bluuebunny/crossref_metadata_2025 huggingface dataset:
Anatomy of Cyberattacks on Hybrid Clouds
Authors: Bhardwaj Akashdeep
Published: August 2024

With the ascent in digital assaults on cloud conditions like Brute Force, Malware, or Distributed Denial of Service assaults, data security officials and server farm directors have a huge task to implement security and provide solutions to such unique threats presented and prerequisites for attacks and more explicitly for Distributed Denial of Service (DDoS) security in enormous facilitating condition arrangements. This part proposes the utilization of a multi-layered system configuration dependent on a Hybrid cloud arrangement including an On-premises arrangement just as an open cloud framework equipped for taking care of DDoS storms. Single-level standard server farm configuration is compared with a three-level server farm engineering plan. The creator performed DDoS assaults on the two structures to decide the strength to withstand DDoS assaults by estimating the Real User Monitoring parameters and afterward approved the information acquired utilizing a Parametric T-Test.
Organizational Ecology and Its Implications on Organizational Ecological Innovation
Authors: Vargas-Hernandez José G. , Rodriguez-Maillard Carlos A. , Gonzàlez-Àvila Francisco J. , Vargas-González Omar C.
Published: August 2024

This study has the objective to analyze the implications of the organizational ecology on organizational ecological innovation, from the assumption that the theoretical and conceptual framework of organizational ecology positively associates with ecological innovation in organizations. The method employed is analytical and reflective based on the theoretical and conceptual literature review. It is concluded that major organizational ecological innovations leading to technical changes are pioneered by entrepreneurial organizations shedding lights on the positive relationships between the concepts and theories of organizational ecology and the competing mechanisms posited in the results of the organizational innovative ecology.
An Analysis on the Benefits and Difficulties of the Circular Economy of Building and Demolition Waste for Sustainable Environmental Development
Authors: Bahadur Preeti Singh , Bais Pratima , Bhutani Vijay
Published: August 2024

Construction and demolition (C&D) waste was traditionally viewed as zero value material in a linear economy, leading to the majority of C&D waste materials ending up in landfills. Many nations have begun investigating new models to reduce the use of finite resources that are currently being misused, overused, or rapidly depleting in recent years due to a rise in awareness of sustainability and resource management. In this sense, implementing the ‘circular economy' (CE) has become a viable model to reduce the damaging effects of C&D waste on the environment. A complete transition to CE in the building and demolition industries is, however, hampered by certain issues. As a result, the purpose of this review paper is to examine various facets of C&D waste and the ways in which CE can be included in building projects. A review of the literature indicated that there are five primary domains in which barriers to the adoption of CE in C&D waste sectors exist: legal, technical, social, behavioural, and economic aspects.
Designing Exhibitions That Ensure Enjoyment and Wellbeing
Authors: González-Valerio Beatriz Guerrero
Published: August 2024

Fashion as part of contemporary culture is an asset to be preserved and disseminated. This chapter addresses the need for fashion to be exhibited within specific architectural designs and environments in order to be able to express itself in its entirety. It also highlights the fact that paying attention to certain aspects such as light, route, and staging not only contributes to making the designs and creations stand out, but also provides the public with a greater sense of well-being. All of this improves the experience of the visit to the exhibition space and visitor satisfaction, given that there is a significant relationship between the quality with which a museum is perceived, and the emotions aroused.
Interplay of Artificial Intelligence and Recruitment
Authors: Saloni Shikha , Gupta Neema , Agarwal Ambuj Kumar , Tiwari Raj Gaurang , Jain Vishal
Published: August 2024

In this chapter, the authors focus on different ways in which AI is incorporated in the process of recruitment. Along with the above stated objective, they also explore the forms of AI based recruitment, the benefits of AI based recruitment, and the challenges that might be encountered during the process with an emphasis on gender bias. In the findings, they aim to describe the gender bias in professional functions in businesses. On the other hand, they hope to gain insight into potential gender discrepancies between operational and leadership positions, as well as between departments. The findings of this chapter will benefit researchers, academics, and managers in analyzing gender-related practices and policies. Organizations can become more aware of their gendered practices, which affect the recruitment procedure and the varied roles and responsibilities assigned to men and women, by giving voice to the prejudices that generate gender biases. Along with this, they provide the implications, limitations, and future scope of the study.
Sovereignty Over Personal Data
Authors: Primec Andreja , Pastirk Gal , Perko Igor
Published: August 2024

The contribution focuses on an individual's right to control their personal data and the right to privacy, emphasizing the legal aspects and developments in the context of modern technological and digital challenges. These rights become especially critical in the context of swift advancements in technology and digitalization, enabling the accumulation, examination, distribution, and sharing of extensive data volumes in previously unattainable manners. There has been a surge in sensors embedded within smart devices, gathering data from various aspects of our professional and personal lives. Furthermore, artificial intelligence (AI) has emerged, capable of analysing and interpreting data in unthinkable ways. These shifting dynamics present a challenge to reconsider how data is regulated. It's not merely about updating the rules themselves; it involves reimagining the process of creating regulatory frameworks.
Navigating the Personalization Pathway
Authors: Tariq Muhammad Usman
Published: August 2024

