Search Results (574)

Climate change has prompted the adoption of sustainable measures to reduce greenhouse gas (GHG) emissions, particularly in urban transportation. The integration of renewable energy sources, such as solar energy, offers a promising strategy to enhance sustainability in urban transit systems. This study assessed solar irradiation along the tram route in Cuenca—an Andean city characterized by distinctive topographic and climatic conditions—with the aim of evaluating the technical feasibility of integrating solar energy into the tram infrastructure. A descriptive, applicative, and longitudinal approach was adopted. Solar irradiation was monitored using a system composed of a fixed station and a mobile station, the latter installed on a tram vehicle. Readings carried out over fourteen months facilitated the analysis of seasonal and spatial variability of the available solar resource. The fixed station recorded average irradiation values ranging from 3.80 to 4.61 kWh/m²·day, while the mobile station reported values between 2.60 and 3.41 kWh/m²·day, revealing losses due to urban shading, with reductions ranging from 14.7% to 18.8% compared to fixed-site values. It was estimated that a fixed photovoltaic system of up to 1.068 MWp could be installed at the tram maintenance depot using 580 Wp panels, with the capacity to supply approximately 81% of the annual electricity demand of the tram system. Complementary solar installations at tram stops, stations, and other related infrastructure are also proposed. The results demonstrate the technical feasibility of integrating solar energy—through fixed and mobile systems—into the tram infrastructure of Cuenca. This approach provides a scalable model for energy planning in urban transport systems in Andean contexts or other regions with similar characteristics. Full article

Proton therapy enables precise dose delivery to tumors while sparing healthy tissues, offering significant advantages over conventional radiotherapy. Accurate prediction of biological doses requires detailed knowledge of radiation interactions with biological targets, especially DNA, a key site of radiation-induced damage. While most biophysical models (LEM, mMKM, NanOx) rely on water as a surrogate, this simplification neglects the complexity of real biomolecules. In this work, we calculate the stopping power and range of protons in liquid water, dry DNA, and hydrated DNA using semi-empirical cross sections for ionization, electronic excitation, electron capture, and electron loss by protons and neutral hydrogen in the 10 keV–100 MeV energy range. Additionally, ionization cross sections for uracil are computed to explore potential differences between DNA and RNA damage. Our results show excellent agreement with experimental and ab initio data, highlighting significant deviations in stopping power and range between water and DNA. Notably, the stopping power of DNA exceeds that of water at most energies, reducing proton ranges in dry and hydrated DNA by up to 20% and 26%, respectively. These findings provide improved input for Monte Carlo simulations and biophysical models, enhancing RBE predictions and dose accuracy in hadrontherapy. Full article

Induction motors are a common component of electromechanical equipment in mining operations, yet they are susceptible to failures resulting from frequent start–stops, overloading, wear and tear, and component failure. It is evident that such failures can result in severe ramifications, encompassing industrial accidents and economic losses. The present paper proposes a detailed study of engine fault diagnosis technology. It has been demonstrated that prevailing intelligent engine diagnosis algorithms exhibit a limited diagnostic efficacy under variable operating conditions, and the reliability of diagnostic outcomes based on individual signals is questionable. The present paper puts forward the proposition of an investigation into a fault diagnosis algorithm for induction motors. This investigation utilized a range of analytical methods, including signal analysis, deep learning, transfer learning, and information fusion. Currently, the methods employed for fault diagnosis based on traditional machine learning are reliant on the selection of statistical features by those with expertise in the field, resulting in outcomes that are significantly influenced by human factors. This paper is the first to integrate a multi-branch ResNet strategy combining three-phase and single-phase currents. A range of three-phase current input strategies were developed, and a deep learning-based motor fault diagnosis model with adaptive feature extraction was established. This enables the deep residual network to extract fault depth features from the motor current signal more effectively. The experimental findings demonstrate that deep learning possesses the capacity to automatically extract depth features, thereby exceeding the capabilities of conventional machine learning algorithms with regard to the accuracy of motor fault diagnosis. Full article

