bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimal Design of Damped Structure with Inerter System Based on Modified Harmony Search Algorithm
Authors: Pan Chao , Han Xiao , Wang Hao , Yang Dengke , Zhang Li
Published: August 2022

An inerter system can amplify the deformation of its internal energy dissipation device, thereby improving the efficiency of energy dissipation and shock absorption. This is the so‐called damping enhancement mechanism, one of the key mechanisms of the inerter system. Although the theoretical framework for damping enhancement of inerter systems has been established, the implementation of this principle for the design of an inerter system requires solving a complicated constrained optimization problem, which is not easy to be figured out using traditional approaches. To obtain valid design results through a lucid and robust method, it is proposed to optimize the damping parameters through a metaheuristic algorithm named harmony search algorithm in order to maximize the damping enhancement degree of the inerter system with the satisfaction of structural performance. First, the closed‐form seismic response solutions of a single‐degree‐of‐freedom (SDOF) structure with an inerter system are derived based on the theory of random vibration. Then, the mathematical expression of the constrained optimization problem is established. Due to the inefficiency of the original harmony search algorithm to solve the constrained optimization problem, the algorithm is modified by introducing a new harmony generating method and an adaptive strategy for parameter adjustment. The modified harmony search algorithm is compiled to solve the optimal design problem of the inerter system. The algorithm is verified by designing a structure with an inerter system. It is found that the number of iterations and time consumption until convergence required by the modified harmony search algorithm can be reduced by about 20%∼90% compared with the original algorithm, which confirms the effectiveness of the modified algorithm. The results of dynamic analyses show that the structure have achieved the preset performance demands under different cases and the damping enhancement characteristic of the inerter system is fully utilized.
A View of Banhatti and Revan Indices in Chemical Graphs
Authors: Mahboob Abid , Muhiuddin G. , Siddique Imran , Alam Sajid Mahboob
Published: August 2022

A special type of graph invariant called topological index is the collection of data on algebraic graphs and provides a mathematical way to understand chemical structural features. One of the driving factors behind the wide public attention according to these indices is their remarkable ability to correlate and predict the properties of a wide range of molecular species. Our concern is basically with the study of molecular structures, its shapes, geometries, number of atoms, vertices, bond length, and bond strength. It is very expensive to find the properties of compounds in laboratories due to different expensive apparatus and expensive rare material. It is also time consuming and requires an expert person to perform the experiments. So with the help of graphs and topological index, we want to give an easy approach to find the different characteristics of molecular structures with practical lab experiments. It is a mathematical and theoretical method to estimate the physicochemical properties. Many physicochemical features of a molecular compound can be predicted using topological indices. In this article, we will determine some topological invariants of silicon carbide SiC3–I[t, u] for all values of t and u.
Post-Myocardial Infarction Ventricular Septal Defect Successfully Treated with Impella as Bridge to Cardiac Transplantation
Authors: Giudicatti Lauren , Silbert Benjamin , Xu Xiao-Fang , Putrino Anthony , Lee Felicity , Shah Amit , Larbalestier Robert , Slimani Karim , Laycock Andrew , Lam Kaitlyn
Published: August 2022

A 63-year-old female presented late with anterior ST-elevation myocardial infarction and cardiogenic shock. This was complicated by acute ventricular septal defect with large left-to-right shunt. An Impella CP was inserted on day seven with rapid haemodynamic improvement. This facilitated bridge to cardiac transplant on day twelve post-MI.
Quantitative Inversion Model Design of Mine Water Characteristic Ions Based on Hyperspectral Technology
Authors: Li Yunbo
Published: August 2022

In view of the problems of low measurement accuracy and repeated calibration during the use of coal mine water quality analysis, the hyperspectral reflection noncontact measurement technology was proposed to solve the existing problems. KCl, NaCl, pH, NaHCO3, and CaCl2were used to indicate the characteristic ion information of Na+, K+, Ca2+, Cl−, HCO3−, and pH mine water in the laboratory, and 2220 spectral data were obtained by spectral determination. Savitzky–Golay convolution smoothing was used to smooth and denoise the original spectral data of each ion, and the relationship between the spectrum and the concentration of each reagent was obvious after smoothing and denoising pretreatment. The principal component regression method was used to build the inversion model of each ion content, and through the modeling study, the prediction set of KCl was found: the coefficient R∧2 reaches 0.907, RPD is up to 2.7; the prediction set of NaCl was found: the coefficient R∧2 reaches 0.957, RPD is up to 3.1; the PH prediction set was found: the coefficient R∧2 reaches 0.785, RPD is up to 2.1; the prediction set of NaHCO3 was found: the coefficient R∧2 reaches 0.137, RPD is up to 1.2; the prediction set of CaCl2 was found: the coefficient R∧2 reaches 0.622, and RPD is up to 1.7. The results show that the hyperspectral method can play a better role in the extraction of K+, Cl−, Na+, Ca2+, and pH. It is difficult to extract HCO3− ions.
Innovation of Visual Communication Design of Interactive Packaging for Internet-Famous Food Based on Artificial Intelligence
Authors: Wang Lina
Published: August 2022

