bluuebunny/crossref_metadata_2025 huggingface dataset:
Methane Biogas Production in Malaysia: Challenge and Future Plan
Authors: Amin Muhd Arshad , Shukor Hafiza , Yin L. S. , Kasim Farizul Hafiz , Shoparwe Noor Fazliani , Makhtar Muaz Mohd Zaini , Yaser Abu Zahrim
Published: October 2022

Biomethane is a sustainable energy that is produced from an organic and renewable resource. As the second-largest oil palm producer in the world, palm oil mill effluent (POME) is the primary source of biomethane generation in Malaysia. POME is the by-product of palm oil extraction and is extensively employed as a feedstock for the production of biomethane. Malaysia has an equatorial environment with humid and hot weather; this climate is conducive to the cultivation of numerous agricultural crops. A considerable number of agricultural wastes and residues are produced by agricultural crops, however, only 27% of them are used as fuel or to create useable products. Several publications have been published on the production of biomethane from POME; nevertheless, additional research is required on the use of other bioresources and technologies for biomethane production in Malaysia. In addition, there is a lack of comprehensive information on the future development of biomethane production in Malaysia; thus, to fill this gap, this review paper focuses on the challenges and future of Malaysia, which puts an emphasis on POME and also includes other alternative options of bioresources that can be the future feedstock for biomethane production in Malaysia. To the best of our knowledge, this is the first paper to provide a comprehensive overview of the biogas trend in Malaysia in terms of challenges and current biomethane development, as well as detailed information on a number of leading companies that are currently active in Malaysia biogas industry.
Identifying and Targeting Prediction of the PI3K-AKT Signaling Pathway in Drug-Induced Thrombocytopenia in Infected Patients Receiving Linezolid Therapy: A Network Pharmacology-Based Analysis
Authors: Xie Jiao , Chen Hongli , Li Youjia , Liu Chenwei , Zheng Xiaowei , Feng Siping , Wang Haitao
Published: October 2022

The pharmacological mechanisms underlying the adverse effects of linezolid on thrombocytopenia have not been conclusively determined. This network pharmacology study aimed at investigating the potential pharmacological mechanisms of linezolid-induced adverse reactions in thrombocytopenia. In this study, target genes for linezolid and thrombocytopenia were compared and analyzed. Overlapping thrombocytopenia-associated targets and predicted targets of linezolid were imported to establish protein-protein interaction networks. Gene Ontology and the Kyoto Encyclopedia of Genes and Genome pathway enrichment analyses were performed to determine the enriched biological terms and pathways. The mechanisms involved in linezolid-induced thrombocytopenia were established to be associated with various biological processes, including T cell activation, peptidyl serine modification, and peptidyl serine phosphorylation. The top five relevant protein targets were obtained, including ALB, AKT1, EGFR, IL6, and MTOR. Enrichment analysis showed that the targets of linezolid were positively correlated with T cell activation responses. The mechanism of action of linezolid was positively correlated with the PI3K-AKT signaling pathway and negatively correlated with the Ras signaling pathway. We identified the important protein targets and signaling pathways involved in linezolid-induced thrombocytopenia in anti-infection therapy, providing new information for subsequent studies on the pathogenesis of drug-induced thrombocytopenia and potential therapeutic strategies for rational use of linezolid in clinical settings.
Anisotropic Hypoplastic Model Considering the Relation between Fabric Tensor and Anisotropic Parameters
Authors: Jong Yong-Gwang , Chen Zixuan , Ma YongMin , Liu Yang
Published: October 2022

The fabric anisotropy and anisotropic parameters have great effect on the mechanical property of sand. Based on the existing anisotropic model, a modified anisotropic hypoplastic model is developed by incorporating the relation between fabric tensor and anisotropic parameters into the nonlinear part of the constitutive model. The applicability of the improved anisotropic hypoplastic model is validated by comparing with the existing model and experimental data. As a result, it is found that the modified model can well predict the mechanical response of anisotropic sand.
Cyclosporine (0.05%) Combined with Diclofenac Sodium Eye Drops for the Treatment of Dry Eye Disease
Authors: Bai Run , Liu Li-ping , Chen Zhen , Ma Qiong
Published: October 2022

Objective. To assess the clinical efficacy of cyclosporine (0.05%) combined with diclofenac sodium eye drops (0.1%) in the treatment of dry eye disease. Methods. A prospective analysis was performed on clinical information of 128 patients diagnosed with dry eye at the ophthalmic clinic of the General Hospital of Xinjiang Military Command of the Chinese People’s Liberation Army from August 2020 to August 2021. Specifically, patients were randomly divided into a control group and a study group. In addition to conventional treatment, patients in the control group were treated with cyclosporine (0.05%) eye drops; while in the study group, patients received cyclosporine (0.05%) combined with diclofenac sodium eye drops (0.1%). Subsequently, comparisons and analysis were performed before and after treatment between the two groups in the clinical symptom questionnaire score of dry eye disease, the corneal fluorescein staining (CFS) score, determination of tear film break-up time (BUT), Schirmer I test (SIT) score, and curative effect. Results. After treatment, the clinical symptom scores and CFS scores were decreased while the BUT and SIT scores were increased in both groups; besides, compared with the control group, the clinical symptom scores and CFS scores were much lower while the BUT and SIT scores were higher in the study group. Moreover, the overall response rate in the study group (96.9%) was much better than that in the control group (79.7%); and the differences between the two groups were statistically significant (

p

 < 0.05). Conclusion. The combination of cyclosporine (0.05%) and diclofenac sodium eye drops (0.1%) based on conventional treatment can be applied to the clinical treatment of dry eye disease due to its good clinical effects on relieving dry eye symptoms.
Serum Lipopolysaccharide Is Associated with the Recurrence of Atrial Fibrillation after Radiofrequency Ablation by Increasing Systemic Inflammation and Atrial Fibrosis
Authors: Wang Meng , Xiong Hua , Lu Li , Zhu Tongjian , Jiang Hong
Published: October 2022

