bluuebunny/crossref_metadata_2025 huggingface dataset:
A Hybrid Search Model for Constrained Optimization
Authors: Gao Xiaoli , Yuan Yangfei , Li Jie , Gao Weifeng
Published: September 2022

This paper proposes a hybrid model based on decomposition for constrained optimization problems. Firstly, a constrained optimization problem is transformed into a biobjective optimization problem. Then, the biobjective optimization problem is divided into a set of subproblems, and different subproblems are assigned to different Fitness functions by the direction vectors. Different from decomposition‐based multiobjective optimization algorithms in which each subproblem is optimized by using the information of its neighboring subproblems, the neighbors of each subproblem are deFined based on corresponding direction vector only in the method. By combining three main components, namely, the local search model, the global search model, and the direction vector adjusting strategy, the population can gradually move toward the global optimal solution. Experiments on two sets of test problems and Five real‐world engineering design problems have shown that the proposed method performs better than or is competitive with other compared methods.
Effect of Dry‐Wet Cycles on Strength Properties and Microstructure of Lime‐Metakaolin‐Modified Soil
Authors: Li Xinming , Zhang Haoyang , Guo Yanrui , Yin Song , Ren Kebin
Published: September 2022

To explore the feasibility of replacing natural hydraulic lime (NHL) with lime‐metakaolin (L‐MK) in the restoration of soil sites, the samples of L‐MK‐modified silty sand (hereinafter L‐MK‐modified soil) underwent 0, 5, 10, and 15 dry‐wet cycles and were then tested for mass loss, unconfined compressive strength, and splitting tensile strength. Some samples were tested using XRD, TG and SEM microscopic tests to study the strength mechanism for L‐MK‐ and NHL‐modified soil. The results showed that the mass loss ratios of the L‐MK‐ and NHL‐modified soils after 15 dry‐wet cycles were within 2%. The compressive and tensile strengths of the L‐MK‐modified soil decreased with more dry‐wet cycles, but the tensile strength decreased sharply initially and then to be stable after five dry‐wet cycles. The attenuation characteristics were different obviously for the failure mode of compressive and tensile strength and the unevenness of the specimen caused by dry‐wet cycles. The compressive and tensile strengths of L‐MK‐modified soil were significantly higher than those of NHL‐modified soil after the same dry‐wet cycle, and the decreased range of compressive and tensile strength was smaller than that of NHL‐modified soil. The strength formation and attenuation characteristics of L‐MK‐modified soil are closely related to the influence of dry‐wet cycles on the hydration products (e.g., CSH and C4AH13) generated by hydration reaction. The mix proportion of 6% L + 4% MK can effectively replace 8% and 10% NHL to protect soil sites.
A Regulatory Network Analysis of the Importance of USP15 in Breast Cancer Metastasis and Prognosis
Authors: Ling Jun , Qin Chenhui , Li Tao , Wang Baozhen , Cai Weiji , Ma Lei , Wang Yanfeng , Chen Jing , Xu Fang
Published: September 2022

Background. Ubiquitin-specific protease15(USP15), is the 16th identified protease in the USP family and is a key protein in tumorigenesis. However, the predictive value and regulatory mechanism of USP15 in breast cancer are unclear. Methods. The GEPIA, UALCAN, GeneMANIA, and STRING databases were applied to explore the expression of USP15 in breast cancer and associated proteins. In addition, the TIMER database was evaluated for immune infiltration patterns. Moreover, protein immunoblotting assay, cell scratching assay, small compartment invasion assay, 3D stromal gel assay, immunoprecipitation assay, and immunohistochemistry (IHC) were used to USP15 regulatory mechanisms in breast cancer. Results. In BRCA, several databases, including GEPIA and UALCAN, describe the upregulation of total protein levels and USP15 phosphorylation. In addition, the expression of USP15 was significantly correlated with gender and clinical stage. Overall survival (OS) was lower in patients with high USP15 expression. Functional network analysis showed that USP15 is involved in tumor-associated pathways, DNA replication, and cell cycle signaling through TGFβRI. In addition, USP15 expression was positively correlated with immune infiltration, including immune score, mesenchymal score, and several tumor-infiltrating lymphocytes (TIL). In addition, IHC results further confirmed the high expression of USP15 in breast cancer and its prognostic potential. Conclusions. These findings demonstrate that high USP15 expression indicates poor prognosis in BRCA and reveal potential regulatory networks and the positive relationship with immune infiltration. Thus, USP15 may be an attractive predictor for BRCA.
Observer-Based Robust Finite-Time Trajectory Tracking Control for a Stratospheric Satellite Subject to External Disturbance and Actuator Saturation
Authors: Huang Shurui , Yang Yueneng , Yan Ye , Zhang Shifeng , Liu Zhiyang
Published: September 2022

