bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Antischistosomal Activities of Crude Aqueous Extracts of Artemisia annua, Nigella sativa, and Allium sativum against Schistosoma mansoni in Hamsters
Authors: Fadladdin Yousef Abdal Jalil
Published: March 2022

Schistosomiasis is a neglected disease, as the World Health Organization classified it in the second category after malaria. World Health Organization approved praziquantel (PZQ) as the only chemotherapy to treat schistosomiasis. Over the years, some problems have arisen with PZQ, as it showed poor efficacy in the early stages of infection as well as the emergence of some resistance to it. In searching for new alternative drugs to treat schistosomiasis, the researchers intensified their efforts to find a new drug. The present study focuses on evaluating the effect of three plant extracts Artemisia annua, Nigella sativa, and Allium sativum at different doses of 31.25, 62.5, 125, 250, and, 500 μg/ml; in vitro study was accomplished on the Schistosoma mansoni adult worms. The results declared that the concentration of 500, 250, and 125 of Artemisia annua was more effective on adult worms, and the same concentrations of Nigella sativa and Allium sativum gave less effect on the adult worms than the previous plant. In vivo study was accomplished on the hamster’s tissue after exposing it to doses of the plants’ extracts with different concentrations; it showed the presence of calcifications and damage to the worm eggs in the liver and spleen, as well as reducing the size of granulomas. After conducting many confirmatory studies Artemisia annua extract can be used as an effective and safe treatment for Schistosoma disease.
Examination of New Electrocardiographic Repolarization Markers in Diabetic Patients with Noncritical Coronary Artery Disease
Authors: Gürler Müjgan , İnanır Mehmet
Published: March 2022

Background. Diabetes mellitus (DM) is a multisystemic, chronic disease that affects many organs. Coronary artery disease (CAD) is the leading cause of death in patients with DM. The electrocardiogram’s new ventricular repolarization parameters can predict mortality and morbidity. The ventricular repolarization indices were examined in diabetic patients with a CAD diagnosis in this study. Methods. The study group consisted of 84 DM patients (51 males; mean age 58.8 ± 6.6) with noncritical CAD. The control group consisted of 84 DM patients (47 males; mean age 58.7 ± 8.8) with a normal coronary artery. The intervals of QT, QRS, JT, and Tp-e were all measured. Tp-e/QT, Tp-e/QTc, Tp-e/JT, and Tp-e/JTc ratios were determined with QTc, QTd, QTdc, and JTc intervals. Results. Heart rate (74.4 ± 13.1 vs. 70.0 ± 13.6 bpm,

p

: 0.036), QT (381.0 ± 30.3 vs. 368.6 ± 29.1 ms,

p

: 0.008), QTc (407.5 (359–450) vs. 389 (339–430) ms,

p
<
0.001

), QTd (25.1 ± 6.2 vs. 21.9 ± 9.9 ms,

p

: 0.013), QTdc (26.7 ± 6.1 vs. 23.1 ± 10.8 ms,

p

: 0.010), Tp-e (95.7 ± 12.2 vs. 73.6 ± 9.8 ms,

p
<
0.001

), JT (293.8 ± 22.0 vs. 283.5 ± 30.9 ms,

p

: 0.014), and JTc (313.6 ± 12.3 vs. 302.4 ± 33.7 ms,

p
=
0.005

) intervals, and Tp-e/QT (0.25 ± 0.03 vs. 0.20 ± 0.03 ms,

p
<
0.001

), Tp-e/QTc [0.23 (0.19–2.33) vs. 0.19 (0.14–0.25) ms,

p
=
0.007

], Tp-e/JT (0.33 ± 0.04 vs. 0.26 ± 0.04 ms,

p
<
0.001

), and Tp-e/JTc (0.30 ± 0.03 vs. 0.24 ± 0.03 ms,

p
<
0.001

) ratios were all found to be significantly higher in diabetic patients with noncritical CAD. Conclusion. In this study, ventricular repolarization markers on the surface ECG were found to be elevated in diabetic CAD patients. These variables may be related to fatal arrhythmic events. To be sure, large-scale, randomized controlled trials are required.
A Rare Cause of Haemorrhagic Shock: Rupture of Gastric Wall Seeding of Hepatocellular Carcinoma
Authors: Ishimaru Naoki , Fujikawa Hirohisa , Niwa Kazuya , Kobayashi Yoshifumi
Published: March 2022

Ruptured hepatocellular carcinoma (HCC) can lead to peritoneal dissemination. However, gastric wall seeding from HCC is exceedingly rare, and little is known about its clinical course. Herein, we report a case of an 88-year-old man who presented with a four-hour history of nausea, vomiting, and upper abdominal pain. He has a history of ruptured HCC during surgery. The patient underwent an emergency laparotomy on account of haemorrhagic shock, which confirmed the diagnosis of ruptured HCC with gastric wall seeding. The findings from this study showed that the ruptured HCC can seed into the stomach wall, and the implanted lesions may rupture and lead to life-threatening haemorrhagic shock. Surgery is an effective treatment for bleeding from the implanted lesions.
MAE-CAD: An IP-Based Core Network Asset Discovery Technology Based on Multiple Autoencoders
Authors: Wang Yijing , Chang Peng , Wang Han , Ding Yong , Sun Rui
Published: March 2022

