bluuebunny/crossref_metadata_2025 huggingface dataset:
Double-Shelled NiCo-LDH@MSiO3(M = Ni, Cu, Mn) Hollow Polyhedral Cages for Efficient Adsorption Toward Anionic Organic Pollutant
Authors: Li Shang-Bo , Liu Ling , Zhang Yu-Jie , Xia Zhi-Qiang , Wang Ning , Wang Quan-De
Published: April 2022

Hierarchical hollow nano-/micro-architectures with complex multishelled structures are highly desirable for achievement of various functionalities. However, it is still a great challenge to integrate multinary compositions into a nonspherical hollow structure with complex multishells. Herein, we developed an effective strategy to construct hierarchical double-shelled NiCo-layered double hydroxides@MSiO3(NiCo-LDH@MSiO3, M = Ni, Cu, Mn) hollow polyhedral nanocages with inner NiCo-LDH and outer MSiO3layers. Our design principle involved a one-pot synthesis of Co-based zeolitic imidazolate framework-67@SiO2(ZIF-67@SiO[Formula: see text] core@shell polyhedrons and a subsequent two-step in situ chemical conversion reaction process. Benefiting from the structural merits including high specific surface area, abundant mass diffusion pathways, and the unique hierarchical hollow structures, the double-shelled NiCo-LDH@MSiO3hollow polyhedral cages exhibited significant adsorption capacity of Congo red. Especially, the maximum adsorption capacity of the double-shelled NiCo-LDH@NiSiO3hollow polyhedral cages could reach up to 510.6[Formula: see text]mg/g, implying a high efficiency for the removal of Congo red from aqueous solution and a potential application in water treatment.
SARS-CoV2 and Air Pollution Interactions: Airborne Transmission and COVID-19
Authors: Chung Kian Fan , Abubakar-Waziri Hisham , Kalaiarasan Gopinath , Adcock Ian M , Dilliway Claire , Fang Fangxin , Pain Christopher , Kumar Prashant , Ransome Emma , Savolainen Vincent , Bhavsar Pankaj , Porter Alexandra
Published: April 2022

A link between outdoor pollution of particulate matter (PM) and the mortality from COVID-19 disease has been reported. The potential interaction of SARS-CoV2 emitted from an infected subject in the form of droplets or as an aerosol with PM[Formula: see text] (PM of 2.5 [Formula: see text]m or less in aerodynamic diameter) may modulate SARS-CoV2 replication and infectivity. This may represent an important airborne route of transmission, which could lead to pneumonia and a poor outcome from COVID-19. Further studies are needed to assess the potential infectivity and severity of such transmission.
The Impact of Route Descriptions on Human Expectations for Robot Navigation
Authors: Rosenthal Stephanie , Vichivanives Peerat , Carter Elizabeth
Published: April 2022

As robots are deployed to work in our environments, we must build appropriate expectations of their behavior so that we can trust them to perform their jobs autonomously as we attend to other tasks. Many types of explanations for robot behavior have been proposed, but they have not been fully analyzed for their impact on aligning expectations of robot paths for navigation. In this work, we evaluate several types of robot navigation explanations to understand their impact on the ability of humans to anticipate a robot’s paths. We performed an experiment in which we gave participants an explanation of a robot path and then measured (i) their ability to predict that path, (ii) their allocation of attention on the robot navigating the path versus their own dot-tracking task, and (iii) their subjective ratings of the robot’s predictability and trustworthiness. Our results show that explanations do significantly affect people’s ability to predict robot paths and that explanations that are concise and do not require readers to perform mental transformations are most effective at reducing attention to the robot.
TagFocus: Towards Fine-Grained Multi-Object Identification in RFID-based Systems with Visual Aids
Authors: Yin Junjie , Yang Zheng , Liao Sicong , Duan Chunhui , Ding Xuan , Zhang Li
Published: April 2022

Obtaining fine-grained spatial information is of practical importance in Radio Frequency Identification (RFID)-based systems for enabling multi-object identification. However, as high-precision positioning remains impractical in commercial-off-the-shelf (COTS)-RFID systems, researchers propose to combine computer vision (CV) with RFID and turn the positioning problem into a matching problem. Promising though it seems, current methods fuse CV and RFID through converting traces of tagged objects extracted from videos by CV into phase sequences for matching, which is a dimension-reduced procedure causing loss of spatial resolution. Consequently, they fail in harsh conditions like small tag intervals and low reading rates. To address the limitation, we propose TagFocus to achieve fine-grained multi-object identification with visual aids in RFID systems. The key observation is that traces generated through different methods shall be compatible if they are of one identical object. Accordingly, a Transformer-based sequence-to-sequence (seq2seq) model is trained to generate a simulated trace for each candidate tag-object pair. And the trace of the right pair shall best match the observed trace directly extracted by CV. A prototype of TagFocus is implemented and extensively assessed in lab environments. Experimental results show that our system maintains a matching accuracy of over 91% in harsh conditions, outperforming state-of-the-art schemes by 27%.
An Integrated Topic Modelling and Graph Neural Network for Improving Cross-lingual Text Classification
Authors: Vo Tham
Published: April 2022