The combination of visual communication design of food packaging and digital media art is the new trend. In this study, the visual communication design of interactive packaging of Internet-famous food is analyzed, and an innovative identification method of the interactive packaging visual communication design based on artificial intelligence is put forward. The weighted fusion rule of conjunction disjunction double operators is used to fuse the innovative evaluation information of multiple expert groups on interactive packaging, and finally, the innovative multigroup discrimination results of interactive packaging are obtained. Considering both the consistency of evaluation information and the contradiction of evaluation information, the weight factor is introduced to achieve the balance between conjunction operator and disjunction operator. The designed conjunctive disjunctive double operator weighted fusion rule can support the contradiction handling in the innovative discrimination of interactive packaging and has the advantages of convenient application and easy programming.
Relationship between Intestinal Microflora and Hepatocellular Cancer Based on Gut‐Liver Axis Theory
Authors: Li Shipeng , Han Wenfeng , He Qichen , Zhang Wei , Zhang Youcheng
Published: August 2022

The intestinal microflora is a bacterial group that lives in the human digestive tract and has a long‐term interdependence with the host. Due to the close anatomical and functional relationship between the liver and the intestine, the intestinal flora affects liver metabolism via the intestinal‐hepatic circulation, thereby playing an extremely important role in the pathological process of liver inflammation, chronic fibrosis, and liver cancer. In recent years, the rapid development of technologies in high‐throughput sequencing and genomics has opened up possibilities for a broader and deeper understanding of the crosstalk between the intestinal flora and the occurrence and development of liver cancer. This review aims to summarize the mechanisms by which the gut microbiota changes the body’s metabolism, through the gut‐liver axis, thereby affecting the occurrence and development of primary liver cancer. In addition, the potential regulation of intestinal microflora in the treatment of liver cancer is discussed.
Improved Genetic Algorithm for English Information Education and Teaching Quality Impact Analysis
Authors: Li Yuanbo , Song Wanhe
Published: August 2022

With the increase of the number of college students, in order to improve the quality of teaching, the professional setting is more and more detailed, the course content is more and more rich, the information teaching system will be increasingly perfect. This paper proposes an improved genetic algorithm for English informatics education scheduling system to address the problem of the contradiction between the rapidly increasing number of students and the limited teaching resources in the current English scheduling in universities. The system first proposes an improved parallel mechanism which computes the probability of population changing evolutionary strategies according to the algebra of population maintaining the optimal solution invariant. The evolution strategy needed by the population can be obtained by fuzzy reasoning according to the evolution degree and individual difference of the population so as to get rid of the local extreme value and search for the global optimal value. Secondly, an improved algorithm initialization method, fitness transformation method, and competition strategy are proposed to adjust the balance between convergence speed and population diversity. Finally, based on Baldwin evolution theory, a new local search strategy is proposed. The algorithm has been tested on famous international data sets and a school’s English curriculum. Experimental results show that this algorithm has higher efficiency and quality than other algorithms with better performance, and the proposed teaching system can improve the teaching quality of English information education.
Data Mining Algorithm for College Students’ Mental Health Questionnaire Based on Semisupervised Deep Learning Method
Authors: Li Maoning
Published: August 2022

In recent years, there are many cases of college students who have psychological problems affecting their studies, dropping out of school, and even committing suicide. College students, as a part of high-level talents, have always been regarded as outstanding members of society. They default to have strong psychological qualities, but the reality is disappointing. Various pressures such as academics, social relations, and employment make college students the mental exhaustion has led to many bloody tragedies. The timely detection of psychologically abnormal students has become one of the most concerned and thorny issues in major universities. By constructing a mental health state perception model for college students and optimizing the model parameters, it can be seen that the

f

score of the internal and external tendency model has increased by 3.3%, the

f

score of the depression binary model has increased by 2.5%, and the anxiety binary model of the

f

score has increased by 2.5%. The score increased by 8%. The established model has an obvious effect and can quickly analyze the difference between the behaviors of psychologically abnormal students and normal students in school and also provide a management decision-making basis for college student managers and psychological counselors.
Research on Site Safety Management by BIM Technology Based on Fuzzy Intuition Set
Authors: Liu Peng
Published: August 2022

The application of building information modeling technology on a smart site can take advantage of the coordination, visualization, and simulation capabilities of this technology, allowing the engineering design and construction of the site to be based on a BIM model. In the construction of any engineering project, an accurate BIM model can be created using engineering information and data gathered at an early stage. Even though the “smart site” management system and BIM technology have some positive effect on safety production management, there is still room for improvement. This paper introduces the characteristics of safety and security management through the analysis of BIM big data and then selects evaluation indices. Using administrator experience and fuzzy inference tools, inference rules are then established and inference models are designed. Lastly, example validation and comparative analysis verify the method’s rationality and efficacy, providing a basis for the administrator’s deployment decision for the safety production command. Experiments show that the method proposed in this paper can be better applied to site safety management.
Competency Evaluation Model of English Teaching Position Based on Nonlinear Random Matrix
Authors: He Xuan , Song Xiaochuan
Published: August 2022

