bluuebunny/crossref_metadata_2025 huggingface dataset:
Concurrence of Rheumatoid Arthritis and Ankylosing Spondylitis: Analysis of Seven Cases and Literature Review
Authors: Flores-Robles Bryan-Josué , Labrador-Sánchez Eztizen , Andrés-Trasahedo Estíbaliz , Pinillos-Aransay Valvanera , Joven-Zapata María-Yasmina , Torrecilla Lerena Laura , Salazar-Asencio Osman-Alberto , López-Martín Juan-Antonio
Published: May 2022

Introduction. The association of rheumatoid arthritis (RA) and ankylosing spondylitis (AS) in a single patient is a rarely described phenomenon. AS and RA are conditions that can have a high impact on the morbidity and mortality of patients. Methods. We described the clinical, epidemiological, analytical, and radiological characteristics of 81 patients with concomitant diagnosis of rheumatoid arthritis (RA) and ankylosing spondylitis (AS). Of these patients, seven were diagnosed at our hospital. A literature review was carried out using Medline, Embase, Scopus, and virtual hospital libraries, including the period from January 1950 to April 2020. Results. Regarding the results, 71% of the patients were men, with a mean age of 53 years (±14.83). RA was the first disease diagnosed in 52% of the cases. Approximately 53% of the patients had rheumatoid nodules, and 83% reported inflammatory lumbar pain during their evaluation. Erosions were observed on radiographs of the hands and/or feet in 85% of the cases, and almost all the patients (80/81) had sacroiliitis on imaging studies. Approximately 92% of the cases were rheumatoid factor (RF) positive and 90% HLA B-27 positive. Conclusions. The coexistence of RA and AS is highly uncommon. With the data obtained in this review, it seems that there exist erosive radiological patterns, positivity for RF, involvement of the axial skeleton, and rheumatoid nodules at a higher frequency than those patients with a single diagnosis of the two entities. More data are needed to corroborate this association.
Alterations in Immune-Related Defensin Alpha 4 (DEFA4) Gene Expression in Health and Disease
Authors: Basingab Fatemah , Alsaiary Abeer , Almontashri Shahad , Alrofaidi Aisha , Alharbi Mona , Azhari Sheren , Algothmi Khloud , Alhazmi Safiah
Published: May 2022

Defensin Alpha 4 (DEFA4) is the fourth member of the Alpha Defensins family known as a part of antimicrobial peptides in the innate immune system. DEFA4 has a strong preference to kill Gram-negative bacteria more than Gram-positive bacteria. In addition, DEFA4 exhibits antiviral activity against human immunodeficiency virus type 1 (HIV-1) in vitro. Moreover, DEFA4 can act as an inhibitor of corticosterone production (Corticostatin). On the other hand, alternations in DEFA4 gene expression have been reported in different disorders such as diseases related to inflammation and immunity dysfunction, brain-related disorders, and various cancers. The up-regulation of DEFA4 appears to be involved in the malignant transformation or aggressive form of cancer. Interestingly, the modified version of DEFA4 fragment (1–11) was potent and efficient against antibiotic-resistant bacteria. This review provides a general background abSaudi Arabia out DEFA4 and sheds light on changes in DEFA4 gene expression in different diseases. The paper also discusses other aspects related to DEFA4 as an antimicrobial and antiviral agent. The research was conducted based on available articles obtained from databases starting from 1988 to the present.
Predictive Value of CT-Based Radiomics in Distinguishing Renal Angiomyolipomas with Minimal Fat from Other Renal Tumors
Authors: Han Zhiwei , Zhu Yuanqiang , Xu Jingji , Wen Didi , Xia Yuwei , Zheng Minwen , Yan Tao , Wei Mengqi
Published: May 2022

Objectives. This study is aimed at determining whether CT-based radiomics models can help differentiate renal angiomyolipomas with minimal fat (AMLmf) from other solid renal tumors. Methods. This retrospective study included 58 patients with a postoperative pathologically confirmed AMLmf (observation group) and 140 patients with other common renal tumors (control group). Non-contrast-enhanced CT and contrast-enhanced CT data were evaluated. Radiomics features were extracted from manually delineated volume of interest (VOIs). The least absolute shrinkage and selection operator (LASSO) regression was used for feature screening. Five classifiers, including logistic regression, multilayer perceptron (MLP), support vector machine (SVM),

k

-nearest neighbor (KNN), and logistic regression (LR), were used, with leave-out validation (128 training, 60 testing). The diagnostic performance of the classifier was evaluated and compared by receiver operating characteristic curve (ROC) analysis. Results. Among the 1029 extracted features, prediction models of AMLmf were composed, by 2, 10, 4, and 9 selected features for precontrast phase (PCP), corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP), respectively. Models of CMP and NP achieved adequate performance after using MLP classifier, with prediction accuracy of 0.767 (AUC 0.85, sensitivity 0.76, and specificity 0.78) and 0.783 (AUC 0.83, sensitivity 0.79, and specificity 0.78), respectively. MLP model of features selected from the combination of the all features had the best diagnostic performance (accuracy 0.8500, sensitivity 0.8095, specificity 0.9444, and AUC 0.9193). Conclusions. Radiomics features may help to distinguish benign AMLmf from common malignant kidney masses, which may contribute to the selection of interventions for renal tumors.
Chairside Screening for Undiagnosed Diabetes and Prediabetes in Patients with Periodontitis
Authors: Masiero Silvia , Alberti Alice , Corbella Stefano , Francetti Luca
Published: May 2022