In recent years, along with the dramatic developments of deep learning in the natural language processing (NLP) domain, notable multilingual pre-trained language techniques have been proposed. These recent multilingual text analysis and mining models have demonstrated state-of-the-art performance in several primitive NLP tasks, including cross-lingual text classification (CLC). However, these recent multilingual pre-trained language models still suffer limitations regarding their adaptation for specific task-driven fine-tuning in the context of low-resource languages. Moreover, they also encounter problems related to the capability of preserving the global semantic (e.g., topic, etc.) and long-range relationships between words to better fine-tune and effectively handle the cross-lingual text classification task. To meet these challenges, in this article, we propose a novel topic-driven multi-typed text graph attention–based representation learning method for dealing with the cross-lingual text classification problem called TG-CTC. In the proposed TG-CTC model, we utilize a novel fused topic-driven multi-typed text graph representation to jointly learn the rich-schematic structural and global semantic information of texts to effectively handle the CLC task. More specifically, we integrate the heterogeneous text graph attention network with the neural topic modelling approach to enrich the semantic information of learned textual representations in the context of multiple languages. Extensive experiments in benchmark multilingual datasets showed the effectiveness of the proposed TG-CTC model compared with the contemporary state-of-the-art baselines.
Does Parkinson’s disease affect peripheral circulation and vascular function in physically active patients?
Authors: Martignon Camilla , Pedrinolla Anna , Laginestra Fabio Giuseppe , Giuriato Gaia , Saggin Paolo , Tinazzi Michele , Schena Federico , Venturelli Massimo
Published: April 2022

Our study verified an intact peripheral circulation in patients with Parkinson's disease (PD). However, using the single passive leg movement, we observed a similar reduction of the vascular integrity in physically active patients and matched elderly, compared to young, likely induced by aging but independent on the pathology. This comparable effect confirmed that the disease, at early stage, with a dynamic lifestyle does not worsen the vascular system but reveals the cardinal symptoms of PD.
The Impact of Big Data Technology on the Scale Management of China’s Animation Film Industry
Authors: Wang Kai , Li Guojun
Published: April 2022

With the development of science and technology, social networking and digital media technology are also constantly developing. Handheld mobile smart terminals have been continuously promoted and popularized. The promotion and popularization of network and digital media technology bring convenience to people’s life and work. People can use their leisure time to shop online and watch related videos. The advent of the Internet era has promoted the development of China’s industry. The era of big data is coming. Big data analysis brings enormous value to all occupations. At the same time, it has brought new developments to Chinese animation films, realizing the marketing and development design of animated films. At the same time, big data has also brought opportunities for the development of the Chinese animation film industry. The application of big data brings a foundation for China’s animation film industry. From investment and financing, topic development and design, marketing promotion, and distribution and development of derivative products, big data brings huge value to the entire industry chain. However, big data is not omnipotent, and there are certain problems in the use of big data in Chinese animation films. At present, the application of big data in China’s animation film industry also has limitations and practical difficulties. Based on this, this paper focuses on discussing and analyzing the historical status of China’s animation industry. Moreover, it analyzes the future development prospects of the animation industry and other related contents. It is hoped that the brief analysis in this article provides some reference and inspiration for relevant practitioners.
Reduced Incidence of Necrotizing Enterocolitis due to the Anti-Inflammatory Effects of CXCL14 in Intestinal Tissue
Authors: Sun Xu , Xu Lingqi , Ma Shurong , Chen Lulu , Tang Ruze , Li Dashuang , Hu Fangjie , Wang Ting , Gong Yuan , Zhou Huiting , Wang Jian
Published: April 2022

Bioinformatic analysis indicated that downregulated CXCL14 will occur in the intestinal tissue of patients with necrotizing enterocolitis (NEC). To reveal the relationship between CXCL14 and mucosal immune regulation, we designed and implemented the experiments to explore the potential function of CXCL14 in the pathogenesis of NEC. Firstly, this study confirmed that the expression of CXCL14 decreased in the intestinal tract of NEC children. Secondly, the experiments results showed that CXCL14 could ameliorate the inflammatory injury of intestinal tissue through the suppressive effect on the expression of TNF-α and INF-γ in vivo. Finally, we explained that activation of the TLR4 can reduce the expression level of CXCL14 in the intestinal tissue of mouse pups. Collectively, our study suggested that CXCL14 may negatively regulate the inflammatory response in intestinal tissue and play an essential role in NEC development and progression.
Research Progress on the Mechanism of Persistent Low-Level HBsAg Expression in the Serum of Patients with Chronic HBV Infection
Authors: Wu Jie , Yu Yu , Dai Yuzhu , Zhang Yingjie , Cheng Jun
Published: April 2022