Nowadays, the economic globalization is advancing day by day. As the most widely used language for international communication, English is becoming more and more important. English education is the most important part of the whole English basic education, and English teachers play an important role in developing the cause of English basic education. Job competency assessment is a technical method to assess the staff’s working ability, which can comprehensively and objectively evaluate the English teaching position competency. Based on the in-depth study of the relevant theories of the job competency model, the nonlinear random matrix is applied to the job competency evaluation, and the components of English teachers’ job competency are deeply explored. The competency evaluation elements are constructed from three levels of knowledge ability, technical ability, and potential ability. Based on the established job competency evaluation model, this paper evaluates and analyzes the English teachers’ job competency. The results showed that the model had good structural validity, and the correlation between factors and the scale was 0.3∽0.8, with no significant difference in each factor, which provided strong support for the assessment of English teaching competence.
Analytical Model of Action Fusion in Sports Tennis Teaching by Convolutional Neural Networks
Authors: Li Huiguang , Guo Hanzhao , Huang Hong
Published: August 2022

In order to improve the effectiveness of tennis teaching and enhance students’ understanding and mastery of tennis standard movements, based on the three-dimensional (3D) convolutional neural network architecture, the problem of action recognition is deeply studied. Firstly, through OpenPose, the recognition process of human poses in tennis sports videos is discussed. Athlete tracking algorithms are designed to target players. According to the target tracking data, combined with the movement characteristics of tennis, real-time semantic analysis is used to discriminate the movement types of human key point displacement in tennis. Secondly, through 2D pose estimation of tennis players, the analysis of tennis movement types is achieved. Finally, in the tennis player action recognition, a lightweight multiscale convolutional model is proposed for tennis player action recognition. Meanwhile, a key frame segment network (KFSN) for local information fusion based on keyframes is proposed. The network improves the efficiency of the whole action video learning. Through simulation experiments on the public dataset UCF101, the proposed 3DCNN-based KFSN achieves a recognition rate of 94.8%. The average time per iteration is only 1/3 of the C3D network, and the convergence speed of the model is significantly faster. The 3DCNN-based recognition method of information fusion action discussed can effectively improve the recognition effect of tennis actions and improve students’ learning and understanding of actions in the teaching process.
Pitch Evaluation of Matouqin Chamber Music Performance Based on Artificial Neural Network
Authors: Hu Guangying
Published: August 2022

In order to study the pitch evaluation of Matouqin chamber music performance based on artificial neural network, this paper puts forward the relevant theories in the fields of human ear auditory perception system, auditory psychology, music theory knowledge, and pattern recognition. This paper extracts the auditory image features of chords and then establishes a sparse representation classifier model for chord recognition and classification. Scale-invariant feature transformation (SIFT) and spatial pyramid matching (SPM) are used to extract the detailed features of chord auditory images. The experimental results show that the highest correct recognition rate of the chord recognition algorithm based on the auditory image proposed in this paper is 76.2%, which is 20.4% higher than that of MFCC feature based on human auditory characteristics.
Targeting Oxidative Stress Mechanisms to Treat Alzheimer’s and Parkinson’s Disease: A Critical Review
Authors: Aborode Abdullahi Tunde , Pustake Manas , Awuah Wireko Andrew , Alwerdani Mariam , Shah Parth , Yarlagadda Rohan , Ahmad Shahzaib , Silva Correia Inês F. , Chandra Ayush , Nansubuga Esther Patience , Abdul-Rahman Toufik , Mehta Aashna , Ali Omar , Amaka Shekinah Obinna , Zuñiga Yves Miel H. , Shkodina Anastasiia D. , Inya Oko Christian , Shen Bairong , Alexiou Athanasios
Published: August 2022

Neurodegenerative disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) are becoming more frequent as the age increases. Contemporary therapies provide symptom resolution instead of targeting underlying pathological pathways. Consequently, there is considerable heterogeneity in response to treatment. Research has elucidated multiple potential of pathophysiological mechanisms contributing to neurodegenerative conditions, among which oxidative stress pathways appear to be suitable drug targets. The oxidative stress pathway has given rise to numerous novel pharmacological therapies that may provide a new avenue for neurodegenerative diseases. For example, SKQ (plastoquinone), MitoVitE, vitamin E, SOD mimic, MitoTEMPO (SOD mimetic), and bioactive molecules like curcumin and vitamin C have indeed been examined. To better understand how oxidative stress contributes to neurodegenerative diseases (such as Alzheimer’s and Parkinson’s), we analyzed the medicinal qualities of medicines that target markers in the cellular oxidative pathways. The specific pathway by which mitochondrial dysfunction causes neurodegeneration will require more investigation. An animal study should be carried out on medications that tackle cellular redox mechanisms but are not currently licensed for use in the management of neurodegenerative conditions.
Specific Surface Area Enhancement of Waste Tire-Based Activated Carbon by Demineralization Technique and Adsorption of Methylene Blue
Authors: Kassahun Estifanos , Mekuria Sintayehu , Beyan Surafel Mustefa
Published: August 2022

