bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on Adsorption Behavior of a New Type Gemini Surfactant Onto Quartz Surface by a Molecular Dynamics Method
Authors: Lyu Weifeng , Zhou Zhaohui , Huang Jia , Yan Kai
Published: December 2021

The adsorption mechanism of the branched quaternary ammonium salt Gemini surfactant (Gemini C3) at the water-surfactant-quartz interfaces for both neutral and negatively charged quartz surfaces was studied by a molecular dynamics (MD) method. Initial and final configurations, distributions of the surfactant and its interaction with surfaces, the radial distribution function (RDF) of water molecules, and the mean square displacement (MSD) of the surfactant in bulk phase have been elucidated at the molecular level. The results showed that the adsorption of Gemini surfactants onto the hydrophilic quartz surface was driven by electrostatic interaction, which increased the hydrophobicity of the solid surface when the surfactant concentration was lower than critical micelle concentration (CMC). However, the contact angle only slightly increased since the surface tension decreased simultaneously with growing concentration. Monolayers were formed during the adsorption process of Gemini C3 molecules on the quartz surface rather than a double layer when the concentration reached the CMC, indicating a gradual transformation of an extended monolayer adsorption configuration into a more compact one. The solid-liquid interfacial tension increased with the surfactant concentration and led to a significant increase of the contact angle. The simulation results were consistent with the experiments, which further revealed the microscopic adsorption mechanism of the Gemini C3 surfactant onto the quartz surface, and provided theoretical guidance for controlling the wetting properties and surface modification of the rock.
The dire straits of Paratethys: gateways to the anoxic giant of Eurasia
Authors: Palcu Dan V. , Krijgsman Wout
Published: December 2021

A complex interplay of palaeoclimatic, eustatic and tectonic processes led to fragmentation and dissipation of the vast Tethys Ocean in Eocene–Oligocene times. The resulting Paratethys Sea occupied the northern Tethys region on Eurasia, grouping water masses of various sub-basins, separated from each other and from the open ocean through narrow and shallow gateways and land bridges. Changes in marine gateway configuration and internal connectivity affected regional hydrology, shifting most Paratethyan basins to extreme carbon-sink anoxic environments, anomalohaline evaporitic or brackish conditions, or even endorheic lakes. Paratethys gateway restriction triggered the onset of a long-lasting (
c.
20 myr) giant anoxic sea, characterized by stratified water masses and anoxic bottom-water conditions, resulting in thick hydrocarbon source rocks. Here, we review the geological evolution of the ‘dire straits’ of Paratethys that played a crucial role in the Eocene–Oligocene connectivity history of the Central Eurasian seas and we show that the main anoxic phases (Kuma and Maikop) correspond to restricted connectivity with the global ocean and a period of CO
2
depletion in the atmosphere. Paratethys represents one of the largest carbon sinks in Earth's history and may thus have played a prominent role in global climate change.
Neurodevelopmental Preparedness for Language in the Neonatal Brain
Authors: Nallet Caroline , Gervain Judit
Published: December 2021

Neonates show broad-based, universal speech perception abilities, allowing them to acquire any language. Moreover, an increasing body of research shows that prenatal experience with speech, which is a low-pass signal mainly preserving prosody, already shapes those abilities. In this review, we first provide a summary of the empirical evidence available today on newborns’ universal and experience-modulated speech perception abilities. We then interpret these findings in a new framework, focusing on the role of the prenatal prosodic experience in speech perception development. We argue that the chronological sequence of infants’ experience with speech, starting before birth with a low-pass filtered signal and continuing with the full-band signal after birth, sets up the prosodic hierarchy and a cascade of embedded neural oscillations as its brain correlate, laying the foundations for language acquisition. Prosody, constituting infants’ very first experience with language, may thus play a fundamental role in speech perception and language development.
Characterization of Electroactive Amino Acids with Fast-Scan Cyclic Voltammetry
Authors: Weese-Myers Moriah E. , Ross Ashley E.
Published: December 2021

Small molecules and signaling peptides are extensively involved in controlling basic brain function. While classical neurotransmitters can be detected with a variety of techniques, methods for measurement of rapidly-released neuropeptides remain underdeveloped. Fast-scan cyclic voltammetry (FSCV) is an electrochemical technique often used for subsecond detection of small molecule neurotransmitters, in vivo. A few peptides have been detected with FSCV; however, a detailed analysis of the electrochemical signature of all electroactive amino acids with FSCV has not been fully investigated. Because the mechanisms, locations, and timescales for signaling peptide release in the brain are relatively unexplored, developing sensitive and selective tools capable of quantitating neuropeptide signaling is essential. To bridge this gap, we used FSCV to characterize the electroactive amino acids: cysteine, methionine, histidine, tryptophan, and tyrosine. We show that tyrosine, tryptophan, and histidine are easily oxidized on carbon fiber surfaces with FSCV, while detection of the sulfur-containing amino acids is more difficult. This study provides critical information for electrochemical waveform design and optimization for detection of peptides containing these amino acids.
The Stable 3D Zn Electrode for High-Power Density Zn Metal Batteries
Authors: Zhu Yun Guang , Narayanan Thaneer Malai , Katayama Yukihisa , Shao-Horn Yang
Published: December 2021

