Eng

Accurate prediction of ship fuel consumption is essential for improving energy efficiency, optimizing mission planning, and ensuring operational integrity at sea. However, during complex tasks such as high-speed maneuvers, fuel consumption exhibits complex dynamics characterized by the coexistence of baseline drift and transient peaks that conventional models often fail to capture accurately, particularly the abrupt peaks. In this study, a hybrid prediction model, DGM-SVR, is presented, combining a rolling dynamic grey model (DGM (1,1)) with support vector regression (SVR). The DGM (1,1) adapts to the dynamic fuel consumption baseline and trends via a rolling window mechanism, while the SVR learns and predicts the residual sequence generated by the DGM, specifically addressing the high-amplitude fuel spikes triggered by maneuvers. Validated on a simulated dataset reflecting typical fuel spike characteristics during high-speed maneuvers, the DGM-SVR model demonstrated superior overall prediction accuracy (MAPE and RMSE) compared to standalone DGM (1,1), moving average (MA), and SVR models. Notably, DGM-SVR reduced the test set’s MAPE and RMSE by approximately 21% and 34%, respectively, relative to the next-best DGM model, and significantly improved the predictive accuracy, magnitude, and responsiveness in predicting fuel consumption spikes. The findings indicate that the DGM-SVR hybrid strategy effectively fuses DGM’s trend-fitting strength with SVR’s proficiency in capturing spikes from the residual sequence, offering a more reliable and precise method for dynamic ship fuel consumption forecasting, with considerable potential for ship energy efficiency management and intelligent operational support. This study lays a foundation for future validation on real-world operational data. Full article

Background: Electroencephalography (EEG) signals play a crucial role in diagnosing epilepsy by reflecting distinct patterns associated with normal brain activity, ictal (seizure) states, and interictal (between-seizure) periods. However, the manual classification of these patterns is labor-intensive, time-consuming, and depends heavily on specialized expertise. While deep learning methods have shown promise, many current models suffer from limitations such as excessive complexity, high computational demands, and insufficient generalizability. Developing lightweight and accurate models for real-time epilepsy detection remains a key challenge. Methods: This study proposes a novel dual-channel deep learning model to classify epileptic EEG signals into three categories: normal, ictal, and interictal states. Channel 1 integrates a bidirectional long short-term memory (BiLSTM) network with a Squeeze-and-Excitation (SE) ResNet attention module to dynamically emphasize critical feature channels. Channel 2 employs a dual-branch convolutional neural network (CNN) to extract deeper and distinct features. The model’s performance was evaluated on the publicly available Bonn EEG dataset. Results: The proposed model achieved an outstanding accuracy of 98.57%. The dual-channel structure improved specificity to 99.43%, while the dual-branch CNN boosted sensitivity by 5.12%. Components such as SE-ResNet attention modules contributed 4.29% to the accuracy improvement, and BiLSTM further enhanced specificity by 1.62%. Ablation studies validated the significance of each module. Conclusions: By leveraging a lightweight design and attention-based mechanisms, the dual-channel model offers high diagnostic precision while maintaining computational efficiency. Its applicability to real-time automated diagnosis positions it as a promising tool for clinical deployment across diverse patient populations. Full article

Nanomaterials are present everywhere today and represent the new industrial revolution. Depending on the application, there are many ways to synthesize nanomaterials with different properties. The industrial production of nanomaterials faces various challenges at different stages, going from conception and design to implementation and scaling-up of the production process, which can limit the growth of practical application at a large-scale scope, such as due to the lack of reproducibility, safety, and environmental impact. Here, we discuss current advances achieved for nanomaterial production at a large scale, encompassing a range of synthetic strategies and post-treatment modifications used to enhance the nanomaterials’ performance. A particular interest is devoted to highlighting the progress of MoS₂ nanomaterials’ application. Thus, overcoming those discussed challenges becomes a new prospect for the future perspectives of industrial nanomaterials and nanotechnologies. Full article

