bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unknown Building Exploration via Frontier Incentivazition under Various Communication Scenarios
Authors: Kocabas Huzeyfe , Harper Mario 
Published: December 2023

Abstract
Exploring unknown indoor environments poses significant challenges for robotic systems, especially in restricted communication areas. We propose a new and computationally lightweight approach for multi-robot indoor exploration by integrating frontier-based exploration, Voronoi sub-partitioning, and an agent detection mechanism that significantly improves exploration time. Unlike existing studies, our research systematically investigates three communication scenarios between robots: no communication, limited communication (within sensor range and line of sight), and oracle-based communication (central monitoring). Through extensive simulations in diverse map complexities, this study shows the improved performance of the proposed agent detection mechanism in unknown building exploration under limited communication.
Anti CFH-associated HUS: do we still need Plasma Exchange?
Authors: Ferri Marion , Zotta Frederica , Donadelli Roberta , Dossier Claire , Duneton Charlotte , El-Sissy Carine , Fremeaux-Bacchi Veronique , Kwon Theresa , Quadri Lisa , Pasini Andrea , Sellier-Leclerc Anne-Laure , Vivarelli Marina , Hogan Julien 
Published: December 2023

Abstract
Background
Five to 50% of atypical hemolytic and uremic syndrome (aHUS) in children are caused by autoantibodies against complement Factor H (CFH). Given the acquired autoimmune nature of the disease, plasma exchange and various immunosuppressive treatments have been used. More recently, eculizumab has been proposed.
Methods
In this multicenter, retrospective study, we report the outcome of 12 children with anti-CFH antibody-associated HUS treated with eculizumab associated with various immunosuppressive regimens.
Results
Patients were treated with eculizumab for 15.5 [9.5;23.0] months and 3 received plasma exchange (PE) or IgG adsorption. Three patients received MMF alone, 1 patient received an association of MMF and steroids, 1 patient received an association of MMF and rituximab, 3 patients received MMF/steroids and rituximab and 4 patients did not receive any immunosuppression. Anti-CFH ab levels significantly decreased but no difference was observed based on the immunosuppressive regimen. ECZ was discontinued in 7/10 patients after 11 [7.5–15.5] months and MMF in 6/8 patients after 36 [35;40] months. Anti-CFH titers at MMF discontinuation ranged from 257 to 3425 UI/L. None of these patients relapsed and eGFR at last follow-up was above 70mL/min/1.73m2 in all patients.
Conclusion
Eculizumab is effective and safe in inducing and maintaining remission in aHUS secondary to anti-CFH antibodies and renders anti-CFH titers reduction less urgent. Anti-CFH antibody titers decreased in most patients irrespective of the immunosuppressive treatment chosen, so that a strategy consisting of associating eculizumab with MMF monotherapy seems sufficient at least in non-Indian or less severe forms of anti-CFH antibody-associated HUS.
Assessment of conjunctival microvessels for patients with coronary artery disease using Optical Coherence Tomographic Angiography
Authors: Liu Jing , Sun Jiayi , Zhang Yue , Gu Xiaoya , Yu Xue , Yu Xiaobing 
Published: December 2023

