bluuebunny/crossref_metadata_2025 huggingface dataset:
Simulation of Field Oriented Control Algorithm of Permanent Magnet Synchronous Motor Based on SVPWM
Authors: Wang Bin
Published: May 2022

Aiming at the problems of large flux pulsation and unstable operation of permanent magnet synchronous motors affected by motor parameters and magnetic field harmonics, this paper proposes a permanent magnet synchronous motor vector control strategy based on SVPMW directional control algorithm to improve motor control performance. This control strategy realizes the effective control of the motor under harmonic interference by establishing a motor model oriented along the air gap magnetic field and the SVPMW regulator algorithm. At the same time, an online query algorithm for current reference values based on mathematical models is established to achieve fast dynamic response of the motor. A simulation model of directional control is established in MATLAB/Simulink. The simulation results show that the modeling method can effectively reduce the interference of harmonics on the flux linkage. It proves that under the interference of harmonics, the directional control of this algorithm has a better control effect, which is practical. The design and debugging of the motor control system provide the basis and ideas.
A Bridgeless LUO Converter with Glowworm Swarm Optimized Tuned PI Controller for Electrical Applications
Authors: Sivaramkrishnan M. , Ramkumar M. Siva , Subramanian S Siva , Ladu Nyagong Santino David
Published: May 2022

The synchronous reluctance motor (Sync-RM) is known for its higher reliability, faster dynamic response, higher efficiency, higher speed range, higher power density, and higher torque per ampere. Consequently, the scope of research into emerging Sync-RMs drive system has been extended. For different kinds of mechanical and electrical applications, motors are important components of any machine or tool. There are several types of motors available for various kinds of industrial applications, but the benefits of Sync-RM in medium- and low-power applications have eventually been very efficient compared to other motor types, regarding the factors such as high flux density per unit volume, high efficiency, requirements of low maintenance, and low electromagnetic obstruction. This research proposed a Sync-RM drive fed by a bridgeless LUO converter (BLLC) using the Glowworm Swarm Optimization (GSO) tuned proportional integral (PI) controller. High competence, higher power density, and a small structure of the motor drive are delivered by the PI-tuned converter. The output voltages are related to the Sync-RM motor of the proposed converter. Due to its minimum effort and modest development, the Sync-RM here is chosen for the drive. The proposed Sync-RM drive framework based on the LUO converter was evaluated by simulation using the Matlab/Simulink tool.
Heart Rate Variability and Chronic Kidney Disease in Patients with Type 2 Diabetes
Authors: Shi Wei , Zhang Jing , Chen Dan , Chen Xiaolei , Duan Wei , Zhang Hongmei
Published: May 2022

To evaluate whether heart rate variability (HRV) as a measure of cardiac autonomic neuropathy (CAN) is associated with chronic kidney disease (CKD) in Chinese adults with type 2 diabetes mellitus (T2DM) in China. 392 individuals of T2DM were entered in this study, all these subjects undertook the Holter electrocardiogram for 24 hours to get the HRV parameters. Of these T2DM patients, 126 (37.3%) had CKD, and most of the HRV parameters were lower in this group than in those without CKD. Decreased HRV parameters were strongly related with CKD in Spearman’s correlation analysis. After adjustments for variables, the logistic regression showed that standard deviation of the averaged normal RR intervals for all 5-minute segments (SDANN) was independently associated with decreased estimated glomerular filtration rate (

eGFR
<
60

 mL/min/1.73 m2) (

OR
=
0.988

; 95% CI, 0.978-0.998;

P
=
0.015

) and increased urine albumin : creatinine

ratio
 

UACR

≥
30

 mg/g Cr (

OR
=
0.992

; 95% CI, 0.985-0.998;

P
=
0.015

). A decreased 24-hour time domain HRV parameter, SDANN, was strongly associated with both eGFR and UACR among Chinese T2DM.
Is Sustainable Korean Economic Growth Centered on Small-Sized and Medium-Sized Companies Possible? Focused on Innovative Small and Medium Companies
Authors: Yeon Ju Han , Jang Seung-Hwan
Published: May 2022

Korea should grow small-sized and medium-sized companies and promote the development of the Korean economy in order to respond appropriately to the environment such as the fourth industrial revolution, COVID-19, and rapid changes in the international market. Under these circumstances, it is necessary to categorize innovative small and medium companies to establish hypotheses, compare, and validate their performance in order to consider the possibility of a small and medium-sized business-oriented economy. Among the policies for supporting small and medium companies, the government focused on the economic and technical performance of the small and medium companies, focusing on the certified small and medium companies. Through this, not only South Korea but also other countries want to provide insights to consider one of the various measures for economic growth.
Effect Comparison of Assisted Surgery Simulated by Preoperative 3D Reconstruction and Minimally Invasive Surgery with the Assist of Knee Arthroscopy in the Treatment of Tibial Plateau Fracture under the Background of Intelligent Medicine
Authors: Lu Lei , Chai Leizi , Wan Deyu , Li Peng , Chen Duozi
Published: May 2022

