bluuebunny/crossref_metadata_2025 huggingface dataset:
A new derivation of the Hénon’s isochrone potentials
Authors: Saa Alberto , Venegeroles Roberto
Published: September 2022

We revisit in this note the Hénon’s isochrone problem. By using the standard Abel inversion technique for one-dimensional motion, we recover in a simple way the Hénon’s parabolae and get all isochrone central potentials under mild smoothness assumptions on the potential function. Our approach also allows us to conclude that isochronous radial periods with explicit energy dependence are necessarily Keplerian, i.e., T2 ∝ | E|−3, and that their corresponding orbits can be easily integrated by mapping them into the usual Kepler problem. It can also be employed to study some other inverse central-force problems and, in particular, it provides a proof of Bertrand’s theorem.
Research on Edge Detection and Image Segmentation of Cabinet Region Based on Edge Computing Joint Image Detection Algorithm
Authors: Gao Huixin , Zhou Gang , Cao Yang , Luo Zhiyuan , Shen Zhicheng , Malar A. Jasmine Gnana
Published: September 2022

Image segmentation (IE) in several disciplines of image processing and computer vision is an essential topic. Segmentation splits a picture into the areas or items that it constitutes. Image segmentation may be achieved with many approaches, some easier than others because of sophisticated programming requirements. The most common technique for segmenting pictures is edge detection (ED) based on sudden (locomotive) intensity fluctuations. This paper aims to study edge detection approaches for the division of images and acquired experimental findings, Sobel, Prewitt, Robert, CannyLoG (Laplacian of Gaussian). It is vital to ensure that picture segmentation algorithms deliver correct results quickly and efficiently for computer vision to reach its full potential. Computer vision approaches require more investigation in hierarchical architectural IoT networks created for seeing the world. In this work, the new way to provide joint image detection (JID) algorithm is to provide multi-scaling approaches for edge detection and segmentation using IoT edge computing (EC). This JID-EC method avoids the requirement to choose and track the edge explicitly. This study provides an overview of fundamental ideas, techniques, and algorithms common to segment images and edge detection, focusing on the segmentation and visualization of joint-articular cartilage images. The reason for this failure is that it is an image noise-sensitive high pass filter. The need for improved algorithms to meet a suitable value of low and high thresholds should thus be stressed for picture noise such as a canny edge, and the performance is achieved with an efficiency of 95.2%.
Distance Optimally Edge Connectedness of Arrangement Graph Based on Subgraph Fault Pattern
Authors: Yu Zhengqin , Zhou Shuming , Zhang Hong , Liu Xiaoqing
Published: September 2022

Large-scale multiprocessor systems or multicomputer systems based on networking have been extensively used in the big data era and social network. Fault tolerance is becoming an essential attribute in multiprocessor systems with the increase of the system scale. For any distinct vertices [Formula: see text], the local connectivity of [Formula: see text] and [Formula: see text], denoted by [Formula: see text], is the maximum number of independent [Formula: see text]-paths in system graph [Formula: see text]. The local edge connectivity of [Formula: see text], [Formula: see text], [Formula: see text], is defined similarly. For any [Formula: see text], [Formula: see text], if [Formula: see text] (or [Formula: see text], then [Formula: see text] is [Formula: see text]-distance optimally (edge) connected, where [Formula: see text] is the diameter of [Formula: see text] and [Formula: see text] is the degree of [Formula: see text]. For any integers [Formula: see text] subject to [Formula: see text], if [Formula: see text] is [Formula: see text]-distance optimally (edge) connected, then we call [Formula: see text] is [Formula: see text]-distance local optimally (edge) connected. In this work, we show that [Formula: see text] ([Formula: see text] is [Formula: see text]-arrangement graph) is [Formula: see text]-distance local optimally edge connected for [Formula: see text] and [Formula: see text].
A Hybrid Method for the Primary Resonance Response of Harmonically Forced Strongly Nonlinear Oscillators
Authors: Du Hai-En , Li Li-Juan , Er Guo-Kang , Iu Vai Pan
Published: September 2022

A hybrid method is presented to obtain the analytical approximate solution to the primary resonance of harmonically forced strongly nonlinear oscillators. This hybrid method combines the classical perturbation method and the classical harmonic balance method. With the proposed splitting procedure some free parameters are introduced, more accurate and reliable analytical approximation compared to the results obtained by the classical harmonic balance method are presented. The proposed method is not based on the small parameter assumption when perturbation method is applied. It is found that the corrections to erroneous solution when harmonic balance method and Floquet theory are adopted in stability analysis is necessary. The proposed method gives excellent stability results compared to those obtained by using harmonic balance method and Floquet theory. Two examples are presented to illustrate the applicability, validity and convergence of the proposed method. The convergence of the solution in stability analysis by the proposed hybrid method are compared with that obtained by using the Floquet theory and the harmonic balance method. The results obtained by the proposed method are verified by the numerical simulations.
On (1,r)-ideals of commutative rings
Authors: Anebri Adam , Khalfi Abdelhaq El , Mahdou Najib
Published: September 2022