Abstract
Background
To quantify conjunctival microvascular characteristics obtained by optical coherence tomographic angiography (OCTA) and investigate their relationship with the presence and severity of coronary artery disease (CAD).
Methods
This cross-sectional study included 103 consecutive CAD patients confirmed by coronary angiography and 125 non-CAD controls. The temporal conjunctivas along the limbus of each participant were scanned using OCTA. Quantification of conjunctival microvasculature was performed by AngioTool software. The severity of the disease was evaluated using SYNTAX and Gensini scores.
Results
Compared to the controls, the CAD group exhibited significantly lower vessel area density (30.22 ± 3.34 vs. 26.70 ± 4.43%, p < 0.001), lower vessel length density (6.39 ± 0.77 vs. 5.71 ± 0.89/m, p < 0.001), lower junction density (3.44 ± 0.56 vs. 3.05 ± 0.63/m, p < 0.001), and higher lacunarity (0.11 ± 0.03 vs. 0.14 ± 0.05, p < 0.001). Among all participants, lower vessel area density, lower vessel length density, lower junction density, and higher lacunarity were associated with greater odds of having CAD; the adjusted ORs (95% confidence intervals) per one SD decrease were 2.71 (1.71, 4.29), 2.51(1.61, 3.90), 2.06 (1.39, 3.05), and 0.36 (0.23, 0.58), respectively. Among CAD patients, junction density was negatively associated with the Gensini score (r=-0.359, p = 0.037) and the Syntax score (r=-0.350, p = 0.042) in women but not in men (p > 0.05).
Conclusions
Conjunctival microvascular characteristics were significantly associated with the presence of CAD. Junction density significantly associated with the severity of CAD among women patients.
Examining the Impact of Social Media Use on University Students' Learning, Sleeping Habits, and Social Interactions: A Quantitative Analysis
Authors: Olugbade Damola 
Published: December 2023

Abstract
Social media use has grown prevalent across the world, affecting the lives of people of all ages, including university students. People may now create, share, and interact with others across long distances because to the increased acceptance of social media platforms. The goal of this study was to investigate the consequences of social media use on university students' learning, sleeping habits, and social interactions. A cross-sectional study approach was used using a sample of 200 students from First Technical University in Ibadan, Nigeria. A questionnaire was utilized to collect data, and statistical analysis was performed using the Chi-squared (Fisher's exact test) method. According to the data, 98% of the participants used social networking sites. Furthermore, 66% of students said that social media addiction had a detrimental influence on their academic performance since they were more drawn to social media engagement. Tiktok was the most widely used social media platform (41%), followed by Snapchat (24%), WhatsApp (19%), Instagram (10%), Telegram (2%), and Twitter (2%). In addition, the survey examined students' sleeping patterns, with 50% reporting bedtime between 10 p.m. and 11 p.m., and 44% sleeping between 1 a.m. and 2 a.m. Notably, 71% of students blamed their later bedtime on their use of social media. Finally, half of the respondents admitted that social media had an impact on their social interactions. In conclusion, this study highlights the extensive application of social media among university students and its potential impact on various aspects of their lives. The findings highlight the need for more study and interventions to address the addictive nature of social media, encourage good sleeping habits, and buffer any harmful effects on interactions among this demographic.
Application of multi-angle spaceborne observations in characterizing the long-term particulate organic carbon pollution in China
Authors: Hang Yun , Pu Qiang , Zhu Qiao , Meng Xia , Jin Zhihao , Liang Fengchao , Tian Hezhong , Li Tiantian , Wang Tijian , Cao Junji , Fu Qingyan , Dey Sagnik , Li Shenshen , Huang Kan , Kan Haidong , Shi Xiaoming , Liu Yang 
Published: December 2023

Abstract
Ambient PM2.5 pollution is recognized as a leading environmental risk factor, causing significant mortality and morbidity in China. However, the specific contributions of individual PM2.5 constituents remain unclear, primarily due to the lack of a comprehensive ground monitoring network for constituents. This issue is particularly critical for carbonaceous species such as organic carbon (OC) and elemental carbon (EC), which are known for their significant health impacts, and understanding the OC/EC ratio is crucial for identifying pollution sources. To address this, we developed a Super Learner model integrating Multi-angle Imaging SpectroRadiometer (MISR) retrievals to predict daily OC concentrations across China from 2003 to 2019 at a 10-km spatial resolution. Our model demonstrates robust predictive accuracy, as evidenced by a random cross-validation R² of 0.84 and an RMSE of 4.9 μg/m³, at the daily level. Although MISR is a polar-orbiting instrument, its fractional aerosol data make a significant contribution to the OC exposure model. We then use the model to explore the spatiotemporal distributions of OC and further calculate the EC/OC ratio in China. We compared regional pollution discrepancies and source contributions of carbonaceous pollution over three selected regions: Beijing-Tianjin-Hebei, Fenwei Plain, and Yunnan Province. Our model observes that OC levels are elevated in Northern China due to industrial operations and central heating during the heating season, while in Yunnan, OC pollution is mainly contributed by local forest fires during fire seasons. Additionally, we found that OC pollution in China is likely influenced by climate phenomena such as the El Niño-Southern Oscillation. Considering that climate change is increasing the severity of OC concentrations with more frequent fire events, and its influence on OC formation and dispersion, we suggest emphasizing the role of climate change in future OC pollution control policies. We believe this study will contribute to future epidemiological studies on OC, aiding in refining public health guidelines and enhancing air quality management in China.
12-Methoxy Carnosic acid efficacy determinationand optimization through computational tools for 5alpha reductase enzyme and using finasteride as refrence molecule.
Authors: bahaa Ahmed 
Published: December 2023

