bluuebunny/crossref_metadata_2025_split huggingface dataset:
JIM-Studie 2023
Authors: Struckmeyer Kati 
Published: December 2024

2023 waren Jugendliche im Durchschnitt 224 Minuten täglich online. Elementar war dabei vor allem die Nutzung von Messengern und Social Media. WhatsApp wird von 94 Prozent folgt von TikTok (59 %) und Snapchat (49 %). So lauten einige der Ergebnisse der Anfang Dezember 2023 veröffentlichten JIM-Studie. Weiterhin wurde ermittelt, dass jedes dritte Mädchen und jeder vierte Junge 2023 im Netz schon einmal sexuell belästigt worden sind. Besorgniserregend ist weiterhin, dass 23 Prozent der Jugendlichen im letzten Monat vor der Befragung ungewollt mit pornografischen Inhalten konfrontiert wurden. Zudem gaben 14 Prozent an, innerhalb des letzten Monats im Internet angefeindet oder beleidigt worden zu sein.
Wenn es um onlinebasierte Möglichkeiten geht, Serien, Sendungen und Filme anzusehen, ist die Nutzung von Netflix (63 %) gestiegen (2022: 50 %), während die Nutzung von YouTube (2023: 50 %, 2022: 53 %) fast gleich blieb. Es sind nach wie vor die beliebtesten Portale Jugendlicher.
Die JIM-Studie 2023 (Jugend, Information, Medien) des Medienpädagogischen Forschungsverbund Südwest ist eine Kooperation der Landesanstalt für Kommunikation Baden-Württemberg und der Medienanstalt Rheinland-Pfalz, in Zusammenarbeit mit dem Südwestrundfunk. Dafür wurden in ganz Deutschland im Zeitraum vom 30. Mai bis 9. Juli 2023 insgesamt 1.200 Jugendliche im Alter zwischen 12 und 19 Jahren befragt.
Primary Changes in Corneal Nerve Fiber Structure in Patients with Primary Glaucoma and Related Influencing Factors
Authors: Cai Mingming , Zhang Jie , Xie Lin 
Published: December 2024

Objective:
The aim of this study was to explore the primary changes in corneal nerve
fiber structure and its influencing factors in patients with primary glaucoma.

Method:
A retrospective analysis was conducted in this study. A total of 51 patients with primary
glaucoma who were diagnosed and treated in our hospital from March 2020 to March 2022
were selected as the research objects and designated as the glaucoma group. In addition, 51 patients
with normal eyes were chosen as the control group. The characteristic changes of corneal
nerve fibers, the thickness of the nerve fiber layer, and the number of ganglion cell complexes
and dendritic cells were measured. Multivariate logistic regression analysis was performed to
analyze the influencing factors of ganglion fiber structure change.

Result:
Compared with the control group, the length of corneal nerve fibers and the density of
corneal nerve fibers in the glaucoma group were significantly shortened, the number of branches
was significantly reduced, the curvature was significantly increased, and the number of dendritic
cells was significantly increased (P <0.05). Compared with the control group, the thickness of
the upper, lower, nasal, temporal, and peripheral nerve fiber layers in the glaucoma group was
obviously reduced (P <0.05). Compared with the control group, the thickness of the above inferior,
nasal, temporal, and peripheral nerve fiber layers in the glaucoma group was significantly
reduced (P <0.05). Compared with the control group, the above, below, and mean ganglion cell
complex thickness in the glaucoma group was significantly reduced (P <0.05). Logistic regression
analysis showed that intraocular pressure and the number of dendritic cells were risk factors
for ganglion fiber structure change. In contrast, nerve fiber layer thickness and ganglion cell
complex were protective factors for ganglion fiber structure change (P <0.05).

