bluuebunny/crossref_metadata_2025 huggingface dataset:
Relationship between Major SatisfactionㆍStressㆍResilience of College Students
Authors:   The K Association of Education Research, Jeong Seong-Ran
Published: March 2024

The purpose of this study is to examine the relationship between college students' major satisfaction, stress, and resilience, and to determine whether college students' major satisfaction and stress affect their resilience. For this purpose, major satisfaction, stress, and resilience tests were conducted on 221 students at K University and the results were analyzed. Data analysis included descriptive statistics, frequency analysis, correlation analysis, multiple regression analysis, and reliability analysis using the SPSS program. As a result of the study, college students' major satisfaction and stress showed a negative correlation, and the negative correlation was especially high with academic stress and future problem stress. In terms of stress and resilience of college students, the correlation between positive resilience and stress was particularly high. College students' major satisfaction and resilience were found to be positively correlated, and resilience's interpersonal skills were found to be highly positively correlated with major satisfaction. Factors that affect the resilience of college students are both major satisfaction and stress. It was found that the higher the major satisfaction and the lower the stress, the higher the resilience. The relative influence of major satisfaction and stress showed that major satisfaction had a greater impact on resilience than stress. In future research, qualitative research such as FGI (Focus Group Interview) will be needed in addition to research to improve college students' stress management ability and resilience and quantitative research on major satisfaction, stress, and resilience targeting more diverse groups.
A Simple Linear Controller Design to Ensure Global Exponential Stability for a Class of Uncertain Fifth-Order Nonlinear Systems
Authors: Yeong-Jeu Sun  , Sheng-Chieh Chen  , Ting-Chia Chang  , Yu-Chi Ho  , Wei-Chun Liao  , Sheng-Wei Huang
Published: March 2024

This paper intends to explore the controller design problem for a class of uncertain fifth-order nonlinear control systems. Combining the theory of differential and integral inequalities, a linear controller will be proposed to promote a class of nonlinear control systems with multiple uncertainties to achieve the goal of global exponential stability. In addition, the guaranteed exponential convergence rate of such uncertain nonlinear systems will also be precisely calculated. Finally, some numerical simulation results will also be presented to verify and illustrate the correctness of this main theorem and the design process of the controller
Formulation and Evaluation of Floating Microspheres
Authors: Mr. Manish V. Bagade  , Mr. Shubham J. Sahdeo  , Mr. Amit S. Sawad  , Mr. Akhil Maske  , Mrs. K. Raja Rajeshwari
Published: March 2024

Gastro-retentive dosage forms have potential for use as controlled- release drug delivery systems. Gastro retentive floating drug delivery systems have a bulk density lower than that of gastric fluids and thus increase residence time of drug in stomach and provide controlled delivery of many drugs. The aim of the present study is formulation and characterization of floating microspheres using model drug. Floating microspheres were prepared by oil-in-water emulsion solvent evaporation technique using ethyl cellulose as release retarding polymers. The floating microspheres were evaluated for percentage yield (%), particle size, drug content, drug entrapment efficiency, in-vitro floating ability and in-vitro drug release studies. The surface morphology of prepared microspheres was characterized by scanning electron microscopy. The microspheres were found to be spherical in shape and porous in nature. Compatibility studies were performed by fourier transform infrared (FTIR) technique. The prepared microspheres showed prolonged drug release of 12 h and remain buoyant for more than 12 h. In-vitro release kinetics were studied in different release kinetics models like zero order, first order model. It was concluded that developed floating microspheres offers a suitable and practical approach for prolonged release of drug over an extended period of time and thus oral bioavailability, efficacy and patient compliance is improved
Encyclopedia of Medieval Literature
Authors: Lambdin Robert Thomas , Lambdin Laura
Published: March 2024

Often misleadingly called the Dark Ages, the period between the fall of Rome and the Renaissance was a time of great creativity. The Middle Ages gave rise to some of the world's most enduring and influential literary works, including Dante'sCommedia, Chaucer'sThe Canterbury Tales, and a large body of Arthurian lore and legend. This reference is a comprehensive guide to literature written between 500 and 1500. While the volume is primarily devoted to the early literature of England, it also includes entries for historical persons and subjects of cultural relevance which would have been discussed in literary works or which might have affected their creation. Multicultural in scope, the book also covers Islamic, Hispanic, Celtic, Mongolian, Germanic, Italian, and Russian literature and culture of the Middle Ages.

