bluuebunny/crossref_metadata_2025 huggingface dataset:
Galvanic Corrosion and Fatigue Behavior of a SM480C Welded Joint Steel in a Sea-Crossing Suspension Bridge
Authors: Zhou Xudong , Zhang Xinmin , Xu Ruiwen , Li Bin , Ren Xuechong , Zhu Jinyang , Jin Ying
Published: July 2022

The corrosion tendency and fatigue behavior of a SM480C welded joint in a sea-crossing suspension bridge after twenty-year exposure to a marine environment was investigated in this work. It was found that the corrosion product on the whole surface of the welded joint is loose, with many holes and cracks, which allowing corrosive media enter and reach the surface of the substrate. Localized corrosion occurred in the weld zone (WZ) and the heat-affected zone (HAZ), the maximum depth of localized corrosion in the HAZ reached 1.8 mm, and the maximum local corrosion rate is 0.082 mm/y. By using Bimetallic Conjugation Theory calculations, the galvanic effect of the welded joint was qualified, indicates that HAZ was the most corrosion susceptible area in the welded joint. The galvanic corrosion current on HAZ reached approximately 2 μA, which is much higher than the corrosion of isolated HAZ by about 6.5 times. The corrosion has an obvious influence on the fatigue performance, the elongation of the bridge deck decreases by 40%~70%, and the tensile strength decreases by 4.5%~31.33%. In order to ensure the service safety and avoid premature failure, the average thickness of the corroded bridge deck should not be less than 10 mm under the stress amplitude of 115 MPa.
Spherical Linear Diophantine Fuzzy TOPSIS Algorithm for Green Supply Chain Management System
Authors: Alshammari Ibtesam , Parimala Mani , Ozel Cenap , Riaz Muhammad
Published: July 2022

Enhancing competitive pressure is one of the most significant roles of supply chain management. The competitive environment and customer perception have shifted in favour of an ecological mentality. As a result, green supplier selection (GSS) has emerged as a critical problem. The challenge of green supplier selection striving for agility, durability, ecological sensitivity, leanness, and sustainability is tackled in this paper. In terms of recycling applications, environmental applications, carbon footprint, and water consumption, the environmental parameters evaluated in GSS and traditional supplier selection differ. Because of the form of the problem, a resolution is defined, which comprises an algorithm entrenched in the spherical linear Diophantine fuzzy sets (SLDFSs) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique. Before discussing the approach of the SLDF model, some background information on SLDF sets is provided. To assure the uniqueness of this robust extension, different operations on SLDFSs are described, along with some concise interpretations to help the reader comprehend these ideas. A robust TOPSIS approach has been utilized in the issue of GSS by taking into consideration the multicriteria decision-making (MCDM) technique particularly useful in several areas, like analyzing and choosing traditional and environmental conventionalities. Due to linguistic criteria and the inability to assess all criteria, the fuzzy technique must be used with the TOPSIS method to lessen the consequences of instability and ambiguity. The spherical linear Diophantine fuzzy TOPSIS approach is employed, as it simplifies the evaluation of decision-makers and criteria. The hybrid technique resulting from integrating the SLDFS and TOPSIS is extremely successful in selecting which provider is more suited among the alternatives established on the criteria set by the order of significance, and this method may also be incorporated into similar issues.
Trajectories and Singular Points of Two-Dimensional Fractional-Order Autonomous Systems
Authors: Yang Bohui , Luo Ziyang , Zhang Xindong , Tang Quan , Liu Juan
Published: July 2022

In this paper, we study the trajectories and singular points of two-dimensional fractional-order planar autonomous linear system involving the Caputo-Fabrizio fractional derivative. By the corresponding fractional integral of the Caputo-Fabrizio fractional derivative, we obtain the analytical solutions for the fractional-order planar autonomous linear system, and then, we discuss the behavior of the trajectories for the mentioned autonomous linear system. Furthermore, we consider the existence of singular points in the trajectories. We discuss the conditions under which the singular point is stable or unstable. By determining the value range of the parameters, we obtain the theorems on the type of singular points. Finally, some examples are given to verify the analysis for the mentioned autonomous linear system.
Wisdom Tourism Management Mode in the Background of Big Data of the Internet of Things
Authors: Huang Zong , Wen Qiong , Song Yanqiong
Published: August 2022