Conclusion:
There were primary changes in the structure of corneal nerve fibers in patients with
primary glaucoma, which were more slender, tortuous, and sparse, and the primary changes in
nerve fiber structure could be affected by intraocular pressure, the number of dendritic cells, the
thickness of the nerve fiber layer, and the ganglion cell complex.
Design of a Low-Complexity Deep Learning Model for Diagnosis of Type 2 Diabetes
Authors: Soltanizadeh Soroush , Mobini Majid , Naghibi Seyedeh Somayeh 
Published: December 2024

Background:
Recent research demonstrates that diabetes can lead to heart problems,
neurological damage, and other illnesses.

Method:
In this paper, we design a low-complexity Deep Learning (DL)-based model for the
diagnosis of type 2 diabetes. In our experiments, we use the publicly available PIMA Indian Diabetes
Dataset (PIDD). To obtain a low-complexity and accurate DL architecture, we perform
an accuracy-versus-complexity study on several DL models.

Result:
The results show that the proposed DL structure, including Convolutional Neural Networks
and Multi-Layer Perceptron models (i.e., CNN+MLP model) outperforms other models
with an accuracy of 93.89%.

Conclusion:
With these features, the proposed hybrid model can be used in wearable devices
and IoT-based health monitoring applications.
Review of Key Technology and Latest Research Progress of Deflector Jet Servo Valve
Authors: LI Jianying , Guo Qi , Wang Yaqian , Zhang Xiaoqiu 
Published: December 2024

Abstract:
The deflector jet servo valve has the advantages of strong anti-pollution ability, high reliability
and better dynamic performance, so it is widely used in aviation, military and other fields.
The research on deflector jet servo valve theory and production is not yet mature. It is expected that
through the collation and analysis of the existing data, the method of improving the performance of
this kind of valve will be obtained, which will provide the basis for future research and development.
By summarizing the research status of the static and dynamic characteristics, pre-stage optimization,
mathematical modeling and other parts of the deflector jet servo valve, the technical points and development
rules are obtained. By sorting out the relevant patent information in recent years, the vacancy
in the patent analysis of the deflector jet servo valve is filled. Based on the summary of the
research status of the deflector jet servo valve, combined with the summary of the research status of
the deflector jet servo valve, it is pointed out that there are few studies on the performance of deflector
jet servo valve under cavitation, noise and extreme temperature. These parameters are of great
significance to improve the performance of servo valve, which will be an important research direction
in the future. The research status and development trend of the deflector jet servo valve are
summarized and analyzed, and various methods that can improve the performance of the servo valve
are described, which is of great significance for the subsequent performance improvement and indepth
study of such valves.
Enhancing Clinical History Taking Through the Implementation of a Streamlined Electronic Questionnaire System at a Pediatric Headache Clinic: Development and Evaluation Study
Authors: Cho Jaeso , Han Ji Yeon , Cho Anna , Yoo Sooyoung , Lee Ho-Young , Kim Hunmin 
Published: December 2024

Abstract

Background
Accurate history taking is essential for diagnosis, treatment, and patient care, yet miscommunications and time constraints often lead to incomplete information. Consequently, there has been a pressing need to establish a system whereby the questionnaire is duly completed before the medical appointment, entered into the electronic health record (EHR), and stored in a structured format within a database.

Objective
This study aimed to develop and evaluate a streamlined electronic questionnaire system, BEST-Survey (Bundang Hospital Electronic System for Total Care-Survey), integrated with the EHR, to enhance history taking and data management for patients with pediatric headaches.

Methods
An electronic questionnaire system was developed at Seoul National University Bundang Hospital, allowing patients to complete previsit questionnaires on a tablet PC. The information is automatically integrated into the EHR and stored in a structured database for further analysis. A retrospective analysis compared clinical information acquired from patients aged <18 years visiting the pediatric neurology outpatient clinic for headaches, before and after implementing the BEST-Survey system. The study included 365 patients before and 452 patients after system implementation. Answer rates and positive rates of key headache characteristics were compared between the 2 groups to evaluate the system’s clinical utility.

