Abstract: Drag reduction forms a key area of focus in aerodynamics with a significant emphasis on delaying the laminar to turbulent transition of boundary layers over the wing of aircraft. There is enough evidence to suggest that achieving such transition delays is particularly challenging for backward swept wings with large leading edge sweep angles, which give rise to crossflow and attachment line instabilities, in addition to the Tollmien-Schlichting waves. The sustenance of extended laminar flow regions at high sweep angles has been demonstrated in recent studies, by designing airfoils with specially curated leading edge profiles, which generate pressure distributions that can suppress crossflow. Such airfoils are called Crossflow Attenuating Natural Laminar Flow (CATNLF) airfoils. However, the design of such airfoils is presently restricted to inverse methodologies due to the inability of the conventional geometry parameterization techniques in representing the specialized leading edge profiles of CATNLF airfoils. The aim of this study is to illustrate that a parametric representation of CATNLF airfoils can be realized using Bezier curves, thereby enabling their forward multi-point design using gradient-free Bayesian optimization. The developed design framework in terms of geometry parameterization and optimization formulation is able to deliver airfoils that can sustain natural laminar flow up to around 50% chord length on the upper surface, with a leading edge sweep angle greater than 27 degrees at a Mach number of 0.78 and a Reynolds number of 20 million within a range of lift coefficients Cl = 0.5 ± 0.1, making them a suitable design choice for a medium-range transport aircraft.

Abstract: This paper presents a hardware-aware field-programmable gate array (FPGA) implementation of a layered 2-dimensional corrected normalized min-sum (2D-CNMS) decoder for quasi-cyclic low-density parity-check (QC-LDPC) codes in very small aperture terminal (VSAT) satellite communication systems. The main focus of this work is leveraging Xilinx Vitis high-level synthesis (HLS) to design and generate an LDPC decoder IP core based on the proposed algorithm, enabling rapid development and portability across FPGA platforms. Unlike conventional NMS and 2D-NMS algorithms, the proposed architecture introduces dyadic, multiplier-free normalization combined with two-level magnitude correction, achieving near-belief propagation (BP) performance with reduced complexity and latency. Implemented entirely in HLS and integrated in Vivado, the design achieves real-time operation on Zynq UltraScale+ multiprocessor system-on-chip (MPSoC) with throughput of 116-164 Mbps at 400 MHz and resource utilization of 8.7K-22.9K LUTs, 2.6K-7.5K FFs, and zero DSP blocks. Bit-error-rate (BER) results show no error floor down to 10⁻⁸ across additive white gaussian noise (AWGN) channel model. Fixed scaling factors are optimized to minimize latency and hardware overhead while preserving decoding accuracy. These results demonstrate that the proposed HLS-based 2D-CNMS IP core offers a resource-efficient, high-performance solution for multi-frequency time division multiple access (MF-TDMA) satellite links.

Abstract: This study presents a comparative analysis of the economics of batch and continuous lubricant supply process strategies in internal combustion engines (ICEs). A phenomenological model based on mass balance equations was developed to describe the dynamics of lubricant precursor depletion, film formation, and film removal under both supply strategies. The results demonstrate that the continuous supply system achieves a steady-state condition that ensures stable film thickness and a significant reduction in lubricant consumption compared with the batch strategy. Sensitivity analyses reveal that both the kinetic constant and the film removal rate strongly influence lubricant make-up requirements, defining a feasibility region for process operation. Under supercritical conditions, the batch strategy exhibits rapid precursor overconsumption; in contrast, the continuous strategy maintains minimal excess. The findings suggest that continuous lubrication process strategy can substantially improve economic and environmental performance in ICEs when properly designed and operated within feasible kinetic and mechanical limits.