A stable Zn metal electrode can develop rechargeable zinc metal batteries (RZMBs) which have the high theoretical capacity (820 mAh g−1), low redox potential, and intrinsic safety. However, the corrosion of Zn metal in aqueous electrolytes and Zn dendrite formation during the plating process lead to poor cycling and thus hinder the development of RZMBs. Here, we employed ionic liquid-based gel polymer (poly(vinylidene fluoride)-co-hexafluoropropylene, PVDF-HFP) and acetylene black (AB) to achieve a stable and flexible three-dimensional (3D) Zn/AB/PVDF-HFP film electrode with ionic and electronic conductive networks and high surface area, showing 26 times higher plating/stripping current than planar Zn plate. By developing a continuous structure between the ionic liquid-based gel polymer membrane and the flexible 3D Zn/AB/PVDF-HFP electrode, the low resistance, high rate capability and long cycle life (> 800 h) was obtained. Our work shows a flexible Zn film electrode and ionic liquid-based gel polymer electrolyte, could pave the path for rechargeable and high-cycle life thin-film RZMBs.
Recent COVID-19 vaccination has minimal effects on the physiological responses to graded exercise in physically active healthy people
Authors: Batatinha Helena , Baker Forrest L. , Smith Kyle A. , Zúñiga Tiffany M. , Pedlar Charles R. , Burgess Shane C. , Katsanis Emmanuel , Simpson Richard J.
Published: December 2021

Recent COVID-19 vaccination does not affect a large number of physiological responses to graded exercise, indicating that vaccination is unlikely to impair exercise capacity in normal healthy people. Heart rate and norepinephrine levels were elevated in response to exercise after the two-dose Pfizer mRNA vaccination compared to controls. Small elevations in cardiovascular and neuroendocrine responses to exercise after recent COVID-19 vaccination could have implications for exercise performance in elite athletes and warrants investigation.
Functional morphometry to estimate the alveolar surface area using a premature baboon model
Authors: Dassios Theodore , Rüdiger Mario , McCurnin Donald , Seidner Steven R. , Williams Emma E. , Greenough Anne , Möbius Marius Alexander
Published: December 2021

The main morphological characteristic of chronic respiratory disease in prematurely born infants is the impaired/arrested alveolar growth that corresponds to a smaller aggregated alveolar surface area (SA). This decreased SA might be the limiting factor later in life affecting exercise capacity and quality of life. There is paucity of sensitive, noninvasive biomarkers to monitor the evolution of neonatal respiratory disease. Our noninvasive functional morphometric SA might help to bridge the gap between pathophysiology and clinical monitoring.
Current‐Fed Bidirectional DC‐DC Converter Topology for Wireless Charging System Electrical Vehicle Applications
Authors: Subudhi Partha Sarathi , Thilagaraj M. , Sundar Ganesh C. S. , Diwakaran S. , Naveen P. , Gurusamy Saravanakumar , Pallikonda Rajasekaran M.
Published: December 2021

This paper compares the efficiency of a modified wireless power transfer (WPT) system with a current‐fed dual‐active half‐bridge converter topology and a complete bridge converter topology for a current‐fed resonate compensation network with current sharing and voltage doubler. Full‐bridge topologies are widely used in current WPT structures. The C‐C‐L resonate compensation networks for dual‐active half‐bridge converter and full‐bridge converter topologies are built in this paper on both the transmitter and receiver sides. Due to higher voltage stress around inverter switches, series‐parallel (S‐P) tanks are not recommended for current‐fed topologies because they are not ideal for medium power applications. A series capacitor is connected to reduce the reactive power absorbed by the loosely coupled coil. As a consequence, the C‐C‐L network is used as a compensation network. Dual‐active half‐bridge topology is chosen over full‐bridge topology due to the system’s component count and overall cost. Soft‐switching of the devices is obtained for the load current. The entire system is modelled, and the effects are analysed using MATLAB simulation.
Combined Catheter Ablation and Left Atrial Appendage Closure in Atrial Fibrillation Patients with and without Prior Stroke
Authors: Mo Bin-Feng , Zhang Rui , Yuan Jia-Li , Sun Jian , Zhang Peng-Pai , Li Wei , Chen Mu , Wang Qun-Shan , Li Yi-Gang
Published: December 2021