Longer entries provide thorough coverage of major English authors such as Chaucer and Malory, and of entire genres, such as drama, lyric, ballad, debate, saga, chronicle, and hagiography. Shorter entries examine particular literary works; significant kings, artists, explorers, and religious leaders; important themes, such as courtly love and chivalry; and major historical events, such as the Crusades. The entries are written by scholars and each entry concludes with a brief bibliography. The volume closes with a list of the most valuable general works for further reading.
Program Madrasah Diniyah Dalam Meningkatkan Pemahaman Keagamaan Mahasiswa Universitas Islam Negeri Sayyid Ali Rahmatullah Tulungagung
Authors: Awaliah Lutfiatul , Hikam Nurul , Rofiq Ainur , Hakim Tri Fahad Lukman
Published: March 2024

Penelitian ini berlatarbelakang karena adanya  berbagai input mahasiswa perguruan tinggi islam yang berbeda-beda maka penelitian ini bertujuan untuk mengetahui adanya program madrasah diniyah di Universitas Islam Negeri Sayyid Ali Rahmatullah Tulungagung untuk meningkatkan pemahaman keagamaan mahasiswa yang nantinya diharapakan menjadi mahasiswa yang berintelektual luas, berakhlakul karimah, dan berjiwa sosial yang tinggi. Penelitian ini dilaksanakan di kampus Universitas Islam Negeri Sayyid Ali Rahmatullah Tulungagung, yang menggunakan jenis penelitian kualitatif dengan pendekatan studi kasus,kehadiran peneliti sangatlah berpengaruh dalam penelitian karena peneliti melaksanakan pengumpulan data melalui Observasi, Wawancara dan Dokumentasi dengan penguatan keabsahan data dengan menggunakan Triangulasi, yang digunakan peneliti yaitu Triangulasi teknik dan Triangulasi Sumber yang dilakukakan selama proses penelitian. Hasil penelitian yang diperoleh peneliti di Universitas Islam Negeri Sayyid Ali Rahmatullah Tulungagung diantaranya adanya Program Kelas BTQ( Baca Tulis Al-Qur’an) yang menggunakan metode An-nahdliyah, kelas Tilawah dengan menggunakan beberapa lagu yang sudah ditentukan oleh pengelola, kelas Tahfidz menggunakan metode sorogan dan setoran hafalan, sedangkan pada program Kelas Kitab Turots yang terdiri dari tiga tingkatan yaitu Ula, Wustho dan Ulya yang menggunakan metode bandongan, lalaran, setoran hafalan dan menggunakan penulisan arab pegon bahasa jawa. Dalam program Madrasah diniyah yang sudah terlaksana di kampus Universitas Islam Negeri Sayyid Ali Rahmatullah Tulungagung selalu mengadakan Evalusi pada setiap pembelajaran yang nantinya juga akan berpengaruh pada program yang sudah terlakasana menjadi lebih baik, yaitu diantaranya menggunakan dua bentuk evaluasi yaitu evaluasi formatif yang dilaksanakan setiap pertemuan dan evaluasi sumatif yang dilaksankan di setiap akhir semester.
Role of Computational Biology in Oral Science
Authors: T.A Deepak , C.K Anulekha , Suchindra Suchindra , Tejasvi Avinash , Nadhira Ms. Mariyam
Published: March 2024

DNA sequence Cigarette Smoking, Betel leaf chewing, and alcohol consumption are major cause of oral cancer in Asia. The difficulty in quitting, coupled with patients’ economic conditions affects the inability to get diagnosed early, driving death rate higher. There has been major advancement in molecular sciences, computational biology, and other fields today, but we are not still able to pinpoint the causes of oral cancer, also known as Squamous Cell Carcinoma (OSCC). Early detection leads to better survival rate, therefore, education on yearly check-ups plays a vital role. Computational analysis at the genomic (DNA sequence) can help patients with targeted cellular treatment and hopefully a cure. In this paper, we would look at computation tools used in detecting OSCC and various analysis. Analysis includes detecting abnormality in the cell and other molecular reactions which later morph into a cancerous cell. Later, we investigate all computational tools or techniques from local and global sequence alignment, protein structure, gene, functional structure analysis which help medical staff detect cancer, which in turn can help with oral cancer treatment, prognosis and hopefully a cure.
Next Generation Sequencing in Detecting Oral Cancer Due to Tobacco Consumption
Authors: Nguyen Ngoc Hoi , Nguyen Quang-Trung , T.A Deepak , Suchindra Suchindra , Tejasvi Avinash
Published: March 2024

DNA sequence DNA Sequencing is the first step in establishing phylogenetic trees, protein structure prediction, diagnosis of cancer, discovery of drugs and hence its importance cannot be underestimated. DNA sequencing finds its use in the diagnosis of oral squamous cell carcinoma (OSCC). Oral Cancer is the most common occurring malignancies in the world, especially in India where the prevalence for smoking, Areca nut chewing coupled with a lifestyle that encourages these two activities as fashion are left many people diagnosed with OSCC. Patients with this OSCC are more likely unaware of its side effects and over time might suffer from facial deformity. The importance to understanding the symptoms, prevention and treatment of oral cancer is very much essential today. In this paper, we looked at over 2000 odd papers published and look at the correlation between the next Generation DNA sequencing algorithms (NGS) play an important role in diagnosis of OSCC. This is a further study on some of the papers which have highlighted NGS role in OSCC Diagnosis. We did like to see a comprehensive review on the papers published so far. In the discussion, we will see frequently mutated genes in the OSCC, recent discoveries and OSCC treatment based on the findings.
Integrating Machine Learning in Clinical Decision Support Systems
Authors: Subramanian Tanay
Published: March 2024

