bluuebunny/crossref_metadata_2025 huggingface dataset:
Safety and Efficacy of Medicinal Plants Used to Manufacture Herbal Products with Regulatory Approval in Uganda: A Cross-Sectional Study
Authors: Kaggwa Bruhan , Kyeyune Henry , Munanura Edson Ireeta , Anywar Godwin , Lutoti Stephen , Aber Jacqueline , Bagoloire Lynn K. , Weisheit Anke , Tolo Casim Umba , Kamba Pakoyo Fadhiru , Ogwang Patrick Engeu
Published: April 2022

Introduction. The Uganda National Drug Authority requires phytochemical screening, freedom from microbial contamination, and evidence of safety and efficacy of the constituent plants to register herbal products. Since Uganda has no pharmacopeia, safety, efficacy, and plant processing information are not readily available. We documented the plant materials used to manufacture products in Uganda and established evidence of their safety and efficacy and availability of monographs. Methods. The NDA register of herbal products was reviewed, and a product list was extracted. The herbal products were purchased from local pharmacies, and their labels were studied to identify plant ingredients and drug use. Literature was reviewed to document evidence of the safety and efficacy of the plant materials concerning manufacturer’s claims. Also, the WHO and available African Pharmacopeia were searched to establish the availability of the plant monographs. Results. Of the 84 NDA-registered local products, only 18 were obtained from the market; 82% were indicated for respiratory tract disorders. Thirty-three plant materials were listed with Eucalyptus globulus Labill, being the commonest. Several in vitro and in vivo studies demonstrate efficacy, thus supporting the use of the selected plant species for empirical treatment as stated on the product label. While most plants were safe, some species such as Albizia coriaria Oliv. had dose-dependent toxicities that cannot be predicted in combinations. The WHO, African Pharmacopoeia, and West African Herbal Pharmacopoeia had only 16 plant monographs of the 33 plants of interest. Nevertheless, Aloe vera (L.) Burm.f., Azadirachta indica A.Juss., Zingiber officinale Roscoe, and Allium sativum L. monographs were published by all three pharmacopoeias. Conclusions. Preclinical evidence of safety and efficacy exists in the literature for most of the plants used to manufacture registered herbal products in Uganda. More specific bioassays and clinical trials are required for the products to provide conclusive evidence of safety and toxicity. Monographs are urgently needed for the Ugandan plants.
Optimization of Parameters Using Taguchi Orthogonal Array Design for an Intensified Per-Pass Conversion of Alphabutol® Technology in Butene-1 Production
Authors: Alenezi Hamoud , Haidar Zainab , Das Mirdul
Published: April 2022

Taguchi orthogonal design was used in this study to investigate the effects of three operational parameters (i.e., reactor temperature, reactor pressure, and catalyst ratio on per-pass conversion (PPC)). The optimal PPC was calculated from the predicted response function at 48°C of reactor temperature, 21 kg/cm2g of reactor pressure, and catalyst mole ratio of 2.50. Under these conditions, the PPC was estimated to be 87.1 per cent, with a maximum SNR of 38.95. In line with the delta ranking, the decreasing order of significance of each process parameter on the average per-pass conversion (PPC) was x1 > x2 > x3, with percentage contributions of 50.6%, 26.1%, and 23.3% for reactor temperature, temperature, and catalyst mole ratio, respectively. The optimal production condition can therefore be attained at a larger scale with the higher per-pass conversion of butene-1.
Combination Blockade of the IL6R/STAT-3 Axis with TIGIT and Its Impact on the Functional Activity of NK Cells against Prostate Cancer Cells
Authors: González-Ochoa S. , Tellez-Bañuelos M. C. , Méndez-Clemente A. S. , Bravo-Cuellar A. , Hernández Flores G. , Palafox-Mariscal L. A. , Haramati J. , Pedraza-Brindis E. J. , Sánchez-Reyes K. , Ortiz-Lazareno P. C.
Published: April 2022

Background/Aims. Prostate cancer (PCa) is one of the neoplasms with the highest incidence and mortality rate in men worldwide. Advanced stages of the disease are usually very aggressive, and most are treated with chemotherapeutic drugs that generally cause side effects in these patients. However, additional therapeutic targets such as the IL6R/STAT-3 axis and TIGIT have been proposed, mainly due to their relevance in the development of PCa and regulation of NK cell-mediated cytotoxicity. Here, we evaluate the effect of inhibitors directed against these therapeutic targets primarily via an analysis of NK cell function versus prostate cancer cells. Methods. We analyzed the secretion of cytokines, chemokines, and growth factors in 22Rv1, LNCaP, and DU145 cells. In these cells, we also evaluated the expression of NK ligands, IL6R, STAT-3, and phosporylated STAT-3. In NK-92 cells, we evaluated the effects of Stattic (Stt) and tocilizumab (Tcz) on NK receptors. In addition, we assessed if the disruption of the IL6R/STAT-3 pathway and blockade of TIGIT potentiated the cytotoxicity of NK-92 cells versus DU145 cells. Results. DU145 abundantly secretes M-CSF, VEGF, IL-6, CXCL8, and TGF-β. Furthermore, the expression of CD155 was found to increase in accordance with aggressiveness and metastatic status in the prostate cancer cells. Stt and Tcz induce a decrease in STAT-3 phosphorylation in the DU145 cells and, in turn, induce an increase of NKp46 and a decrease of TIGIT expression in NK-92 cells. Finally, the disruption of the IL6R/STAT-3 axis in prostate cancer cells and the blocking of TIGIT on NK-92 were observed to increase the cytotoxicity of NK-92 cells against DU145 cells through an increase in sFasL, granzyme A, granzyme B, and granulysin. Conclusions. Our results reveal that the combined use of inhibitors directed against the IL6R/STAT-3 axis and TIGIT enhances the functional activity of NK cells against castration-resistant prostate cancer cells.
Urban Ecological Monitoring and Prediction Based on Deep Learning
Authors: Yang Jun He , Chan Chia Pang
Published: April 2022