This chapter delves into the revolutionary potential of adaptive learning technologies (ALTs) within the realm of higher education, with a particular focus on their ability to tailor growth opportunities to diverse student needs. Against the backdrop of evolving higher education landscapes characterised by technological advancements and shifting student demographics, ALTs emerge as crucial enablers of flexible, student-centered learning environments. The discussion explores the fundamental principles of adaptive learning, encompassing key concepts, theories, and the theoretical foundations that underpin these technologies, such as constructivism and cognitive load theory. Readers will gain insights into the current adaptive learning landscape, including a survey of existing platforms and the significant roles played by artificial intelligence and machine learning. The section also addresses the challenges associated with ALT adoption, ranging from technical and academic barriers to ethical considerations such as data privacy and equity issues.
Ask New and Challenging Questions Towards Reasoning Skills
Authors: Rosado Susana , Ribeiro Jorge Tavares
Published: August 2024

This chapter explores innovative approaches to fostering reasoning skills in higher education. It delves into various dimensions of active learning, emphasizing the importance of inquiry-based methods, outdoor experiences, and technology integration. The chapter highlights examples from the Lisbon School of Architecture, University of Lisbon, across different study cycles. Notable examples include using Kahoot and MathCityMap in the bachelor's, student-generated challenges in architecture and design master's, and teacher-led research project challenges in the Ph.D. course. The methodologies and outcomes of these active approaches are discussed, emphasizing lifelong learning and metacognition. Overall, the chapter advocates for a holistic view of education that transcends traditional boundaries and prepares students for complex, real-world challenges. The results obtained in all students' degrees reveal improvements in students' commitment, motivation, and engagement in learning and the production of solid and well-founded knowledge.
Institutional Support and Leadership for Transdisciplinary Education
Authors: Rajnath Yadav KrishnaKumar
Published: August 2024

Transdisciplinary education, transcending traditional disciplinary boundaries to tackle complex real-world problems, hinges on robust institutional support and visionary leadership. This chapter delves into the pivotal roles institutions and leaders play in fostering an environment conducive to transdisciplinary education. It examines strategies for embedding transdisciplinary approaches within institutional frameworks, curriculum design, faculty development, and student engagement. Drawing upon theoretical frameworks and practical examples, the chapter underscores the importance of institutional commitment, resource allocation, and cultural change in advancing transdisciplinary education initiatives. Furthermore, it explores the leadership's crucial role in championing transdisciplinary education, fostering interdisciplinary collaboration, and nurturing a culture of innovation and creativity. In conclusion, institutional backing and leadership are imperative for advancing transdisciplinary education, equipping students to confront real-world challenges effectively
Battery Revolution
Authors: Kumar M. Sunil , Krishna R. V. V. , Satyanarayana V. , Ranjit P. S.
Published: August 2024

The combination of amount computing and machine intelligence is a significant step forward in battery technology, and it is the driving force behind what can be referred to as the battery revolution. The purpose of this community is to improve battery design, optimization, and performance by utilizing the computational capacity of amount computing and the literacy capabilities of machine intelligence. Through the utilization of the capacity of amount computing to simulate intricate molecular structures and responses with an unknown level of delicacy and speed, in conjunction with the capability of machine intelligence to analyze vast datasets and forecast the most effective battery configurations, significant progress has been made in the enhancement of battery efficiency, capacity, and longevity. The purpose of this study is to investigate the synergistic link between amount computing and machine intelligence in the field of battery technology. It focuses on pressing vital advances, obstacles, and prospects.
Efficient Power Grid Management Using Quantum Computing and Machine Learning
Authors: Aslam S. , Tabita G. , Krishna J. S. V. Gopala , Palav Manesh R.
Published: August 2024

This research work aims to explore a new approach to enhance power grid management efficiency by combining machine learning with quantum computing. This groundbreaking research aims to resolve the many problems associated with power distribution, load balancing, and resilience to fully optimise these areas in modern energy systems. The proposed method makes use of quantum algorithms to accomplish accurate and speedy computations by leveraging the inherent parallelism of quantum computing. for optimizing power grid management tasks such as energy distribution, load balancing, and grid stability. Development of novel quantum-inspired optimization algorithms capable of efficiently solving power grid management tasks, demonstrating improvements in energy efficiency, grid stability, and cost reduction compared to traditional methods. Integration of machine learning models for demand forecasting, anomaly detection, and predictive maintenance, enabling proactive and data-driven decision-making in power grid operations.
Enabling Smart Power Grids Through Quantum Computing and Artificial Intelligence
Authors: Kumar M. Sunil , Krishna R. V. V. , Satyanarayana V. , Rao A. Purna Chandra
Published: August 2024

