bluuebunny/crossref_metadata_2025 huggingface dataset:
An IoT Based RFID Enabled Automatic Waste Segregator and Monitoring System
Authors: Anand Abhishek , Shrivastav Abhinav , Aggrawal Gunjan
Published: February 2025

The increasing population has led to a significant environmental issue: the
proper disposal of a combination of biodegradable and non-biodegradable garbage.
This issue is expected to persist as the global population continues to expand. The issue
of waste treatment and management is a significant global concern across all
geographical areas. The magnitude of its detrimental effects on our society and its
environment is immense. The consequences of our haphazard and unregulated garbage
disposal practices have become a significant issue for the well-being of both humans
and the biodiversity of plant and animal species. By employing appropriate methods of
segregation, it is possible to reuse and recycle trash efficiently. Indian Railways carries
a daily average of 23 million passengers in India. This project presents a feasible
prototype of an automated garbage segregator that efficiently manages the entire
system using a microcontroller. The device is equipped with a range of sensors,
primarily including an infrared (IR) sensor for detecting the presence of trash, a
moisture sensor for detecting water content in the waste, and a metal sensor for
detecting metallic waste. Therefore, these sensors are used to detect and identify the
specific type of trash that is being disposed of. The microcontroller can regulate and
oversee all operations of the system, including the proper disposal of garbage into
certain bins categorized by metal, moist, and other materials. The project also focuses
on implementing an incentive system that encourages the public to maintain a clean
environment. It is achieved using a user-friendly RFID card scanning device, which
rewards individuals with points for their efforts in disposing of waste properly. In
addition, the entire system is supervised the Wi-Fi module. The objective of this
project is to offer a cost-efficient and financially viable alternative for waste
management, therefore guaranteeing a pristine and healthful atmosphere. This
application is a highly efficient tool for managing and organizing waste, as well as for
separating different types of waste.
Electrical Insulating Properties of Epoxy Modified Shellac Polyamide Resin Blends
Authors: Jha Praveen Chandra , Mishra Pankaj Kumar
Published: February 2025

The electrical insulating properties of various blends of epoxy-modified
Shellac Polyamide resin blends were investigated and it has been found that all the
weaknesses of shellac can be overcome. It has also been observed that these blends
retain (or rather enhance) good electrical insulating properties of shellac, by suitably
blending with these resins.
Electrical Insulating Properties of Epoxy-Modified Shellac Alkyd Resin Blends
Authors: Jha Praveen Chandra , Kumar Shalin
Published: February 2025

The electrical insulating properties of various blends of epoxy-modified
Shellac Alkyd resin blends were investigated and it was found that all the weaknesses
of shellac can be overcome. It has also been observed that these blends retain (or rather
enhance), all the good electrical insulating properties of shellac, by suitably blending
with these resins.
Recognition of Characters of New-born Baby's Fingerprinting Using Machine Learning
Authors: Singh Arun Kumar
Published: February 2025

The ability to recognize fingerprints properly and quickly has been possible
thanks to the development of machine learning (ML) techniques, which have
revolutionized the biometric identification field. In this research work, we offer a
machine-learning method for character recognition in newborn fingerprints. A
collection of newborn fingerprint photos with known demographics (gender, age, and
ethnicity) was gathered, and the images were pre-processed to improve contrast and
reduce noise. Relevant information was extracted from the photos using feature
extraction techniques, and machine learning (ML) algorithms like support vector
machines (SVM), decision trees (DT), and neural networks (NN) were trained to
identify the distinctive fingerprint traits of a newborn. The results of the study showed
that the recommended method, which employs ML algorithms, can correctly recognize
the characteristics of a newborn baby's fingerprints. Just a few of the metrics utilized to
assess each ML model's performance in a hold-out validation situation were precision,
recall, and F1 score. The decision tree achieved an 89% success rate, the neural
network achieved 94% success rate, and the SVM algorithm achieved the success rate
of 92%. These findings suggest that ML algorithms can quickly and accurately
recognize the characters in a newborn baby's fingerprints. The suggested approach has
numerous uses in security and healthcare systems where precise identification is
essential. Accurate infant identification is essential in the healthcare industry to
guarantee proper medical care and avoid medical errors. Access control in security
systems can be implemented with fingerprint recognition. This study advances the use
of machine learning (ML) to recognize characters in newborn baby fingerprints more
accurately and efficiently. The results could have a big impact on security and
healthcare systems. The suggested technique, which makes use of machine learning
techniques, can quickly and precisely identify the characters in a newborn baby's
fingerprints. This study adds to the development of more precise and effective
techniques for the recognition of newborn baby fingerprint characters, highlighting the
potential uses of machine learning in healthcare and security systems.
IoT Based on Accident Detection and Alert System
Authors: Sharma Sheelesh Kumar , Sharma Avinash Kumar , Garg Srishti , Singh Priyansha , Yashaswi
Published: February 2025

