bluuebunny/crossref_metadata_2025 huggingface dataset:
On the Edge of the Deployment: A Survey on Multi-access Edge Computing
Authors: Cruz Pedro , Achir Nadjib , Viana Aline Carneiro
Published: April 2022

Multi-Access Edge Computing (MEC) attracts much attention from the scientific community due to its scientific, technical, and commercial implications. In particular, the European Telecommunications Standards Institute (ETSI) standard convergence consolidates the discussions around MEC. Still, the existing MEC practical initiatives are incomplete in their majority, hardening or invalidating their effective deployment. To fill this gap, it is essential to understand a series of experimental prototypes, implementations, and deployments. The early implementations can reveal the potential, the limitations, the related technologies, and the development tools for MEC adoption. In this context, this work first brings a discussion on existing MEC initiatives regarding the use cases they target and their vision (i.e., whether they are more network-related or more distributed systems). Second, we survey MEC practical initiatives according to their strategies, including the ETSI MEC standard. Besides, we compare the strategies according to related limitations, impact, and deployment efforts. We also survey the existing tools making MEC systems a reality. Finally, we give hints to issues yet to be addressed in practice. By bringing a better comprehension of MEC initiatives, we believe this survey will help researchers and developers design their own MEC systems or improve and simplify the usability of existing ones.
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining
Authors: Pellegrina Leonardo , Cousins Cyrus , Vandin Fabio , Riondato Matteo
Published: April 2022

“I’m an MC still as honest” – Eminem, Rap God

We present
MCRapper
, an algorithm for efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families of functions exhibiting poset (e.g., lattice) structure, such as those that arise in many pattern mining tasks. The MCERA allows us to compute upper bounds to the maximum deviation of sample means from their expectations, thus it can be used to find both
(1)
statistically-significant functions (i.e., patterns) when the available data is seen as a sample from an unknown distribution, and
(2)
approximations of collections of high-expectation functions (e.g., frequent patterns) when the available data is a small sample from a large dataset. This flexibility offered by
MCRapper
is a big advantage over previously proposed solutions, which could only achieve one of the two.
MCRapper
uses upper bounds to the discrepancy of the functions to efficiently explore and prune the search space, a technique borrowed from pattern mining itself. To show the practical use of
MCRapper
, we employ it to develop an algorithm
TFP-R
for the task of True Frequent Pattern (TFP) mining, by appropriately computing approximations of the negative and positive borders of the collection of patterns of interest, which allow an effective pruning of the pattern space and the computation of strong bounds to the supremum deviation.
TFP-R
gives guarantees on the probability of including any false positives (precision) and exhibits higher statistical power (recall) than existing methods offering the same guarantees. We evaluate
MCRapper
and
TFP-R
and show that they outperform the state-of-the-art for their respective tasks.
Metabolomic signatures of low- and high-adiposity neonates differ based on maternal BMI
Authors: Aydogan Mathyk Begum , Piccolo Brian D. , Alvarado Fernanda , Shankar Kartik , O’Tierney-Ginn Perrie
Published: April 2022

Using untargeted metabolomics in 100 newborns, we found that cord blood metabolite signatures associated with neonatal adiposity differed between offspring of women with and without obesity.
Adhesion G protein-coupled receptors: structure, signaling, physiology, and pathophysiology
Authors: Lala Trisha , Hall Randy A.
Published: April 2022

Adhesion G protein-coupled receptors (AGPCRs) are a family of 33 receptors in humans exhibiting a conserved general structure but diverse expression patterns and physiological functions. The large NH2 termini characteristic of AGPCRs confer unique properties to each receptor and possess a variety of distinct domains that can bind to a diverse array of extracellular proteins and components of the extracellular matrix. The traditional view of AGPCRs, as implied by their name, is that their core function is the mediation of adhesion. In recent years, though, many surprising advances have been made regarding AGPCR signaling mechanisms, activation by mechanosensory forces, and stimulation by small-molecule ligands such as steroid hormones and bioactive lipids. Thus, a new view of AGPCRs has begun to emerge in which these receptors are seen as massive signaling platforms that are crucial for the integration of adhesive, mechanosensory, and chemical stimuli. This review article describes the recent advances that have led to this new understanding of AGPCR function and also discusses new insights into the physiological actions of these receptors as well as their roles in human disease.
Heart Rate Variability during Auricular Acupressure at Heart Point in Healthy Volunteers: A Pilot Study
Authors: Trinh Dieu-Thuong Thi , Nguyen Que-Chi Thi , Bui Minh-Man Pham , Nguyen Van-Dan , Thai Khac-Minh
Published: April 2022

