Search Results (38,290)

Background: Diabetes mellitus is associated with worse outcomes after cardiac arrest. Hyperglycemia, diabetes treatments and other long-term sequalae may contribute to this association. We sought to determine the acute effect of diabetes on the return of spontaneous circulation (ROSC) and post-arrest cardiac function in a rat cardiac arrest model. Methods: Eighteen male Wistar rats were utilized, and 12 underwent the induction of type II diabetes for 10 weeks through a high-fat diet and the injection of streptozotocin. The carotid artery flow and femoral arterial pressure were measured. Seven minutes of asphyxial cardiac arrest was induced. An external cardiac compression was performed via an automated piston. Post-ROSC, epinephrine was titrated to a mean arterial pressure (MAP) of 70 mmHg. Data was analyzed using the Mann–Whitney test. The significance was set at p ≤ 0.05. Results: The rate of the ROSC was significantly lower in animals with diabetes, 50% compared to 100% in non-diabetics. Additionally, it took significantly longer to achieve the ROSC in diabetics, p = 0.034. In animals who survived, the cardiac function was reduced, as indicated by an increased epinephrine requirement, p = 0.041, and a decreased cardiac output at the end of the experiment, p = 0.017. The lactate, venous and arterial pressures, heart rate and carotid flow did not differ between groups at 2 h. Conclusions: Diabetes negatively affects the survival from cardiac arrest. Here, the critical difference was the rate of the conversion to a life-sustaining rhythm and the achievement of the ROSC. The post-ROSC cardiac function was depressed in diabetic animals. Interventions targeted at improving defibrillation success may be important in diabetics. Full article

The force ripple of a permanent magnet synchronous linear motor (PMSLM) caused by multi-source disturbances in practical applications seriously restricts its high-precision motion control performance. The traditional single-mechanism model has difficulty fully characterizing the nonlinear disturbance factors, while the data-driven method has real-time limitations. Therefore, this paper proposes a hybrid modeling framework that integrates the physical mechanism and measured data and realizes the dynamic compensation of the force ripple by constructing a collaborative suppression algorithm. At the mechanistic level, based on electromagnetic field theory and the virtual displacement principle, an analytical model of the core disturbance terms such as the cogging effect and the end effect is established. At the data level, the acceleration sensor is used to collect the dynamic response signal in real time, and the data-driven ripple residual model is constructed by combining frequency domain analysis and parameter fitting. In order to verify the effectiveness of the algorithm, a hardware and software experimental platform including a multi-core processor, high-precision current loop controller, real-time data acquisition module, and motion control unit is built to realize the online calculation and closed-loop injection of the hybrid compensation current. Experiments show that the hybrid framework effectively compensates the unmodeled disturbance through the data model while maintaining the physical interpretability of the mechanistic model, which provides a new idea for motor performance optimization under complex working conditions. Full article

The piston-type expander (PTE), as the primary output component, significantly influences the performance of an energy storage system. This paper proposes a non-cam variable valve actuation system for the PTE, supported by a mathematical model. An enhanced S-curve trajectory planning method is used to design the valve lift curve. The study investigates the effects of various valve lift design parameters on output power and efficiency at different rotational speeds, employing orthogonal design and SPSS Statistics 27 (Statistical Product and Service Solutions) simulations. A grey comprehensive evaluation method is used to identify optimal valve lift parameters for each speed. The results show that valve lift parameters influence PTE performance to varying degrees, with intake duration having the greatest effect, followed by maximum valve lift, while intake end time has the least impact. The non-cam PTE outperforms the cam-based PTE. At 800 rpm, the optimal design yields 7.12 kW and 53.5% efficiency; at 900 rpm, 8.17 kW and 50.6%; at 1000 rpm, 9.2 kW and 46.8%; and at 1100 rpm, 12.09 kW and 41.2%. At these speeds, output power increases by 18.37%, 11.42%, 11.62%, and 9.82%, while energy efficiency improves by 15.01%, 15.05%, 14.24%, and 13.86%, respectively. Full article

