Objective: Standardizing image acquisition protocols and image quality across cameras is an important need in imaging, in particular in multi-center clinical trials and the use of image analysis and machine learning algorithms. The objective of this study was to examine the effect of ordered subset expectation maximization (OSEM) reconstruction parameters on the quantitative image quality of cardiac perfusion SPECT images in different typical SPECT cameras and therefore assess the need to change the parameter values across cameras. Methods: The analysis was carried out by comparing the defect contrast-to-noise ratio (CNR) at 12 OSEM subset-iteration combinations. Eight frames were reconstructed using the SIMIND Monte Carlo Simulation package. An activity of 370 MBq (10mCi) and projection acquisition interval of 20 seconds per projection were used. Attenuation (AC) and scatter corrections (SC) were performed in this study for all images. Results: The 16-2 subset-iteration combination yielded the highest CNR and defect contrast values for both cameras. The difference between CNR values for two cameras was found to be close to 5%. Conclusions: Monte Carlo simulations can be useful to investigate how quantitative image quality behaves with respect to reconstruction parameters and correction algorithms in a controlled environment. In this study, the use of different camera brands did not seem to significantly affect the lesion detectability. Further simulations with more extended range of parameters and camera brands may be conducted in the future to quantify further the variability between different brands of cameras.

Objective: This study synthesizes current evidence on the role of Artificial Intelligence (AI) and, where relevant, Open Science (OS) practices in enhancing Human Resource Management (HRM) performance. It focuses on recruitment processes, ethical considerations, and employee participation. Methodology: A systematic literature review was conducted in Scopus covering the period 2019–2024, following PRISMA guidelines. The initial search yielded 1486 records. After de-duplication and screening using Rayyan, 66 studies (≈ 4.4%) met the inclusion criteria, which targeted peer-reviewed works addressing AI-supported HR decision-making. A combined content and bibliometric analysis was performed in R (Bibliometrix) to identify thematic patterns and conceptual structures. Results: Analysis revealed four thematic clusters: 1) Implementation and employee participation emphasizing human-in-the-loop approaches and effective change management; 2) ethical challenges including algorithmic bias, transparency gaps, and data privacy risks; 3) data-driven decision-making delivering higher accuracy, fewer errors, and personalized recruitment and performance assessment; 4) operational efficiency enabling faster workflows and reduced administrative workloads. AI tools consistently improved selection quality, while OS practices promoted transparency and knowledge sharing. Implications: The successful adoption of AI in HRM requires employee engagement, strong ethical safeguards, and transparent data governance. Future research should address the long-term cultural, organizational, and well-being impacts of AI integration, as well as its sustainability.

The study acknowledges empirical, conceptual, and policy-driven papers that address emotional assertiveness, assertive communication, and assertive training as means of improving employee performance in Chinese banking, which is a significant contributor to the Chinese economy. Most banking enterprises have suffered from poor performance and a lack of aggressiveness in operation. It can be used by both managers and employees to create a good interaction process and a favorable work environment, which can help elevate performances. The research employs a quantitative approach, utilizing a questionnaire survey and simple random sampling. The sample comprises 381 employees from the Chinese banking industry, with a response rate above 70%. The regression analysis confirms that emotional assertiveness, assertive training, and assertive communication significantly impact employee performance. In conclusion, this study contributes to academia and industries by addressing the importance of assertiveness in improving performance. The policy-driven evidence on the conceptual framework of HR literacy in emotional, training, communication, and job performance should be adopted and reviewed in the country’s existing management by objective policy and legal framework in resolving employee job performance and training that are still underutilized and have a great deal of potential to satisfy the employees and management needs by establishing and emerging nations.

In the rapidly evolving landscape of contemporary business, the strategic alignment of employees with their designated roles is a pivotal determinant of organizational success. Employee misfit, characterized by a misalignment between employees’ skills, interests, and assigned roles, poses formidable challenges to individual and collective performance. This comprehensive research report delves into the intricate implications of employee misfit, explores evolving trends in career consciousness among job seekers, outlines the multifaceted challenges HR managers face, and fervently advocates for implementing a comprehensive selection process to address this prevalent issue effectively. The report underscores the proactive role of management in cultivating a supportive work environment, fostering diverse career pathways, and embedding an inclusive selection framework to confront and mitigate the persistent issue of employee misfit.