Eng

Understanding the flow characteristics of tight sandstone reservoirs is crucial for improving resource recovery efficiency. During fluid flow in porous media, surfactant components in the fluid can adsorb onto solid surfaces, forming a boundary layer. This boundary layer has a pronounced impact on fluid movement within tight sandstone formations. In this study, digital core analysis is employed to investigate how the boundary layer influences non-Darcy flow behavior. A computational model is first developed to quantify the thickness and viscosity of the boundary layer, followed by the construction of a mathematical flow model based on the Navier–Stokes equations that incorporates boundary layer effects. Using CT scan data from actual core samples, a pore network model is then built to represent the reservoir’s complex pore structure. The impact of boundary layer development on microscale flow is subsequently analyzed under varying pore conditions. The results indicate that both boundary layer thickness and viscosity significantly influence fluid transport in microscopic pores. When the relative boundary layer thickness is 0.5, and the relative viscosity reaches 10, the actual outlet flow rate drops to only 12.89% of the value obtained without considering boundary layer effects. Furthermore, in tight reservoirs with smaller pore throat sizes, the boundary layer introduces considerable flow resistance. When boundary layer effects are incorporated into the pore network model, permeability initially increases with pressure gradient and then stabilizes. Full article

Underground hydrogen storage (UHS) in saline aquifers offers a viable alternative to surface-based storage systems, which are limited by capacity constraints, high operational pressures, complex thermal regulation, low energy densities, and potential safety hazards. This study uses a fully compositional reservoir simulation model to evaluate hydrogen behavior in the Mt. Simon Sandstone in the Illinois Basin. The analysis focuses on the effects of hysteresis, solubility, diffusivity, and production well perforation location on recovery efficiency. Cyclic injection and withdrawal scenarios were simulated to assess storage performance and operational strategies. The results show that accounting for hydrogen diffusivity shows essentially unchanged withdrawal efficiency at 79%, the same as the base case. Solubility causes a slight decrease to 78%, while hysteresis leads to a more significant reduction to 63%. The location of injection well perforations also influences recovery: top-perforated wells increase efficiency from 60% after the first cycle to 74% after six cycles, whereas bottom-perforated injection wells increase efficiency from 56% to 79% over the same period. These findings emphasize the importance of accounting for multiphase flow dynamics and strategic well placement in optimizing UHS system performance. The insights contribute to advancing reliable, large-scale hydrogen storage solutions essential for supporting renewable energy integration and long-term energy security. Full article

This study introduces a hybrid optimization method to enhance the melting characteristics and weld bead morphology of flux-cored wire hardfacing with exothermic addition into the core filler. The Taguchi Design of Experiments (L9 orthogonal array) was used to analyze the effects of key conditions on multiple melting characteristics. The hybrid Taguchi-GRA-PCA effectively identified the best parameter combination, resulting in a significant improvement in overall melting performance. The impact of welding modes on weld bead parameters and melting characteristics was examined. It was determined that the optimal amount of the exothermic addition CuO-Al introduced into the flux-cored wire filler should be a medium level (EA = 28 wt.%). Results showed that wire feed speed WFS and EA had the greatest effect on MOR and DR, while EA and CTWD mainly influenced SF and De. It has been determined that the content of the exothermic additive has a significant impact on the melting process of filler materials, affecting the melting characteristics and weld bead morphology. It has been found that the melting characteristics of deposition rate and spattering factor can be used to optimize welding modes and characterize most output parameters of the welding/surfacing process. Full article