Introduction. Globally, it is estimated that half of all people with diabetes are undiagnosed. Because of the well-documented bidirectional link between diabetes and periodontitis, dentists and dental hygienists may have the possibility to screen a targeted population for diabetes during routine dental visits. The aim of the present study is to investigate the effectiveness of one device for diagnosis of diabetes/prediabetes used in one private dental practice and investigate the correlation between the levels of HbA1c and periodontal parameters. Methods. Periodontal patients that were never diagnosed with diabetes were asked to fill a risk assessment questionnaire for type 2 diabetes mellitus. PD, CAL, FMBS%, FMPS%, and HbA1c through a prick-finger test were measured before and after periodontal therapy or only once in patients following supportive periodontal therapy. Results. A total of 98 subjects were screened, and among them, one had diabetes and 30 had prediabetes. The mean value of HbA1c was 5.62% for patients with untreated periodontitis and 5.42% for periodontally treated patients. The diagnosis of diabetes resulted to be correlated to FMBS% and FMPS%, while HbA1c levels were correlated to FMBS%, FMPS%, and periodontitis grade. Conclusion. The present chairside diabetes-screening protocol allowed a consistent proportion of patients to become aware of their pathological or prepathological condition and to seek proper and timely medical care. Thus, dentists and dental hygienists could provide health promotion services and preventive measures.
Peripapillary Pachychoroid Syndrome (PPS): Diagnosing and Treating a Rare Entity
Authors: Bouzika Peggy , Georgalas Ilias , Sotirianakou Maria-Evanthia , Karamaounas Aristotelis , Symeonidis Chrysanthos , Tyrlis Konstantinos , Blounas Stylianos , Mani Ekaterini , Rotsos Tryfon
Published: May 2022

Two cases with peripapillary pachychoroid syndrome (PPS) along with the challenges concerning correct diagnosis and treatment are presented. In the first case, the patient presented with painless unilateral gradual visual loss. Fundoscopy and optical coherence tomography (OCT) revealed cystoid macular edema (CME) in the left eye (LE), extending from the temporal optic disc margin towards the fovea, with no additional findings. Enhanced-depth imaging- (EDI-) OCT provided additional information and increased choroidal thickness nasally to the macula and pachyvessels in the outer choroidal layer, findings supportive of PPS. Photodynamic therapy (PDT) was applied at the leakage sites. Two months later, CME and subretinal fluid (SRF) had resolved, and VA had significantly improved. In the second case, a patient presented with reduced vision and metamorphopsia bilaterally over the previous 5 days. Fundoscopy revealed CME in both eyes. OCT confirmed the presence of CME in the papillomacular area in the right eye; similarly, CME was recorded in the macula of the LE with SRF located subfoveally. EDI-OCT showed increased choroidal thickness in both eyes. Treatment was administered, originally with dorzolamide eye drops along with eplerenone tablets, and then dexamethasone eye drops that eventually led to significant anatomic and functional improvement. It is important for ophthalmologists to be able to recognize the unique clinical entity of PPS, as its resemblance to disorders with similar features may lead to misdiagnoses and unnecessary, or even incorrect, interventions.
Evaluation of Short-Term Insulin Pump for Treatment of Patients with Type 2 Diabetes Mellitus Complicated with Lower Extremity Arterial Disease in Endocrinology by Ultrasonography
Authors: Guo Qiongyao , Li Zhouyue , Chen Hua
Published: May 2022

This research aimed to explore the curative effect of short-term insulin pump in the treatment of type 2 diabetes mellitus (T2DM) patients with lower extremity arterial disease (LEAD) based on ultrasonography. 422 patients (220 males and 202 females) with T2DM in the hospital were selected, and they were randomly divided into control group (

n
=
211

, oral hypoglycemic drugs or diet control, appropriate exercise to lower blood glucose) and experimental group (

n
=
211

, insulin pump was used to reduce blood glucose). After 2 weeks, the therapeutic effect was evaluated by ultrasonography. The results showed that after two weeks of treatment, the difference in lumen intima between the two groups was statistically significant (

P
<
0.05

). The intima-media thickness (IMT) values of the experimental group were

0.83
±
0.03
 
mm

,

0.62
±
0.03
 
mm

, and

0.41
±
0.04
 
mm

, respectively, which were significantly different from those of the control group (

1.62
±
0.54
 
mm

,

1.23
±
0.14
 
mm

, and

0.78
±
0.11
 
mm

) (

P
<
0.05

). There was obvious difference in low-density lipoprotein cholesterol (LDL-C) level between the experimental group (

2.22
±
0.46
 
mmol
/
L

) and the control group (

3.21
±
0.62
 
mmol
/
L

) (

P
<
0.05

). The LEAD score of the experimental group was

5.51
±
1.11

, which was significantly different from that of the control group (

7.08
±
2.73

) (

P
<
0.05

). There was clear difference in LEAD score between the two groups under different course of disease (CD) (