Abstract: In this paper a folding wing based on gear meshing deformation mechanism is developed, focusing on structural analysis and further optimization of the folding wing. Compared with existing folding wing concepts, the deformation mode of this wing is easier to manufacture and implement in engineering. A dynamic contact finite element model of gear meshing is established in ABAQUS, achieving the transmission of motion. The meshing simulation on the gear pair and dynamic strength analysis on the gear mechanism is conducted to obtain stress analysis. The results shows that the mechanism meets the strength requirements. Further dynamic numerical simulations are conducted on the outboard wing to determine the hazardous area of the load, indicating that the folding wing meets the strength requirements. At the same time, the analysis is conducted on the displacement at the tip of the outboard wing, indicating that the folding motion is relatively gentle. Finally, based on the stress analysis results, a weight reduction topology design is carried out for the spoke area of the gear and the rib structure of the folding wing using the variable density method. While ensuring strength, the optimal distribution of materials is sought by using as little material as possible, and the model is reconstructed according to the optimization results. The optimization results show that the weight reduction effect is significant.

Abstract: This paper presents the implementation of an early stage fault detection and health monitoring system for electric motors and their drive units. The study focuses on developing a cost-effective system capable of identifying abnormal behavior in both drive electronics and mechanical components before a major failure occurs. The proposed design integrates multiple sensing parameters such as vibration, acoustic signals, and electrical quantities including voltage and current. These inputs are processed using data-driven techniques to assess motor condition and identify fault patterns. A microcontroller-based platform is used for real-time monitoring and signal processing, providing early warnings through an intuitive serial interface. Experimental observations confirm that this approach can effectively detect drive faults, motor imbalance, and bearing wear at an early stage, reducing downtime and maintenance costs. This work demonstrates a practical and scalable method to enhance the reliability and operational safety of motor-driven systems, contributing to improved industrial efficiency and predictive maintenance strategies.

Abstract: Si MOSFETs are widely used in power conversion systems; however, long-term operation under repetitive switching and electro-thermal stress leads to progressive degradation and eventual failure. Two representative failure modes are commonly observed: gate-oxide degradation and packaging-related degradation, which often exhibit different evolution patterns. This paper proposes an AI-based diagnosis and prognostics framework that jointly leverages steady-state time-series information and fixed-length features extracted from turn-off transients. The study utilizes the NASA Open Accelerated-Aging dataset and reorganized/preprocessed data supported by MATLAB/Simulink measurement cir-cuit modeling. Physics-informed rule-based labeling is applied to discriminate normal, gate-oxide, and packaging-related conditions based on degradation indicators such as R_{ds_on} evolution. The trained model is further interpreted via permutation importance to quantify whether gradual/abrupt degradation indicators and transient features contribute to decision-making. Performance is assessed on held-out tests and synthesized cases sampled from baseline operating distributions to examine consistency under previously unseen conditions.

Abstract: Land subsidence is a critical geohazard that threatens infrastructure stability, environmental sustainability, and public safety, particularly in data-scarce and terrain-sensitive regions. This study presents a GIS-based framework for mapping land subsidence susceptibility using a slope-based polynomial regression approach. Twenty national and international case studies were reviewed to identify key environmental and hydrological factors influencing subsidence. Statistical correlation and regression analysis revealed slope as the most influential parameter, exhibiting a strong non-linear relationship with subsidence occurrence. The developed regression model was applied to a selected road corridor in Meghalaya using slope data derived from Digital Elevation Models (DEMs) within a GIS environment. The resulting susceptibility map categorizes the study area into low, moderate, and high-risk zones, which show strong agreement with known patterns of terrain instability. The proposed approach offers a cost-effective and scalable method for preliminary subsidence risk assessment in regions lacking extensive field monitoring and provides valuable insights for infrastructure planning and hazard mitigation.

Abstract:

The construction environment of hydraulic engineering is complex, while traditional safety monitoring methods suffer from low efficiency and delayed response. Although static recognition models based on improved YOLOv5s have enhanced detection accuracy, they still cannot assess behavioral persistence and struggle to achieve proactive early warning. To address this, this study integrates the improved YOLOv5s with the DeepSORT algorithm to construct an integrated real-time "detection-tracking-warning" system. The system utilizes DeepSORT to achieve stable personnel tracking in complex scenarios and triggers dynamic warnings based on spatiotemporal behavioral logic. A desktop prototype system was developed using PyQt5/PySide6. Experimental results show that the system achieves a Multiple Object Tracking Accuracy (MOTA) of 86.2% in multi-object occlusion scenarios; the accuracy of unsafe behavior warning exceeds 95%, with an average delay of less than 1.5 seconds. This research accomplishes a transition from passive recognition to proactive warning, providing an intelligent solution for safety management in hydraulic construction.

