bluuebunny/crossref_metadata_2025 huggingface dataset:
Forecasting of Musical Equipment Demand Based on a Deep Neural Network
Authors: Zhang Jiemin
Published: March 2022

With the popularity of music, the music equipment market has ushered in a new round of explosion. But at the same time, the market environment is changing rapidly, and music equipment companies are greatly affected by market changes. This type of enterprise has a wide variety of products, fast update, short delivery time and urgent time, which is a severe challenge to its production and operation. In this environment, in order to quickly respond to changes in the market environment, and to formulate plans for corporate procurement, production, and sales in an orderly manner, music equipment companies must make accurate predictions of market demand. At present, the extended research based on LSTM is a relatively mainstream deep neural network in the research methods of time series problems. This article is based on LSTM model to conduct an in-depth study on the prediction of music equipment demand. In order to solve the problems of overfitting, disappearance of gradient, model collapse and other problems in previous experimental studies, this paper proposes an improved LSTM prediction model. In terms of model structure selection, Dropout mechanism is used, and L2 regular term is introduced. In the selection of the activation function, MReLU function is proposed, which can improve the prediction effect of the model and enhance the applicability of the model. To measure the prediction effect of the improved LSTM model established in this paper, this paper selects RMSE and MAE as evaluation indicators, and compares experiments with other mainstream prediction models. The research results show that the improved LSTM network prediction model is superior to other models in the prediction of music equipment demand, which verifies the effectiveness.
Saudi Teachers’ Self-Efficacy in Implementing the Arabic Language Integrative Curriculum
Authors: Alfayez Arwa Fahad
Published: March 2022

A set of beliefs and knowledge about teaching and learning lies at the heart of every teacher. Curriculum methods are modified as a teacher’s knowledge grows, and self-efficacy grows. Teachers’ effectiveness and views influence literacy teaching in their courses to various degrees. However, they are frequently obligated to teach in a particular manner in order to obtain desired learning outcomes, even if they do not completely agree with the techniques or approaches. The objective of this study is to examine the relationship between Saudi elementary school teachers’ efficacy beliefs and practices regarding implementing the Arabic language integrative curriculum and the factors that affect and impede teachers’ instructional classroom practice. This qualitative case study uses Bandura’s (1997) self-efficacy framework to provide rich, thick descriptions of Saudi teachers’ self-efficacy source experiences and how those experiences shaped self-efficacy beliefs. It highlights three elementary Saudi teachers’ efficacy through observations and interviews. The results indicate that teachers’ high efficacy beliefs led them to make changes in the curriculum based on their students’ needs. Evidence also supported the tension between whether teachers want to implement the integrative curriculum or the separated one. The participants in the study found new ways to maneuver through the scripted curriculum and what they think is best for their students.
Investigation of Cinnamaldehyde Derivatives as Potential Organic UV Filters
Authors: Pan Monica B. , Hughes Chloe S. , Lynch Hailey N. , Schilling Marcia M. , Liyana Pathiranage Anuradha
Published: March 2022

Long-term exposure to ultraviolet (UV) rays has been attributed to irreversible health defects at the cellular level. Most importantly, damage to DNA by UVA and UVB rays can result in uncontrolled cellular growth, leading to skin cancer. As a result, topical treatments have been developed over time to protect the skin from UVA and UVB rays. The active ingredients in sunscreens or sun creams are sometimes unsaturated, aromatic organic compounds capable of absorbing harmful UV photons at a great range of wavelengths. Absorption capabilities of these species depend on their degree of conjugation and their molar absorptivity. With this knowledge, two cinnamaldehyde derivatives were synthesized into five potential organic UV filters by the aldol condensation reaction. The products were identified using nuclear magnetic resonance (NMR) and attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopies, and ultraviolet-visible (UV-vis) spectroscopy was used to determine the UV absorption range and intensity of absorption for each compound. Since the compounds would hypothetically be utilized in topical ointments to aide in skin protection, these compounds were assessed in the presence of Pseudomonas aeruginosa, a representative bacterium of the skin’s natural flora. A time-course assay was conducted to detect growth effects of P. aeruginosa in the presence of the organic compounds. According to the spectroscopic and bacterial analyses of these UV-blocking compounds, three compounds were determined to be potential UV filters that cover UVA region while demonstrating no apparent harm to the natural skin bacteria P. aeruginosa, while the other two likely diminished bacterial growth by simple niche inhibition.
Influence of Management Efficiency of Sports Equipment in Colleges and Universities Based on the Intelligent Optimization Method
Authors: Wang Shuai
Published: March 2022

China has always considered the improvement of youth physical quality as one of the key steps in the talent training program, and universities have always followed the national policy to change the quantity and quality of sports equipment. This study mainly focuses on the development of sports equipment management system in colleges and universities. By building a scientific sports equipment management system, the efficiency of sports equipment management process is improved and the difficulty of managers’ work is reduced. The construction of the sports equipment management system is a comprehensive information service platform created specifically for sports administrators. The main function is to help sports equipment administrators obtain information on equipment management, and it is an electronic system that can obtain the status of sports equipment use through certain instructions, providing an example for sports equipment management in China’s universities.
Scientific Evidence of Chinese Herbal Medicine (Gegen Qinlian Decoction) in the Treatment of Ulcerative Colitis
Authors: Huang Jinke , Zhang Jiaqi , Wang Yifan , Ma Jing , Yang Xuefei , Guo Xiaoxue , Lv Mi , Ma Jinxin , Zheng Yijun , Wang Fengyun , Tang Xudong
Published: March 2022

