bluuebunny/crossref_metadata_2025 huggingface dataset:
Cosmological constant and AdS spacetimes from Minkowski spheres
Authors: Boskoff Wladimir-Georges , Capozziello Salvatore
Published: April 2022

A theory of gravity without masses can be constructed starting from Minkowski [Formula: see text] spaces. The corresponding adapted (p, q) Minkowski potentials, gradients and Laplacians built on each signature lead to a field equation similar to the Newton-Laplace one. In this framework, the anti-de Sitter spacetime is a hypersurface described by a constant potential of Minkowski gravitational force in absence of matter. We show that the cosmological constant of the anti-de Sitter spacetimes is related to a geometric property of Minkowski spheres: the centro-affine conservation of volumes defined from the so-called Minkowski–Tzizteica affine spheres. It is possible to show the connection between the anti-de Sitter AdS(2, 3) space, the non-Euclidean geometry and the difference between Minkowski [Formula: see text] sphere and the pseudosphere seen as a surface of the 3-Euclidean space. According to a suitable parameterization, the relation between de Sitter and anti-de Sitter spacetimes in any dimension is fixed. As a consequence, the light can travel in any anti-de Sitter spacetime defining the geodesic structure. These considerations can be recast in terms of Einstein field equations with cosmological constant and extended to f(R) gravity.
A curative and preventive treatment fractional model for plant disease in Atangana–Baleanu derivative through Lagrange interpolation
Authors: Abdullah Tariq Q. S. , Huang Gang , Al-Sadi Wadhah
Published: April 2022

The growth of the world population’s number leads to increasing food needs. However, plant diseases can decrease the production and quality of agricultural harvests. Mathematical models are widely used to model and interpret plant diseases, showing viruses’ transmission dynamics and effects. In this paper, we investigate the dynamics of the treatments of plant diseases via the Atangana–Baleanu derivative in the sense of Caputo (ABC). We study the existence and uniqueness of solutions of curative and preventive treatment fractional model for plant disease. By using Lagrange interpolation, we give numerical simulations and investigate the results at various fractional orders under specific parameters. The results show that the increase of the roguing rate for the most infected plant or the decrease of the rate of planting in the infected area will reduce the plant disease transmissions. For balancing the plant production, the decision-makers can plant in other areas in which there are no infected cases.
Attack-resilient Fusion of Sensor Data with Uncertain Delays
Authors: Chen Yanfeng , Zhang Tianyu , Kong Fanxin , Zhang Lin , Deng Qingxu
Published: April 2022

Malicious attackers may disrupt the safety of autonomous systems through compromising sensors to feed wrong measurements to the controller. This article proposes attack-resilient sensor fusion that combines local sensor readings and shared sensing information from multiple sources. The method results in higher resilience against sensor attacks through jointly considering sensing noise and uncertain communication delay. To be specific, we first identify the considerable impact of the delay on determining attacked sensors. Second, we present a novel two-dimensional abstract sensor model, where each measurement is augmented as a probabilistic interval based on the convolution of the noise and delay. Third, we propose a fusion algorithm that admits the fused value with highest joint probability distribution of the intervals to tolerate corrupted measurements. Finally, we demonstrate the effectiveness of our method in a vehicle-platoon case study using extensive simulations and testbed experiments.
Outsourced Professional Development for Online Instructors: Recommendations from research
Authors: Zhao Ling , Dixon Raymond , Dousay Tonia , Carr-Chellman Ali
Published: April 2022

To improve the quality of online teaching, institutions typically provide structured professional development in the form of institutional teaching or learning center programming. This programming typically focuses on teaching and learning quality, transitions to online teaching, pedagogies, and new technologies. This article reports on the use of outsourced professional development along with faculty responses to the program. Use of an outsourced program was attractive to faculty who wanted to have another institution's name on their vita. Seventeen faculty completed the professional development program and eight shared their reflections on this inquiry. With a focus on translating research to practice, the article lays out a series of recommendations for future professional development for online instruction including attention to prior knowledge, personalizing approaches, targeting both introductory and advanced learner needs, integrating a community of practice with specific roles, reviewing all materials prior to implementation to ensure tailored program, allowing faculty choice across content, formative and summative evaluation to determine effectiveness including learner perceptions, learner analytics, and content review for current and emerging practice and innovation.
Microglia and Neurodevelopmental Disorders
Authors: Lukens John R. , Eyo Ukpong B.
Published: April 2022

Mounting evidence indicates that microglia, which are the resident immune cells of the brain, play critical roles in a diverse array of neurodevelopmental processes required for proper brain maturation and function. This evidence has ultimately led to growing speculation that microglial dysfunction may play a role in neurodevelopmental disorder (NDD) pathoetiology. In this review, we first provide an overview of how microglia mechanistically contribute to the sculpting of the developing brain and neuronal circuits. To provide an example of how disruption of microglial biology impacts NDD development, we also highlight emerging evidence that has linked microglial dysregulation to autism spectrum disorder pathogenesis. In recent years, there has been increasing interest in how the gut microbiome shapes microglial biology. In the last section of this review, we put a spotlight on this burgeoning area of microglial research and discuss how microbiota-dependent modulation of microglial biology is currently thought to influence NDD progression.
Klotho Ameliorates Podocyte Injury through Targeting TRPC6 Channel in Diabetic Nephropathy
Authors: Yao Xingmei , Guo Hengjiang , Sun Mengyao , Meng Sixuan , Zhu Bingbing , Fang Ji , Huang Jiebo , Wang Hao , Xing Lina
Published: April 2022