Objectives. The gut microbiota and its metabolites are linked to inflammation and contribute to the progression of atrial fibrillation (AF), but the predictive value of the gut microbiota-derived metabolite lipopolysaccharide (LPS) for AF recurrence (RAF) is unknown. This study is aimed at investigating (1) the correlation between LPS and RAF and (2) its relationship with inflammation and atrial fibrosis. Method. We performed a single-centre retrospective analysis in 159 AF patients. Fasting plasma samples were collected, and an enzyme-linked immunosorbent assay was used to determine the levels of serum LPS, interleukin-6 (IL-6), collagen type-1 C-terminal telopeptide (CITP), and transforming growth factor-β1 (TGFβ1). The cumulative risk for RAF was evaluated with Kaplan–Meier analysis. Cox proportional hazard analysis was carried out to predict the hazard of RAF. The correlations among LPS and IL-6, CITP, TGFβ1, and left atrial diameter (LAD) were analysed by Pearson’s correlation coefficient. Subsequent univariate and multivariable linear regression analyses were carried out to evaluate the connection between clinical variables and Log-LPS. Results. All 159 AF patients were included in this study. The proportion of persistent atrial fibrillation was 40.3%, the mean age was

61.9
±
10.1

years, the proportion of males was 61.6%, and the mean LPS was

56.5
±
29.5

 pg/mL. After all patients were divided into tertiles according to the circulating LPS level, a total of 44 RAF occurred: 10 in the first tertile, 15 in the second tertile, and 19 in the third tertile (log-rank test

P
=
0.037

). Heart failure (hazard ratio 2.029,

P
=
0.041

), LAD (hazard ratio 1.064,

P
=
0.022

), Log-LPS (hazard ratio 5.686,

P
=
0.043

), and CITP (hazard ratio 6.841,

P
=
0.033

) independently predicted the risk of RAF. In all patients, univariate analysis showed that heart failure, LAD, hs-CRP, IL-6, CITP, and TGF-β1 were connected with Log-LPS. Multivariate linear regression analysis indicated that IL-6 and hs-CRP were independently and positively connected with Log-LPS. Conclusions. Our results indicated that circulating LPS was a predictor of RAF and may contribute to RAF incidence after ablation by increasing systemic inflammation and atrial fibrosis.
Deep Learning BP Neural Network Guided Evaluation of Students’ Innovation and Entrepreneurship Education
Authors: Yi Jingyi , Cui Xiao
Published: October 2022

With the promotion and development of the “Internet+,” the computer major has become a hot major in innovation and entrepreneurship education. It is more and more necessary to carry out the refined differences between majors. This is a training issue, but not an employment issue. It has become a major measure for the development of international education. The international vocational training system of “three combinations and five drives” has been established and implemented. In February 2020, the State Department of Higher Education issued the key work points of the Department of Higher Education of the Ministry of Education in 2020. The document makes it clear that it must be implemented in the whole process of talent training. BP neural net model will be a brand-new research and development idea. By constructing a scientific and reasonable training assessment index system, the efficiency of computer professional technology training can be improved. Taking the development of computer specialty as the main research objective, this paper firstly establishes the evaluation index system of computer specialty for the first time, then makes a scientific evaluation of computer specialty by using BP neural net model and then carries out an empirical study of innovative employment mode through the evaluation index system and makes an empirical quantitative analysis. It is expected to be an effective basis for the social policy research of developing computer specialties.
Prognostic Assessment of Oxidative Stress-Related Genes in Colorectal Cancer and New Insights into Tumor Immunity
Authors: Chen Zilu , Mei Kun , Xiao Yao , Xiong Yan , Long Wei , Wang Qin , Zhong Jiang , Di Dongmei , Ge Yunxi , Luo Yi , Li Ziyun , Huang Yan , Gu Renjun , Wang Bin
Published: October 2022

