bluuebunny/crossref_metadata_2025 huggingface dataset:
Approach to Predict Software Vulnerability Based on Multiple-Level N-gram Feature Extraction and Heterogeneous Ensemble Learning
Authors: Zhang Bing , Gao Yuan , Wu Jingyi , Wang Ning , Wang Qian , Ren Jiadong
Published: September 2022

Software vulnerabilities are one of the roots of computer security problems. The traditional static analysis and dynamic analysis methods based on software source code mainly have some deficiencies, such as high false positive rate, high false negative rate and insufficient semantic information captured. Nevertheless, the application of machine learning, Natural Language Processing and other technologies in software vulnerability prediction can effectively mitigate such issues. This paper proposed a vulnerability prediction method based on multiple-level N-gram feature extraction and heterogeneous ensemble learning. First, by code intermediate representation and constructing a multiple-level N-gram feature generation model, two kinds of N-gram semantic features with different window size and different granularity at word and char level were extracted to retain the semantic and structural information of code. Second, TF–IDF was used to construct the vector space model as the input of prediction model. As a single classifier was prone to overfitting and poor generalization, this paper conducted benchmark testing on five classical machine learning algorithms (NB, SVM, DT, LR, RF), and then combined four (SVM, DT, LR, RF) among them, which had better performance as the base classifiers to form the stacking heterogeneous ensemble method to build the vulnerability prediction model. Finally, the proposed method was verified on buffer overflow vulnerability and resource management vulnerability datasets, with a lowest false positive rate and false negative rate which can reach 1.58% and 4.06%, respectively.
The Persistent Homology of Cyclic Graphs
Authors: Adams Henry , Coldren Sophia , Willmot Sean
Published: September 2022

We give an [Formula: see text] algorithm for computing the [Formula: see text]-dimensional persistent homology of a filtration of clique complexes of cyclic graphs on [Formula: see text] vertices. This is nearly quadratic in the number of vertices [Formula: see text], and therefore a large improvement upon the traditional persistent homology algorithm, which is cubic in the number of simplices of dimension at most [Formula: see text], and hence of running time [Formula: see text] in the number of vertices [Formula: see text]. Our algorithm applies, for example, to Vietoris–Rips complexes of points sampled from a curve in [Formula: see text] when the scale is bounded depending on the geometry of the curve, but still large enough so that the Vietoris–Rips complex may have non-trivial homology in arbitrarily high dimensions [Formula: see text]. In the case of the plane [Formula: see text], we prove that our algorithm applies for all scale parameters if the [Formula: see text] vertices are sampled from a convex closed differentiable curve whose convex hull contains its evolute. We ask if there are other geometric settings in which computing persistent homology is (say) quadratic or cubic in the number of vertices, instead of in the number of simplices.
HOMFLYPT homology over ℤ2 detects unlinks
Authors: Wu Hao
Published: September 2022

We apply the Rasmussen spectral sequence to prove that the [Formula: see text]-graded vector space structure of the HOMFLYPT homology over [Formula: see text] detects unlinks. Our proof relies on a theorem of Batson and Seed stating that the [Formula: see text]-graded vector space structure of the Khovanov homology over [Formula: see text] detects unlinks.
THE CONTRIBUTION OF FINANCIAL DEVELOPMENT TO FORMALIZATION THROUGH ECONOMIC GROWTH: EVIDENCE FROM CROSS-COUNTRY DATA
Authors: SIRISANKANAN AEGGARCHAT , KANANURAK PAPAR
Published: September 2022

The aim of the paper is to formulate empirical specification models to examine whether financial development stimulates economic growth and encourages formalization. Cross-country data analysis of 140 sample countries during the period from 2000 to 2018 were utilized, together with new indexes of financial development. The income decomposition method and the two-step estimation approach, together with the multiple linear regression with interactions effect, were employed as empirical methods. It was found that financial development can lower informal self-employment through economic growth. Financial development also contributes to economic growth. Therefore, development of financial markets and financial institutions should be an alternative policy to control informal employment.
Maximum max-k-clique subgraphs in cactus subtree graphs
Authors: Gavril Fanica
Published: September 2022

In this paper, we analyze the intersection graphs of subtrees in a cactus, called cactus subtree graphs. A max-k-clique subgraph of a graph is an induced subgraph whose maximum clique has at most [Formula: see text] vertices. We describe a polynomial time algorithm to find a maximum weight max-[Formula: see text]-clique subgraph in a cactus subtree graph when [Formula: see text] is fixed. In perfect graphs this problem is equivalent to the maximum weight [Formula: see text]-colorable subgraph problem. In many applications like scheduling production lines and communication networks on imperfect graphs, a maximum max-[Formula: see text]-clique subgraph solution is better than a maximum [Formula: see text]-colorable subgraph solution. In addition, we describe cactus subtree graphs polynomial time algorithms for recognition, maximum independent sets, maximum cliques, maximum induced bipartite graphs, maximum induced forests and minimum feedback vertex sets.
Multiagent Autonomous Source Search Using Submodularity and Branch-and-Bound
Authors: Xu Xiaoling , Marelli Damián , Meng Wei , Cai Qianqian , Fu Minyue
Published: September 2022

