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A truth inference scheme for crowdsourcing using NLP and swin transformers
Abstract Crowdsourcing has become a prevalent method for data collection across various domains, offering a scalable and cost-effective solution. However, ensuring the reliability of crowdsourced data remains a significant challenge due to the varying expertise of contributors and the complexity of tasks. Truth inference aims to derive high-quality and accurate answers from heterogeneous and noisy responses for crowdsourcing tasks. In order to address these challenges, we propose a truth inference model that integrates Natural Language Processing with transfer learning using Swin transformers. Unlike traditional transformer architectures, the Swin transformer employs a shifted windowing technique that effectively captures both local and global contextual features in textual data. This approach helps to generate more accurate embedding representations, specifically fine-tuned for nuances of crowdsourced tasks. By incorporating the Swin transformer, our model dynamically refines contributor reliability scores and task difficulty estimates, resulting in a more accurate truth inference. Experimental evaluations on multiple crowdsourcing datasets demonstrate that our approach consistently outperforms state-of-the-art methods in accuracy, scalability, and robustness, particularly under noisy and complex task conditions.
Enhancing 1D ionic conductivity in lithium manganese iron phosphate with low-energy optical phonons
Rendering algorithm for 3D model of goods in power warehouse based on linear interpolation and 2D texture mapping
Elevated plasma level of PAI-1 is associated with severe COVID-19
Abstract Evidence indicates endothelial dysfunction in severe coronavirus disease (COVID-19). Plasminogen activator inhibitor-1 (PAI-1) is a marker of endothelial injury and could be a prognostic marker for COVID-19-related hospitalization and outcomes. The association between PAI-1 levels and the severity of COVID-19-related outcomes was investigated in this study. This single-center retrospective chart review included 113 hospitalized adults from 6.29.2020 to 8.1.2021 with confirmed COVID-19. Plasma PAI-1 levels were measured by ELISA. The primary endpoint was the difference in PAI-1 levels between severe and non-severe COVID-19 groups. Severe COVID-19 was defined as the need for ventilator assistance and/or death. The mean age was 60.78 (22 to 103, SD ± 16.93), and 52 were female and 63 male. There was a significant positive correlation between age and PAI-1 levels. PAI-1 levels were significantly higher in patients with hyperlipidemia. PAI-1 levels in patients requiring ventilator assistance and who died versus those who did not are significantly higher. High plasma PAI-1 levels are associated with severe COVID-19, defined as requiring ventilator use and/or death. Thus, PAI-1 may be a biological marker for severe COVID-19.
Non-linear relationship between serum iron levels and 28-day mortality in sepsis patients: a retrospective study
A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface temperature
Abstract This research paper delves into the realm of quantum machine learning (QML) by conducting a comprehensive study on time-series data. The primary objective is to compare the results and time complexity of classical machine learning algorithms on traditional hardware to their quantum counterparts on quantum computers. As the amount and complexity of time-series data in numerous fields continues to expand, the investigation of advanced computational models becomes critical for efficient analysis and prediction. We employ a time-series dataset that include temperature records from different nations throughout the world spanning the previous half of the century. The study compares the performance of classical machine learning algorithms to quantum algorithms, which use the concepts of superposition and entanglement to handle subtle temporal patterns in time-series data. This study attempts to reveal the different benefits and drawbacks of quantum machine learning in the time-series domain through rigorous empirical analysis. The findings of this study not only help to comprehend the applicability of quantum algorithms in real-world contexts, but they also open the way for future advances in utilizing quantum computing for increased time-series analysis and prediction. This study’s findings could have ramifications in industries ranging from finance to healthcare, where precise forecasting using time-series data is critical for informed decision-making.
RETRACTED ARTICLE: Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques
Force fluctuations regulation and the role of neurophysiological mechanisms throughout different isometric contraction intensities
Abstract Force complexity is a key indicator of the neuromuscular system’s adaptability and motor control. Although an inverted U-shaped relationship between force complexity and contraction intensity is established, its underlying mechanisms remain unclear. To investigate whether changes in motor unit behaviour (recruitment and firing rate) would accompany and explain this relationship, 25 young male adults performed a 30-second knee extensors’ hold-isometric task at 50%, 75%, 100%, 150% and 175% of their End-Test Torque (ETT), at individual’s optimal angle. Force complexity and motor unit behaviour were assessed through Sample Entropy (SampEn) and high-density surface electromyography, respectively. We demonstrated a trend for an inverted U-shaped relationship between force complexity and contraction intensity, with SampEn at ETT and 150%ETT being significantly higher than at 50%ETT and 75%ETT (all p < 0.05). This pattern was accompanied by an increase in motor unit actions potentials and firing rate as the intensity increased up to 150%ETT (all p < 0.05). A multiple linear regression analysis showed that force complexity was explained in 18% by the vastus lateralis’ motor unit behaviour. The findings suggest that changes in force complexity depend on contraction intensity and are partly explained by alterations in motor unit behaviour, influencing the neuromuscular system’s adaptability to meet task demands.
