bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of students’ employability using clustering algorithm: A hybrid approach
Authors: Premalatha N. , Sujatha S.
Published: March 2022

Data Mining is a process of exploring the huge data in search of reliable patterns and methodical relationship among variables. As a result, the findings may be validated through applying the detected patterns to a novel subset of the data. In simple words, Data Mining is referred as extracting the useful information as large dataset and transforming into reliable structure for future use. Data Mining has shown its incredible performance in various fields to a greater extent, out of which, Educational Data Mining (EDM) is one among them. Many researchers have addressed huge number of problems in EDM and applied various techniques to reveal the useful and hidden information that helped in the process of decision making. Students getting employed during and after graduation are one of the important parts of their life. Students, based on their academic performances, are getting employed in companies they deserve. But still, the probability of getting employed is very less in this competitive world. In this paper, a real-time scenario has been chosen for analyzing various factors for getting employed/unemployed. Various clustering and classification techniques have been implemented and their performances are studied. A hybrid approach is presented in this paper that integrates the benefits of particle swarm optimization (PSO) and fuzzy clustering means (FCMs). The results obtained show that the proposed technique helps in obtaining higher accuracy to other clustering techniques. The proposed clustering algorithm PSO-FCM, accuracy is 34.4%, 36.45% and 28.45% higher than the existing method, time complexity shows 45%, 33% and 49% lower than the existing [Formula: see text]-means clustering, Naïve Bayes clustering and SVM clustering algorithms, respectively.
Opportunistic Transmission for Video Streaming over Wild Internet
Authors: Huang Jiawei , Su Qichen , Li Weihe , Liu Zhuoran , Zhang Tao , Liu Sen , Zhong Ping , Jiang Wanchun , Wang Jianxin
Published: March 2022

The video streaming system employs adaptive bitrate (ABR) algorithms to optimize a user’s quality of experience. However, it is hard for ABR algorithms to choose the right bitrate consistently under highly dynamic bandwidth fluctuations in wild Internet. In this article, we propose a building block on the client side named
Opportunistic Chunk Replacement Mechanism
(OCRM) to help existing ABR algorithms make full use of the available bandwidth to improve the network utilization and viewing experience of users. Specifically, the servers take advantages of the spare bandwidth to opportunistically transmit high-quality chunks (called
opportunistic chunks
) with low priority to the client, without incurring any extra delay. Then, the client player replaces the low-quality chunks with the opportunistic ones that have high quality. We compare OCRM with state-of-the-art ABR algorithms by using trace-driven experiments spanning a wide variety of quality of experience metrics and network conditions. The test results show that OCRM effectively achieves high network utilization and improves the user’s viewing experience by up to 35%.
Spherical Convolution Empowered Viewport Prediction in 360 Video Multicast with Limited FoV Feedback
Authors: Li Jie , Han Ling , Zhang Chong , Li Qiyue , Liu Zhi
Published: March 2022

Field of view (FoV) prediction is critical in 360-degree video multicast, which is a key component of the emerging virtual reality and augmented reality applications. Most of the current prediction methods combining saliency detection and FoV information neither take into account that the distortion of projected 360-degree videos can invalidate the weight sharing of traditional convolutional networks nor do they adequately consider the difficulty of obtaining complete multi-user FoV information, which degrades the prediction performance. This article proposes a spherical convolution-empowered FoV prediction method, which is a multi-source prediction framework combining salient features extracted from 360-degree video with limited FoV feedback information. A spherical convolutional neural network is used instead of a traditional two-dimensional convolutional neural network to eliminate the problem of weight sharing failure caused by video projection distortion. Specifically, salient spatial-temporal features are extracted through a spherical convolution-based saliency detection model, after which the limited feedback FoV information is represented as a time-series model based on a spherical convolution-empowered gated recurrent unit network. Finally, the extracted salient video features are combined to predict future user FoVs. The experimental results show that the performance of the proposed method is better than other prediction methods.
Generative Metric Learning for Adversarially Robust Open-world Person Re-Identification
Authors: Liu Deyin , Wu Lin (Yuanbo) , Hong Richang , Ge Zongyuan , Shen Jialie , Boussaid Farid , Bennamoun Mohammed
Published: March 2022

The vulnerability of re-identification (re-ID) models under adversarial attacks is of significant concern as criminals may use adversarial perturbations to evade surveillance systems. Unlike a closed-world re-ID setting (i.e., a fixed number of training categories), a reliable re-ID system in the open world raises the concern of training a robust yet discriminative classifier, which still shows robustness in the context of unknown examples of an identity. In this work, we improve the robustness of open-world re-ID models by proposing a generative metric learning approach to generate adversarial examples that are regularized to produce robust distance metric. The proposed approach leverages the expressive capability of generative adversarial networks to defend the re-ID models against feature disturbance attacks. By generating the target people variants and sampling the triplet units for metric learning, our learned distance metrics are regulated to produce accurate predictions in the feature metric space. Experimental results on the three re-ID datasets, i.e., Market-1501, DukeMTMC-reID, and MSMT17 demonstrate the robustness of our method.
Deep Uncoupled Discrete Hashing via Similarity Matrix Decomposition
Authors: Wu Dayan , Dai Qi , Li Bo , Wang Weiping
Published: March 2022

