Unique Prediction Research Articles

Intelligent Renewable Energy Source Allocation Scheme for Internet of Things Enabled PHEVs Systems

Plug-in hybrid electric vehicles (PHEVs) have received a lot of interest because of their ability to cut fuel usage by using electricity from the grid as an alternative energy source. PHEVs differ from typical hybrid electric vehicles (HEVs) in terms of increased battery capacity, the availability of plug-in recharging, and improved battery state of charge (SOC) management systems. Efficient energy consumption and excellent performance are critical issues for hybrid powertrains. The estimation of SOC in PHEVs is a difficult NP-hard problem that is frequently tackled utilising metaheuristic techniques. In this paper, we offer an enhanced dingo optimisation with deep learning-based prediction (EDOA-DLP) model specifically tailored for PHEVs. The EDOA-DLP model includes an efficient SOC estimate algorithm for real-time energy management. The EDOA-DLP technique's performance has been thoroughly proven through considerable experimentation in a variety of circumstances. The results show that the proposed EDOA-DLP method beats other current methods in terms of predicting SOC accurately and reaching the targeted goals for PHEVs. Overall, this research presents a unique EDOA-DLP SOC prediction approach for PHEVs, with promising results in terms of energy management and performance optimisation. The experimental results highlight the EDOA-DLP model's improved performance. Overall, this research presents a unique EDOA-DLP SOC prediction approach for PHEVs, with promising results in terms of energy management and performance optimisation. The experimental results illustrate the EDOA-DLP model's improved performance when compared to competing methodologies, highlighting its potential to improve the efficiency and efficacy of PHEV systems.

A Conceptual Aquila Merged Arithmetic Optimization (AIAO) Integrated Auto-Encoder Based Long Short Term Memory (AUE-LSTM) For Sentiment Analysis

Sentiment analysis is a branch of analysis that uses disorganized written language to infer the opinions and emotions of people's critiques and attitudes toward entities and its features. In order to produce acceptable results, the majority of sentiment analysis models that employ supervised learning algorithms require a large amount of labeled information during the training stage. This is typically costly and results in significant labor expenses when used in practical applications. In this study, an intelligent and unique sentiment prediction system is developed for accurately classifying the positive, negative, and neutral comments from the social media dataset. Data preprocessing, which entails noise reduction, tokenization, standardization, normalization, stop word removal, and stemming, is done to ensure that the data is of a high enough quality for efficient sentiment prediction and analysis. The preprocessed data is then used to extract a mix of features, including hash tagging, Bag of Words (BoW), and Parts of Speech (PoS). Consequently, in order to choose the best features and speed up the classifier, a new hybrid optimization method called Aquila merged Arithmetic Optimization (AIAO) is used. Furthermore, an Auto-Encoder based Long Short Term Memory (AuE-LSTM), an innovative and clever ensemble learning technique, is used to precisely anticipate and classify user feelings based on the chosen data. This study uses a variety of open source social media datasets to evaluate the performance of the suggested AIAO integrated AuE-LSTM model.

Critical evaluation of established risk prediction models for acute respiratory distress syndrome in adult patients: A systematic review and meta-analysis.

To assess the performance of validated prediction models for acute respiratory distress syndrome (ARDS) by systematic review and meta-analysis. Eight databases (Medline, CINAHL, Embase, The Cochrane Library, CNKI, WanFang Data, Sinomed, and VIP) were searched up to March 26, 2023. Studies developed and validated a prediction model for ARDS in adult patients were included. Items on study design, incidence, derivation methods, predictors, discrimination, and calibration were collected. The risk of bias was assessed by the Prediction model Risk of Bias Assessment Tool. Models with a reported area under the curve of the receiver operating characteristic (AUC) metric were analyzed. A total of 25 studies were retrieved, including 48 unique prediction models. Discrimination was reported in all studies, with AUC ranging from 0.701 to 0.95. Emerged AUC value of the logistic regression model was 0.837 (95% CI: 0.814 to 0.859). Besides, the value in the ICU group was 0.856 (95% CI: 0.812 to 0.899), the acute pancreatitis group was 0.863 (95% CI: 0.844 to 0.882), and the postoperation group was 0.835 (95% CI: 0.808 to 0.861). In total, 24 of the included studies had a high risk of bias, which was mostly due to the improper methods in predictor screening (13/24), model calibration assessment (9/24), and dichotomization of continuous predictors (6/24). This study shows that most prediction models for ARDS are at high risk of bias, and the discrimination ability of the model is excellent. Adherence to standardized guidelines for model development is necessary to derive a prediction model of value to clinicians.