Background. Combined atrial fibrillation (AF) ablation and left atrial appendage closure (LAAC) has been practiced for management of both the symptoms and the high stroke risk of AF. Data of the combined procedure in selected patients with prior stroke are limited. The aim of this study is to compare the safety and efficacy of combined catheter ablation and LAAC between AF patients with and without prior stroke. Methods and Results. This retrospective study enrolled 296 patients who underwent combined procedures of AF ablation and LAAC. Patients were divided into two groups: 81 patients with prior stroke (Stroke group) and 215 patients without prior stroke (Control group). Combined procedures were successfully performed in all the patients. Patients in the Stroke group had higher CHA2DS2-VASc scores (4.9 ± 1.2 vs. 3.2 ± 1.0,P<0.001) and higher HAS-BLED scores (3.5 ± 1.1 vs. 3.0 ± 1.0,P<0.001) compared with those in the Control group. Procedure-related complications in the Stroke group included two pericardial effusions and two groin hematomas, which did not differ significantly fromthe Control group (4.9% vs. 4.2%,P=0.778). After a mean follow-up of 20 months, the AF-free rate of the Stroke group was comparable with that of the Control group (64.2% vs. 68.4%,P=0.495). The relative risk reductions in stroke and bleeding (observed rate compared to that predicted from the CHA2DS2-VASc and HAS-BLED scores) were 80% and 79%, respectively, in the Stroke group, and 62% and 62%, respectively, in the Control group. Conclusions. The combination of catheter ablation and LAAC is safe and efficient in selected AF patients with prior stroke. It was observed that patients with prior stroke may benefit more from risk reductions of stroke and bleeding following the combined procedure.
The Temporal Pattern of Arterial Stiffness during Aging: A Large-Scale Cross-Sectional Study
Authors: Tang Zhengli , Lu Yuanyuan , Hao Yiming , Morris Robert , Kang Di , Wang Fang , Fan Lin , Wang Weijian , Wang Yiqin , Cheng Feng
Published: December 2021

Brachial-ankle pulse wave velocity (baPWV) is a noninvasive clinical test that provides quantification for the stiffness of both the aorta and peripheral arteries by measuring the brachial and tibial arterial wave velocities. The temporal pattern of baPWV values during aging was investigated in this paper. A gradual increase in baPWV with respect to age was observed, suggesting an increase in the stiffness of arterial vessels as age increases. The ΔbaPWV value, defined as the absolute value of the difference between bilateral baPWV, also showed a positive correlation with aging. Many underlying physiological conditions such as hyperlipidemia, hypertension, diabetes, and hyperglycemia have previously been shown to elevate baPWV and contribute to the decline of arterial function. The effect of factors including biological sex, blood pressure, and blood glucose levels on the baPWV temporal pattern were also investigated. Between the ages of 18 and 50, men in the study had significantly higher baPWV readings than females of comparable age on average. However, after the age of 50, mean baPWV values increased at a greater rate in females than in males. In addition, blood pressure and blood glucose were shown to be associated with baPWV values. The results will improve existing prediction models for future cardiovascular episodes induced by arterial hardening in different age groups.
Urban Public Sports Information‐Sharing Technology Based on Internet of Things
Authors: Li Youliang , Li Fenglei , Xiong Yujun
Published: December 2021

With the continuous development of social economy, sports has become one of the important ways of physical exercise, and the demand for corresponding sports facilities is also increasing. The Internet of Things technology is introduced in this paper. Through combining the current status of urban public sports, an urban public sports sharing system is built by trial, to promote the sharing of urban public sports information through the continuous development of new technologies such as the Internet and improve the publicity and popularization of public sports information. Simulation experiments prove that the Internet of Things technology is effective and can effectively support the sharing of urban public sports information.
Enterprise Financial Leverage and Risk Assessment Based on Mobile Payment under Artificial Intelligence
Authors: Xu Xiaoling , Song Jianghao
Published: December 2021

To better promote the healthy and long-term development of corporate financial management, the basement is established on the perspective of artificial intelligence (AI). Initially, based on the theories of modern mobile payment (MP) and corporate financial leverage, the corresponding data set is obtained through the questionnaire method as the research data. The reliability coefficients obtained after the test are all above 0.65, indicating that the reliability and stability of the entire data are relatively good. Besides, it is also found from the data of the questionnaire that some residents believe that MP will bring harm such as information leakage. Next, a new multilevel evaluation analysis method is introduced. After evaluating the financial management risk, operation risk, and network security risk existing in enterprise MP, it is found that the financial management risk accounts for the largest proportion of the three, with a risk weight of about 0.54, and the capital risk occupies the main position in the financial management risk. Finally, through the analysis of the risks existing in the whole operation process of the enterprise, it is found that about 50% of the financial management risk of the enterprise in the market belongs to the advanced risk, about 30% of the operational operation risk belongs to the low risk, and about 20% of the network security risk belongs to the advanced risk, which indicates that the financial management risk and network security risk are the top priority of the enterprise MP risk. Although the operational operation risk belongs to the low risk, it cannot be ignored. Subsequently, feasible suggestions and opinions are put forward on these phenomena from the perspectives of the government, enterprises, and residents. Therefore, there is great reference significance for the current financial risk assessment of enterprises based on MP.
Study on Dynamic Mechanical Properties and Failure Mechanism of Sandstones under Real-Time High Temperature
Authors: Zhang JiaZhi , Li Ming , Lin Gang , Zhang Lianying , Yu Hao , Di Kui
Published: December 2021

