bluuebunny/crossref_metadata_2025 huggingface dataset:
A Personalized Recommendation Method for Ancient Chinese Literary Works Based on a Collaborative Filtering Algorithm
Authors: Chen Chanjuan
Published: October 2022

The works of ancient Chinese literature that rely on Internet technologies are developing quickly. Through mobile phone inspection, ancient Chinese literary masterpieces are becoming more well-known, which encourages readers to read them more regularly. Providers of output literary works are confronted with a conundrum and a challenge, enabling users to quickly discover and attract their own works in the vast body of ancient Chinese literature. Providers of output literature can rely on personalized recommendation technology to find a solution to this issue. The production of works of literature is one of the most significant signs of human civilisation. As Internet technology becomes more widespread, the suggestion of the platform will become a more important factor in determining how the general audience reacts to works of literature. A collaborative filtering (CF) algorithm is offered as a method for making the recommendation algorithm for ancient Chinese literature more accurate. The personalized recommendation system’s technological support helps to increase the accuracy of recommendations. This system predicts and scores the reading preferences of the readers in a thorough manner, which helps to improve the recommendation’s accuracy. It is hoped that the user community will find the analysis and discussion contained in this article to be useful as a reference. The compelling experimental findings show that the recommendation algorithm suggested in this study greatly improves the accuracy of the intelligent recommendation system. These results were found by analyzing the final experimental data.
Evolution Mechanism of Thickly Sticky Loose Layered Arch Structure and Prediction Method of Initial Ground Fissure Caused by Underground Coal Mining
Authors: Yingming Yang , Quansheng Li , Shiliang Liu , Kai Zhang , Yongqiang Zhao , Guojun Zhang , Weilong Zhang , Gang Liu
Published: October 2022

In view of the problem of taking the loose layer as the uniform cloth load while ignoring thickly sticky loose layered arch (TSLLA) of the traditional mining rock overburden structure analysis, combined with the geological conditions of shallow buried thickly sticky loose layer in Shendong mining area, the mechanical model of the mining TSLLA bearing structure is established, the variation law of arch height under different loose layers is analyzed, and the formation conditions of TSLLA are given. The first theory deduced the dynamic evolution mechanism of TSLLA structure advancing. The dynamic evolution of TSLLA structure in 22207 coalface is simulated. The formula for the limit height of TSLLA is derived. A classification method of mining loose layer structure is proposed. The loose layer structure type is divided into load type, arch beam type damage type, and limit arch height type. On this basis, the mechanical model of “arch beam” is constructed. The influence mechanism of the TSLLA on the surface cracks is revealed. The methods and calculation steps for initial surface cracks are presented. The results are verified by the measured data of surface subsidence of the typical thickly sticky layer in Shendong mining area.
Privacy-Enhanced Intrusion Detection and Defense for Cyber-Physical Systems: A Deep Reinforcement Learning Approach
Authors: Lin Qingyuan , Ming Rui , Zhang Kailing , Luo Haibo
Published: October 2022

Cyber-physical systems (CPSs) will play an important role in future real-world applications through the deep integration of computing, communication, and control technologies. CPSs are increasingly deployed in critical infrastructure, industry, and homes to achieve a smart grid, smart transportation, and smart healthcare and to bring many benefits to citizens, businesses, and governments. However, the openness and complexity brought by network and wireless communication technology, as well as the intelligence and dynamic of network intrusions make CPS more vulnerable to network intrusions and bring more serious threats to human life, enterprise productivity, and national security. Therefore, intrusion detection and defense in CPS have attracted considerable attention and have become a fundamental aspect of CPS security. However, a new challenging problem arises: how to improve the efficiency and accuracy of intrusion detection while protecting user privacy during the intrusion detection process. To address this challenge, we propose a deep reinforcement learning-based privacy-enhanced intrusion detection and defense mechanism (PIDD) for CPS. The PIDD is composed of three modules: privacy-enhanced topology graphs generation module, graph convolutional networks-based user evaluation module, and the deep reinforcement learning-based intruder identification and handling module. The experimental results show that the proposed PIDD achieves excellent performance in intrusion detection accuracy, intrusion defense percentage, and privacy protection.
Clinical Comparative Study of Different Fundus Imaging Methods in the Acute Phase of Vogt-Koyanagi-Harada Disease
Authors: Wu Hongyun , Hu Liqun , Tang Wei , Chen Guishang , Liu Jinrong , Li Shaojun , Xu Zhe
Published: October 2022

Background. The diversification of follow-up ophthalmic imaging examination methods, and whether there are differences in clinical characteristics of VKH at the acute stage under different images. Our study aims to compare the imaging characteristics of the acute phase of Vogt-Koyanagi-Harada disease (VKH) under different fundus imaging methods to deepen clinical knowledge. Methods. A retrospective case study was performed on fundus images of 62 eyes from 31 patients with acute phase VKH and a disease duration ≤2 months who were treated at Ganzhou People’s Hospital from January 2013 to December 2020. Fundus photography (FP), optical coherence tomography (OCT), and fundus fluorescein angiography (FFA) were performed on all 62 eyes. The fundus presentations were divided into an optic disc swelling (ODS) group, a serous retinal detachment (SRD) group, and a mixed type (MT) group (both ODS and SRD), and the proportions of patients in these groups and the coincidence rate of ODS, SRD, and MT identified by the three fundus imaging modes were determined. Results. The proportion of patients with ODS was highest under FP, and the proportion of patients with MT was highest under OCT. The proportions of patients with ODS and MT in the three fundus imaging modes differed significantly (

