bluuebunny/crossref_metadata_2025 huggingface dataset:
The System for Managing Electricity Bills
Authors: Rohini Bhowate  , Mamta Pimpalkar  , Minal Solanki
Published: April 2024

The remarkable advances in science and technology have raised the bar for human life expectancy. Without these breakthroughs, the globe will be overloaded. In contrast to other ongoing projects, this one is innovative in that it simplifies the process of paying an electrical bill. MySQL served as the back end and Java Swing served as the front end for this project's implementation. The project's goal is to create an application software that will automate the process of tracking clients' unit consumption and creating electricity bills based on whether they are business or individual customers. It shows client information, the number of units they have used, and past bills. As you pay the bill, the date of payment will be updated. It effortlessly integrates upcoming advancements and modifications while maintaining an error-free database.
Understanding effective pillars of mentorship for student leader protégés
Authors: Jarred A. Shellhouse  , Cecelia E. Suarez  , Benge Matt , J. C. Bunch
Published: April 2024

We examined the mentor-protégé relationship and the foundational pillars that make the relationship effective. Through a phenomenological design, we conducted interviews with student leaders to discuss their relationship with their self-identified most significant mentor. Using leader-member exchange and mentor relationship theory as a lens, we used the protégé’s perspective to find what was important for them as they were developed by a mentor. We found respect, trust, direct engagement and feedback, relationship maintenance, and shared values to be important components of effective mentoring. We used inductive analysis to develop a model that illustrates the qualities and phases that mentoring relationships go through, indicating which pillars should be present at each stage. We discuss implications of this for leader-member exchange theory and mentor relationship theory, as well as implications for practice in leadership education.
AN AYURVEDIC MANAGEMENT OF VANDHYATVA ASSOCIATED WITH POLYCYSTIC OVARIAN DISEASE: A CASE REPORT
Authors: Dr. Akshita
Published: April 2024

In Ayurveda, Infertility is correlated with 'Vandhyatva,' which can result from various factors like 'Yoni Pradosha' (abnormalities in the female genital tract), 'Manaso Abhigata' (psychological issues), 'Sukra-Asrik Dosha' (abnormalities in sperm and ovum) and 'Ahara-Vihara Dosha' (improper diet and lifestyle). PCOS is a common hormonal disorder in women, causing irregular periods, excessive hair growth and weight gain. It affects fertility due to anovulation or oligovulation. In classical texts, PCOS is correlated to 'Pushpaghni Jathaharini,' denoting fertility challenges with symptoms like obesity ('Sthulya') and hirsutism ('Lomasa Ganda'). Vata and Kapha are the key Doshas involved with 'Shukra,' 'Asrik,' 'Ahara,' and 'Manasa' as causative factors. A 22-year-old female patient was reported to PTSR OPD with complaints of irregular menses with increased intervals and wanted to conceive irrespective of unprotected sexual intercourse since 3 years. Her USG was suggestive of B/L Polycystic ovarian disease. Semen analysis of her husband was normal. Aims and Objectives: To improve the qualities of Garbhasambhava Samagri with the help of Samshodhan Karma, Uttar Basti along with Shaman Chikitsa. Results: After Ayurvedic management, patient conceived and carrying a healthy pregnancy. Conclusion: The study concluded that the treatment administered was highly effective in treating infertility related to PCOS.
Business Approaches in the Energy Sector - a Review
Authors:   Petroleum-Gas University of Ploiesti, 39 Blvd. București 100680 Ploiești, România, Popescu Cătălin , Uță Daniela Steluța ,   Petroleum-Gas University of Ploiesti, 39 Blvd. București 100680 Ploiești, România, Mitu Augustin Constantin ,   Petroleum-Gas University of Ploiesti, 39 Blvd. București 100680 Ploiești, România, Anghel Denisa Andreea ,   Master Student, Petroleum-Gas University of Ploiesti, 39 Blvd. București 100680 Ploiești, România
Published: April 2024

The significance of enhancing business models within the energy sector has markedly increased in recent years due to the growing demand for sustainable energy sources and the requirements created by regulations and imposed on companies in relation to environmental protection. The purpose of this research is to provide suggestions for the development of business models corresponding to the present, which take into account the principles of sustainable development, in a competitive environment characterized by heightened volatility. By analyzing the literature and industry examples, it is argued that business models in the energy sector have to adapt in three main directions: adopting green technologies, rethinking production using digital platforms and tools, but also based on knowledge-based services. In order to generate a comprehensive analysis, the PRISMA methodology was used and the Web of Science database was queried by scanning all the publications on the business models in energy. The basic idea in the case of business models in the field of energy forces companies to a permanent and flexible adaptation of the business to the developments in the economic, social, legal, technological and ecological spheres by promoting energy efficiency solutions and the large-scale use of energy renewable sources, but also in relation to the realities already present regarding high energy prices, uncertain long-term supply and the huge environmental impact of fossil energy consumption.
Evaluating Symmetric Causality between External Debt and Macroeconomic Variables in Nigeria
Authors:   Department of Economics, Faculty of Social Sciences, Olabisi Onabanjo University, Ago-Iwoye, Nigeria, Okutimiren Adeteji Olusegun , Maku Olukayode Emmanuel ,   Department of Economics, Faculty of Social Sciences, Olabisi Onabanjo University, Ago-Iwoye, Nigeria, Adelowokan Oluwaseyi Adedayo ,   Department of Economics, Faculty of Social Sciences, Olabisi Onabanjo University, Ago-Iwoye, Nigeria, Ogunjobi Festus Olalekan ,   Department of Economics, Faculty of Social Sciences, Olabisi Onabanjo University, Ago-Iwoye, Nigeria
Published: April 2024