Relativistic Time-of-Arrival Measurements: Predictions, Post-Selection and Causality Problems

We analyze time-of-arrival probability distributions for relativistic particles in the context of quantum field theory (QFT). We show that QFT leads to a unique prediction, modulo post-selection that incorporates properties of the apparatus into the initial state. We also show that an experimental distinction of different probability assignments is possible especially in near-field measurements. We also analyze causality in relativistic measurements. We consider a quantum state obtained by a spacetime-localized operation on the vacuum, and we show that detection probabilities are typically characterized by small transient non-causal terms. We explain that these terms originate from Feynman propagation of the initial operation, because the Feynman propagator does not vanish outside the light cone. We discuss possible ways to restore causality, and we argue that this may not be possible in measurement models that involve switching the field–apparatus coupling on and off.

A KRR-UKF robust state estimation method for distribution networks

State estimation is an integral component of energy management systems. Employing a state estimation methodology that is both accurate and resilient is essential for facilitating informed decision-making processes. However, the complex scenarios (unknown noise, low data redundancy, and reconfiguration) of the distribution network pose new challenges for state estimation. In the context of this study, we introduce a state estimation technique known as the kernel ridge regression and unscented Kalman filter. In normal conditions, the non-linear correlation among data and unknown noise increases the difficulty of modeling the distribution network. Thence, kernel ridge regression is developed to map the data into high-dimensional space that transforms the non-linear problem into linear formulations base on the data rather the complicate grid model, which improves model generalization performance and filters out unknown noises. In addition, with the unique prediction correction mechanism of the Kalman method, the kernel ridge regression-mapped state value can be revised by the measurement, which further enhances model accuracy and robustness. During abnormal operating conditions and taking into account the presence of faulty data within the measurement system, we initiate the use of a long short-term memory network and combined convolutional neural network (CNN) model, referred to as the ATT-CNN-GRU. This model is utilized for the prediction of pseudo-measurements. Subsequently, we use an outlier detection method known as ordering points to identify the clustering structure to effectively identify and substitute erroneous data points. Cases on the IEEE-33 bus system and 109-bus system from a city in China show that the method has superior accuracy and robustness.

Bargaining as a Struggle Between Competing Attempts at Commitment

Abstract The strategic importance of commitment in bargaining is widely acknowledged. Yet disentangling its role from key features of canonical models, such as proposal power and reputational concerns, is difficult. This paper introduces a model of bargaining with strategic commitment at its core. Following Schelling (1956, The American Economic Review, vol. 46, 281–306), commitment ability stems from the costly nature of concession and is endogenously determined by players’ demands. Agreement is immediate for familiar bargainers, modelled via renegotiation-proofness. The unique prediction at the high concession cost limit provides a strategic foundation for the Kalai bargaining solution. Equilibria with delay feature a form of gradualism in demands.

