bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection and Quantification of Resting Tremor in Parkinson’s Disease Using Long-Term Acceleration Data
Authors: Yuan Han , Liu Sen , Liu Jiali , Lin Hai , Yang Cuiwei , Cai Xiaodong , Zeng Lepeng , Li Siman
Published: October 2021

Long-term monitoring of resting tremor is key to assess the status of patients suffering from Parkinson’s disease (PD), which is of vital importance for reasonable medication. The detection and quantification of resting tremor in reported works rely heavily on specified movements and are not appropriate for long-term monitoring in real-life condition. The purpose of this study is to develop a detection model for long-term monitoring of resting tremor and explore an effective indicator for tremor quantification. This study included long-term acceleration data from PD patients and proposed a resting tremor detection model based on machine learning classifiers and Synthetic Minority Oversampling Technique (SMOTE). Four machine learning classifiers, K-Nearest Neighbor (KNN), Random Forest (RF), Adaptive Boosting (AdaBoost), and Support Vector Machine (SVM), were compared. Furthermore, an indicator called tremor timing ratio (TTR) was defined and calculated for tremor quantification. The detection model with RF classifier achieved the highest overall accuracy of 94.81%. The sample entropy of the acceleration signal was proved most influential in the classification by exploring the feature importance. Through the Kruskal-Wallis test and the Mann-Whitney U test, the TTR had a strong correlation with the subscore of resting tremor in Unified Parkinson Disease Rating Scale (UPDRS). Such two-step evaluation process for resting tremor can detect the tremor effectively and is expected to be applied in long-term monitoring of PD patients in daily life to realize a more comprehensive assessment of PD.
The Health Hazards of Volcanoes: First Evidence of Neuroinflammation in the Hippocampus of Mice Exposed to Active Volcanic Surroundings
Authors: Navarro-Sempere A. , Martínez-Peinado P. , Rodrigues A. S. , Garcia P. V. , Camarinho R. , García M. , Segovia Y.
Published: October 2021

Neuroinflammation is a process related to the onset of neurodegenerative diseases; one of the hallmarks of this process is microglial reactivation and the secretion by these cells of proinflammatory cytokines such as TNFα. Numerous studies report the relationship between neuroinflammatory processes and exposure to anthropogenic air pollutants, but few refer to natural pollutants. Volcanoes are highly inhabited natural sources of environmental pollution that induce changes in the nervous system, such as reactive astrogliosis or the blood-brain barrier breakdown in exposed individuals; however, no neuroinflammatory event has been yet defined. To this purpose, we studied resting microglia, reactive microglia, and TNFα production in the brains of mice chronically exposed to an active volcanic environment on the island of São Miguel (Azores, Portugal). For the first time, we demonstrate a proliferation of microglial cells and an increase in reactive microglia, as well an increase in TNFα secretion, in the central nervous system of individuals exposed to volcanogenic pollutants.
Sublemma‐Based Neural Machine Translation
Authors: Nguyen Thien , Nguyen Huu , Tran Phuoc
Published: October 2021

Powerful deep learning approach frees us from feature engineering in many artificial intelligence tasks. The approach is able to extract efficient representations from the input data, if the data are large enough. Unfortunately, it is not always possible to collect large and quality data. For tasks in low‐resource contexts, such as the Russian ⟶ Vietnamese machine translation, insights into the data can compensate for their humble size. In this study of modelling Russian ⟶ Vietnamese translation, we leverage the input Russian words by decomposing them into not only features but also subfeatures. First, we break down a Russian word into a set of linguistic features: part‐of‐speech, morphology, dependency labels, and lemma. Second, the lemma feature is further divided into subfeatures labelled with tags corresponding to their positions in the lemma. Being consistent with the source side, Vietnamese target sentences are represented as sequences of subtokens. Sublemma‐based neural machine translation proves itself in our experiments on Russian‐Vietnamese bilingual data collected from TED talks. Experiment results reveal that the proposed model outperforms the best available Russian ⟶ Vietnamese model by 0.97 BLEU. In addition, automatic machine judgment on the experiment results is verified by human judgment. The proposed sublemma‐based model provides an alternative to existing models when we build translation systems from an inflectionally rich language, such as Russian, Czech, or Bulgarian, in low‐resource contexts.
Elaborate Modeling and Fragility Assessment of a Multiframe PC Box‐Girder Bridge with Intermediate Hinges in California
Authors: Wu Tong , Li Zhan , Liu Shengchun
Published: October 2021