The objective of this study was to evaluate the feasibility and efficacy of the ultrasound-guided thoracolumbar erector spinae plane (TL-ESP) block in standing horses. A total of 24 injections (n = 12) were performed at the L1 level using either 0.1 mL/kg of saline solution (SS group) or 2% lidocaine (LID group). The block feasibility was assessed based on needle visualization and injection time, while efficacy was evaluated through craniocaudal and dorsoventral (DV) spread using the pinprick technique over 270 min. Desensitization was observed at least once in 100% of horses in the LID group and in 75% in the SS group (p = 0.001). However, in the SS group, desensitization was primarily limited to the Th18 metamer at the 2 cm DV position, with a shorter duration compared to the LID group. The block onset occurred at 22.5 (11.25–60) min in the LID group and at 5 (5–30) min in the SS group (p = 0.069). The number of affected metamers was significantly higher in the LID group (2 [¹,²,³]) compared to the SS group (1 [1–2.25], p = 0.014). At the 2 cm DV point, the end of the block effect occurred at 135 (120–210) min in the LID group and at 60 (3.75–60) min in the SS group (p = 0.001). Needle visualization was excellent in 95.8% of cases, and the mean injection time was 2.5 (2–3) min. These findings confirm that the TL-ESP block is a feasible technique in standing horses. However, its effect is predominantly localized to dorsal dermatomes with a limited ventral spread. Future studies evaluating larger volumes and multiple injection sites are warranted to enhance its clinical applicability. Full article

This study focuses on Shanghai, a pioneer city in China’s community renewal practices. In recent years, community renewal driven by civic participation has become a prominent research topic, leading to the emergence of numerous exemplary cases in Shanghai. However, field investigations revealed that many projects have experienced varying degrees of physical deterioration and a decline in spatial vitality due to insufficient maintenance, reflecting unsustainable outcomes. In response, this study examines a bottom-up community renewal project led by the research team, aiming to explore how broad civic participation can promote sustainable community renewal. A multidisciplinary approach incorporating perspectives from ecology, the humanities, economics, and sociology was used to guide citizen participation, while participatory observation methods recorded emotional shifts and maintenance behavior throughout the process. The results showed that civic participatory actions under the guidance of sustainability principles effectively enhanced citizens’ sense of community identity and responsibility, thereby facilitating the sustainable upkeep and operation of community spaces. However, the study also found that bottom-up efforts alone are insufficient. Sustainable community renewal also requires top-down policy support and institutional safeguards. At the end, the paper concludes by summarizing the practical outcomes and proposing strategies and mechanisms for broader application, aiming to provide a reference for related practices and research. Full article

Accurately predicting how quickly people will adopt electric vehicles (EVs) is vital for planning charging stations, managing supply chains, and shaping climate policy. We present a forecasting model that uses three separate Long Short‑Term Memory (LSTM) branches—one for past EV sales, one for infrastructure and policy signals, and one for economic trends. An attention mechanism first highlights the most important weeks in each branch, then decides which branch matters most at any point in time. Trained end‑to‑end on publicly available data, the model beats traditional statistical methods and newer deep learning baselines while remaining small enough to run efficiently. An ablation study shows that every branch and both attention steps improve accuracy, and that adding policy and economic data helps more than relying on EV history alone. Because the network is modular and its attention weights are easy to interpret, it can be extended to produce confidence intervals, include physical constraints, or forecast adoption of other clean‑energy technologies. Full article