Within the global economic environment, the common pursuit of sustainable growth, infrastructure development, and poverty reduction prevails. However, the Nigerian government has encountered significant hurdles in its efforts to attain macroeconomic stability and the necessary conditions for economic prosperity. Consequently, the government has turned to external sources, primarily through debt acquisition, as a solution. This reliance on external debt is rooted in Nigeria’s struggle to effectively address the savings-investment gap, a challenge driven by various factors, including limited domestic savings, insufficient foreign exchange earnings, poor productivity levels, and inadequate tax revenues. Despite the visible increase in Nigeria’s external debt in recent times, noticeable improvements in macroeconomic indicators have remained elusive, raising questions about the effective allocation of these borrowed resources. In response to this discord, this study delves into the complex interaction between external debt and macroeconomic variables within the Nigerian economy. By employing a thorough analysis using the symmetric - Granger (1981) causality framework, covering the period from 1986 to 2020. Except for economic growth, which shows a causal relationship with external debt, the results showed no causal relationship between external debt and investment, economic growth, or exchange rate. Consequently, these results emphasize the importance for the government to explore alternative means of obtaining funds, rather than relying extensively on external debt, especially when pursuing projects with productive objectives.
Knowledge Management and Organisational Performance in selected Foam Manufacturing Firms in Lagos State, Nigeria
Authors:   Osun State University, Oke Bale Road , Osogbo, Osun State, Nigeria, Onikoyi Idris Adegboyega , Fetuga Omoshalewa Maryam ,   "Redeemer's University, Ede, Osun State, Nigeria, Odeh Callistus Destiny ,   Osun State University, Oke Bale Road , Osogbo, Osun State, Nigeria, Onaolapo Opeyemi Samuel ,   Osun State University, Oke Bale Road , Osogbo, Osun State, Nigeria
Published: April 2024

The study examined the impact of knowledge management and organisation performance in selected foam manufacturing firms in Lagos state, Nigeria. The study made use of survey research design to obtain information from the staff of Vita-foam Nigeria Plc. and Mouka Limited. The population of the study was the 792 staff of Vitafoam Nigeria Plc. Ikeja, and Mouka Limted, Ikeja, Lagos State, Nigeria. The study made use of simple random sampling technique. Result of the first hypothesis showed the R2 value of 0.608 which means that knowledge management technology variables have positive influence on workforce effectiveness. Also, findings of the second hypothesis showed an R2 value of 0. 660 which means that knowledge management strategy variables have positive influence on workforce effectiveness. In addition, findings of hypothesis three showed that R2 value = (0. 791) which means that knowledge management training variables have positive influence on workforce effectiveness. Finally, hypothesis four resulted in an R2 value of 0.734, indicating a favorable impact of knowledge application factors on workforce efficiency. The research ultimately establishes that variables such as knowledge application, training in knowledge management, the strategic approach to knowledge management, and the utilisation of knowledge management technology all exhibit statistically significant relationships with workforce effectiveness. The research suggests that it would be beneficial for business organisations to adopt different knowledge management approaches. These approaches are valuable as they provide guidance on effectively overseeing and consolidating a company's information, data, and expertise, leading to enhanced productivity and efficiency.
VÍNCULOS AFETIVOS NO CÁRCERE: IMPACTO NA MOTIVAÇÃO E APRENDIZAGEM DE ESTUDANTES PRIVADAS DE LIBERDADE
Authors: Queiroz Clésia Carneiro da Silva Freire , Souza Cláudia Maria de
Published: April 2024

Os vínculos afetivos são essenciais para a motivação e aprendizagem das estudantes no cárcere, apesar dos desafios inerentes ao ambiente prisional a escola pode ser um espaço de crescimento pessoal para as estudantes privadas de liberdade. Neste contexto, este artigo tem como objetivo principal, avaliar como as relações afetivas estabelecidas no espaço prisional feminino influenciam a motivação das estudantes nos processos de aprendizagem. Para tanto, utilizou-se da metodologia do ensaio acadêmico, que consistiu em uma síntese e análise crítica das ideias e pontos de vista de diversos autores sobre o tema. Ao avaliar a dinâmica das relações afetivas no ambiente prisional, foi possível identificar como a motivação das estudantes é influenciada por esses laços emocionais. A análise revelou nuances importantes sobre como a construção de vínculos afetivos pode ser um catalisador fundamental para o engajamento e o interesse nas atividades educacionais. Portanto, conclui-se que a promoção consciente de vínculos afetivos no sistema prisional feminino não apenas impacta positivamente o processo de aprendizagem, mas também se revela essencial para o estabelecimento de práticas educacionais mais humanizadas. Essa constatação ressalta a necessidade de políticas e abordagens educacionais que reconheçam e promovam ativamente a construção de relações afetivas como parte integrante do desenvolvimento integral das estudantes privadas de liberdade.
Translating deposition rates into erosion rates with landscape evolution modelling
Authors: van der Meij W. Marijn
Published: April 2024

