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Our mission is to inspire and empower the scientific exchange between scholars around the world, especially those from emerging countries. We provide a virtual library for knowledge seekers, a global showcase for academic researchers, and an open science platform for potential partners.

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Research article
Enhancing Board Effectiveness in Maltese Public Sector Entities: An Analytical Study
lauren ellul ,
marilyn scicluna ,
peter j. baldacchino ,
norbert tabone ,
simon grima
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Available online: 06-20-2024

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This study critically evaluates Board Effectiveness (BE) within Maltese Public Sector Entities (MPSEs), with a focus on five key aspects: board selection and appointment, board role, board composition, board remuneration, and board performance evaluation. Semi-structured interviews were conducted with twenty-two participants, including eighteen MPSE board members (BMs), a representative from the Malta Institute of Directors, two corporate lawyers, and one corporate advisor. The findings indicate significant deficiencies in BE, particularly due to a lack of transparency in the selection and appointment process. This process is often influenced by political loyalties, which exclude new talent and discourage competent individuals. The identification of BMs as Politically Exposed Persons (PEPs) further restricts the inclusion of diverse talent, particularly among entrepreneurs. Additionally, insufficient training for BMs and persistent political pressures have been found to hinder the fulfilment of fiduciary duties. Female representation on MPSE boards is notably low, and foreign appointments are rare, thereby weakening the overall board composition. Moreover, the remuneration for MPSE BMs is significantly lower than that in the private sector, adversely impacting the quality of BMs. Resistance to implementing performance evaluations, which could potentially reduce political protection, has also been observed to impede BE. This study underscores the necessity of strengthening corporate governance (CG) practices to enhance BE in MPSEs, which is crucial for fostering a thriving economy and creating a positive legacy for future generations.

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The study aims to examine the current state of property tax administration in Zimbabwean local authorities under the conditions of digitalization. Property taxes within the Zimbabwean local tax system are significantly under-collected, necessitating an urgent enhancement of their contribution to local authority budgets. A quantitative research approach was adopted, collecting data through questionnaires from a target population of 60 staff members within an urban local authority. Purposive sampling was employed to select Chief Executive Officers, Heads of Departments, and staff directly involved with Information and Communication Technology (ICT) and Property Tax Administration, including ICT departments, accounting and finance staff, and engineering departments. Additionally, residential and commercial property owners were conveniently sampled based on availability and willingness to participate, resulting in a total sample size of 46 respondents. The findings reveal a significant positive relationship between Information Technology and property tax administration, suggesting that policymakers should prioritize digitization to enhance effective tax administration. Furthermore, control variables such as population, trade, and GDP were found to have significant relationships with tax administration in Zimbabwe. The introduction of ICTs has been shown to improve the efficiency and effectiveness of property tax administration, underscoring its critical role in the fiscal decentralization of local governments.

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In recent decades, the demand for electricity has continuously increased. Power generation facilities are predominantly situated at substantial distances from consumption centers, necessitating transmission over extensive, high-voltage lines. Such configurations lead to significant energy losses and diminished capacity and capability of transmission systems. Consequently, enhancements in transmission line performance have become a focal point for power system operators. The integration of the flexible alternating current transmission system (FACTS) technology has emerged as a pivotal solution, facilitating dynamic control over power flow and amplifying the existing capacity of power lines without the need for constructing new infrastructure. Among various FACTS devices, the static synchronous series compensator (SSSC) plays a crucial role by injecting variable capacitive or inductive reactance as required, thereby optimizing power flow and enhancing voltage stability. This review paper meticulously examines the functionality of different FACTS technologies, with a specific focus on the SSSC. Comparative analyses of transmission line performance, uncompensated, compensated through traditional series capacitors, and enhanced via SSSC, were conducted. The findings underscore the versatility of SSSC in reducing transmission losses and stabilizing network operations. This investigation not only details the operational benefits of SSSC but also explores its potential in addressing contemporary challenges in power transmission systems.
Open Access
Research article
Advancements in Image Recognition: A Siamese Network Approach
Jiaqi Du ,
Wanshu Fu ,
Yi Zhang ,
ziqi wang
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Available online: 06-13-2024

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In the realm of computer vision, image recognition serves as a pivotal task with extensive applications in intelligent security, autonomous driving, and robotics. Traditional methodologies for image recognition often grapple with computational inefficiencies and diminished accuracy in complex scenarios and extensive datasets. To address these challenges, an algorithm utilizing a siamese network architecture has been developed. This architecture leverages dual interconnected neural network submodules for the efficient extraction and comparison of image features. The effectiveness of this siamese network-based algorithm is demonstrated through its application to various benchmark datasets, where it consistently outperforms conventional approaches in terms of accuracy and processing speed. By employing weight-sharing techniques and optimizing neural network pathways, the proposed algorithm enhances the robustness and efficiency of image recognition tasks. The advancements presented in this study not only contribute to the theoretical understanding but also offer practical solutions, underscoring the significant potential and applicability of siamese networks in advancing image recognition technologies.

