bluuebunny/crossref_metadata_2025 huggingface dataset:
Implications of Adnexal Invasions in Primary Extramammary Paget’s Disease: A Systematic Review
Authors: Aryal Sabita , Ye Qiang Liu
Published: April 2024

Extramammary Paget’s disease (EMPD) is an erratic malignant skin disorder primarily affecting skin areas abundant with skin appendages like hair follicles. The vulva is most involved site, followed by genital areas, penoscrotal regions and axillary skin. EMPD presents as erythematous skin lesions resembling eczema, typically progressing slowly, either primary or secondary manifestations. Primary EMPD originates as an intraepithelial neoplasm of the epidermis, often leading to local lymph node metastases and distant metastases. A systematic literature search using targeted keywords across multiple databases was conducted. Studies focusing on EMPD, adnexal involvement, depth, recurrence, and prognosis were included by keeping in view the objective which is to determine the significance of adnexal involvement and depth concerning recurrence and prognosis in the primary EMPD. Adnexal involvement, especially in hair follicles and eccrine ducts, is prevalent in primary EMPD. However, its correlation with tumor progression or recurrence rates remains inconclusive. Surgical excision, including Mohs micrographic surgery, is the primary therapeutic approach, with topical agents and systemic treatments used in advanced cases. Future studies regarding understanding adnexal involvement's depth and significance are essential in designing effective targeted therapeutic approaches in EMPD.
Ragi Pest Control
Authors: Prabhu Upase Sharanesh , . Nikhil , G S Rakesh , R Chandru , L V Vedashree
Published: April 2024

The rise of global population has put increasing pressure on the agriculture industry to meet the demand for food. However, the growing use of pesticides and insecticides in conventional farming practices has caused significant harm to the environment and human health. Thus, there is a growing interest in using sustainable agriculture practices that reduce the use of these harmful chemicals. One such practice is pest detection, which enables farmers to detect pests in their crops before they cause significant damage. In this context, this project aims to develop a pest detection system using IoT and Arduino. The system will be designed to detect pests in crops through a combination of sensors and machine learning algorithms. The system will consist of an Arduino microcontroller, soil moisture sensors, temperature and humidity sensors, infrared sensors or camera modules, and a Wi-Fi or Bluetooth module. The sensors will collect data on soil moisture levels, temperature, humidity, and pest activity. The data will be sent to a cloud-based server or database for analysis and visualization. The infrared sensor or camera module will detect the presence of pests in the crops. The system will use machine learning algorithms to distinguish between pests and other objects, such as leaves or debris. When pests are detected, the system will alert the farmer through a buzzer or LED connected to the Arduino board. The farmer can then take appropriate action, such as applying pesticide or removing infested plants. The pest detection system has the potential to reduce the use of harmful pesticides and insecticides in agriculture, as farmers will be able to identify pests before they cause significant damage. The system will also provide farmers with real-time information on pest activity, enabling them to take proactive measures to control pests and reduce crop damage. Additionally, the system can track and store the pest detection data over time, allowing farmers to monitor trends and patterns in pest activity. In conclusion, this project proposes the development of a pest detection system using IoT and Arduino that will enable farmers to monitor pest activity in their crops in real-time. The system has the potential to reduce the use of harmful chemicals in agriculture and improve crop yield while ensuring sustainable and environmentally friendly practices.
Sosialisasi dan pendampingan pembuatan rencana anggaran biaya Pembangunan Jembatan Gombengsari
Authors: Purnomo Dimas Aji , Prisilia Harliwanti , Hindratmo Astria
Published: April 2024

Rencana anggaran biaya adalah perkiraan biaya yang dibutuhkan untuk setiap pekerjaan pembangunan atau proyek konstruksi. Era saat ini masyarakat sangat membutuhkan pengetahuan tentang bagaimana menyusun anggaran biaya dan metode pelaksanaan konstruksi jembatan dikarenakan desa diberi kewenangan untuk mengelola dana desa. Dana desa tersebut digunakan untuk pekerjaan kontruksi. Dengan adanya pelatihan ini masyarakat lebih memahami tentang bagaimana cara menyusun rencana anggaran biaya yang benar dan bisa mengaplikasi metode pekerjaan konstruksi jembatan sesuai dengan standar PU
Appropriate Technology Machine Innovation Training for High School-Vocational School Students as an Important Role in Increasing the Productivity of MSMEs in Benowo District
Authors: Muharom Muharom , Kholili Navik , Nugroho Alfi , Muchid Muhammad
Published: April 2024

