Predictors Of Success Research Articles

Prognosis and ablation success in thyroid cancer: overcoming the challenges of incomplete clinical profiles.

Differentiated thyroid carcinoma (DTC) is managed by surgery followed by radioiodine (RAI) therapy in most intermediate and high-risk patients. Most nonmetastatic patients have excellent treatment responses and have long-term disease-free status. A lack of comprehensive medical services in resource-limited nation leads to attrition of critical clinical prognostication information. This study aimed to identify readily available clinical, biochemical, and histopathological parameters to predict remnant ablation success and long-term outcomes. The study included DTC patients who underwent RAI after surgery. Ablation success was determined by thyroglobulin (Tg) and whole-body radioiodine scan. Patients were followed for at least 5 years to assess biochemical incomplete response (BIR) and structural recurrence. The study included 383 patients (a mean age of 37.8 ± 12.9 years). Successful ablation was noted in 251 (65.5%). High preablative stimulated serum Tg (presTg), papillary variants, and central and lateral compartment lymph nodal metastases were associated with ablation failure. PresTg (P < 0.001) was the most significant predictor. After a 102.9 ± 34.5 months follow-up, 280 (73.1%) patients were disease-free. BIR and structural recurrence were noted in 103 and 32 patients. PresTg (8.1 ± 27.7 vs. 92.3 ± 99.9 ng/ml), ATg (112.9 ± 389.8 vs. 43.2 ± 89.8 IU/ml), papillary variant, central [109 (66.1%) vs. 56 (33.9%)], and lateral compartment [65 (63.7%) vs. 37 (36.3%) lymph nodal metastases were associated (P < 0.05) with BIR. PresTg >10.5 has a sensitivity and specificity of 86.6 and 86.0% for predicting BIR. Patients with successful remnant ablation and a presTg level <10.5 ng/ml had a low risk of long-term disease recurrence (less than 5%). This ambispective study found that successful ablation and long-term disease-free survival were achievable in a significant proportion of DTC patients. BIR (26.9%) and structural recurrence (8.4%) were not uncommon. PresTg levels emerged as a crucial predictor of ablation success and subsequent outcomes. In resource-limited regions, presTg levels and ablation failure can aid in optimizing treatment strategies and improving patient care.

Factors of Weight Loss for Telemedically Supported Metabolic Syndrome Patients in a Controlled Trial

Metabolic syndrome (MetS) is a complex of interrelated risk factors, associated with several serious chronic diseases like diabetes. The goal of this study was to find dietary factors of successful weight loss for MetS outpatients. We performed a 90-day dietary intervention in a telemedically supported, pre- and post-test, controlled trial in Hungary involving 132 MetS patients; 67 were in the intervention, and 65 were in the control group. Patients in the intervention group used wireless smart devices, a dietary logger, and a lifestyle app. During the trial, we recorded the patients’ weight loss and diet composition. For analysis, t-tests were used, and the temporal trends of diet composition in the intervention group were analyzed between two sub-groups according to weight loss success. Correlation and regression models were used to find predictors of success. The intervention group achieved more weight loss, and the success in this group was linked with more consumption of raw fruits/vegetables, poultry and potato dishes, while age had a negative effect. We conclude that telemedically supported dietary coaching is an efficient alternative for interventions directed at weight loss. Future trials should investigate the therapeutic application of diets rich in raw fruits, especially apples, and vegetables, as well as poultry dishes.