Since the rapid development of the global economy, tourism has become an indispensable entertainment and relaxation item in people’s lives. However, the traditional tourism management mode is slow in conveying tourism information, and there is no reasonable planning of tourism projects for tourists, and it is even more difficult to accurately locate the travel preferences of travelers and make specific travel plans, so that travel resources cannot be fully utilized. Therefore, the Internet of Things big data technology and travel management are combined. For this reason, a wisdom tourism management model based on the big data background of Internet of Things (IoT) is developed. In order to timely arrange and adjust work and travel plans, wisdom tourism makes use of cloud computing, IoT, and other new technologies, with the help of portable mobile terminal devices, so that the tourists can receive timely information about tourism resources, tourism activities, and tourism surroundings to achieve intelligent perception of tourism information, timely arrangement effect. Wisdom tourism can make use of undeveloped tourism resources as much as possible and drive the development of surrounding industries, and promote the economic development of tourist areas. This paper mainly compares the smart tourism management mode with the traditional tourism management mode. The experiment shows that the development degree of the wisdom tourism management mode accounts for 82.8% of the total tourism resources. The traditional tourism management model is only 70.1%, and the GDP of the economy around the tourist area in the wisdom tourism management model in one year is 1.4 times higher than that of the traditional tourism management model. In terms of tourism experience, the wisdom tourism management model is also much better than the traditional tourism management model. And the lower the average age of the experimenter, the higher the favorability of wisdom tourism, up to 98%. It highlights the advantages of the wisdom tourism management model in personalized tourism, tourism development, and surrounding economic growth.
Efficacy Analysis of Team-Based Nursing Compliance in Young and Middle-Aged Diabetes Mellitus Patients Based on Random Forest Algorithm and Logistic Regression
Authors: Qian Dongni , Gao Hong
Published: July 2022

Objective. Long-term hyperglycemia in young and middle-aged diabetic patients can be complicated with diabetic ketoacidosis, stroke, myocardial infarction, infection, and other complications. The objective was to explore the application value of machine learning in predicting the recurrence risk of young and middle-aged diabetes patients with team-based nursing intervention. Methods. Clinical data of 80 patients with diabetes treated in the Department of Endocrinology from 2019 to 2020 were retrospectively collected. The data set was divided into 70% training set (

n

 =56) and 30% test set (

n

 =24). All the selected research cases were intervened by the team-based management mode involving family and clinical doctors and nurses. The degree of diabetes knowledge learning, the level of blood glucose changes, and the psychological state of the patients were evaluated. The random forest (RF) algorithm and logistic regression prediction model were constructed to predict the risk factors of diabetes recurrence. Results. There was no significant difference in the degree of diabetes knowledge learning, the level of blood glucose changes, and the psychological state between the training set and the test set (

P
>
0.05

). The FPG, HbA1c, and 2hPG of recurrence group patients were significantly higher than those of nonrecurrence group patients, and the difference was statistically significant (

P
<
0.05

). In descending order of importance based on the RF algorithm prediction model were glucose, BMI, age, insulin, pedigree function, skin thickness, and blood diastolic pressure. The accuracy of RF and logistic regression prediction models is 81.46% and 80.21%, respectively. Conclusion. The team-based nursing model has a good effect on the blood glucose control level of middle-aged and young diabetic patients. Age, BMI, and glucose values are risk factors for diabetes. The SF algorithm has a good effect on predicting the risk of diabetes, which is worthy of further clinical application.
Interest and Utility of MC1R Testing for Melanoma Risk in Dermatology Patients with a History of Nonmelanoma Skin Cancer
Authors: Hay Jennifer L. , Lee Erica H. , Christian Stephanie N. , Schofield Elizabeth , Hamilton Jada G. , Yang Ciyu , Hedayati Bobak , Sadeghi Keimya , Robson Mark E. , Halpern Allan , Zhang Liying , Orlow Irene
Published: August 2022

Public access to genetic information is increasing, and community dermatologists may progressively encounter patients interested in genetic testing for melanoma risk. Clarifying potential utility will help plan for this inevitability. We determined interest and uptake of genetic risk feedback based on melanocortin receptor gene (MC1R) variants, immediate (two weeks) responses to risk feedback, and test utility at three months in patients (age ≥ 18, with a history of nonmelanoma skin cancer). Participants (N = 50) completed a baseline survey and were invited to consider MC1R testing via the study website. Testing interest and uptake were assessed through registration of test decision, request of a saliva test kit, and kit return (all yes/no). Immediate responses to risk feedback included feedback-relevant thoughts, emotions, communication, and information seeking after result receipt; test utility outcomes included family and physician communication and information seeking. Results indicated good retention at both time points (76%; 74%). Half (48%) logged onto the study website, and of these, most (92%) chose testing and (95%) returned a saliva sample. After two weeks, most (94%) had read all the risk feedback information and distress was low (M = 8.81, 7–28, SD = 2.23). Many (69%) had talked with their family about the results. By three months, most had spoken with family (92%) and physicians (80%) about skin cancer risk. Physician communication was higher (70%) in those tested versus those not tested (40%,

p
=
0.02

). The substantial interest and promising outcomes associated with MC1R genetic testing in dermatology patients inform intervention strategies to enhance benefits and minimize risks of skin cancer genetic testing.
Design and Production of Visual Image of Print Advertisement Based on Mobile Media
Authors: Zhang Yue , Hong Kyungmin
Published: August 2022