Reducing the fatality rate resulting from vehicle accidents is a significant
concern in our society. Delivering prompt and excellent emergency healthcare services
poses a difficulty. The response time of emergency personnel to the accident location is
significantly prolonged, resulting in an occasional inability to preserve lives. In order to
address this scenario, it is imperative to minimize the interval between the occurrence
of the accident and the provision of medical services. We have developed a
meticulously structured Android application that will be utilized for the purpose of
identifying accidents and promptly alerting emergency services. Additionally, it will
carry out timely rescue operations. A built-in sensor designed to detect car accidents
and transmit the data to the Android Application. The system will promptly notify the
user through an alarm regarding the generated accident notification. It will then allow a
brief period, known as the buffer time, for the user to cancel any erroneous
notifications. If the user fails to cancel, the message will be transmitted by GSM to the
nearest hospital and police station, along with the precise position of the accident
determined by GPS technology. The execution of this Android application has the
potential to preserve the lives of millions of individuals annually.
Blockchain for Electronic Health Record
Authors: Mishra Shubham Kumar , Prashar Pratyush , Tyag Priyanka
Published: February 2025

Blockchain has the potential to revolutionize the sharing and preservation of
patient's electronic health records by offering secure means for information exchange
during care transitions and ensuring their safety on a decentralized peer-to-peer
network. An exhaustive and methodical examination of the literature was undertaken to
locate the latest study on blockchain technology in the healthcare sector. The objective
was to identify the present obstacles and unanswered queries in order to facilitate and
streamline comprehension of this era of decentralized record-keeping. The
reassessment was prompted by the increasing number of examination questions related
to electronic health records (EHR) within a blockchain. The primary objectives of our
organized framework are to initially leverage blockchain technology for electronic
health records (EHR) and then establish user-friendly electronic data storage
capabilities through the implementation of detailed access controls.
Too Late Fascism: A Review of Alberto Toscano’s Late Fascism
Authors: Aerts Alexander
Published: February 2025

Review of Alberto Toscano. 2023. Late Fascism: Race, Capitalism and the Politics of Crisis. London: Verso.
Can We Do Without Progress?
Authors: Van Hulzen B.N.
Published: February 2025

Review of Rahel Jaeggi. 2023. Fortschritt und Regression. Berlin: Suhrkamp.
STANDARDISASI PARAMETER SPESIFIK DAN NON SPESIFIK SERBUK KULIT JERUK LEMON (Citrus limon (L.) Osbeck)
Authors: Wulandari Ari Susiana
Published: February 2025

Latar Belakang: Jeruk Lemon (Citrus limon (L.) Osbeck) merupakan salah satu varietas jeruk lemon yang ditanam secara komersial di Indonesia. Kebanyakan limbah kulit jeruk lemon hanya dibuang begitu saja, tampa dimanfaatkan secara optimal. Untuk menangani masalah sampah, salah satu usaha yang sering dilakukan yaitu mengolah atau mendaur-ulang sampah menjadi produk atau bahan yang berguna. Tujuan Penelitian penelitian ini bertujuan untuk mengetahui apakah serbuk kulit jeruk lemon  (Citrus limon (L.) Osbeck) telah memenuhi standarisasi parameter spesifik dan non spesifik. Metode Penelitian menggunakan metode eksperimental laboratorium. Standarisasi yang dilakukan ada dua yaitu standarisasi spesifik yang terdiri dari uji identitas, organoleptik, dan kadar senyawa larut air dan etanol, parameter non spesifik meliputi uji susut pengeringan, kadar air, dan kadar abu.Hasil parameter spesifik organoleptis hasil berbentuk serbuk, warna kuning pucat, bau khas dan rasa pahit, penetapan senyawa larut air dengan hasil 9,3382% dan senyawa larut etanol dengan hasil 5,480%. Parameter non-spesifik didapatkan nilai rata-rata susut pengeringan 3,09 %, kadar air 6,26%, dan penetapan kadar abu 1,315%.
Exploring Metadata Catalogs in Health Care Data Ecosystems: Taxonomy Development Study (Preprint)
Authors: Scheider Simon , Mallick Mostafa Kamal
Published: February 2025

BACKGROUND

In the European health care industry, recent years have seen increasing investments in data ecosystems to “FAIRify” and capitalize the ever-rising amount of health data. Within such networks, health metadata catalogs (HMDCs) assume a key function as they enable data allocation, sharing, and use practices. By design, HMDCs orchestrate health information for the purpose of findability, accessibility, interoperability, and reusability (FAIR). However, despite various European initiatives pushing health care data ecosystems forward, actionable design knowledge about HMDCs is scarce. This impedes both their effective development in practice and their scientific exploration, causing huge unused innovation potential of health data.

OBJECTIVE

This study aims to explore the structural design elements of HMDCs, classifying them alongside empirically reasonable dimensions and characteristics. In doing so, the development of HMDCs in practice is facilitated while also closing a crucial gap in theory (ie, the literature about actionable HMDC design knowledge).

METHODS

We applied a rigorous methodology for taxonomy building following well-known and established guidelines from the domain of information systems. Within this methodological framework, inductive and deductive research methods were applied to iteratively design and evaluate the evolving set of HMDC dimensions and characteristics. Specifically, a systematic literature review was conducted to identify and analyze 38 articles, while a multicase study was conducted to examine 17 HMDCs from practice. These findings were evaluated and refined in 2 extensive focus group sessions by 7 interdisciplinary experts with deep knowledge about HMDCs.