Heart rate variability (HRV) is the variation in time between each heartbeat. Increasing HRV may contribute to improving autonomic nervous system dysfunctions. Acupuncture stimulation through the vagus plexus in the ear is considered as a method that can improve HRV. In this pilot study, we examined 114 healthy volunteers at the Faculty of Traditional Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, from January to May 2020. During a 20-minute interval, participants were stimulated two times at the acupoint in the left ear with Semen seed. The heart rate and HRV values were monitored before, during, and after acupressure every 5 minutes. When we compared the experimental group with the control group, HRV significantly increased in the stage of ear-stimulated acupressure compared with the stage before and after the auricular acupressure (

p
=
0.01

,

p
=
0.04

,

p
=
0.04

and

p
=
0.02

) and the difference was not statistically significant compared with the phase of nonstimulated (

p
=
0.15

,

p
=
0.28

). The changes in other values including SDNN (standard deviation of the average NN), RMSSD (root mean square of successive RR interval differences), LF (low-frequency power), and HF (high-frequency power) in all stages were not statistically significant

p
=
>
0.05

between groups. Based on the results, we can determine the increase in HRV when conducting auricular acupressure with stimulation at the heart acupoint on the left ear. This leads to a direction in further studies for clinical application for patients with autonomic nervous disorder.
Design of Mayer Wavelet Neural Networks for Solving Functional Nonlinear Singular Differential Equation
Authors: Sabir Zulqurnain , Zahoor Raja Muhammad Asif , Guirao Juan L. G. , Saeed Tareq
Published: April 2022

In the present work, an advance computational intelligence paradigm based on functional Mayer artificial neural network (FM-ANN) is accessible for solving the singular nonlinear functional differential equation (NFDE) numerically. The solution of singular NFDE is performed by using the artificial neural networks (ANNs) optimized with global search genetic algorithm (GA) enhanced by local refinements of sequential quadratic (SQ) programming and the hybrid of GASQ programming. The proposed scheme is applied for solving three types of second-order singular NFDEs. In order to validate the correctness of the designed scheme, the comparison of the proposed and exact solutions has been performed. Moreover, the statistical interpretations are used to prove the worth, convergence, accuracy, stability, and robustness of FM-ANN-GASQP for the solution of singular NFDEs.
e-Commerce Personalized Recommendation Based on Machine Learning Technology
Authors: Liu Liping
Published: April 2022

As e-commerce offers more and more choices for users, its structure becomes more and more complicated. Inevitably, it brings about the problem of information overload. The solution to this problem is an e-commerce personalized recommendation system using machine learning technology. People often seem confused when facing extensive information and cannot grasp the key points. This paper studies the personalized recommendation technology of e-commerce: deeply analyzes the related technologies and algorithms of the e-commerce recommendation system and proposes the latest architecture of the e-commerce recommendation system according to the current development status of the e-commerce recommendation system. The system recommends accuracy and real-time requirements and divides the system into two parts: offline mining and online recommendation and analyzes and implements the functions and technologies of each part. User-based recommender systems, collaborative filtering recommender systems, and content-based recommender systems are analyzed, respectively. The personalized recommendation cannot only quickly help customers find the required commodity information in a wide range of complex information but also can compare more commodity information to help customers to judge. However, the existing recommendation system has some problems such as the lack of recommendation personality, the reduced relevance of recommendation, and the poor timeliness of recommendation. Finally, a recommendation system that combines three recommendation algorithms is designed, and experiments are carried out. The newly designed recommendation system is compared with three different recommendation systems, and a summary and outlook are made. Based on the introduction of the relevant theories, characteristics, and mainstream technologies of personalized recommendation based on machine learning, this document presents a constructive example of a model based on the factors that influence personalized e-commerce information recommendations in the retail sector. Through questionnaire surveys, we analyze and design the influencing factors for consumers to purchase personalized products after the survey and build a project using state-of-the-art field learning techniques. Through the model to test the eight hypotheses proposed in this paper, the results show that customer income level, customer online shopping experience, commodity prices, product quality, recommendation relevance, credit evaluation, and service quality will have a significant positive impact on shopping willingness and ultimately affect the customer’s shopping behavior. e-commerce platform can use this influencing factor to establish personalized information recommendation service mode.
User Sensitive Information Protection Scheme Based on Blockchain Technology
Authors: Luo Sheng
Published: April 2022

With the continuous development of mobile applications and location technology,

k

-anonymity technology is usually used to protect the privacy and security of users in location-based services that are used by mobile users. Due to the self-interest of users, in the cooperative construction of a

k

-anonymity privacy protection scheme, it is difficult to guarantee the honest cooperation behavior of the requesting and cooperating users. To solve the above problems, this paper proposes a distributed

k

-anonymous location privacy protection scheme based on blockchain. In this paper, we record the requesting users, cooperative users, and their location information as evidence, and we punish the users with location leakage and deception. An anonymous zone cannot be constructed when the user is a requesting user. An interactive record mechanism is designed to constrain the self-interest behavior of the requesting and cooperating users in the process of anonymous region construction. Security analysis and experiments show that this scheme can effectively protect the user’s location privacy, promote the honest cooperation between the requesting user and the cooperative user, and encourage more users to participate in the construction of an anonymous zone.
A Detection Algorithm for Audio Adversarial Examples in EI-Enhanced Automatic Speech Recognition
Authors: Huang Ying , Liu Jie
Published: April 2022