Oxidative stress is crucial to the biology of tumors. Oxidative stress’ potential predictive significance in colorectal cancer (CRC) has not been studied; nevertheless here, we developed a forecasting model based on oxidative stress to forecast the result of CRC survival and enhance clinical judgment. The training set was chosen from the transcriptomes of 177 CRC patients in GSE17536. For validation, 65 samples of colon cancer from GSE29621 were utilized. For the purpose of choosing prognostic genes, the expression of oxidative stress-related genes (OXEGs) was found. Prognostic risk models were built using multivariate Cox regression analysis, univariate Cox regression analysis, and LASSO regression analysis. The outcomes of the western blot and transcriptome sequencing tests were finally confirmed. ATF4, CARS2, CRP, GPX1, IL1B, MAPK8, MRPL44, MTFMT, NOS1, OSGIN2, SOD2, AARS2, and FOXO3 were among the 14 OXEGs used to build prognostic characteristics. Patients with CRC were categorized into low-risk and high-risk groups according on their median risk scores. Cox regression analysis using single and multiple variables revealed that OXEG-related signals were independent risk factors for CRC. Additionally, the validation outcomes from western blotting and transcriptome sequencing demonstrated that OXEGs were differently expressed. Using 14 OXEGs, our work creates a predictive signature that may be applied to the creation of new prognostic models and the identification of possible medication candidates for the treatment of CRC.
Performance of Spectrum Sharing in Hybrid Satellite Terrestrial Network with Opportunistic Relay Selection
Authors: Xue Guanchang , Yang Mingchuan , Guo Qing , Yuan Shuai
Published: October 2022

In this paper, we consider the uplink of a hybrid satellite-terrestrial spectrum sharing system, in which satellite terminal communicates with satellite through terrestrial relay-assisted transmission. To accommodate expanding networks within limited spectrum, spectrum sharing is considered a promising candidate. For the system in which satellite terminals and terrestrial terminals share spectrum, we propose opportunistic relay selected spectrum sharing method based on decoding and forwarding protocol. Selecting the optimal relay station is aimed at minimizing the outage probability of the satellite terminal and maximizing the throughput of the system. Due to spectrum sharing policy and imperfections in the RF front end, we also consider the effects of cochannel interference (CCI) and hardware impairments (HIs). The satellite link is modeled as Shadowed-Rician fading, and the terrestrial link uses Nakagami-m fading. In addition, we deduce the closed-form analytical expressions of the satellite terminal outage probability, deduce the asymptotic expressions under the condition of high signal-to-noise ratio, and analyze their achievable diversity orders. Numerical and simulation results validate the performance gain of opportunistic relay selection compared to partial relay selection. Monte Carlo simulations confirm the correctness of the theoretical analysis and illustrate the effects of CCI and HIs on the system.
Identification of Key Genes in the HBV-Related HCC Immune Microenvironment Using Integrated Bioinformatics Analysis
Authors: Ding Wei , Zhang Zheng , Ye Nianyuan , Zhuang Ling , Yuan Zhiping , Xue Wenbo , Tan Yulin , Xu Xuezhong
Published: October 2022

Purpose. Hepatocellular carcinoma (HCC) has poor prognosis and high mortality among gastrointestinal tumors because of its insidious onset and strong invasiveness. However, there was little understanding of their pathogenesis. The purpose of this study was to use bioinformatics analysis to identify genes associated with the immune microenvironment in HBV-related HCC and to develop new therapeutic targets to prevent and treat cancer. Methods. RNA-seq data of HBV-related HCC cases were downloaded from TCGA-LIHC database. ESTIMATE and Deseq2 algorithms were used to screen out differentially expressed genes (DEGs). WGCNA was used to construct gene coexpression networks. In key modules, functional enrichment analysis was performed. Protein-protein interaction (PPI) was used to screen hub genes, and survival analysis was conducted to assess their prognostic significance. Following, we search for key genes differentially expressed between cancerous and paracancerous tissues in GSE136247 and GSE121248 datasets. Reveal the potential links between key genes in immune infiltration by using TIMER. Finally, in TCGA-LIHC database, integration of key genes with clinical data were used to further validate their correlation with prognosis. Results. In the cohort of HBV-related HCC patients, immune/stromal/ESTIMATE scores were not significantly associated with patient prognosis. After bioinformatics analysis, screening out five key genes was significantly related to the prognosis of HBV-related HCC. Downregulation of SLAMF1 and TRAF3IP3 suggested poor prognosis and was related to a variety of immune cell infiltration. Furthermore, compared with adjacent nontumor tissues, TRAF3IP3 and SLAMF1 were highly expressed in tumor tissues and were linked to tumor recurrences. Conclusion. In conclusion, SLAMF1 and TRAF3IP3 were identified with higher expression in tumor tissues and associated with tumor recurrence. It will be a new research direction of tumor progress and treatment.
The Value of GeneXpert MTB/RIF for Detection in Tuberculosis: A Bibliometrics-Based Analysis and Review
Authors: Li Zhiyi
Published: October 2022

With the continuous development of medical science and technology, especially with the advent of the era of precision diagnosis and treatment, molecular biology detection technology is widely valued and applied as an aid to early diagnosis of tuberculosis. The GeneXpert Mycobacterium tuberculosis Branching (MTB) technology is a suite of semi-nested real-time fluorescent quantitative PCR in vitro diagnostic technologies developed by Cepheid Inc. It targets the rifampicin resistance gene, rpoB, and can detect both MTB and resistance to rifampicin within 2 h. This review analyzed the papers related to GeneXpert using bibliometric software CiteSpace and Bibliometrix. A total of 151 articles were analyzed, spanning from 2011 to 2021. This bibliometrics-based review summarizes the history of the development of GeneXpert in tuberculosis diagnosis and its current status. Contributions of different countries to the topic, journal analysis, key paper analysis, and clustering of keywords were used to analyze this topic.
Genetic Mechanisms and Identification of Low-Resistivity Pay Zones: A Case Study of Pengyang Area, Ordos Basin, China
Authors: Zhao Peiqiang , Hou Yuting , Ma Fengqing , Huang Jixin , Wang Xiaoyu , Xie Jiarui , Deng Chengxiang , Mao Zhiqiang
Published: October 2022

