bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of the deep residual networks at different depths on expression recognition
Authors: Pan Yufeng
Published: October 2023

Facial expression recognition plays a critical role in numerous applications like emotion analysis, human-computer interaction, and surveillance systems. Given the importance of this task, this study aims to investigate the effectiveness of different depths of Residual Networks (ResNet). The primary objective is to scrutinize and compare these ResNet models in terms of their training and validation losses and performance metrics like accuracy, recall, and F1 scores. In this research, a thorough comparative analysis is conducted by setting up exhaustive experiments using these models. The experiment is carried out on a popular facial expression dataset. Despite the depth differences, ResNet101 emerged as the model demonstrating superior performance. It struck the most effective balance between model complexity and generalization capacity, leading to the lowest validation loss and better performance. Experimental results show that a more complex model does not necessarily yield optimal results. The optimal balance between model complexity and generalisation needs to be investigated. These findings can provide essential guidance in the design of deep learning models for facial expression recognition and other similar tasks.
Face expression recognition based on residual network and support vector machine
Authors: Liu Shuiyuan
Published: October 2023

In recent years, Facial expression recognition (FER) has become an outstanding research field, which content academia and industry alike. In order to alleviate the gradient disappearance in facial expression recognition caused by increasing network depth, this paper proposes joint residual network and support vector machine (SVM) to build a model. The algorithm uses a small convolutional kernel and a deep network structure. In addition, a multi-task cascaded convolutional network (MTCNN) is constructed for data pre-processing. Meanwhile, migration learning is introduced to overcome the shortcomings of insufficient data and prevent overfitting. Extensive experiments are conducted on the FER-2013 dataset. The model achieves a recognition rate of 98.6% in the test, meanwhile the accuracy of SVMs category is about 1% better than that of SoftMax. The experimental results proof that the model hereby propose outperforms different networks and previous methods. It further perceives that the method hereby propose is effective in recognizing facial expressions.
Research on improving the generalization ability of NLP categorization models: A comparison of Mixup and generative adversarial networks
Authors: Chen Jiyan
Published: October 2023

The application of deep neural networks in tasks related to natural language processing has grown in recent years. However, training deep learning models often requires a large amount of data to produce accurate and generalizable results. When the data are insufficient, categorization models may suffer from overfitting or poor performance. This paper compares two methods for enhancing the generalization ability of natural language processing categorization models when data are insufficient: Mixup and Generative Adversarial Networks (GANs). This paper uses the Internet Movie Database (IMDB) dataset for a binary classification task, Mixup method, and Relational Generative Adversarial Networks (RelGAN) model to generate new data, respectively, and compares them with the original dataset. Experimental results indicate that Mixup method can reduce overfitting risk effectively and enhance the models robustness, while the GAN model does not show obvious advantages. This paper supports the idea that using noise-adding operations to enhance the dataset is feasible in the case of small samples rather than using generative models.
An overview of big data mining and data privacy protection technologies
Authors: Liu Jinyang
Published: October 2023

With the advent of the era of big data, data mining techniques have significantly improved their ability to extract valuable information from data. However, privacy dangers are growing. Consequently, securing the protection of personal privacy during the mining of massive amounts of data has become a significant challenge. This paper examines the relationship between data mining techniques and privacy protection measures through a review of the pertinent literature. It provides a concise analysis of the benefits and drawbacks of commonly utilized classification algorithms in data mining. In addition, it examines the interplay between data mining techniques and privacy protection and summarizes important privacy protection techniques. In addition, this paper provides a summary of the most important privacy protection methods. These techniques include data anonymization, association rule concealing, data perturbation, etc. By comprehending these privacy protection techniques, appropriate privacy safeguards can be selected to ensure the privacy and security of the data when conducting data mining.
Creating Zhudi and Guzheng sampler based on Nyquist
Authors: Li Zihan
Published: October 2023

Nyquist is a strong and basic software for making music with algorithms. With the western instruments available as functions inside the software, this research aims to create sampler of traditional Chinese instruments that can read Musical Instrument Digital Interface (MIDI) files based on the present functions. This research mainly focuses on two instruments: Guzheng, a traditional plucked instrument in China and Zhudi, the oldest Chinese musical instrument found so far and the most representative woodwind in the Chinese musical instrument family. The methods used in this research is built on original functions, envelopes, filters in the software. After creating the samplers, the MIDI file users uploaded will be converted into scores inside Nyquist for the samplers to read. The separation of the MIDI file is done by if and else clauses, splitting the samplers to each focus on a certain part of the song. Based on the methods above, this research yields a sample song that has a MIDI file originally in the timbre of piano applied to both samplers at the same time. The outcome yielded applies two samplers separated at a certain pitch, forming a contrast between the main melody and the chords. This research successfully achieves its intended purpose, creating a bridge that connects western music culture with traditional eastern music culture.
A comparative analysis of traditional and AI-based routing algorithms in electronic design automation
Authors: Shen Zheyi
Published: October 2023

