External Predictors Research Articles

Modelling net primary productivity using near real-time land cover data and soil moisture information

ABSTRACT The objective of this study is to develop a methodological approach to assess ecosystem functioning in terms of carbon uptake and sequestration. To achieve this, a model was developed to forecast Net Primary Productivity (NPP) and tested in a rural area, the Thrace region (NE Greece). Monthly net photosynthesis data from July 2015 to February 2024 was estimated using the MODerate Resolution Imaging Spectroradiometer (MODIS) Aqua MYD17A2H v061 dataset for the study area. This dataset was used as the dependent variable in an Autoregressive Integrated Moving Average (ARIMA) model. The predictive ability of monthly Land Cover (LC) and Soil Moisture (SM) data at various time lags was examined, and variables found to be significant predictors were introduced into the model as external predictors. The study found that crop areas and SM conditions exert the greatest influence on NPP, with areas containing trees exerting a lesser impact. Additionally, NPP was estimated for seven different LC and SM scenarios from March 2023 to February 2024.

Teacher‐Student Rapport: An Essential Mediator in Creating a Learning Climate Conducive to Psychological Well‐Being of Chinese Students

ABSTRACTThe success of students is closely tied to their well‐being and those students with high degrees of well‐being are more likely to excel in academic environments. Accordingly, the internal and external factors that cause positive changes in students' mental states and lead them to higher levels of psychological well‐being (PWB) need to be divulged. In response to this necessity, many scholars have explored the internal and external predictors of students' PWB such as teacher–student rapport (TSR) and learning climate in different educational domains. To narrow these gaps, this research inspects the role of TSR as a mediator in creating a learning climate conducive to PWB of Chinese Students. To do so, three scales were given to 588 Chinese students, among them 560 were valid. Then, structural equation modelling (SEM) was performed to analyse participants' answers. The results disclosed strong and close connections between TSR, learning climate and students' PWB. The results also uncovered that TSR was a significant mediator between learning climate and PWB. The outcomes of this investigation might be illuminating for teachers, teacher trainers and educational managers.

Unveiling Chinese EFL students’ academic burnout and its prediction by anxiety, boredom, and hopelessness: a latent growth curve modeling

ABSTRACT Due to the undesirable influences of academic burnout on students’ overall well-being, multitudes of studies have examined this psychological distress and its potential antecedents. Nevertheless, earlier studies have predominantly employed a linear, monolithic research method to probe students’ academic burnout. Moreover, the majority of existing studies have exclusively concentrated on the external predictors of this phenomenon. To address these gaps, adopting the ‘latent growth curve modeling (LGCM)’ as a dynamic research approach, the current inquiry examined Chinese EFL students’ academic burnout and its potential variations over a course of a semester. With this approach, this inquiry assessed the role of boredom, hopelessness, and anxiety as internal predictors of students’ academic burnout. For this purpose, 621 EFL students were randomly chosen from two Chinese universities and four self-scoring questionnaires were given to participants at the onset, middle, and conclusion of the semester. The outcomes of the LGCM disclosed that the rate of students’ academic burnout reduced throughout the semester. The findings further revealed that students’ unpleasant feelings, boredom, hopelessness, and anxiety, positively predicted their academic burnout. Overall, the results of this longitudinal research would be invaluable for language teachers seeking to reduce academic burnout among their learners.

Testing the determinants of job satisfaction among police administrative officers in Taiwan

PurposeThe current paper aims to provide insights into the determinants associated with job satisfaction among police administrative (personnel) officers in Taiwan, especially both internal organizational predictors and unique external predictors related to the Chinese cultural context.Design/methodology/approachData were gathered from police administrative officers across major and medium-sized police agencies in Taiwan. Multiple regression models were employed to analyze the relationship between both internal factors to the organization (e.g. workplace fairness, supervisor support, self-efficacy) and external factors (related to traditional Chinese culture and its expectations) and job satisfaction.FindingsThe external factors of work-family life balance and financial benefits are strong predictors, emphasizing the cultural significance of family harmony and financial stability in Taiwanese society. This finding challenges the prevailing notion in the literature that the primary source of job satisfaction among police officers is derived from internal organizational factors. Collectively, the findings concluded the multi-faceted determinants of job satisfaction among administrative officers in Taiwan, intertwining both individual and internal organizational factors with broader external cultural influences.Practical implicationsThis study investigated the job satisfaction among administrative officers who play a key role in a police department. The findings showed that external factors exert a significant impact on job satisfaction. This offers a new frontier to examine job satisfaction among not only administrative officers but also patrol officers in Taiwan and Asian countries. In addition, training courses can be developed and focus on work-family relations when officers are off duty.Originality/valueWhile previous research has extensively explored job satisfaction among police officers in various roles and countries, by integrating internal organizational and external predictors, this study pioneers the focus on “police administrative officers” within Taiwanese police agencies.

