The Asian Bulletin of Big Data Management
Recognization by HEC's Journals Recognition System (HJRS) The Asian Bulletin of Big Data Management (ABBDM) is a peer-reviewed journal recognized by the Higher Education Commission of Pakistan in Y category . The Asian Bulletin of Big Data Management (ABBDM) is a peer-reviewed journal for research in social science and related disciplines. The Journal seeks to include empirical, conceptual, and methodological articles across the full range of green management in Asian countries. The ABBDM provides a forum for academics and practitioners to share the latest developments and advances in the knowledge and practice of global business dimensions. It publishes scholarly articles of an interdisciplinary nature with contemporary global relevance. It also aims to foster the exchange of ideas on a range of important international issues and, in doing so, provide a stimulus for further research and the development of global perspectives. To this end, our objective is to attract quality scholarship that can practically benefit corporations and other institutions in their overall development as well as society at large. We encourage contributions from around the globe, with a special emphasis on emerging economies. The Asian Bulletin of Big Data Management (ABBDM) © 2021 by ASIAN ACADEMY OF BUSINESS AND SOCIAL SCIENCE RESEARCH is licensed under Attribution 4.0 International
ASIAN ACADEMY OF BUSINESS AND SOCIAL SCIENCE RESEARCH
Optimizing Deep Learning Parameters for Enhanced Image Classification Accuracy: A Theoretical and Empirical Analysis
This study investigates the,impact of key deep learning,parameters—algorithm,type, dataset size, learning,rate, batch size, and number,of epochs—on image,classification accuracy, a,critical concern,in industries such as,healthcare, security, and,technology. Despite,advancements in deep learning, optimizing,these parameters for maximum,accuracy remains,challenging. To address,this issue, a,survey was conducted,among 381 professionals and,academics experienced,in deep learning, and,the data was analyzed,using Partial Least Squares,Structural Equation,Modeling (PLS-SEM). The,results confirm that all,five parameters significantly,influence image classification,accuracy, with optimized,settings leading to substantial,improvements. This,study contributes to,the theoretical understanding,of the Bias-Variance,Tradeoff Theory in the,context of deep learning,and offers practical guidelines,for enhancing model,performance. However, limitations,such as reliance on,self-reported data suggest,the need for further,experimental research. The,findings provide a comprehensive,framework for optimizing,deep learning models, offering,both academic insights and,practical solutions for,improving accuracy in,critical applications.
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Stock Markets during Different Waves of COVID-19 Pandemic: An Empirical Study on the Comparative Significance of Information and Sentiment
This study has investigated the impact of Covid-19 on stock markets in perspective of cases, deaths, and sentiments during different waves of pandemic in emerging countries. In order to run analysis OLS technique have been applied. The study found that information and sentiment might have different implications for different markets, where information seemed to be. Sentiment was deemed important for USA and India, whereas information aspect was deemed important for India and Pakistan, while both sentiment and information failed to explain stock returns in China. This implied that sentiment was important for countries where pandemic was not managed or controlled, whereas information was important for countries with some control on the pandemic, and both information and sentiment were not important for the country with stringent control on the pandemic and also on media. It is recommended on findings of the study that both information and sentiment could be important in different markets, during different situations and investors should consider dynamics of individual markets for their investment decisions
Blockchain Espousal in Legal Compliance with Corporate Governance
There is much exhilaration about blockchain technology in practically every business, including financial, and significant investments are being made worldwide. In order to uncover critical criteria for blockchain espousal in corporate governance, we undertook a systematic examination of a sample of 52 articles (10 industrial and 42 academics) through a preliminary research period from 2012 to 2020. We perform textual and empirical research to build a paradigm for decentralised autonomous governance. Additionally, we relate classic corporate governance theories to blockchain espousal using this framework. Our research also focuses on how blockchain technology may be used to improve the way corporations are run, now and in the future, as well as its possible consequences. By doing a systematic study and conducting an analysis of the text, we uncovered other gaps in the earlier academic and industrial literature and common tendencies. In addition, as part of our empirical research, we are compiling a one-of-a-kind blockchain investment database to make future investment projections. In addition, in the course of and after the COVID-19 conference, we researched the many ways blockchain technology may be used to improve corporate governance. According to our findings, previous academic publications prefer to concentrate on regulation and Initial Coin Offerings (ICOs). In contrast, previous publications in the sector have focused on exchanges and cryptocurrencies. Final thoughts on the impact of blockchain technology on corporate governance from the perspectives of behaviour, law & regulatory requirements, ethical considerations and managerial practices.