Detecting previously unseen anomalies in sensor data is a challenging problem for artificial intelligence when sensor-specific and deployment-specific characteristics of the time series need to be learned from a short calibration period. From the application point of view, this challenge becomes increasingly important because many applications are gravitating towards utilising low-cost sensors for Internet of Things deployments. While these sensors offer cost-effectiveness and customisation, their data quality does not match that of their high-end counterparts. To improve sensor data quality while addressing the challenges of anomaly detection in Internet of Things applications, we present an anomaly detection framework that learns a normality model of sensor data. The framework models the typical behaviour of individual sensors, which is crucial for the reliable detection of sensor data anomalies, especially when dealing with sensors observing significantly different signal characteristics. Our framework learns sensor-specific normality models from a small set of anomaly-free training data while employing an unsupervised feature engineering approach to select statistically significant features. The selected features are subsequently used to train a Local Outlier Factor anomaly detection model, which adaptively determines the boundary separating normal data from anomalies. The proposed anomaly detection framework is evaluated on three real-world public environmental monitoring datasets with heterogeneous sensor readings. The sensor-specific normality models are learned from extremely short calibration periods (as short as the first 3 days or 10% of the total recorded data) and outperform four other state-of-the-art anomaly detection approaches with respect to F1-score (between 5.4% and 9.3% better) and Matthews correlation coefficient (between 4.0% and 7.6% better). Full article

Carbon deposits on the crown of engine pistons can markedly reduce combustion efficiency and shorten service life. Conventional cleaning techniques often fail to simultaneously ensure a high carbon removal efficiency and maintain optimal surface integrity. To enable efficient and precise carbon removal, this study proposes the application of hybrid laser cleaning—combining continuous-wave (CW) and pulsed lasers—to piston carbon deposit removal, and employs response surface methodology (RSM) for multi-objective process optimization. Using the N52B30 engine piston as the experimental substrate, this study systematically investigates the combined effects of key process parameters—including CW laser power, pulsed laser power, cleaning speed, and pulse repetition frequency—on surface roughness (Sa) and carbon residue rate (RC). Plackett–Burman design was employed to identify significant factors, the method of the steepest ascent was utilized to approximate the optimal region, and a quadratic regression model was constructed using Box–Behnken response surface methodology. The results reveal that the Y-direction cleaning speed and pulsed laser power exert the most pronounced influence on surface roughness (F-values of 112.58 and 34.85, respectively), whereas CW laser power has the strongest effect on the carbon residue rate (F-value of 57.74). The optimized process parameters are as follows: CW laser power set at 625.8 W, pulsed laser power at 250.08 W, Y-direction cleaning speed of 15.00 mm/s, and pulse repetition frequency of 31.54 kHz. Under these conditions, the surface roughness (Sa) is reduced to 0.947 μm, and the carbon residue rate (RC) is lowered to 3.67%, thereby satisfying the service performance requirements for engine pistons. This study offers technical insights into the precise control of the hybrid laser cleaning process and its practical application in engine maintenance and the remanufacturing of end-of-life components. Full article

High-strength steel has high strength and low thermal conductivity, and its thin-walled parts are very susceptible to residual stress and deformation caused by cutting heat during the drilling process, which affects the machining accuracy and quality. High-strength steel thin-walled components are widely used in aerospace and other high-end sectors; however, systematic investigations into their temperature fields during drilling remain scarce, particularly regarding the evolution characteristics of the temperature field in thin-wall drilling and the quantitative relationship between drilling parameters and these temperature variations. This paper takes the thin-walled parts of AF1410 high-strength steel as the research object, designs a special fixture, and applies infrared thermography to measure the bottom surface temperature in the thin-walled drilling process in real time; this is carried out in order to study the characteristics of the temperature field during the thin-walled drilling process of high-strength steel, as well as the influence of the drilling dosage on the temperature field of the bottom surface. The experimental findings are as follows: in the process of thin-wall drilling of high-strength steel, the temperature field of the bottom surface of the workpiece shows an obvious temperature gradient distribution; before the formation of the drill cap, the highest temperature of the bottom surface of the workpiece is distributed in the central circular area corresponding to the extrusion of the transverse edge during the drilling process, and the highest temperature of the bottom surface can be approximated as the temperature of the extrusion friction zone between the top edge of the drill and the workpiece when the top edge of the drill bit drills to a position close to the bottom surface of the workpiece and increases with the increase in the drilling speed and the feed volume; during the process of drilling, the highest temperature of the bottom surface of the workpiece is approximated as the temperature of the top edge of the drill bit and the workpiece. The maximum temperature of the bottom surface of the workpiece in the drilling process increases nearly linearly with the drilling of the drill, and the slope of the maximum temperature increases nearly linearly with the increase in the drilling speed and feed, in which the influence of the feed on the slope of the maximum temperature increases is larger than that of the drilling speed. Full article