With the rapid development of science and technology, various fields (such as information and multimedia) have been combined with each other into a complete field of print advertising design. The visual image is the main part of showing the expressive power of graphic advertising, which intuitively explains the design creativity of the advertisement. It uses visual expression to attract the audience’s first attention and stimulate consumer emotions. However, with the continuous change of people’s aesthetic art and aesthetic needs, ordinary print advertisements can no longer meet the needs of consumers. The continuous development of science and technology has promoted the spread of mobile media. Mobile media applications not only integrate with people’s daily life but also bring people various mobile media advertisements. It realizes the multimedia and diversification of advertising. To meet the ever-changing visual aesthetic needs of users, it is necessary to use mobile media technology to innovate the visual image of print advertisements and provide consumers with a unique visual experience. Therefore, this paper will study the design and production of print advertisement visual image based on mobile media. This paper takes the design process of mobile media as the research process and summarizes the visual image design method of print advertising that meets the needs of users, conforms to the development of the times and aesthetic trends, and has high cultural information dissemination efficiency. It then tests the proposed theoretical method through the comparison of works and finally realizes the second leap of design theory. Experiments show that the visual image of mobile media advertising is more in line with consumers’ aesthetics and can increase product sales. The highest sales of its products increased by 9.26% compared with ordinary products.
Parameter Optimization and State Evaluation of Basketball Teaching Based on BPNN
Authors: Wang Zhanqi
Published: August 2022

BPNN is a multi-layer network with forward and backward error algorithms. It is currently the most widely used NN. Especially when it is applied to the basketball teaching scene, it can improve the research on the parameter optimization and state evaluation of the BP neural network in the teaching scene. This paper is mainly to study how to optimize the parameters of basketball teaching scene to improve the teaching effect and to evaluate the state. This paper improves the BPNN technology to study the basketball teaching scene and then proposes a genetic algorithm on the basis of the BPNN. It optimizes the parameters of basketball teaching scene through genetic algorithm and then analyzes the experimental data. The experimental results of this paper show that the BPNN is not as good as the BPNN based on the genetic algorithm in optimizing the parameters of the basketball teaching scene. The BPNN based on genetic algorithm has a successful recognition rate of more than 93% for basketball dribbling and shooting, which is 7-8% higher than that of the BPNN.
Buildings, Causalities, and Injuries Innovative Fuzzy Damage Model during Earthquakes
Authors: Amiri Shahmirani Mohammad Reza , Nikghalb Rashti Abbas Akbarpour , Adib Ramezani Mohammad Reza , Golafshani Emadaldin Mohammadi
Published: August 2022

One of the human concerns has always been to estimate the damage caused before the earthquake and predict the extent of injuries and causalities. An effective model should be developed based on the field survey data for appropriate prediction. In this study, the degree of damage to the structure is first determined and the potential damage is then predicted using field data and fuzzy logic (FL). Effective parameters in the model include the structure height, building age, shear wave velocity in the soil, the plan equivalent moment of inertia, distance to the fault, earthquake factor, the number of inhabitants in the building, and the building height-to-width ratio (HWR). The parameters are fuzzily divided into five classifications: bad, relatively bad, medium, relatively good, and good. The model output parameter, which is the degree of damage to the building, is fuzzy and is divided into five classifications: complete damage, extensive damage, moderate damage, slight damage, and no damage. It should be noted that buildings with steel and concrete structures and moment frames, in the night, day, and traffic time scenarios, have very limited type 3 and 4 injuries with 32, 24, and 16 people, respectively, but type 1 and 2 injuries are significant. During the earthquake at night, the number of people with type 1 and 2 injuries is 975607 and 58757, in the event of the earthquake during the day, the number of people with type 1 and 2 injuries is 739096 and 44513, and during the earthquake at traffic time, the number of people with type 1 and 2 injuries is 492731 and 29675, respectively.
Comparative Investigation on the Properties and Molecular Mechanisms of Natural Phenolic Compounds and Rubber Polymers to Inhibit Oxidative Aging of Asphalt Binders
Authors: Yang Fenghua , Hu Zhuang , Xi Hongyi
Published: July 2022

Using bio-based antioxidants and industrial wastes to inhibit asphalt aging is a sustainable practice in the pavement industry. To investigate the inhibitory effects and potential mechanisms of phenolic compounds and crumb tire rubber on the aging deterioration of asphalt binders, the aging characteristics of base asphalt, catechin modified asphalt, and crumb rubber modified asphalt were evaluated using Fourier transform infrared spectroscopy and reactive molecular dynamics simulations. The results showed that both catechins and crumb rubber exerted resistance to asphalt aging and that the former performed better. Specifically, catechins block asphalt aging at the chemical level by providing active H atoms to react with O2 and free radicals, thus preventing them from oxidizing asphalt molecules. In contrast, crumb rubber exerts its antiaging properties mainly in physical aspects, firstly by limiting the movement of asphalt molecules to reduce the contact and reaction of asphalt with O2 and secondly by counteracting the aging-induced asphalt hardening through its own degradation, thus partially restoring the mechanical properties of aged asphalt. This study provides molecular insight into the antiaging mechanisms of natural phenolic compounds and rubber polymers and is expected to provide theoretical guidance for the development of targeted antiaging technologies for asphalt binders.
Dichotomy Condition and Periodic Solutions for Two Nonlinear Neutral Systems
Authors: Mesmouli Mouataz Billah , Attiya Adel A. , Elmandouha A. A. , Tchalla Ayékotan M. J. , Hassan Taher S.
Published: August 2022