This paper is concerned with near-optimal source search problem using a multiagent system in cluttered indoor environments. The goal of the problem is to maximize the detection probability within the minimum search time. We propose a two-stage strategy to achieve this goal. In the first stage, a greedy approach is used to define a set of grid cells with the aim of maximizing the detection probability. In the second stage, an iterative branch-and-bound procedure is used to design the search paths of all agents so that all grid cells are visited by one agent and the largest search path among all agents is minimized. Simulation results show that the proposed search algorithm has better performance in terms of exploration time compared to other existing methods.
Patients with Ankylosing Spondylitis Can Maintain Clinical and Functional Improvement after Switching from Infliximab Reference Product to Infliximab Biosimilar (REMSIMA): 12 Months Comparative Open-Label Study
Authors: Mathkhor Abdulsatar J. , Altaqi Ali , Abdullah Abdulnasser , Khudhairy Amer
Published: September 2022

Objective: Efficacy, tolerance, and safety of infliximab biosimilar are the same as infliximab reference product (RP) in the management of ankylosing spondylitis (AS) patients previously were on infliximab RP. We aimed to evaluate the biosimilar CT-P13 (Remsima) in terms of efficacy, tolerance, and safety to its RP.

Materials and methods: Seventy-eight consecutive randomly selected patients were recruited for the study. All patients were naïve to any other biologics before receiving infliximab RP, and all were in clinical remission. The sample of patients was divided into two subgroups: 40 patients were continued on infliximab RP and 38 patients were switched to infliximab biosimilar. All patients underwent clinical evaluation and investigation. Both groups followed up for further 12 months. The disease activity was calculated utilizing Ankylosing Spondylitis Disease Activity Score (ASDAS), using the C-reactive protein (CRP), and Bath Ankylosing Spondylitis Disease Activity Index (BASDAI). Remission considered when BASDAI < 4 and ASDAS < 1.3. Functional scores for all patients were assessed using the Bath Ankylosing Spondylitis Functional Index (BASFI).

Results: Sustained clinical remission was observed after 12 months of treatment in the infliximab RP continued and switched groups. At the end of the study, erythrocyte sedimentation rate (ESR), CRP, visual analog scale (VAS), ASDAS, BASDAI, and BASFI were 15.04 ± 2.37, 2.10 ± 0.88, 3.10 ± 0.78, 1.52 ± 0.40, 2.80 ± 0.37, and 3.05 ± 0.24 in the infliximab RP continued group, respectively, and were 15.15 ± 1.45, 2.29 ± 0.89, 3.21 ± 0.69, 1.59 ± 0.57, 2.76 ± 0.45, and 2.89 ± 0.92 for the switching group, respectively; the difference was statistically not significant ([Formula: see text] values > 0.05). No significant adverse events were noted in the switching group compared to the continuous group groups.

Conclusion: We found infliximab biosimilar CT-P13 (Remsima) was not inferior to infliximab RP and can maintain patients with ankylosing spondylitis in clinical remission.
Developing an “India in the World” Framework: Modi Regime’s Political Economy in a Changing World
Authors: SINHA ASEEMA
Published: September 2022

India’s political economy and economic policies can no longer be understood in a purely domestic-oriented or closed economy framework. This paper offers an “India in the world” framework that analyzes how traditional borders of domestic political economy and international politics interact in shaping domestic policy and political economy. In contrast to existing political economy approaches on India, such a framework pays attention to the changing nature of domestic political economy but also how international factors affect and shape domestic imperatives and goals. Simultaneously, domestic developments have important global consequences in terms of increasing (or decreasing) global attention and external economic flows to India, which must be attended to in understanding India’s domestic political economy. I also suggest that the global world is not only a set of exogenous structures and constraints, but rather, the changing global order is deployed and used by state actors to refurbish their political and state power to achieve both domestic and global aims. However, if state actors fail to renew the domestic sources of growth or contribute to crony capitalist or debt-laden domestic growth, then, even attending to global alliances may fail to renew the economic sources of state power and create serious contradictions in India’s domestic and global strategies despite new realignments favoring India at the global level. These ideas help us understand Modi regime’s political economy in a new way.
Sverdrup Basin Composite Tectono-Sedimentary Element, Canadian Arctic Islands
Authors: Embry Ashton , Beauchamp Benoit , Dewing Keith , Harrison Christopher
Published: September 2022

The Sverdrup Basin Composite Tectono-Sedimentary Element (CTSE) covers 210 000 km
2
in the Canadian Arctic Archipelago. The CTSE was initiated in the Early Carboniferous by rifting of highly deformed Early Paleozoic strata and contains a maximum of 15 km of Carboniferous–Eocene strata. Eight phases of basin development have been recognized, with each being characterized by a specific combination of tectonic and depositional regimes. The phases are separated by intervals of uplift and tectonic reorganization, and each resultant first-order sequence is regarded as a separate TSE. Carbonate sedimentation was dominant in the Late Carboniferous and Early Permian, with clastic sedimentation becoming more common in the Middle Permian. Source areas lay to the east, south and north. In the Triassic, clastic sedimentation rates increased; and by the end of the Triassic, the central basin was filled. A shallow seaway was present throughout the Jurassic. In the Early Cretaceous, subsidence rates and clastic supply increased significantly, and basic volcanism occurred over most of the CTSE. Subsidence rate, sediment supply and volcanism decreased in the Late Cretaceous. The basin was progressively deformed in the Paleogene (Eurekan Orogeny), with local foreland deposits reaching 3 km. Seventeen oil and gas fields have been discovered on salt-cored, Eurekan anticlines. A combination of stratigraphic and structural traps, involving Triassic–Jurassic strata, has the greatest potential for future hydrocarbon discoveries.
Dynamic Structure Discovery Applied to the Ion Transport in the Ubiquitous Lithium-ion Battery Electrolyte LP30
Authors: Andersson Rasmus , Borodin Oleg , Johansson Patrik
Published: September 2022