Results
Implementation of the BEST-Survey system significantly increased the mean data acquisition rate from 54.6% to 99.3% (P<.001). Essential clinical features such as onset, location, duration, severity, nature, and frequency were obtained in over 98.7% (>446/452) of patients after implementation, compared to from 53.7% (196/365) to 85.2% (311/365) before. The electronic system facilitated comprehensive data collection, enabling detailed analysis of headache characteristics in the patient population. Most patients (280/452, 61.9%) reported headache onset less than 1 year prior, with the temporal region being the most common pain location (261/703, 37.1%). Over half (232/452, 51.3%) experienced headaches lasting less than 2 hours, with nausea and vomiting as the most commonly associated symptoms (231/1036, 22.3%).

Conclusions
The BEST-Survey system markedly improved the completeness and accuracy of essential history items for patients with pediatric headaches. The system also streamlined data extraction and analysis for clinical and research purposes. While the electronic questionnaire cannot replace physician-led history taking, it serves as a valuable adjunctive tool to enhance patient care.
Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study
Authors: Cardamone Nicholas C , Olfson Mark , Schmutte Timothy , Ungar Lyle , Liu Tony , Cullen Sara W , Williams Nathaniel J , Marcus Steven C 
Published: November 2024

Abstract

Background
Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured EHR text into structured features, which can then be integrated into statistical prediction models, ensuring that the results are both clinically meaningful and interpretable.

Objective
This study aims to compare the classification decisions made by clinical experts with those generated by a state-of-the-art LLM, using terms extracted from a large EHR data set of individuals with mental health disorders seen in emergency departments (EDs).

Methods
Using a dataset from the EHR systems of more than 50 health care provider organizations in the United States from 2016 to 2021, we extracted all clinical terms that appeared in at least 1000 records of individuals admitted to the ED for a mental health–related problem from a source population of over 6 million ED episodes. Two experienced mental health clinicians (one medically trained psychiatrist and one clinical psychologist) reached consensus on the classification of EHR terms and diagnostic codes into categories. We evaluated an LLM’s agreement with clinical judgment across three classification tasks as follows: (1) classify terms into “mental health” or “physical health”, (2) classify mental health terms into 1 of 42 prespecified categories, and (3) classify physical health terms into 1 of 19 prespecified broad categories.

Results
There was high agreement between the LLM and clinical experts when categorizing 4553 terms as “mental health” or “physical health” (κ=0.77, 95% CI 0.75-0.80). However, there was still considerable variability in LLM-clinician agreement on the classification of mental health terms (κ=0.62, 95% CI 0.59‐0.66) and physical health terms (κ=0.69, 95% CI 0.67‐0.70).

Conclusions
The LLM displayed high agreement with clinical experts when classifying EHR terms into certain mental health or physical health term categories. However, agreement with clinical experts varied considerably within both sets of mental and physical health term categories. Importantly, the use of LLMs presents an alternative to manual human coding, presenting great potential to create interpretable features for prediction models.
Coexistence of accompanying autoimmune diseases in adolescents with autoimmune thyroiditis
Authors: Pankiv V.I. 
Published: November 2024

