Miracle-dz/UniFAR-FTUs huggingface dataset:
Seroprevalence and associated risk factors of avian influenza virus subtype H9N2 in backyard poultry of Peshawar Pakistan

The current study was conducted to estimate the serological prevalence and risk factors associated with the transmission of avian influenza subtype H9N2 among backyard poultry from different villages of semi-arid climate district Peshawar Pakistan between January to May 2019. In total, 240 blood samples of healthy backyard poultry older than two months were collected from 30 different villages of district Peshawar. A predesigned questionnaire was used to collect the data related to risk factors. Se-rum samples were tested through the haemagglutination inhibition (HI) test further confirmed by the micro-neutralizing test (MN). Antibody body titer ≤ 8 considered negative for prevalence of H9N2. In order to classify risk factors, Chi-square and Logistic regression analyses were performed. Out of 240, 150 were found to be positive. The overall seroprevalence was 62.5%, mean antibody titer for avian influenza virus in all villages was 6.8 and (95%) confidence interval ranges from 35.33 to 51.70% re-spectively. The high prevalence of antibodies to avian influenza viruses in bird serum emphasizes that avian influenza plays an important role in the respiratory infections of backyard chickens in the area, and possibly throughout Pakistan. In order to avoid the introduction of H9N2, biosecurity initiatives, surveillance, monitoring systems, and to some extent, vaccinations are useful tools.
The Creation of Intention To Buy through Online Social Ties and Social Media Advertisement

Objective: The growing number of social media users makes many companies want to get subscribers through their social media activities. There are several choices of strategies that can be done by the company to be able to take the huge market. Research Design & Methods: This study compares the effect of advertising activity on social media and user group development that has a strong tie to intention to buy. Findings: This research found that the social media advertisements has a better impact to intention to buy compared to social ties. Implications & Recommendations: This study was conducted on social media users on land Transportation Company that already has a group in social media, Facebook, and does advertising activities on it. Contribution & Value Added: The results of this study provide theoretical implications in the verification of the previous research results and managerial implications that can help the company in conducting marketing activities through social media.
In vitro evaluation of the antimicrobial activity and diffusion capacity of solutions used for canine ear cleaning

Otitis is a common condition among dogs and requires appropriate treatment. Given the importance of the otitis externa and the use of products to combat this condition, this study evaluated the in vitro antimicrobial abilities of six commercial ear cleaners solutions (EC1 to EC6) and solutions of 3% boric acid, 3% lactic acid, 0.11% salicylic acid, 0.5% chlorhexidine, and 3% propylene glycol against Staphylococcus spp. , Pseudomonas spp. , Proteus spp. , and Malassezia spp. The in vitro diffusion capacity in synthetic cerumen was also assessed. Dogs with clinical signs of otitis externa were selected and samples were collected from the external ear canals. The microbiological study was performed to identify the microorganisms from samples collected and the microorganisms isolated were used in the study. One ceruminolytic for human use and five commercial solutions used for canine ear cleaning were selected based on the diversity of the components used in each formula. The results show a variation of antimicrobial activity and diffusion capacity ear cleaners and its compounds. Lactic acid, chlorhexidine, EC1, EC2, EC4, and EC5 showed the best results for microbiological growth inhibition; boric acid, salicylic acid, propylene glycol and EC6 had little or no effect on microorganism growth. The ECs tested demonstrated diffusion capacity using the SSC. EC1 was the solution with the most significant responses, both as an antimicrobial agent and with regards to diffusion capacity. Among the commercial veterinary products tested, EC4 was found to have the best results.
Design and Analysis of High Beam forming MIMO Antenna for 5 G Applications

in upcoming days wireless communication products and electronic gadgets are becoming a necessity to human life. Communication systems need antennas that work with multiband and wide band with required parameters like polarization and gain. The main motto of this work is to produce high beam forming with the aid of mutual coupling among the four antenna elements in order to encourage enhanced transit capacity and empower the communication bandwidths at very large data rates for 5G Technology. In the view of mitigating the multipath fading with above mentioned principles. The designed antenna is developed a MIMO patch antenna with wide characteristics. It operates the frequency band from 2.2 GHz to 4.8 GHz. The antenna is developed with FR4 material with a dielectric consistent of 4.4, loss tangent of 0.02 and a density of 1.6mm. The recommended design has 4 monopole antennas. Each monopole antenna has a circular patch with radius of 5mm to avoiding interference. The simulation results sparameter, VSWR, TARC, ECC, CCL and diversity gain are obtained and verified with the aid of Ansys HFSS and CST studio. Keywords—Beam forming, MIMO, Mutual Coupling, 5G applications.
DEVELOPING A HOLISTIC BUSINESS PROCESS MANAGEMENT MATURITY MODEL ( BPM 3 )

