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Recent work has highlighted that a large number of participants are needed to reproducibly predict individual behavior traits based on characteristics in brain structure or function. However, having enough data per participant is also critical for prediction. Here, we review recent evidence that the limited performance of current brain-behavior prediction models is driven by two major causes, which are noisy data and small effects. We offer a framework to tackle these challenges through 'precision' brain and behavioral approaches that collect more per-participant data and implement within-subject experimental designs. We discuss how integrating precision approaches with consortium studies may provide improved brain-behavior predictions necessary to achieve more powerful clinical applications. Predicting individual behavioral traits from brain idiosyncrasies has broad practical implications, yet predictions vary widely. This constraint may be driven by a combination of signal and noise in both brain and behavioral variables. Here, we expand on this idea, highlighting the potential of extended sampling 'precision' studies. First, we discuss their relevance to improving the reliability of individualized estimates by minimizing measurement noise. Second, we review how targeted within-subject experiments, when combined with individualized analysis or modeling frameworks, can maximize signal. These improvements in signal-to-noise facilitated by precision designs can help boost prediction studies. We close by discussing the integration of precision approaches with large-sample consortia studies to leverage the advantages of both. [ABSTRACT FROM AUTHOR]

This paper showcases shear testing conducted on seven reinforced concrete deep beams featuring square openings, aimed at demonstrating the efficacy of a shear strengthening approach utilizing textile-reinforced concrete (TRC). This experimental program researches two types of openings of 150 × 150 mm and 200 × 200 mm at the centre of the shear spans. The solid beam undergoes testing for comparison with both the unstrengthened and strengthened beams containing openings. The study evaluates the effectiveness of TRC through a comparison of load-displacement curves, cracking patterns, and strains in reinforcements between the strengthened and un-strengthened beams. The experimental results indicate that the TRC strengthening technique effectively boosts the shear-carrying capacity of beams. Examination of the tested beams reveals a significant enhancement in stiffness at the point of maximum force, with improvements ranging from 22% to 86%, depending on the opening size and the number of reinforced textile layers. Rupture in TRC and crushing in the concrete strut are observed in the strengthened beams, whereas the un-strengthened beams show severe crushing in concrete at failure. The test results are compared with calculations based on code provisions, revealing that the prediction model consistently yields values 11 to 44% higher than the experimental results. [ABSTRACT FROM AUTHOR]

Objective. This study proposes a real-time tumor position prediction-based multi-dimensional respiratory motion compensation puncture method to accurately track real-time lung tumors and achieve precise needle puncture. Approach. A hybrid model framework integrating prediction and correlation models is developed to enable real-time tumor localization. A Long Short-Term Memory neural network with bidirectional and attention modules (Bi-LSTM-ATT) is employed for predicting external respiratory signals. Subsequently, a backpropagation neural network is constructed to correlate these signals with tumor positions. Tumor trajectory decomposition and the determination of an optimal puncture window based on multiple criteria ensure accurate needle puncture. Main results. When the delay time of Bi-LSTM-ATT model is 500 ms, its RMSE, MAE, and R 2 are 0.0482 mm, 0.0414 mm, and 97.90% respectively. The correlation model locates lung tumors in 10 cases with a target registration error within 0.74 mm. The proposed puncture method achieves a puncture error ranging from 1.00 mm to 1.32 mm, with an average error of 1.2 mm. Significance. The proposed method is validated for its high accuracy and robustness, establishing it as a promising tool for percutaneous biopsy procedures within the lung. Clinical trial registration Clinical trial registration was not required for this research. [ABSTRACT FROM AUTHOR]

The article focuses on the predictive value of the ABCD2 score in assessing three-year stroke risk following a transient ischemic attack (TIA), including the study's findings from Denmark's National Patient Registry showing a higher stroke rate in high-risk individuals.

High-dimensional (HD) data, such as images and profiles, are commonly collected from complex systems and contain significant explanatory and predictive information for effective systems monitoring and control. Therefore, developing accurate and robust predictive models based on HD data is crucial. In the literature, various methods, including linear scalar-on-tensor regression, are developed to model a complex system based on HD data. However, existing estimation techniques ignore the presence of outliers and are prone to biased estimations. This article proposes a robust scalar-on-tensor regression framework that handles multi-dimensional HD input data when the data contain outliers. Our proposed estimation method is constructed using maximum Lq-likelihood estimation instead of the classic maximum likelihood estimation. The asymptotic analysis under the Gaussian distribution assumption and the guideline on choosing the tuning parameter of our proposed method is provided. Several simulations and case studies evaluate the proposed method's efficacy compared to several benchmark methods in the literature. [ABSTRACT FROM AUTHOR]

Compared with the point prediction of the wind power, the interval prediction provides more information to assist power dispatching and optimize quotation strategies in power trading. To improve the accuracy of the prediction intervals, this paper proposes a novel model based on the lower upper bound evaluation (LUBE) by the gate recurrent unit (GRU) network. First, the Pearson correlation coefficient is selected to screen out the variables related to wind power, which can simplify the input variables of the prediction model while preserving the feature information as much as possible. Afterwards, with the consideration of the prediction interval relative deviation (PIRD), the improved objective function is introduced. Based on the GRU network, the prediction intervals of the wind power can be obtained to minimize the new objective function. Finally, by choosing several mainstream neural networks, experiments are conducted towards a certain wind farm in China. The results show that the proposed model has a significant improvement in both prediction interval width and PIRD under the given prediction interval coverage probability (PICP). [ABSTRACT FROM AUTHOR]

The popularity of deep learning in time series prediction has significantly increased compared to the past. In this article, we utilize deep learning methods, which encompass long short term memory (LSTM) networks, simple recurrent neural network (SimpleRNN) networks, and gated recurrent units (GRU) networks. This research introduces a hybrid foundational model for forecasting future closing prices of EUR_USD in financial time series and Bitcoin, combining SimpleRNN with LSTM, referred to as SimpleRNNLSTM. To improve the precisions of our hybrid model, we incorporate twenty-one technical indicators into the training data. Then, we compute four measures to evaluate the performance of various prediction models. When predicting currency pairs EUR_USD and Bitcoin, our hybrid foundational model outperforms SimpleRNN, LSTM, and GRU models. [ABSTRACT FROM AUTHOR]

The hydromatereological processes are examined through analysis of temporal rainfall variability. India is an agricultural land and its economy is mainly dependent on timely rains to produce good harvest. The amount of rainfall varies with regional and temporal variation in distribution. The present research has been conducted to predict the temporal variations in rainfall in Udupi district, Karnataka, India using support vector regression (SVR) model and to validate the findings using actual rainfall records. The data has been collected from the statistical department, Udupi district, Government of Karnataka, India. The prediction accuracy of SVR based rainfall prediction model depends on tuning of algorithmic-based parameters. The parameter optimization is performed using grid search to select the optimal values of hyperparameters. The analysis was performed for the year 2018 based on the training dataset from 2000-2017. It is observed that there is a decreasing trend in total annual rainfall in 2018 and it is concluded that the average yearly rainfall has declined during the years 2018 and 2019. The rainfall predicted results were validated with actual records. The SVR based rainfall prediction model will predicts the rainfall accurately for application in agricultural sector. [ABSTRACT FROM AUTHOR]