Quantum computers make it possible to solve hard planning problems, accurately simulate how grids change over time, and improve the efficiency of grid activities. Quantum algorithms have changed the way grids are managed, how demand is predicted, and how energy is distributed in real time. In addition, AI technologies like machine learning, deep learning, and others improve quantum computing by looking at the huge amounts of data that smart power grids create. Using quantum mechanics in AI models and algorithms makes it possible for advanced control systems to handle grid processes automatically, adapt to changing conditions, and quickly fix problems. This research looks into how these technologies could be used, what problems they might cause, and what areas of research should be done in the future to help build safe, resilient, and efficient energy infrastructure. When quantum computing and artificial intelligence come together, they will change the energy business right away. This will create a power grid environment that is smarter, cleaner, and more resilient.
Harnessing Quantum Computers for Efficient Optimization in Chemical Engineering
Authors: Sushama C. , Krishna R. V. V. , Satyanarayana V. , Ganesan T.
Published: August 2024

Quantum computing (QC) has made it possible for optimization and machine learning to get better. These improvements could have big effects on many areas, like medicine, technology, communication, and finance. There will probably soon be a huge rise in the use of QC in the chemistry, pharmaceutical, and bio-molecular fields. Improvements in quantum hardware and software have sped up the process of putting QC into action. It is very important to find real-life chemical engineering problems that cutting-edge quantum methods could help solve, no matter if they are used in computers today or in the future. The authors go over some basic QC ideas while also talking about the problems with current quantum computers. There is also an outline of quantum algorithms that, when used with current quantum computers, could help chemical engineers with machine learning and optimization. There are also plans for future quantum devices because this research looks into linked uses that could use quantum algorithms run on them.
Integrated Quantum Computing and Machine Intelligence for Sustainable Energy Solutions
Authors: Subbiah Muthuraman , Krishna R. V. V. , Satyanarayana V. , Kataria Abhinav
Published: August 2024

The synergistic integration of amount computing and machine intelligence to address challenges in sustainable energy results. By using the computational power of amount computing and the adaptive literacy capabilities of machine intelligence, the study aims to optimize energy product, distribution, and application in a sustainable manner. Through advanced algorithms and optimisation ways, the exploration explores how intertwined amount computing and machine intelligence can enhance the effectiveness, trustability, and environmental sustainability of energy systems. The findings offer perceptivity into the eventuality of this interdisciplinary approach to revise the energy sector, paving the way for the development of innovative results for renewable energy integration, smart grid operation, and energy-effective technologies. Eventually, the exploration contributes to the advancement of sustainable energy results by employing the combined power of amount computing and machine intelligence to address complex challenges in energy optimisation and resource operation.
Machine Learning-Driven Optimization of Battery Materials via Quantum Computing
Authors: Kumar Loveleen , Krishna R. V. V. , Radhakrishnan S. , Bagal Yudhishther Singh
Published: August 2024

This research explores the possible synergy between machine learning algorithms and quantum computers to advance the progress of battery materials. To streamline the investigation of materials suitable for high-performance batteries, we introduce a novel framework that employs optimization approaches guided by machine learning. This comprehensive collection of properties for Mg-ion and Li-ion battery electrode materials allows machine learning algorithms to accurately forecast their voltage, capacity, and energy density. This advancement is anticipated to expedite the exploration of more effective materials for energy storage. The results showed a strong relationship between energy density and capacity, but no such relationship was found between average voltage and the aforesaid factors. Implementing this technique in high-throughput systems has the potential to greatly expedite breakthroughs in computational materials research.
Quantum Algorithms for Accelerated Simulation in Materials Chemistry
Authors: Arya Aayushi , Banjare Ramesh Kumar , Yashan Noushad , Joshi Harish Chandra
Published: August 2024

Quantum computing has the potential to revolutionize computational chemistry by providing algorithms that can model chemical processes ten times quicker than we can today. This report examines the present state of research and development in quantum algorithms for materials chemistry modeling. A thorough investigation is conducted into the benefits and drawbacks of quantum simulation techniques utilized in materials research. This research examines the benefits and drawbacks of applying quantum algorithms on both current and future quantum hardware platforms, including error-corrected quantum computers and noisy intermediate-scale quantum (NISQ) devices. Finally, the authors discussed potential future research directions and challenges in the field of quantum algorithms for quicker models of materials chemistry. These strategies included actions to reduce the likelihood of errors, approaches to improve quantum circuits, and the development of novel quantum methods expressly for use in materials research.
Quantum Algorithms for Intelligent Optimisation in Battery Manufacturing
Authors: Kumar C. Anil , Krishna R. V. V. , Satyanarayana V. , Ranjit P. S.
Published: August 2024

