bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial intelligence correctly identifies perfect lateral knee X-rays: a pilot study
Authors: Oettl Felix Conrad , Shariatnia Moein , Kunze Kyle , Allen Answorth , Ranawat Anil , Pearle Andrew , Pareek Ayoosh 
Published: September 2024

Abstract

Background
Accurate assessment of lateral knee radiographs is crucial for evaluating knee biomechanics and guiding treatment decisions. However, manual evaluation is time-consuming and subject to variability. This study aims to develop a machine learning model that can automatically classify lateral knee X-rays, paving the way for automated measurement of important biomechanical parameters like posterior tibial slope and patella tendon indices.
Methods
929 random lateral knee X-rays, with Kellgren Lawrence (KL) grade 0 and 1 were extracted from the Osteoarthritis Initiatives (OAI) publicly accessible database. We randomly split the 929 images into 729 images for the training set and 200 images for the test set. The images were evaluated for quality and classified into three categories: 'Excellent', 'Good', and 'Bad'. Region of interest was identified and cropped using a deep learning object detector. The images were resized to 320 * 320 and augmented. We utilized fine-tuning versions of Convolutional Neural Networks (CNN) architectures, with subsequent 5-fold cross validation to help with hyperparameter tuning. Model performance was evaluated with area under the receiver operating characteristic curve (AUC) and Accuracy.
Results
ResNet was the most accurate model, with a composite AUC of 0.979 (CI= [0.964–0.99]). The highest accuracy was achieved correctly classifying the ‘Bad’ class. The confusion matrix showed that classifying 'Bad' and 'Excellent' classes was simpler than 'Good,' consistent with expert human annotators. Saliency maps highlighting the most predictive area of the X-ray exhibit a focus on alignment of the posterior femoral condyle.
Conclusion
We successfully developed and validated a high-performing deep learning model for classifying lateral knee X-ray images. Ultimately, the ability to extract accurate biomechanical data from routine lateral radiographs through automated analysis has potential to revolutionize orthopedic care delivery, enabling cost-effective assessments, aiding surgical planning, and facilitating outcome evaluation – paving the way for improved diagnostic capabilities and better patient outcomes.
Pillar-MTNet: A Point Cloud Vehicle Target Detection Algorithm Based on Pillar and Swin Transformer
Authors: Liang Jiabao , Guan Zhiwei , Chen Xiaoyan , Su Wei , Hou Zeyao 
Published: September 2024

Abstract

Object detection in point clouds is a crucial component of numerous robotics applications, including autonomous driving. Developing an efficient yet deployable backbone network capable of extracting feature is a foundational challenge within the domain of 3D perception. In contrast to convolution, the attention mechanism employed in Transformers is better suited for effectively modeling long-range relationships and is more readily deployable in real-world applications. In this paper, we designed a new point cloud vehicle target detection algorithm called Pillar-MTNet for better detection accuracy. We introduced Mobile Block into the network to replace the convolution operation of the traditional backbone network to reduce the network parameters and improve the feature extraction capability of the network. Additionally, for the purpose of extracting more profound semantic features, we also incorporate Swin Transformer Block (STB) to process the feature map subsequent to the final down-sampling and Mobile Block. RDIoU is adopted to optimize detection results and use it to guide bounding box regression and supervise classification. Extensive comparative experimentations on the classic Open Dataset Kitti and Waymo shows that Pillar-MTNet outperforms previous classical methods in vehicle target detection accuracy.
Structural Design and Optimization of Egg Carrier for Dynamic Egg Slit Detection Platforms
Authors: Meng Ronghua , Tian Yuxiang , Huang Siwei 
Published: September 2024