Abstract: In disaster scenarios where communication infrastructure is damaged, Unmanned Aerial Vehicle (UAV)-assisted wireless networks can provide temporary connectivity and hence the indispensable mobile edge computing functionality. However, limited resources on UAVs require prioritization of critical data in such scenarios. This research addresses re-liable transmission and task offloading by modeling user tasks as layered compositions, where the base layer is essential and enhancement layers are optional. TDMA-based prioritization is employed to ensure reliable decoding of high-priority layers of the computational tasks (i.e., intra-user priority) along with inter-user priority needed for urgent users like rescue teams. Under these reliability constraints, the work formulates a joint communication-computation optimization problem to allocate transmission power and UAV CPU cycles efficiently in order to minimize total weighted offloading latency. The original problem is non-convex and thus we leverage epigraph and perspective functions to recast the problem into convex. We also derive analytically, using KKT conditions, the optimal water-filling-like solutions for the reformulated problem. The numerical results show that, at a signal-to-noise ratio of 5 dB, the proposed algorithm achieves relative latency reductions vs the baseline algorithms (39.99% reduction vs Equal Allocation, 49.99% reduction vs Enhancement First, and 69.99% reduction vs No Priority) which reflect considerable latency reduction with priority-aware offloading.

Abstract: Ultrasonic vibration-assisted grinding (UVAIG) is a continuous-contact grinding process. In this process, the arc length of engagement for a single abrasive grain is longer compared to conventional grinding, which enhances the quality of the processed surface and improves processing efficiency. This study aims to establish a three-dimensional model of abrasive grains in space and to theoretically deduce the trajectory of abrasive grains during axial ultrasonic vibration-assisted internal grinding (UVAIG), as well as the resulting surface quality, measured as Ra. A three-dimensional simulation tool for ultrasonic vibration grinding micro-surfaces is developed using MATLAB. This tool enables the analysis of how various processing parameters affect workpiece surface morphology. Additionally, a predictive model is established for UVAIG simulations, allowing theoretical calculation of surface topography changes induced by different processing parameters, vibration settings, and abrasive grain models.

Abstract: Small hydro power plants (HPPs) play an important role in managing fluctuating energy requirements. This article presents a real-world case study where model predictive control (MPC) utilizing lightGBM-based machine learning (ML) forecasts of energy demand and water availability is employed to optimize the scheduling of a small HPP for peak shaving. A comparative analysis is conducted between the current non-predictive control strategy, which relies on operator decisions for peak shaving, and a fully automatic controller that optimally schedules the utilization of available water resources based on ML predictions. Preliminary results show that the MPC can outperform the operator’s decisions and that this has the potential of improving peak shaving capabilities of small HPPs, emphasizing the role of predictive control methodologies for exploiting energy storage resource in the management of the distribution grid. This approach offers a pragmatic solution that small utilities can adopt with minimal effort using their own data.

Abstract: The engine system requirements for different engine cycles significantly influence the design of the mixing head. A literature review of fuel-injection technology for hydro-gen and methane is presented. The literature review aimed to answer proposed questions specific to the liquid rocket engine fuel injector design. The current review methodology accounts for the engine system effect. Thus, a comprehensive literature review of the working principles of startup-staged combustion cycle engines based on original patents is provided. At the end of the review, the research gaps and suggestions for further work are summarised. At high mass flow rate and injection pressure in the supercritical regime (> 50 MPa), experience is limited to the staged combustion cycle developed in Russia and the US. It is necessary to consider a fluid-dynamic heat transfer coupling study for the multi-injection element design in the supercritical state. Cryogenic spray atomisation experiments need to be designed with research significance. It is still needed to study how the similarity of the spray flow field to the combustion performance affects a liquid rocket engine problem. Moreover, scaling stoichiometric mixing theory needs to be expanded to different injector types, such as tri-coaxial and pintle injectors, to validate the correlation between the nonreactive mixing length and flame length.

