bluuebunny/crossref_metadata_2025 huggingface dataset:
An Explainable Vision Transformer Model Based White Blood Cells Classification and Localization
Authors: Katar Oguzhan , Yildirim Ozal
Published: July 2023

White blood cells (WBCs) are crucial components of the immune system that play a vital role in defending the body against infections and diseases. The identification of WBCs subtypes is useful in the detection of various diseases, such as infections, leukemia, and other hematological malignancies. The manual screening of blood films is time-consuming and subjective, leading to inconsistencies and errors. Convolutional neural networks (CNN)-based models can automate such classification processes, but are incapable of capturing long-range dependencies and global context. This paper proposes an explainable Vision Transformer (ViT) model for automatic WBCs detection from blood films. The proposed model uses a self-attention mechanism to extract features from input images. Our proposed model was trained and validated on a public dataset of 16,633 samples containing five different types of WBCs. As a result of experiments on the classification of five different types of WBCs, our model achieved an accuracy of 99.40%. Moreover, the model’s examination of misclassified test samples revealed a correlation between incorrect predictions and the presence or absence of granules in the cell samples. To validate this observation, we divided the dataset into two classes, Granulocytes and Agranulocytes, and conducted a secondary training process. The resulting ViT model, trained for binary classification, achieved impressive performance metrics during the test phase, including an accuracy of 99.70%, recall of 99.54%, precision of 99.32%, and F-1 score of 99.43%. To ensure the reliability of the ViT model’s, we employed the Score-CAM algorithm to visualize the pixel areas on which the model focuses during its predictions. Our proposed method is suitable for clinical use due to its explainable structure as well as its superior performance compared to similar studies in the literature. The classification and localization of WBCs with this model can facilitate the detection and reporting process for the pathologist.
A Review of Machine Learning Techniques for the Classification and Detection of Breast Cancer from Medical Images
Authors: Jalloul Reem , Chethan H. K. , Alkhatib Ramez
Published: July 2023

Cancer is an incurable disease based on unregulated cell division. Breast cancer is the most prevalent cancer in women worldwide, and early detection can lower death rates. Medical images can be used to find important information for locating and diagnosing breast cancer. The best information for identifying and diagnosing breast cancer comes from medical pictures. This paper reviews the history of the discipline and examines how deep learning and machine learning are applied to detect breast cancer. The classification of breast cancer, using several medical imaging modalities, is covered in this paper. Numerous medical imaging modalities’ classification systems for tumors, non-tumors, and dense masses are thoroughly explained. The differences between various medical image types are initially examined using a variety of study datasets. Following that, numerous machine learning and deep learning methods exist for diagnosing and classifying breast cancer. Finally, this review addressed the challenges of categorization and detection and the best results of different approaches.
The Predictive Capabilities of Artificial Intelligence-Based OCT Analysis for Age-Related Macular Degeneration Progression—A Systematic Review
Authors: Muntean George Adrian , Marginean Anca , Groza Adrian , Damian Ioana , Roman Sara Alexia , Hapca Mădălina Claudia , Muntean Maximilian Vlad , Nicoară Simona Delia
Published: July 2023

The era of artificial intelligence (AI) has revolutionized our daily lives and AI has become a powerful force that is gradually transforming the field of medicine. Ophthalmology sits at the forefront of this transformation thanks to the effortless acquisition of an abundance of imaging modalities. There has been tremendous work in the field of AI for retinal diseases, with age-related macular degeneration being at the top of the most studied conditions. The purpose of the current systematic review was to identify and evaluate, in terms of strengths and limitations, the articles that apply AI to optical coherence tomography (OCT) images in order to predict the future evolution of age-related macular degeneration (AMD) during its natural history and after treatment in terms of OCT morphological structure and visual function. After a thorough search through seven databases up to 1 January 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 1800 records were identified. After screening, 48 articles were selected for full-text retrieval and 19 articles were finally included. From these 19 articles, 4 articles concentrated on predicting the anti-VEGF requirement in neovascular AMD (nAMD), 4 articles focused on predicting anti-VEGF efficacy in nAMD patients, 3 articles predicted the conversion from early or intermediate AMD (iAMD) to nAMD, 1 article predicted the conversion from iAMD to geographic atrophy (GA), 1 article predicted the conversion from iAMD to both nAMD and GA, 3 articles predicted the future growth of GA and 3 articles predicted the future outcome for visual acuity (VA) after anti-VEGF treatment in nAMD patients. Since using AI methods to predict future changes in AMD is only in its initial phase, a systematic review provides the opportunity of setting the context of previous work in this area and can present a starting point for future research.
A First Computational Frame for Recognizing Heparin-Binding Protein
Authors: Zhu Wen , Yuan Shi-Shi , Li Jian , Huang Cheng-Bing , Lin Hao , Liao Bo
Published: July 2023

Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.
Blue-Light Fundus Autofluorescence (BAF), an Essential Modality for the Evaluation of Inflammatory Diseases of the Photoreceptors: An Imaging Narrative
Authors: Mantovani Alessandro , Herbort Carl P. , Hedayatfar Alireza , Papasavvas Ioannis
Published: July 2023