Drilling fluid losses into formation voids are among the major issues that lead to increases in the costs and nonproductive time of operations. Lost circulation materials have been widely used to stop or mitigate losses. In most cases, the size of the loss zone is not known, making conventional lost circulation materials unsuitable for plugging the loss zone. In this study, novel temperature-sensitive LCM (TS-LCM) particles composed of diglycidyl ether of bisphenol A (DGEBA) and 4,4′-diaminodiphenyl methane were prepared. It is a thermal-response shape-memory polymer. The molecular structure was analyzed by Fourier transform infrared spectroscopy. The glass transition temperature (T_g) was tested by Different scanning calorimetry (DSC). The shape-memory properties were evaluated by a bend-recovery test instrument. The expansion and mechanical properties of particles were investigated under high temperature and high pressure. Fracture sealing testing apparatus was used to evaluate sealing performance. The mechanism of sealing fracture was discussed. Research results indicated that the T_g of the TS-LCM was 70.24 °C. The shape fixation ratio was more than 99% at room temperature, and the shape recovery ratio was 100% above the T_g. The particle was flaky before activation. It expanded to a cube shape, and the thickness increased when activated. The rate of particle size increase for D90 was more than 60% under 120 °C and 20 MPa. The activated TS-LCM particles had high crush strength. The expansion of the TS-LCM particles could self-adaptively bridge and seal the fracture without knowing the width. The addition of TS-LCM particles could seal the tapered slot with entrance widths of 2 mm, 3 mm and 4 mm without changing the lost circulation material formulation. The developed TS-LCM has good compatibility with local saltwater-based drilling fluid. In field tests in the Yan’an area of the Ordos Basin, 15 shale oil horizontal wells were plugged with excellent results. The equivalent circulating density of drilling fluid leakage increased by an average of 0.35 g/cm³, and the success rate of plugging malignant leakage increased from 32% to 82.5%. The drilling cycle was shortened by an average of 14.3%, and the effect of enhancing the pressure-bearing capacity of the well wall was significant. The prepared TS-LCM could cure fluid loss in a fractured formation efficiently. It has good prospects for promotion. Full article

Background: Brain tumors can severely impair neurological function, leading to symptoms such as headaches, memory loss, motor coordination deficits, and visual disturbances. In severe cases, they may cause permanent cognitive damage or become life-threatening without early detection. Methods: To address this, we propose an interpretable deep ensemble model for tumor detection in Magnetic Resonance Imaging (MRI) by integrating pre-trained Convolutional Neural Networks—EfficientNetB7, InceptionV3, and Xception—using a soft voting ensemble to improve classification accuracy. The framework is further enhanced with a Light Gradient Boosting Machine as a meta-learner to increase prediction accuracy and robustness within a stacking architecture. Hyperparameter tuning is conducted using Optuna, and overfitting is mitigated through batch normalization, L2 weight decay, dropout, early stopping, and extensive data augmentation. Results: These regularization strategies significantly enhance the model’s generalization ability within the BR35H dataset. The framework achieves a classification accuracy of 99.83 on the MRI dataset of 3060 images. Conclusions: To improve interpretability and build clinical trust, Explainable Artificial Intelligence methods Grad-CAM++, LIME, and SHAP are employed to visualize the factors influencing model predictions, effectively highlighting tumor regions within MRI scans. This establishes a strong foundation for further advancements in radiology decision support systems. Full article

IP spoofing is a critical component in many cyberattacks, enabling attackers to evade detection and conceal their identities. This study rigorously compares eight deep learning models—LSTM, GRU, CNN, MLP, DNN, RNN, ResNet1D, and xLSTM—for their efficacy in detecting IP spoofing attacks. Overfitting was mitigated through techniques such as dropout, early stopping, and normalization. Models were trained using binary cross-entropy loss and the Adam optimizer. Performance was assessed via accuracy, precision, recall, F1 score, and inference time, with each model executed a total of 15 times to account for stochastic variability. Results indicate a powerful performance across all models, with LSTM and GRU demonstrating superior detection efficacy. After ONNX conversion, the MLP and DNN models retained their performance while achieving significant reductions in inference time, miniaturized model sizes, and platform independence. These advancements facilitated the effective utilization of the developed systems in real-time network security applications. The comprehensive performance metrics presented are crucial for selecting optimal IP spoofing detection strategies tailored to diverse application requirements, serving as a valuable reference for network anomaly monitoring and targeted attack detection. Full article

