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Medical education continually adapts to the needs of future health care professionals, with student motivation in research being a pivotal aspect. This study at Alfaisal University aimed to explore the motivations, benefits, and challenges faced by medical students in extracurricular research activities. Using a mixed-method approach, we combined quantitative surveys with qualitative group interviews. Findings revealed that both extrinsic (e.g., enhancing postgraduate training prospects) and intrinsic (e.g., personal interest and skill refinement) factors significantly motivate students to be involved in research activities. Participants unanimously acknowledged skill enhancement, particularly in literature comprehension, creative ideation, and networking. However, challenges such as conflicts with course scheduling, lack of hands-on experiences, and mentorship issues were identified as potential barriers to research participation. Addressing these barriers and understanding motivations can inform the design of research programs, enhancing the overall student research experience. This study underscores the importance of research in medical education, emphasizing the need for institutions to prioritize addressing challenges and leveraging benefits to prepare medical students for a research-integrated clinical future.

Abstract Non-financial companies listed on the Frankfurt Stock Exchange face considerable difficulties due to expensive funding and the need to make complex decisions about their capital structure. These problems impact their judgments about dividend policy, resulting in ambiguity and possible inefficiency. This study draws on the bird-in-hand theory to investigate the influence of the cost of capital on dividend policy decisions among non-financial firms listed on the Frankfurt Stock Exchange, focusing on 227 companies from 2005 to 2022. The data for this study were sourced from Thomson Reuters Datastream. Employing the common shock autoregressive distributed lag (CS-ARDL) and two-step generalized method of moments (GMM) estimations, findings reveal that the cost of debt consistently negatively impacts dividend payouts and coverage ratios. In contrast, the cost of equity has an insignificant effect. Conversely, the weighted average cost of capital positively influences dividend payouts and coverage ratios but negatively influences dividend yield. All the findings supported the bird-in-hand theory except for the negative impact of WACC on dividend yield. These insights highlight the importance of considering the cost of capital and market share dynamics in dividend policy formulation. Firms should prioritize efficient debt management to sustain dividend distributions, and aligning dividend policies with overall capital structure management is crucial in this regard, providing a sense of reassurance and confidence to the firms and their stakeholders. This study provides valuable guidance for financial decision-makers navigating dividend-related strategies within corporate environments.

This study aimed at figuring out the effect of full e-learning on the students' academic performance in accounting courses, which include multiple mathematical calculations compared with the English Language courses, which are free of mathematical calculations. A questionnaire was designed and distributed among a random sample of students during the second semester of the academic year 2019-2020, during which full e-learning was implemented due to the Coronavirus Pandemic. The sample included (302) out of (1411) male and female students at the Department of Administrative Sciences, whose major is accounting and business administration. Besides, they study English language as part of the general courses at the Community College in Khamis Mushait. The study found that, there is a statistically significant effect regarding the features of full e-learning on the students' academic performance with respect to the accounting and English Language courses. The problems related to e-learning did not affect the students' academic performance in accounting courses, while it impacted negatively on the students' academic performance in the English language courses. The study recommended that, the educational institutions should continuously develop the e-learning atmosphere in order to become conducive, attractive and creative.

This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security.

The knowledge of the enhancement of the journey to educational leadership in the Further Education (FE) sector in the United Kingdom (UK) has attracted the attention of academics. The majority of the experiments reviewed illustrate the formal educational mechanisms that leaders have experimentally learned via their experiences in FE. However, this study proposed that in order to lead admirably, senior academic leaders need additional assistance for their training and hands-on leadership experience. With an emphasis on Gronn’s theory of leadership model, the major objective is to critically evaluate educational leadership development at FE institutions in the UK. This supports the recognition of leadership in Gronn’s model in career terms and explores the significant impacts of the four phases of Gronn’s model in the development of academic leaders. Additionally, this study is interested in learning about leaders’ experiences and how they view leadership in FE in general. This study used a mixed-method approach to ensure that the two main research questions were consistently addressed. Twelve leaders at the FBT participated in face-to-face interviews for the data-gathering process, while 61 leaders in the UK completed a questionnaire. The results showed that developing into an effective leader requires a number of factors, or what Gronn’s concept of the leadership journey refers to as four sequential phases. Four areas of critical impact were identified during the professional pathway: family influence, educational influence, influential people, and significant experiences. The leader’s major experiences, character traits, and leadership aspirations were all shaped by fostering knowledge. The research on educational leadership supports the idea that a school leader’s capacity is greatly influenced by their leadership, which is the implication of this study.