Multiframe PC box‐girder bridge with intermediate hinges is a kind of bridge having complex structural characters, which is very quintessential in California. In this study, a typical bridge was adopted to establish a nonlinear dynamic model through OpenSees platform. Intermediate hinge and inhibiting devices in it were elaborately simulated. Meanwhile, pushover analysis was used to reinstate a specimen of column test, which has the similar ratio of reinforcement to the typical bridge, and the hysteretic model parameters of the longitudinal steels inside columns were obtained. The damage indexes of column and hinge, which are primary components, under different limit states were acquired by moment‐curvature analysis. Taking into account the uncertainty, nonlinear time‐history analysis of the bridge was carried out through a suite of synthetic ground motions. Subsequently, a probabilistic seismic demand model was developed, and fragility curves were further focused on. According to fragility assessment, the conclusion shows that columns and hinge restrainers exhibit high fragility, and bridge system fragility is gradually determined by column fragility along with aggravating of the damage state. Unseating of girder can hardly occur at abutments and intermediate hinges. Moderate limit state could be exceeded in the positions of plug‐type concrete structures in intermediate hinges, which tends to create transverse and vertical cracks, furthermore causing reinforcements yield. It indicates that it would severely underestimate the seismic fragility of intermediate hinges without considering the elaborate simulation of hinges.
RB Allocation Scheme for eMBB and URLLC Coexistence in 5G and Beyond
Authors: Zhang Xuefen , Guo Xiaodong , Zhang Huan
Published: October 2021

In this paper, we studied resource allocation in a dynamic multiplexing scenario of two 5G services, namely, enhanced mobile broadband (eMBB) and ultrareliable low‐latency communication (URLLC). Considering that eMBB services focus on high data rates while URLLC is very strict in terms of latency and reliability, we formulate a joint eMBB/URLLC resource allocation problem. To solve the formulated problem, in particular, we propose a joint resource allocation algorithm that can satisfy both eMBB user rate and URLLC interrupt probability requirements. We propose an RB allocation scheme that takes into account the needs of eMBB and URLLC users. The program consists of two phases as follows: (1) establish an RB allocation for eMBB users and (2) assign the minislot for URLLC users and calculate the transmission power of URLLC users that ensures the reliability constraint. We also realize and verify the algorithm through the simulation.
Natural Language Processing Algorithms for Normalizing Expressions of Synonymous Symptoms in Traditional Chinese Medicine
Authors: Zhou Lu , Liu Shuangqiao , Li Caiyan , Sun Yuemeng , Zhang Yizhuo , Li Yuda , Yuan Huimin , Sun Yan , Xu Fengqin , Li Yuhang
Published: October 2021

Background. The modernization of traditional Chinese medicine (TCM) demands systematic data mining using medical records. However, this process is hindered by the fact that many TCM symptoms have the same meaning but different literal expressions (i.e., TCM synonymous symptoms). This problem can be solved by using natural language processing algorithms to construct a high-quality TCM symptom normalization model for normalizing TCM synonymous symptoms to unified literal expressions. Methods. Four types of TCM symptom normalization models, based on natural language processing, were constructed to find a high-quality one: (1) a text sequence generation model based on a bidirectional long short-term memory (Bi-LSTM) neural network with an encoder-decoder structure; (2) a text classification model based on a Bi-LSTM neural network and sigmoid function; (3) a text sequence generation model based on bidirectional encoder representation from transformers (BERT) with sequence-to-sequence training method of unified language model (BERT-UniLM); (4) a text classification model based on BERT and sigmoid function (BERT-Classification). The performance of the models was compared using four metrics: accuracy, recall, precision, and F1-score. Results. The BERT-Classification model outperformed the models based on Bi-LSTM and BERT-UniLM with respect to the four metrics. Conclusions. The BERT-Classification model has superior performance in normalizing expressions of TCM synonymous symptoms.
Φ-Haar Wavelet Operational Matrix Method for Fractional Relaxation-Oscillation Equations ContainingΦ-Caputo Fractional Derivative
Authors: Sunthrayuth Pongsakorn , Aljahdaly Noufe H. , Ali Amjid , Shah Rasool , Mahariq Ibrahim , Tchalla Ayékotan M. J.
Published: October 2021

This paper proposes a numerical method for solving fractional relaxation-oscillation equations. A relaxation oscillator is a type of oscillator that is based on how a physical system returns to equilibrium after being disrupted. The primary equation of relaxation and oscillation processes is the relaxation-oscillation equation. The fractional derivatives in the relaxation-oscillation equations under consideration are defined in theΦ-Caputo sense. The numerical method relies on a novel type of operational matrix method, namely, theΦ-Haar wavelet operational matrix method. The operational matrix approach has a lower computational complexity. The proposed scheme simplifies the main problem to a set of linear algebraic equations. Numerical examples demonstrate the validity and applicability of the proposed technique.
Degradation of Shotcrete Materials Subjected to Sulfate and Chloride Attack in Varying Exposure Regimes
Authors: Zhao Ruiqiang , Xu Lihao , Yang Jun , Zou Yang , Zhang Zhongya
Published: October 2021

