bluuebunny/crossref_metadata_2025 huggingface dataset:
Robot Path Planning in a Dynamic Environment Using Deep Q-Learning
Authors: Ali Rifaqat , Chandrakar Preeti
Published: October 2024

Robot path planning is a necessary requirement for today’s autonomous
industry as robots are becoming a crucial part of the industry. Planning a path in a
dynamic environment that changes over time is a difficult challenge for mobile robots.
The robot needs to continuously avoid all the obstacles in its path and plan a suitable
trajectory from the given source point to the target point within a dynamically changing
environment. In this study, we will use Deep Q-Learning (Q-Learning using neural
networks) to avoid the obstacles in the environment, which are being dynamically
created by the user. The main aim of the robot is to plan a path without any collision
with any of the obstacles. The environment is simulated in the form of a grid that
initially contains information on the starting and the target location of the robot. Robots
need to plan an obstacle-free path for the given points. The user introduces obstacles
whenever he/she wishes during the simulation to make the environment dynamic. The
accuracy of the path is judged by the path planned by the robot. Various architectures
of neural networks are compared in the study that follows. Simulation results are
analyzed for the evaluation of an optimized path, and the robot is able to plan a path in
the dynamic environment.
The Impact of Industry 4.0 on Manufacturing: Challenges and Opportunities
Authors: Goswami Ramesh Chandra , Joshi Hiren , Gautam Sunil
Published: October 2024

The concept of Industry 4.0 emerged in Europe a decade ago, and later, it
was investigated and adopted by academics and industries throughout the world. Due to
significant technical improvements in various industries over the past few years, the
world's industrial systems have transformed. The major technologies, such as IoT and
Big Data, have a large impact on Industry 4.0. They affect each and every sector of the
economy. The main advantages include advances in productivity, efficiency, flexibility,
decision-making process, and quality of goods and services. The challenges include
analyzing the data produced and integrating new technology with the staff and
equipment that are already in place. Our goal is to summarize the potential and
challenges associated with adopting Industry 4.0 in the manufacturing sector.
Social Welfare and Artificial Intelligence's Role: A Comprehensive Summary of the Study
Authors: Nayak Manjushree , Tiyadi Jagannath
Published: October 2024

AI is a strong instrument with the potential to transform social welfare
systems and improve individuals' and communities' overall well-being. This research
study examines the uses, advantages, ethical issues, and possible obstacles of artificial
intelligence in social welfare in depth. The concept of social welfare and its
fundamental objectives of promoting social justice, equality, and overall quality of life
are explained in detail at the beginning of the paper. It then digs into the different ways
in which artificial intelligence is being utilized to resolve complex social issues and
improve social government performance. Eliminating poverty is one crucial area where
AI has significant potential. By utilizing artificial intelligence innovations, for example,
information examination, prescient display, and choice of emotionally supportive
networks, state-run administrations, and associations can more readily grasp destitution
elements, distinguish weak populations, and configure designated mediations to ease
neediness and upgrade social versatility. Man-made intelligence likewise assumes a
vital role in further developing medical care access and conveyance. AI-powered
systems have the potential to revolutionize healthcare by enhancing diagnostic
accuracy, lowering costs, and facilitating personalized and preventive care, all the way
through to optimizing treatment plans and drug discovery. Training is another space
where computer-based intelligence can have a groundbreaking effect. AI-based
assessment tools, adaptive learning platforms, and intelligent tutoring systems can meet
individual learning needs, make it easier to get a personalized education, and close
educational gaps, making education more accessible and fairer to everyone. In addition,
AI technologies aid in crime prediction and prevention, which contributes to public
safety. AI algorithms can identify high-risk areas, detect suspicious activities, and
enable proactive law enforcement strategies by analyzing vast amounts of data, such as
crime patterns, social media feeds, and surveillance footage. This enables improved
outcomes for public safety. While the advantages of man-made intelligence in friendly
government assistance are critical, moral contemplations cannot be neglected. The
transparency and accountability of AI systems, algorithmic bias, and privacy concerns
are all examined in depth in this paper. It stresses the significance of developing and implementing AI in a manner that upholds social values, fairness, and individual rights.
In addition, the research paper discusses potential obstacles to applying AI to social
welfare, such as the requirement of trustworthy data, digital infrastructure, and
workforce upskilling.
Signs Unveiled: The Power and Promise of AI-Based Sign Recognition Systems
Authors: Dubey Parul , Bhagat Priti Nilesh , Anjum Gousia Hazra , Akhter Parvin
Published: October 2024

Artificial intelligence (AI) has revolutionized the field of sign recognition,
enabling machines to accurately interpret and respond to signs, symbols, and gestures.
This chapter explores the dynamic landscape of AI-based sign recognition systems,
unveiling their components, functionalities, applications, and advantages over
traditional methods. By leveraging machine learning and computer vision, these
systems offer remarkable accuracy, adaptability, and real-time responsiveness. Their
impact extends across social and business domains, enhancing inclusivity for the
hearing impaired, enabling cross-cultural communication, and transforming industries
such as transportation and healthcare. Challenges and ethical considerations are also
addressed. The future trajectory of AI-based sign recognition promises a world where
machines seamlessly bridge the gap between human communication and technological
innovation.
Various Applications of Internet of Things-Based Artificial Intelligence in the Agriculture Sector
Authors: Sheikh Ruhi Uzma , Gupta Madhuri , Bhonsle Devanand , Swarnakar Veenita , Kshatri Sapna Singh , Verma Shailendra
Published: October 2024