RESULTS

The artifact generated by the study is an iteratively conceptualized and empirically grounded taxonomy with elaborate explanations. It proposes 20 dimensions encompassing 101 characteristics alongside which FAIR HMDCs can be structured and classified. The taxonomy describes basic design characteristics that need to be considered to implement FAIR HMDCs effectively. A major finding was that a particular focus in developing HMDCs is on the design of their published dataset offerings (ie, their metadata assets) as well as on data security and governance. The taxonomy is evaluated against the background of 4 use cases, which were cocreated with experts. These illustrative scenarios add depth and context to the taxonomy as they underline its relevance and applicability in real-world settings.

CONCLUSIONS

The findings contribute fundamental, yet actionable, design knowledge for building HMDCs in European health care data ecosystems. They provide guidance for health care practitioners, while allowing both scientists and policy makers to navigate through this evolving research field and anchor their work. Therefore, this study closes the research gap outlined earlier, which has prevailed in theory and practice.
Enhancing the Clinical Relevance of Al Research for Medication Decision-Making (Preprint)
Authors: Wang Qi , Chen Mingxian
Published: February 2025

UNSTRUCTURED

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Authors’ Reply: Enhancing the Clinical Relevance of Al Research for Medication Decision-Making (Preprint)
Authors: Vordenberg Sarah E , Nichols Julianna , Marshall Vincent D , Weir Kristie Rebecca , Dorsch Michael P
Published: February 2025

UNSTRUCTURED

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Long-Term Feasibility and Outcomes of a Digital Health Program to Improve Liver Fat and Cardiometabolic Markers in Individuals with Non-Alcoholic Fatty Liver Disease: A Prospective Single-Arm Feasibility Study (Preprint)
Authors: Björnsdottir Sigridur , Ulfsdottir Hildigunnur , Gudmundsson Elias Freyr , Dobies Bartosz , Sveinsdottir Kolbrun , Isberg Ari Pall , Magnusdottir Gudlaug E. A. , Gunnarsdottir Thrudur , Karlsdottir Tekla , Bjornsdottir Gudlaug , Sigurdsson Sigurdur , Oddsson Saemundur , Gudnason Vilmundur
Published: February 2025

BACKGROUND

A 3-month digital health program for NAFLD previously showed feasibility in engagement, program retention and clinical outcomes. This study investigates whether improvements in cardiometabolic risk factors achieved during a 3-month active program were sustained over a subsequent 6-month follow-up period.

OBJECTIVE

To evaluate whether the clinical improvements achieved after 3 months program were maintained over the subsequent 6-month period.

METHODS

In a 9-month, single-arm study using the Sidekick app, individuals with NAFLD and BMI>30 or metabolic syndrome, or type 2 diabetes (T2D) were included. The initial 3 months focused on providing education about diet, physical activity, stress management, and sleep followed by 6 months without coaching. The measured outcomes encompassed demographics, body composition, liver fat and blood markers.

RESULTS

Of 38 participants enrolled, 28 (82%) completed the nine-month study measurements 63.0 years (IQR 53.5-71.0) , 57.1% female). At nine months, the mean weight loss was 4.0 kg (SD 5.0, P<.001) compared to baseline. Liver fat decreased by 2.5% (SD 4.5, P<.001), with an 18.4% relative reduction. Systolic blood pressure decreased by 8.3 mmHg (SD 13.4, P<.001) and diastolic by 1.0 mmHg (SD 2.4, P=.02). Waist circumference decreased by 4.7 cm (SD 7.1, P<.001) and median HbA1c decreased by 18.5 mmol/mol (P<.001).

CONCLUSIONS

Sustained improvements in liver fat and metabolic markers suggest that Sidekick Health’s digital program is a promising strategy for managing NAFLD without requiring continuous coaching.

CLINICALTRIAL

Clinicaltrials.gov NCT05426382; https://clinicaltrials.gov/study/NCT05426382
Using Exploratory Graph Analysis in validating the structure of the Technology Readiness Index2.0 in the health context (Preprint)
Authors: Pan Xin-yun , Wang Wen-yi , Sun Er-hong , Ye Xu-chun , Nong Yan-ning
Published: February 2025

BACKGROUND

With a continual interest in technology adoption in the health context, a good understanding of psychological determinants is of great importance. Although Technology Readiness Index2.0 (TRI2.0) is currently the most comprehensive measure of technology readiness (TR), there are discrepancies between the factorial structure of the TRI2.0 found in the initial study (four factors) and other studies (two factors).

OBJECTIVE

This study aimed to translate TRI2.0, determine its construct, and validate the Chinese version of TRI2.0 in patient sample exposed to artificial intelligence technology.

METHODS

TRI2.0 was translated, back-translated, and cross-culturally adapted using the Brislin translation model to form the Chinese version. Baseline data of 326 participants (age 30.92±11, 45.1% males) in the outpatient department were analyzed. Content validity was checked by calculating the score content validity index (S-CVI). Exploratory graph analysis (EGA) was conducted to evaluate the dimensionality of the scale, and a bootstrap exploratory graph analysis approach (bootEGA) was employed to assess the dimensionality and item stability. Confirmatory factor analysis (CFA) was computed to test the measurement models. Cronbachɑ and test-retest reliability were used to evaluate the scale's internal consistency.

