Search Results (20,690)

An efficient Battery Management System (BMS) specifically for Electric Vehicles is crucial for improving battery run time performance. A primary function of an effective BMS is accurately determining the State of Charge (SOC) and State of Health (SOH) of lithium-ion batteries in Electric Vehicles (EVs). However, many existing studies have concentrated on examining sensor malfunctions in batteries to avert problems such as overcharging and overheating and are lacking in terms of effective handling of non-linear behaviors. To overcome these limitations, the proposed work introduces a hybrid approach for estimating the state of lithium-ion batteries. It employs an Extended Kalman Filter (EKF) for SOC estimation and modified Multi-Layer Perceptron (MLP) for SOH estimation in batteries. It can handle the non-linear characteristics often exhibited by sensor readings and fault behaviors. The EKF algorithm involves initialization, prediction, and correction phases, allowing for accurate state estimation based on measurements. For SOH estimation, the NASA battery dataset, which includes various battery conditions across different temperatures, is analyzed using a modified MLP regression process. This modified MLP employs a gradient shift bias adjustment technique to minimize error rates by refining the gradients and biases introduced during the training process. It also effectively adjusts the model’s weights for better SOH estimation. The results demonstrate improved accuracy in battery performance, as indicated by lower RMSE, MSE, MAE and R² values. Furthermore, the study highlights the effectiveness of this hybrid method for significant battery management at different temperatures, which emphasizes the potential of this model, with enhanced state estimation for EV applications. Full article

This paper presents an experimental and numerical investigation of submerged vegetation flow, with a particular focus on vegetation-related terms, especially in the vicinity of the free end. Experimental results indicate that substantial shear stress is observed near the top of vegetation, where the drag coefficient increases significantly due to the disturbance caused by the free end. Furthermore, wake generation is notably suppressed, particularly at heights where wake-generated turbulence dominates, leading to a reduction in turbulent kinetic energy (TKE). A numerical model based on the volume-averaged Reynolds-averaged Navier–Stokes (VARANS) equations was developed, incorporating a vertically varying drag coefficient. The two-scale $⟨k⟩-⟨\epsilon ⟩$ turbulence model is further modified with the inclusion of a new damping function to capture the suppression of wake generation. The model accurately simulates both unidirectional and oscillatory flows, as well as the associated turbulence structures, with good agreement with experimental measurements. The influence of the tips on wave-induced currents, mass transport and TKE distribution is also investigated. It was found that the tip effects play a significant role in strengthening wave-induced currents at the top of loosely arranged, short, and sparse vegetation, with shear kinetic energy (SKE) serving as a critical component of TKE, contributing to the nonuniform distribution. Both Eulerian currents and Stokes drift contribute to streaming in the direction of wave propagation near the vegetation top, which intensifies with increasing solid volume fraction, while tip effects further enhance the onshore mass transport. Within the vegetation, mass transport is more sensitive to wave period and wave height, shifting from onshore to offshore as wavelength increases under constant water depth. Full article

Sound speed profiles (SSPs) must be detected simultaneously to perform a multibeam depth survey. Accurate real-time sound speed profile (SSP) acquisition remains a critical challenge in deep-sea multibeam bathymetry due to the limitations regarding direct measurements under harsh operational conditions. To address the issue, we propose a joint inversion framework integrating World Ocean Atlas 2023 (WOA23) temperature–salinity model data, historical in situ SSPs, and surface sound speed measurements. By constructing a high-resolution regional sound speed field through WOA23 and historical SSP fusion, this method effectively mitigates spatiotemporal heterogeneity and seasonal variability. The artificial lemming algorithm (ALA) is introduced to optimize the inversion of empirical orthogonal function (EOF) coefficients, enhancing global search efficiency while avoiding local optimization. An experimental validation in the northwest Pacific Ocean demonstrated that the proposed method has a better performance than that of conventional substitution, interpolation, and WOA23-only approaches. The results indicate that the mean absolute error (MAE), root mean square error (RMSE), and maximum error (ME) of SSP reconstruction are reduced by 41.5%, 46.0%, and 49.4%, respectively. When the reconstructed SSPs are applied to multibeam bathymetric correction, depth errors are further reduced to 0.193 m (MAE), 0.213 m (RMSE), and 0.394 m (ME), effectively suppressing the “smiley face” distortion caused by sound speed gradient anomalies. The dynamic selection of the first six EOF modes balances computational efficiency and reconstruction fidelity. This study provides a robust solution for real-time SSP estimation in data-scarce deep-sea environments, particularly for underwater autonomous vehicles. This method effectively mitigates the seabed distortion caused by missing real-time SSPs, significantly enhancing the accuracy and efficiency of deep-sea multibeam surveys. Full article