Hashing has been drawing increasing attention in the task of large-scale image retrieval owing to its storage and computation efficiency, especially the recent asymmetric deep hashing methods. These approaches treat the query and database in an asymmetric way and can take full advantage of the whole training data. Though it has achieved state-of-the-art performance, asymmetric deep hashing methods still suffer from the large quantization error and efficiency problem on large-scale datasets due to the tight coupling between the query and database. In this article, we propose a novel asymmetric hashing method, called
D
eep
U
ncoupled
D
iscrete
H
ashing (DUDH), for large-scale approximate nearest neighbor search. Instead of directly preserving the similarity between the query and database, DUDH first exploits a small similarity-transfer image set to transfer the underlying semantic structures from the database to the query and implicitly keep the desired similarity. As a result, the large similarity matrix is decomposed into two relatively small ones and the query is decoupled from the database. Then both database codes and similarity-transfer codes are directly learned during optimization. The quantization error of DUDH only exists in the process of preserving similarity between the query and similarity-transfer set. By uncoupling the query from the database, the training cost of optimizing the CNN model for the query is no longer related to the size of the database. Besides, to further accelerate the training process, we propose to optimize the similarity-transfer codes with a constant-approximation solution. In doing so, the training cost of optimizing similarity-transfer codes can be almost ignored. Extensive experiments on four widely used image retrieval benchmarks demonstrate that DUDH can achieve state-of-the-art retrieval performance with remarkable training cost reduction (30× - 50× relative).
esDNN: Deep Neural Network Based Multivariate Workload Prediction in Cloud Computing Environments
Authors: Xu Minxian , Song Chenghao , Wu Huaming , Gill Sukhpal Singh , Ye Kejiang , Xu Chengzhong
Published: March 2022

Cloud computing has been regarded as a successful paradigm for IT industry by providing benefits for both service providers and customers. In spite of the advantages, cloud computing also suffers from distinct challenges, and one of them is the inefficient resource provisioning for dynamic workloads. Accurate workload predictions for cloud computing can support efficient resource provisioning and avoid resource wastage. However, due to the high-dimensional and high-variable features of cloud workloads, it is difficult to predict the workloads effectively and accurately. The current dominant work for cloud workload prediction is based on regression approaches or recurrent neural networks, which fail to capture the long-term variance of workloads. To address the challenges and overcome the limitations of existing works, we proposed anefficient supervised learning-basedDeepNeural Network (esDNN) approach for cloud workload prediction. First, we utilize a sliding window to convert the multivariate data into a supervised learning time series that allows deep learning for processing. Then, we apply a revised Gated Recurrent Unit (GRU) to achieve accurate prediction. To show the effectiveness of esDNN, we also conduct comprehensive experiments based on realistic traces derived from Alibaba and Google cloud data centers. The experimental results demonstrate that esDNN can accurately and efficiently predict cloud workloads. Compared with the state-of-the-art baselines, esDNN can reduce the mean square errors significantly, e.g., 15%. rather than the approach using GRU only. We also apply esDNN for machines auto-scaling, which illustrates that esDNN can reduce the number of active hosts efficiently, thus the costs of service providers can be optimized.
Social Network Analytic-Based Online Counterfeit Seller Detection using User Shared Images
Authors: Cheung Ming , Sun Weiwei , She James , Zhou Jiantao
Published: March 2022

Selling counterfeit online has become a serious problem, especially with the advancement of social media and mobile technology. Instead of investigating the products directly, one can only check the images, tags annotated by the sellers on the images, or the price to decide if a seller sells counterfeits. One of the ways to detect counterfeit sellers is to investigate their social graphs, in which counterfeit sellers show different behaviour in network measurements, such as those in centrality and EgoNet. However, social graphs are not easily accessible. They may be kept private by the operators, or there are no connections at all. This article proposes a framework to detect counterfeit sellers using their connection graphs discovered from their shared images. Based on 153 K shared images from Taobao, it is proven that counterfeit sellers have different network behaviours. It is observed that the network measurements follow Beta function well. Those distributions are formulated to detect counterfeit sellers by the proposed framework, which is 60% better than approaches using classification.
Social Public Opinion Communication and Network Legal Management Based on Artificial Intelligence Cognitive Wireless Network
Authors: Bai Heju
Published: March 2022

