE CFD solver is employed for these two-phase simulations, where a tabulation-based approach ensures accurate representation of thermodynamic and transport properties across multiphase regimes. Simulations are performed for varying impurity concentrations, enabling systematic evaluation of their influence on flow rate, and decompression wave propagation and associated flow variables, such as temperature. The results demonstrate strong agreement with experimental observations while providing insights into impurity-driven phase change behavior. The study investigates the effect of outlet geometry, dimensions, and role of Equation of State as well. CPA shows a better fit to the experimental results compared to PR and PC-SAFT for the simulations of supercritical CO₂. It is found that for nozzle geometry where there is smooth change in cross-section area, the simulations prediction were quite close to experiment. However, for the case of orifice venting where there is sharp change in cross-section area, the simulations under predict the leakage mass flow rate, implying the influence of head loss due to geometry. Finally, the feasibility of simulating a 50 km industrial pipeline transporting sCO₂ was investigated. The role of venting towers and gravity prove to be predominant in this specific case.

Abstract:

In the Philippine agricultural setup, pre-harvest cacao (Theobroma cacao) fruits are wrapped with low-density polyethylene (LDPE) for moisture retention and damage protection. Responding to the growing concern for its waste volume and scarcity of treatment, this research explores the co-hydrothermal carbonization (co-HTC) of cacao shells (CS) and LDPE as a method to convert agricultural waste with plastic into hydrochar of potential energy applications. Thus, observations on the thermal, physicochemical, and morphological changes from feedstocks to hydrochar are carried out. Optimal conditions of 200 °C for 60 minutes resulted in hydrochar with 21.11 MJ/kg and appreciable thermal properties. SEM micrographs show rough and porous structures of hydrochar powder and presence of cracks on oversized LDPE film, while EDX analysis reveals C, K, Ca, and Zn metals that affects chemical pathway. FTIR analysis further supports chemical synergy by preservation of functional groups innate from both parent materials, as well as relative LDPE degradation due to chain scissoring and oxidative reactions. Kinetic and thermal evolutions are also investigated to reveal influence of pretreatment to the stability of cacao shells-dominated hydrochar and the effectivity of biomass integration to facilitate relatively easier degradation of LDPE. The findings support co-HTC as a viable technology to enhance the circular economy by valorizing LDPE and cacao shells while promoting energy recovery.

Abstract: Based on a spatial clustering and partitioned stacking ensemble model, this study addresses the limitations of traditional geoweighting regression in capturing nonlinear location premiums and submarket heterogeneity within urban real estate markets. It proposes a two - stage modeling framework: “spatial clustering → partitioned differentiated stacking ensemble.” Using long - term multi - source transaction data for Beijing's secondary housing market, the study divides the market into three spatially heterogeneous submarkets: core, near - suburban, and far - suburban. Stacked ensemble models based on ElasticNet, XGBoost, LightGBM, and Random Forest are constructed within each submarket.Factor analysis extracts interpretable common factors, which are combined with Lasso and SHAP for feature selection and impact mechanism analysis.Results indicate that the zoned stacking model performs exceptionally well across all three submarkets, achieving an R² of 0.916 in the core urban area. Significant nonlinear location premiums exist within the core urban area.The multi - level interpretability framework reveals the differentiated effects of location and scale factors across different submarkets.This study advances from “global modeling” to “spatial zoning + adaptive ensemble,” providing a viable tool for refined valuation and risk management in highly heterogeneous markets.

Abstract: This study describes the development and mechanistic analysis of a coaxial jet antisolvent process for the continuous production of nanocarriers. A single experimental platform was used to generate both curcumin-based nanoparticles and nanoliposomes, enabling direct comparison of how mixing regime and formulation variables influence product characteristics. Fluid-dynamic behavior was first characterized using tracer and micromixing experiments, revealing a strong dependence of mixing time and composition gradients on flow conditions. Nanoparticles and liposomes obtained under optimized conditions exhibited submicron sizes and controlled polydispersity. To rationalize these observations, a preliminary computational framework was implemented, combining Reynolds-averaged computational fluid dynamics with a population balance formulation solved by the method of moments. The model provided spatially resolved insight into solvent exchange, supersaturation development, and nucleation–growth dynamics, offering qualitative agreement with experimental trends. Although simplified, the modeling approach establishes the basis for future extensions toward full population-balance distribution simulations capable of predicting complete particle size distributions. Overall, the coaxial jet mixer emerges as a versatile and informative tool for continuous nanocarrier production and for advancing a rational, model-assisted design of pharmaceutical nano-systems.