The research on dynamic mechanical properties of rocks under high temperature is the basis for safe and efficient implementation of deep coal mining and underground coal gasification engineering. In this paper, the split Hopkinson bar (SHPB) with real-time high-temperature function was adopted to systematically study dynamic mechanical properties of sandstones. The research showed that under the condition of a fixed temperature, with the increase of strain rate, the dynamic compressive strength and dynamic peak strain of sandstone increased gradually, and the variation of dynamic elastic modulus with strain rate was not obvious. With the increase of temperature, the dynamic compressive strength of sandstone increased first and then decreased, the dynamic peak strain increased gradually, and the dynamic elastic modulus decreased overall. The variation law of macroscopic failure mode and energy dissipation density with temperature was revealed, and the change mechanism was explained considering the influence of high temperature on the internal structure of sandstone. Based on the principle of component combination and the theory of micro-element strength distribution, the dynamic statistical damage constitutive model was established, and its parameters had certain physical significance. Compared with the experimental results, the established model can well describe the dynamic stress-strain relationship of sandstone under real-time high temperature.
The Effect of Celiac Neurolysis and Splanchnicectomy on Survival in Unresectable Pancreatic Cancer: A Systematic Review and Meta-Analysis
Authors: Ye Linhan , Schorn Stephan , Pergolini Ilaria , Safak Okan , Demir Elke , Istvanffy Rouzanna , Friess Helmut , Ceyhan Güralp O. , Demir Ihsan Ekin
Published: December 2021

Background: Intractable pancreatic pain is one of the most common symptoms of patients with pancreatic ductal adenocarcinoma (PDAC). Celiac neurolysis (CN) and splanchnicectomy were already described as effective methods to manage abdominal pain in unresectable PDAC, but their impact on overall survival (OS) has not yet been established. Objective: We aimed to investigate the impact of CN and splanchnicectomy on the survival of patients with unresectable pancreatic cancer. Methods: A systematic review of PubMed and Cochrane Library according to predefined searching terms was conducted in March 2020. Hazard ratios (HR) of OS data were calculated using the Mantel-Haenszel model for random effects or fixed effects. Result: Four randomized-controlled trials (RCTs) and 2 non-RCTs with a total of 2,507 patients were identified. The overall pooled HR did not reveal any relevant effect of CN and splanchnicectomy on OS (HR: 1.03; 95% CI: 0.81–1.32), which was also underlined by the sensitivity analysis of RCTs (HR: 1.0; 95% CI: 0.72–1.39) and non-RCTs (HR: 1.07; 95% CI: 0.71–1.63). However, subgroup analyses depending on tumor stage revealed that CN or splanchnicectomy was associated with a worsened OS in AJCC (American Joint Committee on Cancer) stage III patients with unresectable PDAC (HR: 1.22; 95% CI: 1.03–1.45), but nor for AJCC stage IV patients (HR: 1.27; 95% CI: 0.9–1.80). Conclusion: Although only few data are currently available, this systematic review with meta-analysis showed that in unresectable PDAC, CN or splanchnicectomy is associated with a worsened survival in stage III PDAC patients, with no effect on stage IV PDAC patients. These data call for caution in the usage of CN or splanchnicectomy in stage III PDAC and for further studies addressing this observation.
Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios as Noninvasive Predictors of the Therapeutic Outcomes of Systemic Corticosteroid Therapy in Ulcerative Colitis
Authors: Endo Katsuya , Satoh Tomonori , Yoshino Yuki , Kondo Shiho , Kawakami Yoko , Katayama Tomofumi , Sasaki Yoshiteru , Takasu Atsuko , Kogure Takayuki , Hirota Morihisa , Meguro Takayoshi , Satoh Kennichi
Published: December 2021

Introduction: Predictive biomarkers for the therapeutic outcome of induction therapy with systemic corticosteroid for active ulcerative colitis (UC) have not been established. This study aimed to investigate whether neutrophil-to-lymphocyte ratio (NLR) and/or platelet-to-lymphocyte ratio (PLR) can be predictive biomarkers for the therapeutic outcomes of systemic corticosteroid therapy in UC. Methods: This was a single-center retrospective cohort study. In total, 48 patients with UC who received induction therapy with systemic corticosteroid were enrolled. Based on the achievement of clinical remission after 8 weeks of treatment, the patients were divided into the remission group (n = 28) and the nonremission group (n = 20). Clinical characteristics, NLR, and PLR at baseline between the remission and nonremission groups were compared via a univariate analysis. The independent risk factors of nonremission were identified via a multivariate analysis. Results: The baseline Mayo score, platelet count, lymphocyte count, C-reactive protein (CRP) levels, NLR, and PLR between the 2 groups significantly differed. The nonremission group had higher NLR and PLR than the remission group (4.70 [3.04–11.3] vs. 3.10 [1.36–16.42]; p < 0.05, and 353.6 [220.3–499.8] vs. 207.2 [174.4–243.6]; p < 0.001, respectively). A multivariate analysis revealed that a Mayo score of ≥9, CRP level of ≥1.26 mg/dL, and PLR of ≥262 (hazard ratio: 23.1, 95% confidence interval: 1.29–413.7, p = 0.033) were considered independent risk factors for nonremission. Conclusion: This report first identified the efficacy of NLR and PLR as candidate biomarkers for predicting the therapeutic outcomes of systemic corticosteroid therapy in UC.
Impact of Diagnostic Delay on Disease Course in Pediatric- versus Adult-Onset Patients with Ulcerative Colitis: Data from the Swiss IBD Cohort
Authors: Schoepfer Alain M. , Tran Vu Dang Chau , Rossel Jean-Benoit , Sokollik Christiane , Spalinger Johannes , Safroneeva Ekaterina , von Graffenried Thea , Godat Sébastien , Hahnloser Dieter , Vavricka Stephan R. , Braegger Christian , Nydegger Andreas ,   on behalf of the Swiss IBD Cohort Study Group
Published: December 2021