Durability of in situ shotcrete under external sulfate attack was investigated, taking into consideration the addition of mineral admixtures, along with the presence of chloride ions. Three water‐to‐binder ratios (w/b), i.e., 0.35, 0.45, and 0.55, and two types of supplementary cementitious materials (SCMs), namely, fly ash (FA) and silica fume (SF), were considered in the current study. Two different laboratorial immersion regimes (continuously full immersion and partial immersion with cycling temperature and relative humidity) were carried out to induce chemical/physical sulfate attack. Results show that loss of strength was the typical feature of chemical sulfate attack on shotcrete, while surface spalling dominated in deterioration caused by physical sulfate attack. The presence of chloride ions can globally mitigate these deteriorations. Meanwhile, the lower w/b ratio proved to be efficient in increasing the resistance to both sulfate attacks. Adding fly ash (FA) in shotcrete mixtures enhanced the long‐term performance but invited massive white efflorescence on surface layer under partial‐immersion exposure condition. Silica fume (SF) admixture can compensate the undesired reduction of early‐age strength caused by FA addition, but make these specimens more susceptible to sulfate attack. Mercury intrusion porosimetry (MIP) analysis, scanning electron microscopy (SEM), and X‐ray diffraction (XRD) tests reveal that these consequences were strongly related to the refinement of microstructure resulted from pozzolanic reactions and hydration kinetics.
Research on Anchor Cable and C‐Shaped Tube Support Method in Deep Layers Roadway, Experimental Study, and Numerical Simulation
Authors: Shan Renliang , Zhang Shupeng , Xiao Shengchao , Liang Junqi
Published: October 2021

In roadways with high ground stress or burial depths, the joints distributed within rock formations are subject to complex stresses and interlayer misalignments frequently. Rock bolts and cable bolts anchored in the rock formations are subject to tensile and shear forces. Most of the bolts used in roadway engineering are local anchored, resulting in insufficient shear strength at the bolt free end close to roadway surface and increasing bolts breaking. The anchor cable and C‐shaped tube (ACC) is a highly prestressed cable bolt that can withstand high shear force in its free end. This paper examines the effect of the relationship between C‐shaped tube length and joint location on the shear resistance of ACC by double shear tests. To fully exploit the ACC’s shear resistance, the C‐shaped tube ends should be at least 30 cm beyond the joint. The effect of preload and concrete spray thicknesses on roadway deformation and plastic zone is investigated by numerical simulation. Results show that ACC and concrete spraying layer can form a stable extruded arch structure, so that the broken and soft rock within the loosen zone is in three‐dimensional‐stress state, effectively improving surrounding rock properties and controlling its deformation size. Based on these results, the ACC support design method is proposed.
Detection for Dangerous Goods Vehicles in Expressway Service Station Based on Surveillance Videos
Authors: Huang Kai , Zhao Qinpei
Published: October 2021

To improve the safety capabilities of expressway service stations, this study proposes a method for detecting dangerous goods vehicles based on surveillance videos. The information collection devices used in this method are the surveillance cameras that already exist in service stations, which allows for the automatic detection and position recognition of dangerous goods vehicles without changing the installation of the monitoring equipment. The process of this method is as follows. First, we draw an aerial view image of the service station to use as the background model. Then, we use inverse perspective mapping to process each surveillance video and stitch these videos with the background model to build an aerial view surveillance model of the service station. Next, we use a convolutional neural network to detect dangerous goods vehicles from the original images. Finally, we mark the detection result in the aerial view surveillance model and then use that model to monitor the service station in real time. Experiments show that our aerial view surveillance model can achieve the real-time detection of dangerous goods vehicles in the main areas of the service station, thereby effectively reducing the workload of the monitoring personnel.
An Edge Computing‐Enabled Train Obstacle Detection Method Based on YOLOv3
Authors: Li Song , Zhao Hongli , Ma Jinmin
Published: October 2021

Rail transit is developing towards intelligence which takes lots of computation resource to perform deep learning tasks. Among these tasks, object detection is the most widely used, like track obstacle detection, catenary wear, and defect detection and looseness detection of train wheel bolts. But the limited computation capability of the train onboard equipment prevents running deep and complex detection networks. The limited computation capability of the train onboard equipment prevents conducting complex deep learning tasks. Cloud computing is widely utilized to make up for the insufficient onboard computation capability. However, the traditional cloud computing architecture will bring in uncertain heavy traffic load and cause high transmission delay, which makes it fail to complete real‐time computing intensive tasks. As an extension of cloud computing, edge computing (EC) can reduce the pressure of cloud nodes by offloading workloads to edge nodes. In this paper, we propose an edge computing‐based method. The onboard equipment on a fast‐moving train is responsible for acquiring real‐time images and completing a small part of the inference task. Edge computing is used to help execute the object detection algorithm on the trackside and carry most of the computing power. YOLOv3 is selected as the object detection model, since it can balance between the real‐time and accurate performance on object detection compared with two‐stage models. To save onboard equipment computation resources and realize the edge‐train cooperative interface, we propose a model segmentation method based on the existing YOLOv3 model. We implement the cooperative inference scheme in real experiments and find that the proposed EC‐based object detection method can accomplish real‐time object detection tasks with little onboard computation resources.
Simulation and Prediction of Fungal Community Evolution Based on RBF Neural Network
Authors: Cai Xiao-Wei , Bao Ya-Qian , Hu Ming-Feng , Liu Jia-Bao , Zhu Jia-Ming
Published: October 2021