Podocyte damage is vital for the etiopathogenesis of diabetic nephropathy (DN). Klotho (KL), a multifunctional protein, has been demonstrated to have renoprotective effects; nevertheless, the mechanism for protective effect has not been completely elucidated. Transient receptor potential cation channel subfamily C, member 6 (TRPC6), a potential target of KL, is implicated in glomerular pathophysiology. Here, we sought to determine whether KL could protect against podocyte injury through inhibiting TRPC6 in DN. We found that high glucose (HG) triggered podocyte injury as manifested by actin cytoskeleton damage along with the downregulation of KL and Synaptopodin and the upregulation of TRPC6. KL overexpression reversed HG-induced podocytes injury, whereas cotreatment with TRPC6 activator flufenamic acid (FFA) significantly abrogated the beneficial effects conferred by KL. Moreover, KL knockdown in podocytes resulted in actin cytoskeleton impairment, decreased Synaptopodin expression, and increased TRPC6 expression. In db/db mice, KL overexpression inhibited TRPC6 expression and attenuated diabetes-induced podocyte injury, which was accompanied by decreased albuminuria and ameliorated glomerulosclerosis. Our data provided novel mechanistic insights for KL against DN and highlighted TRPC6 as a new target for KL in podocytes to prevent DN.
Adaptive Clustering Algorithm for Teaching Resources of Chinese Flower and Bird Painting Practice and Theory Course
Authors: Yang Gao , Jiang Yixiang
Published: April 2022

Painting is a two-dimensional visual language that expresses ideas and emotions while also creating aesthetic aspects. Flower and bird painting embodies an aesthetic ideal. For Chinese people, it serves as a harmonious coexistence with natural objects. Based on the contemporary context and the concept of environmental aesthetics, flower and bird painting should go beyond the sensual portrayal of plants and animals, individualistic lyricism, and the tendency of commercialization. It helps to examine the important value of plants and animals in the spiritual life of human beings from the perspective of the community of human destiny. It also raises the concern of contemporary people for the natural environment and the survival status of natural objects. Free from one’s sorrow and happiness, the painting focuses on the pressing ecological issues of universal concern to all mankind and becomes art with world significance expressed in Chinese aesthetic symbols and artistic forms. It is important to classify the teaching resources of Chinese flower and bird painting theory and time. The traditional Chinese flower and bird painting theory and practice course teaching resources classification method are inefficient; for this reason, the adaptive clustering algorithm of teaching resources of Chinese flower and bird painting theory and practice is proposed. Using mutual information technology to obtain incremental data, the digital Chinese flower and bird painting theory and practice course teaching resources features are extracted. It features a tree to be constructed according to the two-step clustering algorithm. The leaf nodes of the feature tree are grouped according to the hierarchical coalescence algorithm. The Euclidean square distance is used to obtain the Chinese flower and bird painting theory and practice course teaching resources square root measure continuous variables. The number of clusters with the maximum growth distance represents the final number of clusters. The results show that the method can realize the effective grouping and classification of Chinese flower and bird painting theory and practice course teaching resources.
Potential Impact of Cancer Susceptibility Genes on Lung Cancer Metastasis
Authors: Wang Jiaqing , Peng Bin , Sun Xuefeng , Ding Peikun , Li Shixuan , Li Guofeng , Shi Xiaoshun , Wang Guangsuo
Published: April 2022

Background. Studies of prognosis-related molecular markers are an important tool to uncover the mechanism of tumour metastasis. Cancer susceptibility gene testing is an important tool for genetic counselling of cancer risk. However, the impact of lung cancer susceptibility genes (LCSGs) on lung cancer metastasis and prognosis has not been well studied. Methods. The list of lung cancer susceptibility genes was retrospectively analysed and updated. After expression profiling and functional analysis, LCSG-based signatures for prognosis were identified by Cox regression and LASSO regression analyses. For translational purposes, nomograms integrating LCSGs and clinical characteristics were constructed. Results. A total of 301 LCSGs were employed for modelling. For lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), 10-gene and 7-gene signatures were created and independently validated. The LCSG-based risk score could stratify LUAD survival (univariate: hazard ratio

HR

=
1.076

, 95% confidence interval

CI

=
1.049

–1.103,

P
<
0.001

; multivariate:

HR
=
1.066

, 95%

CI
=
1.037

–1.095,

P
<
0.001

) and LUSC survival (univariate:

HR
=
1.149

, 95%

CI
=
1.066

−1.239,

P
<
0.001

; multivariate:

HR
=
1.129

, 95%

CI
=
1.038

−1.228,

P
=
0.005

). One of the processes affected by differentially expressed genes in both LUAD and LUSC was the negative regulation of epithelial cell differentiation. Conclusions. Overall, novel LCSG-based gene signatures for LUAD and LUSC were constructed. These findings could expand the understanding of the impact of LCSG expression on cancer metastasis and prognosis.
Visual Analysis of Research Hotspots in Geriatric Nursing Education in China and Abroad
Authors: Li Qingyun , Bu Zihan , Xue Mengting , Huang Anle , Tu Wenjing , Xu Guihua
Published: April 2022

Background. This study aimed to conduct a literature search to determine research hotspots in the field of gerontological care education in China and abroad. A knowledge of the focus of research conducted abroad may assist Chinese educators in determining the shape of gerontological care education in the future. Methods. The “Web of Science Core Collection” and “CNKI” databases were searched for literature on gerontological care education published from 2010 to 2020. CiteSpace software was used to display the knowledge map of co-occurrence of keywords, and an evolution trend map of research hotspots in recent 10 years was constructed. Results. From 2010 to 2020, the focus of foreign gerontological nursing education research was on the training of gerontological nursing personnel; the development of elderly care services; and education and training in dealing with patients with senile delirium and dementia. The focus of gerontological nursing education research in China was primary care education, training of senior elderly nursing personnel, talent training mode reform, training of nursing staff, and career development paths for geriatric nursing staff. Conclusions. Foreign geriatric nursing education research focuses on cultivating talents, mental health services for the elderly, innovating the mode of pension, and the care of patients with disorders such as dementia and delirium, while in China, the emphasis remains on gerontological nursing personnel training. Attention to research developments in other countries may assist Chinese educators to promote the development of geriatric nursing education in our country.
Multirobot Adaptive Task Allocation of Intelligent Warehouse Based on Evolutionary Strategy
Authors: Liu Yifan , Liu Fei , Tang Li , Bai Chuanzheng , Liu Li
Published: April 2022

To solve the dynamic and real-time problem of multirobot task allocation in intelligent warehouse system under parts-to-picker mode, this paper presents a combined solution based on adaptive task pool strategy and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) algorithm. In the first stage of the solution, a variable task pool is used to store dynamically added tasks, which can dynamically divide continuous and large-scale task allocation problems into small-scale subproblems to solve them to meet dynamic requirements. And an adaptive control strategy is used to automatically adjust the total number of tasks in the task pool to achieve a trade-off among throughput, energy consumption, and waiting time, which has better adaptability than manually adjusting the size of the task pool. In the second stage of the solution, when the task pool is full, tasks in the task pool will be assigned to robots. For the task allocation problem, this paper regards it as an optimization problem and uses the CMA-ES algorithm to find the optimal task assignment solution for all the robots. By comparing with fixed threshold method under 56 different task pool sizes, the experimental results show that the throughput can be close to reaching the optimal level, and the average distance traveled by robots to handle each unit is lower using adaptive threshold method; so, adaptive task pool solution has better adaptability and can find the optimal task pool size by itself. This method can satisfy the dynamic and real-time requirements and can be effectively applied to the intelligent warehouse system.
In Silico Establishment and Validation of Novel Lipid Metabolism-Related Gene Signature in Bladder Cancer
Authors: Sun Xianchao , Zhang Ying , Chen Yilai , Xin Shiyong , Jin Liang , Liu Xiang , Zhou Zhen , Zhang Jiaxin , Mei Wangli , Zhang Bihui , Yao Xudong , Yang Guosheng , Ye Lin
Published: April 2022

Background. Aberrant lipid metabolism is an alteration common to many types of cancer. Dysregulation of lipid metabolism is considered a major risk factor for bladder cancer. Accordingly, we focused on genes related to lipid metabolism and screened novel markers for predicting the prognosis of bladder cancer. Methods. RNA-seq data for bladder cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The nonnegative matrix factorization (NMF) algorithm was used to classify the molecular subtypes. Weighted correlation network analysis (WGCNA) was applied to identify coexpressed genes, and least absolute shrinkage and selection operator (LASSO) multivariate Cox analysis was used to construct a prognostic risk model. External validation data and in vitro experiments were used to verify the results from in silico analysis. Results. Bladder cancer samples were grouped into two clusters based on the NMF algorithm. A total of 1467 genes involved in coexpression modules were identified in WGCNA. We finally established a 5-gene signature (TM4SF1, KCNK5, FASN, IMPDH1, and KCNJ15) that exhibited good stability across different datasets and was also an independent risk factor for prognosis. Furthermore, the predictive efficacy of our model was generally higher than the predictive efficacy of other published models. Distinct risk groups of patients also showed significantly different immune infiltration cell patterns and associations with clinical variables. Moreover, the 5 signature genes were verified in clinical samples by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry, which were in agreement with the in silico analysis. For in vitro experiments, knockdown of IMPDH1 markedly inhibited cell proliferation in bladder cancer. Conclusion. We established a 5-gene prognosis signature based on lipid metabolism in bladder cancer, which could be an effective prognostic indicator.
The Predictive Levels of Serum Anti-Müllerian Hormone and the Combined Index of the Number of Retrieved Oocytes and Good-Quality Embryos in Advanced-Age Infertile Women
Authors: Sun Tie-Cheng , Chen Xi , Shi Cheng , Tian Li , Zhou Shan-Jie
Published: April 2022