Introduction: Given the lack of data, we aimed to assess the impact of the length of diagnostic delay on the natural history of ulcerative colitis (UC) in pediatric (diagnosed <18 years) and adult patients (diagnosed ≥18 years). Methods: Data from the Swiss Inflammatory Bowel Disease Cohort Study were analyzed. Diagnostic delay was defined as the interval between the first appearance of UC-related symptoms until diagnosis. Logistic regression modeling evaluated the appearance of the following complications in the long term according to the length of diagnostic delay: colonic dysplasia, colorectal cancer, UC-related hospitalization, colectomy, and extraintestinal manifestations (EIMs). Results: A total of 184 pediatric and 846 adult patients were included. The median diagnostic delay was 4 [IQR 2–7.5] months for the pediatric-onset group and 3 [IQR 2–10] months for the adult-onset group (p = 0.873). In both, pediatric- and adult-onset groups, the length of diagnostic delay at UC diagnosis was not associated with colectomy, UC-related hospitalization, colon dysplasia, and colorectal cancer. EIMs were significantly more prevalent at UC diagnosis in the adult-onset group with long diagnostic delay than in the adult-onset group with short diagnostic delay (p = 0.022). In the long term, the length of diagnostic delay was associated in the adult-onset group with colorectal dysplasia (p = 0.023), EIMs (p < 0.001), and more specifically arthritis/arthralgias (p < 0.001) and ankylosing spondylitis/sacroiliitis (p < 0.001). In the pediatric-onset UC group, the length of diagnostic delay in the long term was associated with arthritis/arthralgias (p = 0.017); however, it was not predictive for colectomy and UC-related hospitalization. Conclusions: As colorectal cancer and EIMs are associated with considerable morbidity and costs, every effort should be made to reduce diagnostic delay in UC patients.
Understanding the role of individual learner in adaptive and personalized e-learning system
Authors: Muhammad Alva Hendi , Ariatmanto Dhani
Published: December 2021

Dynamic learning environment has emerged as a powerful platform in a modern e-learning system. The learning situation that constantly changing has forced the learning platform to adapt and personalize its learning resources for students. Evidence suggested that adaptation and personalization of e-learning systems (APLS) can be achieved by utilizing learner modeling, domain modeling, and instructional modeling. In the literature of APLS, questions have been raised about the role of individual characteristics that are relevant for adaptation. With several options, a new problem has been raised where the attributes of students in APLS often overlap and are not related between studies. Therefore, this study proposed a list of learner model attributes in dynamic learning to support adaptation and personalization. The study was conducted by exploring concepts from the literature selected based on the best criteria. Then, we described the results of important concepts in student modeling and provided definitions and examples of data values that researchers have used. Besides, we also discussed the implementation of the selected learner model in providing adaptation in dynamic learning.
Prototype mobile contactless transaction system in traditional markets to support the covid-19 physical distancing program
Authors: Afrianto Irawan , Sasmita Mouhamad Hatta Hiroshi , Atin Sufa
Published: December 2021

One way to prevent and reduce the spread of the covid-19 pandemic is through physical distancing program. This research aims to develop a prototype contactless transaction system using digital payment mechanisms and QR code technology that will be applied in traditional markets. The method used in the development of electronic market systems is a prototype approach. The application of QR code and digital payments are used as a solution to minimize money exchange contacts that are common in traditional markets. The results showed that the system built was able to accelerate and facilitate the buying and selling transaction process in traditional market environment. Alpha testing shows that all functional systems are running well. Meanwhile, beta testing shows that the user can very well accept the system that was built. The results of the study also show acceptance of the usefulness of the system being built, as well as the optimism of its users to be able to take advantage of this system both technologically and functionally, so its can be a part of the digital transformation of the traditional market to the electronic market and has become one of the solutions in reducing the spread of the current covid-19 pandemic.
Supervised machine learning based liver disease prediction approach with LASSO feature selection
Authors: Afrin Saima , Shamrat F. M. Javed Mehedi , Nibir Tafsirul Islam , Muntasim Mst. Fahmida , Moharram Md. Shakil , Imran M. M. , Abdulla Md
Published: December 2021