Objective. To explore the effect comparison of the assisted surgery simulated by preoperative 3D reconstruction and the minimally invasive surgery with the assist of knee arthroscopy in the treatment of tibial plateau fracture (TPF) under the background of intelligent medicine. Methods. 100 patients with TPF admitted to our hospital from January 2021 to January 2022 were selected as the study subjects. According to the order of admission, the patients were divided into the simulation group with 3D reconstruction (n = 50) and the auxiliary group with knee arthroscopy (n = 50), and the clinical indicators were compared between the two groups. Results. There was no significant difference in any other clinical treatment indexes between the two groups except the surgery time (

P
>
0.05

), and there was no significant difference in knee flexion ability, walking ability, and Rasmussen scores between the two groups after treatment (

P
>
0.05

). However, compared with the auxiliary group with knee arthroscopy, the mean posterior slope angle and varus angle of the patients were significantly higher (

P
<
0.001

), and the total incidence of complications was significantly lower (

P
<
0.05

). Conclusion. Based on the analysis under the background of intelligent medicine, it is found that the assisted surgery simulated by preoperative 3D reconstruction has a better effect and a higher safety, but they have the similar effects on improving the knee joint function of patients.
MicroRNA-503 Exacerbates Myocardial Ischemia/Reperfusion Injury via Inhibiting PI3K/Akt- and STAT3-Dependent Prosurvival Signaling Pathways
Authors: He Yanjing , Cai Yin , Sun Tianhao , Zhang Liangqing , Irwin Michael G. , Xu Aimin , Xia Zhengyuan
Published: May 2022

Acute myocardial infarction is a leading cause of death worldwide, while restoration of blood flow to previously ischemic myocardium may lead to ischemia/reperfusion (I/R) injury. Accumulated evidence shows that microRNAs play important roles in cardiovascular diseases. However, the potential role of microRNA-503 (miR-503) in myocardial I/R injury is little known. Thus, this study is aimed at determining whether and how miR-503 affects myocardial I/R injury in vivo and in vitro. A mouse model of myocardial I/R injury and H9c2 cell model of hypoxia/reoxygenation (H/R) injury were established. The postischemic cardiac miR-503 was downregulated in vivo and in vitro. Mechanistically, PI3K p85 and Bcl-2 are miR-503 targets. The post-ischemic cardiac PI3K p85 protein level was decreased in vivo. Agomir-503 treatment exacerbated H/R-induced injuries manifested as decreased cell viability, increased lactate dehydrogenase activity, and cell apoptosis. Agomir-503 treatment reduced cell viability under normoxia as well and reduced both PI3K p85 and Bcl-2 protein levels under either normoxia or H/R condition. It reduced phosphorylation of Stat3 (p-Stat3-Y705) and Akt (T450) in cells subjected to H/R. In contrast, Antagomir-503 treatment attenuated H/R injury and increased p-Stat3 (Y705) under normoxia and increased p-Akt (T450) under either normoxia or H/R condition. It is concluded that miR-503 exacerbated I/R injury via inactivation of PI3K/Akt and STAT3 pathways and may become a therapeutic target in preventing myocardial I/R injury.
Finite Volume Method for a Time-Dependent Convection-Diffusion-Reaction Equation with Small Parameters
Authors: Ahmed Uzair , Mashat Daoud Suleiman , Maturi Dalal Adnan
Published: May 2022

Convection, diffusion, and reaction mechanisms are characteristics of transient mass-transfer phenomena that occur in natural and industrial systems. In this article, we contemplate a passive scalar transport governed by the convection-diffusion-reaction (CDR) equation in 2D flow. The efficiency of solving computationally partial differential equations can be illustrated by using a precise numerical method that yields remarkable precision at a low cost. The accuracy and computational efficiency of two second-order finite difference methods were investigated. The results were compared to a finite volume technique, which has a memory advantage and conserves mass, momentum, and energy even on coarse grids. For various diffusion coefficient values, numerical simulation of unsteady CDR equation are also performed. The techniques were examined for consistency and convergence. The effectiveness and accuracy of these approaches for solving CDR equations are demonstrated by simulation results. Efficiency is measured usingL2andL∞, and the estimated results are compared to the corresponding analytical solution.
Automated Detection Model Based on Deep Learning for Knee Joint Motion Injury due to Martial Arts
Authors: Xue Meng , Liu Yan , Cai XiaoMei
Published: May 2022

Objective. Develop a set of knee joint martial arts injury monitoring models based on deep learning, train and evaluate the model’s effectiveness. Methods. This paper mainly collects knee MRI images of 1546 patients with knee joint martial arts injuries from 2015 to 2020. Through manual annotation, the data set is divided into six categories: meniscus injury, tendon injury, ligament injury, epiphyseal cartilage injury and synovial joint capsule loss. The human knee collaborative MRI image database is established, and the data set is divided into the training and validation sets. And test set. Establish a deep neural network, train the model using the training set and validation set, locate the knee joint injury location, and classify the specific injury type. The model’s validity was validated using the test set, and the model’s sensitivity, specificity, and mean accuracy for detecting lesions were evaluated. Results. In the test set, the accuracy of meniscus injury, tendon injury, ligament injury, bone and bone cartilage injury and synovial joint capsule injury were 83.2%, 89.0%, 88.0%, 85.9%, 85.6% and 83.5%, respectively, and the overall average accuracy value was 86.0%. The sensitivity and specificity of the model were 91.3% and 87.3%, respectively. Conclusion. The application of the deep learning method in the classification and detection of knee joint martial arts injuries can significantly improve the diagnosis effect, reduce the diagnosis time and misdiagnosis rate, and provide decision support for surgery.
Design and Implementation of a Radioactive Source Intelligent Search Robot Based on Artificial Intelligence Edge Computing
Authors: Wang Pin , Gao Zhijian , Li Yimin , Zeng Lingyu , Zhong Hongmei
Published: May 2022