The Triassic Yanchang Formation in the Pengyang area of Ordos Tianhuan depression is an important oil and gas formation. However, most of the oil pays in the study formations are low-resistivity or low-contrast reservoirs with low permeability, bringing challenges to the reservoir identification and evaluation by well logs. In this paper, we first measured the nuclear magnetic resonance (NMR), phase permeability, cation exchange capacity (CEC), and X-ray diffraction for core samples. Then, the genetic types for the low-resistivity pays were analyzed based on the experiment results, water analysis, and well log data collected. It was found that large variations of formation water salinity, high irreducible water saturation, and clay conductivity are the primary genetic types. Further, the random forest (RF) algorithm with sensitive parameter inputs was used to identify the oil, oil and water, and water layers. The anomaly of spontaneous potential (∆SP) that characterizes water salinity, the relative value of gamma ray log (∆GR) that describes the bound water content, resistivity, density, and acoustic logs were taken as sensitive logs according to the genetic analysis. Finally, this identification method was verified by comparison with the traditional crossplot method and oil test results. The identification accuracy of the RF is 90%, far higher than that by the crossplot method.
What Affects Safety Perception of Female Ride-Hailing Passengers? An Empirical Study in China Context
Authors: Yang Yazao , Hu Shixingyue , Liao Dingling , Huang Xianbo
Published: October 2022

Ride-hailing services provide an efficient way to travel, but they also cause some safety incidents, which make female passengers uneasy. Analyzing the safety perceptions of female passengers, particularly their psychological and emotional responses, can assist operators in developing effective solutions and safe travel environments for them. This study explores factors that are likely to affect the safety perception of female ride-hailing passengers using a subjective method (data was obtained from 596 Chinese female passengers). The methodologies adopted mainly include confirmatory factor analysis (CFA) and a maximum-likelihood structural equation model (ML-SEM). A passenger safety perception model is developed by considering various elements such as safety expectation, platform trust, perceived environment, and safety awareness. The results revealed that safety perception is positively influenced by perceived environment and safety expectation (containing three subdimensions, namely, driver behavior, traveling together, and mobile phone dependence). The effects of safety awareness and platform trust on safety perception are mediated by perceived environment and safety expectation, respectively. Regarding overall effects, safety expectation is the most powerful predictor for safety perception of female ride-hailing passengers, followed by platform trust, perceived environment, and safety awareness. Finally, countermeasures are offered from the perspectives of operators, drivers, and passengers to enhance the safety perception of female ride-hailing passengers. A high level of ride-hailing safety would undoubtedly boost the female passenger’s trust and consequently ridership.
Efficacy of Modified Nonpneumatic Transaxillary Approach in the Treatment of Thyroid Cancer and Its Effect on Immune Function and Parathyroid Function
Authors: Huang Tao , Sang Yiming , Zhang Jizong
Published: October 2022

Objective. The aim of the study is to investigate the effect of modified nonpneumatic transaxillary approach in the treatment of thyroid cancer and its effect on immune function and parathyroid function. Methods. A total of 96 patients with thyroid cancer who were diagnosed and treated in our hospital from January 2018 to December 2020 were selected and randomly divided into the control group of 48 cases and the observation group of 48 cases. The control group was given open surgery, and for the observation group, modified nonpneumatic transaxillary approach was used for treatment. The perioperative related indicators, the incidence of complications, as well as the changes of immune function indicators, parathyroid hormone (PTH), and calcium before and after surgery were compared between the two groups. Results. The time of flap separation and cavity construction, operation time, and hospital stay in the observation group were significantly longer than those in the control group (

P

 < 0.05). After operation, CD3+, CD4+, and CD4+/CD8+ in the two groups were lower than those before operation (

P

 < 0.05), but the observation group was significantly higher than that in the control group (

P

 < 0.05). The serum PTH and calcium at 1 h, 1 d, 3 d and 7 d after operation were lower than those before operation in this group (

P

 < 0.05), but the observation group was significantly higher than that in the control group (

P

 < 0.05). Compared with the control group, the incidence of complications in the observation group (4.17% vs. 6.25%) was not statistically significant (

P

 > 0.05). Conclusion. Compared with open surgery, the modified nonpneumatic transaxillary approach in the treatment of thyroid cancer is more effective in reducing immune function decline, hypoparathyroidism, and hypocalcemia; although the operation time and recovery time are longer, and it is safe. Sex is also high.
Astragalus Polysaccharides Promote Wound Healing in Diabetic Rats by Upregulating PETN and Inhibiting the mTOR Pathway
Authors: Ma Le
Published: October 2022