Abstract: In low-Earth-orbit (LEO) satellite networks, the requirement for intelligent parameter-adjustment strategies has become increasingly critical due to the presence of highly dynamic channel conditions, limited spectrum resources, and complex interference environments. In this paper, a method for optimizing LEO satellite communication links based on deep reinforcement learning (DRL) is proposed. Through the optimization of the transmit power, the modulation and coding scheme (MCS), the beamforming parameters, and the retransmission mechanisms, adaptive link control is achieved in dynamic operational scenarios. A multidimensional state space is constructed, within which the channel state information, the interference environment, and the historical performance metrics are integrated. The spatio-temporal characteristics of the channel are extracted by means of a hybrid neural architecture that incorporates a convolutional neural network (CNN) and a long short-term memory (LSTM) net-work. To effectively accommodate both continuous and discrete action spaces, a hybrid DRL framework that combines proximal policy optimization (PPO) with a deep Q-network (DQN) is employed, thereby enabling cross-layer optimization of the physical-layer and link-layer parameters. The results demonstrate that substantial improvements in throughput, bit error rate (BER), and transmit-power efficiency are achieved under severely time-varying channel conditions, which provides a new idea for resource management and dynamic-environment adaptation in satellite communication systems.

Abstract: Urban curbside loading and unloading zones are increasingly affected by competing non logistics uses, such as outdoor terraces or resident parking, leading to reductions in effective curbside length. These design decisions can significantly alter service capacity and generate environmental externalities in urban freight operations that are rarely quantified. This study introduces the Factor of Occupancy (Fo) as a space–time design indicator for curbside unloading zones, defined as the product of effective curbside length and the maximum authorised dwell time. Using direct observational data from an urban block in Zaragoza (Spain), the analysis focuses on a loading and unloading zone whose effective length was reduced by approximately 6 m due to the installation of a restaurant terrace. Two curbside configurations are compared: a reduced configuration (8 m) and a restored configuration (14 m), keeping demand and temporal constraints constant. Fo is integrated into a loss based queueing model (M/M/1/1) to estimate blocking probabilities and the number of served and rejected freight operations. To capture the environmental implications of curbside capacity loss, the paper proposes the Hidden Carbon Emissions (HCE) indicator, which quantifies the additional CO₂ emissions generated by rejected vehicles through block recirculation and idling during illegal occupancy, based on observed behaviour and publicly available emission factors. Results show that restoring curbside length substantially increases effective service capacity and reduces rejected vehicles, leading to a marked decrease in hidden CO₂ emissions per operation. The findings highlight that minor curbside design decisions can produce measurable impacts on both urban freight efficiency and environmental performance.

Abstract: Ice accretion along aircraft leading edges, particularly at stagnation line parting strips, remains difficult to remove using conventional electrothermal anti-icing systems. These systems require continuous high-power heating to maintain the stagnation region above the melting point, often exceeding 10–12 kW/m². This study introduces an Ice Cavitation Deicer (ICD) that removes ice through rapid, localized cavitation generated within a thin melt layer formed at the ice–surface interface. In the proposed approach, a short pulse of electric current melts a 1–10 µm interfacial layer and causes a cavitation impulse of approximately 1–10 MPa. This impulse ejects the stagnation line ice in a direction normal to the surface, often against the external airflow, enabling the immediate aerodynamic removal of the remaining ice. Analytical modeling based on the energy conservation principle was used to determine the optimal foil geometry, thermal pulse parameters, thermal stress, and material selection. Experiments with various metallic foils and substrate materials validated the predicted ejection behavior. Compared with conventional thermal anti-icing, the ICD concept reduces power consumption by approximately two orders of magnitude while offering rapid and reliable leading-edge deicing.