Objectives. Gegen Qinlian decoction (GQD), a Chinese herbal compound, has been widely used in the treatment of ulcerative colitis (UC) in China. However, evidence from systematic reviews (SRs)/meta-analyses (MAs) of GQD in UC remains highly controversial. To collate, evaluate, and synthesize the current evidence, we carried out this study. Methods. SRs/MAs of GQD for UC were obtained from eight databases. Methodological Quality of Systematic Reviews 2 (AMSTAR-2) was utilized to appraise the methodological quality, Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) for reporting quality, and Grading of Recommendations Assessment, Development, and Evaluation (GRADE) for evidence quality. Results. Four eligible SRs/MAs were obtained. According to AMSTAR 2, all SRs/MAs were graded as critically low quality. According to PRISMA checklist, all SRs/MAs failed to report the information of protocol and registration. With GRADE, no outcome measure with high-quality evidence was found, and the evidence quality for outcome measures was in the moderate to critically low levels. Conclusions. GQD with conventional medicine (CM) seems to be more effective in UC than CM alone. This finding provides a new alternative strategy for the treatment of UC. However, owing to the limitations of the evidence provided by the included SRs/MAs, this conclusion must be treated with caution.
Optimal Deep Learning Model for Olive Disease Diagnosis Based on an Adaptive Genetic Algorithm
Authors: Alshammari Hamoud , Gasmi Karim , Krichen Moez , Ammar Lassaad Ben , Abdelhadi Mohamed Osman , Boukrara Ammar , Mahmood Mahmood A.
Published: March 2022

Though many researchers have studied plant leaf disease, the timely diagnosis of diseases in olive leaves still presents an indisputable challenge. Infected leaves may display different symptoms from one plant to another, or even within the same plant. For this reason, many researchers studied the effects of those diseases on, at most, two plants. Since olive crops are affected by many pathogens, including bacteria welt, olive knot, Aculus olearius, and olive peacock spot, the development of an efficient algorithm to detect the diseases was challenging because the diseases could be defined in many different ways. For this purpose, we introduce an optimal deep learning model for diagnosing olive leaf diseases. This approach is based on an adaptive genetic algorithm for selecting optimal parameters in deep learning model to provide rapid diagnosis. To evaluate our approach, we applied it in three famous deep learning models. For the comparative evaluation, we also tested other well-known machine learning methods. The experimental results presented in this paper show that our model outperformed the other algorithms, achieving an accuracy of approximately 96% for multiclass classification and 98% for binary classification.
Network Coding-Based D2D Transmission for Public Safety Networks over LTE HetNets and 5G Networks
Authors: Tata Chafika , Kadoch Michel
Published: March 2022

Device to Device (D2D) communications appear like an emergency solution for the Public Safety Network (PSN) when the LTE cell range is limited. D2D networks can use the unlicensed frequency bands, as this makes their transmissions cheaper and easier to deploy. Therefore, the development of this technology must deal with the security challenge. On the one hand, it is important to know how to design a secure D2D solution within the small cells, and on the other hand, the new scheme needs to deal with the problem of radio resources limit, since it will be used during emergency situations. This paper develops a new algorithm, named Generalized Secure Network Coding-based Data splitting algorithm (G-SNCDS), to ensure a secure data transmission for Public Safety D2D communications over LTE Heterogeneous Networks (HetNets) and 5G networks, without using additional radio resources. Our approach consists of performing Network Coding (NC) data packets transmissions based on a new Data Splitting (DS) technique, which we developed, based on a constructed butterfly effect that uses a new Butterfly network algorithm that we propose. Our solution enhances the security without affecting the level of Quality of Service (QoS). Thus, it is more suitable when network resources are limited. The simulation results show that our approach provides a secure D2D communication without increasing the overhead in the network.
Regional Economy Using Hybrid Sequence‐to‐Sequence‐Based Deep Learning Approach
Authors: Peng Bo
Published: March 2022

In recent times, the role of the regional economy changed significantly under certain conditions of globalization and structural adjustment. The process of changing must be crucial to analyse regional economy and develop the planning of regional economy. Developing economies depend often on industries and country policies. Modern studies tend to participate in important factors in this field such as energy intensity, labour skills, local industries, resources, and local expertise. Furthermore, in this study, to start developing the regional economy and make the revolution in this field to connect it with new technology, we train the deep learning algorithm of gathering factors to manage them perfectly and make a good prediction for the future economy. Hybrid sequence to sequence (seq2seq) algorithms of deep learning fed with previous information from past years and run the system to compare the predicted result data with current information to evaluate the method to be certified for the coming years.
Effects of Electroacupuncture on the Gut Microbiome in Cisplatin-Induced Premature Ovarian Failure Mice
Authors: He Qi-da , Guo Jing-jing , Zhang Qi , Yau Yuen-ming , Yu Yue , Zhong Zheng-hong , Tong Zi-yan , Yang Zong-bao , Chen Min
Published: March 2022