P
<
0.05

). Studies indicated that short-term application of insulin pump therapy could effectively improve the pathological changes of lower limbs in patients with T2DM, which had clinical application value.
Automatic Scoring of English Essays Based on Machine Learning Technology in a Wireless Network Environment
Authors: Zhang Fuzhuang , Yu Lan , Shen Jun
Published: May 2022

The English composition is an important indicator of English learners’ overall language skills and is asked in large-scale English examinations, both in China’s college entrance examinations and graduate examinations and in the TOEFL, GRE, and IELTS examinations in Europe and the United States. Some automatic scoring systems for English writing have been created in the United States and internationally, however the systems still have issues with generalization, accuracy, and error correction. In this paper, we present a method to improve the accuracy of existing automatic composition scoring systems through deep learning techniques in a wireless network environment. Experiments reveal that the method can accurately assess the quality of English learners’ writings, paving the way for the creation of an automated composition scoring system for large-scale machine testing and web-based self-learning platforms.
Promotion Strategy of Low-Carbon Consumption of Fresh Food Based on Willingness Behavior
Authors: Zhao Zhao , Zhong Xiaqing , Zhu Yuqing
Published: May 2022

The research on the influencing factors of residents’ low-carbon consumption willingness and low-carbon consumption behavior of fresh food has certain practical guiding significance. Existing studies have analyzed the low-carbon consumption willingness, but the factors considered are not comprehensive and the degree of fit needs to be improved. Therefore, this paper starts with 37 variables from six aspects: demographic factors, psychological factors, low-carbon related knowledge, external factors, policy norms, and product factors. The binary logistic model is used to carry out regression analysis on low-carbon consumption willingness and low-carbon consumption behavior, and the fitting degree is higher and reaches about 90%. The regression results show that sense of responsibility, government tax, low-carbon product quality, and low-carbon product price have a significant impact on residents’ low-carbon consumption willingness. Whether there are fake and shoddy products in the market and whether the products are really of low carbon have a significant impact on low-carbon consumption behavior. Finally, starting from the three subjects of government, enterprises, and residents, this paper puts forward targeted suggestions to improve residents’ low-carbon consumption willingness and promote residents’ low-carbon consumption behavior, in order to promote low-carbon consumption.
Computing Fault‐Tolerant Metric Dimension of Connected Graphs
Authors: Ahmad Uzma , Ahmed Sara , Javaid Muhammad , Alam Md Nur
Published: May 2022

For a connected graph, the concept of metric dimension contributes an important role in computer networking and in the formation of chemical structures. Among the various types of the metric dimensions, the fault‐tolerant metric dimension has attained much more attention by the researchers in the last decade. In this study, the mixed fault‐tolerant dimension of rooted product of a graph with path graph with reference to a pendant vertex of path graph is determined. In general, the necessary and sufficient conditions for graphs of order at least 3 having mixed fault‐tolerant generators are established. Moreover, the mixed fault‐tolerant metric generator is determined for graphs having shortest cycle length at least 4.
Application of the Whole Optimization of Emergency Nursing Model United and Its Influence on Patients’ Stress Response and Nursing Satisfaction
Authors: Ai Dannan , Xu Sumin
Published: May 2022

Objective. To investigate the use of an integrated emergency nursing model with a multidisciplinary team (MDT) teaching method for practice of nursing towards multiple trauma in the emergency department and its influence on patients’ stress response and nursing satisfaction. Methods. The research subjects were 120 multiple trauma patients hospitalized to our hospital’s emergency department between January 2019 and January 2020, who were evenly divided into groups A (

n
=
60

) and B (

n
=
60

) based on the sequence of admission. For patients in group A, on the basis of whole optimization of the emergency nursing model, the MDT teaching and training were given to the nursing staff in group A. Patients in group B had their emergency nursing model completely optimized. The assessment scores of nursing staff were compared. The patients’ C-reactive protein (CRP) levels in peripheral circulation, first-aid time indices, treatment effect, risk of complications & nursing contentment were all investigated. Results. Nursing personnel in group A had considerably higher achievement scores than staff nurses in group B (

P
<
0.001

). CRP levels in group A were considerably lower following therapy (

P
<
0.05

) than those in group B. The time it took for group A to receive first assistance was considerably less than that for group B (

P
<
0.001

). Group A had a considerably superior treatment effect than group B (

P
<
0.05

). Complications occurred at a lower rate in group A (

P
<
0.05

) than in group B. Group A nurses were more satisfied than group B nurses (

P
<
0.05

). Conclusion. The entire optimization of the emergency nursing model combined with the MDT way of teaching can abbreviate the rescue process, reduce stress, improve treatment effect & reduce the possibility of complications in multiple trauma patients in the emergency department, and patients seem to be more comfortable with this nursing model. As a result, it should become more well known.
Laboratory prediction energy control system based on artificial intelligence network
Authors: Desmira Desmira , Bakar Norazhar Abu , Hashim Mohd Ruzaini , Wiryadinata Romi , Hamid Mustofa Abi
Published: May 2022