45 Gender Differences in Mathematics and its Cognitive and Non-Cognitive Predictors in Community College Students.

Objective:Historically, numerous studies have supported a male advantage in math. While more recent literature has shown that the gender gap is either decreasing or non-significant, a gender difference remains for higher level math (high school and college) (Hyde et. al. 1990; Casey et. al. 1995). It is known that both cognitive and non-cognitive factors influence math performance. There is little evidence for gender differences in working memory (Miller & Bichsel, 2004), which is a key predictor for mathematics. There is, however, evidence for gender differences in the non-cognitive domain, including math anxiety, with females having higher levels (Miller & Bichsel, 2004; Goetz, et. al. 2013). This study evaluates gender differences in both standardized and everyday math performances, and the way that cognitive and non-cognitive factors impact math. The study is focused on a very understudied group with high levels of math difficulty, namely community college students. We expected to find gender differences in math, and expect these to be in part accounted for by gender differences in strong mathematical predictors, particularly non-cognitive factors.Participants and Methods:Participants included 94 community college students enrolled in their first math class (60 female; 34 male). Participants were administered the Kaufman Test of Educational Achievement - 3rd edition (KTEA3): Math Computation (MC) and Math Concepts Application (MCA) subtests, as well as an original Everyday Math (EM) measure which assessed their math ability in the context of common uses for math (e.g., financial and health numeracy). Additional measures included math anxiety, self-efficacy, and confidence. Finally, complex span working memory tasks were administered to assess verbal and spatial working memory. Analyses were performed using correlation and regression to examine relationships between the cognitive and non-cognitive variables and standardized and everyday math measures.Results:Correlations showed that all cognitive and non-cognitive variables are significantly correlated with all three math measures (all p < .05). There were no significant gender differences for any of the math measures, nor the working memory, or non-cognitive measures. Regression showed that across all three math outcomes, math anxiety and verbal working memory are significantly predictive of math performance. Overall R2 values were significant (range 27% to 37%, all p < .001). Working memory and math anxiety were unique predictors in all three regressions (all p < .05), but other non-cognitive variables such as self-efficacy did not show unique prediction (all p >.05).Conclusions:There was no evidence for gender differences on any studied variable. This stands in contrast to prior studies, although few studies have included community college students. On the other hand, both cognitive and non-cognitive factors were complimentary in the prediction of math outcomes, which is consistent with prior work. Among non-cognitive predictors, math anxiety was particularly prominent. This study clarifies prior conflicting work regarding gender differences, and highlights the role of both math anxiety and working memory as relevant for multiple math outcomes.

Experimental Predictions for Norm-Conserving Spontaneous Collapse.

Previous work has shown that nonlocal collapse in quantum mechanics can be described by a deterministic, non-unitary operator added to the standard Schrödinger equation. In terms of key aspects, this term differs from prior work on spontaneous collapse. In this paper, we discuss the possible predictions of this model that can be tested by experiments. This class of collapse model does not intrinsically imply unique experimental predictions, but it allows for the possibility.

Equilibrium uniqueness in entry games with private information

AbstractWe study equilibria in static entry games with single‐dimensional private information. Our framework embeds many models commonly used in applied work, allowing for firm heterogeneity and selective entry. We introduce the notion of strength, which summarizes a firm's ability to endure competition. In environments of applied interest, an equilibrium in which entry strategies are ordered according to the firms' strengths always exists. We call this equilibrium herculean. We derive simple and testable sufficient conditions guaranteeing equilibrium uniqueness and, consequently, a unique counterfactual prediction.

Unique stable matchings

In this paper we consider the issue of a unique prediction in one-to-one two-sided matching markets, as defined by Gale and Shapley (1962), and we prove the following: TheoremLet P be a one-to-one two-sided matching market and letP⁎be its associated normal form, a (weakly) smaller matching market with the same set of stable matchings that can be obtained using procedures introduced inIrving and Leather (1986)andBalinski and Ratier (1997). The following three statements are equivalent:(a)P has a unique stable matching.(b)Preferences onP⁎are acyclic, as defined byChung (2000).(c)InP⁎every market participant's preference list is a singleton.

The Representation of Third-Party Helping Interactions in Infancy

Despite numerous findings on the sophisticated inferences that human infants draw from observing third-party helping interactions, currently there is no theoretical account of how infants come to understand such events in the first place. After reviewing the available evidence in infants, we describe an account of how human adults understand helping actions. According to this mature concept, helping is a second-order, goal-directed action aiming to increase the utility of another agent (the Helpee) via reducing the cost, or increasing the reward, of the Helpee's own goal-directed action. We then identify the cognitive prerequisites for conceiving helping in this way and ask whether these are available to infants in the interpretation of helping interactions. In contrast to the mature concept, we offer two simpler alternatives that may underlie the early understanding of helping actions: ( a) helping as enabling, which requires second-order goal attribution but no utility calculus, and ( b) helping as joint action, which requires efficiency (i.e., utility) evaluation without demanding second-order goal attribution. We evaluate the evidence supporting these accounts, derive unique predictions from them, and describe what developmental pathway toward the mature concept they envisage. We conclude the article by outlining further open questions that the developmental literature on the interpretation of helping interactions has not yet addressed.

