Browse Articles
Discover research articles across all indexed journals
The silence of opioids-dependent chronic pain patients: A text mining analysis from sex and gender perspective
Existing evidence indicates sex-related differences in Prescription Opioid Use Disorder (OUD) in Chronic Non-Cancer Pain (CNCP). However to date, there is scant evidence for other socioeconomic factors in these differences. Our aim was to enquire about the influence of gender and drug copayment of OUD narratives by the text mining analysis. A prospective mixed-methods study was designed and performed at Pain Unit (PU) including 238 real world patients with CNCP divided in controls (n = 206) and OUD cases (n = 32) due to DSM-5 diagnosis Variables related to pain, sleep, mental and health status were collected in together with sex and gender interaction, in pain status, along 30-45 min face-to-face interviews. Sex differences were observed due to women’s significantly older ages, with a stronger impact on mental health, and an even stronger one for the OUD women. Globally, OUD cases were more unemployed vs the CNCP controls, and on a significantly higher median opioid daily dose of 90 [100] mg/day. Although OUD participants did more social activities, they tended to use less vocabulary to express themselves regardless of their sex, gender role or economic status. In contrast, the CNCP participants presented more differences driven by their incomes, with “limited” being the most discriminating word for those on low income, followed by “less” and “help”. Here, the most significant word of CNCP women was “husband”, followed by “tasks”. In contrast, gender reproductive roles shared similarities in both sexes, being one of the most discriminatory words “help”. The data show that OUD patients seem to have a marked influence of OUD on poorer lexicon and simpler narrative, together with a significant impact of socioeconomic factors on the CNCP narratives. The conclusion suggests to extend the research to better understand the effect of sex, gender and socioeconomic status in CNCP especially on OUD women’s health.
Innovative hand pose based sign language recognition using hybrid metaheuristic optimization algorithms with deep learning model for hearing impaired persons
Yes, you can do global, cross-cultural behavioral science research using existing survey firms
Assessing the sustainability of combined heat and power systems with renewable energy and storage systems: Economic insights under uncertainty of parameters
The escalating challenges posed by fossil fuel reliance, climate change, and increasing energy expenses have underscored the critical importance of optimizing energy systems. This paper addresses the economic dispatch (ED) challenge, which directs the optimization of the output of generation units to satisfy electricity and heat requirements while reducing operational expenses. In contrast to conventional economic dispatch methods, this research incorporates renewable energy sources (RESs), energy storage systems (ESSs), and combined heat and power (CHP) systems. This integrated strategy facilitates the concurrent optimization of electrical and thermal generation, culminating in a more comprehensive and efficient solution. A sophisticated scheduling model for combined heat, power, and electrical energy dispatch (CHPEED) has been devised, minimizing generation expenses. The suggested model accounts for practical constraints inherent in real-world power systems, such as prohibited operating regions, while also addressing the intricate relationships between heat and power generation in CHP units. Also, the nature of wind energy, photovoltaic systems, and load requirements within the realm of stochastic dynamic ED are considered. The general algebraic modeling system (GAMS) was utilized to solve the optimization problem. The cost without RES or ESS is $250,954.80, indicating a high reliance on costly energy sources. Integrating RES reduces costs to $247,616.42, highlighting savings through decreased fossil fuel dependency. The combination of RES and ESS achieves the lowest cost of $245,933.24, showcasing improvements in efficiency and supply-demand management via optimized energy utilization. Hence, the findings demonstrate the model’s effectiveness in addressing uncertainties associated with renewable generation, ensuring reliability in meeting energy demands and validating the possible capability to enhance the sustainability and efficiency of energy systems.
Performance analysis and optimization design of variant CR-CR 9-speed automatic transmission
Abstract With the growing demand of people for fuel economy, driving comfort and environmental friendliness of automobiles, the development of high-gear automatic transmissions (ATs) with outstanding performance has become a research focus in the automotive field. However, the lack of systematic research methods has impeded the progress in this field. This paper presents a performance analysis and optimization method for high-gear variant CR-CR 9-speed AT. Firstly, the lever method was employed to calculate the transmission ratios of each gear position, the relative rotational speeds of each component, and the internal and external torques of the transmission, and a general formula for transmission efficiency was derived. Secondly, in order to enhance the performance and efficiency of the transmission, a scaled optimization algorithm was programmed, obtaining the optimal scheme of transmission structure with the highest efficiency and the optimal speed ratio step value ranges for the reduction and acceleration gears. Finally, three-dimensional modeling and simulation were carried out to verify the correctness of the theoretical derivation and the feasibility of the most efficient structure scheme. This method can provide a theoretical foundation and technical support for the improvement and preferred application of high-gear ATs.