Catalyzing IVF outcome prediction: exploring advanced machine learning paradigms for enhanced success rate prognostication

This study addresses the research problem of enhancing In-Vitro Fertilization (IVF) success rate prediction by integrating advanced machine learning paradigms with gynecological expertise. The methodology involves the analysis of comprehensive datasets from 2017 to 2018 and 2010–2016. Machine learning models, including Logistic Regression, Gaussian NB, SVM, MLP, KNN, and ensemble models like Random Forest, AdaBoost, Logit Boost, RUS Boost, and RSM, were employed. Key findings reveal the significance of patient demographics, infertility factors, and treatment protocols in IVF success prediction. Notably, ensemble learning methods demonstrated high accuracy, with Logit Boost achieving an accuracy of 96.35%. The implications of this research span clinical decision support, patient counseling, and data preprocessing techniques, highlighting the potential for personalized IVF treatments and continuous monitoring. The study underscores the importance of collaboration between gynecologists and data scientists to optimize IVF outcomes. Prospective studies and external validation are suggested as future directions, promising to further revolutionize fertility treatments and offer hope to couples facing infertility challenges.

Disagreement Predicts Startup Success: Evidence from Venture Competitions

This paper examines the relationship between disagreement surrounding a startup proposition and its future success. I find that the more venture competition judges disagree on the quality of a startup, the more likely the startup is to succeed, particularly when its proposition is unique. To explain this correlation, I build on the notion that (i) entrepreneurs pursue opportunities based on their subjective beliefs and (ii) common opinion alone cannot be a source of competitive advantage. Therefore, value is disproportionately created and captured by founders with unconventional ideas that spark disagreement, and potential investors should harness disagreement as a predictor of success. I leverage data from 67 venture competitions to show that the empirical implications of this theoretical framework are supported by the data, whereas alternative explanations (e.g., that judges disagree more about risky ventures) are not. Additionally, I provide insights into which evaluators tend to disagree more often (e.g., former entrepreneurs) and which aspects of a startup (e.g., business model) are most polarizing. This work has broad implications for investors and institutions that strive to evaluate the potential of startup ideas. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsc.2024.0169 .

Social Support and Expectancy as Predictors of Licensure Examination Success Among Professional Teachers

This study aimed to determine the Licensure Examination for Teachers (LET) profile, the Level of Social Support and Expectancy and their relationships and identify the dimensions that predict LET Success Rating. This quantitative study utilized descriptive-correlational design among LET Passers and Licensed Professional Teachers produced by the University of Mindanao from 2019–2023. 303 LPTs composed of 109 Elementary and 194 Secondary teachers selected via convenience and snowball sampling responded to the virtual survey. The collected data were analyzed using mean, standard deviation, spearman correlations, and multiple regressions. Results revealed that 87% of the LPTs were top-performing and had high social support and expectancy levels during their LET preparation. The correlation between social support and the LET Success Rating is positive but very weak. Likewise, the correlation between expectancy and LET Success Rating is positive but very weak. Moreover, social support predicts the LET Success Rating by 1.9%; expectancy predicts it by 0.4%. Furthermore, support from Friends and/or classmates has moderate and positive correlations and has a strong positive predictive power with LET Success Rating.

Barriers and Facilitators in Lactation Support for the Preterm Mother-Infant Dyad: An Integrated Approach.

Breastfeeding offers significant health benefits for both mothers and infants, particularly preterm infants, where it serves as a therapeutic strategy to reduce mortality and morbidities. However, breastfeeding practices are threatened globally by societal norms and systemic barriers at both micro and macro levels. This paper explores the complex interplay of these barriers and facilitators, focusing on the Neonatal Intensive Care Unit (NICU) setting. Preterm infants face specific feeding challenges due to immature physiological functions, yet evidence supports that strategies like cue-based feeding and individualized care can enhance feeding success and health outcomes. For mothers, initiating and maintaining lactation after preterm birth is critical but challenging, with early lactation support and achieving sufficient milk volume being key predictors of success. Partner support significantly influences lactation outcomes, although more inclusive research is needed for diverse family structures. In the NICU, a multidisciplinary approach to lactation care is vital, emphasizing the need for experienced staff and family-centered practices. NICU design, policies promoting parental presence, and adherence to Baby-friendly Hospital Initiative guidelines further support breastfeeding. This paper aims to provide directives for local breastfeeding policies through an integrated approach, considering societal attitudes and healthcare practices. The findings advocate for improved lactation support in NICUs, inclusive language and policies, and further research into diverse familial and gender roles in breastfeeding.

