bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study of salivary gland cytopathological lesions and categorized according to Milan reporting system at tertiary care centre Rajkot
Authors: Patel Disha Bipinbhai , Gandhi Shilpa H , Dhruva Gauravi H 
Published: July 2024

Classification of salivary gland lesions into 6 diagnostic categories by using Milan Reporting System. Calculate risk of malignancy (ROM) of each category in present study and compare with other similar study. : The Milan System for Reporting Salivary Gland Cytopathology (MSRSGC) developed in 2015, aims of MSRSGC to standardize the reporting of salivary gland cytology. It provides a uniform diagnostic terminology that enhances communication between pathologists and clinicians. Moreover, it facilitates the evaluation of data and ensures a precise cytologic and histologic correlation of cases. Ultimately, this system is designed to improve patient care by streamlining the diagnosis process and enabling more accurate treatment decisions. Clinical history and cytology smears of salivary gland lesions, which were diagnosed from October 2022 to September 2023, were retrieved in a P.D.U. medical hospital in Rajkot, Gujarat. The cytology smears were all meticulously reviewed and subsequently classified into one of the six categories as prescribed by MSRSGC. With HPE considered the gold standard, the sensitivity, specificity, positive predictive value, and negative predictive value of FNAC for detecting malignant lesions were meticulously calculated.: A total of 59 salivary gland lesions underwent examination, of which 38 (64.4%) were found in males, and 21 (35.6%) were identified in females. The patients had a median age of 45 years, with ages ranging from 10 to 80 years. It was revealed through statistical analysis that the sensitivity, specificity, positive predictive value, and negative predictive value were determined to be 80%, 100%, 100%, and 88%, respectively. : MSRSGC serves as an effective framework for the categorization of salivary gland tumors, be they benign or malignant. Our findings reflect the positive contribution of the MSRSGC towards accurately identifying the malignant lesions and thus further helping the clinicians regarding the specific management decisions.
Gastrointestinal carcinomas in young adults- Case series
Authors: Yadav Ankita , Zaidi Noorin , Irfan Sumaiya , Lal Nirupma , Sheikh Iqra 
Published: July 2024

Gastrointestinal (GI) carcinomas is a term for group of cancers that affect the digestive system. This includes oesophageal, gallbladder, liver, pancreatic, stomach, and bowel cancers. Young-onset colorectal cancer (CRC) is defined as CRC that manifests in people under the age of 50 years. The incidence of CRC in young people has increased by 2% to 8% annually. Small bowel cancer is one of the rare types of cancer as its incidence accounts for less than 1% of all other types of cancer in the United States. Most of these tumors occur in older adults; &#62; 90% of cases occur in people older than 40 years. Here, we are presenting four cases of young onset GI carcinomas. GI carcinomas is now being reported in younger population, therefore it is important to be aware of symptoms and risk factors in them. Here, we are presenting four cases of young onset GI carcinomas in female patients aged 22 and 24 and two male patients aged 21 and 32.
Analisa Pengaruh Normalisasi Sungai Kemuning Terhadap Stabilitas Tanggul Corrugated Concrete Sheet Pile (CCSP) pada Kondisi Beban Normal dan Beban Gempa
Authors: Putra Paksitya Purnama , Ganesha Khalid Pijar , Wicaksono Luthfi Amri , Lestari Rini Trisno 
Published: July 2024