This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML algorithms, such as regression, random forest, and neural networks, the review aims to showcase their potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge, and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a collaborative approach to refine these systems for safety, efficacy, and equity.
Brief Commentary: Using a Logic Model to Integrate Public Health Informatics Into Refinements of Public Health Surveillance System
Authors: Fant GV
Published: March 2024

The COVID-19 pandemic has been a watershed moment in public health surveillance, highlighting the crucial role of data-driven insights in informing health actions and policies. Revisiting key concepts— public health, epidemiology in public health practice, public health surveillance, and public health informatics—lays the foundation for understanding how these elements converge to create a robust public health surveillance system framework. Especially during the COVID-19 pandemic, this integration was exemplified by the WHO efforts in data dissemination and the subsequent global response. The role of public health informatics emerged as instrumental in this context, enhancing data collection, management, analysis, interpretation, and dissemination processes. A logic model for public health surveillance systems encapsulates the integration of these concepts. It outlines the inputs and outcomes and emphasizes the crucial actions and resources for effective system operation, including the imperative of training and capacity development.
Leveraging Machine Learning Algorithm to Enable Access to Credit for Small Businesses in the United States of America
Authors: T. Yussuph Toyyibat
Published: March 2024

The research centers on the essential function of loans in driving economic expansion and the intrinsic danger of loan defaults. Small businesses play a crucial role in generating employment and fostering economic growth, highlighting the significance of precise loan eligibility evaluations. The Small Business Administration's (SBA) 7(a) loan program seeks to assist small businesses by providing loan guarantees to mitigate risks for financial institutions (Mini et al., 2018). The study utilizes sophisticated machine learning methods, particularly the Random Forest and XGBoost algorithms, to forecast loan defaults and ascertain ideal loan amounts for small businesses. A dataset comprising 27 variables is analyzed, encompassing loan attributes, borrower details, and loan outcomes. The results highlight the effectiveness of both Random Forest and XGBoost in generating loan default predictions, with a slight edge for XGBoost. Additionally, Linear Regression is used to estimate loan amounts for qualified borrowers. The analysis identifies key factors contributing to loan defaults, with variables such as Term, Disbursement Gross, SBA Approval, and Gross Approval playing a significant role in Random Forest predictions. The study reveals intriguing patterns in loan defaults across various industrial sectors, emphasizing the complex nature of assessing loan performance within these industries (Zhou et al., 2023). This research aims to improve lending practices, benefiting both lenders and borrowers while enhancing our understanding of risk management in the context of small businesses.
An Ethical Study of Generative AI from the Actor-Network Theory Perspective
Authors: li Yuying , zhu Jinchi
Published: March 2024

The widespread use of Generative Artificial Intelligence in the innovation and generation of communication content is mainly due to its exceptional creative ability, operational efficiency, and compatibility with diverse industries. Nevertheless, this has also sparked ethical problems, such as unauthorized access to data, biased decision-making by algorithms, and criminal use of generated content. In order to tackle the security vulnerabilities linked to Generative Artificial Intelligence, we analyze ChatGPT as a case study within the framework of Actor-Network Theory. We have discovered a total of nine actors, including both human and non-human creatures. We examine the actors and processes of translation involved in the ethical issues related to ChatGPT and analyze the key players involved in the emergence of moral issues. The objective is to explore the origins of the ethical issues that arise with Generative Artificial Intelligence and provide a particular perspective on the governance of Generative Artificial Intelligence.
Synthetic Brain Images: Bridging the Gap in Brain Mapping With Generative Adversarial Model
Authors: Mourad Drici , Oseni Kazeem Oluwakemi
Published: March 2024

Magnetic Resonance Imaging (MRI) is a vital modality for gaining precise anatomical information, and it plays a significant role in medical imaging for diagnosis and therapy planning. Image synthesis problems have seen a revolution in recent years due to the introduction of deep learning techniques, specifically Generative Adversarial Networks (GANs). This work investigates the use of Deep Convolutional Generative Adversarial Networks (DCGAN) for producing high-fidelity and realistic MRI image slices. The suggested approach uses a dataset with a variety of brain MRI scans to train a DCGAN architecture. While the discriminator network discerns between created and real slices, the generator network learns to synthesise realistic MRI image slices. The generator refines its capacity to generate slices that closely mimic real MRI data through an adversarial training approach. The outcomes demonstrate that the DCGAN promise for a range of uses in medical imaging research, since they show that it can effectively produce MRI image slices if we train them for a consequent number of epochs. This work adds to the expanding corpus of research on the application of deep learning techniques for medical image synthesis. The slices that are could be produced possess the capability to enhance datasets, provide data augmentation in the training of deep learning models, as well as a number of functions are made available to make MRI data cleaning easier, and a three ready to use and clean dataset on the major anatomical plans.
"Predictive Modelling of Air Quality Index (AQI) Across Diverse Cities and States of India using Machine Learning: Investigating the Influence of Punjab's Stubble Burning on AQI Variability"
Authors: Sidhu Kamaljeet Kaur , Balogun Habeeb , Oseni Kazeem Oluwakemi
Published: March 2024