Analyzing predictors of pearl millet supply chain using an artificial neural network

PurposeThe internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.Design/methodology/approachDescriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.FindingsThe MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R2 values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.Research limitations/implicationsTo the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.Originality/valueThe literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

DeepO-GlcNAc: a web server for prediction of protein O-GlcNAcylation sites using deep learning combined with attention mechanism.

Protein O-GlcNAcylation is a dynamic post-translational modification involved in major cellular processes and associated with many human diseases. Bioinformatic prediction of O-GlcNAc sites before experimental validation is a challenge task in O-GlcNAc research. Recent advancements in deep learning algorithms and the availability of O-GlcNAc proteomics data present an opportunity to improve O-GlcNAc site prediction. This study aims to develop a deep learning-based tool to improve O-GlcNAcylation site prediction. We construct an annotated unbalanced O-GlcNAcylation data set and propose a new deep learning framework, DeepO-GlcNAc, using Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) combined with attention mechanism. The ablation study confirms that the additional model components in DeepO-GlcNAc, such as attention mechanisms and LSTM, contribute positively to improving prediction performance. Our model demonstrates strong robustness across five cross-species datasets, excluding humans. We also compare our model with three external predictors using an independent dataset. Our results demonstrated that DeepO-GlcNAc outperforms the external predictors, achieving an accuracy of 92%, an average precision of 72%, a MCC of 0.60, and an AUC of 92% in ROC analysis. Moreover, we have implemented DeepO-GlcNAc as a web server to facilitate further investigation and usage by the scientific community. Our work demonstrates the feasibility of utilizing deep learning for O-GlcNAc site prediction and provides a novel tool for O-GlcNAc investigation.

External and Internal Predictors of Student Satisfaction with Online Learning Achievement

Building and testing a framework of interactive and indirect predictors of student satisfaction would help us understand how to improve student online learning experience. The current study proposed that external predictors such as poor technological, environmental, and pedagogical factors would be internalized as negative psychological traits and indirectly predict student satisfaction in online learning. Results of multivariate regressions with 5824 Chinese undergraduate students demonstrated that instructors’ online teaching experience and communication with students had a stronger predictive effect on student satisfaction than wireless network quality and learning environment. Providing after-class reviewing materials to students or having longer self-learning time would not buffer students from negative external factors. Structural equation modeling analysis results showed that inferior technological, environmental, and pedagogical factors would be internalized into negative attitudes and emotions toward online learning and indirectly predict student satisfaction. Our study has implications for better understanding the extensive influence of online learning barriers caused by external conditions and building preventive mechanisms through the improvement of instructors’ teaching experience and communication with students.

Redundancy analysis includes analysis of variance-simultaneous component analysis (ASCA) and outperforms its extensions

Chemometrics and statistical ecology share interest in the analysis of multivariate data. In ecology, unconstrained and constrained ordination are popular methods to analyze and visualize multivariate data, with principal component analysis (PCA) and redundancy analysis (RDA) as prototype methods. Constraints give more insight and power by focusing on the response of the variables to particular external predictors or experimental factors, after optional adjustment for covariates. In chemometrics, analysis of variance - simultaneous component analysis (ASCA) was proposed decades later, with particular emphasis on the multivariate main and interaction effects in factorial experiments. This paper shows the similarities and differences between ASCA, its extensions, and (partial) RDA, alias reduced-rank regression. ASCA and RDA (understood as a sequence of partial RDAs, just as ASCA uses a sequence of PCAs) are shown to be mathematically identical for equireplicated designed experiments. Differences appear with unequal replication. As a corollary we show that, with equal replication, a particularly attractive form of ASCA, which displays a main effect together with an interaction, is a special case of principal response curve analysis. RDA is a least-squares method and uses the optimal weights in the dimension reduction of the treatment effects, whereas ASCA extensions for unbalanced data use alternative, sub-optimal weights.