Our purpose is to describe blue-light fundus autofluorescence (BAF) features of inflammatory diseases of the outer retina characterised by photoreceptor damage. BAF from patients diagnosed with secondary and primary inflammatory photoreceptor damage were retrospectively analyzed and compared to other imaging modalities including fluorescein angiography (FA), indocyanine green angiography (ICGA), and spectral domain optical coherence tomography (SD-OCT). Multiple evanescent white dot syndrome (MEWDS), idiopathic multifocal choroiditis (MFC), acute posterior multifocal placoid pigment epitheliopathy (APMPPE), serpiginous choroiditis (SC), and acute syphilitic posterior placoid chorioretinitis (ASPPC), all cases corresponding to secondary photoreceptor diseases caused by inflammatory choriocapillaris nonperfusion, were included and compared to primary photoreceptor disease entities, including acute zonal occult outer retinopathy (AZOOR) and cancer-associated retinopathy (CAR). Both groups showed increased BAFs of variable intensity. In severe cases of APMPPE and ASPPC, BAF also showed hypoautofluorescent areas. In group 1 (secondary diseases) BAF hyperautofluorescent areas were associated with colocalized ICGA hypofluorescent areas, indicating choriocapillaris nonperfusion; whereas in group 2 (primary diseases), no ICGA signs were detected. The associated colocalized areas of hypofluorescence on ICGA in the first group, which were absent in the second group, were crucial to allow the differentiation between primary (photoreceptoritis) and secondary (choriocapillaritis) photoreceptor diseases. BAF patterns in inflammatory diseases of the outer retina can give relevant information on the photoreceptor and RPE involvement, with ICGA being crucial to detect concurring choriocapillaris damage and differentiating the two pathologies.
The Effect of a Zinc-L-Carnosine Mouthwash in the Management of Oral Surgical Wounds: Preliminary Results of a Prospective Cohort Study
Authors: Dell’Olio Fabio , Siciliani Rosaria Arianna , Novielli Gianluigi , Tempesta Angela , Favia Gianfranco , Limongelli Luisa
Published: July 2023

Background: Zinc L-carnosine promotes the transition from the inflammatory to the proliferative phase of wound healing by reducing the expression of pro-inflammatory signals and enhancing the expression of anti-inflammatory signals. This prospective cohort study aims to test the effect of a zinc–L-carnosine mouthwash in promoting oral surgical wound healing. Methods: From October 2022 to February 2023, the authors enrolled healthy adult volunteers who needed the extraction of bilateral molars at the Unit of Dentistry of the University of Bari. The authors studied the baseline wound healing of each patient after the first extraction. Three months later, the patients underwent the second extraction and rinsed their mouths with zinc–L-carnosine mouthwash twice per day for the following 28 postoperative days. For a month after each extraction, the patients received weekly follow-up visits by an oral surgeon blinded about the study to record the modified healing index score of the wounds (range 0–6 points). For statistical analysis, we used the one-tailed t-test for paired samples with a significance level set at p < 0.05 to compare the baseline scores with those recorded during the exposure to the zinc–L-carnosine mouthwash. Results: The authors enrolled four women and six men (mean age = 44.60 ± 19.22 years). On the seventh and fourteenth postoperative days, the mean difference between the modified healing index scores obtained by using the zinc–L-carnosine mouthwash and the baseline was not significant. On the twenty-first postoperative day, the mean score obtained by using the mouthwash was 5.2 ± 1.3 points and was significantly higher than the 4.7 ± 1.8 points of the baseline (p = 0.026). On the twenty-eighth postoperative day, the mean difference was significant as well (5.9 ± 0.3 points and 5.4 ± 1.1 points, respectively). Conclusion: The preliminary results of this study showed that the zinc–L-carnosine mouthwash improved the quality of oral surgical wound healing.
Analysis of Students’ Emotional Patterns Based on an Educational Course on Emotions Management
Authors: Edara Inna Reddy
Published: July 2023

Based on the current research trends and academic applications, which suggest that emotional intelligence can be learned and enhanced through education and training, this academic project coded and analyzed the emotional patterns of 46 undergraduate students who attended an 18-week academic course on emotional management. A paired-sample t-test showed a significant increase from pre-course to post-course emotional intelligence scores, suggesting the importance of designing an academic course in formal educational settings. Analysis of students’ emotions journals indicated a total of 18 negative emotions, with the emotion of irritability occurring most frequently, followed by anger and anxiety. Loneliness, impatience, and guilt were some of the least felt negative emotions. The triggers for the arousal of negative emotions spanned family matters, interpersonal relations, academics, and communication skills, among others. The majority of the students used cognitive-behavioral and family systems theories in understanding and analyzing their emotional patterns. Students used various strategies to deal with the negative emotions, including developing communication skills, talking with family and friends, better time management, improving self-efficacy, cultivating a positive attitude, engaging in physical activities, etc. These results are discussed in this paper and the possible implications for practical purposes and further research are suggested.
Use of an Artificial Intelligence-Driven Digital Platform for Reflective Learning to Support Continuing Medical and Professional Education and Opportunities for Interprofessional Education and Equitable Access
Authors: Cohen Brian , DuBois Sasha , Lynch Patricia A. , Swami Niraj , Noftle Kelli , Arensberg Mary Beth
Published: July 2023