Benefiting from the development of big data, edge computing, and deep learning, splendid breakthroughs have been made in automatic speech recognition (ASR) in recent years. Since then, more and more smart products have chosen speech as the interface for human-computer interaction, which causes popularity of edge intelligence (EI) enhanced automatic speech recognition. While people are enjoying the social changes brought by speech recognition technology, a factor of instability quietly emerged called audio adversarial example which is a type of audio deliberately generated by attackers via adding subtle perturbations to the original audio signal. The added perturbations which sound like certain noise that cannot be precepted by human but will cause ASR system make wrong transcription. Three detection algorithms for audio adversarial examples are proposed in this thesis, namely, the robust detection algorithm based on WER (word error rate), the feature detection algorithm based on ADR (adversarial ratio), and the collaborative detection algorithm based on neural network. The experiment results show that three detection algorithms proposed in this thesis have a great discrimination on audio adversarial examples and achieve high AUC scores. Among them, the cooperative detection is the best and the feature detection is the worst. In addition, we found that robust detection algorithm tends to have a higher accuracy score but a lower recall score, while feature detection algorithm tends to have the converse performance. Moreover, since the proposed collaborative detection method combines the advantages of the robust detection and feature detection methods, it presents a better performance with respect to accuracy, recall, and F1 score.
Blocking TGF-β Expression Attenuates Tumor Growth in Lung Cancers, Potentially Mediated by Skewing Development of Neutrophils
Authors: Fang Yifei , Li Xiyao , Jiang Yuxuan , Ge Zhihan
Published: April 2022

In the tumor microenvironment (TME), cells secrete a cytokine known as transforming growth factor-β (TGF-β), which polarizes tumor-associated neutrophils (TANs) towards a protumor phenotype. In this work, C57BL/6 mice with TGF-β1 gene knocked out selectively in myofibroblasts receive orthotopic implantation of Lewis lung carcinoma (LLC). Then, TANs’ differentiation and tumor growth are studied both in vivo and in vitro, to examine the potential effects of TGF-β levels in TME on neutrophil polarization and cancer progression. Possible results are anticipated and discussed from various aspects. Though tumor suppression via inhibition of TGF-β signaling has been widely studied in this field, this study is the first to present a detailed experimental design for evaluating the potential antitumor effects of blocking TGF-β expression. This work provides a creative approach for cancer treatment targeting specific cytokines, and the experimental design presented here may apply to future research on other cytokines, promoting the development of novel cancer-treating strategies.
Study on the Anticancer Activity of Prodigiosin from Variants of Serratia Marcescens QBN VTCC 910026
Authors: Nguyen Sy Le Thanh , Nguyen Tien Cuong , Do Thi Tuyen , Vu Trong Luong , Nguyen Thi Thao , Do Thi Thao , Nguyen Thi Hien Trang , Le Thanh Hoang , Trinh Dinh Kha , Nguyen Thi Anh Tuyet
Published: April 2022

Prodigiosin (Pg), a secondary metabolism produced by numerous bacterial species, is known as anticancer, antibacterial, antifungal, immunosuppressant, antioxidant, antimalarial properties. Pg has been tested for antitumor activity in many different cancer cell lines but studies in LU‐1, KB cell lines, and tumor‐bearing mice are still limited. In this study, Serratia marcescens QBN VTCC 910026 strain (GenBank: KX674054.1) was mutated using Ethyl Methanesulfonate (EMS) to increase the production of Pg. One strain known as EMS 5 was capable of increasing prodigiosin biosynthetic yield by 52% when compared to the wild‐type strain. Red bacterial pigmented colonies containing Pg were collected from solid media, lysed with acetone, purified with toluene: ethyl acetate at a ratio of 9: 1 (v/v), and then used to evaluate the potential anticancer activity. The purity of Pg was confirmed using a high‐performance liquid chromatography (HPLC) method which indicated a 98% rate. Pg chemical formula which was determined using 1H‐NMR and 13C‐NMR spectroscopy, confirmed as prodigiosin (Pg). Human breast cancer cell lines MCF‐7, oropharyngeal cancer KB, and particularly lung cancer LU‐1 in vitro were used to test the anticancer activity of purified Pg compound. It showed a strong inhibitory ability in all the cancer cell lines. Furthermore, the isolated Pg had capable of inhibiting tumor growth, the tumor volume decreased by 36.82%, after 28 days. The results indicated that the bacterial prodigiosin from variants Serratia marcescens QBN VTCC 910026 strain is an encouraging fragment suitable for therapeutic applications.
G/M/1-Based DDoS Attack Mitigation in 5G Ultradense Cellular Networks
Authors: Gao Qinghang , Wang Hao , Wan Liyong , Xiao Jianmao , Wang Long
Published: April 2022