The electrolytes of the today omnipresent lithium-ion batteries (LIBs) have for more than 25 years been based upon 1 M LiPF6 in a 50:50 EC:DMC mixture—commonly known as LP30. The success of the basic design of the LP30 electrolyte, with many variations and additions made over the years, is unchallenged. Yet, some molecular level fundamentals of LP30 are surprisingly elusive: the structure of the first solvation shell of the Li+ cation is still a topic of current debate; the details of the dynamics are not fully understood; the interpretation of structural and dynamic properties is highly dependent on the analysis methods used; the contributions by different species to the ion transport and the energetics involved are not established. We here apply dynamic structure discovery analysis as implemented in CHAMPION to molecular dynamics simulation trajectories to bring new light on the structure and dynamics within LP30 and especially the (Li+) ion transport to rationalize further development of LIB electrolytes.
A Physical Derivation of the Capacitive Brick Layer Model in Polycrystalline Ceramics from Fundamental Electrodynamic Equations
Authors: Uhlenbruck Sven
Published: September 2022

The brick layer model in electrochemical impedance spectroscopy has been successfully used for decades to describe the behavior of polycrystalline materials. Equivalent circuits were created to obtain information about grain and grain boundary properties from electrochemical impedance spectroscopy measurements. Previous publications have expanded on initial interpretations and described grains and grain boundaries as plate capacitors filled with the grain and grain boundary material, respectively. However, this approach poses a number of significant issues, since key assumptions for the equivalent circuits do not match with the actual experimental situation of the material when exposed to an outer electric field, thus calling into question the entire approach for the interpretation.
FeO-Co3O4 Hybrid Composite on Oxidized Graphite: Single-Stage Hydrothermal Synthesis and Application as an Electrocatalyst for Oxygen Evolution Reaction
Authors: Niyitanga Theophile , Kim Haekyoung
Published: September 2022

The sluggish oxygen evolution reaction (OER) is one of the major factors limiting the application of water splitting for hydrogen production. This study used a single-stage hydrothermal method to synthesize highly porous FeO-Co3O4 nanoparticles on oxidized graphite (GOX) sheets. The fabrication of FeO-Co3O4@GOX nanoparticles depends on the oxidation time. The optimal catalyst (FeO-Co3O4@GOX-10) showed higher activity toward the OER than Co3O4, FeO-Co3O4, FeO-Co3O4@GOX-5, and FeO-Co3O4@GOX-24. The FeO-Co3O4@GOX-10 catalyst exhibited a low overpotential and small Tafel slope of 0.23 V and 64 mV dec−1, respectively, at a current density of 10 mA cm−2, demonstrating enhanced specific activity and turnover frequency. The optimal catalyst played a synergistic role in accelerating the electron transfer process and accelerating the OER with faradaic efficiency of 95.3%. Moreover, the improved activity of the FeO-Co3O4@GOX-10 catalyst was attributed to enhanced active site exposure, fast charge transfer, and synergistic effect of FeO-Co3O4 and GOX. This study provides a new approach for the development of efficient, stable, and environmentally friendly hybrid catalysts for energy conversion applications.
Algebraic Properties of

ω
,
θ

-Complex Fuzzy Subgroups
Authors: Al-harshni Ahad Abdullah , Alghazzawi Dilshad
Published: September 2022

This paper defined the notion of

ω
,
θ

-complex fuzzy sets,

ω
,
θ

-complex fuzzy subgroupoid, and

ω
,
θ

-complex fuzzy subgroups and described important examples under

ω

,

θ

-complex fuzzy sets. Additionally, we discussed the conjugacy class of group with respect to

ω
,
θ

-complex fuzzy normal subgroups. We define

ω
,
θ

-complex fuzzy cosets and elaborate the certain operation of this analog to group theoretic operation. We prove that factor group with regard to

ω
,
θ

-complex fuzzy normal subgroup forms a group and establishes an ordinary homomorphism from group to its factor group with regard to

ω
,
θ

-complex fuzzy normal subgroup. Moreover, we create the

ω
,
θ

-complex fuzzy subgroup of factor group.
Infrared Technology-Based Sensor Data Analysis for Thermal Fault Identification of Electrical Equipment in Intelligent Substations
Authors: Wang Xuan , Zhang XiaoFeng , Zhou Feng , Xu Xiang
Published: September 2022

The electrical equipment of intelligent substation cannot deal with the problem of load mutation, and the load adjustment is not timely, which leads to the poor performance of thermal fault identification of electrical equipment. Therefore, a study on thermal fault identification of the electrical equipment of intelligent substation based on infrared technology is proposed. First, under the background of infrared technology, according to the thermal fault feature extraction model of substation electrical equipment, the principle of double station cross location is proposed; combined with the sampling results of thermal fault characteristic parameters of electrical equipment in intelligent substation, the thermal fault characteristics are analyzed; through the process of thermal fault identification of electrical equipment in intelligent substation, the preliminary classification structure of thermal fault is obtained and the thermal fault identification is completed. The experimental results show that the designed method has good performance of thermal fault identification, high output stability, better identification effect after optimization, and highly sensitive identification ability.
Pharmacological Effects of Hydrocotyle bonariensis Comm. ex Lam (Araliaceae) Extract on Cardiac Activity
Authors: Komla Kaboua , Tcha Pakoussi , Aklesso Mouzou , Balakiyém Kadissoli , Mindede Assih , Patrick Bois , Aurelien Chatelier
Published: September 2022