PREDICTING STUDENT PERFORMANCE IN ONLINE LEARNING PLATFORMS: ANALYZING ENGAGEMENT METRICS WITH MACHINE LEARNING MODELS

The emergence of online learning platforms harbours both opportunities and challenges, this paper focuses on machine learning algorithms for deep diving into key predictors of students success in online environments, captured by engagement metrics such as quiz scores, participation in forums, and time spent on learning tasks. Data was collected through a survey of 50 students and then run through the Random Forest model. Major predictors of success include the assignment approach, quiz score, and motivation. The prediction from this model can be interesting because it can pick important features, but the overall accuracy is quite low at about 20%, which implies that larger datasets and more features might be necessary to enhance the predictions. Time management, motivation, and staying in touch are such variables that the research holds key to determining results for students. The results are important not only for online learning platforms but also for interventions suggested, which include early warning systems, personalised learning paths, and elements of gamification to support performance for improved student support at the right time for those most at risk. Future studies should consider more sophisticated machine learning models and the use of more objective performance data in their attempt to make more refined predictions.

A Heatmap-Supplemented R-CNN Trained Using an Inflated IoU for Small Object Detection

Object detection architectures struggle to detect small objects across applications including remote sensing and autonomous vehicles. Specifically, for unmanned aerial vehicles, poor detection of small objects directly limits this technology’s applicability. Objects both appear smaller than they are in large-scale images captured in aerial imagery and are represented by reduced information in high-altitude imagery. This paper presents a new architecture, CR-CNN, which predicts independent regions of interest from two unique prediction branches within the first stage of the network: a conventional R-CNN convolutional backbone and an hourglass backbone. Utilizing two independent sources within the first stage, our approach leads to an increase in successful predictions of regions that contain smaller objects. Anchor-based methods such as R-CNNs also utilize less than half the number of small objects compared to larger ones during training due to the poor intersection over union (IoU) scores between the generated anchors and the groundtruth—further reducing their performance on small objects. Therefore, we also propose artificially inflating the IoU of smaller objects during training using a simple, size-based Gaussian multiplier—leading to an increase in the quantity of small objects seen per training cycle based on an increase in the number of anchor–object pairs during training. This architecture and training strategy led to improved detection overall on two challenging aerial-based datasets heavily composed of small objects while predicting fewer false positives compared to Mask R-CNN. These results suggest that while new and unique architectures will continue to play a part in advancing the field of object detection, the training methodologies and strategies used will also play a valuable role.

Determining the influence of data on working with video materials on the accuracy of student success prediction models

The object of this study is models for predicting students’ success, constructed on the basis of machine learning methods. The paper reports results of research into the problem of improving their accuracy by expanding the data set for training the specified models. The most available are data on student actions, which are automatically collected by learning management systems. Entering additional information about students’ work increases time and resources but allows the improvement of the accuracy of the models. In the study, information about students’ work with video materials, particularly the number and duration of views, was entered into the original data set. To automate the collection of this data, the plugin for the Moodle system has been developed, which stores information about user’s actions with the video player and the duration of watching video materials in the database. Model training was carried out using Naive Bayes (NB), logistic regression (LR), random forest (RF), and neural networks (NN) algorithms with and without video data. For the models using video viewing data, accuracy increased by 10 %, balanced accuracy by 15 %, and overall performance, expressed as area under the curve (AUC), increased by 14 %. The highest prediction accuracy, with a difference of 1.8 %, was obtained by models built using RF algorithms – 87.1 % and NN – 85.3 %. At the same time, the accuracy of the models obtained by the NB and LR algorithms was 70.7 % and 76.5 %. The increase in accuracy for them was 2.3 % and 8.1 %, respectively. Analysis of calculations confirms the assumption that students’ work with educational video materials is correlated with their success. The results make it possible to find a reasonable compromise between model development costs and its accuracy at the stage of data preparation for model training.