Obtaining comprehensive and accurate air quality information is conducive to people’s daily travel and living arrangements, especially to protect people’s health from air pollutants. Due to the limited number of air quality monitoring stations and the lack of training samples, the generalisation performance of air quality estimation model is often not good enough. Therefore, we propose an urban air quality index (AQI) prediction and AQI level estimation method based on deep multi-task learning. We consider various urban big data information related to air quality (meteorology, transportation, enterprise self-test, POI, road network, etc.), and use machine learning methods such as deep learning and graph embedding learning to learn the representation of relevant information, and establish the relationship between these related representations and air quality. Experiments show that this scheme can estimate the level of urban air quality index joint prediction task and air quality index, and the model has generalisation performance.
On Nonunique Fixed Point Theorems via Interpolative Chatterjea Type Suzuki Contraction in Quasi‐Partial b‐Metric Space
Authors: Gautam Pragati , Singh Shiv Raj , Kumar Santosh , Verma Swapnil
Published: April 2022

In the present research paper, Chatterjea type contraction is defined and discussed in the framework of quasi‐partial b‐metric space. Further, some common fixed point results are proved using the notion of interpolation. The results are extended to fixed point theorems for modified Chatterjea type Suzuki contraction using w‐admissible maps. The results proved are new and unique supported by application which will enrich the existing literature.
Liquidity, Credit Risk, and Their Interaction on the Spreads in China’s Corporate Bond Market
Authors: Wu Zijian , Yang Baochen , Su Yunpeng
Published: April 2022

This paper investigates the impact of multidimension liquidity, credit risk, and the interaction between liquidity and credit risk on corporate bond spreads based on a large transaction data set from July, 2006 to June, 2016, including the monthly data of 3716 bonds in China. Our main findings reveal that liquidity premiums are the main parts of corporate bond spreads. The interaction between liquidity and credit risk plays a significant role in determining corporate bond spreads. In addition, the differences between the interbank market and the exchange market have a significant impact on corporate bond spreads in normal period, and the interaction between liquidity and credit risk has an enhanced impact on corporate bond spreads during financial crisis. We also find that the interaction between liquidity and credit risk will increase with the increase of liquidity risk and credit risk and it is a time‐varying dynamic process.
A Model Study on Raw Material Chemical Composition to Predict Sinter Quality Based on GA-RNN
Authors: Li Yifan , Zhang Qunwei , Zhu Yi , Yang Aimin , Liu Weixing , Zhao Xinfeng , Ren Xinying , Feng Shilong , Li Zezheng
Published: April 2022

The quality control process for sintered ore is cumbersome and time- and money-consuming. When the assay results come out and the ratios are found to be faulty, the ratios cannot be changed in time, which will produce sintered ore of substandard quality, resulting in a waste of resources and environmental pollution. For the problem of lagging sinter detection results, Long Short-Term Memory and Genetic Algorithm-Recurrent Neural Networks prediction algorithms were used for comparative analysis, and the article used GA-RNN quality prediction model for prediction. Through correlation analysis, the chemical composition of the sintered raw material was determined as the input parameter and the physical and metallurgical properties of the sintered ore were determined as the output parameters, thus successfully establishing a GA-RNN-based sinter quality prediction model. Based on 150 sets of original data, 105 sets of data were selected as the training sample set and 45 sets of data were selected as the test sample set. The results obtained were compared to the real value with an average prediction error of 1.24% for the drum index, 0.92% for the low-temperature reduction chalking index (RDI), 0.95% for the reduction index (RI), 0.40% for the load softening temperature T10%, and 0.43% for the load softening temperature T40%, with all within the running time thresholds. The study of this model enables the prediction of the quality of sintered ore prior to sintering, thus improving the yield of sintered ore, increasing corporate efficiency, saving energy, and reducing environmental pollution.
Data-Specific Anisotropic Mexican Hat Wavelets for Structure-Preserving Image Processing
Authors: Wang Qingzheng , Wu Huixin , Yang Hua , Liu Yue , Zhang Chenming , Li Bin
Published: April 2022