Abstract. Soil erosion is one of the main threats to agricultural food production due to the loss of fertile soil. Determination of erosion rates is essential to quantify the degree of land degradation, but it is inherently challenging to determine temporally dynamic erosion rates over agricultural time scales. Optically Stimulated Luminescence (OSL) dating can provide temporally-resolved deposition rates by determining the last moment of daylight exposure of buried colluvial deposits. However, these deposition rates may differ substantially from the actual hillslope erosion rates. In this study, OSL-based deposition rates were converted to hillslope erosion rates through inverse modelling with soil-landscape evolution model ChronoLorica. This model integrates geochronological tracers into the simulations of soil mixing and redistribution. The model was applied to a kettle hole catchment in north-eastern Germany, which has been affected by tillage erosion over the last 5000 years. The initial shape of the landscape and the land use history are well-constrained, enabling accurate simulations of the landscape evolution that incorporate uncertainties in the model inputs. The calibrated model reveals an increase in erosion rates of almost to orders of magnitude from pre-historic ard ploughing up to recent intensive land management. The simulated rates match well with independent age controls from the same catchment. Uncertainty in the reconstructed initial landscape and land use histories had a minor influence of 12–16 % on the simulated rates. The simulations showed that the deposition rates were on average 1.5 higher than the erosion rates due to the ratio of erosional and depositional area. Recent artificial drainage and land reclamation have increased deposition rates up to five times the erosion rates, emphasizing the need of cautious interpretation of deposition rates as erosion proxies. This study demonstrates the suitability of ChronoLorica for upscaling experimental geochronological data to better understand landscape evolution in agricultural settings.
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Authors: Di Santo Dario , He Cenlin , Chen Fei , Giovannini Lorenzo
Published: April 2024

Abstract. The accurate calibration of parameters in atmospheric and Earth system models is crucial for improving their performance, but remains a challenge due to their inherent complexity, which is reflected in input-output relationships often characterized by multiple interactions between the parameters and thus hindering the use of simple sensitivity analysis methods. This paper introduces the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a new tool designed with the aim of providing a simple and flexible framework to estimate the sensitivity and importance of parameters in complex numerical weather prediction models. This tool leverages the strengths of multiple regression-based and probabilistic machine learning methods including LASSO, Support Vector Machine, Classification and Decision Trees, Random Forest, Extreme Gradient Boosting, Gaussian Process Regression, and Bayesian Ridge Regression. These regression algorithms are used to construct computationally inexpensive surrogate models to effectively predict model outputs from input parameters, thereby significantly reducing the computational burden of running high-fidelity models for sensitivity analysis. Moreover, the multi-method approach allows for a comparative analysis of the results. Through a detailed case study with the Weather Research and Forecasting (WRF) model coupled with the Noah-MP land surface model, ML-AMPSIT is demonstrated to efficiently predict the behavior of Noah-MP model parameters with a relatively small number of model runs, by simulating a sea breeze circulation over an idealized flat domain. This paper points out how ML-AMPSIT can be an efficient tool for performing sensitivity and importance analysis also for complex models, guiding the user through the different steps and allowing for a simplification and automatization of the process.
Modelling human impacts on surface and subsurface hydrological drought
Authors: Senbeta Tesfaye Belay , Kochanek Krzysztof , Karamuz Emilia , Napiorkowski Jaroslaw Jan
Published: April 2024

Abstract. The scientists tend to assume that climatic factors are the only drivers in the development of drought. However, this classical assumption is no longer valid in the Anthropocene, as most catchments have been significantly modified by human factors. Recent theoretical developments have shown that there are mutual interactions and feedback mechanisms – drought shapes humans and vice versa. Therefore, this study aims to assess the role of human impacts on hydrological drought (HD) characteristics by considering runoff and baseflow separately. For this purpose, the two rainfall-runoff models: SWAT (Soil and Water Assessment Tool) and HBV (Hydrologiska Byrans Vattenbalansavdelning) models are compared. The results show significant differences in the magnitude of change between the two models, suggesting that the ensemble method of multiple models need to be considered for modelling human impact. However, the results confirm that despite significant differences in the magnitude of change, both hydrological models indicate similar impacts, i.e. positive and negative.
Seasonal precipitation forecasting with large scale climate predictors: a hybrid ensemble empirical mode decomposition-NARX scheme
Authors: Ouachani Rim , Bargaoui Zoubeida , Ouarda Taha
Published: April 2024