With the continuous development of the network, the number of network assets continues to increase. Despite the convenience diversified network assets bring, it also poses new challenges to IP-based network asset management. Traditional asset discovery technologies mainly analyze network traffic, and detect relevant information (operating system, running software, etc.) of IP-based assets through methods such as active discovery, passive discovery, and discovery methods based on cyberspace search engines. These methods assign the same weight to all network IP-based network assets, and it is difficult to effectively analyze diversified network assets. In this paper, we propose the concept of IP-based core network assets, and collect the data of the relevant network assets based on this concept. Then, we construct a dataset and establish feature engineering for data preprocessing. As there is currently no relevant IP-based core network asset detection method, we propose an IP-based core network asset discovery technology based on pretraining of multiple autoencoders, MAE-CAD. The results show that our method can achieve 95.74% in Acc and 95.04% in F1 in the experimental environment (Acc = 98.11% and F1 = 97.16% in the actual network environment because of duplicate samples). In addition, MAE-CAD has excellent robustness. In an environment where the proportion of data is extremely unbalanced, when the IP-based core network asset data in the training set only accounts for 1/200 (0.5%), MAE-CAD can still obtain 92.91% in Acc and 91.57% in F1.
CDK Inhibition Reverses Acquired 5-Fluorouracil Resistance in Hepatocellular Carcinoma Cells
Authors: Pu Yiyi , Yan Dongmei , Tu Linglan , Cheng Liyan , Yu Jie , Li Zhuduo , Zheng Xiaoliang , Wang Xinbao
Published: March 2022

Background. 5-Fluorouracil (5-FU) has been widely applied in treating cancers. However, its usage is largely limited in hepatocellular carcinoma (HCC), due to acquired resistance. Here, we aim to identify target proteins and investigate their roles in 5-FU sensitivity of HCC cells. Methods. Mass spectrometry (MS) proteomics was performed on 5-FU-resistant cell line (BEL7402/5-FU) and its parental cell line (BEL7402) with 5-FU treatment. In order to identify potential targets, we compared the proteomics between two cell line groups and used bioinformatics tools to select hub proteins from all differentially expressed proteins. Results. We finally focused on a group of cell cycle-related kinases (CDKs). By CCK8 assay, we confirmed that the CDK inhibitor significantly decreased the IC50 of 5-FU-resistant cells. Conclusions. Our study verified that CDK inhibition can reverse 5-FU resistance of HCC cells.
The Co‐Movement between International and Emerging Stock Markets Using ANN and Stepwise Models: Evidence from Selected Indices
Authors: Al-Najjar Dania
Published: March 2022

In the past two decades, especially after the financial crisis of 2007–09, the literature for examining the availability of integration between the stock exchanges in developed and developing markets has grown. The importance of this topic stems from the significant implications of the linkage between exchange markets on various decisions taken by interested parties, such as policymakers and investors, in the decisions for portfolio diversification. This study examines the relationship between a developing stock exchange index, Amman Stock Exchange Index (ASEI), and the number of international indices, including S&P 500, NASDAQ, Nikkei, DAX, CAC, and HSI for 2008‐2019. To validate the availability of the linkage between the indices, the author includes various tests of a correlation coefficient, stepwise regression analysis, and artificial neural network (ANN). Despite the results indicating that the ANN is more efficient than linear regression in investigating the availability of the relationship between ASEI and international indices, stepwise regression and neural network support this relationship. Furthermore, ANN results revealed that the S&P 500 index and year have the most substantial relationship with ASEI. Our research is theoretically and practically important; policymakers and investors can benefit from our findings. Future studies may explore the effect of different international stock market indices on ASEI or other developing markets. Further studies can use macroeconomic factors to build prediction models for stock market indices.
A Secure Two-Factor Authentication Framework in Cloud Computing
Authors: kaur Sandeep , kaur Gaganpreet , Shabaz Mohammad
Published: March 2022

Cloud computing technology has brought tremendous evaluation in the arena of IT (information technology). This technology paves the path of starting business with lowest investment by availing infrastructure as a service (IAAS), platform as a service (PAAS) and software as a service (SAAS) pay per uses model. Cloud computing services can be quickly and easily provisioned and discharged with minimum efforts and service provider (SP) relationship. Cloud computing characteristics such as on demand self-service, broad network access, resource pooling, and rapid elasticity lead the demand of computing. Despite these features, this platform is free to security issues and attacks specifically in terms of communication because of unsecure authentication and privacy. However, strong user authentication procedure impedes illegal access to the SP which is the principal requirement for securing cloud computing ecosystem. In this regard, we attempt to propose possible counter measures for the cloud ecosystem. Hence, this paper presented a novel one way hash and nonce-based two-factor secure authentication scheme with traditional user IDs, password, and OTP verification procedure that resist brute force attack, session and account hijacking attack, MITM attacks, and replay attacks.
Design of HHO-PID Controllers for Load Angle of Power Plant Synchronous Generators
Authors: Abdolhosseini Morteza , Abdollahi Rohollah
Published: March 2022