Public opinion supervision is an important part of the national security supervision system. In the Internet era, especially with the rapid development of mobile social media, the position of online public opinion supervision in public opinion supervision is becoming more and more prominent. Internet public opinion supervision has a wide range of subjects, diverse objects, multiple channels, instant and efficient communication speed, and two-way interaction and has its distinctive features, but at the same time there are phenomena such as online rumors, invasion of others' privacy, and venting private anger. The existence of these problems requires strengthening the legal system of public opinion supervision, enhancing the network literacy of Internet users, government management departments, website operators, and other subjects of online public opinion supervision, continuously improving the technical level of online public opinion monitoring, and public opinion guidance ability of network supervision departments, and strengthening the regulation of online public opinion supervision through the improvement and improvement of legal system and coordinated promotion of cyberspace governance. Meanwhile, mobile Internet is the soil for social opinion dissemination. With the development of technology, cognitive wireless network is becoming an important infrastructure in the mobile Internet. Therefore, this paper studies social opinion dissemination and cyber law management in cognitive wireless networks and proposes a cognitive engine model based on artificial intelligence algorithms for cognitive wireless networks for enhancing opinion dissemination and law management. The extended experiments demonstrate the advanced performance and effectiveness of the proposed method in this paper and provide a new technical approach for enhancing public opinion dissemination and legal management.
Peripheral Purinergic Modulation in Pediatric Orofacial Inflammatory Pain Affects Brainstem Nitroxidergic System: A Translational Research
Authors: Borsani Elisa , Ballini Andrea , Buffoli Barbara , Muzio Lorenzo Lo , Di Domenico Marina , Boccellino Mariarosaria , Scacco Salvatore , Nocini Riccardo , Dibello Vittorio , Rezzani Rita , Cantore Stefania , Rodella Luigi Fabrizio , Cosola Michele Di
Published: March 2022

Physiology of orofacial pain pathways embraces primary afferent neurons, pathologic changes in the trigeminal ganglion, brainstem nociceptive neurons, and higher brain function regulating orofacial nociception. The goal of this study was to investigate the nitroxidergic system alteration at brainstem level (spinal trigeminal nucleus), and the role of peripheral P2 purinergic receptors in an experimental mouse model of pediatric inflammatory orofacial pain, to increase knowledge and supply information concerning orofacial pain in children and adolescents, like pediatric dentists and pathologists, as well as oro‐maxillo‐facial surgeons, may be asked to participate in the treatment of these patients. The experimental animals were treated subcutaneously in the perioral region with pyridoxalphosphate‐6‐azophenyl‐2 ′,4 ′‐disulphonic acid (PPADS), a P2 receptor antagonist, 30 minutes before formalin injection. The pain‐related behavior and the nitroxidergic system alterations in the spinal trigeminal nucleus using immunohistochemistry and western blotting analysis have been evaluated. The local administration of PPADS decreased the face‐rubbing activity and the expression of both neuronal and inducible nitric oxide (NO) synthase isoforms in the spinal trigeminal nucleus. These results underline a relationship between orofacial inflammatory pain and nitroxidergic system in the spinal trigeminal nucleus and suggest a role of peripheral P2 receptors in trigeminal pain transmission influencing NO production at central level. In this way, orofacial pain physiology should be elucidated and applied to clinical practice in the future.
Convolutional Neural Network and Computer Vision-Based Action Correction Method for Long-Distance Running Technology
Authors: Juan Wang
Published: March 2022

For long-distance running tactics, a computer vision-based motion correction solution is presented. The depth information is merged into the KCF algorithm to enhance it, which overcomes the classic KCF method’s incapacity to tackle the tracking drift issue caused by occlusion and extracts the technical features of long-distance running motions. Based on computer vision, the posture area of long-distance runners is detected, and the technical movements of long-distance runners are recognized. Calculate the centroid coordinates of the wrong technical movement correction area in long-distance running, and generate a tracking image of the wrong technical movement in long-distance running. The foreground and background information of the image is separated by the optical flow feature of machine vision, and the motion trajectory of the long-distance running error technique is extracted, and the motion correction of the long-distance running technique based on computer vision is realized. The experimental results show that the method in this paper has better accuracy in extracting long-distance running motion features and can accurately identify and correct the technical movements of the long-distance running, the technical movements of the head, and the technical movements of the body balance, and the correction efficiency is high.
Trailing Edge Flap Effects on Dynamic Stall Vortex and Unsteady Aerodynamic Forces on a Pitching Airfoil
Authors: Xing Shi-Long , Xu He-Yong , Zhang Wei-Guo
Published: March 2022

This study focuses on the effect of the upward deflection of trailing edge flap (TEF) on the strength and trajectory of dynamic stall vortex (DSV) around a pitching airfoil by means of numerical simulations based on unsteady Reynolds-averaged Navier-Stokes (URANS). The effect of the upward deflection of the TEF on the unsteady aerodynamic forces due to DSV is analyzed. The numerical simulation method for large mesh deformation is constructed. Radial basis function- (RBF-) based mesh deformation algorithm, as well as Laplacian and optimization-based mesh smoothing algorithm, is adopted to ensure the mesh quality in flow field simulations. The results reveal that the upward deflection of the TEF can reduce the peaks of drag and pitching moment coefficients. Although the maximum lift coefficient of the airfoil is slightly reduced, its maximum drag and pitching moment coefficients are significantly reduced by up to 34.8% and 31.8%, respectively. The vorticity transport behavior in a planar control region during the DSV formation and detachment is analyzed. It is found that the TEF can change the development process of the DSV. The upward deflection of the TEF reduces the vorticity flux from the leading edge shear layer, which causes the circulation of the DSV and the translational velocity of the vortex center to decline. The peaks of the unsteady aerodynamic forces on the airfoil induced by the DSV are reduced. The upward deflection of the TEF plays the role of alleviating the pitching moment load. The longer TEF can result in a better control effect. The bigger the upward deflection angle of the TEF, the better its control effect.
Simultaneous Determination of Lamotrigine, Oxcarbazepine, Lacosamide, and Topiramate in Rat Plasma by Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry
Authors: Qiu Erjie , Yu Lu , Liang Qishun , Wen Congcong
Published: March 2022