Descriptive Data Analysis on the Effect of General Anesthesia Versus Epidural Anesthesia in Postoperative Patients Regarding Pain
This study aims to investigate the impact of different anesthesia techniques, specifically regional anesthesia (RA) and general anesthesia (GEA), on postoperative pain, recovery, and overall experience in women undergoing cesarean sections. The primary objective is to compare pain levels and analgesic needs between RA and GEA, using tools like the Short-Form McGill Pain Questionnaire (SF–MPQ), Visual Analog Scale (VAS), and the Pain Quality Scale. Additionally, the study will assess recovery outcomes, including the time to first independent mobilization and the onset of lactation, alongside the emotional and psychological effects of each anesthesia method. A sample of 120-150 patients, selected via convenience sampling from private hospitals, will complete a questionnaire designed to collect both quantitative data (pain levels, mobilization time) and qualitative data (emotional experiences, satisfaction). The study hypothesizes that RA will result in lower pain levels and reduced analgesic consumption compared to GEA, as well as faster recovery, including quicker mobilization and lactation. The findings aim to provide valuable insights into optimizing postoperative care and anesthesia choices for cesarean deliveries, potentially improving patient outcomes and guiding clinical practices.
Acceptance and Commitment Therapy for the Treatment of Postpartum Depression: A Case Study Approach
The prevalence of postpartum depression (PPD) has been linked to contradictory outcomes for mothers, infants, and mother-child relationships. Pakistan has the greatest prevalence percentage among the Asian countries which ranges from 28%-63% (Habiba et al., 2020). When it comes to treating postpartum depression, one of the best methods is Acceptance and Commitment Therapy. (Hayes et al., 2006). However, a large number of new mothers choose not to seek expert assistance, which highlights the need for new platforms to be developed in order to provide health care services in Pakistan. As noted by Waters et al. (2020) the primary goal of this research is to develop and evaluate the ACT for postpartum depression in postpartum women. The present work is based on replication of the study conducted by Branquinho et al., (2022), titled "A blended cognitive–behavioral intervention for the treatment of postpartum depression”. The treatment approach used in the present study addresses mood and anxiety problems related to perinatal diseases. Utilizing the single-case study design. Our main resource who woman who volunteered, from teenage mothers to women in their forties who give birth. The intervention protocols and the strategy were developed in close collaboration with seasoned clinical psychologists and psychiatrists who are experts in the sector. The program was conducted weekly for a minimum of 12 weeks, during which the technique and treatment plan were explained in full. Compiling the patient's progress report and compiling the data received during treatment were done using computation. At the conclusion of the session, there was a notable decline in the severity of anxiety and depression symptoms. The goal is to compile positive evidence supporting the feasibility, efficacy, and proof-of-concept of ACT in reducing depressive symptoms in the postpartum phase. The advantages and disadvantages of this format were examined, along with their prospective applications in clinical health care and future research.
Early Detection and Classification of Apple Leaf Diseases Using Deep Learning Technique
Apple leaf diseases represent a critical challenge for apple crop production, threatening both the quality and quantity of harvests. Leaves are essential to the growth and health of apple trees, as they play a pivotal role in photosynthesis and nutrient transport. When these leaves become infected, the diseases can rapidly spread across the entire tree, leading to significant damage that can impact the yield and the overall health of the orchard. The consequences of such outbreaks can be devastating, with severe economic losses for farmers and potentially reduced marketability of the crops. To mitigate these risks, timely and accurate detection of apple leaf diseases is crucial. Early intervention allows farmers to apply targeted treatments and prevent the spread of infection to other parts of the tree or adjacent trees in the orchard. This research proposes an improved deep learning approach, utilizing the VGG16 model, to detect early signs of apple leaf diseases. By focusing on small spots and widespread lesions, the model can identify symptoms at an early stage, allowing for prompt intervention to prevent further spread. The study employs a dataset from Kaggle and a custom repository called Apple Fungal Diseases Dataset, with the classification results compared to other leading techniques to ensure accuracy. By providing a reliable method for early detection, this research aims to support farmers in managing apple leaf diseases more effectively, ultimately contributing to healthier orchards and improved crop yields.
The Integration of Fintech in Energy Markets: Economic Benefits and Policy Considerations
This study investigates the impact of, financial technology (fintech) integration, on the efficiency, cost reduction, and, sustainability of energy markets, with a, particular focus on, Pakistan. Employing, the Technology-Organization-Environment, (TOE) framework, the research, explores how fintech, adoption, investment in, fintech, regulatory support, technological, infrastructure, and market transparency, influence key market, outcomes. A structured questionnaire, was distributed to professionals, and stakeholders in the, energy sector, yielding 320,responses. The analysis, conducted using, Structural Equation Modeling (SEM) ,in Smart PLS, reveals that fintech, adoption significantly, enhances market efficiency, while, investment in fintech and market, transparency contribute to, cost reduction. Regulatory, support and technological, infrastructure are crucial, for market efficiency, and sustainability, respectively. The, study underscores the, need for a balanced approach that, includes supportive regulatory, frameworks and robust, technological infrastructure to fully, leverage fintech's potential. The findings, offer valuable insights for, policymakers and energy, market participants, highlighting, the critical role of fintech in driving, economic benefits and addressing, market inefficiencies. Future, research should expand, to multiple countries to validate these findings and explore additional contextual factors.