Emotional Intelligence and Workplace Spirituality in Predicting Career Success of High-Tech Leaders

In the highly competitive environment of the high-tech industry, leadership roles are often filled with numerous challenges and stressors. Success in the workforce requires a combination of high emotional intelligence and a connection to workplace spirituality. This study aimed to compare demographic differences among high-tech leaders regarding emotional intelligence, workplace spirituality, and career success. It also sought to examine the relationships and effects of emotional intelligence and workplace spirituality on career success, as well as the moderating role of workplace spirituality in these relationships. A survey was conducted with 139 leaders from various high-tech companies in Taiwan. The results showed that male leaders demonstrated higher levels of self-awareness and self-motivation in emotional intelligence, while leaders with higher education levels exhibited greater self-awareness and empathy. Additionally, both emotional intelligence and workplace spirituality were significant predictors of career success. Notably, workplace spirituality moderated the relationship between emotional intelligence and career success. The implications of this study highlight the importance of enhancing emotional intelligence and workplace spirituality to foster leaders’ career success and gain a competitive advantage in the high-tech industry.

3DSTarPred: A Web Server for Target Prediction of Bioactive Small Molecules Based on 3D Shape Similarity.

Target identification plays a critical role in preclinical drug development. The in silico approach has been developed and widely applied to assist medicinal chemists and pharmacologists in drug target identification. There are many target prediction web servers available today that have revealed both advantages and shortcomings in practical applications. Here, we present 3DSTarPred, a web server for three-dimensional (3D) shape similarity-based target prediction of small molecules. A benchmark study showed that 3DSTarPred achieved a target prediction success rate of 76.27%, which was higher than that of existing target prediction web servers. In addition, the performance of 3DSTarPred in the target prediction of diverse substructures/superstructures was also better than that of the existing target prediction web servers. In case studies, 3DSTarPred was used to identify the potential targets of two small molecules, one being kaempferol, a natural lead compound for the treatment of Alzheimer's disease (AD), and the other being sildenafil, a candidate for drug repurposing in AD. The case studies further demonstrated the reliability and success of 3DSTarPred in practice. The 3DSTarPred server is freely available at http://3dstarpred.pumc.wecomput.com.

Zero-shot counting with a dual-stream neural network model.

To understand a visual scene, observers need to both recognize objects and encode relational structure. For example, a scene comprising three apples requires the observer to encode concepts of "apple" and "three." In the primate brain, these functions rely on dual (ventral and dorsal) processing streams. Object recognition in primates has been successfully modeled with deep neural networks, but how scene structure (including numerosity) is encoded remains poorly understood. Here, we built a deep learning model, based on the dual-stream architecture of the primate brain, which is able to count items "zero-shot"-even if the objects themselves are unfamiliar. Our dual-stream network forms spatial response fields and lognormal number codes that resemble those observed in the macaque posterior parietal cortex. The dual-stream network also makes successful predictions about human counting behavior. Our results provide evidence for an enactive theory of the role of the posterior parietal cortex in visual scene understanding.

Multimodal machine learning-based technical success prediction in patients undergoing transcatheter aortic valve implantation