The exploration chapter investigates the operation of amount algorithms for intelligent optimisation in battery manufacturing processes. Using the principles of amount computing, the study aims to enhance the effectiveness of battery manufacturing operations. Quantum algorithms offer new approaches to working optimisation problems by employing amounts of marvels similar to superposition and trap. By exercising amount-inspired optimisation ways, the exploration explores how to streamline colourful aspects of battery manufacturing, including material conflation, electrode fabrication, and assembly processes. Through theoretical analyses and computational simulations, the paper evaluates the performance of amount algorithms in optimising manufacturing workflows, chapter product costs, and perfecting battery performance criteria. The findings give perceptivity into the eventuality of amount computing to revise battery manufacturing processes and pave the way for advancements in energy storehouse technology.
Quantum Computing and Artificial Intelligence in Materials Discovery for Batteries
Authors: Harish S. , Krishna R. V. V. , Satyanarayana V. , Pattanaik Bala Chandra
Published: August 2024

The synergistic integration of amount computing and artificial intelligence (AI) in the field of accoutrements discovery for batteries. Using the computational power of amount computing and the pattern recognition capabilities of AI, this study aims to accelerate the identification and design of new battery accoutrements with enhanced performance characteristics. By employing amount-calculating algorithms for accurate simulation of infinitesimal relations and electronic structures, coupled with AI-driven prophetic modelling ways, experimenters can efficiently explore vast accoutrement libraries and prognosticate material parcels applicable to battery performance. The chapter investigates colourful methodologies and case studies where an amount of computing and AI have been applied to expedite the discovery of high-capacity, stable, and environmentally sustainable battery accoutrements.
Quantum Computing Machine Intelligence for Optimal Battery Performance
Authors: Sarao Pushpender , Krishna R. V. V. , Ranjit P. S. , E. R. Babu
Published: August 2024

This research improves batteries using AI and quantum processing. Quantum computing uses quantum physics to quickly search for many solutions to manage large amounts of data. Deep learning, reinforcement learning, and other machine intelligence use massive datasets to uncover patterns and improve algorithms for quantum computing. To test alternative configurations simultaneously, the authors record operating parameters, ambient variables, and battery attributes in a quantum state. They want to utilize reinforcement learning algorithms to improve charging and draining methods so they operate well and can be used in many situations. This research aims to reduce degradation, improve energy efficiency, and extend battery life. Machine intelligence and quantum computation are used to analyze batteries and optimize performance. Bringing together experts from different sectors could help construct strong, environmentally friendly power networks. This modification may affect energy storage technology greatly. The research's findings could impact electric cars, power grid security, and renewable energy.
Realizing Sustainable Energy Quantum Computing Applications in Power Grids
Authors: Sushama C. , Jain Sonal , Parija Soma , Aslam S.
Published: August 2024

Climate change and the desire to cut carbon emissions have increased demand for renewable energy. Quantum computing's revolutionary potential and unmatched processing capability could benefit capacity grids and other businesses. This research examines how quantum computing could improve power network efficiency to achieve sustainable energy goals. Quantum computing can solve complex power grid optimization problems. Current conventional approaches to power grid improvement have limits, but quantum computing can assist overcome them. Power grid optimization incorporates quantum methods like Grover's algorithm and the quantum approximate optimization algorithm. Quantum-resistant cryptographic techniques and quantum key distribution could secure power grid communication and data transfer. Given that quantum computing is still developing, the research examines the present obstacles to implementing quantum power grid solutions. Finally, quantum computing could improve power grid operations, security, and renewable energy consumption, helping accomplish sustainable energy goals.
Revolutionizing Battery Design Through Quantum Computing and Machine Intelligence
Authors: Sushama C. , Krishna R. V. V. , Srimathi J. , Anil C. H.
Published: August 2024

There is a growing demand for longer-lasting and more efficient batteries due to the increasing number of portable electronic gadgets, electric cars, and renewable energy sources. It can be expensive and time-consuming to use traditional methods for designing and optimizing batteries because they rely on trial and error. Nevertheless, there are substantial chances to enhance battery design because of recent advances in AI and quantum computing. Complex chemical interactions and materials can be recreated using quantum computing. Researchers may examine huge swaths of chemical space and anticipate the characteristics of new battery materials with unparalleled accuracy by applying the concepts of quantum mechanics. Predictions made by machine learning (ML) are data-driven and have the potential to be valued. Instead of going into detail about each machine learning technique, the authors will focus on the scientific problems related to electro chemical systems that can be solved with the use of machine learning.
Towards Greener Power Grids Quantum Computing Solutions for Energy Efficiency
Authors: Babu Kuppam Mohan , Bhaskaraiah M. , Roja Kasaram , Chandraiah T.
Published: August 2024

The research's goal is to look into how quantum computing might be able to help solve the difficult problems that come up when trying to make power grid processes more environmentally friendly. Power networks are currently facing a number of major problems, including transmission losses, changes in demand, and the addition of green energy sources. By using quantum coherence, entanglement, and interference, quantum computers make it possible to effectively explore large solution spaces. In this way, real-time optimization and flexible decision-making can be carried out. Finally, the authors look at the pros and cons of using quantum computing to make power systems work better. This research shows that quantum computing has the potential to make power sources more reliable and long-lasting. This will help make the future more environmentally conscious. It is possible to make an energy system that lasts longer, uses less energy, and damages the world less by using the special properties of quantum mechanics.
Cardiotocogram Data Analysis for Obstetric Risk Stratification
Authors: Dwivedi Dwijendra Nath , Mahanty Ghanashyama , Mahanty Meghna
Published: August 2024