The primary objective of the study was to assess the values of serum anti-Müllerian hormone (AMH) levels and the combined index for the prediction of number of oocytes retrieved (NOR) and number of good-quality embryos (GQE) in infertile women undergoing IVF/ICSI treatment. A group of 521 infertile women aged 21–46 years were recruited as subject in this study. Serum AMH, hormones, and antral follicle count (AFC) were measured. The infertile women were categorized into three groups: 21–34 years (reproductive age), 35–39 years (reproductive age), and 40–46 years (advanced-age infertile). The predictive accuracy of variables was analyzed by the receiver operating characteristic (ROC) curve. AFC, AFC/age ratio, AMH/age ratio, and ovarian response prediction index (ORPI) decreased gradually, while AMH decreased significantly with increase in age. Moreover, NOR and GQE were positively correlated with AFC, AMH, AFC/age ratio, AMH/age ratio, and ORPI (

P
<
0.001

). A statistical significance was observed in predicted oocyte retrieval including AMH, AMH/age ratio, and ORPI between 21–34 years and 35–46 years; especially in the 35–46 years group, these variables reached a “high” grade in the diagnostic accuracy because area under curve (AUC) ranged from 0.982 to 0.988 significantly. No statistical significance was observed for FSH, AMH, AFC, and related combined index predicting GQE. The predictive value of AFC and AFC/age ratio was limited regarding oocyte retrieval; however, AMH, AMH/age ratio, and ORPI concurrently had an excellent value for predicting NOR in reproductive-age women, especially in advanced-age infertile women.
Analysis of Reasonable Respiratory Efficiency in Tennis Competition and Training Environment Based on Cloud Computing
Authors: Ren Honghua , Dan Wang
Published: April 2022

Competitive tennis is developing in the direction of quantification. How to use and give full play to all positive factors, in order to attack actively and give full play to the limits of body and psychology, breathing, as the basic metabolic function of human body, also plays a vital role in tennis. This paper studies that it plays an important role in the rationality and explosiveness of sports and the psychological and physiological regulation in competition. The characteristics of tennis events determine the importance of scientific and rational breathing. Reasonable breathing during exercise is conducive to maintaining the basic stability of the internal environment, improving the training effect, and giving full play to the functional ability of the human body, so as to create excellent sports results. First, reduce respiratory resistance. Second, there are two methods to improve alveolar ventilation efficiency and pulmonary ventilation: increasing respiratory rate and increasing respiratory depth. When the inhalation volume is constant, the alveolar gas freshness rate depends on the functional residual volume in the alveolar cavity at the end of expiratory or before inhalation. The less functional the residual air, the more fresh air inhaled, and the higher the oxygen partial pressure in alveolar gas. An effective way to reduce the functional residual volume in the alveolar cavity is to exhale as deeply as possible, so as to ensure that more oxygen enters the body. Reasonable breathing methods can not only accelerate the excitation of the body, increase movement strength, reduce fatigue, and promote recovery but also play a vital role in the rational allocation of physical fitness and the improvement of sports performance. The purpose of this study is to provide a theoretical basis for scientific tennis training by analyzing the characteristics of tennis events, the form of breathing in tennis and the efficiency of reasonable breathing in tennis.
A K-Means and Ant Colony Optimization-Based Routing in Underwater Sensor Networks
Authors: Bai Qiuchan , Jin Chunxia
Published: April 2022

Reliable data transfer seems a quite challenging task in underwater sensor networks (UWSNs) in comparison with terrestrial wireless sensor networks due to the peculiar attributes of UWSN communication. Therefore, K-means and ant colony optimization-based routing (KACO) is proposed in this paper. In KACO, network area under water is divided into layers with regard to the depth level. And nodes of each layer are divided into clusters by the optimized K-means algorithm. The K-means algorithm is used to cluster nodes. Considering the shortcoming of K-means clustering, an improved K-means clustering is used to select the initial cluster center. In the stage of selecting cluster heads, the remaining energy of nodes and the distance from the sink node are used to calculate the competing factors of nodes, and then, the cluster heads are selected according to the competing factors. In the intercluster routing, the ant colony optimization (ACO) was improved by introducing the Gini coefficient, and the intercluster routing based on improved ACO is proposed. The simulation results show that the proposed KACO routing can effectively reduce the energy consumption of nodes and improve the efficiency of packet transmission.
High-Throughput Sequencing Reveals CXCR4 and IGF1 Behave Different Roles in Weightlessness Osteoporosis
Authors: Wang Dong , Li Weihang , Ding Ziyi , Shi Quan , Zhang Shilei , Zhang Zhuoru , Liu Zhibin , Wang Xiaocheng , Yan Ming
Published: April 2022