This study established an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method to study the pharmacokinetics of four antiepileptic drugs, lamotrigine, oxcarbazepine, lacosamide, and topiramate, in rats after oral administration. The gradient elution was performed on a UPLC HSS T3 (2.1 mm × 100 mm, 1.8 μm) column with acetonitrile-0.1% formic acid as the mobile phase at a flow rate of 0.4 mL/min. Protein precipitation by acetonitrile was adopted for plasma sample pretreatment. Electrospray- (ESI-) positive/negative ion switching and multiple reaction monitoring (MRM) modes were adopted for ion quantitative determination of antiepileptic drugs. UPLC-MS/MS detection and Drug and Statistics (DAS) software fitting were performed to blood samples collected from rats after oral administration of lamotrigine, oxcarbazepine, lacosamide, and topiramate (5 mg/kg). All drugs examined showed linearity within 5–5000 ng/ml (R2 > 0.9987), the intraday accuracy was within 92%–108%, and the interday accuracy was within 93%–109%. The relative standard deviations (RSD) of intraday and interday were less than 15%. The matrix effect was within 91%–105%, and the recovery was better than 88%. The established UPLC-MS/MS method was successfully applied to the pharmacokinetic study of lamotrigine, oxcarbazepine, lacosamide, and topiramate in rats.
A Robust and Privacy-Preserving Anonymous User Authentication Scheme for Public Cloud Server
Authors: Khan Naveed , Zhang Jianbiao , Jan Saeed Ullah
Published: March 2022

Everyone desires to avail online services provided by different service providers securely, efficiently, and effectively. In this regard, security is still a significant concern for them. However, no one guarantees secure communication by browsing different applications remotely. To ensure confidentiality, authorization, availability, nonrepudiation, and removing eavesdropping, without a robust authentication scheme, nothing will go right. Therefore, we attempted to design a robust and privacy-preserving authentication scheme for end-users to securely access public cloud servers’ services remotely without losing performance. Our proposed scheme security has been evaluated formally using the random oracle model (ROM) and ProVerif2.03 and informally using proposition and discussion. At the same time, the performance metric has been analyzed by considering the scheme’s computation and communication costs. Upon comparing the proposed scenario with state-of-the-artwork, it has been demonstrated that the scheme is much better in terms of security and performance, as these are contradicting metrics, and the change in one conversely affects the other.
Eryngium foetidum L. (Apiaceae): A Literature Review of Traditional Uses, Chemical Composition, and Pharmacological Activities
Authors: Rodrigues Thiara L. M. , Silva Maria E. P. , Gurgel Ely S. C. , Oliveira Mozaniel S. , Lucas Flavia C. A.
Published: March 2022

Eryngiumfoetidum L. is popularly known as chicória-do-Pará, is native to the Amazon region, and is widely distributed in Northern Brazil. It is considered a versatile species due to its diversified uses in ethnomedicine, gastronomy, and pharmaceutical industry. The objective of this study was to review the literature on the traditional uses, chemical composition, and pharmacological activities of E. foetidum based on information published in national and international scientific articles between the years 2011 and 2021. Literature searches were performed with the combination of the expressions “Eryngium foetidum L.,” “chicória-do-Pará,” “traditional uses,” “ethnobotany,” “volatile compounds,” and “essential oil.” The species is widely used as a flavoring condiment in beans, meat, duck, and fish, and in the preparation of tucupi (cassava sap), showing to have great importance for the Amazonian food culture. In traditional medicine, it has analgesic, antibacterial, antiflu, and antipyretic applications. The chemical profile is characterized by the presence of aromatic and aliphatic aldehydes, mainly (2E)-2-dodecenal in leaves and 2,3,4-trimethylbenzaldehyde in roots, in addition to fixed compounds such as carotenoids, flavonoids, and phenols. These compounds have shown diverse biological activities and potential antibacterial, anthelmintic, and antioxidant applications, confirming their potential for use in folk medicine. Therefore, it is inferred that this aromatic plant has vast potential uses and is an important alternative as a natural resource for the food and pharmaceutical industries in view of its antioxidant capacity and bioactive compounds.
Effects of Vitamin D on Respiratory Function and Immune Status for Patients with Chronic Obstructive Pulmonary Disease (COPD): A Systematic Review and Meta-Analysis
Authors: Yang Huan , Sun Deyang , Wu Fengqing , Xu Xiao , Liu Xi , Wang Zhen , Zhou Linshui
Published: March 2022

