Nanoparticle drug delivery systems are engineered technologies that use nanoparticles for the targeted delivery and controlled release of therapeutic agents. Cisplatin-loaded nanoparticle formulations were optimized utilizing response surface methods and the central composite rotating design model. This study employed a central composite rotatable design with a three-factored factorial design with three tiers. Three independent variables namely drug polymer ratio, aqueous organic phase ration, and stabilizer concentration were used to examine the particle size, entrapment efficiency, and drug loading of cisplatin PLGA nanoparticles as responses. The results revealed that this response surface approach might be able to be used to find the best formulation for the cisplatin PLGA nanoparticles. A polymer ratio of 1:8.27, organic phase ratio of 1:6, and stabilizer concentration of 0.15 were found to be optimum for cisplatin PLGA nanoparticles. Nanoparticles made under the optimal conditions found yielded a 112 nm particle size and a 95.4 percent entrapment efficiency, as well as a drug loading of 9 percent. The cisplatin PLGA nanoparticles tailored for scanning electon microscopy displayed a spherical form. A series of in vitro tests showed that the nanoparticle delivered cisplatin progressively over time. According to this work, the Response Surface Methodology (RSM) employing the central composite rotatable design may be successfully used to simulate cisplatin-PLGA nanoparticles.

The study aims to examine the labor market challenges and motivational factors for employee retention through the example of a small machinery company in Hungary. In recent years, Hungary’s labor market has faced significant difficulties, particularly due to the COVID-19 pandemic, which resulted in temporary unemployment followed by labor shortages. The research aims to identify the motivational, welfare, and financial factors that contribute to employee retention. Due to the small sample size, we did not investigate the relationships concerning loyalty, commitment, and performance. The research methods included comprehensive data collection at a machinery company employing 24 people located near the Austrian-Hungarian border. During the data collection, we conducted a questionnaire survey that included questions related to benefits, performance, commitment, and loyalty. The collected data were processed by calculating weighted averages and differences. The results indicate that flexible working hours and easy accessibility to the workplace are of utmost importance to employees. There is also a significant demand for performance-based pay and diverse, flexible benefit packages. Employees require both formal and informal professional recognition, such as praise and awards. The research has practical significance for both organizational management and employee well-being. Understanding employee opinions and implementing measures based on these can have four primary effects: improvement in employee performance, reduction in turnover, increase in organizational commitment, and enhancement of the company’s positive perception.

In the dynamic landscape of modern education, it is essential to understand and recognize the psychological habits that underpin students’ learning processes. These habits play a crucial role in shaping students’ learning outcomes, motivation, and overall educational experiences. This paper shifts the focus towards a more nuanced exploration of these psychological habits in learning, particularly among secondary school students. We propose an innovative assessment model that integrates multimodal data analysis with the quality function deployment theory and the subjective-objective assignment method. This model employs the G-1-entropy value method for an objective evaluation of students’ psychological learning habits. The G-1-entropy method stands out for its comprehensive, objective, and practical approach, offering valuable insights into students’ learning behaviors. By applying this method to assess the psychological aspects of learning, this study contributes to educational research and informs educational reforms. It provides a robust framework for understanding students’ learning habits, thereby aiding in the development of targeted educational strategies. The findings of this study offer strategic directions for educational management, teacher training, and curriculum development. This research not only advances theoretical knowledge in the field of educational psychology but also has practical implications for enhancing the quality of education. It serves as a scientific foundation for educators, administrators, and policymakers in shaping effective educational practices.

Richard’s equation was approximated by finite-difference numerical scheme to model water infiltration profile in variably unsaturated soil^[1]. The published data of Philip’s semi-analytical solution was used to validate the simulated results from the numerical scheme. A discrepancy was found between the simulated and the published semi-analytical results. Morris method as a global sensitivity tool was used as an alternative to local sensitivity analysis to assess the results discrepancy. Morris method with different sampling strategies were tested, of which Manhattan distance method has resulted a better sensitivity measures and also a better scan of input space than Euclidean method. Moreover, Morris method at p = 2 , r = 2 and Manhattan distance sampling strategy, with only 2 extra simulation runs than local sensitivity analysis, was able to produce reliable sensitivity measures (μ^*, σ). The sensitivity analysis results were cross-validated by Sobol’ variance-based method with 150,000 simulation runs. The global sensitivity tool has identified three important parameters, of which spatial discretization size was the sole reason of the discrepancy observed. In addition, a high proportion of total output variance contributed by parameters β and θ_s is suggesting a greater significant digits to reduce its input uncertainty range.

The fifth-generation technology standard (5G) is the cellular technology standard of this decade and its adoption leaves room for research and disclosure of new insights. 5G demands specific skillsets for the workforce to cope with its unprecedented use cases. The rapid progress of technology in various industries necessitates a constant effort from workers to acquire the latest skills demanded by the tech sector. The successful implementation of 5G hinges on the presence of competent individuals who can propel its progress. Most of the existing works related to 5G explore this technology from a multitude of applied and industrial viewpoints, but very few of them take a rigorous look at the 5G competencies associated with talent development. A competency model will help shape the required educational and training activities for preparing the 5G workforce, thereby improving workforce planning and performance in industrial settings. This study has opted to utilize the Fuzzy Delphi Method (FDM) to investigate and evaluate the perspectives of a group of experts, with the aim of proposing a 5G competency model. Based on the findings of this study, a model consisting of 46 elements under three categories is presented for utilization by any contingent of 5G. This competency model identifies, assesses, and introduces the necessary competencies, knowledge, and attributes for effective performance in a 5G-related job role in an industrial environment, guiding hiring, training, and development. Companies and academic institutions may utilize the suggested competency model in the real world to create job descriptions for 5G positions and to develop curriculum based on competencies. Such a model can be extended beyond the scope of 5G and lay the foundation of future wireless cellular network competency models, such as 6G competency models, by being refined and revised.