Al-Kindi Center for Research and Development (KCRD) (E-Journals)
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Digital Leadership in Realizing Bureaucratic Reform in the South Sulawesi Provincial Government
No full textBureaucratic reform efforts over the years have yet to yield optimal results, leaving government bureaucracy associated with pathologies such as corruption, collusion, nepotism (KKN), slow service performance, and an inability to meet public satisfaction. Digital leadership among heads of local government agencies is expected to contribute significantly to achieving bureaucratic reform. This study aims to analyze the role of digital leadership in facilitating bureaucratic reform in South Sulawesi Province and to formulate a digital leadership model for effective bureaucratic reform. The research employs digital leadership theory as outlined by Klein (2020), which encompasses business characteristics, attitudinal characteristics, and general characteristics, serving as the analytical framework to assess the state of digital leadership among local government leaders in South Sulawesi. The study adopts a qualitative descriptive method, with primary data collected through observations and interviews with key informants. Data analysis techniques include data reduction, data display, and drawing conclusions. The findings conclude that: 1) Not all local government leaders possess effective digital leadership skills, which results in suboptimal contributions to bureaucratic reform. This is evident through the evaluation of business characteristics, attitudinal characteristics, and general characteristics that have yet to support reform in institutional structuring, administrative processes, human resource management, and service delivery. 2) The researcher formulated the “S4P Digital Leadership Model,” developed from Klein’s (2020) digital leadership model, which is believed to contribute to more effective bureaucratic reform. The novelty of this model lies in the incorporation of local wisdom dimensions—Sipakatau, Sipakalebbi, Sipakinge, and Sirri na Pacce—as well as digital leadership prerequisites such as digital literacy, innovation, and collaboration. This comprehensive, contextually relevant, and responsive model can be implemented across different regions in Indonesia, utilizing local cultural values as guiding principles for governance and public service delivery
Level of Essential Best Practices and the Implementation of Inclusive Education among Regular Elementary Teachers
No full textThe study aimed to investigate the level of essential best practices and the implementation of inclusive education among regular elementary teachers in identified schools in the five districts of the Division of Capiz for the school year 2020-2021. The quantitative technique was employed, and convenient random sampling was used to determine 50 respondents. An adapted research survey questionnaire was used to gather data. Frequency, percentage, weighted mean, standard deviation, chi-square, and correlation coefficient were used. The findings revealed that the participants perceived themselves as having a "moderate" level of essential best practices of inclusive education and having a "moderate" implementation of inclusive education. Furthermore, results revealed no significant relationship between the level of essential best practices in inclusive education and its profile as to age and gender, teaching position, number of years in service, number of LSENs in class, and number of special/inclusive education trainings attended. There was no significant relationship between implementation of inclusive education as to age and gender, number of years in service, number of LSENs in class, number of special/inclusive education training attended, and number of hours in special/inclusive education training attended. It is concluded that essential best practices of inclusive education have influenced inclusive school practice. To address the issue, an action plan was formulated for implementation
The Application of Semiotics in Cross-Boundary Cobranding for the Brand Identity Design of Luckin Coffee
No full textAs a coffee chain brand, Luckin Coffee entered the Chinese market in 2017, positioning itself as a domestic brand offering "affordable and accessible" quality coffee for the general public. With the continuous expansion of the consumer market and increasingly fierce competition among brands, Luckin Coffee has adopted comprehensive strategies to stabilize its market position and strengthen consumer purchasing intent. In addition to leveraging a new retail model enabled by mobile internet and big data, Luckin Coffee has actively pursued cross-boundary cobranding. This emerging marketing strategy disrupts traditional brand design paradigms by integrating cultural and visual elements through collaboration with diverse IPs or artists. Such cobranded initiatives foster innovation in brand design, enhancing both the image and cultural value of the brand while offering a novel approach to redefining brand identity
Optimizing Lung Cancer Risk Prediction with Advanced Machine Learning Algorithms and Techniques
No full textLung cancer is among the leading causes of cancer death in the U.S.A. as well as globally and causes more deaths than breast, prostate, and colorectal cancers combined. It thus presents a significant health burden globally, with an estimated new case diagnosed and death toll at 2.2 and 1.8 million annually, respectively. Given the complexity of the etiology of lung cancer, there is a real urgent need for more accurate and reliable prediction models with the capability to integrate diverse risk factors. While current modalities for screening and imaging clinical conditions are effective, they are often costly and invasive. The study\u27s main objective was to develop and evaluate machine learning models, using integrated demographic, environmental, and lifestyle variables for predicting lung cancer risk. The source of dataset for lung cancer risk prediction was retrieved from multiple sources, particularly, Cleveland hospital