Artificial intelligence is a very broad science, which consists of different fields, such as machine learning, and computer vision. In recent years, the world nuclear industry has developed vigorously. At the same time, incidents of loss of radioactive sources also occur from time to time. At present, most of the search for radioactive sources adopt manual search, which is inefficient, and the searchers are vulnerable to radiation damage. Sending a robot to the search an area where there may be an uncontrolled radioactive source is different. Not only does it improve efficiency, it also protects people from radiation. Therefore, it is of great practical significance to design a radioactive source search robot. This paper mainly introduces the design and implementation of a radioactive source intelligent search robot based on artificial intelligence edge computing, aiming to provide some ideas and directions for the research of radioactive source intelligent search robot. In this paper, a research method for the design and implementation of a radioactive source intelligent search robot based on artificial intelligence edge computing is proposed, including intelligent edge computing and gamma-ray imaging algorithms, which are used to carry out related experiments on the design and implementation of radioactive sources, an intelligent search robot based on edge computing. The experimental results of this paper show that the average resolution of the radioactive source search robot is 90.55%, and the resolution results are more prominent.
Inhibiting Microglia-Derived NLRP3 Alleviates Subependymal Edema and Cognitive Dysfunction in Posthemorrhagic Hydrocephalus after Intracerebral Hemorrhage via AMPK/Beclin-1 Pathway
Authors: Zhang Zhaoqi , Guo Peiwen , Huang Suna , Jia Zhengcai , Chen Tunan , Liu Xin , Feng Hua , Chen Yujie
Published: May 2022

For posthemorrhagic hydrocephalus (PHH) patients, whether occur subependymal edema indicates poor outcomes, partially manifested as cognitive impairment. In the brain, NLRP3 inflammasome mainly derived from microglia/macrophages is involved in proinflammatory and neurodeficits after hemorrhage, and autophagy is vital for neuronal homeostasis and functions. Accumulating evidence suggest that NLRP3 inflammasome and autophagy played an essential role after intracerebral hemorrhage (ICH). We aimed to dissect the mechanisms underlying subependymal edema formation and cognitive dysfunction. Here, based on the hydrocephalus secondary to ICH break into ventricular (ICH-IVH) in rats, this study investigated whether microglia/macrophage-derived NLRP3 induced subependymal edema formation and neuron apoptosis in subventricular zones (SVZ). In the acute phase of ICH-IVH, both the expression of NLRP3 inflammasome of microglia/macrophages and the autophagy of neurons were upregulated. The activated NLRP3 in microglia/macrophages promoted the release of IL-1beta to extracellular, which contributed to excessive autophagy, leading to neurons apoptosis both in vivo and in vitro through the AMPK/Beclin-1 pathway combined with transcriptomics. Administration of MCC950 (NLRP3 inflammasome specific inhibitor) after ICH-IVH significantly reduced edema formation and improved cognitive dysfunction. Thus, inhibiting NLRP3 activation in SVZ may be a promising therapeutic strategy for PHH patients that warrants further investigation.
Explore the Effect of Asthma Regulating HIF‐1 Pathway on Sperm Quality Based on Rat Model
Authors: Feng Junlong , Tang Yuan , Yang Zhen , Bao Binghao , Liu Yichen , Deng Sheng , Li Haisong , Li Jiangbin , Wang Jisheng
Published: May 2022

This study is to verify the effect of asthma on sperm quality and explore its potential underlying mechanism. We randomly categorized the Sprague‐Dawley (SD) rats into control (Group C) and asthma model (Group M) groups. Rats in the asthma model group were induced allergic asthma by intraperitoneal injection of ovalbumin solution. We evaluated the sperm motility and sperm concentration. The expression of the Interleukin‐6 (IL6), phosphorylation‐signal transducer and activator of transcription 3 (p‐Stat3), and hypoxia‐inducible factor‐1α (HIF‐1α) proteins and mRNAs in the testicular tissue was detected by western blotting and RT‐qPCR. Compared with group C, sperm concentration and sperm motility in group M rats were significantly decreased (P < 0.05). Meanwhile, compared with group C, the expression levels of IL6, Stat3, and HIF‐1α proteins and mRNAs in group M rats were significantly increased (P < 0.05). Asthma can regulate the HIF‐1 signaling pathway, promoting the expression of IL6, Stat3, and HIF‐1α protein and mRNAs, so as to promote sperm apoptosis and ultimately causing male infertility.
Relationship between Emotional Eating, Consumption of Hyperpalatable Energy-Dense Foods, and Indicators of Nutritional Status: A Systematic Review
Authors: Fuente González Cristina Elizabeth , Chávez-Servín Jorge Luis , de la Torre-Carbot Karina , Ronquillo González Dolores , Aguilera Barreiro María de los Ángeles , Ojeda Navarro Laura Regina
Published: May 2022