Growing evidence showed that the gut microbiota was associated with premature ovarian failure (POF). Many clinical types of research had shown that electroacupuncture was effective in the treatment of POF. However, there was little research on regulating the gut microbiome of POF mice by electroacupuncture. Therefore, this study attempted to verify whether electroacupuncture could regulate the gut microbiome in POF mice. POF mice were established by being injected intraperitoneally with cisplatin (2 mg/kg) for 2 weeks. Guanyuan (CV4) and Sanyinjiao (SP6) were selected in the electroacupuncture-at-the-acupoints group (EA group). Nonacupoints around CV4 and SP6 were selected in the electroacupuncture-at-the-nonacupoints group (EN group). The EA group and EN group were treated for 3 weeks. The ovarian function was evaluated by histopathological and molecular assays. Meanwhile, the gut microbiome of all mice was detected by 16S rDNA sequencing. The results showed that EA could restore the estrous cycle and reduce the number of atresia follicles in POF mice. The levels of serum follicle-stimulating hormone and luteinizing hormone were decreased by EA. As well, the levels of serum estradiol, anti-Mullerian hormone, and β-glucuronidase were increased by EA. The relative expressions of PI3K, AKT, and mTOR were increased to promote the proliferation of ovarian cells in the EA group. According to the results of 16S rDNA sequencing, the abundance and diversity of the gut microbiome could be regulated by EA. The relative abundance of beneficial bacteria was increased by EA. The KEGG pathway analysis showed that the gut microbiome associated with the estrogen signaling pathway, oocyte maturation, and PI3K-AKT signaling pathway was regulated by EA.
Discovery of a Conditionally Activated IL-2 that Promotes Antitumor Immunity and Induces Tumor Regression
Authors: Nirschl Christopher J. , Brodkin Heather R. , Hicklin Daniel J. , Ismail Nesreen , Morris Kristin , Seidel-Dugan Cynthia , Steiner Philipp , Steuert Zoe , Sullivan Jenna M. , Tyagi Ethika , Winston William M. , Salmeron Andres
Published: March 2022

IL-2 is a cytokine clinically approved for the treatment of melanoma and renal cell carcinoma. Unfortunately, its clinical utility is hindered by serious side effects driven by the systemic activity of the cytokine. Here, we describe the design and characterization of a conditionally activated IL-2 prodrug, WTX-124, that takes advantage of the dysregulated protease milieu of tumors. WTX-124 was engineered as a single molecule containing an inactivation domain and a half-life extension domain that are tethered to a fully active IL-2 by protease-cleavable linkers. We show that the inactivation domain prevented IL-2 from binding to its receptors in nontumor tissues, thereby minimizing the toxicity associated with systemic exposure to IL-2. The half-life extension element improves the pharmacokinetic profile of WTX-124 over free IL-2, allowing for greater exposure. WTX-124 was preferentially activated in tumor tissue by tumor-associated proteases, releasing active IL-2 in the tumor microenvironment. In vitro assays confirmed that the activity of WTX-124 was dependent on proteolytic activation, and in vivo WTX-124 treatment resulted in complete rejection of established tumors in a cleavage-dependent manner. Mechanistically, WTX-124 treatment triggered the activation of T cells and natural killer (NK) cells, and markedly shifted the immune activation profile of the tumor microenvironment, resulting in significant inhibition of tumor growth in syngeneic tumor models. Collectively, these data demonstrate that WTX-124 minimizes the toxicity of IL-2 treatment in the periphery while retaining the full pharmacology of IL-2 in the tumor microenvironment, supporting its further development as a cancer immunotherapy treatment.

See related Spotlight by Silva, p. 544.
Fenofibrate Exerts Anticancer Effects on Human Cervical Cancer HeLa Cells via Caspase-Dependent Apoptosis and Cell Cycle Arrest
Authors: Song Soo Youn , Lee Si Yeo , Ko Young Bok , Kim Jinju , Choi Tae-Young , Lee Ki Hwan , Yoo Heon Jong , Yuk Jae Min
Published: March 2022

