Search Results (61,676)

Deep coalbed methane (CBM) resources possess significant potential. However, their development is challenged by geological characteristics such as high in situ stress and low permeability. Furthermore, existing production strategies often prove inadequate. In order to achieve long-term stable production of deep coalbed methane reservoirs and increase their final recoverable reserves, it is urgent to construct a scientific and reasonable drainage system. This study focuses on the deep CBM reservoir in the Daning-Jixian Block of the Ordos Basin. First, a thermal–hydraulic–mechanical (THM) multi-physics coupling mathematical model was constructed and validated against historical well production data. Then, the model was used to forecast production. Finally, key control measures for enhancing well productivity were identified through production strategy adjustment. The results indicate that controlling the bottom-hole flowing pressure drop rate at 1.5 times the current pressure drop rate accelerates the early-stage pressure drop, enabling gas wells to reach the peak gas production earlier. The optimized pressure drop rates for each stage are as follows: 0.15 MPa/d during the dewatering stage, 0.057 MPa/d during the gas production rise stage, 0.035 MPa/d during the stable production stage, and 0.01 MPa/d during the production decline stage. This strategy increases peak daily gas production by 15.90% and cumulative production by 3.68%. It also avoids excessive pressure drop, which can cause premature production decline during the stable phase. Consequently, the approach maximizes production over the entire life cycle of the well. Mechanistically, the 1.5× flowing pressure drop offers multiple advantages. Firstly, it significantly shortens the dewatering and production ramp-up periods. This acceleration promotes efficient gas desorption, increasing the desorbed gas volume by 1.9%, and enhances diffusion, yielding a 39.2% higher peak diffusion rate, all while preserving reservoir properties. Additionally, this strategy synergistically optimizes the water saturation and temperature fields, which mitigates the water-blocking effect. Furthermore, by enhancing coal matrix shrinkage, it rebounds permeability to 88.9%, thus avoiding stress-induced damage from aggressive extraction. Full article

The growing need for environmentally friendly separation processes has motivated the search for alternative solvents to petroleum-derived chemicals for the recovery of biosynthesized products. Although effective, conventional petroleum-based solvents pose major environmental and sustainability concerns, including pollution, ecotoxicity, human health risks, and high costs and energy demands for recycling. Consequently, current research and industrial practice increasingly focus on their replacement with safer and more sustainable alternatives. This study investigates the use of natural oils (i.e., grapeseed, sweet almond, and flaxseed oils) as renewable, biodegradable, and non-toxic diluents in reactive extraction systems for the separation of 7-aminocephalosporanic acid (7-ACA). The combination of these oils with tri-n-octylamine (TOA) as extractant enabled high extraction efficiencies, exceeding 50%. The system comprising 120 g/L tri-n-octylamine in grapeseed oil, an aqueous phase pH of 4.5, a contact time of 1 min, and a temperature of 25 °C resulted in a 7-ACA extraction efficiency of 63.4%. Slope analysis suggests that complex formation likely involves approximately one molecule each of tri-n-octylamine and 7-ACA, although the apparent order of the amine is reduced in systems using natural oils. This study highlights the potential of natural oil-based reactive extraction as a scalable and environmentally friendly method for 7-ACA separation, aligning with the principles of green chemistry and environmental biotechnology. Full article

This review examines the transformative impact of innovative artificial intelligence (AI) methods on energy productivity, industrial transformation, and digitalization in the context of energy economics, energy efficiency, and sustainability. AI-based tools are revolutionizing energy systems by optimizing production, reducing waste, and enabling predictive maintenance in industrial processes. Integrating AI increases operational efficiency across various sectors, significantly contributing to energy savings and cost reductions. Using deep learning (DL), machine learning (ML), and generative AI (genAI), companies can model complex energy consumption patterns and identify efficiency gaps in real time. Furthermore, AI supports the renewable energy transition by improving grid management, forecasting, and smart distribution. The review highlights how AI-assisted digitalization fosters smart production, resource allocation, and decarbonization strategies. Economic analyses indicate that AI implementation correlates with improved energy intensity indicators and long-term sustainability benefits. However, challenges such as data privacy, algorithm transparency, and infrastructure investment remain key barriers. This article synthesizes current literature and case studies to provide a comprehensive understanding of AI’s evolving role in transforming energy-intensive industries. These findings highlight AI’s crucial contribution to sustainable economic development through improved energy efficiency and digital innovation. Full article

