Abstract: Maintaining the integrity of waterproof strata (WPS) between mine workings and overlying aquifers is critical, because water-conducting cracks (WCC) may cause mine flooding and surface subsidence. In the Upper-Kama potash deposit, the WPS is a 50-140 m thick stratified sequence of evaporites and clays overlying mined-out cham-bers. Under long-term loading, salt rocks tend to creep, soften, and localize damage, which can cause WPS failure. In this paper the Concrete damage-plasticity model, supplemented by the N2PC-MCT viscoplastic creep model, is applied to simulate WCC initiation and evolution in the Upper-Kama WPS. Model parameters are obtained from published laboratory tests, in-cluding uniaxial and triaxial compression and tension, and then validated using ob-served ground-surface subsidence. A plane-strain finite-element model incorporates stratified lithology, interface elements between layers, and stepwise excavation. Long-term simulations up to 50 years investigate two operational scenarios: with and without backfilling. The calibrated model reproduces the main stages of surface subsidence and chamber closure. Without backfilling, simulations indicate that tensile damage localizes mainly in a stiff central salt layer of the WPS. Most cracks appear approximately between 33 and 37 years after the beginning of mining. With backfill, tensile crack propagation stops and damage remains stable. A hypothetical homogeneous WPS case confirms that the observed central-layer cracking is associated with stiffness contrasts and composite bending in the stratified system. An approximate analytical multilayer beam solution, based on energy minimization, predicts bending stress concentration in stiff intermediate layers and is consistent with the numerical stress distribution. The combined numerical and analytical results clarify the mechanisms of long-term WCC initiation in stratified WPS and may be used for hazard assessment and planning of mitigation measures, including backfilling and focused monitoring of stiff central layers.

Abstract: Monitoring the progression of large wildfires in near-real-time is essential for active-fire situational awareness and emergency response management. Current satellite-based wildfire monitoring systems face a trade-off between temporal and spatial resolution: geostationary satellites such as GOES offer frequent (~5 minutes) but coarse observations (~2 km), while low earth orbit (LEO) instruments such as VIIRS provide fine spatial detail (∼375 m) with limited temporal coverage (twice per day). To bridge this gap, this study introduces a deep learning (DL) approach that enables near real-time, high-resolution wildfire monitoring using GOES data. The proposed approach consists of two main steps: a segmentation step to distinguish active fire regions from background areas and a regression step to estimate the active fire pixels brightness temperature (BT) across a region of interest. The output of these steps is combined to generate a high-resolution fire location and BT maps. To train the DL model, multi-spectral GOES inputs are paired with VIIRS-derived fire observations from several wildfires across the United States. Spatial consistency between GOES and VIIRS data is achieved through parallax correction, reprojection, resampling, and per-image normalization. Ablation studies are performed to demonstrate the impact of different assumptions (e.g., background values in the VIIRS ground truth) and strategies (e.g., loss functions) throughout the development process. The results show that the proposed DL approach effectively enhances GOES imagery, improving both BT estimation and fire boundary localization. Overall, the proposed method offers a practical and scalable solution for wildfire boundary detection and thermal mapping using existing satellite systems.

Abstract: The significant heat generation during refueling of hydrogen pressure tanks might exceed the permissible 85 °C temperature limit for type IV tanks consisting of a thermoplastic liner and a carbon fiber composite overwrap. Common countermeasures like hydrogen pre-cooling or long filling times are energy and time consuming, hence in this paper passive means through thermally better suited materials are examined. Therefore state of the art and alternative materials are first characterized and finally compared using a transient heat model. The different material combinations are compared for maximum temperature and weight in a typical filling scenario. As alternative liner materials thermoplastics filled with short carbon fibres, minerals and graphite and concerning the composite overwrap copper coated carbon fibres were chosen to improve thermal properties. The findings show that the liner is the bottleneck while transferring heat from the inner to the outer tank surface. Using graphite filled thermoplastic as liner material shows the highest potential regarding thermal optimization with only little weight increase. Using additionally copper coated carbon fibres reduces the maximum temperature further, but at a high weight increase. This article is a revised and expanded version of a paper, which was presented at the 15th EASN International Conference, in Madrid, Spain, in October 2025 [1].

Abstract: This paper provides a rigorous examination of eight fundamental architectural deficiencies that render the Linux kernel unsuitable for deployment in safety-critical avionics. These deficiencies include inadequate temporal determinism, the absence of physical memory isolation, driver-induced contamination of global kernel state, an excessively large and unbounded Trusted Computing Base (TCB), open and nondeterministic system semantics, insufficient inter rocess fault containment, unstable kernel behavior due to continuous patching, and a highly complex toolchain that imposes prohibitive DO-330 qualification burdens. Through a technical and standards-aligned analysis, this paper demonstrates that Linux cannot satisfy the determinism, verifiability, isolation, and lifecycle stability required for airworthiness certification, making it inherently incompatible with certifiable airborne platforms.

