Core Objective Research Articles

Cradle Care

Abstract: In a rapidly evolving landscape where digital solutions are revolutionizing healthcare, the advent of artificial intelligence (AI) has ushered in a new era of comprehensive support for expectant mothers throughout their pregnancy journey. This abstract introduces an innovative Pregnancy App with AI that aspires to bridge gaps in traditional healthcare systems, offering proactive, accessible, and personalized guidance to address the critical needs of both the mother and the unborn child. Pregnancy represents a pivotal phase in a woman's life, and ensuring the well-being of both mother and child is paramount. This groundbreaking Pregnancy App with AI is meticulously designed to transform the way we approach prenatal care, empowering expectant mothers with the tools they need for a healthy and informed pregnancy experience. This presentation unfolds the core objectives of the Pregnancy App, shedding light on its integration of cutting-edge AI technologies, and the research underpinning its development. By harnessing collective knowledge from an extensive literature survey, we have identified key trends, challenges, and opportunities that have profoundly influenced the features and functionalities of our application. In summary, the Pregnancy App with AI stands at the forefront of digital healthcare solutions, redefining the pregnancy journey by offering personalized, proactive, and accessible support. Through the integration of cutting-edge AI technologies and the insights garnered from in-depth research, our application promises to transform the way we approach prenatal care, ensuring the well-being of both mother and child throughout this critical phase of life

Personalized Health Recommendation System using Machine Learning

Abstract: The core objective of the project is to develop a health website capable of harnessing individual health data to generate personalized health recommendations, fostering early detection and more effective management of health issues. Utilizing Machine Learning (ML), the team aims to predict diseases like arrhythmia, Sleep Apnea, Insomnia, and Stroke, pivotal for early intervention and adapting diagnosis strategies. In their approach, ML algorithms, including Logistic Regression, Random Forest, and Voting classifier, analyze diverse health data sources to build a comprehensive recommendation system. It is noteworthy that all models exhibit an accuracy exceeding 90%, which underscores the reliability and effectiveness of the system. By seamlessly integrating various health data sources and emphasizing proactive health management, this initiative holds transformative potential, empowering individuals to make informed decisions about their well-being and promoting improved health outcomes.

An Investigation into the Relationship between Transformational Leadership Style and Organizational Commitment in a College of Administration and Economics: An Empirical Research

The current research aims to demonstrate the relationship and impact between transformational leadership and its dimensions (Idealized Influence Behaviour, Idealized Influence Attributed, Intellectual Stimulation, Inspiring Motivation, and Individual Consideration) on organizational commitment and its dimensions (continuous organizational commitment, emotional organizational commitment, and normative organizational commitment) at the College of Administration and Economics. Descriptive-analytical approach used to achieve the core object of the research. The research adopted the questionnaire as the core tool for this purpose. A research sample included various administrative levels (director, chief inspector, supervisor, and assistant supervisor). The questionnaire distributed randomly, and 70 valid questionnaires collected for statistical analysis. The research adapted a set of statistical methods such as arithmetic mean, coefficient of variation, standard deviation, and simple regression coefficient by the statistical program (SPSS V26) to analyze data and test hypotheses. The research reached a set of essential results, the most important of which was that transformational leadership is the main factor for building an organization's culture to achieve growth and success and motivate individuals. Paper type: Research paper