Objective. Presently, astragalus polysaccharide (APS) is being investigated for its therapeutic potential in various diseases; however, its underlying mechanism has not yet been clarified. This study was aimed at observing the effects of APS on wound healing in diabetic rats and at exploring its underlying mechanism. Methods. Streptozotocin was injected into the tail vein of SD rats to induce diabetic animal models, in which an incision on the back was made. Rats were treated with different dosages of APS to observe their wound healing. Additionally, RT-qPCR and Western blot assay were conducted to observe the expression levels of PTEN and mTOR pathway-associated factors. Results. Diabetic rats had a prolonged wound healing process, fewer blood vessels, and increased inflammatory response, in which decreased PTEN and elevated mTOR phosphorylation were also identified. APS effectively improved wound healing in a dose-dependent manner by inhibiting the release of inflammatory mediators and attenuating endothelial injuries. Suppression of PTEN could effectively increase the phosphorylation of mTOR and diminish the therapeutic functions of APS on wound healing in diabetic rats. Conclusion. This study highlighted that APS could promote wound healing in diabetic rats by upregulating PTEN and suppressing the mTOR pathway activation.
An Analysis of the Effect of Noninvasive Positive Pressure Ventilation on Patients with Respiratory Failure Complicated by Diabetes Mellitus
Authors: Sun Jia-an , Wang Xiaoman , Liu Ying , Liu Chang
Published: October 2022

Objective. To observe the clinical effectiveness of noninvasive positive pressure ventilation in patients with respiratory failure complicated by diabetes. Methods. From May 2021 to May 2022, 90 patients with respiratory failure complicated by diabetes treated in our hospital were recruited and randomly assigned to receive either medication (control group) or noninvasive positive pressure ventilation (study group), with 45 patients in each group. The clinical endpoint was therapeutic outcomes. Results. Noninvasive positive pressure ventilation resulted in significantly lower Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) scores versus medications (

P
<
0.05

). Patients with noninvasive positive pressure ventilation showed better pulmonary function indices versus those with medications (

P
>
0.05

). There was no significant difference in arterial oxygen (PaO2), carbon dioxide partial pressure (PaCO2), and arterial oxygen pressure/inspired fraction of O2 (PaO2/FiO2) between the two groups prior to the intervention (

P
>
0.05

). However, patients in the study group had significantly elevated PaO2 and PaO2/FiO2 and lower PaCO2 levels than those in the control group (

P
<
0.05

). Following the intervention, noninvasive positive pressure ventilation resulted in significantly lower inflammatory factor levels versus medications (

P
>
0.05

). After the intervention, markedly better glucose control was observed in the study group versus the control group (

P
<
0.05

). The incidence of complications in the control group was 2.38%, which was significantly lower than that of the control group (16.67) (

P
<
0.05

). Conclusion. Noninvasive positive pressure ventilation effectively suppresses the inflammatory response, improves the blood gas analysis index, and eliminates the negative emotions of patients, thereby maintaining hemodynamic stability and improving clinical efficacy with a better safety profile. Further studies are recommended prior to clinical promotion.
Using Trajectory Subclustering to Improve Destination Prediction
Authors: Van Hinsbergh James , Griffiths Nathan , Xu Zhou , Mouzakitis Alex
Published: October 2022

Destination prediction is an active area of research, especially in the context of intelligent transportation systems. Intelligent applications, such as battery management in electric vehicles and congestion avoidance, rely on the accurate prediction of the future destinations of a vehicle. Destination prediction methods can utilise mobility patterns and can harness the latent information within vehicle trajectories. Existing approaches make use of the spatial information contained within trajectories, but this can be insufficient to achieve an accurate prediction at the start of an unfolding trajectory, since several destinations may share a common start to their trajectories. To reduce the prediction error in the early stages of a journey, we propose the Destination Prediction by Trajectory Subclustering (DPTS) method for iteratively clustering similar trajectories into groups using additional information contained within trajectories, such as temporal data. We show in our evaluation that DPTS is able to reduce the mean distance error in the first 40–60% of journeys. The implication of reducing the distance error early in a journey is that location-aware applications could provide more accurate functionality earlier in a journey. In this article, we (i) propose the Destination Prediction by Trajectory Subclustering (DPTS) method by extending an existing destination prediction method through incorporating an iterative clustering stage to decompose groups of similar trajectories into smaller groups and (ii) evaluate DPTS against the baseline performance of the existing method.
A Lip Repositioning Technique Using Polyester Threads for Gummy Smile Treatment
Authors: Horn Renata Oliveira Ribeiro , Elias Carlos Nelson , Joly Júlio César
Published: October 2022

A new technique is proposed in this study to correct the gummy smile (GS) with myotomy, combining lip repositioning with the insertion of polyester threads at the surgical site to act as a physical barrier and control relapse. 11 patients were clinically assessed (30.2 ± 7.43 years old, 90.9% females and 9.10% males). All patients presented gingival display (GD) greater than 4 mm. Hypermobile upper lip (HUL), vertical maxillary excess (VME) + HUL, altered passive eruption (APE) + HUL, and VME + APE were the etiologies identified. Three polyester threads were inserted in each patient one month after the surgery. The GS was measured before, 6 months, and 12 months after the surgery. The results showed a reduction in the mean GD of the patients, 4.42 mm after 6 months (

p

value = 0.000) and 4.13 mm after 12 months (

p

value = 0.000). The largest relapse was 0.29 mm and was not statistically significant (

p

value = 0.07). The Friedman test with pairwise comparisons was used to determine the existence of statistically significant differences in GD between the periods analyzed. The results showed that the proposed technique was successful in treating GS, presenting significant reductions in the GD 12 months after surgery and controlling the relapse.
Supervised Reinforcement Learning for ULV Path Planning in Complex Warehouse Environment
Authors: Li Shoulin , Guo Weiya
Published: October 2022