P
<
0.05

), while the proportion of patients with SRD did not (

P
>
0.05

). The proportion of patients with subretinal fluid with positive OCT results was significantly higher than those with positive FFA results (81.08% vs. 59.46%) (

P
<
0.05

). Conclusion. Clinically significant positive signs could be obtained for acute VKH under different imaging methods. However, compared with FP and FFA, OCT tomography is more intuitive for the observation of lesions.
Miniaturized Multiband Bandpass Filters Based on a Single Multimode Resonator Loading Branches
Authors: Luo Jie , Shi Kaibo , Gao Shanshan
Published: October 2022

In this paper, the operating mechanism of proposed bandpass filters with a single multimode resonator loaded with branches is introduced. Based on the design procedure, the center frequencies of the proposed bandpass filters can be controlled due to the design freedom. Meanwhile, the proposed bandpass filters (BPFs) feature compact sizes and small insertion loss. To validate the design and analysis, a prototype was fabricated and measured with six passbands centered at 1.23/1.76/2.38/4.24/5.23/6.75 GHz. The measured result of the fabricated filter agrees well with the simulation, which indicates that the proposed structure can serve as a potential candidate for multiband BPF designs.
Structure Optimization and Governance of Multilevel Rail Transit Integration under the Background of a Metropolitan Area Based on the Industrial Internet of Things Security Data Fusion Method
Authors: Dai Xin , Ma Tianshan , Zhu Wenying
Published: October 2022

In recent years, the urban rail transit network architecture has gradually grown, the contradiction between rail transit passenger flow and transport load has been deepening, and its carrying capacity has also been tested. Passenger flow risk has become the most important source of risk in rail transit. The key to restricting rail transit service quality is how to effectively monitor and manage rail transit passenger flow and provide accurate and convenient early warning to staff. In order to effectively manage the complex passenger flow scene, the premise is to distinguish the real-time state of its moving target. The time series data feature extraction and LSTM data fusion were used to analyze the traffic data sequence in the multilevel rail transit network model. The multilevel rail transit integration of the Internet of things is modeled by the method of data fusion. It can be seen from the experimental data that in the data fusion mode, the network comprehensive evaluation prediction value fitting effect can quickly converge, and the error rate is less than 4%. By comparing the mean square error (MSE) and mean absolute error (MAE) data of the traditional method and the experimental method used in this paper through two different datasets, it was understood that the MAE under the data fusion method was reduced by 8 compared with the traditional method, and the MSE was decreased by 33, indicating that this method can bring better simulation effect to the model. The improvement of the synergistic and complementary functional network and the acceleration of the efficient and convenient flow of elements are the inevitable results of the integration of multilayer rail transit in the metropolitan area.
3D Face Recognition Neural Network for Digital Human Resource Management
Authors: Dong Yiming
Published: October 2022

Digital human resource management can improve the organizational and operational efficiency of enterprises.In order to improve the efficiency of enterprise digital management and solve the problems of low security level and insufficient stability of 2D face recognition, we introduce 3D face recognition into the digital human resource management system. We propose a face recognition method based on a multistream convolutional neural network and local binary pattern and build a digital face recognition management system. We first build the system computer vision scene. Then a local binary mode facial expression feature extraction scheme is designed according to the depth camera image extraction method. Considering that face 3D features are easy to be missed, we build a multistream convolutional neural network to learn facial 3D features. Finally, we validate the effectiveness of the method in selecting a public face dataset. Experiments prove that our method can reach 98% face recognition accuracy, which is significantly better than other methods.
Identification of Drug-Disease Associations Using a Random Walk with Restart Method and Supervised Learning
Authors: Liu Xiaoqing , Yi Wenjing , Xi Baohang , Dai Qi
Published: October 2022

Drug-disease correlations play an important role in revealing the mechanism of disease, finding new indications of available drugs, or drug repositioning. A variety of computational approaches were proposed to find drug-disease correlations and achieve good performances. However, these methods used a variety of network information, but integrated networks were rarely used. In addition, the role of known drug-disease association data has not been fully played. In this work, we designed a combination algorithm of random walk and supervised learning to find the drug-disease correlations. We used an integrated network to update the model and selected a gene set as the start of random walk based on the known drug-disease correlations data. The experimental results show that the proposed method can effectively find the correlation between drugs and diseases, and the prediction accuracy is 82.7%. We found that there are 8 pairs of drug-disease relationships that have not yet been reported, and 5 of them have pharmacodynamic effects on Parkinson’s disease. We also found that a key linkage between Parkinson’s disease and phenylhexol, a drug for the treatment of Parkinson’s disease α-synuclein and tau protein, provides a useful exploration for the effectiveness of the treatment of Parkinson’s disease.
Research on CT Lung Segmentation Method of Preschool Children based on Traditional Image Processing and ResUnet
Authors: Li Zheming , Yang Li , Shu Liqi , Yu Zhuo , Huang Jian , Li Jing , Chen Lingdong , Hu Shasha , Shu Ting , Yu Gang
Published: October 2022