Continuing medical education (CME) and continuing education (CE) provide frameworks for assimilating and disseminating new advancements and are mainstays of clinicians’ professional development and accreditation. However, traditional CME/CE approaches may be challenged in providing opportunities for integrated and interprofessional learning and helping clinicians effectively translate innovations into individual practice. This Commentary describes the reflective learning approach, including its integration into CME/CE and how it can support interprofessional education. Also identified are barriers to reflective and interprofessional learning implementation and CME/CE access. The Commentary provides insights based on point-of-care reflection data and outlines considerations in trialing the use of an artificial intelligence (AI)-driven digital platform for reflective learning. Further, the Commentary describes how the AI-driven digital platform may help overcome barriers to reflective learning and interprofessional education and support equitable CME/CE program access.
Cross-Modality Person Re-Identification Algorithm Based on Two-Branch Network
Authors: Song Jianfeng , Yang Jin , Zhang Chenyang , Xie Kun
Published: July 2023

Person re-identification is the technique of identifying the same person in different camera shots, known as ReID for short. Most existing models focus on single-modality person re-identification involving only visible images. However, the visible modality is not suitable for low-light environments or at night, when crime is frequent. In contrast, infrared images can reflect the nighttime environment, and most surveillance systems are equipped with dual-mode cameras that can automatically switch between visible and infrared modalities based on light conditions. In contrast to visible-light cameras, infrared (IR) cameras can still capture enough information from the scene in those dark environments. Therefore, the problem of visible-infrared cross-modality person re-identification (VI-ReID) is proposed. To improve the identification rate of cross-modality person re-identification, a cross-modality person re-identification method based on a two-branch network is proposed. Firstly, we use infrared image colorization technology to convert infrared images into color images to reduce the differences between modalities and propose a visible-infrared cross-modality person re-identification algorithm based on Two-Branch Network with Double Constraints (VI-TBNDC), which consists of two main components: a two-branch network for feature extraction and a double-constrained identity loss for feature learning. The two-branch network extracts the features of both data sets separately, and the double-constrained identity loss ensures that the learned feature representations are discriminative enough to distinguish different people from two different patterns. The effectiveness of the proposed method is verified by extensive experimental analysis, and the method achieves good recognition accuracy on the visible-infrared image person re-identification standard dataset SYSU-MM01.
Dynamic Health Monitoring of Aero-Engine Gas-Path System Based on SFA-GMM-BID
Authors: Li Dewen , Li Yang , Zhang Tianci , Cai Jing , Zuo Hongfu , Zhang Ying
Published: July 2023

This paper proposes a dynamic health monitoring method for aero-engines by extracting more hidden information from the raw values of gas-path parameters based on slow feature analysis (SFA) and the Gaussian mixture model (GMM) to improve the capability of detecting gas-path faults of aero-engines. First, an SFA algorithm is used to process the raw values of gas-path parameters, extracting the effective features reflecting the slow variation of the gas-path state. Then, a GMM is established based on the slow features of the target aero-engine in a normal state to measure its health status. Moreover, an indicator based on the Bayesian inference distance (BID) is constructed to quantitatively characterize the performance degradation degree of the target aero-engine. Considering that the fixed threshold does not suit the time-varying characteristics of the gas-path state, a dynamic threshold based on the maximum information coefficient is designed for aero-engine health monitoring. The proposed method is verified using a set of actual operation data of a certain aero-engine. The results show that the proposed method can better reflect the degradation process of the aero-engine and identify aero-engine anomalies earlier than other aero-engine fault detection methods. In addition, the dynamic threshold can reduce the occurrence of false alarms. All these advantages give the proposed method high value in real-world applications.
Research on Road Sign Detection and Visual Depth Perception Technology for Mobile Robots
Authors: Zhao Jianwei , Liu Yushuo
Published: July 2023

To accomplish the task of detecting and avoiding road signs by mobile robots for autonomous running, in this paper, we propose a method of road sign detection and visual depth perception based on improved Yolov5 and improved centroid depth value filtering. First, the Yolov5 model has a large number of parameters, a large computational volume, and a large model size, which is difficult to deploy to the CPU side (industrial control computer) of the robot mobile platform. To solve this problem, the study proposes a lightweight Yolov5-SC3FB model. Compared with the original Yolov5n model, the Yolov5-SC3FB model only loses lower detection accuracy, the parameter volume is reduced to 0.19 M, the computational volume is reduced to 0.5 GFLOPS, and the model size is only 0.72 MB, making it easy to deploy on mobile robot platforms. Secondly, the obtained depth value of the center point of the bounding box is 0 due to the influence of noise. To solve this problem, we proposed an improved filtering method for the depth value of the center point in the study, and the relative error of its depth measurement is only 2%. Finally, the improved Yolov5-SC3FB model is fused with the improved filtering method for acquiring centroid depth values and the fused algorithm is deployed to the mobile robot platform. We verified the effectiveness of this fusion algorithm for the detection and avoidance of road signs of the robot. Thus, it can enable the mobile robot to correctly perceive the environment and achieve autonomous running.
Quick Identification of Open/Closed State of GIS Switch Based on Vibration Detection and Deep Learning
Authors: Zhang Kun , Zhang Yong , Wu Junjie , Li Zhizhong
Published: July 2023

