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Gross Domestic Product (GDP) is significant for measuring the strength of national and global economies in urban profiling areas. GDP is significant because it provides information on the size and performance of an economy. The real GDP growth rate is frequently used to indicate the economy's health. This paper proposes a new model called Pearson Correlation-Long Short-Term Memory-Recurrent Neural Network (PC-LSTM-RNN) for predicting GDP in urban profiling areas. Pearson correlation is used to select the important features strongly correlated with the target feature. This study employs two separate datasets, denoted as Dataset A and Dataset B. Dataset A comprises 227 instances and 20 features, with 70% utilized for training and 30% for testing purposes. On the other hand, Dataset B consists of 61 instances and 4 features, encompassing historical GDP growth data for India from 1961 to 2021. To enhance GDP prediction performance, we implement a parameter transfer approach, fine-tuning the parameters learned from Dataset A on Dataset B. Moreover, in this study, a preprocessing stage that includes median imputation and data normalization is performed. Mean Square Error, Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, Median Absolute Error, and determination coefficient (R2) evaluation metrics are utilized in this study to demonstrate the performance of the proposed model. The experimental results demonstrated that the proposed model gave better results than other regression models used in this study. Also, the results show that the proposed model achieved the highest results for R2, with 99.99%. This paper addresses a critical research gap in the domain of GDP prediction through artificial intelligence (AI) algorithms. While acknowledging the widespread application of such algorithms in forecasting GDP, the proposed model introduces distinctive advantages over existing approaches. Using PC-LSTM-RNN which achieves high R2 with minimum error rates. [ABSTRACT FROM AUTHOR]

Background: Previous studies have shown that the Gaming Disorder Test (GDT) and Gaming Disorder Scale for Adolescents (GADIS-A) have promising validity and reliability when assessing symptoms of gaming disorder among young adults. However, validity and reliability properties of the two scales have not been established among a Vietnamese population. Objective: The present study translated the GDT and GADIS-A into Vietnamese and examined their factor structures, measurement invariance, convergent validity, concurrent validity, and divergent validity among university students. Methods: A total of 610 young adults (mean age = 21.09 years; 63.4% females) were recruited using convenience sampling and who completed a paper-and-pencil survey between April and June 2023. All participants completed a demographic questionnaire, GDT, GADIS-A, and six standardized scales related to gaming disorder, social media addiction, smartphone addiction, and psychological distress. Confirmatory factor analysis (CFA), internal consistency testing, and Pearson's correlations were performed. Results: CFA showed that the GDT had a one-factor structure and the GADIS-A had a two-factor structure. The internal consistency was excellent for both scales among this population. Moreover, both GDT and GADIS-A showed convergent, concurrent, and divergent validity with other standardized scales. Conclusion: The Vietnamese versions of the GDT and GADIS-A have good psychometrics, which may be utilized in future research regarding gaming disorder among Vietnamese populations. [ABSTRACT FROM AUTHOR]

The vast volume of redundant and irrelevant network traffic data poses significant hurdles for intrusion detection. Effective feature selection is crucial for eliminating irrelevant information. Presently, most filtering and embedded methods rely on fixed thresholds or ratios, necessitating prior knowledge. Conversely, wrapper methods are computationally intensive, and individual feature selection methods may introduce biases in evaluation. To address these challenges, this study introduces Adaptive Neighborhood based Feature Selection (AN-SFS), a dynamic feature selection approach that adapts to local statistical properties of the data. Unlike traditional methods, AN-SFS adjusts its threshold based on the characteristics of the current feature subset and incorporates statistical measures of neighboring features, capturing subtle relationships and dependencies. This adaptability enables AN-SFS to achieve robust and effective feature selection outcomes. Using NSL-KDD and UNSW-NB15 datasets, our model demonstrates superiority over conventional ML classifiers in detection rate, precision, and recall, achieving outstanding accuracy rates of 99.3% on NSL-KDD and 97.5% on UNSW-NB15, significantly outperforming contemporary methods. To further demonstrate the effectiveness of our feature selection approach, we conducted a series of comparisons with other statistical methods, including ANOVA, Pearson correlation, and the chi-square test. In each comparison, our approach consistently outperformed these alternatives, underscoring its superior efficacy. Highlights: This paper introduces a new method for choosing important features from a dataset, called Adaptive Neighborhood-based Statistical Feature Selection (AN-SFS). This technique picks the most relevant features by using statistical data and information from neighbouring data points. We also perform feature selection specifically for different types of attacks by dividing the dataset into subsets that represent various attack categories. We then identify and choose the most relevant features for each type of attack based on their importance. We test our method using the NSL-KDD and UNSW-NB15 datasets. Our experiments demonstrate that our approach effectively distinguishes between normal IoT traffic and different attack scenarios. Notably, it achieves these results with low model complexity and a compact size. [ABSTRACT FROM AUTHOR]

A large data set of over 30 thousand organic compounds containing carbon, nitrogen, oxygen, fluorine, and hydrogen was collected, and the density of each compound was predicted by 1D descriptors derived from its molecular formula and 2D descriptors derived from its constitutional structural features. The 2D structural features are composed of Benson's groups, corrected groups, and 2D structural features of the whole molecular structures. All the descriptors were extracted by an in-house program in Java with a function to ensure that each atom (or bond) of molecules is represented by Benson's groups once for atom-based (or bond-based) descriptors. Partial least square (PLS) and random forest (RF) methods were used separately to build models to predict the density. Further, the variable selection of descriptors was performed by variable importance of RF. For partial least square, the combination of the models constructed by descriptors based on the atoms and the bonds achieved the best results in this paper: for the cross-validation of the training set, the Pearson correlation coefficient (R) = 0.9270, mean absolute error (MAE) = 0.0270 g·cm−3, and root mean squared error (RMSE) = 0.0426 g·cm−3; for the prediction of the test set, R = 0.9454, MAE = 0.0263 g·cm−3, and RMSE = 0.0375 g·cm−3. [ABSTRACT FROM AUTHOR]

Due to the high incidence and inconspicuous initial characteristics of rotor unbalance faults, the detection of incipient unbalance faults is becoming a very challenging problem. In this paper, a new method of small rotor unbalance fault diagnosis based on RIME-VMD and modified wavelet kernel network (modified-WKN) is proposed. Firstly, in order to extract the small unbalance fault information from the vibration signals with low signal-to-noise ratio (SNR) more efficiently, the RIME algorithm is used to search for the optimal location of the penalty factor and decomposition layer in the variable mode decomposition (VMD). Secondly, the most relevant decomposition components to the small unbalance fault information are selected by using Pearson Correlation Coefficients and utilized to reconstruct the signal. Finally, the modified-WKN diagnostic model that is used for multi-sensor data fusion is constructed. The model can acquire features of vibration signals from multiple position sensors, which enhances the ability of the modified WKN diagnostic model to deal with incipient fault modes. Based on the experimental analysis of rotor unbalance fault datasets with different SNRs, it is verified that the detection performance of the proposed method is better than the traditional WKN and VMD-WKN methods. Specifically, the proposed method is more sensitive to the initial unbalance faults. [ABSTRACT FROM AUTHOR]

This paper presents a unique advanced statistical approach based on Artificial Intelligence (AI) to examine factors affective on phonological awareness and print awareness of preschool children. Artificial Neural Network (ANN) models were created and correlations between the independent and dependent (outcome) variables were analyzed. The ANN models were trained using the data for phonological awareness and print awareness of children. According to the findings, the created ANN model had an excellent fit to the actual data (R2 = 0.934 and 0.940). Furthermore, the ANN model results were tested with a traditional analysis technique, Pearson correlation analysis. The ANN models yielded similar results to the Pearson correlation analysis but with more detail as expected. The ANN models were run for user-generated synthetic datasets and the relationships between the dependent and independent variables were discussed using model results. Demographic variables, namely, children's age, mother's age, mother's education, and family income were found to be not effective on children's print and phonological awareness skills. On the other hand, home literacy environment-related variables were found to be very effective. In conclusion, this paper introduces a methodology for implementing ANN modeling in educational data. A novel and powerful approach is provided to assess and estimate essential components of early literacy skills. The study has important implications for advancing our understanding of potential benefits of employing an AI-based modeling techniques in the field of education. The utilization of machine learning methods in educational research, as presented in this paper, has the potential to fundamentally reshape our approaches in categorizing and analyzing educational data. [ABSTRACT FROM AUTHOR]