Three-dimensional measurement technologies based on computer vision have been developed with the aim of achieving perceptual speeds equivalent to humans (30 fps). However, in a highly mechanized society, there is no need for computers and robots to work slowly to match the speed of human perception. From this kind of circumstance, high-speed 3D vision with speeds far beyond that of humans, such as 1000 fps, has emerged. High-speed 3D measurement has great applicability not only for accurately recognizing a moving and deforming target but also for enabling real-time feedback, such as manipulation of the dynamic targets based on the measurement. In order to accelerate 3D vision and control the dynamic targets in real time, high-speed vision devices and high-speed image processing algorithms are essential. In this review, we revisit the basic strategy, triangulation as a suitable measurement principle for high-speed 3D vision, and introduce state-of-the-art 3D measurement methods based on high-speed vision devices and high-speed image processing utilizing structured light patterns. In addition, we introduce recent applications using high-speed 3D measurement and show that high-speed 3D measurement is one of the key technologies for real-time feedback in various fields such as robotics, mobility, security, interface, and XR. Full article

Methane is a powerful greenhouse gas and short-lived climate pollutant generated in landfills. In this work, five first-order decay models were implemented to estimate the methane emissions from a landfill near Oaxaca city. The five models were the simple first-order decay model, the modified first-order decay model, the multiphase model, the LandGem model, and the Intergovernmental Panel on Climate Change (IPCC) model. An autoregressive integrated moving average (ARIMA) model was built to predict waste generation, and a gravimetric method was used to estimate the volume of stored waste. The ARIMA model correctly predicted the generation of municipal solid waste, calculating 108,202 tons of solid waste in the landfill for the year 2022. In terms of the models and considering the experimental data measured in 2020, the simple model and the simple modified model were more accurate, with 3.50 × 10⁶ m³ (relative error = 1.0) and 3.76 × 10⁶ m³ of methane (relative error = 6.3), respectively. The multiphase model calculated a value of 3.09 × 10⁶ m³ of methane (relative error = 12.6), the LandGEM model calculated a value of 4.97 × 10⁶ m³ (40.7), and the IPCC model calculated a value of 3.19 × 10⁶ m³ (relative error = 9.7). The LandGEM model was improved when the standard values proposed by the Environmental Protection Agency (EPA) were considered. According to the simple model and the simple modified model, by 2050, the landfill will emit 1.22 × 10⁶ m³ and 1.37 × 10⁶ m³, demonstrating that important methane emissions will be released in the decades to come. This information is important for the implementation of methane mitigation strategies, to which Mexico has committed in the Global Methane Initiative. Full article

Accurate prognosis at first treatment for bovine respiratory disease (BRD) is essential for timely interventions and management decisions. This cross-sectional observational study evaluated 819 commercial beef feedyard cattle at chute-side for first BRD treatment. Logistic regression models examined potential associations between two outcomes—first treatment failure (requiring additional treatment) and unfinished treatment (due to mortality or culling)—and several explanatory variables, including sex, days on feed, bodyweight, breed, pulse oximetry, lung auscultation scores, and ultrasound lung scores (ULS) measured in the caudo-dorsal lung region. Animals that ultimately did not finish treatment were significantly more likely to present a ULS of 5 (74%) compared with those scored 1–4 (18–38%). Similarly, cattle with a ULS of 5 had a much higher probability of first treatment failure (74%) than those with scores of 1–3 (35–41%). Moreover, three or more B-lines in the ultrasound image or a “moth sign” finding were both strongly associated with increased probability of negative outcomes. These results highlight key ultrasound-based and demographic factors that serve as practical prognostic indicators for cattle at the onset of BRD treatment. Full article

With the rapid development of micro-machining technology, the feature size of object parts becomes smaller whilst the roundness tolerance must be critically verified at sub-micrometers or nanometers. Therefore, establishing a method is critically important for evaluating roundness errors to guarantee the machining quality. A progressive search method is proposed based on the minimum zone criterion in the Cartesian coordinate system in this paper. The least-square center of the measured points is used as the initial reference center by establishing a search circle model for progressively approaching the minimum zone circle center. To testify to the feasibility and performance of the proposed method, comparison and simulation experiments are implemented. The results demonstrated that the progressive search method is effective, reliable, and can evaluate roundness error accurately and quickly with not more than 0.1 s and 10 times. Full article