Empowering appropriate technology is very important to support the sustainability of small businesses in the region. This activity is essential to introduce appropriate technology to high school/vocational school students as young technology drivers. This community service program aims to teach students how to use relevant technology in everyday life. Guiding in completing appropriate technology according to the needs the small business desires properly and correctly. The method for implementing community service activities is carried out by directing the Implementation and completion of the tools used at the regional level. The first stage is developing and applying TTG machines in natural resource management. The second stage is implementing the TTG machine for community empowerment (strategic opportunities and challenges). The third stage is implementing TTG machines to manage abundant natural resource potential. The fourth stage is the promotion of TTG machines for community empowerment in the Implementation of Law No. 6 of 2014. The fifth stage is the design and method of making the TTG machine.
Analysis Of The Design And Construction Of A Water Spinach (Ipomoea Aquatica) Vegetable Drying Chamber With A Spiral Type Heater
Authors: Wahid Okon Agustyan , Nugroho Wahyu , Kholili Navik , Khusna Dwi , Nugroho Alfi , Muchid Mochammad
Published: April 2024

The food drying process is an innovative method to change the process of reducing the water content of fresh food products of plant origin to slow down the emergence and growth of unwanted microorganisms. This heating system has four heating elements, where the heating elements used have a power of 1200W because each heating element has a power of 300W. During the drying process, it is known that the average air temperature in the drying room is higher when using solar energy compared to a heating system. The average temperature measurement during the day is 38°C. The measurement results tend to be constant, with an average of 34°C for 7 hours using the heating system. The battery voltage shows an increase every hour. At 08:30 am the battery voltage was 12.3V, and until 16.00 the voltage was 13.57V. This dryer is sourced from solar energy because using solar for drying requires a longer time, namely 8 hours (08.30-16.00), compared to using a heating system for 7 hours.
Centenary Paper: La difusión de la poesía china en la cultura catalana durante la dictadura franquista
Authors: ZHIMENG BAI
Published: April 2024

La literatura catalana sufrió una terrible agresión durante la dictadura de Francisco Franco (1939–1975), representada principalmente por la censura impuesta a los libros editados en catalán. En este período de represión cultural, la misión de traducir poesía extranjera en las regiones de habla catalana se caracterizó por unos objetivos y unas características determinados por las circunstancias sociohistóricas especiales. En el presente artículo se realiza una investigación panorámica de la difusión de poesía china en la cultura catalana durante la dictadura franquista. Se han estudiado las diversas cuestiones relacionadas con este itinerario: las publicaciones, las formas de traducción y de edición, los motivos de los traductores catalanes al efectuar su labor, y la influencia de los elementos sociohistóricos en las actividades divulgativas.
Help to climb up: impacts of modern education among the Gǀui and Gǁana
Authors: Noguchi Tomoe , Takada Akira
Published: April 2024

Out of School Education and Training (OSET) launched in New Xade – the largest settlement for San in Botswana – in 2007. It is geared towards training children who dropped out of formal education. The present study aimed to describe the impacts of modern education among the Gǀui and Gǁana by comparing the practice of formal schooling and OSET. We conducted semi-structured interviews with school-aged children who dropped out of formal school, as well as with teachers from both the primary school and OSET. The interviews focused on the lives of school-aged children inside and outside of educational facilities. Children were asked about their reasons for dropping out of primary school, their perspectives on the activities at OSET and their expectations about their futures. Teachers were asked about their educational experiences, their assessments of child education and their expectations about their futures. We found that non-formal education such as OSET can provide opportunities for self-development for children who dropped out of formal education. Further, unemployed youth who had achieved secondary school qualifications found employment at OSET as teachers, thus increasing their self-reliance. Because the present research sheds light on previously unknown issues surrounding the education of Gǀui and Gǁana, it stands to contribute to the improvement of basic education policy in Botswana.

This article was published open access under a CC BY licence:
https://creativecommons.org/licences/by/4.0
.
Audio tagging of avian dawn chorus recordings in California, Oregon and Washington
Authors: Weldy Matthew , Denton Tom , Fleishman Abram , Tolchin Jaclyn , McKown Matthew , Spaan Robert , Ruff Zachary , Jenkins Julianna , Betts Matthew , Lesmeister Damon
Published: April 2024

Declines in biodiversity and ecosystem health due to climate change are raising urgent concerns. In response, large-scale multispecies monitoring programmes are being implemented that increasingly adopt sensor-based approaches such as acoustic recording. These approaches rely heavily on ecological data science. However, developing reliable algorithms for processing sensor-based data relies heavily on labelled datasets of sufficient quality and quantity. We present a dataset of 1,575 dawn chorus soundscape recordings, 141 being fully annotated (n = 32,994 annotations) with avian, mammalian and amphibian vocalisations. The remaining recordings were included to facilitate novel research applications. These recordings are paired with 48 site-level climatic, forest structure and topographic covariates. This dataset provides a valuable resource to researchers developing acoustic classification algorithms or studying biodiversity and wildlife behaviour and its relationship to environmental gradients. The dawn chorus recordings were collected as part of a long-term Northern Spotted Owl monitoring program; this demonstrates the complementary value of harnessing existing monitoring efforts to strengthen biodiversity sampling.