Air pollution is a common and serious problem nowadays and it cannot be ignored as it has harmful impacts on human health. To address this issue proactively, people should be aware of their surroundings, which means the environment where they survive. With this motive, this research has predicted the AQI based on different air pollutant concentrations in the atmosphere. The dataset used for this research has been taken from the official website of CPCB. The dataset has the air pollutant concentration from 22 different monitoring stations in different cities of Delhi, Haryana, and Punjab. This data is checked for null values and outliers. But, the most important thing to note is the correct understanding and imputation of such values rather than ignoring or doing wrong imputation. The time series data has been used in this research which is tested for stationarity using The Dickey-Fuller test. Further different ML models like CatBoost, XGBoost, Random Forest, SVM regressor, time series model SARIMAX, and deep learning model LSTM have been used to predict AQI. For the performance evaluation of different models, I used MSE, RMSE, MAE, and R2. It is observed that Random Forest performed better as compared to other models.
Evaluation of Medium-Sized Language Models in German and English Language
Authors: Peinl René , Wirth Johannes
Published: March 2024

Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative question answering, which requires models to provide elaborate answers without external document retrieval. The paper introduces an own test dataset and presents results from human evaluation. Results show that combining the best answers from different MLMs yielded an overall correct answer rate of 82.7% which is better than the 60.9% of ChatGPT. The best MLM achieved 71.8% and has 33B parameters, which highlights the importance of using appropriate training data for fine-tuning rather than solely relying on the number of parameters. More fine-grained feedback should be used to further improve the quality of answers. The open source community is quickly closing the gap to the best commercial models.
Document Author Classification using Parsed Language Structure
Authors: K. Moon Todd , H. Gunther Jacob
Published: March 2024

Over the years there has been ongoing interest in detecting authorship of a text based on statistical properties of the text, such as by using occurrence rates of noncontextual words. In previous work, these techniques have been used, for example, to determine authorship of all of The Federalist Papers. Such methods may be useful in more modern times to detect fake or AI authorship. Progress in statistical natural language parsers introduces the possibility of using grammatical structure to detect authorship. In this paper we explore a new possibility for detecting authorship using grammatical structural information extracted using a statistical natural language parser. This paper provides a proof of concept, testing author classification based on grammatical structure on a set of “proof texts,” The Federalist Papers and Sanditon which have been as test cases in previous authorship detection studies. Several features extracted from the statistical natural language parser were explored: all sub trees of some depth from any level; rooted subtrees of some depth, part of speech, and part of speech by level in the parse tree. It was found to be helpful to project the features into a lower dimensional space. Statistical experiments on these documents demonstrate that information from a statistical parser can, in fact, assist in distinguishing authors.
Exploring Self-Disclosure and Its Impact on Psychological Well-Being among Homosexuals (Gay)
Authors: Prabandari Ruby Bulan , Huwae Arthur
Published: March 2024

Progress over time has encouraged the gay community to be more open about their sexual identity. However, there is still stigma and societal disacceptance because they are considered deviant from religion, law and culture. This research aims to determine the relationship between self-disclosure and the psychological well-being of homosexuals (gay) in Central Java. The method used is quantitative with a correlational design. A total of 237 homosexuals (gay) in Central Java were used as research participants using incidental sampling techniques. Research measurements used the Revised Self-Disclosure Scale (α = .92) and the Ryff Psychological Well-Being Scale (α = .543-.828). The research data analysis method uses the product-moment correlation test from Karl Pearson. The research results show a significant, but not strong, positive relationship between self-disclosure and psychological well-being (r = .147 and sig. = .012). This indicates that self-disclosure is one of the factors related to increasing the psychological well-being of homosexuals (gays) in Central Java. The implication of this research is the need for self-disclosure for homosexuals (Gay) to vent all the feelings and inner pressure they experience and make them feel more relieved to improve their psychological well-being to live their lives well for the sake of past goals. Future research should be able to develop research by looking for other factors related to self-disclosure and psychological well-being.
A Case of Forgotten Poisoning in a Patient Presenting with Speech Disorder
Authors: Hançer Çelik Fatma , Aytekin Rukiye , Yıldırım Haticenur , Baykan Necmi , Salt Ömer
Published: March 2024

