Search Results (5,843)

Inertial navigation systems (INSs) exhibit distinctive characteristics, such as long-duration operation, full autonomy, and exceptional covertness compared to other navigation systems. However, errors are accumulated over time due to operational principles and the limitations of sensors. To address this problem, this study theoretically explores a numerically simulated integrated inertial navigation system consisting of a single-axis cold atom interferometer gyroscope (CAIG) and a conventional inertial measurement unit (IMU). The system leverages the low bias and drift of the CAIG and the high sampling rate of the conventional IMU to obtain more accurate navigation information. Furthermore, an adaptive gradient ascent (AGA) method is proposed to estimate the variance of the measurement noise online for the Kalman filter. It was found that errors of latitude, longitude, and positioning are reduced by 43.9%, 32.6%, and 32.3% compared with the conventional IMU over 24 h. On this basis, errors from inertial sensor drift could be further reduced by the online Kalman filter. 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

This study conducts an in-depth analysis of the dynamic characteristics of a single reheat steam turbine generator unit and its speed control system under variable operating conditions. A comprehensive simulation model was constructed to comprehensively evaluate the impact of the heat extraction system on the dynamic behavior of the unit, which integrates the speed control system, actuator, single reheat steam turbine body, and once-through boiler dynamic coupling. This model focuses on revealing the mechanism of the heat extraction regulation process on the core operating parameters of the unit and the system frequency regulation capability. Based on the actual parameters of a 300 MW heat unit in a power plant in Guangxi, the dynamic response of the established model under typical dynamic conditions such as extraction flow regulation, primary frequency regulation response, and load step disturbance was simulated and experimentally verified. The results show that the model can accurately characterize the dynamic characteristics of the heat unit under variable operating conditions, and the simulation results are in good agreement with the actual engineering, with errors within an acceptable range, effectively verifying the dynamic performance of the heat system module and the rationality of its control parameter design. This study provides a reliable theoretical basis and model support for the accurate simulation of the dynamic behavior of heat units in the power system and the design of optimization control strategies for system frequency regulation. Full article

A square patch metasurface is designed, simulated, fabricated, and experimentally tested to investigate polarization characteristics quantitatively. The metasurface consists of one layer unit cell in the form of a square patch with one via and a lumped resistor, which is used for harvesting RF (radio frequency) energy. FR4 dielectric is used as a substrate supported by a metal ground plane. Polarization-dependent properties with specific surface current patterns and voltage dip are obtained when simulating under normal incidence of a plane wave. This characteristic results from changes in surface current conditions when the polarization angle is varied. A voltage dip appears at a specific polarization angle when the surface current pattern is symmetrical. This condition occurs when the position of the lumped resistor from the center of the patch is perpendicular to the linearly polarized incident electric field. A couple of 10 × 10 arrays with different resistor positions are fabricated and tested. The experimental results are in good agreement with the simulated results. The proposed design demonstrates a symmetric unit cell structure with one via and a resistor that exhibits polarization-dependent behavior for linear polarization. An asymmetric patch design is explored through both simulation and measurement to mitigate polarization dependence by suppressing the dip behavior, albeit at the expense of reduced absorption efficiency. This study provides a complete polarization analysis for both symmetric and asymmetric patch metasurfaces with a single via and a single lumped resistor, and introduces a predictive relation between the position of the resistor relative to the center of the patch and the resulting voltage dip behavior. Full article

