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L'arrivée inattendue du cancer chez l'adolescent et le jeune adulte (AJA) constitue une expérience difficile et complexe qui entraîne des répercussions profondes et durables sur la famille et son fonctionnement. Dans ce contexte, la famille est amenée à modifier son fonctionnement habituel pour s'adapter à la maladie et à ses thérapeutiques. Notre objectif est de décrire les interactions entre le jeune et ses proches – parents et fratrie – pour appréhender l'impact du cancer sur la famille et son fonctionnement. Nous avons réalisé une étude qualitative, proposant un entretien de recherche de manière individuelle aux AJA, aux mères, aux pères et à la fratrie. Les entretiens (AJA = 13 ; parents = 11 ; fratrie = 2) ont été soumis à une analyse thématique. Dix thèmes majeur composés de sous-thèmes auprès des AJA, 10 thèmes majeurs composés de sous-thèmes auprès des parents et 6 thèmes majeurs avec des sous-thèmes auprès de la fratrie ont été identifiés. L'analyse montre que l'AJA malade, à la frontière entre l'infantile et l'âge adulte, régresse vers des positions infantiles devenant dépendant de l'entourage familial (les parents notamment et la fratrie) pour les soins nécessaires. L'entourage familial a également tendance à valoriser cette dépendance par les soins apportés au jeune malade. Les interactions qui se mettent en place dans la famille ont tendance à l'acculer dans un fonctionnement régressif pour assurer sa survie physique et psychique. Le fonctionnement régressif comme un processus permet à la famille de faire face au cancer, et de ce fait l'évolution des liens familiaux est remise à plus tard. Un accompagnement davantage centré sur la famille est nécessaire, en mettant l'accent sur les besoins spécifiques des AJA, en termes d'individuation et d'autonomisation. Adolescent and young adult (AYA) cancer is a difficult and complex experience, which has major and ongoing consequences for the family and its functioning. In this context, the family need change its usual functioning to cope with the disease and its treatments. The aim of this study is to describe the interactions between the young patient and the family members – parents and siblings – in order to understand the impact of cancer on his/her family system and its functioning. We conducted a qualitative study, with individual research interviews with AYAs, mothers, fathers and siblings. The interviews (AJA = 13; parents = 11; siblings = 2) were subjected to thematic analysis. Ten major themes with sub-themes for AYAs, 10 major themes with sub-themes for parents and 6 major themes with sub-themes for siblings were identified. The results show that the patient AYA, transitioning from childhood to adulthood, can easily regress to childhood positions, becoming dependent on his family (parents and siblings), for daily care. Relatives also tend to promote this dependency by providing care for the young patient. The interactions, which develop in the family, tend to push him into a regressive functioning in order to maintain his psychological and physical survival. The regressive functioning helps families to cope with cancer, and as a result the evolution of family bonds are postpone. More family-centered support is needed, focusing on the specific needs of AYAs, particularly in terms of individuation and autonomization. [ABSTRACT FROM AUTHOR]

We design four Artificial Neural Network (ANN) models, namely a Multilayer Perceptron (MLP), a Convolutional Neural Network (CNN), an Extreme Learning Machine network (ELM) and an ensemble model based on stacking (STACKED) to forecast the ionospheric vertical Total Electron Content (vTEC) from 1 to 24 hours in advance on a single station at mid latitude. The time series used, spanning from January 2006 to December 2018, includes vTEC, provided by the Global Navigation Satellite System (GNSS) receiver at Tsukuba (TSKB), Japan (36.06 o N, 140.05 o E) and suitable external drivers selected among several helio-geophysical parameters. The selection of appropriate external drivers is made by rating their relevance on the vTEC at forecasting timescales (from 1 to 24 hours). The process, based on eleven machine learning models, highlights that the most important external drivers are: the 10.7 cm Solar Flux (F10.7), the magnitude B T of the Interplanetary Magnetic Field (IMF), and the Auroral Electrojet (AE) index. The forecasting performance of the four models (MLP, CNN, ELM, STACKED) is then analysed. The analysis relies on three statistical metrics to compare actual and forecasted vTEC: the coefficient of determination ( R 2 ), the Mean Absolute Error (MAE), and the Root Mean Squared Error (RMSE). Additionally, descriptive statistics are presented using box and whisker plots. The four ANN models show a quite satisfactory capability to forecast vTEC when applied to the test dataset which represents 10% of the available data from 2006 to 2018. Furthermore, by conducting a Wilcoxon signed rank test, it is shown that statistically significant improvements are achieved by the STACKED model with regard to MLP, CNN and ELM. On average, by analysing the forecasted (from 1 to 24 hours in advance) vs the actual vTEC, the STACKED model achieves R 2 = 0.816 , RMSE = 0.426 TECu, and MAE = 0.296 TECu (1 TECunit = 10 16 electrons/ m 2 ) whilst MLP, CNN and ELM show respectively R 2 = 0.808 , 0.812 , 0.803 , RMSE = 0.436 TECu, 0.431 TECu, 0.441 TECu and MAE = 0.304 TECu, 0.299 TECu, 0.312 TECu. [ABSTRACT FROM AUTHOR]

• Serum RC levels were positively and independently associated with the severity of hepatic steatosis and liver fibrosis. • During follow-up, baseline serum RC levels were independently associated with increased risk of development and decreased likelihood of regression of MASLD. • Baseline serum RC levels were positively correlated with the incident of liver fibrosis. The aim of this study was to explore the associations of serum remnant cholesterol (RC) levels with the progression and regression of metabolic dysfunction-associated steatotic liver disease (MASLD) in Chinese adults. We conducted a cross-sectional study in 13,903 individuals who underwent transient elastography tests (cohort 1) and a longitudinal study in 17,752 individuals who underwent at least two health check-up exams with abdominal ultrasound (cohort 2). Anthropometric and biochemical parameters were collected. Serum RC levels were calculated. Noninvasive fibrosis indices such as FIB-4 were evaluated in cohort 2. In cohort 1, serum RC levels were positively and independently associated with the severity of hepatic steatosis and liver fibrosis according to logistic regression analysis. In cohort 2, baseline serum RC levels were increased in participants with the incidence of MASLD and decreased in participants with the regression of MASLD during the follow-up period. Baseline serum RC levels were independently associated with an increased risk of development and a decreased likelihood of regression of MASLD: the fully adjusted hazard ratios (HR) were 2.785 (95 % CI 2.332–3.236, P < 0.001) and 2.925 (95 % CI 2.361–3.623, P < 0.001), respectively. In addition, when we used FIB-4 to evaluate liver fibrosis, baseline serum RC levels were positively correlated with the incidence of high-intermediate probability of advanced fibrosis. However, we did not find an association between serum RC levels and the regression of liver fibrosis. Serum RC levels are independently correlated with the progression and regression of MASLD in Chinese adults, suggesting that RC may participate in the pathophysiological process of MASLD. [ABSTRACT FROM AUTHOR]

An accurate prognosis of renal function decline in Autosomal Dominant Polycystic Kidney Disease (ADPKD) is crucial for early intervention. Current biomarkers used are height-adjusted total kidney volume (HtTKV), estimated glomerular filtration rate (eGFR), and patient age. However, manually measuring kidney volume is time-consuming and subject to observer variability. Additionally, incorporating automatically generated features from kidney MRI images, along with conventional biomarkers, can enhance prognostic improvement. To address these issues, we developed two deep-learning algorithms. Firstly, an automated kidney volume segmentation model accurately calculates HtTKV. Secondly, we utilize segmented kidney volumes, predicted HtTKV, age, and baseline eGFR to predict chronic kidney disease (CKD) stages > =3A, > =3B, and a 30% decline in eGFR after 8 years from the baseline visit. Our approach combines a convolutional neural network (CNN) and a multi-layer perceptron (MLP). Our study included 135 subjects and the AUC scores obtained were 0.96, 0.96, and 0.95 for CKD stages > =3A, > =3B, and a 30% decline in eGFR, respectively. Furthermore, our algorithm achieved a Pearson correlation coefficient of 0.81 between predicted and measured eGFR decline. We extended our approach to predict distinct CKD stages after eight years with an AUC of 0.97. The proposed approach has the potential to enhance monitoring and facilitate prognosis in ADPKD patients, even in the early disease stages. [ABSTRACT FROM AUTHOR]

Obesity is associated with nonalcoholic steatohepatitis (NASH), which leads to an increased rate of primary liver cancers, cirrhosis, and decreased life expectancy. Metabolic/bariatric surgery (MBS) determines long-term weight loss and the resolution of obesity-related medical problems. The aim of this study was to evaluate the impact of MBS on liver histologic features in individuals with obesity. Tertiary referral university hospital. We retrospectively analyzed data on 37 patients undergoing MBS from a prospectively held database. All patients had a liver biopsy at the time of MBS and a second liver biopsy in case of further surgery or for NASH follow-up. Eighteen patients had NASH on the first liver biopsy. The primary endpoint was the resolution of steatohepatitis without worsening of fibrosis on the second liver biopsy. Secondary endpoints were the evolution of liver steatosis, hepatocyte ballooning, nonalcoholic fatty liver disease activity score, and biochemical parameters from the time of the first to the second liver biopsy. Fifteen (83.3%) patients had significant resolution of steatohepatitis (P <.001) without fibrosis worsening. There was a statistically significant improvement of all blood tests except for low-density lipoprotein, alkaline phosphatases, and bilirubinemia. The Homeostatic Model Assessment (HOMA) index was significantly improved after MBS (P <.001), and circulating insulin and leptin concentrations were significantly reduced. Mean weight loss was 47 kg, with a 16.6 kg/m2 body mass index reduction and a % of total weight loss (%TWL) of 40.3 ±14% from the moment of MBS to the last follow-up. MBS is effective in determining NASH regression without fibrosis worsening and in reducing HOMA index and leptin and insulin concentrations. • Bariatric surgery induces hepatitis remission in individuals with obesity and NASH. • Bariatric surgery causes HOmeostatic Model Assessment for Insulin Resistance reduction. • Bariatric surgery reduces circulant leptin and insulin concentrations. [ABSTRACT FROM AUTHOR]