Abstract Background Procedural success is fundamental to achieving optimal clinical outcome, and prediction of procedural success and failure is of key importance in the selection of the appropriate treatment strategy in patients with severe aortic stenosis requiring aortic valve replacement. Purpose We aimed to develop a prediction model for procedural success in patients undergoing transcatheter aortic valve implantation (TAVI) using multimodal information and employing machine learning algorithms. Method In a prospective TAVI registry, patient baseline demographics, clinical history, medication, laboratory, procedural characteristics, electrocardiogram, angiography, transthoracic and transesophagel echocardiography, and computed tomography information were collected prospectively and extracted as numeric data. Data preprocessing included the imputation of missing values and the elimination of features with high collinearity. We incorporated several feature selection (FS) and classification algorithms for model development. Datasets for each target were randomly split into train/validation sets (70/10%) and hold-out test sets (20%). All machine learning processes, including preprocessing, feature selection, parameter, and hyperparameter optimization, were carried out on training and validation sets in 10-fold cross-validation. Different metrics, including area under the curve (AUC) in receiver operating characteristic curve analyses, sensitivity, and specificity, were calculated for model evaluation on the holdout test set. Procedural success was defined as technical success in the Valve Academic Research Consortium-3 (VARC-3). Technical failure differentiated between vascular and cardiac complications. Results In a total of 2,576 patients undergoing TAVI with a contemporary device between March 2012 and June 2023, 242 (9.4%) had technical failure: 70 (2.7%) had cardiac and 178 (6.9%) had vascular technical failure. The sensitivity and specificity for overall technical failure were 0.71 and 0.77, respectively, with AUC of 0.76 (95% CI: 0.69-0.84) achieved by the recursive feature elimination and AdaBoost algorithm. For cardiac technical failure, the ANOVA FS and Random Forest (RF) model yielded sensitivity and specificity of 0.71 and 0.77, with an AUC of 0.76 (95% CI: 0.65 to 0.87). Vascular technical failure showed AUC of 0.76 (95% CI: 0.69 to 0.84), with sensitivity and specificity of 0.72 and 0.73, respectively, with achieved using the ANOVA FS and RF model. Conclusion The initial results of the novel prediction model for technical success are promising. Further improvements and external validation are warranted to support the use of the proposed model in clinical practice.Central Illustration

Right ventricular free wall longitudinal strain as a predictor of myocardial recovery in patients with advanced cardiogenic shock supported with the Impella device

Abstract Background Right ventricular (RV) dysfunction is a known independent factor influencing the outcome of patients in cardiogenic shock (CS). The aim of this monocentric retrospective study was to investigate the impact of left ventricular (LV) unloading on the right heart function and its predictive role regarding weaning following Impella device implantation in patients with profound CS due to LV failure. Methods We performed a retrospective analysis of 41 patients admitted to intensive care unit over a period of 3 years with CS due to LV dysfunction requiring support with Impella. Clinical and echocardiographic data were collected and correlated with weaning success. Finally, a multivariate analysis to identify independent predictors of weaning success was performed. Results After Impella implantation, the following was observed: right ventricular fractional area change (RVFAC) increased from a mean value of 28% to 38% (p=0.01), the tricuspid annular systolic velocity (TASV) increased from 9 cm/s to 10 cm/s (p=0.01), the velocity time integral right ventricular outflow tract (VTI RVOT) increased from 10 to 13 cm3/stroke (p=0.01), the right ventricular free wall longitudinal strain (RVFWLS) increased from -12% to -21% (p&amp;lt; 0.001). However, following LV unloading, a decrease in both RVFAC from 38% to 30% (p 0.01) and in VTI RVOT from 13 to 11 cm3/stroke (p&amp;lt;0.01) was observed. Moreover, there was a significant decrease in RVFWLS from -21% to -14% (p &amp;lt; 0.001). Notably, the percentage of change in RVFWLS between maximum and minimum (Δ%RVFWLSPmax-Pmin) support with Impella during the weaning process was significantly lower in weanable patients (34.5% in weaned vs 86% in non-weaned; p=0.038). Investigating the predicting ability of these parameters, Δ%RVFWLSPmax-Pmin emerged showing an AUC of 0.826 (0.690-0.962) in identifying non-weaned patients with a cut-off value of 38%. According to the multivariate analysis, Δ%RVFWLSPmax-Pmin in non-weaned patients remained the only significant parameter even considering possible confounders. Conclusions LV unloading with Impella in advanced CS patients improved RV function echocardiographic parameters. RVFWLS increased significantly after Impella implantation and its percentage of change between maximum and minimum support was significantly lower in weanable compared to non-weanable patients. Our findings suggest that RVFWLS could be used as a predictor of myocardial recovery allowing to identify weanable patients with adequate accuracy.