This dataset of dawn chorus soundscape recordings is one of the few open-access acoustic datasets annotated with non-biotic and both interspecific (across species) and intraspecific (within species) bird, mammal and amphibian sonotypes and the first that is paired with climatic, forest structure and topographical covariates extracted at recorder locations. This makes it a valuable resource for researchers studying the dawn chorus and its relationship to the environment.
Inclusiveness in mental health research: a survey of attitudes, awareness, and actions among journal editors.
Authors: Pezzoli Patrizia , Zhai Weili , Marsh Joan , Viding Essi
Published: April 2024

Background: Improving inclusiveness in mental health research merits attention as we seek to reduce inequalities in mental health. Academic journals can promote inclusiveness through editorial practices related to the selection of content and the composition of journal editorial boards.

Objectives: To investigate the attitudes, awareness, and actions of journal editors con-cerning inclusiveness in mental health research and editorial practices.

Methods: We surveyed 74 chief and senior editors, representing 55 prominent journals in neuroscience, psychiatry, and psychology (2021 impact factor M = 8.04, SD = 10.76).

Results: Most respondents (74–99%) acknowledged the importance of inclusiveness in mental health research, and a majority (62–78%) were familiar with existing guide-lines. Half or fewer of the journals (49–50%) had policies for selecting content that is diverse, and 20% had policies regarding inclusion of individuals with lived experi-ence of mental health challenges. Well over half the journals (57–72%) had policies to widen diversity among their editorial boards and roughly half (43–53%) among peer reviewers, although only a few (18–23%) included among their editors or peer review-ers individuals with lived experience of dealing with mental health challenges.

Conclusions: This study highlighted an intention-action gap, with positive attitudes and awareness but limited editorial practices promoting inclusiveness in mental health research. Inclusion of individuals with lived experience emerged as an area in particular need of improvement. We discuss potential strategies that journals might consider to foster inclusiveness, such as diversity training, publication checklists, and infrastructure that supports participatory approaches.
Embracing Circular Economy Principles for Sustainable Green Supply Chain Management in Manufacturing Industries
Authors: Moorthy K. Sankara , Balakrishnan G. , Kumar S. Satheesh , Raja L. , Vijayalakshmi A.
Published: April 2024

The circular economy is a sustainable approach in the manufacturing sector that focuses on long-lasting product design, remanufacturing, recycling, and waste reduction. This approach optimizes resource utilization, reduces reliance on finite resources, and minimizes waste generation, contributing to environmental conservation and mitigating climate change's effects. Digital technologies and data analytics play a crucial role in promoting a circular economy in manufacturing supply chains, with IoT, AI, and blockchain enhancing visibility, traceability, and collaboration. The challenges of implementing circular economy principles are discussed, along with the potential economic benefits of sustainable production and consumption patterns. Case studies from successful manufacturing companies are also examined.
Leveraging HR Metrics for an Effective Recruitment and Selection Process With Special Reference to the Manufacturing Sector in Coimbatore
Authors: Durai Subhashini , Krishnaveni R. , Manoharan Geetha , Nivedha M.
Published: April 2024

Any organization's growth and stability depend on its recruitment and selection process, which attracts top talent. HR metrics quantify and rate human resource processes. This study aims to integrate human resource metrics into the recruitment and selection process throughout. In this exploratory study, human resource managers discuss metrics and their use in evaluating recruitment and selection procedures. Manufacturing HR managers are being surveyed about the KPIs in question. Most HR managers (out of 15, at least 10 HR managers) have reported nine metrics as actively used in their firm, while a minority (out of 15, less than 10 HR managers) have reported 10 metrics as rarely used and six calculated metrics. This chapter will interest researchers and practitioners who identify, design, and deploy metrics to measure an organization's recruitment and selection process. The chapter identifies metrics to demonstrate how metrics encourage HR practitioners to adopt and measure skilled workforce in recruitment and selection. This contributes to the HR metrics debate.
The Deployment of Automated Parcel Lockers in Urban Logistics
Authors: Vaca Jorge Chicaiza , Rabe Markus , Gonzalez-Feliu Jesus
Published: April 2024

This chapter reviews the notion of automated parcel lockers (APL) and proposes a simulation-optimization methodology for APL adoption as last-mile delivery scheme, reducing the risk of failures in their implementations. After addressing the main issues in APL deployment and simulation and optimization related to them, a method combining a system dynamics simulation model (SDSM) with a facility location problem (FLP) optimization model is proposed. Then, it is applied to a case in the city of Dortmund (Germany) assessing three scenarios of customer demand. The three scenarios (pessimistic, realistic, and optimistic) are simulated through a planning horizon of 60 months. Results show functional indicators (number of parcel lockers used and their coverage in the territory) and economic ones, mainly the net present value (NPV). The NPV determines the investment required to implement each scenario. From these investments, third-party logistics providers can decide about implementing APLs as delivery scheme.
Future Trends in Sustainable Urban Logistics
Authors: Tepecik Aylin
Published: April 2024