Retraction is the process of removing an article from the scientific records after it has been published. The objective of the study is to explore the influence of the number of retracted papers on the journal quality parameters in the subject of Psychology. The study has applied Pearson Coefficient Correlation to establish the relationship between the number of retracted papers and bibliometric parameters of journal evaluation. All the retracted journal articles indexed in the Scopus database up to the year 2022 in the subject of Psychology, were considered for the study. The Software tools, i.e. MS Excel, and statistical software R Studio® (version 3.6.1) along with the Biblioshiny bibliometrics package were used for data analysis. Bibliometrics indicators, i.e. SJR, total citations count, and h index of the journals, were retrieved from the website of Scimago Journal Ranking (www.scimagojr.com), and the Journal Impact Factor (IF) 2022 was used for the analysis. The results revealed a set of 180 journal articles scattered across 99 journals, that were retracted in the subject of Psychology. Among these 36% of the retracted papers were published as open access. From the analysis, it was concluded that there is a positive correlation between the number of retracted papers and bibliometric indicators i.e., impact factor, h-index, total citation counts, and JCR. On the other hand, there is the least influence of the number of retracted articles with SJR (0.024) and IF (0.072). However, a visible and normal co-relation was found among the total citation counts (0.170) and h-index (0.136), respectively. The findings of the study revealed that there is the least correlation between the number of retracted journal articles and the journal evaluation indicators; therefore, journal publishers must include a retraction policy to enhance the journal's reputation. [ABSTRACT FROM AUTHOR]

Edible insects are perceived as an incredible opportunity to mitigate the major challenge of sustainably producing healthy foods for a growing world population in the face of climate change uncertainties over the coming decade. In this study, we assessed the nutrient composition and sensory properties of Acheta domesticus, Apis mellifera, Gnathocera trivittata, Gryllotalpa africana, Imbrasia epimethea, Imbrasia oyemensis, Locusta migratoria, Macrotermes subhylanus, Nomadacris septemfasciata, Rhyncophorus phoenicis, Ruspolia differens and Rhynchophorus ferrugineus consumed in Eastern D. R. Congo. The investigated edible insects are highly appreciated and nutritious, with proteins (20.67–43.93 g/100 g) and fats (14.53–36.02 g/100 g) being the major macro-nutrients, proving their potential to improve diets through food enrichment. The high potassium (24–386.67 mg/100 g), sodium (152–257.82 mg/100 g), magnesium (32–64 mg/100 g), iron (5.3–16.13 mg/100 g), calcium (25–156.67 mg/100 g) and zinc (11–19.67 mg/100 g) content make the assessed edible insects a useful mineral-containing ingredient for preventing undernutrition in countries which are plagued by micronutrient deficiencies. A scatter plot of matrices and Pearson's correlations between sensory attributes and nutritional composition showed a negative correlation (r = − 0.45) between protein and appearance. While no strong correlation was observed between nutritional attributes and sensory acceptance, a positive correlation was observed between potassium and aroma (r = 0.50), after-taste (r = 0.50) and acceptability (r = 0.52). Principal component analysis results indicated that the two axes accounted for up to 97.4% of the observed variability in the nutrient composition and sensory attributes of commonly consumed edible insects in the Eastern D. R. Congo. Given the significant delicacy and nutritional potential of edible insects highlighted in this paper, households can rely on the latter to meet their nutritional needs rather than conventional livestock, thus contributing to environmental and financial security through local business opportunities. [ABSTRACT FROM AUTHOR]

The lives of caregivers can be deeply impacted by having a child with a developmental disability (DD). To offset those impacts, caregivers may engage in accommodations, or strategies to bolster everyday functioning. The nature and extent of these accommodations can provide insight into how the family is doing and what supports are needed from a family-centered perspective. This paper presents the development and preliminary validation of the Accommodations & Impact Scale for Developmental Disabilities (AISDD). The AISDD is a rating scale that measures day-to-day accommodations and impacts of raising a child with a DD. A sample of 407 caregivers of youth with DDs (Mage = 11.7 years; 63% males) completed the AISDD, along with measures of caregiver strain, daily challenges, child adaptive behavior, and behavior and emotional regulation. The AISDD is a unidimensional, 19-item scale with excellent internal consistency (ordinal alpha =.93) and test–retest (ICC =.95) reliability. Scores were normally distributed and sensitive to age (r = −.19), diagnosis (ASD + ID > ASD > ID), adaptive functioning (r = −.35), and challenging behaviors (r =.57). Finally, the AISDD showed excellent convergent validity with similar measures of accommodations and impacts. These findings support the use of the AISDD as a valid and reliable tool for measuring accommodations among caregivers of individuals with DDs. This measure shows promise in its ability to identify which families may need additional support for their children. [ABSTRACT FROM AUTHOR]

To execute seamless computing along with information sharing, Cloud Computing (CC) plays an important role in measurable resource sharing. But, owing to its open and distributed framework, privacy is a major risk as it is open to attackers. To accomplish their malicious goals, cloud services are abused by intruders. In this paper, using Linear Sigmoid Singleton Deep Neural Network (LS2DNN)with Pearson correlation and Brownian motion induced K-Anonymity (PBKA)centered lightweight privacy preservation technique in the cloud server, an efficient federated learning-based Intrusion Detection System (IDS) is proposed. From the publically available sources, NSL-KDD datasets are downloaded; also, Feature Extraction (FE), null feature removal, and feature mapping are executed. Subsequently, for feature selection, Chebyshev Chaotic Mapping adapted Jaya Optimization Algorithm (C2MJOA) is wielded. Then, for classifying attacked and normal data, the LS2DNNclassifier is utilized.The incoming data from the data owners are privacy preserved via federated learning-centered Pearson correlation and Brownian motion-induced K-Anonymity (PBKA) while testing; moreover, the data is verified for intrusion. Finally, when compared with the prevailing models, the proposed model attained better results. [ABSTRACT FROM AUTHOR]

The health of young people is crucial for the future and development of a nation. It is the collective responsibility and imperative mission of society to ensure the holistic well-being, both physically and mentally, of young individuals. Therefore, it is essential to thoroughly comprehend the factors that influence their health in order to expedite the exploration of effective solutions. The objective of this study is to comprehend the mechanisms that underlie the correlation between physical exercise behavior and psychological resilience among teenagers, while also examining the mediating role played by social sensitivity and need to belong. So put forward the hypothesis: (1) physical exercise behavior can positively predict the psychological resilience. (2) Social sensitivity and need to belong plays a mediating role between physical exercise behavior and psychological resilience. (3) Social sensitivity and need to belong plays a chain mediating role between physical exercise behavior and psychological resilience. Using the cluster sampling method, a total of 1106 students (with an average age of 15.7 and a standard deviation of 0.598) who met the requirements were surveyed from Shandong Province in China. Standard scales were utilized to assess Physical Exercise Behavior, Psychological Resilience, Social Sensitivity, and Need to Belong. For data analysis, Pearson's correlation analysis and bias-corrected percentile Bootstrap method were sequentially conducted. (1) The present study did not find any significant methodological bias, and the observed correlations between physical exercise behavior, psychological resilience, social sensitivity, and need to belong were all statistically significant; (2) Based on the self-determination theory, this study elucidates the relationship between physical exercise behavior and psychological resilience among teenagers. The findings indicate that physical exercise behavior positively predicts the need to belong and psychological resilience, while negatively predicting social sensitivity. Similarly, social sensitivity negatively predicts the need to belong and psychological resilience. Moreover, the need to belong directly and positively predicts psychological resilience. Importantly, all hypotheses proposed in this paper were empirically supported. (3) The indirect effect of the path mediated by social sensitivity is 0.009, while the indirect effect of the path mediated by need to belong is 0.033. Additionally, the combined indirect effect of both social sensitivity and need to belong as mediating variables is 0.014. (4) The cumulative sum of all these indirect effects amounts to 0.056. Based on the self-determination theory, we propose a chain mediation model, specially, physical exercise behavior can significantly positively predict psychological resilience, among which, social sensitivity and need to belong play a significant mediating role between Physical exercise behavior and psychological resilience. In addition, the adoption of good physical exercise behavior can enhance the psychological resilience of adolescents by diminishing social sensitivity and augmenting the need to belong. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent healthcare framework by incorporating modern computing technologies like machine learning and deep learning. The sole motivation of this paper is to predict diabetes status intelligently so that patients can be made aware of their diabetes status regularly. A novel ensemble machine learning-based diabetes prediction framework, namely "iDP", has been proposed in this paper. Six different machine learning approaches have been used for the iDP framework, namely random forest, decision tree, neural network, AdaBoost, support vector machine, and XGBoost. Training and testing of ensemble modeling have been performed based on these learning techniques. A comprehensive preliminary data analysis and screening of statistical analysis have been systematically demonstrated in this paper. The recursive feature elimination and fivefold cross-validation methods have been used to select the best model. All experiments have been performed using R-Studio 1.2.5 with the help of the Rattle library 5.4.0. The preliminary data analytic and Pearson correlation coefficient values have been computed to verify the linearity relationship among the data. Results have been reported in eight performance metrics, including accuracy, specificity, sensitivity, area under curve, area under convex hull, minimum error rate, minimum weighted loss, and precision. The proposed iDP framework improves the results on different aspects in order to compare them with individual learning approaches. Despite this, a comparative performance analysis of the iDP framework has also been performed with state-of-the-art prevailing models. As per experiment, the iDP framework gives better results in most cases, like accuracy: 95.26%, AUC: 91.15%, sensitivity: 96.81%, specificity: 97.72% and precision: 90.72%. [ABSTRACT FROM AUTHOR]