In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.
Effect of repeated electrical breakdowns on mineral and natural ester insulating oils
Authors: Ghani Sharin Ab , Ahmad Khiar Mohd Shahril , Chairul Imran Sutan , Zamir Muhammad Imran
Published: December 2021

Transformer insulating oils are exposed to repeated electrical discharge or breakdowns inside power transformers. Durability tests are conducted to analyze the ability of oil to resist decomposition due to such high electrical stresses. With the increasing demand for alternative insulating oils for oil-immersed transformers, it is worthy to compare the performance of different types of insulating oils (conventional mineral-based insulating oil and natural ester-based insulating oil) under repeated electrical breakdown. In this paper, the AC breakdown voltage of different mineral-based and natural ester-based insulating oils is reported. Durability tests were conducted based on the AC breakdown voltage behavior of insulating oils after 50 electrical breakdown shots. The AC breakdown voltage of each insulating oil sample was assessed according to the ASTM D1816 standard test method. Based on the results, it can be concluded that the dissimilarity in chemical composition of the insulating oils has a significant effect on the AC breakdown voltage behavior of these oils under repeated electrical breakdowns.
Review of impedance source power converter for electrical applications
Authors: Saravanan V , Venkatachalam K M , Arumugam M , Borelessa M.A.K. , Hemapala K.T. M.U.
Published: December 2021

Power electronic converters have been actively researched and developed over the past decades. There is a growing need for new solutions and topography to increase the reliability and efficiency of alternatives with lower cost, size and weight. Resistor source converter is one of the most important power electronic converters that can be used for AC-DC, AC-AC, DC-DC and DC-DC converters which can be used for various applications such as photovoltaic systems, wind power systems, electricity. Vehicles and fuel cell applications. This article provides a comprehensive overview of Z-source converters and their implementation with new configurations with advanced features, emerging control strategies and applications.
An experimental study of PV/T system using parabolic reflectors and heat exchanger
Authors: Ismail Benlaria , Mohammed Belhadj , Abdelkhalek Othmane , Elhadj Sabouni
Published: December 2021

Photovoltaic (PV) systems can be made more efficient by forcing the PV panel to operate at its maximum point power due to the electrical properties of photovoltaic generators, which are substantially non-linear (MPP). This study examines the effectiveness of using a combination of parabolic concentrator Bi-reflector and heat exchanger as a cooling system on the performance of photovoltaic generators to get a photovoltaic/thermal (PV/T) system, and their effect on the direct current (DCDC) converter using matrix laboratory (MATLAB) simulink. The experimental tests were carried out under various temperature values and sun irradiation. The results demonstrated that the use of parabolic Bi-reflectors, to further illumine te the panels, and the use of the cooling system to absorb excess heat to get heat water, could increase and enhances performances of the photovoltaic generator.
Consideration of contribution of hot-electron injection to the resistive switching of sputter-deposited silicon oxide film
Authors: Omura Yasuhisa
Published: December 2021

This paper considers the contribution of hot electrons to the resistive switching of sputter-deposited silicon oxide films based on experiments together with semi-2D Monte Carlo simulations. Using various device stack structures, this paper examines the impact of hot-electron injection on resistive switching, where conduction-band offset and fermi-level difference are utilized. Support is found for the predictions that hot-electron injection reduces the switching voltage and this should reduce the dissipation energy of switching. It is predicted that two-layer metal stacks can significantly reduce the number of oxygen vacancies in the sputter-deposited silicon oxide film after the reset process. It is also demonstrated that, in unipolar switching, the number of E’ or E” centers of the sputter-deposited silicon oxide film is relatively large.
A novel design for hardware interface board with reduced resource utilization
Authors: Ananth G. S. , Shylashree N. , Tunga Satish , N. Latha B.
Published: December 2021

The final cost of an integrated circuit (IC) is proportional to its testing time. One of the main goals of test engineers when building an IC test solution is to reduce test time. Reduction of Test time is achieved by multi-site testing where multiple ICs are tested simultaneously using automated test equipment (ATE). During multi-site testing, if a certain test requires abundant resources, it is accomplished by testing one set of ICs at a time while the other ICs remain idle, thus lengthening the total test time. In digital-analog hybrid ICs, both analog and digital tests need to be performed, increasing the tester resource requirement and causing digital resource shortage. This paper describes a hardware interface board (HIB) design for a test case of a digital-analog IC on Teradyne’s ETS-364 ATE. The HIB's design allows the ATE to perform multi-site I2C based tests, which usually require lot of tester resources, utilizing only two digital resources and one measurement resource. This design achieves halving the I2C test time while lowering the number of resources necessary for multi-site testing compared to set-by-set testing. The proposed work has achieved up to 90.625% of resource reduction for multisite testing for a single test.
Applications of artificial intelligence with cloud computing in promoting social distancing to combat COVID-19
Authors: Al-Hamiri Mohammed Ghadhban , Abdulsada Hayder Fadhil , Abdul-Rahaim Laith A.
Published: December 2021

