bluuebunny/crossref_metadata_2025_split huggingface dataset:
GSCW-YOLO：A Real-Time Lightweight Behavior Recognition Model for Multi- Dairy-Goat
Authors: Wang Xiaobo , Wang Meili , Hu Yufan , Li Mei , Zhao Wenxiao , Mao Rui 
Published: October 2024

Abstract

Livestock behavior serves as a crucial indicator of physiological health. Using deep learning methods to automatically recognize dairy goat behaviors, especially abnormal ones, can aid in the early detection of potential health and environmental issues. This study proposed a multi-scale and lightweight behavior recognition model for dairy goats, known as GSCW-YOLO, which integrates Gaussian Context Transformation (GCT) and the Content-Aware Reassembly of Features upsampling operator (CARAFE). This integration aims to enhance the YOLOv8n model's attention to behavioral features, mitigate complex background interferences and concurrently enhance the model's capability to discern subtle differences among behaviors. Furthermore, the model includes a small target detection layer and optimization of the Wise-IoU loss function to enhance its adaptability in identifying distant small target behaviors and transient abnormal behaviors in surveillance videos. A new dataset called GoatABRD was also established, encompassing four abnormal behaviors (limping, attacking, death and gnawing) and six common behaviors (standing, lying, eating, drinking, scratching and grooming). The experimental results demonstrate that the precision, recall and mean Average Precision (mAP) of the GSCW-YOLO model reached 93.5%, 94.1% and 97.5%, respectively, marking improvements of 3, 3.1 and 2 percentage points over the YOLOv8n model. Moreover, the GSCW-YOLO model has a considerably smaller size of only 5.9MB, with a frame per second (FPS) of 175. Meanwhile, it outperforms popular models like CenterNet, Efficientdet, and other networks in the YOLO series. GSCW-YOLO provides significant technical support for the intelligent management and welfare-focused breeding of dairy goats, thereby facilitating the modernization of the dairy goat industry.
DeepEnhancerPPO: An Interpretable Deep Learning Approach for Enhancer Classification
Authors: Mu Xuechen , Chen Qiufen , Shi Bocheng , Xu Long , Huang Zhenyu , Xu Ying , Zhang Kai 
Published: October 2024

Abstract

Enhancers are short genomic segments located in non-coding regions in a genome that help to increase the expressions of the target genes. Despite their significance in transcription regulation, effective methods for classifying enhancer categories and regulatory strengths remain limited. To address the issue, we propose a novel end-to-end deep learning architecture named DeepEnhancerPPO. The model integrates ResNet and Transformer modules to extract local, hierarchical, and long-range contextual features. Following feature fusion, we employ the proximal policy optimization (PPO), a reinforcement learning technique, to reduce the dimensionality of the fused features, retaining the most relevant ones for downstream classification. We evaluate the performance of DeepEnhancerPPO from multiple perspectives, including ablation analysis, independent tests, and interpretability of classification results. Each of these modules contributes positively to the model's performance, with ResNet and PPO being the top contributors. Overall, DeepEnhancerPPO exhibits superb performance on independent datasets compared to other models, outperforming the second-best model by 6.7% in accuracy for enhancer category classification. The model also ranks within the top five classifiers out of 25 in enhancer strength classification without the need to re-optimize the hyperparameters, indicating that the DeepEnhancerPPO framework is highly robust for enhancer classification. Additionally, the inclusion of PPO enhances the interpretability of the classification results. The source code is openly accessible at https://github.com/Mxc666/DeepEnhancerPPO.git.
Colloquial Persian POS (CPPOS) Corpus: A Novel Corpus for Colloquial Persian Part of Speech Tagging
Authors: Rabiei Leila , Rahmani Farzaneh , Khansari Mohammad , Rajabi Zeinab , Salimi Moein 
Published: October 2024