Development of offsite construction skill profile prediction models using mixed-effect regression analysis

Offsite construction (OSC) transfers onsite construction activities to factory-based processes utilising technological advancements, resulting in new and emerging skills while causing some existing skills to be changed and others to be redundant. However, there are no established methods to systematically quantify these OSC skill requirements. This paper presents OSC skill prediction models while highlighting the process of model development for future research. The aim of these models is to predict skills using a comparable measure, manhours/m2. A skill classification with six skill categories was used to analyse OSC skills. Numerical model development methods were reviewed, and mixed-effect regression modelling was selected for model development. The skills data needed for regression modelling was collected using eight case studies. Predominantly panelised and modular OSC projects were used to collect skills data. The skill prediction models were validated using further case study data and an expert forum. Comparatively, modules OSC type requires higher skill quantities than panels, for all the six skill categories analysed. Onsite and offsite skill requirements vary for different OSC types. Additionally, complex, non-linear relationships were recognised between OSC types and the utilisation of their skills. This research presents unique OSC skill prediction models that can provide early-stage advice to policymakers, project planners and manufacturers on OSC skill requirements. It also provides a novel methodology to develop predictive models for specific industry scenarios that have non-linear and complex relationships.

No Evidence for Transactional Effects Between Religiosity and Self-Esteem in a Secular Country

This research tests the unique predictions of three different theoretical perspectives on the self-esteem benefits of religiosity: the religiosity-as-a-personal-relationship-with-a-higher-power perspective, the religiosity-as-a-resource perspective, and the religiosity-as-social-value perspective. To do so, we used random-intercept cross-lagged panel models and examined the between- and within-person associations between three indicators of religiosity (belief in God, service attendance, and prayer frequency) and self-esteem across 11 annual assessments in a nationally representative sample of Dutch individuals (N = 12,915). The results reveal largely nonsignificant associations between all three religiosity indicators and self-esteem at the between- and the within-person levels. This finding supports the religiosity-as-a-social-value perspective, suggesting that the self-esteem benefits of religiosity are restricted to religious cultures only. This research extends previous research by showing that the results hold across different measures of religiosity and by providing some initial evidence that the power of the religiosity-as-a-social-value perspective might be larger than formerly assumed.

Impostor Phenomenon and Grit as Predictors of Job Satisfaction in Female Pharmacy Faculty.

Impostor phenomenon (IP), grit, and other factors impact job satisfaction for faculty, particularly female faculty. The Impostor Phenomenon Research Collaborative (IPRC) evaluated IP, grit, and job satisfaction in pharmacy faculty. A cross-sectional study was conducted with a convenience sample of faculty using a survey, which included demographic questions and validated instruments: Clance Impostor Phenomenon (CIPS), Short GRIT Scale, and Overall Job Satisfaction Questionnaire. Differences between groups, relationships, and prediction were evaluated using independent t-tests, ANOVA, Pearson correlation, and regression analyses. A total of 436 participants completed the survey; 380 self-identified as pharmacy faculty. Two hundred and one (54%) reported intense or frequent feelings of IP. The mean CIPS score was above 60, indicating a risk of negative outcomes related to IP. There were no differences in the prevalence of IP or job satisfaction levels when female and male faculty were compared. Female faculty had higher GRIT-S scores. Faculty reporting more IP had lower grit and lower job satisfaction. Job satisfaction in faculty was predicted by IP and grit; however, grit did not provide a unique prediction when combined with IP for male faculty. IP was not more prevalent in female faculty. Female faculty were grittier than male faculty. Higher grit was associated with less IP and higher job satisfaction. IP and grit predicted job satisfaction for female and male pharmacy faculty. Our findings suggest that improving grit may help mitigate IP and impact job satisfaction. Further research on evidence-based IP interventions is needed.