The rapid and accurate identification of the opening and closing state of the knife switch in a gas insulated switchgear (GIS) is very important for the timely detection of equipment faults and for the reduction of related accidents. However, existing technologies, such as image recognition, are vulnerable to weather or light intensity, while microswitch, attitude sensing and other methods are unable to induce equipment power failure with sufficient speed, which brings many new challenges to the operation and maintenance of a GIS. Therefore, this research designs a GIS shell vibration detection system for knife switch state discrimination, introduces a deep learning algorithm for knife switch vibration signal analysis, and proposes a fast convolutional neural network (FCNN) to identify the knife switch state. For the designed FCNN, a normalization layer and a nonlinear activation layer are used after each convolution layer to obviously reduce feature quantity and increase algorithm efficiency. In order to test the recognition performance based on the vibration detection system, this study carried out two kinds of knife switch opening and closing experiments. One group with artificial noise was added, the other group did not include artifical noise, and a corresponding data set was constructed. The experimental results show that the recognition accuracy for both datasets reaches 100%, and the FCNN algorithm is better than the five classical algorithms in terms of prediction efficiency. This study shows that the vibration detection technology based on deep learning can be used to effectively identify the opening and closing state of a GIS knife switch, and is expected to be promoted and applied.
A Novel Method for Constructing Spatiotemporal Knowledge Graph for Maritime Ship Activities
Authors: Xie Cunxiang , Zhang Limin , Zhong Zhaogen
Published: July 2023

This study focused on the construction of a spatiotemporal knowledge graph for ship activities. First, a ship activity ontology model was proposed to describe the entities and relations of ship activities. Then, maritime event text data were utilized as the ship activity dataset, where entities and relations were extracted to form triplets. Thus, the data layer was populated, completing the construction of the ship activity spatiotemporal knowledge graph. The process of extracting triplets involved initially inputting the text sentences into the Bidirectional Encoder Representations from Transformers (BERT) model for pretraining to obtain vector representations of characters. These representations were then fed into a lattice long short-term memory network (Lattice-LSTM) for further processing. The resulting hidden vectors h1,h2,⋯,hn were input into the conditional random field (CRF) to perform named entity recognition. The recognized entities were then labeled in the original sentences and input into another BERT-Lattice-LSTM network. The resulting hidden vectors h′1,h′2,⋯,h′n were fed into a relation classifier, which output the relation between the two labeled entities, completing the extraction of entity–relation triplets. In experiments, the proposed method achieved triplet extraction performance exceeding 90% for three different evaluation metrics: Precision, Recall, and F1-measure.
Risevi: A Disease Risk Prediction Model Based on Vision Transformer Applied to Nursing Homes
Authors: Zhou Feng , Hu Shijing , Wan Xiaoli , Lu Zhihui , Wu Jie
Published: July 2023

The intensification of population aging has brought pressure on public medical care. In order to reduce this pressure, we combined the image classification method with computer vision and used audio data that is easy to collect in nursing homes. Based on MelGAN, transfer learning, and Vision Transformer, we propose an application called Risevi (A Disease Risk Prediction Model Based on Vision Transformer), a disease risk prediction model for nursing homes. We first design a sample generation method based on MelGAN, then refer to the Mel frequency cepstral coefficient and the Wav2vec2 model to design the sample feature extraction method, perform floating-point operations on the tensor of the extracted features, and then convert it into a waveform. We then design a sample feature classification method based on transfer learning and Vision Transformer. Finally, we obtain the Risevi model. In this paper, we use public datasets and subject data as sample data. The experimental results show that the Risevi model has achieved an accuracy rate of 98.5%, a precision rate of 96.38%, a recall rate of 98.17%, and an F1 score of 97.15%. The experimental results show that the Risevi model can provide practical support for reducing public medical pressure.
A Tree Point Cloud Simplification Method Based on FPFH Information Entropy
Authors: Hu Chenming , Ru Yu , Fang Shuping , Zhou Hongping , Xue Jiangkun , Zhang Yuheng , Li Jianping , Xu Guopeng , Fan Gaoming
Published: July 2023