Lung segmentation using computed tomography (CT) images is important for diagnosing various lung diseases. Currently, no lung segmentation method has been developed for assessing the CT images of preschool children, which may differ from those of adults due to (1) presence of artifacts caused by the shaking of children, (2) loss of a localized lung area due to a failure to hold their breath, and (3) a smaller CT chest area, compared with adults. To solve these unique problems, this study developed an automatic lung segmentation method by combining traditional imaging methods with ResUnet using the CT images of 60 children, aged 0-6 years. First, the CT images were cropped and zoomed through ecological operations to concentrate the segmentation task on the chest area. Then, a ResUnet model was used to improve the loss for lung segmentation, and case-based connected domain operations were performed to filter the segmentation results and improve segmentation accuracy. The proposed method demonstrated promising segmentation results on a test set of 12 cases, with average accuracy, Dice, precision, and recall of 0.9479, 0.9678, 0.9711, and 0.9715, respectively, which achieved the best performance relative to the other six models. This study shows that the proposed method can achieve good segmentation results in CT of preschool children, laying a good foundation for the diagnosis of children’s lung diseases.
Application of Digital Painting Technology in the Creation of an Oil Painting Containing Graphene
Authors: Wu Yani
Published: October 2022

Art and science and technology are two different ways people understand and reflect the objective world. The mutual penetration of science, technology, and art has promoted the development of art and the innovation of forms. Technology has become the carrier of social and cultural exchanges and spiritual world exchanges for artists, providing new ideas for modern art. In recent years, with the active development of various new media technologies, new media technologies represented by image technology and digital technology have gradually developed and are closely integrated with various fields. With the development and progress of image and digital technology, digital painting technology has had a great impact on the field of artistic creation. Of course, oil painting creation in the field of art has also encountered unprecedented challenges. This article introduces various functional methods in digital painting technology, and also introduces the preparation method of graphene graphite for printing. And through experiments, graphite with good performance was prepared, which showed that when ethylene glycol was used as a solvent and the mass fraction of graphene was 37.5%, the performance of the ink was relatively best. Finally, this paper designs a digital painting system and compares it with ordinary digital painting systems. It is concluded that the average satisfaction of painters in the designed system is 1.5 points higher than that of ordinary ones. These data show that the application of digital painting technology in the creation of graphene oil paintings has better applications than ordinary digital painting systems.
Student-Centered Learning Environment Based on Multimedia Big Data Analysis
Authors: Zhao Erxi , He Jian , Jin Zhou , Wang Yue
Published: October 2022

The application of current information technology in education, especially multimedia network technology, has brought about major changes in the content and methods of instruction. It has replaced the conventional teacher-centered, textbook-centered, and classroom-centered teaching environment with a student-centered, information technology-based learning environment that includes a rich network of multimedia learning resources and virtual reality. Through the interaction between students and students and between students and the learning environment, students can acquire knowledge on the basis of observation, understanding, and cognition, so as to grasp the essence of things. It is an effective cognitive tool for students to explore freely and visualize various knowledge and skills. Therefore, the teacher is no longer the authority of knowledge imparting, but the learner’s guide and helper or even the senior partner in the learner’s learning activities. This shift will allow teaching staff to focus more on the design and development of learning environments and resources. This paper proposes a new clustering algorithm CURE, which overcomes the shortcomings of the detection rate and stability of the classical clustering algorithm and is suitable for solving the clustering problem in the learning environment of big data analysis. Experiments are carried out on some international standard network security dataset KDDCUP101, and the running time of the algorithm is 1230 s. The results show that the stability of the proposed algorithm is increased by 30.22% and the detection rate is increased by 10.98% compared with the common algorithm. Compared with the global K-means algorithm, the time complexity is also greatly enhanced.
Functional Capacity, Lipid Profile, and Associated Factors in Older Adults Living in Urban and Rural Areas
Authors: Comelli Medeiros Francieli Cristina , De Sá Clodoaldo Antônio , Chielle Eduardo Ottobeli , Ferretti Fátima , Ascari Rosana Amora , Peretro Gabriela , da Silva (Corralo) Vanessa
Published: October 2022

This study aimed to analyze the relationship between sociodemographic and lifestyle variables, functional strength, aerobic capacity, and lipid profile of older adults living in urban and rural areas. For this purpose, 208 older adults were evaluated (132 living in rural areas and 73 living in urban areas). Sociodemographic data were collected, and functional strength, aerobic capacity, and lipid profile of older adults living in the southern region of Brazil were evaluated. Only total cholesterol and LDL cholesterol were associated with place of residence (

p
<
0.05

), and living in the countryside was associated with low aerobic capacity (

p
=
0.010

). The use of medication (OR = 3.01;

p
=
0.012

) and smoking (OR = 0.30;

p
=
0.027

) were the only variables that explained aerobic performance, regardless of place of residence. In conclusion, place of residence is not a factor that, alone or in combination with other conditions, affects the functional performance or lipid profile of the older adult population.
Circulating IgA Antibodies AgainstFusobacterium nucleatumAmyloid Adhesin FadA are a Potential Biomarker for Colorectal Neoplasia
Authors: Baik Jung Eun , Li Li , Shah Manish A. , Freedberg Daniel E. , Jin Zhezhen , Wang Timothy C. , Han Yiping W.
Published: October 2022