Abstract
It has been known for many decades that rosemary is used as treatment for Alopecia and hair loss
This paper is addressing the efficacy of 12-Methoxy carnosic acid found in rosemary as 5 alpha reductase inhibitor compared to finasteride through molecular docking techniques. Using docking tools like Auto dock tools to assign enzyme configuration box and then using vina tool for molecular docking and getting reports for both ligands. Also chemsketch was used to optimize the drug and ensure the docking of the 2 pockets of 5 alpha reductase . Also pymol was used for visualization and ligand-enzyme interactions different orientations with different scores. Also open babel was used to handle the many conversions in cheminformatics nature. The results were very promising for a natural molecule like 12-methoxy carnosic acid molecule as 5 alpha reductase inhibitor . This review authenticates that rosemary has appeared as a reliable source for curing hair loss, by inhibiting 5 alpha reductase enzyme. It also opens the door for further studies on single isolated constituents, especially carnosic acid,and support drug discovery with computational and cheminformatics techniques.
Creation of Iğdır geological map with geographic information systems (GIS) and comparison with large soil groups
Authors: KARAOĞLU Mücahit 
Published: December 2023

Abstract
The geological map of Iğdır was prepared using the abolished General Directorate of Rural Services (GDRS, 1/25000), General Directorate of Land Reform (GDLR, 1/25000), General Directorate of Mineral Research and Exploration (GDMRE, 1/500000) maps and Geographic Information System (GIS). A Digital Elevation Model (DEM) of the Iğdır province land and hydrographic map was created using the 1/25000 scale topographic map of the General Directorate of Mapping (GDM) of the Iğdır province. On the hydrographic map, 6 bifurcation sequences and 13 river sub-basins were determined and the basin map of Iğdır province was created. Iğdır province SPI distribution map was prepared with Stream Power Index (SPI) calculations. Area and percentage calculations were performed with ArcGIS Pro 3.0.1. The geological map of Iğdır province was compared with the map of large soil groups of Iğdır province prepared previously. Consequently, it was determined that on the 4th time formations Basaltic, Maroon, Colluvial, Hydromorphic and Alluvial soils; on the 3rd time Miosen formations Brown, Regosol, Basaltic and Colluvial soils; on the 2nd time Mesozoic formations Brown soils and Bare Rocks and Slope Debris; on the 1st time common formations of Permian and Carboniferous periods Alluvial soils were located. SPI values were calculated between 14.6 and − 13.8 from high mountainous areas to the depression plain, and it was determined that seasonal stream flows have high erosive power.
Examining Relationships Between Nonprofit Organizations and Board Member Affiliations: A Social Network Study
Authors: Dong Weichuan 
Published: December 2023

Abstract
This paper investigates the relationship between nonprofit organizations (NPOs) and third-party organizations (TPOs) in Western New York by examining the composition of NPO boards. While past research has explored board diversity's impact on NPO performance, this study focuses on the identities and affiliations of board members, specifically their roles in external TPOs. Three key questions guide the investigation: 1) the overall composition of NPO boards in terms of external TPO affiliations, 2) variations in board composition across NPO categories, and 3) the broader NPO-TPO relationship structure and characteristics of key-position NPOs. The findings shed light on the current state of relationships between NPOs and external resources, providing insights for NPOs in board member selection and support attraction, as well as guidance for TPOs interested in specific social activities. The paper employs theoretical perspectives such as weak ties, reviews relevant literature, introduces network models, and discusses results within the research framework, contributing practical insights to decision-making in Western New York's nonprofit sector.
AI-based Wildfire Prevention, Detection and Suppression System
Authors: Shroff Prisha 
Published: December 2023