The stratospheric satellite is regarded as an ideal stratosphere flight platform and is able to accomplish various missions such as surveillance, earth observation, and remote sensing, which requires a robust and effective trajectory tracking control method to support these tasks. A novel observer-based robust finite-time control scheme is proposed to address the trajectory tracking control problem dedicated to a stratospheric satellite in the presence of external disturbance and actuator saturation. Firstly, an extended state observer (ESO) is adopted to observe the unavailable velocity states and unknown disturbances simultaneously, and the estimated data are utilized in the robust control law design. Then, an auxiliary system based on anti-windup compensator is developed to directly compensate for the actuator saturation difference. After that, a backstepping nonsingular fast terminal sliding mode control (BNFTSMC) strategy is designed to track the desired trajectory with high accuracy, fast convergence rate, and finite-time convergence. Then, a stability analysis using Lyapunov-based theory is performed, in which the stabilization of the stratospheric satellite system and finite-time convergence are proven. Furthermore, a number of simulations are conducted further to verify the excellent performance of the designed control strategy.
Analysis of Factors Affecting the Success of Sustainable Development Projects with the Help of Machine Learning Tools
Authors: Chen Zhi-Jun , Hsieh Tsung-Shun , Mousavi Davoudi Seyed Mehdi
Published: September 2022

Sustainable development projects are a group of development projects created with the aim of sustainable urban growth and development. To achieve development, it is essential to pay attention to the existence of projects. The point to consider is the threat of these expensive assets by all kinds of risks, such as floods, earthquakes, wars, mistakes, and price fluctuations, during the life cycle of projects from the beginning of their idea to the end of their useful life. Hence, the main objective of the study is to analyze the criteria and factors of project success and different machine learning strategies to achieve success and predict specific construction performance. To meet that aim, the research employs the descriptive approach, and analytical and logical aspects are derived from various sources such as research articles, published materials, online websites, books, and articles. The study’s results reveal that employing machine learning tools and algorithms to create a link between project success factors and criteria and prediction can bring multiple advantages, including high accuracy, ease of use, and inference for decision‐making. It can be concluded that algorithmic solutions could be integrated in a manner that project managers can adequately utilize to enhance project success by eliminating potential risks and guiding the project toward attaining its objectives.
Blockchain-Aided Searchable Encryption-Based Two-Way Attribute Access Control Research
Authors: Xu Zhigang , Zhang Shiguang , Han Hongmu , Dong Xinhua , Zheng Zhiqiang , Wang Haitao , Tian Wenlong
Published: September 2022

In the Internet of Things (IoT), data sharing security is important to social security. It is a huge challenge to enable more accurate and secure access to data by authorized users. Blockchain access control schemes are mostly one-way access control, which cannot meet the need for ciphertext search, two-way confirmation of users and data, and secure data transmission. Thus, this paper proposes a blockchain-aided searchable encryption-based two-way attribute access control scheme (STW-ABE). The scheme combines ciphertext attribute access control, key attribute access control, and ciphertext search. In particular, two-way access control meets the requirement of mutual confirmation between users and data. The ciphertext search avoids information leakage during transmission, thus improving overall efficiency and security during data sharing. Moreover, user keys are generated by the coalition blockchain. Besides, the ciphertext search and pre-decryption are outsourced to cloud servers, reducing the computing pressure on users and adapting to the needs of lightweight users in the IoT. Security analysis proves that our scheme is secure under a chosen-plaintext attack and a chosen keyword attack. Simulations show that the cost of encryption and decryption, keyword token generation, and ciphertext search of our scheme are preferable.
Adaptive Coordinated Variable Speed Limit between Highway Mainline and On-Ramp with Deep Reinforcement Learning
Authors: Cheng Ming , Zhang Chenghao , Jin Hui , Wang Ziming , Yang Xiaoguang
Published: September 2022

Highway merging bottleneck is challenged with serious traffic conflicts between on-ramp and mainline vehicles, causing significant capacity drop and drastic speed changes. The paper proposes an adaptive coordinated variable speed limit model to manage highway speed of on-ramp and mainline continuous sections without priority to mainline. That helps to remove the speed difference between the vehicles from on-ramp and mainline flooding into the merging zone, and to sustain actual traffic density close to critical density to counteract capacity drop as indicated with macroscopic fundamental diagram. The method of deep reinforcement learning based on deep deterministic policy gradient is employed to solve the proposed model with a row of continuous control variables. Simulation platform with VISSIM 5.3 is established, and the proposed method can enhance traffic flow through the merging zone by around 10% and 19% under static and dynamic demand, respectively, in addition to reduced density and speed variation by around 30%. This research provides insights into the management of highway capacity so as to secure traffic efficiency and reliability for the merging zone.
Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods
Authors: Ullah Zahid , Saleem Farrukh , Jamjoom Mona , Fakieh Bahjat , Kateb Faris , Ali Abdullah Marish , Shah Babar
Published: September 2022

Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, eye damage, and nerve damage. There are several risk factors involved in causing this disease, with some of the most common being obesity, age, insulin resistance, and hypertension. Therefore, early detection of these risk factors is vital in helping patients reverse diabetes from the early stage to live healthy lives. Machine learning (ML) is a useful tool that can easily detect diabetes from several risk factors and, based on the findings, provide a decision-based model that can help in diagnosing the disease. This study aims to detect the risk factors of diabetes using ML methods and to provide a decision support system for medical practitioners that can help them in diagnosing diabetes. Moreover, besides various other preprocessing steps, this study has used the synthetic minority over-sampling technique integrated with the edited nearest neighbor (SMOTE-ENN) method for balancing the BRFSS dataset. The SMOTE-ENN is a more powerful method than the individual SMOTE method. Several ML methods were applied to the processed BRFSS dataset and built prediction models for detecting the risk factors that can help in diagnosing diabetes patients in the early stage. The prediction models were evaluated using various measures that show the high performance of the models. The experimental results show the reliability of the proposed models, demonstrating that k-nearest neighbor (KNN) outperformed other methods with an accuracy of 98.38%, sensitivity, specificity, and ROC/AUC score of 98%. Moreover, compared with the existing state-of-the-art methods, the results confirm the efficacy of the proposed models in terms of accuracy and other evaluation measures. The use of SMOTE-ENN is more beneficial for balancing the dataset to build more accurate prediction models. This was the main reason it was possible to achieve models more accurate than the existing ones.
A BP Neural Network-Based Early Warning Model for Student Performance in the Context of Big Data
Authors: Shi Chengxiang , Tan Yun
Published: September 2022

Nowadays, educational data mining technology has received more and more attention from scholars in China, and the application of correlation between student behavior data and student achievement to teaching management has become a hot research topic. Starting from the study of the potential association between book borrowing and student achievement in the big data environment, the paper analyzes the correlation between book borrowing and student achievement based on the Apriori algorithm and concludes that there is a strong correlation rule between book borrowing and student achievement. Based on BP neural network prediction algorithm, the paper constructs an early warning model for student performance by predicting book borrowing through course performance. The absolute value of the error between the predicted value of book borrowing and the real value of borrowing is used as a basis to make early warning for students’ performance, so as to realize the monitoring of students’ learning situation, thereby providing theoretical suggestions for teachers’ teaching and promoting the school’s management of students.
Study on the Clinical Value of Noninvasive Prenatal Testing in Screening the Chromosomal Abnormalities of the Fetus in the Elderly Pregnant Women
Authors: Gu Zhiping , Du Mengmeng , Xu Tianhui , Jin Chunyan
Published: September 2022

Introduction. To explore the clinical value of noninvasive prenatal testing (NIPT) in screening the chromosomal abnormalities of the fetus in the elderly pregnant women. Materials and Methods. Between January 2020 and December 2021, 1949 elderly pregnant women underwent NIPT in our hospital. At the same time, 236 elderly pregnant women received invasive prenatal diagnosis, and the pregnancy outcomes were followed-up. Results. When NIPT was used for prenatal screening of fetal chromosomal aneuploidy, its diagnostic coincidence rate for trisomy 21 was the highest, with a coincidence rate of 90.00%, and the diagnostic coincidence rate for other chromosomal abnormalities was the lowest, only 22.22%. The sensitivity, specificity, positive predictive rate, and negative predictive rate for T21 by NIPT were 100%, 99.97%, 94.28%, and 100%; for T18 were 100%, 99.92%, 72.22%, and 100%, respectively; and for T13 were 100%, 99.95%, 50%, and 100%, respectively. Patients with high risks according to NIPT results further received invasive prenatal diagnosis, and 18 cases were excluded from the follow-up. For the remaining 1933 cases in the NIPT group, there was an incidence of 2.28% of adverse pregnancy outcomes. For the remaining 234 cases in the Amniocentesis group, there was an incidence of 1.28% of adverse pregnancy outcomes. There was no significant difference between the two groups (

P
>
0.05

). The diagnostic rate of fetal chromosomal abnormalities in pregnant women under 40 years old was about 0.39-0.79%; however, the risk for people over 40 is relatively high at 1.32-4.44%. Conclusion. The noninvasive prenatal screening of fetal DNA in the second trimester of pregnancy for elderly pregnant women has high application value in the prediction of pregnancy outcome. The high risk of pregnancy can be determined by detecting trisomy 21, 18, and 13 syndromes, and the probability of adverse pregnancy outcome increases.
Bibliometric Survey on Particle Swarm Optimization Algorithms (2001–2021)
Authors: Ajibade Samuel-Soma M. , Ojeniyi Adegoke
Published: September 2022