Coronary computed tomography angiography (CCTA) is used to evaluate components of atherosclerosis. Either adaptive or diverse, fixed Hounsfield Units (HU) are used to define components such as low attenuation (LAP), mixed (MP) and calcified plaque (CP). Comparisons of different platforms and different thresholding approaches have not been extensively evaluated. We compare two fixed threshold options to an adaptive threshold option within a specific platform and to fixed threshold options measured with another platform. Coronary segments (n ​= ​24) of good image quality, with well-defined boundaries and representing a broad range of atheroma were analyzed for LAP, MP and CP. Thresholds for LAP vs MP and MP vs CP were either Fixed 30/350 , Fixed 75/350 or based on an automatically determined Adaptive option. Pearson correlation and Bland-Altman analyses were undertaken. Within a single platform, measures were highly correlated irrespective of use of Adaptive or Fixed 30/350 and Fixed 75/350 thresholds (R ​≥ ​0.819, p ​< ​0.000001). The correlation slope for measures of LAP progressively diminished comparing the Adaptive versus Fixed 30/350 and the Fixed 75/350 versus the Fixed 30/350 approaches but bias was small. Between-platform comparisons yielded less optimal results, particularly with respect to measures of LAP and with one platform yielding both very small LAP volumes and very small ranges of volumes. Measures of plaque components are highly correlated irrespective of use of Adaptive or Fixed threshold approaches within a given platform. But measures are more affected by the specific proprietary algorithms employed than by specific thresholding options, especially for LAP. [ABSTRACT FROM AUTHOR]

• Proposed-1: Forecast of Solar Cycle 25 using Hybrid CNN-Bidirectional-GRU(CNN-BiGRU). • Proposed-2: Novel Gradient Residual Correction (GRC) model to enhance the proposed-1. • Models are compared with deep-learning traditional models over 4-types of SSN data. • Models' performances are validated based on Friedman ranking for measures. In this study, a hybrid deep-learning(DL) model is proposed, which consists of a Convolution-Neural-Network (CNN) and Bidirectional-Gated-Recurrent-Unit (Bi-GRU) for the prediction of sunspot numbers(SSN) of different frequencies along with novel post-processing techniques of Gradient-Residual-Correction (GRC) to enhance the accuracy of the predictions further. GRC is utilized to reduce the residual present in the forecasted values. The AdaBoost regression model is implemented over the residual obtained from the prediction of training data using a hybrid CNN-BiGRU model with respect to the gradient of the training data points to predict the residual for the test data points. Ultimately, the predicted residual for test data points is summed up with the predicted test data points to achieve the final predictions. The results obtained from the proposed methods are compared with the results obtained from traditional DL models. The validation of the proposed method is carried out based on four performance metrics, namely Root Mean Squared Error(RMSE), Mean Absolute Scaled Error(MASE), Mean Absolute Error(MAE), and Mean Absolute Percentage Error(MAPE). A significant percentage of improvement is observed while using the GRC technique in comparison to all other experimented models for all four variants of SSN data. A maximum improvement of 85.71% has been achieved in comparison to the BiLSTM model over the 13-month smoothed SSN dataset on the basis of MAPE. Friedman Ranking is also performed as a non-parametric statistical test over the results of the performance measures. This model has been utilized for the forecast of solar cycle 25(SC25) over the annual mean of total SSN. It has been observed that the SC25 is expected to reach its peak in the year 2024 with an annual average peak value of 143.641. Comparative analysis of SC25 and the peak of SSN in the ongoing cycle with the previous works are also carried out. [ABSTRACT FROM AUTHOR]

To investigate the best predictor of muscle strength assessed with both Modified Sphygmomanometer Test (MST) methods (bag adaptation and non-adapted) and to provide normative values for these two MST methods for the strength assessment of the trunk, upper, and lower limb muscles. Cross-sectional study. The strength of 42 muscle groups were assessed with the MST in the bag adaptation and non-adapted methods in 120 healthy individuals, 50 % males, divided into three age groups (20–39, 40–59, 60–79) with 40 subjects per group. Stepwise multiple regression analysis was performed to investigate which independent variables (sex, age, and limb dominance) is the best predictor of muscle strength (α = 5 %). Sex was the best independent predictor for all muscle groups for both MST methods (8.8 % < R2<57.8 %, p < 0.0001), except for the ankle plantar flexors assed with the non-adapted sphygmomanometer, in which age was the best independent predictor (R2 = 25.6 %; p < 0.0001). The normative values of muscle strength were reported for both MST methods considering the subgroups (sex, age, and limb dominance). Ceiling effect was observed when the MST bag adaptation was used to assess some muscles (8.8 %). Sex was the best predictor of muscle strength, as commonly found for muscle strength assessment with the dynamometer. The normative values provided have high clinical utility and can be used to interpret results of muscle strength assessment using both MST methods. For the MST in the bag adaptation method, caution is advised for the assessment of some muscles. [ABSTRACT FROM AUTHOR]

Rainfall patterns are vital in shaping ecosystems and supporting life on Earth. However, changing rainfall distribution and intensity due to environmental factors and climate change have become increasingly evident in recent years. This study aims to utilize machine learning (ML) and Artificial intelligence (AI) techniques to predict rainfall trends in the Kokrajhar region, a district in Assam, India. Accurate rainfall prediction is crucial for effective water resource management, agriculture planning, and disaster preparedness in the area. To achieve this objective, 31 years of historical rainfall data from Kokrajhar have been collected. Various machine learning algorithms, including Random Forests, MLP, ETS, and MEDIAN, have been employed. The models were trained and evaluated using the data set, with performance metrics mean absolute percentage error (MAPE). The results suggest that the ETS model (Average MAPE 424.11% after removing outliers) exhibited the best performance among the other models in the second fold. In the third fold, the MLP model outperformed the others. These findings demonstrate the effectiveness of AI and ML techniques in predicting rainfall trends in the Kokrajhar area. [ABSTRACT FROM AUTHOR]

Soil fertility's significance in agriculture profoundly affects the well-being of farmers. A novel technique is being introduced to gauge soil categorization levels, based on soil chemical data involving elements like "Nitrogen, Phosphorus, Potassium, PH value, Organic Carbon, Electrical Conductivity, Sulphur, Zinc, iron", and more. Classification and regression methodologies are employed to predict soil categorization levels like low level, medium, and high level. The dataset for soil categorization analysis was collected from a farm within the Bhandara region of Maharashtra. The ultimate model's efficacy was ascertained through a combination of soil analysis and meticulous refinement of hyper parameters in both random forest classification and regression approaches. In pursuit of an improved model, various alternative strategies for classification and regression were explored. Evaluation metrics, including OOB error rate, AUC accuracy, error rate, recall, precision, and F-score, were applied in the classification analysis. For the regression aspect, metrics such as mean squared error, r2 score, and error rate were employed. After subjecting the experimental dataset to thorough analysis using the random forest algorithm, accurate predictions of soil fertility levels were achieved with 93% accuracy of training and 83% accuracy of testing. This method offers a reliable pathway to enhance crop productivity. [ABSTRACT FROM AUTHOR]

Cancer which is caused by abnormal growth or tumour snatches away millions of lives each year. Depending on the organ affected, we find various cancer types like blood, skin, lung, kidney, breast, prostrate, liver etc. Some of these are curable if diagnosis of the disease can be made early. Others like pancreatic cancer are very difficult to cure. Hence early detection is one of most important aspects of treatment. Cancer affected patient goes through several stages of the disease. There are usually four stages of cancer. The Stage 1 of cancer being the preliminary stage and stage 4 being the last stage of the disease. It has been observed that the deadliness of the disease aggravates with each stage. There is no fixed or well defined time limit for the change in the stages. It may depend on the type of cancer, the physical attributes of the patient and on external factors and life-style habits. It is usually observed that symptoms change with change in stages of the disease. We intend to find out whether there is any change in the overall relationship between the genes amongst the various stages of lung cancer patients. Long Short Term Memory (LSTM), Bidirectional Long Short Term Memory (BiLSTM) and Feed Forward Neural Network (FNN) are used for analyzing the gene dependencies. The comparative analysis of the results obtained is done using various statistical methods of correlation and regression. Analysis of the results reveal significant variance in correlation among the various stages of lung cancer, showing Stage 2 and Stage 3 to be the most strongly correlated and Stage 1 and Stage 2 being the most weakly correlated indicating the inconsistency of genetic relationship between the various stages of lung cancer. [ABSTRACT FROM AUTHOR]