People’s health is closely linked to their diet. Diet can be defined as the set of foods that are consumed in a day, and it is susceptible to being altered by various factors, such as physiological, environmental, psychological, and social. These, in turn, can be affected by an inadequate diet and/or a dysregulation of emotions. Emotions are an immediate response by the organism informing it of the degree of favorability of a certain stimulus or situation. Moods are similar to emotions but more intense and prolonged. Some studies indicate that the consumption of hyperpalatable energy-dense foods may be related to emotional eating. Emotional eating is characterized by the excessive consumption of hyperpalatable energy-dense foods, rich in sugars and fats, in response to negative emotions. But several reports also indicate that emotional eating may be associated with the presence of positive emotions, so further analysis of the available information is necessary. Consuming higher amounts of hyperpalatable energy-dense foods can lead to the accumulation of energy in the body that results in an increase in body weight, as well as other associated diseases. Obesity is the world’s leading diet-related health problem. The objective of this work was to carry out a systematic review of the available literature using the Cochrane methodology, in accordance with the PRISMA guidelines, to evaluate the relationship between emotional eating, the consumption of hyperpalatable energy-dense foods, and indicators of nutritional status. An exhaustive search in different databases yielded 9431 scientific articles, 45 of which met the inclusion criteria. This review underscores the fact that knowing and understanding the reasons why people consume hyperpalatable energy-dense foods and the possible connection with their emotional eating can provide key data for improving and personalizing patients’ nutritional treatment. This in turn can encourage compliance with treatment plans to improve people’s health and quality of life using an interdisciplinary approach.
Toxicity and Antiulcer Properties of Ipomoea wightii (Wall.) Choisy Leaves: An In Vivo Approach Using Wistar Albino Rats
Authors: Sathyanarayanan Saikumar , Sreeja Puthanpura Sasidharan , Arunachalam Karuppusamy , Parimelazhagan Thangaraj
Published: May 2022

Humans have been using herbs to prevent and cure various ailments since antiquity, and Ipomoea wightii is a significant medicinal plant known for its wide ethnobotanical uses. Although the plant is known to treat ulcers, there is no significant scientific validation. The present study aimed to assess the acute toxicity, subacute toxicity, and antiulcer properties of the leaf methanol extract of I. wightii (IWL). In the subacute study, the extracts were given orally at 100, 200, and 400 mg/kg doses for 28 days, and we analyzed the biochemical and histological parameters to evaluate the toxicity of IWL. Two different models were assessed to explore antiulcer properties, such as indomethacin- and ethanol-induced ulcer model. Ulcer areas and ulceration percentage histopathology of the stomach were used to study the efficacy of extracts. The acute toxicity study showed that IWL was safe to the maximum dose of 2000 mg/kg body weight. In a subacute toxicity study, the oral administration of IWL did not produce any mortality in the tested animals. The analysis of haematological, liver biochemical, kidney profile, lipid profile, and in vivo antioxidant parameters depicted that all the values were within the control limits after the experimental period and were considered nontoxic to animals. Additionally, the antiulcer study demonstrated a positive response of IWL in a dose-related manner (indomethacin- and ethanol-induced models). Macroscopic analysis showed that pretreatment with I. wightii leaf methanol extract significantly reduced the gastric lesion and decreased the ulceration area (14.52 mm2), demonstrating superior results to the positive control group (27.71 mm2). The histopathological analysis revealed that pretreatment with a high dose of 400 mg/kg of I. wightii leaf methanol extract and positive control group (omeprazole) markedly protected pathological effects, and the gastric mucosa appeared normal. In conclusion, I. wightii has solid nontoxic potential as a promising native herb for an integral therapy for the treatment of ulcers.
The Effects of CD73 on Gastrointestinal Cancer Progression and Treatment
Authors: Sun Pengcheng , Zheng Xiao , Li Xiaodong
Published: May 2022

Gastrointestinal (GI) cancer is a common and deadly malignant tumor. CD73, a cell-surface protein, acts as a switch of the adenosine-related signaling pathway that can cause significant immunosuppression. Recent evidence has emerged that CD73 is a promising immunotherapy target for regaining immune cell function and restraining tumorigenesis, and a growing stream of research indicates that combining immunotherapy with other therapies can effectively improve the prognosis and survival of GI cancer patients. Several immune checkpoint inhibitors have been approved for use in GI cancer recently; however, they have demonstrated limited efficacy. Solving the problem of immunosuppression in GI cancer is the key to developing an effective therapeutic option and the modulation of CD73 expression may provide an answer. In this review, we discuss current research on CD73 in gastric, liver, pancreatic, and colorectal cancer to evaluate its therapeutic potential as an immunotherapy target in GI cancers.
A Two-Stage Unrelated Question Randomized Response Model for Estimating the Prevalence of Stigmatized Attribute
Authors: Narjis Ghulam , Shabbir Javid , Onyango Ronald
Published: May 2022