Amyloid-β (Aβ) and tau are important biomarkers to predict the progression of cognitively unimpaired (CU) to dementia due to Alzheimer's disease (AD), according to the diagnosis framework from the US National Institute on Aging and the Alzheimer's Association (NIA-AA). However, it is clinically difficult to predict those subjects who were already with Aβ positive (A +) or tau positive (T +). As a typical characteristic of neurodegeneration in the diagnosis framework, the hypometabolism of the posterior cingulate cortex (PCC) has significant clinical value in the early prediction and prevention of AD. In this paper, we proposed the glucose metabolism in the PCC as a biomarker supplement to Aβ and tau biomarkers. First, we calculated the standard uptake value ratio (SUVR) of PCC based on fluorodeoxyglucose positron emission computed tomography (FDG PET) imaging. Secondly, we performed Kaplan–Meier (KM) survival analyses to explore the predictive performance of PCC SUVR, and the hazard ratio (HR) was calculated. Finally, we performed Pearson correlation analyses to explore the physiological significance of PCC SUVR. As a result, the PCC SUVR showed a consistent downward trend along the AD continuum. KM analyses showed better predictive performance when we combined PCC SUVR with cerebro-spinal fluid (CSF) Aβ42 (from HR = 2.56 to 3.00 within 5 years; from HR = 2.76 to 4.20 within 10 years) and ptau-181 (from 2.83 to 3.91 within 5 years; from HR = 2.32 to 4.17 within 10 years). There was a slight correlation between Aβ42/Aβ40 and PCC SUVR (r = 0.14, p = 0.02). In addition, several cognition scales were also correlated to PCC SUVR (from r = –0.407 to 0.383, p < 0.05). Our results showed that glucose metabolism in PCC may be a potential biomarker supplement to the Aβ and tau biomarkers to predict the progression of CU to AD. [ABSTRACT FROM AUTHOR]

Routability estimation identifies potentially congested areas in advance to achieve high-quality routing solutions. To improve the routing quality, this paper presents a deep learning-based congestion estimation algorithm, which serves to guide the initial pattern routing of a global router to reduce unexpected overflows. Unlike existing methods based on traditional compressed 2D features for model training and prediction, our algorithm extracts appropriate 3D features from the placed netlists. Furthermore, an improved RUDY (Rectangular Uniform wire DensitY) method is developed to estimate 3D routing demands. For the model selection, we employ the U-net model with good image prediction ability as a routability estimator, and the prediction results are used for guiding the initial routing process. Since our training set is formed by cropping training designs, the structural information between two adjacent cropped designs may be missed. Hence, we divide experiments into two parts, experiments based on normal designs and experiments based on big designs. Compared with the state-of-the-art method, experimental results show that our routability estimator can significantly improve the Pearson Correlation Coefficient (PCC) index and reduce the Mean Absolute Normalized Error (MANE) and the Standard Deviation in the Normalized Error (SDNE). Furthermore, our congestion-guided global routing can reduce the routing overflows, wirelength, and via count both on normal designs and big designs, compared to CUGR. [ABSTRACT FROM AUTHOR]

The parts with integrated design technology are widely used in aerospace field because of their advantages such as high strength and high reliability compared with riveted structures, which often present multiple cavities and thin walls, and milling occupies a large amount of machining time, while it is very easy to cause rapid degradation of tool performance because their materials are mostly made of hard-to-machine metals. To address the failure of traditional methods in monitoring the tool trajectory of complex cavity milling process, this paper proposes a tool wear prediction method based on a short-time Fourier transform and an improved depth residual network. Firstly, the short-time Fourier transform is used to convert the signal into a time-frequency map. Then, to solve the problem that the depth residual network describes the machining state from a single perspective, the original model is improved by adding a feature fusion layer. Finally, the signal and the time-frequency map are simultaneously input into the improved deep residual network, and the tool wear sensitive features filtered by Pearson correlation coefficient are extracted from the time domain and frequency domain of the signal, and the deep features in the time-frequency domain are extracted from the residual block structure of the time-frequency map, and the tool wear sensitive features and the deep features in the time-frequency domain are fused in the feature fusion layer to complete the model training. The experimental results show that the average prediction deviation of tool wear by the regression model established in this paper is 0.76%, which is lower than that of the original deep residual network, shallow convolutional neural network, and artificial feature-based machine learning model. [ABSTRACT FROM AUTHOR]

Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous fluctuations of the brain activity during the resting-state as captured by functional MRI (rsfMRI) contain rich non-stationary dynamics over a relatively fixed structural connectome. Among the modeling approaches, graph diffusion-based methods with single and multiple diffusion kernels approximating static or dynamic functional connectivity have shown promise in predicting the FC given the SC. However, these methods are computationally expensive, not scalable, and fail to capture the complex dynamics underlying the whole process. Recently, deep learning methods such as GraphHeat networks and graph diffusion have been shown to handle complex relational structures while preserving global information. In this paper, we propose a novel attention-based fusion of multiple GraphHeat networks (A-GHN) for mapping SC-FC. A-GHN enables us to model multiple heat kernel diffusion over the brain graph for approximating the complex Reaction Diffusion phenomenon. We argue that the proposed deep learning method overcomes the scalability and computational inefficiency issues but can still learn the SC-FC mapping successfully. Training and testing were done using the rsfMRI data of 1058 participants from the human connectome project (HCP), and the results establish the viability of the proposed model. On HCP data, we achieve a high Pearson correlation of 0.788 (Desikan-Killiany atlas with 87 regions) and 0.773 (AAL atlas with 86 regions). Furthermore, experiments demonstrate that A-GHN outperforms the existing methods in learning the complex nature of the structure-function relation of the human brain. [ABSTRACT FROM AUTHOR]

Triangular γ -graphyne has a special carbon–carbon bonding arrangement, which results in outstanding electrical characteristics. It is a potential material for semiconductors and conductors in nanoelectronic devices. The number of vertices at a distance of 2 from a vertex is known as the connection number (CN) of that vertex. In this paper, we computed Zagreb-type indices based on connection numbers. In order to give us a better knowledge of the structural properties of molecules or networks, these indices are calculated. Following the computation of these indices, we investigated their use in computing entropy, providing important new information about the thermodynamic characteristics and complexity of the understudied systems. We used Python language to find the Pearson correlation coefficient between indices and entropy and show its heat map. [ABSTRACT FROM AUTHOR]

Abstract—The paper presents the algorithm for eliminating structured noise (SN) appearing in the transient electromagnetic (TEM) response curves during correlation processing of recorded pseudo noise signals. The algorithm is based on the detection and elimination of pulses of structured noise (SNP) with energies exceeding optimal detection threshold (calculated from the TEM curve) against the background of the smoothly (slowly) varying TEM sounding signal. The method provides reliable SNP detection with high correct recognition and low false alarm rate as well the subsequent elimination of SN field from the recorded TEM curves. For automatic calculation of SNP optimal detection threshold, it is proposed to use the criterion that the noise and interference signals remaining in the TEM curve after SNP removal are maximally close to normal distribution. Selection and setting of the optimal detection threshold is performed by successive sweep through its values (from maximum to minimum) with a given step which determines the accuracy of detection threshold. The procedure of selecting the detection threshold is reduced to finding, at each step, the maximum value of the Pearson correlation coefficient between the probability density distribution functions of amplitudes of noise and interference signals remaining in TEM curve after SN removal and the calculated normal distribution. This approach has made data processing maximally automated and increased its speed, which is very important in the field work. The algorithm was tested on TEM signals recorded at Chunkurchak experimental site in the field work with electromagnetic measurement system using pseudo noise signals (EMS PNS). Application of the algorithm has significantly improved (approximately by a factor of 450) the signal to noise ratio in the TEM response curve in the interval of late times. [ABSTRACT FROM AUTHOR]

Martensitic AISI 420 stainless steel is favored for hardness and strength due to the hard phase of martensite and chromium carbide. However, machining this martensitic steel is difficult since the heat conductivity of this material is poor, which can cause an increase in temperature in the machining zone, resulting in a high amount of tool wear and poor machinability of the work material. Therefore, this paper proposes an experiment to monitor end mill tool wear while cutting MSS AISI 420 through cutting frequency analysis, focusing on IPFs and their associated sidebands. This study investigates the correlation between vibration signal frequencies and tool wear during the milling process on martensitic stainless steel (AISI 420) using the Pearson correlation method. The research yields insights into this relationship, with significant implications for tool wear monitoring. Findings revealed a variety of different correlations between vibration frequencies and tool wear progression, including the emergence of the natural frequency as a dominant indicator of severe wear or damage, strong correlations between sideband signals and tool wear through high-frequency sampling, and optimal sensor placement along the Y-axis with the spindle area as a secondary location. [ABSTRACT FROM AUTHOR]