Simulation and prediction of the scale change of fungal community. First, using the experimental data of a variety of fungal decomposition activities, a mathematical model of the decomposition rate and the relationship between the bacterial species was established, thereby revealing the internal mechanism of fungal decomposition activity in a complex environment. Second, based on the linear regression method and the principle of biodiversity, a model of fungal decomposition rate was constructed, and it was concluded that the interaction between mycelial elongation and moisture resistance could increase the fungal decomposition rate. Third, the differential equations are used to quantify the competitive relationship between different bacterial species, divide the boundaries of superior and inferior species, and simulate the long-term and short-term evolution trends of the community under the same initial environment. And an empirical analysis is made by taking the sudden change of the atmosphere affecting the evolution of the colony as an example. Finally, starting from summer, combining soil temperature, humidity, and fungal species data in five different environments such as arid and semiarid, a three-dimensional model and RBF neural network are introduced to predict community evolution. The study concluded that under given conditions, different strains are in short-term competition, and in the long-term, mutually beneficial symbiosis. Biodiversity is important for the biological regulation of nature.
Transient Response of a Simulated Aeroengine with a Fusing Structure during a Fan-Blade Out Event
Authors: Ma Chi , Chen Wei , Han Jiaqi , Liu Lulu , Zhao Zhenhua , Luo Gang
Published: October 2021

A fan-blade out (FBO) event may cause complex vibrations in an aeroengine. A fusing structure protects the structural integrity of the whole aeroengine after an extreme accident, such as a blade-loss event. In this paper, we investigate not only the transient and steady responses of a simulated aeroengine model with a fusing structure after an FBO event but also the changes made to the model because of the fusing structure. Our simulated model of an aeroengine includes two rotors, bearings, and a casing. The results for the dynamic response of the simulated model show that the fusing structure can reduce the steady-state response and the impact load on the support bearings in the rotor system. The rubbing impact between the blades and casing was accounted for. A fast method for calculating the response of fused structures was developed, which may be useful when designing the stiffness of the fusing structure.
A Randomized, Double-Blind, Placebo-Controlled Study to Assess the Efficacy and Safety of a Nutritional Supplement (ImmuActiveTM) for COVID-19 Patients
Authors: Majeed Muhammed , Nagabhushanam Kalyanam , Shah Kalpesh , Mundkur Lakshmi
Published: October 2021

Background. SARS-CoV-2 has emerged as a global threat due to its infectivity and rapid transmission. We evaluated the safety and efficacy of herbal and mineral formulation (ImmuActive) as an adjunct therapy in COVID-19 patients. Methods. A randomized, double-blind, placebo-controlled study was conducted in 100 COVID-19 patients in three centers in Southern India, and 92 subjects completed the study. Subjects were followed up until they were discharged from the hospital or for a maximum of 28 days, whichever was earlier. The primary outcome parameters were the mean change and time required to change the ordinal scale of disease severity by one unit. The secondary outcomes were the time required to turn RT-PCR negative or get discharged from the hospital, change in modified Jackson’s Symptom Severity score, and COVID-19 quality of life questionnaire. Results. The ordinal scale at the end of the study was significantly lower in COVID-19 patients supplemented with ImmuActive (0.57) than placebo (1.0), with a

p

value of 0.0043. The ordinal scale decreased by one unit within 2.35 days in ImmuActive-supplemented patients, while it took 3.36 days in placebo-supplemented patients. Days of hospitalization and time required to turn RT-PCR negative were comparatively lower in the ImmuActive arm than the placebo arm. Change in modified Jackson’s Symptom Severity Score and COVID-19 QOL were significant from screening to the end of the study in both ImmuActive and placebo arms. There were no adverse events observed during the study period. Conclusion. The study results suggest that ImmuActive could be a beneficial and safe adjunct treatment for effectively managing COVID-19 infection symptoms.
Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms
Authors: Wang Yong , Zhang Liang , Qi Lin , Yi Xiaoping , Li Minghao , Zhou Mao , Chen Danlei , Xiao Qiao , Wang Cikui , Pang Yingxian , Xu Jiangyue , Deng Hao , Liu Longfei , Guan Xiao
Published: October 2021

Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.
CBO-IE: A Data Mining Approach for Healthcare IoT Dataset Using Chaotic Biogeography-Based Optimization and Information Entropy
Authors: Ahirwar Manish Kumar , Shukla Piyush Kumar , Singhai Rakesh
Published: October 2021