This study was focused on the synthesis of activated carbon from a waste tire and the enhancement of its specific area by a demineralization technique that can be used for the removal of methylene blue dye (MB). Maximum MB removal (89.41%) was attained at an impregnation ratio value of 0. A maximum yield (42.65%) was found at 48 h. Waste tire-based activated carbon (WTAC)'s ability to remove MB was increased by large values up to an impregnation time of 24 h. WTAC has a maximum MB removal of 90.13% at 4 M of KOH. The sample had a surface area of 53 m2/g. This sample was demineralized by using NaOH and H2SO4 in a 1 : 1 ratio, and the surface area was enhanced to 257 m2/g. In the demineralization process, a massive decrement of metals from the waste was seen; for each metal, namely, Zn2+, Al3+, Ca2+, and Mg2+, 43.79%, 32.45%, 27.95%, and 6.843% reductions were achieved, respectively. After this process, the maximum removal of MB was found at 1.2 g adsorbent dosage, 120 min, pH 8, the temperature of 20°C, and an initial dye concentration of 10 mg/L. The adsorption mechanism revealed that the process of adsorption happens at a specific site of homogeneous adsorption on the surface of the adsorbent. The kinetics study showed that the adsorption process of the dye is mainly affected by the chemical reaction.
Innovation and Digital Construction of Cultural Tourism Industry under the Background of Big Data and Internet of Things
Authors: Liu Bo
Published: August 2022

In recent years, the concept of the Internet of Everything has gradually entered all walks of life, and the combination of big data technology and the Internet of Everything applied to other industries has become the current trend of industrial innovation. The integration between the tourism industry and the cultural industry is an inevitable trend for the sustainable development of the tourism industry. New science and technology are constantly innovating the way of life of human beings. If the tourism industry wants to maintain a state of rapid development, it must focus on the innovative development and digital construction of cultural tourism, develop new cultural tourism products, and propose new marketing innovation models. This promotes faster and better development of the tourism industry. Based on this, this paper designs a questionnaire and uses statistical methods to analyze the data obtained by the questionnaire, summarizes the current problems in the innovation and digital construction of the cultural tourism industry, and proposes solutions in a targeted manner, in order to improve the cultural tourism industry. Healthy development provides incremental contributions.
Operating of DSIM without Current and Speed Sensors Controlled by ADRC Control
Authors: Oumar Aichetoune , Ahmed Yarba , Cherkaoui Mohamed
Published: August 2022

This paper presents an operation of the double start induction machine (DSIM) without current and speed sensors controlled by active disturbance rejection control (ADRC). The operation of the machine, without current and speed sensors, is an economic and simple method. The main advantages of this method are the reconstruction of stator current phases and rotor speed using only one DC voltage. The method is very simple and effective. It is based on the information provided by a DC voltage and the switching states of the converters to reconstruct the stator voltages. After we use two voltages observers to estimate the current stator and the rotor speed. The performance and the effectiveness of this method are verified under different conditions of simulations in MATLAB/Simulink. The results of the simulation prove the ability of this method to produce the same performances of DSIM with the current and speed sensors.
Study on Roof Presplitting Mechanism and Deformation Control of Reused Roadway in Compound Soft Rock by Roof Presplitting Approach
Authors: Qi Fuzhou , Yang Dangwei , Li Xinming , Li Bin , Zhai Yingying , Yu Shuisheng
Published: August 2022

Based on the compound soft rock movement law, the rock formation structural features for a mining roadway were examined. Furthermore, the presplitting, extension, and coalescence mechanism of rock formation and the engineering needs for reusing an abandoned underground roadway were studied. According to engineering and geological conditions, an analytic expression of the inertial force, the moment of inertia, and the vertical stress acting on the breakage are obtained by constructing a structural mechanical model of the overlying strata. The transfixion law, the tensile shear failure characteristics, the release rate in the fracture expansion process of presplitting fissures, and the evolution law of the plastic zone in reused roadway surrounding rock on conditions of 0 m, 0.5 m, 1 m, 1.5 m, and 2 m fissure intervals are analyzed by creating a three-dimensional numerical model. The stress distribution and deformation laws of the roadway under mining disturbance are obtained. The results show that when the fissure interval is 1 m, the peak value of the horizontal stress reaches 10~12 MPa, and the stress concentration factor is 1.8~2.2, which promotes fracture expansion and transfixion and controls plastic damage development on both sides of a fracture. The elastic strain energy of the reused roadway is reduced to 32.7% of the peak value. The deformation of the roof is coordinated and deformed. It is helpful for the compound soft rock strata to fall and plug the area, forming a stable composite reused roadway surrounding the rock structure. A 1 m presplitting fissure interval is adopted for field testing, and the monitoring results show that 0~3.0 m behind the mining face, presplitting fissures will be connected and begin to fall. Moreover, 3.0~6.0 m behind the mining face, compound soft rock strata will fall and plug the area fully, forming a reused roadway structure. Severe deformation of reused roadway was reduced, and the stability of surrounding rock was improved by implementing the roof presplitting approach.
Related Factors of Cerebral Hemorrhage after Cerebral Infarction and the Effect of Atorvastatin Combined with Intensive Nursing Care
Authors: Yang Qian , Yang Yuedong , Li Xiaoting
Published: August 2022