In this article, we consider two nonlinear neutral systems with multiple delays. Our main tool here is to use dichotomy theory to construct an implicit solution for these two systems. Utilizing Krasnoselskii’s fixed point theorem, we obtain sufficient criteria for the existence of periodic solutions, as well as for the uniqueness of solutions. The main results expand and generalize certain previously published findings.
Emerging Actions and Energy Strategies for Sustainable Development of Sakarya City, Turkey: A SWOT Analysis
Authors: Aksoy Tırmıkçı Ceyda
Published: July 2022

Turkey has been one of the earliest participants of the international climate policy process, since the Ministerial Conference on Atmospheric Pollution and Climate Change held in 1989. The country has prepared strategy documents, actions plans, sectoral policies, and projects to detect and adapt climate change effects. However, any of this has not turned into a main plan to support climate change mitigation on an international scale. The purpose of this paper is to identify local climate change mitigation strategies of Sakarya city, Turkey, by strengths, weaknesses, opportunities, and threats (SWOT) analysis. For this purpose, relevant information were gathered from Covenant of Mayors, greenhouse gas inventories of the city, National Energy Efficiency Action Plan, online workshop with 44 local stakeholders from private sector and local universities held on 13.10.2020. The results emphasized the importance of the cross-link between local adaptation and mitigation in terms of energy demand and energy-based emissions on national and international scale.
Protective Effect of Curcuma Extract in an Ex Vivo Model of Retinal Degeneration via Antioxidant Activity and Targeting the SUMOylation
Authors: Hassanzadeh Kambiz , Vahabzadeh Zakaria , Bucarello Lucia , Dragotto Jessica , Corbo Massimo , Maccarone Rita , Feligioni Marco
Published: July 2022

Retinal degeneration is the major and principal cause behind many incurable blindness diseases. Several studies indicated the neuroprotective effect of Curcuma longa in eye pathologies, specifically retinopathy. However, the molecular mechanism behind its effect has not been completely elucidated. Using an ex vivo model of retinal degeneration obtained from an ex vivo optic nerve cut (ONC), we demonstrated that Curcuma extract (Cur) exerted a neuroprotective effect. Importantly, Cur was able to modulate apoptosis and MAPK signaling pathway activation and prevent retinal ganglion cell (RGC) loss. Other well-known neuroprotective pharmacological tools, including memantine (Mem), citicoline (Cit), and ginkgolic acid (GA), were used to compare the potential mechanisms of Cur. The antioxidant activity of retinas treated with Cur following optic nerve cut was significantly higher than control, but Cur failed to change the retina glutamate content. Considering the antioxidant effect of Cur and taking advantage of our recent findings on the crosstalk between oxidative stress and post-translational protein modifiers, in particular, small ubiquitin-related modifier (SUMO), we were interested in exploring the effect of Cur on SUMOylation. We found that Cur significantly prevented the increase of protein SUMOylation, confirming our previous in vitro data indicating the cytoprotective effect of curcumin through modulating the oxidative stress and SUMO-JNK axis. Altogether, these results suggest that Curcuma protects the retina from degeneration via antioxidant activity and targets SUMOylation. Therefore, it might be considered for the combination therapy with other neuroprotective agents with different mechanisms in preclinical studies on retinal degeneration.
Inflación de costo
Authors: García Carlos
Published: July 2022

Durante el último año, el valor de los alimentos subió en forma dramática. Los precios del trigo, azúcar, maíz y la soja aumentaron entre 25%-40% en los mercados internacionales. Todos productos claves en la alimentación humana. Continuar leyendo...
Clinical Outcomes and Immune Markers by Race in a Phase I/II Clinical Trial of Durvalumab Concomitant with Neoadjuvant Chemotherapy in Early-Stage TNBC
Authors: Foldi Julia , Kahn Adriana , Silber Andrea , Qing Tao , Reisenbichler Emily , Fischbach Neal , Persico Justin , Adelson Kerin , Katoch Anamika , Chagpar Anees , Park Tristen , Blanchard Adam , Blenman Kim , Rimm David L. , Pusztai Lajos
Published: July 2022

Purpose:

The incidence of triple-negative breast cancer (TNBC) is higher among Black or African American (AA) women, yet they are underrepresented in clinical trials. To evaluate safety and efficacy of durvalumab concurrent with neoadjuvant chemotherapy for stage I–III TNBC by race, we enrolled additional AA patients to a Phase I/II clinical trial.

Patients and Methods:

Our study population included 67 patients. The primary efficacy endpoint was pathologic complete response (pCR; ypT0/is, N0) rate. χ2 tests were used to evaluate associations between race and baseline characteristics. Cox proportional hazards models were used to assess association between race and overall survival (OS) and event-free survival (EFS). Multivariate logistic regression analyses were used to evaluate associations between race and pCR, immune-related adverse events (irAE) and recurrence.