Background/Objectives: Urinary tract infections (UTIs) represent a considerable challenge within the field of clinical medicine, as they are responsible for significant morbidity and intensify the operational pressures encountered by healthcare systems. Conventional diagnostic approaches, which include symptom evaluation, dipstick urinalysis, and standard urine culture, often demonstrate inadequacies in identifying atypical clinical manifestations, infections with low bacterial counts, or pathogens that show growth difficulties under typical laboratory conditions. These limitations undermine diagnostic accuracy and hinder timely therapeutic measures. Methods: The present manuscript is a systematic review conducted in accordance with PRISMA guidelines. A structured search was performed in PubMed, Scopus, and Google Scholar, yielding 573 records, of which 107 studies were included for qualitative synthesis. The primary aim of this systematic review is to evaluate both conventional and emerging diagnostic methods for UTIs, with specific objectives of assessing their clinical applicability, limitations, and potential to improve patient outcomes. Results: Recent progress in diagnostic technologies offers promising alternatives. Molecular-based assays, such as multiplex polymerase chain reaction, matrix-assisted laser desorption ionization mass spectrometry, and next-generation sequencing, have substantially improved both the precision and efficiency of pathogen identification. Furthermore, contemporary techniques for evaluating antimicrobial susceptibility, including microfluidic systems and real-time phenotypic resistance assays, enable clinicians to execute targeted therapeutic strategies with enhanced efficacy. Results of this synthesis indicate that while conventional diagnostics remain the cornerstone for uncomplicated cases, innovative molecular and phenotypic approaches demonstrate superior performance in detecting low-count bacteriuria, atypical pathogens, and resistance determinants, particularly in complicated and recurrent infections. These innovations support antimicrobial stewardship by reducing dependence on empirical antibiotic treatment and lessening the risk of resistance emergence. Conclusions: Nonetheless, the incorporation of these technologies into clinical practice requires careful consideration of implementation costs, standardization protocols, and the necessary training of healthcare professionals. In conclusion, this systematic review highlights that emerging molecular diagnostics and resistance-profiling tools offer substantial promise in complementing or enhancing traditional methods, but their widespread adoption will depend on robust validation, cost-effectiveness, and integration into clinical workflows. Full article

Traditional maritime target element resolution, relying on manual experience and uniform sampling, lacks accuracy and efficiency in non-uniform sampling, missing data, and noisy scenarios. While large language models (LLMs) offer a solution, their general knowledge gaps with maritime needs limit direct application. This paper proposes a fine-tuned LLM-based adaptive optimization method for non-uniform sampling maritime target element resolution, with three key novelties: first, selecting Doubao-Seed-1.6 as the base model and conducting targeted preprocessing on maritime multi-source data to address domain adaptation gaps; second, innovating a “Prefix tuning + LoRA” hybrid strategy (encoding maritime rules via Prefix tuning, freezing 95% of base parameters via LoRA to reduce trainable parameters to <0.5%) to balance cost and performance; third, building a non-uniform sampling-model collaboration mechanism, where the fine-tuned model dynamically adjusts the sampling density via semantic understanding to solve random sampling’s “structural information imbalance”. Experiments in close, away, and avoid scenarios (vs. five control models including original LLMs, rule-only/models, and ChatGPT-4.0) show that the proposed method achieves a comprehensive final score of 0.8133—37.1% higher than the sub-optimal data-only model (0.5933) and 87.7% higher than the original general model (0.4333). In high-risk avoid scenarios, its Top-1 Accuracy (0.7333) is 46.7% higher than the sub-optimal control, and Scene-Sensitive Recall (0.7333) is 2.2 times the original model; in close and away scenarios, its Top-1 Accuracy reaches 0.8667 and 0.9000, respectively. This method enhances resolution accuracy and adaptability, promoting LLM applications in navigation. Full article