The prevalence of Gestational Diabetes Mellitus (GDM) is increasing at a rapid pace globally. This is concerning because GDM can lead to serious health problems like Type 2 Diabetes, Cardiovascular Diseases, and Depression in both the mother and her baby. GDM also increases healthcare expenditure during and after pregnancy and this is particularly worrisome for women belonging to households with poor healthcare affordability. Given the rising prevalence of GDM and its adverse impact, it is crucial to identify its risk factors and explore the factors associated with healthcare affordability among pregnant women. Our work attempts to identify the association between lifestyle (exercise, fruit, and vegetable consumption), sociodemographic factors, and Gestational Diabetes. Second, attempts to investigate the determinants of Health care affordability among pregnant women. Multivariate stepwise logistic regression is used for investigation purposes. We found Depression, higher BMI, and lower fruit intake to be associated with higher odds of GDM among pregnant women. Whereas the presence of GDM, lower socioeconomic position, absence of health insurance, and single motherhood were found to be associated with poor healthcare affordability. Lifestyle and mental health are associated with the GDM risk in pregnant women. Marital status, health insurance, and socioeconomic position are associated with healthcare affordability among pregnant women. The study can aid in better health outcomes for pregnant women and the creation of inclusive health policies. [ABSTRACT FROM AUTHOR]

L'environnement est un concept qui permet de réinterroger la psychopathologie psychanalytique classique, à commencer par la nature du symptôme et les processus qui le structurent, puis les notions de normal et pathologique. Cet article propose ainsi les principaux axes d'une psychopathologie psychanalytique de l'environnement dans une dimension intersubjective. Nous opérons une lecture et une analyse singulières des théories de Donald W. Winnicott sur l'environnement et des concepts qui lui sont liés, en particulier la dépendance, le self , l'agressivité et la réparation. Puis nous décrivons leurs complémentarités et leurs ruptures avec les théories anglo-saxonnes, comme elles furent portées par Sándor Ferenczi, Mélanie Klein et Michael Balint. Il existe différents types de défaillances selon que l'environnement empiète affectivement et corporellement sur les besoins du nourrisson par la séduction, le tourment ou la disparition. Ce qui importe étant le moment de dépendance durant lequel elles ont lieu, générant soit une privation affective et potentiellement une organisation psychotique ou maniaco-dépressive au moment de la dépendance absolue, soit une déprivation affective et potentiellement une organisation antisociale au moment de la dépendance relative. Les défaillances de l'environnement (Winnicott) peuvent ainsi se retrouver complémentaires aux positions paranoïde-schizoïde et dépressive (Klein), comme aux concepts d'ocnophilie et de philobatisme (Balint). Le concept d'environnement montre également combien c'est la motricité qui constitue une expérience intersubjective dans la construction du self. Chez les parents, elle se manifeste avant tout dans le maintien ordinaire, chez le nourrisson, dans l'agressivité à l'origine de son geste spontané, tout à la fois destructeur et créateur, puis plus largement réparateur dans les environnements familial et social. La régression à la dépendance pourra devenir nécessaire à l'exploration d'expériences primaires inhérentes à l'ensemble individu-environnement et avoir une fonction de réintégration intersubjective. L'esquisse pour une psychopathologie psychanalytique de l'environnement est donc pertinente pour interpréter les souffrances du self comme des « troubles » intersubjectifs et analyser les cas cliniques de régression à la dépendance dans la situation analytique. The environment is a concept that allows us to re-examine classical psychoanalytic psychopathology, starting with the nature of the symptom and the processes that structure it, then the notions of the normal and the pathological. This article thus proposes the main axes of a psychoanalytic psychopathology of the environment in an intersubjective dimension. We carry out a singular reading and analysis of Donald W. Winnicott's theories on the environment and the concepts related to it, in particular dependence, the self, aggressiveness, and repair. Then we describe their complementarities and their breaks with Anglo-Saxon theories, as developed by Sándor Ferenczi, Melanie Klein, and Michael Balint. There are different types of failures depending on whether the environment impinges affectively and bodily on the needs of the infant through seduction, torment, or disappearance. What matters is the moment of dependence during which they take place, generating either potentially a psychotic or manic-depressive organization at the moment of absolute dependence, or potentially an antisocial organization at the moment of relative dependence. The failures of the environment (Winnicott) can thus be considered complementary to the paranoid-schizoid and depressive positions (Klein), as well as to the concepts of ocnophilia and philobatism (Balint). The concept of environment also shows how motor skills constitute an important intersubjective experience in the construction of the self. In parents, this manifests itself above all in ordinary holding; in infants, in the aggressiveness at the origin of their spontaneous gestures, both destructive and creative, then becoming more broadly restorative in the family and social environments. Regression to dependence may become necessary for the exploration of primary experiences inherent to the individual-environment dyad, and may also have a function of intersubjective reintegration. This sketch for a psychoanalytic psychopathology of the environment is therefore relevant for interpreting the sufferings of the self as intersubjective "disorders" and for analyzing clinical cases of regression to dependence in the analytic situation. [ABSTRACT FROM AUTHOR]

Hybrid and ramjet engines became now effective propulsion systems with great development potential in different fields. They require using proper high-energy components. At high-speed blowing conditions conventional polymer fuels exhibit relatively low regression rate. As an alternative, it is proposed to use paraffin and based on it compositions. Paraffin is high-energy hydrocarbon with a low melting point leading to a high regression rate in high-speed blowing conditions. The results of experimental study of regression behavior of paraffin mixtures with various additives under intensive hot air blowing at 1 MPa pressure are presented. Stearic acid, nanoaluminum Alex, aluminum hydride AlH 3 , B and mechanoactivated aluminum boride AlB 2 were used as 10 % by mass additives. Experiments were conducted on cylindrical perforated samples. Using original design microwave gauge the current diameter of a sample bore was measured during experiment with subsequent calculation of regression rate. Based on experimental data and the results of heat balance calculation, the temperature on the gasified surface of paraffin fuel was estimated. • Paraffin based mixtures regression rate was studied under hot air blowing at 1 MPa. • Original microwave gauge was used to record permanently paraffin regression rate. • Additives of B, AlH 3 and AlB 2 essentially increased the paraffin regression rate. • Additive of Alex practically did not change the paraffin regression rate level. • Need to refine heat transfer coefficient for a liquid collapsing layer is formulated. [ABSTRACT FROM AUTHOR]

Stress is a critical risk factor for various health issues, but an objective, non-intrusive and effective measurement approach for stress has not yet been established. Gait, the pattern of movements in human locomotion, has been proven to be a valid behavioral indicator for recognizing various mental states in a convenient manner. This study aims to identify the severity of stress by assessing human gait recorded through an objective, non-intrusive measurement approach. One hundred and fifty-two participants with an average age of 23 years old (SD = 1.07) were recruited. The Chinese version of the Perceived Stress Scale with 10 items (PSS-10) was used to assess participants' stress levels. The participants were then required to walk naturally while being recorded with a regular camera. A total of 1320 time-domain and 1152 frequency-domain gait features were extracted from the videos. The top 40 contributing features, confirmed by dimensionality reduction, were input into models consisting of four machine-learning regression algorithms (i.e., Gaussian Process Regressor, Linear Regression, Random Forest Regressor, and Support Vector regression), to assess stress levels. The models that combined time- and frequency-domain features performed best, with the lowest RMSE (4.972) and highest validation (r = 0.533). The Gaussian Process Regressor and Linear Regression outperformed the others. The greatest contribution to model performance was derived from gait features of the waist, hands, and legs. The severity of stress can be accurately detected by machine learning models using two-dimensional (2D) video-based gait data. The machine learning models used for assessing perceived stress were reliable. Waist, hand, and leg movements were found to be critical indicator in detecting stress. • Stress severity can be identified using time- and frequency- domain gait features. • The machine learning models used for assessing perceived stress were reliable. • Waist, hand, and leg movements are critical for detecting stress in young adults. [ABSTRACT FROM AUTHOR]

The process of urbanization is inevitable and its higher rates have been observed in the less developed nations. It has impacted the socio-economic as well as environmental condition of the urban system. The present study intends to demonstrate a methodological framework to utilize the computer applications, like GIS and remote sensing, for analyzing the pattern of urbanization and its impact on the environmental and climatic condition of the study region. The geospatial data has been collected from the satellite imagery and the data pertaining to social and environmental conditions has been collected from the reports obtained from the government agencies. The overlay analysis using GIS has been performed to understand the interactive pattern of the land use and land cover change. Further, the interdependence among variables has been established using regression analysis method. The results of the analysis have been presented using tabulation and graphical methods. [ABSTRACT FROM AUTHOR]

Mutual funds, a cornerstone of modern investment portfolios, offer investors a diversified and professionally managed approach to financial growth. These investment vehicles collect small amount from multiple investors and invests the collective investment further in diversified portfolio of stocks, bonds, or other securities. Mutual funds are characterized by their net asset value (NAV), which represents the per-share value of the fund's assets minus its liabilities. Investors benefit from the expertise of seasoned fund managers who make investment decisions based on their analysis of market trends and company performance. By providing an accessible and diversified investment option, mutual funds have become a popular choice for both novice and seasoned investors seeking to achieve their financial goals. Apart from that, the role of AI is increasing exponentially in various areas. Such AI based tools and algorithms are utilized by various MFs expert to identify the suitable investment portfolios. This paper proposes a technique to analyse the performance of the different stock's portfolios based on the past data. Combining the isolated analysis of different stocks portfolios may be utilized to predict the net asset value prediction (NAV) of MFs. The experiments carried out in this research work uses linear regression analysis to analyse the stock's performance at sub level of the MFs. A case study for the Axis Bluechip fund's is also carried out by using the proposed ML approach. The Yahoo Finance dataset is used for the case study. Previously, machine learning algorithms like linear regression (LR), decision tree regression (DTR), and multivariate regression (MR) are taken to predict and analyse the dataset. These algorithms calculate the NAV of the mutual fund with good accuracy and analyse the best result for the test dataset. The various models are being created from different portfolios to analyse the NAV of the mutual funds. This work proposed hierarchical method to perform the linear regression analysis for the NAV predicting. This work uses R-Square (R2) statistical measures to determine the goodness of models. The obtained R2 is 0.86, implying that the created models have good accuracy and performance on the dataset. [ABSTRACT FROM AUTHOR]