AIRLINE PRICING: HOW AIRLINES USE PRICING STRATGEIES

Airline pricing strategies play a pivotal role in the aviation industry's revenue management. This abstract explores the intricate mechanisms behind airline pricing, delving into the fundamental principles, factors, and techniques utilized by airlines to set fares. Drawing from a combination of academic research, industry reports, and real-world examples, this abstract provides a concise overview of the dynamic landscape of airline pricing. The abstract begins by elucidating the core objectives of airline pricing strategies, emphasizing the dual goals of maximizing revenue and optimizing capacity utilization. It then delineates the key determinants that shape pricing decisions, including demand forecasting, market segmentation, competition analysis, cost considerations, and consumer behavior. Furthermore, the abstract elucidates various pricing tactics employed by airlines, such as dynamic pricing, yield management, fare differentiation, and ancillary revenue generation. It examines how advanced analytics and revenue management systems enable airlines to dynamically adjust fares in response to fluctuating demand patterns, market conditions, and competitor actions. Moreover, the abstract explores the role of distribution channels in airline pricing, highlighting the importance of direct bookings, online travel agencies (OTAs), global distribution systems (GDS), and partnerships with intermediaries. It also discusses the emergence of personalized pricing strategies, leveraging big data analytics and machine learning algorithms to tailor fares based on individual preferences and purchasing behavior. Additionally, the abstract touches upon the regulatory frameworks and ethical considerations surrounding airline pricing practices, including transparency, fairness, and consumer protection. It underscores the need for airlines to strike a balance between revenue optimization and customer satisfaction, fostering trust and loyalty among passengers. In conclusion, this abstract offer valuable insights into the complex ecosystem of airline pricing strategies, shedding light on the multifaceted interplay between market dynamics, technological advancements, and business objectives. By understanding the underlying principles and tactics of airline pricing, stakeholders can navigate the competitive landscape more effectively, driving sustainable growth and profitability in the aviation industry.

Impact of Ceramic Art Therapy on the Psychological Health of Socially Disadvantaged Groups

With the progress of society, there is an increasing concern for mental health issues, especially regarding the mental well-being of socially disadvantaged groups. Due to various factors, socially disadvantaged groups are more susceptible to psychological stress and distress, thereby requiring more attention and assistance. As an emerging therapeutic approach, ceramic art therapy has gradually gained widespread attention from scholars and psychotherapists. The core objective of this study is to delve into the impact of ceramic art therapy on the mental health of socially disadvantaged groups, to provide valuable insights to enhance the mental well-being of these vulnerable populations.

A linguistic evaluation of the South African higher education sector: A reflection on 30 years of democracy (1994–2024)

The core objective of this article is to evaluate the progress made in linguistic development over the past three decades, with a specific focus on the role of language and its philosophical underpinnings in reshaping and decolonising South African higher education landscape. Linguistic imperialism as a conceptual framework alongside the Framework for Policy Analysis were employed to inform, guide and support the author’s contention that linguistic hegemony is still dominating the discourse within the higher education sector. The research adopts a qualitative approach, utilising content analysis for data analysis purposes. The findings of the article reveal that despite 30 years of democracy, the influence of colonial-era language philosophy and western epistemologies persists. It is suggested that comprehensive implementation of language policies could effectively address the transformation and decolonisation agenda. It further advocates for alignment between policy and practice through pragmatic, intentional and transformative initiatives aimed at identifying, interrogating and disrupting the coloniality of power and its western epistemologies, as far as language policy and practice are concerned. In conclusion, the article emphasises the urgent need to counteract existing linguistic imperialism and hegemony embedded within colonial knowledge systems by enacting transformative policies that will prioritise student access, success, linguistic diversity, linguistic justice, epistemic justice and social cohesion within the framework of higher education.Contribution: The aim is to provide guidance to language policy implementers in South African universities by offering an overview of the linguistic advancements achieved between 1994 to 2024.

Worry-YING and Worry-YANG: A Critical Feminist Study with Women Who Worry

Gender is a critical determinant of mental health and well-being. Women are at greater risk of mental illness due to gender-based violence, economic disadvantage, and low social status. Women experience higher levels of acute and chronic stress and are twice as likely to be diagnosed with anxiety disorder. Anxiety disorder is a serious public health problem affecting 1:3 people worldwide, and according to WHO's International Classification of Diseases (ICD-10), a key symptom of anxiety is excessive worry. Few studies, however, have explored worry as a precursor to anxiety, and fewer have explored worry in relation to gender. This critical feminist study aimed to explore this phenomenon and identify the impact of worry on women and their mental health and well-being. Methods: Forty-five women living in Melbourne, Australia, participated in this qualitative study. They attended a 1-h semi-structured interview with the researcher to define and discuss worry and identify any impact or effect worry or worrying has had on their life as women. Results: The findings revealed that women worry about children, money, work, study, and violence against women. Conclusions: This study builds on the existing knowledge of the mental health and well-being of women, supports early intervention strategies to prevent anxiety disorder and long-term health risks; and meets the core objectives of the National Women’s Health Strategy 2020-2030 to ‘Improve the health and wellbeing of all women and girls in Australia’.