Abstract: Manual assembly of multi-pin cable harnesses remains vulnerable to miswiring when conductors are visually indistinguishable. This paper presents an industrial case study of a quick-connect harness composed of two connectors (receptacle-type and pin-type) linked by 16 black conductors (2.5 mm²; 200 mm length), where the dominant failure mode is a two-wire swap that breaks correct pin-to-pin mapping and may cause downstream equipment damage. In the baseline state, end-of-line verification relied on visual inspection only (1 min/unit), resulting in an internal nonconformity rate of 4% (repairable). To achieve the operational goal of zero defects (zero escapes), we propose and integrate an electronic pin-to-pin continuity and mapping fixture as a deterministic End-of-Line (EOL) quality gate implementing poka-yoke logic (“no PASS—no shipment”) and enabling structured traceability records. Using a before–after workload model that includes mandatory retest after rework, the fixture reduces test time to 0.33 min/unit. For a monthly volume of 1500 units, total quality workload (test + rework + retest) decreases from 31 h/month to 13.58 h/month, releasing 17.42 h/month. Global quality productivity increases from 48.39 units/h to 110.46 units/h (+128%). The proposed architecture couples deterministic electrical verification with data logging aligned to digital thread and data-driven quality management concepts to sustain continuous improvement and prevent customer escapes.

Abstract: The paper states that biogas plants are of particular importance in the development of renewable energy sources, and their efficiency is largely determined by the accuracy and reliability of parameter measurements during the production process. Sensors that determine temperature, pressure, pH, humidity, methane (CH₄) and hydrogen sulfide (H₂S) concentrations, gas flow, and oxidation-reduction potential (ORP) form the basis of the monitoring system. However, during operation, they are affected by nonlinear dependence, noise, drift, and errors that reduce the reliability of measurements. To solve this problem, mathematical modeling and sensor optimization methods are proposed. The study proposes a mathematical model that describes the correlations between the physicochemical characteristics of the environment and the output signals of the sensors. Based on this model, an analysis of the sensitivity of the measurement channels was carried out, critical areas where accuracy is significantly reduced were identified, and methods for compensating for errors were proposed. To improve the reliability of the results, intelligent data processing was used, including artificial neural networks, which allow adaptive adjustment of output data and calibration in real-time monitoring mode. The proposed approach improves measurement accuracy and the stability of the sensor system to external influences, which is also of practical importance for monitoring and controlling biogas plants. A mathematical model was proposed that takes into account the physicochemical dependence on environmental parameters (temperature, pressure, pH, Ch₄ and H₂S concentrations, humidity, gas flow, and redox potential) and sensor response. Based on this, a sensitivity analysis of the measurements was performed to identify areas of maximum error. Intelligent data processing using artificial neural networks was used to compensate for systematic errors and sensor drifts, which allowed for real-time calibration and correction of sensor readings.

Abstract: In this study, formation water sample №1082 from the Narimanov OGPD, together with crude oil samples from the Bulla-Deniz and Muradkhanli fields, were examined under laboratory conditions to evaluate the efficiency of chemical reagents. The Alkan-318 demulsifier, Marza-1 inhibitor, Difron-4201 depressor additive, and the combined ADM composition (Alkan-318 + Difron-4201 + Marza-1 in a 1:1:1 ratio) were tested for their effects on water separation, corrosion inhibition, sulfate-reducing bacteria activity, paraffin deposition, and pour point depression. Comparative experiments showed that the ADM composition demonstrated superior performance over individual reagents at equal concentrations. At an optimal dosage of 600 g/t, Alkan-318 and the ADM composition reduced residual water in Bulla-Deniz (75% water cut) and Muradkhanli (41% water cut) oils to 0.1% and 0.8%, respectively. For pour point depression, Difron-4201 (900 g/t) and ADM (600 g/t) achieved efficiencies of 169.2% and 176.9% in Bulla-Deniz oil, and 151.4% and 170.0% in Muradkhanli oil. Regarding deposit prevention, ADM reached 95.4% and 96.9% efficiency, significantly exceeding individual reagents. Corrosion tests revealed that Marza-1 and ADM provided up to 99.9% protection in aggressive H₂S and CO₂ environments, while ADM also exhibited a nearly complete bactericidal effect (99.8%) against sulfate-reducing bacteria, highlighting its multifunctional efficiency.