Introduction: Carbapenem-resistant Klebsiella pneumoniae (CRKP) represents a critical public health threat due to its rapid nosocomial dissemination, limited therapeutic options, and elevated mortality rates. This study aimed to characterize the epidemiology, carbapenemase profiles, and antimicrobial susceptibility patterns of CRKP isolates, as well as the clinical features and outcomes observed in infected or colonized patients. Materials and Methods: We conducted a retrospective analysis of clinical and microbiological data from patients with CRKP infections or colonization admitted between January 2023 and January 2024. Descriptive statistics were used to assess prevalence, resistance patterns, and patient outcomes. Two binary logistic regression models were applied to identify independent predictors of sepsis and in-hospital mortality. Results: Among 89 CRKP isolates, 45 underwent carbapenemase typing. More than half were metallo-β-lactamase (MBL) producers, with 44.4% co-harbouring NDM and OXA-48-like enzymes. Surgical intervention was associated with a significantly lower risk of sepsis (p < 0.01) and in-hospital mortality (p = 0.045), whereas intensive care unit (ICU) stay was a strong predictor of both outcomes. ICU admission conferred a 10-fold higher risk of sepsis (95%Cl 2.4–41.0) and a 40.8-fold higher risk of in-hospital death (95% Cl 3.5–473.3). Limitations: This single-center retrospective study included a limited number of isolates in certain groups. Additionally, cefiderocol (FDC) susceptibility was assessed by disk diffusion rather than by the broth microdilution method. Conclusions: Our study underscores the increasing prevalence of metallo-beta-lactamase-producing CRKP, particularly strains harbouring dual carbapenemases. Timely recognition of high-risk patients, combined with the implementation of targeted infection control measures and the integration of novel therapeutic options, is crucial to optimize clinical management and reduce mortality associated with CRKP. Full article

This study investigates the bond behavior of fabric-reinforced cementitious matrix (FRCM) composites with three common masonry substrates—solid clay bricks (SBs), perforated bricks (PBs), and concrete hollow blocks (HBs)—using knitted polyester grille (KPG) fabric. Through uniaxial tensile tests of the KPG fabric and FRCM system, along with single-lap and double-lap shear tests, the interfacial debonding modes, load-slip responses, and composite utilization ratio were evaluated. Key findings reveal that (i) SB and HB substrates predominantly exhibited fabric slippage (FS) or matrix–fabric (MF) debonding, while PB substrates consistently failed at the matrix–substrate (MS) interface, due to their smooth surface texture. (ii) Prism specimens with mortar joints showed enhanced interfacial friction, leading to higher load fluctuations compared to brick units. PB substrates demonstrated the lowest peak stress (69.64–74.33 MPa), while SB and HB achieved comparable peak stresses (133.91–155.95 MPa). (iii) The FRCM system only achieved a utilization rate of 12–30% in fabric and reinforcement systems. The debonding failure at the matrix–substrate interface is one of the reasons that cannot be ignored, and exploring methods to improve the bonding performance between the matrix–substrate interface is the next research direction. HB bricks have excellent bonding properties, and it is recommended to prioritize their use in retrofit applications, followed by SB bricks. These findings provide insights into optimizing the application of FRCM reinforcement systems in masonry structures. Full article

Background: Right ventricular (RV) dysfunction is associated with poor clinical outcomes in critically ill sepsis patients, but its pathophysiology and predictors are incompletely characterized. We aimed to investigate the predictors of RV dysfunction and its outcomes in sepsis patients admitted to the intensive care unit (ICU). Methods: This is a single-center retrospective cohort study of adult patients admitted to the ICU for sepsis who had echocardiography within 72 h of diagnosis. Patients with acute coronary syndrome, acute decompensated heart failure, or significant valvular dysfunction were excluded. RV dysfunction was defined as the presence of RV dilation, hypokinesis, or both. Demographics and clinical outcomes were obtained from electronic medical records. Results: A total of 361 patients were included in our study—47 with and 314 without RV dysfunction. The mean age of the population was 66.8 years and 54.6% were females. Compared to those without RV dysfunction, patients with RV dysfunction were more likely to require mechanical ventilation (63.8% vs. 43.9%, p = 0.01) and vasopressor support (61.7% vs. 36.6%, p < 0.01). On multivariate logistic regression analysis, increasing age (OR 1.03, 95% C.I. 1.00–1.06), a history of HIV infection (OR 5.88, 95% C.I. 1.57–22.11) and atrial fibrillation (OR 4.34, 95% C.I. 1.83–10.29), and presence of LV systolic dysfunction (OR 14.40, 95% C.I. 5.63–36.84) were independently associated with RV dysfunction. Patients with RV dysfunction had significantly worse 30-day survival (Log-Rank p = 0.023). On multivariate Cox regression analysis, older age (HR 1.02, 95% C.I. 1.00–1.04) and peak lactate (HR 1.16, 95% C.I. 1.11–1.21) were independent predictors of 30-day mortality. Conclusions: Among other findings, our data suggests a possible association between a history of HIV infection and RV dysfunction in critically ill sepsis patients, and this should be investigated further in future studies. Patients with evidence of RV dysfunction had poorer survival in this population; however this was not an independent predictor of mortality in the multivariate analysis. A larger cohort with a longer follow-up period may provide further insights. Full article