Prediction of postoperative pancreatic fistula and pancreatitis after pancreatoduodenectomy or distal pancreatectomy: A review.

Postoperative pancreatic fistula (POPF) is the leading cause of morbidity and early mortality in patients undergoing pancreatic resection. In addition, recent studies have identified postoperative acute pancreatitis (POAP) as an independent contributor to morbidity. Most perioperative mitigation strategies experimented for POPF have been shown to be in vain with no consensus on the best perioperative management. Clinical prediction models have been developed with the hope of identifying high POPF risk patients with the leading idea of finding subpopulations possibly benefiting from pre-existing or novel mitigation strategies. The aim of this review was to map out the existing prediction modeling studies to better understand the current stage of POPF prediction modeling, and the methodology behind them. A narrative review of the existing POPF prediction model studies was performed. Studies published before September 2022 were included. While the number of POPF prediction models for pancreatoduodenectomy has increased, none of the currently existing models stand out from the crowd. For distal pancreatectomy, two unique POPF prediction models exist, but due to their freshness, no further external validation or adoption in clinics or research has been reported. There seems to be a lack of adherence to correct methodology or reporting guidelines in most of the studies, which has rendered external validity-if assessed-low. Few of the most recent studies have demonstrated preoperative assessment of pancreatic aspects from computed tomography (CT) scans to provide relatively strong predictors of POPF. Main goal for the future would be to reach a consensus on the most important POPF predictors and prediction model. At their current state, few models have demonstrated adequate transportability and generalizability to be up to the task. Better understanding of POPF pathophysiology and the possible driving force of acute inflammation and POAP might be required before such a prediction model can be accessed.

Machine learning algorithms in wood ash-cement-Nano TiO2-based mortar subjected to elevated temperatures

Mortar is subjected to high temperatures during fire attacks or when it is near heat-radiating equipment like furnaces and reactors. The physical and microstructure of mortar were considerably altered by high temperatures. In this investigation, the effects of elevated temperatures on the flexural and compressive strengths of wood ash (WA) cement mortar modified with green-synthesised Nano titanium oxide (NT) were examined. In order to produce mortar samples, the cement was replaced with 10% WA, and 1–3% NT by weight of binder were added at constant water-binder ratio. The specimens were heated to 105, 200, 400, 600, and 800 °C with an incremental rate of 10 °C per min in the electric furnace for a sustained period of 2 h to measure their strengths. The machine learning algorithm of artificial neural networks with Levenberg-Marquardt backpropagation training techniques of different network architectures was engaged to predict the compressive strength of WA-cement-NT-based mortar produced. The findings showed that higher temperatures reduced compressive strength after 400 °C and flexural strength after 200 °C. The mortar specimen with a 3% NT addition showed the highest residual compressive strength increase, ranging from 18.75 to 27.38%. Compared to compressive strength, flexural strength is more severely affected by high temperatures. The backpropagation training algorithm revealed that each hidden layer displayed its unique strong prediction. However, Levenberg-Marquardt backpropagation training technique of 7–10-10-1 network structures yielded the best performance metrics for training, validation, and testing compared to 7-10-10-10 and 7-10-1 network architectures.

External Validation of the Japanese Clinical Score for Mortality Prediction in Patients With Acute Heart Failure.