The emergence of Coronavirus disease 2019 (COVID-19) disease and its rapid spread around the world has serious impacts on people's lives in addition to its effects on many aspects, including the economic and educational sectors. Researches have proved that social distance is effective in combating COVID-19. Maintaining social distance is hard to be handled by humans especially in crowded areas such as airports and campuses. So, there is a need to apply a robust and proactive design to manage this process automatically and smartly. This paper presents a design system to fight COVID-19 by maintaining the social distance with effective monitoring for suspected cases. This has been done using cloud computing and a framework including Arduino (node microcontroller unit (NodeMCU)) with several sensors. The operational aspects of this design system using cloud computing have been discussed. Generally, NodeMCU has been involved in checking the conditions, comparison processing, and communication with the webserver. Moreover, the webserver has been used for determining the maximum number of persons allowed to enter. The results state that this design system is effective in combating COVID-19 through maintaining the social distance and collecting information about suspected cases. This system is valuable, dependable, and stable since the whole process is contactless.
Deadlock detection in distributed system
Authors: Rout Kshirod Kumar , Mishra Debani Prasad , Salkuti Surender Reddy
Published: December 2021

In highly automated devices, deadlock is a case that occurs when no system can permit its event which may give irrelevant economic losses. A process can request or release resources that are either available or are on hold by others. If a process requesting a resource is not available at any time, then that process enters into the waiting state. But if a waiting state is not converted into its present state, it enters more than two processes are having an indefinite waiting state. The proposed algorithm gives an efficient way for deadlock detection. For the implementation of this work, C++ and python as the basic programming language are used. It gives an idea about how resources are allocated, and how few processes result in deadlock.
An enhanced framework for solving cold start problem in movie recommendation systems
Authors: Elzeheiry Salma Adel , Mekky N. E. , Atwan A. , Hikal Noha A.
Published: December 2021

Recommendation systems (RSs) are used to obtain advice regarding decision-making. RSs have the shortcoming that a system cannot draw inferences for users or items regarding which it has not yet gathered sufficient information. This issue is known as the cold start issue. Aiming to alleviate the user’s cold start issue, the proposed recommendation algorithm combined tag data and logistic regression classification to predict the probability of the movies for a new user. First using alternating least square to extract product feature, and then diminish the feature vector by combining principal component analysis with logistic regression to predict the probability of genres of the movies. Finally, combining the most relevant tags based on similarity score with probability and find top N movies with high scores to the user. The proposed model is assessed using the root mean square error (RMSE), the mean absolute error (MAE), recall@N and precision@N and it is applied to 1M, 10M and 20M MovieLens datasets, resulting in an accuracy of 0.8806, 0.8791 and 0.8739.
Sophisticated and modernized library running system with OCR algorithm using IoT
Authors: Karthikeyan D. , P. Arumbu V. , Surendhirababu K. , Selvakumar K. , Divya P. , Suhasini P. , Palanisamy R.
Published: December 2021

An internet of things (IoT) is an exclusive method, were its impact on the enactments of human life is very trendy. This research on library control system operates on the basis of IoT and optical character recognition (OCR) algorithm rules and its training. A closed-circuit television (CCTV) watched mechanism is created to control the book issuing and returning phenomenon via tag studying system in the library. In this proposed work text file is converted into an audio file. This audio file is being played and the contents of the book can be heard via the headset. This unique function of the OCR helps blind people. Now a days OCR widely focused in machine processes such as machine transformation, text to speech extraction and text data mining. It utilized in various area of research in artificial intelligence, computer vision and pattern recognition. Using OCR to scan the damaged book in the library converted into pdf format the book gets new life and sharing the contents to multiple readers. In this paper aims to implement IoT based library management system to maintaining books in digital format.
An approach to categorize chest X-ray images using sparse categorical cross entropy
Authors: N. Chaithanya B. , J. Swasthika Jain T. , Ruby A. Usha , Parveen Ayesha
Published: December 2021

The Coronavirus disease (COVID-19) pandemic is the most recent threat to global health. Reverse transcription-polymerase chain reaction (RT-PCR) testing, computed tomography (CT) scans, and chest X-ray (CXR) images are being used to identify Coronavirus, one of the most serious community viruses of the twenty-first century. Because CT scans and RT-PCR analyses are not available in most health divisions, CXR images are typically the most time-saving and cost-effective tool for physicians in making decisions. Artificial intelligence and machine learning have become increasingly popular because of recent technical advancements. The goal of this project is to combine machine learning, deep learning, and the health-care sector to create a categorization technique for detecting the Coronavirus and other respiratory disorders. The three conditions evaluated in this study were COVID-19, viral Pneumonia, and normal lungs. Using X-ray pictures, this research developed a sparse categorical cross-entropy technique for recognizing all three categories. The proposed model had a training accuracy of 91% and a training loss of 0.63, as well as a validation accuracy of 81% and a validation loss of 0.7108.
Grey wolf optimization algorithm for hierarchical document clustering
Authors: Jabbar Ayad Mohammed , Ku-Mahamud Ku Ruhana
Published: December 2021