Hydrocotyle bonariensis is one of the medicinal plants used in traditional medicine for the management of hypertension in Africa, Asia, and Latin America. However, the real impact of the traditional use of this plant on arterial hypertension has not yet been the subject of conclusive scientific information in the literature. This study aimed essentially to evaluate the potential cardiomodulatory effect of the hydroethanolic extract of Hydrocotyle bonariensis. In other to do so, the hydroethanolic extract of H. bonariensis was studied in vivo on the Wistar rat ECG and then in vitro on the isolated perfused Wistar rat heart using the Langendorff system. The extract was also tested on isolated guinea pig atria kept alive in the organ-specific vessel under physiological conditions similar to those of a living organism. At the cellular level, the effects of the extract were evaluated on the human cardiac sodium current INav1.5 and on the human cardiac pacemaker current If. We noted that the extract caused a decrease in P wave and T wave amplitudes and heart rate and an increase in the duration of the RR interval on the in vivo rat ECG. On the isolated perfused Langendorff heart as well as on the isolated atria, a decrease in the RR interval and in the heart rate was noted. The extract had no effect on human cardiac sodium current, but it did reduce human cardiac pacemaker current. In conclusion, the present study demonstrated that Hydrocotyle bonariensis, a medicinal plant traditionally used to prevent and treat hypertension, has an overall cardiomoderating effect. This effect would contribute to the reduction of blood pressure.
A Smoothed Matrix Multivariate Elliptical Distribution-Based Projection Method for Feature Extraction
Authors: Qiu Hong , Wang Renfang , Sun Dechao , Liu Xinwei , Zhang Liang , Liu Yunpeng
Published: September 2022

Big data has the traits such as “the curse of dimensionality,” high storage cost, and heavy computation burden. Self-representation-based feature extraction methods cannot effectively deal with the image-level structural noise in the data, so how to character a better relationship of reconstruction representation is very important. Recently, sparse representation with smoothed matrix multivariate elliptical distribution (SMED) using structural information to handle low-rank error images caused by illumination or occlusion has been proposed. Based on SMED, we present a new method named SMEDP for feature extraction. SMEDP firstly utilizes SMED to automatically construct an adjacency graph and then obtains an optimal projection matrix by maximizing the ratio of the local scatter matrix and the total scatter matrix in the PCA subspace. Experiments on the COIL-20 object database, ORL face database, and CMU PIE face database prove that SMEDP works well and can achieve considerable visual and recognition performance than the relevant methods.
Research on the Predictive Analysis of Park Landscape Design and Cost Based on RNN Model
Authors: Lyu Guang , Zhang Dan , Liu ZuoLin
Published: September 2022

As people’s awareness of the environment gradually increases and their requirements for the comfort of living space become higher, landscape design has also ushered in a golden period of development. With the increasing investment in landscape construction in urban development, the area of park green space has been increasing. A park is a place that provides recreation and relaxation for the public. However, the mere pursuit of landscape quality and artistic effects without effective cost control will eventually lead to a rise in construction costs. Therefore, this study explores the main influencing factors that lead to high park landscape costs by analyzing the current development of park landscape design. Based on the comprehensive analysis, a park landscape cost prediction model based on recurrent neural networks is proposed in order to better control the construction costs of park landscapes. This study applies advanced deep learning technology to the project management of park landscapes, which effectively improves the accuracy of cost prediction. In addition, an artificial bee colony algorithm is introduced to update the weights of the recurrent neural network, resulting in a globally optimal ABC-RNN prediction model. The experimental results show that the proposed ABC-RNN prediction model has higher prediction accuracy and stability than the commonly used prediction models.
Molecular Analysis of Prognosis and Immune Infiltration of Ovarian Cancer Based on Homeobox D Genes
Authors: Chen Buze , Gao Cui , Wang Haihong , Sun Jieyun , Han Zhengxiang
Published: September 2022

Background. Homeobox D (HOXD) genes were associated with cancer pathogenesis. However, the role of HOXD genes in ovarian cancer (OC) and the possible mechanisms involved are unclear. In this study, we analyzed the function and regulatory mechanisms and functions of HOXD genes in OC based on comprehensive bioinformatics analysis. Methods. Expression of HOXD1/3/4/8/9/10/11/12/13 mRNA was analyzed between OC tissue and normal tissue using ONCOMINE, GEO, and TCGA databases. The relationship between HOXD expression and clinical stage was studied by GEPIA. The Kaplan-Meier plotter was used to analyze prognosis. cBioPortal was used to analyze the mutation and coexpression of HOXDs. GO and KEGG analyses were performed by the DAVID software to predict the function of HOXD coexpression genes. Immune infiltration analysis was used to evaluate the relationship between the expression of HOXD genes and 24 immune infiltrating cells. Results. The expression of HOXD3/4/8/9/10/11 was significantly lower in OC tissues than in normal ovarian tissues, while the expression of HOXD1/12/13 was significantly higher in OC tissues. The expression of HOXD genes was associated with FIGO stage, primary therapy outcome, tumor status, anatomic neoplasm subdivision, and age. The expression levels of HOXD1/3/4/8/9/10 correlated with tumor stage. HOXD1/8/9 could be served as ideal biomarkers to distinguish OC from normal tissue. Low HOXD9 expression was associated with shorter overall survival (OS) (HR: 0.75; 95% CI: 0.58–0.98;