Many commercial wearable sensor systems typically rely on a single continuous cardiorespiratory sensing modality, photoplethysmography (PPG), which suffers from inherent biases (i.e., differences in skin tone) and noise (e.g., motion and pressure artifacts). In this research, we present a wearable device that provides robust estimates of cardiorespiratory variables by combining three physiological signals from the upper arm: multiwavelength PPG, single-sided electrocardiography (SS-ECG), and bioimpedance plethysmography (BioZ), along with an inertial measurement unit (IMU) providing 3-axis accelerometry and gyroscope information. We evaluated the multimodal device on 16 subjects by its ability to estimate heart rate (HR) and breathing rate (BR) in the presence of various static and dynamic noise sources (e.g., skin tone and motion). We proposed a hierarchical approach that considers the subject’s skin tone and signal quality to select the optimal sensing modality for estimating HR and BR. Our results indicate that, when estimating HR, there is a trade-off between accuracy and robustness, with SS-ECG providing the highest accuracy (low mean absolute error; MAE) but low reliability (higher rates of sensor failure), and PPG/BioZ having lower accuracy but higher reliability. When estimating BR, we find that fusing estimates from multiple modalities via ensemble bagged tree regression outperforms single-modality estimates. These results indicate that multimodal approaches to cardiorespiratory monitoring can overcome the accuracy–robustness trade-off that occurs when using single-modality approaches. Full article

Research on image sensor (IS)-based visible light positioning systems has attracted widespread attention. However, when the receiver is tilted or under a single LED, the positioning system can only achieve two-dimensional (2D) positioning and requires the assistance of inertial measurement units (IMU). When the LED is not captured or decoding fails, the system’s positioning error increases further. Thus, we propose a novel three-dimensional (3D) visible light positioning system based on image sensors for various environments. Specifically, (1) we use IMU to obtain the receiver’s state and calculate the receiver’s 2D position. Then, we fit the height–size curve to calculate the receiver’s height, avoiding the coordinate iteration error in traditional 3D positioning methods. (2) When no LED or decoding fails, we propose a firefly-assisted unscented particle filter (FA-UPF) algorithm to predict the receiver’s position, achieving high-precision dynamic positioning. The experimental results show that the system positioning error under a single LED is within 10 cm, and the average positioning error through FA-UPF under no light source is 6.45 cm. Full article

Urban health planning requires a multi-scalar understanding of the territory, capable of capturing socio-economic inequalities and health needs at the local level. In the case of Rome, current administrative subdivisions—Urban Zones (Zone Urbanistiche)—are too large and internally heterogeneous to serve as effective units for equitable health planning. This study presents a methodology for the territorial redefinition of Rome’s Municipality III, aimed at supporting healthcare planning through an integrated analysis of census sections. These were grouped using a combination of census-based socio-demographic indicators (educational attainment, employment status, single-person households) and real estate values (OMI data), alongside administrative and road network data. The resulting territorial units—21 newly defined Mesoareas—are smaller than Urban Zones but larger than individual census sections and correspond to socio-territorially homogeneous neighborhoods; this structure enables a more nuanced spatial understanding of health-related inequalities. The proposed model is replicable, adaptable to other urban contexts, and offers a solid analytical basis for more equitable and targeted health planning, as well as for broader urban policy interventions aimed at promoting spatial justice. Full article