Particle swarm optimization algorithms (PSOA) is a metaheuristic algorithm used to optimize computational problems using candidate solutions or particles based on selected quality measures. Despite the extensive research published, studies that critically examine its recent scientific developments and research impact are lacking. Therefore, the publication trends and research landscape on PSOA research were examined. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and bibliometric analysis techniques were applied to identify and analyze the published documents indexed in Scopus from 2001 to 2021. The published documents on PSOA increased from 8 to 1,717 (21,362.50%) due to the growing applications of PSOA in solving computational problems. “Conference papers” is the most common document type, whereas the most prolific researcher on PSOA is Andries P. Engelbrecht (South Africa). The most active affiliation (Ministry of Education) and funding organization (National Natural Science Foundation) are based in China. The research landscape on PSOA revealed high levels of publications, citations, and collaborations among the top authors, institutions, and countries worldwide. Keywords co-occurrence analysis revealed that “particle swarm optimization (PSO)” occurred more frequently than others. The findings of the study could provide researchers and policymakers with insights into the prospects and challenges of PSOA research relative to similar algorithms in the literature.
Multisource Information Fusion Algorithm for Personalized Tourism Destination Recommendation
Authors: Yun Laiyan , Luo Zhenrong
Published: September 2022

In this paper, the existing scenic spot recommendation algorithms ignore the implicit trust and trust transmission of users when dealing with user relationships, and the lack of historical browsing behavior data of users in new city scenes leads to an inaccurate recommendation. In this paper, a personalized scenic spot recommendation method combining user trust relationship and tag preference is proposed. Firstly, the trust degree is introduced when the recommendation quality is poor only considering the similarity of users. By mining the implicit trust relationship of users, the problem that the existing research cannot make recommendations when the direct trust is difficult to obtain is solved, and the data sparsity and cold start problems are effectively alleviated. Secondly, in the process of user interest analysis, the relationship between scenic spots and tags is extended to the relationship among users, scenic spots and tags, and users’ interest preferences are decomposed into long-term preferences for different scenic spots tags, which effectively alleviates the problem of poor recommendation quality when users’ historical tour records are lacking. The personalized tourism recommendation method proposed in this paper effectively integrates many features of social networks and effectively alleviates the problems of data sparseness and feature learning in tourism recommendation based on social networks by using vectorization and deep learning technology. Its research has very important usage scenarios and commercial value in the tourism industry. This model can efficiently mine the association rules between scenic spots in multisource information data. The experimental results show that mining the correlation between the scenic spots selected by tourists can provide effective information for tourism decision-making.
A SEIR Epidemic Model of Whooping Cough‐Like Infections and Its Dynamically Consistent Approximation
Authors: Alqarni M.M. , Nasir Arooj , Alyami Maryam Ahmed , Raza Ali , Awrejcewicz Jan , Rafiq Muhammad , Ahmed Nauman , Sumbal Shaikh Tahira , Mahmoud Emad E.
Published: September 2022

Whooping cough is a highly transmitted disease around the world. According to the World Health Organization (WHO), 0.15 million cases had reported globally in 2018. Most of the Asian and African states are infected regions. Through the study, we investigated the whole population into the four classes susceptible (S), exposed (E), infected (I), and vaccinated or recovered (R). The transmission dynamics of whooping cough disease are studied analytically and numerically. Analytical analyses are positivity, boundedness, reproduction number, equilibria, and local and global stabilities. In numerical analysis, we developed an implicit numerical integration scheme consistent with the biological problem’s properties. The analysis of the implicit method for the said model is dynamically consistent, positive, and bounded. Furthermore, an implicit numerical integration scheme is suitable for studying a particular epidemic model such as the whooping cough disease.
Pharmacological Potential of the Standardized Methanolic Extract of Prunus armeniaca L. in the Haloperidol-Induced Parkinsonism Rat Model
Authors: Saleem Uzma , Hussain Liaqat , shahid Faiza , Anwar Fareeha , Chauhdary Zunera , Zafar Aimen
Published: September 2022

Parkinson’s disease (PD) is a complex, age-related neurodegenerative disease that causes neuronal loss and dysfunction over time. An imbalance of redox potential of oxidative stress in the cell causes neurodegenerative diseases and dysfunction of neurons. Plants are a rich source of bioactive substances that attenuate oxidative stress in a variety of neurological disorders. The aim of the present study was to evaluate the Prunus armeniaca L. methanolic extract (PAME) for anti-Parkinson activity in rats. PD was induced with haloperidol (1 mg/kg, IP). The PAME was administered orally at 100, 300, and 800 mg/kg dose levels for 21 days. Behavioral studies (catalepsy test, hang test, open-field test, narrow beam walk, and hole-board test), oxidative stress biomarkers (SOD, CAT, GSH, and MDA) levels, neurotransmitters (dopamine, serotonin, and noradrenaline) levels, and acetylcholinesterase activity were quantified in the brain homogenate. Liver function tests (LFTs), renal function tests (RFTs), complete blood count (CBC), and lipid profiles were measured in the blood/serum samples to note the side effects of PAME at the selected doses. Histopathological analysis was performed on the brain (anti-PD study), liver, heart, and kidney (to check the toxicity of PAME on these vital organs). Motor functions were improved in the behavioral studies. Dopamine, serotonin, and noradrenaline levels were significantly increased (