The randomized response technique (RRT) is an effective method designed to obtain the stigmatized information from respondents while assuring the privacy. In this study, we propose a new two-stage unrelated question RRT model to estimate the prevalence of sensitive attribute

π

when the proportion of unrelated innocuous attribute is known and unknown. A simulation study is carried out to validate the theoretical results of proposed estimators. The utility of proposed two-stage unrelated question RRT model under stratification is also explored. An efficiency comparison between the proposed two-stage unrelated question RRT model and the Singh and Suman RRT model is carried out numerically under simple and stratified random sampling.
Numerical Simulation of Rock Breaking by High-Temperature and High-Pressure Water under Thermal Driving
Authors: Hu Shaobin , Cai Yukang , Zhang Lin , Yan Zhengyong , Pang Shuogang
Published: May 2022

To solve the difficult problems in the field of unconventional oil and gas extraction and hard rock excavation in urban underground spaces, this paper proposes a rock-breaking technique with high-temperature and high-pressure water under thermally driven conditions and establishes a coupled thermal-fluid-solid model with COMSOL Multiphysics (COMSOL Co., Ltd. Shanghai, China). Different simulation groups are established by controlling variables to explore the effects of the surrounding rock load, heat source power, and Biot coefficient on the damage evolution during thermally driven rock breaking. To make the results relevant to practical engineering, the damage evolution results under the maximum normal stress criterion, maximum normal strain criterion, and Coulomb-Navier damage criterion are considered, and a comparative analysis is performed. The results of this study show that an increase in unilateral load and heat source power accelerates the damage evolution rate, while an increase in bilateral load and Biot coefficient has the opposite effect. The damage evolution rate controlled by the maximum normal stress criterion is the fastest under general conditions. Finally, the advantages in rock breaking provided by the established method are verified by a comparison of results from the proposed model and a conventional hydraulic fracturing model.
Cooperative Scheme ToA‐RSSI and Variable Anchor Positions for Sensors Localization in 2D Environments
Authors: Zaidi Monji , Bouazzi Imen , Usman Mohammed , Mohammed Shamim Mohammed Zubair , Singh Ninni , Gunjan Vinit Kumar
Published: May 2022

To rich good accuracy in the 2D area for wireless sensor network (WSN) nodes, a localization method has to be selected. The objective of this paper is first to select which localization technique is required (Received Signal Strength Indicator (RSSI)) or (Time of Arrival (ToA)) against anchors placement in a 2D area. Depending on whether the anchor nodes are spaced or not and inspired by the idea of using the RSSI method for small distances and the ToA method for greater distances, we will show which method should be used for the positioning process which mainly guarantees a minimal localization error. Second, a two‐dimensional localization scheme for WSN which is called Combined Advantages of ToA‐RSSI (CA ToA‐RSSI), hereafter, ranging methods, is designed in this work, to make the accuracy better during the positioning process. Results provided through MATLAB simulations show that our new technique improves considerably the positioning accuracy compared with the traditional RSSI and ToA ranging method. The proposed scheme can be run under Line of Sight and (LOS) and Nonline of Sight (NLOS) conditions taking into account a difference in the measurement error.
A Decision Support System for Optimal Building Cold Source Selection
Authors: Li Qing , Dong Yan , Cao Rong-Guang , Li Le , Chen Zhi-Bin
Published: May 2022

Building energy consumption is increasingly becoming a matter of global concern. A key aspect of this is the nature of building cold source systems and their effectiveness. However, choosing the cold source for a building is a complex decision-making process. The traditional evaluation method is relatively simple, and it is difficult to comprehensively consider multiple factors and their mutual influences, and it is even more difficult to increase the consideration of the whole life-cycle cost on this basis. Especially in China, the cost of extra-large public projects invested and constructed by the government plays a significant role in the decision-making process, and it should not be affected by other factors to change the decision-making. Therefore, this paper proposes a new decision-making system, which is based on value engineering (VE) and combined with analytic network process (ANP) to conduct a comprehensive study of project decision-making. Based on existing approaches to construction project evaluation and the selection of cold source systems, we establish an evaluation index for building cold source system functional models and network structures. This approach can take into account the impact of cost on decisions. We then present the results of a simulation-based case study of potential central air-conditioning cold source system solutions for the new Xiang’an New Airport in Xiamen, China. The corresponding value coefficients for conventional electric refrigeration, ice storage, and water storage were found to be 0.8262, 1.0049, and 1.2442, respectively. This suggests that water storage-based cooling offers the best technical and economic benefits for this project. It also confirms that the proposed decision support system can identify the modes with the highest value coefficients, which is one of the most effective methods for selecting a building cold source system scheme. Therefore, the system has the potential to support the effective selection of other building cold source schemes.
Image Deblurring Algorithm Based on the Gaussian-Scale Mixture Expert Field Model
Authors: Zhang Jing , Zhang Tao
Published: May 2022