Data mining is mostly utilized for a huge variety of applications in several fields like education, medical, surveillance, and industries. The clustering is an important method of data mining, in which data elements are divided into groups (clusters) to provide better quality data analysis. The Biogeography-Based Optimization (BO) is the latest metaheuristic approach, which is applied to resolve several complex optimization problems. Here, a Chaotic Biogeography-Based Optimization approach using Information Entropy (CBO-IE) is implemented to perform clustering over healthcare IoT datasets. The main objective of CBO-IE is to provide proficient and precise data point distribution in datasets by using Information Entropy concepts and to initialize the population by using chaos theory. Both Information Entropy and chaos theory are facilitated to improve the convergence speed of BO in global search area for selecting the cluster heads and cluster members more accurately. The CBO-IE is implemented to a MATLAB 2021a tool over eight healthcare IoT datasets, and the results illustrate the superior performance of CBO-IE based on F-Measure, intracluster distance, running time complexity, purity index, statistical analysis, root mean square error, accuracy, and standard deviation as compared to previous techniques of clustering like K-Means, GA, PSO, ALO, and BO approaches.
Fractal‐Dimension‐Based Walking Trajectory Analysis: A Case Study in Museum J. Armand Bombardier
Authors: Han Xueying , Zhan Changhong , Li Guanghao
Published: October 2021

A comprehensive understanding of the randomness, arbitrariness, and complexity of the visitors’ behavior and interaction in a museum is important because it is associated with the design. There is still uncertainty about how to characterize the visitors’ behavior and interaction. The fractal dimension was used in this study to indicate the geometrical form of the aged’s, the families’, and the students’ walking trajectories. The study results represented that all three sorts of the walking trajectory fractal‐dimension‐time curves fluctuated in the early stage. A remarkable exponential converges could then be observed. The mean fractal dimension after the convergence of the aged’s, the families’, and the students’ walking trajectory was nearly 1.8, 1.6, and 1.2, respectively. Furthermore, the behavior characteristics of these three sorts of visitors were quantified and the reasons were speculated and inferred. The comprehensive consideration of fractal geometry can aid in visitors’ behavior modeling and museum design.
Dynamic Decision Model of Real Estate Investment Portfolio Based on Wireless Network Communication and Ant Colony Algorithm
Authors: Li Ming , Wu Yousong
Published: October 2021

From reform and opening to the comprehensive construction of a well‐off society, the rapid growth of the national economy and the advancement of urbanization have promoted the rapid development of China’s real estate industry. The real estate industry has become a pillar growth point in the development of the national economy. At the same time, China’s real estate markets also continue to mature. However, due to the short development time of my country’s real estate market, imperfect management mechanism, irregular organization, and other issues, coupled with the fierce competition and internationalization of the market investment environment, the risk of investment accumulation in the real estate industry is also increasing. Therefore, in real estate investment decision‐making, it is of far‐reaching significance to study how to control real estate investment risks and promote the healthy and stable development of the real estate industry. The purpose of this article is to build a set of investment portfolios based on the ant colony algorithm to diversify risks and obtain returns, so that the constructed investment portfolios will minimize the risk when the return reaches a certain amount of time. This article first gives a general introduction to wireless network communication and then analyzes the risk of real estate project investment. First, the variance is used as a measure of risk to establish a dynamic model of the real estate development project portfolio, and the ant colony algorithm is introduced to the investment risk of real estate development projects. In the dynamic analysis, an improved portfolio model was established, and the two were compared through case analysis. The experimental results show that under the condition of the same net present value and investment payback period, the ant colony algorithm based on variance is invested in lot H. The ratio is obviously higher, and the capital investment ratio of lot H based on the ant colony algorithm is obviously lower. The difference between the two is 30.1%.
Joint Channel Pruning and Quantization‐Based CNN Network Learning with Mobile Computing‐Based Image Recognition
Authors: Liu Huanyu , Luo Qing , Shao Mingmei , Pan Jeng-Shyang , Li Junbao
Published: October 2021

The development of the Internet and communication technology has ushered in a new era of the Internet of Things (IoT). Moreover, with the rapid development of artificial intelligence, objects are endowed with intelligence, such as home automation and smart healthcare, which are typical applications of artificial intelligence technology in IoT. With the rise of convolutional neural network (CNN) in the field of computer vision, more and more practical applications need to deploy CNN on mobile devices. However, due to the large amount of CNN computing operations and the large number of parameters, it is difficult to deploy on ordinary edge devices. The neural network model compression method has become a popular technology to reduce the computational cost and has attracted more and more attention. We specifically design a small target detection network for hardware platforms with limited computing resources, use pruning and quantization methods to compress, and demonstrate in VOC dataset and RSOD dataset on the actual hardware platform. Experiments show that the proposed method can maintain a fairly accurate rate while greatly speeding up the inference speed.
Capacitive Deionization for the Removal of Paraquat Herbicide from Aqueous Solution
Authors: Alfredy Tusekile , Elisadiki Joyce , Jande Yusufu Abeid Chande
Published: October 2021