Rapid advances in artificial intelligence (AI) and machine learning have had a significant impact on various fields, including electronic design automation (EDA). This study aims to compare current EDA routing algorithms with AI-based routing algorithms, highlighting their respective advantages and limitations. Through a comprehensive analysis of existing EDA routing algorithms and artificial intelligence-based technologies, this study explores the applicability of these algorithms in different EDA scenarios, with a focus on their effectiveness and efficiency in routing tasks, and aims to compare and contrast traditional and AI-based routing algorithms in the context of electronic design automation. It also shows that the current AI technology will play an important role in the future development of EDA software. Therefore, the combination of AI technology may be the focus of EDA software development, so as to help elucidate the evolving landscape of EDA and provide insights into the potential future direction, and provide a a comprehensive understanding of their underlying methodologies, advantages, and limitations of traditional and AI-based algorithms in the context of electronic design automation.
Facial emotion recognition based on improved ResNet
Authors: Du Wei
Published: October 2023

A common and difficult task in computer vision is the identification of facial expressions of emotion. Facial emotion recognition has shown great promise in many areas including healthcare, robotic communication and customer service. However, the variability of human appearance and muscle movement leads to difficulties in facial emotion recognition. Therefore, deeper convolutional neural networks are introduced to recognize facial emotions. The residual network (ResNet) can be built in a deep architecture that can solve the degradation problem when the depth of the network increases. In this paper, an improved ResNet50 is proposed to implement facial emotion recognition. Specifically, the proposed model appends two blocks consisting of fully connected layers to the ResnNet50. The layers are stacked with shortcut connections to solve the degradation problem and to make the training process smoother. The accuracy achieved by the improved model on the Facial Emotion Recognition 2013 dataset (FER-2013) is 13.31% higher than that of the ResNet50. Experimental data indicate that the improved model performs efficiently in facial emotion recognition due to shortcut connectivity and the addition of fully connected layers. Meanwhile, the degradation and gradient disappearance problems are improved.
The analysis of electric vehicle integration based on big data technology
Authors: Chen Xinyao
Published: October 2023

The potential of electric vehicles (EVs) to reduce greenhouse gas emissions in the transportation sector has given the adoption of EVs a considerable boost in recent years. Concurrently, the field of big data analytics has witnessed exponential growth, providing unprecedented opportunities for extracting valuable insights and optimizing various industrial sectors. This paper presents a comprehensive overview of the intersection between electric vehicles and big data analysis. Various EV-related data sources are explored along with the discussion of data computing platforms. Followed by this, this paper analyzes different use cases of big data analysis in EVs, covering key areas such as energy management, charging infrastructure optimization, and vehicle condition monitoring, which demonstrates how big data can be crucial for the successful integration of EVs into green smart cities. Finally, the author provides future research insight and opportunities for the use of big data techniques in EV adoptions. In particular, this paper serves as a roadmap for future research in the area of data analytics in EV integration.
Investigation of multiple convolutional neural network models on emotion detection
Authors: Gao Weijin
Published: October 2023

The accurate detection of emotions holds significant importance in the field of psychology, necessitating the careful selection of an appropriate model for facial expression classification. In this study, emotion detection is the classification task to compare the performance of MobileNet, ResNet, and DenseNet. For the detailed model, MobileNet, ResNet50, and DenseNet169 are selected for comparative analysis. The dataset FER-2013 is from Kaggle, which contains a training set and test set consisting of 29, 709 samples and 3589 samples, respectively, with seven facial expression categories. In terms of preprocessing, normalization, and data augmentation are considered. The whole dataset is normalized by dividing 255 and augmented with a Keras image generator. In the model-building step, the structure of the test models is controlled in the same structure. The pre-trained model from the Keras application connects with one global average pooling layer and adds one dense layer at the last as the output layer with the SoftMax activation function. Moreover, this study kept all hyper all parameters the same during the training period. After the model training, the confusion matrix is used to show the class relativity and the loss and accuracy of each model are plotted for analysis. Experimental results demonstrated that the MobileNet achieves 56.08% accuracy on test set which is more competitive than the DenseNet169 and ResNet50 and provides a relatively stable loss.
Facial expression recognition based on ResNet and transfer learning
Authors: Chen Yixuan
Published: October 2023

With its potential to revolutionize a wide range of applications, including lie detection, social robotics, and driver fatigue detection, facial expression recognition is a field that is rapidly expanding. However, traditional machine learning methods have struggled with facial expression recognition due to limitations such as manual feature selection and limited representation capabilities. Additionally, these methods require large amounts of annotated data, which can be time-consuming and expensive to obtain. In order to overcome these difficulties, this paper suggests a novel method that builds recognition models using a multi-layer perceptron (MLP) and ResNet. This hybrid model offers improved performance over conventional CNN models, achieving an impressive accuracy rate of 85.71% on the FER_2013 dataset. Additionally, migration learning is used to increase the model's precision and avoid over-fitting. The FER_2013 dataset is used to train and test the model. The results of the trials show that the suggested model can recognize facial expressions while minimizing the overfitting problem typically associated with deep learning. The model will eventually include a self-attentive mechanism in the study in an effort to improve model resolution. By using it with color images, the team also hopes to increase the model's capacity for generalization.
Realistic evaluation of the prospect of replacing silicon with other IC materials: Take molybdenum disulfide and graphene as examples
Authors: Chen Zhexuan
Published: October 2023