Background/Objectives: The optimal protein dose for critically ill cancer patients, especially for muscle mass preservation and survival, remains unclear. This study evaluated whether a higher protein intake, compared to usual intake, was associated with improved clinical outcomes in this population. Methods: This was a prospective analysis of critically ill adult cancer patients admitted to an oncological intensive care unit (ICU). Patients were initially assigned to receive protein prescriptions of either 1.5 or 2.0 g per kilogram per day (g/kg/day), but due to common limitations in achieving prescribed targets in this setting, a prespecified per-protocol analysis was conducted. After three days of exclusive nutritional therapy, patients were reclassified into two groups based on actual protein intake: >1.5 g/kg/day (higher intake group, IG) and ≤1.5 g/kg/day (usual intake group, CG). The primary outcome was muscle mass preservation, measured by quadriceps muscle thickness (QMT) via ultrasound on days 1, 7, and 14. Secondary outcomes included ICU survival, hospital and ICU length of stay, mechanical ventilation duration, dialysis requirement, and 60-day survival. Results: From June 2019 to September 2023, 117 patients were included. Following the planned interim analysis, the study was stopped after meeting the Pocock criterion for ICU survival (p = 0.0013). After reclassification, 68.4% (n = 80) were in the IG and 31.6% (n = 37) in the CG. ICU stay was similar (both medians 13 days), but the IG had shorter hospital stays [21.0 vs. 27.5 days, p = 0.020], less QMT loss, and improved ICU (HR = 0.31, 95% CI 0.15–0.64), hospital (HR = 0.43, CI 0.23–0.80), and 60-day survival (HR = 0.43, CI 0.23–0.80), along with shorter ventilation duration (HR = 0.54, CI 0.30–0.99). Conclusions: Higher protein intake (>1.5 g/kg/day) was associated with better muscle mass preservation and improved clinical outcomes in critically ill cancer patients. Full article

Progestins have shown to be highly effective, adequately safe contraceptives and a real alternative in the cases with contraindications to estrogen use. This review assesses the effectiveness of treatments for managing uterine bleeding due to the use of progestin-only contraceptives. A systematic review of databases such as Embase, LILACS, CINAHL, and PubMed, with a result of 701 studies published in English between 2005 and 2022, was carried out. 21 randomized clinical trials were analyzed. There were effective non-hormonal alternatives for managing progestin-related bleeding. While several treatments show promise, results must be replicated by studies with longer follow-up periods. Differences came from the five new studies and their good methodological quality—specifically, blinding and assessment of population losses. Benefits were the stop of uterine bleeding and a shortened duration of these episodes, with studies generally reporting satisfaction among their patients. There were few adverse effects seen, with the most common being headaches and nausea, which should be addressed by future studies. These results are expected to be of use for health professionals and midwives working on contraceptive management. Full article

To alleviate the pressure on grid regulation and ensure grid safety, pumped storage power stations need to frequently start and stop and change operating conditions, leading to the pump-turbine easily entering the hump characteristic zone, causing flow oscillation within the unit and significant changes in its input power, resulting in increased vibration and grid connection failure. The spatial distribution of energy losses and the hydrodynamic flow features within the hump zone of a pump-turbine under pumped storage operation are the focus of the study. The SST k-ω turbulence model is applied in CFD simulations of the pump-turbine within this work, focusing on the unstable operating range of the positive slope, with model testing providing experimental support. The model test method combines numerical simulation with experimental verification. The LEPR method is used to quantitatively investigate the unstable phenomenon in the hump zone, and the distribution law of energy loss is discussed. The results show that, at operating points in the hump zone, up to 72–86% of the energy dissipation is attributed to the runner, the guide vane passage, and the double vane row assembly within the guide vane system. The flow separation in the runner’s bladeless area evolves into a vortex group, leading to an increase in runner energy loss. With decreasing flow rate, the impact and separation of the water flow intensify the energy dissipation. The high-speed gradient change and dynamic–static interference in the bladeless area cause high energy loss in the double vane row area, and energy loss mainly occurs near the bottom ring. In the hump operation zone, the interaction between adverse flows such as vortices and recirculation and the passage walls directly drive the sharp rise in energy dissipation. Full article

As an important representation form of three-dimensional scenes, the point cloud contains rich geometry and attribute information. The video-based point cloud compression standard (V-PCC) divides and projects three-dimensional data directionally onto a two-dimensional plane. The generated geometric and attribute graphs contain occupied pixels obtained by projection and unoccupied pixels used for smooth filling. Among them, the non-occupied pixels have no practical effect on the reconstructed point cloud. However, in the process of encoding bitrate allocation, V-PCC still uses the original bitrate control method, resulting in insufficient bitrate utilization efficiency. To this end, this paper proposes a method for optimizing the unoccupied pixels of point cloud videos based on V-PCC and jointly controlling the coding rate of geometries and attribute graphs. For geometric graphs, this paper improves the allocation of bitrate weights based on whether the encoded blocks contain non-occupied pixels and the proportion of occupied pixels, and stops allocating bitrates to encoded blocks that are all non-occupied pixels. For the attribute graph, the input pixel improvement algorithm is designed by using the occupation map, and the invalid unoccupied pixel information is cavitation. Experiments show that under the All Intra configuration, compared with the original scheme, this method reduces the Geom.BD-GeomRate by an average of 15.67% and 16.68%, respectively, in the point-to-point D1 and point-to-face D2 metrics. The end-to-end BD-AttrRate is reduced by an average of 4.38%, 0.68%, and 1.74%, respectively. Overall, the average savings are 29.88%, 31.50%, 5.50%, 2.66%, and 3.34%, respectively, achieving bitrate optimization and effectively controlling encoding loss. Full article