Let [Formula: see text] be a commutative ring with nonzero identity. In this paper, we introduce and investigate a new class of ideals that is closely related to the class of [Formula: see text]-ideals. A proper ideal [Formula: see text] of [Formula: see text] is said to be a [Formula: see text]-ideal if whenever nonunit elements [Formula: see text] and [Formula: see text], then [Formula: see text] or [Formula: see text] Some basic properties of [Formula: see text]-ideals are studied. For instance, we give a method to construct [Formula: see text]-ideals that are not [Formula: see text]-ideals. Among other things, it is shown that if [Formula: see text] admits a [Formula: see text]-ideal that is not an [Formula: see text]-ideal, then [Formula: see text] is a local ring. Also, we provide a necessary and sufficient condition in term of [Formula: see text]-ideals for a ring to be a total quotient ring and we determinate the [Formula: see text]-ideals of a chained ring. Finally, we give an idea about some [Formula: see text]-ideals of the localization of rings, the trivial ring extensions and the power series rings to construct nontrivial and original examples of [Formula: see text]-ideals.
Tuning the Thermal Spin Transport Properties via Redox Reactions and Force
Authors: Tan Xingyi , Li Qiang , Ren Dahua
Published: September 2022

Spin caloritronic devices, multifunctional devices combining spintronics and caloritronic, are crucial for the sustainable development of humans. A novel spin caloritronic device is presented using anthraquinone-based molecular sandwiched among two semi-infinite zigzag graphene nanoribbons containing asymmetrical edge hydrogenation electrodes. We demonstrate that the temperature gradient between the right and the left electrodes can generate spin-up and spin-down currents flowing in opposite orientations. Besides, the mentioned currents possess approximately the same magnitudes, indicating a nearly nondissipative spin Seebeck effect. We also find that these currents are significantly dissimilar for the redox reactions and force at different temperature gradients, demonstrating that the developed system can provide acceptable switching action. The obtained results reflect that the redox reactions and force can control the spin Seebeck effect.
A Review of Self-Sensing in Carbon Fiber Structural Composite Materials
Authors: Chung D. D. L.
Published: September 2022

Sensing is a basic ability of smart structures. Self-sensing involves the structural material sensing itself. No device incorporation is needed, thus resulting in cost reduction, durability enhancement, sensing volume increase and absence of mechanical property diminution. Carbon fiber renders electrical conductivity to a composite material. The effect of strain/damage on the electrical conductivity enables self-sensing. This review addresses self-sensing in structural composite materials that contain carbon fiber reinforcement. The composites include polymer-matrix composites with continuous carbon fiber reinforcement (relevant to aircraft and other lightweight structures) and cement–matrix composites with short carbon fiber reinforcement (relevant to the civil infrastructure). The sensing mechanisms differ for these two types of composite materials, due to the difference in structures, which affects the electrical and electromechanical behaviors. For the polymer–matrix composites with continuous carbon fiber reinforcement, the longitudinal resistivity in the fiber direction decreases upon uniaxial tension, due to the fiber residual compressive stress reduction, while the through-thickness resistivity increases, due to the fiber waviness reduction; upon flexure, the tension surface resistance increases, because of the reduction in the current penetration from the surface, while the compression surface resistance decreases. These strain effects are reversible. The through-thickness resistance, oblique resistance and interlaminar interfacial resistivity increase irreversibly upon fiber fracture, delamination or subtle irreversible change in the microstructure. For the cement–matrix composites with short carbon fiber reinforcement, the resistivity increases upon tension, due to the fiber–matrix interface weakening, and decreases upon compression; upon flexure, the tension surface resistance increases, while the compression surface resistance decreases. Strain and damage cause reversible and irreversible resistance changes, respectively. The incorporation of carbon nanofiber or nanotube to these composites adds to the costs, while the sensing performance is improved marginally, if any. The self-sensing involves resistance or capacitance measurement. Strain and damage cause reversible and irreversible capacitance changes, respectively. The fringing electric field that bows out of the coplanar electrodes serves as a probe, with the capacitance decreased when the fringing field encounters an imperfection. For the cement-based materials, a conductive admixture is not required for capacitance-based self-sensing.
Card Nim
Authors: Shasha Dennis
Published: September 2022

Ordering the Nim sum.
Securing the company jewels
Authors: Neville-Neil George
Published: September 2022

GitHub and runbook security.
The Role of Nature-Based Solutions in Supporting Social-Ecological Resilience for Climate Change Adaptation
Authors: Turner Beth , Devisscher Tahia , Chabaneix Nicole , Woroniecki Stephen , Messier Christian , Seddon Nathalie
Published: September 2022