LiDAR technology has been widely used in forest survey and research, but the high-resolution point cloud data generated by LiDAR equipment also pose challenges in storage and computing. To address this problem, we propose a point cloud simplification method for trees, which considers both higher similarity to the original point cloud and the area of the tree point cloud. The method first determines the optimal search neighborhood using the standard deviation of FPFH information entropy. Based on FPFH information entropy and Poisson disc sampling theory, the point cloud is partitioned and sampled. By optimizing the separation thresholds of significant feature points and less significant feature points using a genetic algorithm with the Hausdorff distance and point cloud area as the objective function, the final simplified point cloud is obtained. Validation with two point cloud data sets shows that the proposed method achieves good retention of the area information of the original point cloud while ensuring point cloud quality. The research provides new approaches and techniques for processing large-scale forest LiDAR scan point clouds, reducing storage and computing requirements. This can improve the efficiency of forest surveys and monitoring.
Assessing the Use of Burn Ratios and Red-Edge Spectral Indices for Detecting Fire Effects in the Greater Yellowstone Ecosystem
Authors: Szpakowski David M. , Jensen Jennifer L. R. , Chow T. Edwin , Butler David R.
Published: July 2023

Burn severity is commonly assessed using Burn Ratios and field measurements to provide land managers with estimates of the degree of burning in an area. However, less commonly studied is the ability of spectral indices and Burn Ratios to estimate field-measured fire effects. Past research has shown low correlations between fire effects and Landsat-derived Burn Ratios, but with the launch of the Sentinel-2 constellation, more spectral bands with finer spatial resolutions have become available. This paper explores the use of several red-edge-based indices and Burn Ratios alongside more ‘traditional’ spectral indices for predicting fire effects, measured from the Maple and Berry fires in Wyoming, USA. The fire effects include ash depth, char depth, post-fire dead lodgepole pine (Pinus contorta; PICO) density/stumps, mean basal diameter, cone density on dead post-fire trees, coarse wood percent cover/volume/mass, percent cover of ghost logs and initial regeneration of post-fire PICO/aspen density. All-possible-models regression was used to determine the best models for estimating each fire effect. Models with satisfactory R2 values were constructed for post-fire dead PICO stumps (0.663), coarse wood percent cover (0.691), coarse wood volume (0.833), coarse wood mass (0.838), ash depth (0.636) and percent cover of ghost logs (0.717). Red-edge-based indices were included in all of the satisfactory models, which shows that the red-edge bands may be useful for measuring fire effects.
SegForest: A Segmentation Model for Remote Sensing Images
Authors: Wang Hanzhao , Hu Chunhua , Zhang Ranyang , Qian Weijie
Published: July 2023

The accurate estimation of forest area is of paramount importance for carbon sequestration projects, ecotourism and ecological safety. Forest segmentation using remote sensing images is a crucial technique for estimating forest area. However, due to the complex features, such as the size, shape and color of forest plots, traditional segmentation algorithms struggle to achieve accurate segmentation. Therefore, this study proposes a remote sensing image forest segmentation model named SegForest. To enhance the model, we introduce three new modules: multi-feature fusion (MFF), multi-scale multi-decoder (MSMD) and weight-based cross entropy loss function (WBCE) in the decoder. In addition, we propose two new forest remote sensing image segmentation binary datasets: DeepGlobe-Forest and Loveda-Forest. SegForest is compared with multiple advanced segmentation algorithms on these two datasets. On the DeepGlobe-Forest dataset, SegForest achieves a mean intersection over union (mIoU) of 83.39% and a mean accuracy (mAcc) of 91.00%. On the Loveda-Forest dataset, SegForest achieves a mIoU of 73.71% and a mAcc of 85.06%. These metrics outperform other algorithms in the comparative experiments. The experimental results of this paper demonstrate that by incorporating the three proposed modules, the SegForest model has strong performance and generalization ability in forest remote sensing image segmentation tasks.
Analysis of the Operating Parameters of Wood Transport Vehicles from the Point of View of Operational Reliability
Authors: Kováč Ján , Gregor Igor , Melicherčík Ján , Kuvik Tomáš
Published: July 2023

The aim of the research was to create a universal system for monitoring and evaluating the operating parameters of the haulage vehicles used for the haulage of wood with self-maintenance. The article presents partial results from the entire research. Data for research into the operational reliability of IVECO, SCANIA, and TATRA vehicles were obtained from the real-world operating conditions of two companies dealing with the mining/transportation process. Information from the operating conditions was obtained according to the test plan [n, R, t], according to which n objects were simultaneously tested, and the objects that were damaged during the tests were replaced with new ones; the tests ended after the test time t for each of the n positions. Based on the results and statistical analyses, it can be said that the best operational reliability is achieved by IVECO, followed by SCANIA, and only then by TATRA. The resolution of the above conclusions in operating conditions will contribute to the efficiency of the operation of the investigated facilities and the extension of the technical life of the means of transportation.
Fermentation Regulation and Ethanol Production of Total Mixed Ration Containing Apple Pomace
Authors: Fang Jiachen , Du Zhumei , Cai Yimin
Published: July 2023