Abstract
The flood that occurred in the Sampang area was caused by the low capacity of the Kemuning River. One of the possible methods to solve flood overflow is to increase the capacity using normalization by dredging sediments in the river channel. To carry out normalization, it is necessary to consider the impact on the stability of the existing CCSP (Corrugated Concrete Sheet Pile) embankment. This study is intended to analyze the impact of normalization on the stability of the CCSP embankment, which is 500 m long, using the finite element method (FEM) program. This study consisted of several stages, namely soil data analysis, lateral pile analysis, and safety factor analysis. Based on the calculation results, all stationing segments are safe for normalization because they have a faktor keamanan (SF) value above the SF limit required by SNI 8460:2017, that is, SF = 1.5 under normal conditions and SF = 1.1 under earthquake conditions. Likewise, when normalization is carried out at the depth limit of the river bed elevation, the CCSP embankment still meets the stability requirements, with an average SF value in earthquake conditions of SF = 1.27 and SF = 2.46 in non-earthquake conditions. The stability of the CCSP reaches its limit when the average dredging depth elevation is between 4.71 m and 5.92 m below the river's normal water level in earthquake and non-earthquake conditions.
Keywords: Normalization, Stability, CCSP, Kemuning River, FEM.
Abstrak
Banjir yang terjadi di daerah Sampang disebabkan oleh debit banjir yang melebihi kapasitas tampung Sungai Kemuning. Salah satu upaya untuk mengatasi terjadinya luapan banjir adalah dengan meningkatkan kapasitas tampung melalui normalisasi dengan cara mengeruk sedimen pada alur sungai. Pelaksanaan normalisasi perlu mempertimbangkan dampak terhadap stabilitas tanggul CCSP (Corrugated Concrete Sheet Pile) eksisting yang sudah ada. Penelitian ini dimaksudkan untuk menganalisis pengaruh normalisasi terhadap stabilitas tanggul CCSP sepanjang 500 m menggunakan program bantu berbasis elemen hingga (FEM). Penelitian terdiri dari beberapa tahap yakni analisis data tanah, analisis tiang lateral, dan analisis faktor keamanan. Berdasarkan hasil perhitungan, seluruh segmen stasioning aman untuk dilakukan normalisasi karena memiliki nilai faktor keamanan (SF) diatas SF limit yang disyaratkan SNI 8460:2017 yaitu (SF=1,5) pada kondisi normal dan (SF=1,1) pada kondisi gempa. Saat normalisasi dilakukan pada batas kedalaman elevasi dasar sungai, tanggul CCSP masih memenuhi syarat stabilitas dengan nilai SF rata-rata pada kondisi gempa sebesar SF=1,27 dan SF=2,46 pada kondisi normal. Stabilitas CCSP mencapai batasnya saat elevasi pengerukan rata-rata sedalam 4,71 m dan 5,92 m dibawah muka air normal sungai pada kondisi gempa dan tanpa gempa.
Kata kunci: Normalisasi, Stabilitas, CCSP, Sungai Kemuning, FEM.
A Systematic Literature Review on Leaf Disease Recognition Using Computer Vision and Deep Learning Approach
Authors: Yani Nik Afiqah N. Ahmad , Fauzi Shukor Sanim Mohd , Zaki Nurul Ain Mohd , Ismail Mohammad Hafiz 
Published: July 2024

Background: Plant diseases affect agricultural output, quality and profitability, making them serious obstacles for agriculture. It is essential to detect diseases early in order to reduce losses while retaining sustainable practices. Plant disease detection has benefited greatly from the use of computer vision and deep learning in recent years because of their outstanding precision and computing capability.
Objective: In this paper, we intend to investigate the role of deep learning in computer vision for plant disease detection while looking into how these techniques address complex disease identification problems. A variety of deep learning architectures were reviewed, and the contribution of frameworks such as Tensorflow, Keras, Caffe and PyTorch to the researchers' model construction was studied as well. Additionally, the usage of open repositories such as PlantVillage and Kaggle along with the customized datasets were discussed.
Methods: We gathered the most recent developments in deep learning techniques for leaf disease detection through a systematic literature review of research papers published over the past decade, using reputable academic databases like Scopus and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method for guidance.
Results: This study finds that researchers consistently enhance existing deep learning architectures to improve prediction accuracy in plant disease detection, often by introducing novel architectures and employing transfer learning methods. Frameworks like TensorFlow, Keras, Caffe, and PyTorch are widely favored for their efficiency in development. Additionally, most studies opt for public datasets such as PlantVillage, Kaggle, and ImageNet, which offer an abundance of labelled data for training and testing deep learning models.
Conclusion: While no singular ‘best' model emerges, the adaptability of deep learning and computer vision demonstrates the dynamic nature of plant disease recognition area, and this paper provides a comprehensive overview of deep learning's transformative impact on plant disease recognition by bringing together information from different studies.