Nowadays, with the fierce competition, increasingly more companies begin to pay more attention to efficient management which can help companies to make quick decision, fast information transfer and shorter cycle times. In order to achieve this, many companies start internally to improve their business process continuously in different aspects to respond the changes in environment and customers’ needs. So business process management (BPM) becomes the most popular business and technology management method in the recent years, it is important for Business Success and improving organizational performance, to identify current Status of BPM and its weaknesses and strengths. In this paper, we developed a holistic BPM maturity model (BPM3) by analysis BPM maturity models. This model provides a framework for the detailed evaluation of BPM capabilities and achievements within organizations and better BPM implementation in the future.
Deep Learning Based Analysis of Ophthalmology: A Systematic Review

INTRODUCTION: Diagnosis of medical-related problems is the biggest issue in every era. In past decades, due to less number of technologies and equipment’s it was challenging to diagnose it. As time passes, technologies i.e. Artificial Intelligence (AI) grow and became popular in the medical field especially in ophthalmology at proliferating rate. But, still, many of the diseases are diagnose manually. (i.e., eye disease disorder) which is time consuming, expensive, and tedious task. Existing prediction systems can resolve medical issues such as ocular disorder but the accuracy of prediction is very less. OBJECTIVES: This study gives a brief overview of the analysis of traditional systems with modern approaches. Further, this study highlights the different allied techniques and impact of transfer learning on ophthalmology for the detection of various eye diseases i.e., Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Glaucoma, etc. METHODS: AI and Machine Learning Technique are used to conduct this research for analysis. RESULTS: The result of this paper concludes AI with allied techniques may reshape and revolutionize the medical community especially in the area of ophthalmology. CONCLUSION: This paper presented a comprehensive review of AI with allied techniques in ophthalmology. In ML and DL-based approaches, CNN provides the most promising results.
Social Trust-based Blockchain-enabled Social Media News Verification System

In the current digital era, information exchanges can be done easily through the Internet and social media. However, the actual truth of the news on social media platforms is hard to prove, and social media platforms are susceptible to the spreading of hoaxes. As a remedy, Blockchain technology can be used to ensure the reliability of shared information and can create a trusted communications environment. In this study, we propose a social media news spreading model by adapting an epidemic methodology and a scale-free network. A Blockchain-based news verification system is implemented to identify the credibility of the news and its sources. The effectiveness of the model is investigated by utilizing agent-based modelling using NetLogo software. In the simulations, fake news with a truth level of 20% are assigned a low News Credibility Indicator (NCI ± -0.637) value for all of the different network dimensions. Moreover, the Producer Reputation Credit is also decreased (PRC ± 0.213) so that the trust factor value is reduced. Our epidemic approach for news verification has also been implemented using Ethereum Smart Contract and several tools such as React with Solidity, IPFS, Web3.js, and Metamask. By showing the measurements of the credibility indicator and reputation credit to the user during the news dissemination process, this proposed smart contract can effectively limit user behaviour in spreading fake news and improve the content quality on social media.
CMAR_A_254897 4667..4675

1Department of Blood Transfusion, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin 300060, People’s Republic of China; 2Department of Immunology, Key Laboratory of Educational Ministry of China, Tianjin Key Laboratory of Cellular and Molecular Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, People’s Republic of China; 3Department of Nephrology, General Hospital of Tianjin Medical University, Tianjin 300052, People’s Republic of China
Total Knee Replacement in Patients with Ankylosis . Report of Three Cases and Literature Review

AbstrAct Introduction: Performing a Total Knee Replacement (TKR) in patients with ankylosed knees is technically demanding and associated with considerable complications. The purpose of this study is to report three cases of patients with ankylosed knees treated with TKR and present an updated literature review. Materials and Methods: We evaluated etiologies, preoperative range of motion, surgical technique, and type of implant utilized in each case. Complications and postoperative range of motion were also analyzed. Radiographs were used to evaluate loosening or osteolysis. Lastly, we performed an updated literature review. results: The etiologies were juvenile rheumatoid arthritis in two cases and chronic osteomyelitis in one. The patients did not have a range of motion at all and the ability to perform daily life activities was severely affected. A medial parapatellar approach was used in all cases followed by an extensive soft tissue release. A primary posterior-stabilized design was used in two cases and a rotatinghinge design in one case. The mean postoperative range of motion was 90° and all three patients reported a high satisfaction rate. No complications were reported. conclusions: TKR in patients with ankylosed knees has substantially improved the clinical outcome and the arc of motion. Due to its complexity, it must be approached as revision surgery, with careful preoperative planning.
Isolation and Identification of Pathogenic Bacteria Associated with Raw Meat from Different Locations in Ado-Ekiti