This paper proposes a novel approach for structure-sensitive image processing based on the rigorous mathematical derivation of data-specific anisotropic Mexican hat wavelets (DAM). Our DAM is derived from the negative first-order derivative of the fundamental solution of heat diffusion equation with respect to time, which not only shares similar properties with Mexican hat wavelet but also intrinsically embeds the image-specific properties. Through the scale-aware DAM transform and its inverse transform, we are capable of conducting structure-sensitive image processing. Our key idea is to represent the images as undirected graphs, whose edge weights are governed by the normalized intensity/color differences within the local neighboring pixel window. Based on the rigorous theory of global graph Laplacian and heat diffusion, our original DAM can also encode the local/global structure of images. We employ the Krylov subspace technique to reduce the computational cost of our DAM transform. Furthermore, aiming at various structure-preserving image processing applications such as filtering, detail enhancement, tone manipulation, and stylization, we conduct comprehensive experiments and make quantitative comparisons with other state-of-the-art methods, which demonstrate the versatility and superiority of our method.
Selection of Compatible Coal Seam for Methane Drainage Operation Based on Uncertain Geological Conditions: A Hybrid Fuzzy Approach
Authors: Jafarpour Amir , Najafi Mehdi
Published: April 2022

Geological studies are very important at different steps of mining activities. Different uncertain geological criteria and factors show a significant impact during the underground mining operations and mineral extraction process. The current study reports on the evaluation and classification of coal seams for methane drainage-ability (MDA) through a fuzzy hybrid approach. This problem was investigated due to the importance of uncertain geological factors in the process of MDA and the necessity to evaluate safety operations in underground coal mines. The important criteria involved are depth, thickness, and uniformity of coal seam, joints and cleats conditions, roof quality, coal seam gas content, underground water condition, and permeability of coal seam. The two-stage fuzzy classification approach was used to analyze the effectiveness of the uncertain geological criteria. Also, fuzzy cognitive map (FCM) method was used to calculate weights for geological criteria. The used FCM method is based on the Hebbian algorithm and metaheuristic methods. The Hebbian learning algorithm is made from a hybrid learning algorithm of nonlinear heuristics and differential evolution. In addition, fuzzy intervals of criteria were calculated based on technical reports and other scientific studies. Then, the rank of each coal seam calculates by fuzzy T-norms. The proposed system was employed to classify the coal seams for MDA in Parvadeh coalfield, Iran. The results showed that the C1, C2, B2, B1, and D coal seams were classified in the “very good,” “good,” “moderate,” “poor,” and “very poor” categories, respectively. The proposed fuzzy hybrid approach provides a new logical tool in the selection of coal seam for methane drainage operation and can reduce the risk of methane drainage projects in underground coal mines.
Application of Contrast-Enhanced Ultrasound Combined with Elastic Imaging Technology in Differential Diagnosis of Salivary Gland Tumors
Authors: Chen Hong , Bao Xinyu , Wan Long
Published: April 2022

This paper proposes the effect of contrast-enhanced ultrasound combined with real-time elastic imaging technology in the differential diagnosis of salivary gland tumors. 200 patients were selected, including 120 males and 60 females. The age ranged from 9 to 83 years, with an average of 55.4 years. Among the 200 cases, there were 90 cases of single parotid gland on the right, 77 cases of single parotid gland on the left, 2 cases of bilateral (single parotid gland on each side), 2 cases of multiple parotid gland on the right (2 lesions), 1 case of 2 lesions on the left and 1 lesion on the right, and 1 case of multiple parotid gland on the left (4 lesions). 135 cases were located in the superficial lobe (78%) and 38 cases (22%) in the deep lobe of parotid gland. The ARIETTA 70 color Doppler ultrasound diagnostic instrument is used. The equipment is equipped with high-frequency contrast probe, real-time elastic imaging technology, and related software. The results showed that the detection rate of salivary gland tumors by ultrasound was 100% and the diagnostic coincidence rate was 71% (123/173). Ultrasound can not only identify the tumors in and around the parotid gland but also identify the location, size, and internal structure of the tumors. Combined with CDFI, it can make qualitative diagnosis of most benign and malignant salivary gland tumors and provide help for clinical treatment and operation plan. It is proved that contrast-enhanced ultrasound and real-time elastic imaging technology have advantages over gray-scale ultrasound in differentiating benign and malignant superficial enlarged lymph nodes, and the combined use can effectively improve the diagnostic efficiency.
Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model
Authors: Duan Bo , Xu Zhengmin , Pan Lili , Chen Wenxia , Qiao Zhongwei
Published: April 2022

In order to compare magnetic resonance imaging (MRI) findings of patients with large vestibular aqueduct syndrome (LVAS) in the stable hearing loss (HL) group and the fluctuating HL group, this paper provides reference for clinicians' early intervention. From January 2001 to January 2016, patients with hearing impairment diagnosed as LVAS in infancy in the Department of Otorhinolaryngology, Head and Neck Surgery, Children's Hospital of Fudan University were collected and divided into the stable HL group (n = 29) and the fluctuating HL group (n = 30). MRI images at initial diagnosis were collected, and various deep learning neural network training models were established based on PyTorch to classify and predict the two series. Vgg16_bn, vgg19_bn, and ResNet18, convolutional neural networks (CNNs) with fewer layers, had favorable effects for model building, with accs of 0.9, 0.8, and 0.85, respectively. ResNet50, a CNN with multiple layers and an acc of 0.54, had relatively poor effects. The GoogLeNet-trained model performed best, with an acc of 0.98. We conclude that deep learning-based radiomics can assist doctors in accurately predicting LVAS patients to classify them into either fluctuating or stable HL types and adopt differentiated treatment methods.
Novel Visual Secret Sharing Scheme for the QR Code with Meaningful Shares
Authors: Pan Jeng-Shyang , Liu Tao , Yan Bin , Yang Hong-Mei , Chu Shu-Chuan , Wang Meng-Xi
Published: April 2022