Data on the Sense of Coherence Among Nurses
The purpose of this study is to determine the sense of coherence among nurses and its effect on nurses’ relationship with patients. And how therapeutic relationship affected by the coping mechanism of the nurses. The data were collected by using the questionnaires adapted from the previous studies. The study is cross sectional and quantitative using a deductive approach. Population of the study was the nurses and convenient sampling method used for collecting data. Sample size was 191 nurses. Finally, 119 valid questionnaires were used for data analysis. The result of my study is positive that is nurses with stronger sense of coherence can successfully develop a nurse patient interaction which benefits the both the nurses and the patients. Because stress and anxiety directly affect the nurses performance those who can manage the work related stressors more productively can do better as compare to those who fails to cope with depression. From the results of the study, it is concluded that the stronger sense of coherence resulted in the better coping mechanism in response to any stress or fears related to work and eventually they maintained good nurse patient therapeutic relationship.
Data on Violence and Abuse Among Nurses and the Use of Antidepressant Medications
This study investigates the impact of physical and emotional violence on emotional exhaustion among nurses and explores the moderating role of antidepressant use. Conducted at Mayo Hospital in Lahore, the study surveyed 126 nurses to examine the relationships between violence exposure, emotional exhaustion, task interruptions, and the use of antidepressants. The research utilized a structured questionnaire covering demographic details, experiences of violence, emotional exhaustion levels, task interruptions, and antidepressant use. The results reveal that both physical and emotional violence significantly contribute to emotional exhaustion, with emotional exhaustion itself playing a substantial role in predicting antidepressant use. Emotional exhaustion was found to partially mediate the relationship between physical and emotional violence and the use of antidepressants. Additionally, task interruptions were identified as a mediator between violence exposure and emotional exhaustion, though they did not significantly moderate the relationship between emotional violence and antidepressant use. The study's findings underscore the severe impact of violence on nurses' emotional well-being and the crucial role of antidepressants in managing emotional exhaustion. The results suggest the need for targeted interventions to address workplace violence and its psychological effects, as well as recommendations for reducing task interruptions to improve overall nurse well-being and efficiency.
Impact of Total Quality Management and Agile Manufacturing on Performance in the Apparel Industry: A Moderating Role of Higher Management Commitment
This study investigates the impact of Total Quality Management (TQM) and Agile Manufacturing (AM) on the performance of apparel mills, while also exploring the moderating role of Higher Management Commitment (HMC). The goal is to assess whether TQM and AM enhance mill performance and whether HMC amplifies these effects. The research adopts a quantitative approach using data from 104 respondents across various apparel mills in Karachi, Pakistan. A structured questionnaire was employed, measuring TQM, AM, Operational Safety (OS), Training, and HMC using a 5-point Likert scale. Regression and correlation analyses were conducted to evaluate relationships between the variables. The results indicate that both TQM and AM have significant positive effects on apparel mill performance, with training also showing a strong correlation. However, OS did not demonstrate a significant impact. Furthermore, HMC significantly moderates the relationship between TQM and training on mill performance but does not moderate the relationship between AM and OS. The findings suggest that managers in the apparel industry should focus on implementing TQM and AM to enhance performance, while also fostering strong managerial commitment to maximize these benefits. This study contributes to the existing literature by integrating TQM, AM, and HMC, providing a comprehensive understanding of their effects on apparel mill performance
Efficient Malware Investigation and Recognition Using Machine Learning Algorithms
Malware is unique of the biggest problems that modern internet users have to deal with. Polymorphic malware is a new type of harmful software that is extra pliable than prior peers of bugs. Polymorphic malware continuously alters its signature characteristics in order to evade detection by conventional malware detection techniques. We applied various machine learning algorithms to detect malware or dangerous threats. A high detection ratio meant that the most accurate algorithm had been chosen to be used within the system. One advantage of the confusion matrix is its ability to track false positives and false negatives, providing deeper insights into the system’s performance. In particular, it revealed that machine learning algorithms like Naïve Bayes, Support Vector Machine (SVLM), Random Forest (RF), and K-Nearest Neighbor (kNN) can be used to detect harmful traffic on computer systems by calculation changes in correlation patterns. This approach enhances the effectiveness of malware detection and overall security in computer networks. The findings demonstrated that NB (87%), kNN (91.76%), SVM (92.41%), and RF (98.07%) performed well in terms of detection accuracy when compared to other classifiers. These findings are important as malicious software is growing more prevalent and sophisticated.