The Journey of the Asian Forensic Sciences Network (AFSN) through Fifteen Years

The Asian Forensic Sciences Network was formed in 2008 by a small group of six forensic institutes from six countries in Asia, with the vision to create a common platform for scientists in the region to come together to advance forensic science, raise quality standards, and foster collaboration. Since its inception, the network has experienced remarkable growth, now comprising 70 member institutes from 18 countries across Asia. An Annual Meeting & Symposium, hosted by a member institute each year, serves as a cornerstone event for the network. In addition, the network runs nine technical workgroups covering areas in Crime Scene Investigation, Digital Forensics, DNA, Fingerprint, Forensic Medicine, Illicit Drugs, Questioned Document, Toxicology, and Trace Evidence, alongside a dedicated Quality Assurance and Standards Committee. These workgroups and committee work in tandem with the AFSN Board to formulate strategies aligned with the network’s core objectives. This paper chronicles AFSN’s journey over the past fifteen years, highlighting the pivotal role of the Board and the Workgroups, as well as the dedicated passion and unwavering commitment of the members in shaping the network through numerous activities.

Drug Dosage Control System Using Reinforcement Learning

This project introduces a pioneering approach for optimizing drug dosage control strategies through the utilization of reinforcement learning (RL), a sophisticated subset of machine learning techniques. The core objective is to dynamically adjust drug dosages in real-time based on patient responses, thereby maximizing therapeutic efficacy while minimizing potential adverse effects. By integrating reinforcement learning algorithms, including Q-learning, Deep Q-Networks (DQN), and actor-critic methods, the system learns from patient data to make precise dosage adjustments considering individual patient characteristics, disease progression, and response to treatment. The framework promises to revolutionize personalized medicine by providing tailored drug dosages, enhancing treatment outcomes, and ensuring patient safety. The project's scope covers not only the development and implementation of this innovative RL- based system but also addresses significant challenges such as model interpretability, scalability, and regulatory compliance, ensuring its practical applicability in healthcare settings. Through this work, we aim to bridge the gap between conventional drug prescription methodologies and the potential for personalized, optimized care, making a substantial contribution to the advancement of healthcare systems.

Policía, ver para creer: Variación en percepciones y expectativas sociales sobre la Policía Militar

The present research had as core objective to identify the perception of the citizens about the performance of the military state police institutions, according to the opinion of the students of a private institution of higher education. It was a qualitative research, using questionnaires for data collection. The answers to the questions were evaluated both in terms of their classification, and in a more profound way, by identifying the structuring nuclei of the discourse. It was found that the general perceptions about the military state police were evenly distributed, between positive, negative or neutral. It was also found that the military state police had a better assessment of the participants' place of residence, in relation to other corporations in other states. It was noticed that, in general, considering the specificities of the studied group, the population is well informed about the attributions of the military state police. Regarding the formation of opinion, it was possible to identify a greater influence of information that is accessed through the media, than of real and personal interactions with the military state police. Finally, it was possible to verify the improvement of opinion about such institutions, when exposed to their positive actions, as well as the worsening of negative judgment, when exposed to situations of problematic or disastrous performance.

Designing a framework for entrepreneurship education in Chinese higher education: a theoretical exploration and empirical case study

Entrepreneurship education (EE) has rapidly evolved within higher education and has emerged as a pivotal mechanism for cultivating innovative and entrepreneurial talent. In China, while EE has made positive strides, it still faces a series of practical challenges. These issues cannot be effectively addressed solely through the efforts of universities. Based on the triple helix (TH) theory, this study delves into the unified objectives and practical content of EE in Chinese higher education. Through a comprehensive literature review on EE, coupled with educational objectives, planned behavior, and entrepreneurship process theories, this study introduces the 4H objective model of EE. 4H stands for Head (mindset), Hand (skill), Heart (attitude), and Help (support). Additionally, the research extends to a corresponding content model that encompasses entrepreneurial learning, entrepreneurial practice, startup services, and the entrepreneurial climate as tools for achieving the objectives. Based on a single-case approach, this study empirically explores the application of the content model at T-University. Furthermore, this paper elucidates how the university plays a role through the comprehensive development of entrepreneurial learning, practices, services, and climate in nurturing numerous entrepreneurs and facilitating the flourishing of the regional entrepreneurial ecosystem. This paper provides important contributions in its application of TH theory to develop EE within the Chinese context, and it provides clear guidance by elucidating the core objectives and practical content of EE. The proposed conceptual framework serves not only as a guiding tool but also as a crucial conduit for fostering the collaborative development of the EE ecosystem. To enhance the robustness of the framework, this study advocates strengthening empirical research on TH theory through multiple and comparative case studies.