Abstract: This paper presents a conceptual framework, the AI-Augmented Interview Framework (AAIF), requiring empirical validation before deployment. No interviews have been conducted; all thresholds, weights, and KPI linkages are conjectures pending empirical testing. The accelerating adoption of AI-powered development tools (GitHub Copilot, ChatGPT, Claude) is transforming software engineering practice. Industry surveys indicate that over 75% of professional developers now use AI coding assistants regularly (noting potential self-selection bias in survey samples), yet fewer than one in four organizations assess AI fluency during technical interviews. AAIF proposes a structured five-stage interview methodology (Stage 0 fundamentals gate plus four AI-augmented stages) for evaluating developer competencies in AI-mediated environments. The framework assesses: (1) toolchain fluency and prompt engineering, (2) AI output evaluation and critical reasoning, (3) system-oriented problem solving with AI integration, and (4) meta-reasoning about AI limitations, ethics, and failure modes. We develop evaluation rubrics with behaviorally anchored rating scales, propose configurable decision thresholds, and provide an integrated risk framework addressing bias, fairness, legal compliance, and ethical dimensions. The novelty lies in the systematic integration of established methods from industrial-organizational psychology, software engineering, and risk management for the specific and underexplored problem of assessing developers who use AI tools. A detailed four-phase empirical validation protocol is proposed as a key contribution.

Abstract: The high viscosity of biodiesel fuel, caused by the presence of saturated fatty acid esters, limits its application, particularly at low temperatures. Supercritical fluid extraction (SFE) using carbon dioxide represents a promising method for selective fractionation, enabling the removal of high-viscosity saturated components and the enrichment of the fuel with less viscous unsaturated esters. However, the rational design of such processes requires a deep understanding of the interrelationship between flow hydrodynamics, thermodynamic conditions, and mass transfer in a supercritical medium. In this work, a comprehensive computational fluid dynamics (CFD) modeling study of the fractionation process was performed for a model ethyl oleate/ethyl palmitate mixture (25.28:74.72 wt.%) in supercritical CO2 at pressures of 11 and 14 MPa and a temperature of 40 °C. A three-dimensional model of a laboratory-scale extractor was developed using the ANSYS Fluent software environment. Since the target esters are absent from the standard material database, a custom property library and compiled User-Defined Function (UDF) routines were developed. These describe the temperature dependence of density, viscosity, heat capacity, and thermal conductivity for both the individual components and their mixture using established mixing rules. The calculations employed an Eulerian multiphase model, the realizable k–ε turbulence model, and species transport equations. The modeling revealed pronounced selectivity: under the chosen thermodynamic conditions, ethyl palmitate is extracted preferentially over ethyl oleate, with this difference becoming more pronounced as pressure increases. The developed and verified CFD model deepens the fundamental understanding of hydrodynamics and mass transfer during supercritical fractionation and serves as a basis for optimizing process parameters to produce biodiesel with reduced viscosity. The regime at P=14 MPa and t=40 °C provides the most favorable thermodynamic and hydrodynamic conditions for the selective removal of saturated esters.

Abstract: This study evaluates the effects of Al2O3 and CeO2 nanoparticles as additives to standard diesel and biodiesel fuels on the combustion and emissions characteristics of a CR diesel engine with split injection (pilot and main injections). Three nanoparticle dosing levels (50 ppm, 100 ppm, and 150 ppm) were compared with undoped standard diesel and bio-diesel fuels. The results showed that the presence of both Al2O3 and CeO2 in biodiesel in-creased the ignition delay of the pilot fuel by about 8.0% at low load and about 3.5% at high load. The addition of both nanoparticles to diesel and biodiesel fuels had an insig-nificant effect on the main injection fuel's ignition delay, MBF50 position and combustion duration. The thermal efficiency was up to 1.0% lower. Al2O3 additive in diesel had no significant effect on NOx emissions. CO emissions were higher by 4.4-7.5% in most cases. The Al2O3 additive in biodiesel reduced NOx emissions by an average of 38%, 17.1%, and 9.4% at low, medium, and high engine loads, respectively. The reduction in CO emissions was on average 15%. The addition of CeO₂ nanoparticles to diesel fuel reduced NOₓ emis-sions by 22.5%, 8.5%, and 3.1% on average at low, medium, and high engine loads, re-spectively. When the engine was operated on CeO₂ doped biodiesel, NOₓ emissions were lower by an average of 25.7%, 9.6%, and 2.5% at low, medium, and high loads, respective-ly. Adding CeO₂ nanoparticles to diesel fuel increased CO emissions, whereas adding them to biodiesel significantly reduced CO emissions.

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.