Fusobacterium nucleatum (Fn) is a gram-negative oral anaerobe and prevalent in colorectal cancer. Fn encodes a unique amyloid-like adhesin, FadA complex (FadAc), consisting of intact pre-FadA and cleaved mature FadA, to promote colorectal cancer tumorigenesis. We aimed to evaluate circulating anti-FadAc antibody levels as a biomarker for colorectal cancer. Circulating anti-FadAc IgA and IgG levels were measured by ELISA in two study populations. In study 1, plasma samples from patients with colorectal cancer (n = 25) and matched healthy controls (n = 25) were obtained from University Hospitals Cleveland Medical Center. Plasma levels of anti-FadAc IgA were significantly increased in patients with colorectal cancer (mean ± SD: 1.48 ± 1.07 μg/mL) compared with matched healthy controls (0.71 ± 0.36 μg/mL; P = 0.001). The increase was significant in both early (stages I and II) and advanced (stages III and IV) colorectal cancer. In study 2, sera from patients with colorectal cancer (n = 50) and patients with advanced colorectal adenomas (n = 50) were obtained from the Weill Cornell Medical Center biobank. Anti-FadAc antibody titers were stratified according to the tumor stage and location. Similar as study 1, serum levels of anti-FadAc IgA were significantly increased in patients with colorectal cancer (2.06 ± 1.47 μg/mL) compared with patients with colorectal adenomas (1.49 ± 0.99 μg/mL; P = 0.025). Significant increase was limited to proximal cancers, but not distal tumors. Anti-FadAc IgG was not increased in either study population, suggesting that Fn likely translocates through the gastrointestinal tract and interact with colonic mucosa. Anti-FadAc IgA, but not IgG, is a potential biomarker for early detection of colorectal neoplasia, especially for proximal tumors.

Significance:

Fn, an oral anaerobe highly prevalent in colorectal cancer, secretes the amyloid-like FadAc to promote colorectal cancer tumorigenesis. We report that circulating levels of anti-FadAc IgA, but not IgG, are increased in patients with both early and advanced colorectal cancer compared with the healthy controls, and especially in those with proximal colorectal cancer. Anti-FadAc IgA may be developed into a serological biomarker for early detection of colorectal cancer.
Does the Augmentation of Trauma Informed Hatha Yoga Increase the Effect of Dialectical Behavior Therapy for Substance Use Disorders on Psychopathological Strain of Patients with Borderline Personality Disorder and Comorbid Substance Use Disorder? Results of a Quasi-Experimental Study
Authors: Riegler Alisa , Bumb Jan Malte , Wisch Christian , Schuster Rilana , Reinhard Iris , Hoffmann Sabine , Frischknecht Ulrich , Enning Frank , Schmahl Christian , Kiefer Falk , Koopmann Anne
Published: October 2022

Background: Borderline personality disorder (BPD) is one of the most common personality disorders among persons with substance use disorders (SUDs) and is characterized by severe clinical symptoms. The aim of this study was to investigate if the effect of dialectical behavior therapy for substance use disorders (DBT-S) inpatient treatment on psychopathological symptom load in patients suffering from both BPD and SUD can be augmented by weekly 60-min “Trauma Informed Hatha Yoga” sessions. Materials and Methods: Thirty-nine patients suffering from comorbid BPD and SUD were consecutively in time included in this quasi-experimental pilot study (first intervention then control group). In the intervention group, weekly Trauma Informed Hatha Yoga sessions were added to standard DBT-S for 8 weeks. The participants of the control group received standard DBT-S. All participants completed several self-report questionnaires to assess symptoms of depression, anxiety, symptoms of BPD, and their subjective stress perception at three points in time during the study course. Results: A repeated measures analysis of variance with patients’ psychopharmacological medication as covariate revealed a significant main effect of time for each of the psychometric scales (State and Trait Anxiety Inventory subscale for state anxiety [STAI-S] p = 0.001, Beck Depression Inventory [BDI] p < 0.001; Borderline Symptom List 23 [BSL] p = 0.036) indicating that the psychopathological symptom load of the patients was significantly lower at the end of the DBT-S therapy compared to the beginning in both study groups. Moreover, there was a significant interaction effect of group*time on the psychometric scales STAI-T (subscale for trait anxiety) sum score (p = 0.010) and the sum score of the Perceived Stress Scale (PSS) (p = 0.043). This was expressed by the fact that the participants of the intervention group showed a significant reduction of the STAI-T sum score as well as the sum score of the Perceived Stress Scale (PSS), while the control group did not. Due to the exploratory nature of this study, correction for multiple testing was omitted. Conclusion: Although they are very preliminary, our results suggest that practicing Trauma Informed Hatha Yoga on a regular basis in addition to DBT-S inpatient treatment seems to reduce the level of trait anxiety and perceived stress significantly more than DBT-S inpatient treatment alone. Nevertheless, the effectiveness of Trauma Informed Hatha Yoga in reducing trait anxiety and perceived stress in patients suffering from SUD und BPD must be tested in large randomized controlled trials.
Operation of biogas-solar-diesel hybrid renewable energy system with minimum reserved energy
Authors: Syam Fathy Abdelaziz , El-Sebah Mohamed I. Abu , Sakkoury Khaled S. , Sweelem Emad A.
Published: October 2022

