Abstract: The mobile industry has experienced long-run changes in its knowledge structure, including identifiable transition points observable through meaning-based analysis. Using abstracts from 86,674 mobile-industry publications published between 2005 and 2024, we embed documents with SPECTER2, build year-specific embedding distributions, and derive knowledge regimes by combining change-point detection with inter-year distribution distances. We then extract regime-specific topics via clustering and reconstruct topic lineages by aligning topic similarities to classify inheritance, differentiation, convergence, and disappearance. The analysis delineates three regimes spanning 2005 to 2012, 2013 to 2019, and 2020 to 2024, with pronounced transitions around 2012 to 2013 and 2019 to 2020. Regime 1 centers on foundational technologies such as wireless communication, power, sensors, and reliability. Regime 2 expands toward platforms, apps, and data analytics alongside cross-domain convergence. Regime 3 is characterized by strengthened 5G operations and data-driven services, together with the independent rise of policy, governance, and regulation topics. Transitions reflect recombination built on inherited knowledge rather than abrupt replacement, and post-transition topics display distinct growth typologies by network position and growth pattern. By integrating embedding-based change-point detection with topic-lineage reconstruction, we provide a reproducible account of regime transitions and quantitative evidence to inform the timing of corporate R&D, standard and platform strategies, and policy and regulatory design.

Abstract: This paper presents a comprehensive diagnostic framework for electrical machines, based on the application of artificial neural networks (ANNs) for the analysis of electrical and vibration signals. The proposed method leverages deep learning architectures to automatically extract informative features and achieve high fault classification accuracy. The framework integrates signal pre-processing, neural network training, and a condition evaluation module, enabling the implementation of a predictive maintenance system suitable for industrial applications. A multi-sensor diagnostic system is proposed, combining CNN-LSTM architectures with a graph neural network (GNN) for correlational analysis of currents, vibrations, and thermal parameters. This approach allows early detection of inter-turn short circuits and bearing faults, improving diagnostic accuracy by 7–12% compared to existing state-of-the-art methods. The framework demonstrates robustness under varying operating conditions, including transient and self-excitation regimes, and provides physically interpretable results, bridging the gap between data-driven and physics-informed diagnostics.

Abstract: As service robots increasingly enter public buildings such as hospitals and offices, human-robot sharing space has emerged as a pivotal topic in architectural design field, yet its relevant theoretical framework remains underdeveloped and incomplete. Existing frameworks—including Human-Robot Interaction (HRI), Human-Robot Collaboration (HRC), and Human-Robot Coexistence—have advanced research on interaction, coordination, and safety, but most regard the built environment as a passive backdrop, overlooking its active design value. This review retrieved literatures from 2000 to 2026 across four databases (Web of Science, Scopus, IEEE Xplore, and ScienceDirect) and analyzed 183 core publications using CiteSpace, systematically synthesizing the interdisciplinary knowledge in this field. The study introduces "Human-Robot Sharing Space (HRSS)" as an independent conceptual framework, repositioning the built environment from an interactive background to a core design variable while clarifying its boundaries with other traditional frameworks. Through bibliometric analysis, it reveals the field’s evolutionary trajectory from basic technical exploration to scenario-specific refinement. Finally, five systematic gaps in current research are identified: interdisciplinary theoretical integration, transferability to real-world scenarios, multidimensional evaluation indicators, coverage of architectural typology, and longitudinal empirical studies. This review bridges the gap between robotic technology and architectural design needs, providing a theoretical foundation for constructing an environment-centric, scale-inclusive, and practical design framework for HRSS.

Abstract: In this study, we developed an improved electrical impedance method for measuring oil film thickness with a correction for surface roughness effects. Statistical analysis of the oil film thickness distribution revealed that rough surfaces exhibit higher capacitance values than those predicted by the ideal parallel-plate model, despite having the same mean film thickness. Consequently, a corresponding roughness correction formula was derived. The accuracy of the method was verified in ball-on-disc type apparatus using balls with a rough surface. The corrected oil film thickness agreed more closely with the Hamrock-Dowson equation and with optical interferometry measurements than did the uncorrected result. These outcomes confirm that oil film thickness can be estimated considering surface roughness. The technique is therefore expected to facilitate the optimization of lubrication conditions and enable more reliable bearing-life prediction.