The percentage of and factors associated with the regression of Barrett's esophagus (BE) or its characteristic intestinal metaplasia (IM) remain unclear, and conflicting results have been reported because of diverse regression and sampling error definitions. Thus, we investigated the rates of IM regression, sampling error, and associated factors. Forty-two patients with proven short-segment BE with IM who underwent two follow-up endoscopies with biopsies of Barrett's mucosa were retrospectively analyzed. Additional Alcian blue and MUC2 staining were done on the biopsy specimens without IM in hematoxylin–eosin staining. Only patients with negative hematoxylin–eosin, Alcian blue, and MUC2 staining for IM in both follow-up endoscopies were considered to have true regression. When all three stains were negative for IM in the first, but positive in the second follow-up endoscopy, we considered IM persisting and declared sampling error. Among the 18 patients without IM at the first follow-up endoscopy, only five (11.9%) were judged to have true regression. Prolonged proton-pump inhibitor use was significantly associated with regression. Limited experience of the endoscopist, and insufficient biopsy number were significantly related to sampling error. Receiver operating characteristic (ROC) curve analysis showed the best cut-off value of the biopsy number/maximal-length (cm) ratio to predict sampling error was 2.25. In our patients with short-segment BE, 11.9% experienced regression of IM. Prolonged proton-pump inhibitors treatment was associated with regression. An insufficient biopsy number was related to a missed IM, which may be eliminated by maintaining biopsy number/maximal-length (cm) ratio ≥2.25. [ABSTRACT FROM AUTHOR]

The two-phase Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) campaign over India has generated datasets of numerous natural and artificial surroundings with high signal-to-noise ratios and higher spatial and spectral consistencies. However, the first phase AVIRIS-NG datasets of urban areas have 8.1 m spatial resolution, which is insufficient for producing level-4 urban material and natural cover maps required to model Earth's processes at the micro-scale or constantly monitor urban processes. The current work addresses this drawback by demonstrating a part of the proposed proof-of-concept approach on the AVIRIS-NG dataset of Ahmedabad, India, wherein super-resolution (SR) is proposed to enhance the hyperspectral image's spatial resolution and preserve its rich spectral content at the targeted spatial scale before utilizing it for producing detailed urban land cover maps. A step-wise account of the SR process has been provided herein to aid the reader in reproducing these experiments. Super-resolved products generated using four SR algorithms have been compared and validated using a composite framework based on processing time, qualitative visual investigation of selected image patches and spectral profiles, re-interpretation of Wald's reduced resolution protocol and full-reference image quality metrics. Results suggest the AVIRIS-NG dataset's successful spectral and spatial fidelity at a larger spatial scale, albeit varying in each super-resolved output. Sparse regression and natural prior (SRP), and anchored neighbourhood regression (ANR) produce the best and poorest super-resolved outputs among the tested SR algorithms. Although gradient profile prior (GPP) generated super-resolved product is the second best, it is also the most computationally expensive algorithm. An additional yardstick in the form of a spectral similarity analysis of randomly selected unknown spectral signatures on super-resolved output against a reference spectral library has been proposed to ascertain spectral content preservation further before utilizing the super-resolved product for urban information extraction. This exercise's findings reveal the presence of the target urban land cover feature at the unknown pixels with matching scores of greater than 0.5 in both best and worst super-resolved products, thereby advocating the extension of the proposed proof-of-concept to recently launched spaceborne hyperspectral missions' datasets. [ABSTRACT FROM AUTHOR]

• AI and other predictive models are becoming increasingly common in criminal law. • Some decisions made by judges show high levels of predictability. • All predictions on petitions for new criminal trials in Sweden in 2021 were accurate. • Other types of legal decisions e.g. legal precedent are probably less predictable. Predictability of legal decisions is usually considered a prerequisite for the rule of law, following the maxim 'like cases should be treated alike'. Yet, this presupposes that the case outcome can be predicted based on the merits of the case, rather than other factors. The purpose of this study was to test whether and to what extent legal decisions on petitions for new criminal trials can be predicted on the basis of other fairly superficial criteria that one could access without even reading the case file, e.g. which Court decided, whether the applicant had legal representation etc. To this end, all petitions for new criminal trials submitted to the Swedish Supreme Court and the six Courts of Appeal in the time period 2010–2020 (n = 3915) were reviewed. This data formed the basis of a regression model which was then used to predict decisions regarding petitions in 2021. On the basis of access to legal representation and crime type, the regression model predicted accurately 100 % of the decisions made in 2021. This raises questions about the evidentiary basis for the decisions and also the role of judges in situations where their decisions are fully predictable. [ABSTRACT FROM AUTHOR]

A set of hands-on activities, that were proposed in an introduction course to machine learning in a Chemical Engineering undergraduate course, are presented. The activities aimed to introduce basic concepts of unsupervised learning (e.g., clustering) and supervised learning (e.g., classification and regression). Google Colaboratory, a cloud service provided by Google for free to promote research in Artificial Intelligence and Machine Learning, was used to develop these activities, but the proposed activities can be run similarly in a local Python environment. The datasets used in the activities are publicly available on websites such as Kaggle and University of California (UCI), and a specific example in chemical engineering for the ore grinding process was also used. The student's response to the ML topic within the course was very positive. • Hands-on activities are presented viewing the learning of Machine Learning in Chemical Engineering. • Clustering, classification, regression, and Artificial Neural Networks (ANN) are evaluated. • The tool used for the development of activities was Google Colaboratory. • The datasets used are available on public sites such as Kaggle and the University of California (UCI). • The activities form an interesting set to be presented for a Chemical Engineering undergraduate course. [ABSTRACT FROM AUTHOR]

La métapsychologie de la création musicale élaborée à partir de l'approche clinique directe de l'artiste musicien, instrumentiste ou chanteur est rarement explorée au sein de la littérature psychanalytique. Cet article a pour visée d'exposer en quoi l'écoute clinique des musiciens permet de poser quelques spécificités relatives à la création musicale et la nécessité d'élaborer un dispositif d'écoute aménagé. Ce faisant, il s'agit de répondre de façon adaptée aux demandes de soins psychiques adressées au clinicien. Ce sujet convoque l'interdisciplinarité : identité et pratique de l'art musical, théories et pratiques de la clinique analytique. Au regard de cette complexité et amplitude, il ambitionne aussi un propos synthétique et circonscrit. L'article se structure à partir d'une sélection de travaux de Freud et de D. Anzieu dédiés à la création artistique. À partir de notre lecture critique intra-textuelle, des correspondances sont établies avec le sujet. D'autres apports théoriques psychanalytiques et musicologiques s'insèrent dans notre propos en guise de complément. Ce tout s'articule à nos observations cliniques issues d'implications psychothérapeutiques auprès de musiciens professionnels ayant reçu une formation dispensée par les conservatoires. Freud a signifié ses distances et réserves quant à la compréhension de l'art musical et ce positionnement est généralement pensé en termes d'a-musicalité. Cependant, le tissage de ses recherches associées à celles de D. Anzieu, de même que la métapsychologie de la musique et le recueil de nos données cliniques permettent de dégager les particularités de la réalité psychique engagée dans la création musicale. Il est question d'angoisses quasi constantes et névralgiques. Ces dernières se rapportent d'une part aux mécanismes de régression constituant l'une des propriétés psychiques de la musique, laquelle issue du sonore est un prolongement du milieu intra-utérin, et d'autre part, aux mécanismes de dissociation psychique fortement mobilisés en situation scénique. La formation musicale fréquemment investie dans l'enfance, voire la petite enfance, et son contexte socio-affectif refoulés, déniés ou révélés par les musiciens reçus en consultation viennent compléter ce tableau clinique. Ces particularités apportent un éclairage analytique sur la nature de la plainte et des symptômes à l'œuvre chez certains musiciens. Elles instruisent également la rencontre entre le clinicien et le musicien, précisément la dimension transférentielle, quand celle-ci est chargée de résistances. En outre, il apparaît que le traitement de la souffrance psychique s'oriente le plus souvent en première intention vers une psychothérapie de soutien, dans le cadre de dispositifs aménagés, cela sans écarter les possibilités d'une psychothérapie analytique et d'une cure-type. L'un des enjeux thérapeutiques consisterait essentiellement à réguler des angoisses de perte relatives aux mouvements d'aller et retours constants entre les mécanismes de dissociation du moi et les processus de réunification de cette instance. Notre propos se situe dans le registre de la métapsychologie de la création musicale. Il a donc circonscrit notre approche au sujet musicien dans ses liens à cet objet culturel. Cependant, la lecture des demandes de soins psychiques émanant de musiciens professionnels peut être complétée par les travaux existants qui explorent le registre de la souffrance au travail. Les demandes de soins adressées au clinicien provenant de sujets musiciens, révèlent la nécessité de poursuivre un travail de recherche existant sur la métapsychologie de la création musicale. Cette perspective d'approfondissement comporte plusieurs arborescences d'évolution dans le sens d'implications cliniques certes individuelles, mais aussi groupales tant pour des formations instrumentales/vocales que des institutions culturelles. The metapsychology of musical creation elaborated from the direct clinical approach of the musician-artist is rarely explored within the psychoanalytical literature. The aim of this article is to explain how clinical listening to musicians allows us to posit some specificities related to musical creation and to suggest the need to elaborate a particular practice of listening. In doing so, clinicians can respond in an adapted way to the requests for psychic care that are addressed to them. This subject calls for interdisciplinarity: identity and practice of musical art, theories and practices of the analytical clinical practice. In view of this complexity and amplitude, this essay also aims to be synthetic and circumscribed. The article is based on a selection of works by Freud and D. Anzieu dedicated to artistic creation. From our intra-textual critical reading, correspondences are established. Other theoretical contributions are inserted in filigree in our proposal as a complement. All of this is articulated with our clinical observations resulting from psychotherapeutic work with conservatory-trained professional musicians. Freud expressed his distance and reservations concerning the understanding of musical art and this position is generally thought of in terms of a-musicality. However, the weaving of his research associated with that of D. Anzieu, as well as the metapsychology of music and the collection of our clinical data, allow us to identify the particularities of the psychic reality involved in musical creation. It is a question of almost constant and neuralgic anxieties. These last ones relate, on the one hand, to the mechanisms of regression constituting one of the psychic properties of music, which stems from the status of sound as an extension of the intra-uterine environment, and on the other hand, to the mechanisms of psychic dissociation strongly mobilized in performance situations. The musical training frequently committed to in childhood, even early childhood, and the repression, denial, or revelation of its socio-affective context by the musician-patients received in my consultation complete this clinical picture. These particularities shed analytical light on the nature of the complaint and symptoms at work in certain musicians. They also inform the encounter between the clinician and the musician, precisely the transferential dimension, when this is loaded with resistance. Moreover, it appears that the treatment of psychological suffering is most often oriented in the first instance towards supportive psychotherapy, within the framework of adapted devices, without ruling out the possibilities of analytical psychotherapy and a typical cure. One of the therapeutic stakes would consist essentially in regulating the anxieties of loss relating to the movements of constant coming and going between the mechanisms of dissociation of the self and the processes of reunification of this authority. Our proposal is situated in the register of the metapsychology of musical creation. It has therefore circumscribed our approach to the musician-subject in her/his links to this cultural object. However, the reading of the requests for psychotherapy emanating from professional musicians can be completed by existing research that explores the register of the suffering at work. The requests for care addressed to the clinician coming from musician-subjects reveal the need to pursue existing research on the metapsychology of musical creation. Further research could explore the sense of the clinical implications – both on an individual and a group level – for vocal or instrumental ensembles as well as for cultural institutions. [ABSTRACT FROM AUTHOR]