Anticholinergic poisoning is one of the most common causes of poisoning in the emergency department. Beautiful hawthorn (atropa belladonna), which is found in our country, is one of the plants that can cause anticholinergic syndrome. It is important in the differential diagnosis in cases progressing with general status disorder and loss of consciousness and psychotic findings. The onset of anticholinergic intoxication varies depending on the toxin and occurs within one to two hours following oral ingestion. The diagnosis of anticholinergic poisoning is based on clinical findings. Anticholinergic poisoning is easily diagnosed when a history of exposure to an anticholinergic substance is obtained and the patient shows altered mental status, delirium or hallucinations. However, in cases with no history and unknown exposure, the diagnosis of anticholinergic intoxication is considered when clinical signs and symptoms caused by mental status disorder and anticholinergic effects are detected on physical examination.
A 75-year-old woman was admitted to the emergency department with complaints of slurred speech, altered consciousness and facial shifting that started about 2 hours ago. On physical examination, the patient was incoopere and disoriented, GCS:12 eyes were spontaneously open, obeying orders but making unintelligible sounds, nuchal rigidity was suspiciously positive, bilateral babinski reflex was positive. On admission vital signs were blood pressure 144/72 mmHg, pulse 98/min, temperature 37 °C, SO2 94%, fingerstick blood glucose 104 mg/dl and ECG was in normal sinus rhythm. There were no acute pathologic findings on both brain CT and brain MR imaging. In the control physical examination, both pupils were mydriatic, IR -/- and the patient had dry mouth. According to the anamnesis obtained from another relative of the patient, it was learned that they ate spinach in the evening and speech disorder started afterwards. The patient was diagnosed with anticholinergic intoxication considering that the spinach eaten by the patient with clinical findings might have been mixed with the beautiful hawthorn weed.
In this case report, we aimed to emphasize the importance of detailing the anamnesis and the necessity of a complete systemic examination in patients presenting with confusion and speech disorder.
BASIC BENEFITS OF DIGITAL TRANSFORMATION IN HIGHER EDUCATION
Authors:   Faculty of Political Theory, Hanoi University of Science and Technology, Hanoi, Vietnam, Nguyen Thi Huyen , Luong Minh Hanh , Nguyen Quang Truong , Hoang Anh Dong
Published: March 2024

Digital transformation is an important issue for economic, cultural, social, and educational development...in the current context, it stands out as an important aspect of the Fourth Industrial Revolution. Therefore, the 13th National Party Congress (2021) identified 6 key tasks, including implementing national digital transformation, developing the digital economy. Not an exception to the above trend, the education sector, especially higher education, has also been very actively implementing the digital transformation process in recent years because of the benefits brought by digital transformation. In the article, the author pointed out some basic benefits of digital transformation in higher education to help managers, lecturers, and students understand the importance and benefits that digital transformation brings, thereby actively participating in the digital transformation process in higher education.
IMPROVING THE LABOR MARKET TOWARD SUSTAINABLE DEVELOPMENT – SUGGESTIONS FOR LABOR LAW
Authors:   ", Dang Hoang Minh ,   University of Economics and Law, Ho Chi Minh City, Vietnam and  Vietnam National University, Ho Chi Minh City, Vietnam"
Published: March 2024

Gender inequality negatively impacts the opportunities of female workers in the labor market and the society's sustainability. Since the participation of women in the workforce characterizes more difficulties compared to men, empowering female workers with fair access to job opportunities means advancing gender equality as well as sustainable development. Against this backdrop, this study aims to provide critical suggestions for perfecting the Vietnam Labor Code to increase the workforce’s gender equality. Accordingly, although the new Labor Law is updated with innovative provisions about protecting and empowering female workers, the gender gap in Vietnam's Labor market still pervasively remains and this problem therefore prevents sustainable development. Based on the theoretical relationship between empowering and protecting female workers, gender equality, and sustainable development goals, a critical view of establishing the new approach to gender equality and the accountability of businesses toward this issue is presented.
CHARACTERISTICS OF TRANSFORMATIVE INNOVATION POLICY AND IMPLICATIONS FOR STAKEHOLDER ENGAGEMENT IN SCIENCE TECHNOLOGY AND INNOVATION POLICYMAKING PROCESS FOR SUSTAINABLE DEVELOPMENT
Authors:   Vietnam Institute of Science Technology and Innovation, Ministry of Science and Technology, Dang Thu Giang
Published: March 2024

The article aims to contribute to the development of the application of new policy paradigm to science technology and innovation (STI) policy development in order to improve innovation capacity and cooperation among stakeholders, minimizing socio-technical constraints, promoting resilience and prevention against risks, uncertainties and lack of knowledge, recommending that following a new policy paradigm, STI policy makers in Vietnam expand the participation of stakeholders in policy development towards sustainable development goals.
IS SOUTH KOREA SUSTAINABLE
Authors:   Director of the Institute of Ocean and Civilizations at Asian Humanity Foundation, Korea, Kim Seong Beom
Published: March 2024

"While Korea has experienced significant economic development, a large portion of the population does not feel happiness. It is meaningless to achieve economic development if the people are not happy. Now, Korea must reflect on its rapid development process and independently find answers to open a sustainable future. In this situation, the presenter believes that, for Korea to be sustainable, it needs to engage in deep exchanges with Vietnam in various aspects, including culture and ideology. Korea now needs to develop its own strategies for creating a good society, moving beyond accepting and adapting other countries' cultures and ideologies. Vietnam is expected to provide important advice as a comprehensive strategic partnership for Korea to reflect, learn, and achieve sustainable development."
DX AND HUMAN RESOURCES DEVELOPMENT IN JAPAN
Authors:   Takushoku University, Japan, Yoshino Fumio
Published: March 2024