There is a need to reduce the negative effects caused by load distribution in urban areas as a result of the increase in population and urbanization rate. At this point, it is necessary to eliminate the negativities related to transportation in order to make transportation activities in urban areas sustainable. Recently, the importance of the concept of urban logistics has increased, and approaches that facilitate urban life and logistics actions have become more important. However, logistics service providers have begun to look for ways to respond to consumer demands with fast, reliable, economic, and innovative methods. In order to maintain urban logistics, different methods can be used to deliver products to consumers. The study offers a perspective on technological developments affecting urban logistics and draws attention to sustainable and innovative delivery methods.
A Systematic Review of K-12 School Shooting Research
Authors: Joseph Justin J. , Aguado N. Alexander , Purser Christoper W.
Published: April 2024

High-profile school shootings (i.e., rampage school shootings) have fueled empirical and general inquiry into school shooting characteristics and have been a point of emphasis for stakeholders (e.g., parents, teachers, and students) nationwide. The current chapter's goals are to provide a systematic overview of the available evidence examining school shooting incidents. Specifically, this review is intended to gather, summarize, and review relevant peer-reviewed studies on the topic, integrate and identify key findings and themes, identify gaps and limitations in the literature, and discuss implications for directions for future research. Further, it is expected this systematic review can inform practitioners, academics, researchers, policymakers, and stakeholders on the current state of school shooting research and promote innovation in methodological approaches for investigating school shooting incidents.
Impact of Gun Violence on Students' Mental Health
Authors: Pegadraju Sivani , Kachhi Zidan
Published: April 2024

This chapter explores how gun violence affects students' mental health, highlighting the need of comprehending the perspectives of both direct and indirect victims. It highlights the complicated issue's significance for public health by addressing rising rates of anxiety, despair, and PTSD among students. Less than 1% of firearm-related deaths in schools are caused by school shootings, despite an increase in these incidents. Economic instability, inequality, COVID-19, and gun suicides are all contributing issues. The chapter emphasises how critical it is to address issues related to mental health, particularly the racial disparities in treatment availability. School counsellors are essential in putting ideas into action, helping traumatised children, and encouraging academic achievement. To improve school safety, recommendations include tightening gun laws, involving the community, and providing mental health care. In reaction to gun violence, the chapter promotes a holistic strategy that gives students' mental health top priority.
School Violence
Authors: McNeal Ramona Sue , Kunkle Susan M. , Bryan Lisa Dotterweich , Schmeida Mary
Published: April 2024

Florida responded to the Parkland shooting with the Public Safety Act, which included measures restricting the sale of firearms to individuals with mental illness. As suggested by this act, mental illness has become a focal point in the debate on how to address school shootings and has increased the call for “red flag” legislation. These laws target those persons believed to be in psychological crisis, having potential to harm self or others and permits law enforcement to remove the person's access to the firearm. Not all states officials support red flag initiatives; some argue that having one's firearms removed without the chance for an initial court hearing is in violation of one's due process. Which states are more likely to adopt stringent gun laws related to mental illness? In examining this topic, this chapter explores state-level legislative action concerning the passage of red flag laws, 2018 through 2022.
Long After the Sound of Gun Fire Is Silenced
Authors: Mudd-Fegett Kimberly
Published: April 2024

Teachers enter the helping profession to mold the lives of children and young adults, not to arm themselves against intruders or their students. Furthermore, teachers and administrators do not knowingly enter into their professions with the mindset that they will one day look into the eyes of their students as they lay before them shot, dying, or picking up the pieces from a traumatic event. In this chapter, the author will highlight data from interviews with three teachers who have vast levels of experience teaching and exposure to trauma. This author will use a narrative perspective in conducting these interviews to truly understand their experience, calling, and need for change. These interviews will highlight teachers who taught when active shooters were a foreign term and teachers who have taught throughout the longevity of their careers in a world where active shooting training and preparedness were a part of their required yearly training. The stark contrast between these participants' interviews will give insight into the vast difference in teaching in today's classroom.
A President's Leadership Retrospective
Authors: Hellmich David Michael
Published: April 2024

This chapter presents the leadership reflections of a community college president as he nears the end of his career. His professional writing has focused on three topics: cooling out, strategic planning, and ethical leadership. These topics as well as the topic of organizational frames have been important in his professional maturation and have influenced the leadership strategies he has employed at Sauk Valley Community College in rural Illinois. He summarizes his interest in these topics and ties them to leadership strategies that have been most impactful for him as president, at times juxtaposing them with effective and ineffective leadership strategies he encountered along the road of his community college career. While all of these leadership strategies are specific to his experiences on community college campuses, they can be adapted to other organizations and situations.
Data Privacy, Compliance, and Security Including AI ML
Authors: Singhal Sangeeta
Published: April 2024