Abstract. Much of northern Tunisia regularly experiences extremes of drought and flooding, with high rainfall variability. The development of reliable and accurate seasonal rainfall forecasts can provide valuable information to help mitigate some of the outcome of floods and enhance water management and monitoring, particularly for agriculture. Whether climate indices oscillations contain some information to be useful for hydrological forecasting is worth investigating. Ensemble monthly rainfall forecasts are carried out using a hybrid neural network model. The hybrid model called EEMD-NARX based on a non-linear autoregressive network with exogenous inputs (NARX) coupled to Ensemble Empirical Mode Decomposition (EEMD) method is developed in this work. First, the EEMD is performed to extract significant information from modes of variability (IMF) associated to climate indices and precipitation. Each IMF of selected indices as well as precipitation IMFs are then used as inputs to the NARX forecasting model to forecast each IMF of precipitation. To make forecasts operational, we reconstruct precipitation by summing of all forecasted IMFs to make comparison with observed precipitation in the Medjerda river basin located in north Tunisia. Results show that IMFs of MEI and SOI indices can be distinguished from a white noise at the 95 % level. It is also found that an oscillatory forcing coming from the Atlantic influences the precipitation in the Mediterranean basin. The results indicate that exogenous inputs like climatic indices improve the accuracy of forecasts in some in some precipitation stations. The correlation coefficient between observed and forecasted monthly precipitation is ranging from 0.7 to 0.8. EEMD allows extracting significant components from exogenous inputs like climate indices that help reducing predictive uncertainty as well as improving forecasts of a NARX model at longer lead-times.
Encefalitis límbica: clínica, patogenia y problemas diagnósticos
Authors: Giacomantone Eduardo G.
Published: April 2024

La encefalitis límbica es una enfermedad infrecuente y potencialmente grave, que puede o no ser paraneoplásica y se caracteriza por déficit de la memoria reciente, alteraciones psiquiátricas y convulsiones. De origen autoinmunitario, está asociada a anticuerpos séricos e intratecales contra antígenos neuronales intracelulares y de superficie, con especial afectación de zonas límbicas.

En este artículo se revisan aspectos históricos y epidemiológicos, patogenia, síndromes más frecuentes y mejor delimitados, histopatología y es- tudios complementarios. Se repasan también las dificultades del diagnóstico diferencial y la necesidad de descartar siempre un tumor subyacente. La detección de autoanticuerpos neuronales es importante para el diagnóstico, la planificación terapéutica y el pronóstico. La inmunoterapia y, si corresponde, el tratamiento de la neoplasia son cruciales para lograr una recuperación neurológica sustancial.

La encefalitis límbica es una entidad probablemente subdiagnosticada, con un pronóstico más favorable si se trata de forma temprana. El actual conocimiento de su patogenia puede además aportar claridad para la mejor comprensión de otros síndromes neurológicos y psiquiátricos que puedan compartir mecanismos autoinmunitarios, como algunos trastornos psicóticos y epilepsias farmacorresistentes.
ASSESSMENT OF CLIMATE CHANGE IMPACTS ON PRECIPITATION AND TEMPERATURE: A CASE STUDY ON THE KRISHNA RIVER BASIN
Authors:   Visvesvaraya National Institute of Technology, Nagpur, India, SYAM N.S.S. , SUNIL A. ,   Indian Institute of Technology, Bombay, India, PICHUKA S. ,   National Institute of Technology Andhra Pradesh, Tadepalligudem, India, MANDAL A. ,   Visvesvaraya National Institute of Technology, Nagpur, India
Published: April 2024

In this study, the statistical downscaling model (SDSM) is employed for downscaling the precipitation (PREC), maximum temperature (Tmax), and minimum temperature (Tmin) over the Krishna River Basin (KRB). The Canadian Earth System Model, version 2 (CanESM2) General Circulation Model (GCM) outputs were considered as predictor variables. First, the SDSM was calibrated using the data for a 30-year period (1961-1990) and subsequently validated with the data for a 15-year period (1991-2005). Upon perceiving a satisfactory performance, the SDSM was then used for projecting the predictand variables (PREC, Tmax, and Tmin) for the 21st century considering three representative concentration pathway (RCP) scenarios viz. RCP2.6, RCP4.5, and RCP8.5. The future period was divided into three 30-year time slices named epoch-1 (2011-2040), epoch-2 (2041-2070), and epoch-3 (2071-2100), respectively. The period 1976-2005 was considered as baseline period and all the future results were compared with this data. The results were analysed at various temporal scales, i.e., monthly, seasonal, and annual. The study has reveals that the KRB is going to become wetter during all the seasons. The results are discussed for the worst-case scenario, i.e., RCP8.5 epoch-3. The average annual maximum and minimum temperature is expected to increase. The extreme event analysis is also carried out considering the 90th and 95th percentile values. It is noticed that the extreme (90th and 95th percentiles) are going to increase. There are extreme events that go beyong extreme values. The outcome of this study can be used in flood modeling for the KRB and also for the modeling of future irrigation demands along with the planning of optimal irrigation in the KRB culturable area.
ESTIMATION OF GROUNDWATER FLOW FOR THE CENTRAL EASTERN SAYAN RIVERS
Authors:   Institute of the Earth’s Crust, Siberian Branch, Russian Academy of Sciences, BADMINOV P.S. , ORGIL'YANOV A.I. ,   Institute of the Earth’s Crust, Siberian Branch, Russian Academy of Sciences, PAVLOV S.KH. ,   Institute of the Earth’s Crust, Siberian Branch, Russian Academy of Sciences, KRYUKOVA I.G. ,   Institute of the Earth’s Crust, Siberian Branch, Russian Academy of Sciences
Published: April 2024

