bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of the Bioaccumulated Metal Contaminants in Broiler Chicken -a Case Study From Skardu Pakistan
Authors: Butt Fazila , Jamil Ishrat , Parveen Kousar , Hussain Muhammad , Hussain Shafqat , Ali Hasnain 
Published: April 2024

Abstract

Chicken meats are an important source of nutrients and are consumed all over the world therefore regular screening is of utmost need for food safety to enjoy good health. The present study is designed to assess the quality of broiler chicken meat available in Skardu City. The meat samples were collected from different poultry shops situated in the commercial market of Skardu. A total of five samples of chicken meat were collected, 4 samples of broiler chicken, and 1 sample of frozen chicken meat. The samples were prepared using the wet digestion method and analyzed using standardized atomic absorption spectroscopy. The results showed that lead, nickel, and chromium are all present in chicken meat at different concentrations except cadmium. However, the concertation of all metals are above the permissible limit recommended by World Health Organization. Health risk assessment showed that meat is cotaminaed with toxic metal and belong to IARC class 1(human carcinogen) and class 2B (possible human cancer).
Does time and experience matter in pediatric arterial ischemic stroke intervention? Long term follow up experience
Authors: Ozturk Gulten , Bıyıklı Erhan , Unver Olcay , Dogru Omer , Saygi Evrim Karadag , Baltacıoglu Feyyaz , Turkdogan Dilsad 
Published: April 2024

Abstract

Introduction:
Acute ischemic stroke is an important cause of mortality in childhood with variable long term neurological sequelae. This study presents long term follow up outcome data of our center which is a tertiary clinic with a well-organized pediatric stroke team.
Method:
Medical records of patients diagnosed at pediatric age (28 days-18 years) with acute ischemic stroke between years 2011-2018 in Marmara University Pediatric Neurology Clinic were retrospectively evaluated. Patients whose initial medical intervention was performed by Marmara University Pediatric Emergency unit and had been followed by our clinic for at least 5 years after initial diagnoses were included in this study. The modified Rankin Scale(mRS) and Pediatric Stroke Outcome Measure Short Neuro Exam (PSE-SEN) were performed to all of the patients in addition to final neurological examination.
Results:
The mean first ischemic stroke age of 32 patients (20 male,62,5%) included in the study was 77,39±61,93 months. First stroke age was grouped as early (≤60 months; n=16) and late (>61 months, n=16). The median time passed from initial symptom to emergency room admission was 15,5 (1-720) hours. The mean follow-up interval of the study group was 85,44±20,52 months and the mean age of the group at the time of the study (present age) was 162,62±64,4 months. Affected vascular territory did not have any significant effect on long-term clinical outcomes (p=0,550). Late stroke age group (>61 months) had a significantly higher rate of normal functional long-term outcome(p=0.023).
The younger age group (≤60 months) had longer hospital admission time (24 h vs 9 h). PSOM scores were helpful to detect some mild cognitive and language dysfunctions that did not interfere with daily life.
Conclusion:
Pediatric Stroke Outcome Measure Short Neuro Exam (PSE-SEN) is helpful to catch mild neurocognitive sequela that can be missed with neurological examination.
Considering the fact that time after first symptom is critical for early intervention in acute ischemic stroke patients, stroke teams should be settled in all centrally located medical centers with easy transportation opportunities.
Effect of crosslink density on Sealant Properties Based on Silane- Modified Polyurethane
Authors: Basir Mohammad Hassan Mahdavi , Salimi Ali , Boohendi Hossein , Zohuriaan-Mehr Mohammad 
Published: April 2024