Optimizing Fund Performance: A Communication Systems Approach to Data Mining in Large- Cap Equity Mutual Funds

Mutual funds represent a secure and technologically driven conduit for Indian investors to engage with the dynamic Indian financial markets. These professionally managed funds strategically allocate investments across a spectrum of securities, including equities, bonds, government securities, and gold, leveraging cutting-edge information technology infrastructure for seamless operation and optimization. Within this landscape, performance emerges as a pivotal metric, driving the flow of savings into these digitally enabled funds, indicative of investors' confidence and expectations in the realm of data-driven decision-making. The core objective of this paper is to construct a robust mutual fund performance evaluation model utilizing the Statistical Package for the Social Sciences (SPSS), a sophisticated software platform renowned for its advanced analytics and predictive modeling capabilities in the information technology domain. The work focused specifically on equity mutual funds, this study meticulously examines top-rated funds within the large-cap category, drawing insights from a comprehensive validation period spanning the last fifteen years from April 2007 to March 2021. Through the systematic evaluation of various performance metrics within the context of information technology-driven analysis, this model aims to deliver actionable insights and strategic foresight, benefiting researchers, society, and financial analysts alike, while catalyzing innovation and driving positive impacts in the intersection of technology and finance.

A Deep Learning Approach for Malnutrition Detection

The timely detection of malnutrition in children is of paramount importance, as it allows for early intervention and treatment. This proactive approach not only prevents further health deterioration but also fosters proper growth, minimizing the long-term consequences of malnutrition, such as stunted growth, impaired cognitive development, and increased vulnerability to diseases. Our work encompasses the creation of a new dataset comprising images of children in Healthy, Undernourished, Stunting, and Wasting categories. The core objective is to assess the deep learning model performance in classifying these children images. The experimentation is carried out by varying epochs, batch size, optimizers AdamW, Adamax, and RMSprop; and different values of the learning rate 0.1, 0.01, 0.001, and 0.0001 during model training. The model is trained on image dataset constructed by cleaning images generated by the stable diffusion model. The model is tested on randomly selected child images from websites. The model successfully classified two classes with 95% accuracy, 97.6% F1 score, precision 97.6%, and 97.6% recall with Adam optimizers, 0.0001 learning rate, and Batch size 4. Additionally, for the four-class categorization scenario, the study broadens the classification. The model achieved 88.87% accuracy, 90.3% recall, 90.2% precision, and an F1 score of 90% for four-class categorization with AdamW optimization, 0.0001 learning rate, and batch size 6. These results are satisfactory for prediction of malnutrition category in children.

CAR DAMAGE DETECTION USING CNN

In today's modern society, automobiles play a crucial role, and the automatic classification of car damages holds particular significance for the auto insurance industry. Our proposed solution involves the implementation of two Convolutional Neural Network (CNN) models. Specifically, the VGG16 model is employed to identify and assess the location and severity of car damage, while the Mask R-CNN is utilized to accurately mask the damaged regions. Both models collectively provide valuable insights into the extent. The CNN models effectively filter out images without damages, allowing only those with identified damage to be passed on to the object detection model. This strategic approach enhances the overall performance of the model. The core objective of this research project is to achieve maximum accuracy through the utilization of CNN models. TensorFlow, Key Words: E-commerce, Car Damage, Detection, Classification, VGG, Mask RCNN, Severity, Location, Masking

BlockFog: A Blockchain-based Framework for Intrusion Defense in IOT Fog Computing