Atherosclerosis is a systemic disease that frequently begins early in life. However, knowledge about the temporal disease dynamics (ie, progression or regression) of human subclinical atherosclerosis and their determinants is scarce. This study sought to investigate early subclinical atherosclerosis disease dynamics within a cohort of middle-aged, asymptomatic individuals by using multiterritorial 3-dimensional vascular ultrasound (3DVUS) imaging. A total of 3,471 participants from the PESA (Progression of Early Subclinical Atherosclerosis) cohort study (baseline age 40-55 years; 36% female) underwent 3 serial 3DVUS imaging assessments of peripheral arteries at 3-year intervals. Subclinical atherosclerosis was quantified as global plaque volume (mm3) (bilateral carotid and femoral plaque burden). Multivariable logistic regression models for progression and regression were developed using stepwise forward variable selection. Baseline to 6-year subclinical atherosclerosis progression occurred in 32.7% of the cohort (17.5% presenting with incident disease and 15.2% progressing from prevalent disease at enrollment). Regression was observed in 8.0% of those patients with baseline disease. The effects of higher low-density lipoprotein cholesterol (LDL-C) and elevated systolic blood pressure (SBP) on 6-year subclinical atherosclerosis progression risk were more pronounced among participants in the youngest age stratum (P interaction = 0.04 and 0.02, respectively). Over 6 years, subclinical atherosclerosis progressed in one-third of middle-age asymptomatic subjects. Atherosclerosis regression is possible in early stages of the disease. The impact of LDL-C and SBP on subclinical atherosclerosis progression was more pronounced in younger participants, a finding suggesting that the prevention of atherosclerosis and its progression could be enhanced by tighter risk factor control at younger ages, with a likely long-term impact on reducing the risk of clinical events. (Progression of Early Subclinical Atherosclerosis [PESA; also PESA-CNIC-Santander]; NCT01410318) [Display omitted] [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the prevalence and severity of apical periodontitis (AP) in subjects with different stages of chronic kidney disease (CKD) and its association with systemic markers. In this cross-sectional study, 105 patients with CKD (n = 35 each in the early, predialysis, and hemodialysis groups) and 105 healthy controls were included. The prevalence, number of teeth with AP (endodontic burden [EB]), and the severity of AP were recorded. High- sensitivity C-reactive protein, blood urea, and serum creatinine levels were also recorded. Logistic regression was applied to determine the possible association between CKD and AP in the study population, and linear regression was performed to predict the effect of AP on systemic markers in CKD patients. AP in at least 1 tooth was found in 75.2% of CKD patients and 40.9% of the controls (P <.05). CKD patients were 4 times more likely to have AP than controls (P <.05; odds ratio = 3.954; 95% confidence interval, 2.09–7.45). EB and the severity of AP were also significantly higher in CKD patients than the healthy controls (P <.05). Although higher values of EB and severe AP were observed with the progression of disease, the difference was not significant. The severity of AP was significantly associated with an increase in serum creatinine, blood urea, and a decrease in estimated glomerular filtration rate (P <.05) in CKD patients. AP was significantly more prevalent in the CKD group. The association between the severity of AP and CKD markers suggests that AP could possibly alter the progression of CKD. However, these findings do not establish a cause-and-effect relationship. [ABSTRACT FROM AUTHOR]

The color of road signs is a critical aspect of road safety, as it helps drivers quickly and accurately identify and respond to these signs. Properly colored road signs improve visibility during the day and make it easier for drivers to make informed decisions while driving. In order to ensure the safety and efficiency of road traffic, it is essential to maintain the appropriate color level of road signs. The objective of this study was to analyze the color status and daylight chromaticity of in-use road signs using supervised machine learning models, and to explore the correlation between road sign's age and daylight chromaticity. Three algorithms were employed: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The data used in this study was collected from road signs that were in-use on roads in Sweden. The study employed classification models to assess the color status (accepted or rejected) of the road signs based on minimum acceptable color levels according to standards, and regression models to predict the daylight chromaticity values. The correlation between road sign's age and daylight chromaticity was explored through regression analysis. Daylight chromaticity describes the color quality of road signs in daylight, that is expressed in terms of X and Y chromaticity coordinates. The study revealed a linear relationship between the road sign's age and daylight chromaticity for blue, green, red, and white sheeting, but not for yellow. The lifespan of red signs was estimated to be around 12 years, much shorter than the estimated lifespans of yellow, green, blue, and white sheeting, which are 35, 42, 45, and 75 years, respectively. The supervised machine learning models successfully assessed the color status of the road signs and predicted the daylight chromaticity values using the three algorithms. The results of this study showed that the ANN classification and ANN regression models achieved high accuracy of 81% and R2 of 97%, respectively. The RF and SVM models also performed well, with accuracy values of 74% and 79% and R2 ranging from 59% to 92%. The findings demonstrate the potential of machine learning to effectively predict the status and daylight chromaticity of road signs and their impact on road safety in the Swedish context. • The study used supervised machine learning models to analyze road sign color and daylight chromaticity in Sweden. • The study found a linear relationship between age and daylight chromaticity for blue, green, red, and white sheeting but not for yellow. • The estimated lifespan of red signs is 12 years, while the estimated lifespans of yellow, green, blue, and white sheeting are 35, 42, 45, and 75 years, respectively. • The study found that the relationship between age and daylight chromaticity is affected by color, class, GPS coordinates, direction, and coefficient of retroreflection. • Machine learning models achieved high accuracy in predicting color status and daylight chromaticity, ranging from 74 to 81% for classification and R2 ranging from 59 to 97% for regression. [ABSTRACT FROM AUTHOR]

Clustering and regression are two of the most important problems in data analysis and machine learning. Recently, mixed-integer linear programs (MILPs) have been presented in the literature to solve these problems. By modelling the problems as MILPs, they are able to be solved very quickly by commercial solvers. In particular, MILPs for bivariate clusterwise linear regression (CLR) and (continuous) piecewise linear regression (PWLR) have recently appeared. These MILP models make use of binary variables and logical implications modelled through big- M constraints. In this paper, we present these models in the context of a unifying MILP framework for bivariate clustering and regression problems. We then present two new formulations within this framework, the first for ordered CLR, and the second for clusterwise piecewise linear regression (CPWLR). The CPWLR problem concerns simultaneously clustering discrete data, while modelling each cluster with a continuous PWL function. Extending upon the framework, we discuss how outlier detection can be implemented within the models, and how specific decomposition methods can be used to find speedups in the runtime. Experimental results show when each model is the most effective. [ABSTRACT FROM AUTHOR]

Changes in land use and cover (LULC) are now considered as a significant concern because they have a great impact on biodiversity loss and climate changes. Therefore, the present study was applied to assess the effect of land use dynamic on land surface temperature (LST). LULC maps of years; 1991, 2001, 2014, and 2022 were applied using Maximum likelihood classification MLC) while the LST was developed using thermal bands of Landsat images. LULC maps showed a considerable development and improvement in vegetation cover and urbanization. Results showed a negative and positive correlation between LST with NDVI and NDBI respectively. It was revealed that the estimated and predicted LST confirm the reliability of the present model with R2 = 0.95. The study calculated that the maximum LST in 2022 to simulate current state of Riyadh, Saudi Arabia and the results were 32.1, 45.5, 38.7 and 41.3 in winter, summer, autumn and spring respectively. We are now sounding the alarm because the state must act quickly and decisively to stop intensive forestation if it is to avoid very high temperatures by 2030. Our findings point to the need for environmental protection in response to the study area's quick conversion of LULC and increasing trend of LST. [ABSTRACT FROM AUTHOR]