Abstract

This study investigates the profound influence of crosslink density, stemming from both primary and secondary aminosilanes, on the properties of one-part polyurethane sealants. Through systematic experimentation, we elucidate the consequential effects of varying crosslinking agents on mechanical, thermal, and adhesive characteristics. Our results highlight the distinct impact of primary and secondary aminosilanes on the resulting material, offering valuable insights into tailoring specific properties for diverse applications. The chemical and physical structures of the one-part polyurethane sealants were investigated by nuclear magnetic resonance spectroscopy (NMR), Fourier transform infrared spectroscopy (FT-IR) and the mechanical properties were evaluated by tensile tests. The results reveal that silane-terminated moisture-curable polyurethanes can be successfully synthesized and cured with Dibutyltin Dilaurate catalysts. The comparative analysis underscores the nuanced interplay between crosslink density and material performance, paving the way for optimized formulations. This research enhances the fundamental understanding of crosslinking mechanisms, guiding the formulation of materials suitable for a wide array of applications.
Wind-driven upwelling sustains weakened Atlantic overturning under climate extremes
Authors: Baker Jonathan , Bell Michael , Jackson Laura , Vallis Geoffrey K , Watson Andrew , Wood Richard 
Published: April 2024

Abstract

The Atlantic Meridional Overturning Circulation (AMOC), vital for northward heat transport across the Atlantic Ocean, is projected to weaken due to global warming1, with significant global climate impacts2,3. However, the extent of AMOC weakening is uncertain with wide variation across climate models1,4,5, while some statistical indicators suggest an imminent collapse6. Here, we evaluate the AMOC’s resilience to extreme greenhouse gas and North Atlantic freshwater forcings in 34 climate models by analysing its upwelling pathways that return AMOC deep waters to the surface. We find that upwelling in the Southern Ocean (SO), driven by persistent SO winds, sustains a weakened AMOC in all cases, preventing its complete collapse. Since SO upwelling must be balanced by downwelling in the Atlantic or Pacific, the AMOC can only collapse if a compensating Pacific overturning (PMOC) develops. Remarkably, a PMOC does emerge in almost all models, but it is too weak to balance all of the SO upwelling, suggesting an AMOC collapse is unlikely this century. Our findings reveal novel AMOC stabilising mechanisms with implications for past and future AMOC changes, and hence for ecosystems and ocean biogeochemistry. They suggest that better understanding and estimates of the SO and Indo-Pacific circulations are urgently needed to accurately predict future AMOC change.
GIS-based methodology for culvert location evaluation on railways: a case study of the Qena–Safaga Track in Egypt
Authors: Ashour Mohamed A. , Mahmoud Hazem M. , Abu-Zaid Tarek S. 
Published: April 2024

ABSTRACT
Railway infrastructure plays a critical role in transportation networks, and ensuring its integrity and resilience is of utmost importance. Culverts are vital components of railway tracks, providing drainage and water management to prevent structural damage and disruptions. Identifying suitable locations for culverts requires careful evaluation and consideration of various factors. A study using GIS techniques was conducted on an existing commercial railway track in the Eastern Desert of Upper Egypt to assess the effectiveness of existing culverts in preventing flash floods. The culvert suitability index map revealed that areas with high slopes and large drainage areas were more susceptible to water accumulation, indicating the need for culverts. The study also highlighted areas where culverts could be installed without significantly impacting existing infrastructure. It was recommended to install culverts in 27 locations along the track where they intersect with watercourses. Existing culverts covered just 93 watercourses, while 5 specialized culverts needed to be relocated. The findings have significant implications for railway engineering, as using GIS techniques streamlines the process of culvert location evaluation, saving time and resources. The systematic approach ensures culverts are installed in the most appropriate locations, minimizing flooding risks and ensuring the safety and efficiency of railway operations.
Real-Time Multiple Object Detection Using Raspberry Pi and Tiny-ML Approach
Authors: Jaiswal Tarun , Pandey Manju , Tripathi Priyanka 
Published: April 2024

Introduction:
Object detection has been an essential task in computer vision for decades,
and modern developments in computer vision and deep learning have greatly increased the
accuracy of detecting systems. However, the high computational requirements of deep learningbased
object detection algorithms limit their applicability to resource-constrained systems, such
as embedded devices.

Methods:
With the advent of Tiny Machine Learning (TinyML) devices, such as Raspberry Pi, it
has become possible to deploy object detection systems on small, low-power devices. Due to
their accessibility and cost, Tiny-ML devices, such as Raspberry Pi, a single-board tiny-ML device
that is extremely well-liked, have recently attracted a lot of attention.

Results:
In this study, we present an enhanced SSD-based object detection approach and deploy
the model using a tinyML device, i.e., Raspberry Pi.