With the 5G millimeter wave (mmWave) application, ultradense cellular networks are gradually becoming one of the core characteristics of 5G cellular networks. In the edge computing environment, considering load balancing among edge nodes is beneficial to slow down the process of distributed denial of service (DDoS) attack. However, most existing studies have given less consideration to congestion in the multiuser and multiedge server models. Someone who uses the M/M/1 model also seems to ignore the effect of scheduling algorithms on the Markov property of the task arrival process. In this manuscript, based on ensuring the quality of experience (QoE) for users, the G/M/1 model is introduced to the task scheduling of edge servers for the first time to improve load balancing between edge servers. For the multi armed bandit (MAB) algorithm framework, specific metrics are established to quantify the degree of its equilibrium. The number of users assigned to the edge nodes and each edge node’s processing of specific tasks is taken into account. We experimentally evaluated its performance against two baseline approaches and three state-of-the-art approaches on a real-world dataset. And the experimental results validate the effectiveness of this method.
Effect of Bionic Nonsmooth Surface Vane on the Antiwear Characteristics of Double-Vane Pump
Authors: Ma Longbiao , Gu Yunqing , Xia Ke , Mou Jiegang , Wu Denghao , Yan Muhan
Published: April 2022

In order to improve the antiwear characteristics of the double-vane self-priming pump, the surface structure of the Scapharca subcrenata was extracted and reconstructed according to bionic principles. Three types of nonsmooth surface models were established at the outlet end of the suction surface of the vanes, which is the most severely worn in the double-vane pump. The external characteristics, pressure field distribution, wear area distribution, and wear degree of the volute and vanes at different concentrations of nonsmooth vane structure were investigated by numerical simulation to reveal the mechanism of the nonsmooth surface structure of the wear characteristics of the vanes. The results show that the head and efficiency of pumps with four different vanes decrease and the average wear rate increases as the particle concentration increases. The different vane structures have a very small effect on the wear resistance of the volute, but a larger effect on vane wear. The circular nonsmooth surface structure, which reduces the low pressure area of the inlet section of the impeller while ensuring a smaller drop in head and efficiency, produces the best antiwear effect and improves the antiwear performance of the double-vane pump.
Unification of Two‐Variable Family of Apostol‐Type Polynomials with Applications
Authors: El-Desouky Beih S. , Gomaa Rabab S. , Magar Alia M.
Published: April 2022

In this paper, the two‐variable unified family of generalized Apostol‐type polynomials is introduced, and some implicit forms and general symmetry identities are derived. Also, we obtain new degenerate Apostol‐type numbers and polynomials constructed from the new 2‐variable unified family. We derive explicit formulae of polynomials and identities that include some special numbers and polynomials. In addition, a probabilistic representation of the new family and some statistical properties are obtained.
Plan‐Do‐Check‐Action Circulation Combined with Accelerated Rehabilitation Nursing under Computed Tomography in Prevention and Control of Hospital Infection in Elderly Patients Undergoing Elective Orthopedic Surgery
Authors: Zhao Liguo , Hu Lianghong , Li Zhangxian , Deng Fenyan
Published: April 2022

To explore the adoption of plan‐do‐check‐action (PDCA) circulation combined with accelerated rehabilitation nursing based on gemstone spectral imaging computed tomography (GSICT) in the prevention and control of hospital infection in the elderly patients undergoing the elective orthopedic surgery, 80 elderly patients who underwent the elective orthopedic surgery in the hospital were selected. Then, according to the randomized controlled principle, these 80 patients were divided into control group (40 cases) with conventional nursing and observation group (40 cases) with accelerated rehabilitation surgical nursing combined with PDCA circulation. All the patients underwent the GSICT examination without any contraindicators. Compared with the conventional CT scan, metal artifacts in GSICT were considerably reduced. In the images processed by GSI and metal artifacts reduction system (MARS), metal artifacts were basically eliminated and the positions, forms, and edges of metal artifacts in the human body were clearly presented. Hospital infection occurred in 1 (2.5%) patient in the observation group and 5 (12.5%) patients in the control group, and the difference was statistically significant (P < 0.05). In terms of temperature increase, patients in control group (37.5%) had a remarkably higher value than that of observation group (7.5%). The increase rate of white blood cell (WBC) count in control group (12.5%) was obviously higher than that in observation group (2.5%). Besides, the differences were statistically significant (P < 0.05). After PDCA circulation combined with accelerated rehabilitation nursing mode was applied, the hospitalization time of observation group (5.3 ± 2.4 days) was markedly lower than that of control group (9.7 ± 3.8 days). Moreover, the total hospitalization cost of observation group (791.44 yuan) was notably lower than that of control group (4068.96 yuan), with significant differences (P < 0.05). Nursing satisfaction in observation group (92.5%) was higher than that in control group (77.5%), and the difference was statistically significant (P < 0.05). In short, GSICT could effectively reduce beam hardening artifacts and metal implant artifacts and improve image quality. Furthermore, accelerated rehabilitation nursing combined with PDCA circulation could effectively reduce the incidence of hospital infection and improve nursing satisfaction.
Multi-UAV Cooperative Assisted RSU Data Acquisition Strategy considering Coverage Quality
Authors: Du Xiaoyu , Guo Qicheng , Zhang Yanyu , Li Yinyin , Zhou Yi
Published: April 2022