Abstract: Hyperspectral image (HSI) analysis plays a central role in remote sensing tasks requiring fine-grained material discrimination, vegetation health assessment, and post-disturbance monitoring. Yet, the high dimensionality and strong spectral redundancy in HSIs often reduce the efficiency and reliability of machine learning models. These challenges are especially important in wildfire science and prescribed-fire monitoring, where spectral responses vary due to burn severity, char deposition, canopy structure, and early vegetation recovery. Benchmark datasets such as Indian Pines and Pavia University provide controlled environments for algorithm evaluation, but real-world post-fire forest conditions pose additional complexity. This study presents a unified and comprehensive evaluation of four band-selection strategies: Principal Component Analysis (PCA), Spatial–Spectral Edge Preservation (SSEP), Spectral-Redundancy Penalized Attention (SRPA), and a Deep Reinforcement Learning (DRL)–based selector. These strategies are combined with classical machine learning and deep learning classifiers: Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and 3D Convolutional Neural Networks (3D-CNN). The full pipeline includes exploratory data analysis, preprocessing, patch-based spatial–spectral modeling, consistent train–validation protocols, and multi-dataset evaluation across Indian Pines, Pavia University, and a new custom VNIR hyperspectral dataset collected after prescribed burns at the Lubrecht Experimental Forest in Montana, USA. By systematically comparing statistical, edge-aware, attention-guided, and reinforcement-learning-based band-selection strategies, this work identifies compact yet informative spectral subsets that enhance classification performance while reducing computational cost. Importantly, the inclusion of the Montana prescribed-burn dataset provides a unique real-world testbed for understanding band-selection behavior in fire-affected forest environments. Overall, this study contributes a generalizable and extensible framework for HSI dimensionality reduction and classification, laying the groundwork for future applications in wildfire assessment, vegetation recovery monitoring, and remote sensing.

Abstract: This review paper examines innovative urban design strategies for sustainable noise management through a structured analysis framed by ten guiding questions. It begins with an overview of conventional noise assessment technologies and progresses to advanced mitigation approaches. Core principles of sustainable urban design are explored, alongside evaluations of urban and transportation planning, traffic-reduction measures, green infrastructure, and resilient architectural strategies. Material innovations and modern noise-control technologies are presented as complementary solutions. Community-based methods, including citizen science and participatory planning, are highlighted for fostering inclusive governance. The discussion concludes by addressing key challenges and future directions, underscoring interdisciplinary collaboration to transform urban noise pollution into opportunities for healthier, more livable cities.

Abstract: The Global Navigation Satellite System (GNSS) has emerged as a backbone of modern civilisation, industry, and society. Degradations and disruptions of the GNSS Positioning, Navigation, and Timing (PNT) service performance are caused by natural and adversarial sources. The ionospheric effects form the principal single class of the GNSS PNT performance degradation causes. Traditional GNSS ionospheric correction models appear unable to resolve the problem for their global nature, and the intrinsic lack of agility and flexibility. Here we contribute to the case with the proposal of concept and methodology for tailored GNSS ionospheric correction model development in support of GNSS resilience development, based on: (i) a massive dataset of long-term (annual) GNSS-derived total electron content TEC observations, as target variable (ii) a massive dataset of geomagnetic field density components, as predictors, and (iii) utilisation of statistical/machine learning predictive model development methods. The proposed approach emerges as a component of the previously introduced architecture-agnostic Ambient-Aware Application-Aligned (AA2) GNSS PNT concept, introducing the GNSS positioning environment situation awareness. Proposed concept and methodology is successfully demonstrated in the case of tailored GNSS ionospheric correction model development using the R environment for statistical computing in the case-scenario of mid-latitude single-frequency commercial-grade GNSS rover.

Abstract: Amid U.S. housing and labor shortages, Appalachia needs solutions that strengthen communities. This study examines how establishing an industrialized off-site construction (IOC) ecosystem can address regional housing, workforce, and construction challenges. From March–June 2024, we conducted seven participatory design workshops across Appalachia (n=129). Using a standardized prompt sequence (status quo, opportunities, IOC solutions), affinity clustering, and PICK chart prioritization, participants identified needs, capacities, and gaps, then ranked actions to advance IOC. Validity was tested through independent re-clustering with a shared codebook; inter-rater agreement was substantial (weighted κ=0.80). Five cross-cutting levers emerged: Education & Training; Policy & Regulation; Marketing & Awareness; Financing & Funding; and Technology & Innovation. Marketing & Awareness were consistently viewed as high-impact and easier to implement near term; Education & Training were high-impact but resource-intensive; Policy and Financing were impactful yet harder to shift; Technology & Innovation should be introduced incrementally to fit tradition-bound industry and regional norms. The resulting roadmap emphasizes near-term pilots, targeted talent pipelines, permitting/code alignment, and fit-for-purpose capital. The main contribution is a globally reproducible participatory protocol with transparent prompts, a shared codebook, independent re-clustering, and reliability metrics that enable replication and benchmarking across regions.

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.