The results of estimating the groundwater flow for the rivers of the Eastern Sayan, which includes 89 individual watersheds, are presented. Most of the river basins (left tributaries of the Angara River) are located in a fold-mountain area and are poorly covered by the data of the routine observations of river runoff. The groundwater flow of the rivers was determined by a complex hydrological and hydrogeological method of genetic hydrograph separation, taking into account a removal of a part of the flow for the aufeis formation. For unexplored rivers, the groundwater flow was determined by the method of transition coefficients for unit water discharges obtained during the hydrometric survey in the winter low-water period. In the fold-mountain area, the specific groundwater flow varies over a wide range and depends on the landscape and climatic conditions, as well as on the hydrogeological conditions of specific watersheds associated with the presence of permafrost and differences in tectonic activity in different parts of the study area.
Application of artificial intelligence technology in monitoring students’ health: Preliminary results of Syiah Kuala Integrated Medical Monitoring (SKIMM)
Authors: Pratama Rovy , Suhanda Rachmad , Aini Zahratul , Nurjannah Nurjannah , Geumpana Teuku A.
Published: April 2024

Health promoting university is a holistic approach to health that uses higher education settings to create a learning environment that improves the health and well-being of the campus community in a sustainable manner. The utilization of technology such as artificial intelligence (AI) could be one of the main success factors in the implementation of health-promoting universities to increase the effectiveness and efficiency of all stages of activities carried out in health promotion and prevention efforts. Integrated monitoring with the utilization of AI in this program is conducted to evaluate the health status of the students. The Syiah Kuala Integrated Medical Monitoring (SKIMM) has three components of continuous health status evaluation: vital signs, nutritional status, and burnout status. Health status monitoring was conducted continuously for three months among medical students at the Faculty of Medicine Universitas Syiah Kuala, Banda Aceh, Indonesia. This system uses the WhatsApp application as a platform to monitor health status. Student health monitoring in this program consists of two main activities: the health measurement phase and the health monitoring phase. The use of the SKIMM system to monitor students' vital signs, nutritional status and burnout status through the use of AI significantly raises students' awareness to conduct timely self-examination and enables sustainable healthy lifestyle behavior change. The adoption of AI technology allows for continuous health promotion to the entire academic community, including students in implementing the health promoting university.
Repensando la configuración organizacional: perspectiva de género y conciliación de vida laboral y familiar: Caso de estudio en pyme potosina
Authors: De la Rosa Hernández Martha Angélica , Ortega Velázquez Aída
Published: April 2024

En el presente capítulo se muestran los resultados de investigación aplicada a una pyme potosina cuyo objetivo fue identificar las prácticas laborales con equidad de género, a fin de distinguir acciones concretas que, desde las organizaciones, promuevan el objetivo cinco de igualdad de género, de los objetivos para el Desarrollo Sostenible de la Agenda 2030. Para lograr lo anterior, se diseñó una guía de autoevaluación que posteriormente se aplicó en la empresa del caso de estudio, utilizando la metodología de semáforo. Los resultados obtenidos muestran un fragmento de la realidad que como sociedad debe preocuparnos y llevarnos a repensar en acciones colectivas, con los diferentes actores involucrados, que verdaderamente incidan en la materialización del objetivo en cuestión.
Las empresas familiares y el desarrollo sostenible: Los retos ante la pandemia de COVID-19
Authors: Cervantes Rosas María de los Ángeles , De Anda Montaño Rosa Elena , Rojas Escárrega David Ignacio
Published: April 2024

Las empresas no pueden mantenerse al margen de las exigencias de la sociedad y una de ellas, es comprometerse con el desarrollo sostenible que involucra el logro del crecimiento privilegiando el bienestar común. Sin embargo, la crisis de salud producto del COVID-19, pone en un predicamento a los empresarios y los obliga a elegir entre el equilibro económico, social y ambiental, o preocuparse por la sobrevivencia de la empresa. Es por ello, que el objetivo de la presente investigación es identificar los cambios en la toma de decisiones de propietarios de empresas familiares y su visión de futuro en relación con el desarrollo sostenible. Para ello, por medio del estudio de caso, se entrevista a propietarios de empresas familiares guasavenses. Dentro de los principales hallazgos, en la dimensión económica se menciona la búsqueda de la menor afectación posible, la conservación del cliente y no afectar la calidad de productos y servicios. En la parte social, el fomento de hábitos de salud, cuidado de salud emocional de colaboradores y su conservación. Por último, en la dimensión ambiental, se menciona principalmente el uso racional de recursos. Adicionalmente, han requerido adaptarse a los cambios en el entorno como producto de la pandemia generada por el COVID-19, para poder permanecer en el mercado. A manera de conclusión, se puede señalar que la crisis sanitaria ha afectado en diferentes ámbitos, y las empresas familiares no han sido la excepción. Sin embargo, algunas le han apostado a la resiliencia por medio de estrategias que minimicen los impactos y esperando que mejore la situación.
The impact of digital marketing, social media, and digital transformation on the development of digital leadership abilities and the enhancement of employee performance: A case study of the Amman Stock Exchange
Authors: Hanandeh Ahmad , Haddad Eva , Najdawi Sakher , Kilani Qais
Published: April 2024