Long-term patient clinical benefits and healthcare utilization after pulsed field ablation in paroxysmal atrial fibrillation: sub-analyses from the multicenter inspIRE trial

Abstract Background/Introduction The treatment of atrial fibrillation (AF) with pulsed field ablation (PFA) aims to prevent AF recurrence while reducing complications from the ablation modality. Similar to thermal ablation, PFA has proven to be safe and effective for treatment of paroxysmal AF (PAF); however, there is scarce evidence on patient-related clinical benefits, including healthcare utilization, using this new energy modality. Purpose The objective of this study is to assess long-term clinical improvements in patient care, symptoms, and quality-of-life (QOL) after PFA ablation in PAF. Methods InspIRE was a multicentre study evaluating the safety and efficacy of variable loop PFA catheters integrated with a 3D mapping system for treatment of symptomatic PAF. Patients were followed for up to 12 months for the following QOL and healthcare utilization endpoints: Atrial Fibrillation Effect on Quality-of-Life (AFEQT) score, Class I/III AAD utilization, repeat ablation, incidence of direct current cardioversion (DCCV), and cardiovascular (CV) hospitalization. Post-hoc Kaplan-Meier estimates for clinical benefit success at 12 months were provided. Success was defined as composite freedom from occurrence of the following events post-blanking: repeat ablation, CV hospitalization, AAD utilization, and DCCV post-blanking. Predictors of clinical benefit success were explored via multivariate logistic regression analysis. Results A total of 186 enrolled patients (59±10 years; 70.0% male; CHA2DS2-VASC score 1.3±1.2) underwent PVI. Compared to baseline: 1) improvements in the AFEQT composite score were seen at 12 months (P&amp;lt;0.001); 2) Class I/III AAD use was reduced from 81.7% to 20.1% at 6-12 months (P&amp;lt;0.05); and 3) the proportion of subjects with DCCV decreased from 13.7% up to 12 months before the index procedure to 3.3% up to 12 months after the index procedure (P&amp;lt;0.001, Figure 1). Minimal clinically important difference in QoL (≥5 points improvement in AFEQT) was achieved in 85.2% of patients. Patients free from 12-month AF/AT/AFL recurrence (asymptomatic and symptomatic) had significantly greater improvements in AFEQT scores from baseline to 12 months versus those with recurrence (P=0.01). The clinical benefit success rate at 12 months was 87.5% (Figure 2). Patients who failed the clinical benefit endpoint were counted, including those with repeat ablation (7.5%), those on new or higher dose of AAD (6.5%), those hospitalized for CV (4.8%), and those who had DCCV post-blanking (1.6%). Multivariate logistic regression analysis showed that left ventricle ejection fraction &amp;lt;60% (OR: 0.22; 95% CI: 0.08-0.62; P&amp;lt;0.05) and incidence of diabetes (OR: 7.25; 95% CI: 2.00, 26.3; p&amp;lt;0.05) were associated with a higher risk of clinical benefit failure. Conclusions PFA ablation of PAF patients led to a clinical benefit success rate of 87.5%, with clinically meaningful improvement in QOL, as well as a reductions in AAD use, cardioversion, and hospitalization.Figure 1.Improvements in QoL and HealthFigure 2.Kaplan-Meier Analysis of Time

Analysis of the efficacy of Prostatic Artery Embolization in the treatment of Benign Prostatic Hyperplasia.