Background. Cerebral infarction is a common neurological disease with high incidence, which is the main factor causing death and disability in adults in China. Cerebral hemorrhage transformation is a common clinical complication. High NIHSS score at admission, atrial fibrillation, and small artery occlusion cerebral infarction can increase the risk of cerebral infarction complicated with hemorrhage transformation. Aim. To explore the related factors of cerebral hemorrhage transformation after cerebral infarction and the value of atorvastatin calcium tablets combined with early intensive care measures. Methods. In this study, a case-control study was conducted. Sixty patients with hemorrhagic transformation after cerebral infarction admitted to the Department of Neurology of our hospital from January 2017 to June 2021 were selected as the observation group, and 90 patients with cerebral infarction without hemorrhagic transformation during the same period were selected as the control group. The risk factors of hemorrhagic transformation after cerebral infarction were analyzed. Results. The results of logistic regression model showed that the increased National Institutes of Health Stroke Scale (NIHSS) score at admission, hypertension, atrial fibrillation, TOAST classification of small artery occlusion, and large infarction lesions were the risk factors for hemorrhagic transformation in patients with cerebral infarction (

P
<
0.05

). After 2 weeks and 4 weeks of treatment, the NIHSS scores of the intervention group were lower than those of the conventional group (

P
<
0.05

). NIHSS scores of the two groups after treatment were significantly lower than those before treatment (

P
<
0.05

). After three months of treatment, the patients in the intervention group with GOS score of 5 points accounted for 16.67%, and the patients with GOS score of 4 points accounted for 56.67%. The patients in the conventional group with GOS score of 5 points accounted for 6.67%, and the patients with GOS score of 4 points accounted for 33.33%. The prognosis of the intervention group was better than that of the conventional group on the whole (

P
<
0.05

). Conclusion. Patients with hypertension, large infarction lesions, high NIHSS score at admission, atrial fibrillation, and small artery occlusion cerebral infarction can increase the risk of bleeding transformation in patients with cerebral infarction. For patients with bleeding transformation, atorvastatin calcium tablets combined with early intensive nursing intervention has a certain value for improving the prognosis of patients.
Application of Boiler Optimization Monitoring System Based on Embedded Internet of Things
Authors: Zhang Haiqing , Qiu Mingxiang , Yu Xingjiang , Wu Yujiao , Ma Yinhong
Published: August 2022

In order to study the deficiency of tuning PID controller in traditional boiler combustion air system, this paper proposes an embedded Internet of things monitoring system and puts forward a method of fuzzy PID controller with the help of nonlinear optimization algorithm to reasonably set the initial value of control parameters. Through the simulation comparison with the traditional engineering tuning PID controller, it is found that when the simulation process is t = 2000 s, the step disturbance with amplitude of 0.1 is added to the system input, and there will be slight disturbance in the combustion process. Through fuzzy PID and strong robustness support, the modeling error of the monitoring system model can be controlled between 10%∼25%, which proves that the nonlinear optimized PID has strong anti-interference and can meet the process requirements of the system.
Correlation of TET‐2 Levels with Disease Evaluation in AMI Patients
Authors: Wang Qinglei , Fu Shan , Li Shucheng , Huang Xiansheng , Wang Hong
Published: August 2022

Objective. To investigate the expression levels of Ten‐Eleven Translocation‐2 (TET‐2) in patients with acute myocardial infarction (AMI) and correlations of TET‐2 levels with disease severity. Methods. A total of 150 patients with confirmed AMI were included in this study. According to the number of coronary artery lesions, the patients were divided into a single lesion group, two lesion group, and three lesion group. According to the Gensini scores, these patients were also assigned into three groups: the low‐risk group, middle‐risk group, and high‐risk group. 150 patients without AMI confirmed by coronary angiography were included in the control group in the same period. TET‐2 and cardiac troponin T (cTNT) levels were detected by ELISA analysis and compared among different groups. Pearson’s correlation analysis was used to evaluate the correlations of TET‐2 levels and cTNT levels/Gensini scores. The levels of TET‐2 in AMI patients increased remarkably with the increase of disease severity. Patients in the single lesion group or low‐risk group had the lowest levels of TET‐2. Pearson correlation analysis indicated that TET‐2 levels were positively associated with cTNT levels and Gensini scores, respectively (all P < 0.001). Conclusion. The levels of TET‐2 were upregulated in AMI patients and positively correlated with cTNT levels or Gensini scores, suggesting that the examination of TET‐2 expression levels could be exploited for predicting the disease severity.
Tunicate swarm algorithm based maximum power point tracking for photovoltaic system under non-uniform irradiation
Authors: Sholikhah Evi Nafiatus , Windarko Novie Ayub , Sumantri Bambang
Published: August 2022