Background. Many studies have demonstrated that vitamin D has clinical benefits when used to treat patients with chronic obstructive pulmonary disease (COPD). However, most of these studies have insufficient samples or inconsistent results. The aim of this meta-analysis was to evaluate the effects of vitamin D therapy in patients with COPD. Methods. We performed a comprehensive retrieval in the following electronic databases: PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Data, and Chinese Scientific Journals Database (VIP). Two trained reviewers identified relevant studies, extracted data information, and then assessed the methodical quality by the Cochrane risk of bias assessment tool, independently. Then, the meta-analyses were conducted by RevMan 5.4, binary variables were represented by risks ratio (RR), and continuous variables were represented by mean difference (MD) or standardized mean difference (SMD) to assess the efficacy of vitamin D therapy in patients with COPD. Then, publication bias assessment was conducted by funnel plot analysis. Finally, the quality of evidence was assessed by the GRADE system. Results. A total of 15 articles involving 1598 participants were included in this study. The overall results showed a statistical significance of vitamin D therapy in patients with COPD which can significantly improve forced expiratory volume in 1 second (FEV1) (MD: 5.69, 95% CI: 5.01-6.38,

P
<
0.00001

,

I
2
=
51
%

) and FEV1/FVC (SMD:0.49, 95% CI: 0.39-0.60,

P
<
0.00001

,

I
2
=
84
%

); and serum 25 (OH)D (SMD:1.21, 95% CI:1.07-1.34,

P
<
0.00001

,

I
2
=
98
%

) also increase CD3+ Tcells (MD: 6.67, 95% CI: 5.34-8.00,

P
<
0.00001

,

I
2
=
78
%

) and CD4+ T cells (MD: 6.00, 95% CI: 5.01-7.00,

P
<
0.00001

,

I
2
=
65
%

); and T lymphocyte CD4+/CD8+ ratio (MD: 0.41, 95% CI: 0.20-0.61,

P
=
0.0001

,

I
2
=
95
%

) obviously decrease CD8+ Tcells(SMD: -0.83, 95% CI: -1.05- -0.06,

P
<
0.00001

,

I
2
=
82
%

), the times of acute exacerbation (RR: 0.40, 95% CI: 0.28-0.59,

P
<
0.00001

,

I
2
=
0
%

), and COPD assessment test (CAT) score (MD: -3.77, 95% CI: -5.86 - -1.68,

P
=
0.0004

,

I
2
=
79
%

). Conclusions. Our analysis indicated that vitamin D used in patients with COPD could improve the lung function (FEV1 and FEV1/FVC), the serum 25(OH)D, CD3+ T cells, CD4 + T cells, and T lymphocyte CD4+/CD8+ ratio and reduce CD8+ T cells, acute exacerbation, and CAT scores.
Improvement of Lipotoxicity-Induced Islet β Cellular Insulin Secretion Disorder by Osteocalcin
Authors: Zhang Yafang , Li Ling , Zhang Yongze , Yan Sunjie , Huang Lingning
Published: March 2022

Background. Osteocalcin (OCN) has been proved to be closely related with the development of type 2 diabetes mellitus (T2DM). We aimed to study if OCN could improve the disorder of islet cell caused by lipotoxicity. Methods. Alizarin red staining was used to investigate the mineralization. Western blotting and ELISA methods were used to measure protein expression. Immunofluorescence staining was used to investigate the protein nuclear transfer. Results. High glucose and high fat inhibited the differentiation of osteoblast precursors. Overexpression of insulin receptor (InsROE) significantly promoted the Runx2 and OCN expression. The increase of insulin, Gprc6a, and Glut2 by osteoblast culture medium overexpressing insulin receptor was reversed by osteocalcin neutralizing antibody. Undercarboxylated osteocalcin (ucOC) suppressed the lipotoxic islet β-cell damage caused by palmitic acid. The FOXO1 from intranuclear to extranuclear was also significantly increased after ucOC treatment compared with the group PA. Knockdown of Gprc6a or suppression of PI3K/AKT signal pathway could reverse the upregulation of GPRC6A/PI3K/AKT/FoxO1/Pdx1 caused by ucOC. Conclusion. OCN could activate the FOXO1 signaling pathway to regulate GLUT2 expression and improve the insulin secretion disorder caused by lipotoxicity.
The Development Path and Data Mining Mode of Rural Tourism under the Background of Big Data
Authors: Chen Liying , Hu Hong , He Xun , Lyu Meng
Published: March 2022

With the advent of the era of mass tourism, rural tourism has received increasing attention from tourism-related personnel, such as the wide application of Internet, cloud computing, and big data technology, making it possible to build a development path for rural tourism based on the background of big data. The purpose of this paper is to study the development path and data mining mode analysis of rural tourism under the background of data and propose a clustering integration algorithm. The article gives an overview of the fit relationship between rural tourism and big data. This paper takes the farm tourism data in area A as the research object and through the data analysis of the rural tourism development path using the clustering integration algorithm; it explores the effectiveness of the clustering integration algorithm in the construction of the rural tourism development path. The experimental results show that compared with the traditional rural tourism development model in 2020, the tourism revenue in 2021 will increase by more than 300,000 yuan.
A Deep Learning Approach for Recognizing the Cursive Tamil Characters in Palm Leaf Manuscripts
Authors: Devi S Gayathri , Vairavasundaram Subramaniyaswamy , Teekaraman Yuvaraja , Kuppusamy Ramya , Radhakrishnan Arun
Published: March 2022

