bluuebunny/crossref_metadata_2025 huggingface dataset:
Sodiated Carbon Fibres for Use in Future Multifunctional Structures
Authors: Harnden Ross , Peuvot Kevin , Zenkert Dan , Lindbergh Göran
Published: January 2022

The well-known structural properties of PAN-based carbon fibres (CFs) have previously been combined with electrochemical functionalities such as energy storage, solid-state actuation, and strain sensing. These properties have been created through lithium insertion/intercalation in the turbostratic microstructure of the CFs, with the goal of making truly multifunctional structures with potentially large mass and volume savings.

The presented work investigates the insertion of sodium ions into CFs, and whether these multifunctional effects are still observable and exploitable for applications in future multifunctional structures. The motivation for this work comes from the fact that sodium's ionic radius is larger than that of lithium's, and hence a more pronounced multifunctional effect is hypothesised. A secondary motivation comes from a general increase in interest in sodium-ion batteries (SIBs) as a more environmentally friendly alternative to lithium-ion batteries (LIBs).

In this study it is found that sodium-ion capacity in commercial CFs is considerably less than that of lithium-intercalated CFs, with a maximum first cycle capacity of 180mAh/g in comparison with 327mAh/g for lithium insertion. The cycling stability is found to be less consistent, and the first cycle losses found to be larger when compared with lithium-intercalated CFs in a half-cell set-up.

Using in-situ strain measurements during sodiation and desodiation it is found that sodium insertion creates a lower axial expansion in CFs in comparison with lithium intercalation, even when normalised for capacity. Despite this, the mechanical work available is still significantly larger than that of traditional piezoelectric (PZT) ceramics that are used today for solid-state actuation.

The piezo-electrochemical (PECT) effect, observed in lithium-intercalated CFs - in which mechanical strains produce linearly proportional voltage responses - is also observed for sodiated CFs. The coupling factor k (V/unit strain) is found to be considerably lower in sodiated CFs than in lithium-intercalated CFs for a corresponding state of charge. There is still potential for this effect to be exploited to create self-strain sensing structures, although the lower coupling factor makes sodiated CFs less favourable than lithium-intercalated CFs for this application.

The present work concludes that sodiated CFs have a lower potential for use in multifunctional structures than lithium-intercalated CFs for energy storage, solid-state actuation, and strain sensing. It is acknowledged, however, that continued improvements in electrolytes, and cell design may contribute to an increase in this potential in future.

Figure 1
Flights‐to‐and‐from‐Quality with Islamic and Conventional Bonds in the COVID‐19 Pandemic Era: ICEEMDAN‐Based Transfer Entropy
Authors: Bossman Ahmed , Agyei Samuel Kwaku , Owusu Junior Peterson , Agyei Ellen Animah , Akorsu Patrick Kwashie , Marfo-Yiadom Edward , Amfo-Antiri George
Published: January 2022

We revisit the flight‐to‐quality (FTQ) and flight‐from‐quality (FFQ) occurrences vis‐à‐vis the stock‐bond nexus across differing investment time scales in the COVID‐19 era, using a novel technique hinged on a denoised frequency‐domain transfer entropy. Our findings divulge that flights, both FTQ and FFQ, could be attained during stress periods. Generally, in the intermediate term of the COVID‐19 pandemic, both Islamic and conventional bonds could act as safe havens, diversifiers, and hedges for international equities, and the same could be observed for international equities. We reiterate empirically that flights may improve the financial system’s stability and robustness by allowing diversity to be effective when it is most required. The findings have financial and portfolio implications for investors considering how to deploy their investments in the COVID‐19 era. Our findings may impact policymakers’ responses to changes in various asset classes, allowing them to better monitor financial markets and adjust macroeconomic policies.
p-Cresyl Sulfate Predicts Ischemic Stroke among Patients on Hemodialysis: A Prospective Cohort Study
Authors: Tan Xiao , Zou Jianzhou , Xiang Fangfang , Zhang Pan , Shen Bo , Wang Yaqiong , Ding Xiaoqiang , Cao Xuesen
Published: January 2022

Background and Purpose. Hemodialysis patients face a higher risk of ischemic stroke. p-Cresyl sulfate is a typical protein-bound uremic toxin that contributes to chronic kidney disease and cardiovascular disease progression, as well as mortality in hemodialysis patients. The present study was aimed at elucidating the association between p-cresyl sulfate and the risk of ischemic stroke in hemodialysis patients. Method. Patients on hemodialysis over 6 months were enrolled in this prospective cohort study and were divided into 2 groups based on plasma p-cresyl sulfate level. The primary end point was the first episode of ischemic stroke during follow-up. The association between p-cresyl sulfate and ischemic stroke incidence was analyzed by Kaplan-Meier method and Cox proportional hazard model. Results. 220 patients were enrolled in this study. 44 patients experienced episodes of first ischemic stroke during follow-up for 87.8 (47.6-119.5) months. Kaplan-Meier analysis demonstrated that the incidence of ischemic stroke in the high p-cresyl sulfate group was significantly higher than that in the low p-cresyl sulfate group (Log-Rank