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Recent observations of global warming phenomena have necessitated the evaluation of the service performance of asphalt pavements, which is substantially influenced by surface temperature levels. This study employed twelve distinct machine learning algorithms—K-neighbors, linear regression, multi-layer perceptron, lasso, ridge, support vector regression, decision tree, AdaBoost, random forest, extra tree, gradient boosting, and XGBoost—to predict the surface temperature of asphalt pavements. Data were sourced from the Road Weather Information System of Iowa State University, comprising 12,581 data points including air temperature, dew point temperature, wind speed, wind direction, wind gust, and pavement sensor temperature. These data were segmented into training (80%) and testing (20%) datasets. Analysis of model outcomes indicated that the Extra Tree algorithm was superior, exhibiting the highest R$^2$ value of 0.95, whereas the Support Vector Regression algorithm recorded the lowest, with an R$^2$ value of 0.70. Furthermore, Shapley Additive Explanations were utilized to interpret model results, providing insights into the contributions of various predictors to model outcomes. The findings affirm that machine learning algorithms are effective for predicting asphalt pavement surface temperatures, thereby supporting pavement management systems in adapting to changing environmental conditions.

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In the realm of heat transfer, the phenomenon of boiling heat transfer is paramount, especially given its efficiency in harnessing the latent heat of vaporization for significant thermal energy removal with minimal temperature alterations. This mechanism is integral to various industrial applications, including but not limited to the cooling systems of nuclear reactors, macro- and micro-electronic devices, evaporators in refrigeration systems, and boiler tubes within power plants, where the nucleate pool boiling regime and two-phase flow are prevalent. The imperative to optimize heat exchange systems by mitigating excessive heat dissipation, whilst simultaneously achieving downsizing, has consistently been a critical consideration. This research uses computational, based on Fluent software, to analyze thermal characteristics and cooling mechanisms of different concentrations of nanofluids, in conjunction with surfaces adorned with diverse fin geometries. Specifically, the study scrutinizes the thermal performance of water-based nanofluids, incorporating Copper (II) Oxide (CuO) nanoparticles at concentrations ranging from 0% to 1.4% by volume, under boiling conditions. The analyses extend to the efficacy of different fin shapes—including circular, triangular, and square configurations-within a two-dimensional geometry, under the conditions of forced convection heat transfer in both steady and transient, viscous, incompressible flows. The findings are poised to contribute to the design of more efficient heat exchange systems, facilitating enhanced heat dissipation through the strategic use of nanofluids and meticulously designed surface geometries.

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This investigation addresses the critical challenge of devising robust and sustainable energy infrastructures by integrating renewable energy sources in Makkovik, Newfoundland, and Labrador. A hybrid renewable energy system (HRES) comprising wind turbines, photovoltaic (PV) solar panels, battery storage, and backup diesel generators was evaluated for its viability and efficiency. With the help of the HOMER Pro software, extensive modeling and optimization were conducted, aimed at reducing dependency on fossil fuels, cutting carbon emissions, and enhancing economic benefits via decreased operational costs. The results indicated that the energy demands of Makkovik could predominantly be met by the proposed system, utilizing renewable resources. Significant reductions in greenhouse gas emissions were observed, alongside improved cost-efficiency throughout the system's projected lifespan. Such outcomes demonstrate the system’s capability to provide an environmentally friendly and technically viable solution, marking a substantial step towards energy resilience and sustainability for isolated communities. The integration of diverse renewable energy sources underlines the potential for substantial emission reductions and operational cost savings, highlighting the importance of innovative energy solutions in enhancing the sustainability and resilience of remote areas. This study contributes vital insights into optimizing energy systems for economic and environmental benefits, advancing the discourse on renewable energy utilization in isolated regions.
Open Access
Research article
A Comparative Analysis of Side Effects from the Third Dose of COVID-19 Vaccines in Palestine and Jordan
jebril al-hrinat ,
aseel hendi ,
abdullah m. al-ansi
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Available online: 06-05-2024