Raw meat is any type of uncooked muscle tissue of an animal used as food. Meat is a complex niche that has chemical and physical characteristics that support plethora of microorganisms’ growth. Eighteen (18) samples of fresh and spoilt meat (beef, mutton and chicken) were randomly collected from three (3) different locations in Ado-Ekiti metropolis. Samples were analysed for bacteriological contamination using standard Microbiological methods. Associated pathogenic bacteria recovered from the isolated raw meat samples include Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia, Proteus vulgaris, Bacillus cereus, Staphylococcus epidermidis and Proteus mirabilis with percentage occurrence of 24.2%, 17.7%, 16.1%, 12.9%, 12.9%, 11.3% and 4.9% respectively. The mean aerobic plate count in order of 10 (cfu/mL) was high for spoilt raw meat (Mutton) samples at 1.46 than 1.40 reportedfor fresh raw meat (Mutton) samples.
YOLOv5s-FCG : An Improved YOLOv5 Method for Inspecting Riders’ Helmet Wearing

Manual inspection of riders’ helmets is time-consuming and labor-intensive, low in efficiency and small in coverage. Aiming at this shortcoming, this paper proposed an improved YOLOv5s-FCG(FourLayers, CBAM attention, GhostBottleneck) helmet wearing detection method based on YOLOv5 (You Only Look Once). Based on the smallest volume of YOLOv5s in YOLOv5 series, the network was improved, the shallow feature detection layer was added, the three-scale feature detection was changed to four-scale feature detection, and the up sampling was increased by four times. Add the CBAM attention module; Use lightweight GhostBottleneck instead of Bottleneck structures. The results in our experiments show that YOLOv5s-FCG raises the average detection accuracy (mAP) by 2.0% compared with YOLOv5s on the ourselves’ riding safety helmet data set and 1.5% on the NWPU-VHR-10 public data set. The proposed algorithm not only ensured the detection rate, volume, computation and number of parameters, but also improved the detection accuracy. And it had good adaptability and generalization ability in complex road environments such as poor light and small targets.
A stacking-based classification approach to android malware using host-level encrypted traffic

In recent years, smartphones have been developing fast. Android, a mobile platform convenient and open to all, has attracted more audience than any one of its counterpart. However, mobile devices are frequently attacked by malware, which calls to malware detection. Currently, we are lacking studies of Android malware detection based on ensemble learning. In this work, we propose a model to detect Android malware. The model takes the encrypted traffic that the malware generates as input. Through clustering, the model removes the third-party traffic and retains the purity of the first-party traffic. The model extracts traffic features to construct host-level traffic fingerprint and classifies the malware through stacking-based ensemble learning. We use the publicly available dataset CICAndMal2017 to build the classification model. This dataset successfully classifies malware into different categories. In the controlled experiments we use SVM and Random Forest models. The results show that our model is significantly more accurate in classifying malware than SVM and Random Forest models, with an accurate rate of 96.7% in the optimal condition.
Space-Time Separate Modeling for Efficient Video Classification

Efficient video classification requires the deep neural network models to be much lightweight. Current deep convolutional networks generally adopt 3D convolutions or similar spatio-temporal computational counterparts to process the 3D signal of videos. However, due to the heavy computational load of those 3D units, they suffer from the problems of hard training and inefficient inferring. To address these problems, this paper proposes a novel yet efficient space-time separate (STS) modelling module. STS splits the feature channels into multiple groups and separately model various types of content information from the channel groups using different convolutional operations. Since the channel splitting mechanism can significantly reduce the model complexity, STS is much more lightweight than the existing video models. Particularly, the joint use of spatial/temporal/spatio-temporal convolutions achieves paralleled information modelling in a single block. We conduct experiments on two benchmarked video datasets to evaluate the performance of STS and demonstrate its effectiveness and efficiency on the video classification task.
Research on Target Tracking Algorithm Based on Correlation Filtering

Target tracking has always been an important research direction in the field of computer vision. The target tracking method based on correlation filtering has become a research hotspot in the field of target tracking due to its efficiency and robustness. In recent years, a series of new progress has been made in this field, and it has been widely used in automatic driving,video surveillance, human-computer interaction, national defense security and other fields. Mainly summarize the research status in this field. First of all, the main components and research difficulties of current target tracking are introduced. Secondly, the algorithm of target tracking based on correlation filtering is introduced, and the development of the classical algorithm model and its existing problems are analyzed in detail. Finally, some views on the future research direction and development trend of related filtering algorithms are given.
GSW: A High Performance IQA Index Based on Global and Saliency Window Similarity