Tamil is an old Indian language with a large corpus of literature on palm leaves, and other constituents. Palm leaf manuscripts were a versatile medium for narrating medicines, literature, theatre, and other subjects. Because of the necessity for digitalization and transcription, recognizing the cursive characters found in palm leaf manuscripts remains an open problem. In this research, a unique Convolutional Neural Network (CNN) technique is utilized to train the characteristics of the palm leaf characters. By this training, CNN can classify the palm leaf characters significantly on training phase. Initially, a preprocessing technique to remove noise in the input image is done through morphological operations. Text Line Slicing segmentation scheme is used to segment the palm leaf characters. In feature processing, there are some major steps used in this study, which include text line spacing, spacing without obstacle, and spacing with an obstacle. Finally, the extracted cursive characters are given as input to the CNN technique for final classification. The experiments are carried out with collected cursive Tamil palm leaf manuscripts to validate the performance of the proposed CNN with existing deep learning techniques in terms of accuracy, precision, recall, etc. The results proved that the proposed network achieved 94% of accuracy, where existing ResNet achieved 88% of accuracy.
Walking and Activeness: The First Step toward the Prevention of Strokes and Mental Illness
Authors: An Ning , Chuo Jing
Published: March 2022

Physical activity, especially routine walking, is an imperative factor for the prevention of strokes, mental illness, and cardiovascular diseases (CVDs). The NIH (National Institute of Health) has also acknowledged walking as the most important factor of the stroke rehabilitation program. Many research studies are presented by physicians and researchers in the literature that highlight the positive impacts of walking on human health (physical and mental). This paper has the objective of studying the impact of regular walking, especially on mental illness, CVDs, and strokes. The C-reactive protein (CRP), P-selectin protein, and homocysteine biomarkers are considered to decide the improvement in the health of an individual with respect to CVDs and strokes. The other parameters considered for the recommendations of physicians and healthcare experts for mental health are PSS (perceived stress score) and ESS (Epworth sleepiness score) that control mental illness. The values are measured for the participating subjects before participating in the walking activity and after the end of the walking schedule to see the impact on individuals. The overall mental and physical health of an individual contributes to the chances of occurrence of CVDs, mental illness, and strokes in individuals aged between 40 years and 55 years, as per the study presented in this paper. The results show that the PSS and ESS scores are improved after the walking activity. Eventually, it improved recovery from many kinds of mental illness and also reduced the chances of strokes. Similarly, the levels of the biomarkers that determine the chances of an individual having CVD or stroke also improved. Walking can impact our overall health in many ways, however, in this paper, the focus is given to ailments, such as strokes, CVDs, and mental illness. The results show that stress and improper sleepiness can impact mental health negatively. The research outcome is measured by adding walking in a routine life so that every individual can get rid of many physical and mental ailments. The results presented in the paper reveal that the 90-day walking program has created a good impact on the health of individuals by improving their physical and mental health.
A mathematical model for suitability of smartphone apps for children
Authors: Yadav Savita , Chakraborty Pinaki , Mittal Prabhat
Published: March 2022

Children now get access to smartphones at an early age and gradually acquire skills to use different types of smartphone apps. We developed a mathematical model for the ability and interest of children to use smartphone apps. The model can be used to determine the level of difficulty of apps and identify niche app, i.e. apps that are designed specifically to be used by a narrow age range of children. We used the model to analyze nine apps that are used by children. We found three of the apps to be of suitable difficulty level, four of them to be too easy and two of them to be too difficult for children who are interested in using them. We were also able to identify three niche apps.
Assessment of Groundwater Quality Using Water Quality Index from Selected Springs in Manga Subcounty, Nyamira County, Kenya
Authors: Wekesa Alice Makonjo , Otieno Calford
Published: March 2022

We present the results of groundwater quality assessment that was done during the rainy season in November 2018 in the Manga region of Nyamira County, Kenya. Water samples were collected from three springs, Kiangoso, Kerongo, and Tetema, for the assessment. Water quality index was calculated based on pH, turbidity, nitrate, phosphate, calcium, magnesium, chloride, sulphates, fluoride, iron, total phosphorous, total hardness, total alkalinity, total dissolved solids, and total coliform. These fifteen parameters were analyzed and characterized according to standard methods and with reference to the World Health Organization and Kenya Bureau of Standards for physiochemical and bacteriological parameters which were then used in the calculation of water quality index. The water quality index was 21.32 for Kiangoso, 29.66 for Kerongo, and 25.64 for Tetema. The water quality index was found to be of excellent quality status at Kiangoso, while of good quality status at Kerongo and Tetema. The water quality index of Manga groundwater represented by the three springs therefore is less than 30 and can be used for drinking, irrigation, and industrial purpose. The present results are crucial for future management of groundwater in the Manga region.
Occurrence, Genesis, and Significance of Analcime in Fine-Grained Sedimentary Rocks
Authors: Wang Junran , Liang Chao , Cao Yingchang , Tian Yu
Published: March 2022