In order to improve obstetric risk categorization, this study employs a thorough machine learning approach that makes use of cardiotocogram data. The goal is to find patterns and correlations that are relevant for forecasting fetal well-being by evaluating a dataset that includes several fetal health indicators, such as baseline fetal heart rates, uterine contractions, fetal movements, and decelerations. To lay the groundwork for feature selection and model construction, the authors conducted exploratory data analysis, which yielded important insights into the distributions and correlations of these clinical variables. The authors provide a predictive model for foetal health status classification that makes use of state-of-the-art machine learning methods. With this approach, they can better comprehend fetal distress signals and make more informed clinical decisions, which in turn can help reduce rates of neonatal and perinatal morbidity and mortality. The revolutionary effect of machine learning on improving healthcare efficiency and patient outcomes is highlighted by the results, which stress the need of incorporating data-driven methods into obstetric care.
Logistic Regression for Assessing Childhood ADHD
Authors: Silva Joao Carlos , Brito Rita , Sousa Ines
Published: August 2024

This work aims to offer a quick and easy way to diagnose a child for ADHD (attention-deficit/ hyperactivity disorder), by measuring/ classifying some of the child's features and habits. The importance of an early diagnosis is crucial, and the best time to assess and deal with this problem is in pre-school, avoiding future traumatic situations such as school dropout in the future. So being, in this work several preschool children participated in a survey comprised of thirteen (13) variables that was compiled with the help of a paediatrician, and afterwards were tested for ADHD. With these results, we were able to measure the importance of each variable in determining the likelihood of the child having ADHD using a logistic regression. As a result, we ended up with a smaller set of variables (9 instead of 13), that can be used as an easy method (simple equation) for an early diagnosis of ADHD, just by measuring some of the child's main characteristics.
Construction of the Index System for the Integration of Professional Education and Innovation Entrepreneurship Education in Applied Universities
Authors: Wang Guilian
Published: August 2024

In applied universities, innovation and entrepreneurship education (IEE) has entered a new era of comprehensive integration of professional education. However, as a novel educational activity, the evaluation of professional innovation integration still lacks reasonable appraisal standards. This article is based on the basic theory of the Kirkpatrick evaluation model, combined with the innovation and entrepreneurship practice of China's applied universities, this study initially constructs an index system for professional innovation integration in applied universities, estimates and ascertains the weights of different indicators using the Delphi and analytic hierarchy process approach, and thereafter, builds an index system for professional innovation integration in applied universities, which comprises of 4 primary indicators, namely: reaction layer, learning layer, behavior layer, and achievement layer, supported by 9 secondary indicators and 23 tertiary indicators.
The Multi-Modal Robot Perception, Language Information, and Environment Prediction Model Based on Deep Learning
Authors: Cai Yangchao , Zhao Yiming , Zhang Wenkai , Xing Nian
Published: August 2024

This paper presents a deep learning-based model to enhance robot perception and prediction in complex environments. The model utilizes CNN, LSTM, and attention mechanisms to address multimodal perception and environment prediction tasks. The introduction provides background on the importance of robots in various fields and emphasizes the need for multimodal perception. The methodology section explains the working principles of CNN, LSTM, and attention mechanisms. The experimental results demonstrate significant improvements in robot perception and prediction, with low errors and high accuracy. The proposed model supports robot applications in complex environments such as autonomous navigation and human-robot interaction. It effectively handles multimodal data and enhances robot perception and prediction capabilities. The experimental results validate the model's effectiveness, providing important support for robot decision-making in complex environments.
Multidimensional Financial Metrics for Corporate Financial Risk Assessment and Early Warning Mechanisms
Authors: Wei Ran , Wong Eugene Yin Cheung , Sun Matthew , Wang Zhaoqi
Published: August 2024

By constructing a comprehensive multi-dimensional financial index evaluation system, this study effectively identifies, evaluates, and forewarns the financial risks of enterprises. Utilizing public financial data from S&P Global and Alpha Vantage, a BP neural network-based enterprise financial risk assessment framework is built, and the KMO method analyzes different risk factors. This comprehensive evaluation model, paired with a risk warning mechanism, assesses financial situations through financial leverage, profitability, solvency, operating efficiency, growth indicators, and cash flow indicators. The example analysis shows: 1) The RMSE value ranges from 46.71 to 64.94, indicating high accuracy and stability in predicting financial risk; 2) The R value is very close to 1, demonstrating high stability.
COVID-19’s Effect in Pregnancy and Vertical Transmission: A Systematic Review
Authors: Abbasi Fatemeh , Movahedi Minoo , Seresht Leila Mousavi , Nazari Farzaneh , Naeiji Zahra , Arbabzadeh Taraneh , Khanjani Somayeh
Published: August 2024