Among HBV-infected persons, there is a group of people with hepatitis B surface antigen (HBsAg) showing persistently low levels of expression. The production of low-level HBsAg does not mean a good outcome of chronic HBV infection. Patients still have virus replication and sustained liver damage, and they have the potential to transmit the infection. This risk poses a challenge to clinical diagnosis and blood transfusion safety and is a major concern of experts. However, the mechanism behind persistent low-level HBsAg expression in serum is not completely clear, and complete virus clearance by the host is vital. In this review, we summarize the research progress on the mechanism behind low-level expression of HBsAg in patients with chronic HBV infection in recent years.
Optimization-Based Ensemble Feature Selection Algorithm and Deep Learning Classifier for Parkinson’s Disease
Authors: Sabeena B. , Sivakumari S. , Teressa Dawit Mamru
Published: April 2022

PD (Parkinson’s Disease) is a severe malady that is painful and incurable, affecting older human beings. Identifying PD early in a precise manner is critical for the lengthened survival of patients, where DMTs (data mining techniques) and MLTs (machine learning techniques) can be advantageous. Studies have examined DMTs for their accuracy using Parkinson’s dataset and analyzing feature relevance. Recent studies have used FMBOAs for feature selections and relevance analyses, where the selection of features aims to find the optimal subset of features for classification tasks and combine the learning of FMBOAs. EFSs (ensemble feature selections) are viable solutions for combining the benefits of multiple algorithms while balancing their drawbacks. This work uses OBEFSs (optimization-based ensemble feature selections) to select appropriate features based on agreements. Ensembles have the ability to combine results from multiple feature selection approaches, including FMBOAs, LFCSAs (Lévy flight cuckoo search algorithms), and AFAs (adaptive firefly algorithms). These approaches select optimized feature subsets, resulting in three feature subsets, which are subsequently matched for correlations by ensembles. The optimum features are generated by OBEFSs the trained on FCBi-LSTMs (fuzzy convolution bi-directional long short-term memories) for classifications. This work’s suggested model uses the UCI (University of California-Irvine) learning repository, and the methods are evaluated using LOPO-CVs (Leave-One-Person-Out-Cross Validations) in terms of accuracies, F-measure values, and MCCs (Matthews correlation coefficients).
Effects of Butylphthalide Sodium Chloride Injection Combined with Edaravone Dexborneol on Neurological Function and Serum Inflammatory Factor Levels in Sufferers Having Acute Ischemic Stroke
Authors: Li Keliang , Zhang Qiting , Lu Xuesheng , Yao Shengqi
Published: April 2022

For investigating an influence on butylphthalide sodium chloride injection combined with edaravone dexborneol on neurological function and serum inflammatory factor levels in sufferers having acute ischemic stroke, 120 sufferers having acute ischemic stroke from September 2020 to September 2021 are chosen for the study subjects. In line with the diverse therapies, they took part in a control group and the study group, with 60 examples in each group. The control group is treated with edaravone dexborneol, and the study group is treated with butylphthalide sodium chloride injection, based on the control group. The posttreatment curative efficacy on the two groups is recorded, and treatment of both the two groups is compared. Before and after neurological function indexes (NIHSS and mRS), inflammatory factor indexes (IL-6, CRP, and TNF-α), life quality index (Barthel index), hemorheological indexes (plasma-specific viscosity), and neurological levels of NSE are logged and contrasted between the two groups of adverse reactions during therapy. Postcure, the overall response rate and Barthel index of the study group obviously overtop those of the control group (

p

 < 0.05). IL-6, CRP, TNF-α, NSE, plasma specific viscosity, and NIHSS and mRS scores obviously hypodown those of the control group (

p

 < 0.05), and untoward effects on the two groups during curing are lower, and the discrepancy is not obvious(

p

 > 0.05). Butylphthalide sodium chloride injection combined with edaravone dexborneol can enhance curative efficacy on sufferers having acute ischemic stroke, improve neurological function, blood rheology, and quality of life, and decrease the secretion of cytokine, having a better effect and high medication safety.
Use of Intravenous Paracetamol Preoperatively Favors Lower Risk of Delirium and Functional Recovery in Elderly Patients with Hip Fracture: A Propensity Score-Matched Analysis
Authors: Guo Junfei , Wang Tao , Zheng Xuehong , Long Yubin , Wang Xin , Zhang Qi , Liu Junchuan , Zhang Guolei , Zha Junpu , Hou Zhiyong , Zhang Yingze
Published: April 2022

We aimed to investigate whether the use of intravenous paracetamol (IVP) preoperatively in intertrochanteric fracture (IF) patients aged 65 years or over receiving intramedullary fixation had significantly benefits on the pain score at discharge, delirium incidence, length of hospital stay (LOS), functional outcomes, and mortality. A retrospective analysis of all surgically treated patients presenting with IF was conducted at a single Level I trauma center in China between Jan. 2016 and Jan. 2020. The data concerning patients’ demographics, injury-related data, surgery-related data, operation-related data, in-hospital data, and postoperative outcomes were extracted. To minimize potential confounding and selection bias, the propensity score matching (PSM) method was performed via the caliper matching method by using a 1 : 1 ratio. After PSM, McNemar’s chi-square tests were used to examine the association of using IVP with outcome analyses. The Spearman correlations of IVP using, pain scores, and the factors which may influence them were also computed. After screening 2963 consecutive patients, 2166 were included finally, including 1576 in the non-IVP group and 590 in the IVP group. After PSM, 531 remained in each group. The pain scores at discharge were significantly between the two groups before and after matching (all

