Severe Imbalance Research Articles

Research on the factors affecting rural teachers’ retention and countermeasures: strategies based on the perspective of Herzberg’s two-factor theory

ABSTRACT Rural education is an important component of K–12 education. However, influenced by multiple factors, the situation of rural teachers is far from stable. Therefore, how to retain teachers is the global issue faced by rural education. Based on Herzberg’s two-factor theory, a survey of Herzberg’s two-factor influence was carried out among 323 rural teachers, uncovering the internal factors affecting the stability of teaching staff, to understand the measures needed to improve rural teachers’ retention rates. The study shows that there exists a severe imbalance in the gender ratio of rural teachers, and teachers are highly satisfied with educational policies in spite of their low incomes. With regards to interpersonal relationships, school administrators fail to emphasize a “human-centered” management philosophy. Improvement measures are proposed including expanding the channel of teacher professional development, satisfying the demand for teacher professional development, improving welfare and creating a pleasant work environment.

Polymyxin-B induced bartter-like syndrome: an unusual adverse effect

Introduction and Importance: Bartter syndrome is a rare autosomal recessive disorder affecting renal tubular function, leading to electrolyte and volume homeostasis disturbances. It can also manifest as Bartter-like syndrome, a rare side effect of certain medications. Polymyxin-B, used to treat multidrug-resistant infections, is infrequently associated with BLS, making early recognition crucial to prevent severe electrolyte imbalances. Case Presentation: A 73-year-old female with coronary artery disease, COPD, hyperlipidemia, and other comorbidities presented with fever, respiratory distress, and hypoxia on mechanical ventilation. Initial labs showed leukocytosis, anemia, and normal potassium. Despite broad-spectrum antibiotics, sputum cultures revealed pandrug-resistant Acinetobacter baumannii, sensitive only to Polymyxin-B. After six days of Polymyxin-B, the patient developed fever, hypotension, hypokalemia, hypomagnesemia, and polyuria. Urine studies indicated increased potassium excretion. A diagnosis of Bartter like syndrome was made. Polymyxin-B was discontinued, and the patient’s electrolytes normalized, resolving the polyuria. She was discharged with daily potassium and magnesium supplements. Clinical Discussion: Bartter-like syndrome can result from Polymyxin-B-induced tubular dysfunction, characterized by hypokalemia and hypomagnesemia. Close electrolyte monitoring is essential for patients receiving this antibiotic, particularly in those with complex medical conditions. Early recognition allowed for timely discontinuation of Polymyxin-B, which rapidly reversed the electrolyte disturbances. Conclusion: This case underscores the importance of recognizing Polymyxin-B-induced Bartter-like syndrome. Clinicians should be vigilant for electrolyte disturbances in patients undergoing treatment with Polymyxin-B, ensuring timely interventions to mitigate adverse outcomes.

Impacts of Data Preprocessing and Sampling Techniques on Solar Flare Prediction from Multivariate Time Series Data of Photospheric Magnetic Field Parameters

The accurate prediction of solar flares is crucial due to their risks to astronauts, space equipment, and satellite communication systems. Our research enhances solar flare prediction by employing sophisticated data preprocessing and sampling techniques for the Space Weather Analytics for Solar Flares (SWAN-SF) data set, a rich source of multivariate time series data of solar active regions. Our study adopts a multifaceted approach encompassing four key methodologies. Initially, we address over 10 million missing values in the SWAN-SF data set through our innovative imputation technique called fast Pearson correlation-based k-nearest neighbors imputation. Subsequently, we propose a precise normalization technique, called LSBZM normalization, tailored for time series data, merging various strategies (log, square root, Box–Cox, Z-score, and min–max) to uniformly scale the data set's 24 attributes (photospheric magnetic field parameters), addressing issues such as skewness. We also explore the “near decision boundary sample removal” technique to enhance the classification performance of the data set by effectively resolving the challenge of class overlap. Finally, a pivotal aspect of our research is a thorough evaluation of diverse oversampling and undersampling methods, including SMOTE, ADASYN, Gaussian noise injection, TimeGAN, Tomek links, and random undersampling, to counter the severe imbalance in the SWAN-SF data set, notably a 60:1 ratio of major (X and M) to minor (C, B, and FQ) flaring events in binary classification. To demonstrate the effectiveness of our methods, we use eight classification algorithms, including advanced deep-learning-based architectures. Our analysis shows significant true skill statistic scores, underscoring the importance of data preprocessing and sampling in time-series-based solar flare prediction.