To investigate the safety and efficacy of prostatic artery embolization (PAE) in the treatment of lower urinary tract symptoms (LUTS) caused by benign prostatic hyperplasia (BPH), and to investigate predictors of clinical success of PAE. A retrospective analysis was used to collect 107 patients [median age 81.0 (73.0,85.0)] with BPH-related LUTS treated with PAE from September 2014 to February 2022 in a hospital. Repeated measurement ANOVA was used to compare the efficacy evaluation indicators at different times before and after PAE. Univariate and multivariate analyses were used to identify potential predictors of PAE clinical success and establish the optimal joint prediction model. The Receiver Operating Characteristic curves of the quantitative predictors and multivariate model prediction probability values significantly correlated with clinical success were plotted. Of the 107 cases, 103 (96.3%) successfully underwent PAE. The International Prostate Symptom Score (IPSS) decreased from a baseline mean of 24.94 to 10.19 (P < 0.05) 3 months after PAE, and the mean IPSS at 6 months, 12 months and 24 months was 10.12, 11.30 and 11.86, respectively, which were statistically significant compared with baseline (P<0.05). Predictors of clinical success were greater prostate volume (> 65 ml, P = 0.018), adenomatous-dominant benign prostatic hyperplasia (AdBPH)(P = 0.030), indwelling catheterization due to urine retention (P = 0.028), and bilateral embolization (P = 0.018). PAE was able to significantly improve BPH-related LUTS and the outcome indicators remained stable at long-term follow-up. Preoperative urinary retention catheters, AdBPH, larger prostate volume (> 65ml) and bilateral embolization suggest better clinical efficacy.

The Problem of Differential Importability and Scientific Modeling

The practice of science appears to involve “model-talk”. Scientists, one thinks, are in the business of giving accounts of reality. Scientists, in the process of furnishing such accounts, talk about what they call “models”. Philosophers of science have inspected what this talk of models suggests about how scientific theories manage to represent reality. There are, it seems, at least three distinct philosophical views on the role of scientific models in science’s portrayal of reality: the abstractionist view, the indirect fictionalist view, and the direct fictionalist view. In this essay, I try to articulate a question about what makes a scientific model more or less appropriate for a specific domain of reality. More precisely, I ask, “What accounts for the fact that given a determinate target domain, some scientific models, but not others, are thought to be “appropriate” for that domain?” I then consider whether and the degree to which each of the mentioned views on scientific models institutes a satisfactory response to this question. I conclude that, amongst those views, the direct fictionalist view seems to have the most promising response. I then utilize this argument to develop a more precise account of the problem of differential importability, and ultimately offer a more general and less presumptive argument that the problem seems to be optimally solved by justifying comparative evaluation of model-importabilities solely in terms of comparative evaluations of what I characterize as models’ “holistic” predictive success.

Constraining dark matter from strong phase transitions in a U1Lμ−Lτ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \ extrm{U}{(1)}_{L_{\\mu }-{L}_{\ au }} $$\\end{document} model: implications for neutrino masses and muon g − 2

In this paper, we study a non-minimal gauged U1Lμ−Lτ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \ extrm{U}{(1)}_{L_{\\mu }-{L}_{\ au }} $$\\end{document} model, where we add two complex singlet scalars, three right-handed Majorana neutrinos (RHN), and a vector-like dark fermion to the Standard Model (SM), all non-trivially charged under the extra gauge symmetry. The model offers an easy resolution to the muon (g – 2) anomaly, which fixes the scale of spontaneous symmetry breaking. Furthermore, the two-zero minor structure in the RHN mass matrix provides successful predictions for neutrino oscillation parameters, including the Dirac phase. The extended scalar sector can easily induce first-order phase transitions. We identify all possible phase transition patterns in the three-dimensional field space. We quantify the associated gravitational waves from the sound wave source and demonstrate that the signatures can be observed in future space-based experiments. We find that strong first-order phase transitions require large values of scalar quartic couplings which constrain the scalar dark matter (DM) relic density to a maximum of 10−2 and 10−5 when we consider the DM direct detection bound. Nonetheless, the model successfully explains the DM relic density via contribution from the vector-like dark fermion. We show the allowed range of the model parameters that can address all the beyond SM issues targeted in this study.