Abstract: This study presents a prospective attributional Life Cycle Assessment of the Italian na-tional electricity grid for 2024–2040, aligned with the integrated national climate and en-ergy plan and the country's decarbonization pathway. The main goal was to calculate, analyze, and apply hourly emission factors for electricity and compare them with annual average factors for the same consumption profile to assess temporal effects on environmental outcomes. Hourly factors were derived from a cost-optimization energy model simulating Italy's evolving generation mix. The model projects a sharp decline in fossil-based generation and a significant expansion of solar photovoltaics and wind, which together exceed half of national production by 2040. Natural gas remains essential for system balancing, while electricity imports stabilize the grid when renewable output is low. 16 impact categories were evaluated, revealing decreasing trends in climate change (255 to 141 g CO2-eq/kWhe), acidification, and others, and rising temporal variability in mineral/ metal resource depletion and land use due to renewable intermittency. Applying the method to a positive energy district in Bologna shows that time-resolved factors offer clearer insights than annual averages, especially for season-dependent impacts, and demonstrate substantial reductions in impact by 2040, alongside notable differences between consuming and exporting electricity.

Abstract: Autonomous underwater vehicles (AUVs) face significant challenges in complex dynamic marine environments, where ocean currents, uncertain obstacles, and in-ter-vehicle interactions increase collision and mission failure risks. This study proposes a risk-aware cooperative path planning framework for multiple AUVs that integrates conditional Bayesian networks (CBN) for probabilistic environmental risk assessment directly into a receding horizon optimization scheme. The approach models AUV kinematics under time-varying ocean currents, incorporates collision avoidance, en-ergy consumption, path smoothness, and dynamic risk constraints derived from CBN-inferred probabilities. Risk levels are mapped nonlinearly to enable gradi-ent-based optimization while maintaining continuous sensitivity. The framework is evaluated through Monte Carlo simulations in a realistic South China Sea canyon en-vironment using HYCOM reanalysis current data, with comparisons against baseline methods. Results demonstrate substantial improvements: mission success rate increas-es by up to 35%, energy consumption decreases by 12–18%, path smoothness improves, and risk exposure is significantly reduced across various current intensities and obsta-cle densities. This method enhances operational safety and efficiency for cooperative AUV missions in uncertain dynamic oceans, offering a promising engineering solution for real-world underwater applications. This work presents an engineering-oriented framework that embeds a CBN-derived probabilistic risk index into cooperative re-ceding-horizon trajectory optimization for multi-AUV systems operating under realis-tic, time-varying ocean current fields. The main contributions of this work are summarized as follows: (1) A risk-aware cooperative path planning framework is developed for mul-ti-AUV systems, in which a probabilistic environmental risk model based on a Conditional Bayesian Network (CBN) is directly embedded into a reced-ing-horizon optimization process, rather than used as a post hoc evaluation or external safety filter. (2) Unlike existing deterministic or purely reactive approaches, the proposed CBN-based risk inference mechanism enables the planner to explicitly reason about coupled terrain–current–uncertainty effects, providing a continuous risk gradient that cannot be obtained from binary obstacle representations. (3) The proposed receding-horizon cooperative optimization embeds probabilis-tic risk directly into the planning objective, allowing multi-AUV systems to proactively trade off efficiency and safety in a mathematically tractable manner, rather than relying on post hoc risk filtering. (4) The effectiveness and practical applicability of the proposed method are demonstrated through extensive Monte Carlo simulations in a realistic sub-marine canyon environment using reanalysis-based ocean current data, showing statistically consistent improvements in mission success rate, energy efficiency, trajectory smoothness, and reduction of high-risk exposure com-pared with a baseline cooperative planning strategy. The proposed framework provides a practical and scalable solution for real-world multi-AUV missions, with potential applications in marine environmental monitoring, seabed surveying, underwater inspection, and ocean engineering operations.