Alzheimer’s disease (AD), an intense neurological illness, severely impacts memory, behavior, and personality, posing a growing concern worldwide due to the aging population. Early and accurate detection is crucial as it enables preventive measures. However, current diagnostic methods are often inaccurate in identifying the disease in its early stages. Although deep learning-based bioimaging has shown promising results in medical image classification, challenges remain in achieving the highest accuracy for detecting AD. Existing approaches, such as ResNet50, VGG19, InceptionV3, and AlexNet have shown potential, but they often lack reliability and accuracy due to several issues. To address these gaps, this paper suggests a novel bioimaging technique by developing a custom Convolutional Neural Network (CNN) model for detecting AD. This model is designed with optimized layers to enhance feature extraction from medical images. The experiment’s first phase involved the construction of the custom CNN structure with three max-pooling layers, three convolutional layers, two dense layers, and one flattened layer. The Adam optimizer and categorical cross-entropy were adopted to compile the model. The model’s training was carried out on 100 epochs with the patience set to 10 epochs. The second phase involved augmentation of the dataset images and adding a dropout layer to the custom CNN model. Moreover, fine-tuned hyperparameters and advanced regularization methods were integrated to prevent overfitting. A comparative analysis of the proposed model with conventional models was performed on the dataset both before and after the data augmentation. The results validate that the proposed custom CNN model significantly overtakes pre-existing models, achieving the highest validation accuracy of 99.53% after data augmentation while maintaining the lowest validation loss of 0.0238. Its precision, recall, and F1 score remained consistently high across all classes, with perfect scores for the Moderate Demented and Non-Demented categories after augmentation, indicating superior classification capability. Full article

This study utilized unmanned aerial systems (UAS) and terrestrial laser scanners (TLS) to develop a 3D numerical model of slope anchors and conduct a comprehensive analysis. Initial data were collected using a UAS with 4 K resolution, followed by a second dataset captured 6 months later with 8 K resolution after artificially damaging the anchor. The model analyzed damage factors such as cracks, destruction, movement, and settlement. Cracks smaller than 0.3 mm were detected with an error margin of ±0.05 mm. The maximum damaged area on the anchor head was within 3% of the designed value, and the volume of damaged regions was quantified. A combination analysis examined elevation differences on the anchor’s irregular bottom surface, resulting in an average difference at 20 points, reflecting ground adhesion. The rotation angle (<1°) and displacement of the anchor head were also measured. The study successfully extracted quantitative damage data, demonstrating the potential for an accurate assessment of anchor performance. The findings highlight the value of integrating UAS and TLS technologies for slope maintenance. By organizing these quantitative metrics into a database, this approach offers a robust alternative to traditional visual inspections, especially for inaccessible facilities, providing a foundation for enhanced safety evaluations. Full article

Background/Objectives: Chronic bacterial prostatitis (CBP) is a prevalent urological condition significantly impacting patients’ quality of life. Accurate diagnosis is essential to differentiate bacterial from non-bacterial prostatitis and to guide appropriate antimicrobial therapy. In the context of antimicrobial resistance (AMR), the Meares and Stamey (M&S) test is a valuable diagnostic tool for targeted antibiotic use and a valuable antimicrobial stewardship (AMS) measure. Despite its clinical relevance, its adoption is limited by practical and logistical challenges. Methods: Relevant databases were searched by using methods recommended by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The keywords used included “Meares and Stamey test,” “antimicrobial stewardship and prostatitis,” and “chronic bacterial prostatitis and Meares.” Results: We enclosed seven studies: one single-center prospective observational comparative study, two national surveys, three cross-sectional studies, and one consensus conference. The M&S test remains the gold standard for diagnosing CBP, offering high specificity in identifying bacterial infections localized within the prostate. The test enables precise pathogen identification and facilitates targeted antimicrobial therapy. Despite its clinical relevance, its adoption is hindered by procedural complexity, patient discomfort, and the apparent need for specialized personnel and facilities. Alternative diagnostic methods, such as the two-glass pre- and post-massage test (PPMT), have demonstrated comparable diagnostic sensitivity while being more practical and time-efficient. Additionally, emerging microbiological techniques are under investigation to increase the M&S test’s sensitivity. Conclusions: The M&S test plays a crucial role in AMS by ensuring targeted antimicrobial therapy in CBP. Overcoming its limitations through patient stratification, clinician education, and the integration of emerging microbiological techniques is essential to enhance its applicability in modern urological practice. Full article