To continue space operations with the increasing space debris, accurate characterization of fragment fly-out properties from hypervelocity impacts is essential. However, with limited realistic experimentation and the need for data, available static arena test data, collected utilizing a novel stereoscopic imaging technique, is the primary dataset for this paper. This research leverages machine learning methodologies to predict fragmentation characteristics using combined data from this imaging technique and simulations, produced considering dynamic impact conditions. Gaussian mixture models (GMMs), fit via expectation maximization (EM), are used to model fragment track intersections on a defined surface of intersection. After modeling the fragment distributions, k-nearest neighbor (K-NN) regressors are used to predict the desired characteristics. Using Monte Carlo simulations, the K-NN regression is shown to predict the distributions for both the total number of fragments intersecting a given surface, as well as the expected total fragment velocity and mass associated with that surface. This information can then be used to estimate the kinetic energy of the particle to classify the particle and avoid debris collisions. • Machine learning combines high-fidelity simulations with experimental data. • High speed collisions or explosions can be characterized. • Fragment energy and distribution can be predicted. [ABSTRACT FROM AUTHOR]

[Display omitted] [ABSTRACT FROM AUTHOR]

Determining the oxidation resistance of UHTC carbides in extreme environments is challenging theoretically and experimentally due to the high dimensional complexity of influencing variables and intricate testing setups. Herein we demonstrate the use of machine learning (ML) models trained with experimental literature data to predict the oxide thickness of UHTC carbides exposed to air based on composition, mean grain size, relative densification, holding time, and temperature. A multi-dimensional database with 76 occurrences is created containing experimental results of Hf, Zr, and Ta carbides plus additives. The preprocessed database is then used to train ML models to predict their oxidation behavior. The trained model predicts the oxidation damage in the form of an average oxide thickness in UHTC carbides with a Mean Absolute Error (MAE) of ±65.45 μm for samples in the testing set that developed thicknesses up to 1000 μm. The model successfully predicted oxidation damage for a recession rate lower than 60 μm/min. It is noticed that the ensemble method MAE is increased to ±134.34 μm while forecasting the oxidation of samples with a recession rate higher than the threshold. The unprecedented approach is a novel way to predict the damage through the oxidation of carbide compounds before processing for a smarter design with room for improvement. [ABSTRACT FROM AUTHOR]

The World Bank and many financial analysts predicted that a global recession could occur in 2023. The unpredictability of the global economy, including stock prices, can have a negative impact on people's financial situation. Nonetheless, some investors may view this as an opportunity to purchase stocks at reduced prices with increased risk. Before investing, technology can help people conduct research and formulate strategies. This study aims to develop a predictive model to forecast the current stock price based on historical stock data, web-scraped Google Trends, and the number of daily tweets data. In addition, the objective of this study is to identify the optimal algorithm among Linear Regression, Decision Tree Regressor, and Random Forest. Thus, it has been demonstrated that historical stock data, Google Trends, and the number of daily tweets can be correlated with the current stock price. In addition, the Linear Regression algorithm-trained predictive model has the highest R2 score and the lowest Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error. [ABSTRACT FROM AUTHOR]

The present work aims to design and develop shielded metal arc welding electrode coating fluxes. The thermal properties of the electrode coating fluxes are studied. Ternary phase diagrams of C a F 2 − C a O − A l 2 O 3 and S i O 2 − B a O − A l 2 O 3 are used to determine the range of constituents. 27 flux mixture combinations were created using an extreme vertices design approach. The corrected optical basicity is estimated and utilized for the theoretical estimation of sulfide capacity and water vapor solubility. Thermogravimetric analysis, and differential scanning calorimetry analysis was performed to quantify the thermal response of the flux while heating. Thermogravimetry analysis shows variation of weight loss from 8.49% to 12.25% with the variation in flux composition. Calcite and witherite has positive influence on weight loss. Differential scanning calorimetry shows an overall change in enthalpy from −1076.73 to −7786 J/g. Hot disk analysis reveals fluorspar has a positive effect, whereas calcite has a negative effect on thermal conductivity. The effect of individual flux constituents and their interactions on thermal properties is discussed using regression analysis. The artificial neural network is used for predictive modeling, and the accuracy of its predictions is compared to regression analysis. In comparison to the regression, the model's mean square error improved by nearly 37% on average. The reduction in the mean absolute percentage error of the unseen test data suggests that the artificial neural network approach has better modeling capability. [ABSTRACT FROM AUTHOR]

With the rapid developments of fifth generation (5G) mobile communication networks in recent years, different use cases can now significantly benefit from 5G networks. One such example is high-speed trains found in several countries across the world. Due to the dense deployment of 5G millimetre wave (mmWave) base stations (BSs) and the high speed of moving trains, frequent handovers (HOs) occur which adversely affect the Quality-of-Service (QoS) of mobile users. User association for load balancing is also a key issue in 5G networks. Therefore, HO optimisation and resource allocation are major challenges in the mobility management of high-speed train systems. Handover Margin (HOM) and Time-to-Trigger (TTT) parameters are crucial for the HO process since they affect the key performance indicators (KPIs) of high-speed train systems in 5G networks. To manage system performance from the aspect of predictive analytics, we have modelled system performance of mobility management through machine learning (ML). First, the HO management process of a high-speed train scenario is framed as a supervised ML problem. The inputs for the problem are regression task, HOM and TTT and the outputs are key performance indicators (KPIs). Second, data processing is accomplished after generating a simulation dataset. Several methods are employed for the dataset, such as Adaptive Boosting (AdaBoost), Gradient Boosting Regression (GBR), CatBoost Regression (CBR), Support Vector Regression (SVR), Multi-layer Perceptron (MLP), Kernel Ridge Regression (KRR) and K-Nearest Neighbour Regression (KNNR). Tenfold cross validation is then applied for choosing the best hyperparameters. Finally, the deployed methods are compared in terms of the Mean Absolute Error (MAE), Mean Square Error (MSE), Maximum Error (Max E), and R2 score metrics. From the MAE results, CBR achieves the best outcomes for load level and throughput KPIs with 0.003 and 0.0144, respectively. On the other hand, GBR achieves the best results for call dropping ratio (CDR), radio link failure (RLF) and spectral efficiency KPIs with 0.354, 0.082 and 0.354, respectively. CBR also follows GBR for the three KPIs with 0.356, 0.082 and 0.357, respectively. Only a slight difference in estimations is present. MLP achieves the best results for HO ping-pong (HOPP) and HO probability (HOP) KPIs with 0.0045 and 0.011, respectively. This is followed by GBR and CBR. From the MSE outcomes, CBR and GBR exhibit the best results for load level and throughput KPIs with 2e-5 and 3e-5, respectively. GBR attains the best results for CDR, RLF and spectral efficiency KPIs with 0.25, 0.011 and 0.025, respectively. Accordingly, CBR follows GBR with slightly different errors for the three KPI estimations. MLP achieves the best results for HOPP and HOP KPIs with 5e-5 and 3.6e-5, respectively. Again, this is followed by GBR and CBR for the estimation of these results. This indicates that CBR and GBR can capture relationships between inputs and KPIs for the dataset used in this study, outperforming all other methods generally used for solving this problem. [ABSTRACT FROM AUTHOR]

Identification of a multi-input/single-output system enables the creation of a mathematical model that as accurately as possible represents the input-output relation induced by a physical system. When there is little information about the physical laws related to the system or when the system is too complex, methods such as parametric identification are used to define the system model. In that case, preliminary assumptions can be made about the system leading to a parametric aggregation function based model. This parametric model may be learned by estimating the parameters of this aggregation function from a representative set of inputs/outputs. A major difficulty is to design a model that is relatively simple yet precise enough to meet the user's needs. Linear models are commonly used because they meet these two contradictory constraints. However, use of a linear model is often at the expense of the accuracy of input-output relationship description. In a recent paper, a new modeling approach was proposed, under the name of macsum modeling [31] , which aims at replacing the linear model concept by a set of linear models. The obtained aggregation function leads to an interval-valued output. This represents the lack of accuracy of the model to predict the system output when the inputs are known. An interesting feature of this model is that it is ruled by a single precise parametric vector whereas the output is imprecise. Moreover, the vector dimension is equal to that of the input space. In this paper, we propose a method to learn such a model so that it best reflects the input-output relationship of the considered system. This method is based, as in the case of linear modeling, on a regression method. This is particularly new approach because the macsum aggregation is based on a Choquet integral and very few authors have proposed learning of an aggregation function based on the Choquet integral. [ABSTRACT FROM AUTHOR]

Machine learning methods require massive data collection to produce accurate predictions, raising privacy concerns. Secure Multi-Party Computation (SMPC) is one of the possible techniques to preserve data privacy during the computation process. Despite recent advances, the efficiency and scalability of SMPC in combination with machine learning remains an uncertain area. Moreover, various implementations of protocols exist that were optimized for the particular use cases, making the decision towards a suitable solution more complicated. The question of which protocol should be used under what circumstances naturally arises. In this case, when is defined as a security requirement and participant number. This work focuses on exploring the feasibility of using SMPC and machine learning in different settings. The work presents a comparison of selected multi-party computation protocols with different numbers of participants, malicious, and semihonest security models. Each relevant group of settings was constructed for the given input parameters, and the same inference scenario is performed within those settings. The inference scenario involves using a model trained on private statistical data inside a simulated multi-party computation session with a given number of participants and a security model. The results revealed a noticeable increase in computation time when moving from two to three participants, and the addition of a fourth participant led to a less substantial increase in computation time. Furthermore, as the number of participants continued to increase, the increase pattern became less aggressive. The study also noted that transitioning from a semi-honest to a malicious setting resulted in a significant increase in computation time. The results achieved help to outline the trade-offs between the protocol, the security model, and the number of participants, and help to choose the most efficient approach for future work. [ABSTRACT FROM AUTHOR]