p
<
0.001

). The differences of delirium rate and functional outcomes became significant after propensity score-based matching (

p
=
0.001

and 0.033, respectively), although they were not significant before matching. No significant difference was observed in other operation-related data, LOS, and crude mortality rates at 30-day, 90-day, and 12-month before and after PSM. In conclusion, this study highlights the need for preoperative IVP use to optimize pain control, postoperative functional recovery, and minimize pain-related comorbidities such as delirium in elderly patients with hip fracture.
A New Fast Algorithm for Library Circulation Data Mining Based on FUP
Authors: Han Cunge , Yu Wensen , Li Xiaofei , Lin Hai , Zhao Huanyun
Published: April 2022

As the first incremental association mining algorithm, FUP can well solve the problem, but the algorithm also has the deficiencies to produce a large set of candidates and multiple iterating the database, leading to the algorithm’s low execution efficiency when dealing with some large transactions with fast updates, such as book circulation data. This study proposes an improved FUP algorithm that takes transaction identifier (TID) in the database to scan the database only once, making the computation significantly less than the FUP algorithm. Through detecting the circulation data of a university library, the experimental results show that compared with the standard FUP algorithm and SFUA algorithm, with the increase of borrowing and record transactions, the improved FUP algorithm has significantly improved the operation efficiency, which can help the library to do a good job in book recommendation scientifically.
Lipoprotein Levels in Early Adulthood and NAFLD in Midlife: The Coronary Artery Risk Development in Young Adults (CARDIA) Study
Authors: Khanna Sahil , Wilkins John T. , Ning Hongyan , Allen Norrina B. , Lewis Cora E. , Carr J. Jeffrey , Lloyd-Jones Donald , VanWagner Lisa B.
Published: April 2022

Objective. We evaluated the association of apolipoprotein B (apoB) with low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), and triglycerides (TG) in early adulthood with concordant/discordant associations and midlife NAFLD. Methods. Participants from the CARDIA study were included (n = 2,655; baseline mean age: 25.0, 59.1% female, and 48.6% black). NAFLD was defined as liver attenuation ≤40 Hounsfield units after excluding other causes of liver fat. Logistic regression models assessed the odds of Y25 NAFLD among tertiles of apoB, LDL-C, non-HDL-C, and TG and quartiles of the apoB/TG ratio. Discordance/concordance analyses examined the association of apoB with each lipid marker and Y25 NAFLD. Results. The Y25 NAFLD prevalence was 10%. The high-tertile TG group (OR 1.87, 95% CI, and 1.30–2.69) and the low- (OR 1.98, 95% CI, and 1.30–3.01) and middle-apoB/TG ratio groups (OR 1.78, 95% CI, and 1.17–2.72) had the greatest odds of midlife NAFLD. Using discordance/concordance analysis, the high-apoB/high-TG group had the highest odds of NAFLD (OR 1.69, 95% CI, and 1.09–2.61) followed by the low-apoB/high-TG group. The high apoB/low TG group had the lowest odds of NAFLD. Conclusions. Among the studied lipid markers in early adulthood, TG levels have the strongest and most consistent association with midlife NAFLD.
Mathematical Analysis of Two Waves of COVID-19 Disease with Impact of Vaccination as Optimal Control
Authors: Santra P. K. , Ghosh D. , Mahapatra G. S. , Bonyah Ebenezer
Published: April 2022

This paper is devoted to answering some questions using a mathematical model by analyzing India’s first and second phases of the COVID-19 pandemic. A new mathematical model is introduced with a nonmonotonic incidence rate to incorporate the psychological effect of COVID-19 in society. The paper also discusses the local stability and global stability of an endemic equilibrium and a disease-free equilibrium. The basic reproduction number is evaluated using the proposed COVID-19 model for disease spread in India based on the actual data sets. The study of nonperiodic solutions at a positive equilibrium point is also analyzed. The model is rigorously studied using MATLAB to alert the decision-making bodies to hinder the emergence of any other pandemic outbreaks or the arrival of subsequent pandemic waves. This paper shows the excellent prediction of the first wave and very commanding for the second wave. The exciting results of the paper are as follows: (i) psychological effect on the human population has an impact on propagation; (ii) lockdown is a suitable technique mathematically to control the COVID spread; (iii) different variants produce different waves; (iv) the peak value always crosses its past value.
An Empirical Study on the Construction Path of Sports Public Service Platform under the Background of “Internet +” by Fuzzy Computing
Authors: Wang Jieqing , Shi Deming , Tsai Sang-Bing
Published: April 2022