P
=
0.007

). Cox regression analysis as well proved that p-cresyl sulfate level was significantly associated with the first incidence of ischemic stroke (HR (hazard ratio) 2.332, 95% CI (95% confidence interval) 1.236-4.399,

P
=
0.009

). After being adjusted for other confounding risk factors, the results persisted significant (model 11: HR 2.061, 95% CI 1.030-4.125,

P
=
0.041

). Conclusion. Plasma p-cresyl sulfate predicts the first incidence of ischemic stroke in hemodialysis patients.
Bearing Fault Feature Extraction Method Based on GA-VMD and Center Frequency
Authors: Li Yuxing , Tang Bingzhao , Jiang Xinru , Yi Yingmin
Published: January 2022

To promote the effect of variational mode decomposition (VMD) and further enhance the recognition performances of bearing fault signals, genetic algorithm (GA) is applied to optimize the combination of VMD parameters in this paper, and GA-VMD algorithm is put forward to improve the decomposition accuracy of VMD. In addition, combined with the center frequency, a feature extraction method based on GA-VMD and center frequency is proposed to ameliorate the difficulty of bearing fault feature extraction. Firstly, the bearing signal is decomposed into a series of intrinsic mode components (IMFs) by GA-VMD. Then, the Center Frequency of IMFs is extracted, and the recognition rate is calculated by k-nearest neighbor (KNN) algorithm. Simulation signal experiments state clearly that, compared with manual parameter setting-VMD algorithm and parameter optimization VMD algorithm based on particle swarm optimization (PSO), the decomposition result of GA-VMD has a smaller root mean square error and higher decomposition accuracy, which verifies the effectiveness of GA-VMD. The experimental results demonstrate that, by comparison with the feature extraction method based on envelope entropy, the feature extraction method based on center frequency has better inter class separability and higher mean recognition rate (the highest recognition rate of single feature is 94.5%, and in the case of multiple features, the recognition rate reaches 100% when four features are extracted) and can realize the accurate identification of different bearing fault signals.
The Mechanism Study of Common Flavonoids on Antiglioma Based on Network Pharmacology and Molecular Docking
Authors: Li Taiping , Xiao Yong , Wang Zhen , Xiao Hong , Liu Hongyi
Published: January 2022

Background. Glioma is the most common primary intracranial tumor in adult patients. Among them, glioblastoma is a highly malignant one with a poor prognosis. Flavonoids are a class of phenolic compounds widely distributed in plants and have many biological functions, such as anti-inflammatory, antioxidant, antiaging, and anticancer. Nowadays, flavonoids have been applied to the therapy of glioma; however, the molecular mechanism underlying the therapeutic effects has not been fully elaborated. This study was carried out to explore the mechanism of selected active flavonoid compounds in treating glioma using network pharmacology and molecular docking approaches. Methods. Active ingredients and associated targets of flavonoids were acquired by using the Traditional Chinese Medicine Database and Analysis Platform (TCMSP) and Swiss TargetPrediction platform. Genes related to glioma were obtained from the GeneCards and DisGeNET databases. The intersection targets between flavonoid targets and glioma-related genes were used to construct protein-protein interaction (PPI) network via the STRING database, and the results were analyzed by Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed and displayed by utilizing the Metascape portal and clusterProfiler R package. Molecular docking was carried out by iGEMDOCK and SwissDock, and the results were visually displayed by UCSF Chimera software. Results. Eighty-four active flavonoid compounds and 258 targets overlapped between flavonoid targets and glioma-related genes were achieved. PPI network revealed potential therapeutic targets, such as AKT1, EGFR, VEGFA, MAPK3, and CASP3, based on their node degree. GO and KEGG analyses showed that core targets were mainly enriched in the PI3K-Akt signaling pathway. Molecular docking simulation indicated that potential glioma-related targets-MAPK1 and HSP90AA1 were bounded more firmly with epigallocatechin-3-gallate (EGCG) than with quercetin. Conclusions. The findings of this study indicated that selected active flavonoid compounds might play therapeutic roles in glioma mainly through the PI3K-Akt signaling pathway. Moreover, EGCG had the potential antiglioma activity by targeting MAPK1 and HSP90AA1.
Esophageal Plasmacytoma Revealed by Persistent Hiccups: A Case Report and Literature Review
Authors: Khalil Khaoula , Lahlimi Fatima Ezzahra , Tazi Illias
Published: January 2022

Introduction. Solitary extramedullary plasmacytoma (SEP) is a rare neoplasm that is derived from monoclonal proliferation of plasma cells in the soft tissues or organs arising outside the bone marrow. It is present in about 3% of all plasma cell tumors and originates mainly from the upper respiratory tract and nasopharynx. Involvement of the esophagus is exceptionally seen in cases of SEP. Case Presentation. We report a novel case of a 74-year-old male patient attended with a 6-month history of hiccupping further associated with dysphasia and weight loss all caused by esophageal plasmacytoma. Histological and immunohistochemical examination of the tumor confirmed the diagnosis of plasmacytoma. Workup for the multiple myeloma came out to be negative, thus confirming the diagnosis of SEP. The patient was treated with radiotherapy alone, leading to complete remission (at 30 months of follow-up). Conclusion. Esophageal plasmacytoma, an exceptional presentation of extramedullary plasmacytoma, should be kept in mind while dealing with patients presenting with intractable hiccups.
Calculation Model of the Axial Dispersion Distribution of Cylindrical-Warhead Fragments
Authors: Jiang Ning , Li Wen Bin , Yao Wen Jin , Gao Da Cheng
Published: January 2022

