bluuebunny/crossref_metadata_2025 huggingface dataset:
Pandemic to Endemic: Changing Learning Styles as Coping Mechanism
Authors: Kamal Nurmala Mustaffa , Mohamed Mahfudzah , Ismail Puteh Mariam , Ahmad Asyaari Elmiza , Rahim Roslina Abdul
Published: November 2024

Professional accountancy students face challenges as examinations are conducted by professional bodies separate from their learning institutions. As COVID19 enters the endemic stage, the transition from online distance learning (ODL) back to traditional face-to-face learning raises learning behaviours and preferences issues. This quantitative research on professional accounting students at Universiti Teknologi Mara (UiTM), aims to provide a perspective and critical evaluation of students' learning preferences and its implications on the appropriate teaching and learning pedagogy. The Partial Least Square Structural Equation Modelling (PLS-SEM), a multivariate method, is used to find path models with latent constructs to identify the influencing factors of ACCA students’ examination performances. It is found that ODL classes do not significantly change students’ learning style. It can be deduced that students are comfortable with ODL and traditional classes. Generally, the learning profile of the student population in this study indicates that the ability to adapt is still lacking and this may be a localised scenario. In Malaysia, to address this may require a comprehensive review of the education system that can develop students to become independent and active learners with high self-motivation.
Adaption of Artificial Intelligence (AI) to enhance business and collaboration between countries, focusing on Saudi Arabia
Authors: Zafar Nisar Ahmed
Published: November 2024

Artificial Intelligence (AI) is one of the most ingenious inventions drastically transforming the business world. The question is whether the current growth of AI is all hype or if it has the potential to revolutionize the world is raised by the abundance of intelligent goods and services that have emerged in recent times. Technological developments in AI pave the way for creating technologies that resemble humans and can operate independently and imitate our intellectual processes. Numerous industries have shown considerable interest and enthusiasm in adopting AI due to technological advancements. As a result, numerous businesses are making significant investments to leverage this opportunity through innovative business models based on AI. The study aims to establish a model based on an evidence-synthesis approach that demonstrates how the business sector in Saudi Arabia can incorporate AI to achieve better outcomes and strengthen its relationships with other countries. However, the study also highlights some advantages and disadvantages of adopting AI models in the business industry so a balanced approach can be used to incorporate AI in different aspects of the business. The study recommends incorporating AI in different health, education, and business sectors in Saudi Arabia to foster international relationships in Saudi Arabia.
Numerical Approximation Tool Prediction on Potential Broad Application of Subsurface Vertical Flow Constructed Wetland (SSVF CW) Using Chromium and Arsenic Removal Efficiency Study on Pilot Scale
Authors: Baquir Mohammad , Khalil Nadeem , Ayub Sohail , Kumar Manoj
Published: November 2024

This study investigates the potential broad application of Subsurface Vertical Flow Constructed Wetlands (SSVF CWs) for heavy metal remediation, focusing on Chromium (Cr) and Arsenic (As) removal efficiency. A pilot-scale experimental setup was employed, utilizing a SSVF CW filled with 12 mm gravel and 2 mm coarse sand, planted with Phragmites Australis. The research, conducted over 366 days, aimed to develop a numerical approximation tool to predict the performance and applicability of SSVF CWs in various environmental conditions. The experimental system operated at a hydraulic loading rate of $98-111 \mathrm{~mm} / \mathrm{d}$ and a hydraulic retention time of 6 days. Results showed average removal efficiencies of $44.87 \pm 9.52 \%$ for Cr and $43.16 \pm 9.43 \%$ for As. A mass balance analysis revealed that substrate accumulation was the primary mechanism for heavy metal removal, accounting for $29 \%$ of Cr and $26 \%$ of As removal. Plant uptake contributed to $3.5-9.9 \%$ of Cr and $0.3-$ $8.8 \%$ of As removal. Based on these findings, a numerical model was developed to simulate SSVF CW performance under varying environmental and operational parameters. The model incorporated factors such as influent concentrations, hydraulic loading rates, substrate composition, and plant species. Validation against experimental data showed good agreement, with an $\mathrm{R}^{2}$ value of 0.89 . The numerical tool was then used to predict SSVF CW performance across a range of scenarios, indicating potential broad applications in industrial wastewater treatment, mine drainage remediation, and contaminated groundwater cleanup. This study provides valuable insights into the scalability and versatility of SSVF CWs for heavy metal removal, offering a sustainable and cost-effective solution for water treatment challenges.
The Relationship Between Sensory Processing, Substance Abuse and Impulsivity With Suicide in Schizophrenia: A Narrative Review
Authors:   Student Research Committee, Department of Occupational Therapy, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Shourvazi Roya , MirzaKhani Navid ,   Department of Occupational Therapy, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Rahmanirasa Amir ,   Department of Occupational Therapy, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Pashmdarfard Marzieh ,   Department of Occupational Therapy, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Loabichian Maedeh ,   Student Research Committee, Department of Occupational Therapy, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Published: November 2024