Accurate analysis of load angle stability of the synchronous generator depends on considering mechanical parameters in addition to electrical parameters in synchronous generator modeling, which leads to increasing state-space variables and model complexity and ultimately reduces the accuracy and performance of conventional controllers. Therefore, an intelligent controller is required for the stable and reliable operation of the synchronous load angle generator. With this approach in this paper, first considering the electrical and mechanical parameters, a complete 14-order model of the synchronous generator is obtained. Then, a PID controller based on the Harris optimization algorithm is proposed to stabilize the load angle of the synchronous generator. The HHO optimization algorithm is a population-based optimization technique that optimizes PID controller coefficients. The effectiveness of the new HHO technique in optimizing the controller PID coefficients over known optimization techniques such as GA-PID and Modified GA-PID has been confirmed using simulation results. The simulation results show that the proposed controller has a higher performance in load angle stability compared to the sliding mode and fuzzy-PID controllers.
Research on Oral English Dialogue Understanding Based on Deep Learning
Authors: Li Chan , Tian Rui
Published: March 2022

Oral English dialogue is a crucial part of a dialogue system that enables a computer to “understand” the input language as a human does, so the performance of a dialogue system is closely related to the performance of oral English dialogue understanding. In task-based human-machine dialogue systems, external knowledge bases can provide the machine with valid information beyond the training data, helping the model to better perform the oral English dialogue comprehension task. In this paper, we propose a deep recurrent neural network based on feature fusion, which directly stacks multiple nodes at a single time node to deepen the complexity of nonlinear transformations. The feature fusion network structure is applied to the ATIS dataset for oral English dialogue comprehension experiments, and the experimental results demonstrate that the feature fusion RNN network can further improve the effectiveness of the RNN network and the GRU network structure unit can obtain better results among different RNN node units.
Financial Asset Risk Measurement Based on Smart Sensor Big Data Security Analysis and Bayesian Posterior Probability Model
Authors: Lu Zixin
Published: March 2022

Following the speeding up of a process of financial globalization, the risks faced by financial markets have become more complex and diversified. Correlated patterns among financial assets exhibit characteristics of nonlinearity, asymmetry, and tail correlation. The original linear correlation analysis method is no longer suitable, but relevant information describing financial risks. In order to confirm whether an asset is safe, the key is to study and master its volatility, and this is based on our mastery of volatility measurement skills. This article is based on smart sensor big data security analysis and Bayesian analysis. The risk measurement of financial assets based on the empirical probability model is studied. The GARCH-t(1,1) model is selected according to the Akaike information criterion (AIC) after the generalized autoregressive conditional heteroskedasticity (GARCH) model is established by the EViews software. According to the results of probability integral transformation, a series of correlation coefficients and degrees of freedom oft-copula are obtained by the maximum likelihood estimation method. This paper uses the risk-adjusted return on capital (RAROC) method to evaluate the risk performance of financial assets. Financial institutions can only retain and absorb the financial market risks that cannot be avoided and transferred. The edge user node sends the service request to the edge server node. The edge server uses the model proposed in this paper to evaluate the user’s trust and selects the corresponding service level according to the trust level corresponding to the calculated credibility results. The data show that the edge calculation takes 0.2581 seconds, while the linear search takes about 64 seconds. The results show that intelligent edge computing improves the accuracy and efficiency of financial asset risk measurement.
Mechanical Properties of Plastic Sand Brick Containing Plastic Waste
Authors: Sahani Kameshwar , Joshi Bhesh Raj , Khatri Kabiraj , Magar Abiraj Thapa , Chapagain Sabin , Karmacharya Nabanita
Published: March 2022

The use of plastic has grown extensively in recent years all over the world. It is inexpensive and easily available and can be moulded into any shape. However, plastic is nonbiodegradable; it causes pollution and create difficulties in managing even for a wealthy nation. The purpose of this study was to investigate the environment‐friendly potential use of plastic and demonstrate usefulness of plastic sand bricks as alternative structural elements, replacing standard clay brick. The physical and mechanical properties of plastic sand bricks were studied in different plastic sand ratios of 1 : 3, 1 : 4, and 1 : 5 by their weight, using plastic as a binder. Moreover, the thermal resistance test, split tensile strength test, penetration test, and Fourier transform infrared spectroscopy were performed. The study concluded that the strength of plastic sand brick depends upon the uniformity of the mixture and increases when the ratio of sand and plastic in the mixture is increased to 1 : 4 from 1 : 3. Any increase or decrease in the ratio 1 : 4 is found to reduce the strength. All the bricks in any of the ratios showed zero water absorption and nil efflorescence.
Assessment of Saudi Women’s Adherence and Experience with Venous Thromboembolism Prophylaxis after Cesarean Section Delivery Using Telemedicine Technology
Authors: Elmaghraby Dalia Ahmed , Al-Bassri Zakiah Khalid , AlTuraiki Zainab Ahmed , Alsaleh Jinan Adnan , Alzuwayyid Latifah Saleh , Albanyan Norah Abdulmohsen , Almulhim Abdulaziz Saleh
Published: March 2022