"Ratio of mobile phone/Internet users to bank account holders in many Asian countries. Notable numbers include Hong Kong's high mobile payment rate of 97.9% and a significant bank account holder rate of 80.2% in Cambodia. These statistics provide insight into the varying levels of financial digitization in the region. Along with that, the 2019 B2C E-commerce Index shows Singapore as the leading country with a score of 97, emphasizing the country's strong e-commerce infrastructure. This has left Japan facing challenges in transforming traditional industries such as automobiles and electrical appliances to meet the demands of the digital age. The reason Japan may lose competitiveness in the era of digital transformation is because: Challenges include the reluctance of Japanese automakers and electrical equipment manufacturers to switch from traditional vehicles to electric power (EV), a sector led by companies such as Tesla in China. Impact on advances related to telecommunications and the internet. Meanwhile, the Japanese education system only emphasizes creating diligent workers rather than fostering leadership and creativity. The absence of famous Japanese businessmen comparable to global figures such as Bill Gates or Steve Jobs is arguably due to a system that produces generalists rather than specialists. The Seigniorage system still exists, specifically: The older the employee, the higher their salary, responsibility and status, a lifetime commitment. Lifetime commitment is one of the characteristics of Japanese management. This is not suitable for the digital age. Nowadays, job hopping or changing companies is not a special event in the lives of Japanese workers. However, even today, the longer one works for a company, the better the employee's evaluation."
Why Prevention Fails: Chronicling the Genocide in Artsakh
Authors:   Lemkin Institute for Genocide Prevention, von Joeden Forgey Elisa
Published: March 2024

Azerbaijan’s September 19, 2023 attack on the Republic of Artsakh resulted in the almost total displacement of the indigenous Armenian population, making it one of the most successful genocides in history. For over a year before Azerbaijan’s attack, the Lemkin Institute for Genocide Prevention viewed Artsakh as the “perfect storm” for genocide prevention and was using as many strategies as possible to urge Western leaders to recognize the threat and take effective action. Any leader willing to challenge Azerbaijan diplomatically would have had the work of many genocide scholars and genocide prevention organizations to back them up. We still believe that coordinated pressure from the Western powers could have had a chance of avoiding genocide and may have resulted in finding a secure, and perhaps independent, space for Artsakh Armenians in their ancestral homeland. This article aims to show how the case of genocide in Artsakh is an object lesson in how diplomatic silences, shaped by geopolitical interests, can provide the power framework in which genocide can easily take place, offer diplomatic cover for the state or organization committing the crime, and normalizing the crime within international relations. It proposes that the genocide in Artsakh ushered in a new “New Imperialism”, in which the post-1945 law-based world order is jettisoned for raw power, threatened communities and unwanted peoples are less safe than they were before September 19, 2023, and genocide will become the order of the day – unless we find new mechanisms to prevent it.
Karnig G. Bodourian, Տարագրի յուշեր 1915-1917 [Memoirs of a Deportee 1915-1917], Memoirs of Survivors of the Armenian Genocide, 8. Editor, author of the preface and references Mihran A. Minassian, Yerevan: AGMI Publishing, 2022, 527 pp.
Authors:   Armenian Genocide Museum-Institute Foundation, Minassian Mihran
Published: March 2024

N/A
Harutyun Grigoryan, Իմ կենսագրությունը [My Biography], Memoirs of Survivors of the Armenian Genocide, 10. Editor, author of the preface and references Regina Galustyan. Yerevan: AGMI, 2023, 304 pp.
Authors:   Armenian Genocide Museum-Institute Foundation, Galustyan Regina
Published: March 2024

N/A
EMERGING TRENDS IN CYBERSECURITY FOR CRITICAL INFRASTRUCTURE PROTECTION: A COMPREHENSIVE REVIEW
Authors: Sontan Adewale Daniel  , Samuel Segun Victor
Published: March 2024

As critical infrastructure becomes increasingly interconnected and digitized, the need for robust cybersecurity measures to safeguard essential systems is more pressing than ever. This review article explores the dynamic landscape of cybersecurity for critical infrastructure, focusing on emerging trends, current challenges, and future prospects. The historical overview delves into the evolution of cyber threats, emphasizing the need for adaptive security measures. Key components of critical infrastructure are examined, elucidating the specific challenges each sector faces. The current state of critical infrastructure cybersecurity is analyzed, with a spotlight on frameworks that guide organizations in bolstering their defenses. The heart of the review explores emerging trends in cybersecurity, covering artificial intelligence and machine learning for threat detection, IoT security, blockchain applications, and advancements in cloud computing security. Challenges and threats on the horizon, including advanced persistent threats and quantum computing implications, are scrutinized to provide insights into potential vulnerabilities.