A new maximum power point tracking (MPPT) technique based on the bio-inspired metaheuristic algorithm for photovoltaic system (PV system) is proposed, namely tunicate swarm algorithm-based MPPT (TSA-MPPT). The proposed algorithm is implemented on the PV system with five PV modules arranged in series and integrated with DC-DC buck converter. Then, the PV system is tested in a simulation using PowerSim (PSIM) software. TSA-MPPT is tested under varying irradiation conditions both uniform irradiation and non-uniform irradiation. Furthermore, to evaluate the performance, TSA-MPPT is compared with perturb & observe-based MPPT (P&O-MPPT) and particle swarm optimization-based MPPT (PSO-MPPT). The TSA-MPPT has an accuracy of 99% and has a reasonably practical capability compared to the MPPT technique, which already existed before.
Risk assessment of power system transient instability incorporating renewable energy sources
Authors: Hoballah Ayman , EL-Sayed Salah Kamal , Al Otaibi Sattam , Hendawi Essam , Elkalashy Nagy , Ahmed Yasser
Published: August 2022

Transient stability affected by renewable energy sources integration due to reductions of system inertia and uncertainties associated with the expected generation. The ability to manage relation between the available big data and transient stability assessment (TSA) enables fast and accurate monitoring of TSA to prepare the required actions for secure operation. This work aims to build a predictive model using Gaussian process regression for online TSA utilizing selected features. The critical fault clearing time (CCT) is used as TSA index. The selected features map the system dynamics to reduce the burden of data collection and the computation time. The required data were collected offline from power flow calculations at different operating conditions. Therefore, CCT was calculated using electromagnetic transientsimulation at each operating point by applying self-clearance three phase short circuit at prespecified locations. The features selection was implemented using the neighborhood component analysis, the Minimum Redundancy Maximum Relevance algorithm, and K-means clustering algorithm. The vulnerability of selected features tends to result great variation on the best features from the three methods. Hybrid collection of the best common features was used to enhance the TSA by refining the final selected features. The proposed model was investigated over 66-bus system.
Design methodology for general enhancement of a single-stage self-compensated folded-cascode operational transconductance amplifiers in 65 nm CMOS process
Authors: AL-Qaysi Hayder Khaleel , Saleh Adham Hadi , Mahmood Tahreer
Published: August 2022

The problems resulting from the use of nano-MOSFETs in the design of operational trans-conductance amplifiers (OTAs) lead to an urgent need for new design techniques to produce high-performance metrics OTAs suitable for very high-frequency applications. In this paper, the enhancement techniques and design equations for the proposed single-stage folded-cascode operational trans-conductance amplifiers (FCOTA) are presented for the enhancement of its various performance metrics. The proposed single-stage FCOTA adopts the folded-cascode (FC) current sources with cascode current mirrors (CCMs) load. Using 65 nm complementary metal-oxide semiconductor (CMOS) process from predictive technology model (PTM), the HSPICE2019-based simulation results show that the designed single-stage FCOTA can achieve a high open-loop differential-mode DC voltage gain of 65.64 dB, very high unity-gain bandwidth of 263 MHz, very high stability with phase-margin of 73°, low power dissipation of 0.97 mW, very low DC input-offset voltage of 0.14 uV, high swing-output voltages from −0.97 to 0.91 V, very low equivalent input-referred noise of 15.8 nV/Hz, very high common-mode rejection ratio of 190.64 dB, very high positive/negative slew-rates of 157.5/58.3 V⁄us, very fast settling-time of 5.1 ns, high extension input common-mode range voltages from −0.44to 1 V, and high positive/negative power-supply rejection ratios of 75.5/68.8 dB. The values of the small/large-signal figures-of-merits (𝐹𝑜𝑀s) are the highest when compared to other reported FCOTAs in the literature.
Development and characterization of an automated portable wound irrigation device for diabetic ulcers
Authors: Nadhif Muhammad Hanif , Assyarify Hanif , Irsyad Muhammad , Yunir Em , Edi Tarigan Tri Juli , Sarumpaet Angela , Iswati Eni
Published: August 2022