The parasitic currents issue arises in induction machines operating at higher frequencies. The parasitic current flow is the main cause of premature degradation of winding insulation deterioration and bearing damage. The effect of electromagnetic interference (EMI) requires investigating an equivalent per-phase model of an induction motor (IM) at higher frequencies. The per-phase induction motor mathematical model of the IM drive for common mode (CM) configuration is developed to perform high-frequency analysis for drive operation. The high-frequency per-phase induction motor model MATLAB (2021b) is developed to generate the transfer function of IM drive and generate bode plots for both CM and differential mode (DM) impedance configuration at higher frequency. Initially, the IM typical frequency response without considerations of stray and parasitic effects is presented for normal behavior of the IM. In order to verify the simulated impedance parameters of 0.3 kW and 38 kW IMs with stray and parasitic components, a high-frequency response for magnitude and phase response is generated and compared to analyze per-phase induction motor model performance before and after resonance frequencies. The comparison of CM and DM bode plots validates the dominance of inductance and parasitic capacitance before and after the occurrence of resonance frequency, respectively. The analysis suggests that 38 kW IM resonance occurs in MHz range which exhibits much better performance compared to the 0.3 kW IM model. Full article

The selection of a final operating point from a Pareto front set of marine diesel engine configurations relies on the critical task of translating operator priorities into quantitative criterion weights. This study isolates this pivotal weighting step and introduces an operator-defined fuzzy weighting module that maps linguistic importance ratings to normalized weights. This module systematically maps important ratings for Specific Fuel Consumption (SFC), Nitrogen Oxides (NOx), and Particulate Matter (PM) into a set of normalized weights for the Multi-Criteria Decision-Making method. The module’s core is a Mamdani-type fuzzy logic module that utilizes triangular membership functions and centroid defuzzification. These fuzzy weights are integrated with the TriMetric Fusion algorithm to generate a robust consensus ranking. Validation on a Pareto front from a two-stroke diesel engine demonstrates the framework’s efficacy: a Fuel-Economy priority selected a configuration with SFC advantage, while a Strict Environmental Compliance priority correctly identified dual emissions strengths. Furthermore, the system effectively mediated trade-offs in a high-competition scenario. Rank correlation analysis confirmed that while the Pareto front nature of the alternatives leads to inherent similarities in rankings, the fuzzy weights induce significant and logical divergences. Future work will focus on validation with real operator feedback and comparative studies with traditional weighting methods. Full article

With the deepening of mineral resource exploitation, conical picks are subjected to severe wear under high-stress and high-friction conditions, which has become a critical factor governing rock-breaking efficiency. To address this issue, this study systematically investigates the mechanism by which wear-induced geometric evolution of conical pick tips influences rock-breaking efficiency through controlled indentation tests. Three conical picks with varying wear degrees, characterized by different tip cone angles, were tested to quantify the peak indentation force, specific energy, indentation crater area, and indentation hardness index of rock specimens. The results show that progressive pick wear leads to tip blunting and an increase in cone angle, resulting in monotonic increases in peak indentation force, specific energy, indentation crater area, and indentation hardness index as functions of pick tip geometry. The experimental observations are interpreted using the cavity expansion model based on the Mohr–Coulomb yield criterion, following the Detournay–Huang theoretical framework. Wear-induced changes in pick tip geometry promote the expansion of the plastic zone and increase stress field complexity within the rock during indentation, thereby reducing rock-breaking efficiency. All reported trends are derived from repeated indentation tests and presented as mean values, demonstrating consistent and statistically reliable behavior. Based on these findings, optimizing pick tip geometry and improving wear resistance are identified as effective strategies to minimize energy consumption and enhance rock-breaking efficiency in deep hard-rock mining. This study provides a mechanistic understanding of how conical pick wear degrades rock-breaking efficiency through geometric control of plastic zone evolution, offering both theoretical insight and experimental evidence beyond previous material-focused studies. Full article

Digital telecommunications have become the backbone of modern healthcare, transforming how patients and professionals interact, share information, and deliver treatment. The integration of telecommunications with medicine, biomedical engineering and health services has enabled rapid growth in telemedicine, remote patient monitoring, wearable biomedical devices, and data-driven clinical decision-making. Emerging technologies such as artificial intelligence, big data analytics, virtual and augmented reality and robotic tele-surgery are further expanding the scope of digital health. This review provides a comprehensive overview of the role of telecommunications in medicine and biomedical engineering. We classify key applications, highlight enabling technologies and critically examine the challenges regarding interoperability, data security, latency, and cost. Finally, we discuss future directions, including 5G/6G networks, edge computing, and privacy-preserving medical AI, emphasizing the need for reliable and equitable access to telecommunications-enabled healthcare worldwide. Full article