The use of electrical energy increases globally every year. The laboratory prediction energy control system (LPECS) predicted energy demand. This research was conducted in the Electrical Engineering Vocational Education laboratory by comparing the artificial neural fuzzy system (ANFIS) with the fuzzy logic. The comparison of methods aimed to determine their reliability in the energy demand prediction systems. The results showed that the minimum value of the target data using the conventional method (actual data) was 44.42%. Meanwhile, the prediction data using the ANFIS method was 44.33%, and the prediction data using the fuzzy method was 59.31%. The maximum value of the conventional ways (actual data) of ANFIS and fuzzy was similar by 77.59%. The RMSE ANFIS value was 0.1355%, the mean absolute percentage error (MAPE) was 0.2791%, and the fuzzy logic was 0.1986%. Thus, the ANFIS is applicable to determine the minimum and maximum values. Meanwhile, fuzzy can only show the maximum value but cannot reach the minimum value properly.
A survey on driver drowsiness detection using physiological, vehicular, and behavioral approaches
Authors: Gatea Mustafa Kamel , Gharghan Sadik Kamel , Ibrahim Raed Khalid , Ali Adnan Hussein
Published: May 2022

Drowsiness is a significant reason for street mishaps and has huge ramifications for driver safety. A few lethal mishaps can be prohibited if the sleepy drivers are cautioned in time. There are a number of tiredness identification strategies that screen the drivers’ languor state while driving and caution unfocused drivers. Highlights may be gathered from outward appearances (e.g., yawning and eyes and head movement) to determine the degree of laziness. This paper presents a holistic investigation of current strategies for driver laziness discovery and gives an exploration of widely-used characterization procedures. We begin by organizing the current procedures into three categories: behavior, vehicular, and physiological boundaries-based procedures. Then, we survey top directed learning methods utilized for laziness discovery. Next, we examine the advantages and disadvantages of the various techniques. A similar examination indicated that none of these strategies is entirely precise. However, physiological boundaries-based procedures produce more exact outcomes than other types of procedures. Their non-intrusive nature may be decreased through utilizing remote sensors on various elements including the driver’s body, driver’s seat, seat covers, and steering wheel.
A novel optimizing PI control of shunt active power filter for power quality enhancement
Authors: Hadi Ayad Mahmood , Thajeel Ekhlas M. , Nahar Ali K.
Published: May 2022

In this paper, shunt active power filter (SAPF) is designed to address the problem of current harmonics in the source current arising from a nonlinear load and improve the quality of electric power. That will be by compensating reactive power and harmonic currents. The PI controller responsible for DC-link energy storage tuning was developed using the Lévy flight distribution algorithm (LFA). It is a novel, previously unused optimization approach to suggest relative gain Kp and integration gain Ki gain values for the PI controller. This approach aims to get the best dynamic performance of SAPF, speed up the convergence rate, get the fastest best stability and bypass constant voltage advancement for DC-link. The model was tested and implemented in MATLAB simulation software. The result of total harmonic distortion THD showed the efficiency of this method compared with the results of traditional PI. The design of the model in the current reference frame was based on instantaneous reactive power (pq) theory.
FRIT-based integral action state feedback controller tuning using PSO for a liquid slosh suppression system
Authors: Zulkifli Nurul Najihah , Ramli Mohd Syakirin , Ahmad Hamzah , Irawan Addie
Published: May 2022

This paper presents a model-free approach of controller tuning to a liquid slosh suppression system. The sloshing is the usual occurrence happening to the liquid in a moving container. An integral action state feedback controller was proposed as the selected control structure. A fictitious reference signal was formulated using the recorded input-output data generated from a one-shot experiment and later be used to design the appropriate performance index. The minimization of the performance index of the controlled system was achieved by employing the PSO algorithm. Numerical analyses using MATLAB software have been conducted to evaluate the effectiveness of the proposed model-free approach. The results manifested that the tuned controller had exhibited good transient response performance regarding the trajectory tracking of the cart motion and reduction of slosh level motion.
The performance comparison of artificial intelligence based distance relays for the protection of transmission lines
Authors: Javed Muhammad Rameez , Virk Umar Siddique , Waleed Aashir , Jamal Muhammad Yasir
Published: May 2022

The safe and reliable generation and transmission of electricity is the desirable factor for utilities which arises the need of protection equipment such as relays in the power systems. According to recent studies, the conventional relays are not able to provide the required protection to the power systems, resulting in the emergence of various artificial intelligence (AI) techniques such as (i) artificial neural network (ANN); (ii) adaptive neuro fuzzy interface system (ANFIS); and (iii) fuzzy logic based relays for the protection. This work presents the protection scheme for transmission lines using various AI based distance relays along with performance comparison of these relays with a conventional numerical distance relay (NL). The comparison analysis has been performed by generating a test model in the MATLAB/Simulink environment and comparing “response time” against the same fault occurence for all relays. The comparison analysis found that AI based relays outperformed than conventional relay in terms of response time and accuracy level against the faults.
A comprehensive analysis on IoT based smart farming solutions using machine learning algorithms
Authors: Aliar Ahamed Ali Samsu , Yesudhasan Justindhas , Alagarsamy Manjunathan , Anbalagan Karthikram , Sakkarai Jeevitha , Suriyan Kannadhasan
Published: May 2022