Background. Autoimmune thyroiditis (AIT) is the most common cause of goiter and acquired hypothyroi­dism in children and adolescents living in iodine-sufficient regions. Most studies, which investigated the coexistence of AIT and other autoimmune diseases, were conducted in the adult patient population, and examined the prevalence of AIT in nonthyroid autoimmune diseases. The purpose of the study was to investigate the coexistence of other accompanying autoimmune diseases in adolescents aged 14–18 years who were diagnosed and followed up at the Ukrainian Scientific and Practical Centre of Endocrine Surgery, Transplantation of Endocrine Organs and Tissues of the Ministry of Health of Ukraine. Materials and methods. A total of 110 patients aged 14–18 years diagnosed with AIT were examined. Their age at admission, sex, family history, complaints at admission, comorbidities, physical examination and laboratory fin­dings, clinical follow-up information were retrospectively reviewed. Results. Of the 110 patients, 74.6 % were female and 25.4 % were male, with a mean age of 15.9 ± 2.1 years. A total of 9 (8.2 %) of the patients had overt hypothyroidism, 29 (26.4 %) had subclinical hypothyroidism, and 72 (65.4 %) had euthyroidism. High level of thyroid peroxidase antibo­dies was detected in 108 (98.2 %) patients, and 81 (73.6 %) had high thyroglobulin antibody levels (Tg-Ab). There was a family history of autoimmune disease in 43.6 % of cases. Autoimmune diseases accompanying AIT were present in 29 (26.4 %) of patients. The most common of them were type 1 diabetes (17.3 %), celiac disease (2.7 %), skin diseases (8.2 %), and rheumatologic diseases (2.7 %). The mean age of AIT patients with and without concomitant autoimmune disease was similar. In both groups, female sex was dominant. The rate of subclinical hypothyroidism was statistically significantly elevated in the group without accompanying autoimmune disease (p < 0.001). A statistically significant relationship was found between elevated Tg-Ab and concomitant autoimmune disease. The prevalence of accompanying autoimmune disease was increased 2.34 times in patients with elevated Tg-Ab levels. Conclusions. Autoimmune diseases, especially T1DM and skin diseases, are associated with AIT. There is an increased risk of autoimmune disease in AIT that affects both sexes. Regular follow-up of AIT patients with elevated Tg-Ab levels for autoimmune disease development is important in terms of earlier diagnosis of diseases.
LC-HRMS-Based Metabolomics Approach Reveals Antioxidant Compounds from <i>Centella asiatica</i> Leaves Extracts
Authors: Silvia Riva , Wahyuni Wulan Tri , Rohaeti Eti , Aisyah Siti , Septaningsih Dewi Anggraini , Karomah Alfi Hudatul , Rafi Mohamad 
Published: December 2024

Centella asiatica is a medicinal plant widely used as a traditional medicine due to several biological activities, such as antioxidants in Indonesia. This study aims to identify the active antioxidant compounds of C. asiatica leaves extract using a liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS)-based metabolomics approach. Extracts were prepared using different concentrations of ethanol p.a., i.e. 70% ethanol, 50% ethanol, 30% ethanol, and water. Antioxidant activity was tested using the 2,2-diphenyl-1-picrylhydrazyl method. The results showed the highest antioxidant activity was C. asiatica extracted by 70% ethanol with IC50 of 72.48 ± 2.42 µg/mL. The positive control was ascorbic acid, having an IC50 value of 3.38 ± 0.04 µg/mL. Ascorbic acid and 70% ethanol extract have strong antioxidant activity. Metabolite profiling using LC-MS/MS could identify 35 metabolites consisting of flavonoids, fatty acids, phenolics, terpenes, and several other groups of compounds. Orthogonal partial least squares-discriminant analysis classified the metabolites into active (ethanol 70% and ethanol 50%) and inactive (ethanol p.a., ethanol 30%, and water) antioxidants. Five metabolites have potential as antioxidants, namely 4,5-dicaffeoylquinic acid (11), kaempferol (13), and three unknown compounds.
Immobilization of Cerium(IV) Oxide onto Reduced Graphene Oxide in Epoxy Resin Matrix as Radar Absorbing Composite for X-band Region
Authors: Bolilanga Patricya Inggrid Wilhelmina , Basuki Rahmat , Apriliyanto Yusuf Bramastya , Prasojo Agus Eko , Lazuardy Ardyan , Anitasari Reza , Putri Riyanti , Sasongko Nugroho Adi , Santiko Arief Budi 
Published: December 2024