Background. Telemedicine technology is widely used especially after the COVID-19 pandemic. It can be used to give medical advice as well as follow up with the patients at home in the outpatient setting. Low molecular weight heparin, such as enoxaparin, decreases the risk of venous thromboembolism after cesarean delivery. Objective. assessing postcesarean women’s adherence and experience to enoxaparin thromboprophylaxis regimens in the outpatient setting via telephone calls. Methods. prospective cohort study was conducted in the Maternity and Children Hospital (MCH). Postcesarean women were interviewed at the postnatal ward in the MCH. The participants were followed up for 10 days of the delivery to assess their adherence to and experience with enoxaparin thromboprophylaxis. Suboptimal adherence was defined as the missing of one or more doses of enoxaparin. Results. 170 women participated in this study. The majority of them (78.8%) were fully adherent to enoxaparin while 21.2% missed at least one dose. The most-reported causes for suboptimal adherence were perceived lack of necessity (44.4%), forget to take the injection (30.6%), feeling of high load going to the primary care unit to take injection (27.8%), and fear of injection (11.1%). Conclusion. Telemedicine technology could be used to assess patients’ adherence. The study results showed that the majority of participants were fully adherent to postcesarean enoxaparin but more than 20% of the participants were suboptimal adherent. Proper patient education techniques could influence patients’ adherence and potentially decrease the risk of DVT in those populations.
Architectural and Landscape Garden Planning Integrated with Artificial Intelligence Parametric Analysis
Authors: Wang Yanxia , Chen Leiyi
Published: March 2022

Parametric design, driven by digital technology, has sparked extensive research and debate in the domains of architecture and urban planning, offering a new approach to issue solving. Architecture and landscape architecture, like architecture and urban planning, are disciplines that are part of the artificial environment. Architectural landscape design has begun to be influenced by parametric design. This study presents a more technical parametric design technique of architectural landscape design that involves artificial intelligence parametric analysis and proposes an architectural landscape planning and design method that incorporates artificial intelligence (AI) parametric analysis. This is a new discipline of concurrent design that complements and expands architectural landscape design methodologies and is based on artificial intelligence methods. This study integrates artificial intelligence parametric design theory and methodology into architectural landscape design and presents a parametric method appropriate for landscape architecture design based on architectural landscape architecture characteristics.
Generalized Hermite–Hadamard-Type Integral Inequalities forh-Godunova–Levin Functions
Authors: Ali Rana Safdar , Mubeen Shahid , Ali Sabila , Rahman Gauhar , Younis Jihad , Ali Asad
Published: March 2022

The main objective of this article is to establish generalized fractional Hermite–Hadamard and related type integral inequalities forh-Godunova–Levin convexity andh-Godunova–Levin preinvexity with extended Wright generalized Bessel function acting as kernel. Moreover, Hermite–Hadamard-type and trapezoid-type inequalities for several known convexities including Godunova–Levin function, classical convex,s-Godunova–Levin function,P-function, ands-convex function are deduced as corollaries. These obtained results are analyzed in the form of generalization of fractional inequalities.
Application of Suctioning Ureteral Access Sheath during Flexible Ureteroscopy for Renal Stones Decreases the Risk of Postoperative Systemic Inflammatory Response Syndrome
Authors: Qian Xiaoyuan , Liu Chenqian , Hong Senyuan , Xu Jinzhou , Qian Can , Zhu Jianning , Wang Shaogang , Zhang Jiaqiao
Published: March 2022

Purpose. To clarify the efficiency and outcomes of suctioning ureteral access sheath (UAS) during flexible ureteroscopic lithotripsy (fURL) for the management of renal stones. Methods. Between January 2017 and January 2019, a total of 444 patients with renal stones undergoing fURL were divided into suctioning UAS and nonsuctioning UAS groups. The outcomes of patients in both groups were compared using a matched-pair analysis (1 : 1 scenario). Furthermore, a directed acyclic graph (DAG) was drawn to guide the multivariate logistic regression model and analyze the protective effect of suctioning UAS on the incidence of postoperative systemic inflammatory response syndrome (SIRS). Results. Before propensity score matching, significant differences were observed between the two groups in blood white cell counts, urine white cell counts, preoperative fever, preoperative indwelling stents, and laterality (

P
<
0.05

). Eighty-one patients in the suctioning UAS group were successfully matched with 81 patients in the nonsuctioning group. The stone-free rate (SFR) on postoperative day 1 after fURL in the suctioning group was higher than that in the nonsuctioning group (86.4% vs. 71.6%;

P
=
0.034

), whereas it was comparable between the two groups 1 month after the surgery (88.9% vs. 82.7%;

P
=
0.368

). The incidence of postoperative fever or SIRS was lower in the suctioning group (fever: 3.70% vs. 14.8%;

P
=
0.030

; SIRS: 1.23% vs. 12.3%;