• Non-imaging optics was proved effective for LWPT receiver. • Optical efficiency is the biggest factor to explain the advantages of CPV. • Experiments and non-linear regression was used to extract I-V parameters. • Multi-field characteristics of the CPV module under laser power were investigated. • Rotation, distance and misalignment remain the dominance of LWPT output power. Laser Wireless Power Transmission (LWPT) systems have an extensive prospect in the field of wireless energy transmissions such as the space control on-orbit, spacecraft sensor networks, satellites to satellite communication and power transmission, ground to ground, ground to air, ground to Unmanned Aerial Vehicle (UAV), etc. Previously, Concentrated Photovoltaics(CPV) has been used in solar energy as a solution to lower the size of the photovoltaics and yet increase efficiency. As is known laser beam spot normally presents a Gaussian distribution and laser somehow must match the size of the PV cell or array, that is how lens and concentrators are important to reshape and guide the laser beam towards the photovoltaics and in case of misalignment. The current research aims at improve the output performance of LWPT receiver by typical non-imaging optical device-Cross Compound Parabolic Concentrator (CCPC), which helps to improve the transmission of laser beam from different incident directions. Multi-field characteristics of the CPV module under laser power would be solved based on parameters from non-linear regression and experimental study. The multi-field performances of CPV module and single solar cell as the LWPT receivers have been compared. We have found that transmission distance d, divergence half-angle β, rotation angle θ and misalignment distance d mis are most important parameters for multi-field performances and LWPT conversion. The current research provides important references for LWPT system design and optimizations. [ABSTRACT FROM AUTHOR]

In the early VLSI design stages, as most interest is given to timing closure, various techniques are deployed to satisfy the timing constraints and close design timing. Nevertheless, timing still needs to be accurate at the early stages due to the lack of physical design information. Therefore, the challenge for most EDA Logic Synthesis engines is to improve the overall timing accuracy at higher levels of abstraction. In this paper, we shall reduce the error gap in Cell Delay estimation for a placed gate-level netlist. We will generate the post-placed netlist from two different Siemens EDA tools. The purpose is to improve the accuracy of the cell delays of Oasys-RTL Synthesis tool, based on accurate cell delays from Aprisa PnR. We have trained and tested various Machine Learning models on several 16 nm designs. We achieved the highest accuracy using the Random Forest model, where the average accuracy is 91.25%. Furthermore, as the accuracy of the Cell delay is improved, the average Root-Mean-Square-Error (RMSE) is reduced from 16.958 to 4.469. [ABSTRACT FROM AUTHOR]

Eggshell concrete is an innovative green material that helps to recycle eggshell waste while reducing the environmental harm caused by excessive cement production. However, recent studies on eggshell concrete are limited, and the outcomes may vary due to the variation of mix design. The design of the experiment is used to simplify and optimize the study of sustainable concrete, yet analysis involving eggshell concrete is still scarce. This paper aimed to develop mathematical models for the prediction of eggshell concrete compressive strength using mixed regression (MR) and response surface methodology (RSM). Overall, 43 datasets were collected from available studies in the literature on eggshell powder as partial cement replacement. The input variables used were the percentage of eggshell, percentage of Ground Granulated Blast-furnace Slag (GGBS), cement content, fine aggregate, coarse aggregate, water, and Conplast SP-430 superplasticizer. The analysis of the contour plot concluded that eggshell powder increased the concrete compressive strength at an optimal replacement percentage between 5% and 10%. However, the partial cement replacement with eggshell powder is more optimal for mix design with higher water content. The statistical results of the model, such as R2, adjusted R2, and root-mean-square error (RMSE), indicated that both MR and RSM models are powerful tools to formulate and predict the eggshell concrete compressive strength. However, RSM models showed better accuracy and lower deviation. [ABSTRACT FROM AUTHOR]

• During the lockdown almost all of the participants started working from home and reported to be satisfied with this. • Lockdown changed participants' travel behaviour: They avoided public transport on behalf of private car and cycling. • Travel satisfaction is lowest for public transport and highest for active modes. • Those who walked and cycled to work reported better mood and work performance than passive transport users. Everyday commuting is seen as a burden and an unwanted necessity for people. Recent studies have challenged this notion and have found that certain aspects of commuting can be positive. In particular, research has shown that active commuting can be an important source of everyday physical activity and a pause between arenas for daily routine. The current study uses the Covid-19 lockdown situation in Norway, and the associated travel restrictions, as a backdrop to study the relationship between active travel and self-reported mood and work performance. In a situation where people are strongly encouraged to take up active mobility forms in place of more passive forms, the often-encountered challenge of self-selection is reduced. A convenience sample was recruited via social media (N = 1319) in May 2020 and completed a total of six follow-up surveys over a period of four months, thus allowing for a panel design as well as a within-subjects comparison. The survey covered topics related to commute mode, experience of travel, current mood, and work performance. Background variables related to personality, general wellbeing as well as sociodemographic measures were also captured. Multivariate models show that those who during this period commute with active modes (walking and cycling) report a higher degree of travel satisfaction than users of passive modes (driving and public transport). Further, active modes are associated with being in a better mood, and with reporting higher work performance. Finally, looking at individuals who over time change travel mode (N = 151), we find that they report improved mood and work performance when travelling with active vs passive modes. The results have implications for policy makers and for employers looking for justification to spend company money on measures to increase active travel. [ABSTRACT FROM AUTHOR]

Background: Plasma and RBC zinc values are unrelated in hospitalized patients. The independent association of these values with important patient outcomes is unknown. Objectives: Measure the independent association of plasma and RBC zinc with outcomes in hospitalized patients. Methods: Plasma and RBC zinc concentrations were prospectively measured within 48 h of hospitalization in consenting patients. Data were linked deterministically with population-based health administrative data to measure each association of zinc measures with 2 outcomes (time to death from any cause and likelihood of death or urgent readmission to hospital within 30-d of discharge) after adjusting for validated outcome risk scores. Results: In total, 250 people admitted to medical services were studied. Patients were ill with a 1-y baseline expected death risk (IQR) of 19.9% (6.3%-37.2%). The observed 1-y and 2-y all-cause death risks were 24.5% (95% CI: 19.6%, 30.3%) and 33.2% (95% CI: 27.3%, 39.9%), respectively. Death risk increased significantly as plasma zinc concentrations decreased (P = 0.0001). This association persisted even after adjusting for the baseline expected death risk (P = 0.02) with every 2-µmol/L decrease in plasma zinc concentrations being independently associated with, on average, a 35% increase in the death risk. RBC zinc concentrations were not associated with the death risk. Neither plasma nor RBC zinc concentrations were significantly associated with the 30-d death or urgent readmission rate. Conclusions: Plasma, but not RBC, zinc concentrations are independently associated with the all-cause death risk in hospitalized medical patients. Further study is required to determine whether this association is causal and identify its potential causal pathways. [ABSTRACT FROM AUTHOR]

In this paper, a biosensing absorber based on phase transition material is presented. Different phases of the Ge2Sb2Te5 (GST) substrate have been studied for the suggested absorber with controllable characteristics. The structure has been examined to determine the infrared absorption characteristics. The detection of varying volumes of hemoglobin and urine biomolecules is studied. The graphene-GST material is utilized for spectrum tuning. The tuning for two distinct phases of GST material, amorphous GST and crystalline GST is examined. The results for aGST and cGST are reported in the form of absorption. Different amounts of hemoglobin and urine biomolecules are used to tune these two GST stages. Based on the wavelength shifts at these various concentrations, the sensitivity is computed. The highest achievable sensitivity for hemoglobin and urine biomolecules is 1500 nm/RIU and 1667 nm/RIU. The developed model is observed for various geometrical parameters and incidence angles, from which it is determined that the suggested structure is insensitive to wide angles between 0° and 60°. For urine biomolecules, the aGST design is more sensitive than the cGST design, but similar results are achieved for hemoglobin biomolecules. Experiments are conducted with Machine Learning-based regression models to minimize the simulation time and resource requirements of biosensor design. The findings of the trials indicate that a regression model can accurately estimate the absorption values for intermediate wavelengths with an R2 score of 0.9999. [ABSTRACT FROM AUTHOR]