RESULTS

The content validity of TRI2.0 is satisfactory (S-CVI= 0.9). Through EGA approach, the TRI2.0 contains four factors, which is consistent with the result of the original version. The CFA further confirmed this structure, with a CFI of 0.93, a SRMR of 0.05, a RMSEA of 0.064, TLI of 0.916 and x2/df of 2.38. Overall Cronbachɑ was 0.797. Chinese patients’ technology readiness (TRI2.0= 3.470 ± 0.488) is higher than the general public (TRI2.0= 3.02 ± 0.61).

CONCLUSIONS

The Chinese version of the Technology Readiness Index 2.0 has good reliability and validity and can be used as an assessment tool for technology readiness in the health context. Patients have an open attitude towards new AI technology in the health context compared to attitude of the public toward new technology in 2015.

CLINICALTRIAL

Not Applicable
Putting Patients at the Center of their Imaging Information: Preferences and Perceptions of Patients and Providers (Preprint)
Authors: van den Broek-Altenburg Eline , Cunningham Nicholas , Benson Jamie , Ali Naiim
Published: February 2025

BACKGROUND

Communication of results are increasingly targeted to patients directly, but there are controversies surrounding report communication from the perspectives of patients, ordering providers, and radiologists.

OBJECTIVE

To compare and contrast patients and providers regarding preferred imaging report source of information; imaging reporting method and perceptions of patients’ understanding of imaging reports.

METHODS

We gathered preferences from patients and providers through surveys. In total 91 patients and 75 physicians, 10 physician assistants, and 6 nurse practitioners completed the surveys. We used chi square and t-tests to compare differences in means between the groups. Logistic regression was used to analyze the probability of an ordering provider to prefer online release of imaging results as the first method of communication as a function of provider characteristics.

RESULTS

Of the 94 providers who participated in the study, 56.4% were female and 87% were white. On average, they had more than 15 years of experience. Most providers prefer delaying the release of imaging reports to patients until they have first reviewed the report themselves. There was significant provider preference heterogeneity regarding imaging report communication to patients and timing of release of the reports to patients. The majority of patients who completed the survey were female (76.9%) and 18.7% were non-white. Patients generally prefer to receive their imaging results online as soon as they are available.

CONCLUSIONS

Results of this study suggest that shared decision-making prior to release of imaging results could help establish how, when, and who should deliver the results to patients first. This study can be leveraged to explore options for a differentiated reporting approach more responsive to patient and provider needs.
Cross-sectional study of resident physician recognition of tachypnea in clinical simulation videos in Japan (Preprint)
Authors: Shikino Kiyoshi , Nishizaki Yuji , Fukui Sho , Kataoka Koshi , Yokokawa Daiki , Shimizu Taro , Yamamoto Yu , Nagasaki Kazuya , Kobayashi Hiroyuki , Tokuda Yasuharu
Published: February 2025

BACKGROUND

Traditional assessments of clinical competence using multiple-choice questions (MCQs) have limitations in the evaluation of real-world diagnostic abilities. As such, recognizing non-verbal cues, like tachypnea, is crucial for accurate diagnosis and effective patient care.

OBJECTIVE

This study evaluated how detecting such cues impacts resident physicians’ clinical competence using a clinical simulation video (CSV) integrated into the General Medicine In-Training Examination (GM-ITE).

METHODS

This multicenter cross-sectional study enrolled first- and second-year resident physicians who participated in GM-ITE 2022. Participants watched a five-minute CSV depicting a patient with acute pulmonary thromboembolism and subsequently answered diagnostic questions. Propensity score matching (PSM) was used to create balanced groups of resident physicians who detected tachypnea (detection group) and those who did not (non-detection group). After matching, we compared GM-ITE scores and the proportion of correct CSV-IE answers between the two groups. Subgroup analyses assessed result consistency.

RESULTS

In total, 5,105 resident physicians were included, from which 959 pairs were identified after PSM. Covariates were well balanced between the detection and non-detection groups (standardized mean difference < 0.1 for all variables). Post-matching, the detection group achieved significantly higher GM-ITE scores (mean=47.6, SD=8.4) than the non-detection group (mean=45.7, SD=8.1; mean difference=1.9, 95% CI: 1.1–2.6; p=0.041). The proportion of correct CSV-IE answers was also significantly higher in the detection group (39.2% vs. 3.0%; difference=36.2%, 95% CI: 32.8–39.4). Subgroup analyses confirmed consistent results across sex, postgraduate years, and age groups.