In recent decades, integrated circuits (IC) have played a more significant role in many areas of society, and silicon is one of the most commonly used materials in this industry. However, as times change and with the development of technology, silicon can only partially meet people's requirements for device performance. Hence, people need new materials to produce more effective IC equipment and devices like chips. Here I present two kinds of materials, molybdenum disulfide and graphene, to discuss their properties of them and the realistic assessment of the prospect that they replace silicon for IC.
The development history and future trends of the microchip
Authors: Shen Peijun
Published: October 2023

This essay provides a detailed analysis of the evolution of microchip technology from its inception to its prospects. It starts by discussing the foundational work of pioneers like Jack Kilby and Robert Noyce and the transformative effect of Moore's Law on chip design and manufacturing. It then evaluates the current state of chip technology, including the leading industry players and challenges such as chip shortages and trade disputes. The essay then explores the anticipated trends in chip technology, such as quantum computing and innovative materials like graphene. It concludes by assessing the expected influence of these advancements on various sectors, including Artificial Intelligence (AI), the Internet of Things (IoT), and autonomous vehicles, and their broader social, economic, and environmental implications. This essay presents a comprehensive and thoughtful analysis of microchip technology's past, present, and future and its far-reaching impact on modern society.
Parameter optimization in convolutional neural network for improving performance of facial expression recognition
Authors: Zhang Jiyin
Published: October 2023

The recognition of similar facial expressions presents a notable challenge, necessitating a focus on the parameters within the fundamental Convolutional Neural Network (CNN) architecture, which serves as a cornerstone in the field of image classification. This research endeavor aims to enhance the model's capacity for facial expression recognition by employing a controlled variable method to examine two specific parameters in a self-designed small CNN: the number of filters and convolutional layers. More specifically, while the filters were fixed at 3, the layers varied from 3 to 6 to 9. Similarly, as the number of the filters rose to 6, the number of the layers also incremented from 3 to 6 to 9. Furthermore, while the number of the filters reached 12, the number of the layers went from 3 to 6 to 9 too. Finally, with the filters increasing to 24, the layers rose from 3 to 6 to 9 as well. Experimental results indicate that both increasing the number of filters and convolutional layers can increment the performance of model in facial expression recognition. Furthermore, increasing the number of filters can exert a more prominent influence on improving the accuracy of facial expression recognition.
DenseNet-random forest model based galaxy classification
Authors: Hu Junyi
Published: October 2023

Finding an efficient and accurate adaptive method that can automatically classify galaxies has become an industry consensus. However, most of the current studies on galaxy classification use a single model for direct output, without considering the combination with other models to output more satisfactory prediction results. Through convolutional neural network and classifier, this study studied the possibility of applying the deep learning model to the Galaxy 10 DECals dataset classification, and proposed DenseNet-Random Forest model through comparative analysis. By adjusting and training DenseNet-121 with appropriate hyperparameters, the input tensor is transferred to the basic model through the creation of a shape input layer, where GlobalAveragePooling2D is added to perform an average pooling operation on each feature map, reducing the spatial dimension of each feature map to 1. During the process, a complete connection layer with 64 neurons was added using the ReLU activation function, and a Dropout layer was added to randomly discard 20% of the neurons during training to prevent overfitting. In addition, ReLU Activation function with 32 full connection layers of neurons and softmax Activation function with 10 output layers of neurons are added. By acquiring the feature vector of the training model and the real label of the verification set, assign x and y values respectively, and import them into the Random forest classifier model. The experimental results demonstrated the model ultimately achieved a prediction accuracy of 68% when processing the Galaxy 10 DECals dataset, and achieved nearly 30% improvement in Precision, Recall, and F1 scores.
A survey on federated learning: evolution, applications and challenges
Authors: Shi Lingyu
Published: October 2023

Federated learning, a machine learning technique that enables collaborative model training on decentralized data, has gained significant attention in recent years due to its potential to address privacy concerns. This paper explores the evolution, applications, and challenges of federated learning. The research topic focuses on providing a comprehensive understanding of federated learning, its advantages, and limitations. The purpose of the study is to highlight the importance of federated learning in preserving data privacy and enabling collaborative model training. The study conducted a literature review by systematically analyzing relevant papers from peer-reviewed journals, conference proceedings, and reputable sources. The results reveal that federated learning offers a promising solution for collaborative machine learning while addressing concerns related to data privacy and security. The study emphasizes the need for further research in optimizing communication protocols, scalability, and privacy-preserving techniques. Overall, this paper contributes to the understanding of federated learning and its potential for secure and efficient decentralized learning paradigms.
Review on the influence of machine learning methods and data science on the economics
Authors: Liu Zhekai
Published: October 2023