Suicidal behaviors are relatively common among children, yet literature concerning the associated risks remains limited. Using a cumulative risk model, this study examined the effects of disability status, suspensions/expulsions, school disconnectedness, and negative peer relationships on suicidal behaviors. Data used were from The Future of Families and Child Wellbeing Study, which yielded an analytic sample (N = 2,463) that was majority male (52%), was Black and Hispanic (81%), and had an average age of 9.3 years. Using multivariate logistic regression with subgroup analysis, negative peer relationships and being male were associated with suicidal ideation in children without a history of suspension/expulsion. On the contrary, children with a history of suspension/expulsion and a disability status had three times higher odds of suicidal ideation in comparison to children with no disability status. These findings have important research and practical implications, which are discussed. [ABSTRACT FROM AUTHOR]

Statistical inference for high-dimensional parameters (HDPs) can leverage their intrinsic correlations, as spatially or temporally close parameters tend to have similar values. This is why nonlinear mixed-effects models (NMMs) are commonly used for HDPs. Conversely, in many practical applications, the random effects (REs) in NMMs are correlated HDPs that should remain constant during repeated sampling for frequentist inference. In both scenarios, the inference should be conditional on REs, instead of marginal inference by integrating out REs. We summarize recent theory of conditional inference for NMM, and then propose a bias-corrected RE predictor and confidence interval (CI). We also extend this methodology to accommodate the case where some REs are not associated with data. Simulation studies indicate our new approach leads to substantial improvement in the conditional coverage rate of RE CIs, including CIs for smooth functions in generalized additive models, compared to the existing method based on marginal inference. [ABSTRACT FROM AUTHOR]

This study proposes a novel multi-scale numerical method for thermal-mechanical analysis of mini-channel heat exchangers (MCHEs) under internal fluid pressure and temperature loads. The method comprises a macro-scale model for global analysis and a meso-scale model for detailed submodel analysis, specifically focusing on the internal fluid pressure effects within the MCHEs. The macroscopic model divides the MCHE into cover plate and homogenized regions subjected to pressure and temperature loads. To incorporate internal pressures into the homogenized MCHE model, mathematical equations are formulated to convert internal fluid pressures into equivalent strain loads. Additionally, a novel equivalent thermal expansion method is introduced, integrating internal fluid pressure loads by prescribing equivalent thermal expansion coefficients alongside spatially-varying nodal temperature fields within the MCHE. The meso-scale models with detailed channel patterns are assigned to specific portions of the homogenized region. The integration of the mesoscale model into the macroscopic framework is achieved through the application of the submodel method. Comparisons between the equivalent and actual MCHE models show that the proposed equivalent method can provide accurate predictions for thermal-mechanical deformations and stresses, and significantly reduce the computational expenses. [ABSTRACT FROM AUTHOR]

Accessible Summary: The Relevance to Mental Health Nursing: This research paper explores the intricate relationship between metacognitive dysfunctional beliefs, obsessive‐compulsive symptoms, and psychosocial performance in patients diagnosed with schizophrenia. Understanding these dynamics can help mental health nurses identify and address each patient's needs more effectively. It can guide them in devising personalized care plans that not only manage the symptoms but also improve the underlying mechanism that exacerbates the psychotic symptoms and social functioning and the overall quality of life. Moreover, the findings of this research can contribute to developing training programs for mental health nurses, equipping them with the necessary skills and knowledge to provide optimal care. What the paper adds to existing knowledge?•This study provides empirical evidence of the significant positive correlation between OCS and metacognitive dimensions in individuals with schizophrenia.•It highlights the role of certain demographic factors, such as younger age and single marital status, in increasing the likelihood of elevated OCS.•It underscores the inverse relationship between higher metacognitive dysfunctional beliefs and lower levels of psychosocial functioning.•It identifies age and metacognitive scores as crucial predictors of psychosocial functioning across various domains.What are the implications for practice?•The findings suggest that therapeutic nursing interventions for individuals diagnosed with schizophrenia should address metacognitive dysfunctional beliefs to improve overall functioning and well‐being.•Clinicians, including psychiatrists and psychiatric nurses, should consider the patient's age, marital status, and metacognitive scores when assessing the risk of elevated OCS and devising treatment plans.•The study emphasizes the need for comprehensive psychiatric nursing assessment, including metacognitive dysfunction and OCS evaluation.What are the implications for future research?•Future research could explore the causal relationships between metacognitive dysfunctional beliefs, OCS, and psychosocial functioning in schizophrenia.•Longitudinal studies could provide insights into the progression of these relationships over time and the impact of therapeutic interventions.•Further research could also investigate the effectiveness of specific therapeutic strategies such as Metacognitive Therapy (MCT), Schema Therapy (ST), Cognitive Enhancement Therapy (CET), and Cognitive Behaviour Therapy (CBT) to address this population's metacognitive dysfunctional beliefs. Background: Schizophrenia is a chronic mental health disorder that significantly impacts an individual's cognitive, emotional and social functioning. Recent research has highlighted the role of metacognitive beliefs and obsessive‐compulsive symptoms (OCS) in the psychosocial performance of individuals diagnosed with schizophrenia. Understanding these relationships could provide valuable insights for developing more effective nursing interventions. This study aimed to investigate the relationship between metacognitive beliefs, OCS and psychosocial performance among individuals diagnosed with schizophrenia. Design: A cross‐sectional survey was conducted involving 174 purposively selected participants diagnosed with schizophrenia. Tools: The Meta‐Cognitions Questionnaire‐30, Young Adult Self‐Report Scale for OCS and Specific Level of Functioning Scale were used to gather the necessary data. Results: The study found a significant positive correlation between OCS and metacognitive dimensions. Age was a significant predictor with an Oddis Ratio of 2.471. The metacognitive dysfunction was a highly significant predictor in univariate and multivariate analyses, with Oddis Ratios of 1.087 and 1.106, respectively. The study also discovered that higher levels of metacognitive dysfunctional beliefs were associated with lower levels of psychosocial functioning. Age and the metacognitive dysfunction score were significant predictors of psychosocial functioning scores, accounting for 26.8% of the variance in these scores. Conclusion: The study reveals a compelling inverse relationship between higher metacognitive dysfunctional beliefs and lower levels of psychosocial functioning in individuals diagnosed with schizophrenia. It also identifies certain demographic factors, such as younger age, as significant contributors to elevated OCS. Importantly, metacognitive dysfunction emerged as a critical predictor of psychosocial functioning across various domains. These findings underscore the potential of incorporating metacognitive‐focused interventions in the treatment plans for schizophrenia patients. By addressing these cognitive patterns, healthcare professionals can enhance overall functioning and well‐being in individuals diagnosed with schizophrenia. [ABSTRACT FROM AUTHOR]