Social-ecological systems underpinning nature-based solutions (NbS) must be resilient to changing conditions if NbS are to contribute to long-term climate change adaptation. We develop a two-part conceptual framework linking social-ecological resilience to adaptation outcomes in NbS. Part one determines the potential of NbS to support resilience based on assessing whether NbS affect key mechanisms known to enable resilience. Examples include social-ecological diversity, connectivity, and inclusive decision-making. Part two includes adaptation outcomes that building social-ecological resilience can sustain, known as nature's contributions toadaptation (NCAs). We apply the framework to a global dataset of NbS in forests. We find evidence that NbS may be supporting resilience by influencing many enabling mechanisms. NbS also deliver many NCAs such as flood and drought mitigation. However, there is less evidence for some mechanisms and NCAs critical for resilience to long-term uncertainty. We present future research questions to better understand how NbS can continue to support social-ecological systems in a changing world.
COVID-19 Thrombotic Complications and Therapeutic Strategies
Authors: Fanaroff Alexander C. , Lopes Renato D.
Published: September 2022

Shortly after the emergence of coronavirus disease 2019 (COVID-19) in late 2019, clinicians rapidly recognized an apparent association between the disease and both arterial and venous thrombotic complications, which was confirmed in epidemiologic studies. Based on these data, hospitals empirically developed and implemented protocols with different strategies for anticoagulation of hospitalized COVID-19 patients. Subsequent randomized controlled trials (RCTs) clarified the role of anticoagulation in patients hospitalized with COVID-19 and recently discharged from the hospital. In this review, we discuss the epidemiology and pathophysiology of thrombosis in patients with COVID-19, observational comparative effectiveness analyses that provided hints of a benefit from anticoagulation, and finally the RCTs that established which patients with COVID-19 benefit from treatment-dose anticoagulation. These RCTs have demonstrated that hospitalized, noncritically ill patients with COVID-19 benefit from treatment-dose anticoagulation, but patients who are hospitalized and critically ill, discharged from the hospital, or not hospitalized do not benefit.
Evaluative Conditioning: Past, Present, and Future
Authors: Moran Tal , Nudler Yahel , Bar-Anan Yoav
Published: September 2022

Evaluative conditioning (EC) research investigates changes in the evaluation of a stimulus after co-occurrence with an affective stimulus. To explain the motivation behind this research, this review begins with an overview of the history of EC research, followed by a summary of the state of the art with respect to three key questions. First, how should EC procedures be used to influence evaluation? We provide a guide based on evidence concerning the functional properties of EC effects. Second, how does the EC effect occur? We discuss the possible mediating cognitive processes and their automaticity. Third, are EC effects ubiquitous outside the lab? We discuss the evidence for the external validity of EC research. We conclude that the most important open questions pertain to the relevance of EC to everyday life and to the level of control that characterizes the processes that mediate the EC effect after people notice the stimulus co-occurrence.
An Efficient Signal Processing Algorithm for Detecting Abnormalities in EEG Signal Using CNN
Authors: Syamsundararao Thalakola , Selvarani A. , Rathi R. , Vini Antony Grace N. , Selvaraj D. , Almutairi Khalid M. A. , Alonazi Wadi B. , Priyan K. S. A. , Mosissa Ramata
Published: September 2022

Electroencephalography (EEG) is crucial for epilepsy detection; however, detecting abnormalities takes experience and knowledge. The electroencephalogram (EEG) is a technology that measures brain motion and represents the brain’s function. EEG is an effective instrument for deciphering the brain’s complicated activity. The information contained in the EEG signal pertains to the electric functioning of the brain. Neurologists have typically used direct visual inspection to detect epileptogenic abnormalities. This method is time‐consuming, restricted by technical artifacts, produces varying findings depending on the reader’s level of experience, and is ineffective at detecting irregularities. As a result, developing automated algorithms for detecting anomalies in EEGs associated with epilepsy is critical. The construction of a novel class of convolutional neural networks (CNNs) for detecting aberrant waveforms and sensors in epilepsy EEGs is described in this research. In this study, EEG signals are analyzed using a convolutional neural network (CNN). For the automatic detection of abnormal and normal EEG indications, a novel deep one‐dimensional convolutional neural network (1D CNN) model is suggested in this paper. The regular, pre‐ictal, and seizure categories are detected using this approach. The proposed model achieves an accuracy of 85.48% and a reduced categorization error rate of 14.5%.
Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model
Authors: Zheng Lan
Published: September 2022

At present, there is a phenomenon of network data packet loss in the trajectory tracking control system, which will degrade or even destabilize the system’s performance. Therefore, this work first explains the theory of the deep long-short term memory (LSTM) neural network model, the kinematic model of mobile robots, and the trajectory tracking error model. The reasons for data packet loss in the control system are analyzed. Second, a prediction model based on the LSTM network is designed according to the theory mentioned above. Finally, the training effect of the LSTM model and the robot trajectory tracking effect based on the model are tested by setting up simulation experiments. The research results are as follows: (1) The pose test error of the mobile robot will eventually tend to zero through the simulation curve generated by the pose parameters (x, y, θ) of the mobile robot. (2) The trajectory tracking error of the deep LSTM neural network prediction and compensation method with the packet loss rate of 5% is less than that with the packet loss rate of 10%. (3) The linear velocity