Background: Gait kinematics address the analysis of joint angles and segment movements during walking. Although there is work in the literature to solve the problems of forward (FK) and inverse kinematics (IK), there are still problems related to the accuracy of the estimation of Cartesian and joint variables, singularities, and modeling complexity on gait analysis approaches. Objective: In this work, we propose a framework for two-dimensional gait analysis addressing the singularities in the estimation of the joint variables using quaternion-based kinematic modeling. Methods: To solve the forward and inverse kinematics problems we use the dual quaternions’ composition and Damped Least Square (DLS) Jacobian method, respectively. We assess the performance of the proposed methods with three gait patterns including normal, toe-walking, and heel-walking using the RMSE value in both Cartesian and joint spaces. Results: The main results demonstrate that the forward and inverse kinematics methods are capable of calculating the posture and the joint angles of the three-DoF kinematic chain representing a lower limb. Conclusions: This framework could be extended for modeling the full or partial human body as a kinematic chain with more degrees of freedom and multiple end-effectors. Finally, these methods are useful for both diagnostic disease and performance evaluation in clinical gait analysis environments. Full article

In this work, the utility of multimodal vision–language models (VLMs) for visual product understanding in e-commerce is investigated, focusing on two complementary models: ColQwen2 (vidore/colqwen2-v1.0) and ColPali (vidore/colpali-v1.2-hf). These models are integrated into two architectures and evaluated across various product interpretation tasks, including image-grounded question answering, brand recognition and visual retrieval based on natural language prompts. ColQwen2, built on the Qwen2-VL backbone with LoRA-based adapter hot-swapping, demonstrates strong performance, allowing end-to-end image querying and text response synthesis. It excels at identifying attributes such as brand, color or usage based solely on product images and responds fluently to user questions. In contrast, ColPali, which utilizes the PaliGemma backbone, is optimized for explainability. It delivers detailed visual-token alignment maps that reveal how specific regions of an image contribute to retrieval decisions, offering transparency ideal for diagnostics or educational applications. Through comparative experiments using footwear imagery, it is demonstrated that ColQwen2 is highly effective in generating accurate responses to product-related questions, while ColPali provides fine-grained visual explanations that reinforce trust and model accountability. Full article

Aerosols play an important role in global meteorology and climate, as well as in air transport and human health, but there are still many unknowns on their effects and importance, in particular for the coarser (giant and ultragiant) aerosol particles. In this study, we aim to exploit the synergy between Raman lidar and Ka-band cloud radar to enlarge the size range in which aerosols can be observed and characterized. To this end, we developed an inversion technique that retrieves the aerosol microphysical properties based on cloud radar reflectivity and linear depolarization ratio. We applied this technique to a 6-year-long dataset, which was created using a recently developed methodology for the identification of giant aerosols in cloud radar measurements, with measurements from Potenza in Italy. Similarly, using collocated and concurrent lidar profiles, a dataset of aerosol microphysical properties using a widely used inversion technique complements the radar-retrieved dataset. Hence, we demonstrate that the combined use of lidar- and radar-derived aerosol properties enables the inclusion of particles with radii up to 12 µm, which is twice the size typically observed using atmospheric lidar alone. Full article