In comparison to other conventional methods like adsorption and reverse osmosis (RO), capacitive deionization (CDI) has only been investigated extensively for the removal of inorganic pollutants from water, demonstrating limited practicality. Herein, the study investigated the use of CDI for the removal of paraquat (PQ) herbicide from water by using commercial activated carbon (AC) electrodes. The CDI performance was examined as a function of the initial PQ concentration, applied voltage, flowrate, treatment time, and cycle stability testing in the batch mode approach. The applied voltage had a beneficial effect on the removal efficiency, whereas the removal efficiency of PQ declined as the initial PQ concentration increased. However, the electrosorption capacity gradually increased with the increase of initial feed solutions’ concentration. The maximum removal efficiency and electrosorption capacity achieved at 5 mg/L and 20 mg/L PQ initial concentrations, an applied voltage of 1.2 V, and 5 mL/min flowrate were 100% and 0.33 mg/g and 52.5% and 0.7 mg/g, respectively. Washing the electrodes with distilled water achieved sequential desorption of PQ, and the process produces a waste stream that can be disposed of or treated further. Therefore, the CDI method is considered a promising and efficient method for removing organic pollutants from water including pesticides.
Rapid Fault Diagnosis Method of Elevator System Based on Multiattribute Decision Making
Authors: Niu Dapeng , Qi Chenshu , Li Guanghai , Li Hongru , Pang Hali
Published: October 2021

Fault tree analysis is often used in elevator fault diagnosis because of its simplicity and reliability. However, the traditional fault tree method has the problems of low efficiency due to ignoring location change of bottom events during troubleshooting. This paper proposes a rapid diagnosis method based on multiattribute decision making to solve the problem. The fault tree of the elevator system is constructed based on expert knowledge and multisource data, and the location‐related matrix is constructed according to the complex vertical structure of the elevator. Then, the attributes of bottom events such as the failure probability, search cost, location time cost, and location‐related attributes are comprehensively analyzed in this paper. Finally, the TOPSIS method for dynamic attributes is used based on the work above to achieve the optimal troubleshooting sequence of elevator vibration fault. The results show that the proposed method is more efficient when compared to the optimal troubleshooting sequence obtained by the traditional method.
Failure Probability Modeling of Miniature DC Motors and Its Application in Fault Diagnosis
Authors: Xie Zhiping , Zhao Rongchen , Zheng Jiming , Lang Yancheng
Published: October 2021

This paper proposes a fault diagnosis method for miniature DC motors (MDCMs) in the presence of the uncertainties caused by material and random factors of the production process. In this method, the probability models of fault multiple features are established based on the advantage criterion of the maximum overall average membership to determine the distribution of fault multiple features. The fault diagnosis algorithm is synthesized to obtain the threshold ranges of fault multiple features according to different confidence levels. Experimental test results are presented and analyzed to validate the efficiency and performance of the proposed fault diagnosis method.
Multiobjective Economic Optimal Dispatch for the Island Isolated Microgrid under Uncertainty Based on Interval Optimization
Authors: Zhang Guoping , Wang Weijun , Du Jie , Sheng Haoyun
Published: October 2021

In order to analyse the impact of renewable generation and load uncertainties on the economic operation optimization of the island microgrid, a multiobjective economic optimal dispatch model under uncertainty based on interval optimization is proposed in this paper. The mathematical model of distributed generation and the prediction model of wind speed and wave generation are established. The uncertainties of renewable generation and load are described by the interval mathematical method. On this basis, the interval multiobjective optimal dispatch model is presented. For the “battery disgusting” users on the island, the battery cost is regarded as a separate optimization objective, and a multiobjective optimization objective function to minimize the economic cost, battery cost, and pollution emission of the island microgrid is discussed. An island microgrid, composed of wind turbine, photovoltaic, wave energy generation, diesel generator, and energy storage system, is chosen as a case study. The NSGA-II algorithm is applied to solve the multiobjective optimal problem. The results for deterministic forecast data and load are analysed, and the optimal operation scheme is obtained by the improved multiobjective grey target decision-making method. The influence of renewable generation fluctuations ±10%, ±20%, and ±30% and the load fluctuations ±10% and ±20% on island microgrid operation optimization is discussed in detail, respectively. The relevant research results can provide a reference for formulating the operating scheme of the island microgrid.
Myeloid-Derived Suppressor Cells: A Propitious Road to Clinic
Authors: Grover Amit , Sanseviero Emilio , Timosenko Elina , Gabrilovich Dmitry I.
Published: October 2021

Myeloid-derived suppressor cells (MDSC) are important regulators of immune responses in cancer. They represent a relatively stable form of pathologic activation of neutrophils and monocytes and are characterized by distinct transcriptional, biochemical, functional, and phenotypical features. The close association of MDSCs with clinical outcomes in cancer suggests that these cells can be an attractive target for therapeutic intervention. However, the complex nature of MDSC biology represents a substantial challenge for the development of selective therapies. Here, we discuss the mechanisms regulating MDSC development and fate and recent research advances that have demonstrated opportunities for therapeutic regulation of these cells.