Keywords: Cybersecurity; Critical Infrastructure; Artificial Intelligence; Internet-of-Things; Blockchain.
EVALUATING THE INTEGRATION OF ADVANCED IT SOLUTIONS FOR EMISSION REDUCTION IN THE OIL AND GAS SECTOR
Authors: Chuka Anthony Arinze  , Olakunle Abayomi Ajala  , Chinwe Chinazo Okoye  , Onyeka Chrisanctus Ofodile  , Andrew Ifesinachi Daraojimba
Published: March 2024

The oil and gas sector is a significant contributor to greenhouse gas emissions, making the integration of advanced IT solutions imperative for emission reduction. This study evaluates the effectiveness of integrating advanced IT solutions in mitigating emissions within the oil and gas industry. The research employs a mixed-methods approach, combining qualitative analysis through literature review and quantitative analysis via case studies and data analysis. The literature review identifies various advanced IT solutions currently available, such as Internet of Things (IoT), Artificial Intelligence (AI), and Big Data analytics, and their potential applications for emission reduction in the oil and gas sector. Case studies are conducted to examine real-world implementations of these solutions by leading companies in the industry. These case studies provide insights into the challenges, benefits, and best practices associated with integrating advanced IT solutions for emission reduction. Furthermore, quantitative data analysis is conducted to assess the environmental impact of these IT solutions based on emission reduction metrics, such as carbon dioxide equivalent (CO2e) emissions and energy efficiency improvements. The findings indicate that the integration of advanced IT solutions holds significant promise for emission reduction in the oil and gas sector. IoT-enabled sensors enable real-time monitoring and optimization of operations, reducing emissions from production processes. AI algorithms enhance predictive maintenance, optimizing equipment performance and minimizing emissions from fugitive sources. Big Data analytics facilitate comprehensive emissions tracking and reporting, enabling proactive emissions management strategies. Overall, this research contributes to a deeper understanding of the potential of advanced IT solutions in reducing emissions in the oil and gas sector and provides valuable insights for industry practitioners, policymakers, and researchers alike.

Keywords: IT, Emission, Reduction, Oil and Gas, Review.
THE ROLE OF AGILE PROJECT MANAGEMENT IN DRIVING INNOVATION IN ENERGY-EFFICIENT HVAC SOLUTIONS
Authors: Wisdom Ebirim  , Danny Jose Portillo Montero  , Emmanuel Chigozie Ani  , Nwakamma Ninduwezuor-Ehiobu  , Favour Oluwadamilare Usman  , Kehinde Andrew Olu-lawal
Published: March 2024

This review delves into the significance of Agile Project Management (APM) in fostering innovation within the realm of energy-efficient Heating, Ventilation, and Air Conditioning (HVAC) solutions. As the global focus intensifies on sustainability and energy conservation, the HVAC industry stands at the forefront of addressing environmental challenges through innovative technologies. Agile methodologies, originally developed for software development, have transcended their conventional domains to revolutionize project management across various industries. This review explores how the application of Agile principles can catalyze innovation in energy-efficient HVAC solutions. Firstly, it delineates the core tenets of Agile Project Management, emphasizing iterative development, adaptive planning, and customer collaboration. By prioritizing flexibility and responsiveness to change, Agile methodologies empower HVAC project teams to swiftly adapt to evolving customer needs and technological advancements. This agility is particularly crucial in the HVAC sector, where rapid technological innovations necessitate quick iterations and constant feedback loops to refine energy-efficient solutions. Furthermore, the review elucidates how Agile frameworks such as Scrum and Kanban facilitate cross-functional collaboration and enhance communication within HVAC project teams. By fostering a collaborative environment that encourages transparency and knowledge sharing, Agile methodologies cultivate a culture of innovation wherein team members can experiment with novel ideas and swiftly pivot based on market feedback. Moreover, this review underscores the role of Agile Project Management in mitigating risks associated with innovation in energy-efficient HVAC solutions. Through frequent iterations and incremental delivery, Agile methodologies enable HVAC project teams to identify potential bottlenecks and address them proactively, thereby reducing project risks and enhancing overall project success rates. This review highlights the transformative potential of Agile Project Management in driving innovation within the energy-efficient HVAC industry. By embracing Agile methodologies, HVAC project teams can navigate the complexities of innovation more effectively, accelerate time-to-market for energy-efficient solutions, and ultimately contribute to the sustainable transformation of the HVAC sector.

Keywords: Agile, Project Management, Innovation, Energy-Efficient, HVAC, Review.
STRATEGIC PARTNERSHIPS IN THE SATELLITE AND TELECOMMUNICATIONS SECTORS: A CONCEPTUAL REVIEW OF DATA ANALYTICS-ENABLED IDENTIFICATION AND CAPITALIZATION OF SYNERGIES
Authors: Nneka Adaobi Ochuba  , Enyinaya Stefano Okafor  , Olatunji Akinrinola  , Favour Oluwadamilare Usman  , Olukunle Oladipupo Amoo
Published: March 2024