Conclusion:
The proposed object detection model is lightweight and built utilizing Mobilenet-
V2 as the underlying foundation.
Improving cognitive learning outcomes and communication skills through problem-based learning with lesson study
Authors: Robi Fikri Syahir , Dianti Putri Rahma , Handayani Ning Rahayu 
Published: April 2024

Students' communication skills must be empowered by habituating discussion to increase learning and understanding. This habit can be trained by applying Lesson Study (LS) activities in Problem-Based Learning (PBL). This study analyzed the effectiveness of PBL learning with LS activities in improving cognitive learning outcomes and communication skills of Masters of Biology Education students at Universitas Negeri Malang. This type of research uses a mixed method with a quantitative research approach and a one-group-pretest-posttest research design. The sampling technique used purposive sampling. Data collection uses test, non-test, observation, and documentation techniques. Data analysis used descriptive qualitative, descriptive statistics, and inferential statistics t-tests. The results showed that the PBL learning syntax had been implemented well, and there were significant differences in the learning outcomes of the two cycles, with a significance value of 0.000 (cycle I) and 0.001 (cycle II) 0.05. Furthermore, the results of student communication skills also experienced an increase in scores from cycle I to cycle II by 0.5%. This shows that PBL learning with LS activities can effectively improve students' communication skills and can be recommended for learning. Memperbaiki pembelajaran kognitif dan keterampilan komunikasi melalui pembelajaran berbasis masalah dengan lesson studyPemberdayaan keterampilan komunikasi siswa perlu dilakukan dengan pembiasaan diskusi sebagai upaya peningkatan pemahaman belajar. Kebiasaan ini bisa dilatih dengan menerapkan kegiatan Lesson Study (LS) dalam model pembelajaran Problem Based Learning (PBL). Tujuan penelitian ini adalah untuk menganalisis efektivitas pembelajaran model PBL dengan kegiatan LS dalam meningkatkan hasil belajar kognitif dan keterampilan komunikasi mahasiswa Pascasarjana Pendidikan Biologi Universitas Negeri Malang. Jenis penelitian menggunakan mixed method dengan pendekatan penelitian kuantitatif dan desain penelitian one-group-pretest-postest. Pengambilan sampel menggunakan teknik purposive sampling. Pengumpulan data menggunakan teknik tes, non-test, observasi dan dokumentasi. Pada siklus pertama materi yang digunakan adalah penyusunan hipotesis dan siklus kedua menggunakan materi statistik dan analisis data. Analisis data menggunakan deskriptif kualitatif, statistik deskriptif dan statistik inferensial uji t-test. Hasil penelitian menunjukkan sintak pembelajaran PBL telah terlaksana dengan baik dan terdapat perbedaan nyata hasil belajar kedua siklus dengan nilai signifikansi 0,000 (siklus 1) dan 0,001 (siklus 2) 0,05. Selanjutnya hasil keterampilan komunikasi mahasiswa juga mengalami peningkatan skor dari siklus 1 ke siklus 2 sebesar 0.5%. Hal tersebut menunjukkan bahwa model PBL dengan kegiatan LS secara efektif dapat meningkatkan keterampilan komunikasi mahasiswa dan dapat direkomendasikan untuk pembelajaran.
Resiliensi guru sekolah menengah atas/kejuruan
Authors: Efianingrum Ariefa , Maryani Maryani , Sukardi Joko Sri , Hanum Farida , Dwiningrum Siti Irene Astuti 
Published: April 2024

Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor yang mempengaruhi resiliensi individu guru SMA/SMK di Daerah Istimewa Yogyakarta (DIY), menganalisis, dan menguji secara empiris pengaruh ketujuh aspek resiliensi terhadap resiliensi individu. Analisis dan pengujian menggunakan instrumen skala resiliensi personal dengan mengadopsi dari teori yang dikembangkan oleh Reivich dan Shatte. Populasi dalam penelitian adalah seluruh guru SMA/SMK di DIY, dengan pengambilan sampel minimum menggunakan rumus Lamshow sebanyak 249 guru SMA/SMK dengan teknik purposive sampling dan analisis data menggunakan SEM (Structural Equation Model) dengan bantuan software Lisrel 8.54. Temuan utama penelitian ini menunjukkan tingkat resiliensi individu guru SMA/SMK di DIY dengan mendasarkan pada tujuh faktor resiliensi individu yaitu aspek emotional regulation, impulse control, optimism, causal analysis, empathy, self-efficacy, and reaching out. Hasil analisis SEM menunjukkan bahwa aspek resiliensi individu guru SMA/SMK di DIY yang memiliki pengaruh paling dominan adalah aspek keempat (optimism), aspek kedua (impulse control), aspek keenam (aspect self efficacy), aspek ketiga (aspect empathy), aspek kelima (aspect causal analysis). Adapun aspek resiliensi individu guru SMA/SMK di DIY yang memiliki pengaruh terendah pada aspek ketujuh (reaching out) dan aspek kesatu (emotional regulation). Resilience of high school/vocational school teachersThe purpose of this study was to determine the factors that influence the individual resilience of SMA/SMK teachers in Daerah Istimewa Yogyakarta(DIY), analyze and empirically test the influence of the seven aspects on individual resilience using the individual resilience scale instrument adopted from Reivich and Shatte. The research population was all high school teachers in Yogyakarta, with a minimum sampling using the Lamshow formula of 249 high school teachers with purposive sampling technique and data analysis using SEM (Structural Equation Model) with the help of Lisrel 8.54 software. The main finding of the research is the individual resilience of high school teachers in Yogyakarta, which looks at seven individual resilience factors: emotional regulation, impulse control, optimism, causal analysis, empathy, self-efficacy, and reaching out. Based on the results of SEM analysis, explain that the most dominant aspects of individual resilience of high school teachers in DIY are the fourth aspect (aspect of optimism), the second aspect (aspect of impulse control), the sixth aspect (aspect of self-efficacy), the third aspect (aspect empathy), the fifth aspect (aspect causal analysis), and the lowest in the seventh aspect (aspect reaching out) and the first aspect (aspect emotional regulation.
Intensive Longitudinal Methods Among Adults With Breast or Lung Cancer: Scoping Review
Authors: Geeraerts Joran , de Nooijer Kim , Pivodic Lara , De Ridder Mark , Van den Block Lieve 
Published: April 2024

Background
Intensive longitudinal methods offer a powerful tool for capturing daily experiences of individuals. However, its feasibility, effectiveness, and optimal methodological approaches for studying or monitoring experiences of oncology patients remain uncertain.

Objective
This scoping review aims to describe to what extent intensive longitudinal methods with daily electronic assessments have been used among patients with breast or lung cancer and with which methodologies, associated outcomes, and influencing factors.

Methods
We searched the electronic databases (PubMed, Embase, and PsycINFO) up to January 2024 and included studies reporting on the use of these methods among adults with breast or lung cancer. Data were extracted on population characteristics, intensive monitoring methodologies used, study findings, and factors influencing the implementation of these methods in research and clinical practice.

Results
We identified 1311 articles and included 52 articles reporting on 41 studies. Study aims and intensive monitoring methodologies varied widely, but most studies focused on measuring physical and psychological symptom constructs, such as pain, anxiety, or depression. Compliance and attrition rates seemed acceptable for most studies, although complete methodological reporting was often lacking. Few studies specifically examined these methods among patients with advanced cancer. Factors influencing implementation were linked to both patient (eg, confidence with intensive monitoring system) and methodology (eg, option to use personal devices).

Conclusions
Intensive longitudinal methods with daily electronic assessments hold promise to provide unique insights into the daily lives of patients with cancer. Intensive longitudinal methods may be feasible among people with breast or lung cancer. Our findings encourage further research to determine optimal conditions for intensive monitoring, specifically in more advanced disease stages.
Classification of Patients’ Judgments of Their Physicians in Web-Based Written Reviews Using Natural Language Processing: Algorithm Development and Validation
Authors: Madanay Farrah , Tu Karissa , Campagna Ada , Davis J Kelly , Doerstling Steven S , Chen Felicia , Ubel Peter A 
Published: April 2024

Background
Patients increasingly rely on web-based physician reviews to choose a physician and share their experiences. However, the unstructured text of these written reviews presents a challenge for researchers seeking to make inferences about patients’ judgments. Methods previously used to identify patient judgments within reviews, such as hand-coding and dictionary-based approaches, have posed limitations to sample size and classification accuracy. Advanced natural language processing methods can help overcome these limitations and promote further analysis of physician reviews on these popular platforms.