Early and reliable identification of vertebral metastases on computed tomography remains a major challenge in oncologic imaging due to the morphological complexity of metastatic lesions and the high inter-patient variability of spinal anatomy. In this study, an end-to-end interpretable radiomic-based framework was developed to automatically distinguish healthy from metastatic vertebrae using segmented DICOM data, coupled with an interactive virtual reality (VR) visualization module implemented in Unity 3D. The proposed framework integrates radiomic feature extraction and selection, informed undersampling to address class imbalance, and automatic machine learning-based classification. To facilitate interpretation, patient-specific 3D models with overlapped classifier outputs were integrated into a VR desktop application, enabling advanced exploration of patient-specific spinal models, with color-coded visualization of algorithmic predictions and expert-defined suspicious lesions. The final classification model, trained using a Random Forest algorithm and optimized via stratified 5-fold cross-validation, achieved an overall accuracy of 0.86, an Area Under the Receiver Operating Characteristic Curve of 0.91, and an F1-score of 0.81 for the metastatic class on the independent test set, achieving competitive diagnostic performance while preserving transparency and clinical interpretability. This study represents a foundational step toward intelligent, interactive, and clinically interpretable tools for the diagnosis and follow-up of spinal metastatic disease. Full article

The transition to the 4th Industrial Revolution (4IR) in the electroplating industry necessitates intelligent, real-time monitoring systems to replace traditional, time-consuming offline analysis. In this study, we developed a cost-effective, automated measurement system for hexavalent chromium (Cr(VI)) in plating wastewater using an Arduino-based RGB sensor. Unlike conventional single-variable approaches, we conducted a comprehensive feature sensitivity analysis on multi-sensor data (including pH, ORP, and EC). While electrochemical sensors were found to be susceptible to pH interference, the analysis identified that the Red and Green optical channels are the most critical indicators due to the distinct chromatic characteristics of Cr(VI). Specifically, the combination of these two channels effectively functions as a dual-variable sensing mechanism, compensating for potential interferences. To optimize prediction accuracy, a systematic machine learning strategy was employed. While the Convolutional Neural Network (CNN) achieved the highest classification accuracy of 89% for initial screening, a polynomial regression algorithm was ultimately implemented to model the non-linear relationship between sensor outputs and concentration. The derived regression model achieved an excellent determination coefficient (R² = 0.997), effectively compensating for optical saturation effects at high concentrations. Furthermore, by integrating this sensing model with the chemical stoichiometry of the reduction process, the proposed system enables the precise, automated dosing of reducing agents. This capability facilitates the establishment of a “Digital Twin” for wastewater treatment, offering a practical ICT (Information and Communication Technology)-based solution for autonomous process control and strict environmental compliance. Full article

This article presents an analysis of the effects of braking system damage on the course of the vehicle collision and driving safety. Research was conducted using simulation methods in the V-SIM 7.0 environment, analysing the collision between a car and a truck at three speeds—50, 60, and 70 km/h—under the assumption of a braking system malfunction in the car. The obtained results showed that as the speed of the truck increased, the total kinetic energy of the system nearly doubled, resulting in deformation of the vehicle’s body front of up to 0.6 m. The maximum force acting on the car decreased with increasing speed, which was due to the change in the point of impact. The recorded acceleration values of the car indicate a moderate level of overloads, which should not cause serious injuries to the passengers but do suggest significant stress on the vehicle’s load-bearing structure. The research may serve as a foundation for further work on braking system diagnostics, the development of friction materials, and the modelling of energy absorption processes in collisions involving vehicles of varying mass and geometry. Full article