Since the beginning of the last decade, digital technological achievements have ushered the economies of developed countries into the fourth industrial revolution, transforming industries into smart ones, referred to as “Industry 4.0”, enabling them to innovate as a prerequisite for sustainable development and economic growth. At the same time, the European Union’s institutions are adopting strategies and programs to transform the European industry into a climate-neutral one, aiming to achieve this by 2050. The authors, participating in the introduction of Lean tools and digital technologies into one of the European shipyards using the “CULIS” (Connect Universal Lean Improvement System) methodology, recognize the high potential of its contribution to the European Commission’s guidelines for transitioning the economy to a sustainable one, and for this purpose, they present it in this paper. Namely, the methodology in question not only theoretically results in a “quick” implementation of tools and doctrines—with an approximately 36-month total duration of the process—but also encompasses as many as three transformations: Lean, digital, and green; an analysis of a methodology with such characteristics significantly adds to the originality of this study. The current stage of the observed shipyard’s “triple” transformation process already results in significant improvements—e.g., an increase in productivity by around 21% or a reduction in sales process costs by 38%. However, given its ongoing pilot phase, (further) analyses of improvements in (European) shipbuilding competitiveness and profitability that can be achieved through digital Lean management of projects’ realization process are implied. Full article

Modern fighter aircraft have an increasing need for at least a moderate level of stealth, and the shape design must bear a part of this constraint. However, the high frequency of close range radar makes high-fidelity radar cross-section computation methods such as the boundary element method too expensive to use in a gradient-free optimization process. On the other hand, asymptotic methods are not able to accurately predict the RCS of complex shapes such as intake cavities. Hence, the need arises for efficient and accurate methods to compute the gradient of high-fidelity radar cross-section computation methods with respect to shape parameters. In this paper, we propose an adjoint formulation for the boundary element method to efficiently compute these gradients. We present a novel approach to calculate the gradient of high-fidelity radar cross-section computations using the boundary element method. Our method employs an adjoint formulation that allows for the efficient computation of these gradients. This is particularly beneficial in shape optimization problems where accurate and efficient methods are crucial to designing modern fighter aircraft with stealth capabilities. By avoiding the need for solving the actual adjoint problem in certain cases, our formulation provides a more streamlined solution while still maintaining high accuracy. We demonstrate the effectiveness of our method by performing shape optimization on various shapes, including simple geometries like spheres and ellipsoids, as well as complex aircraft shapes with multiple variables. Full article

This work will present an algorithmic approach for robust control focusing on hydraulic–mechanical systems. The approach is applied to a hydraulic actuator driving a cart with an inverted pendulum. The algorithmic approach aims to satisfy two robust control requirements for single input single output (SISO) linear systems with nonlinear uncertain structure. The first control requirement is robust stabilization, and the second is robust asymptotic command following for arbitrary reference signals. The approach is analyzed in two stages. In the first stage, the stability regions of the controller parameters are identified. In the second stage, a Particle Swarm Optimization Algorithm (PSO) is applied to find suboptimal solutions for the controller parameters in these regions, with respect to a suitable performance cost function. The application of the approach to a hydraulic actuator, driving a cart with an inverted pendulum, satisfies the goal of achieving precise control of the pendulum angle, despite the system’s inherent physical uncertainties. Full article

Raman spectroscopy has become an indispensable analytical technique in pharmaceutical research, offering non-invasive, rapid, and chemically specific insights into pharmaceutical compounds. In this study, we present a comprehensive benchmark of machine learning models for classifying 32 pharmaceutical compounds based on their Raman spectral signatures. A diverse array of algorithms—including Support Vector Machines (SVMs), Random Forests, k-Nearest Neighbors (k-NN), Gradient Boosting (XGBoost, LightGBM), and 1D Convolutional Neural Networks (CNNs)—were evaluated on a publicly available dataset. The results demonstrate outstanding classification performance across models, with linear SVM achieving the highest accuracy of 99.88%, followed closely by CNN (99.26%). Ensemble methods such as Random Forest and XGBoost also yielded high accuracies above 98.3%. In addition to strong predictive performance, SHAP (SHapley Additive exPlanations) analysis was employed to interpret model decisions. CNN models, in particular, revealed well-localized and chemically meaningful spectral regions critical to classification. This combination of high accuracy and interpretability highlights the promise of explainable AI in pharmaceutical analysis and quality control, offering robust, transparent, and scalable solutions for real-world applications. Full article