Background and Objectives: Schizophrenia is a debilitating and complex mental health disorder characterized by symptoms, such as delusions, hallucinations, and cognitive impairments. A high comorbidity rate is observed between substance abuse, schizophrenia and impulsivity. Given the higher rate of suicide among individuals with schizophrenia, this research aims to investigate the correlation between three factors, sensory processing, substance abuse and impulsivity, concerning suicide in individuals diagnosed with schizophrenia. Methods: Research was conducted on PubMed, Google Scholar and Web of Science using the keywords “schizophrenia,” “substance use disorder (SUD),” “sensory processing,” “suicide” and “impulsivity.” Sixteen articles were selected as the most recent and relevant studies for reporting. Results: The reviewed studies demonstrated a high prevalence of suicide in patients with schizophrenia, particularly during the early stages of the illness. Impulsivity plays a contributing role in the elevated rates of suicide and substance abuse, with substance abuse directly influencing the incidence of suicide. Moreover, sensory processing difficulties can lead to self-harm and suicide. Therefore, the presence of these three factors in patients can increase the risk of suicide. Conclusion: Studies have addressed these risk factors to a considerable extent, less attention has been given to sensory processing, impulsivity, and substance abuse, which are significant factors in self-harm and suicide, as well as their predictive value for suicide. Further studies in this area are recommended.
The Effect of Long-term Biofeedback Therapy on Depression of Elderly Stroke Patients
Authors:   Department of Sports Biomechanics, Faculty of Physical Education and Sports Sciences, Central Tehran Branch, Islamic Azad University, Tehran, Iran., Yousefian Molla Razieh , Hajiahmad Tahereh ,   Tabassom Stroke Rehabilitation Clinic, Tehran, Iran., Kazemi Rouzbeh ,   Tabassom Stroke Rehabilitation Clinic, Tehran, Iran.
Published: November 2024

Background and Objectives: Depression is a common problem after stroke, with a high prevalence. Multiple studies have suggested that biofeedback therapy can effectively treat depression without causing side effects. This research aims to investigate the long-term impact of biofeedback therapy on depression in stroke survivors. Methods: A quasi-experimental study was conducted with 40 stroke patients who were divided into a rehabilitation control group and a rehabilitation group that underwent long biofeedback electromyography exercises. All patients were assessed for their level of depression at the start of the study and again after two years of treatment, after completing occupational therapy sessions with and without biofeedback intervention. Statistical analysis was performed using paired t-tests and correlated t-tests with a significance level (P≤0.05). Results: In both control and intervention groups, no significant differences were observed in the level of depression between the pre-test and post-test stages. However, a crucial difference was observed between the intervention and control groups after using biofeedback. The average depression in patients who used long-term biofeedback decreased after two years compared to the control group (P≤0.05). Conclusion: Long-term utilization of biofeedback therapy impacts the depression levels of stroke patients. This method is highly recommended as a complementary approach to conventional rehabilitation.
Navigating Mpox: Call for the Development of Guidelines for Safe and Effective Physiotherapy Practices
Authors:   Department of Physiotherapy, DDT College of Medicine, Gaphatshwa, Botswana., Silumesii Lili
Published: November 2024

I am writing to highlight the need for consensus-based guidelines addressing mpox (formerly known as monkeypox) in the field of physiotherapy. The recent declaration of a public health emergency by the World Health Organization (WHO) due to the rapid spread of mpox [1] emphasizes the necessity for clear protocols to protect patients and healthcare providers.
Математична модель камери згоряння газотурбінного двигуна, який працює на етанолі
Authors: Shevchenko Mykhailo , Ambrozhevich Maya , Selezen Oleh
Published: November 2024