Agriculture and farming are the most important and basic industries that are very important to humanity and generate a considerable portion of any nation's GDP. For good agricultural and farming management, technological advancements and support are required. Smart agriculture (or) farming is a set of approaches that uses a variety of current information and communication technology to improve the production and quality of agricultural products with minimum human involvement and at a lower cost. Smart farming is mostly based on IoT technology, since there is a need to continually monitor numerous aspects in the agricultural field, such as water level, light, soil characteristics, plant development, and so on. Machine learning algorithms are used in smart farming to increase production and reduce the risk of crop damage. Data analytics has been shown through extensive study to improve the accuracy and predictability of smart agricultural systems. Data analytics is utilised in agricultural fields to make decisions and recommend acceptable crops for production. This study provides a comprehensive overview of the different methods and structures utilised in smart farming. It also provides a thorough analysis of different designs and recommends appropriate answers to today's smart farming problems.
Wireless transceiver bit error rate and capacity improvement using advanced decoding techniques
Authors: Ahmed Ahmed Mohammed , Mohamed Wurod Qasim , Ali Israa Hazem
Published: May 2022

This paper considers advanced techniques for multiple-input multiple-output (MIMO) detection and decoding techniques to improve bit error rate (BER) and channel capacity. These are requirements for sixth generation (6G) (the next generation) access networks. A parallel decoding and detection scheme and a soft bit decoding scheme are implemented meregely to boost the overall performance of MIMO communication systems. The proposed new system is called the advanced system which comprises the two mentioned advanced techniques of decoding. For simplicity, these advanced techniques are employed and developed using two antennas at both ends, transmitter, and receiver. Then it is compared with the other different techniques which are spatial multiplexing SM–sequential decoding zero forcing-interference cancelation (ZF-IC) technique and SM–parallel decoding technique. We show that the advanced system outperforms the other two mentioned systems by achieving ultra-reliability and a high capacity simultaneously without employing space time coding and error control coding techniques. Additionally, better BER performance is achieved with less resolution and the quantization error reduced with an increasing the resolution. The new advanced system is simulated and evaluated with three terms, channel capacity, BER, and quantization error.
Short birth intervals classification for Indonesia’s women
Authors: Ningrum Ratih Ardiati , Fahmiyah Indah , Levi Aretha , Syahputra Muhammad Axel
Published: May 2022

Birth interval is closely related to maternal and infant health. According to world health organization (WHO), the birth interval between two births is at least 33 months. This study is the first to discuss the short birth interval (SBI) in Indonesia and used data from the Indonesian Demographic and Health Surveys 2017 with a total of 34,200 respondents. Birth interval means the length of time between the birth of the first child and the second child. Categorized as SBI if the distance between births is less than 33 months. The variables used include mother's age, mother's age at first giving birth, father's age, household wealth, succeeding birth interval, breastfeeding status, child sex, residence, mother's education, health insurance, mother's working status, contraception used, child alive, total children, number of living children, and household members. Machine learning algorithms including logistic regression, Naïve Bayes, lazy locally weighted learning (LWL), and sequential minimal optimization (SMO) are applied to classify SBI. Based on the values of accuracy, precision, recall, F-score, matthews correlation coefficient (MCC), receiver operator characteristic (ROC) area, precision-recall curve (PRC) area, the Naïve Bayes is the best algorithm with scores obtained 0.891, 0.889, 0.891, 0.885, 0.687, 0.972, and 0.960 respectively. Additionally, 18.25% of mothers were classified as still giving birth within a short interval.
Indonesian automatic short answer grading system
Authors: Salim Heinrich Reagan , De Chintya , Pratamaputra Nicholas Daniel , Suhartono Derwin
Published: May 2022

Short answer question is one of the methods used to evaluate student cognitive abilities, including memorizing, designing, and freely expressing answers based on their thoughts. Unfortunately, grading short answers is more complicated than grading multiple choices answers. For that problem, several studies have tried to build an artificial intelligence system called automatic short answer grading (ASAG). We tried to improve the accuracy of the ASAG system at scoring student answers in Indonesian by enhancing the earlier state-of-the-art models and methods. They were the bidirectional encoder representations from transformer (BERT) with fine-tuning approach and ridge regression models utilizing advanced feature extraction. We conducted this study by doing stages of literature review, data set preparation, model development, implementation, and comparison. Using two different ASAG data sets, the best result of this study was an achievement of 0.9508 in pearson’s correlation and 0.4138 in root-mean-square error (RMSE) by the BERT-based model with the fine-tuning approach. This result outperformed the results of the previous studies using the same evaluation metrics. Thus, it proved our ASAG system using the BERT model with fine-tuning approach can improve the accuracy of grading short answers.
Proposed robust ADRC control of a DFIG used in wind power production
Authors: Laafou Abdeslam Jabal , Madi Abdessalam Ait , Moumani Youssef , Addaim Adnane
Published: May 2022