Visual secret sharing (VSS) scheme can share the secret image with several shares. Shares are performed by the decoding operation to restore the secret image. VSS scheme is mainly used for the secure transmission of images. Quick response (QR) code is often used by people on the Internet. It can be encrypted by the VSS scheme to enhance the security of its transmission. The share is meaningless that will arouse the suspicion of the attacker. The security of the VSS scheme is decreased. This paper proposes a novel VSS scheme for the QR code with meaningful images to solve this problem. Two continue tone images are converted to halftone images. The halftone image is modified to generate the share. Every share is meaningful, not meaningless. Two shares are performed the decoding operation to restore the secret QR code. The restored QR code can be correctly decoded by the general decoder. Experimental results prove that the proposed scheme is feasible.
Security Issues in IoT and Cloud Computing Service Models with Suggested Solutions
Authors: Saini Dinesh Kumar , Kumar Krishan , Gupta Punit
Published: April 2022

Cloud computing is a new model for providing computing services, where computing is delivered as a service over the Internet. Cloud computing is a very popular choice among SMEs because computing services are provided at much lower prices compared to their own IT infrastructure. In the cloud computing model, computing services and data storage are outsourced to cloud service providers. Customers do not have full control over applications and their data. Therefore, an added overhead of security risks comes along, and the security of data becomes the primary concern for cloud customers when considering cloud services. This paper explores various deployment models of cloud computing services and IoT (Internet of Things), figures out associated data security issues with them, and suggests a metric-based solution to assess security provided by cloud services.
A Software Defect Prediction Approach Based on BiGAN Anomaly Detection
Authors: Zhang Shenggang , Jiang Shujuan , Yan Yue
Published: April 2022

Software defect prediction usually is regarded as a classification problem, but classification models will face the class imbalance problem. Although there are many methods to solve the class imbalance problem, there is no method that can fundamentally solve the problem currently. In addition, supervised learning algorithms are always used to train defect prediction models, but obtaining a large amount of high-quality labelled data requires a lot of time and labor cost. In order to solve the class imbalance problem and eliminate the disadvantage of supervised learning, this paper attempts to predict software defects from a new perspective of anomaly detection. We propose an Anomaly Detection Model Based on BiGAN for Software Defect Prediction (ADGAN-SDP). The model proposed in this paper not only does not need to consider the class imbalance problem but also uses a semi-supervised method to train the model. Eight classification-based software defect prediction models are used as the baseline models and compared with ADGAN-SDP model. We evaluate ADGAN-SDP on 19 projects from NASA, AEEEM, and ReLink repositories. The experimental results show that the ADGAN-SDP model, which has a higher recall, outperforms all baseline models. It is suggested that the anomaly detection approach can be applied to the software defect prediction to fundamentally solve the class imbalance problem.
A Novel Nanomedicine System TAR‐AuNSs/Dox for the Treatment of Drug‐Resistant Lymphoma
Authors: Ouyang Shujuan , Tang Youqun , Cao Yanming
Published: April 2022

Lymphoma is one of the most common malignancies of blood system, and drug resistance is an important cause of treatment failure. Multidrug resistance gene P‐glycoprotein (P‐gp) plays an important role in lymphoma chemotherapeutic drug resistance. Our previous studies have found that P‐gp is highly expressed in doxorubicin‐resistant lymphoma cells (Daudi/R). With the development of nanotechnology, a large number of nanomaterials have been applied in various biomedical fields. Therefore, in this study, P‐gp inhibitor tariquidar (TAR) and chemotherapy drug doxorubicin were loaded onto gold nanoshells (AuNSs) to construct TAR‐AuNSs/Dox nanodrug system. TAR and Dox were slowly released from TAR‐AuNSs/Dox in an acidic tumor microenvironment. TAR‐AuNSs/Dox increased the uptake of Dox by drug‐resistant lymphoma cells and inhibited P‐gp expression to reduce Dox pumping. Compared to the free Dox, TAR‐AuNSs/Dox had a stronger killing effect on Daudi/R cells, which provided a new therapeutic strategy for the treatment of drug‐resistant lymphoma.
Reconstruction of Wing Structure Deformation Based on Particle Swarm Optimization Ridge Regression
Authors: Wu Xinyi , Xu Zhiwei , Zeng Jie
Published: April 2022