There are methods of supplying the required energy to the load while reducing the cost relatively, but do not take into account the amount of reserved energy; this is not preferred if batteries are not used to store the excess energy produced. The proposed method is a mathematical model to calculate the system size for supplying a load in an area far from the electrical grid where there are huge sources of biomass energy and high rates of solar radiation. The system includes three energy sources to generate electrical energy from solar energy, biogas in addition to a diesel generator supplying a specific load without batteries. The proposed model provides a solution that achieves an optimized system design while minimizing reserved energy taking into account minimizing the cost. The method assumes that the load is supplied first from the solar generator, when the load gets higher, the biogas generator is introduced, and the diesel generator works only if the load exceeds the capacity of the renewable sources. This scenario depends on increasing the contribution of renewable resources compared to fossil fuel.
Integration of an optimized neural network in a photovoltaic system to improve maximum power point tracking efficiency
Authors: Jarmouni Ezzitouni , Mouhsen Ahmed , Lamhamedi Mohamed , Ouldzira Hicham , En-naoui Ilias
Published: October 2022

Due to the variability of weather conditions and equipment properties the maximum power point tracking (MPPT) performance is influenced. MPPT controllers are widely used to improve photovoltaic (PV) efficiency because MPPT can produce maximum power under various weather conditions. Among the most used techniques and representing a satisfactory efficiency are those based on artificial intelligence. Since the use of neural networks requires resources at the implementation level, the optimization of these systems is an important phase. This work represents an optimized system for tracking the maximum power point, the latter based on a multi-layer neural network. The optimized multi layer perceptron (MLP) will ensure a fast convergence to the maximum power point with a low oscillation compared to the classical method.
Elimination of ferroresonance in the distribution zone by high ohmic reactor-shunt limiter
Authors: Abdel-hamed Alaa M. , M. EL-Shafhy Mohamed , A. Badran Ebrahim
Published: October 2022

Electrical network components catastrophically fail as a result of aberrant system oscillations. Also, consequently leads to saturation in the ferromagnetic core elements. These factors induce distortions in the voltage and current waveforms as well as a large rise in voltage or current. This paper presents the role of distributed generation (DG) in contributing to ferroresonance investigation reduction in the distribution sector. A high ohmic reactor-shunt limiter (HOR-SL) is introduced as a novel technique based on a negative sequence component to mitigate ferroresonance. The proposed HOR-SL reduces the ferroresonance in distribution system (DS) by no more than 10.5 msec. A PSCAD/EMTDC software is used to model the ferroresonance phenomenon. The simulation results strongly show the effectiveness of using the proposed HOR-SL for limiting ferroresonant oscillations and creating stable orbit in the distribution sector. Case studies verified the effectiveness of the proposed technique in mitigating ferroresonance oscillations and keeping the security of the distribution zone.
Investigation of partial discharges in non-woody kenaf based pressboard under influence of moisture
Authors: Kurnia Rizda Fitri , Mat Saman Norhafezaidi , Ahmad Mohd Hafizi , Nawawi Zainuddin , Azrin Nur Azalia , Talib Mohd Aizam
Published: October 2022

The most crucial parts that decide the transformer’s lifetime are the solid insulation of the power transformer, especially the pressboard and kraft paper. The life of the insulation is significantly influenced by the presence of water in the insulation. This paper presents a study on partial discharge (PD) on the existing normal pressboard (NP) and the newly developed Kenaf-polypropylene pressboard (KPP). Also, the moisture content of the pressboard varied between 0%, 1%, and 3% by the percentage weight of different methods. After the initiation of PD, the surface deterioration analysis was performed by using scanning electron microscopy (SEM). The results from the study revealed that KPP shows superior performance in mitigating PD. At 0%, 1% and 3% moisture contents, KPP showed 89%, 83% and 25% reductions in average PD magnitude, respectively. Overall, the kenaf composite pressboard has better PD resistance, which is one of the things to think about when insulating a transformer.
Full state feedback and feed forward control of servo smart window using MATLAB/Simulink
Authors: Ayu Lestari Rany , Yusmaniar Oktiawati Unan
Published: October 2022

House as a place to carry out daily activities requires healthy air. The right step to improve air circulation is to use smart window. A control system method is needed so that smart windows can automate air circulation better. Temperature sensor will determine the minimum, maximum, and opening size of the smart window in order to perform the function of opening and closing effectively. Servo motors are used as actuators in the process of opening - closing windows. In this research, the full state feedback and feed forward control method are used to determine the best control theory for servo motor control for smart window systems. The motor control system modeling in smart window is designed by applying the previously studied control theory. Mathematical modeling was carried out to obtain system equations and performance index. The equation is then applied to the MATLAB/Simulink simulation so that the stability of the system can be monitored against disturbances in the form of noise originating from the sensor or the control system itself.
Modified three-term conjugate gradient algorithm and its applications in image restoration
Authors: Ismail Ibrahim Yahya , Mohammed Khudhur Hisham
Published: October 2022