Sugarcane, a key sugar crop in China, is predominantly manually harvested. In the main sugarcane-producing areas of China, typhoons cause canes to become lodged, resulting in high field losses and low harvesting efficiency. This study aimed to reduce these losses by analyzing the causes: ineffective stalk pickup, transfer, and conveyance. The tests showed the stalk–steel static friction coefficient (SFC) was lower than the stalk–soil SFC. Conventional basecutters use raised patterns to enhance friction, but soil adhesion makes them ineffective, hindering lodged stalk pickup. Bent stalks also struggle to enter butt lift rollers or pass through roller trains, increasing losses. The proposed improvements included adding toothed plates on the cutter discs, optimized disc–roller positioning, and using fewer rollers (one butt lift and one feed roller pair). Theoretical analysis confirmed the toothed plates improved pickup via grabbing force, while using fewer rollers stopped the stalks detaching from and blocking the roller train. A prototype was tested via orthogonal experiments, showing a field loss ratio of 1.21%, a feed rate of 13.09 kg/s, and a billet qualification rate of 95.82% with optimal settings (chopper speed: 390 rpm; 10 stalks/group; roller speed: 230 rpm; ground speed: 1.41 m/s). Field tests achieved 2.0% loss, demonstrating effectiveness for severely lodged cane, a significant improvement over the conventional harvesters (15–20% loss). These findings aid low-loss-level harvester development. Full article

Timely, balanced, and transparent detection of retinal diseases is essential to avert irreversible vision loss; however, current deep learning screeners are hampered by class imbalance, large models, and opaque reasoning. This paper presents a lightweight attention-augmented convolutional neural network (CNN) that addresses all three barriers. The network combines depthwise separable convolutions, squeeze-and-excitation, and global-context attention, and it incorporates gradient-based class activation mapping (Grad-CAM) and Grad-CAM++ to ensure that every decision is accompanied by pixel-level evidence. A 5335-image ten-class color-fundus dataset from Bangladeshi clinics, which was severely skewed (17–1509 images per class), was equalized using a synthetic minority oversampling technique (SMOTE) and task-specific augmentations. Images were resized to $150\times 150$ px and split 70:15:15. The training used the adaptive moment estimation (Adam) optimizer (initial learning rate of $1\times {10}^{-4}$, reduce-on-plateau, early stopping), ${\ell }_2$ regularization, and dual dropout. The 16.6 M parameter network converged in fewer than 50 epochs on a mid-range graphics processing unit (GPU) and reached 87.9% test accuracy, a macro-precision of 0.882, a macro-recall of 0.879, and a macro-F1-score of 0.880, reducing the error by 58% relative to the best ImageNet backbone (Inception-V3, 40.4% accuracy). Eight disorders recorded true-positive rates above 95%; macular scar and central serous chorioretinopathy attained F1-scores of 0.77 and 0.89, respectively. Saliency maps consistently highlighted optic disc margins, subretinal fluid, and other hallmarks. Targeted class re-balancing, lightweight attention, and integrated explainability, therefore, deliver accurate, transparent, and deployable retinal screening suitable for point-of-care ophthalmic triage on resource-limited hardware. Full article