υ

of the mobile robot based on the prediction model of the LSTM network varies greatly but is always in the interval (−2, 2). Its angular velocity

ω

initially fluctuates greatly but gradually tends to zero after about 13 s. (4) When the prediction model tracks the trajectory of the robot, the horizontal position x, the vertical position y, and the angle

θ

coincide with the reference trajectory. The exploration is conducted to provide a reference for the research on data packet loss in the networked mobile robot trajectory tracking system.
Improved Glowworm Swarm Optimization Algorithm Based on a Sigmoid Function for the Absolute Value Equation
Authors: Wang Feiran , Chen Chen , Chen Kai , Shen Liang
Published: September 2022

Solving the absolute value equation (AVE) is a nondifferentiable NP-hard and continuous optimization problem with a wide range of applications. Because its solutions have different forms, it is challenging to design the most efficient algorithm that can solve different AVEs without using overcomplicated technical improvement and problem-dependent objectives. Hence, this paper proposed an improved glowworm swarm optimization (GSO) algorithm with an adaptive step size strategy based on the sigmoid function (SIGGSO) that solves the AVEs. Seven test AVEs, including multisolution and high-dimensional AVEs, are selected for testing and compared with seven metaheuristic algorithms. The experimental results show that the proposed SIGGSO algorithm has higher solution accuracy and stability when seeking multiple solution of AVEs compared to the basic GSO. Moreover, it obtains competitive advantages on multisolution and high-dimensional AVEs compared with other metaheuristic algorithms and provides an effective method for engineering and scientific calculations.
Host Immune Response to Clinical Hypervirulent Klebsiella pneumoniae Pulmonary Infections via Transcriptome Analysis
Authors: Lei Langhuan , Zhang Xiaoli , Yang Rui , Jing Haiming , Yuan Yue , Chen Zhifu , Gou Qiang , Zhao Zhuo , Zhang Jinyong , Wang Xingyong
Published: September 2022

Klebsiella pneumoniae (K. pneumoniae), especially those with hypervirulence, is becoming a global concern and posing great threat to human health. Studies on individual immune cells or cytokines have partially revealed the function of the host immune defense against K. pneumoniae pulmonary infection. However, systematic immune response against K. pneumoniae has not been fully elucidated. Herein, we report a transcriptome analysis of the lungs from a mouse pneumonia model infected with a newly isolated K. pneumoniae clinical strain YBQ. Total RNA was isolated from the lungs of mice 48 hours post infection to assess transcriptional alteration of genes. Transcriptome data were analyzed with KEGG, GO, and ICEPOP. Results indicated that upregulated transcription level of numerous cytokines and chemokines was coordinated with remarkably activated ribosome and several critical immune signaling pathways, including IL-17 and TNF signaling pathways. Notably, transcription of cysteine cathepsin inhibitor (stfa1, stfa2, and stfa3) and potential cysteine-type endopeptidase inhibitor (cstdc4, cstdc5, and cstdc6) were upregulated. Results of ICEPOP showed neutrophils functions as the most essential cell type against K. pneumoniae infection. Critical gene alterations were further validated by rt-PCR. Our findings provided a global transcriptional perspective on the mechanisms of host defense against K. pneumoniae infection and revealed some unique responding genes.
Posterior Vertebral  Column  Resection for Severe Spinal Deformity Correction: Comparison of Pediatric, Adolescent, and Adult Groups
Authors: Song Zhibo , Zhang Zhaoquan , Yang Xiaochen , Zhao Zhi , Li Tao , Bi Ni , Xie Jingming , Wang Yingsong
Published: September 2022

We compared the pre-, intra-, and postoperative characteristics among three groups of patients who underwent posterior vertebral column resection (PVCR) to clarify age-related characteristics and to guide patient management, surgical planning, and complication avoiding. We compared and analyzed the etiology, surgical events, outcomes, and complications among pediatric, adolescent, and adult patients who underwent PVCR in a single-center database retrospectively. Patients were categorized into pediatric (0–12 yr), adolescent (13–19 yr), and adult (>20 yr) cohorts. Demographics, surgical events, clinical and radiographic results, and major complications were compared between groups. A total of 87 patients with a mean follow-up 42 (24–96) months were identified. Pediatric group (14) had a high frequency of congenital vertebral and cardiac abnormal, adolescents (47) presented more intracanal malformations, and idiopathic was common in the adult group (26). Although pediatric patients had shorter fusion levels than adolescent and adult, their mean resected vertebrae (1.91), percentage of blood loss (estimated blood loss per total blood volume) (201.9%), and operative time were much higher. The coronal/sagittal correction rate was significantly higher in the pediatric group (73.6%/72.3%). Overall, surgical complications were more frequent in adults, particularly neuromonitoring alert and implant failure. However, more severe complications were noted in younger patients. For pediatric patients with PVCR, poor physiological conditions and frequent comorbidities indicated cautious patient selection and sufficient preoperative preparation. The higher correction rate may be due to the excellent compliance of the spinal cord. For adult patients, preoperative traction and adjusting the tension of the spinal cord during surgery could contribute to neurological safety.
Identification of a Metabolic Reprogramming-Associated Risk Model Related to Prognosis, Immune Microenvironment, and Immunotherapy of Stomach Adenocarcinoma
Authors: Zhao Yan , Zhang Dongsheng , Sun Yueming
Published: September 2022