Keywords: Deep learning, Computer vision, Plant disease, Systematic literature review
Motorcycle Taxi in Shared Mobility and Informal Transportation: A Bibliometric Analysis
Authors: Herawatie Dyah , Siswanto Nurhadi , Widodo Erwin 
Published: July 2024

Background: Motorcycle taxi (MCT) is a form of widely used informal transportation. Over the last few years, MCT has developed from conventional public transportation into a modern business using digital technology. In these services, digitalization has received a positive response from the public and the increasing number creates many challenges. However, there is a gap in the discussion of new service models for shared mobility transportation.
Objective: This research aimed to analyze MCT, providing an overview of the services as informal transportation.
Methods: Bibliometric analysis was used to evaluate 366 articles published in the Scopus database between 2011-2023. In addition, annual publications and citation topics, most productive sources and influential articles, relevant affiliations, productive countries, location research, main topics, and future research options were identified.
Results: Frequently occurring topics were reported with past and present academic developments related to MCT services. Based on the publication themes, the main topics were arranged into five clusters, namely (a) the development of more sustainable transportation services, (b) environmental and health impact, (c) road safety, (d) risky behaviors or risk factors as MCT drivers, and (e) utilization of MCT for medical or health services. Meanwhile, the research topics comprised themes about travel behavior, health, safety-security, customer satisfaction, and advanced mobility topics.
Conclusion: This research increased knowledge about main topics, trends and future analysis options in MCT. The academic developments served as a guide to future topics.

Keywords: Motorcycle taxi, public transportation, informal transportation, shared mobility, bibliometric analysis
Comparison of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Estimating the Susceptible-Exposed-Infected-Recovered (SEIR) Model Parameter Values
Authors: Sa'adah Aminatus , Sasmito Ayomi , Pasaribu Asysta Amalia 
Published: July 2024

Background: The most commonly used mathematical model for analyzing disease spread is the Susceptible-Exposed-Infected-Recovered (SEIR) model. Moreover, the dynamics of the SEIR model depend on several factors, such as the parameter values.
Objective: This study aimed to compare two optimization methods, namely genetic algorithm (GA) and particle swarm optimization (PSO), in estimating the SEIR model parameter values, such as the infection, transition, recovery, and death rates.
Methods: GA and PSO algorithms were compared to estimate parameter values of the SEIR model. The fitness value was calculated from the error between the actual data of cumulative positive COVID-19 cases and the numerical data of cases from the solution of the SEIR COVID-19 model. Furthermore, the numerical solution of the COVID-19 model was calculated using the fourth-order Runge-Kutta algorithm (RK-4), while the actual data were obtained from the cumulative dataset of positive COVID-19 cases in the province of Jakarta, Indonesia. Two datasets were then used to compare the success of each algorithm, namely, Dataset 1, representing the initial interval for the spread of COVID-19, and Dataset 2, representing an interval where there was a high increase in COVID-19 cases.
Results: Four parameters were estimated, namely the infection rate, transition rate, recovery rate, and death rate, due to disease. In Dataset 1, the smallest error of GA method, namely 8.9%, occurred when the value of , while the numerical error of PSO was 7.5%. In Dataset 2, the smallest error of GA method, namely 31.21%, occurred when , while the numerical error of PSO was 3.46%.
Conclusion: Based on the parameter estimation results for Datasets 1 and 2, PSO had better fitting results than GA. This showed PSO was more robust to the provided datasets and could better adapt to the trends of the COVID-19 epidemic.

