The root of the problem in this research is the fact that scientific writing with a national reputation is still low and the publication of scientific writing with a national reputation is also low, thus affecting the quality of lecturers at the University. To overcome this problem, this research developed a training management model that can improve the scientific writing skills of lecturers and familiarize lecturers to actively conduct nationally reputable scientific writing. The training management model in question is called the “National Reputable Scientific Writing Training Management” model. This type of research is development research or R&D to produce a valid, practical, and effective model, as well as all devices and research instruments related to the application of the model at the University. The results showed that: (1) the National Reputable Scientific Writing Training Management model is suitable for improving the scientific writing ability of lecturers; (2) the output of the National Reputable Scientific Writing Training Management model in the model group is significantly higher than the initial group (pre-model); (3) The average value of IP/IO from experts is 4.4 with a high category, from observers at stage I test is 4.0 with a high category, at stage II test is 4.7 with a high category and stage III test is 4.77 with a high category, so it is concluded that the National Reputable Scientific Writing Training Management model meets the criteria of effectiveness, practicality and implementation; (4) The response of university managers and respondents to the implementation of the model is quite satisfactory, both regarding the concept of the model, the application in technical implementation and their perception of the National Reputable Scientific Writing Training Management model; and (5) the National Reputable Scientific Writing Training Management model can be developed as an alternative implementation in training management at the university.

First principles simulation studies using the density functional theory have been performed on (9, 0) Zigzag Singlewalled Carbon Nanotube (SWCNT) to investigate its electronic, optical and thermodynamic properties using CASTEP (Cambridge Sequential Total Energy Package) and DFTB (Density Functional based Tight Binding) modules of the Material Studio Software version 7.0. Various functionals and sub-functionals available in the CASTEP Module (using Pulay Density Mixing treatment of electrons) and various eigen-solvers and smearing schemes available in the DFTB module (using smart algorithm) have been tried out to chalk out the electronic structure. The analytically deduced values of the band gap obtained were compared with the experimentally determined value reported in the literature. By comparison, combination of Anderson smearing scheme and standard diaogonalizer produced best results in DFTB module while in the CASTEP module, GGA (General Gradient approximation) functional with RPBE (Revised-perdew-Burke-Ernzerh) as Sub-functional was found to be the most consistent. These optimized parameters were then used to determine various electronic, optical and thermodynamic properties of (9, 0) Singlewalled Nanotube. (9, 0) Singlewalled Nanotube, which is extensively being used for sensing NH₃, CH₄ & NO₂, has been picked up in particular as it is reported to exhibit a finite energy band gap in contrast to its expected metallic nature. The study is of utmost significance as it not only probes and validates the simulation route for predicting suitable properties of nanomaterials but also throws light on the comparative efficacy of the different approximation and rationalization quantum mechanical techniques used in simulation studies.

The purpose of this study is to determine the impact of internal university social responsibility policies on the administrative personnel of a Colombian university. Under a non-probabilistic sampling method, the study collected information from 58 collaborators selected from a total of 92 working at the university. The information was collected through a structured questionnaire and evaluated using the generalized linear model. The results indicate that administrators perceive that university social responsibility policies have an adverse effect on the work environment. This is justified by the fact that the university’s actions are not oriented towards the welfare of its personnel. In conclusion, universities should concentrate enormous efforts on implementing strategies that foster the commitment of their collaborators, in order to generate a significant impact on their responsibility and motivation.

Purpose: This paper articulates a model that maximizes the use of e-HRM to achieve sustainable competitive advantage. It examines the indirect effects of e-HRM use on sustainable competitive advantage, through job satisfaction, employee performance, and perceived organizational politics. Design/methodology/approach: A survey approach was used to collect data from 30 organizations. A purposive sampling technique was used to select the study sample. The SPSS PROCESS Macro for running mediation analysis was used to analyze data. Findings: The findings show the indirect effect of e-HRM on sustainable competitive advantage through job satisfaction, employee performance, and perceived organizational politics. Job satisfaction has the biggest effect on achieving strategic outcomes. For organizational excellence, e-HRM use should complement other HRM practices. Practical implications: Management should pay attention to employee outcomes during the implementation of e-HRM. This study broadens the scope of the interaction between e-HRM use and sustainable competitive advantage. This study was conducted in a developing economy and demonstrated that the effects of e-HRM use on sustainable competitive advantage are not limited to developed economies. Originality/value: This study is one of the pioneering efforts to develop a model that maximizes organizational outcomes in developing countries. In addition, this study contributes to the understanding of intervening variables necessary to enhance information technology’s potential within the HR function.

To save patients’ lives, it is important to go for an early diagnosis of intracranial hemorrhage (ICH). For diagnosing ICH, the widely used method is non-contrast computed tomography (NCCT). It has fast acquisition and availability in medical emergency facilities. To predict hematoma progression and mortality, it is important to estimate the volume of intracranial hemorrhage. Radiologists can manually delineate the ICH region to estimate the hematoma volume. This process takes time and undergoes inter-rater variability. In this research paper, we develop and discuss a fine segmentation model and a coarse model for intracranial hemorrhage segmentations. Basically, two different models are discussed for intracranial hemorrhage segmentation. We trained a 2DDensNet in the first model for coarse segmentation and cascaded the coarse segmentation mask output in the fine segmentation model along with input training samples. A nnUNet model is trained in the second fine stage and will use the segmentation labels of the coarse model with true labels for intracranial hemorrhage segmentation. An optimal performance for intracranial hemorrhage segmentation solution is obtained.

This study addresses the impact of the tourism sector on poverty, poverty depth, and poverty severity in Indonesia, focusing on the micro-level dynamics in the province. Despite numerous tourism destinations, their strategic contribution to regional progress remains underexplored. The motivation stems from the need to comprehend the nuanced relationship between tourism and poverty at both the national and local levels, with specific attention to the untapped potential at the province level in Indonesia. We hypothesize that a higher tourism sector GRDP will be inversely correlated with poverty levels, and the inclusion of a Covid-19 variable will reveal a structural impact on poverty dynamics. Employing a Panel Regression Model, secondary data from the Central Statistics Agency (BPS) spanning 2011–2020 is utilized. A panel data regression equation model, including CEM, FEM, and REM, is employed to analyze the intricate relationship between tourism and poverty. The findings demonstrate a negative correlation between higher tourism sector GRDP and the number of poor people. The Covid-19 variable, considered a structural break, reveals a significant association between increased cases and elevated poverty and severity across Indonesian provinces. This study contributes a micro-level analysis of tourism’s role, emphasizing its impact at the provincial level. The findings underscore the need for strategic initiatives to harness the untapped potential of tourism in alleviating poverty and promoting regional progress.