Ensuring data privacy, compliance, and security in healthcare settings, particularly with the integration of artificial intelligence (AI) and machine learning (ML), is paramount for safeguarding sensitive patient information and maintaining trust. The intersection of these technologies with healthcare data introduces unique challenges, including patient confidentiality, regulatory compliance (e.g., HIPAA, GDPR), and the risk of data breaches. While AI and ML hold tremendous potential for improving patient care and treatment outcomes, they also raise concerns regarding algorithmic bias, fairness, and interpretability. To address these challenges, healthcare organizations must implement robust data security measures such as encryption, anonymization, and access controls, while also prioritizing transparency and accountability in AI-driven decision-making processes. Emerging trends in privacy-preserving techniques, such as federated learning and differential privacy, offer promising solutions for balancing innovation with patient rights and regulatory requirements.
Demystifying Machine Learning by Unraveling Interpretability
Authors: Kotagiri Anudeep
Published: April 2024

In this chapter, the authors embark on a journey to unveil the complexities of machine learning by focusing on the crucial aspect of interpretability. As machine learning algorithms become increasingly sophisticated and pervasive across industries, understanding how these models make decisions is essential for trust, accountability, and ethical considerations. They delve into various techniques and methodologies aimed at unraveling the black box of machine learning, shedding light on how models arrive at their predictions and classifications. From explainable AI approaches to model-agnostic techniques, they explore practical strategies for interpreting and explaining machine learning models. Through real-world examples and case studies, they illustrate the importance of interpretability in ensuring transparency, fairness, and compliance in decision-making processes. Whether you're a data scientist, researcher, or business leader, this chapter serves as a guide to navigating the complex landscape of machine learning interpretability and unlocking the true potential of AI technologies.
Measuring Psychometric Analysis of Stress Level in Learners Across Multiple Parameters Using Machine Learning
Authors: Bhatia Ashima Bhatnagar , Mittal Kavita
Published: April 2024

This study presents a comprehensive exploration into the analysis of stress levels in learners using machine learning methodologies across diverse parameters. The study investigates stress quantification through multi-dimensional data encompassing different factors. Employing machine learning models, the study achieves an overall predictive accuracy of 85.6% in assessing stress levels. Notably, physiological data analysis yielded an accuracy of 88.9%, highlighting the reliability of identifying stress patterns. Furthermore, a strong negative correlation -0.75 between stress levels and performance was observed, indicating a significant impact of stress on learners' educational outcomes. Environmental factors contribute to 28% of the variability in stress levels in learners, underscoring their influence. Noteworthy features in predicting stress levels include heart rate variability 37.5%, sleep quality 23.8%, and social interactions 18.6%.
Navigating the Essential Fundamentals of Data Processing for Modern Enterprises
Authors: Whig Pawan , Kasula Balaram Yadav , Yathiraju Nikhitha
Published: April 2024

Navigating the contemporary business landscape demands an adept understanding of data processing essentials for informed decision-making and sustainable growth. This comprehensive guide illuminates pivotal aspects of data processing, encompassing batch and real-time processing paradigms. Addressing data quality, assessments revealed that 85% of surveyed enterprises encountered challenges, while robust cleansing methodologies exhibited a 70% improvement in accuracy and a 60% reduction in errors. Additionally, emphasis on seamless data integration through ETL strategies led to a 50% reduction in integration timelines, with data warehousing yielding a 75% enhancement in query performance. Analyses highlighted a 40% comprehension boost in stakeholders using advanced visualization tools. Furthermore, fortified encryption methods showcased a projected 60% reduction in data breach risk, and compliance with regulations stood at a robust 90%.
Smart Data Processing
Authors: Yalamati Sreedhar , Batchu Ravi Kumar
Published: April 2024

In this chapter, the authors delve into the transformative realm of smart data processing, exploring its pivotal role in harnessing the full potential of artificial intelligence (AI) and machine learning (ML). As the volume and complexity of data continue to grow exponentially, the need for intelligent data processing becomes paramount. The authors examine cutting-edge techniques and methodologies that leverage AI and ML algorithms to extract meaningful insights from vast datasets. The chapter unfolds by elucidating the significance of efficient data preprocessing, discussing how it lays the foundation for robust AI and ML models. Furthermore, they explore advanced techniques such as feature engineering, dimensionality reduction, and data normalization, showcasing their pivotal role in enhancing model accuracy and interpretability. The narrative extends to real-world applications, illustrating how smart data processing can revolutionize industries ranging from healthcare and finance to manufacturing and beyond.
Unveiling the Frontiers
Authors: Whig Pawan , Kasula Balaram Yadav , Jain Anupriya , Sharma Seema
Published: April 2024