Cultural and religious norms, as well as trauma-related cognitions and recovery actions, are known to impact the well-being of survivors of intimate partner violence (IPV). Although acknowledged as a key component, there is scant research on the recovery trajectories of women who have experienced IPV, in particular on survivors from collectivistic societies such as the Jewish Ultra-Orthodox (JUO) community in Israel. A mediation model examined the recovery process of 261 Israeli JUO survivors. In particular, it tested whether the normalization of violence and women's endorsement of Jewish religious norms that justify violence would be directly and negatively associated with women's well-being and positively associated with psychopathology. Additionally, it examined whether women's normalization of violence and support of religious norms would positively predict women's negative trauma-related cognitions. In turn, these cognitions were expected to negatively predict women's engagement in recovery actions, help-seeking behaviors, and faith-based responses but positively predict disengagement responses. The model further posited that women's engagement in steps toward recovery, help-seeking behaviors, and faith-based responses would positively predict women's well-being and negatively predict psychopathology. In contrast, women's disengagement responses would negatively predict women's well-being and positively predict their psychopathology. Bootstrap results indicated that supporting religious norms positively predicted women's trauma-related cognitions, which then negatively predicted women's recovery actions, help-seeking behaviors, and faith-based responses but positively predicted women's disengagement responses. Women's recovery actions and faith-based responses positively predicted women's well-being, while disengagement responses positively predicted women's psychopathology. Contrary to expectations, help-seeking behaviors positively predicted psychopathology. [ABSTRACT FROM AUTHOR]

In this study, the problem of severe wheel flange wear and tread rolling contact fatigue (RCF) of metro wheels with two wheel profiles, i.e. the LM-30 profile and the DIN5573-30 profile, is investigated. The contact characteristics of the two profiles are compared to analyse the causes of wheel flange wear and RCF. Indices combining the density distribution of contact points are proposed to evaluate the wheel flange wear and RCF. The optimisation region of the wheel profile is defined by eight arc parameters, which is derived from the tangency of the arcs. A back-propagation neural network is constructed as a surrogate model for the design variables and the objective function. Four intelligent optimisation algorithms are compared and used to solve the optimisation problem. The vehicle—track dynamic model and the wheel wear and RCF prediction model are used to verify the performance of the optimised profile. The results show that the critical speed of the optimised profile is between that of the LM profile and that of the DIN5573 profile, and the curving performance of the optimised profile is significantly improved. At the same time, the optimised wheel profile can reduce wheel flange wear and tread RCF. [ABSTRACT FROM AUTHOR]

Although resting metabolic rate (RMR) is crucial for understanding athletes' energy requirements, limited information is available on the RMR of Paralympic athletes. The aim of this study was to determine RMR and its predictors in a diverse cohort of Paralympic athletes and evaluate the agreement between measured and predicted RMR from both newly developed and pre-existing equations. This cross-sectional study, conducted between September 2020 and September 2022 in the Netherlands and Norway, assessed RMR in Paralympic athletes by means of ventilated hood indirect calorimetry and body composition by means of dual-energy x-ray absorptiometry. Sixty-seven Paralympic athletes (male: n = 37; female: n = 30) competing in various sports, with a spinal cord disorder (n = 22), neurologic condition (n = 8), limb deficiency (n = 18), visual or hearing impairment (n = 7), or other disability (n = 12) participated. RMR, fat-free mass (FFM), body mass, and triiodothyronine (T3) concentrations were assessed. Multiple regression analyses were conducted with height, FFM, body mass, sex, T3 concentration, and disabilities as potential predictors of RMR. Differences between measured and predicted RMRs were analyzed for individual accuracy, root mean square error, and intraclass correlation. Mean ± SD RMR was 1386 ± 258 kcal/d for females and 1686 ± 302 kcal/d for males. Regression analysis identified FFM, T3 concentrations, and the presence of a spinal cord disorder, as the main predictors of RMR (adjusted R 2 = 0.71; F = 50.3; P <.001). The novel prediction equations based on these data, as well as pre-existing equations of Chun and colleagues and Nightingale and Gorgey performed well on accuracy (>60% of participants within 10% of measured RMR), had good reliability (intraclass correlation >0.78), and low root mean square error (≤141 kcal). FFM, total T3 concentrations, and presence of spinal cord disorder are the main predictors of RMR in Paralympic athletes. Both the current study's prediction equations and those from Chun and colleagues and Nightingale and Gorgey align well with measured RMR, offering accurate prediction equations for the RMR of Paralympic athletes. [ABSTRACT FROM AUTHOR]

Seismic and aseismic slip events result from episodic slips on faults and are often chaotic due to stress heterogeneity. Their predictability in nature is a widely open question. In this study, we forecast extreme events in a numerical model. The model, which consists of a single fault governed by rate-and-state friction, produces realistic sequences of slow events with a wide range of magnitudes and interevent times. The complex dynamics of this system arise from partial ruptures. As the system self-organizes, the state of the system is confined to a chaotic attractor of a relatively small dimension. We identify the instability regions within this attractor where large events initiate. These regions correspond to the particular stress distributions that are favourable for near complete ruptures of the fault. We show that large events can be forecasted in time and space based on the determination of these instability regions in a low-dimensional space and the knowledge of the current slip rate on the fault. [ABSTRACT FROM AUTHOR]

The triad of Environmental, Social, and Governance (ESG) determinants in the contemporary global business environment has become integral to corporate longevity and ethical performance. With China's significant economic influence and unique socio-cultural tapestry, understanding its ESG dynamics is crucial for domestic and international stakeholders. This comprehensive research introduces an innovative multidimensional ESG scoring paradigm designed for the Chinese market. Built on a robust data foundation from Bloomberg, this model encompasses an 11-year trajectory of 1,496 companies, making it one of the most exhaustive studies in this domain. Integrating advanced mathematical and machine learning models and techniques augments the model's predictive accuracy and ensures its resilience to rigorous validation processes. By delving deep into the complexities of China's ESG landscape, this study elucidates the intricate interdependencies of these factors, thereby offering a roadmap for sustainable business practices in China. [ABSTRACT FROM AUTHOR]

Background: Early risk stratification can facilitate timely interventions for adverse pregnancy outcomes, including preeclampsia (PE), small‐for‐gestational‐age neonates (SGA), spontaneous preterm birth (sPTB) and gestational diabetes mellitus (GDM). Objectives: To perform a systematic review and meta‐analysis of first‐trimester prediction models for adverse pregnancy outcomes. Search Strategy: The PubMed database was searched until 6 June 2024. Selection Criteria: First‐trimester prediction models based on maternal characteristics were included. Articles reporting on prediction models that comprised biochemical or ultrasound markers were excluded. Data Collection and Analysis: Two authors identified articles, extracted data and assessed risk of bias and applicability using PROBAST. Main results: A total of 77 articles were included, comprising 30 developed models for PE, 15 for SGA, 11 for sPTB and 35 for GDM. Discriminatory performance in terms of median area under the curve (AUC) of these models was 0.75 [IQR 0.69–0.78] for PE models, 0.62 [0.60–0.71] for SGA models of nulliparous women, 0.74 [0.72–0.74] for SGA models of multiparous women, 0.65 [0.61–0.67] for sPTB models of nulliparous women, 0.71 [0.68–0.74] for sPTB models of multiparous women and 0.71 [0.67–0.76] for GDM models. Internal validation was performed in 40/91 (43.9%) of the models. Model calibration was reported in 21/91 (23.1%) models. External validation was performed a total of 96 times in 45/91 (49.5%) of the models. High risk of bias was observed in 94.5% of the developed models and in 58.3% of the external validations. Conclusions: Multiple first‐trimester prediction models are available, but almost all suffer from high risk of bias, and internal and external validations were often not performed. Hence, methodological quality improvement and assessment of the clinical utility are needed. [ABSTRACT FROM AUTHOR]