Scalable Fabrication of High-Performance Perovskite Solar Cell Modules by Mediated Vapor Deposition.

Perovskite solar cells (PSCs) can enable renewable electricity generation at low levelized costs, subject to the invention of an economically feasible technology for their large-scale fabrication, like vapor deposition. This approach is effective for the fabrication of small area (<1 cm2) PSCs, but its scale-up to produce high-efficiency larger area modules has been limited by a severe imbalance between the vapor-solid reaction kinetics and the mass-transport of the volatile ammonium salt precursor. In this study, an amidine-based low-dimensional perovskite is introduced as an intermediate of the solid-vapor reaction to help resolve this limitation. This improves reaction pathway produces unique vertically monolithic grains with no detectable horizontal boundaries, which is used to produce 1.0 cm2 PSCs with an efficiency of 22.1%, as well as 12.5 and 48 cm2 modules delivering 21.1% and 20.1% efficiency, respectively. The modules retain ≈85% of their initial performance after 900 h of continuous operation (ISOS-L-1 protocol) and ≈100% after 2800 h of storage in an ambient environment (ISOS-D-1 protocol).

Multi-Scale Evaluation and Simulation of Livelihood Efficiency in Post-Poverty Mountainous Areas

Promoting the coordination of livelihoods at the county and farmers’ scales is essential for achieving balanced regional development and rural revitalization in post-poverty mountainous areas. Existing studies predominantly focus on farmers’ or regional livelihood capital and livelihood efficiency at a single scale, lacking research on cross-scale coordination between farmers’ and county livelihoods. Consequently, these studies fail to reveal the interactions and synergistic enhancement pathways between the two scales. This study, using the Qinba mountains in southern Shaanxi as a case, employs system dynamics to construct a coupled system dynamics model of farmers’ livelihood efficiency and county livelihood efficiency. From the perspective of livelihood capital, five regulatory modes, comprising a total of 17 scenarios, were designed and simulated. The results indicate the following data: (1) The coupling coordination degree between farmers’ livelihood efficiency and county livelihood efficiency in the Qinba mountains is 0.623, indicating a moderate level of coordination overall. However, the coupling coordination relationship requires further optimization and adjustment. Specifically, Foping exhibits a severe imbalance, while the coupling coordination degree of Shiquan, Zhashui, Baihe, Pingli, and Lan’gao is in a state of basic coordination. Additionally, 19 other counties, including Lueyang, Ningqiang, Yang, and others, exhibit moderate coordination. (2) Enhancing social or financial capital through various means typically promotes the coordinated development of farmers’ and county livelihood efficiency. On average, social capital and financial capital regulation models can increase the coupling coordination degree by 0.08 and 0.17, respectively. Additionally, strategies such as increasing fixed asset investment and regulating other capital types, including reducing arable land, also effectively improve the coupling coordination degree of farmers’ and county livelihood efficiency. This study provides a decision-making basis for improving the coordination of farmers’ and county livelihoods in post-poverty mountainous areas, thereby promoting economic development and intensive resource utilization. It assists in formulating more precise policy measures and offers a reference for sustainable development and rural revitalization in similar regions.

Targeting MuRF1 to Combat Skeletal Muscle Wasting in Cardiac Cachexia: Mechanisms and Therapeutic Prospects.