The rapid development of the logistics industry leads to an urgent need for intelligent equipment to improve warehouse transportation efficiency. Recent advances in unmanned logistics vehicles (ULVs) make them particularly important in smart warehouses. However, the complex warehouse environment poses a significant challenge to ULV transportation path planning. Multiple ULVs need to transport cargoes with good coordination ability to overcome the low efficiency of a single ULV. The ULVs also need to interact with the environment to achieve optimal path planning with obstacle avoidance. In this paper, we propose a supervised deep reinforcement learning (SDRL) approach for logistics warehouses that enables autonomous ULVs path planning for cargo transportation in a complex environment. The proposed SDRL approach is featured by (1) designing the supervision module to imitate the behaviors of experts and thus improve the coordination ability of multiple ULVs, (2) optimizing the generator of the imitation learning based on the proximal policy optimization to boost the learning performance, and (3) developing the policy module via deep reinforcement learning to avoid obstacles when navigating the ULVs in warehouse environments. The experiments over dynamic and fixed-point warehouse environments show that the proposed SDRL approach outperforms its rivals regarding average reward, training speed, task completion rate, and collision times.
Danlou Tablet May Alleviate Vascular Injury Caused by Chronic Intermittent Hypoxia through Regulating FIH-1, HIF-1, and Angptl4
Authors: Rong Yi , Wu Qian , Tang Jingjing , Liu Zhiguo , Lv Qianyu , Ye Xuejiao , Dong Yu , Zhang Yuebo , Li Guangxi , Wang Shihan
Published: October 2022

Background. Danlou tablet (DLT), the traditional Chinese medicine has been commonly used for dyslipidemia, atherosclerosis, and coronary heart disease. Whether it was effective against vascular injury caused by CIH has remained unknown. The aim of the current study was to observe the effects of DLT on chronic intermittent hypoxia (CIH)-induced vascular injury via regulation of blood lipids and to explore potential mechanisms. Methods. Sixteen 12-week-old male ApoE−/− mice were randomly divided into four groups. The sham group was exposed to normal room air, whereas the other three groups were exposed to CIH. Mice in the CIH + normal saline (NS) group were gavaged with NS. Mice in the CIH + Angptl4-ab group were intraperitoneally injected with Angptl4-antibody. Mice in the CIH + DLT group were gavaged with DLT. After four weeks of intervention, serum lipid concentrations, and serum lipoprotein lipase (LPL) activity were detected. The changes in atherosclerosis in vascular tissue were detected by hematoxylin and eosin (H&E) staining. Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot analysis were applied to detect the expression levels of hypoxia-induciblefactor-1 (HIF-1), factor-inhibiting HIF-1 (FIH-1), angiopoietin-like 4 (Angptl4), and LPL in different tissues. Results. CIH exposure increases serum lipid levels, decreases serum LPL activity, and exacerbates atherosclerosis. Both Angptl4-ab and DLT treatment reversed the changes in lipid concentration, LPL activity, and atherosclerosis caused by CIH. In the epididymal fat pad, CIH exposure decreased the expression of FIH-1 and increased the expression of HIF-1, whereas DLT treatment increased the expression of FIH-1 and LPL and inhibited the expression of HIF-1 and Angptl4. In heart tissue, the expression levels of LPL and Angptl4 were not affected by modeling or treatment. Conclusions. DLT improved vascular damage by improving the increase in blood lipids induced by CIH, potentially by upregulating FIH-1 and downregulating HIF-1 and Angptl4 in adipose tissue. Therefore, DLT may be a promising agent for the prevention and treatment of CIH-induced vascular injury.
Formal Modelling of PBFT Consensus Algorithm in Event-B
Authors: Li Jie , Hu Kai , Zhu Jian , Bodeveix Jean-Paul , Ye Yafei
Published: October 2022

The practical Byzantine Fault Tolerance (PBFT) is a classical consensus algorithm that has been widely applied in an alliance blockchain system to make all nodes agree to certain transactions under the assumption that the proportion of Byzantine nodes is no more than 1/3. It is prevalent due to its performance, simplicity, and claimed correctness. However, any vulnerability of the consensus algorithm can lead to a significant loss in finance because no one can change the transaction results after execution. This paper proposes a formal development method of the PBFT algorithm by horizontal refinement in Event-B, which allows us to manage the complexity of the proof process by factoring the proof of correctness into several refinement steps. During the development of PBFT, we have specified the core mechanism like parameterized message types, primary node change, and water-mark interval. Furthermore, we present a mechanical verification of the safety and liveness properties of the model in Rodin, which can be partially and widely used to check the blockchain consensus algorithm vulnerability using a refinement tree of algorithms.
Deep Transfer Learning for COVID-19 Detection and Lesion Recognition Using Chest CT Images
Authors: Zhang Sai , Yuan Guo-Chang
Published: October 2022