The dispersion distribution characteristics of prefabricated fragments from an improvised explosive device (IED) are essential in defense technology development. To improve warhead design, it is essential to predict a fragment’s velocity and dispersion distribution accurately. This paper investigates a fragment’s flying angle from a detonated warhead with a cylindrical charge under a one-end central detonation. A modified formula for calculating the fragment dispersion distribution at each axial position of the warhead was obtained based on experimental data and model analysis. The impacts of the following parameters on the flying angle were considered in the formula: ratio of charge mass to shell mass, charge diameter, axial sparse effect, and relative axial shell position. The formula was verified by experimentation to be suitable for wide applications using different validation samples. Theoretical calculations with the formula show good accuracy in predicting the flying angle of cylindrical-shell fragments, agreeing with experimental data. The proposed model meets the error requirements of engineering applications. This work provides a theoretical foundation for subsequent research on damage effectiveness and IED damage assessment.
A Blockchain-Based Key-Revocation Access Control for Open Banking
Authors: Riad Khaled , Elhoseny Mohamed
Published: January 2022

Open banking allows banks and financial sectors to easily access the customers’ financial data which is revolutionizing. It also provides the customers with excellent cloud access to various providers’ wide range of financial services. The storage of such sensitive services and data on cloud servers is a double-edged sword. It can ease and support fine-grained access to such services/data anywhere and anytime, supporting the open banking system. But, on the other hand, data privacy and secrecy are a challenge. Thus, efficient access control should exist for open banking’s services and data to protect cloud-hosted financial sensitive data from unauthorized customers. This paper proposes a new access control scheme that employs blockchain for the key-revocation process. We implement the smart contract’s functions on the Ethereum platform and test the contract’s code on the Kovan Testnet before deploying it to the Mainnet. Although the customer is authenticated to open banking, his key/s can be revoked according to the status response of the bank branch. Thus, his access to financial services and data is denied. We did comprehensive experiments for the revocation status response time, data exchanged until receiving the revocation status, and the time spent updating the policy. Also, we compared the results of our proposed scheme with two well-known methods—Certificate Revocation List (CRL) and Online Certificate Status Protocol (OCSP). The experimental results show that our proposed scheme (BKR-AC) has a faster response time than Certificate Revocation List (CRL) and Online Certificate Status Protocol (OCSP) in case of nonrevoked keys/certificates and a slower response time in case of revoked keys to avoid nonrevoking a revoked key. But the data exchanged is an average for BKR-AC between CRL and OCSP, which is still a tiny amount and accepted. The security analysis proved that our scheme is secure against some well-known attacks on open banking systems. In addition, it is also secured against the chosen-text attack by employing the challenge-response authentication mechanism.
A Method for Identifying Tor Users Visiting Websites Based on Frequency Domain Fingerprinting of Network Traffic
Authors: Sun Yuchen , Luo Xiangyang , Wang Han , Ma Zhaorui
Published: January 2022

Although the anonymous communication network Tor can protect the security of users’ data and privacy during their visits to the Internet, it also facilitates illegal users to access illegal websites. Website fingerprinting attacks can identify the websites that users are visiting to discern whether they are performing illegal operations. Existing methods tend to manually extract the traffic features of users visiting websites and construct machine learning or deep learning models to classify the features. While these methods can be effective in classifying unknown website traffic, the effect of classification in the use of defensive measures or onion service scenarios is not yet ideal. This paper proposes a method to identify Tor users visiting websites based on frequency domain fingerprinting of network traffic (FDF). We extract the direction and length features of circuit sequences in access traffic and combine and transform them into the frequency domain. The classification of access traffic is accomplished by using a deep learning classification model combining CNN, FC, and Self-Attention. In this paper, the proposed FDF method is experimentally validated in common scenarios of Tor networks. The results show that FDF outperforms the existing methods for classification in different Tor scenarios. It can achieve 98.8% and 94.3% classification accuracy in undefended and WTF-PAD defense scenarios, respectively. In the onion service scenario, the accuracy is improved by 4.7% over the current state-of-the-art Tik-Tok method.
[Retracted] Experiment and Algorithm Research of Coal Direct Liquefaction Residual Oil Pyrolysis and Coking Technology Based on Lumped Kinetic Engineering
Authors: Bo Tian , Yang Chaohe
Published: January 2022