P
=
0.012

). However, the operative duration was similar in both groups (mean (SD)) (72.9 (28.1) min vs. 80.0 (29.5) min;

P
=
0.121

). The result of the multivariate logistic regression model guided by DAG revealed that the application of nonsuctioning UAS (odds ratio: 5.28 [1.38–35.07],

P
=
0.034

) during fURL was associated with postoperative SIRS. Conclusions. The application of suctioning UAS during fURL was associated with higher SFR on day 1 after surgery and a lower incidence of postoperative fever or SIRS.
Bevacizumab Combined with Intensity-Modulated Radiation Therapy on Cognitive and Coagulation Function in Postoperative Glioma Patients
Authors: Lin Guo-Shi , Wang Wei-Wei , Lin Hong , Lin Rui-Sheng
Published: March 2022

To examine the influences of bevacizumab combined with intensity-modulated radiation therapy (IMRT) on postoperative brain glioma, particularly its impact on coagulation function and cognitive function, the complete clinical data of 156 patients undergoing glioma surgery in the neurosurgery department of our hospital between March 2015 and October 2018 were retrospectively analyzed. All patients underwent glioma surgery and were then assigned to the observation group (Obs group, n = 79, received bevacizumab combined with IMRT) or the control group (Con group, n = 77, received IMRT without bevacizumab) for analysis during postoperative treatment. The patients' short-term efficacy was evaluated, and their serum markers and coagulation function were compared, as well as the cognitive function, the occurrence of adverse reactions during treatment, the Karnofsky performance status (KPS) score, and quality of life after treatment. Patients' survival was followed up within 2 years after surgery. The Obs group showed a notably higher clinical remission rate and clinical control rate (DCR) than the Con group after treatment. The Obs group showed notably lower levels of interleukin-2 (IL-2), vascular endothelial growth factor (VEGF), IL-6, and epidermal growth factor (EGF), experienced notably shorter prothrombin time (PT) and activated partial thromboplastin time (APTT), and showed higher fibrinogen (FIB) and D-dimer (D-D) levels than Con group. The Obs group showed notably better cognitive function, KPS score, and quality of life than the Con group, but no notable difference was observed between them in the incidence of adverse reactions (P > 0.0500). The survival rates in the Obs group were higher than in the Con group. For patients with glioma, postoperative bevacizumab combined with IMRT delivers substantially higher clinical efficacy by lowering serum marker levels and improving cognitive function without significantly affecting coagulation function.
Decision Support System for Economic Management of Large Enterprises Based on Artificial Intelligence
Authors: Zhao Yuexia
Published: March 2022

In order to improve the economic management effect of large enterprises, a decision support system for economic management of large enterprises based on artificial intelligence is designed. The system hardware and software are designed, respectively. The system hardware consists of basic information module, business management module, personnel management module, salary and welfare management module, system management module, and database module. With the support of artificial intelligence technology, build a BP neural network model, and the model was trained, through continuous learning rate adjustment; in the process of training error lower sales forecast results, according to the result of prediction in enterprise comprehensive benefit maximization as the goal, set up large enterprises economic management decision-making model, large enterprise economic management decision-making algorithm design. The test results show that the system has good fault tolerance, reliability, robustness, and high efficiency, the system response time is short, the decision accuracy is high, and the practical application effect is good.
Method of Ideological and Political Teaching Resources in Universities Based on School-Enterprise Cooperation Mode
Authors: Xia Tian , Ahmad Muhammad Talal
Published: March 2022

The personalized recommendation system influences the recommendation of ideological and political teaching resources in universities, resulting in a high MAE score. As a result, under the school-enterprise collaboration paradigm, this study proposes a customised recommendation approach for ideological and political teaching resources in colleges and universities. The ideological and political teaching resource bank is developed against the backdrop of the teaching paradigm that combines universities and businesses. Learners’ browsing data history is gathered to create a learning interest model for them. A hybrid collaborative filtering recommendation method was devised, and a recommendation engine was established by Taste component, taking into account individualised resource recommendation needs and information entropy weight distribution mode. When compared to previous techniques, the developed customised recommendation method considerably enhances the recommendation quality of instructional resources and reduces MAE by 29% and 34%, respectively.
Solving Sensor Ontology Metamatching Problem with Compact Flower Pollination Algorithm
Authors: Lian Wenwu , Fu Lingling , Niu Xishuan , Feng Junhong , Wang Jian-Hong
Published: March 2022

To implement co-operation among applications on the Internet of Things (IoT), we need to describe the meaning of diverse sensor data with the sensor ontology. However, there exists a heterogeneity issue among different sensor ontologies, which hampers their communications. Sensor ontology matching is a feasible solution to this problem, which is able to map the identical ontology entity pairs. This work investigates the sensor ontology meta-matching problem, which indirectly optimizes the sensor ontology alignment’s quality by tuning the weights to aggregate different ontology matchers. Due to the largescale entity and their complex semantic relationships, swarm intelligence (SI) based techniques are emerging as a popular approach to optimize the sensor ontology alignment. Inspired by the success of the flower pollination algorithm (FPA) in the IoT domain, this work further proposes a compact FPA (CFPA), which introduces the compact encoding mechanism to improve the algorithm’s efficiency, and on this basis, the compact exploration and exploitation operators are proposed, and an adaptive switching probability is presented to trade-off these two searching strategies. The experiment uses the ontology alignment evaluation initiative (OAEI)’s benchmark and the real sensor ontologies to test CFPA’s performance. The statistical comparisons show that CFPA significantly outperforms other state-of-the-art sensor ontology matching techniques.
An Intelligent Teaching Strategy of Cross-Cultural Business Communication Course Based on Big Data
Authors: Gao Yali , Latif Faiza
Published: March 2022