To predict mortality in patients with acute heart failure (AHF), we created and validated an internal clinical risk score, the KICKOFF score, which takes physical and social aspects, in addition to clinical aspects, into account. In this study, we validated the prediction model externally in a different geographic area.Methods and Results: There were 2 prospective multicenter cohorts (1,117 patients in Osaka Prefecture [KICKOFF registry]; 737 patients in Kochi Prefecture [Kochi YOSACOI study]) that had complete datasets for calculation of the KICKOFF score, which was developed by machine learning incorporating physical and social factors. The outcome measure was all-cause death over a 2-year period. Patients were separated into 3 groups: low risk (scores 0-6), moderate risk (scores 7-11), and high risk (scores 12-19). Kaplan-Meier curves clearly showed the score's propensity to predict all-cause death, which rose independently in higher-risk groups (P<0.001) in both cohorts. After 2 years, the cumulative incidence of all-cause death was similar in the KICKOFF registry and Kochi YOSACOI study for the low-risk (4.4% vs. 5.3%, respectively), moderate-risk (25.3% vs. 22.3%, respectively), and high-risk (68.1% vs. 58.5%, respectively) groups. The unique prediction score may be used in different geographic areas in Japan. The score may help doctors estimate the risk of AHF mortality, and provide information for decisions regarding heart failure treatment.

Light lepton portal dark matter meets the LHC

We examine the sensitivity of the Large Hadron Collider (LHC) to light lepton portal dark matter with its mass below 10 GeV. The model features an extra doublet scalar field and singlet Dirac dark matter, which have Yukawa interactions with left-handed leptons. To correctly produce the dark matter abundance via the thermal freeze-out, a large mass splitting among the extra scalars is required, thus providing a light neutral scalar below mathcal{O}(10)textrm{GeV} and heavy neutral and charged scalars at the electroweak scale. In this paper, we focus on the electroweak pair-production of the extra scalars with subsequent model-specific scalar decays and evaluate the current constraints with the LHC Run 2 data and the discovery potential at the High Luminosity LHC (HL-LHC). It turns out that a large part of the theoretically allowed parameter space can be tested at the HL-LHC by taking into account complementarity between slepton searches and mono-Z plus missing transverse energy search. We also discuss same-sign charged scalar production as a unique prediction of the model, and the implication of the collider searches in the thermal dark matter scenario.

Gene-environment interaction analysis via deep learning.

Gene-environment (G-E) interaction analysis plays an important role in studying complex diseases. Extensive methodological research has been conducted on G-E interaction analysis, and the existing methods are mostly based on regression techniques. In many fields including biomedicine and omics, it has been increasingly recognized that deep learning may outperform regression with its unique flexibility (e.g., in accommodating unspecified nonlinear effects) and superior prediction performance. However, there has been a lack of development in deep learning for G-E interaction analysis. In this article, we fill this important knowledge gap and develop a new analysis approach based on deep neural network in conjunction with penalization. The proposed approach can simultaneously conduct model estimation and selection (of important main G effects and G-E interactions), while uniquely respecting the "main effects, interactions" variable selection hierarchy. Simulation shows that it has superior prediction and feature selection performance. The analysis of data on lung adenocarcinomaand skin cutaneous melanomaoverall survival further establishes its practical utility. Overall, this study can advance G-E interaction analysis by delivering a powerful new analysis approach based on modern deep learning.

Prognosis and personalized medicine prediction by integrated whole exome and transcriptome sequencing of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a clinically and genetically heterogeneous disease. To better describe the clinical value of the main driver gene mutations of HCC, we analyzed the whole exome sequencing data of 125 patients, and combined with the mutation data in the public database, 14 main mutant genes were identified. And we explored the correlation between the main mutation genes and clinical features. Consistent with the results of previous data, we found that TP53 and LRP1B mutations were related to the prognosis of our patients by WES data analysis. And we further explored the associated characteristics of TP53 and LRP1B mutations. However, it is of great clinical significance to tailor a unique prediction method and treatment plan for HCC patients according to the mutation of TP53. For TP53 wild-type HCC patients, we proposed a prognostic risk model based on 11 genes for better predictive value. According to the median risk score of the model, HCC patients with wild-type TP53 were divided into high-risk and low-risk groups. We found significant transcriptome changes in the enrichment of metabolic-related pathways and immunological characteristics between the two groups, suggesting the predictability of HCC immunotherapy by using this model. Through the CMap database, we found that AM580 had potential therapeutic significance for high-risk TP53 wild-type HCC patients.