Objective. This study is aimed at screening the differential expression profiles of mRNA under weightlessness osteoporosis through high-throughput sequencing technology, as well as investigating the pathogenesis of weightlessness osteoporosis at the molecular level especially in bone marrow mesenchymal stem cells (BMSCs). Methods. The mouse bone marrow mesenchymal stem cell line was divided into ground group and simulated microgravity (SMG) group. BMP-2 was used to induce osteogenic differentiation, and SMG group was placed into 2D-gyroscope to simulate weightless condition. Transcriptome sequencing was performed by Illumina technology, DEGs between ground and SMG group was conducted using the DEseq2 algorithm. Molecular functions and signaling pathways enriched by DEGs were then comprehensively analyzed via multiple bioinformatic approaches including but not limited to GO, KEGG, GSEA, and PPI analysis. Results. A total of 263 DEGs were identified by comparing these 2 groups, including 186 upregulated genes and 77 downregulated genes. GO analysis showed that DEGs were enriched in osteoblasts, osteoclasts cell proliferation, differentiation, and apoptosis; KEGG analysis revealed that DEGs were significantly enriched in the TNF signaling pathway and FoxO signaling pathway; the enrichment results from Reactome database displayed that DEGs were mainly involved in the transcription of Hoxb3 gene, RUNX1 recruitment KMT2A gene, and activation of Hoxa2 chromatin signaling pathway. The four genes, IL6, CXCR4, IGF1, and PLOD2, were identified as hub genes for subsequent analysis. Conclusions. This study elucidated the significance of 10 hub genes in the development of weightlessness osteoporosis. In addition, the results of this study provide a theoretical basis and novel ideas for the subsequent research of the pathogenesis and clinical treatment of weightlessness osteoporosis.
Deep Learning and Microscopic Imaging in the Nursing Process of Neurosurgery Operation
Authors: Jiang Wenchun
Published: April 2022

Neurosurgery is mainly for the treatment of head trauma, cerebrovascular disease, brain tumors, and spinal cord disorders. These operations are difficult and risky, so disability and mortality are high. To reduce the risk of surgery, reduce postoperative complications, and improve the treatment effect of patients, this article applies deep learning and microscopic imaging to the nursing process of neurosurgery. Through deep learning and microscopic imaging, doctors can learn about patients during surgery. The specific situation of the trauma site, after which surgery is performed according to the situation, effectively reduces the casualties, reduces the loss of patients, and provides a reference for the research of neurosurgery nursing. Research results prove that deep learning and microscopic imaging can play an important role in the nursing process of neurosurgery. Compared with conventional treatment methods, microscopic imaging treatment can effectively improve the treatment effect, and the operation time for patients is less than that of conventional treatment. About 20% and the incidence of postoperative complications is lower than 30%, which can effectively reduce the cost to patients and improve the quality of treatment.
Multimodal Teaching of College English Based on Similarity
Authors: Li Jingjing
Published: April 2022

There are many disadvantages in traditional English teaching. In order to solve these problems, multimodal teaching mode is applied to college English teaching. This paper combines the similarity model with English multimodal teaching and puts forward corresponding teaching methods based on the current situation of students’ English learning. This paper studies the multimodal similarity learning method, defines different relation vectors according to different physical or semantic meanings contained in sample data, and transforms the calculation of single-modal similarity between samples into the calculation of multimodal similarity between samples under different relations. Based on the multimodal discourse analysis theory, this paper explores the influence of multimodal teaching mode on students’ English achievement and attitude by combining quantitative research with qualitative research, so as to verify the effectiveness of this teaching mode. The results show that this method is effective and feasible. The application of multimodal teaching mode in English classroom can have an important influence and has far-reaching research significance. The research in this paper has certain reference value for innovating English teaching mode and improving students’ English achievement.
Design of Sports Event Evaluation and Classification Method Based on Deep Neural Network
Authors: Zhao Shutong , Sun Jiangang
Published: April 2022

Large-scale sports events with high-level competition as the main content will have a great impact on the host city whether from the economic level or from the social level. With the improvement of human civilization, people realize that the holding of large-scale sports events not only has a positive impact on the economy and society but also brings some negative effects, such as waste of resources and environmental pollution, which have attracted the attention of the government and investors. Therefore, how to scientifically, comprehensively, and reasonably evaluate large-scale sports events, especially the accurate evaluation of their economic and social effects, has become the focus of attention. The evaluation of large-scale sports events mainly includes two levels: economic and social. Through the specific analysis of the evaluation content and the weight calculation of the evaluation index, the overall optimization of the evaluation of large-scale sports events is realized, and the reference experience is provided for the holding and evaluation of large-scale sports events in the future. Based on this, this article proposes a sports event evaluation and classification method based on the deep neural network. Firstly, on the basis of literature review and field investigation, the evaluation index system of sports events is established. Deep learning models have strong fitting power and robustness and have been applied to many real-world tasks. Then the deep neural network is used to evaluate the holding effect of sports events. The experimental results show that the model has high evaluation accuracy and is of great significance to the supervision and guidance of sports events.
An Unusual Case of Francisella tularensis
Authors: Huang Samantha , Kaptur Bradley , Manu Julius , Woldegabriel Elias
Published: April 2022