Cardiac cachexia, the terminal stage of chronic heart failure, is characterized by severe systemic metabolic imbalances and significant weight loss, primarily resulting from skeletal muscle mass depletion. Despite the detrimental consequences, there is no standardized and clinically-approved intervention currently available for cardiac cachexia. In the context of cardiac cachexia, accelerated protein turnover, that is, inhibited protein synthesis and enhanced protein degradation, plays a crucial role in skeletal muscle wasting. This process is primarily mediated by various proteins encoded by atrogenes. Among them, the atrogene Trim63 (tripartite motif family 63) and its encoded protein MuRF1 have been extensively studied. This review article aims to elucidate the pathogenic mechanisms underlying skeletal muscle wasting in cardiac cachexia, describe the biochemical characteristics of MuRF1, and provide an overview of the investigation into MuRF1-targeting inhibitors. The ultimate goal is to offer novel strategies for the clinical treatment for skeletal muscle wasting associated with cardiac cachexia.

8191 Navigating The Maze Of Adrenal Insufficiency, A Complex Case Of Late Presentation

Abstract Disclosure: K.N. Pereira: None. V. Sundaram: None. V. Mehta: None. K. Desai: None. B. Agrawal: None. Adrenal insufficiency, a rare but potentially life-threatening endocrine disorder, arises from the inadequate production or ineffective response to adrenal hormones, particularly cortisol and aldosterone. It poses diagnostic challenges, especially in individuals with intricate medical histories and manifests as either primary (Addison's disease) or secondary. The subtlety of initial symptoms often leads to a delayed diagnosis, emphasizing the importance of a high index of suspicion among healthcare providers. Timely recognition and intervention are crucial to prevent adrenal crises, which includes severe hypotension, electrolyte imbalances and life-threatening complications. This case report delves into the presentation, diagnosis, and management of primary adrenal insufficiency in a 51-year-old female with a myriad of co-morbidities who presented with persistent low blood pressure, syncope episodes and dizziness both at rest and with exertion, despite being on midodrine with systolic blood pressure readings ranging from 60s to 80s mmHg. She was also noted to be bradycardic and laboratory results showed hyponatremia, hyperkalemia, hypoglycemia and anemia. She was given prompt fluid resuscitation, stress dose steroids and fludrocortisone, leading to a remarkable improvement in blood pressure and overall clinical status. Subsequent testing showed low serum cortisol level and elevated ACTH level, confirming the diagnosis of primary adrenal insufficiency. Continued steroids resulted in significant symptomatic improvement and with no further episodes of hypotension or syncope on ensuing follow ups. This case serves as a poignant illustration for the need for a heightened clinical suspicion, as the symptoms of adrenal insufficiency can often masquerade within the intricate tapestry of comorbidities. When the utilization of midodrine, aimed at managing orthostatic hypotension, did not abate symptoms, it prompted a thorough investigation into the possibility of underlying adrenal dysfunction. This case also highlights the collaborative, multidisciplinary approach in the management of adrenal insufficiency, with endocrinology playing a pivotal role. Presentation: 6/1/2024

Analysis of Ring Galaxies Detected Using Deep Learning with Real and Simulated Data

Understanding the formation and evolution of ring galaxies, which possess an atypical ring-like structure, is crucial for advancing knowledge of black holes and galaxy dynamics. However, current catalogs of ring galaxies are limited, as manual analysis takes months to accumulate an appreciable sample of rings. This paper presents a convolutional neural network (CNN) to identify ring galaxies from unclassified samples. A CNN was trained on 100,000 simulated galaxies, transfer learned onto a sample of real galaxies, and applied to a previously unclassified data set to generate a catalog of rings, which was then manually verified. Data augmentation with a generative adversarial network to simulate images of galaxies was also employed. The resulting catalog contains 1967 ring galaxies. The properties of these galaxies were then estimated from their photometry and compared to the Galaxy Zoo 2 catalog of rings. However, the model’s precision is currently limited due to a severe imbalance of rings in real data sets, leading to a significant false-positive rate of 41.1%, which poses challenges for large-scale applications in surveys imaging billions of galaxies. This study demonstrates the potential of optimizing machine learning pipelines for low training data in rare morphologies and underscores the need for further refinements to enhance precision for extensive surveys like the Vera Rubin Observatory Legacy Survey of Space and Time.