Patients with diabetes mellitus may experience peripheral neuropathy and extremity system impairment, which may lead to ulceration if the treatments are delayed. Diabetic ulcers, unfortunately, are chronic, which require proper treatments, including debridement and bacterial removal using an irrigation device. To date, commercial irrigation devices included pulsed-lavage, bulb syringe, and gravity bags. Unfortunately, the devices have limitations in terms of portability, measurability, controllability, and disposability. To tackle the limitations, this study aims to design, fabricate, and characterize an automated portable wound irrigation device (Apdice), which is controllable non-disposable, and portable. The device was designed and fabricated using a lightweight construction, a rechargeable battery, and non-disposable materials to support the portability and non-disposable means. Meanwhile, the proportional-derivative-integral controller with its peripheral components were featured to enable controllability. Furthermore, the device was also tested regarding the contamination using a particle counter, and appeared to be contamination free. In short, Apdice showed a robust technological performance. However, it is also worth the try to test the contamination test against biological agents to guarantee the biocompatibility of the device.
Quantum dot phosphors CaS:Ce3+ and CaS:Pb2+, Mn2+ for improvements of white light-emitting diodes optic characteristics
Authors: Nguyen Thi Dieu An , Nguyen Thi My Hanh , Huu Phuc Dang
Published: August 2022

The goal of this study is to discover a new method that uses standard phosphors and quantum dots to improve the lighting qualities and heat manipulation of white light-emitting diodes (WLEDs). Despite the popularity as a good ingredient that offers good color rendering properties, quantum dots (QDs) have not been widely employed in the fabrication of WLEDs, particularly, the utilization of QDs-phosphor-mixed nanocomposite is limited. We propose a unique packaging design based on the research’s experimental findings. The layer of nanocomposites consisting of QDs and phosphors is horizontally positioned to the WLED for optimal lighting and heating efficiency. This study simulated and used four distinguishing white LEDs forms: mono-layer phosphorus, two double-layer remote phosphors featuring yellowish-red and yellowish-green organizations, and a triple-layer phosphor. In terms of color rendering and luminous outputs, the triple-layer phosphor configuration outperforms the other implementations, as per the finding.
Single phase second order sliding mode controller for complex interconnected systems with extended disturbances and unknown time-varying delays
Authors: Nguyen Cong-Trang , Hien Chiem Trong , Phan Van-Duc
Published: August 2022

Novel results on complex interconnected time-delay systems with single phase second order sliding mode control is investigated. First, a reaching phase in traditional sliding mode control (TSMC) is removed by using a novel single phase switching manifold function. Next, a novel reduced order sliding mode observer (ROSMO) with lower dimension is suggested to estimate the unmeasurable variables of the plant. Then, a new single phase second order sliding mode controller (SPSOSMC) is established based on ROSMO tool to drive the state variables into the specified switching manifold from beginning of the motion and reduce the chattering in control input. Then, a stability condition is suggested based on the well-known linear matrix inequality (LMI) method to ensure the asymptotical stability of the whole plant. Finally, an illustrated example is simulated to validate the feasible application of the suggested technique.
Novel lightweight video encryption method based on ChaCha20 stream cipher and hybrid chaotic map
Authors: Maolood Abeer Tariq , Gbashi Ekhlas Khalaf , Mahmood Eman Shakir
Published: August 2022

In the recent years, an increasing demand for securing visual resource-constrained devices become a challenging problem due to the characteristics of these devices. Visual resource-constrained devices are suffered from limited storage space and lower power for computation such as wireless sensors, internet protocol (IP) camera and smart cards. Consequently, to support and preserve the video privacy in video surveillance system, lightweight security methods are required instead of the existing traditional encryption methods. In this paper, a new light weight stream cipher method is presented and investigated for video encryption based on hybrid chaotic map and ChaCha20 algorithm. Two chaotic maps are employed for keys generation process in order to achieve permutation and encryption tasks, respectively. The frames sequences are encrypted-decrypted based on symmetric scheme with assist of ChaCha20 algorithm. The proposed lightweight stream cipher method has been tested on several video samples to confirm suitability and validation in term of encryption–decryption procedures. The performance evaluation metrics include visual test, histogram analysis, information entropy, correlation analysis and differential analysis. From the experimental results, the proposed lightweight encryption method exhibited a higher security with lower computation time compared with state-of-the-art encryption methods.
Proactive depression detection from Facebook text and behavior data
Authors: Hemtanon Siranuch , Aekwarangkoon Saifon , Kittiphattanabawon Nichnan
Published: August 2022

This paper proposes a proactive method to detect the clinical depression affected person from post and behavior data from Facebook, called text-based and behavior-based models, respectively. For a text-based model, the words that make up the posts are separated and converted into vectors of terms. A machine learning classification applies the term frequency- inverse document frequency technique to identify important or rare words in the posts. For the behavior-based model, the statistical values of the behavioral data were designed to capture depressive symptoms. The results showed that the behavior-based model was able to detect depressive symptoms better than the text-based model. Regarding performance, a detection model using behaviors yields significantly higher F1 scores than those using words in the post. The K-nearest neighbor (KNN) classifier is the best model with the highest F1 score of 1.0, while the highest F1 score of the behavior-based model is 0.88. An analysis of the predominant features influencing depression signifies that posted messages could detect feelings of self-hatred and suicidal thoughts. At the same time, behavioral manifestations identified depressed people who manifested as restlessness, insomnia, decreased concentration.
Gender detection in children’s speech utterances for human-robot interaction
Authors: Badr Ameer Abdul-Baqi , Abdul-Hassan Alia Karim
Published: August 2022