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A cropping system practice is the sequential cultivation of crops in different crop seasons of a year. Cropping system practices determine the land productivity and sustainability of agriculture in regions and, therefore, information on cropping systems of different regions in the form of maps and statistics form critical inputs in crop planning for optimal use of resources. Although satellite-based crop mapping is widely practiced, deriving cropping systems maps using satellites is less reported. Here, we developed moderate-resolution maps of the major cropping systems of South Asia for the year 2014–2015 using multi-temporal satellite data together with a spectral matching technique (SMT) developed with an extensive set of field observation data supplemented with expert-identified crops in high-resolution satellite images. We identified and mapped 27 major cropping systems of South Asia at 250 m spatial resolution. The rice-wheat cropping system is the dominant system, followed by millet-wheat and soybean-wheat. The map showing the cropping system practices of regions opens up many use cases related to the agriculture performance of the regions. Comparison of such maps of different time periods offers insights on sensitive regions and analysis of such maps in conjunction with resources maps such as climate, soil, etc., enables optimization of resources vis-à-vis enhancing land productivity. Thus, the current study offers new opportunities to revisit the cropping system practices and redesign the same to meet the challenges of food security and climate resilient agriculture.

Intracerebral hemorrhage (ICH) is a life-threatening condition associated with significant morbidity and mortality. This study investigates transcriptomic alterations in rodent models of ICH and severe ICH to shed light on the genetic pathways involved in hemorrhagic brain injury. We performed principal component analysis, revealing distinct principal component segments of normal rats compared to ICH and severe ICH rats. We employed heatmaps and volcano plots to identify differentially expressed genes and utilized bar plots and KEGG pathway analysis to elucidate the molecular pathways involved. We identified a multitude of differentially expressed genes in both the ICH and severe ICH models. Our results revealed 5679 common genes among the normal, ICH, and severe ICH groups in the upregulated genes group, and 1196 common genes in the downregulated genes, respectively. A volcano plot comparing these groups further highlighted common genes, including PDPN, TIMP1, SERPINE1, TUBB6, and CD44. These findings underscore the complex interplay of genes involved in inflammation, oxidative stress, and neuronal damage. Furthermore, pathway enrichment analysis uncovered key signaling pathways, including the TNF signaling pathway, protein processing in the endoplasmic reticulum, MAPK signaling pathway, and Fc gamma R-mediated phagocytosis, implicated in the pathogenesis of ICH.

Shrimp farming and exporting is the main income source for the southern coastal districts of the Mekong Delta. Monitoring these shrimp ponds is helpful in identifying losses incurred due to natural calamities like floods, sources of water pollution by chemicals used in shrimp farming, and changes in the area of cultivation with an increase in demand for shrimp production. Satellite imagery, which is consistent with good spatial resolution and helpful in providing frequent information with temporal imagery, is a better solution for monitoring these shrimp ponds remotely for a larger spatial extent. The shrimp ponds of Cai Doi Vam township, Ca Mau Province, Viet Nam, were mapped using DMC-3 (TripleSat) and Jilin-1 high-resolution satellite imagery for the years 2019 and 2022. The 3 m spatial resolution shrimp pond extent product showed an overall accuracy of 87.5%, with a producer’s accuracy of 90.91% (errors of omission = 11.09%) and a user’s accuracy of 90.91% (errors of commission = 11.09%) for the shrimp pond class. It was noted that 66 ha of shrimp ponds in 2019 were observed to be dry in 2022, and 39 ha of other ponds had been converted into shrimp ponds in 2022. The continuous monitoring of shrimp ponds helps achieve sustainable aquaculture and acts as crucial input for the decision makers for any interventions.