Abstract
Wildfires pose a serious threat to the world’s en- vironment. The global wildfire season length has increased by 19% and severe wildfires have besieged nations around the world. Every year, forests are burned by these wildfires, causing vast amounts of carbon dioxide to be released into the atmosphere, contributing to climate change. Therefore, there is a need for a system which prevents, detects, and suppresses wildfires. The AI-based Wildfire Prevention, Detection and Suppression System (WPDSS) is a novel, fully-automated, end-to-end, AI-based so- lution to effectively predict hotspots (areas where wildfires can start) and detect wildfires, deploy drones to spray fire retardant, preventing and suppressing wildfires. WPDSS consists of four steps. 1) Pre-processing: WPDSS loads real-time satellite data from NASA and meteorological data from NOAA of vegetation, temperature, precipitation, wind, soil moisture, and land cover for prevention. For detection, it loads the real-time data of Land Cover, Humidity, Temperature, Vegetation, Burned Area Index, Ozone, and CO2. It uses the process of masking to eliminate not-hotspots and not-wildfires such as water bodies, and rainfall. 2) Learning: The AI model consists of a random forest classifier, a type of supervised machine learning, which uses a series of decision trees to make an accurate decision. It is trained using a labeled dataset of hotspots/wildfires and not-hotspots/not- wildfires. 3) Identification of hotspots and wildfires: WPDSS runs the real-time data through the model to automatically identify hotspots and wildfires. 4) Drone deployment: The drone flies to the identified hotspot/wildfire location. WPDSS attained a 98.6% accuracy in identifying hotspots and a 98.7% accuracy in detecting wildfires. WPDSS will reduce the impacts of climate change, protect ecosystems and biodiversity, avert huge economic losses, and save human lives. The power of WPDSS developed can be applied to any location globally to prevent and suppress wildfires, reducing climate change.
Improving Sentiment Analysis in Online Course Reviews with BERT and Transformer Attention Mechanism
Authors: Meem Raisa Fairooz , Hasan Khandaker Tabin 
Published: December 2023

Abstract
In the field of text mining, sentiment analysis has grown significantly in importance for understanding user reactions. This knowledge contributes to improving several features of given goods or services. Considering its adequacy, sentiment analysis can play a significant role in the educational field, where student input is essential for enhancing curricula and recommending courses, resources, and other elements. Likewise, because of the higher accuracy and decreased complexity, transfer learning models are increasingly being used in a variety of domains, which suggests that the educational sector will substantially benefit from incorporating them into its related domains.
To leverage the advantages of both sentiment analysis and transfer learning techniques, this study suggests a Transfer Learning Model (BERT-base-uncased) based on BERT (Bidirectional Encoder Representations from Transformers) using the Transformer Attention Mechanism to analyse the sentiment of student feedback from various online courses. Two applications of the suggested model have been demonstrated- one with a sequence classifier and another without it. The result shows a significant accuracy of 76% with a high recall value with the sequence classifier.
New Ureas and Amides - An Account of Recent Trends and Developments in Low
Molecular Weight Gelators
Authors: Patel Ajaykumar Manibhai , Bhardwaj Varsha , Ballabh Amar 
Published: December 2023