India is the most populous country in the world hence the challenge of
feeding the growing population is a matter of concern. Indian economy is dependent
upon agriculture, which is affected by various factors viz. climate change, drought,
heavy rain, insect attacks, etc. A few decades ago, agricultural practices were
performed by conventional techniques only but in the present era, Artificial Intelligent
(AI) has been introduced in the agriculture sector which brought a tremendous
revolution in this field. According to the World Economic Forum (WEF), over 7,000
farmers in India use AI-based technologies in the agriculture field to improve the
productivity of crops, crop yield prediction, soil testing, crop monitoring, disease
diagnosis, spraying, prediction of weather, crop infestation alert, precision forming, etc.
There are more than 1000 agriculture-based start-ups in India that provide solutions in
different stages of farming. It may also be used to maintain records of crops, digital
marketing, market prediction, digital finance, micro insurance, etc. However, it takes a
very long time to achieve the goal because of a lack of access to data, a high cost of
operations, and a lack of technical experts. In the long run, AI-powered machines and
other techniques may provide benefits to the farmers in such a way that they can make
better decisions and benefit. In this chapter, various applications have been discussed in
the field of AI-based agriculture. Various challenges have also been discussed so that
the obstacles can be reduced to achieve our goal in the AI-based agriculture sector.
Predictive Analysis: Forecasting Patient's Outcomes and Medical Trends
Authors: Singhal Alka , Gopinathan Dhanalekshmi
Published: October 2024

Predictive analysis is rapidly transforming the healthcare industry by
leveraging advanced data analytics techniques to predict patient outcomes and identify
medical record trends. With the increasing availability of electronic health records
(EHRs), wearable devices, and other healthcare data sources, healthcare organizations
can use the results of predictive analytics to improve patient care, optimize resource
allocation, and enhance overall healthcare delivery.
Predictive analysis is a data-driven approach that utilizes historical data and statistical
algorithms to make informed predictions about future events. In the context of
healthcare, predictive analysis involves extracting valuable patterns from huge amounts
of patient data to anticipate patient outcomes, disease progression, and medical trends.
Predictive analytics can identify individuals at high risk of having specific diseases
based on their past medical records and history, genetics, lifestyle, and environmental
factors. Early detection allows for proactive interventions, such as lifestyle changes,
screenings, or preventive treatments, which can significantly reduce healthcare costs
and improve patient outcomes. Healthcare providers can use this information to tailor
treatment plans, allocate resources efficiently, and prioritize patient care. Predictive
analytics can assess patient adherence to medication regimens by analyzing historical
data and patient behavior.
The chapter explores the significance of predictive analysis in healthcare and its
applications in Healthcare Policy and Planning. Policymakers can use predictive
analysis to anticipate healthcare needs, allocate budgets, and plan for future healthcare
infrastructure requirements. Predictive analysis is revolutionizing healthcare by
enabling the forecasting of patient outcomes and medical trends. By harnessing the
power of data and advanced analytics, healthcare providers, researchers, and
policymakers can make more informed decisions, improve patient care, and contribute
to the overall well-being of populations. As technology continues to advance and more
data becomes available, the usage of predictive analysis in healthcare is expected to
expand, offering even greater opportunities to enhance the quality and efficiency of
healthcare delivery.
Prediction and Analysis of Digital Health Records, Geonomics, and Radiology Using Machine Learning
Authors: Raj Sundeep , Agarwal Arun Prakash , Tripathi Sandesh , Gupta Nidhi
Published: October 2024

Building different machine learning algorithms and their potential
applications to enhance healthcare systems is very important. AI has countless uses in
healthcare, including the analysis of medical data, early disease diagnosis and
detection, evidence-based objectives to minimize human error, reducing errors between
and among observers, risk identification and interventions for healthcare management,
health monitoring in real-time, helping patients and clinicians choose the right
medication, and assessing drug responses. Machine learning techniques have
transformed many facets of healthcare, ranging from new tools that allow people to
better control their health to new models that assist physicians in making more accurate
decisions. Since the advent of the pacemaker and the first computerized records for
blood test results and chest X-ray reports by Kaiser in the 1950s, physicians have seen
the potential of algorithms to save lives. As new developments in image processing,
deep learning, and natural language processing are revolutionizing the healthcare
sector, this rich history of machine learning for healthcare feeds innovative research
today.It is necessary to comprehend the human effects of machine learning, including
transparency, justice, regulation, simplicity of deployment, and integration into clinical
processes, in order to use it to enhance patient outcomes. The application of machine
learning for risk assessment and diagnosis, illness progression modeling, enhancing
clinical workflows, and precision medicine will be covered in this chapter, which starts
with an introduction to clinical care and data. We shall include all methodological
details for each of these covering topics like algorithmic fairness, causal inference, offpolicy reinforcement learning, interpretability of ML models, and the foundations of
deep learning on imaging and natural language.Advances in AI and ML technologies have significantly improved the ability to
forecast and recognize health emergencies, disease conditions, disease populations, and
immunological responses, to name a few. Even though there is still doubt about the
usefulness of ML-based techniques and how to interpret their findings in clinical
contexts, their use is spreading quickly. Here, we provide a succinct introduction to
machine learning-based methodologies and learning algorithms, such as reinforcement
learning, supervised learning, and unsupervised learning, with examples. Subsequently,
we explore the applications of machine learning (ML) in various healthcare domains
such as genetics, neuroimaging, radiology, and electronic health records. Along with
offering ideas for potential future uses, we also skim the surface regarding the dangers
and difficulties associated with applying machine learning to the healthcare industry,
including issues of privacy and ethics.
Data Collection and Preparation for Medical Applications for Machine Learning
Authors: Jain Prateek , Saxena Abhishek , Bathla Rohit , Behl Rachna , Saxena Urvashi Rahul
Published: October 2024