Abstract

This paper presents an improved egg carrier for supporting common types of eggs currently available on the market during the operation of an egg slit inspection platform. It can convey them forward to the corresponding inspection position. The egg carrier consists of three basic modules. The improved egg carrier has the advantages of adaptability to different types of eggs, adjustability, low maintenance and manufacturing costs, stable and efficient operation. Firstly, the functions to be realized by the egg carrier are analyzed, the preliminary design ideas and solutions are proposed through TRIZ theory. Secondly, the structure of the egg carrier is designed, the material properties are analyzed, the key parameters such as deformation are simulated by SolidWorks and Ansys Workbench. Finite element and fatigue analysis are carried out for the key parameters of the structure, the deformation, fatigue and other data appeared in the actual operation process are obtained. Based on the condition of stable carrying eggs, the mathematical model of carrying motion is established, the Genetic Algorithm (GA) is chosen to optimize the structural parameters. Finally, the motion simulation is carried out for the improved egg carrier, which verifies the reasonableness of the optimized structure. The physical object of the stand-alone egg carrier is manufactured.
Genome-wide identification and expression analysis of two-component system genes in switchgrass (Panicum virgatum L.)
Authors: Wu Baolin , Sun Mengyu , Zhong Tao , Zhang Jiawei , Lei Tingshu , Yan Yuming , Chen Xiaohong , Nan Rui , Sun Fengli , Zhang Chao , Xi Yajun 
Published: September 2024

Abstract

Composed of Histidine Kinases (HKs), Histidine Phosphotransfer Proteins (HPs), and Response Regulators (RRs), the Two-Component System (TCS) plays an essential role in guiding plant growth, development, and reactions to different environmental factors. Although TCS genes have been extensively identified in a variety of plants, the genome-wide recognition and examination of TCS in switchgrass remain unreported. Accordingly, this study identified a total of 87 TCS members in the genome of switchgrass, comprising 20 HK(L)s, 10 HPs, and 57 RRs. Detailed analyses were also conducted on their gene structures, conserved domains, and phylogenetic relationships. Moreover, this study analysed the gene expression profiles across diverse organs and investigated their response patterns to adverse environmental stresses. Results revealed that 87 TCS genes were distributed across 18 chromosomes, with uneven distribution. Amplification of these genes in switchgrass was achieved through both fragment and tandem duplication. PvTCS members are relatively conservative in the evolutionary process, but the gene structure varies significantly. Various cis-acting elements, varying in types and amounts, are present in the promoter region of PvTCS, all related to plant growth, development, and abiotic stress, due to the TCS gene structure. Protein-protein interaction and microRNA prediction suggest complex interactions and transcriptional regulation among TCS members. Additionally, most TCS members are expressed in roots and stems, with some genes showing organ-specific expression at different stages of leaf and inflorescence development. Under conditions of abiotic stress such as drought, low temperature, high temperature, and salt stress, as well as exogenous abscisic acid (ABA), the expression of most TCS genes is either stimulated or inhibited. Our systematic analysis could offer insight into the characterization of the TCS genes, and further the growth of functional studies in switchgrass.
An experimental investigation on the turbulent drag reduction effect of polyacrylamide solution in the loop pipe
Authors: Chen Yang , He Minlan , Yang Peng , Li Changjun , Nechval Andrey M. , Valeev Anvar R. 
Published: September 2024

Abstract

Flexible long-chain polymer molecules with linear structures are widely used in the field of turbulent drag reduction. However, there is no single mechanism that can fully explain all the experimental phenomena of polymer drag reduction. In this paper, we have investigated the relationship between polymer macro-microscopic factors and loop pipe(inner diameter 32mm, wall thickness 3mm, length 19.02m) flow parameters to explain the polymer turbulent drag reduction and degradation mechanisms. Our conclusions are as follows: drag reduction exists in saturated concentration, the higher the Reynolds number corresponds to the larger saturated concentration; there is a maximum drag reduction Reynolds number(Remax), the higher the concentration corresponds to the larger Remax; with concentration θ=100ppm (parts per million) and molecular weight increasing, the drag reduction efficiency increases; with θ=100ppm, the solution shows shear thinning behavior, so the polymer molecules are mechanically fractured; in the concentration range θ=2-20ppm, with concentration and polymer particle size in solution increasing, the distribution range becomes wider; the Weissenberg number(Wi) is related to the molecular weight, and the solution shows a better elasticity with the Wi increasing. This paper elucidates the impact of macro-microscopic factors on the elasticity of polymers, thereby enhancing the understanding of the relationship between polymer elasticity and drag reduction efficiency.
Study on effects of acidic environment on structure and property of magnesium oxychloride cement
Authors: An Lingyun , Chang Chenggong , Wang Ziyi , Zhou Zhifu , Yan Fengyun 
Published: September 2024