Although conceptually simple, the application of seismic isolation can pose dangerous pitfalls. It is based on decoupling the dynamic response of a structure from the ground motion by inserting isolation devices at its base. Significant displacements of the devices are typically required in return for increasing the oscillation period and reducing seismic accelerations in the building. To avoid blind guessing, the suitable values for damping and the period of vibration should be defined. The vibration period can vary in a range that depends on the capacity of the superstructure, which influences the maximum allowable acceleration, and the boundary conditions, which limit the maximum allowable displacement. In this regard, a simple and effective procedure is proposed for assessing the feasibility of seismic isolation and providing a preliminary yet reliable evaluation of the basic parameters, such as damping and the isolation period. This procedure could also highlight the need for changing the seismic protection strategy. Some application examples, aimed at including a wide range of design cases, are also provided. Full article

Electrical discharge machining (EDM) is widely used for machining hard and difficult-to-cut materials; however, the complex and nonlinear nature of the process makes the accurate prediction of key performance indicators challenging, particularly when only limited experimental data are available. In this study, a combined Taguchi design and Gaussian process regression (GPR) modeling framework is proposed to predict the surface roughness (Ra), material removal rate (MRR), and overcut (OC) in die-sinking EDM. An L18 Taguchi orthogonal array was employed to efficiently design experiments involving discharge current, pulse duration, and electrode material. GPR models with an automatic relevance determination (ARD) radial basis function kernel were developed to capture nonlinear relationships and varying parameter relevance. Model performance was evaluated using strict leave-one-out cross-validation (LOOCV). The developed GPR models achieved low prediction errors, with RMSE (MAE) values of 0.54 µm (0.41 µm) for R_a, 1.56 mm³/min (1.21 mm³/min) for MRR, and 0.0065 mm (0.0055 mm) for OC, corresponding to approximately 9.8%, 5.4%, and 5.9% of the respective response ranges. These results confirm stable and reliable predictive accuracy within the investigated parameter domain. Based on the validated surrogate models, multi-objective optimization was performed to identify Pareto-optimal process conditions, revealing graphite electrodes as the dominant choice within the feasible operating region. The proposed approach demonstrates that accurate and robust prediction of EDM performance can be achieved even with compact experimental datasets, providing a practical tool for process analysis and optimization. Full article

Multi-material additive manufacturing (MMAM) enables integration of multiple materials within single products, but existing design methodologies lack systematic frameworks linking detailed consolidation decisions to product-level functional requirements while preserving functional independence. This paper presents a methodology that extends the conventional design process model with a reverse-traced workflow connecting part-level decisions to higher-level product architecture. By tracing how Design for MMAM (DfMMAM) affects design decisions in reverse, designers can identify the best opportunities to use MMAM based on their project scope. The methodology introduces a Level of Process Integration (LPI) framework based on design novelty that structures redesign scope according to whether changes affect part geometry, component assembly, or function allocation, enabling designers to balance consolidation benefits against validation complexity at each level. Sequential decision-making workflows systematically determine which functions can be co-located within unified components while maintaining functional independence through zone-specific design parameters. The methodology is illustrated through a qualitative case study on trail running shoe design across three integration levels, identifying substantial consolidation potential while establishing the foundation for future quantitative validation. Unlike existing approaches limited to part-level redesign, this framework traces detailed consolidation decisions back to product architecture trade-offs, clarifying redesign scope and validation rigor required at each integration level. By operationalizing the relationship between functional decomposition, physical architecture, and MMAM capabilities, this framework provides designers with structured decision pathways to balance consolidation benefits against redesign complexity at each design phase. Full article