Significance:

MDSCs are attractive therapeutic targets because of their close association with negative clinical outcomes in cancer and established biology as potent immunosuppressive cells. However, the complex nature of MDSC biology presents a substantial challenge for therapeutic targeting. In this review, we discuss those challenges and possible solutions.
Hypertensive Heart Failure in Asia
Authors: Rismiati Helsi , Lee Hae-Young
Published: October 2021

Hypertension (HT) is an important risk factor for heart failure (HF). The prevalence of HT among the HF population is higher in Asia than in other regions around the world. In Asia, HT is the most common cause of HF after ischemic heart disease. Hypertensive HF (HHF) results from structural and functional adaptations of the heart, which lead to left ventricular (LV) hypertrophy (LVH). Hypertensive LVH can cause ventricular diastolic dysfunction and becomes a risk factor for myocardial infarction, which is a well-known cause of LV systolic dysfunction. Asymptomatic systolic and diastolic LV dysfunction easily progress to clinically overt HF with other precipitating factors. Although the precise pathophysiology of HHF is still unclear, we have known that HHF can be reversed by effective control of blood pressure (BP). Thus, HT control is essential not only for primary prevention but also for the secondary prevention of HF. Here, we reviewed the epidemiology, pathophysiology, outcome, and implication of BP management in HHF patients, especially in the Asian population.
Study of designing regulator for temperature electrical resistance furnace using Kalman stochastic reconstructor
Authors: Kada Benyekhlef , El Kebir Abdelkader , Berka Mohammed , Belhadj Hafida , Chaouch Djamel Eddine
Published: October 2021

Electric resistance furnaces are the most popular and widely used industrial electro thermal equipment which continues to be the subject of many improvements. The aim of this paper is to control the temperature of electrical furnace for noisy thermocouple sensors. It can be assessed by observing some variables, which are very difficult to observe. Due to limitations, mainly the location of thermal sensors and their noises. In this case, the temperature measurement is trained with centered Gaussian white noise. The problem of accurate temperatures estimation for such sensors is solved using Kalman filter, which is an optimized estimator that provides a computationally efficient way to estimate system state. Thus, variables that are not directly measurable can be reconstructed from the algorithm. Kalman stochastic reconstructor (KSR). We cannot use with fixed parameters to control the temperature. For this reason, this paper comes up with a KSR approach based pole placement (PL) hybrid controller to realize an algorithm for the temperature control electrical furnace. Results based on Matlab simulation show that the improved algorithm has well produced an optimal estimate of the temperature. Evolving over time from noisy measurements. Hybrid algorithm KSR approach based PL give good performance compared to PL controllers.
High precision brain tumor classification model based on deep transfer learning and stacking concepts
Authors: Hamdaoui Halima El , Benfares Anass , Boujraf Saïd , Chaoui Nour El Houda , Alami Badreddine , Maaroufi Mustapha , Qjidaa Hassan
Published: October 2021

In this article, we proposed an intelligent clinical decision support system for the detection and classification of brain tumor from risk of malignancy index (RMI) images. To overcome the lack of labeled training data needed to train convolutional neural networks, we have used a deep transfer learning and stacking concepts. For this, we choosed seven convolutional neural networks (CNN) architectures already pre-trained on an ImageNet dataset that we precisely fit on magnetic resonance imaging (MRI) of brain tumors collected from the brain tumor segmentation (BraTS) 19 database. To improve the accuracy of our global model, we only predict as output the prediction that obtained the maximum score among the predictions of the seven pre-trained CNNs. We used a 10-way cross-validation approach to assess the performance of our main 2-class model: low-grade glioma (LGG) and high-grade glioma (HGG) brain tumors. A comparison of the results of our proposed model with those published in the literature, shows that our proposed model is more efficient than those published with an average test precision of 98.67%, an average f1 score of 98.62%, a test precision average of 98.06% and an average test sensitivity of 98.33%.
Kenaf plant pest and disease detection using faster regional based convolutional neural network
Authors: Rakhmandasari Alfita , Mahmudy Wayan Firdaus , Yulianti Titiek
Published: October 2021