This study seeks to analyze the impact of digital marketing, social media, and digital transformation on the development of digital leadership abilities and the resulting enhancement of employee performance. The research examines the complex connections between digital elements and their influence on organizational dynamics, using a case study done at the Amman Stock Exchange as a basis for analysis. The inquiry explores the impact of digital marketing strategies on leadership skills, the use of social media platforms to promote digital leadership, and the transforming influence of digital initiatives on employee performance. In addition, the study evaluates the interaction of these characteristics and their combined impact on organizational success. Evidence suggests that digital methods have a complex and wide-ranging effect on leadership and performance, including factors such as adaptation, communication, and strategic decision-making. The study highlights the capacity of companies, specifically within the framework of the Amman Stock Exchange, to utilize digital tools for the purpose of developing leadership skills and improving performance. The findings obtained from this study provide important insights for enhancing digital ecosystems in comparable organizational contexts, highlighting the importance of a thorough comprehension and strategic integration of digital components in order to attain long-lasting success.
How metaverse can enhance customer awareness, interest, engagement and experience: A practical study
Authors: Alshurideh Muhammad , Kurdi Barween Al , Al-Gasaymeh Anwar , Abuhashesh Mohammad , Jdaitawi Asma , Alzoubi Haitham M. , Hamadneh Samer , Alquqa Enass
Published: April 2024

The future of the Internet lies in the utilization of contemporary virtual technology, the metaverse is a virtual ecosystem for communication and interaction. This study aims to determine how the metaverse can enhance customer awareness, interest, engagement and experience. The study population consists of customers of retail clothing stores. A questionnaire was developed to collect study data and distribute it electronically. The number of respondents reached (77) customers. Using path coefficients, the results indicate that metaverse can enhance customer awareness, interest, engagement and experience.
Analysis of the tourism network post the COVID-19 pandemic: Implications for revitalization
Authors: Kanrak Maneerat , Nonthapot Sakkarin
Published: April 2024

This study analyzes the tourism network and destinations after the COVID-19 pandemic using social network analysis (SNA). Analysis of 789 destinations in Thailand has found that the destinations are connected by 1,1175 tourism routes. The network is a sparse network with a low network density. It seems to have a scale-free property that reflects that most destinations have low connectivity and a small number of destinations have high connectivity. The network has a large average path length and low clustering coefficient. Different roles of destinations are identified based on degree, betweenness and closeness centrality. The findings draw implications for vitalizing the sector.
The effect of artificial intelligence capability on patient satisfaction
Authors: Oraini Badrea Al
Published: April 2024

The study aims at exploring the effect of artificial intelligence as measured by machine learning capability and expert systems capability on patient satisfaction. As informant participants, medical staff workers in healthcare institutions in KSA comprise the population of the study. Hence, a sample consisting of 300 participants was used to gather research data via a questionnaire developed based on previous works. By means of Smart PLS 3.0 software, the results indicate that both machine learning and expert systems have significant effects on patient satisfaction. The study concluded that artificial intelligence capability is a cornerstone of patient satisfaction. such a statement encompasses theoretical and empirical implications as stated at the end of this study.
Machine learning approaches for enhancing smart contracts security: A systematic literature review
Authors: AlShorman Areej , Shannaq Fatima , Sheha Mohammad
Published: April 2024

Smart contracts offer automation for various decentralized applications but suffer from vulnerabilities that cause financial losses. Detecting vulnerabilities is critical to safeguarding decentralized applications before deployment. Automatic detection is more efficient than manual auditing of large codebases. Machine learning (ML) has emerged as a suitable technique for vulnerability detection. However, a systematic literature review (SLR) of ML models is lacking, making it difficult to identify research gaps. No published systematic review exists for ML approaches to smart contract vulnerability detection. This research focuses on ML-driven detection mechanisms from various databases. 46 studies were selected and reviewed based on keywords. The contributions address three research questions: vulnerability identification, machine learning model approaches, and data sources. In addition to highlighting gaps that require further investigation, the drawbacks of machine learning are discussed. This study lays the groundwork for improving ML solutions by mapping technical challenges and future directions.
A Comprehensive Review on the Transmission, Pathogenesis, and Prevention of Monkey Pox
Authors: Sharma Neha , Sharma Sakshi , Bala Madhu , Rana Ritesh , Sharma Neelam
Published: April 2024