In this paper, a typical airfoil aluminum plate structure is taken as the research object. A structural deformation monitoring and reconstruction method, PSO-RR (particle swarm optimization-ridge regression) algorithm, is proposed. A variety of different complex load cases are applied to the airfoil structure, and the strain values at some specific positions of the structure are collected. The ridge regression algorithm is used to construct the theoretical model of the relationship between the strain and structural deformation. Then, the structural displacements with different load cases are monitored and reconstructed. To improve the precision, the PSO algorithm is used to optimize the ridge regression parameters and comparative analysis is carried out with the typical structural deformation reconstruction algorithm, such as the KO theoretical method. Results show that the PSO-RR predicts the deformation of complex wing structures under different kinds of complex load cases accurately. This method, which has high precision, does not depend on the specific structures and load cases.
Optimal Decision-Making on Hybrid Off-Grid Energy Systems for Rural and Remote Areas Electrification in the Northern Cameroon
Authors: Yaouba  , Zieba Falama Ruben , Ngangoum Welaji Felix , Hamda Soulouknga Marcel , Kwefeu Mbakop Fabrice , Dadjé Abdouramani
Published: April 2022

Hybrid renewable energy systems are effective solutions to the problem of lack of electricity in many localities around the world. In this paper, a comparative study of ten different options of standalone hybrid energy systems is done. These systems are used for household energy supply in rural and remote areas. The three regions of the northern Cameroon have been chosen as study sites. HOMER optimization Pro software has been performed for the optimal sizing of the proposed systems. The system reliability, the cost of energy, the renewable energy penetration, and the carbon dioxide emissions are the main comparative indexes considered. For an energy demand of 46418.100 kWh/year and a lifetime project of 25 years, the best optimal system configuration for the sites considered based on economic analysis is the PV/DG/battery system with a cost of energy of 0.378 $/kWh in Garoua, 0.359 $/kWh in Maroua, and 0.394 $/kWh in Ngaoundéré. When considering the environmental criteria, the PV/Battery and the PV/wind/Battery are the best options with 0 kgCO2 emissions per year and 100% renewable energy penetration. The renewable energy penetration of the PV/DG/Battery system is 95.6% in Garoua, 96.3% in Maroua, and 95.1% in Ngaoundéré. Thus, when taking into account both economic and environmental aspects, the PV/DG/Battery could appear as the best optimal system for rural and remote areas electrification in the northern part of Cameroon. The sensitivity analysis revealed that the studied systems are more attractive when increasing the project lifetime (up to 50 years, the COE is 0.375 $/kWh, 0.356 $/kWh, and 0.391 $/kWh, respectively, in Garoua, Maroua, and Ngaoundéré). However, the studied systems are more attractive when reducing the fuel price and the discount rate. When reducing the fuel price to up to 0.01 $/l, the COE is 0.359 $/kWh in Garoua, 0.342 $/kWh in Maroua, and 0.371 $/kWh in Ngaoundéré. When reducing the discount rate to up to 1%, the values of the COE are 0.253 $/kWh, 0.240 $/kWh, and 0.264 $/kWh, respectively, in Garoua, Maroua, and Ngaoundéré.
α-Bisabolol Mitigates Colon Inflammation by Stimulating Colon PPAR-γ Transcription Factor: In Vivo and In Vitro Study
Authors: Venkataraman Balaji , Almarzooqi Saeeda , Raj Vishnu , Dudeja Pradeep K. , Bhongade Bhoomendra A. , Patil Rajesh B. , Ojha Shreesh K. , Attoub Samir , Adrian Thomas E. , Subramanya Sandeep B.
Published: April 2022

The incidence and prevalence of inflammatory bowel disease (IBD, Crohn’s disease, and ulcerative colitis) are increasing worldwide. The etiology of IBD is multifactorial, including genetic predisposition, dysregulated immune response, microbial dysbiosis, and environmental factors. However, many of the existing therapies are associated with marked side effects. Therefore, the development of new drugs for IBD treatment is an important area of investigation. Here, we investigated the anti-inflammatory effects of α-bisabolol, a naturally occurring monocyclic sesquiterpene alcohol present in many aromatic plants, in colonic inflammation. To address this, we used molecular docking and dynamic studies to understand how α-bisabolol interacts with PPAR-γ, which is highly expressed in the colonic epithelium: in vivo (mice) and in vitro (RAW264.7 macrophages and HT-29 colonic adenocarcinoma cells) models. The molecular docking and dynamic analysis revealed that α-bisabolol interacts with PPAR-γ, a nuclear receptor protein that is highly expressed in the colon epithelium. Treatment with α-bisabolol in DSS-administered mice significantly reduced Disease Activity Index (DAI), myeloperoxidase (MPO) activity, and colonic length and protected the microarchitecture of the colon. α-Bisabolol treatment also reduced the expression of proinflammatory cytokines (IL-6, IL1β, TNF-α, and IL-17A) at the protein and mRNA levels. The expression of COX-2 and iNOS inflammatory mediators were reduced along with tissue nitrite levels. Furthermore, α-bisabolol decreased the phosphorylation of activated mitogen-activated protein kinase (MAPK) signaling and nuclear factor kappa B (NFκB) proteins and enhanced colon epithelial PPAR-γ transcription factor expression. However, the PPAR-α and β/δ expression was not altered, indicating α-bisabolol is a specific stimulator of PPAR-γ. α-Bisabolol also increased the PPAR-γ transcription factor expression but not PPAR-α and β/δ in pretreated in LPS-stimulated RAW264.7 macrophages. α-Bisabolol significantly decreased the expression of proinflammatory chemokines (CXCL-1 and IL-8) mRNA in HT-29 cells treated with TNF-α and HT-29 PPAR-γ promoter activity. These results demonstrate that α-bisabolol mitigates colonic inflammation by inhibiting MAPK signaling and stimulating PPAR-γ expression.
Machine Learning Enabled Techniques for Protecting Wireless Sensor Networks by Estimating Attack Prevalence and Device Deployment Strategy for 5G Networks
Authors: Kumar Parmod , Baliyan Anupam , Prasad K. Ramalingeswara , Sreekanth N. , Jawarkar Parag , Roy Vandana , Amoatey Enoch Tetteh
Published: April 2022