Stomach adenocarcinoma (STAD) is one of the most common malignant digestive tumors. Metabolic reprogramming is an essential feature of tumorigenesis. The roles of metabolic reprogramming in STAD patients were investigated to explore the tumor immune microenvironment (TME) and potential therapeutic strategies. STAD samples’ transcriptomic and clinical data were collected from The Cancer Genome Atlas (TCGA) set and the GSE84437 set. The signature based on the metabolism-related genes (MRGs) was built using the Cox regression model to predict prognosis in STAD. Notably, this MRG-based signature (MRGS) accurately predicted STAD patients’ clinical survival in multiple datasets and could serve as an indicator independently. STAD patients with high scores on the MRGS were eligible for generating a type I/II interferon (IFN) response, according to a complete examination of the link between the MRGS and TME. Tumor Immune Dysfunction and Exclusion (TIDE) and immunophenoscore (IPS) analyses revealed that STAD patients with different MRGS scores had different reactions to immunotherapy. Consequently, assessing the pattern of these MRGs increases the understanding of TME features in STAD, hence directing the development of successful immunotherapy regimens.
Experimental Study on the Effect of Taijiquan on the Physical Function and Mental Health of College Students
Authors: Gao Meixing
Published: September 2022

With the continuous development of education and economy, people pay more and more attention to the improvement of comprehensive quality. College students are a special social group. Now they are not only under enormous pressure of study and employment but also pay more attention to physical function and mental health. Maintaining healthy physical fitness is one of the goals pursued by more and more college students. This paper studies the effects of Tai Chi on the physical and mental health of college students. Taijiquan is a physical education class, divided into an experimental group and a control group. The experimental group was trained three times a week, and the control group was trained once a week. Through the evaluation of college students who practice Tai Chi three times a week and college students who practice Tai Chi once a week, the conclusions are: first, there are significant differences in lung capacity, one-minute skipping rope, and sitting forward bending; second, in self-esteem on assessment, significant differences were found in visual perception threshold ability, somatization, and anxiety factors. The final results are as follows: after 8 weeks of Taijiquan practice, the effect of the experimental group and the control group on the spirometry test (

P
>
0.05

) and the difference in sitting flexion index between male and female students were statistically significant (

P
<
0.05

); Taijiquan was practiced with different frequencies for 8 weeks. After that, the number of skipping ropes did not increase significantly (

P
<
0.05

). That is, different frequencies of Taijiquan practice have an impact on the physique of college students. Taijiquan exercise also has a significant impact on the visual perception threshold ability, which can effectively reduce the somatization, paranoia, anxiety, depression, and other psychological states of college students.
Prediction of New Media Information Dissemination Speed and Scale Effect Based on Large-Scale Graph Neural Network
Authors: Qiumeng Chen , Yu Shen
Published: September 2022

In recent years, because of the popularity of the internet and mobile devices, the dissemination of new media in social networks has attracted extensive attention from scholars and the industry. Scale prediction or propagation speed prediction is to use the initial data to predict the propagation scale of the network. In the complex and changeable social network, how to accurately predict the cascading scale of new media information is the biggest problem at present. In the process of new media information transmission, because of the role of new media information transmission in guiding public opinion, the current hierarchical model of new media information transmission lacks the overall and local models. To solve this problem, a global structure modeling method is proposed. In addition, because of the uncertainty of new media information dissemination, a method of bidirectional recurrent neural network prediction and algorithm complexity is used, and a new method based on large-scale graph neural network is constructed. A prediction method of new media information dissemination speed and scale based on large-scale graph neural network. Through comparative experiments with previous research models, it is found that the NWIDF model constructed in this paper has a good prediction effect.
Bacterial Milk Quality along the Value Chain in Smallholder Dairy Production
Authors: Washaya S. , Jakata C. , Tagwira M. , Mupofu T.
Published: September 2022

This study aimed to evaluate the microbial quality of raw milk along the milk value chain at Africa University (AU). Eighteen Holstein-Friesian cows were used in this experiment. A total of 270 milk samples were collected for laboratory analysis at three different stages, during milking (DM), from the bulk tank (BT), and at the dining hall (DH), to determine the total bacterial count (TBC), Escherichia coli, and Salmonella enterica. Samples were cultured in Petri dishes using an appropriate medium for 48 hours. The plate count method was used to determine the quantity of bacteria. Data were analyzed using GLM SPSS. The results indicated that TBC increased (

P

 < 0.05) from one site to the next along the value chain, yet it undulates when measured over time. Escherichia. coli and S. enterica counts increased (