With the development of computer operation technology and algorithms, the lumped dynamic model is more practical, and the development and application are more comprehensive. Among them, the direct coal liquefaction residual oil pyrolysis and coking technology, as a coal‐to‐liquid process, can increase the oil yield of the coal liquefaction process and reduce environmental pollution. The purpose of this paper is to study the experiment and algorithm of coal direct liquefaction residual oil pyrolysis and coking technology based on lumped kinetic engineering. Starting from the lumped kinetic engineering, this paper takes the direct coal liquefaction residual oil pyrolysis and coking technology as the research object. Based on the experiment of small‐ and medium‐sized equipment, the residual oil pyrolysis and coking experiment is carried out. This paper further analyzes the components of the experimental products and explores the factors that affect the yield of residual pyrolysis oil based on the five lumped kinetic model of coking. Experimental data shows that when the pyrolysis temperature is 450°C, the content of liquefied heavy oil HS in the pyrolysis oil is 47.87%, the content of asphaltene A is 44.28%, and the content of preasphaltene PA is 7.85%; the pyrolysis temperature is 500 °C. At this time, the content of liquefied heavy oil HS in the pyrolysis oil is 54.97%, the content of asphaltene A is 40.23%, and the content of preasphaltene PA is 4.8%. It can be seen that, with the increase of pyrolysis temperature, the content of liquefied heavy oil HS increases, and the content of asphaltene A and preasphaltene PA decreases.
BID-Net: An Automated System for Bone Invasion Detection Occurring at Stage T4 in Oral Squamous Carcinoma Using Deep Learning
Authors: Agarwal Pinky , Yadav Anju , Mathur Pratistha , Pal Vipin , Chakrabarty Amitabha
Published: January 2022

Detection of the presence and absence of bone invasion by the tumor in oral squamous cell carcinoma (OSCC) patients is very significant for their treatment planning and surgical resection. For bone invasion detection, CT scan imaging is the preferred choice of radiologists because of its high sensitivity and specificity. In the present work, deep learning algorithm based model, BID-Net, has been proposed for the automation of bone invasion detection. BID-Net performs the binary classification of CT scan images as the images with bone invasion and images without bone invasion. The proposed BID-Net model has achieved an outstanding accuracy of 93.62%. The model is also compared with six Transfer Learning models like VGG16, VGG19, ResNet-50, MobileNetV2, DenseNet-121, ResNet-101 and BID-Net outperformed over the other models. As there exists no previous studies on bone invasion detection using Deep Learning models, so the results of the proposed model have been validated from the experts of practitioner radiologists, S.M.S. hospital, Jaipur, India.
Research on Agricultural Product Traceability Technology (Economic Value) Based on Information Supervision and Cloud Computing
Authors: Wang Rongkuan , Chen Xi
Published: January 2022

Traditional agricultural product traceability system adopts centralized storage, and the traceability process is solidified, which results in the low reliability of traceability results and the poor flexibility of the system. Aiming to solve this problem, blockchain technology is applied to supply chain traceability, and a supply chain traceability system based on sidechain technology is proposed. Goods management, information sharing, and product traceability in supply chain are realized through Ethereum smart contract. The sidechain technology is adopted to expand Ethereum so that it can meet the needs of practical applications. The experiment results show that the proposed system has a transaction function and information sharing function. Compared with similar trading systems, the proposed system has more advantages in throughput and security.
Antiacne Effects of PVA/ZnO Composite Nanofibers Crosslinked by Citric Acid for Facial Sheet Masks
Authors: Jeong Soyeon , Oh Seong-Geun
Published: January 2022

The water-insoluble polyvinyl alcohol (PVA) nanofibers containing ZnO nanoparticles were prepared by an electrospinning technique for antiacne facial masks. Zinc oxide (ZnO) nanoparticles prepared by a sol-gel method were dispersed in aqueous PVA and citric acid solution for electrospinning. The electrospun nanofibers were treated at 160°C to induce the thermal crosslinking between PVA and citric acid. The crosslinked PVA with citric acid become water-insoluble, which can be used for facial masks. PVA/ZnO composite nanofibers were characterized by structural and morphological analysis techniques including Fourier transform infrared spectroscopy (FT-IR), field-emission scanning electron microscopy (FE-SEM), and transmission electron microscopy (TEM). Also, the swelling ratios according to the ZnO content (0, 1, 4, and 7 wt.% with respect to PVA) were measured. Antibacterial activity tests were also studied. The clear zone of Cutibacterium acnes of PVA/ZnO 7% nanofibers was 2.25 mm, indicating antiacne activities of nanofibers.
Design of Metaheuristic Optimization‐Based Vascular Segmentation Techniques for Photoacoustic Images
Authors: Vaiyapuri Thavavel , Dutta Ashit Kumar , Sikkandar Mohamed Yacin , Gupta Deepak , Alouffi Bader , Alharbi Abdullah , Rauf Hafiz Tayyab , Kadry Seifedine
Published: January 2022