China has introduced a high-level school sports policy to solve the major educational problem of the continuous decline of young people's physical health. The attitude of students has a significant impact on the effectiveness of policy implementation. The purpose of this paper is to explore the mechanism of the effect of students' school sports policy attitudes on physical quality to provide a reference for improving students' physical health and promoting the implementation effect of school sports policy. A total of 2676 college students in China voluntarily participated in the survey. Pearson correlation analysis showed that there is a significant positive correlation among college students' school sports policy attitudes, physical quality, and physical exercise (τ = 0.083–0.127, p < 0.001). The results of structural equation modeling showed that college students' physical exercise partially mediates in the influence of school sports policy attitudes on physical quality. The multi-group analysis found that there was a significant gender difference in the mediating model of physical exercise (p < 0.001). This was mainly reflected in the influence of school sports policy attitudes on exercise behavior (βmale = 0.119, βfemale = 0.015, Z = |-2.528| > 1.960). The study concluded that improving college students' school sports policy attitudes can help them develop good physical exercise behavior, and then promote their physical health. For male students, emphasis should be placed on actively guiding school sports policy attitudes. For female students, emphasis should be placed on cultivating physical exercise behavior. [ABSTRACT FROM AUTHOR]

Inherent biases in numerical simulation models of global climate models (GCMs) reduce the scope for accurately assessing future droughts under multimodel ensemble. The aim of this research is to increase the efficiency of multimodel ensembles. Consequently, this paper introduces a two-way hybrid weighting scheme for ensembling multiple GCMs. The proposed weighting scheme enhances the coherence of the multimodel ensemble of climate model simulations with real observed data and minimizes the impact of non-identical seasonal behavior in climate simulations within the multimodel ensemble. In the application, we consider precipitation data from 18 GCMs of CMIP6 from the Tibet Plateau region. To evaluate the effectiveness of the proposed scheme, we compared the proposed ensemble data with simple model averaged (SMA) ensemble using the Pearson product-moment correlation coefficient (r), root mean squared error (RMSE), and mean absolute error (MAE). Furthermore, we assessed the future characteristics of various drought categories using the steady-state probabilities of a Markov Chain. Small values of errors metircs and high correlation indicate that the proposed hybrid weighting scheme exhibits better performance than the SMA. Under the proposed index, the long term probabilities of various drought classe show variations across different time scales of drought. These results provide perspectives on how the the long term probabilities of various drought categories evolve over time. [ABSTRACT FROM AUTHOR]

Trauma refers to an overwhelming experience that can leave lingering and negative memories in individuals. Although it is a precursor to challenging behaviours among students with disabilities, the ability of teachers to manage and support them in overcoming their post-trauma experiences is rarely discussed in the literature. This study aims to understand the perceived competence of teachers in adopting trauma-informed practices (TIP) for students with disabilities in regular classrooms in the United Arab Emirates (UAE). The trauma model of the Substance Abuse and Mental Health Services Administration (SAMHSA) guided the development of the Teacher Trauma Management Scale (TTMS) as well as the Balanced Inventory of Desirable Responding (BIDR-16), which was used for data collection. A total of 244 in-service teachers were recruited from 22 schools in one of the seven Emirates in the UAE. The data were subjected to a t-test, an analysis of variance, Pearson moment-production correlation, hierarchical regression and moderation analyses. The results show an interrelationship between most of the domains of the SAMHSA model, the ambivalence of participants towards the implementation of TIP, a small influence of social desirability and the effect of background variables on TIP in the UAE. The study findings showed neutrality of participants on trauma management and thus, highlighting the need for TIP policy formulation, curriculum reform and other implications, which are discussed in detail in the paper. [ABSTRACT FROM AUTHOR]

Accurate segmentation of the left ventricle myocardium is the key step of automatic assessment of cardiac function. However, the current methods mainly focus on the end-diastolic and the end-systolic frames in cine MR sequences and lack the attention to myocardial motion in the cardiac cycle. Additionally, due to the lack of fine segmentation tools, the simplified approach, excluding papillary muscles and trabeculae from myocardium, is applied in clinical practice. To solve these problems, we propose a motion-aware DNN model with edge focus loss and quality control in this paper. Specifically, the bidirectional ConvLSTM layer and a new motion attention layer are proposed to encode motion-aware feature maps, and an edge focus loss function is proposed to train the model to generate the fine segmentation results. Additionally, a quality control method is proposed to filter out the abnormal segmentations before subsequent analyses. Compared with state-of-the-art segmentation models on the public dataset and the in-house dataset, the proposed method has obtained high segmentation accuracy. On the 17-segment model, the proposed method has obtained the highest Pearson correlation coefficient at 14 of 17 segments, and the mean PCC of 85%. The experimental results highlight the segmentation accuracy of the proposed method as well as its availability to substitute for the manually annotated boundaries for the automatic assessment of cardiac function. [ABSTRACT FROM AUTHOR]

Drought, a highly detrimental natural disaster, poses significant threats to both human populations, wildlife, and vegetation. Traditional methods of monitoring soil moisture levels rely on ground-based measurements from meteorological stations. However, these stations often lack comprehensive coverage in certain agricultural areas, necessitating the use of alternative methods such as satellite remote sensing. This technique provides a reliable means of measuring soil moisture, a critical factor in effective agricultural management. This paper investigates variations in soil moisture and drought using data from the Cyclone Global Navigation Satellite System (CYGNSS) and the Soil Moisture Active and Passive (SMAP) system. To evaluate the accuracy of these data products, we compared both datasets with the Global Land Data Assimilation System (GLDAS) NOAH model from 2018 to 2019. Our findings reveal a strong correlation between the datasets and the model, with Pearson correlation coefficients (r) and Root Mean Square Errors (RMSE) of approximately r = 0.98 and RMSE = 0.03 for SMAP, and r = 0.97 and RMSE = 0.02 for CYGNSS, respectively. We further compared these measurement datasets with drought indicators such as the Standardized Precipitation Index over three months (SPI3), the Normalized Difference Vegetation Index (NDVI), and Total Water Storage (TWS). The correlation coefficients between SMAP and the three indicators (NDVI, SPI3, and TWS) were 0.93, 0.84, and 0.047, respectively, while the coefficients between CYGNSS and the same indicators were 0.86, 0.78, and 0.56, respectively. All the variables also exhibited significant p-values. Despite minor differences, the results demonstrate excellent agreement. Our findings underscore the sensitivity of space-based sensors to drought conditions, highlighting their effectiveness as tools for detecting and monitoring drought (e.g. agricultural drought), particularly in the short term. [ABSTRACT FROM AUTHOR]

The value of the ecosystem's ultimate goods and services for human welfare and long-term economic and social development is known as the gross ecosystem product (GEP). For the study of GEP accounting, the suggested water-energy-food (WEF) nexus offers a fresh viewpoint. This work aims to build a GEP accounting index system based on WEF, investigate its spatio-temporal evolution characteristics, and assess trade-offs and synergies between and within the water, energy, and food subsystems. Using the Three Gorges Reservoir area (TGRA) as an illustration, the findings revealed that, firstly, the comprehensive benefit of GEP based on WEF showed an upward trend in TGRA. Still, it was worth noting that the total production of the food ecosystem decreased. Secondly, the GEP based on WEF in five periods showed a spatial pattern of "high east and west, low middle." Thirdly, the Pearson correlation coefficient indicated that the GEP trade-off relationships based on WEF were dominant in TGRA, with the strongest trade-offs between AQV, SCV, APV, and LEV. In addition, in bivariate local spatial autocorrelation, the value of the six ecosystem service function relationships was dominated by the trade-off relationship, and the distribution of trade-offs and synergies showed significant heterogeneity at the county scale in the TGRA. Finally, hot spot analysis showed that the hot spots of the gross water and energy ecosystem products were scattered in the tail area of the study area. In contrast, the hot spots of the gross food ecosystem product were concentrated in the belly region. The findings of this study provided a basis for the scientific formulation of territorial spatial pattern optimization for water, energy, and agricultural resources in the TGRA and can more accurately reflect the status of the ecological environment and changes of WEF over time. Moreover, this paper also gives full play to the growth advantages of shipping and aquatic products, implements effective soil erosion prevention and control measures, and establishes water-saving mechanisms and other measures in terms of water resources. Subregional plans for industrial structure and strengthening of waste gas and wastewater treatment facilities regarding energy resources are developed. Implement the cultivated land protection system and promote the superiority of crop varieties and other measures in terms of food resources. [ABSTRACT FROM AUTHOR]