P
=
0.034

) and progression-free survival (PFS) (HR: 0.69; 95% CI: 0.54–0.87;

P
=
0.002

). The HOXD coexpression genes were associated with pathways including cell cycle, TGF-beta signaling pathway, cellular senescence, and Hippo signaling pathway. HOXD genes were significantly associated with immune infiltration. Conclusion. The expression of HOXD genes is associated with clinical characteristics. HOXD9 is a new biomarker of prognosis in OC, and HOXD1/4/8/9/10 may be potential therapeutic targets. The members of the HOXD genes may be the response to immunotherapy for OC.
An LSTM-Autoencoder Architecture for Anomaly Detection Applied on Compressors Audio Data
Authors: Mobtahej Pooyan , Zhang Xulong , Hamidi Maryam , Zhang Jing
Published: September 2022

The compressors used in today’s natural gas production industry have an essential role in maintaining the production line operational. Each of the compressors’ components has routine maintenance tasks to avoid sudden failures. Hence, the significant advantages and benefits of performing preventative maintenance tasks in time are decreasing equipment downtime, saving additional costs, and improving the safety and reliability of the whole system. In this paper, anomaly classification and detection methods based on a neural network hybrid model named Long Short-Term Memory (LSTM)-Autoencoder (AE) is proposed to detect anomalies in sequence pattern of audio data, collected by multiple sound sensors deployed at different components of each compressor system for predictive maintenance. In research methodology, this paper has conducted experiments that employed different RNN architectures such as GRU, LSTM, Stacked LSTM, and Stacked GRU with various functions to create a baseline for model evaluation. Each architecture used audio signals dataset received from the compressor system for experiments to consider each neural network model’s performance. According to performance results, an optimal model for anomaly detection with the best performance scores has been proposed in this research. Experiments combined one-dimensional raw audio signal features using SC and Mel spectrogram features were fed to deep learning models to evaluate performance. Hence, such hybrid methods can effectively detect normal and anomaly audio signals collected from a compressor system, increasing the compressor system’s reliability and the sustainability of the gas production line. The combination of multiple-resource features in the proposed hybrid model showed a 100% score in all four-evaluation metrics such as accuracy, precision, recall, and F1 in LSTM-based autoencoder in both test and train results.
Thermal and Wet Comfort of Clothing in Different Environments Based on Multidimensional Sensor Data Fusion and Intelligent Detection
Authors: Hou Xia
Published: September 2022

With the improvement of quality of life, people pay more and more attention to the comfort performance of clothing, of which thermal and wet comfort is an important part of evaluating the comfort of clothing, referring to the performance of keeping the human body in a reasonable thermal and wet state. When the human body sweats a lot or is in a highly humid environment, the clothing fabric will be soaked to make people feel wet, which seriously affects the comfort performance of clothing wear, and with the rapid development of sensing technology, the comfort of human clothing can be comprehensively evaluated by a variety of sensing data (clothing pressure, temperature, humidity, and heart rate). Therefore, how to analyze and process these data and establish an objective and accurate evaluation criterion for clothing comfort is a difficult problem and has attracted the attention of many researchers. In this paper, an improved kernel function fuzzy kernel c-means clustering algorithm is used to analyze the pressure at specific points in human activities. Unsupervised clustering analysis was performed for five clustering metrics (mean, pressure range, temperature range, humidity range, and heart rate variability). The clustered samples were learned and discriminated by a support vector machine to determine the comfort level of the clothing. The method can be applied to multi-indicator and multiclassification problems, providing smart clothing researchers with an intelligent, objective, and accurate method for evaluating clothing comfort. Experiments show that the method designed in this paper has good performance experience in terms of mean value, pressure range, temperature range, humidity range, and heart rate variability.
Kinematics of a Schönflies-Motion Generator Parallel Manipulator with Moving Configurable Platform
Authors: Gallardo-Alvarado Jaime , Garcia-Murillo Mario A. , Rodriguez-Castro Ramon , Cervantes-Sánchez J. Jesús
Published: September 2022

In this work, a parallel manipulator equipped with a moving configurable platform able to perform the Schönflies motion is introduced. The versatility of the robot is such that it is possible to attach to the configurable platform one end-effector endowed with a redundant decoupled rotation or two end-effectors in two opposite corners of the moving configurable platform. The mobility of the robot is explained by means of the theory of screws while the displacement analysis is approached by means of conventional vectorial algebra. In that concern, the forward position analysis leads to five quadratic equations which are solved by applying the homotopy continuation method. Afterwards, the velocity and acceleration analyses of the robot are approached by resorting to the theory of screws. Numerical examples are included with the purpose of validate the reliability of the method of kinematic analysis employed in the contribution.
Risk Predictive Model Based on Three DDR-Related Genes for Predicting Prognosis, Therapeutic Sensitivity, and Tumor Microenvironment in Hepatocellular Carcinoma
Authors: Hu Renzhi , Liang Xiping , Li Qiying , Liu Yao
Published: September 2022