Objective: In the present study, we attempted to identify the effects of fenofibrate on human cervical cancer cells. Methods: The cytotoxicity of fenofibrate in cervical cancer cells was determined by Cell Counting Kit-8. Immunoblotting assay was used to determine the protein expression of caspase-3, poly ADP-ribose polymerase cleavage, B-cell lymphoma 2 family protein expression, microtubule-associated protein 1A/1B-light chain 3 (LC3), as well as cyclins and cyclin-dependent kinases. Immunofluorescence imaging was used to determine the expression of cleaved caspase-3 and LC3. Flow cytometry was used to determine cell cycle and apoptosis. Results: We first showed that fenofibrate treatment reduced cell viability in HeLa cervical cancer cells in a dose-dependent manner at 24 h and 48 h. Importantly, fenofibrate-induced cell death was mediated through cell cycle arrest in the G0–G1 phase and caspase-dependent apoptosis. Moreover, fenofibrate also induced autophagy activation in a dose-dependent manner and pharmacological inhibition of autophagy led to increase of sub-G1 phase and caspase-dependent cell death in HeLa cells. Conclusion: In conclusion, these data demonstrated that fenofibrate initially induced cell cycle arrest, followed by caspase-3-dependent cell death in cervical cancer HeLa cells. However, fenofibrate also induced autophagy activation, which is closely related to the survival of diverse cancer cells, thus reducing the anticancer effects of fenofibrate. Therefore, the combination of an autophagy inhibitor and fenofibrate might have the potential to become a new therapeutic strategy for cervical cancer.
Expanding the Phenotypic Spectrum of HIVEP2-Related Intellectual Disability: Description of Two Portuguese Patients and Review of the Literature
Authors: Quental Rita , Borges Joana Pires , Santos Helena , Leão Miguel
Published: March 2022

Introduction: Pathogenic variants in HIVEP2 have been associated with a neurodevelopmental disorder mainly characterized by intellectual disability, severe language impairment, and motor developmental delay. Since its first description in 2016, only 15 patients have been described in the literature. Methods: Here, we report 2 additional unrelated Portuguese children presenting intellectual disability and motor delay in whom de novo nonsense pathogenic variants in HIVEP2 have been identified by next-generation sequencing analysis. Results: In patient 1, the variant c.2827C>T, p.(Arg943*) was detected, whereas patient 2 carried the variant c.6667C>T, p.(Arg2223*). Interestingly, patient 1 presented with a rapid growth of the occipitofrontal diameter in the first months of life due to external hydrocephalus, a feature that, as far as we know, has never been reported in patients with HIVEP2 pathogenic variants. Conclusion: This report expands the phenotypic spectrum of this rare syndrome and provides deeper insights by comparing the clinical features of our patients with previously reported affected individuals.
BRCA1 Mutation: An Insidious Enemy with Multiple Facets…
Authors: Godin Pierrick , Duhoux Francois P. , Mazzeo Filomena , Rojas Michel , Bollue Emmanuel , François Aline , Galant Christine , Coulie Julien , Coyette Maude , Lentini Audrey , Deswisen Yannick , Perlepe Vasiliki , Fellah Latifa , Leconte Isabelle , Berlière Martine
Published: March 2022

Epidemiological studies suggest that around 10% of breast cancers are due to hereditary predisposition. The risk of cancer is exponentially increased in patients harboring BRCA1 or BRCA2 mutations. Cumulative breast cancer risk by age 80 is estimated to 72% for BRCA1 mutation carriers and 69% for BRCA2. The cumulative risk estimates for developing ovarian cancer by age 80 are 44% for BRCA1 mutation carriers and 17% for BRCA2. We present here the case of a 59-year-old woman who developed a left breast cancer in 2014 treated by conservative surgery, radiotherapy, and endocrine therapy with letrozole. The diagnosis of BRCA1 mutation was performed in 2015. In 2018, the patient was referred to our institution for treatment of an aggressive angiosarcoma developed in the same breast. She had undergone radical hysterectomy by the age of 49 years for a benign uterine pathology. In 2020, she developed a tumor in the gastric wall; histological analysis confirmed a serous papillary carcinoma of ovarian origin. She was treated – after gastrectomy and lymphadenectomy – with 6 courses of carboplatin and paclitaxel followed by olaparib therapy. In 2021, she suffered from a chest recurrence of high grade angiosarcoma. New resection with free margins was performed. We discuss the link between angiosarcomas and BRCA mutations, the therapeutic options for angiosarcoma and ovarian cancer of extra ovarian origin and the follow-up modalities.
Effect of COVID-19 Pandemic on Patient Utilization of the Telemedicine Services in Dubai
Authors: Swidan Ashraf , Alnoon Nouf Ali , Makki Ibtehal , Zidan Marwan , Alhammadi Hanan , Rahmani Najma , Alameeri Asma , Al Falasi Aisha , Fakhroo Aalaa , Al Mulla Jawaher , Al Marzooqi Lamya
Published: March 2022

Introduction: Telemedicine was launched in Dubai in December 2019. The service provided live interaction between patients and physicians through videos simulating a consultation in a clinic. Methods: This study is a multicenter comparative cross-sectional retrospective study that included 13 health centers in Dubai. The data in this study were taken before and during the coronavirus (COVID-19) pandemic retrospectively. All patients from both telemedicine and primary health care (PHC) clinic visits were included. Results and Discussion: A total of 121,035 patients were involved in this study; there were 55,622 patients before COVID-19 and 65,413 patients during the COVID-19 pandemic. During the coronavirus pandemic, patient utilization of telemedicine increased significantly in comparison to patient visits to the PHC centers. Before the COVID-19 pandemic, only 104 (0.2%) patients used telemedicine, while during the pandemic, the utilization of telemedicine increased to 9,183 (14%). More females than males preferred telemedicine use over visiting the PHC center. With the pandemic, elder patients used telemedicine more than the younger age groups. Telemedicine was used for different types of consultations such as acute, chronic, and COVID-19 related. The most common type of consultation in telemedicine during the pandemic was for chronic diseases (67.7%). Conclusion: In this comparative study, more patients used telemedicine in the time of COVID-19 pandemic whether it was for acute, chronic, or CO­VID-19-related consultations. Telemedicine proved to be a very useful tool at the time of disasters such as the COVID-19 pandemic.
A 2.45/5.8 GHz high-efficiency dual-band rectifier for low radio frequency input power
Authors: El Mattar Sara , Baghdad Abdennaceur , Ballouk Abdelhakim
Published: March 2022