Results:

Twenty-one patients (31%) self-identified as AA. No significant associations between race and baseline tumor stage (P = 0.40), PD-L1 status (0.92), and stromal tumor–infiltrating lymphocyte (sTIL) count (P = 0.57) were observed. pCR rates were similar between AA (43%) and non-AA patients (48%; P = 0.71). Three-year EFS rates were 78.3% and 71.4% in non-AA and AA patients, respectively [HR, 1.451; 95% confidence interval (CI), 0.524–4.017; P = 0.474]; 3-year OS was 87% and 81%, respectively (HR, 1.72; 95% CI, 0.481–6.136; P = 0.405). The incidence of irAEs was similar between AA and non-AA patients and no significant associations were found between irAEs and pathologic response.

Conclusions:

pCR rates, 3-year OS and EFS after neoadjuvant immunotherapy and chemotherapy were similar in AA and non-AA patients. Toxicities, including the frequency of irAEs, were also similar.
Photovoltaic parameters estimation of poly-crystalline and mono-crystalline modules using an improved population dynamic differential evolution algorithm
Authors: Hiendro Ayong , Yusuf Ismail , Husin Fitriah , Khwee Kho Hie
Published: August 2022

Photovoltaic (PV) parameters estimation from the experimental current and voltage data of PV modules is vital for monitoring and evaluating the performance of PV power generation systems. Moreover, the PV parameters can be used to predict current-voltage (I-V) behavior to control the power output of the PV modules. This paper aimed to propose an improved differential evolution (DE) integrated with a dynamic population sizing strategy to estimate the PV module model parameters accurately. This study used two popular PV module technologies, i.e., poly-crystalline and mono-crystalline. The optimized PV parameters were validated with the measured data and compared with other recent meta-heuristic algorithms. The proposed population dynamic differential evolution (PDDE) algorithm demonstrated high accuracy in estimating PV parameters and provided perfect approximations of the measured I-V and power-voltage (P-V) data from real PV modules. The PDDE obtained the best and the mean RMSE value of 2.4251E-03 on the poly-crystalline Photowatt-PWP201, while the best and the mean RMSE value on the mono-crystalline STM6-40/36 was 1.7298E-03. The PDDE algorithm showed outstanding accuracy performance and was competitive with the conventional DE and the existing algorithms in the literature.
Hybrid photovoltaic maximum power point tracking of Seagull optimizer and modified perturb and observe for complex partial shading
Authors: Windarko Novie Ayub , Sholikhah Evi Nafiatus , Habibi Muhammad Nizar , Prasetyono Eka , Sumantri Bambang , Efendi Moh. Zaenal , Mokhlis Hazlie
Published: August 2022

Due to natural randomness, partial shading conditions (PSCs) to photovoltaic (PV) power generation significantly drop the power generation. Metaheuristic based maximum power point tracking (MPPT) can handle PSCs by searching PV panels’ global maximum power point (GMPP). However, trapped at local maxima, sluggishness, continuous power oscillations around GMPP and inaccuracy are the main disadvantages of metaheuristic algorithm. Therefore, the development of algorithm under complex PSCs has been continuously attracting many researchers to yield more satisfying results. In this paper, several algorithms including conventional and metaheuristic are selected for candidate, such as perturb and observe (P&O), firefly (FF), differential evolution (DE), grey wolf optimizer (GWO) and Seagull optimizer (SO). From the preliminary study, SO has shown best performance among other candidates. Then, SO is improved for rapid global optimizer. Modified variable step sizes perturb and observe (MVSPO) is applied to enhance the accuracy tracking of SO. To evaluate the performances, high complexity multipeak partial shading is used to test the algorithms. Statistical results are also provided to analyze the trend of performances. The proposed method performances are shown better fast-tracking time and settling time, high accuracy, higher energy harvesting and low steady-state oscillations than other candidates.
Optimal population size of particle swarm optimization for photovoltaic systems under partial shading condition
Authors: Hashim Norazlan , Nik Ismail Nik Fasdi , Johari Dalina , Musirin Ismail , Rahman Azhan Ab.
Published: August 2022

Particle swarm optimization (PSO) is the most widely used soft computing algorithm in photovoltaic systems to address partial shading conditions (PSC). The success of the PSO run heavily depends on the initial population size (NP). A higher NP increases the probability of a global peak (GP) solution, but at the expense of a longer convergence time. To find the optimal value of NP, a trade-off is typically made between a high success rate and a reasonable convergence time. The most used trade-off method is a trial-and-error approach that lacks explicit guidelines and empirical evidence from detailed analysis, which can affect data reproducibility when different systems are used. Hence, this study proposes an empirical trade-off method based on the performance index (PI) indicator, which takes into account the weighted importance of success rate and convergence time. Furthermore, the impact of NP on achieving a successful PSO was empirically investigated, with the PSO tested with 16 different NPs ranging from 3 to 50, and 10,000 independent runs on various PSC problems. Overall, this study found that the best NP to use was 25, which had the best average PI value of 0.9373 for solving all PSC problems under consideration.
Combinational load shedding using load frequency control and voltage stability indicator
Authors: Al-Sadooni Hussein Hadi Abdul-Wahid , Al-Rubayi Rashid Hamid
Published: August 2022