The latest developments in Artificial Intelligence (AI) and Machine
Learning (ML) technology have led to significant progress in foreseeing and detecting
health crises, understanding disease prevalence, and analyzing disease states and
immune responses, to name a few applications. The growing abundance of electronic
health data represents a significant prospect within the healthcare field, offering the
potential for advancements in both research and practical healthcare enhancements.
Nevertheless, to effectively harness these data resources, healthcare epidemiologists
need computational methods capable of handling vast and intricate datasets. Over the
last ten years, the utilization of machine learning (ML) in the healthcare sector has
played a pivotal role in automating tasks for physicians, improving clinical capabilities,
and enhancing the availability of healthcare services. Machine learning (ML), which
focuses on developing tools and techniques for recognizing patterns in data, can be an
asset in this regard. This advancement underscores the critical importance of data at
every stage of ML, from model creation to its implementation. In this chapter, we offer
a perspective that centers around data, examining the innovations and obstacles that are
shaping the landscape of ML in healthcare.
Gezonder leven; een gebed zonder end?
Authors: Hoenders Rogier
Published: October 2024

Het percentage Nederlanders met een psychische aandoening is in 7 jaar tijd van 17% naar 26% toegenomen. Momenteel heeft zelfs 1 op de 3 jongeren psychische klachten. Het gebruik van antidepressiva en tranquilizers neemt toe. De oplossing ligt niet in meer van hetzelfde. De kosten voor de huidige GGZ zijn al niet meer duurzaam op te brengen en er zijn vanwege ziekteverzuim en de vergrijzing onvoldoende behandelaren. Er staan 100.000 mensen op een wachtlijst. Bovendien wachten we met het stellen van de diagnose, met behandeling en vergoeding van behandeling tot de psychische ziekte is ontstaan. Eigenlijk hebben we dus een geestelijke ziektezorg. Er is een transitie nodig naar een werkelijke geestelijke gezondheidszorg door meer te investeren in preventie door het geven van betrouwbare begrijpelijke informatie en het trainen van vaardigheden (emotieregulatie, stressreductie, veerkracht) om met de uitdagingen van het leven te kunnen omgaan. Dit omvat onder meer een gezonde leefstijl: voeding, beweging, ontspanning, slaap, beperking van genotmiddelen, relaties en ook zingeving en spiritualiteit, al dan niet religieus. Hiertoe hebben Lentis GGZ en de faculteit Religie, Cultuur en Maatschappij (RCM) van de Rijksuniversiteit Groningen een nieuwe leerstoel ingesteld: zingeving, leefstijl en geestelijke gezondheid. Doel is het ontwikkelen, onderzoeken en implementeren van zingeving- en leefstijlinterventies voor het vergroten van (geestelijke) gezondheid met unieke multidisciplinaire samenwerking van geestelijke verzorging, religiewetenschappen, sociologie, psychologie, en geneeskunde.
Accounting outsourcing intensity in Vietnam: Empirical evidence from Vietnamese small and medium-sized enterprises
Authors:   School of Accounting and Auditing, National Economics University, Hanoi, Vietnam, Nguyen Linh Ha , Tran Chung Quang ,   School of Accounting and Auditing, National Economics University, Hanoi, Vietnam, Nguyen Thuy Linh ,   Faculty of Finance and Banking, Thuongmai University, Hanoi, Vietnam, Doan Nguyen Minh ,   Faculty of Economics and International Business, Thuongmai University, Hanoi, Vietnam
Published: October 2024

This study focuses on developing a strategic management tool for small and medium-sized enterprises (SMEs), where outsourcing is seen as an effective method. Outsourcing allows SMEs to concentrate on their core strengths and compete despite limited resources. In Vietnam, SMEs make up over ninety percent of all businesses and play a key role in the economy. However, they face challenges due to limited resources, especially in managing accounting functions. This research examines the factors influencing the level of outsourcing among SMEs in Vietnam based on two key theories: Transaction Cost Theory (TCE) and the Resource-Based View (RBV). A sample of 236 questionnaires was analyzed using SPSS and regression models. The findings revealed positive and significant relationships between outsourcing intensity (OS.Intensity) and four factors: asset specificity (AS), frequency of accounting tasks (FREQ), trust (TRUST), and technology competence (TECH). The study supports the trend of accounting outsourcing for Vietnamese SMEs, allowing them to enhance their internal strengths despite resource constraints. Future research to gain a deeper understanding of accounting outsourcing is also suggested.
Intervention to Improve Well-Being, Nutrition, and Physical Activity in Adults: Experimental Study
Authors: Aubert Morghane , Clavel Céline , Le Scanff Christine , Martin Jean-Claude
Published: October 2024

Background

Mindfulness improves well-being, improves emotional regulation, reduces impulses to eat, and is linked to increased physical activity. Mindfulness interventions usually focus on 1 aspect but do not offer an approach to holistically improving lifestyle.