Background/Objectives: This study aimed to compare the effects of low- and medium-frequency NMES, combined with a standard physical therapy (SPT) program, on functional capacity in critically ill patients. Methods: Fifty-four critically ill patients admitted into Intensive Care Unit (ICU) and on mechanical ventilation participated in this randomized, single-blinded, experimental study. Participants were randomly assigned to a Control group, who received a lower limb SPT program; the Low-frequency NMES group received lower limb SPT + NMES at 100 Hz; and the Medium-frequency NMES group received lower limb SPT + NMES at 100 Hz with a carrier frequency of 2500 Hz. The outcomes, encompassing functional capacity in the hospital, included muscle strength, handgrip strength, functional status, degree of independence for activities of daily living, functional and dynamic mobility, quality of life, and total days hospitalized. Results: Both NMES protocols combined with SPT improved functional capacity compared to the control group. Medium-frequency NMES provided additional benefits on dynamic balance, in the degree of independence to perform activities of daily living and quality of life (all p < 0.001) prior to hospital discharge. It also promoted larger gains on functional status prior to ICU discharge and on knee extension strength (both p < 0.05) prior to intermediate care unit discharge. Medium-frequency NMES also enhanced handgrip strength earlier than low-frequency NMES when compared to the control group. Notably, medium-frequency NMES was the only intervention associated with a significant reduction in total hospital stay duration (p < 0.05). Conclusions: Medium-frequency NMES, along with an SPT program in critically ill patients, showed greater benefits on functional capacity during recovery than low-frequency NMES. (Trial registration: This trial is registered on ClinicalTrials.gov: NCT05287919). Implications for rehabilitation: 1. Medium-frequency NMES may enhance physical functionality and quality of life in critically ill patients with ICU-acquired weakness. 2. Medium-frequency NMES can reduce the number of hospitalization days. 3. NMES combined with SPT represents a feasible and effective option for patients unable to engage in active rehabilitation during critical illness. Full article

The impacts of land use transition on ecological environment quality (EEQ) during China’s rapid urbanization have attracted growing concern. However, existing studies predominantly focus on single-scale analyses, neglecting scale effects and driving mechanisms of EEQ changes under the coupling of administrative units and grid scales. Therefore, this study selects Zhejiang Province—a representative rapidly transforming region in China—to establish a “type-process-ecological effect” analytical framework. Utilizing four-period (2005–2020) 30 m resolution land use data alongside natural and socio-economic factors, four spatial scales (city, county, township, and 5 km grid) were selected to systematically evaluate multi-scale impacts of land use transition on EEQ and their driving mechanisms. The research reveals that the spatial distribution, changing trends, and driving factors of EEQ all exhibit significant scale dependence. The county scale demonstrates the strongest spatial agglomeration and heterogeneity, making it the most appropriate core unit for EEQ management and planning. City and county scales generally show degradation trends, while township and grid scales reveal heterogeneous patterns of local improvement, reflecting micro-scale changes obscured at coarse resolutions. Expansive land transition including conversions of forest ecological land (FEL), water ecological land (WEL), and agricultural production land (APL) to industrial and mining land (IML) primarily drove EEQ degradation, whereas restorative ecological transition such as transformation of WEL and IML to grassland ecological land (GEL) significantly enhanced EEQ. Regarding driving mechanisms, natural factors (particularly NDVI and precipitation) dominate across all scales with significant interactive effects, while socio-economic factors primarily operate at macro scales. This study elucidates the scale complexity of land use transition impacts on ecological environments, providing theoretical and empirical support for developing scale-specific, typology-differentiated ecological governance and spatial planning policies. Full article

Objective: This study aimed to evaluate whether modifying the post-processing algorithm of Twin-Spiral Dual-Energy computed tomography (DECT) improves infarct detection compared to conventional Dual-Energy CT (DECT) and Single-Energy CT (SECT) following endovascular therapy (EVT) for large vessel occlusion (LVO). Methods: We retrospectively analyzed 52 patients who underwent Twin-Spiral DECT after endovascular stroke therapy. Ten patients were used to generate a device-specific parameter (“y”) using an AI-based neural network (SynthSR). This parameter was integrated into the post-processing algorithm for edema map generation. Quantitative Hounsfield unit (HU) measurements were used to assess density differences in ischemic brain tissue across conventional virtual non-contrast (VNC) images and edema maps. Results: The median HU of infarcted tissue in conventional mixed DECT was 33.73 ± 4.58, compared to 22.96 ± 3.81 in default VNC images. Edema maps with different smoothing filter settings showed values of 14.39 ± 4.96, 14.50 ± 3.75, and 15.05 ± 2.65, respectively. All edema maps demonstrated statistically significant HU differences of infarcted tissue compared to conventional VNC images ($p<0.001$) while maintaining the density values of non-infarcted brain tissue. Conclusions: Enhancing the post-processing algorithm of conventional virtual non-contrast imaging improves infarct detection compared to standard mixed or virtual non-contrast reconstructions in Dual-Energy CT. Full article

