Salesian University

The study aims to explore the factor structure of government school teachers of Darjeeling district of West Bengal with respect to Person Job Fit (PJF), Person Organization Fit (POF), Job Satisfaction (JS) and Employee Performance (EP). These constructs are considered vital to understand the effectiveness of a teacher, alignment with the workplace as well as overall organizational outcomes in the organization. A total of 275 teachers responded to the Likert scale questionnaire designed to capture their perceptions across the four constructs. An Exploratory Factor Analysis (EFA) was performed to investigate the underlying dimensions of the construct including PJF, POF, JS and EP. Prior to factor extraction, measures of sampling adequacy and suitability were assessed to ensure the robustness of the analysis. The findings of EFA showed the presence of four distinct factors corresponding to the construct. All retained factors showed acceptable factor loadings, and the extracted factors exhibited strong internal consistency and reliability, indicating that the measurement instrument was both valid and reliable for the study population. The findings add value and provide empirical evidence to support the multidimensional nature of the construct and relevance of fit in shaping the employee’s performance among the government school teachers. The study contributes to the existing knowledge of teacher workforce dynamic in Darjeeling district of West Bengal and lays the groundwork for future research by recommending the use of Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM).

Analysing cough sounds is vital in pulmonary medicine. Recently, AI tools are being trained to analyse the acoustic signals of cough sounds so that more cases can be quickly tested, thereby reducing the patient load on primary healthcare systems. In this paper, we evaluate ”Swaasa”, our AI-based platform for rapid respiratory screening, highlighting its efficacy and ease of use. We applied our trained classifier to catch underlying pathologies from cough sound data collected from diverse sources. We then used a pattern classifier to identify specific respiratory conditions based on cough sound patterns. We tested the robustness of our methods by comparing our results with that of a pulmonary physician in 355 cases and show that Swaasa correctly predicted associated risk in 87.32% of those cases. Our platform has a sensitivity of 97.27% with a Positive Predictive Value (PPV) of 88.54%, giving us the potential to revolutionise disease screening, especially for large populations and in isolated rural areas. Our rapid and easy-to-use Software as a Service (SaaS) solution efficiently diagnoses and conserves resources, improves patient outcomes, and establishes a comprehensive and accessible healthcare framework.

Objective: This study evaluated the decision-making of Large Language Models (LLMs) in interpreting firearm examiner testimony by comparing a standard LLM to one enhanced with forensic science knowledge. Method: Following the experimental paradigm of Garrett et al. (2020), we assessed whether LLMs mirrored human decision patterns and if specialized knowledge led to more critical evaluations of forensic claims. We employed a 2 × 2 × 7 between-subjects design with three independent variables: LLM configuration (standard vs. knowledge-enhanced), cross-examination presence (yes vs. no), and conclusion language (seven variations). Each model condition performed 200 repetitions per scenario. This yielded a total of 5,600 measures of binary verdicts, guilt probability ratings, and credibility assessments. Results: LLMs showed low conviction rates (9.4%) across conditions, with logical variations as a function of the way in which the firearm expert’s conclusion was formulated. Cross-examination produced lower guilt assessments and scientific credibility ratings. Importantly, knowledge-enhanced LLMs demonstrated significantly more conservative evaluations of firearm evidence across all match conditions compared to standard LLMs. Conclusions: LLMs, particularly when enhanced with domain-specific knowledge, showed advantages in evaluating complex scientific evidence compared to human jurors in Garrett et al. (2020), suggesting potential applications for AI systems in supporting legal decision-making.

This study conducted a thorough exploration of Human Resource Analytics (HR Analytics), delving into its historical trajectory, multifaceted roles, practical integration within business frameworks, and its relationship with Human Resource Metrics. Employing a descriptive research methodology, an array of data was meticulously gathered from diverse written sources such as books, scholarly journals, and reputable websites. Moreover, invaluable insights from seasoned experts and professionals were seamlessly integrated to enrich the analytical depth. Throughout the study, significant emphasis was placed on elucidating the conceptual landscape of HRM models, theories, and perspectives to serve as a cornerstone for fostering evidence-based decision-making paradigms within organizational contexts. Historically, Human Resource Management (HRM) predominantly operated within the realm of administrative functions, predominantly influenced by historical knowledge and intuitive practices. However, the paradigm shift towards evidence-based management practices, spearheaded by luminaries such as Pfeffer, Sutton, and Briner, underscored the imperative of leveraging data-supported evidence to underpin HRM decision-making processes. This evolution heralded increased investments in HR analytics, empowering organizations to craft decisions rooted in pragmatism, value addition, and sustainability. Consequently, the study illuminates the pivotal evolution of HR analytics as an indispensable cornerstone in nurturing evidence-centric HRM practices, bolstering organizational competitiveness, and orchestrating streamlined administrative procedures.

A large number of children around the world are separated from their parents either by social services or due to parental decisions. The causes are multiple and include child violence, parental abandonment, illegal immigration, parental imprisonment, etc. The purpose of the present study is to review scientific research on the effects on children of being separated from their parents. For this, a Systematic Review was carried out using the PRISMA statement on the scientific bases DOAJ, Scielo and Redalyc. Open access articles in English have been included. The filtering, screening and final selection are expressed in a flowchart and tables. According to the analysis it was identified that the effects are not specific to the type of separation and include a high incidence of neurological changes as a result of psychological trauma, early sexual development, physical and sexual abuse, school dropout , subpar academic performance, strained peer relationships, symptoms of psychosomatic and psychiatric disorders, as well as internalizing and externalizing behavior. It is also associated with anxiety, depression, delinquency, substance abuse, inappropriate sexual behavior and self-harm. Evidence also suggests that separation can lead to ongoing cycles of family division. Research has shown that children are most at risk between the ages of 9 months and 9 years. Particularly high-risk conditions include separation during the preschool years, abrupt separation without prior notice to the child, being cared for by strangers and simultaneous change of residence.

Abstract In this paper we report the microwave assisted synthesis and characterization of a family of Na−Ln coordination polymers (CPs) of formula [NaDy2(MeCOO)2(SALOH)5(chpH)2] (1Ln, Ln=Eu, Gd, Tb, Dy, Ho, Y). The Na+ cations isolate Ln2 units in a one‐dimensional polymer. By changing the lanthanoid ions, we attain Single Molecule Magnet properties (1Dy), luminescent properties (1Eu, 1Tb, 1Ho), a diamagnetic material (1Y) and a material that presents magnetocaloric effect (1Gd).

O presente estudo tem como foco o projeto de iniciação científica ‘‘A autoria feminina no acervo Raymond Cantel: mapeamento e estudo crítico de folhetos de cordel’’, com foco na análise do cordel épico A história do padre Rodolfo e do índio Simão, de Adélia Carvalho de Oliveira, destacando aspectos do plano histórico e o heroísmo presente no folheto.