The recent advancements in high-frequency, high-power switching devices require the development of non-invasive, cost-effective sensors for signal diagnostics. In this context, planar sensors have emerged as promising candidates for voltage and current sensing due to their compatibility with printed circuit boards (PCBs). However, previously proposed voltage planar sensors exhibit trade-offs between high bandwidths and responsivity, limiting their usage to sub-GHz applications. This study introduces a planar voltage sensor that leverages geometric optimization using software-assisted design to enhance bandwidth without compromising sensitivity. The optimized sensors demonstrate an extended bandwidth response up to 4 GHz and accurate recovery of fast transient signals validated through experimental measurements, which represents a significant step forward in broadband sensing for high-power applications. Full article

The present study focused on a comparative analysis of NaOH-treated and untreated Sida acuta fiber-reinforced composites with respect to their wear behavior and compressive strength. The Sida acuta fibers were treated with 5% NaOH, while the untreated fibers were used directly as reinforcement, both comprising 32 ± 1 wt.% of the epoxy matrix composites. The composites were further characterized based on their average density, hardness, and compressive strength. Additionally, weight loss, volume loss, and wear rate were examined under dry wear test conditions across various loads and sliding velocities. The results indicate that the alkali-treated fiber-reinforced composite exhibits superior hardness (84.3 ± 2.0) and compressive strength (99.89 ± 3.92 MPa), representing improvements of 12.57% and 13.5%, respectively, over the untreated fiber-reinforced composite. Moreover, the 5% NaOH-treated fiber-reinforced composite demonstrated lower wear rates compared to its untreated counterpart. Scanning Electron Microscopy (SEM) was employed to examine the dry wear surface morphology of both composite laminates, providing insights that support the observed test results. Overall, the developed Sida acuta composite exhibits promising properties, making it suitable for lightweight and medium-strength structural applications. Full article

In this study, a comprehensive experimental framework was developed to quantitatively characterize the pore structure of a deep coal-rock (DCR; reservoirs below [3000 m]) gas reservoir. Experimentally, petrological and mineral characteristics were determined by performing proximate analysis and scanning electron microscopy (SEM) as well as by measuring vitrinite reflectance and maceral components. Additionally, physisorption and high-pressure mercury injection (HPMI) tests were conducted to quantitatively characterize the nano- to micron-scale pores in the DCR gas reservoir at multiple scales. The DCR in the NQ Block is predominantly composed of vitrinite, accounting for approximately 77.75%, followed by inertinite. The pore space is predominantly characterized by cellular pores, but porosity development is relatively limited as most of such pores are extensively filled with clay minerals. The isothermal adsorption curves of CO₂ and N₂ in the NQ Block and the DJ Block exhibit very similar variation patterns. The pore types and morphologies of the DCR reservoir are relatively consistent, with a significant development of nanoscale pores in both blocks. Notably, micropore metrics per unit mass (pore volume (PV): 0.0242 cm³/g; and specific surface area (SSA): 77.7545 m²/g) indicate 50% lower gas adsorption potential in the DJ Block. In contrast, the PV and SSA of the mesopores per unit mass in the NQ Block are relatively consistent with those in the DJ and SF Blocks. Additionally, the peak mercury intake in the NQ Block occurs within the pore diameter < 20 nm, with nearly 60% of the mercury beginning to enter in large quantities only when the pore size exceeds 20 nm. This indicates that nanoscale pores are predominantly developed in the DCR of the NQ block, which aligns with the findings from physical adsorption experiments and SEM analyses. Overall, the development characteristics of multi-scale pores in the DCR formations of the NQ Block and the eastern part of the Basin are relatively similar, with both total PV and total SSA showing an L-shaped distribution. Due to the disparity in micropore SSA, however, the total SSA of the DJ Block is approximately twice that of the NQ Block. This discovery has established a robust foundation for the subsequent exploitation of natural gas resources in DCR formations within the NQ Block. Full article