A 67-year-old male presented with complaints of weakness, fatigue, and shortness of breath in the context of a recent hospitalization for the same unresolved symptoms. After a largely nonspecific clinical presentation, a chest X-ray revealed a loculated pleural effusion. Culture of the postthoracentesis exudate revealed the culprit to be the aerobic Gram-negative bacterium Francisella tularensis. Amidst reports of potential resurgence, clinicians should be aware of the possible presentations of tularemia and consider it in the case of an ostensibly contributory patient history.
Energy Auditing in Three-Phase Brushless DC Motor Drive Output for Electrical Vehicle Communication Using Machine Learning Technique
Authors: Ramana T. V. , Manaktala S. S. , Valarmathi K. , Doohan Nitika Vats , Shetty Dasharathraj K. , Kumar Harish , Akwafo Reynah
Published: April 2022

Using predictive nonlinear optimal control, this model examines the output power of a three-phase brushless DC motor (BLDC) drive to ensure that it is stabilized (PNOC). A BLDC is a kind of electric motor that is used in a variety of applications and is one of the models of electric motors that are utilized in constant speed applications. In this motor, the movable component of the rotor created torque and the rotor rotated in a position of low reluctance; the location of the rotor is determined by the motor’s maximum inductance value. The BLDC drive controls the motor via the converter circuit, and the converter circuit ensures that the motor receives the appropriate output power. The project manager should have a thorough discussion with the team about the demagnetization of the malfunctioning BLDC motor before beginning this job. It is possible to model a machine using many existing technologies, such as electrical equivalent circuit diagram (EEC), which are based on a number of assumptions that make the analysis process or the analysis approach simpler. Despite numerical methodologies, these approach scenarios give frequency domain loop (FDL) precision frequency domain, using a suitable weight strategy to deliver high power solution creation (NM). The purpose of this essay is to integrate these two technologies in order to make contributions via the development of a new hybrid EEC-FDL model closed-loop brushless DC motor. PNOC is a driving system that uses predictive nonlinear optimal control (PNOC). The generated model is subjected to simulations under both healthy and incorrect settings, respectively. MATLAB software is utilized to construct the simulation of the control circuit, and simulation outputs are validated by experimental findings. Predictive nonlinear optimal control (PNOC) is employed to eliminate torque ripple and improve system stability.
Social Value Evaluation of Ecosystem Services in Global Geoparks Based on SolVES Model
Authors: Zhu Yiting
Published: April 2022

With the increasing demand for diverse ecosystem services, the assessment of ecosystem services has become a hot research topic. Taking Koktokay Global Geopark as the study area, the SoIVES model was used to quantitatively evaluate the various cultural services of the ecosystem in this area from the perspective of social attributes and spatial heterogeneity and to generate corresponding value index (VI) maps. The results show that aesthetic value index is the largest, while entertainment value index is the smallest. With the increase of distance from roads and water bodies, aesthetic value and entertainment value tend to decrease gradually. The value of popular science education still fluctuates slightly in locations far away from roads and water bodies. The value index of health care value fluctuates within a certain distance from the road and gradually decreases as the distance from the water body increases. The application of the SolVES model in a wide range of areas has achieved good results and provided a scientific basis for ecological construction and park planning.
Secure lightweight obfuscated delay-based physical unclonable function design on FPGA
Authors: Haziq Ishak Mohammad , Mispan Mohd Syafiq , Chiew Wong Yan , Kamaruddin Muhammad Raihaan , Korobkov Mikhail Aleksandrovich
Published: April 2022

The internet of things (IoT) describes the network of physical objects equipped with sensors and other technologies to exchange data with other devices over the Internet. Due to its inherent flexibility, field-programmable gate array (FPGA) has become a viable platform for IoT development. However, various security threats such as FPGA bitstream cloning and intellectual property (IP) piracy have become a major concern for this device. Physical unclonable function (PUF) is a promising hardware fingerprinting technology to solve the above problems. Several PUFs have been proposed, including the implementation of reconfigurable-XOR PUF (R-XOR PUF) and multi-PUF (MPUF) on the FPGA. However, these proposed PUFs have drawbacks, such as high delay imbalances caused by routing constraints. Therefore, in this study, we explore relative placement method to implement the symmetric routing in the obfuscated delay-based PUF on the FPGA board. The delay analysis result proves that our method to implement the symmetric routing was successful. Therefore, our work has achieved good PUF quality with uniqueness of 48.75%, reliability of 99.99%, and uniformity of 52.5%. Moreover, by using the obfuscation method, which is an Arbiter-PUF combined with a random challenge permutation technique, we reduced the vulnerability of Arbiter-PUF against machine learning attacks to 44.50%.
Application of green-emitting Sr3WO6:U phosphor to enhance lumen efficiency of WLEDs
Authors: Dang Huu Phuc , That Phung Ton
Published: April 2022