Perceptual image quality assessment (IQA) attracts significant attention in recent years. It is proved that both global score and an image’s visual saliency (VS) are consistent with subjective evaluation. The global quality score reflects the consistency of the overall structure between two images, and VS map contributes complementarily in evaluating perceptual quality. This paper presents an effective IQA index based on global and saliency window similarity, namely GSW. It chooses VS map as an image feature and uses a special strategy to draw the saliency window with largest significancy. Meanwhile, the background area is taken into account to guarantee the robustness of the quality score. Experimental results on four most widely used databases verify that, compared with state-of-the-art IQA methods, GSW performs consistently well can provide more accurate quality prediction with a low computational complexity.
Study of New Technologies for Cultivation of Solanaceous Vegetable Crops in Short Vegetation Period Regions of Armenia

The article considers the impact of new integrated technologies (mulching, drip irrigation, use of wooden sticks for each plant, use of new biofertilizers in the cultivation of solanaceous vegetable crops (tomato, pepper, eggplant) in regions with a short growing season. The study of new technologies in the cultivation of solanaceae crops in regions of short vegetation period shows the effectiveness of the impact of these technologies on the biological characteristics, economic-valuable and qualitative indicators. New technologies contributed to early maturity, increased yield and quality of fruits, resistance to diseases and pests. New technologies can be effectively implemented in the republic and similar regions of the globe.
Distance of Single-Valued Neutrosophic Set and Its Application in Pattern Recognition

Single-valued neutrosophic set (SVNS) is an extension of fuzzy set, which combines the truth, indeterminacy, and falsity information. The measurement of distance of single-valued neutrosophic set will bring new ideas to pattern recognition. This paper introduces the distance definitions and properties of SVNS, and proposes the improved distance definition of single-valued neutrosophic set based on decision maker attitude towards indeterminacy information and applied them to pattern recognition. By assigning a value to decision-maker’s attitude towards indeterminacy information, this attitude comes from the historical experience and risk preference etc. The distance calculated in this way can often get a matching result closer to the reality. The results show that the improved distance formula of SVNSs is more effective and more practical for pattern recognition.
A Recommended Method Based on the Weighted RippleNet Network Mode

User preferences were modeled by the RippleNet network and successfully applied in the recommender systems, but the weight of the entity was not considered. This paper proposes a RippleNet model incorporating the influence of complex network nodes. After the construction of complex networks based on knowledge Graphs, we build the maximum subnet model and calculate the influence of nodes in the graph network. We added it to the RippleNet as the weight of entities. The experimental results showed that new method increased the AUC and ACC values of RippleNet to 92.0% and 84.6%, solve the problem that entity influence was not considered in the RippleNet network, and made the recommended results more in line with users’ expectations.
Motion Target Detection and Recognition Based on YOLOv4 Algorithm

With the application of artificial intelligence more and more widely, the target detection of artificial intelligence “eyes” is becoming more and more important, which can give the machine the ability to detect and recognize image content in the environment. In this paper, YOLOv4 deep learning motion target detection algorithm is used to achieve the positioning and recognition of motion targets, to identify and mark the location and type of objects contained in the image, to achieve the detection of pictures, videos and camera realtime image detection, so that the machine has the most basic visual ability. Using YOLOv4 algorithm, the speed requirement of real-time detection can be realized with certain precision, and the model mAP value reached 69.48%.
Design and Implementation of a Cross-Media Image to Sound Algorithm and Objective Evaluation Algorithm

With the joining of digital media and Internet, the application of the conversion of cross-media information becomes wider and wider, which makes the integration of multimedia information possible. The transformation of image information to sound information is also in the stage of exploration. It can be applied to all aspects of the industry. Most importantly, it can be used to guide the blind. In this paper, an improved image-to-sound algorithm based on original method is proposed to judge whether there are obstacles in front of the road and test the sound segment through human ears. Among different mapping methods, the highest accuracy can reach 84.2%, which is much higher than the original method. At the same time, an objective evaluation algorithm is designed based on the neural network, which can replace the human ear to judge a large number of image information, and the matching degree of this model can reach 68%.
Key Clips and Key Frames Extraction of Videos Based on Deep Learning

The surveillance camera network covering the city, while protecting the safety of people’s lives and property, generate a large amount of surveillance video data every day, but few videos contain useful information. Surveillance cameras in sparsely crowded areas may capture most of the video in the background. A large number of surveillance videos bring greater storage pressure, and also increase the difficulty of the staff’s work. In this paper, we use the auto-encoder network to extract features of video frames. By comparing the feature differences between video frames, we can automatically select key video clips and key frame images that contain useful information to slim down the surveillance video. Through experiments on actual cameras, we found that this method can achieve the purpose of reducing the pressure of storage and traversal.