In this era of extraordinary accessibility to information, business faces both unprecedented obstacles and opportunities. Constantly accumulating data encompasses everything from consumer behavior to market trends. However, the question of how to extract useful information from this enormous quantity of data and apply it to economic decision-making becomes crucial. Complex non-linear relationships and high-dimensional data frequently render conventional statistical methods and economic models ineffective. Integration of data science and machine learning techniques has enabled economists to extract valuable insights from large-scale and complex economic data. By examining the role of data science and machine learning in economics and tracing its historical development from the refinement of statistics to the era of big data with advanced computational power, this paper will discuss the significance of data-driven decision making and forecasting in the economy with specific algorithm in supervised and unsupervised learning and focus on future challenges and developments.
Research on optimization of forest fire early warning task by neural architecture search
Authors: Bai Junwei , Sun Kexin , Yue Song
Published: October 2023

We employed Neural Architecture Search (NAS) technology in this research paper and com-pared it with the classic convolutional neural network structures LeNet-15 and VGG (Visu-al Geometry Group)16. The objective was to optimize the performance of early warning forest fire image classification tasks. We adopted a public dataset "Forest Fire Early Warning 2 Clas-sification" from the Baidu PaddlePaddle platform, which comprises images of fires and no-fires under various environmental conditions. A convolutional neural network (CNN) model was automatically designed through AutoKeras and underwent 20 training epochs. In the ex-perimental results, NAS outperformed with an accuracy rate of 92%, surpassing LeNet-15 (83%) and VGG16 (49%). However, its training time was longer at 33 seconds, and GPU utili-zation was higher, ranging from 28% to 33%. Despite the room for improvement in training time and resource utilization, NAS has proven its superiority in complex image classification tasks due to its high accuracy. Although NAS has room for enhancement in terms of training time and resource utilization, its outstanding performance in the image recognition task of ear-ly forest fire warning shows great potential for future research and application.
Prediction and feature importance analysis for wordle strategy based on machine learning models
Authors: Fan Zhiming
Published: October 2023

The word guessing game, Wordle, has attracted considerable attention from researchers in computer studies and mathematics due to its significant relevance. Many studies have been taken on Wordle to explore its potential in simulating human behaviors during gameplay and devising optimal strategies for players. In this paper, strategies based on mathematics theories or machine learning technologies are compared by their feature importance in order to explore whether there is a loss of feature meaning in machine learning methods. In the whole study, the effect of features and their relations to the expectations of tries of game Wordle are also explored. More specifically, this paper introduces machine learning models such as linear, random forest, bagging as well as gradient boosting decision trees to visually provide a relation between words features (such as repeat frequency, vowels, consonants) and the expectation of attempts in a game. After establishing machine learning models, the feature importance is derived by feature engineering techniques. The importance is then compared with spearman statistical correlations based on the dataset to hence draw the conclusion of the change of feature meaning in machine learning methods. Study results indicate that there is loss of meaning and effect of features in the better fitted prediction models (gradient boosting decision trees, random forest) compared to the statistic approaches.
Prediction and investigation of stock price related to Chinas new energy vehicles after the opening of the pandemic based on the LSTM model
Authors: Peng Yanghan
Published: October 2023

This research examines the repercussions of the COVID-19 pandemic on China's new energy vehicle market through the utilization of machine learning models for stock price prediction. Specifically, the Long Short-Term Memory (LSTM) and Artificial Neural Network (ANN) are employed to forecast stock prices and price changes for BYD, Changan Automobile, and Guangzhou Automobile Group. While the LSTM model successfully captures the patterns in the stock price data, it exhibits a lag of one day in its predicted outcomes, indicating its reliance on the previous day's price. However, both models encounter challenges in accurately predicting stock price changes, displaying notable disparities from actual values. The classification task of forecasting whether prices will rise or fall also yields unsatisfactory accuracy scores, highlighting the models' limitations in comprehending the dynamics of the stock market. This study reveals that understanding the impact of the pandemic on the NEV market holds significant importance for informed decision-making and effective navigation of China's automotive industry in the aftermath of the pandemic. Furthermore, further modification for the model is also required to enhance the precision and dependability of stock price forecasts.
Problems, solutions and improvements on federated learning model
Authors: Huang Leqi
Published: October 2023

The field of machine learning has been stepping forward at a significant pace since the 21century due to the continuous modifications and improvements on the major underlying algorithms, particularly the model named federated learning (FL). This paper will specifically focus on the Partially Distributed and Coordinated Model, one of the major models subject to federated learning, to provide an analysis of the models working algorithms, existing problems and solutions, and improvements on the original model. The identification of the merits and drawbacks of each solution will be founded on document analysis, data analysis and contrastive analysis. The research concluded that both alternative solutions and improvements to the original model can possess their unique advantage as well as newly-emerged concerns or challenges.
Exploring the Impact of Social Exchange Factors on Organizational Commitment: A Study of Development Bank of Ethiopian Amhara Region Branches
Authors: Melkamu Kebede Molla
Published: October 2023