Identifying Credit Card Fraud with Machine Learning: Evaluation of Algorithms and Oversampling Techniques
The physical loss of a credit card or the theft of sensitive credit card data is referred to as credit card fraud. For detection, a variety of machine learning methods can be applied. This study presents several algorithms for identifying transactions as authentic or fraudulent. The study used the credit card fraud detection dataset. The SMOTE approach was utilized for oversampling due to the extremely unbalanced nature of the dataset. Additionally, a feature selection process was carried out, and the dataset was divided into training and test sets. The experiment employed eight machine learning models, including Random Forest, AdaBoost, Support Vector Classifier, Extreme Gradient Boost, and Logistic Regression algorithms. The findings indicate that few algorithms have a high degree of accuracy when detecting credit card fraud. The Random Forest model can be applied to find further anomalies. This approach has proven to be effective in accurately detecting fraudulent activity and reducing the number of false positives and negatives. The results of the experiment also highlight the importance of feature selection in improving the performance of the models by the highest accuracy score with 96%, precision of 100, Recall of 91, and F1Scroe of 95. By using a combination of different machine learning algorithms, financial institutions can enhance their fraud detection systems and better protect their customers from potential financial losses. The findings from this study demonstrate the potential for significant advancements in the field of fraud detection using machine learning techniques.
From Data to Decisions: Predictive Machine Learning Models for Customer Retention in Banking
This research proposes machine learning—a subfield of artificial intelligence-based approaches to forecast bank client attrition. The study promotes investigating the possibility of churn through the examination of consumer behaviour. In this paper, a method to predict customer churn in a bank using machine learning techniques, which is a branch of artificial intelligence, is proposed. The research encourages the exploration of the likelihood of churn by analysing customer behaviour. The number of service providers is increasing very rapidly in every business these days. As a result, customer churn and engagement have become one of the top issues for most of the banks. In this work, the Random Forest, SVC, XGB, LGBM, and Logistic Regression classifiers are employed. A few feature selection strategies have also been applied to validate system performance and determine which features are more relevant. The test was conducted using the churn modelling dataset from IEEE dataset. The results are compared to find an appropriate model with increased precision and predictability. As a result, when utilised after oversampling, the Random Forest model outperforms other models in terms of accuracy. The Random Forest model showed a significant improvement in accuracy compared to the other classifiers, making it the preferred choice for predicting customer churn in the banking sector. The feature selection strategies helped identify the most relevant features that contribute to accurate predictions. Overall, this study highlights the importance of implementing advanced machine learning techniques and feature selection methods to enhance customer retention strategies in the banking industry.
Social Media Use: A Cognitive Dissonance Analysis of Heavy Users and Engagement Dynamics
Abstract Purpose – This research investigates the correlation between the utilization of social networking sites, user engagement, and cognitive dissonance. It explores the mediating influence of e-compulsive buying behavior (ECBB) in the connection between SNS use, user engagement, and cognitive dissonance, while also examining the moderating impact of self-esteem on SNS use, user engagement, and ECBB. Methodology – Utilizing purposive sampling, this study collected cross-sectional data from 232 female participants actively engaged in social networking sites (SNSs) and who had made 4-5 online purchases during the last six months. Data were gathered through an electronically administered questionnaire. Descriptive data analysis was conducted using SPSS version 21, while SmartPLS version 4.0 was employed to test the proposed hypotheses. Findings – The findings indicate a significant association between the utilization of social networking sites (SNSs) and user engagement with cognitive dissonance and ECBB. Additionally, it was observed that self-esteem played a moderating role in the relationship between SNS use, user engagement, ECBB, and cognitive dissonance, revealing a heightened impact of cognitive dissonance at lower levels of self-esteem. Practical Implications – These results carry implications for implementing interventions aimed at mitigating the adverse consequences associated with social networking site (SNSs) use, thereby fostering favorable mental health outcomes within the community. Moreover, this study offers valuable insights for managers seeking to enhance their social media strategies by analyzing the varied impacts of social media content and understanding the dynamics between posts to engage both active and passive users effectively. Originality/Value – This study contributes to the limited body of empirical research by examining the correlation between social networking site (SNSs) use, user engagement, and cognitive dissonance through the lens of ECBB. Additionally, in response to recent calls from various empirical studies in this field, the inclusion of self-esteem as a moderating variable predicting cognitive dissonance enhances the novelty of this research.