The Internet's growth has opened up new avenues for educational reform, and educational informatization has shifted the traditional classroom-teaching model. Teachers' teaching techniques have changed dramatically because of the introduction of a variety of new mobile intelligent terminals to the campus and classroom. This paper examines the design content of intelligent teaching methods against the backdrop of big data, using a cross-cultural business communication course as an example to reform the classroom-teaching mode, with the goal of resolving the current classroom-teaching dilemma and improving teaching effectiveness through the new intelligent teaching mode.
Solar photovoltaic/thermal air collector with mirrors for optimal tilts
Authors: Benkaddour Assia , Boulaich Hanan , Aroudam Elhassan
Published: March 2022

This work is the result of a study of a photovoltaic/thermal air collector that concentrates solar radiation using two mobile mirrors to enhance electrical and thermal energy. The study is made for the site of Tetouan (Morocco) (longitude=-5°, latitude=35.25°) for a daily variation during typical days in May, June, September, and December, days considered as clear sky. To prove the effectiveness of the mirrors on the production of both electrical and thermal energy by the collector, we compared their electrical and thermal efficiency in two cases, without and with mirrors at the optimal positions. We validate the obtained simulation results by comparing them to the results from experimental studies published in the literature, for which a strong agreement was obtained. The model estimates the solar energy received by the hybrid collector during the day, to optimize the performance of the fixed collector, we have searched for the values of the optimal daily tilt angles of the two mirrors which allowed us to enhance the quantity of incoming solar radiation on the collector. The tilt angles depend on the sun’s elevation angle, the azimuth angle for typical days of the year.
Monitoring of power transformers using thermal model and permission time of overload
Authors: Al_Issa Huthaifa Ahmad , Qawaqzeh Mohamed , Kurashkin Serhii , Halko Serhii , Kvitka Serhii , Vovk Oleksandr , Miroshnyk Oleksandr
Published: March 2022

This paper presents the problem of increasing the reliability of electricity supply to consumers. Uninterrupted power supply to electricity consumers depends on the reliability of power supply system in general and power transformers in particular, the accident rate of which is quite high. The causes of the problem are the location of transformer substations at a considerable distance from the service centers, their spreading out over a large area, missing information about the current modes of their operation and so on. One of the ways to solve this problem is development and implementation a system for continuous diagnostics of power transformers. Failure analysis of power transformer based on fault tree is considered, the diagnostic parameters are determined. The insulation wear rate and permission operating time under overload have been defined with help of equivalent heat circuit. It is proposed to use a permission time as a parameter to diagnose the operation mode and increase the efficiency of maintenance of substations through remote monitoring based on the global service mobile (GSM) network. Remote diagnostics allows to receive an information about emergency situation timely. It helps to reduce operating costs, to ensure the reliability and quality of electricity supply for consumers.
Novel asymmetric space vector pulse width modulation for dead-time processing in three-phase power converters
Authors: Yuniantoro Indriarto , Haldi Widianto Mochammad
Published: March 2022

This research analyzes the asymmetric control strategies in multilevel inverters, including asymmetric techniques in space vector modulation of power converters. Modulation parameters such as reference voltage vector (Vref), switching time, and duty cycle are derived in the three-dimensional spatial vector geometry formulation. Asymmetric space vector pulse width modulation (SVPWM) is unique in specifying modulation parameters, has unequal tetrahedron patterns, accompanied by application examples for the upper and lower sector pairs of a tetrahedron. The combination of the switch in the form of an inclined cylinder produces twelve pairs of asymmetric tetrahedrons where the voltage vector positions are in the other twenty-four tetrahedrons. The calculation shows processing dead-time in switching, which is used for current compensation in three-phase power converters.
A multipart distributed modelling approach for an automated protection system in open-air cloth drying
Authors: Onibonoje Moses Oluwafemi , Olukayode Ojo Adedayo
Published: March 2022

There are different methods of drying wet clothes, but drying with direct sunlight is considered the best suited for the preservation of the quality and usability of the cloths. However, sudden rainfall during the drying period constitutes a major drawback of the method. This returns the cloths to a drenched state as it is practically impossible to watch the clothes dry off after washing. This paper has proposed a model for an automated system for controlled open-air fabric drying by detecting the rain and moisture status of the cloths in real-time, and capable of shielding them to safety from the rainfall and excess sun. The modelled part considers the sensing model, drying model, control model, and their validation. The implementation and evaluation stage relates the result of the validated results to the developed prototypes. The simulated results in the sensing unit indicate above 87.5% agreements with the analytical results, and the controller simulated result provides a relatively small overshoot and faster dynamic response. Manufacturers of hanger systems for cloth drying have a basis for the design and implementation of their products in the paper.
Multi-agent system for steel manufacturing process
Authors: Ricardo Angel R. , Benítez Israel F. , González Guillermo , Nuñez José R.
Published: March 2022