This study aimed to evaluate the cardioprotective effects of two different doses of saroglitazar (SAR) in an animal model of cardiotoxicity induced by 5-fluorouracil (5-FU). Thirty-five rats were randomly allocated into five groups: the negative control, which received distilled water; the 5-FU (150 mg/kg as I.P.) group; the N-acetylcysteine (100 mg/kg) group; and the SAR (0.5 and 5 mg/kg) groups. The last three groups received 5-FU on day 10 along with their treatment. An open field test was performed at zero-time and at the end of the study. On day eleven the animals were euthanized and blood samples were used for measuring troponin I, CK-MB, natriuretic peptide, lipid profile, LDH, ALT, AST, CRP, ESR, TNF-α, IL1β, MDA, and total antioxidant capacity (TAOC). Cardiac tissues were sent for histopathological examination. The study revealed that 5-FU elevated the levels of cardiac-specific and injury-related biomarkers, inflammatory and oxidative stress markers, and that the use of SAR, particularly the high dose, decreased all the cardiac- and other injury-related biomarkers as well as attenuating inflammatory and oxidative stress biomarkers. SAR-treated groups exhibited a significant increase in locomotor activity and a decrease in anxiety-like behavior, indicated by a reduction in time spent in one square and an increase in total movement time. Additionally, the histopathological findings greatly supported the biochemical results evidenced by stopping the detrimental effects caused by 5-FU through structural and functional alterations of cardiac tissues manifested as ameliorating congestion, inflammation, degeneration, arterial wall thinning, and endothelial loss. The dual-acting PPAR agonist SAR demonstrated cardiac protection activity, particularly the high dose, by attenuating cardiac-specific and nonspecific injury biomarkers along with anti-inflammatory and antioxidant activities and attenuated anxiety induced by 5-FU. These findings render SAR a promising candidate to be tested in clinical trials. Further studies are warranted with other cardiotoxicants to confirm these findings. Full article

In the original randomised Dietary Intervention to Stop Coronary Atherosclerosis (DISCO-CT) trial, a 12-month Dietary Approaches to Stop Hypertension (DASH) project led by dietitians improved cardiovascular and metabolic risk factors and reduced platelet chemokine levels in patients with coronary artery disease (CAD). It is unclear whether these benefits are sustained. Objective: To determine whether the metabolic, inflammatory, and clinical benefits achieved during the DISCO-CT trial are sustained six years after the structured intervention ended. Methods: Ninety-seven adults with non-obstructive CAD confirmed in coronary computed tomography angiography were randomly assigned to receive optimal medical therapy (control group, n = 41) or the same therapy combined with intensive DASH counselling (DASH group, n = 43). After 301 ± 22 weeks, 84 individuals (87%) who had given consent underwent reassessment of body composition, meal frequency assessment, and biochemical testing (lipids, hs-CRP, CXCL4, RANTES and homocysteine). Major adverse cardiovascular events (MACE) were assessed. Results: During the intervention, the DASH group lost an average of 3.6 ± 4.2 kg and reduced their total body fat by an average of 4.2 ± 4.8 kg, compared to an average loss of 1.1 ± 2.9 kg and a reduction in total body fat of 0.3 ± 4.1 kg in the control group (both p < 0.01). Six years later, most of the lost body weight and fat tissue had been regained, and there was a sharp increase in visceral fat area in both groups (p < 0.0001). CXCL4 decreased by 4.3 ± 3.0 ng/mL during the intervention and remained lower than baseline values; in contrast, in the control group, it initially increased and then decreased (p < 0.001 between groups). LDL cholesterol and hs-CRP levels returned to baseline in both groups but remained below baseline in the DASH group. There was one case of MACE in the DASH group, compared with four cases (including one fatal myocardial infarction) in the control group (p = 0.575). Overall adherence to the DASH project increased by 26 points during counselling and then decreased by only four points, remaining higher than in the control group. Conclusions: A one-year DASH project supported by a physician and dietitian resulted in long-term suppression of the proatherogenic chemokine CXCL4 and fewer MACE over six years, despite a decline in adherence and loss of most anthropometric and lipid benefits. It appears that sustained systemic reinforcement of behaviours is necessary to maintain the benefits of lifestyle intervention in CAD. Full article

In scientific research and engineering practice, the design of deep spiking neural network (DSNN) architectures remains a complex task that heavily relies on the expertise and experience of professionals. These architectures often require repeated adjustments and modifications based on factors such as the DSNN’s performance, resulting in significant consumption of human and hardware resources. To address these challenges, this paper proposes an innovative evolutionary membrane algorithm for optimizing DSNN architectures. This algorithm automates the construction and design of promising network models, thereby reducing reliance on manual tuning. More specifically, the architecture of DSNN is transformed into the search space of the proposed evolutionary membrane algorithm. The proposed algorithm thoroughly explores the impact of hyperparameters, such as the candidate operation blocks of DSNN, to identify optimal configurations. Additionally, an early stopping strategy is adopted in the performance evaluation phase to mitigate the time loss caused by objective evaluations, further enhancing efficiency. The optimal models identified by the proposed algorithm were evaluated on the CIFAR-10 and CIFAR-100 datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, showing significant improvements in accuracy compared to the existing state-of-the-art methods. This work highlights the potential of evolutionary membrane algorithms to streamline the design and optimization of DSNN architectures, offering a novel and efficient approach to address the challenges in the applications of automated parameter optimization for DSNN. Full article