Manily the cases of monkeypox connected to the 2022 breakout are being reported in nations throughout Europe and the Western Hemisphere, where it was formerly endemic to parts of Africa. Numerous groups are working on contact-tracing initiatives, but it is still unclear what started this outbreak. The monkeypox virus disease is a zoonotic viruses is a member of the orthopoxvirus genus and belonging to family poxiviridae. Following the eradication of smallpox worldwide in the 1970s, occurrences of monkeypox attracted attention on a global scale. The monkeypox virus could be protected against by the smallpox vaccine. Monkeypox instances increased once the smallpox vaccine was no longer administered. Monkeypox did not really receive widespread notice until the 2003 US outbreak. Despite the name "monkeypox," the virus did not originate in monkeys. The virus has been linked to a numeral of rodents and small animals, but the exact source of monkeypox is still unknown. The viral infection was originally noticed in macaque monkeys, hence the term "monkeypox." Although transmission of monkeypox from person to person is extremely uncommon, it is frequently assign to respiratory secretions or closely contacted with mucocutaneous abrasion of the contaminated person. Now present no designated cure for contaminated person, although supportive therpy may be used to relieve symptoms; in extremely severe cases, medications such tecovirimat may be used. Because there are no precise recommendations for symptom alleviation, many treatments are arbitrary. In light of the current outbreaks worldwide, we provide updated information on monkeypox for healthcare professionals in this review.
An Ethnopharmacological, Phytochemical and Pharmacological Review on Mangifera indica (Mango)
Authors: Sharma Divya , Gupta Shivani , Kumar Ramesh , Singh Pooja , Singh Arjun , Khan Hema
Published: April 2024

Traditional medicine is a body of knowledge, skills, and practices based on traditional cultures' presumptions, beliefs, and experiences in order to maintain their health. Traditional herbal treatments are highly valued among many rural or indigenous people in many underdeveloped nations. According to the World Health Organization, about 80% of the world's population relies on traditional medicine, with 60% of rural Indians using herbal treatments. During the previous five years, utilization of herbal supplements grew from 2.5 % to 12 percent. Mangifera indica, a plant that is frequently used in ayurvedic medicine. Although there have already been literature reviews on this plant, this one is presented to gather all the most recent data on its pharmacological and phytochemical activities, which have been widely studied using a variety of techniques. Mango is said to have antidiabetic, anti-oxidant, antiviral, cardiotonic, hypotensive, and anti-inflammatory properties, according to studies. Numerous effects have also been investigated, including hypolipidemia, antimicrobial, hepatoprotective, and gastroprotective properties. Other effects include antibacterial, antifungal, anthelmintic, antiparasitic, antitumor, anti-HIV, anti-bone demineralization, antispasmodic, antipyretic, antidiarrheal, and immunomodulatory properties. These studies are extremely positive and suggest that additional research on this herb is needed to confirm these findings and elucidate additional potential therapeutic effects. The use of mango in clinical trials for a range of ailments is also recommended.
Efficient 2-Distance Coloring Method for Maximum and Average Sparse Graphs in Resource Allocation Optimization of Microgrids
Authors: Chao Qin,
Published: April 2024

In microgrids management, optimizing resource allocation and enhancing efficiency are critical challenges. This paper introduces the 2-distance coloring technique as a novel approach to address these issues by focusing on the coloring problem of sparse graphs. Through rigorous theoretical derivation, we establish the minimum number of colors required for 2-distance coloring in sparse graph scenarios. Specifically, we demonstrate that for a graph G with an average maximum degree less than 2+17/20 and a maximum degree of 6, it can be list 2-distance colored using no more than 11 colors. This means that, given any set of 11 colors for each vertex, a valid 2-distance coloring can be achieved, ensuring no two adjacent vertices share the same color. This finding is pivotal, as it sets a precedent for the number of resources needed to efficiently manage and allocate resources in microgrids systems through 2-distance coloring. The application of this technique in microgrids promises to revolutionize resource distribution, reducing overlap and maximizing efficiency across the network.
Aircraft Objection Detection Method of Airport Surface based on Improved YOLOv5
Authors: Rui Zhou,
Published: April 2024

An aircraft object detection method on the basis of improved YOLOv5 was proposed to address the issues of large model size, high number of parameters, and inability to meet real-time monitoring requirements of aircrafts in traditional object detection. Firstly, the basic unit of ShuffleNetv2 network was optimized through replacing 3x3 convolution with 5x5 convolution and removing subsequent 1x1 convolution. Simultaneously, the original ReLU activation function was replaced with PReLU. Secondly, CBAM (Convolutional Block Attention Module) attention mechanism was developed to enhance the detection accuracy of the improved network. Finally, improved ShuffleNetv2 network was applied as the backbone structure of YOLOv5. Experimental results revealed that the parameter number of the improved YOLOv5 method introduced in this paper was decreased by 18 times, with a model size of 1.03M. Therefore, a 20.8% increase was achieved in frames per second (FPS) in GPU environments and a 234.6% increase was observed in FPS in CPU environments, while a mean average precision (mAP@0.5) of 0.99 was maintained compared with traditional YOLOv5 network. Because of the advantages of fewer parameters, faster recognition speed, higher localization accuracy, and smaller memory requirement, the developed method was found to be suitable for real-time monitoring of aircrafts in airport surface.
3D Facial Expression Recognition Based on Geometric Feature Fusion
Authors: Quan Li Jinwei Wang,
Published: April 2024