The radial magnetic bearing system is an open-loop, unstable, strong nonlinear system with a high rotor speed, predisposition to jitter, and poor interference immunity. The system is subjected to the main interference generated by gravity, and rotor imbalance and sensor runout seriously affect the system’s rotor position control performance. A plug-in repetitive control method based on equivalent-input-disturbance (EID) is presented to address the issue of decreased control accuracy of the magnetic bearing system caused by disturbances from gravity, rotor imbalance, and sensor runout. First, a linearized model of the magnetic bearing rotor containing parameter fluctuations due to the eddy current effect and temperature rise effect is established, and a plug-in repetitive controller (PRC) is designed to enhance the rejection effect of periodic disturbances. Next, an EID system is introduced, and a Luenberger observer is used to estimate the state variables and disturbances of the system. The estimates of the EID are then used for feedforward compensation to address the issue of large overshoot in the system. Finally, simulations are conducted for comparison with the PID control method and PRC control method. The plug-in repetitive controller method assessed in this paper improves control performance by an average of 87.9% and 57.7% and reduces the amount of over-shooting by an average of 66.5% under various classes of disturbances, which proves the efficiency of the control method combining a plug-in repetitive controller with the EID theory. Full article

The rapid development of underground engineering contributes significantly to achieving China’s “dual carbon” strategic goals. However, during the construction and operation phases, this engineering project faces diverse risks and challenges related to disasters. Consequently, enhancing the evaluation capability for underground engineering resilience is imperative. Based on the characteristics of resilience evaluation and enhancement in underground engineering, this study defines the concept and objectives of resilience evaluation for underground space engineering and analyzes corresponding enhancement methods. By considering aspects such as the magnitude of collapse disaster risk in underground engineering, its vulnerability, resistance capacity, adaptability to disasters, recovery ability, and economic feasibility, a comprehensive index system for evaluating the resilience of collapse disaster risks in underground engineering has been established. This research suggests that disaster risk management should shift from passive to active prevention. Through resilience evaluation case applications, it is possible to improve the design objectives of underground engineering towards “structural recoverability”, “ease of damage repair”, and “controllable consequences after a disaster”. The integration of intelligent static assessment models based on artificial intelligence algorithms can effectively enhance the accuracy of resilience evaluations. Furthermore, dynamic assessments using multiple data fusion techniques combined with numerical simulations represent promising directions for improving the overall resilience of underground engineering. Full article

This paper presents a validated, cost-effective technique for the wideband characterization of low-height substrate materials in RF circuits. The method utilizes traditional resonant structures to accurately determine essential parameters—relative permittivity and loss tangent—and delivers reliable results even in the presence of unavoidable fabrication imperfections, thereby enhancing its reliability for RF designers. While it covers a broad frequency range (8–16 GHz), the proposed technique can be adapted for other ranges by modifying the resonant structure dimensions. By combining reflection coefficient and input impedance measurements of a multimode patch antenna, substrate properties are accurately extracted using an iterative numerical fitting process, i.e., secant algorithm. This approach provides RF designers with the precise material data necessary to enhance circuit performance and is especially useful for low-height substrates. The technique’s validity is demonstrated through excellent agreement between simulations and measurements, establishing that the technique provides a practical, solution for industrial and research applications. Full article

This manuscript addresses the issue of irrigation water management with high efficiency and effectiveness and focuses on systems associated with significant water losses, which is sprinkler irrigation. This article presents mathematical modeling that enables the application of precision irrigation using a gun sprinkler robot. The sprinkler robot was fabricated in the Faculty of Agriculture and Natural Resources workshop at As-wan University. The experiments were conducted using 12, 14, and 16 mm nozzle sizes and three gun heights, 1.25, 1.5, and 2 m, at three forward speeds, 25, 50, and 75 m/h. The results revealed that at nozzle 12, the actual wetted diameter would be less than the theoretical diameter by a percentage of 2–5%, while at nozzle 14, it ranged from 2 to 7%, but at nozzle 16, it increased from 6 to 9%. The values of evaporation and wind drift losses were always less than 2.8 mm. The highest efficiency was achieved at the lowest forward speed (25 m/h) and using a 1.5 m gun height. The highest water application efficiency was 81.8, 82.5, and 81.1% using nozzle 12, nozzle 14, and nozzle 16, respectively. Precise irrigation control using sensor and variable rate technology will be the preferred option in the future. Full article