Abstract

Introduction: Part-of-Speech (POS) Tagging, the process of classifying words into their respective parts of speech (e.g., verb or noun), is essential in various natural language processing applications. POS tagging is a crucial preprocessing task for applications like machine translation, question answering, sentiment analysis, etc. However, existing corpora for POS tagging in Persian mainly consist of formal texts, such as daily news and newspapers. As a result, smart POS tools, machine learning models, and deep learning models trained on these corpora may not perform optimally for processing colloquial text in social network analysis.
Method: This paper introduces a novel corpus, "Colloquial Persian POS" (CPPOS), specifically designed to support colloquial Persian text. The corpus includes formal and informal text collected from various domains such as political, social, and commercial on Telegram, Twitter, and Instagram more than 520K labeled tokens. After collecting posts from these social platforms for one year, special preprocessing steps were conducted, including normalization, sentence tokenizing, and word tokenizing for social text. The tokens and sentences were then manually annotated and verified by a team of linguistic experts. This study also defines a POS tagging guideline for annotating the data and conducting the annotation process.
Results: To evaluate the quality of CPPOS, various deep learning models, such as the RNN family, were trained using the constructed corpus. A comparison with another well-known Persian POS corpus named "Bijankhan" and the Persian Hazm POS tool trained on Bijankhan revealed that our model trained on CPPOS outperforms them. With the new corpus and the BiLSTM deep neural model, we achieved a 14% improvement over the previous dataset.
Exploratory comparison of shielding performance of foam-core and corrugated-plate-core sandwich panels at hypervelocity impact
Authors: Telichev Igor 
Published: October 2024

Abstract

Over the past two decades, extensive research has demonstrated the effectiveness of foam-core sandwich panels in shielding spacecraft from micrometeoroid and orbital debris (MMOD). This study investigated an alternative solution by comparing the shielding performance of foam-core and corrugated plate-core sandwich panels. Hypervelocity impact tests were conducted using 6-mm aluminum spherical projectiles on both foam-core and corrugated plate-core panel configurations. The results revealed that the corrugated plate core modified the interaction of debris fragments with the rear wall, leading to improved mitigation of hypervelocity impacts. These findings demonstrated the weight efficiency of the corrugated plate core with ballistic inserts for MMOD protection and suggested the need for further research to advance this approach.
Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida
Authors: Zournas Apostolos , Incha Matthew , Radivojevic Tijana , Blay Vincent , Marti Jose , Costello Zak , Schmidt Matthias , Chung Tan , Thompson Mitchell , Pearson Allie , Kinnunen Patrick , Eng Thomas , Lawson Christopher , Tan Stephen , Ogorzalek Tadeusz , Kaplan Nurgul , Forrer Mark , Backman Tyler , Mukhopadhyay Aindrila , Hillson Nathan , Keasling Jay , Martin Hector Garcia 
Published: October 2024

Abstract

Although synthetic biology can produce valuable chemicals in a renewable manner, its progress is still
hindered by a lack of predictive capabilities. Media optimization is a critical, and often overlooked,
process which is essential to obtain the titers, rates and yields needed for commercial viability. Here,
we present a molecule- and host-agnostic active learning process for media optimization that is
enabled by a fast and highly repeatable semi-automated pipeline. Its application yielded 60% and 70%
increases in titer, and 350% increase in process yield in three different campaigns for flaviolin
production in Pseudomonas putida KT2440. Explainable Artificial Intelligence techniques pinpointed
that, surprisingly, common salt (NaCl) is the most important component influencing production. The
optimal salt concentration is very high, comparable to seawater and close to the limits that P. putida
can tolerate. The availability of fast Design-Build-Test-Learn (DBTL) cycles allowed us to show that
performance improvements for active learning are rarely monotonous. This work illustrates how
machine learning and automation can change the paradigm of current synthetic biology research to
make it more effective and informative, and suggests a cost-effective and underexploited strategy to
facilitate the high titers, rates and yields essential for commercial viability.
Transformer-based Deep Learning Model for Fluorescence Lifetime Parameter Estimations using Pixelwise Instrument Response Function
Authors: Erbas Ismail , Pandey Vikas , Nizam Navid Ibtehaj , Yuan Nanxue , Verma Amit , Barroso Margarida , Intes Xavier 
Published: October 2024