Objective
This study aims to train, test, and validate an advanced natural language processing algorithm for classifying the presence and valence of 2 dimensions of patient judgments in web-based physician reviews: interpersonal manner and technical competence.

Methods
We sampled 345,053 reviews for 167,150 physicians across the United States from Healthgrades.com, a commercial web-based physician rating and review website. We hand-coded 2000 written reviews and used those reviews to train and test a transformer classification algorithm called the Robustly Optimized BERT (Bidirectional Encoder Representations from Transformers) Pretraining Approach (RoBERTa). The 2 fine-tuned models coded the reviews for the presence and positive or negative valence of patients’ interpersonal manner or technical competence judgments of their physicians. We evaluated the performance of the 2 models against 200 hand-coded reviews and validated the models using the full sample of 345,053 RoBERTa-coded reviews.

Results
The interpersonal manner model was 90% accurate with precision of 0.89, recall of 0.90, and weighted F1-score of 0.89. The technical competence model was 90% accurate with precision of 0.91, recall of 0.90, and weighted F1-score of 0.90. Positive-valence judgments were associated with higher review star ratings whereas negative-valence judgments were associated with lower star ratings. Analysis of the data by review rating and physician gender corresponded with findings in prior literature.

Conclusions
Our 2 classification models coded interpersonal manner and technical competence judgments with high precision, recall, and accuracy. These models were validated using review star ratings and results from previous research. RoBERTa can accurately classify unstructured, web-based review text at scale. Future work could explore the use of this algorithm with other textual data, such as social media posts and electronic health records.
Medical Education and Artificial Intelligence: Web of Science–Based Bibliometric Analysis (2013-2022)
Authors: Wang Shuang , Yang Liuying , Li Min , Zhang Xinghe , Tai Xiantao 
Published: April 2024

Abstract

Background
Incremental advancements in artificial intelligence (AI) technology have facilitated its integration into various disciplines. In particular, the infusion of AI into medical education has emerged as a significant trend, with noteworthy research findings. Consequently, a comprehensive review and analysis of the current research landscape of AI in medical education is warranted.

Objective
This study aims to conduct a bibliometric analysis of pertinent papers, spanning the years 2013‐2022, using CiteSpace and VOSviewer. The study visually represents the existing research status and trends of AI in medical education.

Methods
Articles related to AI and medical education, published between 2013 and 2022, were systematically searched in the Web of Science core database. Two reviewers scrutinized the initially retrieved papers, based on their titles and abstracts, to eliminate papers unrelated to the topic. The selected papers were then analyzed and visualized for country, institution, author, reference, and keywords using CiteSpace and VOSviewer.

Results
A total of 195 papers pertaining to AI in medical education were identified from 2013 to 2022. The annual publications demonstrated an increasing trend over time. The United States emerged as the most active country in this research arena, and Harvard Medical School and the University of Toronto were the most active institutions. Prolific authors in this field included Vincent Bissonnette, Charlotte Blacketer, Rolando F Del Maestro, Nicole Ledows, Nykan Mirchi, Alexander Winkler-Schwartz, and Recai Yilamaz. The paper with the highest citation was “Medical Students’ Attitude Towards Artificial Intelligence: A Multicentre Survey.” Keyword analysis revealed that “radiology,” “medical physics,” “ehealth,” “surgery,” and “specialty” were the primary focus, whereas “big data” and “management” emerged as research frontiers.

Conclusions
The study underscores the promising potential of AI in medical education research. Current research directions encompass radiology, medical information management, and other aspects. Technological progress is expected to broaden these directions further. There is an urgent need to bolster interregional collaboration and enhance research quality. These findings offer valuable insights for researchers to identify perspectives and guide future research directions.