records as well as public health databases in the U.S; Besides, we also used large-scale epidemiology studies such as the National Lung Screening Trial (NLST) or the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. These sources provided invaluable datasets to which machine learning models were developed, as they contained very valuable information on demographic data, past medical history, lifestyle habits, and clinical symptoms. In this study, the experiment used 3 machine learning algorithms: Logistic Regression, XG-Boost, and Random Forest. Accuracy, precision, recall, as well as F1 score, are used as performance metrics. Overall, the performance of the Logistic Regression model surpassed the Random Forest and XG-Boost models. It had the highest scores in all the metrics, particularly, accuracy, precision, recall, and F1 score. This is indicative that the model Logistic Regression was slightly better at balancing the true positives and false positives and false negatives. The Random Forest model exemplified an intermediate performance, positioning itself second to the Logistic Regression. A significant volume of empirical studies has established that the different machine learning techniques, such as Logistic Regression and Random Forest considerably improve the detection of lung cancer. Although logistic regression, due to its simplicity and interpretability, remains very useful, Random Forest and XG-Boost are much more capable of modeling difficult nonlinear interactions in high-dimensional data. Advanced models like these will provide far more accurate, personalized risk estimates and have the potential to be a powerful contribution to early detection and better clinical decisions regarding lung cancer
The Impact of the Interaction between Intestinal Flora and Intestinal Wall Immune Microenvironment on Ulcerative Colitis
No full textUlcerative colitis (UC) is a chronic, non-specific inflammatory disease of the intestine with an unknown etiology. The primary clinical manifestations include recurrent abdominal pain, diarrhea, mucus and purulent discharge, bloody stools, and tenesmus. Experts generally agree that the onset of this disease is primarily associated with factors such as intestinal flora, dietary composition, genetics, immunity, infections, and systemic inflammation. The intestinal flora constitutes the largest microecological system within the human body, consisting of a vast array of microorganisms that interact dynamically with the immune cells located in the intestinal wall. This interaction is crucial for maintaining a balance with the intestinal mucosa. The intestinal flora plays a significant role in the development of the immune system, sustaining normal immune function, and cooperatively countering the invasion of pathogenic bacteria. Alterations in the composition of the intestinal flora can influence the equilibrium between intestinal tolerance and immunity. Research has demonstrated that the disease process in UC patients is closely linked to disturbances in the structure of the intestinal bacterial flora，and symptoms can be significantly alleviated following the transplantation of normal flora. Therefore,Modulating the structure of the intestinal microbiota may serve as an effective strategy for treating ulcerative colitis (UC). This article examines the relationship between intestinal flora and intestinal wall immunity in the context of UC, while also exploring novel approaches for its treatment
Child Labor and Its Relationship to Family Circumstances
No full textChild labor is a serious indicator of societal structure breakdown, not merely an economic or social problem but also a result of a systemic failure in the education and social welfare systems. This phenomenon cannot be overlooked as it reflects a genuine crisis in our social system, where families are forced to send their children to work at an early age, signaling a weakness in protecting children\u27s fundamental rights. Addressing this issue requires more than just studying the phenomenon itself; attention must be directed toward the root causes: poverty, poor education, and a social system that allows such practices to persist. The aim of this research is to shed light on how family circumstances contribute to the emergence of this issue and how well-designed community policies can help reduce its spread. The researcher adopted the social survey method in the study, applying it to a sample of children aged 10 to 16 years in Libya. The sample consisted of 207 individuals, and a questionnaire was used as the data collection tool. The research concluded several key findings, the most significant of which was that children work between 7 and 9 hours a day. The findings showed that the social factors pushing children to work included parental separation, while economic factors, such as difficult financial conditions, were a primary cause of family separation. Cultural and educational factors were reflected in the negative impact of family separation on the ability of families to provide education for their children. The study also revealed the negative effects of child labor, particularly the exposure to fatigue, stress, and exhaustion
Effectiveness of the Rapid Eye Movement Sequential Therapy Program in Reducing Post-Traumatic Stress Disorders in Individuals with Obsessive-Compulsive Disorder: A Sample of Mothers of Children with Special Needs Referred from Psychological Clinics in Kha