To effectively utilize local fruit residue resources and regulate ethanol production in fermented feed, the impact of moisture adjustment, lactic acid bacteria (LAB) inoculant, and chemical additive on the fermentation characteristics and ethanol production of total mixed ration (TMR) containing apple pomace was studied. The TMR was prepared with apple pomace, corn, wheat bran, soybean meal, timothy, and alfalfa hay. The mixing proportion of apple pomace was 15% based on dry matter (DM). In experiment 1, the moisture in TMR was unadjusted (control) or adjusted to 45, 50, and 55%, respectively. TMR containing 55% moisture was used in experiment 2, and the treatments were control, homo-fermentative LAB (Lactobacillus plantarum, LP), hetero-fermentative LAB (Lactobacillus buchneri, LB), and calcium propionate (CaP). The laboratory-scale fermentation system was used to prepare TMR, and their fermentation characteristics were analyzed after 60 days of ensiling. In experiment 1, the pH of the various TMRs was around 4.1. As the moisture decreased, lactic acid increased (p < 0.05) and ammonia-N decreased (p < 0.05). The ethanol decreased significantly with moisture adjustment compared to the control and the TMR with 50% moisture had the lowest ethanol content (p < 0.05). In experiment 2, LP treatment increased lactic acid, and decreased acetic acid and ammonia-N significantly (p < 0.05), while LB treatment had no effect on fermentation. LP and LB each had no effect on the ethanol content. TMR treated with CaP significantly decreased the ethanol and acetic acid content (p < 0.05), but did not inhibit lactic acid production compared to control. The results confirmed that adjusting the moisture of TMR to 50% and adding CaP could effectively inhibit the excessive production of ethanol in TMR of apple pomace. Homofermentative LAB can better improve the fermentation quality of TMR than heterofermentative LAB, but neither can inhibit the production of ethanol. This is of great significance to the effective utilization of apple residue resources and the promotion of livestock production.
Effects of Processing Conditions and Simulated Digestion In Vitro on the Antioxidant Activity, Inhibition of Xanthine Oxidase and Bioaccessibility of Epicatechin Gallate
Authors: Zhu Miao , Fei Xiaoyun , Gong Deming , Zhang Guowen
Published: July 2023

The bioactivity and gastrointestinal stability of epicatechin gallate (ECG) may be affected by processing conditions. Results showed that the antioxidant ability and inhibitory activity on xanthine oxidase (XO) of ECG were higher at low pH values. Appropriate microwave and heating treatments improved the antioxidant (the scavenging rate increased from 71.75% to 92.71% and 80.88% under the microwave and heating treatments) and XO inhibitory activity (the inhibitory rate increased from 47.11% to 56.89% and 51.85% at the microwave and heating treatments) of ECG. The treated ECG led to a more compact structure of XO. Moreover, there may be synergistic antioxidant and inhibitory effects between ECG and its degradation products. The bioaccessibility of ECG after simulated digestion was untreated > microwave > heating, and the microwave−treated ECG still had good XO inhibitory activity after digestion. These findings may provide some significant information for the development of functional foods enriched in catechins.
Exploring Consumer Perceptions and Changing Consumption Patterns for Smoked Paprika: Implications for Traditional Food Products in Spain
Authors: Lami Olda , Sama-Berrocal Celia , Martín Alberto , Mesías Francisco J. , Velázquez Rocío
Published: July 2023

Changes in the level of income of the population and a reduction in time availability are shifting food consumption from traditional to more convenient food products. The production of traditional food has a significantly positive impact on the territory, as it contributes to preserving cultural traditions and identity and supports rural development and resilience, thus becoming essential for the survival of rural areas. Within traditional food products, spices are among the most usual ingredients in traditional cuisine, extensively used to add flavour and colour to dishes. However, spices in general and paprika in particular—one of the most typical spices in the Spanish cuisine and the subject of this paper—have been rarely the subject of scientific studies. The purpose of this study is to analyse consumers’ perceptions towards paprika and determine the level of its consumption. For this purpose, a survey was conducted on a representative sample of Spanish consumers. The findings indicated that the average consumption of paprika was 154.5 gr/person per year. Although the general consumers’ perceptions toward this spice were generally positive, they proved to be less positive amongst young people and inhabitants of large cities, whose consumption of the spice was also lower. Additionally, the Pimentón de La Vera PDO paprika was the most popular version of paprika, being mentioned by all the participants. The findings from this study confirm the change in food consumption patterns.
Optimizing the Functional Properties of Starch-Based Biodegradable Films
Authors: Frangopoulos Theofilos , Marinopoulou Anna , Goulas Athanasios , Likotrafiti Eleni , Rhoades Jonathan , Petridis Dimitrios , Kannidou Eirini , Stamelos Alexios , Theodoridou Maria , Arampatzidou Athanasia , Tosounidou Alexandra , Tsekmes Lazaros , Tsichlakis Konstantinos , Gkikas Giorgos , Tourasanidis Eleftherios , Karageorgiou Vassilis
Published: July 2023