A number of disadvantages of traditional networks may be attributed to the close relationship that exists between the control plane and the data plane inside proprietary hardware designs, as described above. The problem of security is one of the most difficult to deal with. There are a plethora of network hazards and attacks that might be encountered these days. DDoS attacks are one of the most popular and disruptive attacks on the internet today, and they affect a wide range of organisations. Despite a large number of traditional mitigation solutions now available, the frequency, volume, and intensity of distributed denial-of-service (DDoS) attacks continue to rise. According to the findings of this paper, a new network paradigm is necessary to satisfy the requirements of today’s complex security concerns. It was necessary to develop a software-defined network (SDN) in order to meet the real-time needs of the massive network that was expanding at an exponential rate. Many advantages of SDN exist, including simplicity of administration, scalability, and agility, but one of the most critical is security, which is one of the most important considerations when implementing SDN. SDS may be seen as a paradigm in which the implementation of new security regulations in the computer environment is performed via the use of protected software, which is described further below. The goal is to provide a flexible and extensible architecture for DDoS detection and prevention that is both flexible and extendable; the suggested clustering approach, which is based on the Open Day Light (ODL) Controller, is employed to carry out the experimental findings. In this section, we emphasise DDoS penetration techniques from a range of tools, and we evaluate the vulnerability against various tactics. It is necessary to use a Mininet emulation tool to construct a detection and prevention system against distributed denial of service (DDoS) attacks in order to achieve success. There is a range of other simulation tools that are utilised in conjunction with this research in order to bring it to a conclusion. Integration of industry standards such as SNORT and Flow has been accomplished in a variety of situations and parameter settings. During the creation of a framework capable of detecting and mitigating DDoS attacks at an early stage in both the control and application levels, the implementation of this framework has been shown to be crucial in the development of a framework.
An End-to-End Lane Detection Model with Attention and Residual Block
Authors: Wang Bo , Yan Xiaoting , Li Deguang
Published: April 2022

Lane detection, as one of the most important core functions in the autonomous driving environment, is still an open problem. In particular, pursuing high accuracy in complex scenes, such as no line and multiple lane lines, is an urgent issue to be discussed and solved. In this paper, a novel end-to-end lane detection model combining the advantages of attention mechanism and residual block is proposed to address the problem. A residual block alleviates the possible gradient problem. An attention block can help the proposed model centralize on where to focus in the process of learning feature representation, which can make the model itself more sensitive to the feature representation of lane lines through convolutional operations. Additionally, the U-shaped structure with three downsampling operations preserves the image resolution and the original lane line information in the image to the greatest extent. The U-shaped structure can directly output the prediction results to eliminate many complex or unnecessary calculation processes. The experimental results on two public lane detection datasets show that the lane detection performance of the proposed model can achieve high accuracy, and the corresponding weight sizes are only 2.25 M. Finally, to further explain the effectiveness of the proposed model, the unavoidable troubles encountered in the experiment are discussed.
Correlation Meta-Analysis of the Efficacy of Inhaled Corticosteroids in Children with Asthma Based on Smart Medical Health
Authors: Qin Yu , Wang Jing , Qin Jingmin , Yang Ning , Li Sha , Xu Lijia , Han Yanjun
Published: April 2022

Children are the reserve force for national construction, and children’s health is now being paid more and more attention. In today’s childhood asthma treatment, many are based on the treatment of common cough, and there is no strict division with asthma. Importantly, the efficacy of inhaled corticosteroids in the treatment of childhood asthma has been extensively studied in the medical community. However, there is no clear explanation for its specific efficacy and role. Research on childhood asthma has always been a key topic in medicine. This article aims to conduct a meta-analysis of the correlation between inhaled corticosteroids and asthma in children. This article proposes a more scientific literature screening strategy, which can achieve a higher accuracy rate for meta-analysis. It combines the intelligent medical health system in the analysis of children’s asthma to assist in the analysis of curative effects. Such analysis also provides reference significance for related research. The experimental results indicate that inhaled corticosteroids have a good effect on childhood asthma. As the concentration of inhaled glucocorticoid increases, the effect will be better. Specifically, if the concentration is increased by 10%, the effect will be about 15% better, but there are corresponding side effects.
The Clinical Significance of Detecting Blood Supply to the Inferior Parathyroid Gland Based on the “Layer of Thymus-Blood Vessel-Inferior Parathyroid Gland” Concept
Authors: Wang Jian-Biao , Su Rong , Jin Lei , Zhou Liang , Jiang Xian-Feng , Xiao Gui-Zhou , Chu Ye-Yuan , Li Fei-Bo , Feng Yi-Bing , Xie Lei
Published: April 2022