This article proposes a concurrent rectifier for radio frequency (RF) energy harvesting from the popular ambient RF sources wireless fidelity (WiFi) 2.45 and 5.8 GHz bands. A voltage doubler-based converter circuit with the Schottky SMS7630 diode is used, this chosen diode has shown good results for low power levels. To ameliorate the resulting circuit, we used an interdigital capacitor (IDC) instead of a lumped component; and then we added a filter to reject the 3rd harmonics of each operating frequency. A dual-band impedance transformer with a direct current (DC) block function is used and optimized at low input power points for more harvested DC power. The final circuit was, therefore, more efficient and more reliable. The maximum conversion efficiencies obtained from the resulting circuit are about 60.321% for 2.45 GHz and 47.175% for 5.8 GHz at 2 dBm of input power. Compared to other previous rectifiers presented in the literature, our proposed circuit presents high efficiencies at low power levels and at these operating frequencies.
Hybrid clipping and companding techniques based peak to average power ratio reduction in orthogonal frequency division multiplexing based differential chaos shift keying system
Authors: Alqassab Sejjad Razzaq , Hasan Fadhil Sahib
Published: March 2022

In this paper, a hybrid approach using clipping and companding techniques is introduced to reduce the peak to average power ratio (PAPR) of orthogonal frequency division multiplexing based differential chaos shift keying (OFDM-DCSK), which is the major drawback of the OFDM-DCSK. The hybrid function is processed at the end of the transmitter before transmitting the signal. However, there is no need for an inverse function at the receiver, which decreases the system complexity. Several techniques have been proposed in the literature for decreasing the PAPR value. Clipping and companding are active methods in terms of reducing the PAPR. Finally, the PAPR reduction and bit error rate (BER) performances are evaluated. The simulation results show that this technique gives better performance as compared with the clipping and companding techniques.
Design of Savonius model wind turbine for power catchment
Authors: Fang Liew Hui , Abd Rahim Rosemizi bin
Published: March 2022

In this study, the fossil fuel usage by-product is carbon dioxide, which is known as the primary cause in global warming. Alternatively, wind energy is a clean alternative energy source compared the fuel consumption can cause smoke pollution. The goal of the work is to develop a pollution controller device model Savonius wind turbine to represent the characterized actual speed wind turbine concepts into convert kinetic energy into electric energy from campus and monitoring all output data display on the cloud. The wind speed operation is enabled through the use of ESP8266 as internet of things (IoT) platform and the alternating current (AC) direct current (DC) harvesting circuit into improve stability of the wind energy performance. Secondly, a magnet coil synchronous generator is used, which is a grid coupled through a diode rectifier and voltage source converter. The parameters that have been measured using wireless fidelity (Wi-Fi) module ESP8266 are considering wind speed, current, voltage and power. The wind speed with 7.8 MPH can produce a maximum output voltage and output current of 1.104 V and 4.321 μA, respectively. Blynk applications functional as role present performance monitoring kit wind turbine analysis with more precise and efficient in anywhere and anytime.
A sensorless approach for tracking control problem of tubular linear synchronous motor
Authors: Hong Quang Nguyen , Quang Nguyen Phung , Lanh Nguyen Van
Published: March 2022

As well-known, linear motors are widely applied to various industrial applications
due to their abilities in providing directly straight movement without auxiliary mechanical transmissions. This paper addresses the sensorless control problem of tubular linear synchronous motors, which belong to a family of permanent magnet linear motor. To be specific, a novel velocity observer is proposed to deal with an unmeasurable velocity problem, and asymptotic convergence of the observer error is ensured. Unlike other studies on sensorless control methods for linear motors, our proposed observer is designed by regrading unknown disturbance load in the tracking control problem whereas considering theoretical demonstrations. By adjusting controller parameters properly, the position and velocity tracking error converge in arbitrary small values. Finally, the effectiveness of the proposed method is verified in two illustrative examples.
Robot for plastic garbage recognition
Authors: Bobulski Janusz , Kubanek Mariusz
Published: March 2022