Hepatocellular carcinoma (HCC) is the seventh most common malignancy and the second most common cause of cancer-related deaths. Tumor mutational load, genomic instability, and tumor-infiltrating lymphocytes were associated with DNA damage response and repair gene changes. The goal of this study is to estimate the chances of patients with HCC surviving their disease by constructing a DNA damage repair- (DDR-) related gene profile. The International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) provided us with the mRNA expression matrix as well as clinical information relevant to HCC patients. Using Cox regression and LASSO analysis, DEGs strongly related to general survival were discovered in the differentially expressed gene (DEG) study. In order to assess the model’s accuracy, Kaplan-Meier (KM) and receiver operating characteristic (ROC) were used. In order to compute the immune cell infiltration score and immune associated pathway activity, a single-sample gene set enrichment analysis was performed. A three-gene signature (CDC20, TTK, and CENPA) was created using stability selection and LASSO COX regression. In comparison to the low-risk group, the prognosis for the high-risk group was surprisingly poor. In the ICGC datasets, the predictive characteristic was confirmed. A receiver operating characteristic (ROC) curve was calculated for each cohort. The risk mark for HCC patients is a reliable predictor according to multivariate Cox regression analysis. According to ssGSEA, this signature was highly correlated with the immunological state of HCC patients. There was a significant correlation between the expression levels of prognostic genes and cancer cells’ susceptibility to antitumor therapies. Overall, a distinct gene profile associated with DDR was identified, and this pattern may be able to predict HCC patients’ long-term survival, immune milieu, and chemotherapeutic response.
Can Your Smartphone Make You a Tourist? Mine Does: Understanding the Consumer’s Adoption Mechanism for Mobile Payment System
Authors: Hameed Irfan , Mubarik Muhammad Shujaat , Khan Kamran , Waris Idrees
Published: September 2022

Payment through mobile phones is a vital breakthrough in the arena of online businesses and e-commerce. The purpose of this study was to investigate the determinants, enablers, and barriers involved in the success or failure of the mobile payment system (MPS) for the travel industry. The study employs the constructs operationalized from coping theory, unified theory of acceptance and use of technology (UTAUT 2), and innovation resistance theory (IRT). The data has been collected from the 378 travelers who have used MPS for travel bookings for the first time. The customers of various travel agencies have been approached using an online questionnaire. Data has been analyzed using Statistical Package for Social Sciences (SPSS) and Analysis of Moment Structures (AMOS) version 26. The analysis revealed several interesting findings. All of the direct hypotheses for coping theory constructs were accepted except for the mediating role of satisfaction. However, the factors of UTAUT 2 and IRT revealed very thought-provoking findings, questioning various obvious perceptions. The findings of the study can be used by the media agencies, hotels, and travel and tourism departments of the governments, especially in the context of developing nations.
Research on the Adaptive PID Speed Control Method for Hub Motors
Authors: Zhang Jingjun , Li Chao , Jia Qingbo , Gao Ruizhen
Published: September 2022

With the increase in tightening of carbon neutral policies, electric new energy vehicles have become a top priority in the development of the automotive industry. The integrated electric vehicle wheel (hub motor) can effectively optimize the vehicle structure and reduce the weight of the vehicle and reduce the energy consumption. At the same time, the feature that four tires can be driven separately is also conducive to the application of various new control algorithms. In this paper, a generalized adaptive PID control is experimented with the speed control of the hub motor, and a fuzzy control-based variable step method is proposed for the overshoot or even runaway problem caused by the inertia of the hub motor in the process of adaptive PID control. Experiments have demonstrated that this method can effectively reduce the amount of overshoot triggered by adaptive control and shorten the regulation time into a steady state.
Stakeholder Perceptions of Sustainable Infrastructure Project Delivery: A Comparative Analysis between Guangdong, Hong Kong, and Macao
Authors: Li Hongyang , Jiang Tingting , Mao Peng , Zheng Junwei , Fang Yuan , Skitmore Martin , Wu Di
Published: September 2022

China is experiencing the rapid development of infrastructure projects throughout the country, especially in the Guangdong‐Hong Kong SAR‐Macao SAR Greater Bay Area. This is placing the Chinese construction industry under tremendous pressure to transition to a sustainability orientation due to the various economic, social, and environmental challenges involved. The transition to more sustainable infrastructure projects requires the involvement of more stakeholders, while the multistakeholder context is of less importance when making project decisions, and this may create an adverse effect on the sustainability level of infrastructure projects. Based on the questionnaire data collected, this study addresses this issue by comparatively analysing stakeholder perceptions of sustainable infrastructure delivery between Guangdong province, Hong Kong SAR, and Macao SAR. Through independent sample t‐tests, the disparity in perceptions between paired stakeholder groups in the Greater Bay Area (as a whole) is revealed. Three pairs, i.e., government department and supervising engineers, owners and designers, and supervising engineers and operators, are found to be the most conflicting, while contractors and supervising engineers are the least. Of the 18 sustainability items analysed, shaped local identity and international reputation (SOC, social factor 8) is the most controversial, while value-for-money of the proposed project during its lifecycle (EOC, economic factor 5) and green design and construction (ENV, environmental factor 2) have the greatest consensus towards their roles in achieving project sustainability. Significantly, different stakeholder perceptions between the three are also identified, and the contractor group is found to share the least consensus geographically, while the operators have the least conflict rating of the relevant importance of various sustainability factors. The thorough analysis of sustainability‐related items of economy, society, and environment contributes to understanding the attitudes of the various stakeholder groups involved, which then helps reduce their conflicts during sustainable infrastructure delivery.
Gravitational Wave-Signal Recognition Model Based on Fourier Transform and Convolutional Neural Network
Authors: Zhang Hao , Zhu Zhijun , Fu Minglei , Hu Minchao , Rong Kezhen , Lande Dmytro , Manko Dmytro , Yaseen Zaher Mundher
Published: September 2022