Objective

This study aims to address this gap by designing and evaluating a holistic mindfulness intervention.

Methods

Committing to a 12-week intervention with 2-hour sessions without knowing whether you will enjoy it can be a hindrance for someone completely unfamiliar with mindfulness. For this reason, we decided to design a mindfulness intervention with short sessions over a reduced number of weeks. The aim is to enable novices to discover different aspects of mindfulness while at the same time offering a satisfactory practice for people who are already practicing mindfulness. We designed and evaluated a web-based mindfulness intervention in 5 sessions of 5 to 10 minutes each on well-being, diet, and physical activity to support a healthier lifestyle. The first 2 sessions focus on formal mindfulness meditation to enable novices to discover mindfulness and its main principles. Then there are 2 sessions about food. The first session about food aims to develop a sense of satisfaction with the food we eat and to focus our attention on new sensations. The second session about food aims to develop the ability to resist the lure of unhealthy foods. Finally, there is a session on physical activity. The aim is to develop a particular awareness of the body during movement, to increase satisfaction with physical activity, and to develop regular exercise.

Results

In total, 32 participants completed the intervention. After the intervention, we observed decreases in negative affect, anxiety, and emotional distress, and an increase in dispositional mindfulness. There was no effect on reported healthy eating habits and physical activity habits. Few participants repeated the exercises as recommended. The majority of our participants were new to mindfulness. The majority of our participants reported being satisfied with the different sessions. A few minor difficulties were mentioned, mainly related to the environment in which the participants carried out the sessions. Only 1 session was less satisfactory for one-third of the participants. The session on resistance to unhealthy foods was formulated too strictly and the idea of banning certain foods was a hindrance for one-third of the participants. A reformulation is needed.

Conclusions

The mindfulness exercises were well accepted and promoted a state of mindfulness. It would be interesting to provide easier technical access to the exercises via a mobile app so that they can be repeated easily.
Assessing the Role of the Autonomic Nervous System as a Driver of Sleep Quality in Patients With Multiple Sclerosis: Observation Study
Authors: Moebus Max , Hilty Marc , Oldrati Pietro , Barrios Liliana ,   PHRT Author Consortium, Holz Christian
Published: October 2024

Background

Low sleep quality is a common symptom of multiple sclerosis (MS) and substantially decreases patients’ quality of life. The autonomic nervous system (ANS) is crucial to healthy sleep, and the transition from wake to sleep produces the largest shift in autonomic activity we experience every day. For patients with MS, the ANS is often impaired. The relationship between the ANS and perceived sleep quality in patients with MS remains elusive.

Objective

In this study, we aim to quantify the impact of the ANS and MS on perceived sleep quality.

Methods

We monitored 77 participants over 2 weeks using an arm-worn wearable sensor and a custom smartphone app. Besides recording daily perceived sleep quality, we continuously recorded participants’ heart rate (HR) and HR variability on a per-second basis, as well as stress, activity, and the weather (20,700 hours of sensor data).

Results

During sleep, we found that reduced HR variability and increased motion led to lower perceived sleep quality in patients with MS (n=53) as well as the age- and gender-matched control group (n=24). An activated stress response (high sympathetic activity and low parasympathetic activity) while asleep resulted in lower perceived sleep quality. For patients with MS, an activated stress response while asleep reduced perceived sleep quality more heavily than in the control group. Similarly, the effect of increased stress levels throughout the day is particularly severe for patients with MS. For patients with MS, we found that stress correlated negatively with minimal observed HR while asleep and might even affect their daily routine. We found that patients with MS with more severe impairments generally recorded lower perceived sleep quality than patients with MS with less severe disease progression.

Conclusions

For patients with MS, stress throughout the day and an activated stress response while asleep play a crucial role in determining sleep quality, whereas this is less important for healthy individuals. Besides ensuring an adequate sleep duration, patients with MS might thus work to reduce stressors, which seem to have a particularly negative effect on sleep quality. Generally, however, sleep quality decreases with MS disease progression.
Behavioral Nudging With Generative AI for Content Development in SMS Health Care Interventions: Case Study (Preprint)
Authors: Harrison Rachel M , Lapteva Ekaterina , Bibin Anton
Published: October 2024

BACKGROUND

Brief message interventions have demonstrated immense promise in health care, yet the development of these messages has suffered from a dearth of transparency and a scarcity of publicly accessible data sets. Moreover, the researcher-driven content creation process has raised resource allocation issues, necessitating a more efficient and transparent approach to content development.

OBJECTIVE

This research sets out to address the challenges of content development for SMS interventions by showcasing the use of generative artificial intelligence (AI) as a tool for content creation, transparently explaining the prompt design and content generation process, and providing the largest publicly available data set of brief messages and source code for future replication of our process.