Keywords: Genetic algorithm, Particle swarm optimization, SEIR model, COVID-19, Parameter estimation.
Hepatitis Identification using Backward Elimination and Extreme Gradient Boosting Methods
Authors: Pardede Jasman , Nurrohmah Desita 
Published: July 2024

Background: Hepatitis is a contagious inflammatory disease of the liver and is a public health problem because it is easily transmitted. The main factors causing hepatitis are viral infections, disease complications, alcohol, autoimmune diseases, and drug effects. Some hepatitis variants such as B, C, and D can also cause liver cancer if left untreated.
Objective: This research aims to determine the effect of Backward Elimination feature selection on the performance of hepatitis disease identification compared to cases where Backward Elimination is not applied.
Methods: XGBoost classification, capable of handling machine learning problems, was utilized. Additionally, Backward Elimination was used as a featured selection to increase accuracy by reducing the number of less important features in the data classification process.
Results: The results for training XGBoost model with Backward Elimination, and applying Random Search for hyperparameter optimization, achieved an accuracy of 98.958% at 0.64 seconds. This performance was better than using Bayesian search, which produced the same accuracy of 98.958% but required a longer training time of 0.70 seconds.
Conclusion: The use of features obtained from Backward Elimination process as well as the use of feature average values for missing value treatment, produced an accuracy of 98.958%.the precision in training XGBoost model with hyperparameter Bayesian search achieved accuracy, recall, and F1 score of 98.934%, 98.934%, and 98.934%, respectively. Consequently, the use of Backward Elimination in XGBoost model led to faster training, improved accuracy, and decreased overfitting.

Keywords: Hepatitis, Backward Elimination, XGBoost, Bayesian Search, Random Search
Co-Depedency between Single Mother and Her Son in Mother (2020)
Authors: Rahmawati Devina Nur , Srimulyani Nunuk Endah 
Published: July 2024

Co-dependency can occur in any kind of relationships, such as husband and wife, friends, and even parents and children. This phenomenon is being represented in media, such as movie titled Mother by Tatsushi Omori. This movie is chosen to be research object with the aim of analyzing the depiction of codependency in a single mother named Akiko and her son named Shuhei. The research used qualitative method with descriptive analysis. The results showed that despite the differences in co-dependency behaviors that Akiko and Shuhei have, there are the same goals and desires, both are equally dependent and need each other. Although Akiko is more dominant and harms Shuhei a lot, it does not make Shuhei think of leaving Akiko. The relationship between mother and son, who have always lived together and are accustomed to each other's nature, makes them even more bonded and attached which then creates a co-dependency that is difficult to end. No matter what the circumstances, these two will never want to leave each other or be left behind.
3D STRUCTURE AND FUNCTION ANALYSIS OF RECOMBINANT ALDII PROTEIN FROM Uncultured Acidilobus sp. USING I-TASSER
Authors: Meray Nishia Waya , Suharti Suharti , Akhmaloka Akhmaloka 
Published: July 2024

The AldII protein is a new recombinant protein produced from a novel gene obtained via a metagenome approach. Previous studies showed that this protein is strong and has the same metal binding aspect as the class II Aldolase enzyme. Aldolase is a valuable enzyme used in pharmaceuticals, food processing, and biochemistry. Further investigation is required to comprehend the structure and function of the AldII protein due to its potential. Researchers will conduct sophisticated bioinformatic analysis on the 3D shape and function prediction of AldII using the I-TASSER webserver from Zhanglab.
The AldII protein is a new recombinant protein produced from a novel gene obtained via a metagenome approach. Previous studies showed that this protein is strong and has the same metal binding aspect as the class II Aldolase enzyme. Aldolase is a valuable enzyme used in pharmaceuticals, food processing, and biochemistry. Further investigation is required to comprehend the structure and function of the AldII protein due to its potential. Researchers will conduct sophisticated bioinformatic analysis on the 3D shape and function prediction of AldII using the I-TASSER webserver from Zhanglab. The I-TASSER server is an online tool for the automated prediction of protein structure and annotation of functions based on structure. Analysis of the AldII protein using the I-TASSER webserver shows that this protein has a stable structure with the closest structural homology to deoxyribose-phosphate aldolase from Bacillus thuringiensis with PDB code 6btdA. Additionally, the biological structure analysis shows that this protein shares the biological function of the enzyme L-fuculose-1-phosphate aldolase, which is part of the class II Aldolase enzyme that plays a role in the catabolism of arabinose, L-Fuculose, and Rhamnose. The results align with prior research that states the AldII protein is a stable protein with a catalytic side that is homologous to the class II Aldolase enzyme.
Effect of Different Genetic Backgrounds on Rumen Microbiota and Serum Metabolic Phenotypes in Beef Cattle
Authors: Zhao Yaxing , Zhao Pengfei , Wu Yi , Li Xiaorui , Huangfu Mingke , Chen Zhimeng , Wang Chunjie , Liu Bo , Simujide Huasai , Chen Hao , Chen Aorigele , Zhang Chunhua , Sun Haizhou 
Published: July 2024