Introduction: The World Health Organization has declared carbapenem-resistant organisms a research and development priority. Although ceftazidime–avibactam was approved around a decade ago, there is still a lack of prospective data on the treatment of resistant pathogens with this agent. Methods: We conducted a prospective, observational, single-center, investigator-initiated study of patients treated with ceftazidime–avibactam for infections caused by carbapenem-resistant organisms. The primary outcome was clinical cure 14 days after the initiation of ceftazidime-avibactam treatment. Secondary outcomes, which were assessed on day 30, included microbiological failure, development of resistance, all-cause mortality, and length of stay in the intensive care unit. Results: A total of 50 patients were included in the study. At baseline, the median Charlson Comorbidity Index and Sequential Organ Failure Assessment Score were 5.5 and 7. Approximately three-quarters of the patients were treated in an intensive care unit and had undergone mechanical ventilation within the previous 7 days prior to the commencement of ceftazidime–avibactam treatment. Half of the patients were diagnosed with nosocomial pneumonia. Most infections were caused by Pseudomonas aeruginosa (48%) and Klebsiella pneumonia (28%). Clinical cure at day 14 was achieved in 59% of patients. Four deaths (9%) and two cases of microbiological failure (4%) were observed. The median length of stay in the intensive care unit was 14 days. There was no emergence of resistance to ceftazidime–avibactam. Discussion: Our study contributes to the growing body of evidence supporting the effectiveness of ceftazidime–avibactam in treating infections caused by carbapenem-resistant organisms. In this cohort of critically ill patients, our results in terms of both clinical success and survival are in the upper range compared to those from mainly retrospective and some prospective studies. Although the benefits of ceftazidime–avibactam have been demonstrated in this and other studies, it must be prescribed cautiously to ensure it remains effective. Full article

Background/Objective: In-hospital cardiac arrest has high incidence and poor survival rates, posing a significant healthcare challenge. It is important to intervene in the hours before the cardiac arrest to prevent poor outcomes. The modified early warning score (MEWS) is a validated tool for identifying a deteriorating patient. It is an aggregate of vital signs and level of consciousness. We retrospectively evaluated MEWS for trends that might predict patient outcomes. Methods: We performed a single-center, one-year, retrospective study. A comprehensive review was conducted for patients aged 18 years and above who experienced a cardiac arrest. Cases that occurred within an intensive care unit, emergency department, during a procedure, or outside the hospital were excluded. A total of 87 cases met our predefined inclusion criteria. We collected data at 12 h, 6 h and 1 h time periods prior to the cardiac arrest. A trend analysis using a linear model with analysis of variance with Bonferroni correction was performed. Results: Out of 87 patients included in the study, 59 (67.8%) had an immediate return of spontaneous circulation (ROSC). Among those who achieved ROSC, 41 (69.5%) died during the admission. Only 20.7% of the patients that sustained a cardiac arrest survived to discharge. A significant increase in the average MEWS was noted from the 12 h period (MEWS = 3.95 ± 2.4) to the 1 h period (MEWS = 5.98 ± 3.5) (p ≤ 0.001) and the 6 h period (4.65 ± 2.6) to the 1 h period (5.98 ± 3.5) (p = 0.023) prior to cardiac arrest. Conclusions: An increase in the MEWS may be a valuable tool in identifying at-risk patients and provides an opportunity to intervene at least 6 h before a cardiac arrest event. Further research is needed to validate the results of our study. Full article