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A multi-scale small object detection algorithm SMA-YOLO for UAV remote sensing images

A corpus-based analysis of noun modifiers in L2 writing: The respective impact of L2 proficiency and L1 background

Complex noun phrases, as a distinctive feature of academic writing, pose an important learning task for L2 learners. Noun modifiers are the primary means of constructing complex noun phrases. Due to the development of natural language processing (NLP) technologies in recent years, noun phrase complexity, which is a micro-syntactic complexity indicator reflecting the complexity and diversity of clausal and phrasal structures, has emerged as an important research topic. This study applies Bayesian regression with informative priors to analyze the use of English noun modifiers by L2 learners of different proficiency levels and L1 backgrounds through the exploration of the EF Cambridge Open Language Database (EFCAMDAT) corpus. It finds that L2 proficiency has a significant impact on the development of noun phrase complexity in non-academic writing, while the influence of L1 background is observable but limited. It thus concludes that as second language proficiency increases, learners tend to converge towards a common grammatical competence that transcends their native linguistic frameworks.

A novel fractal fractional mathematical model for HIV/AIDS transmission stability and sensitivity with numerical analysis

Correction for Chen et al., Live-attenuated virus vaccine defective in RNAi suppression induces rapid protection in neonatal and adult mice lacking mature B and T cells

Types, method, and mode of implementation of pain/symptom maps in musculoskeletal pain rehabilitation: A scoping review protocol

Introduction Pain maps are tools used for assessing the extent, location, or distribution of pain or symptoms for clinical or research purposes. Pain mapping involves a transformational representation of patients’ experiences of pain into a graphical, numerical, or descriptive form that typically requires a patient to indicate the affected body regions and may include additional information such as qualitative description or intensity. In preparation for innovative technology-enabled development of quantifiable pain maps, this review will focus on the methodological aspects of recent pain maps in addition to the reported measurement properties of each mapping approach. This will identify current gaps in knowledge, consistencies in implementation, and inform directions for future development of more person-centric and meaningful pain maps. The objective of this scoping review is to explore the commonly used types of pain/symptom maps in musculoskeletal pain by classifying design (types) across five categorical features: scalability, region-specificity, aspect or orientation, segmentation, and sex identification, and investigate their methods and modes of implementation. Methods Key sources of evidence such as Medline, Embase, PsycINFO, CINAHL, Scopus, Web of Science, will be searched from inception to June 5, 2024, including grey literature from reference screening, library and organizational collections such as WorldCat, ProQuest Global Dissertation, Google Scholar, and Google to find descriptions or evaluations of pain/symptom maps in people with pain of a primarily musculoskeletal origin. Studies reporting standard patient-reported pain or body mapping interventions will be considered but studies that present X-ray or CT or MRI scans or artistic body maps will be excluded. Primary outcomes include ‘types’ of design: scale, segments, sex, orientation, region; pain mapping methods: marking, shading, checking; and mode of implementation: paper, digital, etc. Secondary outcomes include axis I: pain location, extent or distribution; and axis II: pain severity, intensity, and quality. Eligibility screening and data extraction will be conducted by two independent reviewers. The review is intended to initiate research that promotes the integration of data-friendly solutions and supports the application of machine learning in musculoskeletal pain evaluation.

MT1H inhibits the growth of gastric cancer by regulating SLC6A19/TTC39B/ADM2 and activating p53-dependent autophagy

Retraction: Relay protection system of transmission line based on AI

Multi-beam multi-slice X-ray ptychography

Abstract X-ray ptychography provides the highest resolution non-destructive imaging at synchrotron radiation facilities, and the efficiency of this method is crucial for coping with limited experimental time. Recent advancements in multi-beam ptychography have enabled larger fields of view, but spatial resolution for large 3D samples remains constrained by their thickness, requiring consideration of multiple scattering events. Although this challenge has been addressed using multi-slicing in conventional ptychography, the integration of multi-slicing with multi-beam ptychography has not yet been explored. Here we present the first successful combination of these two methods, enabling high-resolution imaging of nanofeatures at depths comparable to the lateral dimensions that can be addressed by state-of-the-art multi-beam ptychography. Our approach is robust, reproducible across different beamlines, and ready for broader application. It marks a significant advancement in the field, establishing a new foundation for high-resolution 3D imaging of larger, thicker samples.

Forecasting stock prices using long short-term memory involving attention approach: An application of stock exchange industry

The Stability of the economy is always a great challenge across the world, especially in under developed countries. Many researchers have contributed to forecasting the Stock Market and controlling the situation to ensure economic stability over the past several decades. For this purpose, many researchers have built various models and gained benefits. This journey continues to date and will persist for the betterment of the stock market. This study is also a part of this journey, where four learning-based models are tailored for stock price prediction. Daily business data from the Karachi Stock Exchange (100 Index), covering from February 22, 2008 to February 23, 2021, is used for training and testing these models. This paper presenting four deep learning models with different architectures, namely the Artificial Neural Network model, the Recurrent Neural Network with Attention model, the Long Short-Term Memory Network with Attention model, and the Gated Recurrent Unit with Attention model. The Long Short-Term Memory with attention model was found to be the top-performing technique for accurately predicting stock exchange prices. During the Training, Validation and Testing Sessions, we observed the R-Squared values of the proposed model to be 0.9996, 0.9980 and 0.9921, respectively, making it the best-performing model among those mentioned above.

Dielectric response mechanism and structure–property relationships of SrSn(BO3)2 microwave ceramics with ultra-low permittivity and their application for 5G microstrip patch antenna

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.