This paper addresses the critical challenge of deploying a minimum number of wireless charging pads (WCPs) in obstacle-rich, large-scale Wireless Rechargeable Sensor Networks (WRSNs) to sustain drone operations. We assume a single base station, stationary sensors, convex polygonal obstacles that drones must avoid, and that both the base station and WCPs provide unlimited energy. To solve this, we propose the Outside-to-Inside Onion-Peeling (OIOP) strategy, a novel two-stage algorithm that prioritizes the coverage of the most remote sensors first and then refines the deployment by removing redundant pads while strictly adhering to obstacle constraints. Simulation results demonstrate OIOP’s superior efficiency: it reduces the number of required pads by approximately 10.83% ± 1.30% and 12.16% ± 1.59% compared to state-of-the-art methods (SMC and MC) and achieves execution times that are 58.02% ± 2.44% and 72.09% ± 2.88% faster, respectively. The algorithm also exhibits remarkable robustness, showing the smallest performance degradation as obstacle density increases. Full article

In this research article, a fast and efficient hybrid Maximum Power Point Tracking (MPPT) control technique is proposed for photovoltaic (PV) systems. The method combines two phases—offline and online—to estimate the appropriate duty cycle for operating the converter at the maximum power point (MPP). In the offline phase, temperature and irradiance inputs are used to compute the real-time reference peak power voltage through an Adaptive Neuro-Fuzzy Inference System (ANFIS). This estimated reference is then utilized in the online phase, where the Robust Backstepping Super-Twisting (RBST) controller treats it as a set-point to generate the control signal and continuously adjust the converter’s duty cycle, driving the PV system to operate near the MPP. The proposed RBST control scheme offers a fast transient response, reduced rise and settling times, low tracking error, enhanced voltage stability, and quick adaptation to changing environmental conditions. The technique is tested in MATLAB/Simulink under three different scenarios: continuous variation in meteorological parameters, sudden step changes, and partial shading. To demonstrate the superiority of the RBST method, its performance is compared with classical backstepping and integral backstepping controllers. The results show that the RBST-based MPPT controller achieves the minimum rise time of $0.018$$\mathrm{s}$, the lowest squared error of $0.3015$$\mathrm{V}$, the minimum steady-state error of $0.29$%, and the highest efficiency of $99.16$%. Full article

This study presents a stochastic Markov-based modeling framework for occupant behavior and residential lighting demand in Luxembourg. Integrating demographic data, time-use surveys, Markov chains, and dual-layer optimization, the model enhances the accuracy of non-HVAC energy demand simulations. The Harmonized European Time Use Surveys (HETUS) provide a detailed activity-based energy modeling approach, while Bayesian and constraint-based optimization improve data calibration and reduce modeling uncertainties. A Luxembourg-specific stochastic load profile generator links occupant activities to energy loads, incorporating occupancy patterns and daylight illuminance calculations. This study quantifies lighting demand variations across household types, validating results against empirical TUS data with a low mean squared error (MSE) and a minor deviation of +3.42% from EU residential lighting demand standards. Findings show that activity-aware dimming can reduce lighting demand by 30%, while price-based dimming achieves a 21.60% reduction in power demand. The proposed approach provides data-driven insights for energy-efficient residential lighting management, supporting sustainable energy policies and household-level optimization. Full article

This paper presents the implementation of a real-time nonlinear state observer applied to an erbium-doped fiber laser system. The observer is designed to estimate population inversion, a state variable that cannot be measured directly due to the physical limitations of measurement devices. Taking advantage of the fact that the laser intensity can be measured in real time, an observer was developed to reconstruct the dynamics of population inversion from this measurable variable. To validate and strengthen the estimate obtained by the observer, a Recurrent Wavelet First-Order Neural Network (RWFONN) was implemented and trained to identify both state variables: the laser intensity and the population inversion. This network efficiently captures the system’s nonlinear dynamic properties and complements the observer’s performance. Two metrics were applied to evaluate the accuracy and reliability of the results: the Euclidean distance and the mean square error (MSE), both of which confirm the consistency between the estimated and expected values. The ultimate goal of this research is to develop a neural control architecture that combines the estimation capabilities of state observers with the generalization and modeling power of artificial neural networks. This hybrid approach opens up the possibility of developing more robust and adaptive control systems for highly dynamic, complex laser systems. Full article