Strategic partnerships in the satellite and telecommunications sectors have become increasingly vital as technological advancements and market complexities continue to evolve. This conceptual review delves into the significance of data analytics-enabled identification and capitalization of synergies within these partnerships. The fusion of satellite and telecommunications technologies presents unique opportunities for innovation and growth, but also challenges that necessitate strategic collaboration. By leveraging data analytics, organizations can effectively identify synergies between satellite and telecommunications services, leading to enhanced operational efficiencies, expanded service offerings, and improved customer experiences. Through a comprehensive analysis of available data, companies can pinpoint areas of overlap and complementary strengths, laying the foundation for mutually beneficial partnerships. The review explores the multifaceted nature of strategic partnerships in these sectors, emphasizing the importance of aligning technological capabilities, market strategies, and regulatory frameworks. It underscores the role of data analytics as a catalyst for informed decision-making, enabling stakeholders to anticipate market trends, optimize resource allocation, and mitigate risks. Furthermore, the review examines the implications of emerging technologies such as artificial intelligence, machine learning, and predictive analytics on partnership dynamics. These tools not only streamline data analysis processes but also empower organizations to proactively identify synergistic opportunities and adapt to evolving market dynamics in real-time. Through a synthesis of theoretical frameworks and empirical evidence, this review provides insights into the strategic imperatives guiding partnership formation and management in the satellite and telecommunications sectors. It underscores the need for a holistic approach that integrates technological expertise with business acumen and regulatory compliance to unlock the full potential of strategic collaborations. The review advocates for a proactive and data-driven approach to identifying and capitalizing on synergies within strategic partnerships in the satellite and telecommunications sectors. By harnessing the power of data analytics, organizations can navigate the complexities of the digital ecosystem, drive innovation, and achieve sustainable competitive advantage in an increasingly interconnected world.

Keywords: Partnership, Telecommunication, Data Analytics, Capitalization, Enabled Identification, Review.
INTELLIGENT MONITORING SYSTEMS IN MANUFACTURING: CURRENT STATE AND FUTURE PERSPECTIVES
Authors: Oladiran Kayode Olajiga  , Emmanuel Chigozie Ani  , Kehinde Andrew Olu-lawal  , Danny Jose Portillo Montero  , Adeniyi Kehinde Adeleke
Published: March 2024

Intelligent Monitoring Systems (IMS) have emerged as indispensable tools in modern manufacturing, offering real-time insights into production processes, equipment performance, and quality control. This review provides an overview of the current state and future prospects of IMS in manufacturing environments.  The current state of IMS in manufacturing involves the integration of advanced technologies such as Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Big Data analytics. These systems collect vast amounts of data from sensors, machines, and production lines, enabling real-time monitoring and analysis of various parameters. By employing AI and ML algorithms, IMS can identify patterns, predict anomalies, and optimize production processes, leading to increased efficiency, reduced downtime, and improved product quality. IMS play a crucial role in predictive maintenance, where they can anticipate equipment failures before they occur, thereby minimizing unplanned downtime and maintenance costs. Moreover, IMS facilitate condition-based monitoring, allowing manufacturers to monitor the health and performance of machinery in real-time and schedule maintenance activities accordingly, optimizing resource allocation and prolonging equipment lifespan. Furthermore, IMS contribute to quality control by continuously monitoring production processes and detecting deviations from desired specifications. By leveraging AI-driven algorithms, IMS can automatically adjust process parameters to maintain product quality standards and minimize defects, thereby enhancing overall product reliability and customer satisfaction. Looking ahead, the future perspectives of IMS in manufacturing are promising, with advancements in areas such as edge computing, robotics, and augmented reality poised to revolutionize manufacturing operations further. Edge computing enables data processing and analysis to occur closer to the data source, reducing latency and enhancing real-time decision-making capabilities. Robotics integration with IMS facilitates autonomous manufacturing processes, while augmented reality technologies provide intuitive interfaces for operators to interact with IMS data in real-time.  IMS represent a transformative technology in manufacturing, offering unprecedented levels of visibility, control, and optimization. As technology continues to evolve, IMS are poised to play an increasingly vital role in shaping the future of manufacturing, driving efficiency, productivity, and innovation..

Keywords: Monitoring, System, Intelligent, Manufacturing, Review, Perspectives.
Efficient Removal Of Bromate From Aqueous Solution Through Electrocoagualtion Using Aluminum Electrodes
Authors: M. Abu Ghalwa Nasser , El din El Nazer Hossam , B. Farhat Nader , Sror Mahmud
Published: March 2024

In this study  the removal efficiency of bromate from aqueous solution using the electrocoagulation process was investigated. The effects of operational parameters such as initial pH, initial bromate concentration, current density, type  electrode, salt concentration, and temperature on the removal efficiency have been studied. With an initial pH of 3.7, an initial bromate concentration of 100 mg/L, (current density 22.7 mA/cm2 using Al electrodes, salt concentration of 1.5 g/L NaCl and temperature of 25° C, The results showed that the removal percentage for bromate  was 97% using Al/Al electrodes at 10 min. It can be concluded that electrocoagulation process by Al electrode is very efficient and clean process for bromate removal from Aqueous solution.