With the proliferation of portable digital products, image quality degradation has received a lot of attention. As the most common phenomenon in image degradation, the issue of image deblurring is the focus of much attention. Blind motion blur removal is the main target of this paper. The heavy-tailed distribution is the most dominant statistical feature of natural images. However, most image deblurring methods use a gradient prior with fixed parameters to recover a clear image, which leads to loss of details in the recovered clear image and does not consider the higher order prior of the natural image. Therefore, this paper proposes a new regularized image recovery model based on the Gaussian-scale mixture expert field(GSM-FOE) model. First, the GSM-FoE model learns filters and corresponding parameters with higher order prior information of images by training images in a natural image library; second, these learning results are used to guide the image recovery process. The GSM-FoE model and gradient-fidelity based image recovery model is proposed, which can be used with an iterative re-weighted least squares (IRLS) method. Experiments demonstrate that the suggested recovery approach is simple to use and successful at reducing blur and noise, as well as suppressing ringing effects while preserving image information. Moreover, the image restoration method performs well for large blurring kernels. The results fully reflect the effectiveness and robustness of the proposed method for complex noise scenarios. The quality of the generated images is significantly better than that of several classical methods.
Morphological, Histological, and Physiological Responses of Female Orange Mud Crab, Scylla olivacea (Herbst, 1796), Fed with Different Dietary Lipid Levels
Authors: Aaqillah-Amr Mohd Amran , Hidir Ariffin , Waiho Khor , Fazhan Hanafiah , Ahmad-Ideris Abdul Rahim , Muhammad-Zulhilmi Ramlee , Peng Teoh Hong , Abualreesh Muyassar H. , Noordiyana Mat Noordin , Ma Hongyu , Ikhwanuddin Mhd
Published: May 2022

The main focus of the study was to assess the effect of lipid levels on the reproduction of the female orange mud crab, Scylla olivacea. Four isonitrogenous feed (~420 g/kg) were formulated with different lipid percentages:

T
1
=
60
 
g
/
kg

,

T
2
=
80
 
g
/
kg

,

T
3
=
100
 
g
/
kg

, and

T
4
=
120
 
g
/
kg

. A feeding trial was carried out on 120 matured female mud crabs for 90 days with 30 replicates in each treatment. Based on the morphological, histological, and hormonal analysis of each crab, the performances of each experimental diet were tested. The results of the experiment disclosed positive body weight gain (BWG) and specific growth rate (SGR), with the highest value recorded in crabs that were fed T4 (BWG:

14.43
±
1.14
 
g

; SGR:

0.10
±
0.01
%
da

y

−
1

). The highest gonado-somatic index (GSI) and hepato-somatic index (HSI) were noted in mud crabs that were fed T4, each with a value of

14.51
±
1.13
%

and

5.23
±
0.55
%

, respectively (

P
<
0.05

). The histological assessment revealed that the highest oocyte diameter was recorded in mud crabs fed T4 (

192.80
±
21.93
 

μm), where the oocyte diameter was correlated with the progesterone (Pg) concentrations in the ovary with

r
=
0.73

. Evidence also indicated that Pg and estradiol (E2) are important in the ovarian development in S. olivacea and their concentrations were affected by the lipid levels. An overall analysis revealed that 120 g/kg of lipid in the diet contributes to optimal reproductive performance in Scylla olivacea. Yet, a further experiment is expected to be carried out in the future to determine the nutrient-dose response as 120 g/kg lipid may not be the optimum lipid level for reproductive and growth performance in S. olivacea. Moreover, the extrapolation of data from this study can be used as guidelines in feed formulation for developments in future research in adult crustaceans.
Pre-Exposure to Environmental Enrichment Protects against Learning and Memory Deficits Caused by Infrasound Exposure
Authors: Jiang Shan , Wang Yong-Qiang , Tang Yi-Fei , Lu Xi , Guo Dan
Published: May 2022

With the development of industrialization in recent years, infrasound has become an important component of public noise. To date, diverse studies have revealed the negative effects of infrasound on the central nervous system (CNS), especially the learning and memory ability. It is widely reported that environmental enrichment (EE) ameliorates the learning and memory deficits in different models of brain injury. Therefore, the present study was designed to determine the possible benefits of pre-exposure to EE in preventing functional deficits following infrasound exposure and their related mechanism. Adult male rats were given enriched or standard housing for 30 days. Following enrichment, the rats were exposed to 16 Hz, 130 dB infrasound for 14 days, and then their learning and memory ability was assessed. Changes to neuroinflammation, apoptosis, and oxidative stress in the hippocampus were also detected. Our results showed that the infrasound-induced deficit in learning and memory was attenuated significantly in EE pre-exposed rats. Pre-exposure to EE could induce a decrease in proinflammatory cytokines and increased anti-inflammatory cytokines and antioxidant properties in the hippocampus. Moreover, pre-exposure to EE also exerted antiapoptosis functions by upregulating the B-cell lymphoma/leukemia-2 (Bcl-2) level and downregulating the P53 level in the hippocampus. In conclusion, the results of the present study suggested that EE is neuroprotective when applied before infrasound exposure, resulting in an improved learning and memory ability by enhancing antioxidant, anti-inflammatory, and antiapoptosis capacities.
A Mechanistic Insight into the Pathogenic Role of Interleukin 17A in Systemic Autoimmune Diseases
Authors: Bisoendial Radjesh , Lubberts Erik
Published: May 2022