Real-time facial recognition via multitask learning on raspberry Pi
Stroke burden analysis attributable to ambient and household PM2.5 in China from 1990 to 2021 based on GBD 2021
Initial production prediction for horizontal wells in tight sandstone gas reservoirs based on data-driven methods
Abstract Accurate prediction of the initial production in horizontal wells targeting tight sandstone gas reservoirs (IPHTSG) is critical for assessing the exploitation potential of well locations and identifying reservoir sweet spots. Traditional methods for estimating horizontal well productivity exhibit limited applicability due to reservoir heterogeneity and unfavourable petrophysical properties; therefore, this study proposes the use of machine learning for IPHTSG forecasting by systematically analysing the engineering parameters and production metrics. First, an IPHTSG database is established by categorizing and compiling the collected engineering and production parameters in addition to the classified initial production data. Second, on the basis of the IPHTSG database, prediction models for the IPHTSG are developed by employing various machine learning algorithms. The dimensionality of the input data is reduced via correlation analysis of the feature parameters, and the parameters of each prediction model are optimized using a grid search and 10-fold cross-validation. Finally, the models are applied to make predictions on a test set to validate their reliability, forming a set of methods and procedures for IPHTSG prediction. Then, this work describes a case study that was conducted on the tight gas reservoir of the H8 Member in the Sulige Southeast Field (Ordos Basin). The effective reservoir length, vertical thickness, open-flow capacity, bottom hole pressure, and amount of sand inclusion from 155 horizontal wells were selected as feature parameters, with data from 140 wells used as the training set and data from 15 wells used as the test set. Six machine learning algorithms were utilized to establish models, and the relevant calculation indicators of different models are compared. Ultimately, the XGBoost prediction model, which exhibits superior performance, is selected. This model achieves a training accuracy of 95% and a testing accuracy of 93.33%, with precision, recall, and F1-score values of 95%, 94.12%, and 93.14%, respectively, and it also has a relatively short training time. The method proposed in this paper successfully realizes IPHTSG prediction, providing a decision-making basis for formulating reasonable development plans and optimizing production parameters. This interdisciplinary methodology provides a replicable template for data-intelligent decision-making in tight gas reservoir management.
Diagnostic performance of MUAC and MUACZ in screening acute malnutrition among children aged 6–23 months in Amhara Region, Ethiopia
Modeling and simulation of optical wireless communication channels in IoUT considering water types turbulence and transmitter selection
Abstract The Internet of Underwater Things (IoUT) is revolutionizing underwater communication by enabling real-time data exchange, environmental monitoring, and exploration in aquatic environments. Among emerging technologies, optical wireless communication (OWC) has gained prominence due to its high-speed data rates and superior efficiency compared to traditional acoustic and radio frequency (RF) methods. This paper presents a comprehensive study of OWC channel modeling and simulation tailored for IoUT applications. The research investigates the physical characteristics of underwater optical channels, focusing on the effects of absorption, scattering, turbulence, and various noise sources on light propagation across diverse water types, including pure seawater, clear coastal waters, and turbid harbor waters. A central aspect of the study is the comparative evaluation of two transmitter types—light-emitting diode photo sources (LED-PS) and laser diode photo sources (LD-PS)—both operating at a 520 nm wavelength (green light). Their performance is assessed under varying environmental conditions, incorporating three turbulence models: log-normal, generalized gamma, and Weibull distributions. Simulation models are developed and implemented using MATLAB and Python to analyze key parameters such as transmission distance, water type, transmitter characteristics, wavelength, and turbulence intensity. Performance metrics, including received optical power, signal-to-noise ratio (SNR), and bit error rate (BER), are evaluated to provide in-depth insights into system behavior. Results show that LD-PS consistently outperforms LED-PS across all scenarios. For instance, at a received power threshold of − 53.4 dBm, LD-PS achieves a communication distance of up to 68.39 m in pure seawater (compared to 27.36 m for LED-PS), while in turbid harbor, the range is reduced to 3.08 m. At a BER of 10−5, LD-PS reaches 67.69 m in pure seawater and 3.18 m in turbid harbor conditions. Under a fixed SNR of 50 dB, LD-PS achieves a maximum range of 73.34 m in pure sea. The minimum SNR required to maintain a BER of 10−5 is 12.19 dB in pure seawater and rises to 91.94 dB in turbid harbor conditions. These findings advance the development of OWC systems by providing practical guidelines for optimizing underwater communication performance. The insights presented serve as a foundation for designing robust and efficient IoUT networks capable of reliable data transmission across a range of aquatic environments.