Objective. How to preserve the inferior parathyroid gland (IPTG) in situ during central neck dissection (CND) is the major concern of thyroid surgeons. The “layer of thymus-blood vessel-IPTG” (TBP layer) concept showed to be effective in preserving IPTG. The objective of this study was to identify the origin and course of blood supply to IPTG (IPBS) within the TBP layer and to take key points of operation during CND. Design. This is a retrospective control study. Participants. Patients who underwent thyroidectomy plus CND using the TBP layer concept and conventional technique between 2017 and 2019 were enrolled. Measurements. The origin and course of IPBS in relation to recurrent laryngeal nerve (RLN) and thymus and prevalence of hypoparathyroidism were detected. Results. A total of 71.3% of IPTGs (251 of 352) were supplied by ITA branches, defined as type A. Type A was further divided into Types A1 (branches of ITA, coursing laterally to the RLN (53.1%, 187 of 352)) and A2 (branches of ITA, traversing medially to the RLN (18.2%, 64 of 352)). Type A2 was more common on the right side than on the left side (

P
<
0.001

). Fifty-five (15.6%) IPTG feeding vessels originated from the thymus or mediastinum. Nineteen (5.4%) IPTGs were supplied by branches of the superior thyroid artery. The incidence of transient hypoparathyroidism decreased from 45.7% to 3.6% (

P
<
0.001

), in the TBP layer group compared with the conventional technique group. Conclusion. The origin and course of IPBS follow a definite pattern. This mapping and precautions help surgeons optimize intraoperative manipulations for better preservation of IPBS during CND.
Inhibition of PARP Potentiates Immune Checkpoint Therapy through miR-513/PD-L1 Pathway in Hepatocellular Carcinoma
Authors: Sun Guoqiang , Miao Ganggang , Li Zhitao , Zheng Wubin , Zhou Chunguang , Sun Guangshun , Cao Hongyong , Li Zhouxiao , Tang Weiwei
Published: April 2022

Background. The DNA repair enzyme poly(ADP-ribose) polymerase (PARP) is involved in DNA damage repair and cell death. However, the association between PARP’s biological activities and the immune microenvironment in hepatocellular carcinoma (HCC) is unclear. The present study will explore whether combining a PARP inhibitor with anti-PD1 might improve the anti-HCC impact and explain how it works. Method. The PARP inhibitor olaparib was screened out of 867 drugs through Cell Counting Kit 8 (CCK-8) assay. The expression of PARP was verified through the TCGA and TISIDB databases. The impacts exerted by PARP inhibitor olaparib to HCC cells were assessed via wound healing, Transwell, and proliferation assay. In vivo, experiments were performed in a C57BL/6 mouse model to evaluate the function of PARP inhibitor olaparib combination with anti-PD1 in HCC and mice tumors were further detected by immunohistochemically staining. Result. Olaparib was selected as the research object on the basis of drug screening. The results of the TCGA and Human Protein Atlas databases revealed that PARP was significantly upregulated in carcinoma cell cluster of HCC tissues compared to normal tissues. Higher expression of PARP showed a poorer prognosis based on Kaplan-Meier Plotter. qRT-PCR experiments confirmed that olaparib could increase PD-L1 expression through inhibiting miR-513 in HCC cells. In vivo, experiment confirmed that the combination of olaparib and anti-PD1 could enhance the immunotherapy effect of HCC. Conclusion. The present study reveals that inhibition of PARP potentiates immune checkpoint therapy through the miR-513/PD-L1 pathway in HCC and the combination of PARP inhibitor olaparib and anti-PD1 is beneficial to HCC therapy.
Algorithm of CAD Surface Generation for Complex Pipe Model in Industry 4.0 Background
Authors: Cheng Xiaolei
Published: April 2022

The current pipeline surface generation algorithm cannot get the angle information of the corner of complex pipeline surface, which leads to the poor accuracy of the algorithm, the slow speed of 3D point cloud intelligent mosaic, and the large number of effective points. Therefore, a CAD surface generation algorithm for complex pipeline model under the background of Industry 4.0 is designed, extracting and rendering the wireframe model and extracting background of the complex pipeline video. We obtain the angle information of the corner points of the complex pipeline surface, extract and match the feature of the dense point cloud, and construct the 3D point cloud data mosaic model. The pipe surface is generated by using double-nodal B-spline. The experimental results show that the precision and stability of the proposed method are high. In the early stage, the proposed method uses ISS feature extraction algorithm to extract feature of point cloud data, which improves the positioning accuracy effectively and enhances the 3D point cloud intelligent stitching speed.
Application of Rough Concept Lattice Model in Construction of Ontology and Semantic Annotation in Semantic Web of Things
Authors: Xu Hongsheng , Sun Huijuan
Published: April 2022