Ethanol is one of the most promising alternative fuels for Ukraine. To convert existing projects of power and cogeneration plants based on gas turbine engines (GTEs) operating on petroleum products and natural gas to ethanol, as well as for their design and performance calculation, it is necessary to have a mathematical model of the working process of GTE combustor. The object of the study is the working process of the GTE combustor fueling on ethanol. The subject of the study is a mathematical model of the working process of GTE combustor fueling on ethanol. The work aims to improve a mathematical model of the working process of a GTE combustor fueling on ethanol by changing the algorithm for calculation of the fuel air ratio, considering thermal dissociation, and a correctly formulated equivalent chemical reaction path of the combustion process. To achieve the aim, the following tasks were solved: based on the use of experimental values of specific isobaric heat capacities of combustion products, which are a function of temperature and pressure, a mathematical model of the working process of the GTE combustor was improved ("simplified" mathematical model); based on the solution of the system of equations of chemical thermodynamics, a mathematical model of the working process of the GTE combustor was developed ("complex" mathematical model); the results of calculation by "simplified" mathematical model of the working process of the GTE combustor were compared with the "complex" one. The following results were obtained: the difference in the calculation of the combustion products' thermodynamic parameters between the developed mathematical models was less than 1.2% for the three modes of the General Electric CF6-80A engine. Conclusion: the "simplified" mathematical model of the working process of the GTE combustor fueling on ethanol was improved. A feature of the model is the implicit consideration of the effect of thermal dissociation and correctly formulated equivalent chemical reaction path of the combustion process by using experimental values of specific isobaric heat capacities of combustion products. This will improve the accuracy of fuel air ratio calculation and other thermodynamic parameters of GTE combustor mathematical models fueling on ethanol, without significantly complicating the model algorithm.
Чисельно-експериментальне дослідження високотемпературної багатоциклової втомної міцності монокристалічних лопаток газових турбін
Authors: Nemanezhyn Yevhen
Published: November 2024

The subject of the research is anisotropic single-crystal turbine blades of aircraft gas turbine engines (AGTE), as well as their multi-cycle fatigue strength characteristics at high temperatures. The goal of this study was to determine the endurance limit of single-crystal high-pressure turbine blades of an AGTE at high temperatures of 820°C. The objective of the study is to develop a method for calculating and experimentally investigating the characteristics of high-temperature multi-cycle fatigue strength of gas turbine blades made of single-crystal nickel heat-resistant alloys. The research methods used are: method of computer modelling, which was used to create a 3D model of a cooled high-pressure turbine blade; finite element method, which was used to determine the stress-strain state of the blade according to the selected form of resonant vibrations; strain and resonance methods, which were used to determine the fatigue strength characteristics of single-crystal turbine blades under high-temperature loading. The following results were obtained. Literary sources describing theoretical and experimental methods for determining the strength characteristics of blades under combined dynamic and high-temperature loading were analyzed. The developed special installation for testing turbine blades under conditions of temperature and dynamic (resonant) loading was described. A combined method for assessing the multi-cycle fatigue strength at high temperatures is proposed, which includes studies on full-scale turbine blades and finite element calculations. The dynamic and temperature characterization of single-crystal turbine blades selected for the study was carried out and described. Fatigue tests were performed to determine the endurance limit of the blades at a high temperature equal to  T = 820 °C. Conclusions. The scientific novelty of the obtained results is as follows. A specialized experimental setup was developed to study the endurance limit of single-crystal AGTE blades under high-temperature loading. A method for combining numerical (ANSYS) and experimental (field tests) study of high-temperature multi-cycle fatigue strength of turbine blades made of single-crystal heat-resistant alloys was developed. The direction of further research is presented.
Оцінка можливості виявлення тріщин під час контролю ЛЮМ1-ОВ на лопатках турбін після нанесення комплексних жаростійких покриттів
Authors: Balushok Konstantin , Seliverstov Oleksandr , Prokopenko Oleksandr , Sakhno Sergii
Published: November 2024

Currently, gas turbine engines (GTD) are being improved and made more powerful by increasing the temperature of the gases in front of the turbine and increasing the air compression ratio in the compressor. In the aviation industry, nickel heat-resistant alloys such as ЖС6У-ВІ, ЖС26-ВІ, ЖС32-ВІ, etc. are used to manufacture GTD blades. Despite their high properties in terms of corrosion resistance and heat resistance, the service life of GTD blades and their protection against oxidation and corrosion damage is ensured by applying coatings using various technologies both to the flowing surface of the blade blades (heat-resistant and heat-protective) and to the inner surfaces of the cooled channels (diffusion coatings). Failure of turbine blades is one of the most severe types of damage to turbine blades, so at different stages of serial production, including after applying heat-resistant coatings, LUM1-OV inspection of blades is performed to detect cracks that can form during processing and lead to their failure during engine operation. The paper evaluates the possibility of detecting cracks on GTD turbine blades with complex heat-resistant coatings GCP+SDP-2. The study was carried out on samples of working blades in the form of an experiment. First, fatigue tests of the samples were carried out on a test bench until cracks appeared. Then, a complex heat-resistant coating of GCP (CrAl gas circulation coating) + SDP-2 (heat-resistant alloy of the Ni-Cr-Al-Y system) was applied to the blade profile in stages according to the serial technology. After each coating was applied, the blades were inspected by the LUM1-OV method to determine the possibility of crack detection. The results showed that immediately after the application of the complex heat-resistant coating, cracks on the blades were not detected, because they spread only in the GCP layer and partially pass into the SDP-2 layer without reaching the surface. The results obtained can be the basis for removing the luminescent inspection operation from the technological process, immediately after applying complex heat-resistant coatings. Since this does not allow detecting cracks in the blade material and can lead to unnecessary costs for additional technological operations.
Теорія фантомізації радіозображень РСА: базові відомості та обґрунтування розмірів ділянки фантомізації
Authors: Pavlikov Volodymyr , Pozdniakov Pavlo , Tserne Eduard , Kolesnikov Denys , Peretiatko Maksym , Malashta Pavlo
Published: November 2024