Prevalence, molecular characterization, and histopathological impact of Trichomonas gallinae in domestic pigeons from Northeastern Egypt
Abstract Trichomonas gallinae (T. gallinae) is a single-celled flagellate protozoan that causes trichomoniasis, a serious and widespread infectious disease primarily affecting Columbiformes. This study investigated the prevalence, molecular characterization, and histopathological effects of T. gallinae in domestic pigeons (Columba livia domestica) from different environments and regions in Egypt. A total of 685 pigeons were examined from markets, dovecotes, and houses across Cairo, Giza, and Qalubyya Governorates from February 2022 to November 2024. Microscopic examination confirmed an infection in 533 pigeons, yielding an overall infection rate of 77.8%. Markets exhibited the highest prevalence (91.8%), followed by dovecotes (72.1%) and houses (58.4%). Regionally, Cairo recorded the highest infection rate (80.7%), followed by Giza (76.0%) and Qalubyya (76.0%). Seasonal variation indicated that summer accounted for the highest number of cases (48.2%), while winter had the lowest (11.4%). Age distribution revealed a strong predominance of squabs (70.7%) compared to adults (29.3%), and gender analysis showed a significantly higher prevalence in females (83.4%) than in males (16.6%). Histopathological examination of oropharyngeal mucosa, proventriculus, and gizzard showed distinct pathological changes, including severe necrosis, caseation, and granulomatous tissue reactions, which are pathognomonic for T. gallinae infection. Molecular analysis confirmed the presence of T. gallinae, with PCR amplification of the ITS1/5.8S/ITS2 gene revealing two novel strains, were deposited in GenBank with accession numbers (OR498119) and (OR498120). These strains exhibited high nucleotide sequence identity with isolates from China, Germany, and Spain, indicating a high degree of genetic conservation and a widespread global distribution of T. gallinae. These findings highlight the widespread prevalence of T. gallinae in domestic pigeons, particularly in urban and commercial environments, with seasonal, age, and gender-related variations influencing infection rates. The detection of novel genetic variants and severe tissue damage emphasizes the need for enhanced surveillance, control measures, and further research on the pathogenicity and epidemiology of T. gallinae in pigeons and potential spillovers to other avian hosts.
Study on the service behavior of rivets connected with brake pads of high speed trains
An investigation of improving validity in upper limb measurements for people with tetraplegia using construct specification equations
Abstract The aim of this paper is two-fold: to investigate development of a Construct Specification Equation (CSE) for UL task difficulty, , and a CSE for person UL ability, , in support of the validity of these two constructs. Measurements of UL task difficulty, , and person UL ability, were derived from applying the Rasch model on the Tetraplegia Upper Limb Activity Questionnaire (TUAQ). The formulations of CSEs as explanations of the two constructs were done using Principal Component Regression (PCR). The CSE for UL task difficulty, , was to a large degree explained by the number of joints involved and the CSE for person UL ability, , was dominated by grasp-related variables. Pearson coefficients of 0.94 and 0.73 were obtained between UL task difficulty and UL person ability from the CSE, respectively, when correlated with each empirical measure. The present work has both explored and extended the methodology for using more qualitative explanatory variables. Specifically, for UL measurements for people with tetraplegia a good CSE for task difficulty, , supports the validity of TUAQ when measuring person UL ability. Additionally, the CSE formulated for person ability, , can be used both for validation purposes as well as a clinical tool.
Regional variations in the trend of iron supplementation during pregnancy and its multi-level predictors in Pakistan
Abstract Iron supplementation during pregnancy is a key intervention preventing and treating iron deficiency anemia, which is associated with adverse maternal and neonatal outcomes, including severe maternal anemia, miscarriage, hemorrhage, preterm birth, and low birth weight. Despite this, a comprehensive understanding of the trends and predictors of iron supplementation across different regions and provinces in Pakistan remains limited. This study aims to assess both the temporal trends in iron supplementation among pregnant women and its multi-level determinants. This research utilizes repeated cross-sectional study design using secondary data from four waves of the Pakistan Demographic and Health Survey (PDHS; 2006–07 to 2019) to analyze the regional variations on the trend of iron supplementation. Participants included ever married women of reproductive age who have responded to the question of “uptake of iron supplementation during last pregnancy”. Various individual, community and institutional level factors from the data set of PDHS 2019 were used as independent factors to study the predictors of iron supplementation among women during pregnancy. For studying the trends, rate differences, rate ratios, changes in percentages and differences in percentages of iron supplementation during pregnancy were calculated, while for analyzing the predictors of iron supplementation, binary logistic regression models were used. There has been a 44.1% increase in iron supplementation among pregnant women nationwide, with regional increases of 61.7% in rural areas and 19.9% in urban areas, leading to a current national supplementation rate of 65.4%. Factors such as older age, rural residency, living in Sindh or Baluchistan, smoking history, higher number of pregnancies and losses, and more children born or deceased were associated with lower odds of iron supplementation(p < 0.005). Conversely, higher education, residency in Gilgit Baltistan, Azad Jammu and Kashmi, as well as Khyber Pakhtunkhwa, and lady health worker’s advice regarding antenatal care were the significant factors with antenatal care utilization as the strongest predictor of supplementation in both unadjusted (OR = 30.07; 95% CI: 23.55–38.40) and adjusted models (AOR = 31.29; 95% CI: 14.37–68.11). Although over half of pregnant women in the study population take iron supplements, the rate is still lower compared to many other countries. Significant regional disparities suggest the need for targeted efforts to increase supplementation rates and improve maternal health outcomes, such as increasing healthcare access in underperforming regions, expanding educational campaigns, and strengthening community-based programs to improve supplementation adherence.