This chapter serves as a comprehensive exploration of the cutting-edge developments and innovations in the field of data science. It examines the latest methodologies, algorithms, and applications that are reshaping the landscape of data-driven decision-making and predictive analytics. Beginning with an overview of recent advancements in machine learning, deep learning, and artificial intelligence (AI), this chapter dives into the intricacies of novel techniques such as reinforcement learning, federated learning, and transfer learning. It discusses their applications across various domains, including healthcare, finance, cybersecurity, and beyond, showcasing their transformative potential. Moreover, this chapter sheds light on emerging trends in data preprocessing, feature engineering, and model interpretability, highlighting their crucial role in improving the accuracy, robustness, and interpretability of data science models.
Security Criteria in Financial Systems in Industry 6.0
Authors: Movahed Ali Bakhshi , Movahed Aminmasoud Bakhshi , Nozari Hamed , Rahmaty Maryam
Published: April 2024

The importance of the financial system in the industry is not only related to the organization itself but also depends on the industry's network. Technological developments, growth in industry, and increasing use of new technology in human life have changed the organization's financial system. One of the most important factors in the necessity of financial development is the issue of network security, monetary systems, banking systems, and privacy of customers and users. For this purpose, it will be considered to improve the quality level and speed of this process to pursue financial claims. Along with the industrial developments of different generations and the continuous and universal use of Industry 4.0 technologies, the financial and banking system also needs to be updated and mastered to reduce cybercrime and prevent financial fraud and bank stolen. This chapter examines the financial system by analyzing the financial security criteria and achieving Financial Security 6.0.
A Comprehensive Investigation of Underwater Disaster Prediction Using Machine Learning Algorithms
Authors: R. Nivethitha , Jothiraj Sivasankari , P. Divya Bharathi , D. Sathish Kumar
Published: April 2024

Underwater disasters cause severe consequences to marine ecology and lead to significant loss to the environment. Since the sea water temperature reaches 700° Fahrenheit, disasters like hydrothermal vents and underwater volcano eruptions occur. Due to these seireachests, marine creatures like sharks and different types of living species have been excavating under the ocean. The risk of underwater disasters like underwater volcanic eruption, tsunami, underwater earthquake, submarine accidents and oil spills are crucial, and the preventive measures of these calamities were highly needed. Oceanographic data includes underwater images and videos captured bymarine archaeologists and divers to know the information about the exploited resources. Existing traditional algorithms have practical limitations to predict underwater catastrophes under more depth condition and can be overcome by machine learning (ML) algorithms, since it was accurate and fast in analyzing the oceanographic data. This chapter provides a comprehensive review of predicting underwater disasters using ML algorithms.
Machine Learning Models for Prediction of Landslides in the Himalayas
Authors: Singh Vikram , Tyagi Sanjay
Published: April 2024

The Himalayan region, characterized by its steep terrain and geological complexities, stands vulnerable to the persistent threat of landslides, particularly exacerbated during the monsoon season. The susceptibility to landslides in this region arises from a convergence of factors, including the region's high seismic activity, diverse geological formations, intense monsoonal precipitation, and rapidly changing climate patterns. The impact of landslides extends beyond immediate infrastructure damage, often leading to loss of lives, disruption of livelihoods, and severe environmental degradation. This chapter embarks on a comprehensive exploration of the pivotal role of artificial intelligence and machine learning in revolutionizing the prediction and mitigation of landslides in the Himalayas. It delves into the intricate challenges posed by the region's geological diversity and environmental dynamics, offering insights into AI-driven strategies to enhance predictive accuracy, implement early warning systems, and devise effective mitigation measures. The chapter commences with an overview of the Himalayas, delineating the geological complexities and the profound influence of climatic conditions on landslide occurrences. It elucidates the critical challenges hindering traditional landslide prediction methods, such as inadequate data quality and sparsity, underscoring the dire need for advanced predictive techniques. A meticulous review of existing methods, encompassing both conventional approaches and the utilization of remote sensing technologies and geographic information systems (GIS), sets the stage for introducing AI-based solutions. The chapter unfolds the nuances of machine learning approaches tailored for landslide prediction, spotlighting the selection of pertinent features, the application of supervised and unsupervised learning models, and the integration of real-time environmental data. A pivotal focus lies on AI-driven early warning systems that amalgamate historical data, sensor networks, and predictive models to facilitate timely alerts and risk assessments. Moreover, the chapter elucidates how AI empowers hazard zonation mapping, aiding in the identification of high-risk areas and adaptive planning for resilient infrastructure development. The narrative is enriched with insightful studies and practical implementations showcasing the efficacy of AI-based models in landslide prediction and mitigation within the Himalayan terrain. Lessons gleaned from these studies illuminate both successes and challenges, providing invaluable insights for the field. This chapter endeavours to unravel the transformative potential of AI and machine learning in confronting the formidable challenge of landslides in the Himalayas. It underscores the significance of these technological advancements in fostering resilience, safeguarding lives, and fortifying the region's infrastructure against the omnipresent threat of landslides.
Modernizing Healthcare
Authors: Singh Bhupinder , Vig Komal , Kaunert Christian
Published: April 2024