Structural digital Twin for damage detection of CFRP composites using meta transfer Learning-based approach

Using deep learning approaches to identify and locate defects in composite structures made of Carbon Fiber Reinforced Plastics (CFRP) is becoming increasingly popular. However, when the training data is limited, the existing deep learning methods do not work well. Moreover, the lack of data for damaged conditions of CFRP structures causes a severe data imbalance, which makes this problem even harder. In this paper, we present a novel damage detection method called DFMTR which stands for Digital Twin based Few-shot Meta Transfer Learning. It creates numerical model as the digital twin to generate a large amount of virtual data under different damage conditions for model training. Then, it applies relational network and domain adaptive techniques to achieve effective few-shot transfer. That means, it can detect damage accurately with only a very small amount of real data. This is very useful and practical for damage detection of high-end CFRP equipment. In experiments, our approach outperforms existing methods in all commonly used metrics. The classification accuracy can be improved to more than 82% from 55% by using only 4 real samples, and the accurate localization of damaged area is also successfully achieved.

Rigid-flexible coupling dynamic-assisted imbalanced fault diagnosis for helicopter tail transmission system

Helicopters operate in extreme environments, complicating the balanced and accurate acquisition of operational data. Predicting high-precision failures in helicopter tail-drive systems is particularly challenging due to severe data imbalances. To address this, a fault diagnosis method based on rigid-flexible coupling dynamics is proposed. A high-precision simulation model generates source domain data samples, validated by comparing the frequency domain components and RMS values of simulated and tested vibration responses under healthy conditions. An adaptive Local Maximum Mean Discrepancy (LMMD) mechanism aligns features between simulated and tested data. Additionally, the Coordinate-Separable Convolutional ResNet (CSC-ResNet) network is proposed to efficiently learn high-dimensional feature knowledge. Results show the proposed method outperforms others in fault diagnosis accuracy and smoothness, offering a new perspective for fault diagnosis of helicopter tail transmission systems.

Sustainable development between sports facilities and ecological environment based on the dual carbon background

The sustainable development of sports facilities and their integration with the ecological environment are crucial in addressing global environmental challenges. This study examines the coupling coordination between sports facilities and the ecological environment in nine prefecture-level cities in Fujian Province, China, from 2013 to 2020, within the framework of China’s “Dual Carbon” strategy. Using a multidisciplinary approach that integrates economics, sociology, and geography, the study employs the entropy method and coupling coordination models to analyze the temporal evolution and spatial distribution of the coupling coordination between sports facilities and the ecological environment. The findings reveal that the overall trend of coupling coordination is positive, with the degree of coordination improving over time from severe imbalance in 2013 to high-quality coordination by 2020. Economic factors, such as per capita GDP, positively influence the coupling coordination, while factors like population density and regional GDP have a negative impact. Coastal cities, such as Xiamen and Zhangzhou, demonstrate stronger regional correlations and play a critical role in improving the overall coupling level of the province. This research provides several recommendations for promoting orderly and optimal development, considering the distinct characteristics of sports facilities and the ecological environment.

AOCBLS: A novel active and online learning system for ECG arrhythmia classification with less labeled samples

Electrocardiogram (ECG) is a pivotal determinant of cardiac arrhythmia. In practice, ECG data are often acquired as continuous unlabeled chunks with high labeling costs and severe class imbalances. This poses formidable challenges to existing offline learning methods. To address these issues, we propose a novel model called active online class-specific broad learning system (AOCBLS) to learn streaming imbalanced ECG and classify arrhythmias. First, a two-stage active learning strategy combining diversity and uncertainty queries was designed to select the most valuable beats from the current chunk for further labeling and learning, thereby reducing labeling costs. Subsequently, a stable training weight update rule was developed to address the dynamic imbalances in the streaming data. Finally, an incremental learning algorithm was proposed to update network weights online using selected beats, eliminating the need for retraining from scratch. Thus, the AOCBLS can effectively learn online from imbalanced data streams with less label information. Intra-patient experiments on the benchmark MIT-BIH arrhythmia database showed our model achieved an overall accuracy of 99.10% and a G-mean of 96.35% in identifying the four AAMI-recommended beat types, with only 10% additional annotation, outperforming advanced methods at lower labeling cost. Additionally, patient-specific experimental results on the benchmark database and two other databases, INCARTDB and SVDB, suggest the potential of AOCBLS for personalized ECG monitoring on portable devices.