Natural analcime, an aluminosilicate mineral with multiple genetic mechanisms, widely occurs in fine-grained sedimentary reservoirs rich in oil and gas. Researchers have discussed the source and formation mechanism of reservoirs and the influence of morphology formation, occurrence characteristics associated with minerals, geochemical data, and Si/Al ratio on reservoir properties. The occurrence location, particle size, automorphism, purity, and fracture development can indicate the source of analcime macroscopically. The correlation between the enrichment of associated minerals and the content of analcime indicates that the associated mineral assemblage or correlation provides a material source for the formation of analcime or effectively improves the formation environment. Geochemical data are often used to identify analcime related to primary magmatic crystallization and hydrothermal processes. The genetic source grouping scheme based on the Si/Al ratio, which is a traditional means to identify the source of analcime, has been widely used in the research on analcime. After more than 200 years of study, research has shown that analcime distributed in fine-grained sedimentary rocks was mainly formed by burial alteration of volcanic materials (V-type analcime), conversion of nontuffaceous materials (N-type analcime), hydrothermal deposition mineralization (H-type analcime), and precipitation directly from an alkaline lake or pore water (P-type analcime). Based on reservoir properties, analcime that formed before the organic acid release stage of source rocks can effectively improve the porosity through precipitation–dissolution mechanisms after the release of massive organic acid, whereas the cementation formed by the transition of the fluid from acid to alkaline during the intermediate diagenetic stage would reduce porosity to some extent.
Robust Localization Based on Constrained Total Least Squares in Wireless Sensor Networks
Authors: Mao Qin , Li Youming
Published: March 2022

Source localization based on signal strength measurements has become very popular due to its wide applications. This paper focuses on differential received signal strength-based localization with model uncertainties in case of unknown transmit power. The error caused by the measurement noise and the reference power offset and the first-order approximate error generated in the process of DRSS receiving power linearization are merged into the constrained total least square regression matrix equation after linearization. The location optimization problem is formed by minimizing the influence of the errors. Based on the explicit closed form expressions of the first step vector and the second derivative matrix of the optimization problem, an iterative technique based on Newton’s method is proposed. The simulation results show that the root mean square error of the new algorithm is closer to the Cramér–Rao lower bound.
Web Service Composition Optimization with the Improved Fireworks Algorithm
Authors: Jiang Bo , Qin Yanbin , Yang Junchen , Li Hang , Wang Liuhai , Wang Jiale
Published: March 2022

Even though the number of services is increasing, a single service can just complete simple tasks. In the face of complex tasks, we require composite multiple services to complete them. For the purpose of improving the efficiency of web service composition, we propose a service composition approach based on an improved fireworks algorithm (FWA++). First, we use the strategy of random selection to keep

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individuals for the next generation, and the purpose is to speed up the convergence speed of the FWA++ and enhance the search ability. Second, we randomize the total number of sparks and maximum amplitude of sparks for each generation. In this way, the search ability and the ability to jump out of the local optimal solution are dynamically balanced throughout the execution of the algorithm. Our experimental results show that compared with other existing approaches, the approach proposed in this paper is more efficient and stable.
BaryonicBMeson Decays
Authors: Huang Xiaotao , Hsiao Yu-Kuo , Wang Jike , Sun Liang
Published: March 2022

We review the two- and three-body baryonicBdecays with the dibaryon (BB′¯) as the final states. Accordingly, we summarize the experimental data of the branching fractions, angular asymmetries, andCPasymmetries. Using theW-boson annihilation (exchange) mechanism, the branching fractions ofB⟶BB′¯are shown to be interpretable. In the approach of perturbative QCD counting rules, we study the three-body decay channels. In particular, we review theCPasymmetries ofB⟶BB′¯M, which are promising to be measured by the LHCb and Belle II experiments. Finally, we remark the theoretical challenges in interpretingBB−⟶pp¯ρ−andBB−⟶pp¯μ−ν¯μ.
Analysis of Government Public Management Information Service and Computer Model Construction Based on Smart City Construction
Authors: Liu Yulin
Published: March 2022

With the continuous development of economy, the traditional urban management system cannot meet the needs of the city. In order to solve many inconvenient urban problems brought by the traditional urban management system and provide convenience for urban development more quickly, the concept of “smart city” has been put forward and gradually applied to urban construction. Based on cloud computing and Internet of things, the service system and construction of smart city were studied in detail; on this basis, the demand of construction of smart city in coastal city A and the construction and practical application of smart city of city A in our country were analyzed so as to provide some reference value for the construction of smart city in our country. Through an in-depth test of the data mining algorithm, this paper verifies the calculation effect of the data mining algorithm and the optimization effect of a smart city public management system.
Increasing TB/HIV Case Notification through an Active Case-Finding Approach among Rural and Mining Communities in Northwest Tanzania
Authors: Abeid R. , Mergenthaler C. , Muzuka V. , Goodluck A. , Nkwabi T. , Bigio J. , Vasquez N Aguilera , Pande T. , Haraka F. , Creswell J. , Rahman T. , Straetemans M.
Published: March 2022