Despite the extensive research on organizational commitment, studies that specifically examine the impact of social exchange factors on organizational commitment are needed. More empirical evidence of the effects of social exchange is necessary because most studies have been conducted in developed countries, and less is done in the context of development banks in Ethiopia. Consequently, this research aims to examine the impact of social exchange factors on organizational commitment among Bank employees by investigating the association between work environment, job security, pay satisfaction, and involvement in decision-making with employees' organizational commitment. Using a census sampling technique, 208 employees filled out Likert-scale questionnaires to collect cross-sectional data and utilized multiple linear regression to test the hypothesis. Descriptive and inferential statistics were employed to examine the data using STATA 17. The findings indicate that the mean value for job security, pay satisfaction, Participation in decision-making, and organizational Commitment was above average. Weighted least square estimation was fitted where Payment satisfaction (B=0.202, P_value<0.06), job security (B=0.25, P_value<0.001), Participation in decision making (B=0.28, P_value<0.001) were significant and had a positive effect however work environment (B=0.05, P_value<0.48) is not effective at a 5% level of significance. This W.L.S. result suggests that employees are committed to D.B.E. However, the work environment was insignificantly related to organizational commitment.In conclusion, the results indicate that job security, pay satisfaction, and Participation in decision-making are significant determinants of organizational commitment. However, the work environment has little impact on employees' commitment to the organization; these help the Bank continue its strategy with moderate changes for the best outcome above average, develop strategies to enhance employee commitment and improve organizational performance. The study highlights the importance of job security, fair compensation, and the opportunity for employees to participate in decision-making processes to increase commitments.
Arthur N. Prior and Leśniewski’s Concept of Names
Authors: Rybaříková Zuzana
Published: October 2023

For a certain period, the concept of names that Stanisław Leśniewski and his followers developed had a certain impact on the concept that appeared in Arthur Prior’s temporal ontology. However, this impact seemed to vanish in time. The aim of this paper is to present why Prior was interested in Leśniewski’s concept of names and quantification and to discuss why in Prior’s later works Leśniewski’s influence is not as apparent as it was in the first works on temporal logic. Namely, the paper suggests three possible solutions; the differences that were between Prior and Leśniewski’s views on time and determinism, new concepts of names that occurred at that time, and Leśniewski’s extensionalism that opposed Prior’s preference for intensional logic.
Growth performance and carcass traits of indigenous Nigerian guinea fowl fed on different dietary protein levels
Authors: Alabi Olayinka , Abang Cyril , Olajide Olasunkanmi , Animashahun Rasaq , Oyong Stephen
Published: October 2023

Objective: This work examined the results of different dietary protein levels on indigenous Nigerian guinea fowl growth performance and carcass yield.
Materials and Methods: One hundred and eight unsexed indigenous keets were randomly assigned to three treatments with experimental diets containing 22%, 24%, and 26% crude protein (CP) in a completely randomized design, with 3 replicates with 12 keets in each replicate. The parameters were measured, including proximate composition, feed intake, growth rate, feed
conversion ratio (FCR), and carcass yield.
Results: The birds fed diets containing 24% CP gained the most body weight at 619.83 gm and had the highest FCR of 3.45. The presence of CP had an impact that was significant (p < 0.05) on most carcass traits and prime cuts but not on gastrointestinal tract weight, head weight, dress percentage, or organ yield.
Conclusion: The study concluded that the dietary CP level of 24% was optimal for body weight gain and carcass yield.
Zeolite-supplemented diets in the prenatal period affected postpartum reproductive parameters, colostrum production, and body condition score of dairy cows
Authors: Movahedi Nader , Foroudi Farhad , Karimi Naser , Abedini Mohammad , Karimi Kazem
Published: October 2023

Objective: The effect of zeolite on Ca plasma concentration, reproductive parameters, body condition score (BCS), and colostrum properties was evaluated in Holstein dairy cows during the postpartum period.
Materials and Methods: Sixty pregnant cows were allocated to three experimental groups, including 1) a control (CON) diet; 2) a CON diet + 0.75% dry matter (DM) zeolite (100 gm/day/cow); and 3) a CON diet + 1.5% DM zeolite (200 gm/day/cow). Experimental diets were fed to cows during the last 4 weeks of pregnancy. Blood sample evaluation for Ca concentration was done. The prevalence of hypocalcemia and reproductive parameters, including BCS and colostrum properties, was also measured.
Results: Total Ca and ionized calcium (Ca++) concentrations in plasma at 6 and 12 h after calving were higher in zeolite-consuming cows than those in CON s (p < 0.01). Mean frequencies for severe and subclinical hypocalcemia in zeolite-consuming cows were 11.64% and 19.36% lower than those in the CON group, respectively (p < 0.01). The mean pregnancy efficiency of these cows was also 12.94% higher than the CON (p < 0.01). The mean BCS (p ≤ 0.05) and colostrum quality (p < 0.01) of zeolite-consuming cows were at maximum, but their mean colostrum yield was lower than that of the CON group (p < 0.01).
Conclusion: Zeolite dietary supplementation at levels of 0.75% and 1.5% DM of the preparturient diet is recommended for better CON of hypocalcemia, improved BCS and colostrum quality, and better prevention of postpartum reproductive disorders in dairy cows.
Investigation of the influence of xanthan on mozzarella cheese characteristics focusing on its antimicrobial effect
Authors: Salam Ayah , Saad Mena , Ahmed Rania , Soliman Neveen
Published: October 2023