The Internet is the product of the rapid popularization of high technology. With the higher and higher configuration of computers, the functions are more and more complete, and the scope of coverage is more and more wide. With the continuous development and improvement of the Internet, computer network technology has been widely used in sports. Based on the background of “Internet +”, this paper builds the framework of Internet sports public service platform and uses websites, mobile apps, WeChat, and other platforms to build sports public service platform. Communication system is the most critical path of Internet construction. Based on the application path of sports public service communication system, the basic content of sports public service system is analysed. Through the research on the practicability of constructing the path, the results show that the Internet channels and media terminals passively receive the content released by the information subject so that the information can achieve the node transmission with high sharing and provide theoretical reference and practical guidance for the informatization development of sports public service.
Research on the Application of Bayesian-Optimized XGBoost in Minor Faults in Coalfields
Authors: Wang Xin , Ding Changwei , Chen Tongjun , Yu Ting
Published: April 2022

This paper proposed a Bayesian optimized extreme gradient boosting (XGBoost) model to recognize small-scale faults across coalbeds using reduced seismic attributes. Firstly, the seismic attributes of the mining area were preprocessed to remove abnormal samples and high-noise samples. Secondly, chi-square bins were performed for each feature of the processed attributes. The weight of evidence (WOE) was calculated in each container, and each element's information value (IV) was obtained to characterize the importance of each feature. Features with low information values are reduced to remove high-noise attributes. Thirdly, the reduced attributes are decomposed by variational modal decomposition (VMD) to obtain new features. Finally, the optimized XGBoost model and traditional methods were constructed to identify and locate faults across coalbeds. Here, the Bayesian-optimized XGBoost objective function was used to balance the training weights of asymmetric examples. As the Bayesian optimization algorithm quickly falls into local optimums, it does not easily balance the “exploit” and “explore” approaches. Therefore, this paper proposed an adaptive balance factor change algorithm to overcome this shortcoming. Comparing the identification outcomes, the optimized XGBoost model has a higher prediction accuracy than the BP neural network, support vector machines (SVMs), K-means clustering, extreme learning machines (ELMs), and random forests (RFs). In summary, the proposed method can improve the identification accuracy of small-scale faults in coal mining areas.
Gut Microbiota Variations between Henoch-Schonlein Purpura and Henoch-Schonlein Purpura Nephritis
Authors: Zhou Fang , Shao Qimin , Jia Lihong , Cai Chunyan
Published: April 2022

Background. In China, little is known regarding the differences between children with Henoch-Schonlein purpura (HSP) and Henoch-Schonlein purpura nephritis (HSPN) concerning their gut microbiota. Methods. We recruited 25 children with HSP, 25 children with HSPN, and 25 healthy children to investigate the differences. Fecal samples were collected and analyzed by sequencing the V3-V4 region of the 16S rRNA gene. The diversity of the fecal gut microbiota was compared between the patient groups. Results. Rarefaction curves showed that the gut microbial diversity between the three groups differed significantly (

P
=
0.0224

). The top five most abundant gut microbial genera were Bacteroides, Faecalibacterium, Prevotella, Ruminococcaceae, and Megamonas in children with HSP; Bacteroides, Faecalibacterium, Prevotella, Bifidobacterium, and Ruminococcaceae in children with HSPN; and Bacteroides, Prevotella, Faecalibacterium, Ruminococcaceae, and Bifidobacterium in healthy children. Children with HSP had the lowest Bifidobacterium abundance among the three groups (

P
<
0.05

). Children with HSPN had a lower abundance of Akkermansia than children with HSP (

P
<
0.05

), whereas children with HSPN had a higher Alistipes abundance than children with HSP (

P
<
0.05

). Fecal microbial community composition did not differ significantly between groups (ANOSIM,

R
=
−
0.002

,

P
=
0.46

). Despite the small sample size, our results indicate that children with HSP or HSPN displayed dysbiosis of the gut microbiota. Conclusion. This study provides valuable insights that will benefit the development of future microbe-based therapies to improve clinical outcomes or prevent the incidence of HSP or HSPN in children.
A Revolutionary Blueprint for Mitigation of Hypertension via Nanoemulsion
Authors: Kumar Girish , Virmani Tarun , Pathak Kamla , Alhalmi Abdulsalam
Published: April 2022

Hypertension is one of the most important causes of mortality, affecting the health status of the patient. At the same time, hypertension causes a huge health and economic burden on the whole world. The incidence and prevalence of hypertension are rising even among young people in both urban as well as rural communities. Although various conventional therapeutic moieties are available for the management of hypertension, they have serious flaws such as hepatic metabolism, reduced dose frequency, poor aqueous solubility, reduced bioavailability, and increased adverse effects, making the drug therapy ineffective. Therefore, it is required to design a novel drug delivery system having the capability to solve the constraints associated with conventional treatment of hypertension. Nanotechnology is a new way of using and manipulating the matter at the molecular level, whose functional organization is measured in nanometers. The applications of nanotechnology in the field of medicine provide an alternative and novel direction for the treatment of cardiovascular diseases and show excellent performance in the field of targeted drug therapy. Various nanotechnologies based drug delivery systems, such as solid lipid nanoparticles, nanosuspension, nanoemulsion, liposome, self‐emulsifying systems, and polymeric nanoparticles, are available. Among them, nanoemulsion has provided a niche to supplement currently available therapeutic choices due to numerous benefits like stability, ease of preparation, enhanced drug absorption, reduced hepatic metabolism, increased dose frequency, enhanced bioavailability, and encapsulation of hydrophilic as well as hydrophobic drugs. This present review provides an in‐depth idea about progression in treatment of hypertension, constraints for antihypertensive drug therapy, need of nanoemulsions to overcome these constraints, comparative analysis of nanoemulsions over other nanostructure drug delivery systems, pharmacodynamics studies of nanoemulsions for treatment of hypertension, recent patents for drug‐loaded nanoemulsions meant for hypertension, and marketed formulations of nanoemulsions for hypertension.
Research on Optimization of Intelligent Logistics Agile Distribution Model in Supply Chain Networks
Authors: Ai Yun Ping , Liu Dong Yan , Liu Jia-Bao , Liu Yun
Published: April 2022