Medical schools with social missions have the potential to increase minority student interest in health disparities research. In previous work, the authors looked at the missions of medical schools to determine if they were associated with minority student representation. In this paper, the authors look at the representation of full-time faculty and senior leaders who are underrepresented in medicine in US medical schools. This study included all MD-granting medical schools in the US with available data on mission statement Social Mission Content (SMC) and faculty demographics. Data were analyzed for representation of faculty underrepresented in medicine (URM) among all faculty, among junior as compared to senior faculty, and among department chairs. In the 2013 data, Pearson correlation coefficients were calculated to characterize the association between SMC and contemporaneous URM faculty representation. In the 2014–2020 data, hierarchical linear models were used to estimate the association between SMC and the annual rate of change in URM faculty representation. In 2013, URM faculty accounted for 7.4% of all faculty at the median medical school, increasing to 8.4% in 2020. As of 2013, URM representation among junior faculty was 9.2% at the median school, 5.6% among senior faculty, and 4.3% among department chairs. The authors found a slow increase in the percentage of URM faculty members (but not department chairs). This trend did not vary between schools with lower vs. higher emphasis on a social mission (based on the mission statement). The increase in chair representation was determined to be associated with the type of the school, whether historically Black or Puerto Rican, and not precisely its mission. [ABSTRACT FROM AUTHOR]

Purpose: Magnetic Resonance Imaging (MRI) is frequently utilised to aid in the comprehensive assessment of back pain, while dual-energy x-ray absorptiometry (DEXA) is the gold standard test for the assessment of bone density. Assessing bone density on MRI could reduce costs and avoid exposing patients to ionising radiation. The aim of this paper is to investigate whether the relative signal intensity of vertebral bodies compared to other structures can detect osteoporosis on MRI. Methods: 100 patients that had undergone both a lumbar spine MRI and a DEXA scan were identified. The T1 and T2 signal intensity of L1–L4 vertebral bodies (VB), cerebro-spinal fluid (CSF), and psoas muscle were measured within a 1-cm2 region of interest (ROI), and the signal intensity ratios were calculated. The ratios were stratified as normal, osteopenic, or osteoporotic based on DEXA T-scores. Results: The T1 VB /T1 CSF ratio was significantly higher in the osteoporotic group than the normal and osteopenic groups (p < 0.001). The T1 VB /T1 CSF ratio had excellent discrimination (AUC = 0.841) for the presence of osteoporosis. The Pearson correlation coefficient between the DEXA T-score and the T1 VB/T1 CSF ratio was -0.474 (p < 0.001). The intra-observer (ICC = 0.910, 95% CI = 0.757–0.966) and inter-observer reliability (ICC = 0.927, 95% CI = 0.824–0.970) were excellent. In our cohort, a T1 VB / T1 CSF ratio of greater than 4 is 66.7% sensitive but 90.0% specific for the presence of osteoporosis. Conclusion: A high T1 VB/T1 CSF ratio suggests osteoporosis on MRI. Prospective validation is needed to confirm these findings. [ABSTRACT FROM AUTHOR]

Groundwater serves as a primary water source for various purposes. Therefore, aquifer pollution poses a critical threat to human health and the environment. Identifying the aquifer's highly vulnerable areas to pollution is necessary to implement appropriate remedial measures, thus ensuring groundwater sustainability. This paper aims to enhance groundwater vulnerability assessment (GWVA) to manage aquifer quality effectively. The study focuses on the El Orjane Aquifer in the Moulouya basin, Morocco, which is facing significant degradation due to olive mill wastewater. Groundwater vulnerability maps (GVMs) were generated using the DRASTIC, Pesticide DRASTIC, SINTACS, and SI methods. To assess the effectiveness of the proposed improvements, 24 piezometers were installed to measure nitrate concentrations, a common indicator of groundwater contamination. This study aimed to enhance GWVA by incorporating new layers, such as land use, and adjusting parameter rates based on a comprehensive sensitivity analysis. The results demonstrate a significant increase in Pearson correlation values (PCV) between the produced GVMs and measured nitrate concentrations. For instance, the PCV for the DRASTIC method improved from 0.42 to 0.75 after adding the land use layer and adjusting parameter rates using the Wilcoxon method. These findings offer valuable insights for accurately assessing groundwater vulnerability in areas with similar hazards and hydrological conditions, particularly in semi-arid and arid regions. They contribute to improving groundwater and environmental management practices, ensuring the long-term sustainability of aquifers. [ABSTRACT FROM AUTHOR]

Forecasting China's carbon price accurately can encourage investors and manufacturing industries to take quantitative investments and emission reduction decisions effectively. The inspiration for this paper is developing an error-corrected carbon price forecasting model integrated fuzzy dispersion entropy and deep learning paradigm, named ICEEMDAN-FDE-VMD-PSO-LSTM-EC. Initially, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to primary decompose the original carbon price. Subsequently, the fuzzy dispersion entropy (FDE) is conducted to identify the high-complexity signals. Thirdly, the variational mode decomposition (VMD) and deep learning paradigm of particle swarm optimized long short-term memory (PSO-LSTM) models are employed to secondary decompose the high-complexity signals and perform out-of-sample forecasting. Finally, the error-corrected (EC) method is conducted to re-modify and strengthen the above-predicted accuracy. The results conclude that the forecasting performance of ICEEMDAN-type secondary decomposition models is significantly better than the primary decomposition models, the deep learning PSO-LSTM-type models have superiority in forecasting China carbon price, and the EC method for improving the forecasting accuracy has been proved. Noteworthy, the proposed model presents the best forecasting accuracy, with the forecasting errors RMSE, MAE, MAPE, and Pearson's correlation are 0.0877, 0.0407, 0.0009, and 0.9998, respectively. Especially, the long-term forecasting ability for 750 consecutive trading prices is outstanding. Those conclusions contribute to judging the carbon price characteristics and formulating market regulations. [ABSTRACT FROM AUTHOR]

Knowledge of how to define and estimate the dependence among multivariate hydrological series is essential for detecting abrupt changes in the dependence. In this paper, a new method (BMCTC) is proposed to detect all possible abrupt change points in the dependence among multivariate hydrological series. The total correlation estimated by the matrix-based Renyi's alpha-order entropy functional is firstly introduced to define and measure the dependence strength among multivariate hydrological series. Then, the moving cut total correlation (MCTC) sequence is built by the moving window technique, which is used to measure changes in the dependence strength among multivariate hydrological series. Finally, the Bernaola-Galvan algorithm is used to detect all change points of the MCTC sequence. Simulations are performed to compare the effectiveness of BMCTC with Pearson correlation (BMCPC) and Spearman correlation (BMCSC), Cramer-von Mises (CvM) and copula-based likelihood-ratio (CLR). The results show that all change points are detected by BMCTC regardless of the samples size, but wrong change points or no change points are detected by other methods in most cases. BMCTC is applied to detect change points in the dependence among annual runoff, precipitation and sediment discharge series in the Xiliugou and the Kuyehe River, China. It is found that the dependence among runoff, precipitation and sediment discharge changed abruptly in 1980 and 1996 in the Kuyehe River and in 1999 in the Xiliugou River. These changes are mainly caused by human activities such as construction of water conservancy projects and coal mining. [ABSTRACT FROM AUTHOR]

Vegetation is an essential component of terrestrial ecosystems, influenced by climate change and human activities. Quantifying the relative contributions of climate change and human activities to vegetation dynamics is crucial for addressing global climate change. Sichuan Province is one of the essential ecological functional areas in the upper reaches of the Yangtze River, and its vegetation change is of great significance to the environmental function and ecological security of the Yangtze River Basin and southwest China. In this paper, the modified Carnegie-Ames-Stanford Approach(CASA) model was used to estimate the monthly NPP (Net Primary Productivity) of vegetation in Sichuan Province from 2000 to 2018, and the univariate linear regression analysis was used to analyze the temporal and spatial variation of vegetation NPP in Sichuan Province from 2000 to 2018. In addition, taking vegetation NPP as an index, Pearson correlation analysis, partial correlation analysis, and second-order partial correlation analysis were carried out to quantitatively analyze the contribution of climate change and human activities to vegetation NPP. Finally, the Hurst index and nonparametric Man-Kendall significance test were used to predict the future change trend of vegetation NPP in Sichuan Province. The results show that (1) from 2000 to 2018, the NPP of vegetation in Sichuan Province has a significant increasing trend (Slope = 6.09gC·m−2·a−1), with a multi-year average of 438.72 gC·m−2·a−1, showing a trend of low in the east and high in the middle. The response of vegetation NPP to altitude is different at different elevations; (2) the contribution rates of climate change and human activities to vegetation NPP change are 4.12gC·m−2·a−1 and 1.97gC·m−2·a−1, respectively. In contrast, the impact of human activities on NPP is more significant than climate change. Human activities are the main factors affecting vegetation restoration and degradation in Sichuan Province. However, the positive contribution to NPP change is less than climate change; (3) the future vegetation NPP change trend in Sichuan Province is mainly rising, and the same direction change trend is much larger than the reverse change trend. The areas with an increasing trend in the future account for 89.187% of the total area. This research helps understand the impact of climate change and human activities on vegetation change in Sichuan Province. It offers scientific bases for vegetation restoration and ecosystem management in Sichuan and the surrounding areas. [ABSTRACT FROM AUTHOR]