In order to solve the problem of interoperability in Internet of Things, the Semantic Web technology is introduced into the Internet of Things to form Semantic Web of Things. Ontology construction is the core of Semantic Web of Things. Firstly, this paper analyzes the shortcomings of ontology construction methods in the Semantic Web of Things. Then, this paper proposes construction of semantic ontology based on improved rough concept lattice, which provides theoretical basis for semantic annotation of the sensing data attributes. In addition, this paper describes the semantic annotation system for the Internet of Things based on semantic similarity of ontology. The system consists of three steps: ontology mapping integration module, information extraction module, and semantic annotation of sensing data. Finally, the experimental results show that this semantic annotation method effectively improves the flexibility of sensor information and data attributes and effectively enhances the expression ability of sensor information and the use value of data.
Study on the Rural Revitalization and Urban-Rural Integration Efficiency in Anhui Province Based on Game Cross-Efficiency DEA Model
Authors: Sun Shanhui , Zhang Ni-Ni , Liu Jia-Bao
Published: April 2022

By taking the 16 cities in Anhui Province for evaluation, the main influencing factors and indicator system for integrated urban-rural development in the new era were explored, to build the BCC model, cross-efficiency model, and game cross-efficiency model of DEA. The above models were applied for empirical analysis and comparative study on the rural revitalization and urban-rural integration efficiency in Anhui Province, to summarize the conclusions efficiency and give suggestions based on the above calculations.
Numerical Simulation of a Two-Dimensional Groundwater Pollute Transport Problem Using Incompressible Steady-State Navier-Stokes Equations and Diffusion-Convection Equations
Authors: Nyende Jacob , Enyogoi Isaac , Mango John , Kasumba Henry
Published: April 2022

Most of the real contaminant problems are defined domains that are geometrically complex and can have different boundary conditions in different areas. Therefore, it is usually difficult to find a solution analytically, so we use the approximate method to generate an approximate function. One answer to this problem is the finite element approach (FEM). This study presents a partial differential equation (PDE) simulation system that uses numerical techniques for the distribution of pollutant concentrations in groundwater in space and time. The movement of the liquid is described by the incompressible steady-state Navier-Strokes equation, while the transport of pollutants is described by the diffusion-convention equation. The variation formulation that forms the basis of FEM and MATLAB is discussed along with the selection of the abstract approximation space and the welfare of the weak formulation. The motivation for this study comes from a specific and considered water body with the discharge of factory effluents on the ground that ends up reducing the quality of groundwater. First, the fluid flow equation is solved to obtain velocity and pressure profiles. Steady-state concentration profiles were obtained for various values of diffusion coefficient (D), baseline, and input concentrations. The results showed that decreasing the diffusion coefficientDincreased the number of pollutants for convective transport and decreased the number of pollutants that diffused from the entrance. Although groundwater is not completely safe, it is concluded that experimental studies are necessary decision-making basis for water resource protection, especially in water pollution emergencies.
IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud
Authors: Mir Mahmood Hussain , Jamwal Sanjay , Mehbodniya Abolfazl , Garg Tanya , Iqbal Ummer , Samori Issah Abubakari
Published: April 2022

COVID-19 is the repugnant but the most searched word since its outbreak in November 2019 across the globe. The world has to battle with it until an effective solution is developed. Due to the advancement in mobile and sensor technology, it is possible to come up with Internet of things-based healthcare systems. These novel healthcare systems can be proactive and preventive rather than traditional reactive healthcare systems. This article proposes a real-time IoT-enabled framework for the detection and prediction of COVID-19 suspects in early stages, by collecting symptomatic data and analyzing the nature of the virus in a better manner. The framework computes the presence of COVID-19 virus by mining the health parameters collected in real time from sensors and other IoT devices. The framework is comprised of four main components: user system or data collection center, data analytic center, diagnostic system, and cloud system. To point out and detect the COVID-19 suspected in real time, this work proposes the five machine learning techniques, namely support vector machine (SVM), decision tree, naïve Bayes, logistic regression, and neural network. In our proposed framework, the real and primary dataset collected from SKIMS, Srinagar, is used to validate our work. The experiment on the primary dataset was conducted using different machine learning techniques on selected symptoms. The efficiency of algorithms is calculated by computing the results of performance metrics such as accuracy, precision, recall, F1 score, root-mean-square error, and area under the curve score. The employed machine learning techniques have shown the accuracy of above 95% on the primary symptomatic data. Based on the experiment conducted, the proposed framework would be effective in the early identification and prediction of COVID-19 suspect realizing the nature of the disease in better way.
Formal Credit-Assisted New Agricultural Business: A Multifactor Analysis Based on BP Neural Network
Authors: Zhang Lihuan , Fan Jing
Published: April 2022

In recent years, the new agricultural business entities have grown rapidly in all parts of the country and have become an effective carrier to achieve the basic stability of China’s rural family operation system and moderate-scale agricultural operation. However, the systemic defects of rural financial market and the credit rationing of formal financial institutions make the capital needs of rural areas unsatisfied for a long time. Financial demand determines the direction of supply reform. Therefore, in order to improve rural financial supply and promote the transformation and development of agricultural economy under the current situation, it is necessary to effectively understand the formal credit demand and the impact factors of accessibility of the new agricultural business entities, which represents the direction of China’s agricultural development in the future. Based on back propagation (BP) neural network, this paper constructs the farmers’ formal credit availability prediction model and studies the formal credit demand and the impact factors of the availability of new agricultural business entities. The experimental results show that the farmers’ formal credit availability prediction model has good performance in prediction accuracy, prediction time, and mean squared error.