Retraction: Research on the inheritance and protection of folk art and culture from the perspective of network cultural governance
Spatial-frequency feature fusion network for small dataset fine-grained image classification
Real-world goal-directed behavior reveals aberrant functional brain connectivity in children with ADHD
Functional connectomics is a popular approach to investigate the neural underpinnings of developmental disorders of which attention deficit hyperactivity disorder (ADHD) is one of the most prevalent. Nonetheless, neuronal mechanisms driving the aberrant functional connectivity resulting in ADHD symptoms remain largely unclear. Whereas resting state activity reflecting intrinsic tonic background activity is only vaguely connected to behavioral effects, naturalistic neuroscience has provided means to measure phasic brain dynamics associated with overt manifestation of the symptoms. Here we collected functional magnetic resonance imaging (fMRI) data in three experimental conditions, an active virtual reality (VR) task where the participants execute goal-directed behaviors, a passive naturalistic Video Viewing task, and a standard Resting State condition. Thirty-nine children with ADHD and thirty-seven typically developing (TD) children participated in this preregistered study. Functional connectivity was examined with network-based statistics (NBS) and graph theoretical metrics. During the naturalistic VR task, the ADHD group showed weaker task performance and stronger functional connectivity than the TD group. Group differences in functional connectivity were observed in widespread brain networks: particularly subcortical areas showed hyperconnectivity in ADHD. More restricted group differences in functional connectivity were observed during the Video Viewing, and there were no group differences in functional connectivity in the Resting State condition. These observations were consistent across NBS and graph theoretical analyses, although NBS revealed more pronounced group differences. Furthermore, during the VR task and Video Viewing, functional connectivity in TD controls was associated with task performance during the measurement, while Resting State activity in TD controls was correlated with ADHD symptoms rated over six months. We conclude that overt expression of the symptoms is correlated with aberrant brain connectivity in ADHD. Furthermore, naturalistic paradigms where clinical markers can be coupled with simultaneously occurring brain activity may further increase the interpretability of psychiatric neuroimaging findings.
Characterizing the effect of impeller design in plant cell fermentations using CFD modeling
Leadership development as a novel strategy to mitigate burnout among female physicians
Background Female physicians are more likely to experience burnout and less likely to hold leadership positions. Effective interventions are needed to support women physicians in the workforce. Objective To determine if a shared learning, social-based leadership development program will impact burnout and career trajectory for female physicians. Design Cohort study. Setting Multispecialty healthcare system and state medical society members. Participants Burnout and Engagement surveys were emailed to 5000 physicians within the Baylor Scott & White Health System (BSWH). The external control group consisted of 516 female physicians within the Texas Medical Association (TMA) and not associated with BSWH. Internal controls included both male (670) and female physicians (240) who did not participate in the program. Intervention The Women Leaders in Medicine (WLiM) program included twice-annual in person summits and support programs throughout the 2-year study period. Measurements The Maslach Burnout Index (MBI) was utilized to evaluate burnout. Surveys were conducted at three separate points and included interest in leadership, intent to retain current employment, and open comments. Results Participants in WLiM had decreased frequency of high emotional exhaustion (mean 2.9 decreased to 2.5), decreased occurrence of high depersonalization (mean 1.6 decreased to 1.3), and improved levels of personal accomplishment (mean 4.7 improved to 5.1) and leadership aspiration (mean 7.4 to 7.8). Intention to stay went from 4.0 to 4.1. Conclusions Burnout can be improved, and leadership aspirations fostered with a group leadership development in a cohort of female physicians.