This work was carried out in the company ACINOX Las Tunas, Cuba, to design an integrated automation architecture based on intelligent agents for control, monitoring, and decision-making in the production process that guarantees an improvement in planning and management of the process in the steelwork plant. The great differences of technologies and systems of each steel mill and the multiple restrictions, methods, and techniques, within a wide dynamic strongly concatenated, do not generalize automation systems feasibly. In our research, we use international research results and the experience of the plant technologists to create three levels of distributed intelligent architecture: business, production planning-control, and steel manufacturing. Each level manages to integrate and balance the particular and general interests for efficient decision-making combined between hierarchy and heterarchy in this steelwork plant, which will be reflected in a reduction of at least 99% of the time used for decision-making concerning the current system, which can lead to a decrease in refractory costs, energy consumption, and production cost. The effectiveness of the solution is demonstrated with scenario validation and expert evaluation.
Design and implementation of an oil leakage monitoring system based on wireless network
Authors: Hameed Jamal A. , Saeed Amer T. , Sultan Mohammed M. , Hameed Musa A.
Published: March 2022

Monitoring pipeline leaks is one of the recent hot studies. Leakage may occur because of time corrosion in the tube raw materials. To reduce the negative consequences of this leak, an effective leak detection system is used to prevent serious leakage accidents and damage in oil pipelines. Buildings, ecosystems, air pollution, and human life are all at risk in case of leakage occurs which could lead to fires. This paper introduces one of the research methods for the detection of pipeline leaks with a particular focus on software-based methods. The computer board interface (CBI) and wireless sensor networks have been used beside Arduino as a micro-monitor for the entire system. ZigBee is also utilized to send read data from sensors to the monitoring system displayed on the LabVIEW graphical user interface (GUI). The operator can take direct action when a leak occurs. The effectiveness of the leakage monitoring process and its practical use are demonstrated by the introduction of computerized techniques based on pressure gauge analysis on a specific pipeline in the laboratory. The result showed that the system is widely covered, accurate data transmission and robust real-time performance which reduces economic losses and environmental pollution.
Design and development of anonymous location based routing for mobile ad-hoc network
Authors: Mahendrakar Shaymrao Swetha , Krishnaraju Pushpa Sothenahalli , Mahalingappa Thungamani , Narayanappa Manjunath Thimmasandra
Published: March 2022

Mobile ad-hoc network (MANET) consists of wireless nodes interacting with each other impulsively over the air. MANET network is dynamic in nature because of which there is high risk in security. In MANET keeping node and routing secure is main task. Many proposed methods have tried to clear this issue but unable to fully resolve. The proposed method has strong secure anonymous location based routing (S2ALBR) method for MANET using optimal partitioning and trust inference model. Here initially partitions of network is done into sectors by using optimal tug of war (OTW) algorithm and compute the trustiness of every node by parameters received signal strength, mobility, path loss and co-operation rate. The process of trust computation is optimized by the optimal decided trust inference (ODTI) model, which provides the trustiness of each node, highest trust owned node is done in each sector and intermediate nodes used for transmission. The proposed method is focusing towards optimization with respect to parameter such as energy, delay, network lifetime, and throughput also above parameter is compared with the existing methods like anonymous location-based efficient routing protocol (ALERT), anonymous location-aided routing in suspicious MANET (ALARM) and authenticated anonymous secure routing (AASR).
Enhancing highly-collaborative access control system using a new role-mapping algorithm
Authors: Abdelfattah Doaa , Hassan Hesham A. , Omara Fatma A.
Published: March 2022

The collaboration among different organizations is considered one of the main benefits of moving applications and services to a cloud computing environment. Unfortunately, this collaboration raises many challenges such as the access of sensitive resources by unauthorized people. Usually, role based access-control (RBAC) Model is deployed in large organizations. The work in this paper is mainly considering the authorization scalability problem, which comes out due to the increase of shared resources and/or the number of collaborating organizations in the same cloud environment. Therefore, this paper proposes replacing the cross-domain RBAC rules with role-to-role (RTR) mapping rules among all organizations. The RTR mapping rules are generated using a newly proposed role-mapping algorithm. A comparative study has been performed to evaluate the performance of the proposed algorithm with concerning the rule-store size and the authorization response time. According to the results, it is found that the proposed algorithm achieves more saving in the number of stored role-mapping rules which minimizes the rule-store size and reduces the authorization response time. Additionally, the RTR model using the proposed algorithm has been implemented by applying a concurrent approach to achieve more saving in the authorization response time. Therefore, it would be suitable in highly-collaborative cloud environments
A novel risk management model in the Scrum and extreme programming hybrid methodology
Authors: Afshari Mahnaz , Javdani Gandomani Taghi
Published: March 2022