TROPIC: Transfascial Oblique Posterior Interbody Correction: Technical Note

Adult spinal deformity is a common problem in today's aging adult population, particularly in the thoracolumbar spine. This can lead to severe pain and disability, leading to a poorer quality of life. Traditionally, open deformity correction has been the mainstay of treatment for these patients as it provides an excellent operative corridor; however, this comes with severe risk and high complication rates. There has been a trend toward more minimally invasive approaches to correct the deformity while preserving the muscle and soft tissue surrounding the bony spine across the fusion. We describe a minimally invasive surgical technique through a Wiltse approach without invading the paraspinal musculature to gain access to the bony spine to perform lumbar interbody fusions and osteotomies. A total of 3 patients were identified for this technical note who underwent the transfascial oblique posterior interbody correction procedure for adult spinal deformity. The patients had severe coronal and rotational imbalances. We were able to achieve adequate correction through an approach without disturbing the spinal musculature and soft tissues. This technique provides benefits of spinal rotational and coronal plane correction and restores lordosis with current advancements of today's technologies without the downsides of an open surgical approach.

Comparative analysis of early versus late surgical intervention for lumbosacral hemivertebra: a minimum 2-year follow-up retrospective study

BACKGROUND CONTEXTLumbosacral hemivertebra (LSHV) is a complex and unique congenital spinal deformity characterized by early severe trunk imbalance and progressive compensatory curve. Previous studies have proved the efficiency of posterior LSHV resection. However, the optimal timing for surgical intervention of LSHV still remains controversial. Few studies compare the surgical outcomes in patients of different age groups. PURPOSETo evaluate the influence of posterior-only LSHV resection surgery timing on clinical and radiographic results. STUDY DESIGNRetrospective analysis. PATIENT SAMPLEWe retrospectively analyzed 58 LSHV patients undergoing posterior-only LSHV resection with short-segment fusion at our institution between 2010 and 2020, with a mean follow-up of 7.5 years. OUTCOME MEASUREThe following data were observed for all cases: patient demographics, clinical outcomes measured by operating time, intraoperative blood loss, complications, and Health-Related Quality of Life, radiographic parameters included Cobb angles, trunk shift and sagittal spinal parameters. METHODSFrom 2010 to 2020, a consecutive series of 58 LSHV patients treated by posterior LSHV resection with short segmental fusion were investigated retrospectively, with a 7.5-year average follow-up period. Patients were stratified into 2 groups based on the timing of surgery: Group E (≤6 years old, representing the early-surgery) and Group L (>6 years old, representing the late-surgery). Radiographic assessments included pre- and postoperative measurements of main scoliosis, compensatory scoliosis, trunk shift, and sagittal balance parameters. Operative data, perioperative complications and SRS-22 questionnaires were also collected. RESULTSCompared to Group L, Group E exhibited a lower intraoperative blood loss (p<.001), higher final main curve correction rate (p=.037), smaller post-op compensatory curve (p=.031), higher sagittal vertical axis correction rates at immediate post-op (p=.045) and last follow-up (p=.027), and lower implant failure complications incidence (p=.006). CONCLUSIONThis study suggested that early surgical intervention in LSHV patients can achieve better correction outcomes, while reducing blood loss and postoperative complications in a large-scale cohort.