The article's subject of study is the creation of the foundations of the theory of the formation of false elements in the images of aerospace radio vision systems by injecting informational signs of objects absent on the underlying surface. The goal is to substantiate the dimensions of the subsurface areas on which it is possible to simulate objects with different methods of viewing the exploring area from the aerospace radar carrier. Task: to develop the geometry of the problem, which will reflect the interaction between the radar with the synthesis of the Synthetic-aperture radar (SAR) and the radar of phantomization (FR); to develop a general structural diagram of the FR; determine the main parameters, taking into account which will allow planning an "interaction" session between the SAR and the FR; to determine the sizes of the underlying surface areas, where it is possible to provide phantomization of radio images formed by SAR with different methods of examination. The methods used are mathematical and functional analysis, navigation, and remote sensing. The following results were obtained. The geometry of the problem of the interaction between the SAR and the FR was defined; it allowed us to calculate the interaction time between the systems and to establish the law of change of the distance between them at any placement of the SAR in orbit. The influence of the interposition of the considered systems on the expected shape of the SAR sounding signal and the size of the area on which the problem of radio image phantomization arises has been investigated. Examples of calculations of the specified parameters for various types of exploring of the earth's surface by radar from an aerospace carrier are given. Conclusions. The article summarizes and develops the theory of phantom radio images constructed using SAR. The obtained results establishing the dependence between the phantomization geometry and such parameters as the potential and actual time of interaction between the systems, the envelope of the bundle of received radio pulses, and the number of separation bands by range are important for further research in this direction, which at the moment it is advisable to focus on solving the optimization problem phantomization of separate blocks of the radar and determining the conditions for planning the processes of injecting objects into the transmitter signal and into the radio image formed by the SAR.
Аналіз застосування технологій ПЛІС в складі IoT
Authors: Kulanov Vitaliy , Perepelitsyn Artem
Published: November 2024

The subject of study in this article and work is the modern technologies of programmable logic devices (PLD) classified as FPGA, and the peculiarities of its application in Internet-of-Things domain at different architectural layers of the implementation, as well as the elements of decision making during choosing of appropriate solutions based on FPGA in the context of implementation of IoT tasks with the requirements of intensive computations. The goal is to analyze the possibilities and peculiarities of the application of technology of programmable logic devices as part of the modern architecture of the Internet of Things with the improvement of decision making process during the choosing of appropriate technical solutions for the use of FPGA with taking into account the types of tasks. Tasks: to analyze the current state of demands regarding the use of FPGA technology in IoT projects; to analyze the challenges and limitations for the application of FPGA technology at different layers during the prototyping of IoT infrastructure; to propose ways of the use of FPGA technology in the IoT infrastructure with the use of high-speed computations; to analyze the advantages and disadvantages of the proposed approaches of integration of FPGA technology; to provide recommendations and determine the further direction of the research of the use of FPGA technology as a part of the IoT infrastructure considering of AI/ML tasks; to provide a practical example of the use of FPGA technology during the creation of a high-performance IoT system. According to the tasks, the following results were obtained. The analysis of the current state of the use of FPGA technology in IoT projects is performed, that showed the significant potential of these technologies for the implementation of high-speed computations in Internet of Things systems. A classification of the ways of applying FPGA in the tasks of real-time data processing and implementation of neural networks is proposed. The set of limitations of the possibility of FPGA technology applying at different layers of IoT infrastructure is defined. It includes high equipment costs, the need for specific design skills, limitations in energy consumption, and the complexity of the integration with existing systems. The practical example of the use of the IoT system with the implementation of high-intensive computations is provided. Conclusions. The main contribution and scientific novelty of the obtained results is that the conducted analysis allows to make a decision about the possibility of applying programmable logic in the construction of IoT projects and home automation systems. Based on the proposed classification of types of IoT tasks, there is a possibility of making a decision on the application of FPGA technology as a separate integrated circuit in the system or using a complete instance of FPGA as a Service in the cloud environment.
Створення надійних ноутбуків на основі простих змінних компонентів
Authors: Tetskyi Artem , Zheltukhin Olexander , Perepelitsyn Artem
Published: November 2024