Background: Globally, caesarean births (CB), including emergency caesareans births (EmCB), are rising. It is estimated that nearly a third of all births will be CB by 2030. Objectives: Identify and summarise the results from studies developing and validating prognostic multivariable models predicting the risk of EmCBs. Ultimately understanding the accuracy of their development, and whether they are operationalised for use in routine clinical practice. Search Strategy: Studies were identified using databases: MEDLINE, CINAHL, Cochrane Central and Scopus with a search strategy tailored to models predicting EmCBs. Selection Criteria: Prospective studies developing and validating clinical prediction models, with two or more covariates, to predict risk of EmCB. Data Collection and Analysis: Data were extracted onto a proforma using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results: In total, 8083 studies resulted in 56 unique prediction modelling studies and seven validating studies, with a total of 121 different predictors. Frequently occurring predictors included maternal height, maternal age, parity, BMI and gestational age. PROBAST highlighted 33 studies with low overall bias, and these all internally validated their model. Thirteen studies externally validated; only eight of these were graded an overall low risk of bias. Six models offered applications that could be readily used, but only one provided enough time to offer a planned caesarean birth (pCB). These well‐refined models have not been recalibrated since development. Only one model, developed in a relatively low‐risk population, with data collected a decade ago, remains useful at 36 weeks for arranging a pCB. Conclusion: To improve personalised clinical conversations, there is a pressing need for a model that accurately predicts the timely risk of an EmCB for women across diverse clinical backgrounds. Trial Registration: PROSPERO registration number: CRD42023384439. Linked article: This article is commented on by Eldamanhoury pp. 241–242 in this issue. To view this article visit https://doi.org/10.1111/1471‐0528.17992. [ABSTRACT FROM AUTHOR]

This paper focuses on the estimation of parameters in linear models with fixed regressors and exchangeable errors, especially, these errors are assumed to be a subset of an infinite exchangeable sequence. We propose a modified M-estimation to estimate the slope parameter, which outperforms the traditional M-estimation when the random errors are exchangeable. The modified M-estimation utilizes a design point centralization technique, leading to a more suitable estimator with improved theoretical properties. To establish the asymptotic properties of the modified M-estimator, we also develop a weighted central limit theorem for exchangeable sequences, which is of independent interest. Regarding the intercept, we prove that there is no consistent estimator for the intercept when the exchangeable errors have non-zero covariance, and we propose an unbiased estimator for the intercept as a viable compromise. Based on our theoretical findings, we develop a simulation-based approach to approximate the confidence set of the slope, and we establish a prediction model specifically designed for linear models with exchangeable errors. Additionally, we illustrate the finite sample performance of the proposed method through various numerical simulations and a real data application. [ABSTRACT FROM AUTHOR]

We tackle the problem of building a prediction interval in heteroscedastic Gaussian regression. We focus on prediction intervals with constrained expected length in order to guarantee interpretability of the output. In this framework, we derive a closed-form expression of the optimal prediction interval that allows for the development of a data-driven prediction interval based on plug-in. The construction of the proposed algorithm is based on two samples, one labelled and another unlabelled. Under mild conditions, we show that our procedure is asymptotically as good as the optimal prediction interval both in terms of expected length and error rate. In particular, the control of the expected length is distribution-free. We also derive rates of convergence under smoothness and the Tsybakov noise conditions. We conduct a numerical analysis that exhibits the good performance of our method. It also indicates that even with a few amount of unlabelled data, our method is very effective in enforcing the length constraint. [ABSTRACT FROM AUTHOR]

This study evaluates techniques to identify high-quality teachers. Since tenure restricts dismissals of experienced teachers, schools must predict productivity and dismiss those expected to perform ineffectively prior to tenure receipt. Many states rely on evaluation scores to guide these personnel decisions without considering other dimensions of teacher performance. I use predictive models to rank teachers based on expected value-added and summative ratings. I then simulate revised personnel decisions and test for changes in average retained teacher performance. In this exercise, I adjust two factors that impact the quality of the predictions: the number of predictors and the length of the pretenure period. Both factors impact the precision of the predictions, though extended pretenure periods also negatively impact selection into teaching. I estimate optimal weights on each performance measure to maximize measures of teacher quality using a range of utility parameters. These improvements are a product of using additional information (value-added) rather than advanced algorithms, as OLS regressions and advanced machine learning techniques produce similar gains. In comparison, prediction models that extend the pretenure period beyond one year do not provide enough additional information to significantly improve average retained teacher performance unless dismissal rates increase dramatically. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Targeted reproductive management (TRM) aims to improve the fertility efficiency of the dairy herd by applying group-level management strategies based on expected reproductive performance. Key to the utility of TRM is the accuracy with which an animal's reproductive performance can be predicted. Automatic milking systems (AMS) allow for the collection of data relating to milk quantity, quality, and robot visit behavior throughout the transition period. In addition to this, auxiliary data sources, such as rumination and activity monitors, as well as historical cow-level data, are often readily available. The utility of this data for the prediction of fertility has not been previously explored. The first objective of this study was to assess the accuracy with which the likelihood of expression of estrus between 22 and 65 DIM and conception to first insemination between 22 and 80 DIM could be predicted using data collected by AMS from 1 to 21 DIM. Our second objective was to assess the change in model performance following the addition of 2 auxiliary data sources. Using data derived solely from the AMS (RBT dataset) a binary random forest classification model was constructed for both outcomes of interest. The performance of these models was compared with models constructed using AMS data in conjunction with 2 auxiliary sources (RBT+ dataset). Expression of estrus was classified with an area under the receiver operator curve (AUC-ROC) of 0.6 and 0.65, conception to first insemination with an AUC-ROC of 0.56 and 0.62 for the RBT and RBT+ datasets, respectively. No statistically significant improvement in classification accuracy was achieved by the addition of auxiliary data sources. This is the first study to report the utility of data collected by AMS for the prediction of reproductive performance. Though the performance described is comparable with previously reported models, their utility for the implementation of TRM is limited by poor classification accuracy within key subgroups. Of note within this study is the failure of the addition of auxiliary data sources to increase the accuracy of prediction over models built using AMS data alone. We discuss the advantages and limitations the integration of additional data sources imposes on model training and deployment and suggest alternative methods to improve performance while preserving model parsimony. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to build a model‐based predictive approach to evaluate the gastrointestinal side effects following an initial metformin medication. Materials and Methods: The model was developed from data from four randomised clinical cohorts. A prediction model was established using integrated or simplified indicators. Ten machine learning models were used for the construction of predictive models. The Shapley values were used to report the features' contribution. Results: Four randomised clinical trial cohorts, including 1736 patients with type 2 diabetes, were first included in the analysis. Seventy percent of participants (1216) were allocated to the training set, 15% (260) were assigned to the internal validation set and 15% (260) were assigned to the test set. The Extra Tree model had the highest area under curve (AUC) (0.87) in the validation and test set. The top five crucial indicators were blood urea nitrogen (BUN), sex, triglyceride (TG), high‐density lipoprotein‐cholesterol (HDL‐C) and total cholesterol (TC), and these five indicators were selected for constructing a simplified predictive model (AUC = 0.76). An online web‐based tool was established based on the predictive model with integrated 17 features and top five indicators. Conclusions: To predict gastrointestinal side effects in diabetic patients for initial use of metformin, a few easily obtained features are needed to establish the model. The model can be applied to the Chinese population in clinical practice. [ABSTRACT FROM AUTHOR]