In the supply chain environment, agile distribution refined the division of operations, responded quickly to the customer needs of the mobile terminal, and promoted the construction of an intelligent logistics distribution system. In order to integrate the distribution resources of the distribution system of the logistics industry and maximize the overall benefits of distribution, this paper designs an intelligent logistics agile distribution model and optimizes the distribution strategy. Firstly, this paper puts forward a rationalization evaluation system based on the established logistics agile distribution model. Make full use of the fuzzy subsets of each level to quantify the fuzzy indexes to evaluate, and then comprehensively clarify the index attributes through the fuzzy transformation criteria to obtain the evaluation results. Secondly, aiming at the networked agile distribution organization form, the scientific location mathematical model of agile distribution center and the path mathematical model of the agile distribution are established. Finally, the uncertain influence of many objective conditions on distribution agility has been analyzed, and the dynamic optimization model of an agile distribution path with the concept of time window under random demand is constructed. Through the experimental analysis, the rationalization evaluation strategy and index of verification test results show that the model has significant optimization advantages, which not only improve the rationality and economic benefits of distribution, but also meet the time requirements of all users.
Astaxanthin as a Potential Antioxidant to Improve Health and Production Performance of Broiler Chicken
Authors: Pertiwi Herinda , Nur Mahendra Mohamad Yusril , Kamaludeen Juriah
Published: April 2022

Recent interest in carotenoids has increased due to their antioxidant and production performance. Astaxanthin (AST) is a xanthophyll carotenoid abundantly distributed in microalgae, which is described as a highly potent antioxidant. Therefore, recent studies have tended to investigate the role of antioxidants in improving metabolic processes and physiological functioning of the body. It is now evident that AST could significantly reduce free radicals and oxidative stress and help to maintain a healthy state. Moreover, AST also could improve the performance of broiler chicken by increasing the daily feed intake, followed by improvement in the food conversion rate.
Characteristics and Health Risk Assessment of Cd in Soil around a Municipal Solid Waste Incineration Plant in Chengdu
Authors: Wei Chunmei , Gong Zhilian , Luo Jie
Published: April 2022

Taking the Chengdu Municipal Solid Waste Incineration (MSWI) Plant in China as object, totally 100 ambient soil samples from 20 sampling points were taken for experiment. This paper analysed the characteristics of Cd in soil. By analyses, the distribution and health risk assessment were taken into consideration. The results demonstrated that the Cd value in soil in the surroundings of the MSWI plant was beyond the acceptable level according to standards (GB15618-2018). The risk assessment in this experiment showed that the carcinogenic and noncarcinogenic risks via inhalation exposure were in the accessible levels.
Enriched Environment Effects on Myelination of the Central Nervous System: Role of Glial Cells
Authors: Gao Zhen-Kun , Shen Xin-Ya , Han Yu , Guo Yi-Sha , Yuan Mei , Bi Xia
Published: April 2022

Myelination is regulated by various glial cells in the central nervous system (CNS), including oligodendrocytes (OLs), microglia, and astrocytes. Myelination of the CNS requires the generation of functionally mature OLs from OPCs. OLs are the myelin-forming cells in the CNS. Microglia play both beneficial and detrimental roles during myelin damage and repair. Astrocyte is responsible for myelin formation and regeneration by direct interaction with oligodendrocyte lineage cells. These glial cells are influenced by experience-dependent activities such as environmental enrichment (EE). To date, there are few studies that have investigated the association between EE and glial cells. EE with a complex combination of sensorimotor, cognitive, and social stimulation has a significant effect on cognitive impairment and brain plasticity. Hence, one mechanism through EE improving cognitive function may rely on the mutual effect of EE and glial cells. The purpose of this paper is to review recent research into the efficacy of EE for myelination and glial cells at cellular and molecular levels and offers critical insights for future research directions of EE and the treatment of EE in cognitive impairment disease.
Positioning Accuracy and Numerical Analysis of the Main Casting Mechanism of the Hybrid Casting Robot
Authors: Li Long , Chen Binyang , Wang Chengjun
Published: April 2022