The multiple Unmanned Aerial Vehicle (multi-UAV) assisted roadside unit (RSU) data acquisition problem considering the coverage quality is a multiobjective optimization problem, which is a NP-hard problem. Heuristic and hyperheuristic algorithms are effective to solve problems of this type. These algorithms can find the optimal or suboptimal solution in a reasonable time. However, such algorithms still have the problems of low convergence accuracy, slow convergence speed, and being easy to fall into the local optimal solution. In this paper, firstly, according to the specific problem scenarios of roadside unit data collection, minimum cost and maximum coverage models based on task cost and coverage quality are established. Then, to solve the optimization model, combined with the update characteristics of the gray wolf optimization algorithm (GWO) and the whale optimization algorithm (WOA), a hybrid weighted gray wolf and whale optimization algorithm (HWGWOA) is proposed. Finally, to verify the effectiveness of the proposed algorithm, extensive simulation experiments are conducted under four different task acquisition scenarios, and the results are compared with those of genetic algorithm (GA), GWO, and WOA. Simulation results show that the algorithm proposed in this paper not only can get lower task cost and higher coverage quality but also has faster convergence speed and better robustness. Specifically, in terms of task cost, the HWGWOA is about 9.54% lower than the GA, about 7.31% lower than the GWO, and about 5.8% lower than the WOA. In terms of coverage, the HWGWOA is up to about 27.87% higher than the GA, about 15.19% higher than the GWO, and about 9.86% higher than the WOA. Therefore, the algorithm is more suitable for large-scale optimization problems.
Protective Effect and Possible Mechanisms of Artemisinin and Its Derivatives for Diabetic Nephropathy: A Systematic Review and Meta-Analysis in Animal Models
Authors: Feng Haoyue , Wu Tingchao , Zhou Qi , Li Hui , Liu Tianyi , Ma Xitao , Yue Rensong
Published: April 2022

Background. Artemisinin and its derivatives have potential antidiabetic effects. There is no evaluation of reported studies in the literature on the treatment of diabetic nephropathy (DN), one of the commonest diabetic microangiopathies, with artemisinins. Here, we aimed to evaluate preclinical evidence for the efficacy and possible mechanisms of artemisinins in reducing diabetic renal injury. Methods. We conducted an electronic literature search in fourteen databases from their inception to November 2021. All animal studies assessing the efficacy and safety of artemisinins in DN were included, regardless of publication or language. Overall, 178 articles were screened according to predefined inclusion and exclusion criteria. Finally, 18 eligible articles were included in this systematic review. The SYstematic Review Center for Laboratory animal Experimentation (SYRCLE) risk-of-bias tool was used to assess the risk of bias in the included studies. The primary outcomes were kidney function, proteinuria, and renal pathology. Secondary endpoints included changes in fasting plasma glucose (FPG) levels, body weight, and relevant mechanisms. Results. Of the 18 included articles involving 418 animal models of DN, 1, 2, 6, and 9 used dihydroartemisinin, artemether, artesunate, and artemisinin, respectively. Overall, artemisinins reduced indicators of renal function, including blood urea nitrogen (

P
<
0.00001

), serum creatinine (

P
<
0.00001

), and kidney index (

P
=
0.0001

) compared with control group treatment. Measurements of proteinuria (

P
<
0.00001

), microalbuminuria (

P
<
0.05

), and protein excretion (

P
=
0.0002

) suggested that treatment with artemisinins reduced protein loss in animals with DN. Artemisinins may lower blood glucose levels (

P
=
0.01

), but there is a risk of weight gain (

P
<
0.00001

). Possible mechanisms of action of artemisinins include delaying renal fibrosis, reducing oxidative stress, and exerting antiapoptotic and anti-inflammatory effects. Conclusion. Available evidence suggests that artemisinins may be protective against renal injury secondary to diabetes in preclinical studies; however, high-quality and long-term trials are needed to reliably determine the balance of benefits and harms.
Psychiatry Meets Cardiology: A Case Report on the Need for Mental Health Assessment in the Evaluation of Cardiovascular Symptoms
Authors: Asamoah Kofi Tekyi , Dei-Asamoa Richard
Published: April 2022

Cardiovascular symptoms like chest pain and palpitations are among the commonest reasons for outpatient hospital visits. Physician preoccupation with ruling out sinister cardiological diagnoses often results in a failure to recognise mental health-related disorders, which account for over 40% of such cases, especially among young women. These disorders can independently cause cardiovascular symptoms or worsen preexisting cardiovascular disease, worsening morbidity. The pathophysiology of mental stress-induced myocardial ischaemia involves increased levels of neurotransmitters, as opposed to anatomical obstruction seen in conventional coronary artery disease. This results in a battery of tests (including invasive assessments) which yield normal results, deepening the patient’s psychological stress. There is therefore an increased expenditure on healthcare with little assurance of wellness. Detection of these conditions is poorer in developing countries due to limited capacity in appreciating mental health disorders, though over 70% of mental health disorders occur in these countries. Two young ladies with no comorbidities who presented with chest pain and palpitations are reported in this paper. Laboratory investigations and specific cardiology-based tests were normal, but a thorough family and social history revealed underlying mental stresses, corroborated by a mental state examination. A diagnosis of panic disorder was made and once managed, symptoms abated and quality of life improved. We seek to emphasise that mental health disorders are an important cause of cardiovascular symptoms among young adults and must be actively sought by physicians to reduce the associated morbidity, as conventional tests for ischaemia are not useful in their diagnosis. Mental state examination should be done routinely in all clinical assessments to identify patients with subtle signs and improve their clinical outcomes. There must be commitment to build capacity among nonpsychiatrists to reduce the treatment gap.
Predicting Overburden-Induced Failure Height of Large Mining Face in Shallow Coal Seam Based on Half-Plane Theory
Authors: Di Shuai
Published: April 2022