The article demonstrates a sample of spectrum in the phosphor-covered WLED (abbreviated as p-WLED) containing one blue chip, one red chip along with phosphors of green and yellow colors. We acquired the p-WLEDs’ ideal spectrum in the CCTs, which is short for correlated color temperature range from 2700 K to 6500 K using a nonlinear program in order to optimize the radiation’s lumen efficiency (LER) when the R9 strong red's color rendering indexes (CRIs) and special CRIs exceed 98. From the outcomes of the recreation, p-WLEDs containing an InGaN blue chip with 450 nm wavelength, an AlGaInP red chip with 634 nm wavelength, along with green and yellow silicate phosphors with the value of 507 and 580 nm wavelength correspondingly; can produce white lights with CRI values of around 98 and particular CRI values of R9 for intense reds above 98. For saturated red, yellow, green, and blue colors, the average values of the particular CRIs R9 through R12 exceed 95. In CCT values of 2700 K to 6500 K, the R13 value in female figures is around 100, with LER values reaching 296 lm/W.
Storage and encryption file authentication for cloud-based data retrieval
Authors: Alsudani Mustafa Qahtan , Fakhruldeen Hassan Falah , Abdul-Jaleel Al-Asady Heba , Jabbar Feryal Ibrahim
Published: April 2022

The amount of data that must be processed, stored, and modified rises as time passes. An enormous volume of data from a wide range of sources must be stored on a safe platform. Maintaining such a large volume of data on a single computer or hard drive is impracticable. As a result, the cloud is the ideal platform for storing any quantity of data. An advantage of storing data in the cloud is that it may be accessed at any time and from any device. However, the security of data stored in the cloud is a big concern. Because of this, despite the benefits, most users are reluctant to move their papers to the cloud. The data should be encrypted before sending it off to the cloud service provider to avoid this issue. It's a great way to increase the security of your papers. According to a new technique presented in the system, data may be searched across encrypted files without compromising the privacy and security of various data owners. Implementing the pallier homomorphic encryption method makes it possible to perform computations on encrypted data without decryption.
High efficient and high gain boost converter with soft switching capability connected to grid using dq axis current control
Authors: Sirisha Bogimi , Akhilesh Saieni
Published: April 2022

Renewable sources like solar PV arrays are generally operated at low voltages due to safety issues. For applications such as grid connected systems, it requires boosting to high voltage which leads to reduction in efficiency. To solve this, a new high gain and efficient boost converter which is a combination of buffer capacitor, passive clamp recovery circuit to restore leakage energy in coupled inductor is presented. The magnetic field of the linked inductor stores the energy obtained from the supply. A passive clamp network recovers the energy that is stored in the leakage inductance, enhances the gain of voltage and improves overall system efficiency. High duty ratios are not essential to achieve higher voltage gain, hence the reverse recovery problem of diode is prevented. Moreover, passive clamp network decreases the voltage stress of switch, thus a minimum rating switch be used, as a result, the system's total efficiency improves. This converter output is fed as input to a single phase full bridge inverter and also synchronized to a single phase grid by using d-q axis current controller. The performance and powers injected are analyzed by connecting a resistive load.
Comparative evaluation of SiC/GaN “MOSFET” transistors under different switching conditions
Authors: Hasan Ghanim Thiab , Mutlaq Ali Hlal , Ali Kamil Jadu
Published: April 2022

The aim of this paper is to conduct a mutual comparison of switching energy losses in cascade gallium nitride (GaN) and silicon "super junction" MOSFET” transistor, in both cases designed for a maximum operating voltage of (650 V). For the analysis of switching characteristics of transistors used double pulse test method by using detailed SPICE simulation model. Data on transient on and off processes were generated using the “LTspice” simulation package in a wide range of drain currents with two different gate resistance values of the tested transistors. The total energy losses in the GaN have been simulated during one transistor at (on and off cycle). The obtained results indicate that the superior switching characteristics of GaN devices for a drain current of (30 A) is five to eight times less than the switching characteristics of silicon “MOSFET” transistor when compared to silicon components, especially during operation of transistors with high drain currents.
Profiling DNA Sequence of SARS-Cov-2 Virus Using Machine Learning Algorithm
Authors: Muflikhah Lailil , Rahman Muh. Arif , Wahyu Widodo Agus
Published: April 2022

Corona virus disease-19 (COVID-19) is growing rapidly because it is an infectious disease. This disease is caused by a virus belonging to the type of DNA virus with very diverse genetics. This study proposes a feature extraction method using k-mer to obtain nucleotide frequencies in protein coding. In profiling viral DNA sequences, this study proposes to obtain similarity by country using hierarchical k-means, where the results are averaged by the hierarchical clustering method and then find the initial cluster center. The experimental results show that the silhouette, purity, and entropy are 0.867, 0.208, and 0.892, respectively. Then, we apply the Gini index feature selection to find the important components as characteristics in each country. The selected components are implemented using the ensemble method, Random Forest, to evaluate their performance. The experimental results showed high performance, including sensitivity, accuracy, specificity, and area under the curve (AUC).
Hyper parameter tuning based gradient boosting algorithm for detection of diabetic retinopathy: an analytical review
Authors: Datta Parul , Das Prasenjit , Kumar Abhishek
Published: April 2022