CONCLUSIONS

Overall, this study showed that detecting non-verbal cues like tachypnea significantly affects clinical competence, as evidenced by higher GM-ITE scores among resident physicians. Integrating video-based simulations into traditional MCQ examinations enhances the assessment of diagnostic skills by providing a more comprehensive evaluation of clinical abilities. Thus, recognizing non-verbal cues is crucial for clinical competence. Video-based simulations offer a valuable addition to traditional knowledge assessments by improving clinicians’ diagnostic skills and preparedness.
An Optimization Method for Perioperative ERAS Decision-making in Total Hip Arthroplasty through Bayesian network model: a multicenter study (Preprint)
Authors: Yi Bin , Zhu Yiziting , Liu Xiang , Shu Xin , He Wenquan , Batkhuder Munkhzul , Lin Xiaoguang , Liu Jiang , Chen Yuwen , Zhang Xuhao , Tang Xi , Yang Zhiyong , Yang Chunyong , Lei Zhen , Han Yan , Li Jiaxue , Zha Xingsheng , Li Yujie
Published: February 2025

BACKGROUND

Background: With the growing demand for Total Hip Arthroplasty (THA), there is an urgent need to devise individualized perioperative management strategies to expedite patient recovery, particularly in scenarios involving multiple disciplines and concurrent decision-making.

OBJECTIVE

Objective:This study aims to construct a decision network and provide decision recommendations based on Bayesian Networks (BNs) for stratified risk groups.

METHODS

Methods: The MIMIC-IV database was employed to establish a developing dataset by extracting patients who underwent THA, followed by K-means clustering for patient phenotyping. A BN model for THA decisions were developed, and support degree was calculated according to conditional probability for decision recommendation. An external validation dataset from the multiple center big data platform was then utilized to validate the BN model's recommendations by calculating the conformity of risk-recommended protocols after phenotype classification. An online tool was developed for convenient application in clinical practice.

RESULTS

Results: The developing dataset comprised 1701 admissions, while the validation dataset included 418 admissions. Patients in the developing dataset were divided into three phenotypes: a low-risk group (n=722), a moderate-risk group (n=673), and a high-risk group (n=306). Tailored recommendation protocols were formulated for each phenotype, covering preoperative and postoperative periods, based on support degree. Consistency in the distribution of differences among clustered indicators was observed in both datasets. Adherence to the recommended decisions was associated with reduced postoperative length of stay (PLOS) across all three decision protocols.

CONCLUSIONS

Conclusions: Decisions recommended by Bayesian Networks for patients of different risk levels can decrease the PLOS and hasten recovery. This systematic approach, integrating risk stratification through clustering with an indicator network constructed by the BN model for decision recommendations, is adept at handling multi-decision tasks and supports clinical physicians in making informed choices regarding perioperative risk assessment and treatment plan selection.

CLINICALTRIAL

The registration has been completed on the website of the China Clinical Trial Registry Center (ChiCTR1900023927).
Evaluating the Credibility of Online Health Information from Medical and Computer Science Perspective: A Systematic Literature Review (Preprint)
Authors: Nabożny Aleksandra , Chlabicz Małgorzata , Wierzbicki Adam , Jatowt Adam , Jemielniak Dariusz
Published: February 2025

BACKGROUND

To combat online health misinformation effectively, bridging knowledge across computer science and medical sciences disciplines is required.

OBJECTIVE

This paper provides a comprehensive overview of research supporting the credibility evaluation of medical content from both perspectives. It aims to illuminate critical gaps between the two approaches and propose solutions to fill them.

METHODS

First, we reviewed existing toolkits and guidelines created by medical experts to assess the credibility and quality of Online Health Information (OHI). We reviewed n1=28 papers from the medical domain. Second, we examined n2=82 articles describing the efforts of computer scientists to assess OHI automatically or semi-automatically. We grouped those articles by their aim, algorithms, types of utilized datasets, and features extracted from sample OHI.

RESULTS

Among our most essential findings lies the conclusion that few recent computational studies leverage expert-developed medical credibility assessment tools in constructing datasets. Datasets have significant differences in annotation protocols, basic definitions, data structures, and more. Last but not least, many classification models focus solely on textual rather than multimodal OHI features, despite the rise of image and video misinformation.

CONCLUSIONS

This survey highlights the urgent need to integrate credibility evaluation techniques from medicine into computational pipelines and unify methodologies for future classification experiments. Additionally, the survey provides a foundation to guide future cross-disciplinary efforts combining medical expertise with AI scalability.
Examining Stigma of Dementia During World Alzheimer’s Awareness Month: Infodemiology Study (Preprint)
Authors: Bacsu Juanita-Dawne , Mah Jasmine Cassy , Jamali Ali Akbar , Conanan Christine , Lautrup Samantha , Berger Corinne , Fiske Dylan , Fraser Sarah Anne , Virani Anila : , Fehr Florriann , Chasteen Alison L. , Rahemi Zahra , Vellani Shirin , Andrew Melissa K. Andrew , Cammer Allison , Gowda-Sookochoff Rory , Nanson Kate , Grewal Karl S. , Spiteri Raymond J.
Published: February 2025

BACKGROUND

Dementia is a significant global health concern. However, public awareness and education about dementia-related stigma remain limited, especially on social media. Examining dementia-related stigma on social media is critical because it impacts how the public perceives people living with dementia. By understanding dementia-related stigma on social media, we can develop educational strategies to target false stereotypes, beliefs, and misinformation to improve the quality of life of people living with dementia.

OBJECTIVE

This study examines dementia-related stigma on the X platform (formerly Twitter) during World Alzheimer’s Month, a global advocacy campaign organized by Alzheimer’s Disease International.