In image restoration, the goal is often to bring back a high-quality version of an image from a lower-quality copy of it. In this article, we will investigate one kind of recovery issue, namely recovering photos that have been blurred by noise in digital photographs (sometimes known as "salt and pepper" noise). When subjected to noise at varying frequencies and intensities (30,50,70,90). In this paper, we used the conjugate gradient algorithm to Restorative images and remove noise from them, we developed the conjugate gradient algorithm with three limits using the conjugate condition of Dai and Liao, where the new algorithm achieved the conditions for descent and global convergence under some assumptions. According to the results of the numerical analysis, the recently created approach is unequivocally superior to both the fletcher and reeves (FR) method and the fletcher and reeves three-term (TTFR) metod. Use the structural similarity index measure (SSIM), which is used to measure image quality and the higher its value, the better the result. The original image was compared with all the noisy images and each according to the percentage of noise as well as the images processed with the four methods.
Optimized multibeam synthesis of time-modulated microstrip arrays
Authors: Alaydrus Mudrik , Attamimi Said , Umaisaroh Umaisaroh
Published: October 2022

The capability to form the main beam of antennas improves the energy efficiency of wireless systems and enhances radar systems' effectiveness in detecting and tracking objects. The time-modulated array can generate multibeam with arbitrary directions in different spectral parts. This work synthesizes several linear arrays to achieve particular main beams and additional restrictions. We modify the pulse durations and the switch-on instants of the switches in each antenna element in an optimization procedure. The parameters are varied through the simulated annealing by minimizing certain cost functions. We designed three linear arrays: model 1 consists of six elements and has two beams. Model 2 is the improved version of model 1 by rejecting the sidelobes of other spectral contributions at the main beam under consideration. Model 3 consists of sixteen elements and has three different beams. The analytical results were verified by modeling the arrays in the form of microstrip arrays in a numerical simulation with HFSS. We obtained very accurate verification. Distinctive devitations at the region ϑ<-60o and ϑ>60o originate from the radiation characteristics of microstrip antennas, which focus the energy upwards to the region around ϑ=0o.
Analysis of the current state of deepfake techniques-creation and detection methods
Authors: A. Abu-Ein Ashraf , M. Al-Hazaimeh Obaida , M. Dawood Alaa , I. Swidan Andraws
Published: October 2022

Deep learning has effectively solved complicated challenges ranging from large data analytics to human level control and computer vision. However, deep learning has been used to produce software that threatens privacy, democracy, and national security. Deepfake is one of these new applications backed by deep learning. Fake images and movies created by Deepfake algorithms might be difficult for people to tell apart from real ones. This necessitates the development of tools that can automatically detect and evaluate the quality of digital visual media. This paper provides an overview of the algorithms and datasets used to build deepfakes, as well as the approaches presented to detect deepfakes to date. By reviewing the background of deepfakes methods, this paper provides a complete overview of deepfake approaches and promotes the creation of new and more robust strategies to deal with the increasingly complex deepfakes.
Diabetes diagnosis system using modified Naive Bayes classifier
Authors: Alwan Jwan Kanaan , Jaafar Dhulfiqar Saad , Ali Itimad Raheem
Published: October 2022

In today’s world, Diabetes is one of these diseases and is now a big growing health problem. The techniques of data mining have been widely applied to extract knowledge from medical databases. In this work, a Medical Diagnosis system of Diabetes is proposed for the ‎diagnosis of diabetes in a manner ‎that is rapid and cost-effective. three stages are ‎involved in the proposed diabetes diagnosis system (DDS) including: dataset constructing, preprocessing and classification algorithm using traditional Naïve Bayesian ‎‎(TNB) and modified Naïve Bayesian (MNB)). MNB Classifier is a modified NB that is used to ‎enhance the accuracy of ‎diagnosis, by adding a proposed modest model to help separate ‎the overlapping diagnosis classes. The outcome‎ ‎showed that the accuracy of MNB classifier is generally higher than that of ‎TNB ‎classifier for all sets of features. An accuracy of about (63%) was achieved for the TNB ‎model, whereas ‎that of the MNB model is (100%). The experimental results showed that ‎the MNB is better than the traditional ‎NB in both two cases of constructed medical ‎datasets; the first case of filling the missing values by experiences and ‎the second case of filling ‎missing values by K-nearest neighbor (KNN) algorithm.
Building an efficient content based image retrieval system by changing the database structure
Authors: Jassim Mohammed Rana , J. Y. Aldarwish Abdulla , A. Yassin Ali
Published: October 2022

The amount of interest in digital images is growing because of the advent of the World Wide Web. Many different industries are making use of digital images today, including medicine, design, journalism, education, and more. Images can be stored and retrieved using a variety of techniques and methods. Nevertheless, the majority of those engines are dependent on Meta data (key-words, descriptions, tags). Although efficient, costly, and able to target keywords and tags with each image, and these engines have their shortcomings. Though it may be possible to use a filtering system to give more accurate results, there are additional issues.
Cost-effective and user-friendly vehicle tracking system using GPS and GSM technology based on IoT
Authors: Akanda Nazmul Islam , Hossain Md. Alomgir , Fahad Md. Mazharul Islam , Rahman Md. Nur , Khairunnaher Khairunnaher
Published: October 2022