Algal biorefineries constitute an emerging platform for the sustainable production of renewable bioproducts; however, their economic viability remains constrained by the high costs associated with microalgal cultivation and biomass harvesting. This study investigated an integrated strategy combining macronutrient optimization with electrocoagulation–flocculation (ECF) harvesting for Chlorella vulgaris. A Central Composite Design (CCD) was employed to optimize concentrations of NaNO₃, KH₂PO₄, and MgSO₄ with the dual objective of maximizing biomass yield and enhancing biocompound content. Subsequently, the ECF process parameters—current density, electrolysis duration, pH, and electrolyte concentration—were optimized to improve harvesting efficiency. Under the optimal macronutrient conditions (NaNO₃: 100.00 mg/L; KH₂PO₄: 222.12 mg/L; MgSO₄: 100.84 mg/L), the model predicted a maximum biomass concentration of 0.475 g/L, along with 32.79% w/w carbohydrates and 6.79 mg/L chlorophyll-a. Optimal ECF harvesting conditions (current: 0.57 A; pH: 4.00; electrolysis time: 12.70 min; electrolyte: 1.74 g/L) achieved a biomass recovery efficiency of 89.51% w/v. These results demonstrate that coupling nutrient optimization with ECF-based harvesting offers a synergistic, scalable, and cost-effective pathway to improve the sustainability of algal biorefineries. Full article

Skin cancer is one of the most prevalent and potentially lethal cancers worldwide, highlighting the need for accurate and timely diagnosis. Convolutional neural networks (CNNs) have demonstrated strong potential in automating skin lesion classification. In this study, we propose a multi-class classification model using EfficientNet-B0, a lightweight yet powerful CNN architecture, trained on the HAM10000 dermoscopic image dataset. All images were resized to 224 × 224 pixels and normalized using ImageNet statistics to ensure compatibility with the pre-trained network. Data augmentation and preprocessing addressed class imbalance, resulting in a balanced dataset of 7512 images across seven diagnostic categories. The baseline model achieved 77.39% accuracy, which improved to 89.36% with transfer learning by freezing the convolutional base and training only the classification layer. Full network fine-tuning with test-time augmentation increased the accuracy to 96%, and the final model reached 97.15% when combined with Monte Carlo dropout. These results demonstrate EfficientNet-B0’s effectiveness for automated skin lesion classification and its potential as a clinical decision support tool. Full article

Background: To overcome the esthetic limitations of dental monolithic zirconia restorations, multichromatic systems were developed to combine improved structural integrity with a natural shade gradient that mimics the optical properties of natural teeth. In response to the clinical demand for time-efficient, i.e., chairside fabrication of zirconia restorations, rapid sintering protocols have become necessary to adjust clinical efficiency along with material performance. This study addresses the challenges of a rapid sintering protocol related to optical performance and phase transformation of the final restoration and the zirconia–cell interaction. Methods: The influence of a rapid sintering protocol on the color stability of the final dental restoration was evaluated by the CIE L*a*b* color space. Phase transformation was assessed through X-ray diffraction analysis. Cellular behavior was evaluated by measuring wettability, the material’s surface energy, and a cell mitochondrial activity assay on human gingival fibroblasts. Results: Optical measurements demonstrated that the total color change in all layers after rapid sintering was above the perceptibility threshold (ΔE* > 1.2), while only the polished enamel layer (ΔE* = 3.01) exceeded the acceptability threshold (ΔE* > 2.7), resulting in a clinically perceptible mismatch. Results of X-ray diffraction analysis, performed for fixed occupancy at Z_0.935Y_0.065O_0.984, revealed that rapid sintering caused a decrease in the cubic (C-) phase and an increase in the total amount of tetragonal (T-) phases. Conventionally sintered zirconia consists of 54% tetragonal (T-) and 46% cubic (C-) phase, whereas in the speed-sintered specimens, an additional T1 phase was detected (T = 49%; T1 = 27%), along with a reduced cubic fraction (C = 24%). Additionally, a small amount of the monoclinic (M) phase is noticed. Although glazing as a surface finishing procedure resulted in increased hydrophilicity, both polished and glazed surface-treated specimens showed statistically comparable cell adhesion and proliferation (p > 0.05). Conclusions: Rapid sintering induced perceptible color changes only in the enamel layer of multichromatic zirconia, suggesting that even layer-specific alterations may have an impact on the overall esthetic outcome of the final prosthetic restoration. Five times higher heating and cooling rates caused difficulty in reaching equilibrium, leading to changes in lattice parameters and the formation of the metastable T1 phase. Full article