Examining the predictors of multitasking information behavior among library and information science professionals in Pakistan

PurposeThe multitasking phenomenon has been prevailing in the technology-driven information environment. People are engaged in multitasking to process information and deal with personal and professional information tasks. This study aims to explore the external predictors of multitasking information behavior (MIB) of library and information science (LIS) professionals from Pakistan.Design/methodology/approachThis is a quantitative study based on a questionnaire survey, and data was collected through Google Form; the link was shared via e-mail and WhatsApp to get maximum responses. The sampling includes the LIS professionals working in Higher Education Commission-recognized universities of Khyber Pakhtunkhwa (KPK), Pakistan. A total of 126 responses were received from 41 universities of KPK.FindingsPearson correlation and regression were applied by using SPSS for data analysis. The findings revealed that time pressure (TP) is a good predictor of multitasking because when professionals got tasks with deadlines, they try to perform multiple activities at a time. Therefore, TP is a predictor of human MIB. This study also revealed that the work environment is a less significant predictor of MIB, and the use of multiple information resources is a weak predictor of MIB. It is noted that individual covariates were not predictors of the multitasking information. However, TP was the most significant predictor among all the contextual and individual factors predicting MIB.Research limitations/implicationsThis research line is significant because MIB is a new dimension of human information behavior among LIS professionals. The findings are beneficial for LIS professionals to increase their work productivity and performance by rationalizing the significant predictors.Originality/valueTo the best of the authors’ knowledge, no such study is available that highlighted the MIB among LIS professionals. Therefore, this study will highlight external factors’ effects on LIS professionals’ MIB. This study will contribute to the literature on libraries and information management as this study describes the LIS professionals’ behavior.

Forecasting Long-Horizon Factor Volatility

This article investigates forecasts of long-term volatility for the fast-growing field of long–short factor strategies in an extensive in-sample and out-of-sample framework. The author follows previous work by empirically comparing various forecast configurations to provide guidance for academics and practitioners on how to form accurate predictions of future volatility for various established factors. The set spans 21 factor return time series over multiple asset classes, factor styles, and a long historical data period. Both in-sample and out-of-sample results suggest monotonically increasing forecast accuracy for longer historical lookback periods, longer forecasting windows, and more-sophisticated models (considering short-term volatility clustering and external predictors motivated by the asset-pricing literature), while the findings appear less pronounced in a real-time setting than observed in-sample. Moreover, investors engaging in carry-styled factor strategies and multifactor portfolios (rather than single factors) achieve more-reliable forecasts, on average, as confirmed by the out-of-sample analysis.

Predictors of Psychedelic Experience: A Thematic Analysis

ABSTRACT Research on the therapeutic potential of psychedelic substances is expanding. A limitation within this field is the unpredictability of individual responses to psychedelics. Better understanding of factors predicting psychedelic experience is essential to clinical progress and wider harm reduction frameworks. Ketamine, MDMA, LSD and psilocybin were selected for comparison due to their promising therapeutic effects and different mechanisms of action. This study aimed to (a) identify factors that produce positive and adverse psychedelic experience, and (b) compare these potential predictors across four psychedelic substances. A thematic analysis was conducted on twenty-two first-person reports of psychedelic use (six per substance), sourced from the Erowid database. This revealed three external predictors (nature, music, and preparation) and three internal predictors (understanding, mind-set, and motivation). Each factor identified contained two sub-themes that further elucidated meaning and impact. Nature and music emerged as potential tools for de-escalating adverse reactions to psychedelics. Substance-specific perceptual and sensorial effects were also examined. Finally, the importance of, and interrelationship between, preparation, mind-set, understanding, and motivation was examined as common themes that emerged. The broader clinical and sociological implications are discussed, with reference to developing harm reduction frameworks. These findings constitute an early step in developing a more nuanced understanding of factors shaping psychedelic experience.

Internal versus External Predictors of Physical Activity in Youth

Physical activity (PA) participation has been linked to broad health benefits including reduced risk of chronic diseases. PA participation is important in adolescence as it sets the precedent for continued engagement and improved long-term health. At baseline, adolescents have some of the lowest rates of physical activity. Subsequently obesity rates in youth have exponentially increased in the past few decades. The existing literature on predictors of youth PA focuses on various internal (e.g., depressive symptoms, anxiety, self-esteem, chronic pain, Body Mass Index) and external (e.g., parental behaviors, risky behaviors) predictors. Some research suggested that internal predictors are more consistent predictors of youth PA, when compared to external predictors. The current study investigated the relationship between both internal and external factors (as well as both facilitators and inhibitors) and PA in youth. Two separate ordinal logistic regression models were utilized. Five internal variables (i.e., depressive symptoms, anxiety, self-esteem, pain, BMI) were significantly related to PA, whereas one of the three external variables were significantly related to PA (i.e., time with friends). Implications include determining important points of intervention to increase PA in youth.

