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The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

This paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.

Using the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.

Our results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.

This research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.

In response to growing concerns over the negative consequences of Internet addiction on adolescents’ mental health, coupled with conflicting results in this literature stream, this meta-analysis sought to (1) examine the association between Internet addiction and depressive symptoms in adolescents, (2) examine the moderating role of Internet freedom across countries, and (3) examine the mediating role of excessive daytime sleepiness.

In total, 52 studies were analyzed using robust variance estimation and meta-analytic structural equation modeling.

There was a significant and moderate association between Internet addiction and depressive symptoms. Furthermore, Internet freedom did not explain heterogeneity in this literature stream before and after controlling for study quality and the percentage of female participants. In support of the displacement hypothesis, this study found that Internet addiction contributes to depressive symptoms through excessive daytime sleepiness (proportion mediated = 17.48%). As the evidence suggests, excessive daytime sleepiness displaces a host of activities beneficial for maintaining mental health. The results were subjected to a battery of robustness checks and the conclusions remain unchanged.

The results underscore the negative consequences of Internet addiction in adolescents. Addressing this issue would involve interventions that promote sleep hygiene and greater offline engagement with peers to alleviate depressive symptoms.

This study utilizes robust meta-analytic techniques to provide the most comprehensive examination of the association between Internet addiction and depressive symptoms in adolescents. The implications intersect with the shared interests of social scientists, health practitioners, and policy makers.

This paper aims to explore the trends in academic research on elder abuse from 1990 to 2023 using bibliometric analysis. It seeks to identify research trends, hotspots and gaps and proposes future research directions.

Using bibliometric analysis method, this study analyzes 2,404 documents related to elder abuse from the Scopus database. Visual analysis is conducted using VOSviewer software to reveal research trends, thematic clusters and their interrelationships.

The study shows a rising concern for elder abuse, especially in nursing homes, domestic settings and among dementia patients. However, research on prevention and intervention measures is lacking, despite increasing international collaboration. Yet, deeper exploration of cross-cultural and regional differences remains limited.

This study reveals that improving care conditions for nursing home residents and dementia patients requires increased funding, professional training for caregivers, the strengthening of regulations and the establishment of clear guidelines for reporting abuse. Additionally, promoting international cooperation, sharing best practices, raising public awareness and supporting ongoing research are essential measures to ensure the safety and dignity of older adults.

To the best of the authors’ knowledge, this study represents the first systematic review of elder abuse research using bibliometric analysis, providing researchers and policymakers with a comprehensive knowledge framework of the field’s development trends and research hotspots.

As a response to challenges that globalization poses, governments often utilize an expansionary fiscal policy, a mix of increased compensation spending and capital tax cuts. To account for these policy measures that are consistent with neither the compensation nor the efficiency hypothesis, this study examines government fractionalization as the key conditional factor.

We test our hypothesis with a country-year data covering 24 OECD countries from 1980 to 2011. To examine how a single country juggles compensation spending and capital taxation policies jointly, we employ a research strategy that classifies governments into four categories based on their implementation of the two policies and examine the link between imports and fiscal policy choices conditioned on government fractionalization.

This study shows that highly fractionalized governments are more likely to implement an expansionary fiscal policy than marginally fractionalized governments as a policy response to economic globalization and import shock.

Our findings imply that fractionalized governments are likely to face budget deficits and debt crises, as the expansionary fiscal policy persists over time.

By examining government fractionalization as one of the critical factors that constrain the fiscal policy choice, this study enhances our understanding of the relationship between economic globalization and compensation or efficiency policies. The arguments and findings in this study explain why governments utilize the seeming incompatible policy preferences over increased compensation spending and reduced capital tax burdens as a response to globalization, potentially subsuming both hypotheses.

This paper shares findings from a multi-year literacy professional development partnership between a school district and university (2014–2019). We share this case of a Literacy Cohort initiative as an example of cross-institutional professional development situated within several of NAPDS’ nine essentials, including professional learning and leading, boundary-spanning roles and reflection and innovation (NAPDS, 2021).

We asked, “In what ways did the Cohort initiative create conditions for community and collaboration in the service of meaningful literacy reforms?” Drawing on social design methodology (Gutiérrez & Vossoughi, 2010), we sought to generate and examine the educational change associated with this multi-year initiative. Our data set included programmatic data, interviews (N = 30) and artifacts of literacy teaching, learning and leading.

Our findings reflect the emphasis areas that are important to educators in the partnership: diversity by design, building relationships through collaboration and rooting literacy reforms in teacher leadership. Our discussion explores threads of reciprocity, simultaneous renewal and boundary-spanning leadership and their role in sustaining partnerships over time.

This paper contributes to our understanding of building and sustaining a cohort model of multi-year professional development through the voices, perspectives and experiences of teachers, faculty and district administrators.