This work details the development of a 10-stage solid-stage linear transformer driver (SSLTD) capable of producing 24 kV, 1 kA pulses with a rise-time of ∼10 ns utilizing SiC MOSFET switches. Throughout the development process, various design parameters were investigated for their influence on the LTD’s performance. Among these considerations was an evaluation of the behavior of several nanocrystalline magnetic core materials subject to high-voltage pulsed conditions, with an emphasis on minimizing energy losses. Another design parameter of interest lies in the physical layout of the LTD structure, particularly the diameter of the central stalk and the dielectric material, which together define the characteristics of the coaxial transmission line, as well as the overall height of each stage. The influence of each of these parameters was weighed to optimize the final design for fastest output pulse rise-time, highest efficiency, and cleanest output pulse waveform profile across varying load resistance. This work also introduces a pulsed reset technique, where repetition-rated burst testing was used to find the maximum operational frequency of the LTD without driving the magnetic cores into saturation. Full article

In response to the growing demand for automation in aerospace harness manufacturing, this study proposes a digital twin-based intelligent monitoring system for robotic wiring operations. The system integrates a seven-degree-of-freedom robotic platform with an adaptive servo gripper and employs a five-dimensional digital twin framework to synchronize physical and virtual entities. Key innovations include a coordinated motion model for minimizing joint displacement, a particle-swarm-optimized backpropagation neural network (PSO-BPNN) for adaptive gripping based on wire characteristics, and a virtual–physical closed-loop interaction strategy covering the entire wiring process. Methodologically, the system enables motion planning, quality prediction, and remote monitoring through Unity3D visualization, SQL-driven data processing, and real-time mapping. The experimental results demonstrate that the system can stably and efficiently complete complex wiring tasks with 1:1 trajectory reproduction. Moreover, the PSO-BPNN model significantly reduces prediction error compared to standard BPNN methods. The results confirm the system’s capability to ensure precise wire placement, enhance operational efficiency, and reduce error risks. This work offers a practical and intelligent solution for aerospace harness production and shows strong potential for extension to multi-robot collaboration and full production line scheduling. Full article

Shipping in complex marine environments requires a balance between navigational safety, minimising travel time and optimising logistics management, which is particularly challenging in areas with geometric obstructions and Offshore Wind Farms (OWFs). This study focuses on the maritime route networks in the Croatian ports of Pula and Rijeka, including the main access routes to OWFs and zones characterised by multiple navigational challenges. The aim of the research is to develop an empirically based and practically applicable framework for the optimisation of sea routes that combines analytical precision with operational efficiency. The parallel application of Dijkstra and A* algorithms enables a comparative analysis between deterministic and heuristic approaches in terms of reducing navigation risk, optimising route costs and ensuring fast logistical access to OWFs. The applied methods include the analysis of real and simulated route networks, the evaluation of statistical route parameters and the visualisation of the results for the evaluation of logistical and operational efficiency. Adaptive heuristic modifications of the A* algorithm, combined with the parallel implementation of Dijkstra’s algorithm, enable dynamic route planning that takes into account real-world conditions, including variations in wind speed and direction. The results obtained provide a comprehensive framework for safe, efficient and logistically optimised navigation in complex marine environments, with direct applications in the maintenance, inspection and operational management of OWFs. Full article