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In this cross-sectional study, the prevalence and characteristics of adverse effects following the administration of the third dose of the coronavirus disease 2019 (COVID-19) vaccines were compared between recipients in Palestine and Jordan. Data were collected via an online survey targeting random samples from both countries. In Palestine, the primary factors predisposing individuals to side effects after the third dose were prior adverse reactions to earlier vaccinations and a history of COVID-19 infection before vaccination. Minor contributing factors included food sensitivities, weight, and drug sensitivities. In Jordan, gender, smoking, and food sensitivities emerged as the most significant variables influencing the development of side effects, with age being a secondary factor. Weight, COVID-19 infection post-vaccination, and prior adverse reactions to earlier doses were less significant. In Palestine, individuals with diabetes and respiratory diseases were more prone to adverse effects, followed by those who are obese, and those with cardiovascular diseases, osteoporosis, thyroid disorders, immune diseases, cancer, arthritis, and hypertension. In Jordan, participants with arthritis were the most likely to develop side effects, followed by those who are obese, and those with respiratory conditions and thyroid disorders. These findings confirm that COVID-19 vaccines authorized for use are generally safe, and vaccination remains a crucial tool in curbing the spread of the virus. Acceptance of the third dose has been notable in both Palestine and Jordan, underscoring the value of booster doses in enhancing immunity.

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A retrospective analysis was conducted to assess potential drug-drug interactions (pDDIs) in the management of cardiovascular diseases, evaluating 500 prescriptions from hospitalized patients between January 1 and April 1, 2023. Using Medscape online software for the identification of drug-drug interactions (DDIs) and SPSS version 21 for statistical analysis, the study documented a 93% occurrence rate of pDDIs across the prescriptions. These interactions were categorized as serious (15% of cases, n=760, maximum per encounter: 4, mean: 1.52 ± 1.064), significant (75.6% of cases, n=3855, maximum per encounter: 30, mean: 7.71 ± 4.583), and minor (9.5% of cases, n=485, maximum per encounter: 4, mean: 0.95 ± 1.025). On average, 9.5 medications were prescribed per patient. Factors significantly associated with the incidence of pDDIs included age (r= 0.921, P < 0.01), presence of concurrent diseases (r= 0.782, P < 0.01), length of hospital stay (r= 0.559, P < 0.01), and the number of prescribed drugs (r= 0.472, P < 0.01). The most frequent interacting combinations were identified, with Clopidogrel + Enoxaparin (38.15%, n=290) and Enoxaparin + Aspirin (26.92%, n=210) being the most common, followed by other notable combinations. The study recorded adverse drug reactions in 15 patients. This investigation highlights a significant prevalence of pDDIs, particularly in cases of polypharmacy among cardiovascular patients. It underscores the critical need for systematic analysis and vigilant monitoring of prescriptions prior to drug administration by healthcare professionals.

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New aggregation operators (AOs) for interval-valued intuitionistic fuzzy sets (IVIFS) have been developed, offering advancements in multi-attribute group decision-making (MAGDM). IVIFS employs intervals for membership and non-membership grades, providing a robust framework to handle uncertainties inherent in real-world scenarios. This study introduces operational laws for interval-valued intuitionistic fuzzy values (IVIFVs), formulated on the Frank T-norm and T-conorm, and presents a generalization of the Maclaurin symmetric mean (MSM) operator tailored for these values. Named the interval-valued intuitionistic fuzzy Frank weighted MSM (IVIFFWMSM) and interval-valued intuitionistic fuzzy Frank MSM (IVIFFMSM), these operators incorporate new operational principles that enhance the aggregation process. The effectiveness of these operators is demonstrated through their application to a MAGDM problem, where they are compared with existing operators. This approach not only enriches the theoretical landscape of fuzzy decision-making models but also provides practical insights into the optimization of market risk.

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Through the deployment of bibliometric techniques and network visualizations, this analysis synthesizes the evolution and trajectories of autonomous driving research from 2002 to May 2024, as captured in the Scopus database encompassing 342 scholarly documents. This study was conducted to delineate the developmental contours, thematic emphases, and the expansive growth trajectory within this field, particularly noting a surge in scholarly outputs since 2017. Such growth has been primarily facilitated by breakthroughs in artificial intelligence and sensor technologies, along with burgeoning interdisciplinary collaborations and escalating academic and industrial investments. A meticulous examination of publication trends, document types, subject areas, and geographic distributions elucidates the multidisciplinary and international nature of this burgeoning field. Key thematic clusters identified—comprising foundational technologies, environmental sustainability, safety measures, regulatory frameworks, user experience, connectivity, and technological innovations—underscore the prevailing research directions and emerging focal areas poised to shape future autonomous mobility solutions. Notably, increased emphasis on environmental sustainability and regulatory frameworks has been observed, highlighting their critical roles in advancing and integrating autonomous driving systems. This study provides pivotal insights for researchers, policymakers, and industry stakeholders, fostering a nuanced understanding of the field’s dynamics and facilitating strategic alignments and innovations in autonomous mobility. The emergent research domains and collaborative networks revealed herein not only map the current landscape but also guide future scholarly endeavors in autonomous driving systems globally.

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