Research on the overburden-induced failure height in large mining faces is important for solving the problems of sand rupture and water permeability when mining in shallow depth. In order to solve the safety and high-efficiency mining problems, this study used the 7 m-high face #52306 of the Daliuta mine as the object of research for theoretical analysis and field measurements. The results show that the traditional empirical formula is not suitable for predicting overburden on three zones of the large mining face in shallow depth. We use elastic half-plane theory to establish a mechanical model to calculate overburden-induced stress and analyze the law of transmission of abutment pressure in the overburden. We then use maximum shear stress theory to develop a method to predict the height of the overburden in the “three zones” of the mining face based on the law of change in the shear stress. According to the characteristic whereby shear stress at the rear of the working face increases first and then decreases, the area where the shear stress was greater than zero was defined as the zone of water conduction-induced fracture, the area where the shear stress increased was identified as the zone of collapse, and the area where the shear stress decreased was defined as the fracture zone. A comparison of the theoretically calculated values with empirically measured data showed that the relative error was controlled to within 6%, verifying the reliability of the proposed method. The results here can provide useful information for the efficient and environmentally friendly mining of shallow coal seams.
COX-2/sEH Dual Inhibitor PTUPB Attenuates Epithelial-Mesenchymal Transformation of Alveolar Epithelial Cells via Nrf2-Mediated Inhibition of TGF-β1/Smad Signaling
Authors: Zhang Chen-Yu , Guan Xin-Xin , Song Zhuo-Hui , Jiang Hui-Ling , Liu Yu-Biao , Chen Ping , Duan Jia-Xi , Zhou Yong
Published: April 2022

Background. Arachidonic acid (ARA) metabolites are involved in the pathogenesis of epithelial-mesenchymal transformation (EMT). However, the role of ARA metabolism in the progression of EMT during pulmonary fibrosis (PF) has not been fully elucidated. The purpose of this study was to investigate the role of cytochrome P450 oxidase (CYP)/soluble epoxide hydrolase (sEH) and cyclooxygenase-2 (COX-2) metabolic disorders of ARA in EMT during PF. Methods. A signal intratracheal injection of bleomycin (BLM) was given to induce PF in C57BL/6 J mice. A COX-2/sEH dual inhibitor PTUPB was used to establish the function of CYPs/COX-2 dysregulation to EMT in PF mice. In vitro experiments, murine alveolar epithelial cells (MLE12) and human alveolar epithelial cells (A549) were used to explore the roles and mechanisms of PTUPB on transforming growth factor (TGF)-β1-induced EMT. Results. PTUPB treatment reversed the increase of mesenchymal marker molecule α-smooth muscle actin (α-SMA) and the loss of epithelial marker molecule E-cadherin in lung tissue of PF mice. In vitro, COX-2 and sEH protein levels were increased in TGF-β1-treated alveolar epithelial cells (AECs). PTUPB decreased the expression of α-SMA and restored the expression of E-cadherin in TGF-β1-treated AECs, accompanied by reduced migration and collagen synthesis. Moreover, PTUPB attenuated TGF-β1-Smad2/3 pathway activation in AECs via Nrf2 antioxidant cascade. Conclusion. PTUPB inhibits EMT in AECs via Nrf2-mediated inhibition of the TGF-β1-Smad2/3 pathway, which holds great promise for the clinical treatment of PF.
An Intelligent Analysis System for Traditional Arts and Crafts Based on Digital technology: Taking the Guangdong-Hong Kong-Macao Greater Bay Area as the Observation Center
Authors: Wen Tingting , Fan Sijie
Published: April 2022

In order to improve the analysis effect of traditional arts and crafts, this paper analyzes traditional arts and crafts combined with digital technology, builds an intelligent analysis system to improve the digital processing effect of traditional arts and crafts, and takes the Guangdong-Hong Kong-Macao Greater Bay Area as an example to verify the system effect. In order to improve the accuracy of subsequent image alignment and defect detection, this paper compares the effects of the pixel-level edge detection algorithm and the subpixel-level edge detection algorithm and finally selects the subpixel-based edge detection algorithm to extract the edge of the image. In addition, this paper compares the traditional defect detection algorithm through research and experiment and proposes an improved image phase difference method according to the actual situation. The experimental research shows that the traditional arts and crafts intelligent analysis system based on digital technology proposed in this paper has a very good effect. At the same time, with the support of this system, the intelligent analysis of traditional arts and crafts in the Guangdong-Hong Kong-Macao Greater Bay Area can be carried out efficiently.
Antioxidant, Antimicrobial, and Protein Kinase Inhibition Profiling of C. ambrosioides Seed Extracts along with RP-HPLC
Authors: Bano Saira , Baig Muhammad Waleed , Okla Mohammad K. , Zahra Syeda Saniya , Akhtar Nosheen , Al-Qahtani Wahidah H. , AbdElgawad Hamada , Haq Ihsan-Ul
Published: April 2022