Exploring the Construction of Angela Merkel's Identity by Global Print Media across International Platforms
This study is an attempt to observe and analyze the intricate operations of media discourses in the identity construction of “world leader” i.e. Angela Merkel. It highlights that how through the explicit and implicit discourse layers’ construction, the macro and micro semantic structures are created in a newspaper, for portraying the desired positive or negative image of a world leader. The theoretical framework has been structured over the basics of the Van Dijk’s (1980) The Theory of Semantic Macrostructures. The macro- level analysis focuses the core themes and main propositions publicized in the selected data. The micro-level analysis concentrates on the lexicalization and syntactic patterns employed by media experts in creating the larger positive image as world leader through smaller level discourse language constructions, focusing her social and political posterities. Furthermore, the study highlights the gradually constructed personal, social and political identities’ portrayal of Angela Merkel as a powerful world leader, through the world newspaper articles discourse and discursive practices.
Security Risk Assessment of IoT for Remote Patient Monitoring System
Remote Patient Monitoring (RPM), an essential component of telehealth, is among the biggest changes marked in healthcare. It facilitates patients’ treatment, remotely at home or in some distant location, without traditional clinical settings. The RPM plays a key role in data acquisition, data analysis and insights, and improved healthcare management. It collects real-time data using wearable sensors and mobile apps, and furnish crucial health metrics. Advanced algorithms and prognostic modelling than process the data, pattern and likely health risks to predict any disease more precisely and accurately for early action. RPM provides real-time watch and remote consultation that help in improved disease control and better patients’ care. Improved accuracy, reduced cost, real time interaction, and refined patient well-being are the significant healthcare benefits of RPM. Prognosis Health & Management (PHM) system is used for predicting the remaining useful life (RUL) of healthcare assets such as sensors, pacemakers, defibrillators and other medical equipment. The PHM leads to pre-emptive maintenance, lowers downtime, and tracks the life span of such healthcare equipment. The IoT enables PHM to monitor remote assets and gather instant data to foresee the RUL of such equipment. Providing the facilitations, PHM also creates potential vulnerabilities of exposure of device, network and data, and poses data security and privacy challenges. Therefore, strong security controls are required to keep patients’ data confidential and safe from uneven approaches to technology and data breaches. This paper evaluates risk associated with RPM using OCTAVE ALLGERO, a risk assessment framework, to mitigate the data security concerns.
Enhancing Autonomous Agents Through Narrative Memory: A Comprehensive Framework for Improved Performance and Interaction
The process of organizing information and experiences into a narrative style is known as narrative memory, and it presents a cutting-edge method for improving the performance of autonomous agents. This essay describes the idea of narrative memory, as well as its elements, construction of narrative structures, and uses. The four main elements of narrative memory are timelines, characters, events, and context. Events are important things that happen, such interactions or acts, and characters are people, other agents, and the autonomous agent. The context gives the underlying knowledge required to comprehend these events and individuals, while the chronology sets the order of occurrences, creating a cause-and-effect link. The process of creating narrative frameworks includes gathering data, identifying events, crafting stories, and refreshing memories. Three methods are used to collect data: external sources, user input, and sensors. This data is used to identify significant events, which are subsequently arranged into logical tales. New situations and experiences are constantly added to the narrative memory. Improved predictive analysis, easier learning and adaptability, and improved human-robot interaction are just a few of the uses of narrative memory in autonomous agents. Robots can interact with people more successfully if they can comprehend human narratives and reply with responses that are contextually relevant. Agents may learn and adapt by identifying patterns in their prior experiences, and they can make better decisions by forecasting future occurrences with the help of historical narratives. Managing the complexity of real-world events, addressing ambiguity and uncertainty, and guaranteeing the ethical use of narrative data are challenges in the implementation of narrative memory. In order to improve narrative memory frameworks, future research options include creating sophisticated story generating tools, integrating narrative memory with other memory systems, and working across disciplines. The goal of this roadmap is to enhance autonomous systems' comprehension, communication, and decision-making skills by utilizing the power of tales.
Linkage between HLA genotypes and COVID-19 susceptibility, severity and mortality-A Systematic Review
HLA is vital for effective immune responses against viruses. The virus responsible for COVID-19, SARS-CoV-2, highlights the significance of this immune response. The unpredictable nature of COVID-19 prompts investigation into how HLA, a component of host genetics, impacts disease susceptibility and outcomes. Understanding HLA variants' influence on COVID-19 susceptibility and severity can aid in identifying at-risk individuals. Significant HLAs that had been previously investigated for their association with COVID-19 were sought. Articles from January 2001 to February 2022 were reviewed in 2 databases: Science Direct and PubMed, employing the search terms "COVID-19" AND "HLA." Studies of genetic association measuring the severity or susceptibility to COVID-19 were analyzed in accordance with PRISMA guidelines. A total of 1326 studies related to the novel coronavirus and HLA conducted between the first month of 2001 and the second month of 2022 were identified in our search. Following the screening process, 15 relevant publications were selected. With the exception of one publication that did not demonstrate any connection, significant correlations between the incidence and mortality due to COVID-19 were found in all investigations. In severe cases, HLA class II, DRB113, was notably more prevalent. Among Black hospitalized patients positive for HLA-B53, there was an observed 7.4-fold higher risk of mortality, while HLA-B51:01 and HLA-A*26:01 were indicative of protective effects. HLAs play an essential role in immunological responses, potentially contributing to varying COVID-19 infection rates and outcomes across populations. These findings underscore the need for national networks to collect patient samples for HLA typing, identifying HLA genotypes associated with disease vulnerability.