The pipelines of approaches for classifying diabetic retinopathy were examined in this study. The effort entails developing appropriate transformations and estimators that can be used to automate the process of diabetic retinopathy detection. The segmentation of the blood vessels was done using a hybrid algorithm that uses Otsu and median filter to get the region of interest. Further, ten classifiers were investigated in order to develop an automated pipeline for diabetic retinopathy detection. The ten classifiers were reviewed based on earlier work in a similar setting and on an exploration of new ways for identifying diabetic retinopathy. To overcome the challenge of low volume of dataset, data argumentation was done so that a generic classifier can be configured. Extensive hyper parameter tuning was performed, and it was shown that the gradient boosting approach is the most stable technique for detecting diabetic retinopathy. This was validated using a 10K fold cross validation method on many metrics (accuracy, recall, precision, and v-measure score). Hyper-parameter tuning helped in achieving accuracy of 0.96.
Controlling the degree of observability in GPS/INS integration land-vehicle navigation based on extended Kalman filter
Authors: Dahmane Bendehiba , Lejdel Brahim , Clementini Eliseo , Harrats Fayssal , Nassar Sameh , Abderrahmane Lahcene Hadj
Published: April 2022

Experimental setup implements the concept of degree of observability (DoO) adequate a land-vehicle navigation application with noised inertial measurement unit (IMU) and global positioning system (GPS) sensors based on a loosely coupled approach. The navigation systems such as IMU-GPS require extensive evaluations of nonlinear equations as used in an extended Kalman filter (EKF). According to DoO and during our test, we have implemented a method for measuring the DoO of all states continuously. Where, the results showed that applying the fusion IMU-GPS system based on EKF be enhanced the DoO measure. The real dataset consists of outputs a high sampling rate for IMU sensor at each (0.01s) and GPS receiver at each (1s). In addition, an aloft category IMU was put together with differential GPS (DGPS) information to produce a real trajectory. GPS has acceptable long-term accuracy, it is used to update the position and velocity in IMU outputs before processing in the EKF algorithm. The implementation consists of three main algorithms: Strapdown (dead reckoning DR), DoO and EKF algorithms. The results are shown, implementation of both approaches based on EKF and the concept of DoO in GPS/INS integrated systems are sufficient robustness to use with low-cost sensors.
Solving flexible job-shop scheduling problem using harmony search-based meerkat clan algorithm
Authors: Jawad Muna Mohammed , Younis Muhanad Tahrir , Sadiq Ahmed T.
Published: April 2022

The classical job shop scheduling (JSS) problem can be extended by allowing processing of an operation by any machine from a given set. This type of scheduling is known as flexible job shop scheduling (FJSS) problem. It incorporates all the difficulties and complexities of its predecessor classical problem. However, it is more complex as it is required to determine the assignment of operations to the machine. Swarm intelligence techniques proved their effectiveness in solving a wide range of complex NP-Hard real world problems. One of these techniques is the meerkat clan algorithm (MCA) that has been successfully applied to various optimization problems. This paper presents a modified MCA for solving the FJSS problem. The modification is based on using harmony search (HS). The introduction of HS provides more exploitation and intensification. HS generates various solutions, which are provided to the MCA. As a result, the exploitation of the local optimum is increased, which in turn increases the convergence rate. The experimental results show that the improved method achieves higher quality schedules. Additionally, the convergence rate is speeded up compared with the standalone algorithm. This gives the proposed method the superiority over the original algorithm.
Online news popularity prediction before publication: effect of readability, emotion, psycholinguistics features
Authors: Rajagopal Suharshala , Kadan Anoop , Gangadharan Prappanadan Manjary , Vimala Lakshmanan Lajish
Published: April 2022

The development of world wide web with easy access to massive information sources anywhere and anytime paves way for more people to rely on online news media rather than print media. The scenario expedites rapid growth of online news industries and leads to substantial competitive pressure. In this work, we propose a set of hybrid features for online news popularity prediction before publication. Two categories of features extracted from news articles, the first being conventional features comprising metadata, temporal, contextual, and embedding vector features, and the second being enhanced features comprising readability, emotion, and psycholinguistics features are extracted from the articles. Apart from analyzing the effectiveness of conventional and enhanced features, we combine these features to come up with a set of hybrid features. We curate an Indian news dataset consisting of news articles from the most rated Indian news websites for the study and also contribute the dataset for future research. Evaluations are performed over the Indian news dataset (IND) and compared with the performance over the benchmark mashable dataset using various supervised machine learning models. Our results indicate that the proposed hybrid of enhanced features with conventional features are highly effective for online news popularity prediction before publication.