The validation of underexplored traditional plant remedies represents a reservoir of novel leads for drug discovery. In line with this, in vitro total phenolics and flavonoids content, multimode antioxidants, antimicrobial, cytotoxicity, and protein kinase inhibition assays were conducted on C. ambrosioides seed extracts in addition to RP-HPLC. Methanol extract exhibited highest total phenolics (

64.6
±
0.6
 
μ
g

gallic acid equivalent/mg) and flavonoids (

50.9
±
0.5
 
μ
g

quercetin equivalent/mg) content. RP-HPLC quantified rutin (1.98 μg/mg) in methanol extract whereas quercetin (0.322 μg/mg) and kaempferol (2.86 μg/mg) in methanol-distilled water extract. Methanol extract exhibited highest ascorbic acid equivalent (AAE) free radical (DPPH) scavenging (IC50 of

110.7
±
5
 
μ
g
/
ml

), total antioxidant capacity (

110.6
±
2.2
 
μ
g

AAE/mg), and total reducing power (

94.30
±
0.46
 
μ
g

AAE/mg). Highest antibacterial activity against K. pneumonia (

14
±
1.61
 
mm

ZOI) and antifungal activity against F. solani (

17
±
1.38
 
mm

ZOI) were shown by n-hexane and chloroform extracts, respectively. Ethyl acetate extract exhibited highest brine shrimps cytotoxicity (LC50 of 125 μg/ml). A noteworthy protein kinase inhibitory potential was shown by ethanol extract with a

20
±
1.27
 
mm

bald zone. Therapeutic potential of medicinal plants can be completely explored by using multiple solvent system. This study makes C. ambrosioides, a resourceful prospect for the bioactivity-guided isolation of lead compounds.
Distributed Continuous‐Time Containment Control of Heterogeneous Multiagent Systems with Nonconvex Control Input Constraints
Authors: Li Xue , Wang Lulu , Zhang Yinsen
Published: April 2022

This paper focuses on studying containment control problem with switching communication graphs of continuous‐time heterogeneous multiagent systems where the control inputs are constrained in a nonconvex set. A nonlinear projection algorithm is proposed to address the problem. We discuss the stability and containment control of the system with switching topologies and nonconvex control input constraints under three different conditions. It is shown that all agents converge to the convex hull of the given leaders ultimately while staying in the nonconvex set under the premise that at least one directed path from leaders to the agents exists in each bounded time interval. Finally, the validity of the results obtained in this paper is verified by simulation.
Optimized Synthesis of Xanthan gum/ZnO/TiO2Nanocomposite with High Antifungal Activity against Pathogenic Candida albicans
Authors: Ghorbani Fatemeh , Gorji Pourya , Mobarakeh Mohammad Salmani , Mozaffari Hamid Reza , Masaeli Reza , Safaei Mohsen
Published: April 2022

Increased resistance of fungal pathogens to common antimicrobial agents is known as one of the most important human problems. Due to the limited variety of antifungal drugs available, the identification and use of new antifungal drugs are essential. This study aimed to determine the optimal conditions for synthesizing a novel nanocomposite of xanthan gum/ZnO/TiO2with the highest antifungal activity against Candida albicans (C. albicans). For this purpose, nine experiments were designed using the Taguchi method. In the designed experiments, three factors of xanthan gum, ZnO, and TiO2nanoparticles have been investigated at three different levels, and the best ratio with the highest antifungal activity was determined. The results showed that in the presence of the synthesized nanocomposite in experiment 3 (xanthan gum 0.01 M, ZnO 0.09 M, and TiO2 0.09 M), the inhibition of fungal growth reached 92.51%. The properties of the synthesized nanocomposite and its components were investigated using different characterization methods, which confirmed the formation of nanocomposites with desirable properties. The antifungal activity results showed that the synthesized nanocomposite as an antifungal agent has an effective performance and can be used well in various fields.
The Optimized Anomaly Detection Models Based on an Approach of Dealing with Imbalanced Dataset for Credit Card Fraud Detection
Authors: Zhang Yan-Feng , Lu Hong-Liang , Lin Hong-Fan , Qiao Xue-Chen , Zheng Hao
Published: April 2022

Credit card fraud is a major problem in today’s financial world. It induces severe damage to financial institutions and individuals. There has been an exponential increase in the losses due to fraud in recent years. Hence, effectively detecting fraudulent behavior is of vital importance for either financial institutions or individuals. Since credit fraud events account for a small proportion of all transaction events in real life, the datasets about credit fraud are usually imbalanced. Some common classifiers, such as decision tree and naïve Bayes, are unable to detect fraud. Furthermore, in some cases, traditional strategies for dealing with an imbalanced problem, such as the synthetic minority oversampling technique (SMOTE), are not effective for the fraud detection datasets. To accurately detect fraud behavior, this study uses anomaly detection on imbalanced data, as well as Isolation Forest (IForest) with kernel principal component analysis. A one-class support vector machine (OCSVM) with AdaBoost is used as two models to detect outliers which significantly improves detection accuracy and efficiency. The model achieved 96% accuracy, 100% precision, 96% recall, and 98%