While Tanzania is among the high TB burden countries to reach the WHO’s End TB 2030 milestones, 41% of the people estimated to have had TB in 2020 were not diagnosed and notified. As part of the response to close the TB treatment coverage gap, SHDEPHA+ Kahama conducted a TB REACH active case-finding (ACF) intervention among rural and mining communities in Northwest Tanzania to increase TB/HIV case notification from July 2017 to June 2020. The intervention successfully linked marginalized mining communities with integrated TB/HIV screening, diagnostic, and referral services, screening 144,707 people for TB of whom 24,200 were tested for TB and 4,478 were tested for HIV, diagnosing 1,499 people with TB and 1,273 people with HIV (including at least 154 people with TB/HIV coinfection). The intervention revealed that community-based ACF can ensure high rates of linkage to care among hard-to-reach populations for TB. Providing integrated TB and HIV screening and diagnostic services during evening hours (Moonlight Events) in and around mining settlements can yield a large number of people with undiagnosed TB and HIV. For TB, this is true not only amongst miners but also FSW living in the same communities, who appear to be at similar or equally high risk of infection. Local NGOs can help to bridge the TB treatment coverage gap and to improve TB and HIV health outcomes by linking these marginalized groups with public sector services. Capturing the number of referrals arriving at CTCs is an important next step to identify how well the integrated TB/HIV outreach services operate and how they can be strengthened.
Power Optimization of IRS-Assisted D2D System Based on Imperfect Channel
Authors: Chen Yun , Zhang Guoping , Xu Hongbo , Ren Yinshuan , Chen Xue , Li Ruijie
Published: March 2022

Intelligent reflecting surface (IRS) is a promising technology that can help wireless communications achieve efficient spectrum and energy efficiency. However, because of its weak signal processing ability, it is difficult to get ideal channel state information (CSI). Under the imperfect channel state information hypothesis, we investigate a device-to-device (D2D) offload network. And an IRS is used to help calculate offloading from one set of task-intensive users to another set of idle users. We aim to jointly optimize transmit beamforming and IRS phase shifts to minimize system transmit power while requiring each user’s rate to meet the minimum rate constraint in the presence of channel errors. Unfortunately, the problem presented is nonconvex, and the imperfection of CSI makes it even more difficult to solve. Therefore, we apply the S-Procedure to convert the original problem to two effectively solvable semidefinite programming (SDP) subproblems and then solve them through the convex-concave procedure (CCP) algorithm and the alternate optimization method. Numerical results show the effectiveness of the algorithm and verify that the assistance of the IRS can greatly save the system transmit power.
HuR Promotes the Progression of Gastric Cancer through Mediating CDC5L Expression
Authors: Cui Jing , Cao Nanjing , Wang Guochao , Wang Fuhua , Yang Bin , Wang Jian , Lv Yongqiang , Chen Yunqing , Li Feng
Published: March 2022

Background and Objectives. Gastric cancer (GC) is one of the common gastrointestinal tumors and the third in the mortality rate among tumors. Studies have shown that the human antigen receptor (HuR) is associated with the malignant degree of GC. Methods. We performed qRT-PCR, cell cycle assay, cell migration, and mouse transplantation model analysis in our experiments. It has been clarified that HuR and microRNAs (miRNAs) have important interplays in the regulation of tumor progression. Results. This study found microRNA-133b (miR-133b), as a HuR-sponged miRNA in GC cells. Downregulation of HuR can promote the expression of miR-133b and further affect the downstream cyclin CDC5L. The expressions of miR-133b were slightly lower in GC tissues than adjacent normal tissues. Conclusion. Our studies suggest that HuR and miR-133b are involved in the development and pathological process of GC cells.
Research on Pattern Recognition Method of Transformer Partial Discharge Based on Artificial Neural Network
Authors: Xi Yu , Yu Li , Chen Bo , Chen Guangqin , Chen Yimin
Published: March 2022

Power transformer is pivotal equipment in a power system, which is responsible for energy transmission and transformation, and its operating condition is related to the safe operation of the power system. In the 21st century, computer science has entered a stage of rapid development, advanced network structures and algorithms have been applied to the field of artificial intelligence, and pattern recognition theory and technology have also made great progress. In the past, the identification of partial discharge type mainly relied on the experience of operation and maintenance personnel, and manual analysis and judgment were made based on partial discharge mapping, which was not very accurate. The application of the computer pattern recognition method in the field of partial discharge type identification has changed the status quo of manual identification, and this method has substantially improved the accuracy and efficiency of identification. Pattern recognition using computer technology has been applied to the field of partial discharge analysis. Compared with manual recognition, its recognition results are accurate, recognition speed is fast, and it has great potential for development. This paper proposes an artificial neural network-based model for transformer partial discharge pattern recognition, which combines the advantages of artificial neural networks with accurate extraction of local spatial higher-order features and provides a new solution for transformer partial discharge pattern recognition. Extended experiments show that the method proposed in this paper achieves leading performance and has practical application value.