The positioning accuracy is a key index to measure the performance of the robot. This paper studies the positioning accuracy of the main pouring mechanism of the hybrid truss pouring robot and analyzes that the main error sources affecting the positioning accuracy are machining error, assembly error, and thermal deformation error. Error transfer matrix is constructed to describe the influence of machining errors and assembly errors on the position and pose of the terminal, and the error parameters have physical significance. The probability distribution of sensitive errors is discussed. A joint regression prediction model based on sensitive error sets is established to determine the thermal deformation error on the basis of fully considering the contribution rate of component error. The results show that the position error has a wide range of influences on the end pose, but the angle error is more sensitive, and the probability distribution of the sensitive error is concentrated. The reliable data can be obtained without reorganizing the measurement in the calibration process. The joint regression model considering the contribution rate of component error can effectively eliminate the collinearity problem in the prediction of thermal deformation from a single heat source. Compared with the single regression model, it has better prediction accuracy and effect.
Customer Relationship Management Based on SPRINT Classification Algorithm under Data Mining Technology
Authors: Sun Yazhou , Tan Xueqing
Published: April 2022

Under the advance of computational intelligence, customer relationship management system based on data mining technology can not only bring more economic benefits to an enterprise but also improve the management and decision-making level of Chinese enterprises. In this paper, the application of data mining technology in customer relationship management (CRM) is analyzed, and four data mining modes are realized: customer classification, cross-marketing, customer acquisition, and customer retention. In the data mining module, SPRINT classification algorithm is used in customer classification. At the same time, FP-growth, an association rule algorithm without candidate set, is applied in cross-marketing, which enhances the practicability of the system. The algorithm of optimal customer retention strategy under digital intelligence technology is adopted in customer retention, which makes up for the shortcomings of traditional CRM system and helps enterprises to better operate and adjust marketing strategies.
Analysis of the Molecular Mechanism of Evodia rutaecarpa Fruit in the Treatment of Nasopharyngeal Carcinoma Using Network Pharmacology and Molecular Docking
Authors: Xu Runshi , Yang Ximing , Tao Yangyang , Luo Wang , Xiong Yu , He Lan , Zhou Fangliang , He Yingchun
Published: April 2022

Background. Nasopharyngeal carcinoma (NPC), a neoplasm of the head and neck, has high incidence and mortality rates in East and Southeast Asia. Evodia rutaecarpa is a tree native to Korea and China, and its fruit (hereafter referred to as Evodia) exhibits remarkable antitumour properties. However, little is known about its mechanism of action in NPC. In this study, we employed network pharmacology to identify targets of active Evodia compounds in nasopharyngeal carcinoma and generate an interaction network. Methods. The active ingredients of Evodia and targets in NPC were obtained from multiple databases, and an interaction network was constructed via the Cytoscape and STRING databases. The key biological processes and signalling pathways were predicted using Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses. Molecular docking technology was used to identify the affinity and activity of target genes, and The Cancer Genome Atlas and Human Protein Atlas databases were used to analyse differential expression. Cell Counting Kit-8 (CCK-8) and Annexin V-fluorescein isothiocyanate (FITC)/propidium iodide (PI) dual-fluorescence staining were used for experimental verification. Results. Active Evodia compounds included quercetin, isorhamnetin, and evodiamine, and important NPC targets included MAPK14, AKT1, RELA, MAPK1, JUN, and p53, which were enriched in lipid and atherosclerosis signalling pathways. Additionally, we verified the high affinity and activity of the active compounds through molecular docking, and the target proteins were verified using immunohistochemistry and differential expression analyses. Furthermore, CCK-8 assays and Annexin V-FITC/PI dual-fluorescence staining showed that isorhamnetin inhibited the proliferation of NPC cells and induced apoptosis. Conclusion. Our results identified the molecular mechanisms of Evodia and demonstrated its ability to alter the proliferation and apoptosis of NPC cells through multiple targets and pathways, thereby providing evidence for the clinical application of Evodia.
A Deep Convolutional Neural Network Based Risk Identification Method for E-Commerce Supply Chain Finance
Authors: Tang Qian , Lu Yan , Wang Bin , Li Zhen
Published: April 2022

With the popularity of the Internet, the rise of e-commerce platforms has led to the rapid development of supply chain (SC) financial services in China, and the competitiveness of commercial banks and core enterprises in the supply chain is now gradually increasing, rapidly expanding into an important area of competition between the two. As an emerging force rebounding from the economic downturn, e-commerce platform transactions, with their unique characteristics of informatization, diversification, and convenience, have provided a broad space for Internet SC finance. The article mainly analyzes the risk identification method of e-commerce SC finance, analyzes its risk from the financing process, gives corresponding data support for the matters or processes that may cause financing risk based on DCNN model, and takes Jingdong SC finance as an example and analyzes its main financing methods and risk identification process; based on different experimental comparisons, a multigroup experimental study shows that the accuracy of supply chain finance risk identification using deep convolutional neural network models can reach 95.36%, which demonstrates the effectiveness of the proposed method by providing better performance compared to traditional BP and SVM networks.
Antioxidant, Enzyme Inhibitory, and Molecular Docking Approaches to the Antidiabetic Potentials of Bioactive Compounds from Persicaria hydropiper L.
Authors: Ayaz Muhammad , Sadiq Abdul , Mosa Osama F. , Zafar Tariq Abdalla , Eisa Hamdoon Alashary Adam , Elkhalifa Modawy Elnour Modawy , Elawad Mohammed Ahmed , Ahmed Alshebli , Ullah Farhat , Ghufran Mehreen , Kabra Atul
Published: April 2022