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. A decision-grade pipeline is presented that converts observable environmental signals into (i) spatial prioritization 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 prioritization surface integrating land, water, and hydrologic dimensions. This prioritization 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 (minimum 5% royalty case; Δ = −US$71.9 million vs baseline; modeled royalty PV = US$93.8 million), US$503.0 million under Exp. 24.675 (Δ = +US$95.0 million), and US$510.3 million under Law No. 8904 (Δ = +US$102.3 million). Royalty-rate sensitivity cases for Exp. 24.717 yield deterministic policy-adjusted net PV of US$242.3 million (10%) and US$148.5 million (15%). 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 (5% royalty case; P(>US$400 million) = 0.3542; P(>US$500 million) = 0.0153), with further reductions at higher take-rates (10%: P(>US$400 million) = 0.0375; P(>US$500 million) = 0.0007; 15%: P(>US$400 million) = 0.0028; P(>US$500 million) = 0.0000), while non-mining pathways shift the distribution upward. The results support 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: CubeSats are used as a platform in modern space missions due to their standardized form factor, reduced development cost, and shortened launch timelines. Earth observation, space weather monitoring and even re-entry applications make use of the CubeSat standard. Despite their advantages, CubeSats are constrained by limited onboard resources, with electrical power availability being one of the most critical bottlenecks. This work presents a dynamic, hybrid offline/online task scheduling and power management algorithm for a re-entry CubeSat, combining pre-computed schedules with real-time adaptation to changing flight conditions. The algorithm employs a heuristic-based approach, ranking tasks by parameters including priority, execution delay, duration, and power consumption. It adapts to varying flight conditions and system failures. In critical battery State of Charge (SoC) scenarios, only high-priority tasks above a defined threshold are executed, conserving power. A simulation suite was developed to evaluate performance under realistic mission profiles and stress tests with high loads and numerous tasks. Metrics included average and maximum task delay and average power consumption. Results show that appropriate heuristic weight selection can yield significant improvements in reliability and efficiency. The proposed algorithm offers a flexible, scalable solution for CubeSat power management, capable of maintaining operational reliability under dynamic conditions.

Abstract: Telemedicine, driven by the Internet of Things (IoT) and next-generation mobile networks, is essential for managing cardiovascular diseases, where hypertension remains the primary risk factor. In preclinical research, rabbits are superior biological models compared to rodents due to their human-like lipid metabolism. However, conventional blood pressure monitoring in this species is hindered by significant limitations: existing systems are non-portable, lack real-time capabilities, and often necessitate terminal procedures (euthanasia). To address these challenges, this study presents a portable, minimally invasive monitoring system utilizing a pressure transducer in the central auricular artery. The device integrates IoT technology for digital signal processing and seamless wireless data transmission to cloud platforms. This development enables continuous, real-time hemodynamic tracking throughout the experimental period without requiring permanent tethering to desktop hardware. By reducing invasiveness and enhancing data mobility, this system provides a robust framework for the preclinical evaluation of antihypertensive agents and cardiovascular mechanisms, bridging the gap between edge computing and remote clinical diagnostics.

Abstract: In recent years, artificial intelligence has been systematically integrated into public environmental decision-making. It increasingly influences risk classification, the distribution of resources, and the exercise of regulatory authority. While policy attention often focuses on predictive performance and ethical principles, less scrutiny has been directed toward the institutional conditions under which algorithmic outputs acquire decision relevance. This policy review addresses that gap by framing environmental artificial intelligence as decision-making infrastructure rather than as neutral analytical software. It introduces the concept of algorithmic sustainability, defined not as a technical property of algorithms but as a governance condition that aligns lifecycle environmental impacts, enforceable accountability, and procedural legitimacy. Drawing on international policy frameworks and regulatory developments, the review shows how current governance instruments insufficiently integrate lifecycle environmental footprints into decision justification. To operationalize algorithmic sustainability, this paper proposes environmental algorithmic impact assessment as a gatekeeping and renewal mechanism for artificial intelligence used in environmental governance. The review concludes that aligning algorithmic deployment with sustainability and the rule of law depends on institutional design choices made before and during system use rather than on technical optimization alone.

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.