This paper proposes a load shedding program for evaluating and distributing the minimum load power to be curtailed required to bring the frequency and voltage, after the system was subjected to a heavy disturbance, to the allowable range for each load bus. The quantity of load shedding was estimated to restore the power system's frequency, taking into account the turbine governor's primary control and the generators' reserve power for secondary control. Calculation and review of the load bus's voltage stability indicator (Li) to prioritize the load shedding quantity at these locations. The lower the voltage stability indicator on the load bus, the less load shedding can occur, and vice versa. The frequency and voltage values are still within allowable ranges with this approach, and a significant amount of load shedding can be prevented, resulting in a reduction in customer service interruption. The proposed method's efficacy was demonstrated when it was checked against the IEEE 30 bus 6 generators power system standard simulated in MATLAB environment and it minimize the power to be shed by around 20% of the conventional load shedding schemes.
An optimal artificial neural network controller for load frequency control of a four-area interconnected power system
Authors: Rameshappa Basavarajappa Sokke , Shadaksharappa Nagaraj Mudakapla
Published: August 2022

In this paper, an optimal artificial neural network (ANN) controller for load frequency control (LFC) of a four-area interconnected power system with non-linearity is presented. A feed forward neural network with multi-layers and Bayesian regularization backpropagation (BRB) training function is used. This controller is designed on the basis of optimal control theory to overcome the problem of load frequency control as load changes in the power system. The system comprised of transfer function models of twothermal units, one nuclear unit and one hydro unit. The controller model is developed by considering generation rate constraint (GRC) of different units as a non-linearity. The typical system parameters obtained from IEEE press power engineering series and EPRI books. The robustness, effectiveness, and performance of the proposed optimal ANN controller for a step load change and random load change in the system is simulated through using MATLAB-Simulink. The time response characteristics are compared with that obtained from the proportional, integral and derivative (PID) controller and non-linear autoregressive-moving average (NARMA-L2) controller. The results show that the algorithm developed for proposed controller has a superiority in accuracy as compared to other two controllers.
Presentation of a fault tolerance algorithm for design of quantum-dot cellular automata circuits
Authors: Dadgar Seyed Mehdi , Farazkish Razieh , Sahafi Amir
Published: August 2022

A novel algorithm for working out the Kink energy of quantum-dot cellular automata (QCA) circuits and their fault tolerability is introduced. In this algorithm at first with determining the input values on a specified design, the calculation between cells makes use of Kink physical relations will be managed. Therefore, the polarization of any cell and consequently output cell will be set. Then by determining missed cell(s) on the discussed circuit, the polarization of output cell will be obtained and by comparing it with safe state or software simulation, its fault tolerability will be proved. The proposed algorithm was implemented on a novel and advance fault tolerance full adder whose performance has been demonstrated. This algorithm could be implemented on any QCA circuit. Noticeably higher speed of the algorithm than simulation and traditional manual methods, expandability of this algorithm for variable circuits, beyond of four-dot square of QCA circuits, and the investigation of several damaged cells instead just one and special cell are the advantages of algorithmic action.
Strategy to reduce transient current of inverter-side on an average value model high voltage direct current using adaptive neuro-fuzzy inference system controller
Authors: Ginarsa I Made , Ari Nrartha I Made , Budi Muljono Agung , Sultan Sultan , Nababan Sabar
Published: August 2022

Growing-up of high voltage direct current (HVDC) penetration into modern power systems (PS) makes difficulty on the PS operation. The HVDC produces high and slow transient current (TC) at start-time, especially for higher up-ramp rate (Urr>20 pu/s). Its condition makes the HVDC cannot be linked and synchronized into the PS rapidly. A strategy to reduce the TC is proposed by an adaptive network based fuzzy inference system (ANFIS) control on inverter HVDC-link to cope up this problem. The ANFIS control is tuned with the help of conventional control in various train-data by using offline mode. Response of ANFIS scheme is improved by suppressing TC at the values of 3.75% for the both phases (A and C), and 3.95% for phase B, for the Urr=30 pu/s. While the conventional control achieved at 9.1% for the both phases (A and B), and 9.2% for the phase C. The ANFIS control gives shorter settling time (0.553 s) than the conventional control (0.584 s) for all phases. The proposed control is more effective than the conventional control at all the scenario.
Hybrid winding function method for inductance analysis of a line start synchronous reluctance machine
Authors: Rebbah Redjem , Kaikaa Mohamed Yazid , Boudjelida Loubna
Published: August 2022

In recent years, there has been renewed interest in line-start synchronous reluctance machines (LSSRMs) due to their simple construction, magnet-free rotor, and low cost. To improve control performance, design optimization, and fault diagnostic analysis of these machines, it requires accurate estimation of their electromagnetic characteristics using detailed and time-consuming finite element analyses (FEAs). In this paper, inductances and electromagnetic torque of the LSSRM were calculated using the combination of winding function analysis and conformal mapping instead of FEA. The hybrid approach can be applied to the prediction of motor behavior, taking into account all space harmonics of the air-gap permeance without any restriction on the rotor saliencies and topologies. The influence of the core saturation, stator slots, and rotor bars were also considered. The results obtained by simulations were compared with FEA in terms of accuracy and computational time.
Real-time remote monitoring and control system for underground pipelines
Authors: Abdulwahab Noor Hazim , Abed‬‏ ‪Ali Ahmed , Jaber Mohammed Ali
Published: August 2022