Introduction. Natural products are among the most useful sources for the discovery of new drugs against various diseases. Keeping in view the ethnobotanical relevance ethnopharmacological significance of Polygonaceae family in diabetes, the current study was designed to isolate pure compounds from Persicaria hydropiper L. leaves and evaluate their in vitro and in silico antidiabetic potentials. Methods. Six compounds were isolated from the chloroform-ethyl acetate fractions using gravity column chromatography and were subjected to structure elucidation process. Structures were confirmed using 1H-NMR, 13C-NMR, and mass spectrometry techniques. Isolated phytochemicals were subjected to in vitro antidiabetic studies, including α-glucosidase, α-amylase inhibition, and DPPH, and ABTS antioxidant studies. Furthermore, the in silico binding mode of these compounds in the target enzymes was elucidated via MOE-Dock software. Results. The isolated compounds revealed concentration-dependent inhibitions against α-glucosidase enzyme. Ph-1 and Ph-2 were most potent with 81.84 and 78.79% enzyme inhibitions at 1000 µg·mL−1, respectively. Ph-1 and Ph-2 exhibited IC50s of 85 and 170 µg·mL−1 correspondingly. Likewise, test compounds showed considerable α-amylase inhibitions with Ph-1 and Ph-2 being the most potent. Tested compounds exhibited considerable antioxidant potentials in both DPPH and ABTS assays. Molecular simulation studies also revealed top-ranked confirmations for the majority of the compounds in the target enzymes. Highest observed potent compound was Ph-1 with docking score of −12.4286 and formed eight hydrogen bonds and three H-pi linkages with the Asp 68, Phe 157, Phe 177, Asn 241, Glu 276, His 279, Phe 300, Glu 304, Ser 308, Pro 309, Phe 310, Asp 349, and Arg 439 residues of α-glucosidase binding packets. Asp 68, Glu 276, Asp 349, and Arg 439 formed polar bonds with the 3-ethyl-2-methylpentane moiety of the ligand. Conclusions. The isolated compounds exhibited considerable antioxidant and inhibitory potentials against vital enzymes implicated in T2DM. The docking scores of the compounds revealed that they exhibit affinity for binding with target ligands. The enzyme inhibition and antioxidant potential of the compounds might contribute to the hypoglycemic effects of the plant and need further studies.
[Retracted] Analysis of Gait Characteristics of Patients with Knee Arthritis Based on Human Posture Estimation
Authors: Lv Xinyu , Ta Na , Chen Tao , Zhao Jing , Wei Haicheng
Published: April 2022

A gait feature analysis method based on AlphaPose human pose estimation fused with sample entropy is proposed to address complicated, high‐cost, and time‐consuming postoperative rehabilitation of patients with joint diseases. First, TensorRT was used to optimize the inference of AlphaPose, which consists of the target detection algorithm YOLOv3 and the pose estimation algorithm. It can speed up latency and throughput by about 2.5 times while maintaining the algorithm’s accuracy. Second, the optimized human posture estimation algorithm AlphaPose_trt was used to process gait videos of healthy people and patients with knee arthritis. The joint point motion trajectories of the two groups were extracted, and the sample entropy algorithm quantified the joint trajectory signals for feature analysis. The experimental results showed significant differences in the entropy of the heel and ankle joint motion signals between healthy people and arthritic patients (p < 0.01), which can be used to identify patients with knee arthritis. This technique can assist doctors in determining needed postoperative joint surgery rehabilitation.
Prediction of Pile Bearing Capacity Using Opposition‐Based Differential Flower Pollination‐Optimized Least Squares Support Vector Regression (ODFP‐LSSVR)
Authors: Hoang Nhat-Duc , Tran Xuan-Linh , Huynh Thanh-Canh
Published: April 2022

Pile foundations are widely used for high‐rise structures constructed in soft ground. The bearing capacity of pile is a crucial parameter required during the design and construction phase of pile foundation engineering projects. In practice, accurate predictions of pile bearing capacity are challenging due to a complex interplay of various geotechnical engineering factors including pile characteristics and ground conditions. This study proposes a data‐driven model for coping with the problem of interest that hybridizes machine learning and metaheuristic approaches. Least squares support vector regression (LSSVR) is used for analyzing a dataset containing historical records of pile tests. Based on such datasets, LSSVR is capable of generalizing a multivariate function that estimates values of pile bearing capacity based on a set of variables describing pile characteristics and ground conditions. Moreover, opposition‐based differential flower pollination (ODFP) metaheuristic is proposed to optimize the LSSVR learning process. Experimental results supported by the statistical test showed that the proposed ODFP‐optimized LSSVR can achieve a good predictive performance in terms of root mean square error, mean absolute percentage error mean absolute error, and coefficient of determination. These results confirm that the ODFP‐optimized LSSVR can be a potential alternative to assist civil engineers in the task of pile bearing capacity estimation.