A definitive screening design was used in order to evaluate the effects of starch, glycerol and montmorillonite (MMT) concentrations, as well as the drying temperature, drying tray type and starch species, on packaging film’s functional properties. Optimization showed that in order to obtain films with the minimum possible thickness, the maximum elongation at break, the maximum tensile strength, as well as reduced water vapor permeability and low opacity, a combination of factors should be used as follows: 5.5% wt starch concentration, 30% wt glycerol concentration on a dry starch basis, 10.5% wt MMT concentration on a dry starch basis, 45 °C drying temperature, chickpea as the starch species and plexiglass as the drying tray type. Based on these results, starch films were prepared, and fresh minced meat was stored in them for 3 days. It was shown that the incorporation of MMT at 10.5% wt on a dry starch basis in the packaging films led to a decreased mesophilic and psychrotrophic bacteria growth factor compared to commercial packaging. When assessed for their biodegradability, the starch films disintegrated after 10 days of thermophilic incubation under simulated composting conditions. Finally, to prove their handling capability during industrial production, the starch films were rewound in a paper cylinder using an industrial-scale rewinding machine.
A Space-Time Finite Element Method for the Fractional Ginzburg–Landau Equation
Authors: Liu Jincun , Li Hong , Liu Yang
Published: July 2023

A fully discrete space-time finite element method for the fractional Ginzburg–Landau equation is developed, in which the discontinuous Galerkin finite element scheme is adopted in the temporal direction and the Galerkin finite element scheme is used in the spatial orientation. By taking advantage of the valuable properties of Radau numerical integration and Lagrange interpolation polynomials at the Radau points of each time subinterval In, the well-posedness of the discrete solution is proven. Moreover, the optimal order error estimate in L∞(L2) is also considered in detail. Some numerical examples are provided to evaluate the validity and effectiveness of the theoretical analysis.
Geraghty Type Contractions in Relational Metric Space with Applications to Fractional Differential Equations
Authors: Alamer Ahmed , Eljaneid Nidal H. E. , Aldhabani Musaad S. , Altaweel Nifeen H. , Khan Faizan Ahmad
Published: July 2023

The present manuscript is devoted to investigating some existence and uniqueness results on fixed points by employing generalized contractions in the context of metric space endued with a weak class of transitive relation. Our results improve, modify, enrich and unify several existing fixed point theorems, The results proved in this study are utilized to find a unique solution of certain fractional boundary value problems.
Some New Properties of Exponential Trigonometric Convex Functions Using up and down Relations over Fuzzy Numbers and Related Inequalities through Fuzzy Fractional Integral Operators Having Exponential Kernels
Authors: Khan Muhammad Bilal , Macías-Díaz Jorge E. , Althobaiti Ali , Althobaiti Saad
Published: July 2023

The concept of convexity is fundamental in order to produce various types of inequalities. Thus, convexity and integral inequality are closely related. The objectives of this paper are to present a new class of up and down convex fuzzy number valued functions known as up and down exponential trigonometric convex fuzzy number valued mappings (UDET-convex FNVMs) and, with the help of this newly defined class, Hermite–Hadamard-type inequalities (H–H-type inequalities) via fuzzy inclusion relation and fuzzy fractional integral operators having exponential kernels. This fuzzy inclusion relation is level-wise defined by the interval-based inclusion relation. Furthermore, we have shown that our findings apply to a significant class of both novel and well-known inequalities for UDET-convex FNVMs. The application of the theory developed in this study is illustrated with useful instances. Some very interesting examples are provided to discuss the validation of our main results. These results and other approaches may open up new avenues for modeling, interval-valued functions, and fuzzy optimization problems.
Performance Analysis of Hydrogen Production for a Solid Oxide Fuel Cell System Using a Biogas Dry Reforming Membrane Reactor with Ni and Ni/Cr Catalysts
Authors: Nishimura Akira , Hayashi Yuki , Ito Syogo , Kolhe Mohan Lal
Published: July 2023

The present study aims to analyze the performance characteristics of the biogas dry reforming process conducted in a membrane reactor using Ni/Cr catalysts and to compare these characteristics with those obtained using pure Ni catalysts. The effect of the pre-set reaction temperature, the molar ratio of CH4:CO2 and the pressure difference between the reaction chamber and the sweep chamber on the characteristics of biogas dry reforming is analyzed. In the present work, the molar ratio of the supplied CH4:CO2 is varied to 1.5:1, 1:1 and 1:1.5. In this case, CH4:CO2 = 1.5:1 simulates a biogas. The pressure difference between the reaction chamber and the sweep chamber is varied to 0 MPa, 0.010 MPa and 0.020 MPa. The reaction temperature is changed to 400 °C, 500 °C and 600 °C. It is revealed that the highest concentration of H2 is achieved using a Ni/Cr catalyst when the molar ratio of CH4:CO2 is 1.5:1 at the differential pressure of 0.010 MPa and the reaction temperature of 600 °C. Under this condition, the H2 yield, H2 selectivity and thermal efficiency are 12.8%, 17.5% and 174%, respectively. The concentration of the H2 produced using a Ni/Cr catalyst is larger than that produced using a Ni catalyst regardless of the pre-set reaction temperature, the molar ratio of CH4:CO2 and the differential pressure.
Searching for Short-Timescale Transients in Gamma-ray Telescope Data
Authors: Jaitly Annanay , Kostunin Dmitriy , Cescon Karin
Published: July 2023