Underground pipelines suffer from corrosion in soil layers, and this corrosion is accelerated with the increasing of soil thickness due to more water contained. Cathodic protection (CP) is one of the most common methods for controlling corrosion of metals. Its popularity returns to the fact that CP system is simple, cheap, and suitable for many industrial applications. The drawback of the available CP systems is the need to go to the site for gathering data using classical instruments and methods, which is tedious, dangerous, uneconomic, and inaccurate. The main objective of this paper is to present a real-time remote monitoring and control (RT-RMC) system for any CP platform. The work started with implementation of an industrial-like CP prototype to realize the desired task. The implemented CP system consists of two famous CP methods, the sacrificial anodes (SACP) and the impressed current (ICCP). After that, the RT-RMC system is implemented with two techniques, global system for mobile communications(GSM), and web of things (WoT) to facilitate monitoring and control tasks. Experimental results are obtained for voltage and current measurements with different environments, disturbance, and pipe coatings.
Electrocardiograph signal recognition using wavelet transform based on optimized neural network
Authors: Jawad Ali Talib , Abdul-Zahra Dalael Saad , Gheni Hassan Muwafaq , Abdullah Ali Najim
Published: August 2022

Due to the growing number of cardiac patients, an automatic detection that detects various heart abnormalities has been developed to relieve and share physicians’ workload. Many of the depolarization of ventricles complex waves (QRS) detection algorithms with multiple properties have recently been presented; nevertheless, real-time implementations in low-cost systems remain a challenge due to limited hardware resources. The proposed algorithm finds a solution for the delay in processing by minimizing the input vector’s dimension and, as a result, the classifier’s complexity. In this paper, the wavelet transform is employed for feature extraction. The optimized neural network is used for classification with 8-classes for the electrocardiogram (ECG) signal this data is taken from two ECG signals (ST-T and MIT-BIH database). The wavelet transform coefficients are used for the artificial neural network’s training process and optimized by using the invasive weed optimization (IWO) algorithm. The suggested system has a sensitivity of over 70%, a specificity of over 94%, a positive predictive of over 65%, a negative predictive of more than 93%, and a classification accuracy of more than 80%. The performance of the classifier improves when the number of neurons in the hidden layer is increased.
Automated prediction of sudden cardiac death using statistically extracted features from electrocardiogram signals
Authors: Reddy Karna Viswavardhan , Kumar Navin
Published: August 2022

Sudden cardiac death (SCD) is becoming a severe problem despite significant advancements in the usage of the information and communication technology (ICT) in the health industry. Predicting an unexpected SCD of a person is of high importance. It might increase the survival rate. In this work, we have developed an automated method for predicting SCD utilizing statistical measures. We extracted the intrinsic attributes of the electrocardiogram (ECG) signals using Hilbert-Huang and wavelet transforms. Then utilizing machine learning (ML) classifier, we are using these traits to automatically classify regular and SCD existing risks. Support vector machine (SVM), decision tree (DT), naive Bayes (NB), discriminate k-nearest neighbors (KNN), analysis (Disc.), as well as an ensemble of classifiers also utilized (Ens.). The efficiency and practicality of the proposed methods are evaluated using a standard database and measured ECG data obtained from 18 ECG records of SCD cases and 18 ECG records of normal cases. For the automated scheme, the set of features can predict SCD very fast that is, half an hour before the occurrence of SCD with an average accuracy of 100.0% (KNN), 99.9% (SVM), 98.5% (NB), 99.4% (DT), 99.5% (Disc.), and 100.0% (Ens.)
Super resolution image reconstruction via dual dictionary learning in sparse environment
Authors: Kiran Seetharamaswamy Shashi , Veeranna Suresh Kaggere
Published: August 2022

Patch-based super resolution is a method in which spatial features from a low-resolution (LR) patch are used as references for the reconstruction of high-resolution (HR) image patches. Sparse representation for each patch is extracted. These coefficients obtained are used to recover HR patch. One dictionary is trained for LR image patches, and another dictionary is trained for HR image patches and both dictionaries are jointly trained. In the proposed method, high frequency (HF) details required are treated as combination of main high frequency (MHF) and residual high frequency (RHF). Hence, dual-dictionary learning is proposed for main dictionary learning and residual dictionary learning. This is required to recover MHF and RHF respectively for recovering finer image details. Experiments are carried out to test the proposed technique on different test images. The results illustrate the efficacy of the proposed algorithm.
Performance analysis in text clustering using k-means and k-medoids algorithms for Malay crime documents
Authors: Mohemad Rosmayati , Mohd Muhait Nazratul Naziah , Mohamad Noor Noor Maizura , Othman Zulaiha Ali
Published: August 2022