This study explores the optimization of solar energy harvesting in Truro City in the UK using PVSyst simulations integrated with real-time meteorological data. Focusing on panel orientation, tilt angle, shading, and albedo, the research aimed to enhance both energy efficiency and economic viability of photovoltaic (PV) systems in green buildings. A 100 kWp rooftop solar installation served as the case study. Energy outputs derived from spreadsheet-based models and PVSyst simulations were compared to validate results. Optimal tilt angles were identified between 35° and 39°, and the azimuth angle of 0° yielded the highest energy gain without requiring solar tracking. Fixed configurations with a 5 m pitch showed only a 10% shading loss, requiring 1680 m² of space and generating an average of 646.83 kWh/m² monthly. Compared to recent works, our integration of real-time climate data improved simulation accuracy by 6–9%, refining operational planning and decision-making processes. This included better timing of high-load activities and enhanced prediction for grid feedback. The study demonstrates that data-driven optimization significantly improves performance reliability and system design, offering practical insights for solar infrastructure in similar temperate climates. These results provide a benchmark for urban energy planners seeking to balance performance and spatial constraints in PV deployment. Full article

Pervaporation is a proven technology that overcomes distillation to produce high-quality ethanol. The process is modeled and simulated in the literature, but the model’s accuracy with industrial data is rarely discussed. In this work, a commercial pervaporation membrane was modeled and simulated in UniSIM^® (R490) to purify ethanol from water after the azeotrope point of distillation. The pervaporation system was developed manually using a component splitter, adjust functions, and a spreadsheet. Results show that the simulation calculations were acceptable and differed from pilot-plant data by 4.6%. A correction factor was used to reduce the error further to 0.7%. Full article

In distribution systems, limited measurement configurations and communication constraints often result in a low success rate of data acquisition, posing challenges to both system observability and the real-time performance required by state estimation (SE). Consequently, the distribution system SE (DSSE) relies heavily on pseudo-measurements to supplement real-time data. However, existing pseudo-measurement models generally fail to adequately account for topology changes and may lead to numerical instability issues. To resolve these challenges, this paper presents a graph computing-based DSSE method with enhanced numerical stability for low-observability distribution systems. Specifically, a graph neural network (GNN) is employed to dynamically learn the coupling relationships between bus and branch electrical quantities to improve the credibility of pseudo-measurements. Additionally, a loop belief propagation (LBP) algorithm based on factor graphs is designed to capture the statistical discrepancies between real-time and pseudo-measurements, thus avoiding the impact of pseudo-measurement modeling on numerical stability. Numerical results on IEEE 33-bus and 95-bus test systems demonstrate that the proposed method effectively adapts to topology variations and significantly improves the accuracy of both pseudo-measurement modeling and DSSE. Full article

Many communities in the Brazilian Amazon region remain without reliable access to electricity due to geographical barriers and the high cost of connecting to the national grid. This study aims to evaluate the techno-economic feasibility of implementing battery storage systems in an existing isolated solar–diesel microgrid located in Tunui-Cachoeira, in the district of São Gabriel da Cachoeira (AM). The analysis uses an energy balance methodology, implemented through the HOMER Pro simulation platform, to assess three scenarios: (i) without batteries, (ii) with lithium-ion batteries, and (iii) with lead–acid batteries. Technical and economic indicators such as net present cost (NPC), levelized cost of energy (LCOE), diesel consumption, and renewable fraction were compared. The results indicate that incorporating lead–acid batteries yields the lowest LCOE (1.99 R$/kWh) and the highest renewable fraction (96.8%). This demonstrates that adding energy storage systems significantly enhances the performance and cost-effectiveness of microgrids, offering a viable path to electrify remote and hard-to-reach communities in the Amazon. Full article