Abstract

Fluorescence lifetime imaging (FLI) is an important molecular imaging modality that can provide unique information for biomedical applications. FLI is based on acquiring and processing photon time of arrival histograms. The shape and temporal offset of these histograms depends on many factors, such as the instrument response function (IRF), optical properties, and the topographic profile of the sample. Several inverse solver analytical methods have been developed to compute the underlying fluorescence lifetime parameters, but most of them are computationally expensive and time-consuming. Thus, deep learning (DL) algorithms have progressively replaced computation methods in fluorescence lifetime parameter estimation. Often, DL models are trained with simple datasets either generated through simulation or a simple experiment where the fluorophore surface profile is mostly flat; therefore, DL models often do not perform well on samples with complex surface profiles such as ex-vivo organs or in-vivo whole intact animals. Herein, we introduce a new DL architecture, MFliNet (Macroscopic FLI Network), that takes an additional input of IRF together with TPSF, addressing discrepancies in the photon time-of-arrival distribution. We demonstrate the model’s performance through carefully designed, complex tissue-mimicking phantoms and preclinical in-vivo cancer xenograft experiments.
Impact of symptomatic COVID-19 on the oral health of pediatric patients in Tbilisi City
Authors: Mania Lia , Nanobashvili Ketevan , Manjavidze Tinatin , Benashvili Mamuka , Astamadze Ia 
Published: October 2024

Abstract

Background/Purpose: Coronavirus disease 2019 (COVID-19) has become the cause of a global health crisis during the current pandemic. This research aimed to study the impact of symptomatic COVID-19 on children’s oral health indices and salivary microbiome composition duringthe post-COVID-19 period.
Methods: An observational, cross-sectional study was conducted in Tbilisi (Georgia) among those aged 7-12 years. A total of421 children included in the study had a history of laboratory-confirmed COVID-19 within one year of exposure. No participants met the criteria for comorbidconditions or for PCC. A stratified simple random selection of schools and among selected clusters was used. The selected children were divided into two groups: the exposed group, patients with a history of symptomatic COVID-19; and the control group, patients with a history of asymptomatic COVID-19. The data were collected from August 2022 to December 2023. Oralscreening, microbiological examination of saliva, and administration of questionnaires were also performed. Logistic regression was used to calculate ORs with 95% confidence intervals. The statistical processing of the data was performed with SPSS-23. The study was approved by the Biomedical Research Ethical Council of the University of Georgia (UGREC –04 –22/09.03.2022).
Results: Statistically significant differences in the means of the oral health indicators between the studied groups were detected (exposed: DMFT+deft=5.9; MGI=0.92; S-OHI=1.9; control: DMFT+deft=3.8; MGI=0.56; S-OHI=1.4). According to the logistic regression, symptomatic COVID-19 had a significant effect on the following oral health indicators: DMFT+deft (OR=1.26; 95% CI=1.14-1.39); MGI (OR=2.31; 95% CI=1.50-3.55); and S-OHI (OR=3.43; 95% CI=2.03-5.76). The effect of symptomatic COVID-19 on the frequency of eradication of the studied microbiome was also significant (OR=2.12; 95% CI=1.23-3.63).
Conclusion: A close association was established between symptomatic COVID-19 and microbiome changes in the oral saliva of children as well as between oral health indicators and symptomatic COVID-19. Considering the research results, it is assumed that a symptomatic course of COVID-19 may be an additional risk factor associated with poor oral health in the pediatric population in the post-COVID-19 period.
Automated Token-Level Detection of Persuasive and Misleading Words in Text Using Large Language Models
Authors: Nijodo David , Schmidt Daniel , Costa Samuel , Martins Andrew , Johnson Nicholas 
Published: October 2024