The subject of study in this article and research is principles of creating of reliable and repairable notebooks. The goal is to determine the structural and circuitral peculiarities of the construction of portable computers and laptops, which improve fault tolerance, repairability, allow possibility of power supply using wider voltage range, and also reduce the cost of repairing, which makes it possible to create more reliable solutions with fault tolerance. The task is to analyze the existing architectures of computers built on modern chipsets; to analyze cases of failure of the computer equipment regarding the causes and consequences of faults; to analyze the possible reasonability of repairing and the computer repair options; to propose recommendations of the structural organization of notebook components and the recommendations for the support of a wide range of notebook voltages of power supply. According to the tasks, the following results were obtained. The analysis of the constructional design features of modern laptops and portable computers is performed. The main components of notebook motherboard, including the processor, multicontroller, and host bridge, are analyzed. The possible causes of faults of system board components are analyzed. The interaction of the multicontroller with BIOS and UEFI of notebooks is analyzed. The causes and solutions of possible issues of battery replacement are identified. The disadvantages of direct wired connection of peripheral devices to the processors implemented as a system on chip are discussed. The recommendations of the creation of repairable circuits of notebooks are proposed. The proposed circuits have a higher cost, weight and size characteristics with higher power consumption, but the obtained fault-tolerance allows the use in critical industries. Conclusions. The scientific novelty of the obtained results is in the fact that the performed analysis of solutions from leading manufacturers and the practical experience of laptop failures diagnostics allowed to propose a set of recommendations of the reliable laptops creation and modification of the existing components including the suppor of a wide range of power supply voltages, that will make it possible the charging from power sources without the use of an additional converters. The proposed use of discrete components and controllers for each interface port for connection of peripheral devices increases the system durability to the influence of external factors with maintaining of workability of the notebook and the rest ports of motherboard in case of the fault of one of them.
Аналіз номенклатури мікроконтролерів для побудови елементів домашньої автоматизації
Authors: Vdovichenko Oleksandr , Perepelitsyn Artem
Published: November 2024

The subject of study and research in this article is the list of modern microcontrollers and technologies of the programming for creation elements of home automation, embedded systems, and components of Internet of Things. The goal is to analyze the product range and nomenclature of modern microcontrollers for creation of components of IoT systems and home automation considering an available resources and hardware implementation of interfaces. The tasks: to analyze a significant parameters of microcontroller for practical use; to analyze the product range and a modern manufacturers list of the most widely used microcontrollers; to analyze the main features of AVR microcontrollers; to perform an analysis of the advantages of STM32 microcontrollers; to analyze the capabilities of ESP32 microcontrollers; to perform the comparison of hardware resources of these three groups of microcontrollers. According to the tasks, the following results were obtained. Fourteen leading manufacturers of microcontrollers are analyzed with taking into account the bit length and a set of series and families of devices. The main characteristics and requirements for the practical application of microcontrollers for both the creation of embedded systems and for training and prototyping are highlighted. The reasons of the great popularity of the use of AVR microcontrollers by the community for training and learning of programming are identified. The capabilities of hardware resources and the advantages of using for embedded systems of the set of models of STM32 microcontrollers series are analyzed. The reasonability of the use of ESP32 microcontrollers for building of components of IoT systems and devices with Internet access is concluded. The comparison of the hardware resources of microcontrollers of three manufacturers is performed with taking into account the available size of program memory, the number of interfaces and pins. Conclusions. The main contribution and scientific novelty of the obtained results is that the performed analysis allows to simplify the process of a decision making about the selection of the required product range of modern microcontrollers for the creation of the components of the systems of home automation and Internet of Things with taking into account the available hardware resources, the number of pins, and the presence of hardware interfaces. This reduces the required efforts and costs at the initial stages of prototyping such systems, as well as during the porting to new microcontrollers and integrated environments of an already existing project.
Development and Implementation of a “Virtual Team Coordination Application” for Enhanced Remote Work Efficiency
Authors: Rajan  , Sukhmanpreet Singh Virk  , Loveleen Kaur  , Dr. Rajbir Kaur
Published: November 2024