Course Success Prediction and Early Identification of At-Risk Students Using Explainable Artificial Intelligence

Artificial Intelligence (AI) is increasingly used in online education platforms to provide valuable insights into students’ performance and success. However, the complexity of AI models makes it challenging for educators to interpret the specific factors that influence whether a student is going to pass or fail. Utilizing the Open University Learning Analytics Dataset (OULAD), this study employs various machine learning and deep learning techniques for predicting students’ success, along with SHapley Additive exPlanations (SHAP) as an Explainable Artificial Intelligence (XAI) technique, to understand the key factors behind success or failure. Unlike traditional statistical methods that explore variable relationships, this AI-driven approach uses advanced deep learning techniques to identify patterns and insights, allowing for a better understanding of the factors influencing student success. Additionally, this study focuses on identifying students at risk of failure using XAI techniques, specifically SHAP, to interpret model outputs by breaking down how specific factors contribute to a student’s success. This method enables targeted interventions to support their success. Results reveal that student engagement and registration timelines are critical factors affecting performance. The customized models achieve up to 94% accuracy for the designed tasks, outperforming traditional approaches. This study contributes to the use of AI in education and offers practical insights not only for educators but also for administrators and policymakers to enhance the quality and effectiveness of online learning.

Investigating the Role of Cover-Crop Spectra for Vineyard Monitoring from Airborne and Spaceborne Remote Sensing

The monitoring of grape quality parameters within viticulture using airborne remote sensing is an increasingly important aspect of precision viticulture. Airborne remote sensing allows high volumes of spatial consistent data to be collected with improved efficiency over ground-based surveys. Spectral data can be used to understand the characteristics of vineyards, including the characteristics and health of the vines. Within viticultural remote sensing, the use of cover-crop spectra for monitoring is often overlooked due to the perceived noise it generates within imagery. However, within viticulture, the cover crop is a widely used and important management tool. This study uses multispectral data acquired by a high-resolution uncrewed aerial vehicle (UAV) and Sentinel-2 MSI to explore the benefit that cover-crop pixels could have for grape yield and quality monitoring. This study was undertaken across three growing seasons in the southeast of England, at a large commercial wine producer. The site was split into a number of vineyards, with sub-blocks for different vine varieties and rootstocks. Pre-harvest multispectral UAV imagery was collected across three vineyard parcels. UAV imagery was radiometrically corrected and stitched to create orthomosaics (red, green, and near-infrared) for each vineyard and survey date. Orthomosaics were segmented into pure cover-cropuav and pure vineuav pixels, removing the impact that mixed pixels could have upon analysis, with three vegetation indices (VIs) constructed from the segmented imagery. Sentinel-2 Level 2a bottom of atmosphere scenes were also acquired as close to UAV surveys as possible. In parallel, the yield and quality surveys were undertaken one to two weeks prior to harvest. Laboratory refractometry was performed to determine the grape total acid, total soluble solids, alpha amino acids, and berry weight. Extreme gradient boosting (XGBoost v2.1.1) was used to determine the ability of remote sensing data to predict the grape yield and quality parameters. Results suggested that pure cover-cropuav was a successful predictor of grape yield and quality parameters (range of R2 = 0.37–0.45), with model evaluation results comparable to pure vineuav and Sentinel-2 models. The analysis also showed that, whilst the structural similarity between the both UAV and Sentinel-2 data was high, the cover crop is the most influential spectral component within the Sentinel-2 data. This research presents novel evidence for the ability of cover-cropuav to predict grape yield and quality. Moreover, this finding then provides a mechanism which explains the success of the Sentinel-2 modelling of grape yield and quality. For growers and wine producers, creating grape yield and quality prediction models through moderate-resolution satellite imagery would be a significant innovation. Proving more cost-effective than UAV monitoring for large vineyards, such methodologies could also act to bring substantial cost savings to vineyard management.