Fundraising Appeals for the COVID-19 Epidemic Fight: A Cross-Country Study of Donor Responses

This research explores the intrinsic and extrinsic motivations driving donors to engage in fundraising appeals launched through social networking sites (SNSs) to mitigate COVID-19’s impact on vulnerable communities from a cross-national perspective. The research adopted a quantitative approach through a web-based survey; a total of 801 donors were obtained from Kuwait and Bahrain and were useable for the analysis. Smart PLS structural equation modelling was used to validate the research model and derive significant insights. In the Kuwaiti sample, we found that humanitarian projects, internet technology, SNSs and religiosity significantly drive donor attitudes towards online donation. All these relationships are indirectly related to the intention to give via SNS through the mediating role of attitudes. As for the Bahraini sample, humanitarian projects, non-profit organizations (NPOs), SNSs, and religiosity significantly influence donors’ attitudes. Attitudes, on the other hand, have a visible mediating role in the relationships between these predictors and behavioral intentions. The findings could be useful for the development of appropriate policies that boost online monetary donations to support emergency aid for communities crushed by the pandemic. This research differs from the existing literature in that its multi-national study scrutinizes the incorporation of both internal and external predictors of fundraising activities into a distinctive related context such as SNSs, particularly in a time of epidemiological crises such as COVID-19.

Long‐term prediction intervals with many covariates

Accurate forecasting is one of the fundamental focuses in the literature of econometric time‐series. Often practitioners and policymakers want to predict outcomes of an entire time horizon in the future instead of just a single k‐step ahead prediction. These series, apart from their own possible nonlinear dependence, are often also influenced by many external predictors. In this article, we construct prediction intervals of time‐aggregated forecasts in a high‐dimensional regression setting. Our approach is based on quantiles of residuals obtained by the popular LASSO routine. We allow for general heavy‐tailed, long‐memory, and nonlinear stationary error processes and stochastic predictors. Through a series of systematically arranged consistency results, we provide theoretical guarantees of our proposed quantile‐based method in all of these scenarios. After validating our approach using simulations, we also propose a novel bootstrap‐based method that can boost the coverage of the theoretical intervals. Finally analyzing the EPEX Spot data, we construct prediction intervals for hourly electricity prices over horizons spanning 17 weeks and contrast them to selected Bayesian and bootstrap interval forecasts.

External macroeconomic shocks and stock price behavior in Nigeria

This research work investigates the relationship between external macroeconomic shocks and stock price behavior in Nigeria. Variables such as exchange rate (EXR), US real interest rate (USRINTR), and world oil price (WOP) are adopted to capture external macroeconomic shocks while all share price index is used to proxy stock price. The research work uses Johansen cointegration and structural vector autoregressive model as the estimation method. Findings from the study confirm that no long-term co-movement exists between the stock price and the selected external shocks. Findings from the study equally show that both US real interest rate (USRINTR) and world oil price (WOP) are the major external shock predictors of the stock price in Nigeria.

Regularization of Predictors of GDP and Individual Sectors of the Economy of the Republic of Kazakhstan

Today, the point of view is widespread that a developed stock market is a necessary component of sustainable development of the entire economy of a country, region. How true is this approach in the conditions of the frontier market and the developing economy, to which Kazakhstan belongs? In our study, we tried to quantify this approach. Compression (regularization) of predictors was carried out. The constructed model was tested on the predictors of civil aviation in Kazakhstan. The following results are obtained. The use of regularization with the goal of: data mining, identifying external predictors of development of various sectors of the economy, we consider it necessary and useful. Stock market capitalization is directly related to the money supply and, to a lesser extent, to household deposits. The view is partially confirmed that pumping money into the economy only leads to financial bubbles. The stock market index was sensitive to a wide range of social and macroeconomic indicators: population growth, unemployment, poverty, inflation, investment, devaluation. Our conclusion: for the development of the stock market does not require any specific financial measures, it is necessary to deal with the economy as a whole. Volumes of transactions with corporate securities do not have stable external predictors. The main indicators of civil aviation in the republic have stable external predictors. Passenger turnover, sending passengers directly depend on: the level of unemployment, wages, GDP per capita, various types of services and products, money supply. There are no external predictors for a separate type of aerial work - cargo transportation. Consequently, it is possible to obtain positive results through the reform of this particular segment of services.