METHODS

Leveraging the pretrained large language model GPT-3.5 (OpenAI), we generate a collection of messages in the context of medication adherence for individuals with type 2 diabetes using evidence-derived behavior change techniques identified in a prior systematic review. We create an attributed prompt designed to adhere to content (readability and tone) and SMS (character count and encoder type) standards while encouraging message variability to reflect differences in behavior change techniques.

RESULTS

We deliver the most extensive repository of brief messages for a singular health care intervention and the first library of messages crafted with generative AI. In total, our method yields a data set comprising 1150 messages, with 89.91% (n=1034) meeting character length requirements and 80.7% (n=928) meeting readability requirements. Furthermore, our analysis reveals that all messages exhibit diversity comparable to an existing publicly available data set created under the same theoretical framework for a similar setting.

CONCLUSIONS

This research provides a novel approach to content creation for health care interventions using state-of-the-art generative AI tools. Future research is needed to assess the generated content for ethical, safety, and research standards, as well as to determine whether the intervention is successful in improving the target behaviors.
Developing a Quick Isolation Bed Inquiry System During the COVID-19 Outbreak: User-Centered Design Approach Based on Toyota Production System (Preprint)
Authors: Lin Chien-Chung , Shen Jian-Hong , Chang Shan-Li , Huang Hui-Ling , Lin Cheng-Yi Peter , Huang Hung-Meng
Published: October 2024

BACKGROUND

During the COVID-19 outbreak in May 2021, our hospital, designated as a specialized facility for treating severely infected patients, faced critical staff and resource shortages. Efficient bed management and quick bed assignments were paramount to ensuring patient safety and treatment outcomes. Leveraging the Toyota Production System (TPS) mindset and Toyota Business Practice (TBP) problem-solving methodology, we aimed to develop a rapid solution to address this challenge.

OBJECTIVE

To design a Quick Isolation Bed Inquiry System that provides real-time information on hospital bed availability, ensuring timely admissions without increasing manpower or costs.

METHODS

We applied the eight-step problem-solving process of TBP, supported by TPS principles such as just-in-time and autonomation. The steps included clarifying the problem, breaking it down by constructing a value stream map, setting targets, analyzing root causes, and developing and implementing countermeasures. The system was built on the hospital’s existing bed inquiry platform, utilizing Microsoft Excel’s Visual Basic for Applications (VBA) to automate manual tasks. The process evolved through three cycles of problem-solving and iterative improvement.

RESULTS

The Quick Isolation Bed Inquiry System, developed using VBA automation, significantly streamlined the bed inquiry process. It generated three outputs: a PDF for administrators, a large-screen view for bed control physicians, and a mobile-friendly version. Previously reliant on manual phone calls, the system reduced report generation time to 4 seconds, with PDF distribution via the hospital's LINE group (LY Corp) in 7 seconds. Later integrated into the hospital's virtual private network (VPN), it enabled authorized staff to check bed availability in real-time, from anywhere, supporting just-in-time practices. Even after the hospital's COVID-19 duties ended, the system continued to operate efficiently for general wards, with ongoing improvements.

CONCLUSIONS

The eight steps of TBP process, combined with the TPS thinking, enabled the rapid development of a Quick Isolation Bed Inquiry System. The system eliminated unnecessary steps, optimized processes, and could be operated by any authorized staff member. It facilitated the timely admission of COVID-19 patients, meeting authorities' expectations without additional manpower or costs. Continuous improvement opportunities remain, including further automation for report distribution, making the system adaptable for future needs.
Sudden Cardiac Death Reporting in US Patients on Dialysis
Authors: Obi Yoshitsugu , Xu Anna , Wilson Jonathan A. , Ephraim Patti L. , Weiner Daniel E. , Scialla Julia J. , Jaar Bernard G. , Boulware L. Ebony , Goldstein Benjamin , Shafi Tariq
Published: October 2024

Key Points

For patients with ESKD treated with hemodialysis, the causes of death reported by the United States Renal Data System and the National Death Index show substantial disagreement.

In particular, the proportion of sudden cardiac death was almost two-fold higher in the United States Renal Data System (42%) compared with the National Death Index (22%).

Background

Cause-specific mortality data from the United States Renal Data System (USRDS) form the basis for identifying cardiovascular disease (CVD), specifically sudden cardiac death (SCD), as the leading cause of death for patients on dialysis. Death certificate data from the National Death Index (NDI) is the epidemiological standard for assessing causes of death for the US population. The cause of death has not been compared between the USRDS and the NDI.

Methods

Among 39,507 adults starting dialysis in the United States, we identified 6436 patients who died between 2003 and 2009. We classified the cause of death as SCD, non-SCD CVD, cancer, infection, and others and compared the USRDS data with those from the NDI.

Results

The median age at the time of death was 70 years, 44% were female, and 30% were non-Hispanic Black individuals. The median time from dialysis initiation to death was 1.2 years. Most of the deaths occurred in hospital (N=4681, 73%). The overall concordance in cause of death between the two national registries was 42% (κ=0.23; 95% confidence interval, 0.22 to 0.24). CVD, including SCD and non-SCD CVD, accounted for 67% of deaths per the USRDS but only 52% per the NDI; this difference was mainly driven by the larger proportion of SCD in the USRDS (42%) versus the NDI (22%). Of the 2962 deaths reported as SCD by the USRDS, only 35% were also classified as SCD by the NDI. Out-of-hospital deaths were more likely to be classified as SCD in the USRDS (60%) versus the NDI (29%), compared with in-hospital deaths (41% in the USRDS; 25% in the NDI).