P

 < 0.05) at the last site of collection and the highest counts were recorded in week two. In conclusion, the current study indicates the hygiene of the dairy parlor with very low TBC, E. coli, and S. enterica counts during milking and bulk tank storage and that the relationship between TBC and E. coli is nonlinear with respect to time.
Is Vitamin D Deficiency the Cause or the Effect of Systemic Lupus Erythematosus: Evidence from Bidirectional Mendelian Randomization Analysis
Authors: Ding Yang , Yang Shengyi , Fan Shasha , Tang Yi , Teng Yan , Tao Xiaohua , Lu Wei
Published: September 2022

Background. Vitamin D deficiency is common in patients with systemic lupus erythematosus (SLE). Observational studies have reported that it is associated with SLE. In this bidirectional Mendelian randomization (MR) study, we explored the genetic association between serum vitamin D (VD) levels and SLE using two models. Methods. Genetic variants associated with vitamin D (

n
=
304,181

), 25-hydroxyvitamin D levels (

n
=
401,460

), and SLE (

n
=
213,683

) at genome-wide significance (

P
<
5
∗

10

−
8

) derived from large-scale publicly available GWAS data were used as instrumental variables. Bidirectional two-sample MR analyses were performed using the inverse variance weighted method (IVW, random, or fixed effect model). Sensitivity analyses including maximum likelihood, MR-Egger method, penalized weighted median method, MR-PRESSO, MR-RAPS, and MR-radial method were conducted. Results. The findings showed that genetically predicted SLE using the IVW method had a negative effect on the vitamin D and 25-hydroxyvitamin D levels in the two models. The results of sensitivity analyses of different analytical approaches were consistent. Conclusions. These findings indicated that genetically determined SLE had a negative effect on the vitamin D and 25-hydroxyvitamin D levels. Future studies, including random controlled clinical trials, should evaluate the association and mechanisms between serum VD levels and SLE.
Research on Mechanical Model of Canal Lining Plates under the Effect of Frost Heaving Force
Authors: Liang Yantao , Zhang Fuping , Jing Mingming , He Pengfei
Published: September 2022

Frost heaving damage of canal lining in cold and dry areas is one of the important causes of canal leakage. In this paper, based on the bending theory of thin plate, a mechanical model of canal lining under the action of frost heaving force is established and solved. Through parametric and engineering case analysis, the following conclusions are drawn: under the action of frost heaving force, the bending moment, shear force, and internal force of the slope plate show a nonuniform distribution, and the maximum values of bending moment Mx and normal stress σx are close to the bottom third of the slope, which is consistent with the existing research and engineering practice. Compared with the theory of beam, the results of the theory of thin plates show that the internal forces and stresses increase at the free boundary (longitudinal expansion joint). The bending moment Mxy and stress τxy are maximum at the four corners of the plate: although the numerical magnitude is lower than that of Mx, it may cause stress concentration to damage the lining plate and thus cause break. The shear force at the longitudinal expansion joint may lead to fracture of the joint material, which needs to be considered in the design process. Due to the uniform distribution of the frost heaving force on the bottom plate lining, its deflection, internal forces, and stresses also show a uniform distribution. The research results can provide scientific reference for the design and operation and maintenance of water transmission canal lining in cold areas.
Innovative Research on Intelligent Classroom Teaching Mode in the “5G” Era
Authors: Rong Jing
Published: September 2022

The integration of “5 G + education” has become a consensus and trend. The maturity and application of technologies represented by artificial intelligence, virtual reality, and big data in the 5 G era have provided a new and greater impetus for education and teaching reform. Education must adhere to the concept of people-oriented, high-quality talent training is the foundation and core of higher education, and colleges and universities need to quickly adapt to the development trend of education in the new era. Therefore, it is of significance to investigate some innovative strategies to address the existing problems in the intelligent classroom teaching mode in the “5 G” Era. This paper discusses the characteristics of high speed and low latency in the 5 G era, discusses the development of the intelligent classroom teaching promoted by 5 G technology, and creatively puts forward different teaching strategies in combination with the problems existing in an intelligent classroom in colleges and universities. Students interact with the constructed virtual or virtual real scenes in real time. With the help of artificial intelligence technology, can establish artificial intelligence teaching assistants and learning partners, support one-to-one skills training, will also provide teachers with an intelligent learning environment, integrate teaching resources, optimize the teaching path, improve teaching interaction, provide a reference for future teaching activities, explore the application of innovative teaching mode based on 5 G communication. Big data will record the learning behavior in the process and use it for the diagnosis and evaluation of dynamic learning results, providing a basis for educators to practice “people-oriented” and help students grow up.
Education and training strategic management course to improve students’ self-directed learning and learning outcomes
Authors: Soko Imelda Paulina , Samo Damianus Dao
Published: September 2022