Abstract

Species with different genetic backgrounds exhibit distinct metabolic traits. Nine beef cattle were selected for the experiment to study changes in serum metabolic phenotypes, rumen microbiota diversity, and composition in beef cattle from different genetic backgrounds. Three groups were Chinese Simmental (S group), Simmental×Chinese Holstein (SH group), and Simmental × Mongolian (SM group) cattle. We used ELISA to detect serum biochemical indicators. The Short-chain fatty acids (SCFAs) in the rumen were examined, and a significant difference was observed in the acetic acid content of the three experimental groups (p < 0.01). The propionic acid content in the rumen of the S group was significantly higher than that of the SH and SM groups (p < 0.05). The A/P ratios of both the S and SM groups were significantly higher than that of the SH group (p < 0.05). We analyzed rumen microbiota composition and diversity in each group of cattle using 16S rRNA sequencing and found that their composition was generally similar in the three groups of crossbred fattening cattle; however, the f_Bacteroidales_RF16_group and g_norank_f_Bacteroidales_RF16_group were significantly enriched in the SH group, whereas Treponema and Spirochaetia were significantly enriched in the SM group. Spirochaetia was significantly enriched in the SM group. Differences in rumen bacterial enrichment indicated that starch, protein, and cellulolytic abilities differed among the S, SH, and SM groups. The results of Spearman correlation analysis confirmed the correlation between rumen genera and serum biochemical indices. Overall, differences in rumen microflora play an important role in influencing the serum metabolic phenotype.
Modification of zirconia with hydroxyapatite for bioactive enhancement as dental implants
Authors: Xing Zhongyuan , Pang Yongxin , Li Quan Bing Eric , Zhang Jian Yong , Xu Donglai 
Published: July 2024

Abstract

Zirconia is one of the best materials for making the crown in dental implant due to its excellent acid and base resistances and appearance close to natural teeth. In addition, its extraordinary mechanical properties render zirconia to be a potential candidate as dental implant, if its biocompatibility can be improved to promote adhesion to natural tissues. This study is to enhance the bioactivity of zirconia with the aim to improve its adhesion to the gingival when it is used as the crown of the implanted tooth.
Hydroxyapatite is the major component of natural bone and has very high bioactivity and thus is selected as the modifier to improve the bioactivity of zirconia. A series of zirconia/hydroxyapatite composites with varied compositions were prepared under different conditions in order to find out the optimal composites for the target application. Various analytical technologies and mechanical tests are employed to characterise structure and properties of resultant composites. Experimental results show that the component ratio and sintering temperature have significant influence on the composite properties. Increase in hydroxyapatite component tends to enhance bioactivity but decline mechanical strength. Experiments found that the composite containing 10% of hydroxyapatite retains sufficient mechanical strength as crown material whilst possesses excellent bioactivity. Sintering results suggest that the optimal mechanical strength is obtained at 1400°C for 2 hours for the composite containing 10% of hydroxyapatite.
Facile and efficient stereospecific deamination of aziridines using task-specific silica immobilized organosilane-based nitrite ionic liquid
Authors: Valizadeh Hassan , Vesali Esmaeil 
Published: July 2024

Abstract

Various cis and trans-aziridine derivatives were deaminated via the reaction with task-specific silica immobilized organosilane-based nitrite ionic liquid. Corresponding cis or trans-alkenes were produced as a stereospecific products in good to excellent yields. The advantages of this method include the one-pot procedure, operational simplicity, solvent-free and very short reaction times. Simple handling of this nitrite anione sourse nanoparticles and thus lowering the risks and hazards of a chemical process can offer other important advantages.