Biomedical imaging technologies are designed to offer functional, anatomical, and molecular details related to the internal organs. Photoacoustic imaging (PAI) is becoming familiar among researchers and industrialists. The PAI is found useful in several applications of brain and cancer imaging such as prostate cancer, breast cancer, and ovarian cancer. At the same time, the vessel images hold important medical details which offer strategies for a qualified diagnosis. Recently developed image processing techniques can be employed to segment vessels. Since vessel segmentation on PAI is a difficult process, this paper employs metaheuristic optimization‐based vascular segmentation techniques for PAI. The proposed model involves two distinct kinds of vessel segmentation approaches such as Shannon’s entropy function (SEF) and multilevel Otsu thresholding (MLOT). Moreover, the threshold value and entropy function in the segmentation process are optimized using three metaheuristics such as the cuckoo search (CS), equilibrium optimizer (EO), and harmony search (HS) algorithms. A detailed experimental analysis is made on benchmark PAI dataset, and the results are inspected under varying aspects. The obtained results pointed out the supremacy of the presented model with a higher accuracy of 98.71%.
Improved Hybrid Model for Predicting Concrete Crack Openings Based on Chaos Theory
Authors: Xu Yao , Huang Yaoying , Xu Xiaofeng , Xiao Fang
Published: January 2022

Conventional statistical models provide inaccurate predictions of concrete crack openings because they do not consider the nonlinear temperature response and the residual characteristics of concrete. To address this problem, this study introduces a nonlinear temperature factor and develops an improved statistical model of crack openings. The chaotic characteristics of residual time series of the improved statistical model are analyzed based on chaos theory and phase-space reconstruction theory. These theories are integrated with back-propagation (BP) artificial neural networks and genetic algorithms (GAs) to establish a GA-BP neural network model for predicting residuals. Finally, a hybrid model is developed for predicting the concrete crack opening behavior. The predictions of the conventional statistical model, the statistical model considering nonlinear temperature component, and the hybrid model are compared using the case study on the crack openings of a regulating sluice. The results show that the proposed hybrid model in this study for predicting concrete crack openings is significantly more accurate than the conventional statistical model and the statistical model considering nonlinear temperature component.
Clinical Significance of Circulating Tumor Cells (CTCs) and Survivin on Predicting Prognosis in Thyroid Cancer Patients
Authors: Weng Xun , YangYang  , Cai Yujiao
Published: January 2022

Background. Clinical significance of circulating tumor cell (CTC) count, mesenchymal CTCs (MCTCs), and survivin in patients with thyroid cancer remains unclear. We evaluated the relationship between the expression of different CTC subtypes or survivin and the prognosis in patients with thyroid cancer. Patients and Methods. This study enrolled 164 patients with thyroid cancer who were diagnosed from January 2013 to September 2020 in our hospital. Among these patients, there were 73 cases with papillary thyroid cancer (PTC), 60 cases with follicular thyroid cancer (FTC), 12 medullary thyroid cancers (MTC), 10 poorly differentiated thyroid cancers (PDTC), 9 anaplastic thyroid cancers, and 10 control patients with nonmalignant thyroid nodules based on their histopathological characteristics. Only 5 milliliters (mL) of peripheral blood from the patients with thyroid cancer and control was used to detect the CTC cell number via CanPatrol capture technique before treatments. We also isolated mononuclear cells (MNC) from the peripheral blood and performed quantity reverse transcriptase polymerase chain reaction (qPCR) for survivin gene expression among these patients. Results. The overall positive rates of CTC at diagnosis were 56.1%. The relapse and metastasis rates in PTC and FTC patients with more than 6 CTCs and positive MCTCs were significantly higher than those in the patients with 6 or less than 6 CTCs and MCTCs. It was also found that these patients with >6 CTCs and MCTCs had shorter progression-free survival (PFS). Additionally, the survivin level of the patients with thyroid cancer was strongly relative to differentiation grades of thyroid cancers. Conclusions. The detection of more than six of total CTCs and positive MCTCs in the patients with differentiated thyroid cancer is an excellent biomarker for predicting the prognosis of patients. Survivin also is a good biomarker for thyroid cancer differentiation.
Implementation and Optimization of Reverse Suspension Structure Design Model Using Deep Learning
Authors: Yu Xiwen , Wang Kai , Wang Shaoxuan
Published: January 2022

The present work aims to improve the design efficiency and optimize the results in the increasingly complex and diversified material design projects to help architects realize the better performance of building structures. According to the characteristics of comprehensive perception and intelligent processing of the Internet of Things, a reverse suspension structure design model is constructed based on the finite element method and simulated annealing algorithm. Besides, deep learning is adopted to train complex functions for performance correction and to optimize the plane structure of shell structure. Moreover, the force is transformed into shape, and the form-finding process is completed to facilitate the operation of designers. Finally, the spatial anchoring ability of the geographic information system is used to match and calculate the relevant characteristics of spatial elements. On this basis, the index construction strategy based on weight distribution is employed to realize the data fusion diagnosis framework and enhance the intelligence of architectural design. The simulation results show that the maximum tensile stress of the physical suspension experiment is 3.71 MPa and the maximum compressive stress is 14.7 MPa. The compressive stress value is much larger than the tensile stress value. The maximum deformation value’s difference between the compressive and tensile stress is 0.07 and 0.11, respectively. The error is within the acceptable range, which is similar to the compression state results obtained from the actual suspension physical experiment, indicating that the initial design model of the reverse suspension structure based on deep learning is reliable. In addition, the evolutionary optimization effect analysis results demonstrate that the load of the design structure is relatively uniform, which verifies the feasibility of the algorithm reported here. The research significance of the reverse suspension structure model constructed here is to provide an accurate and feasible design idea for the reverse design of some complex structures in the building suspension. It can also shorten the creation and improvement cycle of this kind of structure and optimize the performance and construction cycle of the building structure.
Energy-Aware Routing Protocol with Fuzzy Logic in Industrial Internet of Things with Blockchain Technology
Authors: Mehbodniya Abolfazl , Webber Julian L. , Rani Rashmi , Ahmad Sayed Sayeed , Wattar Ihab , Ali Liaqat , Nuagah Stephen Jeswinde
Published: January 2022