Conclusions

Significant discordance exists in the causes of death for patients on dialysis reported by the USRDS and the NDI. Our findings underscore the urgent need to integrate NDI data into the USRDS registry and enhance the accuracy of cause-of-death reporting.

Podcast

This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2024_12_23_CJASNDec1912122324.mp3
The Market Efficiency Debate: Developments of Financial Market Theories
Authors: Datta Gavin
Published: October 2024

A united hypothesis to explain the price movement of financial assets has been a significant research goal over the past few decades. This paper reviews the existing literature on both Efficient Markets and Behavioral Finance, concluding with more contemporary literature that attempts to combine the two theories. It summarizes the EMH and Behavioral Finance concepts, the debate between them, and the evidence supporting both. By recognizing the vast swaths of evidence in support of both hypotehses, one can conclude that the questions surrounding the efficiency or inefficiency of markets remain unanswered. Instead, the evidence supporting both hypotheses suggests the necessity of a compromise to rationalize these results instead of relying on one hypothesis or another. The compromise between both hypotheses, in turn, makes the debate between efficiency and inefficiency almost moot. Researchers must account for human misbehavior and rational expectations on a spectrum when formulating new market hypotheses, remaining open to changes in market theory as new variables continue to be discovered.
A Multimodal Analysis of Metaphors in Artificial Intelligence-Related Cartoons
Authors: Li Shuqing , Yang Ning , Kiseleva Elena
Published: October 2024

This paper, adopting a multimodal perspective and Critical Metaphor Analysis (CMA) approaches, examines 50 cartoons about artificial intelligence published on American cartoon websites from 2023 to 2024. The study finds that AI cartoons mainly construct metaphors through a cross-modal mapping “text as target domain, image as source domain” structure. These cartoons are based on metonymy, presenting metaphorical scenes through the interaction of metonymy and metaphor. The abstract concept of “artificial intelligence” is often metaphorically represented as robots, thereby deepening readers’ understanding of the role of technological image in the socio-cultural context. By analyzing four major metaphorical scenes—devouring, competing, chasing, and hijacking—this paper elucidates the dual perceptions of the opportunities and challenges brought by technological advancements in AI as well as cautious attitude towards AI development.
Research Hotspots and Development Trends of Theme Activities in Kindergartens since 21st Century—Based On Visual Co-word Analysis
Authors: Xu Huiqing
Published: October 2024

In order to accurately and objectively understand the composition and development trend of research hotspots on kindergarten theme activities in the past decade, China National Knowledge Infrastructure (CNKI) was used as the literature search source. The Co-word analysis software Bicomb2.0 and data statistical software SPSS27.0 were used to conduct time-series, research strength, high-frequency key, cluster analysis, and multidimensional scale analysis on 1674 literature on kindergarten theme activities from 2005 to 2023, and to draw knowledge graphs at different time periods. Research has found that the study of kindergarten theme activities generally revolves around five major areas: research on the selection of theme activity content, research on the connotation and characteristics of theme activities, research on the design of theme activities, research on the implementation and value of theme activities, and research on the theory and reform of theme activities. Strengthening the construction of theme activities in kindergartens is an effective breakthrough for achieving high-quality preschool education curriculum. Therefore, future research should focus on the combination of educational theory workers and educational practice workers to form a research community; Exploring diverse perspectives, deep learning and STEAM courses will become new academic growth points for themed activities; Innovative research paradigm, emphasizing the combination of theoretical research and empirical research.
Assemblages as Ecologies
Authors: Aldazabal Irene
Published: October 2024

In this essay I draw on my art research and installation ‘Earthly Bodies, Subterranean Rhythms’ to posit assemblages as ecologies: platforms where specific agencies and interactions occur at different scales, including microscopic levels. Based on documentation images of my creative process, I delve into my experience exploring sculptural building and creative possibilities in more-than-human collaboration. This research involved observing subterranean bodies and temporalities, rhizomatic growth and mycorrhizal interactions as well as working in interspecies cooperation with oyster mushroom mycelium and wheatgrass roots.
As spatial practices, architecture and sculpture have developed specific building methods. I expand on assemblage, the building method I worked with, seeking to articulate three lenses: sculptural, material and philosophical. Technically, an assemblage is a piece made by bringing together disparate elements. In this case it consisted mainly of ceramics, different earthly substrates, mycelium spawn, soil and seeds. Borrowing from Bennet’s ‘Vibrant Matter’ (2010) and Tsing’s ‘The Mushroom at the End of the World’ (2015) I reflect on living sculptures as interspecies assemblages: spaces of collaboration, cooperation and contamination between living and non-living bodies, matter and forces, human and more-than human agencies. From this perspective, assemblages are more than the sum of their parts as they have the capacity to re-make and transform us through encounters.
A polyphony of worlding processes makes the collective architecture we live in: our shared world. Working in interspecies assemblages may enable more respectful and enjoyable ways of collective dwelling.
Editorial
Authors: Piñeiros Estefania , Vyhmeister Katherine , Said Lama , Olcay Hafsa
Published: October 2024