An Efficient Approach for Shortest Path Planning in Automotive Navigation System
Emerging automotive engineering solutions extensively rely on navigation tools. Shortest path evaluation is core competency of optimal path planning, one of the most critical component of any navigation application. Fibonacci heap variant of state-of-the-art Dijkstra’s algorithm solve single source shortest path problems in asymptotic O(E + V log V) worst-case time. In spite Fibonacci heap is theoretically dominant in time efficiency, it might not work much better empirically with real-world data, because although large and intricate, the real-world road network graphs are sparse and the graph topology greatly affects the performance of the algorithms. SGO algorithm, a more efficient approch to address single source shortest path problem, is applied to solve optimal path planning problem in automotive navigation applications. Each road segment of the road graph leads to a particular location thus will hold unique distance values all the time. The SGO calculates the total distance from start location to the end of the each road segment and evaluates shortest path based on these calculations. The SGO algorithm and the Fibonacci and Binary heap variants of Dijkstra’s algorithm were tested on real-world intercity and intra-city road network graphs to evaluate running time efficiency of the algorithms in automotive navigation systems. The experimental results show that the SGO algorithm outperformed both the variants. The empirical superiority of the SGO algorithm suggests its usefulness in automotive navigation systems.
Navigating Security Challenges: Data Insights into the Foreign Policy Paradigm of Landlocked Nations
Landlocked nations are beholden to their unstable surroundings, imposing insecurity upon domestic matters and international cooperation alike. For states confined within dangerous neighborhoods, like Uzbekistan and Afghanistan, enduring volatility and limited alternatives have become the norm. When navigating such challenges and diplomatic contacts, a delicate balancing act is required. Conversely, landlocked countries situated within stable regions, such as Switzerland, place greater priority upon economic collaboration, spared worries over transit access. Benefiting from this predictability, they concentrate efforts on development and integration. Landlocked nations like Bolivia and Botswana often prioritize nationalism and export dependencies over regional stability. Their relationships with neighboring countries play a crucial role in shaping foreign relations and security strategies, emphasizing the need to understand regional dynamics for effective global geopolitical and international strategies.
Brazil's Foreign Policy in the Emerging Multipolar World: Data-Driven Strategic Positioning within BRICS and Beyond
In the multipolar world, Brazil's foreign policy faces complex geopolitical realities as a part of its strategy to expand its global influence and economic presence. On another front, Brazil considers itself one of the leading countries in the Global South. It has partnered with BRICS to call for better transport, and help has a say in running the world (power balance) instead of being overruled by Western powers. Brazil demonstrates regional leadership by actively participating in initiatives like the New Development Bank and organizations such as MERCOSUR, CELAC, and BRICS, emphasizing its commitment to sustainable development and regional cooperation. The plan is to broaden Brazil's regional power reach, seeking economic and diplomatic relations throughout the planet. Brazilian foreign policy is characterized by a pragmatic search for equidistance, maximizing economic possibilities and strategic ties without ever fully committing to any one bloc or country. These measures will increase Brazil's clout on global issues and solidify the country as a critical factor in shaping a more equitable and representative international order.