METHODS

Posts were scraped from X (formerly Twitter) during the World Alzheimer’s Awareness Month from September 1 - 30, 2022. After filtering the data, 1,981 posts were examined using thematic analysis.

RESULTS

Drawing on our thematic analysis, we found four themes including: 1) dementia stereotypes; 2) discrimination and denied dignity; 3) devaluing the lives of people with dementia; and 4) countering dementia-related stigma.

CONCLUSIONS

By analyzing how stigma manifests on social media, this study sheds light on the dementia education and information needed to combat false beliefs and dementia-related stigma. Findings from this study may help to inform future awareness campaigns to address dementia-related stigma on social media.

CLINICALTRIAL

Not Applicable.
Supporting the Research Data Management Journey of a Postgraduate Student at the University of St Andrews
Authors: Fina Federica , Eckel Haley , Spanou Panagiota , Proven Jackie
Published: February 2025

Most research funders have requirements for data management plans and open data to foster good research data management practices. In order to embed these practices in the postgraduate research (PGR) student journey we have introduced the requirement for a data management plan as part of the first-year progress review and the encouragement to make data underpinning theses publicly available. To support students through these processes we provide a suite of training workshops and are available for one-to-one consultations. User feedback and frequently asked questions are used to review and improve our support offering.
This brief report discusses the planning and implementation processes for data management plan requirement and encouragement of underpinning data. It dives deeper into the workflows, especially for the data deposit, and describes training and support available to students. Statistics on training uptake, data management plan submissions and annual trends for data deposit are also presented. The report concludes with lessons learnt and the team’s plans for the near future.
Reseña Crítica “Corrupción estructural. La teoría del doble fraude y las raíces de la impunidad en México”
Authors: Salgado Remigio Sofía
Published: February 2025

Reseña Crítica, Salgado Remigio, Sofía, “Corrupción estructural. La teoría del doble fraude y las raíces de la impunidad en México”. AKAL México. ISBN 978-607-8898-49-7, 239 páginas, de la autora Irma Eréndira Sandoval Ballesteros.
Improving MRI Image Clarity and Noise Reduction for Enhanced Brain Tumour Detection
Authors: Kaur Mrs. Mandeep
Published: February 2025

Brain diseases are serious conditions that must not be ignored, as brain failure can pose a significant threat to overall
health. Early detection and intervention are critical in managing various brain-related disorders. One of the primary diagnostic
methods for detecting brain tumors and other neurological issues is MRI imaging. MRI is a preferred technique due to its
efficiency, real-time imaging capabilities, and lack of radiation. However, challenges such as speckle noise, Gaussian noise, and
other artifacts continue to compromise the quality of MRI images. As a result, enhancing image quality is crucial for accurate
brain disorder diagnosis. To overcome these challenges, various imaging techniques are employed for preprocessing, noise
reduction, and image enhancement. A key approach to obtaining high-quality images from noisy MRI data is image restoration
and enhancement. Given the high-frequency characteristics of MRI, noise is often present in brain scans. Preprocessing plays
an essential role in improving image quality by applying filters to eliminate noise. Techniques like mean, median, Wiener, and
other filters are commonly used to address issues such as speckle, salt-and-pepper, and Gaussian noise. This research offers a
comprehensive overview of various MRI image preprocessing and enhancement techniques, outlining their objectives and
effectiveness.
Contribuição das universidades brasileiras para a inovação em pesquisa e desenvolvimento
Authors: Lopes Arleson Eduardo Monte Palma , Almeida Oriana Trindade de
Published: February 2025

As universidades desempenham papel fundamental na geração de conhecimento, na formação de profissionais para o mercado de trabalho e no processo de inovação, bem como contribuem para os desenvolvimentos econômico e social do país. Esse artigo tem o objetivo de analisar a contribuição das universidades brasileiras no processo de inovação em pesquisa e desenvolvimento (P&D). Quanto à metodologia, essa pesquisa se classifica como descritiva, com procedimentos técnicos de pesquisas bibliográfica e documental e com abordagem qualitativa. Os dados relacionados aos pesquisadores, envolvidos em P&D, referente ao período de 2000 a 2014, foram coletados no site do Ministério da Ciência, Tecnologia, Inovações e Comunicações. Ainda, foram analisados o Relatório de Pesquisa da Inovação 2017, do Instituto Brasileiro de Geografia e Estatística, o Relatório dos Indicadores Nacionais de Ciência, Tecnologia e Inovação 2021, do Ministério da Ciência, Tecnologia e Inovações, e a Estratégia Nacional de Ciência, Tecnologia e Inovação 2016-2022. Os resultados demonstraram que o setor de ensino é o que apresenta o maior número de pesquisadores envolvidos em P&D, concentrando-se no nível de escolaridade mestrado. Igualmente, foi possível observar que os maiores dispêndios em P&D no país provêm do setor público e que as universidades são as maiores depositantes de patentes de inovação no país.
COMORBIDADES PSIQUIÁTRICAS ASSOCIADAS AO COMPORTAMENTO ALIMENTAR DE PACIENTES COM TRANSTORNOS ALIMENTARES
Authors: Silva Amanda Santos , Teixeira Amanda Pio Autran , Alves Sandymara Coleta , Pessa Rosane Pilot , Manochio-Pina Marina Garcia
Published: February 2025