Astrophysical sources show variability in their emissions over a range of timescales, with transients such as fast radio bursts (FRBs) and magnetar giant flares (MGFs) showing variability on timescales as short as a few milliseconds. Recent advances in gamma-ray astronomy such as telescopes’ high temporal resolution and relatively high uptime, combined with follow-up programs between different facilities, should allow serendipitous observations of burst-like phenomena. Even so, no very-high-energy gamma-ray counterparts for FRBs have been detected so far, and there is a general lack of software tools suited to search for such phenomena. We present a tool capable of searching gamma-ray telescope data for transient phenomena over arbitrary timescales—it is based on the Gammapy package and recursively scans the given field of view for clusters of events within user-defined time and angular-separation intervals. The generalized implementation allows for its application in many other cases and multiple gamma-ray telescopes. The main features and methodology of the developed tool are presented here, along with an analysis of the open gamma ray telescope data performed using it.
Epigenetic Aberrations in Major Psychiatric Diseases Related to Diet and Gut Microbiome Alterations
Authors: Nohesara Shabnam , Abdolmaleky Hamid , Thiagalingam Sam
Published: July 2023

Nutrition and metabolism modify epigenetic signatures like histone acetylation and DNA methylation. Histone acetylation and DNA methylation in the central nervous system (CNS) can be altered by bioactive nutrients and gut microbiome via the gut–brain axis, which in turn modulate neuronal activity and behavior. Notably, the gut microbiome, with more than 1000 bacterial species, collectively contains almost three million functional genes whose products interact with millions of human epigenetic marks and 30,000 genes in a dynamic manner. However, genetic makeup shapes gut microbiome composition, food/nutrient metabolism, and epigenetic landscape, as well. Here, we first discuss the effect of changes in the microbial structure and composition in shaping specific epigenetic alterations in the brain and their role in the onset and progression of major mental disorders. Afterward, potential interactions among maternal diet/environmental factors, nutrition, and gastrointestinal microbiome, and their roles in accelerating or delaying the onset of severe mental illnesses via epigenetic changes will be discussed. We also provide an overview of the association between the gut microbiome, oxidative stress, and inflammation through epigenetic mechanisms. Finally, we present some underlying mechanisms involved in mediating the influence of the gut microbiome and probiotics on mental health via epigenetic modifications.
Evidence for Two Soybean Looper Strains in the United States with Limited Capacity for Cross-Hybridization
Authors: Nagoshi Rodney N. , Davis Jeffrey A. , Meagher Robert L. , Musser Fred R. , Head Graham P. , Portillo Hector , Teran Henry
Published: July 2023

The noctuid moth soybean looper (SBL), Chrysodeixis includens (Walker) is an economically important pest of soybeans (Glycine max (L.) Merr.) in the southeastern United States. It has characteristics that are of particular concern for pest mitigation that include a broad host range, the capacity for annual long-distance flight, and resistance in some populations to important pesticides such as pyrethroids and chitin synthesis inhibitor. The biology of SBL in the United States resembles that of the fellow noctuid fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), a major pest of corn and several other crops. FAW exhibits a population structure in that it can be divided into two groups (host strains) that differ in their host preferences but are broadly sympatric and exhibit incomplete reproductive isolation. In this paper, strategies used to characterize the FAW strains were applied to SBL to assess the likelihood of population structure in the United States. Evidence is presented for two SBL strains that were defined phylogenetically and display differences in the proportions of a small set of genetic markers. The populations exhibit evidence of reproductive barriers sufficient to allow persistent asymmetry in the distribution of mitochondrial haplotypes. The identified molecular markers will facilitate studies characterizing the behaviors of these two populations, with relevance to pest mitigation and efforts to prevent further dispersal of the resistance traits.
The Genetic Diversity of Stallions of Different Breeds in Russia
Authors: Dementieva Natalia , Nikitkina Elena , Shcherbakov Yuri , Nikolaeva Olga , Mitrofanova Olga , Ryabova Anna , Atroshchenko Mikhail , Makhmutova Oksana , Zaitsev Alexander
Published: July 2023

The specifics of breeding and selection significantly affect genetic diversity and variability within a breed. We present the data obtained from the genetic analysis of 21 thoroughbred and warmblood horse breeds. The most detailed information is described from the following breeds: Arabian, Trakehner, French Trotter, Standardbred, and Soviet Heavy Horse. The analysis of 509,617 SNP variants in 87 stallions from 21 populations made it possible to estimate the genetic diversity at the genome-wide level and distinguish the studied horse breeds from each other. In this study, we searched for heterozygous and homozygous ROH regions, evaluated inbreeding using FROH analysis, and generated a population structure using Admixture 1.3 software. Our findings indicate that the Arabian breed is an ancestor of many horse breeds. The study of the full-genome architectonics of breeds is of great practical importance for preserving the genetic characteristics of breeds and managing breeding. Studies were carried out to determine homozygous regions in individual breeds and search for candidate genes in these regions. Fifty-six candidate genes for the influence of selection pressure were identified. Our research reveals genetic diversity consistent with breeding directions and the breeds’ history of origin.