Leaders at the Helm: Shaping the Future of Hospital Administration through Data-Driven Visionary Leadership
This study investigates the impact of various leadership and, management practices on hospital performance, focusing on transformational leadership, effective communication, decision-making ability, change management, emotional intelligence, strategic vision, innovation, employee engagement, resource management, and patient-centred care. Using a structured questionnaire, data were collected from 350, hospital administrators, senior management, department heads, and healthcare professionals. The results highlight the significant, positive effects of these practices on hospital performance, emphasizing the need for a holistic approach to healthcare management. The study fills a critical gap in existing, literature by examining the, combined effects of these factors and their interactions. These findings provide valuable insights for hospital, administrators and policymakers, offering practical strategies for improving healthcare quality and operational efficiency. The implications of the study are significant for addressing, global healthcare disparities and enhancing, healthcare outcomes, particularly in resource-constrained, settings like Pakistan. The study also identifies, challenges and barriers to the, implementation of these, practices, providing a, comprehensive understanding of the factors, influencing hospital performance
Flood Risk Analysis in Sindh, Pakistan: Predicting the Most Affected Tehsil Using Statistical and Machine Learning Models with Comprehensive Data
Extreme weather events pose significant risks and threats to the Sindh region, shedding light on the potential havoc they can wreak on livestock, human lives, and public infrastructure. Unusual monsoon rains in 2022 transformed the geography of Sindh, leading to devastating floods and significant damage. The current flood management strategies are effective, but there is some gap in early warning systems. Different findings suggest a need for refined approaches to improve predictive accuracy and response efficiency. The primary goal is to assess and improve existing flood response measures, identify opportunities for improvement, and strengthen resilience against flood events. In response to these challenges, this study provides actionable insights for policymakers and emergency planners to refine flood management strategies, ultimately aiming to bolster community resilience and preparedness. This paper presents a novel framework for a hybrid predictive model that combines machine learning and statistical analysis to improve the prediction of flood-induced displacement, providing timely warnings and preparing for future flood events. The selected dataset used for this research is from the 'Sindh Flood Data Analysis and Prediction 2022', which includes both demographic and geographical variables. By integrating machine learning and statistical models, the research evaluates their effectiveness in predicting flood impacts and identifying the most affected Tehsils using Python language and Seaborn library for analysis. The Gradient Boosting Classifier achieved the highest accuracy at 97.67%, followed by Logistic Regression at 95%. In contrast, Decision Trees and Lasso Regression demonstrated lower accuracies of 25% and 55%, respectively.
Exploration of PQC-Based Digital Signature Schemes in TLS Certificates
The rapid development in quantum computers brings huge risks to traditional cryptographic systems. This paper talks about the integration of PQC-based digital signature schemes to solve challenges posed on Transport Layer Security certificates. In this paper, we give an analysis of the efficacy, security, and performance implications of various schemes in PQC—particularly lattice-based, hash-based, and multivariate polynomial-based algorithms. We detail more closely the challenges of the real deployment, directly connected with these digital signatures, considering communication overhead and computational costs. Our findings indicate that hybrid certificate chains, which integrate multiple PQC schemes, offer a feasible solution for a seamless transition to quantum-resistant standards with manageable performance trade-offs. Moreover, our study extends to the quantification of security benefits these PQC schemes provide against both quantum and classical computational attacks, underscoring their potential in enhancing the resilience of digital communication systems. This paper aims to contribute valuable insights to ongoing standardization discussions and support the broader adoption of PQC, thereby ensuring robust and future-proof security in digital communications amidst the advancing quantum computing era.
Speaker Recognition: A Comparative analysis Between Deep Learning and Non-Deep Learning Methodologies
This work carries out a comparative study of two methodologies for speaker recognition. It is Deep Learning (DL) and Vector Quantization (VQ). The key area in biometric au-thentication systems involves speaker recognition. This requires durable and efficient algorithms. The aim is to ensure a high accuracy and reliability. The study delves into a deep neural network (DNN) model implementation. It leverages advanced feature extraction. It uses pattern recognition capabilities which are in DL. The study also examines a traditional VQ approach. This method makes use of codebook generation and quantization for speaker ID. Extensive experimentation was done on standard datasets. The project evaluates the performance of two methods. It compares accuracy. It assesses computational complexity. It does so for noise and for variations in speech. The findings of this analysis reveal the strengths and limitations of each technique. Looking at their practical applicability in real-world scenarios provides insights. The comparative results of these techniques aim to guide future developments. This concerns speaker recognition systems - particularly their potential for enhanced performance.
Effect of School Culture on Students’ Socialization at Secondary Level in Punjab
School culture refers to the shared beliefs, values, attitudes, behaviors, and practices that shape how a school operates and interacts. The study's objectives were to explore the school culture and determine the effect of school culture on students' socialization at the secondary level. A survey was conducted to gather data from 24 secondary schools from each randomly selected division of Punjab among 10th-grade students on aspects like academic achievement, socialization, and cooperative learning. A self-developed questionnaire was used to obtain data from the chosen sample. The study evaluated several factors, including socialization, academic achievement, leadership, and student improvement. To investigate the links between these concepts, correlation studies were conducted. In this study, it was found that student's academic performance, the quality of school leadership, and the prevalence of cooperative learning were all positively correlated with a more positive school culture. Students' socialization was positively influenced by their academic performance. The results stress the significance of school culture in determining the diverse experiences and outcomes of students. A positive school culture that encourages socialization, and cooperative learning should be the main goals. Schools can contribute to their overall development and well-being by implementing the recommendations and conducting further research.