Waste and related threats are becoming more and more severe problems in environmental security. There is growing attention in waste management globally, both in developing techniques to decrease their quantity and those correlated to their neutralization and commercial use. The basic segregation process of waste due to the type of material is insufficient, as we can reuse only some kinds of plastic. There are difficulties with the effective separation of the different kinds of plastic; therefore, we should develop modern techniques for sorting the plastic fraction. One option is to use deep learning and a convolutional neural network (CNN). The main problem that we considered in this article is creating a method for automatically segregating plastic waste into seven specific subcategories based on the camera image. The technique can be applied to the mobile robot for gathering waste. It would be helpful at the terrain and the sorting plants. The paper presents a 15-layer convolutional neural network capable of recognizing seven plastic materials with good efficiency.
Threshold adaptation and XOR accumulation algorithm for objects detection
Authors: Rashid Kurmasha Hasan Thabit , Hadi Ali Israa
Published: March 2022

Object detection, tracking and video analysis are vital and energetic tasks for intelligent video surveillance systems and computer vision applications. Object detection based on background modelling is a major technique used in dynamically objects extraction over video streams. This paper presents the threshold adaptation and XOR accumulation (TAXA) algorithm in three systematic stages throughout video sequences. First, the continuous calculation, updating and elimination of noisy background details with hybrid statistical techniques. Second, thresholds are calculated with an effective mean and gaussian for the detection of the pixels of the objects. The third is a novel step in making decisions by using XOR-accumulation to extract pixels of the objects from the thresholds accurately. Each stage was presented with practical representations and theoretical explanations. On high resolution video which has difficult scenes and lighting conditions, the proposed algorithm was used and tested. As a result, with a precision average of 0.90% memory uses of 6.56% and the use of CPU 20% as well as time performance, the result excellent overall superior to all the major used foreground object extraction algorithms. As a conclusion, in comparison to other popular OpenCV methods the proposed TAXA algorithm has excellent detection ability.
Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram
Authors: Ogla Raheem , Saeid Ali Adel , Shaker Shaimaa H.
Published: March 2022

The face recognition process is widely studied, and the researchers made great achievements, but there are still many challenges facing the applications of face detection and recognition systems. This research contributes to overcoming some of those challenges and reducing the gap in the previous systems for identifying and recognizing faces of individuals in images. The research deals with increasing the precision of recognition using a hybrid method of moments and local binary patterns (LBP). The moment technique computed several critical parameters. Those parameters were used as descriptors and classifiers to recognize faces in images. The LBP technique has three phases: representation of a face, feature extraction, and classification. The face in the image was subdivided into variable-size blocks to compute their histograms and discover their features. Fidelity criteria were used to estimate and evaluate the findings. The proposed technique used the standard Olivetti Research Laboratory dataset in the proposed system training and recognition phases. The research experiments showed that adopting a hybrid technique (moments and LBP) recognized the faces in images and provide a suitable representation for identifying those faces. The proposed technique increases accuracy, robustness, and efficiency. The results show enhancement in recognition precision by 3% to reach 98.78%.
Fast discrimination of fake video manipulation
Authors: Hadi Wildan Jameel , Kadhem Suhad Malallah , Abbas Ayad Rodhan
Published: March 2022

Deepfakes have become possible using artificial intelligence techniques, replacing one person’s face with another person’s face (primarily a public figure), making the latter do or say things he would not have done. Therefore, contributing to a solution for video credibility has become a critical goal that we will address in this paper. Our work exploits the visible artifacts (blur inconsistencies) which are generated by the manipulation process. We analyze focus quality and its ability to detect these artifacts. Focus measure operators in this paper include image Laplacian and image gradient groups, which are very fast to compute and do not need a large dataset for training. The results showed that i) the Laplacian group operators, as a value, may be lower or higher in the fake video than its value in the real video, depending on the quality of the fake video, so we cannot use them for deepfake detection and ii) the gradient-based measure (GRA7) decreases its value in the fake video in all cases, whether the fake video is of high or low quality and can help detect deepfake.
Ensemble-based face expression recognition approach for image sentiment analysis
Authors: Gubin Moung Ervin , Chuan Wooi Chai , Mohd Sufian Maisarah , Kim On Chin , Ahmad Dargham Jamal
Published: March 2022

Sentiment analysis based on images is an evolving area of study. Developing a reliable facial expression recognition (FER) device remains a difficult challenge as recognizing emotional feelings reflected in an image is dependent on a diverse set of factors. This paper presented an ensemble-based model for FER that incorporates multiple classification models: i) customized convolutional neural network (CNN), ii) ResNet50, and iii) InceptionV3. The model averaging ensemble classifier method is used to ensemble the predictions from the three models. Subsequently, the proposed FER model is trained and tested on a dataset with an uncontrolled environment (FER-2013 dataset). The experiment demonstrated that ensembling multiple classifiers outperformed all single classifiers in classifying positive and neutral expressions (91.7%, 81.7% and 76.5% accuracy rate for happy, surprise, and neutral, respectively). However, when classifying disgust, anger, and sadness, the ResNet50 model alone is the better choice. Although the Custom CNN performs the best in classifying fear expression (55.7% accuracy), the proposed FER model can still classify fear expression with comparable performance (52.8% accuracy). This paper demonstrated the potential of using the ensemble-based method to enhance the performance of FER. As a result, the proposed FER model has shown a 72.3% accuracy rate.
Comparative and comprehensive study of linear antenna arrays’ synthesis
Authors: Al-Zoubi Asem S. , Amaireh Anas Atef , Dib Nihad I.
Published: March 2022