Distinct phylogeographic distributions and frequencies of precore and basal core promoter mutations between HBV subgenotype C1 rt269L and rt269I types
Variable DPP4 expression in multiciliated cells of the human nasal epithelium as a determinant for MERS-CoV tropism
Transmissibility of respiratory viruses is a complex viral trait that is intricately linked to tropism. Several highly transmissible viruses, including severe acute respiratory syndrome coronavirus 2 and Influenza viruses, specifically target multiciliated cells in the upper respiratory tract to facilitate efficient human-to-human transmission. In contrast, the zoonotic Middle East respiratory syndrome coronavirus (MERS-CoV) generally transmits poorly between humans, which is largely attributed to the absence of its receptor dipeptidyl peptidase 4 (DPP4) in the upper respiratory tract. At the same time, MERS-CoV epidemiology is characterized by occasional superspreading events, suggesting that some individuals can disseminate this virus effectively. Here, we utilized well-differentiated human pulmonary and nasal airway organoid-derived cultures to further delineate the respiratory tropism of MERS-CoV. We find that MERS-CoV replicated to high titers in both pulmonary and nasal airway cultures. Using single-cell messenger-RNA sequencing, immunofluorescence, and immunohistochemistry, we show that MERS-CoV preferentially targeted multiciliated cells, leading to loss of ciliary coverage. MERS-CoV cellular tropism was dependent on the differentiation of the organoid-derived cultures, and replication efficiency varied considerably between donors. Similarly, variable and focal expression of DPP4 was revealed in human nose tissues. This study indicates that the upper respiratory tract tropism of MERS-CoV may vary between individuals due to differences in DPP4 expression, providing an explanation for the unpredictable transmission pattern of MERS-CoV.
Method for building segmentation and extraction from high-resolution remote sensing images based on improved YOLOv5ds
To address challenges in remote sensing images, such as the abundance of buildings, difficulty in contour extraction, and slow update speeds, a high-resolution remote sensing image building segmentation and extraction method based on the YOLOv5ds network structure was proposed using Gaofen-2 images. This method, named YOLOv5ds-RC, comprises three primary components: target detection, semantic segmentation, and edge optimization. In the semantic segmentation module, an upsampling and multiple convolutional layers branch out from the second feature fusion layer of the Feature Pyramid Networks (FPN), producing a category mapping image that matches the original image size. For edge optimization, a Raster compression module is incorporated at the end of the segmentation network to refine the segmentation contours. This approach enables effective segmentation of Gaofen-2 images, achieving detailed results at the individual building scale across urban areas and facilitating rapid contour optimization and extraction. Experimental results indicate that YOLOv5ds-RC achieves an accuracy of 0.8849, a recall of 0.63904, an average precision (AP) at 0.5 of 0.75863, and a mean average precision (mAP) from 0.5 to 0.95 of 0.47388. These metrics significantly surpass those of the original YOLOv5ds, which recorded values of 0.81483 for accuracy, 0.51332 for recall, 0.63552 for AP at 0.5, and 0.34922 for mAP. The algorithm effectively corrects target displacement deviations in non-orthogonal images and achieves more objective and accurate contour extraction, meeting the requirements for rapid extraction. Due to these features, YOLOv5ds-RC can further enhance fully automated rapid extraction and historical change analysis in land use change monitoring.
The Oomplet dataset toolkit as a flexible and extensible system for large-scale, multi-category image generation
Daily briefing: Iguanas from the Americas might have rafted to Fiji
Results from the <i>inSight</i> Mars mission do not require a water-saturated mid crust
A comprehensive survey and comparative analysis of time series data augmentation in medical wearable computing
Recent advancements in hardware technology have spurred a surge in the popularity and ubiquity of wearable sensors, opening up new applications within the medical domain. This proliferation has resulted in a notable increase in the availability of Time Series (TS) data characterizing behavioral or physiological information from the patient, leading to initiatives toward leveraging machine learning and data analysis techniques. Nonetheless, the complexity and time required for collecting data remain significant hurdles, limiting dataset sizes and hindering the effectiveness of machine learning. Data Augmentation (DA) stands out as a prime solution, facilitating the generation of synthetic data to address challenges associated with acquiring medical data. DA has shown to consistently improve performances when images are involved. As a result, investigations have been carried out to check DA for TS, in particular for TS classification. However, the current state of DA in TS classification faces challenges, including methodological taxonomies restricted to the univariate case, insufficient direction to select suitable DA methods and a lack of conclusive evidence regarding the amount of synthetic data required to attain optimal outcomes. This paper conducts a comprehensive survey and experiments on DA techniques for TS and their application to TS classification. We propose an updated taxonomy spanning across three families of Time Series Data Augmentation (TSDA): Random Transformation (RT), Pattern Mixing (PM), and Generative Models (GM). Additionally, we empirically evaluate 12 TSDA methods across diverse datasets used in medical-related applications, including OPPORTUNITY and HAR for Human Activity Recognition, DEAP for emotion recognition, BioVid Heat Pain Database (BVDB), and PainMonit Database (PMDB) for pain recognition. Through comprehensive experimental analysis, we identify the most optimal DA techniques and provide recommendations for researchers regarding the generation of synthetic data to maximize outcomes from DA methods. Our findings show that despite their simplicity, DA methods of the RT family are the most consistent in increasing performances compared to not using any augmentation.