Interleukin 17A (IL-17A) has been put forward as a strong ally in our fight against invading pathogens across exposed epithelial surfaces by serving an antimicrobial immunosurveillance role in these tissues to protect the barrier integrity. Amongst other mechanisms that prevent tissue injury mediated by potential microbial threats and promote restoration of epithelial homeostasis, IL-17A attracts effector cells to the site of inflammation and support the host response by driving the development of ectopic lymphoid structures. Accumulating evidence now underscores an integral role of IL-17A in driving the pathophysiology and clinical manifestations in three potentially life-threatening autoimmune diseases, namely, systemic lupus erythematosus, Sjögren’s syndrome, and systemic sclerosis. Available studies provide convincing evidence that the abundance of IL-17A in target tissues and its prime source, which is T helper 17 cells (Th17) and double negative T cells (DNT), is not an innocent bystander but in fact seems to be prerequisite for organ pathology. In this regard, IL-17A has been directly implicated in critical steps of autoimmunity. This review reports on the synergistic interactions of IL-17A with other critical determinants such as B cells, neutrophils, stromal cells, and the vasculature that promote the characteristic immunopathology of these autoimmune diseases. The summary of observations provided by this review may have empowering implications for IL-17A-based strategies to prevent clinical manifestations in a broad spectrum of autoimmune conditions.
Fluid Dynamics and Numerical Simulation of Exhaled Droplets Containing Infectious Viruses
Authors: Han Jiawen
Published: May 2022

Understanding the movement and transmission patterns of airborne particles is very important to understand the diseases they carry. One of the most common sources of viral infections is sneezing and coughing. This topic explores the theoretical properties of airborne microbes and their dispersal. It aims at developing a model that can predict the movement and transmission of these particles. The model is formulated using the Runge–Kutta (RK) algorithm, which is a 4th-order standard for solving differential equations. It is used to study the effects of various factors such as wind speed and jet velocity on the movement of droplets. The model is compared to the well-known Maxey–Riley equation. It then explains the various factors that influence the dispersal of airborne microbes. The evaporation of airborne microbes has a significant effect on the movement of smaller particles.
Potential Future Directions in Optimization of Students’ Performance Prediction System
Authors: Ahmad Sadique , El-Affendi Mohammed A. , Anwar M. Shahid , Iqbal Rizwan
Published: May 2022

Previous studies widely report the optimization of performance predictions to highlight at-risk students and advance the achievement of excellent students. They also have contributions that overlap different fields of research. On the one hand, they have insightful psychological studies, data mining discoveries, and data analysis findings. On the other hand, they produce a variety of performance prediction approaches to assess students’ performance during cognitive tasks. However, the synchronization between these studies is still a black box that increases prediction systems’ dependency on real-world datasets. It also delays the mathematical modeling of students’ emotional attributes. This review paper performs an insightful analysis and thorough literature-based survey to draw a comprehensive picture of potential challenges and prior contributions. The review consists of 1497 publications from 1990 to 2022 (32 years), which reported various opportunities for future performance prediction researchers. First, it evaluates psychological studies, data analysis results, and data mining findings to provide a general picture of the statistical association among students’ performance and various influential factors. Second, it critically evaluates new students’ performance prediction techniques, modifications in existing techniques, and comprehensive studies based on the comparative analysis. Lastly, future directions and potential pilot projects based on the assumption-based dataset are highlighted to optimize the existing performance prediction systems.
A Probabilistic Spray-and-Wait Routing Algorithm Based on Node Interest Preference in Delay Tolerant Networks
Authors: Chang Yanan , Zhang Ruijie , Cui Jianqun , Zhou Hao , Wan Qiyun , Wu Jike
Published: May 2022

How to select proper relay nodes to ensure the successful delivery of messages is still a hot topic in delay tolerant networks (DTN). In this paper, we propose a probabilistic Spray-and-Wait routing algorithm based on node interest preference (called NIP-PSW). Firstly, considering the influence of the social attributes of nodes, we define a metric called node interest preference (NIP) to measure the probability of nodes becoming friends. Secondly, in view of the influence of node quality and connection time between nodes on message forwarding, we define the delivery probability (DP). Finally, according to the historical information of nodes, the node interest similarity (NIS) is proposed. In spray phase, NIP and DP are used to select the relay node and allocate the number of message copies adaptively. In wait phase, it is judged whether to forward the message to the encountering nodes again according to the NIS and the DP. In addition, the concept of message storage value (MSV) and the acknowledgment (ACK) mechanism are introduced to manage the buffer of nodes. The simulation results show that the NIP-PSW not only can significantly improve the delivery rate and reduce the average delay but also shows good performance in network overhead and average number of hops.
The Efficacy of a Newly Developed Cueing Device for Gait Mobility in Parkinson’s Disease
Authors: Suputtitada Areerat , Chen Carl P.C. , Pongmala Chatkaew , Sriyudthsak Mana , Wilhelm Agnes , Somboon Pakpum , Janssen Jessie , Richards Jim
Published: May 2022