It is the expansion and use of the Industrial Internet of Things (IIoT) in various industrial sectors and applications that are referred to as the Industrial Internet of Things (IIoT). The Industrial Internet of Things includes industrial applications such as robots, medical devices, and software-defined manufacturing processes. In terms of energy conservation, routing is extremely essential. The creation of an energy effectual steering procedure leads to a substantial rise in energy consumption. To minimize network traffic and increase network life, the article presented an Industrial IoT Fuzzy Logic Energy-Aware Routing Protocol (FLEA-RPL), which decreases network traffic as well as improves network life. The most suitable parent for data transfer is selected based on, among other things, the routing parameters charge, residual energy, and expected transmission count. Since the load routing metric is taken into consideration during the construction of the route, the data traffic is spread across the network. This increases network’s lifetime while maintaining a high packet delivery ratio. The proposed work proposes a Multilayer Energy-Aware Aware RPL (MCEA-RPL) cluster for the Internet of Things to decrease network data traffic while increasing the lifetime of the network. It is split into three phases, each including the creation of network rings, intraring divisions, and intercluster routing. First and foremost, the virtual ring is created in the network. Secondly, each ring forms an identical cluster and chooses the CH node. Finally, it is responsible for the maintenance and performance of the DODAG. Data transfer from the lesser sheet to the DODAG root is known as data transfer. By using Blockchain technology, the lifetime of a network may be extended by reducing the number of identical data package transfers. This article offers Enhanced Mobility Support RPL (EM-RPL) in Industrial IoT which enhances mobility support with blockchain and spreads system generation. It comprises two processes: a collection of the parental static node and selection of the parent moving node. The static parent selection method uses routing metrics load and residual energy to identify the parent that is most suited for data transfer. Two phases of mobile parent selection must be distinguished: data transmission and route rediscovery. The mobile node utilizes furious logic to compute the hand-off value of the metric packet errors ratio and the signal strength indication received from the base station. If the hand-off value exceeds the threshold limit, the DODAG route has to be changed to work correctly. The EM-RPL thus increases the package delivery rate by reducing the amount of route interruption caused by mobility, while offering an efficient handling mechanism.
Multimode Analysis of Geometrically Nonlinear Transverse Free and Forced Vibrations of Tapered Beams
Authors: El Hantati Issam , Adri Ahmed , Fakhreddine Hatim , Rifai Said , Benamar Rhali
Published: January 2022

In this study, the geometrically nonlinear free and forced vibrations of tapered beams are investigated on the basis of the Euler-Bernoulli beam theory and the von Karman geometric nonlinearity assumptions. The aim of the analysis is to determine tapered beams nonlinear frequencies and modes, and the associated stress distributions by means of bending moment diagrams. The linear problem is first solved. Then, to tackle the nonlinear problem, the displacement function is expanded as a series of the linear modes and the discrete expressions for the strain and kinetic energies are derived. Assuming a point excitation of the beam, the algebraic nonlinear system obtained based on Hamilton’s principle is solved by an approximate method. The effect of the geometric nonlinearity in both the free and forced cases was illustrated and discussed and the effect of the variation of various tapered beam geometrical parameters. The effect of varying the excitation level was also examined.
Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques
Authors: Ukwuoma Chiagoziem C. , Zhiguang Qin , Bin Heyat Md Belal , Ali Liaqat , Almaspoor Zahra , Monday Happy N.
Published: January 2022

Recent advances in computer vision have allowed broad applications in every area of life, and agriculture is not left out. For the agri-food industry, the use of advanced technology is essential. Owing to deep learning’s capability to learn robust features from images, it has witnessed enormous application in several fields. Fruit detection and classification remains challenging due to the form, color, and texture of different fruit species. While studying the impact of computer vision on fruit detection and classification, we pointed out that till 2018 many conventional machine learning methods were utilized while a few methods exploited the application of deep learning methods for fruit detection and classification. This has prompted us to pursue an extensive study on surveying and implementing deep learning models for fruit detection and classification. In this article, we intensively discussed the datasets used by many scholars, the practical descriptors, the model’s implementation, and the challenges of using deep learning to detect and categorize fruits. Lastly, we summarized the results of different deep learning methods applied in previous studies for the purpose of fruit detection and classification. This review covers the study of recently published articles that utilized deep learning models for fruit identification and classification. Additionally, we also implemented from scratch a deep learning model for fruit classification using the popular dataset “Fruit 360” to make it easier for beginner researchers in the field of agriculture to understand the role of deep learning in the agriculture domain.
Evaluation of Six Preendoscopy Scoring Systems to Predict Outcomes for Older Adults with Upper Gastrointestinal Bleeding
Authors: Li Yajie , Lu Qin , Wu Kexuan , Ou Xilong
Published: January 2022