In order to address the security threats posed by the volatility and stochasticity of large-scale distributed wind power, this paper proposes an attention-based hybrid deep learning approach for more efficient and accurate wind power sequence prediction. Firstly, the Pearson correlation coefficient (PCC) is used to identify the main meteorological variables as input sequences. Secondly, the intrinsic complete ensemble empirical mode decomposition with adaptive noise is used to decompose the sequence of wind power. Then, the hidden information such as wind speed, wind direction, and wind magnitude are extracted by bidirectional temporal convolutional networks (BiTCN), and the acquired information is inputted into bidirectional gated recurrent units (BiGRU) optimized by a multi-head self-attention mechanism for prediction. Finally, the predicted values of each component are summed to obtain the final prediction results. By comparing with the other 12 models, the results show that the two-scale integrated model of BiTCN and BiGRU can obtain better prediction accuracy. Compared with other benchmark models, the RMSE of this paper's model is reduced by more than 9.4%, indicating that this paper's model can fit the wind power data better and achieve better prediction results. [ABSTRACT FROM AUTHOR]

Fetal electrocardiogram (FECG) contains crucial information about the fetus during pregnancy, making the extraction of FECG signal essential for monitoring fetal health. However, extracting FECG signal from abdominal electrocardiogram (AECG) poses several challenges: (1) FECG signal is often contaminated by noise, and (2) FECG signal is frequently overshadowed by high-amplitude maternal electrocardiogram (MECG). To address these issues and enhance the accuracy of signal extraction, this paper proposes an improved Cycle Generative Adversarial Networks (CycleGAN) with integrated contrastive learning for FECG signal extraction. The model introduces a dual-attention mechanism in the generator of the generative adversarial network, incorporating a multi-head self-attention (MSA) module and a channel-wise self-attention (CSA) module to enhance the quality of generated signals. Additionally, a contrastive triplet loss is integrated into the CycleGAN loss function, optimizing training to increase the similarity between the extracted FECG signal and the scalp fetal electrocardiogram. The proposed method is evaluated using the ADFECG dataset and the PCDB dataset both from the Physionet. In terms of signal extraction quality, Mean Squared Error is reduced to 0.036, Mean Absolute Error (MAE) to 0.009, and Pearson Correlation Coefficient reaches 0.924. When validating the model performance, Structural Similarity Index achieves 95.54%, Peak Signal-to-Noise Ratio (PSNR) reaches 38.87 dB, and R-squared (R2) attains 95.12%. Furthermore, the positive predictive value (PPV), sensitivity (SEN) and F1-score for QRS wave cluster detection on the ADFECG dataset also reached 99.56%, 99.43% and 99.50%, respectively. On the PCDB dataset, the positive predictive value (PPV), sensitivity (SEN) and F1-score for QRS wave cluster detection also reached 98.24%, 98.60% and 98.42%, respectively. All of them are higher than other methods. Therefore, the proposed model has important applications in effective monitoring of fetal health during pregnancy. [ABSTRACT FROM AUTHOR]

As the country's primary cash crop and the source of cattle feed, fiber, and oil, cotton is regarded as Pakistan's economic backbone, contributing 0.8% of the national gross domestic product (GDP) and 4.1% of the agricultural value-added amount. South Punjab, Pakistan has remained a significant cotton belt of the country, however, in recent years, the region has witnessed a significant decline in annual cotton production. Cotton production in this region is threatened by several issues-including a decline in area under cultivation, increasing temperature and decreasing rainfall patterns, whitefly and other pest attacks due to changing climate. The objective of this study is to explore the possible impact of climatic variables on the decrease of cotton production in South Punjab, Pakistan over the years 2004 through 2020. Pearson correlation coefficients were used to analyze the correlation between climatic factors and cotton crop yield in the study area. The analysis indicated a weak positive effect of rainfall on cotton production during the flowering and picking stages in the Jhang district, as well as during the vegetative stage in the Vehari and Pakpattan districts. During the picking stage and germination stage in Rahim Yar Khan, minimum temperature has a significantly weak negative effect on cotton production respectively. This study aims to provide a framework for policy, incorporating certain valuable cotton varieties, suitable for the climate of the study area, that will help increase the country's GDP by increasing cotton crop production in this region. [ABSTRACT FROM AUTHOR]

Contrast-free ultrasound quantitative microvasculature imaging shows promise in several applications, including the assessment of benign and malignant lesions. However, motion represents one of the major challenges in imaging tumor microvessels in organs that are prone to physiological motions. This study aims at addressing potential microvessel image degradation in in vivo human thyroid due to its proximity to carotid artery. The pulsation of the carotid artery induces inter-frame motion that significantly degrades microvasculature images, resulting in diagnostic errors. The main objective of this study is to reduce inter-frame motion artifacts in high-frame-rate ultrasound imaging to achieve a more accurate visualization of tumor microvessel features. We propose a low-complex deep learning network comprising depth-wise separable convolutional layers and hybrid adaptive and squeeze-and-excite attention mechanisms to correct inter-frame motion in high-frame-rate images. Rigorous validation using phantom and in-vivo data with simulated inter-frame motion indicates average improvements of 35% in Pearson correlation coefficients (PCCs) between motion corrected and reference data with respect to that of motion corrupted data. Further, reconstruction of microvasculature images using motion-corrected frames demonstrates PCC improvement from 31 to 35%. Another thorough validation using in-vivo thyroid data with physiological inter-frame motion demonstrates average improvement of 20% in PCC and 40% in mean inter-frame correlation. Finally, comparison with the conventional image registration method indicates the suitability of proposed network for real-time inter-frame motion correction with 5000 times reduction in motion corrected frame prediction latency. [ABSTRACT FROM AUTHOR]

The porous underground structures have recently attracted researchers' attention for hydrogen gas storage due to their high storage capacity. One of the challenges in storing hydrogen gas in aqueous solutions is estimating its solubility in water. In this study, after collecting experimental data from previous research and eliminating four outliers, nine machine learning methods were developed to estimate the solubility of hydrogen in water. To optimize the parameters used in model construction, a Bayesian optimization algorithm was employed. By examining error functions and plots, the LSBoost method with R² = 0.9997 and RMSE = 4.18E-03 was identified as the most accurate method. Additionally, artificial neural network, CatBoost, Extra trees, Gaussian process regression, bagged trees, regression trees, support vector machines, and linear regression methods had R² values of 0.9925, 0.9907, 0.9906, 0.9867, 0.9866, 0.9808, 0.9464, and 0.7682 and RMSE values of 2.13E-02, 2.43E-02, 2.44E-02, 2.83E-02, 2.85E-02, 3.40E-02, 5.68E-02, and 1.18E-01, respectively. Subsequently, residual error plots were generated, indicating the accurate performance of the LSBoost model across all ranges. The maximum residual error was − 0.0252, and only 4 data points were estimated with an error greater than ± 0.01. A kernel density estimation (KDE) plot for residual errors showed no specific bias in the models except for the linear regression model. To investigate the impact of temperature, pressure, and salinity parameters on the model outputs, the Pearson correlation coefficients for the LSBoost model were calculated, showing that pressure, temperature, and salinity had values of 0.8188, 0.1008, and − 0.5506, respectively, indicating that pressure had the strongest direct relationship, while salinity had an inverse relationship with hydrogen solubility. Considering the results of this research, the LSBoost method, alongside approaches like state equations, can be applied in real-world scenarios for underground hydrogen storage. The findings of this study can help in a better understanding of hydrogen solubility in aqueous solutions, aiding in the optimization of underground hydrogen storage systems. [ABSTRACT FROM AUTHOR]