Generative Adversarial Networks (GANs) are well-known tools for data generation and semi-supervised classification. GANs, with less labeled data, outperform Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) in classification tasks. The success of GANs in classification tasks motivates the development of GAN-based techniques for semi-supervised regression tasks. However, developing GANs for regression introduces two major challenges: (1) inherent instability in the GAN formulation and (2) performing regression and achieving stability simultaneously. This paper introduces techniques that show improvement in the GANs' regression capability. We bake a differentiable fuzzy logic system at multiple locations in a GAN. The fuzzy logic takes the output of either the generator or the discriminator to predict the output, y , and evaluate the generator's performance. We outline the results of applying the fuzzy logic system across multiple GANs and summarize each approach's efficacy. This paper shows that adding a fuzzy logic layer can enhance GAN's ability to perform regression; the most desirable injection location is problem-specific, and we show this through experiments over various datasets. Besides, we demonstrate empirically that the fuzzy-infused GANs are competitive with the DNNs. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the role of NR4A1 in forskolin (FSK)-induced granulosa cell (GC) differentiation and PGF 2α -induced granulosa-lutein cell (GLC) regression. For experiment 1, primary porcine GCs were pre-cultured for 6 d before induced-differentiation by FSK with or without siNR4A1, and changes in GC proliferation, lipid droplets (LDs), and P 4 level were detected. For experiment 2, the GLC model was established by FSK as in experiment 1, and then PGF 2α was utilized to induce GLC regression with or without siNR4A1, changes in P 4 secretion, apoptosis proteins, and associated signaling pathway members were detected. Results showed that in experiment 1, FSK up-regulated NR4A1 expression during GC differentiation and decreased GC proliferation activity, which was reversed by siNR4A1. siNR4A1 inhibited the FSK-induced decreases in Cyclin B1/D1 and CDK1/ 2 mRNA abundances, and increases in P21/P2 7 mRNA abundances, and FSK-induced LD accumulation. FSK up-regulated P 4 secretion and StAR, CYP11A1 and HSD3B expression, decreased CYP19A1 expression, which were reversed by siNR4A1 except for StAR expression. In experiment 2, PGF 2α induced NR4A1 expression and reduced GLC viability, which were reversed by siNR4A1. Compared with PGF 2α group, the levels of P 4 secretion and StAR expression were higher in PGF 2α +siNR4A1 group, while CYP11A1 and HSD3B expressions held at low levels. siNR4A1 inhibited PGF 2α -induced expression of apoptosis proteins (caspase3, Bax, Fas, TNFa), ATF3, and phosphorylated MAPKs (ERK1/2, P38, JNK). In summary, NR4A1 is involved in regulating porcine GC differentiation and GLC regression as well as the changes in cell proliferation, apoptosis, steroidogenesis, and MAPK pathways, which provide a theoretical basis for further understanding of the mechanism of porcine luteal formation and regression. • NR4A1 is involved in the arrest of cell cycle and differentiation of GCs. • NR4A1 is involved in promoting LDs accumulation and P 4 secretion of GCs. • NR4A1 contributes to PGF 2α -induced GLCs apoptosis and P 4 inhibition. • MAPKs/ATF3 pathway might be associated with the regulation of NR4A1 in GLCs regression. [ABSTRACT FROM AUTHOR]

An approach to predict standard times for upcoming individual customer orders based on historical production orders is developed. The approach follows the CRISP-DM model with regard to the steps data preparation, modeling and evaluation. A precision steel tube straightening machine is provided as a validation example. Furthermore, the paper focuses on how the standard time prediction approach can be successfully integrated into the IT infrastructure of a production focused company. Especially data availability and accessibility in enterprise resource planning systems are discussed. The presented approach offers users the value of more precise scheduling in production than established approaches. [ABSTRACT FROM AUTHOR]

The problem's complexity assessment is an essential element of many topics in the supervised learning domain. It plays a significant role in meta-learning – becoming the basis for determining meta-attributes or multi-criteria optimization – allowing the evaluation of the training set resampling without needing to rebuild the recognition model. The tools currently available for the academic community, which would enable the calculation of problem complexity measures, are available only as libraries of the C++ and R languages. This paper describes the software module that allows for the estimation of 22 classification complexity measures and 12 regression complexity measures for the Python language – compatible with the scikit-learn programming interface – allowing for the implementation of research using them in the most popular programming environment of the machine learning community. [ABSTRACT FROM AUTHOR]

Previous research has found that human voice can provide reliable information to be used for gender identification with a high level of accuracy. In social psychology, perceived masculinity and femininity (masculinity and femininity rated by humans) has often been considered an important feature when investigating the influence of vocal features on social behaviours. While previous studies have characterized the acoustic features that contributed to perceivers' judgements of speakers' masculinity or femininity, there is limited research on developing a machine masculinity/femininity scoring model and characterizing the independent acoustic factors that contribute to perceivers' masculinity and femininity judgements. In this work, we first propose a machine scoring model of perceived masculinity/femininity based on the Extreme Random Forest and then characterize the independent and meaningful acoustic factors that contribute to perceivers' judgements by using a correlation matrix based hierarchical clustering method. Our results show that the machine ratings of masculinity and femininity strongly correlated with the human ratings of masculinity and femininity when we used an optimal speech duration of 7 s, with a correlation coefficient of up to.63 for females and.77 for males. Nine independent clusters of acoustic measures were generated from our modelling of femininity judgements for female voices and eight clusters were found for masculinity judgements for male voices. The results revealed that, for both genders, the F0 mean is the most important acoustic measure affecting the judgement of acoustic-related masculinity and femininity. The F3 mean, F4 mean and VTL estimators were found to be highly inter-correlated and appeared in the same cluster, forming the second most significant factor in influencing the assessment of acoustic-related masculinity and femininity. Next, F1 mean, F2 mean and F0 standard deviation are independent factors that share similar importance. The voice perturbation measures, including HNR, jitter and shimmer, are of lesser importance in influencing masculinity/femininity judgements. • We modelled femininity/masculinity ratings for 129 female/96 male voices. • Extreme Random Forest regression and correlated feature clustering were used. • F0 mean accounted for 23.80% of variance for femininity and 43.5% for masculinity. • A F3 mean/F4 mean/VTL estimators was also a prominent predictor. • Differences in feature weights for femininity and masculinity ratings are discussed. [ABSTRACT FROM AUTHOR]

Background: Depression is a rapidly increasing public health concern, affecting >4% of the global population. Identification of new nutritional recommendations is needed to help combat this increasing public health concern. Objectives: The study aimed to examine the association between vitamin E intake and depressive symptoms. Methods: A retrospective study was conducted by using a nationally representative, modern cohort (NHANES 2017-2020). Depressive symptoms were assessed through the validated 9-item Patient Health Questionnaire (PHQ-9). All adult patients ([≥18 y old], 8091 total adults) who answered the PHQ-9 and daily nutritional values questionnaires were selected for this study. Per literature, patients with PHQ-9 scores ≥10 were considered to have depressive symptoms. Univariable and multivariable logistic regressions were used to investigate the effect of vitamin E on depressive symptoms as ascertained by PHQ-9. The acquisition and analysis of the data within this study was approved by the NCHS ethics review board. Results: After controlling for potential confounders (age, race, sex, and income), we observed that increased vitamin E (up until 15 mg/d) was associated with decreased rates of depressive symptoms, with each 5 mg increase in vitamin E associated with 13% decreased odds of symptoms of depression (OR: 0.87; 95% CI: 0.77, 0.97; P < 0.01). Additional intake above 15 mg/d, the daily recommended amount by the Food and Nutrition Board, did not change the odds of depression (OR: 1.05; 95% CI: 0.92, 1.16; P = 0.44). Conclusions: Increased vitamin E intake (up to 15 mg/d) is associated with decreased depressive symptoms. Further prospective studies are required to ascertain whether increased vitamin E can protect against depressive symptoms and the specific therapeutic dose--response relationship. [ABSTRACT FROM AUTHOR]

Accurate yield prediction is essential for growers, researchers, governments, the farming industry, and policymakers for social peace, food safety, security, and sustainable development. The results of earlier techniques of data collecting and analysis for yield forecasts were typically delayed, expensive, time-consuming, site-specific, and riddled with errors and uncertainties. This study is a novel approach to using high-resolution satellite data in conjunction with environmental and topographic data to predict wheat yield variability at the farm scale using machine learning. In this research, winter wheat yield prediction was based on 36 indicators in machine learning using correlation and different regression models. Winter wheat yield was predicted using linear regression (LR), decision tree (DT), and random forest (RF) regression models with scikit-learn in machine learning. More than 10,000 data points from 45 farms were trained and validated in Fergana valley, Central Asia. Results indicate that at 10 m resolution using Sentinel-2 and other secondary data such as topographic, soil, environmental, and filed data can generate an accurate wheat yield prediction map. The accuracy of all regressions is lowest for LR (R2:95, RMSE: 2.31), highest for RF (R2:98, RMSE: 1.40), and intermediate for DT regression (R2:97, RMSE: 1.85). Results also indicate that prediction in the early stage of the crop is less accurate in comparison to harvesting time as LR (R2:85, RMSE: 2.66), DT (R2:95, RMSE: 2.06), RF (R2:97, RMSE: 1.54) have different R2 and RMSE values. Appling the RF model, the winter wheat prediction is 3.29 to 4.30 t/ha therefore the total wheat production is approximately 100 t in the study area. Thus this study will demonstrate the capability of high-resolution satellite imagery and secondary data for highly accurate real-time crop yield prediction at the field scale, which can be used to assist precision agriculture and will provide a point of reference for crop area extraction, mapping, monitoring, and sustainable development with food security. [ABSTRACT FROM AUTHOR]

Agriculture is essential for the existence of all human beings. It is not only a need, but it also contributes to national growth. Agriculture is the only industry that benefits both itself and the rest of the country. It not only offers food and raw materials to a substantial section of the people but also work prospects. Agriculture is the principal source of income for the majority of people in India. It has been in the country for thousands of years, and modern technology and equipment have substituted ancient farming practices. With the advancement of technology in every industry, it has become increasingly vital to incorporate technology into agriculture. Machine learning facilitates the study of large volumes of data and may deliver faster and more precise results, which can aid in the identification of lucrative possibilities and risky threats. The major goal of this research project is to develop a Machine Learning (ML) model to predict farm production. To estimate crop yields, the data was collected and trained using supervised machine learning with six distinct regression models. With a Mean absolute error (MAE) of 468.16 and a Cross-Validation score of 0.6087, Random Forest Regressor outperformed the other models. [ABSTRACT FROM AUTHOR]