Starting from December 2019, the global pandemic of coronavirus disease 2019 (COVID-19) is continuously expanding and has caused several millions of deaths worldwide. Fast and accurate diagnostic methods for COVID-19 detection play a vital role in containing the plague. Chest computed tomography (CT) is one of the most commonly used diagnosis methods. However, a complete CT-scan has hundreds of slices, and it is time-consuming for radiologists to check each slice to diagnose COVID-19. This study introduces a novel method for fast and automated COVID-19 diagnosis using the chest CT scans. The proposed models are based on the state-of-the-art deep convolutional neural network (CNN) architecture, and a 2D global max pooling (globalMaxPool2D) layer is used to improve the performance. We compare the proposed models to the existing state-of-the-art deep learning models such as CNN based models and vision transformer (ViT) models. Based off of metric such as area under curve (AUC), sensitivity, specificity, accuracy, and false discovery rate (FDR), experimental results show that the proposed models outperform the previous methods, and the best model achieves an area under curve of 0.9744 and accuracy 94.12% on our test datasets. It is also shown that the accuracy is improved by around 1% by using the 2D global max pooling layer. Moreover, a heatmap method to highlight the lesion area on COVID-19 chest CT images is introduced in the paper. This heatmap method is helpful for a radiologist to identify the abnormal pattern of COVID-19 on chest CT images. In addition, we also developed a freely accessible online simulation software for automated COVID-19 detection using CT images. The proposed deep learning models and software tool can be used by radiologist to diagnose COVID-19 more accurately and efficiently.
JGRCAN: A Visual Question Answering Co-Attention Network via Joint Grid-Region Features
Authors: Liang Jianpeng , Xu Tianjiao , Chen Shihong , Ao Zhuopan
Published: October 2022

In recent years, region features extracted from target detection networks have played an important role in visual question answering. The region features only extract the areas that are related to the target, but they lose a lot of nontarget context information and fine-grained details. On the contrary, the grid feature does not lose the details of nontargets but is not conducive to the recognition of the counting question of multiple small targets in the image. To solve this problem, this paper proposes a visual question answering network via joint grid-region features (JGRCAN), which consists of a feature extraction layer, co-attention layer, and fusion layer. The feature extraction layer includes extracting grid features and region features from the image and text features from the question and extracting multivisual feature representation and question feature representation through the co-attention layer to output attention weight and attention feature representation, respectively. The proposed approach effectively integrates grid features and region features, realizes the complementary advantages of region features and grid features, and is able to accurately focus on areas of the image that are relevant to the answer to the question. The results show that the overall classification accuracy of the algorithm on the test-dev and test-std subsets of VQA-v2 is 70.87% and 71.18%, respectively. Compared with baseline models, our proposed JGRCAN has good performance.
Nutritional, Physicochemical, and Organoleptic Properties of Camel Meat Burger Incorporating Unpollinated Barhi Date Fruit Pulp
Authors: Abd El-Hady El Sayed, A. , Alfheeaid Hani A. , Abd El-Razik Mohamed M. , Althwab Sami A. , Barakat Hassan
Published: October 2022

The chemical composition of unpollinated Barhi date fruit (UBDF) (at khalal maturity stage) pulp and its effect as fiber source or fat replacer on the quality characteristics of camel meat burgers were investigated. The UBDF was characterized by high total carbohydrate, fiber, and mineral contents. It contains substantial amounts of fiber (19.60%) and low-fat content. Prepared UBDF pulp was added to camel meat burger as a fiber source at 2.5, 5, 7.5, 10, and 15% instead of camel meat and as a fat replacer at 50, 75, and 100% instead of camel-back fat during manufacturing of camel meat burgers. Chemical composition, mineral content, instrumental color, cooking characteristics, and organoleptic properties were evaluated. Results indicated that adding UBDF as fiber source significantly reduced moisture and protein contents and increased total carbohydrates, including fiber content. In camel meat burgers, K and Ca significantly increased in UBDF level-dependent manure. Adding UBDF as a fat replacer significantly increased moisture and total carbohydrate contents, while a significant reduction in fat content has been remarked. Increasing the levels of UBDF pulp as either fiber source or fat replacer in camel burgers improved (

p
<
0.05

) shrinkage, cooking loss, and cooking yield in roasted camel burger level-dependent manure. The incorporation of UBDF pulp in camel burgers increased the lightness (

L

∗

) values and decreased the redness (

a

∗

) and yellowness (

b

∗

) significantly. The formulated camel burgers with different UBDF pulp levels revealed better organoleptic characteristics than normal camel meat burgers. Interestingly, adding UBDF as fiber source at 7.5-15% presented overall acceptability of more than 90% compared to the control sample of 81.7%. In the same context, adding UBDF as a fat replacer at 100% replacing the level of added fats scored overall acceptability of more than 93.60% compared to the control sample of 67.4%. Generally, according to the results of this study, it could be concluded that UBDF pulp could be used as a functional additive to produce high-fiber and/or low-fat camel meat burgers.
Effectiveness of Feature Extraction by PCA-Based Detection and Naive Bayes Classifier for Glaucoma Images
Authors: Shiny Christobel J. , Vimala D. , Joshan Athanesious J. , Christopher Ezhil Singh S. , Murugan Sivaraj
Published: October 2022

After cataract, glaucoma is one of the second leading retinal diseases in the world. This paper presents the methodology to detect the glaucoma using principal component analysis. The images are involved in dilation as a preprocessing, enhancement using the contrast limited adaptive histogram equalization method, and followed by the extraction of features using principal component analysis. The extracted features are classified using support vector machine, Naive Bayes, and K-nearest neighbor classifiers. Comparing with other classifiers, the Naive Bayes provides high accuracy of 95% which demonstrates the effectiveness of the feature extraction and the classifier.
Fault Diagnosis of Balancing Machine Based on ISSA-ELM
Authors: Li Lei , Liu Kai , Wang Lei , Sun Li , Zhang Zheng , Guo Hao
Published: October 2022