CryoTRANS: predicting high-resolution maps of rare conformations from self-supervised trajectories in cryo-EM

Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, enabling efficient determination of structures at near-atomic resolutions. However, a common challenge arises from the severe imbalance among various conformations of vitrified particles, leading to low-resolution reconstructions in rare conformations due to a lack of particle images in these quasi-stable states. We introduce CryoTRANS, a method that predicts high-resolution maps of rare conformations by constructing a self-supervised pseudo-trajectory between density maps of varying resolutions. This trajectory is represented by an ordinary differential equation parameterized by a deep neural network, ensuring retention of detailed structures from high-resolution density maps. By leveraging a single high-resolution density map, CryoTRANS significantly improves the reconstruction of rare conformations and has been validated on four real-world datasets: alpha-2-macroglobulin, actin-binding protein complexes, SARS-CoV-2 spike glycoprotein, and the 70S ribosome. CryoTRANS can also predict high-resolution structures in cryogenic electron tomography maps using a high-resolution cryo-EM map.

UB-FineNet: Urban building fine-grained classification network for open-access satellite images

Fine classification of city-scale buildings using satellite imagery is a crucial research area with significant implications for urban planning, infrastructure development, and population distribution analysis. However, the task faces great challenges due to low-resolution overhead images acquired from high-altitude space-borne platforms and the long-tailed sample distribution of fine-grained urban building categories, leading to a severe class imbalance problem. To address these issues, we propose a deep network approach to the fine-grained classification of urban buildings using open-access satellite images. A Denoising Diffusion Probabilistic Model (DDPM) based super-resolution method is first introduced to enhance the spatial resolution of satellite images, which benefits from domain-adaptive knowledge distillation. Then, a new fine-grained classification network with Category Information Balancing Module (CIBM) and Contrastive Supervision (CS) technique is proposed to mitigate the problem of class imbalance and improve the classification robustness and accuracy. Experiments on Hong Kong data set with 11 distinct building types revealed promising classification results with a mean Top-1 accuracy of 60.45%, which is on par with street-view image based approaches. A comprehensive ablation study demonstrates that the CIBM and CS modules improve Top-1 accuracy by 2.6% and 3.5%, respectively, over the baseline approach. In addition, these modules can be easily integrated into other classification networks, achieving similar performance improvements. This research advances urban analysis by providing an effective solution for detailed classification of buildings in complex mega-city environments using only open-access satellite imagery. The proposed technique can serve as a valuable tool for urban planners, aiding in the understanding of economic, industrial, and population distribution within cities and regions, ultimately facilitating informed decision-making in urban development and infrastructure planning. Data and code will be publicly available at https://github.com/ZhiyiHe1997/UB-FineNet.

Skeleton-guided multi-scale dual-coordinate attention aggregation network for retinal blood vessel segmentation

Deep learning plays a pivotal role in retinal blood vessel segmentation for medical diagnosis. Despite their significant efficacy, these techniques face two major challenges. Firstly, they often neglect the severe class imbalance in fundus images, where thin vessels in the foreground are proportionally minimal. Secondly, they are susceptible to poor image quality and blurred vessel edges, resulting in discontinuities or breaks in vascular structures. In response, this paper proposes the Skeleton-guided Multi-scale Dual-coordinate Attention Aggregation (SMDAA) network for retinal vessel segmentation. SMDAA comprises three innovative modules: Dual-coordinate Attention (DCA), Unbalanced Pixel Amplifier (UPA), and Vessel Skeleton Guidance (VSG). DCA, integrating Multi-scale Coordinate Feature Aggregation (MCFA) and Scale Coordinate Attention Decoding (SCAD), meticulously analyzes vessel structures across various scales, adept at capturing intricate details, thereby significantly enhancing segmentation accuracy. To address class imbalance, we introduce UPA, dynamically allocating more attention to misclassified pixels, ensuring precise extraction of thin and small blood vessels. Moreover, to preserve vessel structure continuity, we integrate vessel anatomy and develop the VSG module to connect fragmented vessel segments. Additionally, a Feature-level Contrast (FCL) loss is introduced to capture subtle nuances within the same category, enhancing the fidelity of retinal blood vessel segmentation. Extensive experiments on three public datasets (DRIVE, STARE, and CHASE_DB1) demonstrate superior performance in comparison to current methods. The code is available at https://github.com/wangwxr/SMDAA_NET.