Psychologists are emphasizing the importance of predictive conclusions. Machine learning methods, such as supervised neural networks, have been used in psychological studies as they naturally fit prediction tasks. However, we are concerned about whether neural networks fitted with random datasets (i.e., datasets where there is no relationship between ordinal independent variables and continuous or binary-dependent variables) can provide an acceptable level of predictive performance from a psychologist's perspective. Through a Monte Carlo simulation study, we found that this kind of erroneous conclusion is not likely to be drawn as long as the sample size is larger than 50 with continuous-dependent variables. However, when the dependent variable is binary, the minimum sample size is 500 when the criteria are balanced accuracy ≥.6 or balanced accuracy ≥.65, and the minimum sample size is 200 when the criterion is balanced accuracy ≥.7 for a decision error less than.05. In the case where area under the curve (AUC) is used as a metric, a sample size of 100, 200, and 500 is necessary when the minimum acceptable performance level is set at AUC ≥.7, AUC ≥.65, and AUC ≥.6, respectively. The results found by this study can be used for sample size planning for psychologists who wish to apply neural networks for a qualitatively reliable conclusion. Further directions and limitations of the study are also discussed. [ABSTRACT FROM AUTHOR]

Stress is linked to health problems, increasing the need for immediate monitoring. Traditional methods like electrocardiograms or contact photoplethysmography require device attachment, causing discomfort, and ultra-short-term stress measurement research remains inadequate. This paper proposes a method for ultra-short-term stress monitoring using remote photoplethysmography (rPPG). Previous predictions of ultra-short-term stress have typically used pulse rate variability (PRV) features derived from time-segmented heart rate data. However, PRV varies at the same stress levels depending on heart rates, necessitating a new method to account for these differences. This study addressed this by segmenting rPPG data based on normal-to-normal intervals (NNIs), converted from peak-to-peak intervals, to predict ultra-short-term stress indices. We used NNI counts corresponding to average durations of 10, 20, and 30 s (13, 26, and 39 NNIs) to extract PRV features, predicting the Baevsky stress index through regressors. The Extra Trees Regressor achieved R2 scores of 0.6699 for 13 NNIs, 0.8751 for 26 NNIs, and 0.9358 for 39 NNIs, surpassing the time-segmented approach, which yielded 0.4162, 0.6528, and 0.7943 for 10, 20, and 30-s intervals, respectively. These findings demonstrate that using NNI counts for ultra-short-term stress prediction improves accuracy by accounting for individual bio-signal variations. [ABSTRACT FROM AUTHOR]

Detecting out-of-distribution (OOD) inputs plays a crucial role in guaranteeing the reliability of deep neural networks (DNNs) when deployed in real-world scenarios. However, DNNs typically exhibit overconfidence in OOD samples, which is attributed to the similarity in patterns between OOD and in-distribution (ID) samples. To mitigate this overconfidence, advanced approaches suggest the incorporation of auxiliary OOD samples during model training, where the outliers are assigned with an equal likelihood of belonging to any category. However, identifying outliers that share patterns with ID samples poses a significant challenge. To address the challenge, we propose a novel method, Virtual Outlier Smoothing (VOSo), which constructs auxiliary outliers using ID samples, thereby eliminating the need to search for OOD samples. Specifically, VOSo creates these virtual outliers by perturbing the semantic regions of ID samples and infusing patterns from other ID samples. For instance, a virtual outlier might consist of a cat's face with a dog's nose, where the cat's face serves as the semantic feature for model prediction. Meanwhile, VOSo adjusts the labels of virtual OOD samples based on the extent of semantic region perturbation, aligning with the notion that virtual outliers may contain ID patterns. Extensive experiments are conducted on diverse OOD detection benchmarks, demonstrating the effectiveness of the proposed VOSo. Our code will be available at https://github.com/junz-debug/VOSo. [ABSTRACT FROM AUTHOR]

Grouting technology can be selected to strengthen broken rock mass in order to improve its antiseepage and bearing capacity. Based on the working principle of infiltration grouting and grouting pipe technology, experimental and numerical studies are conducted by using high-water grouting material. In terms of numerical analysis and origin software, the polynomial prediction model is provided to establish the relationship between the grouting effect and influencing factors. According to the research findings, the lower the slurry level, the larger the diffusion range. The greatest influencing factors for diffusion radius, grouting volume, and strength are porosity, level height, and water–cement ratio, respectively. Through numerical simulation, corresponding amplification coefficient of diffusion length ranging from 1.21 to 1.33 is obtained, and subsequently, the maximum diffusion length is put forward. Finally, the slurry diffusion theoretical model is improved to explain the slurry diffusion characteristic. [ABSTRACT FROM AUTHOR]

Objective: The aim of the study is to review the literature regarding the current state and clinical applicability of machine learning models in prognosticating the outcomes of patients with mild traumatic brain injury in the early clinical presentation. Design: Databases were searched for studies including machine learning and mild traumatic brain injury from inception to March 10, 2023. Included studies had a primary outcome of predicting post-mild traumatic brain injury prognosis or sequelae. The Prediction model study Risk of Bias for Predictive Models assessment tool was used for assessing the risk of bias and applicability of included studies. Results: Out of 1235 articles, 10 met the inclusion criteria, including data from 127,929 patients. The most frequently used modeling techniques were support vector machine and artificial neural network and area under the curve ranged from 0.66 to 0.889. Despite promise, several limitations to studies exist such as low sample sizes, database restrictions, inconsistencies in patient presentation definitions, and lack of comparison to traditional clinical judgment or tools. Conclusions: Machine learning models show potential in early stage mild traumatic brain injury prognostication, but to achieve widespread adoption, future clinical studies prognosticating mild traumatic brain injury using machine learning need to reduce bias, provide clarity and consistency in defining patient populations targeted and validate against established benchmarks. [ABSTRACT FROM AUTHOR]

The liquid bridge rupture has attracted much attention in various fields such as powder technology, micro gripping, and wet agglomeration. In present study, an artificial neural network (ANN) model was developed to predict the liquid bridge rupture between two plates, focusing on the rupture distance and the transfer ratio. The initial weights and biases of the ANN model were optimized by the grey wolf optimization algorithm (GWO). The GWO-ANN model prediction is compared with the BP-ANN model prediction. Based on the testing dataset, the mean square error (MSE) and correlation coefficient (R2) of the rupture distance for the optimized GWO-ANN model were calculated as 4.65 × 10− 4 and 0.9586, and that of the transfer ratio was 2.15 × 10− 4 and 0.975, respectively. The effectiveness of the constructed GWO-ANN model for the liquid bridge rupture prediction was verified by experimental investigations. The effect of input parameters including contact angles, stretching speed, liquid volume and liquid viscosity on the rupture was discussed in detail. [ABSTRACT FROM AUTHOR]

Different project management processes have been used in software engineering to support managers in keeping project costs manageable. One of the essential processes in software engineering is to accurately and reliably estimate the required effort and cost to complete the projects. The domain of software cost estimation has witnessed a prominent surge in research activities in recent years and being an evolving process, it keeps opening new avenues, each with advantages and disadvantages, making it important to work out better options. This research aims to identify the factors that influence the software effort estimation using the constructive cost model (COCOMO), and artificial neural networks (ANN) model by introducing a novel cost estimation approach, COCOMO-ANN (CANN), utilizing a partially connected neural network (PCNN) with inputs derived from calibrated values of the COCOMO model. A publicly available dataset (COCOMONASA 2), various combinations of activation functions, and layer densities have been systematically explored, employing multiple evaluation metrics such as MAE, MRE, and MMRE. In the PCNN model, the ReLU activation function and a 1000-dense layer have demonstrated better performance. While layer density generally correlates with better outcomes, this correlation is not universally applicable for all activation functions and outcomes vary across different combinations. The use of the relationships between 26 key parameters of COCOMO in PCNN produced better results than FCNN by 0.59%, achieving an MRE of 6.55 and an MMRE of 7.04. The results indicated that the CANN model (COCOMO & ANN) presented better results than existing models. [ABSTRACT FROM AUTHOR]