Accurate prediction of binding free energy is crucial for the rational design of drug candidates and understanding protein-ligand interactions. To address this, we have developed four protocols that combine QM/MM calculations and the mining minima (M2) method, tested on 9 targets and 203 ligands. Our protocols carry out free energy processing with or without conformational search on the selected conformers obtained from M2 calculations, where their force field atomic charge parameters are substituted with those obtained from a QM/MM calculation. The method achieved a high Pearson's correlation coefficient (0.81) with experimental binding free energies across diverse targets, demonstrating its generality. Using a differential evolution algorithm with a universal scaling factor of 0.2, we achieved a low mean absolute error of 0.60 kcal mol-1. This performance surpasses many existing methods and is comparable to popular relative binding free energy techniques but at significantly lower computational cost. Binding free energy calculations are crucial in computational drug discovery, however, the current alchemical free energy perturbation (FEP) requires large computational capabilities to achieve high accuracy. Here, the authors develop an alternative method by combining QM/MM and the mining minima method to predict free energies at lower computational cost and with comparable accuracy to FEP-based methods. [ABSTRACT FROM AUTHOR]

Accurate assessment of body composition is essential for evaluating the risk of chronic disease. 3D body shape, obtainable using smartphones, correlates strongly with body composition. We present a novel method that fits a 3D body mesh to a dual-energy X-ray absorptiometry (DXA) silhouette (emulating a single photograph) paired with anthropometric traits, and apply it to the multi-phase Fenland study comprising 12,435 adults. Using baseline data, we derive models predicting total and regional body composition metrics from these meshes. In Fenland follow-up data, all metrics were predicted with high correlations (r > 0.86). We also evaluate a smartphone app which reconstructs a 3D mesh from phone images to predict body composition metrics; this analysis also showed strong correlations (r > 0.84) for all metrics. The 3D body shape approach is a valid alternative to medical imaging that could offer accessible health parameters for monitoring the efficacy of lifestyle intervention programmes. [ABSTRACT FROM AUTHOR]

The present study sought to examine the association between depression and anxiety with health-related quality of life (HRQoL) among clinical and non-clinical medical students. This study was conducted on newly entrance students (2019 and 2020) in Neyshabur University of Medical Science (NUMS). 471 students participated in this cross-sectional study. The Beck Depression (BDI-II), Anxiety Inventory (BAI), and SF-12 questionnaires were used. To assess the correlation between depression, anxiety, and HRQoL, the Pearson correlation was used. The independent t-test was performed to evaluate the association among different variables with HRQoL. Further, a multiple linear regression model was used to control the confounding effects of the covariates. The mean age of the study population was 21.9 (SD = 6.1), majority of them were female (62.4%). More than 21% and about 32% of students had mild to severe levels of depression and anxiety, respectively. According to correlation analysis (Pearson's r), the correlations between depression, anxiety, and total HRQoL were relatively strong and negative. The multiple linear regression model revealed a statistically significant association between depression and anxiety with different subscales, components, and total HRQoL (p < 0.05). It has been confirmed that the different levels of depression and anxiety in medical students are negatively associated with HRQoL. [ABSTRACT FROM AUTHOR]

Background: Aging results in changes in resting state functional connectivity within key networks associated with cognition. Cardiovascular function, physical activity, sleep, and body composition may influence these age-related changes in the brain. Better understanding these associations may help clarify mechanisms related to brain aging and guide interventional strategies to reduce these changes. Methods: In a large (n = 398) sample of healthy community dwelling older adults that were part of a larger interventional trial, we conducted cross sectional analyses of baseline data to examine the relationships between several modifiable behaviors and resting state functional connectivity within networks associated with cognition and emotional regulation. Additionally, maximal aerobic capacity, physical activity, quality of sleep, and body composition were assessed. Associations were explored both through correlation and best vs. worst group comparisons. Results: Greater cardiovascular fitness, but not larger quantity of daily physical activity, was associated with greater functional connectivity within the Default Mode (p = 0.008 r = 0.142) and Salience Networks (p = 0.005, r = 0.152). Better sleep (greater efficiency and fewer nighttime awakenings) was also associated with greater functional connectivity within multiple networks including the Default Mode, Executive Control, and Salience Networks. When the population was split into quartiles, the highest body fat group displayed higher functional connectivity in the Dorsal Attentional Network compared to the lowest body fat percentage (p = 0.011; 95% CI − 0.0172 to − 0.0023). Conclusion: These findings confirm and expand on previous work indicating that, in older adults, higher levels of cardiovascular fitness and better sleep quality, but not greater quantity of physical activity, total sleep time, or lower body fat percentage are associated with increased functional connectivity within key resting state networks. [ABSTRACT FROM AUTHOR]

Despite its significance, net primary productivity is rarely used as an indicator of climate change. The purpose of this study was to assess the spatiotemporal dynamics of vegetation net primary productivity and its response to climate variability in the Welmel watershed, southeastern Ethiopia. The investigation utilized data from the Moderate Resolution Imaging Spectroradiometer, and the Climate Hazards Group Infrared Precipitation with Stations accessed through the Google Earth Engine cloud computing platform. To evaluate the trend and response of net primary productivity to climatic factors, the Mann-Kendall and Theil Sen's tests, the Pearson correlation coefficient, and stability analysis through the coefficient of variation of net primary productivity were utilized. The analysis of the temporal trend of net primary productivity revealed that the entire watershed, forest, wood, and cultivated land areas exhibited decreasing trends with net primary productivity values of -0.681, -4.361, -1.41, and − 3.25 g C m− 2/year, respectively. On the other hand, shrubs and grazing land displayed increasing trends of 1.15 and 2.04 g C m− 2/year, respectively. The wood and shrubland trends were statistically significant (p < 0.05). In the spatial analysis, an area of 64.6% showed a decreasing trend, whereas 33.78% displayed an increasing trend in net primary productivity values. The areas with stable and unstable variations in net primary productivity accounted for 26.47% and 8.02%, respectively. The net primary productivity of the land cover types and entire watershed were positively correlated with rainfall, however, only forests and woodlands were statistically significantly correlated (p < 0.05). The net primary productivity and land surface temperature were negatively correlated, except for forests and cultivated land, which were positively correlated. The results of this study provide essential information for managing ecosystems, supporting agricultural practices, and enhancing community resilience in a changing climate. [ABSTRACT FROM AUTHOR]

Drought is one of the abiotic stresses that can reduce crop yields. It has a major impact on crop yield reduction. For crops under stress, organic modifiers such as biochar can be useful. Guar (Cyamopsis tetragonoloba L.), an annual legume from the Fabaceae Family, is highly adaptable to arid and semi-arid regions, with many applications in various industries. Field experiments were carried out in a randomized complete block design with three replications using a split-split plots arrangement. The aim was to evaluate the influence of irrigation levels (Ir1 = 10, Ir2 = 14, and Ir3 = 17 days irrigation cycle) and biochar (B1 = 0, B2 = 5, and B3 = 10 tons ha−1) application on physiological traits [(chlorophyll a and b, chlorophyll index (SPAD), relative leaf water content (RWC), electrolyte leakage (EL), canopy temperature, leaf area, water use efficiency (WUE)], morphological parameters (length and diameter of the stem, pod length, fresh weight of root and plant, root length), yield components (seed yield, number of branch plant−1, number of clusters plant−1, pod plant−1, seed pod−1, seed plant−1, 1000-seed weight, and gum contents), and leaf nutrient uptake (Ca, Mg, P, Na, and K) of two commercial and local cultivars (cv1 = RGC-936 and cv2 = Saravan) of the guar plant. It was observed that the Ir3 irrigation treatment produced the highest seed yield (1921.8 kg ha−1) in terms of water stress. However, the maximum pod plant−1 (75.5), seed plant−1 (454.2), seed yield (1871.1 kg ha−1), leaf area (861.8 mm2), SPAD (92.2), Mg (49.8 mg g−1), Na (43.3 mg g−1) and P (0.49 mg g−1) were observed in RGC-936. The results also revealed that biochar was more effective than cultivars in terms of morphological traits. While yield and yield components were affected by cultivar, irrigation at different levels also had a significant effect on functional traits, physiology, and morphology. The addition of biochar appeared to have a positive effect on water stress alleviation and guar growth and leaf nutrient uptake. According to Pearson's correlation analysis, plant weight and length, root weight and length, stem diameter, seed pod−1, branches plant−1, and 1000-seed weight are moderately correlated with seed yield, while pod plant−1 and seed plant−1 are strongly associated with seed yield. On the other hand, the pod length, branches plant−1, and gum content showed a positive but not significant relationship. [ABSTRACT FROM AUTHOR]