Few studies on text clustering for the Malay language have been conducted due to some limitations that need to be addressed. The purpose of this article is to compare the two clustering algorithms of k-means and k-medoids using Euclidean distance similarity to determine which method is the best for clustering documents. Both algorithms are applied to 1000 documents pertaining to housebreaking crimes involving a variety of different modus operandi. Comparability results indicate that the k-means algorithm performed the best at clustering the relevant documents, with a 78% accuracy rate. K-means clustering also achieves the best performance for cluster evaluation when comparing the average within-cluster distance to the k-medoids algorithm. However, k-medoids perform exceptionally well on the Davis Bouldin index (DBI). Furthermore, the accuracy of k-means is dependent on the number of initial clusters, where the appropriate cluster number can be determined using the elbow method.
Classification of plasmodium falciparum based on textural and morphological features
Authors: Setyawan Doni , Wardoyo Retantyo , Wibowo Moh Edi , Herdiana Murhandarwati E. Elsa
Published: August 2022

Malaria is a disease caused by plasmodium parasites transmitted through the bites of female anopheles-mosquito that infect the human red blood cell (RBC). The standard malaria diagnosis is based on manual examination of a thick and thin blood smear, which heavily depends on the microscopist experience. This study proposed a system that can identify the life stages of plasmodium falciparum in human RBC. The image preprocessing process was done by illumination correction using gray world assumption, contrast enhancement using shadow correction, extraction of saturation component, and noise filtering. The segmentation process was applied using Otsuthresholding and morphological operation. The test results showed that the use of artificial neural network (ANN) using a combination of texture and morphological features gives better results when compared to the use of only texture or morphology features. The results showed that the proposed feature achieved an accuracy of 82.67%, a sensitivity of 82.18%, and a specificity of 94.17%, thus improving decision-making for malaria diagnosis.
Appling tracking game system to measure user behavior toward cybersecurity policies
Authors: Alissa Khalid Adnan , Aldeeb Bashar Abedalmohdi , Alshehri Hanan Abdullah , Dahdouh Shahad Abdulaziz , Alsubaie Basstaa Mohammad , Alghamdi Afnan Mohammad , Alkenani Moath Khaireddin , Alsmadi Mutasem Khalil
Published: August 2022

Institutions wrestle to protect their information from threats and cybercrime. Therefore, it is dedicating a great deal of their concern to improving the information security infrastructure. Users’ behaviors were explored by applying traditional questionnaire as a research instrument in data collocate process. But researchers usually suffer from a lack of respondents' credibility when asking someone to fill out a questionnaire, and the credibility may decline further if the research topic relates to aspects of the use and implementation of information security policies. Therefore, there is insufficient reliability of the respondent's answers to the questionnaire’s questions, and the responses might not reflect the actual behavior based on the human bias when facing the problems theoretically. The current study creates a new idea to track and study the behavior of the respondents by building a tracking game system aligned with the questionnaire whose results are required to be known. The system will allow the respondent to answer the survey questions related to the compliance with the information security policies by tracking their behavior while using the system.
New approach on global optimization problems based on meta-heuristic algorithm and quasi-Newton method
Authors: Dasril Yosza , Khang Wen Goh , Bujang Nazarudin bin , Salahudin Shahrul Nizam
Published: August 2022

This paper presents an innovative approach in finding an optimal solution of multimodal and multivariable function for global optimization problems that involve complex and inefficient second derivatives. Artificial bees colony (ABC) algorithm possessed good exploration search, but the major weakness at its exploitation stage. The proposed algorithms improved the weakness of ABC algorithm by hybridized with the most effective gradient based method which are Davidon-Flecher-Powell (DFP) and Broyden-Flecher-Goldfarb-Shanno (BFGS) algorithms. Its distinguished features include maximizing the employment of possible information related to the objective function obtained at previous iterations. The proposed algorithms have been tested on a large set of benchmark global optimization problems and it has shown a satisfactory computational behaviour and it has succeeded in enhancing the algorithm to obtain the solution for global optimization problems.
Security and privacy recommendation of mobile app for Arabic speaking
Authors: Almubarak Hameed , Khouja Mohamed Khairallah , Jedidi Ahmed
Published: August 2022

There is an enormous number of mobile apps, leading users to be concerned about the security and privacy of their data. But few users are aware of what is meant by app permissions, which sometimes do not illustrate what kind of data is gathered. Therefore, users are still concerned about security risks and privacy, with little knowledge and experience of what security and privacy awareness. Users depend on ratings, which may be fake, or keep track of their sense to install an app, and an enormous number of users do not like to read reviews. To solve this issue, we propose a recommender system that reads users' reviews, and which exposes flaws, violations and third-party policies or the quality of a user's experience. In order to design and implement our recommender, we conduct a survey which supports two significant points: to detect the level of security and privacy awareness between users, and to gather new words into a dictionary of a recommender system, which assists to classify each review on the correct level, which can indeed reveal the scale of security and privacy in an app.