Concerns about energy security and environmental sustainability have increased the preference for renewable energy sources. Biodiesel, a renewable alternative to conventional diesel, has gained interest as a potential solution. However, biodiesel's stability during storage and susceptibility to oxidation are significant challenges. Antioxidants are essential to enhance biodiesel's oxidation stability, ensuring fuel quality and sustainability. While various studies have examined the effects of different antioxidant concentrations on biodiesel oxidation stability on trial and error bases, a consensus on the most effective dosage range has yet to be established. This review emphasizes the importance of predictive models for estimating antioxidant amounts in biodiesel. Using mathematical formulations, statistical analyses, and computational simulations, predictive models offer efficient and accurate approaches to understanding the complex relationships between antioxidants, biodiesel properties, and oxidation stability. The review examines the link between antioxidants and biodiesel stability, discusses predictive models' role in determining optimal antioxidant dosages, and identifies factors influencing these decisions. It also highlights the limitations of predictive models and suggests future research directions. The conclusion is that systematically integrating data-driven strategies through predictive models facilitates the cost-effective optimization of antioxidant dosages, advancing high-quality and sustainable biodiesel. [ABSTRACT FROM AUTHOR]

Channel prediction is an effective technology to support adaptive transmission in wireless communication. To solve the difficulty of accurately predicting channel state information (CSI) due to fast time-varying characteristics, a next-generation reservoir calculation network (NGRCN) is combined with CSI, and a channel prediction method for OFDM wireless communication systems based on an adaptive reinforced reservoir learning network (adaptive RRLN) is proposed. An adaptive elastic network (adaptive EN) is used to estimate the output weight matrix to avoid ill-conditioned solutions. Therefore, the adaptive RRLN has echo and oracle properties. In addition, an adaptive singular spectral analysis (adaptive SSA) method is proposed to improve the local predictability of CSI by decomposing and reconstructing CSI to improve the fitting accuracy of the channel prediction model. In the simulation section, the OFDM wireless communication systems are constructed using IEEE802.11ah and the one-step prediction, the multi-step prediction, and the robustness test are implemented and analyzed. The simulation results show that the prediction accuracy of the adaptive RRLN can reach 3 × 10−5 and 8.36 × 10−6, which offers satisfactory prediction performance and robustness. [ABSTRACT FROM AUTHOR]

Small Autonomous Surface/Underwater Vehicles (S-ASUVs) are gradually attracting attention from related fields due to their small size, low energy consumption, and flexible motion. Existing dynamic positioning (DP) control approaches suffer from chronic restrictions that hinder adaptability to varying practical conditions, rendering performance poor. A new three-dimensional (3D) dynamic positioning control method for S-ASUVs is proposed to tackle this issue. Firstly, a dynamic model for the DP control problem considering thrust allocation was established deriving from dynamic models of S-ASUVs. A novel Lyapunov-based model predictive control (LBMPC) method was then designed. Unlike the conventional Lyapunov-based model predictive control (LMPC), this study used multi-variable proportional–integral–derivative (PID) control as the secondary control law, improving the accuracy and rapidity of the control performance significantly. Both the feasibility and stability were rigorously proved. A series of digital experiments using the S-ASUV model under diverse conditions demonstrate the proposed method's advantages over existing controllers, affirming satisfactory performances for 3D dynamic positioning in complex environments. [ABSTRACT FROM AUTHOR]

Model Predictive Control (MPC) is widely adopted for power electronics converters due to its ability to optimize system performance under dynamic constraints. However, its FPGA implementation remains challenging due to the complexity of Hardware Description Language (HDL) programming. This paper addresses this challenge by introducing a straightforward methodology that simplifies FPGA implementation using MATLAB R2022b Simulink HDL Coder. It is shown that HDL Coder yields favorable synthesis outcomes, both in terms of area and time, compared to hand-coded HDL. Notably, the proposed method achieves a significantly reduced sampling step for the MPC algorithm—down to 32 ns—marking a substantial improvement over state-of-the-art implementations. The Integrated Logic Analyzer (ILA) IP available in the Vivado tool is used during the HIL testing phase to facilitate the real-time observation and analysis required for debugging and confirming the FPGA-implemented controller performance. Additionally, this paper discusses the advantages of utilizing HDL Coder for simplifying the FPGA programming process in power electronics applications and addresses the design challenges encountered using this methodology. [ABSTRACT FROM AUTHOR]

TSSC is one of the most important factors affecting loquat flavor, consumer satisfaction, and market competitiveness. To improve the ability to assess the TSSC of loquats, a method leveraging near-infrared spectroscopy and explainable artificial intelligence was proposed. The 900–1700 nm near-infrared spectroscopy of 156 fresh loquat samples was collected and preprocessed using seven preprocessing techniques, significant wavelength extraction utilizing six feature methods to eliminate data redundancy. Linear and nonlinear models were employed to establish the relationship between the feature spectrum and TSSC, with a focus on comparing and analyzing prediction performance. The findings reveal that the combination of 26 spectral bands selected by SPA and the PLSR model yielded the best prediction outcomes (R = 0.9031, RMSEP = 0.6171, RPD = 2.2803). The contribution of key wavelengths can be obtained by SHAP, which explains differences in model prediction accuracy and provides a reference for the application of loquat TSSC determination. [ABSTRACT FROM AUTHOR]

The current research on path tracking primarily focuses on improving control algorithms, such as adaptive and predictive models, to enhance tracking accuracy and stability. To address the issue of low tracking accuracy caused by variable-curvature paths in automatic navigation within agricultural environments, this study proposes a fuzzy control-based path-tracking method. Firstly, a pure-pursuit model and a kinematic model were established based on a Four-Wheel Independent Steering and Four-Wheel Independent Driving (4WIS-4WID) structure. Secondly, a fuzzy controller with three inputs and one output was designed, using the lateral deviation, de; heading deviation, θe; and bending degree, c, of the look-ahead path as the input variables. Through multiple simulations and adjustments, 75 control rules were developed. The look-ahead distance, Ld, was obtained through fuzzification, fuzzy inference, and defuzzification processes. Next, a speed-control function was constructed based on the agricultural machinery's pose deviations and the bending degree of the look-ahead path to achieve variable speed control. Finally, field tests were conducted to verify the effectiveness of the proposed path-tracking method. The tracking experiment results for the two types of paths indicate that under the variable-speed dynamic look-ahead distance strategy, the average lateral deviations for the variable-curvature paths were 1.8 cm and 3.3 cm while the maximum lateral deviations were 10.1 cm and 10.5 cm, respectively. Compared to the constant-speed fixed look-ahead pure-pursuit model, the average lateral deviation was reduced by 56.1% and the maximum lateral deviation by 50.4% on the U-shaped path. On the S-shaped path, the average lateral deviation was reduced by 56.0% and the maximum lateral deviation by 58.9%. The proposed method effectively improves the path-tracking accuracy of agricultural machinery on variable-curvature paths, meeting the production requirements for curved operations in agricultural environments. [ABSTRACT FROM AUTHOR]