The Paradox of Predictability

Scriven’s paradox of predictability arises from the combination of two ideas: first, that everything in a deterministic universe is, in principle, predictable; second, that it is possible to create a system that falsifies any prediction that is made of it. Recently, the paradox has been used by Rummens and Cuypers to argue that there is a fundamental difference between embedded and external predictors; and by Ismael to argue against a governing conception of laws. The present paper defends a new diagnosis of the roots of the paradox. First, it is argued that the unpredictability has to be understood in the light of Turing’s famous results about computability, in particular his proof that there is no solution to the ‘halting problem.’ This allows us to see that previous analyses of the paradox were either mistaken or not fully adequate. Second, the sense of paradox that nevertheless remains is traced to the idea that rational behaviour is not dependent on contingent environmental circumstances: that it is always up to us to engage in activities such as rational prediction or rational belief. The paradox of predictability teaches us that this idea, natural though it may be, is mistaken.

Internal and External Predictors of Vocational High School Students’ Employability: The Role and Functions of School Counselors

The purpose of this study was to depict the vocational high school students’ employability. Family support and self-confidence were examined as predictors that can enhance students’ readiness to enter the working world. Ninety-two students of Class XII of a vocational high school participated in this correlational study. They were recruited using cluster random sampling technique. In collecting the data, employability, self-confidence, and family support scales were administered. The collected data were analyzed using multiple regression technique. The result of the study showed that self-confidence and family support are positively correlated with employability, either partially or simultaneously. This study found that self-confidence, as the internal factor, give greater contribution to students’ employability. The result of this study could be used as a reference for school counselors to provide guidance and counseling services to enhance students’ employability.

Nothing wrong about change: the adequate choice of the dependent variable and design in prediction of cognitive training success

BackgroundEven though investigating predictors of intervention success (e.g Cognitive Training, CT) is gaining more and more interest in the light of an individualized medicine, results on specific predictors of intervention success in the overall field are mixed and inconsistent due to different and sometimes inappropriate statistical methods used. Therefore, the present paper gives a guidance on the appropriate use of multiple regression analyses to identify predictors of CT and similar non-pharmacological interventions.MethodsWe simulated data based on a predefined true model and ran a series of different analyses to evaluate their performance in retrieving the true model coefficients. The true model consisted of a 2 (between: experimental vs. control group) × 2 (within: pre- vs. post-treatment) design with two continuous predictors, one of which predicted the success in the intervention group and the other did not. In analyzing the data, we considered four commonly used dependent variables (post-test score, absolute change score, relative change score, residual score), five regression models, eight sample sizes, and four levels of reliability.ResultsOur results indicated that a regression model including the investigated predictor, Group (experimental vs. control), pre-test score, and the interaction between the investigated predictor and the Group as predictors, and the absolute change score as the dependent variable seemed most convenient for the given experimental design. Although the pre-test score should be included as a predictor in the regression model for reasons of statistical power, its coefficient should not be interpreted because even if there is no true relationship, a negative and statistically significant regression coefficient commonly emerges.ConclusionEmploying simulation methods, theoretical reasoning, and mathematical derivations, we were able to derive recommendations regarding the analysis of data in one of the most prevalent experimental designs in research on CT and external predictors of CT success. These insights can contribute to the application of considered data analyses in future studies and facilitate cumulative knowledge gain.

Forecasting electricity prices with machine learning: predictor sensitivity

PurposeTrading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely depend upon a range of variables such as electricity demand and the feed-in from renewable energy sources. Hence, the purpose of this paper is to provide accurate forecasts..Design/methodology/approachThis paper aims at comparing different predictors stemming from supply-side (solar and wind power generation), demand-side, fuel-related and economic influences. For this reason, this paper implements a broad range of non-linear models from machine learning and draw upon the information-fusion-based sensitivity analysis.FindingsThis study disentangles the respective relevance of each predictor. This study shows that external predictors altogether decrease root mean squared errors by up to 21.96%. A Diebold-Mariano test statistically proves that the forecasting accuracy of the proposed machine learning models is superior.Research limitations/implicationsThe performance gain from including more predictors might be larger than from a better model. Future research should place attention on expanding the data basis in electricity price forecasting.Practical implicationsWhen developing pricing models, practitioners can achieve reasonable performance with a simple model (e.g. seasonal-autoregressive moving-average) that is built upon a wide range of predictors.Originality/valueThe benefit of adding further predictors has only recently received traction; however, little is known about how the individual variables contribute to improving forecasts in machine learning.