With the contemporary increasingly computerized and distanced workplace, effective group coordination is fundamental for maintaining coherence, productivity, and cooperation. The Virtual Group Coordination Application aims to ease communication, task management, and cooperation between staff using Microsoft PowerApps. This application acts as a central platform in which the clients can design and track errands, monitor progress, and cooperate through open dialogue, thereby dealing with common problems remote teams face. The application comes with some of the powerful features found in PowerApps, delivering a native UI, through which employees are able to navigate through several functionalities, including a task and deadline track as well as the exchange of documents. In addition, it integrates tools to update as well as alerts to ensure that tasks are completed timely, which then fosters accountability and better overall crew performance. The application also highlights information security and client jobs to ensure that all sensitive information is only accessible by an authorized work force. Applying the security job idea of PowerApps, the application ensures that its group information remains intact as it provides a customized adjustable experience tailored to the specific needs of various associations. In general, the Virtual Group Coordination Application is a comprehensive solution designed to promote virtual cooperation, collaborate in the best ways possible, and support productivity in an offsite work environment.
The Pervasive Impact of Software Engineering and Architecture: A Multi-Industry Analysis
Authors: Karthik Akinepalli
Published: November 2024

This article presents a comprehensive analysis of the ubiquitous role of software engineering and architecture across diverse industries in the modern digital landscape. Through a combination of literature review, industry case studies, and expert interviews, we examine how core software engineering principles—such as scalability, security, user experience, and data management—are applied and adapted in sectors ranging from e-commerce and fintech to healthcare, transportation, media, manufacturing, and government services. Our findings reveal both common challenges and industry-specific innovations in areas such as blockchain technology, telemedicine, autonomous vehicles, and industrial IoT. We also explore emerging trends, including the integration of artificial intelligence, edge computing, and quantum computing, and their potential impact on future software architectures. Furthermore, this article addresses critical ethical considerations and societal implications, including data privacy, job displacement, and the digital divide. By synthesizing these multifaceted perspectives, we provide valuable insights for both industry practitioners and academic researchers, highlighting the transformative power of software engineering in shaping the technological foundations of contemporary society and identifying key areas for future research and development.
Bias and Its Consequences : A Study of Machine Learning Performance
Authors: Anirudh Kokate  , Madhu Priya
Published: November 2024

This paper addresses the concern about bias affecting the results of machine learning models. For this purpose, it uses the Adult Income dataset from OpenML for income classification. The conditions for bias are induced by underrepresenting people that earn <= $50K in training data, thus checking the behavior of different models when encountering such a skewed distribution. Key metrics, namely accuracy and specificity (True Negative Rate), were analyzed for unbiased and biased training scenarios. The results show that Naive Bayes and Random Forest models were resistant to bias, but others, including SVM and Logistic Regression, suffered major performance drops. This study throws light on the robustness of different classifiers when exposed to biased data, requiring further bias mitigation strategies in real-world applications. This paper actually examines critically how bias in training data can significantly affect the performance of prediction, fairness, and model selection in income classification tasks.
Teledentistry Integration: Revolutionizing Dental Insurance Through Technological Innovation
Authors: Pravesh Nikhare
Published: November 2024

This article examines the transformative impact of teledentistry on the dental insurance industry, exploring how telecommunications technology is revolutionizing dental care delivery, access, and insurance. It discusses the expansion of dental care accessibility, streamlining of insurance processes, enhancement of preventive care, and the generation of data-driven insights through teledentistry. The article also addresses privacy and security considerations, presents case studies of successful implementations, and explores future prospects and innovations in the field. By analyzing the integration of teledentistry with artificial intelligence and wearable technology, the article highlights the potential for more proactive, personalized, and accessible dental care, while emphasizing the need for continued research and development to fully realize these benefits.
The Transformative Impact of Artificial Intelligence and Machine Learning on Marketing Operations
Authors: Sowmya Kotha
Published: November 2024

This comprehensive article explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on modern marketing operations. The research delves into key areas where AI and ML are revolutionizing marketing strategies, including hyper-personalization, predictive analytics, and conversational AI. Through an analysis of recent developments and case studies, the article demonstrates how AI-driven personalization significantly enhances customer engagement and relevance, with some implementations showing up to 25% increase in revenue and 15% improvement in customer retention rates. The article also examines the role of predictive analytics in shifting marketing strategies from reactive to proactive approaches, enabling more accurate forecasting of customer behavior, campaign performance, and market trends. Furthermore, the evolution of chatbots and conversational AI is explored, highlighting their capacity to automate lead qualification, scale customer engagement, and gather real-time insights without increasing manual input. The integration of AI in marketing operations is shown to improve campaign management efficiency, enhance personalization capabilities, and facilitate future-focused campaign development. However, the research also addresses the challenges and ethical considerations associated with AI integration in marketing, including data privacy concerns, skill gaps, and the need to balance automation with human creativity. This article provides a comprehensive overview of how AI and ML are reshaping the marketing landscape, offering valuable insights for marketers, researchers, and business leaders navigating this technological revolution.
Multi-Cloud Automation : A Strategic Approach to Cloud Infrastructure Management
Authors: Saili Krishna Maliye
Published: November 2024