Abstract

Persuasive and misleading language has long been a powerful tool in shaping public opinion, guiding consumer behavior, and influencing political discourse. The complexity of detecting subtle rhetorical strategies, particularly at the token level, presents a significant challenge for traditional methods of text analysis. The novel approach developed in this study leverages token-level processing within transformer-based models to classify words based on their persuasive and misleading potential, providing a granular perspective on language manipulation. Through comprehensive experiments, the analysis demonstrated that tokens linked to sentiment polarity, lexical complexity, and positional importance play key roles in shaping the rhetorical impact of texts. This method provides an efficient and scalable solution for automated content moderation, political discourse analysis, and advertising regulation, with applications extending to media analysis and misinformation detection. The integration of attention mechanisms and contextual embeddings offers a detailed view into how language functions at a deeper structural level, positioning this framework as a significant advancement in automated text analysis.
REIMAGINING CLASSROOM PRACTICES WITH THE LENS OF VIRTUAL REALITY AND AUGMENTED REALITY FOR QUALITY LEARNING TEACHING IN SCHOOLS
Authors: Dr Anjali Bhatnagar  
Published: October 2024

The school education sector is gearing up for Virtual reality (AR) and augmented reality (AR) technologies to find place in classroom learning practices. VR immerses students in a simulated environment, while AR enhances the real-world environment with digital content. This technology allows students to experience situations that may not be possible or safe in real life. It provides a more engaging and interactive learning experience in all subjects. This study evaluates the impact of VR and AR implementation in classrooms on students learning outcomes. The study analysis investigates the use of VR and AR in science subject. Research shows that VR and AR can enhance student motivation, engagement, and retention of knowledge. In science classes, VR simulations have been used to teach complex concepts resulting in improved understanding and higher achievements. Augmented reality has been used to provide students with a visual and interactive experience of processes, leading increased interest and understanding. Moreover, VR and AR can promote active and experiential learning, where students can experiment and explore, leading to deeper learning and critical thinking. Teachers can also use these technologies to personalize learning, adapt to different learning styles, and provide instant feedback to students.
However, challenges remain, including the cost and accessibility of VR and AR devices and the need for teacher training and support. Additionally, the effectiveness of these technologies may vary depending on the subject and the age of the students.
There is no apprehension in accepting the fact that VR and AR technologies have significant potential to redefine educational practices by providing a more immersive, engaging, and interactive learning experience. Future research to explore the long-term impact of these technologies and to identify best practices for implementation is the need of the hour.
SYNTHESIS STRUCTURAL AND MAGNETIC PROPERTIES OF TITANIUM DOPED MAGNESIUM ZINC FERRITE
Authors: Sruthi P.  
Published: October 2024

Magnetic ceramics or ferrites are very well established group of magnetic materials which exhibits ferrimagnetism. It possess high electrical resistivity, low eddy current and dielectric loss which makes it superior than the other ordinary magnetic oxides that contain the ferric oxide (Fe₂O₃) as their basic magnetic compounds. The ceramic magnets are widely used in high frequency transformer application. [(Mg, Mn, F12o4)] spinels are used in square-loop application. Ceramic materials found application in nuclear physics. In nuclear reactors ceramics are used as core components. The studies include the preparation and analysis of Titanium doped Magnesium zinc ferrite. Here, Titanium doped magnesium zinc ferrite nanoparticles (NPS) were synthesized using solid state reaction method. Titanium doping can enhance the magnetic properties of magnesium zinc ferrite in several ways these can contribute to improved magnetic properties , such as increased magnetic moment, saturation magnetization, and magnetic ordering , making titanium – doped magnesium zinc ferrite a promising material for various applications including magnetic storage, sensors, and spintronics. According to the stoichiometric equation [Mg0.9Zn 0.1TixFe2-xO4] at different values of x, (x=0.1, 0.3) are synthesised. Calcination and sintering of the samples is made by high temperature PID controlled muffle furnace which can go up to 1000°C. The structural and magnetic properties are examined using XRD, FTIR and Curie temperature analysis. From the XRD analysis it revels the phase structure and lattice parameters of the samples. XRD analysis gives maximum intensity of (h k l) values as (3 1 1) in both cases. It also reveals that no secondary phases are produced during growth. That means the specimens FCC stacking. FTIR analysis of the samples done using Fourier transformer infrared spectroscopy. From this analysis it revels the detailed atomic structure and bonding of the samples. The bands obtained reveal spinal ferrite structure. Curie temperature of the specimens is determined by Curie temperature apparatus for the study of magnetic properties of the specimens.
RIGHT AND DUTY OF PHYSICIAN-PATIENT IN MEDICAL CONTEXT
Authors: Ranjana Yadav  , Prof. Ameeta Jaiswal  
Published: October 2024