Arm span is the most reliable anthropometric measurement to predict the stature of an individual. Arm span, hand length, foot length, head circumference etc., have been proven to be useful to correctly estimate the height of a person. This study was intended to derive a model for the estimation of height and body mass index from arm span and also to determine the socio-demographics characteristics of the participants. Samples of 500 students in Bowen University were used for this research (250 males and females respectively) with varying ethnicity. Heights were measured accurately while Weight was taken using a standard calibrated weight scale; arm span measurement. Analyzed by Pearson's correlation, the results analysis showed a significant effect of arm span on height on males, and a significant effect of height on BMI in both sexes. There is a significant effect of arm span on BMI on male and no significant effect in females; results from t- test indicated sexual dimorphism in height and arm span but not in BMI. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to determine the psychometric properties of the Spanish-language version of the HIV-Symptom Index (HIV-SI) questionnaire in Spanish patients undergoing antiretroviral therapy. Methods: Between 2014 and 2016, an observational, multicenter, prospective cohort study was conducted in seventeen Spanish hospitals to validate HIV-SI questionnaire in terms of: construct validity (confirmatory factor analysis), internal consistency (Cronbach's alpha), convergent validity (Pearson's correlation coefficient) and Known-group validity. In addition, a sensitivity to change analysis was also performed. Results: A total of 232 patients were included in the study. They had a mean age of 46.17 (SD9.82) and were 75% male. The median overall score for the HIV-SI was 10 (IQR 4– 19.5) and the most common symptoms reported were feelings of nervousness or anxiety, fatigue or energy loss, feeling sad or depressed, stomach pain or bloating, and difficulty sleeping. In the current study, the Spanish HIV-SI questionnaire showed a high internal consistency (α = 0.89) and adequate construct validity (CFI and TLI > 0.90). When contrasted with the MOS-HIV questionnaire, an inverse correlation was found. It showed a good association with the mental (r=-0.61; P < 0.0001) and physical score (r=-0.60; P < 0.0001). In a multivariate analysis, the age of the patient, female condition, hepatitis C coinfection, concomitant treatment and non-adherence resulted in a higher HIV-SI score. Conclusions: Our study has shown that the Spanish HIV-SI is a valid and reliable self-administered PROM for routine measurement of patient- reported symptoms among Spanish patients on antiretroviral treatment. [ABSTRACT FROM AUTHOR]

Because of significant individual differences of brain signals, ensuring accuracy and information transfer rate (ITR) remains a challenge for SSVEP recognition. To address this issue, this study combines innovatively sliding time window method based on covariance analysis with the Task-related Component Analysis (TRCA). Moreover, the adaptive analysis technique is used to match different individuals by adjusting the initial length of time window and changing correlation analysis in TRCA. The results reveal that the smaller the initial time window length, the better the SSVEP recognition performance. With the fixed time window, TRCA using canonical correlation analysis (CCA) exhibits lower accuracy and smaller ITR compared to TRCA using Pearson correlation coefficient (PCC). However, under the sliding time window, TRCA with CCA finishes identification with 97% accuracy comparable to TRCA with PCC, and gets ITR up 20.7%. Furthermore, its accuracy and ITR are better than CCA, FBCCA, ABFCCA, and FBCCA-DW. The above results indicate that, during SSVEP identification, the sliding time window method based on covariance analysis is better paired with CCA than PCC, and the fixed time window method is better paired with PCC than CCA. Code is available. [ABSTRACT FROM AUTHOR]

Kojic acid (KA) has gained significant attention due to its widespread use in the food and cosmetics industries. However, concerns about its potential carcinogenic effects have heightened the need for sensitive detection methods. This study introduces a fluorescence-based optical sensor for the quantification of KA in food samples, utilizing fluorescent carbon dots (CDs) synthesized from pomegranate peel via a hydrothermal method. The Stern–Volmer plot demonstrated a linear response for KA in the range of 120 to 1200 µM, with a Pearson correlation coefficient (r) of 0.9999 and. The sensor exhibited a detection limit of 30 ± 0.04 µM and a limit of quantification (LOQ) of 90 ± 0.14 µM. Application of the developed method to soy sauce and vinegar samples yielded accurate KA determinations, with recoveries of 103.11 ± 0.96% and 104.45 ± 2.15%, respectively. These findings highlight the potential of the proposed sensor for practical applications in food quality and safety assessment, offering valuable insights into the presence of KA in food products. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) hold great promise in summarizing medical evidence. Most recent studies focus on the application of proprietary LLMs. Using proprietary LLMs introduces multiple risk factors, including a lack of transparency and vendor dependency. While open-source LLMs allow better transparency and customization, their performance falls short compared to the proprietary ones. In this study, we investigated to what extent fine-tuning open-source LLMs can further improve their performance. Utilizing a benchmark dataset, MedReview, consisting of 8161 pairs of systematic reviews and summaries, we fine-tuned three broadly-used, open-sourced LLMs, namely PRIMERA, LongT5, and Llama-2. Overall, the performance of open-source models was all improved after fine-tuning. The performance of fine-tuned LongT5 is close to GPT-3.5 with zero-shot settings. Furthermore, smaller fine-tuned models sometimes even demonstrated superior performance compared to larger zero-shot models. The above trends of improvement were manifested in both a human evaluation and a larger-scale GPT4-simulated evaluation. [ABSTRACT FROM AUTHOR]

Bicycle sharing systems are becoming highly popular and there have accumulated a large volume of users' trajectory data. In a bicycle sharing system, most users' borrowing and returning behaviors are arbitrary. Importantly, the bicycle sharing system is affected by weather, time as well as other dynamic factors under non-classical conditions. These factors make the scheduling of shared bicycles imbalanced, which is a typical machine learning problem under non-classical conditions. In addition, imbalanced bicycle scheduling impacts user experiences and causes huge economic losses. In this study, we propose a three-in-one dynamic shared bicycle demand forecasting model under non-classical conditions. The proposed model contains three essential and specific techniques: (1) Clustering stations based on the idea of data field. The activity degree of a station is calculated by constructing a bicycle transition network. The stations' geographical locations and the bicycle transition patterns are taken into consideration. Then, nearby stations with similar transition patterns are aggregated into a cluster based on data field. (2) Modeling dynamic factors under non-classical conditions. The Pearson correlation coefficient is used to choose the most relevant dynamic weather features from real data. These features are transformed into a three-dimensional vector by taking into account the historical demand for bicycles in the station cluster. (3) Predicting bicycle demand using a LSTM (Long Short-Term Memory) network with multiple features that learns the features from the three-dimensional vector. The bicycle demand in each cluster is forecasted in a real-time and dynamic fashion every ten minutes. These three techniques collectively achieve one goal: accuracy prediction with low errors in real-time response. Extensive experiments are conducted on real Citi Bicycle data to compare the proposed model with traditional machine learning algorithms as well as the state-of-the-art approaches. The results demonstrate the superior performance of the proposed model in different evaluation measurements. [ABSTRACT FROM AUTHOR]

Purpose: Measuring trunk extensor muscle endurance is important for identifying non-specific low back pain (LBP) risk factors and prognostic indicators, planning treatment goals, and measuring patient progress. This randomized cross-over study evaluated the reliability and validity of a newly instrumented device for measuring lumbar spine extensor muscle endurance. Methods: Thirty healthy men participated in this study. The Biering-Sørensen test (for the endurance time) and the newly invented device (for the number of repetitions) were applied to measure lumbar spine extensor muscle endurance in two separate weeks at a similar time of a day and with the same rater. Test–retest reliability and validity of the devices were examined via an intra-class correlation coefficient [ICC (2,1)], 95% confidence intervals (95% CI), standard errors of the measurement (SEM), minimal detectable change (MDC), and Pearson's correlation coefficient, respectively. Results: Test–retest reliability of the newly instrumented device demonstrated an excellent level of reliability [ICC (2,1): 0.969; 95% CI: 0.934–0.985; SEM: 2.65 repetition; MDC: 3.75 repetition]; while a moderate to good degree of test–retest reliability was found between the Biering-Sørensen test measurements [ICC (2,1): 0.884; 95% CI: 0.758–0.944; SEM: 13.31 s; MDC: 18.82 s]. Compared to the Biering-Sørensen test, the newly instrumented device had a moderately positive correlation (r = 0.283). Conclusion: The newly instrumented device demonstrated adequate reliability and validity compared with the Biering-Sørensen test. Future studies should assess its clinometric properties in patients with musculoskeletal pain also including female participants. [ABSTRACT FROM AUTHOR]

Reading comprehension is one of the most valuable academic skills taught in school. Selecting the appropriate assessment instrument to ensure early identification and intervention is important as there is an amalgam of cognitive abilities and academic skills involved in reading comprehension. The GORT-5 is the most recent edition of a test that measures reading rate, accuracy, fluency, and comprehension among school-age children and young adults. The current study evaluated the concurrent validity of the GORT-5 with the WJ-IV ACH for a group of referred students. Findings indicate that Pearson correlation coefficients for all GORT-5 and WJ-IV ACH score comparisons were very large (0.70-0.89). Generally, GORT-5 scores were 2-3 points higher than WJ-IV ACH cluster scores. The diagnostic accuracy of the GORT-5 is excellent, with very few students' scores yielding a false negative. [ABSTRACT FROM AUTHOR]