This article examines the evolution and impact of multi-cloud automation strategies in modern enterprise environments, supported by comprehensive industry research and case studies. Drawing from multiple industry reports, including Flexera's 2024 State of the Cloud Report and PwC's Cloud and AI Business Survey, the research reveals that 89% of enterprises now employ multi-cloud strategies, with the global multi-cloud management market projected to grow at a CAGR of 27.3% through 2030. The article analyzes key components of successful multi-cloud automation, including deployment automation, operational excellence through monitoring, and incident management, while providing detailed metrics on their effectiveness. Through examination of real-world implementations across retail, financial services, and SaaS sectors, the research demonstrates how organizations achieve significant improvements in operational efficiency, cost optimization, and business agility through comprehensive automation strategies.
Digital Transformation in Healthcare : Leveraging Technology to Enhance Patient Care
Authors: Sivasatyanarayanareddy Munnangi
Published: November 2024

This article examines the successful implementation of Pega's Healthcare Customer Service and Care Management solutions at a major healthcare provider, demonstrating how digital transformation can revolutionize patient care delivery while optimizing operational efficiency. The implementation, which focused on integrated care coordination, patient case management, and compliance frameworks, resulted in significant improvements, including a 20% reduction in hospital readmissions, a 30% reduction in administrative costs, and a 25-35% reduction in operational costs. The organization achieved enhanced patient outcomes, streamlined operations, and improved regulatory compliance through comprehensive data integration, automated workflows, and user-centric design. The article highlights how strategic digital transformation initiatives, aligned with industry best practices and supported by robust change management, can successfully address healthcare's complex challenges while positioning organizations for future innovation in care delivery.
Artificial Intelligence and Employment Transformation: A Multi-Sector Analysis of Workforce Disruption and Adaptation
Authors: Kanagarla Krishna Prasanth Brahmaji
Published: November 2024

This academic investigation examines the bifurcated impact of artificial intelligence (AI) on contemporary labor markets, analyzing both displacement effects and employment generation across multiple sectors (n=327) during 2020-2024. Through a mixed-methods approach combining econometric analysis of industry-level data, semi-structured interviews with key stakeholders (n=142), and longitudinal case studies of AI-implementing firms (n=47), we demonstrate that while AI automation has led to a 23.4% reduction in traditional middle-skill jobs across manufacturing, logistics, and administrative sectors, it has simultaneously generated a 31.7% increase in new employment categories, particularly in AI development, human-AI collaboration, and digital transformation roles. The findings reveal significant sectoral variations in job displacement rates (ranging from 8.2% to 37.6%) and identify critical factors influencing successful workforce transition, including the timing of reskilling initiatives, the nature of institutional support, and the elasticity of labor market responses. Notably, organizations that implemented proactive reskilling programs achieved a 64% higher retention rate of displaced workers compared to those utilizing reactive approaches. The article also uncovers an emerging "adaptation gap" wherein 42% of displaced workers face significant barriers to transitioning into new roles, primarily due to misaligned skill development programs and insufficient support infrastructure. These findings have important implications for policymakers, business leaders, and educational institutions in developing targeted interventions to facilitate effective workforce adaptation in an AI-driven economy.
Performance Optimization in Distributed SQL Environments : A Comprehensive Analysis of Presto Query Engine
Authors: Santhosh Gourishetti
Published: November 2024

This comprehensive article examines performance optimization techniques for Presto, a distributed SQL query engine widely adopted for large-scale data analytics. Through systematic analysis of both theoretical frameworks and empirical evidence, we present a multifaceted approach to enhancing query performance and resource utilization. The article encompasses critical aspects including memory management strategies, parallel processing optimization, and storage connector configurations across diverse deployment scenarios. Our investigation reveals that strategic implementation of query planning algorithms, coupled with fine-tuned JVM configurations, can yield performance improvements of up to 40% in complex analytical workloads. The article also introduces a novel framework for workload-specific optimization patterns, validated through extensive testing across various data scales and query complexities. Through detailed case studies of large-scale deployments, we demonstrate how combined optimization techniques can significantly reduce query latency while maintaining system stability. Furthermore, we present empirical evidence supporting the effectiveness of adaptive resource allocation strategies in mixed-workload environments. These findings contribute to the growing body of knowledge in distributed query processing optimization and provide practical guidelines for organizations seeking to enhance their Presto deployments.
Demystifying Serverless Architecture for Scalable Web Applications
Authors: Vaibhav Vudayagiri
Published: November 2024