Augmented reality (AR) and virtual reality (VR) technologies have advanced quickly, making them effective tools with the potential to revolutionize healthcare. By providing cutting-edge methods to enhance patient care, medical training, and education; augmented reality (AR) and virtual reality (VR) are transforming the healthcare sector. The patient's experience is being improved by these immersive technologies, which are also giving medical staff cutting-edge tools for training and skill improvement. The digital information is added to the real environment via augmented reality because medical imaging and visualization using this technology are progressing remarkably in the field of healthcare. Now 3D reconstructions of the patient's anatomy are available, surgeons can overlay them in their range of vision for improved guidance during difficult surgeries. This chapter intends to shed light on the bright future of AR and VR in the healthcare arena by examining cutting-edge use cases, technological breakthroughs, and their impact on patient care.
Sensing of Type 2 Diabetes Patients Based on Internet of Things Solutions
Authors: Mijwil Maad M. , Bala Indu , Guma Ali , Aljanabi Mohammad , Abotaleb Mostafa , Doshi Ruchi , Hiran Kamal Kant , El-Kenawy El-Sayed M.
Published: April 2024

Internet of things solutions have brought about a significant revolution in the development of healthcare by providing remote monitoring capabilities and providing doctors with reports on patients in real-time, which leads to developing the care of patients with type 2 diabetes and enhancing their health condition. Through several sensors, IoT devices can collect patients' health data, such as glucose level, blood pressure, heart rate, and physical activity, so that healthcare workers can assess patients' health status and disease development within the body. These devices contribute to saving patients' lives by providing continuous monitoring of vital signs and disease management by physicians and healthcare workers. In this context, this article contributes to reviewing the development of IoT solutions in providing information and mechanisms adopted in monitoring patients with type 2 diabetes, data security issues, privacy concerns, and interoperability.
From Geospatial Data to Insight
Authors: Senbato Genale Assefa , Wako Desalegn Aweke , Ayalew Dessalegn Tsion
Published: April 2024

As technology advances, the potential applications for geospatial data will only continue to grow. However, conventional techniques for evaluating geographic data frequently involve manual interpretation or rule-based strategies, which take a long time and have a limited capacity to handle big datasets. Current technology has significantly enhanced geospatial analysis by providing powerful data collection, processing, and interpretation tools. This study used machine learning to analyze geospatial data and extract insights that would be difficult or impossible to obtain using traditional methods. Literature review, various Python libraries for geospatial data, building and evaluating machine learning models for algorithms like random forest, decision tree, linear regression, and K-means clustering using freely available geospatial data were presented. Machine learning makes analyzing geospatial data more effective for deriving deep understandings and extracting insights.
Topological Insights Into Weather Dynamics in the Indian Context
Authors: S. Azarudheen , Menezes Daphne Julia , Clements Vivan
Published: April 2024

Analysis of day-to-day weather patterns is critical and essential in daily life. Although traditional methods exist, in modern times, we have developed realistic and reliable methods to provide better insights and understanding of complex weather patterns for various surges, especially in these times of global warming. Implementing clustering and topological data analysis in this analysis has looked into a vast understanding of how regions with similar characteristics behave when weather changes occur due to heat, pressure, or wind-related phenomena. The classification model developed using Mapper analysis has produced 95.8% accuracy, concluding that weather follows a transient weather pattern due to various resources and how stagnant conditions affect transient weather patterns, causing rise in sub-clusters. Thus, fitting and interpreting newer models helps us understand weather analysis and classification.
Demand for International Tourism in Sri Lanka: Almost Ideal Demand System
Authors: Jayasooriya S. P. , Bandara R. A. S. P.
Published: April 2024

The paper analyses the tourism demand and its economic determinants for effective management within the tourism sector. Utilising the Almost Ideal Demand System (AIDS), the study aims to estimate tourism expenditure and price responsiveness elasticities. The model employs data from 1995 to 2019, focusing on total share, total expenditure share, and total expenditure share per item. The findings reveal that Sri Lanka emerges as a destination with typical demand characteristics, showcasing a notably higher demand for tourism. Analysis of spending elasticities, both in terms of own and cross prices, underscores the country's favourable position within the tourism market. Specifically, the results indicate that the demand for tourism in Sri Lanka is sensitive to pricing dynamics, with elasticity values suggesting a significant response of tourist demand to price fluctuations. Understanding Sri Lanka's competitive position relative to other destinations underscores the importance of tailoring management strategies to suit the unique demand characteristics of each country. Given the price sensitivity observed within Sri Lanka's tourism demand, careful attention to pricing mechanisms is imperative for sustaining and enhancing the country's tourism sector.
Exploring the career challenges of female tour guides in the Sri Lankan travel and tourism industry: Social Role Theory perspective
Authors: Jayaweera Ruwanthika
Published: April 2024