Due to the uncertainty of meteorological factors and the influence of human activities, the monthly runoff series often exhibit the characteristics of non-stationarity. The appropriate prediction model and the hyperparameters of the model are often difficult to determine, and this affects the model prediction performance. For obtaining the accurate runoff prediction results, a novel prediction model (KVMD-KTCN-LSTM-SA) is proposed. This hybrid model uses Kepler optimization algorithm (KOA)-optimized Variable Mode Decomposition (KVMD), KOA-optimized temporal convolutional network–long short-term memory (TCN-LSTM), and the self-attention (SA) mechanism. KVMD effectively reduces the difficulty of predicting the monthly runoff series, KOA helps to find the optimal hyperparameters of the model, TCN is combined with LSTM, and the SA mechanism effectively increases the performance of the model. Monthly runoff from three hydrological stations in the Hetian River basin and one hydrological station in the Huaihe River basin are predicted with the proposed model, and six models are selected for comparison. The KVMD-KTCN-LSTM-SA model effectively reduces runoff fluctuation and combines the advantages of multiple models and achieves satisfactory runoff prediction results. During the testing period, the proposed model achieves NSE of 0.978 and R2 of 0.982 at Wuluwati station, NSE of 0.975 and R2 of 0.986 at Tongguziluoke station, and NSE of 0.978 and R2 of 0.982 at Jiangjiaji station. The proposed hybrid model provides a new approach for monthly runoff prediction, which is capable of better managing and predicting mid-long-term runoff. [ABSTRACT FROM AUTHOR]

Steady-state and transient hydraulic characteristics of a novel three-plate vertical rotary gate were analysed through physical model experiments and numerical simulations. An experimental gate system was built to analyse the flow characteristics of the gate, and a steady-state flow prediction model was proposed. Steady-state numerical simulations of the gate were conducted to analyse flow field distribution characteristics. A transient numerical model of the gate was established to analyse the flow field distribution characteristics during opening and closing. The discharge coefficient evolution law under different speed conditions was revealed. Under various water levels, the steady-state discharge coefficient of the gate was similar. Within a 0–90° opening, the discharge coefficient grew exponentially. A steady-state flow prediction model for the gate revealed a prediction error of <7%. The discharge coefficient of the gate increased with decreasing opening speed; when the gate was closed, it exhibited asymmetric variation characteristics. The flow hysteresis effect was more evident at higher speeds. Plate 2 experienced the maximum flow force. In the transient state, the flow force acting on the plates exhibited a periodic fluctuation pattern, and the maximum flow force increased with the gate speed. A reference for the design and application of fast opening and closing gates is provided. [ABSTRACT FROM AUTHOR]

We study, both theoretically and experimentally, a communication game with and without seller competition and embed it in a psychological-game framework where players experience costs for lying, misleading others, and being disappointed. We derive the equilibrium predictions of this model, compare them to the setting without psychological payoffs, and test these predictions in a laboratory experiment, in which we induce both material and psychological payoffs. We find that the setting in which players have both material and psychological payoffs features more trade, trades goods of marginally better quality, and does so without welfare losses to either side of the market relative to the setting with material payoffs only. However, the introduction of competition counteracts this improvement and lowers welfare for both sides of the market. This happens due to a surge in dishonesty by sellers in the competitive setting and the buyers' inability to detect this deception. [ABSTRACT FROM AUTHOR]

Accurate prediction and comprehension of off-design turbine performance hold paramount importance in the development of highly efficient turbines capable of operating across a diverse range of conditions. This research concentrates on a highly loaded turbine cascade endwall flow and endeavors to scrutinize the impact of both incidence angle and inlet free stream turbulence intensity (FSTI). The investigation involves a meticulous analysis leading to substantial improvements in the existing endwall loss model (the Benner model correlated with the Moustapha incidence loss correction model), tailored for off-design scenarios. The findings underscore the pronounced influence of both incidence angle and FSTI on the dynamics of the endwall flow. Notably, alterations in the incidence angle have been identified as exerting a discernible impact on the flow structure, particularly when encountering large positive incidence angles. At an incidence angle of +20°, a distinctive vortex known as the Concentrated Shedding Vortex emerges as a pivotal factor in shaping the endwall flow structure, resulting in a substantial escalation in endwall losses. Furthermore, variations in FSTI predominantly affect the intensity of secondary vortices, thereby influencing endwall loss. Leveraging these discernments, an expressive formulation for endwall loss is derived by refining the higher-order terms of the incidence loss correction model and introducing a corrective term associated with FSTI. It has been validated that the proposed model controls the prediction error of endwall loss within a margin of ±0.02. [ABSTRACT FROM AUTHOR]

The objective of temporal link prediction is to forecast potential future connections in a network by analyzing its structural underpinnings and tracking its temporal dynamics. However, existing methods for temporal link prediction are overly reliant on the most recent snapshots of the network, thereby limiting their ability to uncover and utilize the fundamental evolutionary patterns for effective dynamical inference. As a result, the predictive prowess of these models tends to be heightened for proximate future scenarios, as opposed to those farther into the horizon. Furthermore, the majority of the current methodologies overlook the influence of intricate higher-order and overarching structural dynamics, which could potentially enhance predictive accuracy. To tackle these challenges, we introduce a structure-enhanced graph neural ordinary differential equation (SEGODE), a comprehensive framework that leverages neural ordinary differential equations integrated with attention mechanisms to facilitate dynamic inference. The framework enhances the ability to snatch higher-order and global structures. To substantiate the viability of our novel model, we embarked on a comprehensive set of experiments conducted on seven real datasets. The outcomes of these rigorous tests demonstrate that our SEGODE approach not just demonstrates commendable performance in the task of link prediction but additionally has good results even when data is sparse. [ABSTRACT FROM AUTHOR]

Air pollution mitigation is essential to ensure sustainable development, as it directly affects climate change, economic productivity, and social wellbeing. Despite the availability of numerous prediction techniques, machine learning (ML) remains the optimal solution for forecasting air pollution. Constructing a prediction model for a region with limited data poses a challenge. This study presents a novel technique that combines temporal fusion transformer (TFT) with transfer learning to create an inventive air quality index (AQI) prediction model, effectively utilizing temporal insights and prior knowledge. The TFT is an advanced deep neural architecture engineered to enhance time series forecasting through the fusion of sequence modelling and global temporal patterns. By fusing TFT with transfer learning, the research pioneers a fresh approach to AQI prediction for region with data scarcity issue, capitalizing on cross-domain knowledge transfer. Utilizing meteorological and pollutant data from the Cochin region, a hybrid AQI prediction model is constructed through TFT and transfer learning. Employing a preexisting TFT model trained on Trivandrum data, transfer learning technique is utilized to adapt the model for predicting AQI in the Cochin region. The study demonstrates that integrating TFT with transfer learning yields superior accuracy compared to an exclusive TFT-based approach. [ABSTRACT FROM AUTHOR]