EAR 38 No.1 Editorial
CMQ Matrix for Occupational Impact Measurement 4.0
Authors: López Carmona José Luis
Published: October 2024

This article presents its own original methodology to measure the occupational impact of the digitalization of companies, or occupational impact 4.0, by measuring 10 indicators obtained in research carried out by the author on the effects of Industry 4.0 on occupations and jobs. Drawing on different research traditions, the matrix of indicators measures the occupational impact, individually in each job, connecting the substantive content of the work (the tasks and activities carried out in the workplace) with the specific organization of work that takes place in each company or industrial plant because of its digital transformation programs.
resiliencia como parte de la capacidad organizacional en destinos turísticos del vino: Caso en Guanajuato, México
Authors: Montiel Flores Julio César , González Vega Alba María del Carmen , López Salazar Alejandra , Valenzuela Valenzuela Juan Pedro
Published: October 2024

La pandemia de COVID-19 del año 2020 provocó una crisis global sin precedentes, especialmente devastadora para la industria turística mexicana, una fuente crucial de ingresos del país. Las empresas turísticas enológicas en el estado de Guanajuato, parte vital del sector local, enfrentaron desafíos sin precedentes con cierres temporales y una drástica reducción en el turismo. Sin embargo, demostraron una notable capacidad de adaptación al implementar medidas de seguridad, promover el turismo local y desarrollar estrategias innovadoras que, a través de la diversificación de servicios, fortalecimiento de la promoción en línea y alianzas estratégicas, algunas empresas lograron su propio rescate. Este estudio que enmarca un caso singular de la resiliencia organizacional destaca que está ligada al conjunto de capacidades que la organización posee y que refleja un dinamismo para su adaptación al entorno cambiante. Se trata de un trabajo de orden cualitativo que empleó el estudio de caso como método; se aplicaron entrevistas semiestructuradas las observaciones participante y no participante. Entre los resultados, se destaca que la resiliencia y la capacidad organizacional están interrelacionadas, aunque difieren en enfoques y dimensiones, ambos implican considerar riesgos y planificar para abordarlos, pero la capacidad organizacional gestiona recursos para resultados positivos, mientras que la resiliencia implica gestionar riesgos y planificar para mantener la continuidad operativa.
Clima motivacional de clase en la enseñanza media y superior iberoamericana: una revisión sistemática
Authors: Chiarino Nicolás , Curione Karina , Huertas Martinez Juan Antonio
Published: October 2024

El clima motivacional de clase (CMC) es un constructo que permite estudiar los patrones de actuación docente en el aula y su relación con la motivación del estudiantado. En base a la importancia que tiene el CMC para los entornos de enseñanza y aprendizaje se realizó una revisión sistemática con el objetivo de sintetizar la evidencia de estudios iberoamericanos en la educación media y superior durante el período 1992-2023. Se utilizaron las directrices metodológicas definidas en la declaración PRISMA, que permitieron seleccionar 51 artículos que cumplieron con los criterios de elegibilidad. Los resultados revelan una prevalencia de estudios cuantitativos (90 %), en la enseñanza media (65 %). Fueron identificados nueve instrumentos para el estudio del CMC y se encontró que el CMC-Q fue el instrumento más utilizado. El análisis de los estudios señala que existe una varianza explicada común moderada entre el CMC y variables psicológicas personales de estudiantes y docentes, pero baja respecto al rendimiento académico estudiantil. Los hallazgos reportados van en la misma dirección que la evidencia disponible en otras regiones a nivel mundial. Se sugiere avanzar hacia una comprensión multidimensional del CMC, la triangulación de participantes, técnicas y métodos, así como estudios adicionales que integren al CMC como medida de la eficacia en las intervenciones educativas.
A Review on Venous Thrombosis: Management with anticoagulants
Authors: Kishore Kumar Pasupuleti , Varun Pasumala , Ruchitha Thokala , Rohit Kumar Chinta , Rao Rama Tadikonda
Published: October 2024

Thrombosis is the most common condition and the world's leading cause of death due to blood clot development in blood arteries. Thromboembolism includes classification based on where the thrombus gets attached to the blood vessels. Deep vein thrombosis (DVT) and pulmonary embolism (PE) are two of the main causes of vascular death globally, making up venous thromboembolism (VTE). According to estimates, the yearly incidence rates of VTE in individuals with European ancestry vary from 104 to 183 per 100,000 person-years. Thrombus development and propagation are dependent upon the existence of anomalies in blood flow, blood vessel wall, and blood clotting components, which are together referred to as Virchow's triad. A comprehensive diagnostic approach, which includes clinical evaluation, D-dimer testing, ultrasonography, and lung scan, provided a non-invasive diagnosis for most outpatients with suspected venous thromboembolism. Anticoagulant medication is the primary and established treatment for people who have acute venous thromboembolism (VTE). Among the treatment options available, UFH & LMWH are found to be safe for the growing fetus as they appear to not cross the placenta. Oral anticoagulants targeting thrombin or factor Xa have been introduced, replacing vitamin K antagonists. In a study involving DOACs vs VKAs in patients with pre-existing heart conditions, patients receiving DOACs for non-valvular atrial fibrillation had predominantly superior efficacy and safety.
Keywords: Venous thromboembolism, Anticoagulants, Deep vein thrombosis, Pulmonary embolism, Thrombus
Analysis of Prescribing Patterns of Drugs Used in Osteoarthritis in Tertiary Care Center
Authors: Sah Govinda , Malla Bindu
Published: October 2024