No full textThe study aimed to reveal the effectiveness of the Rapid Eye Movement Desensitization and Reprocessing (EMDR) program in reducing post-traumatic stress disorder among patients with obsessive-compulsive disorder (among a sample of mothers of children with special needs referred from psychiatric clinics in Khartoum). The study sample consisted of (14) mothers, who were intentionally selected from those attending psychiatric clinics in Khartoum, and suffering from obsessive-compulsive disorder as a result of post-traumatic stress disorder after being informed that their children have special needs. Their ages ranged between (35-40) years. Participation in the treatment program was approved based on the psychiatrists\u27 orientations for them to attend the program to complete treatment after stopping medication. The Yale-Brown Obsessive Compulsive Scale was used and applied to the study sample before and after the program, as well as during follow-up. To achieve the study objectives, the EMDR program was applied to reduce post-traumatic stress disorder symptoms. It was prepared by the researcher and lasted for two months from January 1st (2023) until the end of February of the same year. The researcher applied the study tools before and after the program. The data was analyzed using the Statistical Package for Social Sciences (SPSS). The study reached the following results: The first hypothesis indicated statistically significant differences at the significance level (α=0.05) in obsessive thoughts among the study sample between the pre-test and post-test due to the EMDR program. The training program showed an effectiveness of (74.7%) in reducing their obsessive thoughts. The result of the second hypothesis also indicated statistically significant differences at the significance level (α=0.05) in compulsive acts among the sample between the pre-test and post-test, attributed to the EMDR program. The training program showed an effectiveness of (74.9%) in reducing the compulsive acts associated with post-traumatic stress disorder among the sample. The follow-up results indicated no statistically significant differences at the significance level (α=0.05) in obsessive thoughts among the sample between the post-test and follow-up test. There were also no statistically significant differences at the significance level (α=0.05) in compulsive acts among the sample between the post-test and follow-up test, indicating the effect and continued effectiveness of the training program during the follow-up stage. In light of these results, the study recommended conducting further studies on the effectiveness of the EMDR program in reducing post-traumatic stress disorder symptoms in patients with obsessive-compulsive disorder with a sample of males or other age groups to reduce their obsessive-compulsive acts.  
Professional Pressures and Their Impact on Marital Compatibility among Women Working in the Health Sector in Kufra City
No full textThis research aims to identify the professional pressures that women working in the health sector are exposed to and the extent of their impact on their marital compatibility based on several variables(years of experience,monthly income, family size) the research community may be from (80) women working in kufra city hospitals, where data was collected through a from developed for this purpose, and statistical analysis was conducted using the SPSS statistical package. The results showed that there is no negative impact of professional pressures on marital compatibility among the respondents,which means that 3.7% of the change in marital compatibility is due to professional pressures, while 96.3% is due to other reasons, and there are no statistically significant differences between the responses of the respondents about both professional pressures and marital compatibility due to the following variables: years of experience – monthly income – family size) while there are differences 3 to 9 for those with a family of 10 or more
Machine Learning Approaches to Identify and Optimize Plant-Based Bioactive Compounds for Targeted Cancer Treatments
No full textMachine learning (ML) represents a breakthrough in drug discovery, markedly increasing efficiency in the search for plant-derived bioactive compounds with anticancer activity. While compounds derived from plants like vincristine and taxol are historical pillars of oncology, the emerging novel therapeutic agents aim to overcome limitations associated with classical therapies, such as toxicity and resistance. Some of the important ML algorithms in this context include decision trees, support vector machines, neural networks, and ensemble learning which allow predictions about bioactivity by managing complicated biological data and determining the effectiveness of different compounds while also optimizing therapeutic profiles. For anticancer compound discovery, supervised as well as unsupervised learning is used whereby activity can be predicted from known properties or compounds just clustered in huge phytochemical databases. Moreover, deep learning models are particularly adept at processing high-dimensional data like multi-omics data and discovering non-linear relationships which furthers our understanding of bioactive compounds at a systems level. While optimizing bioactive compounds, QSAR modeling alongside generative models helps in fine-tuning the molecular design for improved activity and reduced toxicity. ADMET profiling also ensures that the molecules are within the limits of pharmacokinetic and safety standards, thus smoothing out the passage from in silico predictions to experimental validation. The discussion closes with the consideration of some challenges, such as data integration, interpretability of models, computationally intensive tasks, and regulatory demands to be followed versus the promise of the future through cooperative platforms, accessible ML tools together with personalized medicine. Further emphasis is given on the need for continued research interdisciplinary collaborations as well as investments that will help harness the full potential of ML in plant-based anticancer drug discovery to improve treatment outcomes while minimizing adverse effects
Ishmael and Ahab, the Sinister Double at the Limit
No full textIn this article, I will focus on a reading of Moby Dick based on the identification between the two key figures of the work: Ahab, the captain who tries to transgress the limits of the human, according to the terminology of the philosophy of Trias, and a narrator, Ishmael, who seems to know every thought of the Pequod\u27s crew and whose entry into the narrative already raises a doubt as to his identity. To do this, I will use Eugenio Trias\u27 philosophy of the limit as methodology