The recent detection of gravitational waves is a remarkable milestone in the history of astrophysics. With the further development of gravitational wave detection technology, traditional filter-matching methods no longer meet the needs of signal recognition. Thus, it is imperative that we develop new methods. In this study, we apply a gravitational wave signal recognition model based on Fourier transformation and a convolutional neural network (CNN). The gravitational wave time-domain signal is transformed into a 2D frequency-domain signal graph for feature recognition using a CNN model. Experimental results reveal that the frequency-domain signal graph provides a better feature description of the gravitational wave signal than that provided by the time-domain signal. Our method takes advantage of the CNN’s convolution computation to improve the accuracy of signal recognition. The impact of the training set size and image filtering on the performance of the developed model is also evaluated. Additionally, the Resnet101 model, developed on the Baidu EasyDL platform, is adopted as a comparative model. Our average recognition accuracy performs approximately 4% better than the Resnet101 model. Based on the excellent performance of convolutional neural network in the field of image recognition, this paper studies the characteristics of gravitational wave signals and obtains a more appropriate recognition model after training and tuning, in order to achieve the purpose of automatic recognition of whether the signal data contain real gravitational wave signals.
A Case of Hypermucoviscosity Phenotype of Klebsiella pneumoniae Liver Abscess Saved by Damage Control Strategy
Authors: Sato Keita , Takahashi Koji , Kusuta Tsukasa
Published: September 2022

Liver abscess caused by the Hypermucoviscosity phenotype of Klebsiella pneumoniae (HKp) is characterized by high tissue invasiveness and multiple systemic infections. This leads to sepsis, multiple organ failure, and coagulopathy. An 80 year old man came to our hospital with a complaint of malaise and went into hemorrhagic shock after percutaneous transhepatic drainage of a liver abscess caused by HKp. An emergency laparotomy was performed, but the patient suffered from severe coagulopathy and underwent damage control surgery. HKp liver abscesses must be operated on in the presence of multiple organ failure and disseminated intravascular coagulation (DIC) complications when medical treatment is refractory. In these situations, a two-stage damage control strategy should be considered: hemostasis and infection control at the initial surgery and hepatic resection.
Air Quality Prediction Model Using Deep Learning in Internet of Things Environmental Monitoring System
Authors: Feng Yongliang
Published: September 2022

In order to realize the accurate prediction of spatial-temporal air quality index, this paper constructs a STAQI prediction model based on deep learning, including data processing, spatial feature acquisition, temporal feature acquisition, and STAQI prediction. Firstly, the spatial interpolation method is used to optimize the sample data set to provide reliable data; the improved graph convolutional network and the improved long short-term memory are used to effectively extract the spatial and temporal distribution characteristics of AQI data; and then, the extreme learning machine model is used to accurately predict and analyze AQI data. Simulation results show that the evaluation indexes RMSE and MAE of the constructed prediction model are 4.51 and 3.92, respectively, showing excellent curve fitting ability and AQI prediction ability.
A Community-Based Cross-Sectional Study about the Knowledge, Attitude, and Practices of Food Safety Measures among Rural Households in Bangladesh
Authors: Rokshana Rabeya Mst. , Hasan Bin Zihad Md. , Anis Fakir Md. , Sabina Khatun Most. , Rakhi Jinnat Jahan , Islam Ashraful , Islam Rashedul , Abdullah Saeed Khan Md. , Hossain Hawlader Mohammad Delwer
Published: September 2022

Background. Food handlers have been found to play essential roles in transmitting foodborne diseases and can pose a significant public health problem. Our study aimed to assess the knowledge, attitude, and practices (KAP) of food safety measures among the rural households of Bangladesh. Materials and Methods. We conducted this community-based cross-sectional study among women above 18 years involved with food preparation in rural households of four villages in Bangladesh. A total of 400 respondents were selected using the multistage cluster sampling technique. Data were collected using pretested and predesigned questionnaires based on the World Health Organization’s (WHO) five keys for food safety. We used Stata (Version 16) for all statistical analyses. Results. The mean age of the participants was 42.09 ± 12.96 years. The median KAP scores [interquartile range (IQR)] were 7 (21–10), 16 (5–18), and 26 (9–30), respectively. We found the median KAP scores were significantly lower in the age group >55 years than in age groups of 18–25, 26–35, 36–45, and 46–55 years (

P
<
0.05

for all). In addition, the median KAP scores were significantly higher in respondents who were married, literate, employed/active, living in pakka/semipakka house, and with a monthly family income of >5,000 BDT (