Abstract

To reveal the effects of acidic environment on structure and property of magnesium oxychloride cement (MOC), the evolution of macro- and micro-morphologies, phase composition and mechanical property of MOC samples with soaking time in 5 wt.% H2SO4 solution were studied by using SEM, XRD, FT-IR and microelectromechanical electrohydraulic servo-pressure testing machine, respectively. The results show that the deterioration of MOC sample under acidic environment is mainly attributed to the consumption of the sample caused by the reaction between H+ ions and the substances in sample. The longer the soaking time, the more the sample consumed by H+ ions, and the more serious the deterioration of the sample. After dipping in H2SO4 media for 12h, the mass loss rate of MOC sample is up to 77.5%, and its volume decreases by nearly 3/4. Meanwhile, the surface morphology of MOC sample is changed from gel and needle rod to granular, and the content of P5, Mg(OH)2 and MgO reduce, resulting in the compressive strength of MOC samples declining by 92.2%. However, due to the volume effect, H+ ions do not invade the inner core of the sample, so that the micro-morphology and phase composition of the inner core are almost unchanged. In short, MOC sample gradually deteriorates under acidic environment through being consumed by H+ ions from the outside to the inside.
Functional properties, amino acid content, fatty acid, and flavor of buffalo milk Dangke with the addition of Lactobacillus plantarum IIA-1A5 as probiotic
Authors: Hajrawati Hajrawati , Arief Irma Isnafia , Sukma Ade , Wulandari Zakiah , Darmawati Maulita Putri , Ardat Muh. Achyar 
Published: September 2024

Abstract

Dangke, a traditional dairy product originating in Enrekang Regency, is produced by coagulating milk casein sourced from either cow or buffalo, using papain as a coagulating agent. Probiotic bacteria, exemplified by L. plantarum IIA-1A5, shows health benefits by maintaining microbial balance in the digestive system. Therefore, this study aimed to evaluate the physicochemical quality, functional properties, amino acid content, fatty acid, microbiological quality, and flavor of buffalo milk Dangke with the addition of L. plantarum IIA-1A5 as a probiotic at 5% and 10% supplementation levels. The results showed significant differences among treatments in Total Titratable Acidity (TTA), protein, ash, and carbohydrate contents. The highest amino acid content was observed in dangke under the control treatment (without L. plantarum IIA-1A5), particularly in glutamate, while the lowest was identified in alanine and histidine. Furthermore, the highest and lowest fatty acid in the control was Elaidic and Eicosenoic Acid at 0.33% and 0.022%, respectively. The highest total LAB was observed in dangke with a 10% addition of L. plantarum IIA-1A5, reaching 8.50 log CFU/ml. Flavor analysis showed that the major compounds in the 0% dangke group, were silanediol and D-limonene. Meanwhile, in the 5% group, the major compounds were acetoin and methyl formate. It is important to note that the 10% group was affected by ethyl alcohol.
Factors Propelling Financial Inclusion in an Emerging Economy: An analysis through the ARDL Model
Authors: Bhowmik Probir Kumar , Islam Muhammad Rakibul 
Published: September 2024