Hydroxyl-terminated polybutadiene (HTPB) is widely studied and the most used prepolymer for the binder system of composite solid propellants. A suitable functionalization of HTPB with energetic groups greatly improves the performance of the propellant. Therefore, the nitration of HTPB plays an essential role in the obtaining of high-energy binders. Among the reported methods, the nitration of HTPB using nitryl iodide (NO₂I) was distinguished as the most preferable due to the facilitated synthesis and product purity. However, the thus established synthesis is long and laborious; therefore, in the present work we focus our attention on the improved procedure using ultrasonic conditions. The resulted nitro-HTPB was characterized using FTIR, ¹H NMR, GPC, and DSC analyses. Also, based on the recorded IR-spectra a ratiometric analysis for determining the nitration rate was established, which could replace the expensive and time-consuming NMR analysis that was used. Full article

Constructed wetlands (CWs), increasingly adopted as nature-based solutions (NBS) for wastewater treatment, require a rigorous assessment of the durability and structural performance of the materials used in their supporting systems. In contrast to the extensive literature addressing hydraulic efficiency and contaminant removal, the structural behavior of CWs has been scarcely examined, with existing studies offering only general references to reinforced concrete and masonry and lacking explicit design criteria or deterioration analyses. This study integrates evidence from real-world CW installations with a systematic review of 31 studies on the degradation of cementitious materials in analogous environmental conditions, following PRISMA 2020 guidelines, with inclusion criteria based on quantified wastewater-related exposure conditions (e.g., chemical aggressiveness, persistent saturation, and biogenic activity). Results indicate that reinforced concrete, despite its structural capacity, is susceptible to biogenic corrosion, accelerated carbonation, and sulfate–chloride attack under conditions of persistent moisture, with reported degradation rates in analogous wastewater infrastructures on the order of millimeters per year for concrete loss and tens of micrometers per year for reinforcement corrosion. Masonry structures, similarly, exhibit performance constraints when exposed to mechanical overloads and repeated wetting–drying cycles. In contrast, emerging alternatives—such as nanomodified matrices and concretes incorporating supplementary cementitious additives—demonstrate potential to enhance durability while contributing to a reduced carbon footprint, without compromising mechanical strength. These findings reinforce the need for explicit structural design criteria tailored to CW applications to improve sustainability, durability, and long-term performance. Full article

This paper addresses the issues of target unreachability and local optima in traditional artificial potential field (APF) methods for UAV swarm path planning by proposing an improved collaborative obstacle avoidance algorithm. By introducing a virtual target position function to reconstruct the repulsive field model, the repulsive force exponentially decays as the UAV approaches the target, effectively resolving the problem where excessive obstacle repulsion prevents UAVs from reaching the goal. Additionally, we design a dynamic virtual target point generation mechanism based on mechanical state detection to automatically create temporary target points when UAVs are trapped in local optima, thereby breaking force equilibrium. For multi-UAV collaboration, intra-formation UAVs are treated as dynamic obstacles, and a 3D repulsive field model is established to avoid local optima in planar scenarios. Combined with a leader–follower control strategy, a hybrid potential field position controller is designed to enable rapid formation reconfiguration post-obstacle avoidance. Simulation results demonstrate that the proposed improved APF method ensures safe obstacle avoidance and formation maintenance for UAV swarms in complex environments, significantly enhancing path planning reliability and effectiveness. Full article

To address the challenge of simultaneously modelling temporal evolution and static properties in fatigue life prediction, this paper proposes a Hybrid GRU–Attention–DNN model: The Gated Recurrent Unit (GRU) captures time-evolution features, while the attention mechanism adaptively focuses on critical stages. These are then fused with static properties via a fully connected network to generate life estimates. Training and validation were conducted using an 8:2 split, with baselines including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and GRU. Performance was evaluated using the coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), and root mean squared logarithmic error (RMSLE), together with error band plots. Results demonstrate that the proposed model outperforms baseline CNN/GRU/LSTM models in overall accuracy and robustness, and that these improvements remain statistically significant according to bootstrap confidence intervals (CI) of R², RMSE, MAE and RMSLE on the test set. Additionally, this paper conducts an interpretability analysis: attention visualisations reveal the model’s significant emphasis on the early stages of the lifespan. Time window masking experiments further indicate that removing early information causes the most significant performance degradation. Both lines of evidence show high consistency in qualitative and quantitative trends, providing a basis for engineering sampling window design and trade-offs in test duration. Full article