Retrospective, observational, descriptive with quantitative delineation. Data were colected on the start of patient care at the service, with sociodemographic and clinical nature, and information on the need for hospitalization and treatment outcome. Of the patients with AN-B, 51,25% (n = 41) had a diagnosis of psychiatric comorbidities, 17,50% (n = 14) with depression, 31,25% (n = 25) with traits / personality disorder and 23,75% (n = 19) with other psychiatric comorbidities. Of the patients with BN, 66,10% (n = 39) had psychiatric comorbidities, 25,42% (n = 15) with depression, 47,46% (n = 28) with personality traits / disorder and 20.34 % (n = 12) with other comorbidities.
Naming names: an essay on native categories and their political implications
Authors: Gonçalves Ana Luiza Vieira , Rizek Cibele Saliba
Published: February 2025

This article has emerged from an initial concern centered around the naming of peripheral territories and their political and/or depoliticizing dimensions inherent in this process, which is intrinsically tied to the repertoire and categories (both theoretical and political) used to comprehend them. Building on this premise, we explore and elaborate on the implications of adopting the term "occupation" to describe peripheral territories in São Paulo. Our analysis engages with the complex interplay of political, ideological, and economic disputes involving different actors, including the State – expressed through institutional presence, police power, legislators, and laws, which generate diverse impacts on these territories – as well as distinct religious organizations, groups engaged in illicit markets, social movements, non-governmental organizations, private sector companies, residents, and academic institutions at both national and international levels. Drawing on empirical research conducted in the district of Grajaú, in the southern zone of the city of São Paulo, Brazil, this study challenges the uncritical adoption of seemingly native categories. Specifically, in the particular case studied, we advocate for the use of the category "favela" from a dissensual perspective, one that disrupts the sensory landscape, assigning it alternative denominations, and contesting meanings that have been instrumentalized by purportedly virtuous actions.
On call-duty of medical Professionals – Changing the Paradigm of its Assessment as a Results of new Decisions of the Court of Justice of the European Union
Authors: Svec M. , Meszaros P. , Horecky J.
Published: February 2025

Background: New court decisions of the Court of Justice of
the European Union change the paradigm of assessing on-call
duty outside the workplace for healthcare workers so that it can
be considered working time in certain circumstances despite
the current wording of Section 96 of the Labor Code. The transposition of the conclusions of certain court decisions into the
Labor Code would significantly contribute to the improvement
of the working and wage conditions of healthcare workers.
Objectives: The primary goal of the paper is to identify, thoroughly assess, and organize the fundamental theoretical and
legal principles behind the judgments rendered by the Court
of Justice of the European Union, along with decisions from national courts concerning the concept of working hours, particularly on-call duty. This objective aimed to highlight a potential shift in how working time is evaluated.
Methodology: We conducted a search and retrieved national
and European court decisions, encompassing 43 judgments
from the Court of Justice of the European Union and 22 from
national courts. This process involved using the CURIA system, as well as the search systems provided by the Ministry
of Justice in both Slovakia and the Czech Republic, including
commercial databases housing court decisions and legal rulings (APSI, Judikaty.info)
Results: We identified two categories of Court of Justice of the
European Union rulings, which were subsequently mirrored
in national court decisions. Both sets of decisions examined
the evaluation of on-call duty (employee staying outside the
workplace), but they diverged in their interpretation of whether it qualified as working time. If an employer mandated that
an employee on on-call duty (staying outside the workplace)
must be ready to report to work within a specific timeframe
(e.g., 20 minutes) if required for work duties, this represents
a limitation on the employee’s freedom to manage their leisure
time to such an extent that this period could be considered part
of the employee’s working hours.
Conclusion: Based on recent judicial decisions by the Court
of Justice of the European Union, it can be inferred that Section 96 of the Labor Code, which does not categorize on-call
duty if an employee stays outside the workplace as working
time, contradicts Directive no. 2003/88/EC. Given a comprehensive examination of the case’s circumstances and an evaluation of the impact on the employee’s off-duty rest periods,
if the employee is deprived of the autonomy to manage their
leisure time at their own discretion due to the employer’s specific instructions during off-site on-call duty, this time period
may also qualify as working time.
Artificial intelligence in corporate governance
Authors: Ustahaliloğlu Mustafa Kenan
Published: February 2025

Artificial intelligence (AI) has transformed corporate governance, offering unparalleled opportunities for efficiency and decision-making and raising a host of complex legal questions. This article explores the use of AI in corporate governance, addressing the changing role of AI, ethical and legal issues, questions of liability and accountability, considerations of intellectual property, and data privacy issues (Dastani & Yazdanpanah, 2023). The research explores why this topic is of paramount importance, given the increasing adoption of AI in the corporate sector, and identifies the research gap in the form of the legal gray areas surrounding AI. The purpose of the study topic is to clarify how organizations can successfully negotiate the complex web of issues related to AI in corporate governance (Hilb, 2020; Khurshed, 2024; Locke & Bird, 2020). To do this, the article takes a thorough method that incorporates legal analysis with knowledge of business practices. The main takeaway from this work is that it will give readers an understanding of some of the main issues of AI in corporate governance and offer practical advice for businesses doing business in this area.
The PCAOB During the Youth Stage of Its Regulatory Life Cycle
Authors: Keyser John D.
Published: February 2025