Background: Osteoarthritis (O.A.) is a common degenerative lesion of the articular cartilage that corresponds to hypertrophy deterioration of bone structure is osteoarthritis.  The treatment is mainly focused on the management of pain.
Methods:  An Observational study was conducted among 163 patients visiting Orthopedics Out Patient Department   at GMC Hospital. The duration of study was for 6 months from Jan 2024-Jun 2024. The analysis of collected data was done by SPSS version Software. Descriptive Statistics tools like frequency, Percentage were used to express the findings.
Results: We found that female was (68.7%) in comparison to male (31.3%). Oral route (79%) was most commonly preferred route of drugs administration than topical route (21%). Non-Steroidal Anti-Inflammatory Drugs (NSAIDS) (41.78%), Vitamins and minerals (22.5%), Antiulcers (19.5%), Symptomatic Slow-acting Drugs in Osteoarthritis (SYSODA) (6.82%), Muscle Relaxants (5.02%) and Others (4.07%) were Prescribed groups of drugs. Diclofenac (19.52%) was most commonly prescribed Non-Steroidal Anti-Inflammatory Drugs followed by Aceclofenac (11.37%). Average no. of drugs prescribed was 5.12, % of drugs Prescribed from National List of Essential Medicine (NLEM) was 79.88%, % of drugs prescribed by generic name was 0% and Average dispensing time was 225.13 sec.
Conclusion: Osteoarthritis (O.A) was mostly common in female than male. Lumbar was most commonly affected site of Osteoarthritis. Non-Steroidal Anti-Inflammatory Drugs was most commonly prescribed groups of drugs.
Keywords: Osteoarthritis, Prescribing Pattern, Non-Steroidal Anti-Inflammatory Drugs, Diclofenac, Symptomatic Slow-acting Drugs in Osteoarthritis.
The Respiratory Reflex of Cough: A Comparative Study of Contemporary Medicine and Unani Concepts
Authors: SHAIKH AYESHA NAYYER KAMAL , Mir Mahdi Ali S.V. , M Syed Towfeeq Hussain , Ansari Naushaba
Published: October 2024

Cough suddenly and frequently helps to remove mucus, irritants, foreign substances, and bacteria from the airways. It can be a symptom of a respiratory infection like the common cold, bronchitis, tuberculosis, influenza, smoking, or other health issues. Contemporary texts describe several kinds of coughs, including acute, chronic, and subacute coughs, coughs that produce or do not produce mucus, and specific types like Reflex cough, voluntary cough, and evoked cough. In Unani texts, cough is referred to as diseases rather than symptoms. it is also called as “Sual” in Arabic, “Surfa” in Persian, khansi in Hindi, and cough in English. In Unani medicine, Sual is attributed to three causes: Ashab-e-badiya (extrinsic factor), Asbab-i- Wassila (intrinsic factor), and Iltehab  (inflammation). The primary categories of Sual identified in Unani texts are Sual yabis and Sual Ratab, which are further subdivided into various types. This paper examines the concept, causes, types, and treatment approaches to Sual/Cough as described in both classical and modern literature.
Keywords: Cough, Sual, Surfa, Modern concept Unani literature, sual yabis, sual ratab.
Herramientas de Machine Learning y Deep Learning aplicadas al funcionamiento de un Data Center
Authors: D'Angiolo Federico Gabriel , Mayer Roberto , Loiseau Matias , Kwist Ivan , Contreras David , Asteasuain Fernando
Published: October 2024

Hoy en día, debido al gran desarrollo que está teniendo la Inteligencia Artificial, la gran cantidad de datos que se almacenan en la nube y el procesamiento que para ello se requiere, resulta importante contar con Data Centers (DC) que contengan servidores y sistemas de cómputos robustos. Para que dichos servidores puedan procesar adecuadamente, es menester que el ambiente donde se encuentran sea monitoreado teniendo en cuenta variables ambientales como por ejemplo, temperatura, humedad y presión. En este trabajo se comenta la investigación llevada a cabo en áreas como Internet de las Cosas e Inteligencia Artificial (IA), aplicadas a la monitorización de dichas variables en un DC. También se describen las tareas a futuro y la formación de investigadores alrededor de este tema.
Radiation shielding calculations of medical diagnostic X-ray equipmen
Authors: Tsengel Ganzul , Myagmarjav Odsuren , Damdinsuren Bolortuya
Published: October 2024

In this study, we performed radiation shielding calculations for the diagnostic X-ray equipment at Rekha Med Hospital. The lead thickness required for radiation protection, as determined by the methodology outlined in the National Council on Radiation Protection and Measurements (NCRP) Report No. 147, was approximately 2 mm. Air kerma measurements were taken outside the X-ray room to validate the shielding effectiveness with a 2 mm thick lead barrier installed along the walls. The results confirmed that the air kerma dose did not exceed the regulatory dose limit of 0.5 mSv/H in uncontrolled areas, indicating sufficient shielding.