In data mining, the application of grey wolf optimization (GWO) algorithm has been used in several learning approaches because of its simplicity in adapting to different application domains. Most recent works that concern unsupervised learning have focused on text clustering, where the GWO algorithm shows promising results. Although GWO has great potential in performing text clustering, it has limitations in dealing with outlier documents and noise data. This research introduces medoid GWO (M-GWO) algorithm, which incorporates a medoid recalculation process to share the information of medoids among the three best wolves and the rest of the population. This improvement aims to find the best set of medoids during the algorithm run and increases the exploitation search to find more local regions in the search space. Experimental results obtained from using well-known algorithms, such as genetic, firefly, GWO, and k-means algorithms, in four benchmarks. The results of external evaluation metrics, such as rand, purity, F-measure, and entropy, indicates that the proposed M-GWO algorithm achieves better document clustering than all other algorithms (i.e., 75% better when using Rand metric, 50% better than all algorithm based on purity metric, 75% better than all algorithms using F-measure metric, and 100% based on entropy metric).
Rimigs: the impact of gamification on students’ motivation and performance in programming class
Authors: Pratama Ferdian Aditya , Silitonga Riana Magdalena , Jou Yung-Tsan
Published: December 2021

Gamification is described as the art of changing human activity to a game-like environment to gain user engagement. Rimigs is a developed gamification system discussed in this research and was built based on Octalysis framework. This research aims to design and identify the impact of using a gamification system on students’ motivation and performance, especially in programming classes. Through Rimigs, students’ engagement and performance can be enhanced by freely choosing tasks or quests according to the rank points, completing these tasks or quests, getting experience points, getting rewards, avoiding punishment at certain times, and competing with others friends. The proposed research was done by using the iterative methodologies and consists of four phases, starting from i) literature review, which identifies the Octalysis’ core, ii) design, which identifies the game elements used in the system and designing the unified modeling language (UML) diagram of the system, iii) testing, which involve the user in the using of the system, and iv) implementation, which is the analysis step for the impact of Rimigs in students’ performance and engagement. This research finds that Rimigs as a gamification system can positively impact both students’ motivation and performance.
A smart algorithm for fault detection and location in electric power distribution system
Authors: Touijer Hamid , El Alami Mohammed , Zahri Mustapha , Habibi Mohamed
Published: December 2021

In an electric power distribution system (EPDS), fault location accuracy is critical for system stability. In the past, several algorithms have shown that they are inefficient. However, the results of these algorithms have been shown to be inefficient, and they should not be used for every sort of the faults. This paper presents a new algorithm capable to determine the location of fault accurately with low error rate. It is based on the voltage and current calculation at the source station for different types of faults by using either one power supply or double power supply. The work includes the formulation analytical development as simulation test results. The test results are produced by numerical simulation using data from a recognized distribution line in the literature.
Reactive power control of grid-connected photovoltaic micro-inverter based on third-harmonic injection
Authors: Noori Farah T. , Hassan Turki K.
Published: December 2021

This paper presents four interleaved flyback DC-DC converters. Each flyback converter contains a separate maximum power point tracker (MPPT). The MPP tracker is used to collect the maximum power value from photovoltaic panel by using (P&O) algorithm with an output of reference current. In latest years, the active third-harmonic injection circuit have received much interest, this technique contributed to getting a better quality of injection current into the utility grid and control the reactive power. For converting direct current to three-phase sinusoidal currents, a line-commutated current source inverter type (CSI) with filter is used. The developed micro-inverter of (1000 W) offers an expanded range of reactive power control with balanced three-phase output power and good efficiency 95.07%. The effectiveness of the suggested system is clarified through the MATLAB program simulation. The system proposed in this paper has proven its effectiveness in obtaining reactive power control, nearly sinusoidal three-phase output currents and it is compared with the traditional PV micro-inverter system. The comparison shows that the PV micro-inverter is best in more specification than the traditional PV system such as efficiency and total harmonic distortion (THD) and the system losses.
Adaptive robust control design to enhance smart grid power system stabilization using wind characteristics in Indonesia
Authors: Herbandono Khamda , Nandar Cuk Supriyadi Ali
Published: December 2021

This paper is interested to study power system stability in smart grid power system using wind characteristic in south of Yogyakarta, Indonesia. To overcome the intermittent of wind characteristics, this paper presents adaptive robust control design to enhance power system stabilization. The online identification system is used in this research, which updated whenever the estimated model mismatch exceeds predetermined bounds. Then genetic algorithm (GA) is applied to re-tune parameters controller based on the estimated model. The structure of controller is proportional integral (PI) controller due to the most applicable in industry, simple structure, low cost and high reliability. Robustness of controller is guaranteed by taking system uncertainties into consideration. The performance of the proposed controller has been carried out in a hybrid wind-diesel power system in comparison with previous work controller. Simulation results confirm that damping effect of the proposed controllers are much better that of the conventional controllers against various operating.