Abstract

The present study explores the factors that influence financial inclusion in Bangladesh. A time series data set has been constructed by compiling thirty-two years of world development indicators from 1991 to 2022. The Least Squares method was applied, followed by an Autoregressive Distributed Lag (ARDL) model. The investigation finds that macroeconomic and bank-specific factors influence financial inclusion. The study initially considers the number of borrowers (BROW) as the proxy of financial inclusion. BROW was replaced with the number of branches (BRNC) to make the study robust. Both the proxies of financial inclusion provide results that suggest robustness. (The key variables examined include lending interest rates (LIR), GDP growth (GDPg), government expenditure on expenditure (GEOE), inflation (INF), bank lending (LOAN), non-performing loan ratios (NPLR), interest rate spreads (SPRD), and unemployment rates (UNEM). Significant findings reveal that lending interest rates negatively impact the number of borrowers (BORW); on the contrary, GDP growth positively affects the number of borrowers. A noteworthy discovery is the significant negative impact of non-performing loan ratios on the number of borrowers, suggesting that higher bad loans reduce overall lending activity due to increased caution among financial institutions. Additionally, the spread between lending and deposit rates positively influences the number of borrowers, reflecting that higher spreads may incentivize banks to lend more due to greater profitability margins. The study highlights the importance of macroeconomic stability and adequate financial sector management in promoting financial inclusion through increased borrowing. Policymakers should consider these outcomes to develop policies that balance the interest rates, manage economic growth, and control non-performing loans toward fostering a robust borrowing environment. Understanding these dynamics can help formulate strategies that enhance Bangladesh's financial inclusion and economic development.
JEL Classification: G20; G41
Note: The researchers confirm that there is no conflict of interest and that the research paper is for scientific purposes only.
Intelligent optimization of cold radial forging process for 20CrMnTiH alloy based on GA-BP and performance analysis
Authors: Xu Wenxia , Wang Zhaohui , Zhu Xuwen , Zhang Bowen , Zheng Zecheng , Lv Mi , Wang Hongxia 
Published: September 2024

Abstract

For the forming of hollow components, cold radial forging (CRF) is thought to be one of the best manufacturing processes. However, minute modifications to the alloy composition during the stage of cold deformation processing can have an impact on the material's manufacturability. In order to tackle the issues of elevated optimization costs, challenging data acquisition, and forming quality prediction when combining multiple processes, like heat treatment and material composition. In this paper, we propose to build an intelligent prediction model to realize the prediction of the forming quality of CRF of 20CrMnTiH alloy using a back-propagation neural network (GA-BP) optimized by genetic algorithm. The evaluation of forming quality is based on damage, residual stress, and equivalent strain. The effects of alloy composition and spheroidal annealing (SA) process parameters are studied in relation to forming quality. The three prediction models of GA-BP perform better in terms of R2 (coefficient of determination) and MSE (mean square error) than the conventional BP neural network and four other machine learning techniques (gradient-enhanced decision tree, random forest, support vector regression, logistic regression, and logistic regression). Based on a thorough comparative analysis of the evaluation metrics of all three prediction models and multi-objective optimization, it can be concluded that the Pareto solution set produced by NSGA-II has the best distribution uniformity. Remaining stresses were reduced by 12 percent, equivalent strain by 15 percent, and component damage by 30 percent after the obtained process parameters (0.17%C-0.24%Si-0.81%Mn-0.03%P-0.03%S-1.15%Cr-0.05%Ni-0.06%Ti, Te: 60℃, ATi: 4.57h) were optimized using the suggested methodology. The findings demonstrate that the approach presented in this paper improves the CRF molding quality effect in addition to making the optimized model significantly more accurate at predicting molding quality precision.
Morphological, Physicochemical, Nutritional, Technological and Health Characterization of Tubers About Three Nutsedge (Cyperus Esculentus) Cultivars Produced and Marketed in Benin
Authors: Noukpozounkou Zérach Jésugnon , Badoussi Eric Marius , Chabi Ifagbémi Bienvenu , Mamatchi Mélila , Fagla Balbine Akouavi Amoussou , Mitchikpè Evariste Comlan , Chabi Nicodème Worou , Azokpota Paulin 
Published: September 2024