The COVID-19 pandemic has significantly impacted public health and the global economy. It has also been found to have potential effects on pregnancy, neonatal outcomes, and mother-to-infant transmission. This systematic review aims to provide an overview of the maternal and perinatal outcomes associated with pregnancy. A systematic review study was conducted by searching the PubMed, MEDLINE, Embase, and Web of Science databases according to PRISMA guidelines from December 1, 2019, to December 23, 2022. The results indicate that there was an increase in the rate of cesarean delivery among mothers infected with SARS-CoV-2. However, the study found that the mode of delivery for pregnant women infected with SARS-CoV-2 did not increase or decrease the risk of infection for newborns. During the COVID-19 pandemic, there has been an increase in maternal and infant mortality rates, as well as stillbirths and ruptured ectopic pregnancies. Research has shown that SARS-CoV-2 can potentially be transmitted during pregnancy, although vertical transmission is rare. However, additional data are needed to investigate this adverse effect, especially regarding reports of disease recurrence in mothers infected with SARS-CoV-2.
National Quality Policy and Strategy of the Health Services in Health Systems of Developing Countries: A Scoping Review
Authors: Fallah Razieh , Maleki Mohammadreza , Aryankhesal Aidin , Haghdoost Aliakbar
Published: August 2024

It has always been argued that countries should not be inactive about the quality of health services. Therefore, a clear policy needs to be created regarding how quality of health services should be. The present scoping review was aimed to identify and map the available evidence regarding the National Quality Policy and Strategy (NQPS) of the health services in health systems of developing countries, graphically and tabularly. We followed the published methodological guidance of the Joanna Briggs Institute reviews. Also, we employed a narrative thematic synthesis integrated with the systematic analysis using the World Health Organization’s approach of NQPS, and the multiple-streams framework of Kingdon. We included 33 records that met the inclusion criteria; these records were published between 2010 and 2019. Meanwhile, government documents were the most frequent records (61%). Zimbabwe, Indonesia, and Sudan were the most frequent locations (each one 8%). The Ministry of Health was in charge of the ultimate responsibility for developing the NQPS in all identified countries. Besides, 82% of the countries were in the development phase of NQPS formulation, and convergence of three streams was observed in Indonesia, Sudan, and Tanzania. It seems that the African countries were informed about their quality issues, and the need for having NQPS have been more popular with them. We recommend that future research focuses on examining NQPS in terms of prioritizing in the agenda-setting phase of the policy-making cycle, and also, document analysis of all identified NQPS based on the core eight interdependent elements related to the NQPS approach.
Decoding the Systematic Construction of Vālmīki Rāmāyaṇa through Tantrayukti Principles: A Case Study Based on Kaccit Sarga of Rāmāyaṇa
Authors: Panchukrishnan Viswanathan
Published: August 2024

Every research is guided by specific methods and principles as laid out in the respective research methodology. “Tantrayukti” (TY) is an essential tool for research within the context of Indian Knowledge Systems (IKSs). TY was adhered to in the IKS texts of Āyurveda and Arthaśāstra, which systematically expounded principles of sciences and humanities. Extensive research has been conducted on management concepts from Rāmāyaṇa, but these have often been considered poetic descriptions. To bridge this gap, the authors analyze a specific chapter of Vālmīki’s Rāmāyaṇa to assess its adherence to a few Tantrayukti principles. Preliminary findings reveal the application of the rule-based Tantrayukti devices in the 100th sarga of Ayodhyākāṇḍa of Vālmīki Rāmāyaṇa, also referred to as “Kaccit sarga.” This substantiates the systematic documentation and structured exposition of Rāmarājya paradigms by Sage Vālmīki.
Assessment of Nutritional Status among the School-going Adolescents in a Rural Community of Bankura District, West Bengal: A Cross-sectional Study
Authors: Gazi Eashin , Samanta Amit Kumar , Rout Arup Jyoti , Saha Tushar Kanti
Published: August 2024

Background:

Adolescent is a very crucial period of life for the growth and development of the body and mind. Nutrition influences growth and development throughout infancy, childhood and adolescents.

Aims and Objectives:

The aim of the sudy was to assess the nutritional status in terms of thinness, overweight and obesity and stunting amongst the school-going adolescents and to find out the factors related to nutritional status amongst the study subjects.

Methodology:

Community-based descriptive cross-sectional study was conducted amongst six rural schools of Bankura-I community development block in Bankura district from July to September 2017. With the help of a pre-designed, pre-tested, semi-structured schedule, a total of 600 adolescent students were interviewed, and clinical examinations were done. The collected information was entered into Microsoft Excel sheet and analysed using SPSS-20.

Results:

The mean age of the study population was 13.2 ± 2.19 years. About one-third (32.8%) of adolescent students were thin, as per body mass index for age Z-score category. Normal nutritional status was found in the majority of students (63.8%), but overweight and obesity were only 3.4% amongst the adolescent students. Stunting (31.0%) was higher amongst the late adolescents, and also that the percentage of stunting was higher in adolescent girls (35.8%) than boys (15.0%). Stunting was also significantly related with caste, SE class, family type according to number of family members and birth order of study subjects.