In this paper, a comparative and comprehensive study of synthesizing linear antenna array (LAA) designs, is presented. Different desired objectives are considered in this paper; reducing the maximum sidelobe radiation pattern (i.e., pencil-beam pattern), controlling the first null beamwidth (FNBW), and imposing nulls at specific angles in some designs, which are accomplished by optimizing different array parameters (feed current amplitudes, feed current phase, and array elements positions). Three different optimization algorithms are proposed in order to achieve the wanted goals; grasshopper optimization algorithms (GOA), antlion optimization (ALO), and a new hybrid optimization algorithm based on GOA and ALO. The obtained results show the effectiveness and robustness of the proposed algorithms to achieve the wanted targets. In most experiments, the proposed algorithms outperform other well-known optimization methods, such as; Biogeography based optimization (BBO), particle swarm optimization (PSO), firefly algorithm (FA), cuckoo search (CS) algorithm, genetic algorithm (GA), Taguchi method, self-adaptive differential evolution (SADE), modified spider monkey optimization (MSMO), symbiotic organisms search (SOS), enhanced firefly algorithm (EFA), bat flower pollination (BFP) and tabu search (TS) algorithm.
Ultra low phase noise and high output power monolithic microwave integrated circuit oscillator for 5G applications
Authors: El Ftouh Hanae , Moustapha El Bakkali , Naima Amar Touhami , Alia Zakriti
Published: March 2022

novel structure of low phase noise and high output power monolithic microwave integrated circuit (MMIC) oscillator is presented in order to use it in 5G applications. The oscillator is based on the ED02AH process which allows us to design a microwave oscillator keeping a minimum size which is impossible to have it using other technologies since microwave oscillators are sensitive components above 20 GHz. The oscillator is studied, designed, and optimized on a GaAs substrate from the OMMIC foundry using the advanced design system (ADS) simulator in order to obtain a miniaturized oscillator (1.1×1.3 mm2) generating two signals of different frequencies fo1=26 GHz and fo2=30 GHz. The objective is to design an oscillator with high output power and low phase noise while respecting its specifications. The optimization of the proposed microwave oscillator shows satisfying results. Indeed, at 26 GHz and 30 GHz, the output powers are respectively 13.33 dBm and 14.89 dBm. The oscillator produces a sinusoidal signal of 1.5 V and 1.75 V amplitude respectively at 26 GHz and 30 GHz. The oscillator phase noise at fo1 and fo2 resonance frequencies using the liquid crystal (LC) resonator shows respectively -109 dBc/Hz and -110 dBc/Hz at 10 MHz offset.
Improved noisy gradient descent bit-flipping algorithm over Rayleigh fading channel
Authors: Biazaran Reza , Helgert Hermann Joseph
Published: March 2022

Gradient descent bit flipping (GDBF) and its many variants have offered remarkable improvements over legacy, or modified, bit flipping decoding techniques in case of decoding low density parity check (LDPC) codes. GDBF method and its many variants, such as noisy gradient descent bit flipping (NGDBF) have been extensively studied and their performances have been assessed over multiple channels such as binary symmetric channel (BSC), binary erasure channel (BEC) and additive white Gaussian noise (AWGN) channel. However, performance of the said decoders in more realistic channels or channel conditions have not been equally studied. An improved noisy gradient descent bit flipping algorithm is proposed in this paper that optimally decodes LDPC encoded codewords over Rayleigh fading channel and under various fade rates. Comparing to NGDBF method, our proposed decoder provides substantial improvements in both error performance of the code, and in the number of iterations required to achieve the said error performance. It subsequently reduces the end-to-end latency in applications with low or ultra-low latency requirements.
Plasmonic hybrid terahertz photomixer of graphene nanoantenna and nanowires
Authors: Al-Mudhafar Reiam , Jawad Hussein Ali
Published: March 2022

Due to their attractive properties, silver nanowires (Ag-NWs) are newly used as nanoelectrodes in continuous wave (CW) THz photomixer. However, since these nanowires have small contact area, the nanowires fill factor in the photomixer active region is low, which leads to reduce the nanowires conductivity. In this work, we proposed to add graphene nanoantenna array as nanoelectrodes to the silver nanowires-based photomixer to improve the conductivity. In addition, the graphene nanoantenna array and the silver nanowires form new hybrid nanoelectrodes for the CW-THz photomixer leading to improve the device conversion efficiency by the plasmonic effect. Two types of graphene nanoantenna array are proposed in two separate photomixer configurations. These are the graphene nanodisk (GND) array and the graphene bow-tie nanoantenna (GNA) array. The photomixer active region is simulated using the computer simulation technology (CST) Studio Suite® for three optical wavelengths: 780 nm, 810 nm, and 850 nm. From the results, we found that the electric field in the active region is enhanced by 4.2 and 4.8 times for the aforementioned configurations, respectively. We also showed that the THz output power can be enhanced by 310 and 530 times, respectively.
Validity of a graph-based automatic assessment system for programming assignments: human versus automatic grading
Authors: Zougari Soundous , Tanana Mariam , Lyhyaoui Abdelouahid
Published: March 2022