Kenaf plant is a fibre plant whose stem bark is taken to be used as raw material for making geo-textile, particleboard, pulp, fiber drain, fiber board, and paper. The presence of plant pests and diseases that attack causes crop production to decrease. The detection of pests and diseases by farmers may be a challenging task. The detection can be done using artificial intelligence-based method. Convolutional neural networks (CNNs) are one of the most popular neural network architectures and have been successfully implemented for image classification. However, the CNN method is still considered a long time in the process, so this method was developed into namely faster regional based convolution neural network (RCNN). As the selection of the input features largely determines the accuracy of the results, a pre-processing procedure is developed to transform the kenaf plant image into input features of faster RCNN. A computational experiment proves that the faster RCNN has a very short computation time by completing 10000 iterations in 3 hours compared to convolutional neural network (CNN) completing 100 iterations at the same time. Furthermore, Faster RCNN gets 77.50% detection accuracy and bounding box accuracy 96.74% while CNN gets 72.96% detection accuracy at 400 epochs. The results also prove that the selection of input features and its pre-processing procedure could produce a high accuracy of detection. 
Classification of hand gestures from forearm electromyogram signatures from support vector machine
Authors: Albitar Diaa , Jailani R. , Ali Megat Syahirul Amin Megat , Majeed Anwar P. P. Abdul
Published: October 2021

Robotic prosthetics is increasingly adopted as an enabling technology for amputees. These are vital not only for activities of daily living but to display expression and affection. A vital element to this system is an intelligent model that can identify signatures from the remaining limb that can be mapped to specific effector movements. Therefore, the study proposes the use of forearm electromyogram to classify between different types of hand gestures; fingers spread, wave out, wave in, fist, double tap, and relaxed state. Data are acquired from 32 subjects using Myo armband. Initially, a total of 248 time-and frequency-domain features are extracted from the eightchannel device. Neighborhood component analysis has reduced them to a total of fourteen features. A hand gesture classification model based on electromyogram signal has been successfully developed using support vector machine with overall accuracy of 97.4% for training, and 88.0% for testing.
The impact of influencers on the companies reputation in developing countries: Case of Morocco
Authors: Chiny Mohamed , Chihab Marouane , Juiher El Mahdi , Jabari Khaoula , Bencharef Omar , Chihab Younes
Published: October 2021

With the emergence of social networks and their adoption by a large number of users, the importance of influencers continues to grow and companies are in a frantic race to recruit those most likely to promote their reputation and brand image. However, in the existing literature, there is little work that conducts quantitative studies on this subject in developing countries. For this reason, we conducted a study that attempts to understand the importance of influencers in reshaping public opinion of a company or brand. We chose as a subject of study a large Moroccan company operating in the telecommunications sector that hired a popular influencer among young Moroccans. We then adopted an approach based on scraping and analyzing the occurrences of the influencer's posts on Instagram and the content of the company's website and then publishing a questionnaire to 180 respondents in the age range of most of the followers of the influencer in question. The results suggest that a positive relationship exists between the influencer and brand reputation, meaning that if the person is following the influencer who has published content on the brand, that person is expected to be systematically aware of the brand, and vice versa.
Development of mobile and desktop applications for a fingerprint-based attendance management system
Authors: Akinola Olubunmi Adewale , Olopade Sikiru Olatunde , Afolabi Akindele Segun
Published: October 2021

Mobile application technology has been at the forefront of technological advancement in recent years. This has made life easier, and tasks that were considered herculean have been made easier and executable in a much shorter time than ever. One of such tasks is the process of taking attendance during events (such as lectures and conferences) by scribbling one’s signature and other personal details on a central register. This manual process is cumbersome and inconvenient, especially when a large number of participants are involved. To address this problem, this paper presents an automated solution in which a Java-based mobile application was developed and connected wirelessly to a central database that was created using My structured query language (MySQL) application whose task, among others, was to record attendance information. The database was connected to the backend of the web-based software program which was coded in hypertext pre-processor (PHP) programming language. Authentication was achieved through username, password, and fingerprint information. The system was deployedin a university to log students’ details, time absent, time present and cumulative attendances per month and it was realised that the system was highly effective, efficient and 5 times faster than the conventional paper-based attendance logging technique.
Optimized machine learning algorithm for intrusion detection
Authors: Alhayali Royida A. Ibrahem , Aljanabi Mohammad , Ali Ahmed Hussein , Mohammed Mostafa Abdulghfoor , Sutikno Tole
Published: October 2021

Intrusion detection is mainly achieved by using optimization algorithms. The need for optimization algorithms for intrusion detection is necessitated by the increasing number of features in audit data, as well as the performance failure of the human-based smart intrusion detection system (IDS) in terms of their prolonged training time and classification accuracy. This article presents an improved intrusion detection technique for binary classification. The proposal is a combination of different optimizers, including Rao optimization algorithm, extreme learning machine (ELM), support vector machine (SVM), and logistic regression (LR) (for feature selection & weighting), as well as a hybrid Rao-SVM algorithm with supervised machine learning (ML) techniques for feature subset selection (FSS). The process of selecting the least number of features without sacrificing the FSS accuracy was considered a multi-objective optimization problem. The algorithm-specific, parameter-less concept of the proposed Rao-SVM was also explored in this study. The KDDCup 99 and CICIDS 2017 were used as the intrusion dataset for the experiments, where significant improvements were noted with the new Rao-SVM compared to the other algorithms. Rao-SVM presented better results than many existing works by reaching 100% accuracy for KDDCup 99 dataset and 97% for CICIDS dataset.