P
<
0.001

), whereas the level of acetylcholinesterase was decreased significantly (

P
<
0.001

). The levels of superoxide dismutase (SOD), catalase (CAT), and reduced glutathione (GSH) were increased, while malondialdehyde (MDA) and nitrite levels were decreased in the PAME-treated groups significantly compared with the disease control group, hence reducing oxidative stress. The incidence of toxicity was determined by biochemical analysis of LFT and RFT biomarkers testing. The histopathological analysis indicated that neurofibrillary tangles and plaques decreased in a dose-dependent manner in the PAME-treated groups. Based on the data, it is concluded that PAME possessed good anti-Parkinson activity, rationalizing the plant’s traditional use as a neuroprotective agent.
[Retracted] Evaluation of Neutrophil‐Lymphocyte Ratio and Platelet‐Lymphocyte Ratio on Predicting Responsiveness to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients
Authors: Liu Maoxi , Feng Yi , Zhang Yixun , Liu Haiyi
Published: September 2022

Objective. Neutrophil‐lymphocyte ratio (NLR) and Platelet‐lymphocyte ratio (PLR) have been proposed as prognostic biomarkers in multiple cancers. However, the implications of NLR and PLR in the responsiveness to neoadjuvant chemoradiotherapy (nCRT) remain to be clarified in locally advanced rectal cancer (LARC) patients. This retrospective study investigated the prognostic value of NLR and PLR in nCRT responsiveness of LARC patients. Methods. A total number of 86 patients diagnosed with LARC and treated with nCRT and total mesorectal excision were retrospectively followed from 2013 to 2016. Receiver operating characteristic (ROC) curve was used to determine the cutoff values of NLR and PLR, and the patients were divided into NLR elevation and NLR decrease groups, or PLR elevation and PLR decrease groups. The correlation between NLR and PLR changes, and clinicopathological factors were analyzed. The relationship between NLR and PLR changes and the curative responsiveness towards nCRT were further evaluated. Results. NLR and PLR changes after nCRT were significantly correlated with the distance of tumors to the anus and BMI (body mass index) (P < 0.05). The clinical remission rate of patients with NLR reduction was 72.09% (31/43), which was significantly higher than that in patients with NLR increment (22/43, 51.16%). There was no significant difference in the clinic remission rate between the patients with PLR reduction and those with PLR increment (P > 0.05). However, the pathological responsiveness rate was significantly higher in patients with PLR reduction (21/43, 48.84%) when compared to the ones with PLR increment (9/43, 20.9%) (P = 0.036). Conclusion. Our data indicate that in LARC patients with nCRT, the reduction of NLR and the reduction of PLR could serves as predictors for the clinic remission rate and pathological responsiveness rate, respectively. The combination of NLR and PLR changes may be employed as a simple and effective prognostic parameter to predict the treatment outcome of nCRT in LARC.
Prognostic Signature Development on the Basis of Macrophage Phagocytosis-Mediated Oxidative Phosphorylation in Bladder Cancer
Authors: Qu Genyi , Xu Yong , Lu Zhenquan , Nie Haibo , Tang Cheng , Hou Jian , Wen Xiangyang
Published: September 2022