Risk management in software development has always been one of the necessities of software project management. The logical nature of software projects and products has caused several challenges and risks in these projects. On the other hand, with the emergence of Agile methodologies, especially Scrum, and extreme programming (XP) methodologies, in recent years, this issue has become more serious. This is mainly because emphasizing limited documentation in these methodologies has caused these methods to pay little attention to some aspects of project management, particularly risk management. Concentrating on this challenge, the current study has proposed a risk management model in the hybrid methodology, combining Scrum and XP. Using this model in a case study shows this model's success in achieving risk management purposes. The results of this study indicate an appropriate reduction in the number of reworks, change requests, identified risks, and occurred risks. Moreover, the number of eliminated risks and team productivity have increased.
Increasing electrical grid stability classification performance using ensemble bagging of C4.5 and classification and regression trees
Authors: Aziz Firman , Lawi Armin
Published: March 2022

The increasing demand for electricity every year makes the electricity infrastructure approach the maximum threshold value, thus affecting the stability of the electricity network. The decentralized smart grid control (DSGC) system has succeeded in maintaining the stability of the electricity network with various assumptions. The data mining approach on the DSGC system shows that the decision tree algorithm provides new knowledge, however, its performance is not yet optimal. This paper poses an ensemble bagging algorithm to reinforce the performance of decision trees C4.5 and classification and regression trees (CART). To evaluate the classification performance, 10-fold cross-validation was used on the grid data. The results showed that the ensemble bagging algorithm succeeded in increasing the performance of both methods in terms of accuracy by 5.6% for C4.5 and 5.3% for CART.
Automatic recognition of Arabic alphabets sign language using deep learning
Authors: Duwairi Rehab Mustafa , Halloush Zain Abdullah
Published: March 2022

Technological advancements are helping people with special needs overcome many communications’ obstacles. Deep learning and computer vision models are innovative leaps nowadays in facilitating unprecedented tasks in human interactions. The Arabic language is always a rich research area. In this paper, different deep learning models were applied to test the accuracy and efficiency obtained in automatic Arabic sign language recognition. In this paper, we provide a novel framework for the automatic detection of Arabic sign language, based on transfer learning applied on popular deep learning models for image processing. Specifically, by training AlexNet, VGGNet and GoogleNet/Inception models, along with testing the efficiency of shallow learning approaches based on support vector machine (SVM) and nearest neighbors algorithms as baselines. As a result, we propose a novel approach for the automatic recognition of Arabic alphabets in sign language based on VGGNet architecture which outperformed the other trained models. The proposed model is set to present promising results in recognizing Arabic sign language with an accuracy score of 97%. The suggested models are tested against a recent fully-labeled dataset of Arabic sign language images. The dataset contains 54,049 images, which is considered the first large and comprehensive real dataset of Arabic sign language to the furthest we know.
Fake accounts detection on social media using stack ensemble system
Authors: Ali Amna Kadhim , Abdullah Abdulhussein Mohsin
Published: March 2022

In today’s world, social media has spread widely, and the social life of people have become deeply associated with social media use. They use it to communicate with each other, share events and news, and even run businesses. The huge growth in social media and the massive number of users has lured attackers to distribute harmful content through fake accounts, leading to a large number of people falling victim to those accounts. In this work, we propose a mechanism for identifying fake accounts on the social media site Twitter by using two methods to preprocess data and extract the most effective features, they are the spearman correlation coefficient and the chi-square test. For classification, we used supervised machine learning algorithms based on the ensemble system (stack method) by using random forest, support vector machine, and Naive Bayes algorithms in the first level of the stack, and the logistic regression algorithm as a meta classifier. The stack ensemble system was shown to be effective in achieving the best results when compared to the algorithms used with it, with data accuracy reaching 99%.
Hybrid controller design using gain scheduling approach for compressor systems
Authors: Muddenahalli Narasimhaiah Divya , Narayanappa Chikkajala Krishnappa , Lakshmaiah Gangadharaiah Soralamavu
Published: March 2022

The automatic control system plays a crucial role in industries for controlling the process operations. The automatic control system provides a safe and proper controlling mechanism to avoid environmental and quality problems. The control system controls pressure flow, mass flow, speed control, and other process metrics and solves robustness and stability issues. In this manuscript, The Hybrid controller approach like proportional integral (PI) and proportional derivative (PD) based fuzzy logic controller (FLC) using with and without gain scheduling approach is modeled for the compressor to improve the robustness and error response control mechanism. The PI/PD-based FLC system includes step input function, the PI/PD controller, FLC with a closed-loop mechanism, and gain scheduler. The error signals and control response outputs are analyzed in detail for PI/PD-based FLC’s and compared with conventional PD/PID controllers. The PD-based FLC with the Gain scheduling approach consumes less overshoot time of 74% than the PD-based FLC without gain scheduling approach. The PD-based FLC with the gain scheduling approach produces less error response in terms of 7.9% in integral time absolute error (ITAE), 7.4% in integral absolute error (IAE), and 16% in integral square error (ISE) than PD based FLC without gain scheduling approach.