Objectives. To compare the ability of six preendoscopic scoring systems (ABC, AIMS65, Glasgow Blatchford score (GBS), MAP(ASH), pRS, and

T

-score) to predict outcomes of upper gastrointestinal bleeding (UGIB) in older adults. Methods. This was a retrospective study of 602 older adults (

age
≥
65

) presenting with UGIB at Zhongda Hospital Southeast University from January 2015 to June 2021. Six scoring systems were used to analyze all patients. Results. ABC had the largest area under the curve (AUC) (0.833; 95% confidence interval (CI): 0.801–0.862) and was significantly higher than pRS 0.696 (95% CI: 0.658–0.733,

p
<
0.01

) and

T

-score 0.667 (95% CI: 0.628–0.704,

p
<
0.01

) in predicting mortality. MAP(ASH) (0.783; 95% CI: 0.748–0.815) performs the best in predicting intervention and was similar to GBS,

T

-score, ABC, and AIMS65. The AUCs for MAP(ASH) (0.732; 95% CI: 0.698–0.770), AIMS65 (0.711; 95% CI: 0.672–0.746), and ABC (0.718; 95% CI: 0.680–0.754) were fair for rebleeding, while those of GBS (0.662; 95% CI: 0.617–0.694),

T

-score (0.641; 95% CI: 0.606–0.684), and pRS (0.609; 95% CI: 0.569–0.648) were performed poorly. MAP(ASH) performs the best in predicting ICU admission (0.784; 95% CI: 0.749–0.816). All the five scores were significantly higher than pRS (

p
<
0.05

for ABC, AIMS65 and

T

-score,

p
<
0.01

for GBS and MAP). Conclusions. Mortality, intervention, rebleeding, and ICU admission in UGIB for older adults can be predicted well using MAP(ASH). ABC is the most accurate for predicting mortality. Except for rebleeding, GBS has an acceptable performance in predicting ICU admission, mortality, and intervention. AIMS65 and

T

-score performed moderately, and pRS may not be suitable for the target cohort.
[Retracted] Research on the Model of Distance Education Resource Integration from the Perspective of Comparative Education
Authors: Dan Zhao
Published: January 2022

In order to solve the problems of high data loss rate, long time‐consuming resource integration, large amount of redundant data, and incomplete results of resource integration in traditional methods, a design method of distance education resource integration model from the perspective of comparative education is proposed. Association rules are used to mine resources and get effective resources in the context of comparative pedagogy, with the goal of increasing the impact of resource integration. Then, using a three‐tier architecture, a remote education resource integration model is created, and the mining results are combined through the application layer, resource encryption layer, and multicast communication layer to achieve distance education resource integration. Simulation results show that the method of this article has low data loss rate, short resource integration time, less redundant data, and comprehensive integration results, which provides an effective technical support for the development of education.
Stroke Volume Estimation from Respiratory Inductive Plethysmography: Double Empirical Decomposition
Authors: Abdulhay Enas , Gumery Pierre-Yves , Ilango R. , Hariharasitaraman S. , Thilagaraj M. , Arunkumar N. , Ramirez-Gonzalez Gustavo
Published: January 2022

In this study, we have developed a “double-empirical mode decomposition algorithm” to estimate cardiac stroke volume from respiratory inductive plethysmography (RIP) signals. The algorithm consists of first an ensemble empirical mode decomposition (EEMD) to extract the cardiorespiratory components. Then, it is followed by an empirical mode decomposition (EMD) to extract only the cardiac components. This double approach permits (a) solving problems of mixing between cardiac and respiratory components (mode and scale mixing), (b) cardiogenic oscillations extraction in the respiratory inductive plethysmography signal, and (c) subsequent estimation of stroke volume. The algorithm is applied to simulated and real RIP signals. The simulated signals are generated by a cardiorespiratory model previously published by the authors. The real signals are measured via a developed inductive vest. In the real case, the values of estimated stroke volumes are compared to the values obtained by thoracocardiographic filter-based method. In the simulated case, the values are compared to the simulated cardiac activity. The results of comparison through Bland and Altman indicate an error lying in the range ±10%. In contrast to thoracocardiography, the proposed method consists of a promising tool for continuous noninvasive adaptive cardiac monitoring that does not need adjusting parameters or cut-off based on ECG. Also, in comparison to echocardiography and impedance-based methods, it does not necessitate the presence of an expert and is not too sensitive to current penetration.
Fecal Microbiota Transplants for Inflammatory Bowel Disease Treatment: Synthetic- and Engineered Communities-Based Microbiota Transplants Are the Future
Authors: Khan Raees , Roy Nazish , Ali Hussain , Naeem Muhammad
Published: January 2022