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score, respectively. The proposed model is expected to become a helpful tool for finding credit card fraud detection, and the analysis presented in this study will provides useful insights into credit card fraud detection mechanisms.
The Prevalence Rate, Pattern of Antibiotic Resistance, and Frequency of Virulence Factors of Pseudomonas aeruginosa Strains Isolated from Fish in Iran
Authors: Shahrokhi Gholam Reza , Rahimi Ebrahim , Shakerian Amir
Published: April 2022

Pseudomonas aeruginosa (P. aeruginosa) is a pathogenic bacterium and one of the seafood's most common spoilage microorganisms. In this study, 470 fish samples collected randomly were evaluated for the presence of P. aeruginosa, antibiotic resistance, and frequency of virulence factors. Isolation of P. aeruginosa from fish samples was performed on cetrimide agar after an initial enrichment. Representative colonies were selected, and biochemical tests were conducted. An antibiotic resistance test was performed using the disk diffusion method. DNA was extracted, and antibiotic resistance genes, as well as virulence genes, were detected using PCR. Fresh fish showed the highest prevalence of P. aeruginosa (5%). No positive samples contaminated with P. aeruginosa were isolated from frozen fish samples. From smoked, salted, and dried fish, two samples (2.85–4%) were contaminated with P. aeruginosa. The antibiotic resistance against meropenem, imipenem, carbapenem, erythromycin, gentamicin, chloramphenicol, and enrofloxacin was 0%. The lowest antibiotic resistance pattern was observed in fresh fish, and the highest was observed in smoked, salted, and dried fish. Respectively, blaTEM, blaCTX-M, and blaSHV were the most abundant genes encoding antibiotic resistance. The most virulence genes were algD, algU, lasB, toxA, exoS, exoT, and apr. This study suggests that raw seafood could be a source of antibiotic-resistant P. aeruginosa and helps to spread resistance genes through the food chain. It seems that cross-contamination in the fishing, transportation, and supply of seafood can cause increased contamination with Pseudomonas aeruginosa in these products. Therefore, the hygienic principles can effectively reduce contamination by P. aeruginosa. Also, the prophylactic use of antibiotics in these products should be controlled.
Mapping Bus and Stream Travel Time Using Machine Learning Approaches
Authors: Nair Gopikrishnan , Kumar B. Anil , Vanajaskshi Lelitha
Published: April 2022

Collection of travel time data has always been a strenuous task, especially on Indian roads, due to the highly mixed traffic conditions and the absence of rigid driving characteristics. Travel time data collection methods such as on-board GPS devices and Wi-Fi scanners have their own feasibility issues. The GPS devices cannot be installed on all private vehicles, and Wi-Fi scanners cannot be set up at each and every road corridor. However, many transit agencies of Indian cities are installing GPS units in their public transport buses, making them a rich source of data. These buses travel all over the network and record the travel time and location information of vehicles at a certain interval, which can become the best and reliable source of traffic data under such conditions. The only limitation with this data is that the buses differ greatly from the rest of the vehicles in terms of vehicle characteristics, freedom of driving, effect of bus-stops, etc. So, a viable solution is to model and link the stream and bus travel times. Based on this, the present study proposes a method to map bus travel times with stream travel times using machine learning techniques, namely, Gradient Boosting Method and Support Vector Machines. Preliminary analysis showed that a nonlinear relationship exists between the travel times, and other factors like peak/off-peak hours, day of the week, etc. Results show that the proposed methods can efficiently map bus travel times with stream travel times with an average MAPE of 20% and perform better than existing approaches such as linear regression and artificial neural networks.
The Need for New Treatments Targeting MPNST: The Potential of Strategies Combining MEK Inhibitors with Antiangiogenic Agents
Authors: González-Muñoz Teresa , Kim AeRang , Ratner Nancy , Peinado Héctor
Published: April 2022

Malignant peripheral nerve sheath tumors (MPNST) are aggressive soft-tissue sarcomas that represent an important clinical challenge, particularly given their strong tendency to relapse and metastasize and their relatively poor response to conventional therapies. To date, targeted, noncytotoxic treatments have demonstrated limited clinical success with MPNSTs, highlighting the need to explore other key pathways to find novel, improved therapeutic approaches. Here, we review evidence supporting the crucial role of the RAS/MEK/ERK pathway and angiogenesis in MPNST pathogenesis, and we focus on the potential of therapies targeting these pathways to treat this disease. We also present works suggesting that the combination of MEK inhibitors and antiangiogenic agents could represent a promising therapeutic strategy to manage MPNSTs. In support of this notion, we discuss the preclinical rational and clinical benefits of this combination therapy in other solid tumor types. Finally, we describe other emerging therapeutic approaches that could improve patient outcomes in MPNSTs, such as immune-based therapies.