In my duties, the rights of others are included, and in my rights, the duties of others are also included. Just as we can see in the education field where the teacher's duty is to give the right education and it is the right of the learner to get the right education, in the same way, the learner's duty to respect the teacher and the teacher's right to respect of his autonomy. Duty and right are the two parts of one coin (life). Generally, there are some duties to protect human rights (education, information, health, freedom of speech and expression) same some duties (duty of care, not to harm, to benefit, to give good faculties, respect others, to speak the truth) prescribed by medical ethics towards the Patient and Physician to protect rights (right to life, respect to autonomy and dignity, right to health, right to cure, right to select the cases). So we see here, that the Physician's duties become the rights of the Patient, for example, the Physician's duty is to do best for the Patient's health, and the Patient's right is the right to health. In the medical field, the Physician's duty is to respect the Patient's choice and Patient's right is to select an excellent medical option. We always discussed the Patient's rights (Respect, dignity, and autonomy) and the Physician's duties (Doing his best for the Patient's health) and obligations ( not harming the Patient) instead of the Physician's rights (Rights to reject and select treatment and cases) and the Patient's duties ( Respecting the physician and following the physician’s suggestion, Prescriptions) and obligations ( not harming the Physician), If we start to give priority to duties of patients and the rights of physicians, then we will create an actual , mutual partnership model which will connect truly both parties and give equal importance and respect ultimately ending all the conflict related to Physician-Patient relationship. We can also fill the gap in thoughts and motives between both because if both begin to realize their responsibilities and obligations and respect each other's rights, all the clashes between them will come to an end. In this paper, I will discuss the duties and rights of doctors and patients, which can help bridge the gap between both relations and make them equal participants with each contributing actively and not just passively.
Digital Platforms and the Financial Feasibility of Halal Frozen Food Marketing: A Feasibility Assessment
Authors: Gandhi Prima , Yulianti Alma Sophia , Andri Kuntoro Boga , Dewi Sari Putri , Purbawan Ketut Wira 
Published: October 2024

The Indonesian government issued a national policy of large-scale social restrictions in 2020 to 2021. At the same time, there was an increase in internet users in Indonesia which led to an increase in the use of the Tiktok mobile application. Both incidents made halal frozen food producers in Bogor market their products using mobile applications. Until now, there has been no research that calculates the financial feasibility of marketing halal frozen food using mobile applications. This study aims to determine the financial feasibility of marketing halal frozen food produced by PT XYZ using the TikTok mobile application. The financial feasibility analysis in this study uses a quantitative method by calculating income and assessing investment feasibility based on cash flow in the form of an R/C ratio. Researchers conducted observations and interviews as data collection techniques. The research location and respondents were selected purposively. The types of research data used are primary data and secondary data. Based on the results of the study, the net profit received by the company increased by IDR 118,714,448 with an R/C ratio of 2.52, meaning that every cost incurred of IDR 1 will get an income of IDR 2.52. From the explanation above, the results of this study prove that marketing of halal frozen food at PT XYZ Bogor using mobile applications is feasible and profitable. The implication of this study is to recommend halal frozen food producers in countries with increasing internet usage trends to market using mobile applications in order to obtain maximum profit.