The Development of a Novel Nitrate Portable Measurement System Based on a UV Paired Diode-Photodiode.

Nitrates can cause severe ecological imbalances in aquatic ecosystems, with considerable consequences for human health. Therefore, monitoring this inorganic form of nitrogen is essential for any water quality management structure. This research was conducted to develop a novel Nitrate Portable Measurement System (NPMS) to monitor nitrate concentrations in water samples. NPMS is a reagent-free ultraviolet system developed using low-cost electronic components. Its operation principle is based on the Beer-Lambert law for measuring nitrate concentrations in water samples through light absorption in the spectral range of 295-315 nm. The system is equipped with a ready-to-use ultraviolet sensor, light emission diode (LED), op-amp, microcontroller, liquid crystal display, quartz cuvette, temperature sensor, and battery. All the components are assembled in a 3D-printed enclosure box, which allows a very compact self-contained equipment with high portability, enabling field and near-real-time measurements. The proposed methodology and the developed instrument were used to analyze multiple nitrate standard solutions. The performance was evaluated in comparison to the Nicolet Evolution 300, a classical UV-Vis spectrophotometer. The results demonstrate a strong correlation between the retrieved measurements by both instruments within the investigated spectral band and for concentrations above 5 mg NO3-/L.

Infectious Foci, Comorbidities and Its Influence on the Outcomes of Septic Critically Ill Patients.

Sepsis is among the most frequent diagnoses on admission to the intensive care unit (ICU). A systemic inflammatory response, activated by uncontrolled infection, fosters hypoperfusion and multiorgan failure and often leads to septic shock and mortality. These infections arise from a specific anatomic source, and how the infection foci influence the outcomes is unknown. All patients admitted to the ICU of Hospital de Vila Franca de Xira, between 1 January 2017 and 31 June 2023, were screened for sepsis and categorized according to their infection foci. During the study period, 1296 patients (32.2%) had sepsis on admission. Their mean age was 67.5 ± 15.3 and 58.1% were male; 73.0% had community-acquired infections. The lung was the main focus of infection. Septic shock was present in 37.9% of the patients and was associated with hospital mortality. Severe imbalances were noted in its incidence, and there was lower mortality in lung infections. The hospital-acquired infections had a slightly higher mortality but, after adjustment, this difference was non-significant. Patients with secondary bacteremia had a worse prognosis (one-year adjusted hazard ratio of 1.36, 95% confidence interval 1.06-1.74, p = 0.015), especially those with an isolated non-fermenting Gram-negative infection. Lung, skin, and skin structure infections and peritonitis had a worse prognosis, whilst urinary, biliary tract, and other intra-abdominal infections had a better one-year outcome.

Advancing high-speed train gearbox durability: enhanced bearing load and contact stress through transition from helical to herringbone gears

High-speed trains typically utilize helical gear transmissions, which significantly impact the bearing load capacity and fatigue service performance of the gearbox bearings. This paper focuses on the gearbox bearings, establishing dynamic models for both helical gear and herringbone gear transmissions in high-speed trains. The modeling particularly emphasizes the precision of the bearings at the gearbox’s pinion and gear wheels. Using this model, a comparative analysis is conducted on the bearing loads and contact stresses of the gearbox bearings under uniform-speed operation between the two gear transmissions. The findings reveal that the helical gear transmission generates axial forces leading to severe load imbalance on the bearings at both sides of the large gear, and this imbalance intensifies with the increase in train speed. Consequently, this results in a significant increase in contact stress on the bearings on one side. The adoption of herringbone gear transmission effectively suppresses axial forces, resolving the load imbalance issue and substantially reducing the contact stress on the originally biased side of the bearings. The study demonstrates that employing herringbone gear transmission can significantly enhance the service performance of high-speed train gearbox bearings, thereby extending their service life.