Conclusions:

School-based as well as community-based adolescent-friendly health and nutrition education and services and economic development may improve the overall health and nutritional knowledge and status of adolescents.
Stress, Anxiety and Depression in First-year Medical Undergraduates and Coping Strategies Employed during COVID-19-induced Online Learning: A Cross-sectional Study from North India
Authors: Malhotra Rashmi , Naithani Manisha , Verma Anita , Bahurupi Yogesh , Bisht Kanchan , Singh Brijendra , Joshi Tanmay
Published: August 2024

Background:

The COVID-19 pandemic, due to its rising uncertainties, was expected to negatively impact the overall psychological functioning of students undergoing first year of medical professional education with changing teaching styles and environmental restraints.

Objective:

The objective was to assess the impact of this stressful period on the first-year Bachelor of Medicine and Bachelor of Surgery (MBBS) students at an Institute of National importance in North India, by measuring their various emotional states such as stress, anxiety, depression and coping strategies.

Methods:

This was a descriptive cross-sectional study undertaken in first-year MBBS students who were admitted in February 2021 due to delay in the admission process. Their physical classes were suspended due to the onset of second wave of COVID-19 and underwent a significant shift from conventional classes to a hybrid teaching module involving online classes, minimal face-to-face contact with teachers and almost no practical exposure which is in contrast to the conventional teaching practices. Data collection was done during and in the aftermath of second wave of COVID-19 using the Depression, Anxiety and Stress Scale (DASS-21) to assess emotional state and the Brief-Coping Orientation to Problems Experienced Inventory to measure the coping strategies employed.

Results:

One hundred and twenty-five students were approached and 80 consented to participate. The 80 students were analysed. Elevated depressive symptomatology scores were found in 46% of students, of which 14% had scores falling in severe category. More than 62% had elevated anxiety scores, of which 26% had severe range of scores, and 37% students had elevated stress scores, of which more than 10% had severe range of scores. Venting, humour and self-blame were found to be the predominant facets of coping styles employed and they had a significant statistical correlation with all three subscales of DASS-21.

Conclusion:

The study found a significant prevalence of depressive, anxiety and stress symptomatology. These findings emphasise the need for mental health interventions and support programmes for medical college MBBS students to help them cope with the psychological impact of academic and personal stressors.
Dynamic development of microglia and macrophages after spinal cord injury
Authors: Zhou Hu-Yao , Wang Xia , Li Yi , Wang Duan , Zhou Xuan-Zi , Xiao Nong , Li Guo-Xing , Li Gang
Published: August 2024

JOURNAL/nrgr/04.03/01300535-202512000-00029/figure1/v/2025-01-31T122243Z/r/image-tiff

Secondary injury following spinal cord injury is primarily characterized by a complex inflammatory response, with resident microglia and infiltrating macrophages playing pivotal roles. While previous studies have grouped these two cell types together based on similarities in structure and function, an increasing number of studies have demonstrated that microglia and macrophages exhibit differences in structure and function and have different effects on disease processes. In this study, we used single-cell RNA sequencing and spatial transcriptomics to identify the distinct evolutionary paths of microglia and macrophages following spinal cord injury. Our results showed that microglia were activated to a pro-inflammatory phenotype immediately after spinal cord injury, gradually transforming to an anti-inflammatory steady state phenotype as the disease progressed. Regarding macrophages, our findings highlighted abundant communication with other cells, including fibroblasts and neurons. Both pro-inflammatory and neuroprotective effects of macrophages were also identified; the pro-inflammatory effect may be related to integrin β2 (Itgb2) and the neuroprotective effect may be related to the oncostatin M pathway. These findings were validated by in vivo experiments. This research underscores differences in the cellular dynamics of microglia and macrophages following spinal cord injury, and may offer new perspectives on inflammatory mechanisms and potential therapeutic targets.
MicroRNA-301a knockout attenuates peripheral nerve regeneration by delaying Wallerian degeneration
Authors: Fu Lanya , Hu Xiaofang , Xu Jiawei , Li Zhenlin , Cai Jiale , Ma Xinrui , Zou Ying , He Ye , Xu Shuyi , Xu Yizhou , Zhang Jiaqi , Li Yunlun , Liu Jingmin , Fong Tsz Hei , Wang Xianghai , Zhu Lixin , Chen Dongfeng , Liu Aijun , Ma Xiaodong , Guo Jiasong
Published: August 2024