Objectives: The pulmonary artery pulsatility index (PAPI) is a novel hemodynamic parameter that reflects right ventricular (RV) function. PAPI was shown to be useful in predicting outcomes following left ventricular assist device (LVAD) implantation, acute RV infarction, and in patients with chronic RV failure. The standard method to estimate PAPI is during right heart catheterization (RHC); however, echocardiography-based PAPI was also shown to be accurate. In the current study, we evaluated the ability of echocardiography-based PAPI to predict outcomes of patients with acute pulmonary embolism (PE). Methods: A total of 177 patients (mean age 67 ± 15, 54.1% male) with acute PE were included in the study. PAPI was calculated based on measurements from standard transthoracic echocardiography. Results: 27% of patients needed oxygen support, 5.6% were on mechanical ventilation, and 7.3% were on inotropic support. The 30-day mortality rate in the whole cohort was 8.3%. Lower PAPI measurements were associated with increased 30-day mortality (p < 0.05), a higher rate of RV failure (p < 0.001), and the need for inotropic support (p < 0.05). There was no association between PAPI and the need for oxygen support (p = 0.59), mechanical ventilation (0.06), or length of stay (LOS) (p = 0.414). PAPI was superior to tricuspid annular plane systolic excursion (TAPSE) in predicting mortality and RV failure. Conclusions: Echocardiography-derived PAPI is feasible and superior over TAPSE in predicting RV failure and mortality among patients with acute PE. Full article

CFD simulations of annular coolers have often been performed on a single trolley, making it difficult for the method to provide reliable and accurate data for the optimum design of annular coolers. The present paper establishes a three-dimensional model of the entire annular cooler, uses sliding mesh to approach the actual working conditions, and through UDF, realizes the simulations of the continuous feeding process of the annular cooler, and obtains complete data for one run of the annular cooler. By comparing the simulated data with the actual measured data, the reliability of the model was verified. The temperature distribution inside the annular cooler and the temperature variation at the outlet of the waste heat recovery as well as the flow rate are also explored in detail. Subsequently, the temperature distribution inside the annular cooler, the flue gas flow, and the changes in temperature at each outlet were studied under different material layer thicknesses, and the discharge temperature under different thicknesses was obtained. Based upon the proposed method, a lot of data that cannot be obtained by traditional calculation methods can be obtained, thus shortening the cycle of optimizing the design and development of the structure and operating parameters of annular coolers. Full article

Atmospheric aerosols play an important role in the ecological environment, climate change, and human health. Aerosol optical depth (AOD) is the main measurement of aerosols. The next-generation geostationary satellite Himawari-8, loaded with the Advanced Himawari Imager (AHI), provides observation-based estimates of the hourly AOD. However, a highly accurate evaluation of AOD using AHI is still limited. In this paper, we establish a Stacked Denoising AutoEncoder (SDAE) model to retrieve highly accurate AOD using AHI. We explore the SDAE to retrieve AOD by taking the ground-observed AOD as the output and taking the AHI image, the month, hour, latitude, and longitude as the input data. This approach was tested in the Hubei province of China. Traditional machine learning methods such as Extreme Learning Machines (ELMs), BackPropagation Neural Networks (BPNNs), and Support Vector Machines (SVMs) are also used to evaluate model performance. The results show that the proposed method has the highest accuracy. We also compare the proposed method with ground-observed AOD measurements at the Wuhan University site, showing good consistency between the satellite-retrieved AOD and the ground-observed value. The study of the spatiotemporal change pattern of the hourly AOD in the Hubei province shows that the algorithm has good stability in the face of changes in the angle and intensity of sunlight. Full article

In the new energy electric drive system, the thermal stability of IGBT, a core power device, significantly impacts the system’s overall performance. Accurate IGBT temperature measurement is crucial, but traditional methods face limitations in IGBT’s compact working space. Thin-film thermocouples, with their thin and light features, offer a new solution. In this study, Ni 90% Cr 10% and Ni 97% Si 3% thin-film thermocouples were prepared on polyimide substrates via magnetron sputtering. After calibration, the Seebeck coefficient of the thin-film thermocouple temperature sensors reached 40.23 μV/°C, and the repeatability error stabilized at about 0.3% as the temperature rose, showing good stability. Researchers studied factors affecting IGBT temperature. Thin-film thermocouples can accurately monitor IGBT module surface temperature under different conditions. Compared to K-type wire thermocouples, they measure slightly higher temperatures. As the control signal’s switching frequency increases, IGBT temperature first rises then falls; as the duty cycle increases, the temperature keeps rising. This is consistent with RAC’s junction temperature prediction theory, validating the feasibility of thin-film thermocouples for IGBT chip temperature measurement. Thin-film thermocouples have great application potential in power device temperature measurement and may be a key research direction, supporting the optimization and upgrading of new energy electric drive systems. Full article

Localization plays a crucial role in wireless sensor networks (WSNs) and it has sparked significant research interest. GPSs provide quite accurate positioning estimations, but they are ineffective indoors and in environments like underwater. Power usage and cost are further disadvantages, and so many alternatives have been proposed. Many works in the literature still base localization on RSSI measurements and often rely on methods to mitigate the effects of fluctuations in values, so it is important to know real values of RSSIs measured using common devices. This work presents the main localization techniques and exploits a real testbed to collect and evaluate RSSI measurements. An accuracy evaluation and a comparison among several localization techniques are also provided. Full article