Abstract

The edible nutsedge is an weedy plant species whose tuberous roots are comparable to tubers from conventional agriculture. The present study aims to characterize the tubers of three nutsedge cultivars in terms of morphology, technology, nutrition and health. The morphological data reveal average diameter and length of the tubers of the black cultivar greater than those of the brown (twice) and yellow (once) tubers. In addition, the mass of black tubers is 10 times that of brown tubers and 2.5 times that of yellow ones. The black cultivar is on the other hand the least acidic (6.22 ± 0.01) compared to the brown cultivar (5.37 ± 0.01) which is more acidic. Considering the technological parameters, the cultivars are similar according to the tuber densities before and after soaking; CH; IH; CG and IG. Analysis of the nutritional potential of the yellow cultivar reveals high protein (4.04 ± 0.03%) and ash (2.7 ± 0.02%) contents while the highest fat content is contained in the brown nutsedge (21.10 ± 0.12%). The yellow cultivar is richer in Vitamins A and C with 0.62 ± 0.01 mg/L and 0.14%. The same is true for minerals K (8569.01 ± 175 mg/kg); Ca (1333.28 ± 53.4 mg/kg) and P (2000 ± 28.3 mg/kg). The brown cultivar has the highest levels of Iron (136.40 ± 5.30 mg/Kg), Sodium (2217.02 ± 104.2 mg/Kg) although the greatest proportion of Zinc is contained in the cultivar black (185.35 ± 0.28 mg/Kg). FAN are concentrated in the black cultivar with a preponderant content of oxalic acid compared to those of the brown (2 times) and yellow (1.5 times) cultivars. Furthermore, the health assessment presents trace amounts of heavy metals in the three cultivars. Keywords: Yellow nutsedge; cultivar; technological parameters; nutritional potential; heavy metals
Investigating the feasibility of waste cooking oils as a phase change material for low and medium temperature applications: Thermal characterization
Authors: P Saji Raveendran , E Varadha , S Somasundaram 
Published: September 2024

Abstract

The study explores the use of Waste Cooking Oils (WCOs) as Phase Change Materials (PCMs) for thermal energy storage, addressing environmental issues from improper WCO disposal. With global WCO production exceeding 190 million metric tons annually and per capita cooking oil consumption rising from 10.33 liters in 2015 to 11.58 liters in 2021, the need for sustainable solutions is evident. Previous studies have highlighted the thermal energy storage potential of WCOs, making them suitable for room cooling and thermal management applications. This research aims to enhance the thermal properties by blending waste sunflower and coconut oils with soybean oil, optimizing them for low (-20 to 5°C) and medium (5 to 40°C) temperature applications. Advanced characterization techniques are employed to evaluate the chemical composition and thermal properties of these blends. The findings indicate that blending WCOs with soybean oil significantly improves their thermal properties, including latent heat capacity, phase change temperature, and thermal stability. The optimized blends exhibit increased crystallinity and superior thermal conductivity, making them effective PCMs for thermal energy storage. The results validate the feasibility of converting WCOs into eco-friendly thermal storage materials, paving the way for scalable and sustainable energy management practices.
Exploring English Language Instructors’ In-class Practices of Intercultural Communicative Competence through Vignette Cases
Authors: Kumru-Yağmurkaya Zehra Sultan , Aşık Asuman 
Published: September 2024

Abstract

Being an intercultural speaker has been acknowledged one of the essential skills in foreign language teaching. Therefore, Intercultural Communicative Competence of foreign language learners and related in-class practices have gained significance. This study aims to explore English language instructors’ in-class practices of Intercultural Communicative Competence through vignette cases in order to reduce the self-reported ideas. Following the qualitative, phenomenological research design, the data were collected from the ten participants through the vignette-based interview designed within the study. The results revealed that ideal and real versions of Intercultural Communicative Competence practices were different; they ideally believed that it should be in the centre of language teaching; yet, it was not covered much in real situations. The participants were mostly able to determine the problem of teaching Intercultural Communicative Competence throughout the vignette cases offering numerous suggestions and sharing their reactions to the cases. They also provided hypothetical practices for themselves, and shared their in-class experiences. Pedagogical implications and suggestions for further research were provided at the end of the study.