Accurate identification of lithology is considered very important in oil and gas exploration because it has a direct impact on the evaluation and development planning of any reservoir. In complex reservoirs where extreme class imbalance occurs, as critical minority lithologies cover less than 5%, the identification accuracy is severely constrained. Recent deep learning methods include convolutional neural networks, recurrent architectures, and transformer-based models that have achieved substantial improvements over traditional machine learning approaches in identifying lithology. These methods demonstrate great performance in catching spatial patterns and sequential dependencies from well log data, and they show great recognition accuracy, up to 85–88%, in the case of a moderate imbalance scenario. However, when these methods are extended to complex reservoirs under extreme class imbalance, the following three major limitations have been identified: (1) single-scale architectures, such as CNNs or standard Transformers, cannot capture thin-layer details less than 0.5 m and regional geological trends larger than 2 m simultaneously; (2) generic imbalance handling techniques, including focal loss alone or basic SMOTE, prove to be insufficient for extreme ratios larger than 50:1; and (3) conventional Transformers lack depth-dependent attention mechanisms incorporating stratigraphic continuity principles. This paper is dedicated to proposing a geological-constrained multi-scale Transformer framework tailored for 1D well-log sequences under extreme imbalance larger than 50:1. The systematic approach addresses the extreme imbalance by deep-feature fusion and advanced class-rebalancing strategies. Accordingly, this framework integrates multi-scale convolutional feature extraction using 1 × 3, 1 × 5, 1 × 7 kernels, hierarchical attention mechanisms with depth-aware position encoding based on Walther’s Law to model long-range dependencies, and adaptive three-stage class-rebalancing through SMOTE–Tomek hybrid resampling, focal loss, and CReST self-training. The experimental validation based on 32,847 logging samples demonstrates significant improvements: overall accuracy reaches 90.3% with minority class F1 scores improving by 20–25% percentage points (argillaceous siltstone 73.5%, calcareous sandstone 68.2%, coal seams 65.8%), and G-mean of 0.804 confirming the balanced recognition. Of note, the framework maintains stable performance even when there is extreme class imbalance at a ratio of up to 100:1 with minority class F1 scores above 64%, representing a two-fold improvement over the state-of-the-art methods, where former Transformer-based approaches degrade below. This paper provides the fundamental technical development for the intelligent transformation of oil and gas exploration, with extensive application prospects. Full article

Many studies have investigated tree-contact arcing ground faults. However, the effects of branch moisture content and wind speed are still not fully understood. Therefore, this paper addresses the wildfire risk caused by tree-contact arc grounding faults in distribution networks. A 10 kV distribution-line tree-contact arcing fault test platform is built. A two-dimensional multi-physics plasma model is also developed based on magnetohydrodynamics. Experiments and simulations are combined. The effects of wind speed, branch moisture content, and conductor type on arc evolution and fault characteristics are systematically studied. The results show that higher wind speed causes stronger arc-column deformation. The fault current contains more high-frequency components and sharp spikes. At 9 m/s and 16 m/s, the fault current shows strong disturbances and much lower stability. Higher moisture content increases the branch conductivity indirectly. It strengthens the carbonized conductive path and helps sustain stable arcing. For the high-moisture sample (64%), the current waveform is smooth, and its amplitude increases monotonically with fault development. For the low-moisture sample (30%), the current amplitude decreases, and spikes become more frequent. The arc tends to extinguish and reignite repeatedly, which indicates an unstable discharge process. The simulations further reveal the coupling between the arc-root temperature field and the airflow field under different wind speeds and conductivities. They also show clear differences in temperature evolution between bare conductors and insulated conductors. These findings provide experimental evidence and simulation support for identifying wildfires initiated by tree-contact arcing faults. Full article