In the rapidly evolving domain of the Internet of Things (IoT) and fog computing, maintaining security, scalability, and efficient operation poses significant challenges. Addressing these issues, this study introduces "BlockFog," a novel blockchain-based framework designed to bolster intrusion defense in IoT fog computing environments. The core objective of BlockFog is to counteract the vulnerabilities inherent in decentralized IoT ecosystems by leveraging blockchain technology for enhanced security and transparency. The framework's innovative design integrates crucial components such as Device Onboarding & Identity Management, Data Integrity & Logging, Smart Contract-Driven Intrusion Detection, Automated Blockchain Responses, Secure Peer-to-Peer Communication, and a Lightweight Consensus Mechanism. These elements work collectively to ensure the security and functionality of IoT devices within the fog computing paradigm. BlockFog stands out for its meticulous approach to handling high transaction volumes with off-chain computations and layer-2 solutions, ensuring data integrity and facilitating seamless audit processes. The framework's resilience is further demonstrated through its robust response to evolving cyber threats, incorporating Over-the-Air (OTA) updates and advanced data protection mechanisms like zero-knowledge proofs. A comparative analysis highlights BlockFog's superior performance against existing models. The results reveal BlockFog's lower latency rates in normal, high traffic, and attack scenarios, its higher throughput efficiency, and its more effective resource utilization in terms of CPU, memory, and bandwidth usage. Moreover, BlockFog exhibits an enhanced ability to detect and respond to malicious activities, including DDoS attacks, with significantly higher accuracy than its counterparts. These findings underscore BlockFog's potential in redefining security and operational paradigms in IoT fog computing, making it a robust, agile, and transparent framework suitable for the current digital landscape.

Unraveling aquifer dynamics: Time series evaluation for informed groundwater management

Optimizing groundwater monitoring networks is essential for the sustainable management of this critical resource, which is vulnerable to both anthropogenic and natural changes. Focusing on the Hashtgerd aquifer in Iran, this research aims to enhance the efficiency of groundwater level monitoring systems by integrating statistical methods, artificial intelligence (AI), and hydrogeological studies. The study's core objective is to pinpoint and comprehend the factors that cause certain observation wells to become isolated from the main aquifer body, leading to parts of the aquifer being cut off from the central hydrogeological system. Employing clustering techniques on data from 29 observation wells, based on parameters like groundwater level (GWL), groundwater level change (GWLC), groundwater depth (GWD), air temperature (AT), distance (D), ground level (GL), and temperature (T) through Hierarchical Cluster Analysis (HCA), the research categorizes the groundwater information into four distinct groups. Significantly, Cluster 4 encompasses wells with similar characteristics. The analysis reveals that two wells, categorized in Clusters 1 and 2, exhibit unique behaviors, likely due to their connection to local karstic aquifers, as corroborated by geological evidence. Cluster 3's separation is determined through geophysical investigations, identifying confined conditions near specific wells, notably Wells 4 and 8. AI techniques further distinguish differences in meteorological influences and water discharges among the clusters, noting minimal weather impact on Clusters 1 and 2, considerable shallow groundwater influence on Cluster 3, and a pronounced effect of groundwater discharge on Cluster 4. This nuanced identification of aquifer segments disconnected from the main system underscores the need to refine monitoring networks for accurate assessment of groundwater resources. By leveraging a detailed analysis of aquifer complexities through state-of-the-art methodologies, the study guides towards informed, sustainable groundwater management strategies.

Recent Advancements in Continuous-Flow Suzuki-Miyaura Coupling Utilizing Immobilized Molecular Palladium Complexes.

Immobilized Pd-catalyzed Suzuki-Miyaura coupling under continuous-flow conditions using a packed-bed reactor, representing an efficient, automated, practical, and safe technology compared to conventional batch-type reactions. The core objective of this study is the development of an active and durable catalyst. In contrast to supported Pd nanoparticles, the attachment of Pd complexes onto solid supports through well-defined coordination sites is considered a favorable approach for preparing highly dispersed and stabilized Pd species. These species can be directly employed in various flow reactions without the need for pre-treatment. This concept paper explores recent achievements involving the application of immobilized Pd complexes as precatalysts for continuous-flow Suzuki-Miyaura coupling. Our focus is to elucidate the significance of the designed catalyst structures in relation to their catalytic performance under flow conditions. Additionally, we highlight various reaction systems and catalyst packing methods, emphasizing their crucial roles in establishing a practical synthesis process.