Security is very apart important for vehicles to prevent the injury as vehicle theft is very common phenomenon now a days. We can ensure the security of our vehicles via monitoring our vehicles 24/7. There are many possible ways to track a vehicle. A few groups do not concern about the users need. Our research is about the tracking vehicle according to user’s demand. We are concerning on low budget, better geographic coordinate and easy user access. The system needs global positioning system (GPS) and global system for mobile telecommunications (GSM) technology. User can access the system by short message service (SMS) on a mobile phone. GSM module communicate with the user and GPS module communicate with satellite to get latitude and longitude coordinate. Location of vehicle's on earth is determined using Google Maps.
Arrhythmogenic Effects of Cardiac Memory
Authors: Viskin Sami , Chorin Ehud , Schwartz Arie Lorin , Kukla Piotr , Rosso Raphael
Published: October 2022

Cardiac memory is the term used to describe an interesting electrocardiographic phenomenon. Whenever a QRS complex is wide and abnormal, such as during ventricular pacing, the T waves will also be abnormal and will point to the opposite direction of the wide QRS. If the QRS then normalizes, such as after cessation of ventricular pacing, the T waves will normalize as well, but at a later stage. The period of cardiac memory is the phase between the sudden normalization of the QRS and the eventual and gradual return of the T waves to their baseline morphology. Cardiac memory is assumed to be an innocent electrocardiographic curiosity. However, during cardiac memory, reduction of repolarizing potassium currents increases left ventricular repolarization gradients. Therefore, when cardiac memory occurs in patients who already have a prolonged QT interval (for whatever reason), it can lead to a frank long QT syndrome with QT-related ventricular arrhythmias (torsades de pointes). These arrhythmogenic effects of cardiac memory are not generally appreciated and are reviewed here for the first time.
Trends in 1-Year Recurrent Ischemic Stroke in the US Medicare Fee-for-Service Population
Authors: Leifheit Erica C. , Wang Yun , Goldstein Larry B. , Lichtman Judith H.
Published: October 2022

Background:

There have been important advances in secondary stroke prevention and a focus on healthcare delivery over the past decades. Yet, data on US trends in recurrent stroke are limited. We examined national and regional patterns in 1-year recurrence among Medicare beneficiaries hospitalized for ischemic stroke from 2001 to 2017.

Methods:

This cohort study included all fee-for-service Medicare beneficiaries aged ≥65 years who were discharged alive with a principal diagnosis of ischemic stroke from 2001 to 2017. Follow-up was up to 1 year through 2018. Cox models were used to assess temporal trends in 1-year recurrent ischemic stroke, adjusting for demographic and clinical characteristics. We mapped recurrence rates and identified persistently high-recurrence counties as those with rates in the highest sextile for stroke recurrence in ≥5 of the following periods: 2001–2003, 2004–2006, 2007–2009, 2010–2012, 2013–2015, and 2016–2017.

Results:

There were 3 638 346 unique beneficiaries discharged with stroke (mean age 79.0±8.1 years, 55.2% women, 85.3% White). The national 1-year recurrent stroke rate decreased from 11.3% in 2001–2003 to 7.6% in 2016–2017 (relative reduction, 33.5% [95% CI, 32.5%–34.5%]). There was a 2.3% (95% CI, 2.2%–2.4%) adjusted annual decrease in recurrence from 2001 to 2017 that included reductions in all age, sex, and race subgroups. County-level recurrence rates ranged from 5.5% to 14.0% in 2001–2003 and from 0.2% to 8.9% in 2016–2017. There were 76 counties, concentrated in the South-Central United States, that had the highest recurrence throughout the study. These counties had populations with a higher proportion of Black residents and uninsured adults, greater wealth inequity, poorer general health, and reduced preventive testing rates as compared with other counties.

Conclusions:

Recurrent ischemic strokes decreased over time overall and across demographic subgroups; however, there were geographic areas with persistently higher recurrence rates. These findings can inform secondary prevention intervention opportunities for high-risk populations and communities.
Taxonomic study of Myrtaceae in forest fragments in Amazonian Maranhão, Brazil
Authors: AMORIM GABRIELA DOS SANTOS , DE VASCONCELLOS BARBOSA MARIA REGINA , BEZERRA DE ALMEIDA JR. EDUARDO , WAYT THOMAS WILLIAM
Published: October 2022

We present a taxonomic study of the Myrtaceae family in a fragmented landscape known as Amazonian Maranhão. The botanical collections were made from August 2019 to February 2020. Other collections were studied in loco at HST, IAN, IPA, MAR, MG, NY, PEUFR, and SLUI, and others online at virtual herbaria. Myrtaceae are represented in Amazonian Maranhão by 37 species. The most diverse genera are Myrcia with 16 species and Eugenia with 15 species, followed by Myrciaria and Psidium with two species each, and Campomanesia and Calycolpus with one each. Some of the species are cited here from Maranhão for the first time, and have their geographic distribution extended, including Eugenia dittocrepis, E. lambertiana, E. patens, E. patrisii, Myrcia bracteata, M. eximia, and Psidium acutangulum. A taxonomic treatment for the family in the region, with an identification key to the species, detailed descriptions and taxonomic comments for each species is presented.
Kalanchoe ×verdoorniae [K. luciae × K. winteri] (Crassulaceae subfam. Kalanchooideae), a new, large-growing nothospecies from South Africa
Authors: SMITH GIDEON F.
Published: October 2022