P
<
0.05

for all). Among all, 33.75%, 80.25%, and 69.00% had good (≥80% of total) KAP scores, respectively. Multivariable regression analysis revealed that monthly family income >5,000 BDT was a significant predictor of good knowledge [Adjusted Odds Ratio (aOR): 3.51, 95%CI: 1.55–7.98], good attitude (aOR: 5.82, 95%CI: 2.80–11.70), and good practice (aOR: 3.18, 95%CI: 1.67–6.07). Age >55 years was a significant predictor of good attitude (aOR: 0.38, 95%CI: 0.17–0.81) and good practice (aOR: 0.48, 95%CI: 0.21–0.89). Having ≤4 members in the family was a significant predictor of good practice (aOR: 1.85, 95%CI: 1.13–3.03) regarding food safety measures. Conclusion. The study found that KAP among rural Bangladeshi women regarding food safety were relatively satisfactory. However, having a poor monthly income and living in a large family were impediments to good food-safety practices where work can be done. The findings of this study may help develop health intervention programs for food handlers to further improve KAP toward food safety, thereby reducing foodborne illness in households.
Sanzi Yangqin Decoction Alleviates Allergic Asthma by Modulating Th1/Th2 Balance: Coupling Network Pharmacology with Biochemical Pharmacology
Authors: Peng Wenpan , Xu Yong , Zhou Yunhai , Wu Juanjuan , Peng Guiqing , Gu Yanlan , Zhou Xianmei , Pu Mingzhi
Published: September 2022

This study aimed to verify that Sanzi Yangqin Decoction (SYD) can relieve asthma in mice and explore the effect on TH1/Th2 balance. The targets of SYD and asthma were explored from the public database using various methods. The potential targets and signaling pathways were identified by KEGG enrichment analysis from DAVID database. Mice asthma models were established using OVA and aluminum hydroxide. Lung tissues of mice were stained with HE and Masson. The contents of IFN-γ, IL-4, and TNF-α in BALF and IgE in mouse serum were detected using ELISA. In addition, the changes in Th1 and Th2 cells of the spleen were detected by flow cytometry. Fourteen core targets including IL4, IFNG, and MMP9 were identified for the treatment of asthma by SYD. The content of IL-4 in the lung tissue and BALF was gradually decreased with the increase in SYD concentration, while the IFN-γ was gradually increased. The drug significantly reduced IgE levels in serum and TNF-α in BALF. The number of Th1 cells in the spleen increased, while Th2 cells decreased in a concentration-dependent manner. SYD can alleviate pulmonary inflammation, restore Th1/Th2 balance, and relieve asthma.
Design of a Small Quadruped Robot with Parallel Legs
Authors: Lu Ming , Jing Baorui , Duan Hao , Gao Guanbin
Published: September 2022

In this paper, a lightweight and modular design of a quadruped robot with two‐degree‐of‐freedom parallel legs is presented. To reduce the weight and enhance the transmission accuracy, the horizontal layout of the driving end is adopted for designing the legs of the quadruped robot. The rotation angle of each actuator for the quadruped robot is analyzed by the inverse kinematics algorithm. Moreover, the trajectory of the foot‐end, including support and swing phases, is planned to reduce the impact between the foot‐end and the ground. Furthermore, the gait of the four legs of the quadruped robot is designed by considering the conditions of trot, standing, take‐off, and walking. Finally, the effectiveness of the foot‐end trajectory and the stable gait is verified by conducting experiments on a prototype platform.
Cuidados a largo plazo para personas mayores en Chile: revisión de las políticas públicas
Authors: Zegers Vial María , Brahm Smart Magdalena , Jiménez Álvarez Isidora
Published: September 2022

Introducción: en el contexto de un envejecimiento acelerado e inminente, surge la necesidad de implementar cuidados a largo plazo (CLP). Estos son servicios destinados a satisfacer necesidades de personas con afecciones crónicas, que no pueden cuidarse por sí mismas. El objetivo de esta revisión es describir el escenario actual de los CLP para personas mayores en Chile, y revisar políticas públicas relacionadas, utilizando la experiencia de otros países como referencia. Métodos: para describir el escenario actual de los cuidados a largo plazo en Chile, se ingresaron los siguientes términos: “long term care”, “elderly” “older adults”, “Chile”, en tres buscadores (Pubmed, Google Scholar y Web of Science). Luego se realizó una búsqueda sobre políticas públicas de CLP, utilizando los mismos buscadores y los conceptos “long term care”, “elderly” y “policies” como palabras clave. La búsqueda incluyó documentos en español e inglés, priorizando aquellos de publicación más reciente y de mayor relevancia para los objetivos de esta revisión. Resultados: en Chile casi un cuarto de la población mayor tiene dependencia. El acceso a los beneficios formales de CLP llega a menos del 4% de la población que podría necesitarlos, y para los restantes, los cuidados provienen principalmente de las familias. Hay múltiples decisiones que tomar a la hora de crear políticas públicas sobre CLP. En cuanto a las maneras de brindar servicios, destaca la provisión de beneficios en especie. Respecto a quiénes considerar beneficiarios, la “universalidad focalizada” es un concepto transversal a muchos países. También destacan los buenos resultados de la implementación de un sistema integrado. Discusión: en Chile debería extenderse un debate sobre los CLP, ya que es un tema que no ha recibido la atención necesaria e influye a una proporción cada vez mayor de la población.