This work presents the control of the double fed induction generator (DFIG) used in the wind conversion system (WCS). The studied model is based on the control of the two converters grid side converter (GSC) and rotor side converter (RSC), in order to extract the maximum of power and also to keep the direct current (DC) bus voltage constant. The control strategy used here is based on linear active disturbance rejection control (LADRC) controllers. These are known, in the field of control systems, by their performance and robustness and allow a very good rejection of disturbances which can affect the wind system. In addition, this kind of controllers could help to meet the requirements of robustness against the variations of the parameters of the system and the uncertainties of its mathematical model. To validate the effectiveness of the proposed control, the system was modeled and tested by numerical simulation using MATLAB/Simulink software. The results carried out are discussed and analyzed in terms of references tracking, robustness and disturbance rejection. The results obtained have shown shown that the proposed control is promising and useful.
Wideband hybrid precoder for mmWave multiuser MIMO OFDM communications
Authors: Shareef Faez Fawwaz , Al-Kindi Manal Jamil
Published: May 2022

Using millimeter wave (mmWave) transmission for massive multiple input multiple output (MIMO) system can improve system performance and effectively reduce the size of the massive antennas array. However, A wideband beamformer design is needed to take advantage of this wideband channel. In this paper, a downlink multi-user massive MIMO orthogonal frequency division multiplexing (MIMO OFDM) system for mmWave communications is proposed. Each subcarrier channel can be approximated as a narrowband clustered channel, so a narrowband precoder can be applied for each subchannel. The hybrid precoder is implemented in a manner so the digital precoder is obtained for each subcarrier, whilst the analog precoder is common for all subcarriers. A modified “joint spatial division/multiplexing” (JSDM) scheme is used to design the precoder, where each user equipped with more than one antenna. The design of the analog precoder is based on the second order channel statistics to reduce the overhead information need to process and fed back and the subcarrier baseband precoder based on the instantaneous channel state information (CSI). Following the approach of Kronecker channel model, the iteration between the analog beamformers design at both end of link can be avoided. Finally testing the system using various numbers of antennas at base station.
A comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image
Authors: Seno Nezar Ismat , Salman Muntaser Abdul Wahed , Farhan Rabah Nory
Published: May 2022

In our paper, performance of four fuzzy membership function generation methods was studied. These methods were studied in the context of implementing Mamdani fuzzy classification on a set of satellite images for western Iraqi territory. The first method generate triangulate membership functions using mean, minimum (min) and maximum (max) of histogram attribute values (AV), while peak and standard deviation (STD) of these AV were used in the second. On the other hand, in the third and fourth methods, Gaussian membership functions are generated using same mentioned values in the first and second method respectively. The goal was to generate a Mamdani type fuzzy inference system the membership function (MF) of each fuzzy set and implementing the AV of western Iraqi territory training data sets. A pixel-by-pixel comparison of each method with traditional maximum likelihood method (ML) was made on data sets comprising six bands of satellite imagery of the western Iraqi region taken by the Landsat-5 satellite. Simulation results of these performance comparisons singled out that the method using Gaussian MFs together with peak and STD of the AV as the best achiever with a similarity of 83.16 percent for band (3) of the studied area.
MITA interleaver for OFDM-IDMA and SCFDMA-IDMA techniques using QPSK modulation over PLC
Authors: Agarwal Priyanka , Shukla Manoj K.
Published: May 2022

The paper aims to examine the performance of multiplicative interleaving with tree algorithm (MITA) interleaver for grouped IDMA systems i.e., SCFDMA-IDMA and OFDM-IDMA, using QPSK modulation over powerline channel. The prime objective is to evaluate the MITA algorithm for its complexity and throughput over multiple communication systems so that it fulfills the requirements of 5G technology. Higher throughput and low complexity are achieved by the structure of MITA interleaver, in which more users are allotted interleavers per clock cycle as compared to existing interleavers primarily, random, tree, and FLRITI. The analysis is carried out in MATLAB environment for varying parameter values such as data length and user count and is plotted in terms of bit error rate (BER). Next, the effect of Convolutional coding is observed on grouped IDMA systems and lastly comparison in terms complexity is carried out. The simulation results over grouped IDMA systems show that MITA interleaver has better BER performance than FLRITI for large user count and the response further improves with greater number of users. The comparison of interleavers in terms of complexity reveals that MITA has lower complexity than FLRITI. Thus, MITA interleaver can be preferred over FLRITI and is a better option to be implemented in 5G technologies.
Effectiveness evaluation of machine learning algorithms for breast cancer prediction
Authors: Jasim Abdulrahman Ahmed , Jalal Ahmed Adeeb , Abdulateef Nabaa Mohammad , Talib Noor Ali
Published: May 2022

Breast cancer is becoming a global epidemic, affecting predominantly women. As a result, the number of people diagnosed with breast cancer is increasing every day. As a result, it is critical to have certain early detection methods in place that can assist patients in recognizing this condition at an early stage. Therefore, they might begin taking their medication to prevent the sickness from killing them. Different prediction approaches for early diagnosis of such diseases have been created in the machine learning fields. Those algorithms employ a variety of computational classifiers and claim to achieve satisfactory results in a few areas. However, no research was reached to determine which computationally sophisticated approach is more effective in detecting breast cancer. As a result, it is necessary to select the most effective strategy from the available options. This paper makes a contribution to the performance evaluation of 12 alternative classification strategies on datasets of breast cancer. The right explanations for the classifiers' dominance were investigated.
Spectral and energy efficiencies maximization in downlink NOMA systems
Authors: Keti Faris , Atroshey Salih M. , Hamadamin Jalil A.
Published: May 2022