Exploring the Effect of Six Sigma Practices on Quality Improvement of Higher Education: An Exploratory Study
Quality assurance at the higher education level is the most important impacting factor on society and the economy. Six Sigma are key initiative for improving the quality of education in higher education institutions. The objectives of the present study were to explore the application of Six Sigma practices at the university level and determine the relationship between Six Sigma practices and quality assurance at the university level. In the present study, a quantitative research approach was applied to carry out the study. Three sampling regions, Upper, Central, and Southern Punjab of Punjab province were selected to carry out the study. Two public sector universities from each geographic region were selected. In the next stage, four disciplines of above mention universities were selected. A multi-stage random sampling technique was employed to collect data. Data were analyzed by applying SPSS's latest version, structural equation modeling, and regression analysis to analyze the data. The study's findings contribute to understanding the multifaceted nature of QA in university teaching. The proposed model exhibits strong explanatory power, predictive relevance, and goodness of fit. This research underscores the importance of various factors in enhancing quality assurance and informing educational practices and policies.
Smart Cities towards Artificial Intelligence
The creation of smart cities has come a long way in recent years, especially in domains that use AI, such IoT-based components, security systems, smart living options, and environmental monitoring. These advancements seek to build surroundings that meet human requirements by utilizing cutting edge technologies such as automated parking, smart healthcare, and seismic monitoring systems. Even with these encouraging advancements, the current generation of smart cities is still unable to attain genuine autonomy, particularly when it comes to component-level autonomous situation management and decision-making. The transformational perspective proposed in this work views the constituent components of smart environments as self-governing agents with the ability to make decisions. The purpose of these autonomous agents is to improve security and smart living by being able to self-manage and react automatically to different situations. Such autonomous agents can be integrated to accomplish the goal of artificially intelligent smart cities. In smart cities, autonomous agents may behave pro-actively without human involvement by continually monitoring and analyzing data, anticipating possible problems, and taking proactive actions. In the context of smart healthcare, these agents may, for example, track a patient's vitals in real time, anticipate possible health emergencies, notify medical personnel, or even start emergency procedures. Autonomous agents may dynamically assign parking spots in the context of smart car parking based on real-time data, maximizing space use and minimizing traffic congestion. Agents that can autonomously manage emergency actions, such as directing people to safety and managing infrastructure to minimize damage, might be beneficial for earthquake detecting systems. As autonomous entities endowed with decision-making skills are included, we are moving closer to truly intelligent cities. Because of these agents' capacity for independent operation and quick response to changing circumstances, smart cities are able to offer a better quality of security and service while accommodating the changing demands of its residents. As a result, smart cities that have these intelligent agents installed will be more responsive, efficient, and resilient—ultimately capturing the spirit of artificial intelligence in urban planning.
Bridging the Language Gap: Evaluating Text Data Pre-processing and Classification Techniques in Urdu Sentiment Analysis
The advancement of the internet and rapid growth in social media platforms such as Facebook, X , Instagram and others which made it possible for information on review on goods, reaction on events, services from experts, and political beliefs to be easily and globally shared within no time. Due to this fast propagation of information may have both positive and negative impact on thinking and ability to take decision on a particular event or anything related. Due to the generation of such a huge number of date and while the volume of studies directed on slant investigation is quickly extending, these examinations for the most part address English language concerns. But the English the not the only language by which data is got propagated but other such as Urdu, Farsi, Hindi, Pasto, branch etc. The main goal of this research study is to critically assess the data related to Urdu and the Urdu sentiment analysis's progress and problems and present solutions. After critically reviewing the literature and related work following two research directions are identified in accordance with our study— the first one is text pre-processing and the other is sentiment classification. Here, we had the opportunity to describe the progress that has been made in this research field. The pre-processing steps include Tokenisation, stop words removal, word segmentation, text cleaning, transforming into numeric vectors and others then applying machine learning models i.e. regression and SVM. Result is compared out for each of the model use. After thorough investigation, the following results demonstrated that the LDA-TFID model achieved the highest accuracy at 0.923977 as compared to LDA-BOW and SVM-BOW and also outperforming both LDA-BOW and SVM-BOW in term of efficiency and accuracy. This indicates the effectiveness of TF-IDF in enhancing model performance for Urdu sentiment analysis.
Analyzing Data on Students’ Institutional Trust and Its Impact on Emotional Wellbeing
Trust is an important aspect of institutional change initiatives. It reflects the overall practices of an institute. This study investigated the students’ effect of institutional trust on their emotional well-being. It was descriptive research in nature and for the collection of data survey method was used. The questionnaire is used to collect data from participants. The population of this study consisted of the students from University of Okara. Samples were selected by simple random sampling. The findings revealed that institutional trust greatly contributed to the progress of the institute. As well as it can effect positively the academic performance of the students. Recommendations were made that the role of leadership concerning the institution is central. Leaders have been described as the culture founders, their contribution or responsibility being the change of school culture by installing new values and beliefs. This means that the higher the academic reputation and public image of universities the greater is the chance of that university selected by potential students. Students studying in universities and colleges with high academic achievements and recognition expressed higher satisfaction levels than those institutions with relatively lower academic standing. There is always a challenge in branding private higher education institutions.