In order to overcome the problems of missing important information and di-mensionality disaster of three-dimensional (3D) facial expression, a novel method based  on geometric feature fusion is proposed. This approach initially extracts  distance feature vectors and angle feature vectors from key facial regions such as the eyes, mouth, and eyebrows using direct geometric feature. Subsequently, the K-nearest neighbor (K-NN) algorithm is used to obtain the distance feature vector candidate sets and angle feature vector candidate sets separately. Finally, the maximum-minimum rule is utilized to fuse these candidate sets into a single feature vector, completing the recognition of 3D facial expression. Experimental results on the BU-3DFE database demonstrate that this method achieves on overall recognition rate of 97.6%, exhibiting excellent robustness to varia-tions in facial expressions. Furthermore, this approach can serve as a valuable reference for future research in the field of 3D face recognition.
Semi-automatic Knowledge Graph Construction Based on Deep Learning
Authors: Vladimir Y. Mariano, Mideth Abisado, Alexander A. Hernandez Yong Xu,
Published: April 2024

The paper studied course knowledge graph in teaching resources and curriculum knowledge management tasks from the perspective of knowledge management. Considering that the course of Python Language Programming itself has formed a relatively complete knowledge system and knowledge point structure, the paper adopted a top-down approach to build the knowledge graph. Firstly, the paper obtained different types of course-related corpus and data from different sources, and then constructed the ontology layer of Python programming course. At the ontology level, the paper defined the concept type, relation type and attribute type of the course domain respectively. Considering the completeness of knowledge points in the curriculum domain knowledge graph, the paper extracted all entities, relationships, attributes, and its values from the curriculum corpus using a semi-automatic extraction method that takes into account both accuracy and efficiency based on the modeling results of the ontology layer. Then they were transformed into triples in the form of < entity, relationship, entity > or < entity, attribute, attribute value > to build data layer of knowledge graph. Finally, visualization of triplet data was realized through Neo4j graph database.
Airport–Industry–City Integrated Accessibility Using Big Data Case View from Beijing Capital International Airport
Authors: Danyang Shen, Ximeng Liu, Xiudi Li
Published: April 2024

Big data technology can help collect, analyze, and process large-scale transportation, population, economic, and other relevant data, which has been proven to be more accurate in assessing the accessibility of different areas. An aerotropolis is composed of the airport as the core, airport-related industry as the link, and the airport-oriented city as the carrier, airport–industry–city (AIC) system. To better grasp the development quality of aerotropolises, an AIC integrated accessibility measurement model is constructed comprising the accessibility of airport route network, airport ground traffic, and airport information coverage. Using big data of traffic, network and information from 2001 to 2020, we apply the model to the Beijing Capital International Airport (PEK) aerotropolis. The results show that PEK’s AIC connectivity exceeded 1 for the first time in 2008, reaching 1.0890, which the range of the scale is [0, +∞]. It fluctuated significantly from 2017 to 2020, showing a double life-cycle S-curve. So, the PEK aerotropolis’ development can be divided into four stages: germination (before 2001), high-speed development (2002–2011), gradual formation (2012–2019), and “seeking change in the process of change” (after 2020). Our model offers a new tool for identifying the development stages of aerotropolises and for improving connectivity—namely, improving the accessibility of the airport node network, the layout of airside-preferring enterprises, and the network structure of new Internet-based airport cities. Obviously, the AIC integrated accessibility can indeed guide the scientific development of an aerotropolis.
The Primitive Exponent Set of a Class of Representative Twocolored Digraph in Graph Theory
Authors: Meijin Luo, Yusong Lu
Published: April 2024

Graph is simple and intuitive. It can be used to solve many problems in computer science. For a kind of representativedigraph, the edges (arcs) of the digraph are colored with red and blue colors. The range of the primitive exponent are discussed indifferent cases, and the extremal two-colored digraphs are found by coloring all arcs with two colors. Finally, the primitive exponentset is given. The results can provide a reference for the study of primitive exponent of three-colored digraph and the application ofgraph coloring in computer science, such as communication network and coding cache.
Intelligent Management Platform into Equipment Construction Personnel Training Mode Exploration: A Case of Police Equipment Management Information
Authors: Qilei Wang
Published: April 2024

To strengthen the application level of intelligent equipment management information platform for police officers, combined with the relevant requirements of the public security organ, education department and other departments, this paper focusing on the personnel training mode in the field of police equipment management information based on the background of intelligent management application, the connotation of professional development and the current situation of professional demand are analyzed. Through the study of knowledge system, curriculum system, teaching mode and so on, the cultivation of students 'skills and professional quality training are strengthened. The paper also discusses the existing problems in the aspects of the necessity, realistic demand, the promotion path and the evaluation of the school-bureau cooperation training mode. The model construction fully reflects the teaching needs of the police equipment management specialty in the information age, and establishes a professional ability talent training mode that matches the needs of the police equipment management professional posts.