Context: Many patients at the end of life receive antibiotics to alleviate symptoms and improve quality of life; however, clear guidelines supporting decision making about the use of antibiotics are still lacking. Objectives: This study aimed to evaluate the benefits and harms of antibiotic use among patients under a palliative care community support team in Portugal. Methods: An observational, cross-sectional, retrospective study was conducted on 249 patients who died over a two-year period, having been followed for at least 30 days prior to their death. Data included patient demographics, clinical diagnoses, antibiotic prescriptions, and symptomatic outcomes. The effects of commonly prescribed antibiotics—amoxicillin + clavulanic acid, cefixime, ciprofloxacin, and levofloxacin—were compared using statistical analyses to assess survival, symptom intensity, and functional scales. Results: Adverse events, primarily infections and secretions, occurred in 57.8% of cases, with 33.7% receiving antibiotics. No significant difference in survival was observed across the antibiotic groups (p = 0.990). Symptom intensity significantly reduced after 72 h of treatment (p < 0.05), with ciprofloxacin demonstrating the greatest symptom control. The Palliative Outcome Scale decreased uniformly, with higher scores associated with amoxicillin + clavulanic acid (p = 0.004). The Palliative Performance Scale declined post-treatment, with significant changes noted for cefixime and ciprofloxacin (p < 0.05). Conclusions: Antibiotics may improve symptom control and quality of life in the end-of-life stage. While second-line antibiotics may offer additional benefits, the heterogeneity of the sample and limited adverse effect data underscore the need for further research to guide appropriate prescription practices in palliative care. Full article

Accurate and robust spatial perception is fundamental for dynamic 3D city modeling and urban environmental sensing. High-resolution remote sensing data, particularly LiDAR point clouds, are pivotal for these tasks due to their lighting invariance and precise geometric information. However, processing and aligning sequential LiDAR point clouds in complex urban environments presents significant challenges: traditional point-based or feature-matching methods are often sensitive to urban dynamics (e.g., moving vehicles and pedestrians) and struggle to establish reliable correspondences. While deep learning offers solutions, current approaches for point cloud alignment exhibit key limitations: self-supervised losses often neglect inherent alignment uncertainties, and supervised methods require costly pixel-level correspondence annotations. To address these challenges, we propose UnMinkLO-Net, an end-to-end self-supervised LiDAR odometry framework. Our method is as follows: (1) we efficiently encode 3D point cloud structures using voxel-based sparse convolution, and (2) we model inherent alignment uncertainty via covariance matrices, enabling novel self-supervised loss based on uncertainty modeling. Extensive evaluations on the KITTI urban dataset demonstrate UnMinkLO-Net’s effectiveness in achieving highly accurate point cloud registration. Our self-supervised approach, eliminating the need for manual annotations, provides a powerful foundation for processing and analyzing LiDAR data within multi-sensor urban sensing frameworks. Full article

This study introduces a novel methodology for assessing ice-jam flood hazards along river channels. It employs empirical equations that relate non-dimensional ice-jam stage to discharge, enabling the generation of an ensemble of longitudinal profiles of ice-jam backwater levels through Monte-Carlo simulations. These simulations produce non-exceedance probability profiles, which indicate the likelihood of various flood levels occurring due to ice jams. The flood levels associated with specific return periods were validated using historical gauge records. The empirical equations require input parameters such as channel width, slope, and thalweg elevation, which were obtained from bathymetric surveys. This approach is applied to assess ice-jam flood hazards by extrapolating data from a gauged reach at Fort Simpson to an ungauged reach at Jean Marie River along the Mackenzie River in Canada’s Northwest Territories. The analysis further suggests that climate change is likely to increase the severity of ice-jam flood hazards in both reaches by the end of the century. This methodology is applicable to other cold-region rivers in Canada and northern Europe, provided similar fluvial geomorphological and hydro-meteorological data are available, making it a valuable tool for ice-jam flood risk assessment in other ungauged areas. Full article