ExamMate-Online Examination Portal

In today's digital era, the demand for remote learning and online education has surged, necessitating robust platforms for conducting examinations remotely. This research paper introduces ExamMate, an online examination portal tailored to streamline the assessment process within educational institutions and organizations. ExamMate is designed with the core objectives of simplifying examination procedures, enhancing accessibility for administrators and examinees, and ensuring the integrity of assessments. This paper outlines the key features of ExamMate, including its user-friendly interface, customizable exam creation tools, and comprehensive security measures. The platform offers a diverse range of question formats, such as multiple-choice, short answer, and essay questions, catering to various assessment needs. ExamMate prioritizes security through encryption protocols, authentication mechanisms, and anti-cheating measures to maintain the fairness and credibility of assessments. KEYWORD: Exam Mate Online examination portal, Remote proctoring, Exam scheduling, Exam registration, Secure online exams, Student assessment platform, Digital assessment tool, Student performance tracking, Exam grading.

Vacancy-Engineered Nickel Ferrite Forming-Free Low-Voltage Resistive Switches for Neuromorphic Circuits.

Innovations in resistive switching devices constitute a core objective for the development of ultralow-power computing devices. Forming-free resistive switching is a type of resistive switching that eliminates the need for an initial high voltage for the formation of conductive filaments and offers promising opportunities to overcome the limitations of traditional resistive switching devices. Here, we demonstrate mixed charge state oxygen vacancy-engineered electroforming-free resistive switching in NiFe2O4 (NFO) thin films, fabricated as asymmetric Ti/NFO/Pt heterostructures, for the first time. Using pulsed laser deposition in a controlled oxygen atmosphere, we tune the oxygen vacancies together with the cationic valence state in the nickel ferrite phase, with the latter directly affecting the charge state of the oxygen vacancies. The structural integrity and chemical composition of the films are confirmed by X-ray diffraction and hard X-ray photoelectron spectroscopy, respectively. Electrical transport studies reveal that resistive switching characteristics in the films can be significantly altered by tuning the amount and charge state of the oxygen vacancy concentration during the deposition of the films. The resistive switching mechanism is seen to depend upon the migration of both singly and doubly charged oxygen vacancies formed as a result of changes in the nickel valence state and the consequent formation/rupture of conducting filaments in the switching layer. This is supported by the existence of an optimum oxygen vacancy concentration for efficient low-voltage resistive switching, below or above which the switching process is inhibited. Along with the filamentary switching mechanism, the Ti top electrode also enhances the resistive switching performance due to interfacial effects. Time-resolved measurements on the devices display both long- and short-term potentiation in the optimized vacancy-engineered NFO resistive switches, ideal for solid-state synapses achieved in a single system. Our work on correlated oxide forming-free resistive switches holds significant potential for CMOS-compatible low-power, nonvolatile resistive memory and neuromorphic circuits.

Assessing road vulnerability in seismic conditions: a comprehensive study

Road infrastructure is indispensable for societal functionality, yet it is highly susceptible to the devastating impacts of seismic events. This paper focuses on enhancing the resilience of road systems by systematically identifying, quantifying, and assessing factors contributing to their vulnerability during earthquakes. The core objective of this study is to develop and validate a seismic vulnerability index for road sections, which aims to evaluate and classify the susceptibility of road infrastructure to earthquake-induced damage comprehensively. This systematic approach is pivotal for guiding effective mitigation strategies and prioritization efforts. To fulfill this objective, the study employs the Analytic Hierarchy Process (AHP) to introduce a novel methodology for calculating the seismic vulnerability index, incorporating various factors that influence road vulnerability. This method allows for the accurate classification of road sections into distinct levels of susceptibility, providing a solid foundation for implementing targeted interventions and enhancing road resilience. Further, the paper validates the theoretical model through several case studies, demonstrating the practical applicability and effectiveness of the seismic vulnerability index in real-world scenarios. Additionally, the use of Geographic Information System (GIS) technology to simulate earthquake scenarios within an urban road network offers valuable insights into the potential seismic behavior of roads. These simulations are crucial for identifying critical areas that require intervention and for planning resilience-building efforts more effectively. By proposing a comprehensive framework that combines rigorous analysis, empirical validation, and advanced simulation techniques, this paper seeks to make a significant contribution to the field of infrastructure resilience. It aims to advance the understanding of road vulnerability in seismic conditions and supports the development of strategic approaches for enhancing the resilience of road infrastructure against earthquakes. Through the development and validation of a seismic vulnerability index, this study meets its primary objective, providing a valuable resource for researchers, policymakers, and practitioners in disaster management and infrastructure planning.