The human intestine harbors a huge number of diverse microorganisms where a variety of complex interactions take place between the microbes as well as the host and gut microbiota. Significant long-term variations in the gut microbiota (dysbiosis) have been associated with a variety of health conditions including inflammatory bowel disease (IBD). Conventional fecal microbiota transplantations (FMTs) have been utilized to treat IBD and have been proved promising. However, various limitations such as transient results, pathogen transfer, storage, and reproducibility render conventional FMT less safe and less sustainable. Defined synthetic microbial communities (SynCom) have been used to dissect the host-microbiota-associated functions using gnotobiotic animals or in vitro cell models. This review focuses on the potential use of SynCom in IBD and its advantages and relative safety over conventional FMT. Additionally, this review reinforces how various technological advances could be combined with SynCom to have a better understanding of the complex microbial interactions in various gut inflammatory diseases including IBD. Some technological advances including the availability of a gut-on-a-chip system, intestinal organoids, ex vivo intestinal cultures, AI-based refining of the microbiome structural and functional data, and multiomic approaches may help in making more practical in vitro models of the human host. Additionally, an increase in the cultured diversity from gut microbiota and the availability of their genomic information would further make the design and utilization of SynCom more feasible. Taken together, the combined use of the available knowledge of the gut microbiota in health and disease and recent technological advances and the development of defined SynCom seem to be a promising, safe, and sustainable alternative to conventional FMT in treating IBD.
Tumor Necrosis Factor-α–Induced Protein 8-Like 2 Fosters Tumor-Associated Microbiota to Promote the Development of Colorectal Cancer
Authors: Lou Yunwei , Song Miaomiao , Han Meijuan , Zhong Jiateng , Tian Xueqin , Ren Yahan , Song Yaru , Duan Liangwei , Zhao Peiqing , Song Xiangfeng , Zhang Wen , Chen Youhai H. , Wang Hui
Published: January 2022

Although increasing evidence links the gut microbiota with the development of colorectal cancer, the molecular mechanisms for microbiota regulation of tumorigenesis are not fully understood. Here, we found that a member of the TNFα-induced protein 8 (TNFAIP8) family called TIPE2 (TNFAIP8-like 2) was significantly upregulated in murine intestinal tumors and in human colorectal cancer, and colorectal cancer with high expression of Tipe2 mRNA associated with reduced survival time of patients. Consistent with these findings, TIPE2 deficiency significantly inhibited the development of colorectal cancer in mice treated with azoxymethane/dextran sodium sulfate and in Apcmin/+ mice. TIPE2 deficiency attenuated the severity of colitis by successfully resolving and restricting colonic inflammation and protected colonic myeloid cells from death during colitis. Transplantation of TIPE2-deficient bone marrow into wild-type mice successfully dampened the latter's tumorigenic phenotype, indicating a hematopoietic-specific role for TIPE2. Mechanistically, restricting the expansion of Enterobacteriaceae/Escherichia coli (E. coli) decreased intestinal inflammation and reduced the incidence of colonic tumors. Collectively, these data suggest that hematopoietic TIPE2 regulates intestinal antitumor immunity by regulation of gut microbiota. TIPE2 may represent a new therapeutic target for treating colorectal cancer.
Achieving Optimal Correction for Young Myopic Children: A Concept Study
Authors: Rasmussen Sara Bøgelund , Møller Flemming , Jakobsen Trine Møldrup
Published: January 2022

The purpose of this article is to explore alternative ways of achieving optimal correction for myopic children who cannot cooperate to subjective manifest refraction (SR). The study included myopic children aged 9–12 years who underwent non-cycloplegic SR and autorefraction with and without cycloplegia using the Shin-Nippon Nvision-K 5001 autorefractor (AR) as well as non-cycloplegic autorefraction using the Topcon KR-800S AR. There were 21 children (mean age, 10.62 years) included. The spherical equivalent refractive error of SR was not significantly different from that of non-cycloplegic AR measurements, but it was significantly different from that of cycloplegic Shin-Nippon Nvision-K 5001 measurements (p < 0.001). Compared with SR, cycloplegic Shin-Nippon Nvision-K 5001 measured a less myopic refractive error (median: −2.44 D vs. −2.88 D, p < 0.001). For both ARs, the axis measurements and astigmatic dioptre values between SR and autorefraction were not significantly different. Compared with non-cycloplegic SR, cycloplegic measurements showed a lesser degree of myopic refractive error. There was no significant difference between SR and non-cycloplegic autorefraction. Therefore, the Topcon KR-800S and the Shin-Nippon Nvision-K 5001 ARs may be useful for prescribing glasses in myopic children who cannot cooperate during SR. However, caution should be taken with cylinders <0.75 D because the agreement in axis between SR and AR measurement is poor. Therefore, in such cases, we suggest to add half the cylinder to the spherical component.