This paper aims to understand what barriers have resulted in the poor representation of female tour guides in the Sri Lankan travel and tourism industry. The study's design employed a qualitative approach to gain in-depth insights into the experiences of female tour guides, utilising thematic analysis as the analytical strategy. Three main themes emerged from the qualitative research findings of the interviewed female national tour guides: the feminine gender identity of female tour guides, the gender role of women, and gender stereotypes. Firstly, the feminine gender identity of female tour guides presents how femininity contradicts the tour guide's occupational role in the Sri Lankan context. Secondly, gender stereotypes prevalent in familial, work, and broader societal spheres hinder the success of female tour guides in their careers. Finally, socially constructed gender roles impose additional obstacles for women pursuing careers as tour guides. Consequently, these constraints impede women's fulfilment of their social role, creating a challenging atmosphere to be a female tour guide in Sri Lanka. This research suggested that it is the responsibility of government bodies to ensure equal opportunities for females to take up the tour guide occupation.
Innovación institucional y gobernanza transfronteriza en la frontera México-Estados Unidos
Authors: Peña Sergio
Published: April 2024

El artículo analiza la innovación institucional con base en cuatro dimensiones que son el saber, la burocracia, la organización, y la tecnología. El artículo emplea un análisis comparativo de tres modelos de gestión o gobernanza transfronteriza que son el modelo territorial, fronteras abiertas y fronteras móviles y, además, múltiples dimensiones de innovación se analizan y comparan en la frontera México-Estados Unidos. El análisis comparativo se complementa con análisis de discurso. El artículo hace un par de contribuciones al estado del arte: la primera es que cuestiona la idea que la innovación siempre se asocia con aspectos positivos; la innovación produce resultados éticamente cuestionables. Segundo, el artículo señala que no es posible un solo modelo de gobernanza transfronteriza dado que los temas de la agenda son diversos y múltiples algunos más conducentes a la cooperación y otros no tanto.
Biopolítica y vigilancia genética a solicitantes de asilo en la frontera sur de Estados Unidos
Authors: Fuentes Flores César M. , Díaz Carnero Emiliano Ignacio , Barrios De La O María Inés
Published: April 2024

El objetivo del artículo es argumentar que, la toma de muestras de ADN a migrantes y solicitantes de asilo, es un dispositivo biopolítico de vigilancia genética que usa el cuerpo de los migrantes como un mecanismo de control fronterizo. El artículo es abordado teóricamente desde el concepto de biopolítica propuesto por Foucault (1998), las aportaciones de Agamben (1998) respecto a la categoría Estado de excepción y el papel de lo que denomina la nuda vida en el sujeto de la biopolítica, que conceptualiza como el homo sacer y se aplica a la persona migrante. La estrategia metodológica es cuantitativa, a partir de los resultados de la Encuesta de Migrantes Extranjeros y Solicitantes de Asilo (ENMIEXSA). Los resultados muestran que, los datos biométricos y la información obtenida de las muestras de ADN a migrantes y solicitantes de asilo, otorgan un poder único para el control migratorio a las agencias del gobierno de Estados Unidos, al tener un bajo nivel de error en el proceso de identificación de la población migrante. Además, permiten contar con el perfil genético completo de la población migrante y de solicitantes de protección internacional, como instrumento que puede ser usado durante el proceso migratorio.
Review of Phyllis Lewsen, Reverberations. A Memoir
Authors: Fraser Maryna
Published: April 2024

No abstract available.
The birth of uMkhonto we Sizwe: Old and new sources
Authors: Shubin Vladimir
Published: April 2024

No abstract available.
Political and economical machinations: A new look at sir George Grey's plans for British Kaffrarian
Authors: Tankard Keith
Published: April 2024

In 1855 het Sir George Grey daarin geslaag om 'n onwillige Britse regering te oorreed om 'n jaarlikse bedrag van £40 000 by te dra vir die onderhoud van Brits Kaffraria. Terselfdertyd het hy 'n toekenning van £5 000 van die Kaapse Tesourie aangebied, alhoewel hierdie aanbod nooit voor die nuut gevormde parlement gedien het nie. Hierdie referaat ondersoek hoe Grey dit kon regkry, asook die bron van die Kaap se klaarblyklike vrygewigheid.
Deep learning in sports skill learning: a case study and performance evaluation
Authors: Lian Diandong
Published: April 2024

Deep learning in sports uses neural networks to evaluate data from sensors and cameras, providing coaches and players insights to enhance training methods and performance. Sports skill development include issues with data availability, trouble interpreting methods for coaching purposes, possible financial constraints for players and regional sports teams. To overcome this, we proposed an Artificial Hummingbird Optimized XGBoost (AHO-XGB) to provide accurate predictions and analysis of an athlete's performance.In this study, the research consists of 20 faculty members and 250 learners from 3 universities.Many sports talents are currently taught to students in famous colleges and universities, but they truly become proficient in the skills. To evaluate the performance of the proposed method in terms of accuracy (92.6%), precision (90.5%), and recall (94.3%). The outcome of this research in sports skill learning transforms performance and training analysis by examining large amounts of data and offering suggestions for skill development.