The purposes of this study were: i) Developing strategic management of education and training online tutorial; ii) Testing the feasibility of strategic management of education and training online tutorial; iii) Finding out the obstacles in strategic management of education and training online tutorial development process; and iv) Finding the solutions in overcoming obstacles in developing strategic management of education and training online tutorial. This developmental research used the analysis, design, development, implementation, and evaluation (ADDIE) model and involved second year students of teacher training and education faculty in the Kupang region, Indonesia. The instruments used in this study were: i) Validation guidelines for online tutorial material and supporting instruments; ii) Students’ self-directed learning questionnaire; and iii) Online tutorial formative test questions. Data analysis used descriptive and inferential statistical test. The results show that the online tutorial: i) Fulfilled the aspect of validity in the excellent category; ii) Fulfilled the effectiveness aspect in the good category; iii) Limitations of the source, difficulties in compacting the content of material and developing discussion questions are the obstacles in developing the online tutorial; and iv) The solutions are (utilize Universitas Terbuka (UT)'s open educational resources (OER), make concept maps, and keep practicing to make question samples and consult the questions made to the experts).
Determining digital literacy among teacher from gender perspective through the Rasch model
Authors: Wigati Indah , Faisal Faisal , Astuti Resti Tri
Published: September 2022

Advances in technology and the implementation of online learning require all parties in educational institutions, especially teachers, to be able to use technology. Digital literacy skills are needed to support the learning process. The purpose of this study was to measure the digital literacy skills of teachers through the Rasch model from a gender perspective during the coronavirus disease 2019 (COVID-19) pandemic at Madrasah Aliyah Negeri Palembang, Indonesia. A quantitative research method using Rasch analysis was applied. The results showed that the ability of teachers in digital literacy can be categorized as very high (three people), high (28 people), moderate (63 people), and low (two people) and showed that there are no question items that only favor one of the genders. The results showed that the male teachers in this study had a lower level of digital literacy skills than the female teachers.
Electric and magnetic field calculation software in transmission lines
Authors: Guerrero Luis Imbachi , Rubio Fredy Jiménez , Barrera Mario Rodríguez , Giral-Ramírez Diego
Published: September 2022

There is an interest in the biological effects of exposure to low-frequency electromagnetic fields issued by transmission lines on animals and humans. The fields generated by the lines are relevant for the design and operation of power systems. The study of the electric and magnetic fields in the transmission networks implemented commercial simulators bases on the finite element method. These commercial simulators are characterized by accuracy and high hardware and software requirements. This work presents CEM-LT, a tool that accurately precisely the electric and magnetic field in the transmission lines, with simple and intuitive handling and low processing times, making it ideal for being implemented together with optimization methods. The electric and magnetic field in the servant area for two case studies is analyzed to evaluate the accuracy and processing times. The level of accuracy is characterized by comparing the results with COMSOL obtaining errors of less than 2.4%. The case study with the highest computational requirement achieved a processing time of 3,027 seconds.
Comparative analysis of evolutionary-based maximum power point tracking for partial shaded photovoltaic
Authors: Megantoro Prisma , Aldi Kusuma Hafidz Faqih , Awalin Lilik Jamilatul , Afif Yusrizal , Priambodo Dimas Febriyan , Vigneshwaran Pandi
Published: September 2022

The characteristics of the photovoltaic module are affected by the level of solar irradiation and the ambient temperature. These characteristics are depicted in a V-P curve. In the V-P curve, a line is drawn that shows the response of changes in output power to the level of solar irradiation and the response to changes in voltage to ambient temperature. Under partial shading conditions, photovoltaic (PV) modules experience non-uniform irradiation. This causes the V-P curve to have more than one maximum power point (MPP). The MPP with the highest value is called the global MPP, while the other MPP is the local MPP. The conventional MPP tracking technique cannot overcome this partial shading condition because it will be trapped in the local MPP. This article discusses the MPP tracking technique using an evolutionary algorithm (EA). The EAs analyzed in this article are genetic algorithm (GA), firefly algorithm (FA), and fruit fly optimization (FFO). The performance of MPP tracking is shown by comparing the value of the output power, accuracy, time, and tracking effectiveness. The performance analysis for the partial shading case was carried out on various populations and generations.
Optimization of the structure of filter-compensating devices in networks with powerful non-linear power consumers based on fuzzy logic
Authors: Zhilin Evgeniy Vitalievich , Prasol Dmitriy Aleksandrovich , Savvin Nikita Yurievich
Published: September 2022

The article presents a solution to the problem of optimizing the structure of filter-compensating devices (FCD) when installed in high-voltage mine networks with powerful nonlinear electrical receivers. The urgency of the problem of choosing a rational structure of the FCD. The problem of choosing the design and installation location of the FCD is presented. The main technical means of compensation of higher harmonics of currents and voltages in high-voltage networks with powerful nonlinear electrical receivers are considered. Analysis of different types of passive filters (PF) and their frequency properties showed that the choice of specific types of PF refers to the multi-criteria optimization problem. The main methods of optimization of FCD design are considered. The variant of FCD construction based on the solution of multi-criteria optimization problem with the use of fuzzy sets is proposed and justified. To this end, the calculation of PF parameters and frequency characteristics of equivalent systems of the "filter-external network" type for four possible combinations of PF is performed. The optimal is a FCD with two resonant PF tuned to the 11th and 13th harmonics, and a second-order broadband PF tuned to compensate harmonics starting from the 23rd and above. The analysis of simulation results showed effective compensation of higher harmonic currents and voltages.
Anomalies detection for smart-home energy forecasting using moving average
Authors: Mohd Zebara Hoque Jesmeen , Murthy Gajula Ramana , Hossen Jakir , Ganesan Jaya , Aziz Azlan Abd , Tawsif Khan Chy. Mohammed
Published: September 2022