Our recent study demonstrated that knockout of microRNA-301a attenuates migration and phagocytosis in macrophages. Considering that macrophages and Schwann cells synergistically clear the debris of degraded axons and myelin during Wallerian degeneration, which is a prerequisite for nerve regeneration, we hypothesized that microRNA-301a regulates Wallerian degeneration and nerve regeneration via impacts on Schwann cell migration and phagocytosis. Herein, we found low expression of microRNA-301a in intact sciatic nerves, with no impact of the microRNA-301a knockout on nerve structure and function. By contrast, we found significant upregulation of microRNA- 301a in injured sciatic nerves. We established a sciatic nerve crush model in microRNA-301a knockout mice, which exhibited attenuated morphological and functional regeneration following sciatic nerve crush injury. The microRNA-301a knockout also led to significantly inhibited Wallerian degeneration in an in vivo sciatic nerve-transection model and in an in vitro nerve explant block model. Schwann cells with the microRNA-301a knockout showed inhibition of phagocytosis and migration, which was reversible under transfection with microRNA-301a mimics. Rescue experiments involving transfection of microRNA-301a-knockout Schwann cells with microRNA-301a mimics or treatment with the C–X–C motif receptor 4 inhibitor WZ811 indicated the mechanistic involvement of the Yin Yang 1/C–X–C motif receptor 4 pathway in the role of microRNA-301a. Combined with our previous findings in macrophages, we conclude that microRNA-301a plays a key role in peripheral nerve injury and repair by regulating the migratory and phagocytic capabilities of Schwann cells and macrophages via the Yin Yang 1/C–X–C motif receptor 4 pathway.
MicroRNAs as potential biomarkers for diagnosis of post-traumatic stress disorder
Authors: Martinez Bridget , Peplow Philip V.
Published: August 2024

Post-traumatic stress disorder is a mental disorder caused by exposure to severe traumatic life events. Currently, there are no validated biomarkers or laboratory tests that can distinguish between trauma survivors with and without post-traumatic stress disorder. In addition, the heterogeneity of clinical presentations of post-traumatic stress disorder and the overlap of symptoms with other conditions can lead to misdiagnosis and inappropriate treatment. Evidence suggests that this condition is a multisystem disorder that affects many biological systems, raising the possibility that peripheral markers of disease may be used to diagnose post-traumatic stress disorder. We performed a PubMed search for microRNAs (miRNAs) in post-traumatic stress disorder (PTSD) that could serve as diagnostic biomarkers and found 18 original research articles on studies performed with human patients and published January 2012 to December 2023. These included four studies with whole blood, seven with peripheral blood mononuclear cells, four with plasma extracellular vesicles/exosomes, and one with serum exosomes. One of these studies had also used whole plasma. Two studies were excluded as they did not involve microRNA biomarkers. Most of the studies had collected samples from adult male Veterans who had returned from deployment and been exposed to combat, and only two were from recently traumatized adult subjects. In measuring miRNA expression levels, many of the studies had used microarray miRNA analysis, miRNA Seq analysis, or NanoString panels. Only six studies had used real time polymerase chain reaction assay to determine/validate miRNA expression in PTSD subjects compared to controls. The miRNAs that were found/validated in these studies may be considered as potential candidate biomarkers for PTSD and include miR-3130-5p in whole blood; miR-193a-5p, -7113-5p, -125a, -181c, and -671-5p in peripheral blood mononuclear cells; miR-10b-5p, -203a-3p, -4488, -502-3p, -874-3p, -5100, and -7641 in plasma extracellular vesicles/exosomes; and miR-18a-3p and -7-1-5p in blood plasma. Several important limitations identified in the studies need to be taken into account in future studies. Further studies are warranted with war veterans and recently traumatized children, adolescents, and adults having PTSD and use of animal models subjected to various stressors and the effects of suppressing or overexpressing specific microRNAs.
Cerebral venous air embolism – Rare complication after central venous catheterization
Authors: Shah Aditi Vikas , Thaker Nirav , Jaggi Sunila , Shah Sonali H
Published: August 2024

Cerebral venous air embolism (CVAE) is a rarely reported but potentially disastrous complication associated with manipulation or accidental disconnection of a central venous catheter. This case report brings up one such case of a 62-year-old male who presented with altered sensorium post central venous catheterization and was promptly diagnosed to have developed CVAE based on multi-detector computed tomography imaging.
Effect of Anisotropy on the Wear Behavior of Age-Treated Maraging Steel Manufactured by LPBF
Authors: Lim Seung On , Shin Se-Eun
Published: August 2024

Maraging steel has excellent mechanical properties resulting from the formation of precipitates within the matrix through aging treatment. Maraging steel fabricated by the laser powder bed fusion (LPBF) process is suitable for applications including precise components and optimized design. The anisotropic characteristic, which depends on the stacking direction, affects the mechanical properties. This study aimed to analyze the influence of anisotropy on the wear behavior of maraging steel after aging treatment. The features of additive manufacturing tended to disappear after heat treatment. However, some residual cellular and dendrite structures were observed. In the wear tests, a high wear rate was observed on the building direction plane for all counter materials. This is believed to be because the oxides formed on the wear track positively affected the wear characteristics; meanwhile, the bead shape in the stacking direction surface was vulnerable to wear, leading to significant wear.