On May 19, 2009, the Supreme Court of the United States (SCOTUS) agreed to hear a challenge to the constitutionality of the Public Company Accounting Oversight Board (PCAOB). The resulting doubt about the PCAOB’s ability to continue hampered its activities, including recruitment of new Board members. Then, on June 28, 2010, SCOTUS ruled that the PCAOB could continue. Subsequently, the SEC appointed three new board members: Lewis H. Ferguson, Jay D. Hanson, and Chairman James R. Doty. This paper examines how the PCAOB, under the leadership of Chairman Doty, used its powers to regulate the public accounting profession. The Supreme Court ruling in its favor enabled the PCAOB to address broad issues, including the auditor reporting model and naming the engagement partner. The PCAOB also enhanced its international inspection program, reorganized the inspection division, and worked to strengthen audit committees.
Benefits and Drawbacks of Incorporating ChatGPT in Financial Audits
Authors: Otero Angel Rafael , Agu Michael
Published: February 2025

The purpose of this paper is to convey the authors’ perspective regarding the overall use of ChatGPT, a generative artificial intelligence tool, in financial audits. ChatGPT can provide audit efficiencies in data processing, continuous learning, predictive analytics, and cost-effectiveness. However, concerns arise when incorporating ChatGPT in these audits regarding data security and privacy, regulatory compliance, response reliability, industry-specific knowledge, and source data verification. This paper explores the literature and discusses benefits and drawbacks of incorporating ChatGPT in both external and internal financial audits. The paper also presents actionable takeaways for financial auditors to consider and/or implement in their respective audits. Despite the drawbacks highlighted in this paper, the efficiency gains from using ChatGPT in financial audits may outweigh the challenges, ultimately compelling auditors to increasingly rely on this technology in the completion of their financial audits.

JEL Classifications: M42; M150.
Development and validation of a graph convolutional network (GCN)-based automatic superimposition method for maxillary digital dental models (MDMs)
Authors: Pan Yichen , Zhang Zhechen , Xu Tianmin , Chen Gui
Published: February 2025

Objectives

To validate the accuracy and reliability of a graph convolutional network (GCN)-based superimposition method of a maxillary digital dental model (MDM) by comparing it with manual superimposition and quantifying the clinical error from this method.

Materials and Methods

Based on a GCN, learning the features from 100 three-dimensional digital occlusal models under supervision of the palatal stable structure labels that were manually annotated by senior specialists, the palatal stable structure was automatically segmented. The average Hausdorff distance was calculated to assess the difference between automatic and manual segmentations. Tooth position and angulation, including rotation, tip, and torque, of bilateral upper first molars and central incisors were obtained to measure the clinical error of automatic superimposition. Reliability was calculated by intraclass correlation coefficient (ICC).

Results

The average Hausdorff distance was 0.36 mm between automatic and manual segmentations of the palatal stable region and was larger than the intraexaminer and interexaminer deviations. The tooth position deviation was <0.32 mm, and the tooth angulation difference was <0.26° for tip and torque, and 0.46–0.61° in rotation. ICCs, used for assessment of reliability, ranged from 0.82 to 0.99 in all variables.

Conclusions

The GCN-based MDM superimposition is an efficient method for the assessment of tooth movement in adults. The clinical error in tooth position and angulation induced by the method was clinically acceptable. Reliability was as high as manual segmentation.
Acceptance of potential risks in adult orthodontic patients and the influence of personality traits
Authors: Xiang Jie , Luo Min , Liu Jiaqi , Zheng Yunhao , Xiong Xin , Du Shufang , Wang Jun
Published: February 2025

Objectives

To determine acceptance of potential risks in orthodontic treatment and whether it could be affected by personality traits of patients.

Materials and Methods

Two hundred sixty-one adult participants consulting for orthodontic treatment were involved in this cross-sectional study. Acceptance of 18 orthodontic risks was investigated with a structured questionnaire. Personality traits of included patients were assessed with a validated Chinese version of the Big Five Inventory. Statistical analyses were performed as appropriate, and potential correlations were identified using multivariate binary logistic regression analysis.

Results

Acceptability of the investigated orthodontic risks ranged from 47.1% to 87.0%, with periodontitis being the least accepted. Length of treatment (P = .015) and relapse (P = .007) were more unacceptable to older patients. Female participants showed less tolerance to periodontitis (P = .017) than male counterparts. Among the five dimensions of personality traits, conscientiousness was significantly associated with acceptance of tooth extraction and associated risks (odds ratio [OR] = 1.131, P = .002), problems eating (OR = 1.182, P < .001), problems speaking (OR = 1.111, P = .022), and appliance breakage (OR = 1.109, P = .008), while openness was a significant predictor to the acceptance of length of treatment (OR = 1.090, P = .044).

Conclusions

Among patients seeking orthodontic treatment, periodontitis was the least accepted risk. A significant association was found between the acceptance of certain orthodontic risks and personality traits, especially conscientiousness and openness.