Abstract:
The last 20 years have witnessed major advancements in the field of supramolecular
chemistry and have brought us closer to the designing of low molecular
weight gelators with desired properties and applications. In that regard, amide- and ureabased
gelators comprise a unique class as they are extremely versatile in terms of molecular
design and offer a wide range of applications, like anion responsive materials,
selective sensing of heavy metal ions, environmental remediation and many more. Both
sets of compounds have similar molecular scaffolds, making them an excellent tool to
determine the relative importance of the supramolecular interactions involved in the
gelation process. Besides, the concept of crystal engineering can also be employed to
understand the underlying mechanism of gelation by scrutinizing the interactions and
supramolecular assemblies formed by these systems. In this article, we focus on various supramolecular
assemblies formed by various amide and urea derivatives and their recently reported applications to establish
structure-property correlation and their futuristic aspects.
Hypertension Management in Peripheral Artery Disease: A Mini Review
Authors: Kharawala A. , Nagraj S. , Pargaonkar S. , Seo J. , Kokkinidis D.G. , Altin S.E. 
Published: December 2023

Abstract:
Lower extremity peripheral artery disease (PAD) affects over 230 million adults globally, with hypertension being one of the major risk factors for the development of PAD. Despite
the high prevalence, patients with hypertension who have concomitant PAD are less likely to receive adequate therapy. Through this review, we present the current evidence underlying hypertension management in PAD, guideline-directed therapies, and areas pending further investigation. Multiple studies have shown that both high and relatively lower blood pressure levels are
associated with worse health outcomes, including increased morbidity and mortality. Hence,
guideline-directed recommendation involves cautious management of hypertensive patients with
PAD while ensuring hypotension does not occur. Although any antihypertensive medication can
be used to treat these patients, the 2017 American Heart Association/American College of Cardiology (AHA/ACC), 2017 European Society of Cardiology (ESC), and 2022 Canadian guidelines favor the use of angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor
blockers (ARB) as the initial choice. Importantly, data on blood pressure targets and treatment of
hypertension in PAD are limited and largely stem from sub-group studies and post-hoc analysis.
Large randomized trials in patients with PAD are required in the future to delineate hypertension
management in this complex patient population.
Machine Learning Approaches for the Image-Based Identification of Surgical Wound Infections: Scoping Review
Authors: Tabja Bortesi Juan Pablo , Ranisau Jonathan , Di Shuang , McGillion Michael , Rosella Laura , Johnson Alistair , Devereaux PJ , Petch Jeremy 
Published: December 2023

Background
Surgical site infections (SSIs) occur frequently and impact patients and health care systems. Remote surveillance of surgical wounds is currently limited by the need for manual assessment by clinicians. Machine learning (ML)–based methods have recently been used to address various aspects of the postoperative wound healing process and may be used to improve the scalability and cost-effectiveness of remote surgical wound assessment.

Objective
The objective of this review was to provide an overview of the ML methods that have been used to identify surgical wound infections from images.

Methods
We conducted a scoping review of ML approaches for visual detection of SSIs following the JBI (Joanna Briggs Institute) methodology. Reports of participants in any postoperative context focusing on identification of surgical wound infections were included. Studies that did not address SSI identification, surgical wounds, or did not use image or video data were excluded. We searched MEDLINE, Embase, CINAHL, CENTRAL, Web of Science Core Collection, IEEE Xplore, Compendex, and arXiv for relevant studies in November 2022. The records retrieved were double screened for eligibility. A data extraction tool was used to chart the relevant data, which was described narratively and presented using tables. Employment of TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines was evaluated and PROBAST (Prediction Model Risk of Bias Assessment Tool) was used to assess risk of bias (RoB).

Results
In total, 10 of the 715 unique records screened met the eligibility criteria. In these studies, the clinical contexts and surgical procedures were diverse. All papers developed diagnostic models, though none performed external validation. Both traditional ML and deep learning methods were used to identify SSIs from mostly color images, and the volume of images used ranged from under 50 to thousands. Further, 10 TRIPOD items were reported in at least 4 studies, though 15 items were reported in fewer than 4 studies. PROBAST assessment led to 9 studies being identified as having an overall high RoB, with 1 study having overall unclear RoB.

Conclusions
Research on the image-based identification of surgical wound infections using ML remains novel, and there is a need for standardized reporting. Limitations related to variability in image capture, model building, and data sources should be addressed in the future.