Due to the huge connectivity and ever-growing demands of diverse services and high data rate applications, more effective radio access techniques are required for the next generation wireless systems. Non-orthogonal multiple access (NOMA) is a promising candidate which has been recognized as an effective multiple access technique that notably improves the spectral efficiency (SE). In addition to SE, energy efficiency (EE) is also attracting too much interest nowadays due to the limited power of end users (EU) and internet of things (IoT) devices, and the strict environmental concerns related to global carbon dioxide (CO2) emission of communication devices. However, in fact there is a trade-off relationship between SE and EE. In this paper, the SE-EE trade-off relationship in NOMA-based systems is mathematically modeled and analyzed. On the other hand, in order to attain an elastic SE-EE trade-off relationship, the problem is formulated as an optimization problem with an objective of maximizing the EE under the constraint of satisfying a minimum SE demand. Simulation results confirmed the theoretical findings of this study and further asserted the validation of the proposed power allocation scheme which aims to achieve a more flexible trad-off relationship between SE and EE.
Facial expression recognition using HOG and LBP features with convolutional neural network
Authors: Abdulsattar Nadia Shamsulddin , Hussain Mohammed Nasser
Published: May 2022

In computer vision, automatic facial expression recognition (FER) continued a difficult and interesting topic. The majority of extant techniques are based on traditional features descriptors such as local binary pattern (LBP) and histogram of oriented gradient (HOG), in which the classifier's hyperparameters are tailored to produce the best recognition accuracies across a single database or a small set of similar databases. This paper integrates the power of deep learning techniques with the LBP and HOG. The LBP and HOG are estimated from each image in the dataset. The resulting dataset is applied to a convolutional neural network (CNN). The architecture of this CNN constitutes three convolutional layers and three max-pooling layers. The output layers involve BatchNormalization, three dense layers, and two dropout layers. The proposed architecture is validated on the extended cohn-kanade dataset (CK+). We obtain improvement in the accuracy of the CNN model from 0.9593 to 0.967 and 0.975 after using the LBP and HOG respectively.
Convolutional neural network for color images classification
Authors: Mohammed Nora Ahmed , Abed Mohammed Hamzah , Albu-Salih Alaa Taima
Published: May 2022

Artificial intelligent and application of computer vision are an exciting topic in last few years, and its key for many real time applications like video summarization, image retrieval and image classifications. One of the most trend method in deep learning is a convolutional neural network, used for many applications of image processing and computer vision. In this work convolutional neural networks CNN model proposed for color image classification, the proposed model build using MATLAB tools of deep learning. In addition, the suggested model tested on three different datasets, with different size. The proposed model achieved highest result of accuracy, precision and sensitivity with the largest dataset and it was as following: accuracy is 0.9924, precision is 0.9947 and sensitivity is 0.9931, compare with other models.
Variations in phase conductor size and spacing on power losses on the Nigerian distribution network
Authors: Jimoh Abdulrasaq , Ayanlade Samson Oladayo , Ariyo Funso Kehinde , Jimoh Abdulsamad Bolakale
Published: May 2022

Most Nigerian distribution networks are examined on a single-phase basis, which fails to reflect the network's true features. Using three-phase power flow algorithms, this research explores the implications of variations in conductor sizes and spacing on power losses on a Nigerian network. Modified carson's equations were used to model the distribution lines to determine the network's impedance without presuming transposition of the lines. The conductor sizes and spacing were changed to see how they affected network power losses and how they contributed to the distribution network imbalance. The results showed that changing the conductor sizes of certain of the phases increased real power losses by 55.8 and 5.8%, respectively, in phases A and B. Phase C's was reduced by 13.04%. Furthermore, reactive power losses in phases A and B increased by 3.29 and 8.18%, respectively, whereas reactive losses in phase C dropped by 10.32%. Changing the conductor spacing in phases A, B, and C increased real power losses by 825.8, 136.2, and 13.2%, respectively, and reactive power losses by 72.86, 52.30, and 31.89%. Distribution networks should not be evaluated on a single-phase basis since losses differ in each of the three phases. Conductor size and spacing reductions cause huge losses.
Global virtual teams management system: project manager guide
Authors: Yousif Hiba Adil , Gheni Ali Yahya , Jusoh Yusmadi Yah , Ilyana Nur , Shanmugam Mohana
Published: May 2022

Global virtual teams (GVTs) are a recent organizational adaptation created to meet the needs of globalizatized marketplace. GVTs are essentially teams that are distributed across national boundaries and concerned through advanced information and communication technology (ICT) such as email, instant messaging, and video conferencing. The research on GVTs is important in the information system (IS) field because GVTs are dependent on information communication technology and the use of other technologies; GVTs also consists of people from different cultures. This paper tried to answer two research questions. The first one is: what are the GVTs problems facing the project manager (PM). A literature review was conducted to answer the first research question. The second one is: how to develop system as a guide for PM. The system was developed by Visual Studio 2019. Online model verification was conducted within 6 experts from IT industry. After developing the system, it was validated by eleven developers (non-functional test/black-box testing) and 3 developers/programmers (functional test/white-box testing) from the same industry. The result of model verification confirmed that the model can help the PM. Also, the non-functional test/black-box testing and functional test/white-box testing was confirmed.
Two-layer remote phosphor package as a solution to promote color quality scale and lumen in WLEDs
Authors: Huu Phuc Dang , Thi Dieu An Nguyen
Published: May 2022