Serverless computing has revolutionized the development and deployment of scalable web applications by abstracting infrastructure management and enabling automatic scaling. This article aims to demystify serverless architecture by thoroughly analyzing its core concepts, benefits, and practical implementation strategies. We differentiate between Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) models and explore how they contribute to scalability and cost efficiency. Deployment best practices are outlined using AWS Lambda, Azure Functions, and Google Cloud Functions. This article addresses common challenges associated with serverless computing, including cold starts and execution time limitations, and propose solutions to mitigate these issues. Integration with DevOps practices is also discussed, demonstrating how serverless applications can be incorporated into continuous integration and continuous deployment (CI/CD) pipelines. Through case studies, this article illustrates the effective utilization of serverless architecture in building scalable and resilient web applications.
Cloud-Based Machine Learning : Opportunities and Challenges
Authors: Davinder Pal Singh
Published: November 2024

This comprehensive article explores the transformative impact of cloud-based machine learning (ML) on modern enterprises, examining both opportunities and challenges in implementation. The article investigates the rapidly growing cloud computing market and its ML segment, revolutionizing how organizations approach data analytics and business intelligence. Through detailed analysis of enterprise implementations, the article demonstrates how cloud ML solutions have democratized access to advanced analytics, significantly reducing operational costs while improving data processing efficiency. The article examines key aspects, including scalability advantages, cost efficiencies, and technical complexities, while providing evidence-based best practices for successful implementation. Drawing from multiple industry studies and real-world deployments, the article presents a framework for organizations to navigate challenges in data privacy, vendor dependencies, and skill requirements while maximizing the benefits of cloud-based ML solutions.
Role of Artificial Intelligence in IoT
Authors: Jyoti Bolannavar  , Dr. Sumati Ramakrishna Gowda
Published: November 2024

Artificial Intelligence (AI) and the Internet of Things (IoT) are two revolutionary technologies shaping modern industries. AI enables machines to learn from data and make autonomous decisions, while IoT connects devices, allowing them to communicate and exchange information. The convergence of these technologies, known as AIoT (Artificial Intelligence of Things), has immense potential to transform daily life and business operations. AI enhances IoT by adding intelligence to device networks, improving efficiency and automating processes. For example, AI-powered IoT systems can perform predictive maintenance in industrial settings, preventing costly machine failures. In smart buildings, AI can optimize energy usage, reducing both costs and environmental impact. However, integrating AI with IoT poses significant challenges. One major issue is the vast amount of data generated by IoT devices, which AI requires to function effectively. Managing this data efficiently is critical. Additionally, IoT devices are vulnerable to cyber-attacks, making security a key concern. Protecting these systems from potential threats is essential to safely realize the benefits of AIoT. Despite these challenges, the combination of AI and IoT promises to drive future advancements across industries, enabling smarter, more connected systems that optimize decision-making, reduce costs, and improve overall efficiency.
Study of Interleukin 6 in Rheumatoid Arthritis and Anemic Patients in Sudan
Authors: Samar Mirghani Sidahmed Osman  , Olla Mirghani Seidahmed Osman  , Ibrahim Khidir Ibrahim
Published: November 2024

Rheumatoid arthritis is considered one of the most common, and particularly attacks the joint causing significant individual and community burden and resulting from immunological abnormalities. It has been found that IL-6 plays a key role in the development of this disease. Overall anemia of chronic disease and iron deficiency anemia are frequent causes of anemia in RA patients. This study to analysis serum IL6 and to detect frequency of anemia/non-anemia and Rheumatoid arthritis (RA) patients and establish relationship between blood parameters and disease activity among Sudanese patients suffering from rheumatoid arthritis and deficiency anemia in Khartoum State.
Methods
It is a descriptive case-control study that involved 74 patients of both genders of RA, Anemia and non-anemic disease duration was two years. This study was conducted in Khartoum State. Complete Blood Count (CBC) was performed using the Auto Hematology Analyzer. Serum concentrations of IL-6, was measured in all patients and healthy individual using CLIA. The data collection was simple questionnaire and self-interview.
Results
The frequency of the RA within anemic and non- anemic patients was found higher in females 46(62.16%) than in males 28(37.83%). The overall analysis showed significant changes in most of blood parameters between the age groups. A statistically significant association was found between the WBCs, RBCs and MCH blood parameters with IL6 among RA/anemia's patients with Chi square p-value = 0.001,0.013 and 0.014, respectively. The statistic odd ratio was found 5.828 and 5.703 for WBCs and RBCs. This increase was highly significant among RA-Anemia patients in particular. 
Conclusion
Our study reported the blood parameters isolates which harbored the IL6 and their association with rheumatoid arthritis and Anaemia which possibly cause the disease symptoms. Anemia is an important complication in rheumatoid arthritis (RA) which is associated with physical disability and increased mortality. Among various mechanisms, recent studies suggest the central role of hepcidin and IL-6 for the pathophysiology of Anemia.