Neural radiance field (NeRF) is a form of deep learning model that may be used to depict 3D scenes from a collection of photos. It has been demonstrated that NeRF can produce photorealistic photographs of fresh perspectives on a scene even from a small number of input images. However, the optimizer that is employed can have a significant impact on the quality of the final reconstruction. Finding an effective optimizer is one of the biggest challenges while learning NeRF models. The optimizer is responsible for making changes to the model's parameters to minimize the discrepancy between the model's predictions and the actual data. We cover the many optimizers that have been used to train NeRF models in this study. We present research results contrasting the effectiveness of multiple optimizers and examine the benefits and drawbacks of each optimizer. For training NeRF models, four different optimizers viz. Adaptive moment estimation (Adam), AdamW, root mean square propagation (RMSProp), and adaptive gradient (Adagrad) are trained. The most effective optimizer for a given assignment will vary depending on a variety of elements, including the size of the dataset, the complexity of the scene, and the level of accuracy that is required. [ABSTRACT FROM AUTHOR]

Groundwater arsenic (As), contamination is a significant issue worldwide including China and Pakistan, particularly in canal command areas. In this study, 131 groundwater samples were collected, and three machine learning models [Random Forest (RF), Logistic Regression (LR), and Artificial Neural Network (ANN)] were employed to predict As concentration. Descriptive statistics helped to conclude that all of the samples were inside the permitted limit of WHO for pH, Ca, Mg, Turbidity, Cl, K, Na, SO4, NO3, F and beyond limit of WHO for EC, HCO3, TDS, and As. RF suggested a median drop in Gini node impurity across all tree divisions. This predicted As contamination in samples due to presence of TDS, EC, HCO3− and turbidity in upper end of graph which expressed significance of these factors in contaminating water with Arsenic. Moreover, these factors were found positively correlated with Ar contamination. LR model expressed about best fitness of model. ANN classified large data set into two classes i.e. (1) Inside limit of WHO and (2) and outside limit of WHO. Total dissolved solids (TDS), turbidity, sodium (Na) and electrical conductivity (EC) were positively correlated with Ar (Arsenic concentration) in the collected samples. pH and K were negatively associated with Arsenic concentration of the observed samples. Confusion matrices and ROC-AUC scores evaluated that RF, model outperforming than LR, and ANN, in accuracy and sensitivity. Key variables influencing As concentration in the groundwater resources of the study area were identified, such parameters include TDS, chloride (Cl), bicarbonate (HCO3−) and turbidity. The study provided the complete profile of the 131 water samples which can be used to make strategies for the minimization of ground Water contamination for Rohri canal command area. Moreover, the steps can be taken to control the discussed parameters inside the WHO limit. [ABSTRACT FROM AUTHOR]

During train operations, the changing of wheel tread profile due to wheel-rail contact has a significant impact on the safe operation of the train and passenger comfort. Therefore, it is crucial to timely monitor the condition of the wheels and accurately predict wheel wear. In addition, wheel wear prediction can also guide wheel re-profiling, thereby extending the service life of wheels and minimizing operating costs. In this paper, a two-step data-driven wear prediction method was proposed to comprehensively predict wheel wear from two perspectives: the overall train and individual wheels. Firstly, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Radial Basis Function neural network (RBFNN) were used to preprocess historical data, remove outliers, and construct time series data. By analysing the wear trend of wheels, a two-step data-driven prediction method was proposed. The first step is to predict the average Wear of wheels in the future time, and the second step is to predict the deviation between each wheel Wear value and the mean in the future time. Different prediction models were utilized and compared to obtain the best predictive performance. Results show that back propagation neural network (BPNN) optimized by genetic algorithm (GA) and long short-term memory (LSTM) based model yield better performance for predicting the wear trend of the overall train and individual wheels respectively. In addition, comprehensive contour parameters were utilized in the prediction model to enhance the accuracy and robustness. [ABSTRACT FROM AUTHOR]

Steady manoeuvring forces and moments over a generic submarine hull form (SUBOFF) at various angles of incidence were investigated through scaled model tests in the Wind Tunnel Facility at the Naval Science and Technological Laboratory, India. Measurements were performed for a range of angles of incidence in angle of attack (±18°) and drift (±18°) including control surface deflections (±15°) on a 1.372 m model at Reynolds number 1.7 × 106. The present study at angles of incidence is to gather steady-state force, and moment data and validate such load measurements performed in a Wind Tunnel. The particulars of the SUBOFF model, experimental facility and measurement technique, test results for load measurements and estimated hydrodynamic coefficients are highlighted in the paper. The measurement data generated from the above studies will complement the current research on hydrodynamic characteristics for submerged bodies both computationally and experimentally. [ABSTRACT FROM AUTHOR]

To increase energy efficiency and reduce greenhouse gas (GHG) emissions in the shipping industry, an accurate prediction of the ship performance at sea is crucial. This paper proposes a new power prediction method based on minimizing a normalized root mean square error (NRMSE) defined by comparing the results of the power prediction model with the ship in-service data for a given vessel. The result is a power prediction model tuned to fit the ship for which in-service data was applied. A general cargo ship is used as a test case. The performance of the proposed approach is evaluated in different scenarios with the artificial neural network (ANN) method and the traditional power prediction models. In all studied scenarios, the proposed method shows better performance in predicting ship power. Up to 86% percentage difference between the NRMSEs of the best and worst power prediction models is also reported. [ABSTRACT FROM AUTHOR]

Multivariate time series is ubiquitous in real-world applications, yet it often suffers from missing values that impede downstream analytical tasks. In this paper, we introduce the Long Short-Term Memory Network based Recurrent Autoencoder with Imputation Units and Temporal Attention Imputation Model (RATAI), tailored for multivariate time series. RATAI is designed to address certain limitations of traditional RNN-based imputation methods, which often focus on predictive modeling to estimate missing values, sometimes neglecting the contextual impact of observed data at and beyond the target time step. Drawing inspiration from Kalman smoothing, which effectively integrates past and future information to refine state estimations, RATAI aims to extract feature representations from time series data and use them to reconstruct a complete time series, thus overcoming the shortcomings of existing approaches. It employs a dual-stage imputation process: the encoder utilizes temporal information and attribute correlations to predict and impute missing values, and extract feature representation of imputed time series. Subsequently, the decoder reconstructs the series from the feature representation, and the reconstructed values are used as the final imputation values. Additionally, RATAI incorporates a temporal attention mechanism, allowing the decoder to focus on highly relevant inputs during reconstruction. This model can be trained directly using data that contains missing values, avoiding the misleading effects on model training that can arise from setting initial values for missing values. Our experiments demonstrate that RATAI outperforms benchmark models in multivariate time series imputation. [ABSTRACT FROM AUTHOR]

This study tackles rain attenuation in millimeter-wave (mm-wave) communication, a critical concern for the advancement of 5G wireless technology. It examines the variability of raindrop size distribution (DSD) and its impact on specific attenuation, with a focus on tropical environments such as Malaysia. Using the Joss-Waldvogel disdrometer, this study collected and analyzed extensive DSD data over three years, revealing that the highest DSD concentrations do not necessarily result in the greatest specific attenuation. This study adopted a machine learning approach, specifically supervised learning with linear regression, to enhance the accuracy of attenuation prediction models. A new set of coefficients for the power-law model of specific attenuation was derived and benchmarked against the ITU-R P.838-3 standard and similar studies in comparable climates. The findings emphasize the importance of developing regionspecific models that consider local meteorological variations, potentially offering significant improvements to the reliability and design of mm-wave communication systems in the future. [ABSTRACT FROM AUTHOR]