The rGO/CeO2/epoxy composite has been successfully prepared as radar absorbing material (RAM) for the X-band (8–12 GHz) region. The reduced graphene oxide (rGO) originated from pencil graphite oxide (GiO) was synthesized through the modified Hummer method. The synthesis of rGO/CeO2/epoxy was conducted by immobilization of cerium(IV) oxide into rGO (rGO/CeO2) via hydrothermal method and followed by composited the rGO/CeO2 with epoxy resin matrix. Morphological analysis by SEM-EDX indicates that the rGO/CeO2 structure appears to be a tangled layer of edges randomly aggregated, and CeO2 is uniformly anchored on the rGO surface. From the diffractogram result of the XRD instrument, rGO exhibits changes in crystallinity, indicating a transformation of the interlayer structure from multilayer GiO to a single layer of rGO. The presence of Ce–O was indicated at wavenumber 553 cm−1 of rGO/CeO2 by FTIR. The microwave absorbing performance of rGO/CeO2/epoxy conducted by vector network analyzer (VNA) showed that the RL value of the composite was −3.22 dB (47% of electromagnetic wave absorption) at a frequency of 9.25 GHz at the thickness of 1 mm composite. The composite has the promising prospect of being developed as a captivating candidate for the new type of microwave absorptive materials.
Forensic Discrimination of Lipstick Stains Using Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy and Pattern Recognition Techniques
Authors: Azis Mohd Afiq Mohd , Mahat Naji Arafat , Maarof Hasmerya , Azman Sarah Aina 
Published: December 2024

Lipstick stains discovered on objects (such as drinking cups) may be utilized as evidence and/or lead in forensic investigations by chemical analysis. Hence, this study analyzed two brands of lipstick stains' organic composition on paper cups over 0, 24, and 48 h of exposure using attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), coupled with pattern recognition techniques of principal component analysis (PCA) and linear discriminant analysis (LDA). Spectral analysis revealed several functional group differences between the two brands of lipstick, viz. Wardah (W) and Silkygirl (S) stains, particularly in the fingerprint region of 1800–650 cm−1. Application of PCA (variance of ≥ 70%) and LDA successfully provided the suitable means for categorically classifying (a) the different brands of lipstick, (b) the male and female volunteers, as well as (c) the different periods of exposure. The fact that the cross-validation correct classification rates for all the LDA models exceeded 90.0%, their suitability for forensic provenance application of lip stains appears statistically supported. Hence, such an approach to combine the non-destructive ATR-FTIR analysis with PCA and LDA is not only easy and cost-effective but also forensically relevant for enabling the data mining process for forensic intelligence.
Data Analytics Adoption, Social Support, and Internal Auditor Performance
Authors: Bogdani Evisa , Causholli Monika , Rakipi Romina 
Published: December 2024

SUMMARY
Data analytics are regarded as indispensable in enhancing auditor performance in the era of rapid technology advancements and Big Data. We examine the extent to which social support facilitates internal auditor adoption of data analytics, and whether the use of data analytics is associated with performance. Using a unique proprietary dataset from a large insurance company in the U.S., we document that peer social support developed through coparticipation in company trainings, accelerates the adoption of data analytics in various internal audit tasks including continuous auditing, communication, aligning risks and controls, and aligning data analytics with business control objectives. Further, the extent to which data analytics are used is positively associated with several dimensions of auditor performance including internal audit skills, communication skills, and business knowledge. These findings inform research and practice about effective mechanisms that induce auditors’ use of data analytics and the benefits of data analytics in internal audits.
Employees’ Reactions to Algorithmic Performance Evaluation: Threat of Evaluation Bias and Objectivity
Authors: Cha Yunshil 
Published: December 2024

ABSTRACT
Although algorithms are increasingly used for performance evaluation purposes, prior algorithm research shows that individuals tend to trust human decisions more than algorithmic decisions in a subjective domain such as performance evaluation that requires human skills. I conduct an experiment to examine whether the effect of evaluator type (algorithm versus human supervisor) on employees’ perceived trustworthiness of performance evaluations hinges on evaluation bias (positive versus negative) and further investigate whether this interaction effect is mediated by perceived objectivity. I find that employees’ perceived trustworthiness shifts from human evaluations to algorithmic evaluations when negative evaluation bias exists. Further, this reduced algorithm aversion is stronger and significant for people who are vulnerable to evaluation bias because people perceive algorithms as making objective evaluation decisions. Overall, results of this study suggest that the threat of evaluation bias reduces algorithm aversion because of the higher perceived objectivity of algorithmic decisions.
Data Availability: Data are available upon request.