A new nothospecies, Kalanchoe ×verdoorniae (Crassulaceae subfam. Kalanchooideae) is described from South Africa. The parents of the nothospecies are K. luciae and K. winteri, both of which are included in K. [subg. Kalanchoe] sect. Raveta. At maturity, K. ×verdoorniae has large, consistently obovate leaves that in terms of size are intermediate between those of K. luciae and K. winteri. Like the leaves of K. winteri, those of K. ×verdoorniae are basally distinctly auriculate, a feature not well developed in K. luciae. The inflorescences of K. ×verdoorniae are dense-flowered and club-shaped as found in both parents. Kalanchoe ×verdoorniae has considerable architectural appeal and is hardy in mild-climate regions of the world with limited and unpredictable rainfall. When producing kalanchoe material for outdoor cultivation in mild climates, emphasis is often on selecting for highly coloured foliage—strongly maroon to reddish-infused in the case of K. ×estrelae or distinctly orange-infused in the case of K. ×leistneri—with these colours becoming more intense when plants are subject to environmental stress, such as high solar irradiation loads and a limited water supply. However, K. ×verdoorniae has light green leaves that early on appear nearly pure white as a result of the presence of a substantial, long-persisting waxy layer, on virtually all plant parts.
A new Trogoderma from Madagascar (Coleoptera: Dermestidae: Megatominae), with a key to Madagascan species of the genus
Authors: HÁVA JIŘÍ , TRÝZNA MILOŠ
Published: October 2022

Trogoderma banari Háva & Trýzna sp. nov., from central Madagascar is here described. Male genitalia are illustrated, and colour photographs of the species are provided. The new species differs from similar species by the combination of the following characters: colour of the elytral fasciae, the structure of the antennae, and the shape of the male genitalia. A key to the known Madagascan species of the genus Trogoderma is provided.
The genus Hyboella Hancock, 1915 (Orthoptera: Tetrigidae: Metrodorinae) in Indonesia
Authors: STOROZHENKO SERGEY YU.
Published: October 2022

An annotated list of six Indonesian species of the genus Hyboella Hancock, 1915 is given, of them Hyboella sumatrensis sp. nov. is described from Sumatra Island and H. perakensis Günther, 1939 is recorded from Indonesia for the first time. A key to species of Hyboella known from this country is also provided.
Two new species of Commoneria Horak & Komai, 2006 (Lepidoptera: Tortricidae: Olethreutinae) from Thailand
Authors: PINKAEW NANTASAK , MUADSUB SOPITA , JAIKLA SORAYA
Published: October 2022

The genus Commoneria is recorded for the first time from Thailand, represented by two new species: Commoneria sinuata sp. nov. and Commoneria costaplica sp. nov. Illustrations of adults and genitalia are provided. The new taxa increase the number of described Commoneria species to three worldwide.
A Physically Based, Meshless Lagrangian Approach to Simulate Melting Precipitation
Authors: Pelissier Craig , Olson William , Kuo Kwo-Sen , Loftus Adrian , Schrom Robert , Adams Ian
Published: October 2022

An outstanding challenge in modeling the radiative properties of stratiform rain systems is an accurate representation of the mixed-phase hydrometeors present in the melting layer. The use of ice spheres coated with meltwater or mixed-dielectric spheroids have been used as rough approximations, but more realistic shapes are needed to improve the accuracy of the models. Recently, realistically structured synthetic snowflakes have been computationally generated, with radiative properties that were shown to be consistent with coincident airborne radar and microwave radiometer observations. However, melting such finely structured ice hydrometeors is a challenging problem, and most of the previous efforts have employed heuristic approaches. In the current work, physical laws governing the melting process are applied to the melting of synthetic snowflakes using a meshless-Lagrangian computational approach henceforth referred to as the Snow Meshless Lagrangian Technique (SnowMeLT). SnowMeLT is capable of scaling across large computing clusters, and a collection of synthetic aggregate snowflakes from NASA’s OpenSSP database with diameters ranging from 2 to 10.5 mm are melted as a demonstration of the method. To properly capture the flow of meltwater, the simulations are carried out at relatively high resolution (15 μm), and a new analytic approximation is developed to simulate heat transfer from the environment without the need to simulate the atmosphere explicitly.
A CRITIQUE BY DR. NABIL KUKALI ON AN ARTICLE ENTITLED
Authors: Kukali Nabil
Published: October 2022

This essay offers a discussion and critique of Alon Ben‐Meir's (2022) proposal entitled, “The Case for an Israeli‐Palestinian‐Jordanian Confederation: Why Now and How?” Incorporating elements of other viewpoints and prior proposals to add nuance, I explore Ben‐Meir's concerted attempt to think past the exemplary two‐state model for settling the Palestine and Israel conflict and to introduce thoughts on how policy makers and common actors can apply a confederal system later on. Given the entrenched one‐state reality, I argue that the liberation of Palestinians through emancipation in a solitary popularity‐based nation is the most thoughtfully clear option in contrast to many years of failed endeavors.