Objective: This study was designed to show the effect of adding different levels of microbial (lab-produced) and commercial xanthan (CX) for 30 days on the sensory, chemical, and microbiological parameters of mozzarella cheese (MC).
Materials and Methods: The production of xanthan was done in Garcia–Ochoa’s medium. The sensory evaluation of the examined MC was achieved through a tabulated scorecard. The Gerber method was used for the determination of MC fat%. The mean counts of staphylococci [colony forming unit (CFU)/gm], coliforms (most probable number/gm), fungi (CFU/gm), and mesophilic bacteria (CFU/gm) were estimated in different fortified cheeses. Also, mean counts of Escherichia coli O157 and Staphylococcus aureus in artificially contaminated MC were determined.
Results: The microbial xanthan (MX) had a significant (p < 0.05) effect on the sensory parameters of the examined samples with its concentration (0.0007%) after 20 days of storage. The MX (0.0005%) and CX (0.0002%) had a significant effect on moisture, fat in dry matter, and protein percentage of MC throughout the storage period. The high meltability degree of MC was observed in samples with both types of xanthan (0.0002%) at the end of storage.
Conclusion: Both types of xanthan at all concentrations had a significant reducing effect on E. coli O157 and S. aureus in all samples from 10 to 30 days of storage. Xanthan has accepted attentiveness and offers beneficial and safe characteristics that improve its adaptability in MC. In the Middle East, this survival trial of E. coli O157 and S. aureus in the MC supplemented by xanthan is considered a scarce exploratory investigation.
Recent advances in probiotication of fruit and vegetable juices
Authors: Rahman Md , Emon Dwip , Toma Maria , Nupur Asmaul , Karmoker Poly , Iqbal Abdullah , Aziz Mohammad , Alim Md
Published: October 2023

Probiotics are live bacteria beneficial to health when consumed adequately. Health professionals now recommend probiotics on regular diets due to their positive effects on human health. The probiotics that are usually consumed from the market through food products are mostly dairy-based. Fruit and vegetables are gaining popularity as preferred matrices for probiotic carriers to the human body, owing to their high cholesterol content and the lactose intolerance of dairy products. On the other hand, fruits and vegetable juices are rich in nutrient content such as vita¬mins, minerals, and antioxidants and do not contain a starter culture that can compete with the nutrients. The probiotication of fruit and vegetable juices (apple, carrot, citrus fruit, pomegranate, watermelon, tomato, and pineapple) are performing as efficient probiotic bacteria carriers. This review covers the previous works that highlighted the variety of probiotic fruit and vegetable juices as well as the viability of each probiotic in various products after proper fermentation and storage. In addition, physicochemical and sensory changes that occurred during the processing and storage period have been discussed. Furthermore, strategies (microencapsulation, adding prebiotics, antioxidant addition, maintaining optimum pH, temperature, adaptation with resis¬tance, and good packaging) to improve the stability of probiotic bacteria are outlined, as it is difficult to maintain the stability of probiotic bacteria during storage. Finally, the manuscript discusses the effect of probiotic fruit and vegetable juices on human health.
Characteristics of canine oral tumors: Insights into prevalence, types, and lesion distribution
Authors: Satthathum Chakkarin , Srisampane Supreeya , Jariyarangsrirattana Pollawat , Anusorn Pitak , Sattasathuchana Panpicha , Thengchaisri Naris
Published: October 2023

Objective: The escalating prevalence of canine oral tumors has emerged as a considerable health concern. This study examined the prevalence, types, and distributions of lesions linked to canine oral tumors.
Material and Methods: The medical records of 526 dogs diagnosed with oral tumors were analyzed to determine the prevalence, types, and distributions. Tumor stages were classified into four categories using the tumor node metastasis system.
Results: Among the 526 dogs, there were 118 cases of benign tumors and 408 cases of malignant tumors. Acanthomatous ameloblastoma was the most common benign tumor (43.22%), while melanoma was the most common malignant tumor (51.23%). The gingiva was the most common site for both benign and malignant lesions, accounting for 89.83% and 63.73% of cases, respectively. Melanoma, squamous cell carcinoma, and fibrosarcoma were primarily located in the gin¬giva, whereas osteosarcoma was commonly found in the mandible. Most tumors were classified as stage III (ranging from 46.84% to 74.58%). Of the reported cases, 56.08% were males and 43.92% were females, and the most common breed was mixed at 30.41%, followed by Poodle at 14.25% and Shih Tzu at 11.40%. Moreover, patients with malignant oral tumors (11.6 ± 3.1 years) were significantly older than those with benign tumors (8.9 ± 3.4 years, p < 0.0001).
Conclusion: Gingiva was the primary site for oral tumors, and mainly classified as stage III. These findings emphasize the increasing occurrence of oral tumors in senior and geriatric dogs and provide insights into the prevalent types and distribution.
Complete genome sequence analysis of the multidrug resistant Aeromonas veronii isolated for the first time from stinging catfish (Shing fish) in Bangladesh
Authors: Rahman Mohummad , Sadekuzzaman Mohammad , Rahman Md , Siddique Mahbubul , Uddin Mohammad , Haque Md , Chowdhury Md , Khasruzzaman A , Rahman Md , Hossain Muhammad , Islam Md
Published: October 2023