Background. External cues are effective in improving gait in people with Parkinson’s disease (PD). However, the most effective cueing method has yet to be determined. Objective. The aim of this study was to compare the immediate effects of using visual, auditory, or somatosensory cues on their own or in combination during walking compared to no cues in people with PD. Methods. This was a single blinded, randomly selected, controlled study. Twenty people with PD with an age range of 46–79 years and Hoehn and Yahr scores of 1–3 were recruited. Participants were studied under 4 cueing conditions; no cue, visual, auditory, or somatosensory cues, which were randomly selected individually or in a combination. Results. A repeated measures ANOVA with pairwise comparisons using Bonferroni correction showed that any single or combination of the cues resulted in an improvement in gait velocity and stride length compared to no cue. Some significant differences were also seen when comparing different combinations of cues, specifically stride length showed significant improvements when additional cues were added to the light cue. The statistically significant difference was set at

p
<
0.05

. Conclusions. Walking using visual, auditory, or somatosensory cues can immediately improve gait mobility in people with PD. Any or a combination of the cues tested could be chosen depending on the ability of the individual to use that cue.
IIDQN: An Incentive Improved DQN Algorithm in EBSN Recommender System
Authors: Guo Jianan , Wang Yilei , An Hui , Liu Ming , Zhang Yiting , Li Chunmei
Published: May 2022

Event-based Social Networks (EBSN), combining online networks with offline users, provide versatile event recommendations for offline users through complex social networks. However, there are some issues that need to be solved in EBSN: (1) The online static data could not satisfy the online dynamic recommendation demand; (2) the implicit behavior information tends to be ignored, reducing the accuracy of the recommendation algorithm; and (3) the online recommendation description is inconsistent with the offline activity. To address these issues, an Incentive Improved DQN (IIDQN) based on Deep Q-Learning Networks (DQN) is proposed. More specifically, we introduce the agents to interact with the environment through online dynamic data. Furthermore, we consider two types of implicit behavior information: the length of the user’s browsing time and the user’s implicit behavior factors. As for the problem of inconsistency, based on blockchain technology, a new activities event approach on EBSN is proposed, where all activities are recorded on the chain. Finally, the simulation results indicate that the IIDQN algorithm greatly outperforms in mean rewards and recommendation performance than before DQN.
The Performance of Artificial Intelligence Translation App in Japanese Language Education Guided by Deep Learning
Authors: Wang Yi
Published: May 2022

With recent technological advances in wireless networks and the Internet, social media has become a vital part of the daily lives of people. Social media like Twitter, Facebook, and Instagram have enabled people to instantly share their thoughts and ideas about a particular topic or person’s life. Emotion classification in Twitter data remains a hot search topic in the field of artificial intelligence (AI). Though several models have been developed for tweet data in English, it is still needed to develop an effective tweet emotion classification for the Japanese language. In this aspect, this work develops a new artificial intelligence with an Optimal Long Short-Term Memory-Based Japanese Tweet Emotion Classification (OLSTM-JTCC) model in wireless networks. The proposed OLSTM-JTCC technique aims to examine emotions and categorises them into proper class labels. The proposed OLSTM-JTCC technique initially employs the TF-IFD model for the extraction of feature vectors. Besides, the OLSTM model is used to classify the tweet data into different types of emotions that exist within it. In order to improve the classification capability of the LSTM model, the Henry gas solubility optimization (HSGO) algorithm is applied as a hyperparameter optimizer. The performance validation of the OLSTM-JTCC technique took place using Japanese tweets, and the comparative results highlighted the better performance of the OLSTM-JTCC technique in terms of different measures.
Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning
Authors: Senan Ebrahim Mohammed , Jadhav Mukti E. , Rassem Taha H. , Aljaloud Abdulaziz Salamah , Mohammed Badiea Abdulkarem , Al-Mekhlafi Zeyad Ghaleb
Published: May 2022

Cancer is considered one of the most aggressive and destructive diseases that shortens the average lives of patients. Misdiagnosed brain tumours lead to false medical intervention, which reduces patients’ chance of survival. Accurate early medical diagnoses of brain tumour are an essential point for starting treatment plans that improve the survival of patients with brain tumours. Computer-aided diagnostic systems have provided consecutive successes for helping medical doctors make accurate diagnoses and have conducted positive strides in the field of deep and machine learning. Deep convolutional layers extract strong distinguishing features from the regions of interest compared with those extracted using traditional methods. In this study, different experiments are performed for brain tumour diagnosis by combining deep learning and traditional machine learning techniques. AlexNet and ResNet-18 are used with the support vector machine (SVM) algorithm for brain tumour classification and diagnosis. Brain tumour magnetic resonance imaging (MRI) images are enhanced using the average filter technique. Then, deep learning techniques are applied to extract robust and important deep features via deep convolutional layers. The process of combining deep and machine learning techniques starts, where features are extracted using deep learning techniques, namely, AlexNet and ResNet-18. These features are then classified using SoftMax and SVM. The MRI dataset contains 3,060 images divided into four classes, which are three tumours and one normal. All systems have achieved superior results. Specifically, the AlexNet+SVM hybrid technique exhibits the best performance, with 95.10% accuracy, 95.25% sensitivity, and 98.50% specificity.