Background. Macrophages are correlated with the occurrence and progression of bladder cancer (BCa). However, few research has focused on the predictive relevance of macrophage phagocytosis-mediated oxidative phosphorylation (MPOP) with BCa overall survival. Herein, we aimed to propose the targeted macrophage control based on MPOP as a treatment method for BCa immunotherapy. Methods. The mRNA expression data sets and clinical data of bladder cancer originated from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data set. A systematic study of several GEO data sets found differentially expressed macrophage phagocytosis regulators (DE-MPR) between BCa and normal tissues. To discover overall survival-associated DE-MPR and develop prognostic gene signature with performance validated based on receiver operating curves and Kaplan-Meier curves, researchers used univariate and Lasso Cox regression analysis (ROC). External validation was done with GSE13057 and GSE69795. To clarify its molecular mechanism and immune relevance, GO/KEGG enrichment analysis and tumor immune analysis were used. To find independent bladder cancer prognostic variables, researchers employed multivariate Cox regression analysis. Finally, using TCGA data set, a predictive nomogram was built. Results. In BCa, a four-gene signature of oxidative phosphorylation composed of PTPN6, IKZF3, HDLBP, and EMC1 was found to predict overall survival. With the MPOP feature, the ROC curve showed that TCGA data set and the external validation data set performed better in predicting overall survival than the traditional AJCC stage. The four-gene signature can identify cancers from normal tissue and separate patients into the high-risk and low-risk groups with different overall survival rates. The four MPOP-gene signature was an independent predictive factor for BCa. In predicting overall survival, a nomogram integrating genetic and clinical prognostic variables outperformed AJCC staging. Multiple oncological features and invasion-associated pathways were identified in the high-risk group, which were also correlated with significantly lower levels of immune cell infiltration. Conclusion. This paper found the MPOP-feature gene and developed a predictive nomogram capable of accurately predicting bladder cancer overall survival. The above discoveries can contribute to the development of personalized treatments and medical decisions.
A Review of the Role and Challenges of Big Data in Healthcare Informatics and Analytics
Authors: Awrahman Banan Jamil , Aziz Fatah Chia , Hamaamin Mzhda Yasin
Published: September 2022

Healthcare has evolved with the development of technology to improve the quality of life and save lives. Today, big data is considered as one of the most essential and promising future technology areas and has been attracting the medical community’s attention. As a result of big data, we can improve patient outcomes, personalize care, improve relationships between the patient and the provider, and decrease hospital costs. The effect of big data is very large since medical societies are known for their size, diversity of complexity, and a high degree of dynamism. Big data has been discussed from different viewpoints in recent years, protecting its involvement in many aspects, specifically those related to the healthcare system. Assembling health information, sharing data, and integrating health are essential in spreading health care. In addition, the security and privacy of data are critical since the data must be accessed from multiple locations within the distributed system. This paper review aims to understand the role of big data in healthcare issues aggregating data and the challenges associated with big data in healthcare. The papers that have been selected for review are from last year’s research.
Randomized, Open-Label Phase 2 Study of Apalutamide plus Androgen Deprivation Therapy versus Apalutamide Monotherapy versus Androgen Deprivation Monotherapy in Patients with Biochemically Recurrent Prostate Cancer
Authors: Aggarwal Rahul , Alumkal Joshi J. , Szmulewitz Russell Z. , Higano Celestia S. , Bryce Alan H. , Lopez-Gitlitz Angela , McCarthy Sharon A. , Miladinovic Branko , McQuarrie Kelly , Thomas Shibu , Zhang Ke , Small Eric J.
Published: September 2022

Purpose. This randomized phase 2 study sought to assess the treatment effect of a finite duration of apalutamide with and without androgen deprivation therapy (ADT) in biochemically recurrent prostate cancer (BCR PC). Materials and Methods. Patients with BCR PC after primary definitive therapy and prostate-specific antigen (PSA) doubling time ≤12 months were randomized to open-label apalutamide (240 mg/d) alone, apalutamide plus ADT, or ADT alone (1 : 1:1 ratio) for 12 months followed by a 12-month observation period (NCT01790126). Mean changes from baseline in Functional Assessment of Cancer Therapy-Prostate (FACT-P) at 12 months (primary endpoint) and other prespecified assessments of health-related quality of life (HRQoL), PSA nadir, time to PSA progression, time to testosterone recovery, recovered testosterone >150 ng/dL without PSA progression at 24 months, and molecular markers were evaluated. Results. In 90 enrolled patients (apalutamide plus ADT (n = 31), apalutamide (n = 29), ADT (n = 30)), FACT-P at 12 months was not significantly different between apalutamide, ADT and apalutamide, and ADT groups. Addition of apalutamide to ADT prolonged time to PSA progression but this change did not reach statistical significance (hazard ratio (HR): 0.56, 95% confidence interval (CI): 0.23–1.36,

P
=
0.196

); time to testosterone recovery was similar in the ADT-containing groups. In apalutamide plus ADT, apalutamide, and ADT groups, 37.9%, 37.0%, and 19.2% of patients, respectively, had testosterone >150 ng/dL at 24 months without confirmed PSA progression. Of the few biomarkers expressed in blood, EPHA3 was significantly associated with shorter time to PSA progression (

P
=
0.02

) in the overall population. Conclusions. HRQoL was similar in patients treated with apalutamide alone, ADT alone, or their combination, although apalutamide plus ADT did not demonstrate statistically significant noninferiority in change from baseline in overall HRQoL. The aggregated efficacy and safety outcomes support further evaluation of apalutamide plus ADT in BCR PC.
Classification and Quantification of Danmaku Interactions in Online Video Lectures: An Exploratory Study
Authors: Li Shugang , Zhu He , Qian Ying , Ren Shiqi , Fang Bing
Published: September 2022