The human voice speech essentially includes paralinguistic information used in many real-time applications. Detecting the children’s gender is considered a challenging task compared to the adult’s gender. In this study, a system for human-robot interaction (HRI) is proposed to detect the gender in children’s speech utterances without depending on the text. The robot's perception includes three phases: Feature’s extraction phase where four formants are measured at each glottal pulse and then a median is calculated across these measurements. After that, three types of features are measured which are formant average (AF), formant dispersion (DF), and formant position (PF). Feature’s standardization phase where the measured feature dimensions are standardized using the z-score method. The semantic understanding phase is where the children’s gender is detected accurately using the logistic regression classifier. At the same time, the action of the robot is specified via a speech response using the text to speech (TTS) technique. Experiments are conducted on the Carnegie Mellon University (CMU) Kids dataset to measure the suggested system’s performance. In the suggested system, the overall accuracy is 98%. The results show a relatively clear improvement in terms of accuracy of up to 13% compared to related works that utilized the CMU Kids dataset.
Efficient time-series forecasting of nuclear reactions using swarm intelligence algorithms
Authors: Shaker Mehdy Hala , Jabbar Qasim Nariman , Hadi Abbas Haider , Al_Barazanchi Israa , Muwafaq Gheni Hassan
Published: August 2022

In this research paper, we focused on the developing a secure and efficient time-series forecasting of nuclear reactions using swarm intelligence (SI) algorithm. Nuclear radioactive management and efficient time series for casting of nuclear reactions is a problem to be addressed if nuclear power is to deliver a major part of our energy consumption. This problem explains how SI processing techniques can be used to automate accurate nuclear reaction forecasting. The goal of the study was to use swarm analysis to understand patterns and reactions in the dataset while forecasting nuclear reactions using swarm intelligence. The results obtained by training the SI algorithm for longer periods of time for predicting the efficient time series events of nuclear reactions with 94.58 percent accuracy, which is higher than the deep convolution neural networks (DCNNs) 93% accuracy for all predictions, such as the number of active reactions, to see how the results can improve. Our earliest research focused on determining the best settings and preprocessing for working with a certain nuclear reaction, such as fusion and fusion task: forecasting the time series as the reactions took 0-500 ticks being trained on 300 epochs
Performance evaluation of 4-quadrature amplitude modulation over orthogonal frequency division multiplexing system in different fading channels scenarios
Authors: Mohammed Hasan Fadhil , Mughir Ghanim Abdulkareem
Published: August 2022

Orthogonal frequency division multiplexing (OFDM) is a multicarrier modulation (MCM) technique that divides the wide bandwidth into parallel narrow bands, each of which is modulated by orthogonal subcarriers. Currently, OFDM is a high-spectral efficiency modulation technique that is used in a variety of wired and wireless applications. The transmitted signal in a wireless communication channel spreads from transmitter to receiver through multiple reflective paths. This triggers multipath fading, which causes variations in the received signal's amplitude and phase. Slow/fast and frequency-selective/frequency-nonselective are the main types of multipath fading channels. Therefore, in this paper, we proposed new models for modeling multipath fading channels, such as the exponential fading channel and the Gamma fading channel. In addition, new bit-error-rate (BER) derivations have been derived. The performance of the OFDM system over proposed channel models has been evaluated using Monte-Carlo simulation and compared to the Rayleigh fading channel model. The obtained results via simulations show that the exponential fading channel at a rate parameter (λ=0.5) outperforms the Rayleigh fading channel by 6 dB for all values of Eb/No, while the Gamma fading channel at (α=2) outperforms the Rayleigh fading channel by 3 dB for all values of Eb/No.
Game development software engineering: digital educational game promoting algorithmic thinking
Authors: Sungkaew Kornchulee , Lungban Piyamas , Lamhya Sirinya
Published: August 2022

The purpose of this study is to create a digital educational game that promotes algorithmic thinking for elementary school students. However, the processes of game development differ from traditional software development which cannot guarantee its effectiveness in terms of human-machine interfaces. In this article, we propose a new game development software engineering (GDSE) as a model for game development. This new model aims to complement and mitigate the shortcomings of traditional software development. The principles of human-computer interaction are now incorporated in the new model. The GDSE includes design, development, usability inspection, game experience evaluation, educational value evaluation and release. It was used as a research method to develop a game that promotes algorithmic thinking for children. The results of this study are not only a digital educational game that promotes algorithmic thinking for children but also a new game development life cycle that guarantees the performance of digital games in terms of usability enhancement, game experience and educational value.