Balancing machine is a general equipment for dynamic balance verification of rotating parts, whether it breaks down or does not determine the accuracy of dynamic balance verification. In order to solve the problem of insufficient fault diagnosis accuracy of balancing machine, a fault diagnosis method of balancing machine based on the Improved Sparrow Search Algorithm (ISSA) optimized Extreme Learning Machine (ELM) was proposed. Firstly, iterative chaos mapping and Fuch chaos mapping were introduced to initialize the population and increase the population diversity. Secondly, the adaptive dynamic factor and Levy flight strategy were also introduced to update the individual positions and improve the model convergence speed. Finally, the fault feature vector was input to the ISSA-ELM model with the fault type as the output. The experiment showed that the fault diagnosis accuracy of ISSA-ELM is as high as 99.17%, which is 1.67%, 2.50%, 7.50%, and 17.50% higher than that of SSA-ELM, HHO-ELM, PSO-ELM, and ELM, respectively, further improving the prediction accuracy of the operation state of the balancing machine.
On a Mathematical Model of Tumor‐Immune Interaction with a Piecewise Differential and Integral Operator
Authors: Rezapour Shahram , Deressa Chernet Tuge , Mukharlyamov Robert G. , Etemad Sina
Published: October 2022

The representation of mathematical models via piecewise differential and integral operators for dynamic systems has this potential to capture cross‐over behaviors such as a passage from deterministic to randomness which can be exhibited by different systems. A 3D mathematical model, similar to the prey‐predator system, of tumor‐immune interaction with piecewise differential and integral operators is developed and analyzed. Three different scenarios, namely, cross‐overs from deterministic to randomness, the Mittag‐Leffler law to randomness, and a cross‐over behavior from fading memory to the power‐law and a random process, are considered. The existence, uniqueness, positivity, and boundedness of the solutions of the systems are proved via the linear growth and Lipschitz conditions. The numerical approximations by Toufik, Atangana, and Araz are used for approximation of solutions and simulation of the piecewise models in different scenarios. From the nondimensionalized version of the 3D model representation, it is shown that the parameter values have an impact on the growth of tumor cells, and activating the proliferation of the resting cells has negatively affected the development of tumor cells. Moreover, the dynamics of tumor‐immune interaction exhibited a cross‐over behavior, and this behavior is exposed by the piecewise modeling approach used for the representations.
The Hesitation of Anxious Traders in an Agent‐Based Model
Authors: Tang Bao-Jun , Lin Kun-Ben , Huang Jing-Bo , Lin Hung-Wen
Published: October 2022

Anxiety prevails in financial markets. In accordance with psychological research, anxious traders’ hesitant behavior differs from the frequently dissected herding and speculative behaviors. This paper examines the interactions between agent anxiety and price inertia in an artificial financial market. We incorporate an evolutionary mechanism to analyze the strategic benefit of the boundedly rational anxious agent. According to our simulation results, deviations in asset prices from their fundamentals increase with the behavioral hesitation of the anxious agent. The investment rigidity from the anxious agent’s lack of confidence mitigates the possibility of price reversal. Moreover, the average strategic benefit of the anxious agent is close to zero. To ensure the reliability of our finding, we further include the irrationality of the anxious agent in our evolutionary setting. Such an endeavor again demonstrates that the strategic benefit of the fundamentalist agent is inferior to that of the anxious agent. Since the anxious agent is characterized by an intolerance for uncertainty, we also investigate the artificial market under various degrees of risk aversion. We perceive that it is less possible for price reversal to emerge when considering higher levels of hesitation. The behavioral hesitation of the anxious agent enables the agent to cleverly evade the risk raised by the speculator.
PCA-Based Adaptive Training-Feedback Scheme in Time-Varying FDD Massive MIMO Systems
Authors: Huang Yi , Wang Haiquan , Gao Danbei , Zhao Zhijin
Published: October 2022

For massive multiple-input multiple-output (MIMO) systems, the real-time channel state information (CSI) acquisition is crucial but difficult in fast time-varying scenarios, especially for downlink (DL) channels in frequency division duplex (FDD) systems. This paper proposes an adaptive training-feedback scheme to estimate the CSI of DL channels. Specifically, first, base station (BS) determines a subspace containing uplink (UL) channel and an orthogonal normal basis of this subspace by using received signals in UL channel and the principle component analysis (PCA) technique. Second, by using the spatial reciprocity, it is found that the obtained subspace above also contains the DL channel vector. Thus, regarding the basis as pilots, the BS transmits pilots to mobile user (MU), and the user estimates received signals, feeds back them to the BS. Finally, according to information of the feedback, the BS can construct the DL channel vector. In this scheme, the pilots are adaptive to the change of the UL channel. Furthermore, the times of training is the dimension of the subspace, and feedback overhead is the coefficients of linear combination of DL channel vector under the basis only. Thus, cost of training and feedback can be greatly reduced. The simulation results show that the performance of the proposed scheme can approach the optimal scheme with very few training times and feedback overhead at high speed.