Danmaku is an important means of interaction in online education, providing a learning atmosphere of collaboration with peers. Nowadays, there have been more studies on Danmaku interaction. However, there are still some shortcomings in the existing literature for classifying and quantifying the content of Danmaku interactions. Semi-structured interviews were conducted with 35 informants to identify learner-content interaction that met learners’ functional needs and learner-learner interactions that met social, hedonic, and emotional needs. Using content analysis of two video lectures, we found that learner-learner interaction accounted for one-third in both. More learner-content interaction occurred in the lecture with declarative knowledge (in which instructors encouraged learners to practice) than in the lecture with procedural knowledge (in which the learners followed fixed steps). Learner-instructor interaction was also identified—especially when instructors had specific personal characteristics. Learner-interface interaction was also evident—especially in lectures that had quality issues. Since the learner-instructor communication was only one-way (no response from instructors), it was not perceived by learners, indicating that learners’ desire was not satisfied by Danmaku. The results of the study can provide teachers and content creators with advice related to course production and help teaching faculty recognize the importance of one-to-many interactive language and Danmaku interactions for learners.
Design and Implementation of Community Pension Service Quality Evaluation System Based on Clustering Algorithm
Authors: Liu Weiwei
Published: September 2022

The healthy development of community elderly care service is very important. Studying the quality evaluation of community elderly care services and establishing a complete, scientific, and objective community elderly care service method can establish the online reputation of enterprises and help elderly users obtain cost-effective elderly care services. This is very important for healthy development. This paper puts forward a new evaluation model for the quality of community elderly care service. The evaluation method based on the model is designed, the quality evaluation model is constructed, and the community evaluation system is developed. From the aspects of requirement analysis, design, implementation, and testing, the specific implementation process of the system is described. This paper puts forward a community evaluation model which accords with the clustering model: community elderly care service quality evaluation method. AIS-OR mathematical modeling is carried out. Under the guidance of the model, the secondary indicators are analyzed and determined, and the mathematical model of indicator evaluation is constructed. We use text mining technology and emotion analysis to determine the relationship between comment emotion and community elderly care service quality online and build a quality emotion feature library. The naive Bayesian algorithm is used to classify the online evaluation of the service quality of community elderly care services, which verified by experiments and completed the design and development of the community elderly care service quality evaluation system based on the project needs.
An Interactive Traffic Signal Optimization Approach with Dynamic Variable Guidance Lane Control
Authors: Zhao Fei , Fu Liping , Pan Xiaofeng , Zhong Ming , Kwon Tae J.
Published: September 2022

The purpose of setting up variable guidance lanes is to ease the traffic pressure on lanes with more traffic under the condition of ensuring the traffic capacity of the intersection. This study proposes a bi-level model-interactive relationship between variable guidance lane design and signal control and carry out a case study for a road in Wuhan, China. The design plan for the goal, combined with the design plan of the variable guidance lane, optimizes the signal timing plan of the intersection. A real case is modeled in VISSIM to simulate the design scheme of variable guidance lanes to verify the reliability and effectiveness of the bi-level model. The results showed that the average vehicle delay at the intersection was reduced by 20.65% after the bi-level model was optimized. The average error between model calculation results and simulation results is 9.88%. Moreover, the influencing factors of the proposed model are also carried out. The results show that when the traffic flow is greater than 1,000 pcu/h, the optimization effect of the model is more significant. When the traffic capacity gradually increases, the average vehicle delay calculated by the model is smaller. The design scheme and signal timing scheme proposed by the bi-level model can ensure the overall traffic efficiency of the intersection, improve the traffic efficiency of the traffic-stressed lanes, and further promote the space-time resource utilization of the intersection, optimize the space-time resources of the road network, and provide a scientific basis and new ideas and methods.
Pursuing Diabetic Nephropathy through Aqueous Humor Proteomics Analysis
Authors: Chen Huan , Wang Tan , Wang Erqian , Li Ningning , Min Hanyi
Published: September 2022

In order to determine the possible aqueous humor (AH) proteins involved in diabetic nephropathy (DN) progression, we performed gel electrophoresis-liquid chromatography-tandem mass spectrometry protein profiling of AH samples from 5 patients with proliferative diabetic retinopathy (PDR) combined DN and 5 patients with PDR. Function enrichment analyses were carried out after the identification of differentially expressed proteins (DEPs). Protein-protein interaction networks were then built and the Search Tool for the Retrieval of Interacting Genes database and CytoNCA plugin in Cytoscape were utilized for module analysis. Ingenuity Pathway Analysis (IPA) was used to analyze disease and biological function, Tox function enrichment and upstream regulatory molecules/networks. Fifty-four DEPs were finally confirmed, whose enriched functions and pathways covered cell adhesion, extracellular exosome, complement activation, complement and coagulation cascades, etc. Nine hub genes were identified, including NCAM1, PLG, APOH, C3, PSAP, RBP4, CDH2, NUCB1, and GNS. IPA showed that C3 and PLG are involved in renal and urological system abnormalities. Conclusively, DEPs and hub proteins confirmed in this exploratory AH proteomic analysis may help us gain a deeper understanding of the molecular mechanisms involved in DN progression, providing novel candidate biomarkers for the early detection for diagnosis of DN.
Tsbagging: A Novel Cross-Project Software Defect Prediction Algorithm Based on Semisupervised Clustering
Authors: Tang Shiqi , Huang Song , Liu ErHu , Yao YongMing , Wu KaiShun , Ji Haijin
Published: September 2022