In the past few years, the increase in the relation between the physical and digital world over the internet was witnessed. Even though the applications can enhance smart home systems, it is still early stages and challenges in the field of internet of things (IoT). An extreme level of data quality (DQ) system management is essential to produce a meaningful vision. However, in most home energy management system has no straightforward process of removing abnormal data. Hence, the research aims to propose and validate the model of anomaly detection for power consumption in real-time. The moving average (MA) approach identifies and removes abnormal energy consumption data. The results obtained from the forecasting time series auto regressive integrated moving average (ARIMA) model demonstrated that the proposed heuristics effectively enhanced energy usage forecasting. The selection of optimum parameter values for the MA was comprehended for time-series forecasting error minimization by comparing mean squared error (MSE). These outcomes proved the effectiveness of the existing technique and precision of choice of the appropriate. Therefore, the method can effectively route the cleaned sequence data streams in a real-time environment, which is valuable for spotting the anomalies and eliminating for enhancing energy usage time series.
Comparative detection and fault location in underground cables using Fourier and modal transforms
Authors: Nazerian Vahdat , Zakerifar Mohammad Esmail , Zadehbagheri Mahmoud , Kiani Mohammad Javad , Sutikno Tole
Published: September 2022

In this research, we create a single-phase to ground synthetic fault by the simulation of a three-phase cable system and identify the location using mathematical techniques of Fourier and modal transforms. Current and voltage signals are measured and analyzed for fault location by the reflection of the waves between the measured point and the fault location. By simulating the network and line modeling using alternative transient programs (ATP) and MATLAB software, two single-phase to ground faults are generated at different points of the line at times of 0.3 and 0.305 s. First, the fault waveforms are displayed in the ATP software, and then this waveform is transmitted to MATLAB and presented along with its phasor view over time. In addition to the waveforms, the detection and fault location indicators are presented in different states of fault. Fault resistances of 1, 100, and 1,000 ohms are considered for fault creation and modeling with low arch strength. The results show that the proposed method has an average fault of less than 0.25% to determine the fault location, which is perfectly correct. It is varied due to changing the conditions of time, resistance, location, and type of error but does not exceed the above value.
Range-enhanced packet classification to improve computational performance on field programmable gate array
Authors: Ponnuswamy Anita , Devi Manju
Published: September 2022

Multi-filed packet classification is a powerful classification engine that classifies input packets into different fields based on predefined rules. As the demand for the internet increases, efficient network routers can support many network features like quality of services (QoS), firewalls, security, multimedia communications, and virtual private networks. However, the traditional packet classification methods do not fulfill today’s network functionality and requirements efficiently. In this article, an efficient range enhanced packet classification (REPC) module is designed using a range bit-vector encoding method, which provides a unique design to store the precomputed values in memory. In addition, the REPC supports range to prefix features to match the packets to the corresponding header fields. The synthesis and implementation results of REPC are analyzed and tabulated in detail. The REPC module utilizes 3% slices on Artix-7 field programmable gate array (FPGA), works at 99.87 Gbps throughput with a latency of 3 clock cycles. The proposed REPC is compared with existing packet classification approaches with better hardware constraints improvements.
Higher chromatic rendition with Cr3+-doped yellow Y3Al5O12:Ce3+ for double-layer remote phosphor white-light-emitting diodes
Authors: Nguyen Thi Dieu An , Huu Phuc Dang , Nguyen Thi Thuy Hang
Published: September 2022

Remote phosphor designs of white-light-emitting diodes (WLEDs) have been recognized for their high thermal stability and excellent luminous efficiency but not for the chromatic rendering. The study presents an approach for the remote phosphor structure to overcome the low-color-rendering issue by enriching its red-light spectral intensity through co-doping Cr3+ and Ce3+ ions into the yellow  Y3Al5O12 (YAG) phosphor compound. This ion co-doping process probably enhances emission spectra in the far-red range because of the energy transfer of the integrated ions: Ce3+ Cr3+. Additionally, the luminescence and color properties of the phosphor layer significantly depend on the doped concentration of the Cr3+ ion. Here, with 0.008% Cr3+ in the phosphor composite, either internal or external quantum performances of the dual-layer WLED light are enhanced, which achieved the number of 58.9% and 46.7%, respectively. The color rendering index (CRI) would also be higher if Cr3+ is added, about 77.9, compared to the CRI of 63.2 when using the original  YAG:Ce3+ phosphor layer.