This article demonstrates the influence of the red-light LaAsO4:Eu3+ phosphorus on the optical features of the two structures: one-layer remote phosphorus scheme (SRPS) and two-layer remote phosphorus scheme (DRPS). As a result, the Mie hypothesis is used to demonstrate and prove the comparison between color quality and luminosity (LF) between these two factors. The SRPS is a phosphor layer that consists of LaAsO4:Eu3+ particles combined with the YAG:Ce3+ mixture. Meanwhile, DRPS is two phosphor layers of red and yellow separated from each other. To improve the dispersing property, 5% of SiO2 is combined with the phosphorous films. The difference between the structures influences the optical features of WLEDs. The obtained outcomes show that the color rendering index (CRI) rises along with the concentrations of both structures while these values are nearly identical to each other. Meanwhile, at ACCTs (5600 K - 8500 K), the color quality scale (CQS) in DRPS reaches 74, which is higher than SRPS's 71 at 8500 K. Besides, the lumen in DRPS is considerably greater than that in SRPS at 2%-14% LaAsO4:Eu3+. In short, DRPS brings considerable benefits to the color quality and lumen when compared to SRPS. In addition, choosing a suitable concentration also becomes highly vital to achieve desirable CQS and LF.
Multi-input interleaved DC-DC converter for hybrid renewable energy applications
Authors: Alhamrouni Ibrahim , Salem Mohamed , Zahraoui Younes , Ismail Basilah , Jusoh Awang , Sutikno Tole
Published: May 2022

The increasing demand for hybrid energy systems based on renewable energy sources has enabled the new dimension for multi-input converter (MIC). Various topologies have been introduced over the last decade. However, most of these topologies have several drawbacks in terms of design complexity or efficiency. Therefore, this research aims to introduce a multi-input DC-DC converter for hybrid renewable energy applications. The proposed multi-input converter is able to hybridize different sources such as solar PV array and PEMFC. Analysis and simulation have been carried out for the double input two-phase interleaved converter in operating the boost mode. The proposed converter is designed in matlab simulink by using interleaved boost converter method to achieve a boosted and smoothened output. The proposed topology has shown a remarkable performance in terms of output voltage boosting, voltage ripple reduction as well as enhanced efficiency through interleaved boosting technique. From the simulation results, it can be observed that the proposed converter can gain high efficiency which is higher than 97%. The obtained results have been validated with previously published works and the proposed technique has been proven to yield compatible and improved outcomes.
Chattering reduction in step down converter using Robust reaching law
Authors: Somaguddu Ningappa Sridhara , Kondapur Basavarajan Siddesh , Mungasavalli Katappa Sudharashan , Velur Ramareddy Sandeep , Tavane Basavarajappa Lavakumar
Published: May 2022

The proposed robust reaching law for sliding mode control is used for chattering suppression, minimization of steady state error and reaching speed kept fast. With fine tuning parameters of robust reaching law, the system state reaches the sliding mode at the earliest. The mathematical analysis of the proposed reaching law is verifed. In one hand, they guarantee the system states reaches the sliding surface quickly and remain on it, in another way it deteriorate the chattering effectively, even unmatched certainties and disturbances. Such that the system response can better realize the unification of rapidity. A proposed reaching law applied to SMC DC-DC step down converter to analyse the chattering, it reduces the switching losses in the switching devices of the step down converter. In turn efficiency of the buck converter increases. MATLAB/Simulink results gives significant turn down of chattering and dynamic response of the system. This research work effectively improves the performance of the system.
Geometric generative adversarial net based multiple methods for spectrum sensing in cognitive radio networks
Authors: Sadkhan Sattar B. , Mardaw Zaidawi Doaa Jabbar
Published: May 2022

The majority of recently developed approaches require a significant number of labelled samples. The proposed system are dedicated to using less marked samples for automatic modulation detection in the cognitive radio domain. The proposed signal classifier generative adversarial nets (GANs) methodology is a semi-supervised learning framework that focuses on adversarial analysis GANs are a major step forward in the development of competitive generative networks, and they've spawned a slew of apparently unrelated versions. The discovery of a single geometric form in GAN and its derivatives is one of the paper's key contributions. In three geometric stages, by demonstrate how to train an adversarial generative model: updating the discriminator parameter away from the separating hyperplane, looking for the separating hyperplane, and updating the generator along the usual vector route of the separating hyperplane. The shortcomings in current approaches are shown by this geometric intuition, leading us to suggest a new geometric GAN formulation that maximizes the margin using SVM separating hyperplane. An equilibrium is reached between the discriminator and generator in the geometric GAN, according to our theoretical research. Furthermore, detailed computational results showing the superior efficiency of the GAN engineering network were obtained.