Programming is a very complex and challenging subject to teach and learn. A strategy guaranteed to deliver proven results has been intensive and continual training. However, this strategy holds an extra workload for the teachers with huge numbers of programming assignments to evaluate in a fair and timely manner. Furthermore, under the current COVID-19 distance teaching circumstances, regular assessment is a fundamental feedback mechanism. It ensures that students engage in learning as well as determines the extent to which they reached the expected learning goals, in this new learning reality. In sum, automating the assessment process will be particularly appreciated by the instructors and highly beneficial to the students. The purpose of this paper is to investigate the feasibility of automatic assessment in the context of computer programming courses. Thus, a prototype based on merging static and dynamic analysis was developed. Empirical evaluation of the proposed grading tool within an introductory C-language course has been presented and compared to manually assigned marks. The outcomes of the comparative analysis have shown the reliability of the proposed automatic assessment prototype.
Inappropriate machine learning application in real power industry cases
Authors: Khalyasmaa Alexandra , Matrenin Pavel , Eroshenko Stanislav
Published: March 2022

Global digital transformation of the energy sector has led to the emergence of multiple digital platform solutions, the implementation of which have revealed new problems associated with continuous growth of data volumes requiring new approaches to their processing and analysis. This article is devoted to the improper application of machine learning approaches and flawed interpretation of their output at various stages of decision support systems development: data collection; model development, training and testing as well as industrial implementation. As a real industrial case study, the article examines the power generation forecasting problem of photovoltaic power plants. The authors supplement the revealed problems with the corresponding recommendation for industrial specialists and software developers.
Comparison of two deep learning methods for detecting fire hotspots
Authors: Lestari Dewi Putrie , Kosasih Rifki
Published: March 2022

Every high-rise building must meet construction requirements, i.e. it must have good safety to prevent unexpected events such as fire incident. To avoid the occurrence of a bigger fire, surveillance using closed circuit television (CCTV) videos is necessary. However, it is impossible for security forces to monitor for a full day. One of the methods that can be used to help security forces is deep learning method. In this study, we use two deep learning methods to detect fire hotspots, i.e. you only look once (YOLO) method and faster region-based convolutional neural network (faster R-CNN) method. The first stage, we collected 100 image data (70 training data and 30 test data). The next stage is model training which aims to make the model can recognize fire. Later, we calculate precision, recall, accuracy, and F1 score to measure performance of model. If the F1 score is close to 1, then the balance is optimal. In our experiment results, we found that YOLO has a precision is 100%, recall is 54.54%, accuracy is 66.67%, and F1 score is 0.70583667. While faster R-CNN has a precision is 87.5%, recall is 95.45%, accuracy is 86.67%, and F1 score is 0.913022.
An efficient cloudlet scheduling via bin packing in cloud computing
Authors: Chraibi Amine , Ben Alla Said , Ezzati Abdellah
Published: March 2022

In this ever-developing technological world, one way to manage and deliver services is through cloud computing, a massive web of heterogenous autonomous systems that comprise adaptable computational design. Cloud computing can be improved through task scheduling, albeit it being the most challenging aspect to be improved. Better task scheduling can improve response time, reduce power consumption and processing time, enhance makespan and throughput, and increase profit by reducing operating costs and raising the system reliability. This study aims to improve job scheduling by transferring the job scheduling problem into a bin packing problem. Three modifies implementations of bin packing algorithms were proposed to be used for task scheduling (MBPTS) based on the minimisation of makespan. The results, which were based on the open-source simulator CloudSim, demonstrated that the proposed MBPTS was adequate to optimise balance results, reduce waiting time and makespan, and improve the utilisation of the resource in comparison to the current scheduling algorithms such as the particle swarm optimisation (PSO) and first come first serve (FCFS).
An authenticated key management scheme for securing big data environment
Authors: Algaradi Thoyazan Sultan , Rama Boddireddy
Published: March 2022

If data security issues in a big data environment are considered, then the distribution of keys, their management, and the ability to transfer them between server users in a public channel will be one of the most critical issues that must consider on. In which the importance of keys management may outweigh the importance of the encryption algorithm strength. Therefore, this paper raised a new proposed scheme called (AKMS) that works through two levels of security. First, to concerns how the user communicates with the server with preventing any attempt to penetrate senders/receivers. Second, to make the data sent vague by encrypting it, and unreadable by others except for the concerned receiver, thus the server function be limited only as a passageway for communication between the sender and receiver. In the presented work some concepts discussed related to analysis and evaluation as keys security, data security, public channel transmission, and security isolation inquiry which demonstrated the rich value that AKMS scheme carried. As well, AKMS scheme achieved very satisfactory results about computation cost, communication cost, storage overhead which proved that AKMS scheme is appropriate, secure, and practical to use and protect the user's private data in big data environments.