Agriculture consumes the largest share of freshwater globally; therefore, distinguishing between rainfed and irrigated croplands is essential for agricultural water management and food security. In this study, a framework incorporating the Budyko model was used to differentiate between rainfed and irrigated cropland areas in Africa for eight remote sensing landcover products and a high-confidence cropland map (HCCM). The HCCM was generated for calibration and validation of the crop partitioning framework as an alternative to individual cropland masks which exhibit high disagreement. The accuracy of the framework in partitioning the HCCM was evaluated using an independent validation dataset, yielding an overall accuracy rate of 73 %. The findings of this study indicate that out of the total area covered by the HCCM (2.36 million km2), about 461,000 km2 (19 %) is irrigated cropland. The partitioning framework was applied on eight landcover products, and the extent of irrigated areas varied between 19 % and 30 % of the total cropland area. The framework demonstrated high precision and specificity scores, indicating its effectiveness in correctly identifying irrigated areas while minimizing the misclassification of rainfed areas as irrigated. This study provides an enhanced understanding of rainfed and irrigation patterns across Africa, supporting efforts towards achieving sustainable and resilient agricultural systems. Consequently, the approach outlined expands on the suite of remote sensing landcover products that can be used for agricultural water studies in Africa by enabling the extraction of irrigated and rainfed cropland data from landcover products that do not have disaggregated cropland classes.

The goal of this study was to use High performance liquid chromatography (HPLC) with an Analytical Quality by Design (AQbD) approach to measure the amount of caffeine in selected coffee samples available in the local Jazan market in Saudi Arabia. The experiment was performed on a Waters HPLC system equipped with a Raptor C-18 column. To achieve chromatographic separation, a mobile phase of ammonium acetate buffer (10 mM; pH 4.0) and acetonitrile in a 90:10 v/v ratio was used with a flow rate of 1 mL/min for 5 minutes at 274 nm. AQbD was used to optimize chromatographic conditions using a central composite design (CCD). A factorial design of 23 (two factors and three responses) was used with Stat-Ease Inc.'s Design Expert 13.0.3.0 software, Minneapolis, USA. The HPLC method was validated using ICH and USP guidelines, and it met all acceptance criteria. In the dilution range of 2–28 µg/mL, the procedure was shown to be linear. The caffeine content of all five samples (5 g/100 mL) was found to be 20.59–25.38 mg, which was below the level of toxicological concern recommended by the USFDA.

In 2014, the State of Telangana in southern India began repairing and restoring more than 46,000 irrigation water tanks (artificial reservoirs) under the Mission Kakatiya project with an investment in excess of USD 2 billion. In this study, we attempted to map the temporal changes that have occurred in cropland areas and water bodies as a result of the project, using remote sensing imagery and applying land use/land cover (LULC) mapping algorithms. We used 16-day time series data from Landsat 8 to study the spatial distribution of changes in water bodies and cropland areas over the 2013–18 period. Ground survey information was used to assess the pixel-based accuracy of the Landsat-derived data. The areas served by these tanks were identified on the basis of training data and Random Forest algorithms using Google Earth Engine. Our spatial analysis revealed a substantial increase in cropped area under irrigation and expansion of water bodies over the study period. We observed a 20% increase in total tank area in 2017–18 and total cropland and irrigated area expansion of the order of 0.6 M ha and 0.2 M ha, respectively. A comparison of ground survey data and four LULC classes derived from Landsat temporal imagery showed an overall accuracy of 87%, significantly correlated with national agriculture statistics. Periodic monitoring based on remote sensing has proved to be an effective method of capturing LULC changes resulting from the Mission Kakatiya interventions. Higher-resolution satellite data can further improve the accuracy of estimates.