Objective: Whole genome sequencing (WGS) of Aeromonas veronii Alim_AV_1000 isolated from ulcerative lesions of Shing fish (stringing catfish; Heteropneustes fossilis) was performed during the outbreak year 2021.
Materials and Methods: Using next-generation sequencing (Illumina) technology, WGS was accomplished, resulting in the sequencing, assembly, and analysis of the entire genome of the A. veronii strain. Moreover, the genomic features, virulence factors, antimicrobial resistome, and phylogenetic analysis for the molecular evolution of this strain were also examined.
Results: The genome size of the A. veronii Alim_AV_1000 strain was 4,494,515 bp, with an average G+C content of 58.87%. Annotation revealed the known transporters and genes linked to virulence, drug targets, and antimicrobial resistance.
Conclusion: The findings of the phylogenetic analysis revealed that the strain of the present study has a close relationship with the China strain TH0426 and strain B56. This study provides novel information on A. veronii isolated from Shing fish in Bangladesh.
Is there a correlation between the diagnostic test results and the histopathological diagnoses of acute appendicitis in a Turkish hospital in Somalia?
Authors: Yakar Sule , Akan Ayse , Baykan Necmi
Published: October 2023

Appendicitis is the most common cause of acute abdomen. This study aimed to investigate the demographic and clinical data of the patients diagnosed with acute appendicitis in the emergency department of a Turkish tertiary care center in Somalia. The association between the diagnostic test results and the histopathological diagnoses was also analyzed. Data of the patients diagnosed with acute appendicitis at the emergency department between the 1st of January 2021 and the 31st of December 2021 period were retrospectively reviewed. The demographic data, clinical features, laboratory test results and radiological and histopathological findings of the participants were retrieved from the hospital’s electronic medical records for patients in the database. Overall, 83 patients were included. Among those, 59 (71.1%) were male, and the mean patient age was 22±8.2. The mean values of the leukocyte and neutrophil counts and the C-reactive protein levels were higher than the normal range. The laboratory test, abdominal ultrasonography, and enhanced computerized tomography results were compared with the histopathological findings. Only the neutrophil-lymphocyte ratio was found (to be) significant in the diagnosis of complicated appendicitis. The clinical data, laboratory tests, and radiological imaging findings should be evaluated together to diagnose acute appendicitis. Prompt diagnosis is essential for the prevention of postoperative complications and mortality. A statistically significant difference was found in the comparison of the lymphocyte counts, NLR values, and histopathological findings (p=0.01 and p=0.008).
A retrospective- multicenter study: Evaluation of the same brand (Confort) negative pressure wound therapy (NPWT) with silver coated and standard foam in diabetic foot ulcers
Authors: Kiziloglu Ilker , Karaca Banu , Colak Bayram , Cevik Nihal , Ince Bilsev , Sener Alper
Published: October 2023

Diabetic foot ulcers are hard to treat ulcers that are common in population. This situation causes amputations and extremity loss. Among therapeutic options negative pressure wound therapy (NPWT) seems a good option to treat specially non osteomyelitic and non ischemic wounds. This method can be widely used in deep, large and infected wounds. The antibacterial effect of silver on wounds has been used for centuries. Silver wound closure sets, which are also used in NPWT systems, have gained importance in recent years. In this study, the effect of Confort brand NPWT with and without silver on wound healing evaluated retrospectively. This is a multi center study involving five different institution. As a success criterion, a reduction of >50% in the initial dimensions of the wound (width/length/depth) after 7 applications is accepted. When the silver coated and standard foam Confort NPWT groups were compared, the reduction of 50% or more in the wound volume was relatively shorter in the silver group; while it was 3.2 +/- 1.2 weeks in the silver group, it was 5.14 +/- 2.3 weeks in the silver-free group (p=0.08).
Endovascular Reperfusion Therapy in Minor Stroke with Neurologic Deterioration beyond 24 Hours from Onset
Authors: Lee Min A , Hwang Byoung Wook , Ha Sang Woo , Kim Jae Ho , Kim Hak Sung , Ahn Seong Hwan
Published: October 2023

