Aim/Purpose: The study aims to explore the attributes of self-phubbing and partner-phubbing, as well as their impact on marital relationship satisfaction and the quality of communication. Furthermore, it aims to comprehend how these characteristics could impact an individual’s total level of life satisfaction. Background: The study aims to establish a clear association between specific mobile phone usage behaviors and their subsequent impact on relationship satisfaction and the quality of communication. This study investigates the effects of two types of behaviors on interpersonal relationships: self-phubbing, which refers to an individual being deeply absorbed in their own mobile phone use, and partner-phubbing, which refers to witnessing one’s partner being deeply absorbed in a mobile device. Methodology: This study utilizes a quantitative approach. The poll involved 150 smartphone users in Malaysia who are in relationships, and they participated by completing a questionnaire. The data analysis was performed using the Partial Least Squares-based Structural Equation Modeling method. Contribution: This research addresses the gap and gives insight into the consequences of self and partner phubbing and its impact on the relationship and life satisfaction among partners by providing a research model that was validated with primary data. Findings: The results of this survey show that smartphone conflicts harm relationship satisfaction but not communication quality. It was revealed that communication quality does not directly bring a negative impact on life satisfaction, but it directly affects relationship satisfaction, which, in turn, harms life satisfaction. Recommendations for Practitioners: The findings of this study can be used by practitioners to improve relationship counseling and therapy. Through the integration of the notion of phubbing and its impact on relationship happiness, couples can receive guidance on how to reduce the tension that arises from using smartphones. Recommendation for Researchers: Previous research was conducted exclusively on only an individual’s phubbing behavior, but limited work was done on the partner’s phubbing behavior. Future researchers can enhance this model by identifying more factors. Impact on Society: This study addresses broader societal ramifications in addition to the dynamics of particular relationships. This study promotes a more mindful use of smartphones by exposing the complex relationships between technology use, relationship happiness, and general life contentment. This will ultimately lead to healthier relationships and improved societal well-being. Future Research: In the future, we are going to implement an artificial neural network approach to test this data to predict the most important factors that influence phubbing.

Because of lockdown for Covid-19 pandemic online teacher’s training has taken on paramount importance in the academia. Consequently, a key focus of training and education related research has been discovering ways to motivate individuals to engage in more active behavior. This research identifies the factors influencing teachers’ intention to participate in online training. Specifically, we investigated the impact of theory of planned behavior (TPB), ability, opportunity and motivation. Data were collected from 142 respondents using a structured questionnaire from four different universities of Bangladesh. The result suggests high level of impact on teacher’s intention by perceived behavior control, motivation and ability. Our developed research model empirically advances the TPB model with ability, opportunity and motivation.

This paper introduces an intelligent tool with a novel CatML stacking classifier designed to enhance predictive analytics for postgraduate university admission chances. The proposed classifier uses the CatBoost algorithm as a core component of the stacking ensemble method, which integrates CatBoost and Multi-Layer Perceptron (MLP) learners to improve predictive performance. The dataset comprises 13 questionnaire-based surveys, including academic records, standardized test scores (i.e., GRE, IELTS/TOEFL), publication status, extracurricular activities, recommendation letters, and personal statements from Bangladeshi students who applied to various U.S. postgraduate programs. Experimental results demonstrate that the CatML stacking classifier outperforms conventional models, achieving superior accuracy (88.14%) and robustness in predicting admission outcomes. The enhanced performance is attributed to the model's ability to capture complex, non-linear relationships within the data, facilitated by the CatBoost algorithm's handling of categorical features and prevention of overfitting. Finally, this model deploys in a web system developed with HTML, CSS, JavaScript and Flask. This research underscores the efficacy of advanced ensemble techniques in educational data mining and provides a valuable intelligent tool for students aiming to navigate the complexities of U.S. postgraduate admissions. The CatML stacking classifier offers significant improvements in predictive analytics, thereby assisting students in making informed application decisions.

Blockcahin is such a technology that helps us to use a shared ledger. Although the ledger is in shared manner, the total system is quiet secure. Bitcoin is a crypto currency which uses blockchain technology. Value of blockchain is very high than dollar or some other expensive currency. This is one of the reasons of encouraging theft attack on the blockchain technology. In this paper, we want to show the attacks on blockchain, their targeted area, reason and their possible proposed solution as review. Besides this, Double spending attack is a major attack on blockchain which is occurred twice till now and caused a huge loss of crypto currency. In this paper, we also want to represent the reasons of these attacks and propose one solution that can prevent Double Spending Attack. Our findings will provide some future direction for new researchers and also help the crypto business analysts to predict about present security in the aspects of blockchain network.

Joint questionnaire-based grading of insomnia and anxiety severity is practical only if few items are needed. We developed OrdiFuse, a cross-task ordinal stacking model, together with a eakage-controlled, model-aware item-reduction protocol, in 10,322 respondents with physician-assigned grades. Items were ranked by OrdiFuse’s multitask permutation importance, evaluated on folds excluded from the ranking, and reduced under a rule fixed before the locked test set (n = 2,096) was read. The rule retained 24 of 51 items. On the held-out test set, OrdiFuse achieved a multitask macro-F1 of 0.936 and 95.3% exact agreement, significantly outperforming equally tuned single-learner and stacking baselines (∆ = +0.012–0.017; Holm p ≤ 0.004), although its advantage over grid-selected stacking was not significant (∆ = +0.008; p = 0.17). Across three end-to-end re-derivations, all 24 items were consistently re-selected, and OrdiFuse outperformed stacking and histogram gradient boosting. Item selection remained stable across sub-cohorts. However, cross-task fusion showed no measurable benefit, and internal-external validation revealed performance reductions of 0.04–0.09, indicating limited cross-cohort transportability and no consisten comparative advantage. Insomnia grades were near-deterministic functions of the items, so incorporation bias cannot be excluded. The 24 items form a candidate predictive subset, not a validated instrument; independent external validation is required.