Purpose: Patients with minor stroke (National Institutes of Health Stroke Scale score ≤5) and large vessel occlusion (LVO) often experience neurological deterioration >24 hours after onset. However, the efficacy of endovascular reperfusion therapy in these patients remains unclear. The aim of this study was to determine the efficacy and safety of reperfusion therapy in patients with minor stroke and neurological deterioration >24 hours after onset.Materials and Methods: Data were retrospectively reviewed from patients between January 2019 and April 2022 who met the following criteria: (1) minor stroke and small definitive ischemic lesions at initial visit, (2) onset to neurological deterioration >24 hours, (3) cortical signs, Alberta Stroke Program Early computed tomography (CT) Score >6 points, and large artery occlusion confirmed by CT angiography at neurological deterioration. Efficacy and safety outcomes were based on final thrombolysis in cerebral infarction (TICI), incidence of symptomatic intracranial hemorrhage (ICH), and mortality. Outcomes were assessed using the modified Rankin Scale (mRS) at 3 months. Good outcome was defined as a mRS of 0, 1, or 2.Results: Data from 26 patients (38.4% female, mean age 75.8 years) were analyzed; 18 (69.2%) had a good outcome. A final TICI of 2b or 3 was observed in 24 (92.3%) patients. No other adverse events, including dissection, vasospasm or distal embolization, were observed during the procedures. Hemorrhagic events occurred in 8 patients after the procedure; however, there were no symptomatic ICHs. Good prognostic factors were younger age (P=0.062) and carotid stenting (P=0.025).Conclusion: Endovascular reperfusion therapy performed in selected patients with minor stroke, LVO, and neurological deterioration >24 hours after stroke onset demonstrated favorable outcomes and safety.
Salvage therapy for BCG failure with intravesical sequential gemcitabine and docetaxel in patients with recurrent NMIBC
Authors: Garneau Charles-Antoine , Marcotte Nathalie , Lacombe Luois , Fradet Yves , Yves Fradet , Pouliot Frederic , Toren Paul , Lodde Michele
Published: October 2023

Introduction: Bacillus Calmette-Guérin (BCG) failure occurs in approximately 40% of patients with non-muscle-invasive bladder cancer (NMIBC) within two years. We describe our institutional experience with sequential intravesical gemcitabine and docetaxel (gem/doce) as salvage therapy post-BCG failure in patients who were not candidates for or declined radical cystectomy (RC).
Methods: We retrospectively reviewed BCG-failure patients with NMIBC who received gem/doce from April 2019 through October 2022 at the CHU de Québec–Université Laval. Patients received at least five weekly intravesical instillations according to published protocols. Patients who responded to gem/doce had maintenance instillations monthly for up to two years. Primary outcome was progression-free survival (PFS). Secondary outcomes included recurrence-free survival (RFS), cystectomy-free survival (CFS), cancer-specific survival (CSS), overall survival (OS), and treatment adverse events. Survival probabilities were estimated using the Kaplan-Meier method from the first gem/doce instillation.
Results: Thirty-five patients with a median age of 78 years old were included in the study. The median followup time was 21 months (interquartile range 10–29). More than 25% of patients received two or more prior BCG induction treatments. Overall/MIBC PFS estimates at one year were 85%/88% and 60%/70% at two years. Adverse events occurred in 37% of the patients, but only two patients didn’t complete the treatment due to intolerance. Three patients underwent radical cystectomy due to cancer progression. OS was 94% at two years.
Conclusions: With 60% of PFS at two years, gem/doce appears to be a safe and well-tolerated option for BCG failure patients. Further studies are needed to justify widespread use.
基于下一代网络架构的大学网络课程案例教学探索
Authors:   东华大学, 刘 思彬 , 陈 雯 ,   东华大学, 胡 雯静 ,   东华大学, 杨 昱潇 ,   东华大学
Published: October 2023

针对网络通信相关专业的学生，本文提出了开设软件定义网络(Software Defined Networking，SDN)大学生课程，旨在提高他们的创新研究和实践动手能力，以支持其未来的职业发展。课程内容不仅包括理论教学，还提供了一套全新的实验教学方案，其中包括基础实验、进阶实验和综合实验，以帮助学生更好地掌握SDN领域的知识和技能。同时，为了激发学生的积极探索、实践和创新精神，在课程中采用了多种考核方法。通过这些方法，学生将被鼓励主动参与并应用所学知识，同时提高在SDN领域的综合素质。我们认为，通过本课程，学生不仅可以提升其理论知识水平，还能够通过实验教学的方式培养解决问题的能力，而这将为他们在SDN领域的职业发展奠定坚实的基础。
Guns, Construction of Threat, and Lived Ecclesiologies
Authors: Day Katie
Published: October 2023

Recent shootings in houses of worship have led people of faith to interrogate theological understandings of safety and security and implement new practices and policies, including armed protection. These responses to perceived threats and a sense of vulnerability to varying degrees come out of the “lived ecclesiologies” of faith communities. After exploring the meaning of ecclesiology, I argue that lived ecclesiologies are socially constructed; they draw on theological traditions and faith commitments, but also take shape in response to felt threats. I show how Dietrich Bonhoeffer’s own evolving ecclesiology developed out of changing threats, both internal and external, and how this illuminates emergent lived ecclesiologies with respect to the threat of gun violence. Finally, lived ecclesiologies of “fortress” and “hospitality” are explored through sociological research and theological critique.