Software defect prediction (SDP) is an important technology which is widely applied to improve software quality and reduce development costs. It is difficult to train the SDP model when software to be test only has limited historical data. Cross-project defect prediction (CPDP) has been proposed to solve this problem by using source project data to train the defect prediction model. Most of CPDP methods build defect prediction models based on the similarity of feature space or data distance between different projects. However, when the target project has a small amount of label data, these methods usually do not consider this part of data information. Therefore, when the distribution between source project and target project is quite different, these methods are difficult to achieve good prediction performance. To solve this problem, this paper proposes a CPDP method based on a semisupervised clustering (namely, Tsbagging). Tsbagging has two stages; in the first stage, we cluster to the source project data based on the limited labeled data in the target project and assign different weights to these source project data according to the clustering results. In the second stage, we use bagging method to train the prediction model based on the weight assigned in the first stage. The experimental results show that the performance achieved by Tsbagging is better than other existing SDP methods.
Application of Nanoflexible Photoelectric Devices in Welding Tooling Equipment Systems
Authors: Tan Wenping , Liu Honghua , Li Hongmei , Cao Shen
Published: September 2022

With the continuous breakthrough of science and technology, all aspects related to science and technology are constantly updated and changing. Photoelectric devices, also known as photosensitive devices, work on principles based on the photoelectric effect. The application of photoelectric devices in our lives is also diverse. This paper aims to study the flexibility of nanophotoelectric devices, which is a new generation of photoelectric device product technology, making photoelectric devices have more outstanding utility compared with the past. In the traditional sense, photoelectric devices will have their own hardware defects, and the update of nanoflexible photoelectric device technology will solve the above shortcomings. The above breakthroughs in key technologies have improved the hardware of welding tooling in the industrial field. The technique of welding tooling is widely used in the industrial field. The introduction of nanoflexible photoelectric devices into the welding tooling can overcome the defects of conventional processes. This paper proposes the application of nanotechnology in the industrial field. A parameter testing system and a model of dynamic matrix predictive control are established to predict and control the parameters of the experiment throughout the process. The experimental object of this paper is nanoflexible optoelectronic devices; meanwhile, the concentration of nanomaterials in the nanophotoelectric device plays a decisive role in the efficiency of the device, then the final experimental results show that when the concentration of nanomaterials is 8.5

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10, the efficiency of the optoelectric components is the best of the four groups of experiments.
Keyword Extraction for Medium‐Sized Documents Using Corpus‐Based Contextual Semantic Smoothing
Authors: Khan Osama A. , Wasi Shaukat , Siddiqui Muhammad Shoaib , Karim Asim
Published: September 2022

Keyword extraction refers to the process of selecting most significant, relevant, and descriptive terms as keywords, which are present inside a single document. Keyword extraction has major applications in the information retrieval domain, such as analysis, summarization, indexing, and search, of documents. In this paper, we present a novel supervised technique for extraction of keywords from medium‐sized documents, namely Corpus‐based Contextual Semantic Smoothing (CCSS). CCSS extends the concept of Contextual Semantic Smoothing (CSS), which considers term usage patterns in similar texts to improve term relevance information. We introduce four more features beyond CSS as our novel contributions in this work. We systematically compare the performance of CCSS with other techniques, when implemented over INSPEC dataset, where CCSS outperforms all state‐of‐the‐art keyphrase extraction techniques presented in the literature.
Synthesis and Adsorbent Performance of Modified Biochar with Ag/MgO Nanocomposites for Heat Storage Application
Authors: Venkatesh R. , Karthi N. , Kawin N. , Prakash T. , Kannan C. Ramesh , Karthigairajan M. , Bobe Ketema
Published: September 2022

Heat storage is a major problem in the world. Many research is going on the heat storage application. This research investigates the novel Ag/MgO/biochar nanocomposites for heat storage. Ag/MgO/biochar nanocomposites were fabricated using solvent-free ball milling techniques. According to several analytical measurements, the Ag/MgO nanoparticles in biochar are uniformly dispersed across the carbon interface. This type of adsorbent material has been characterized by different techniques such as X-ray diffraction pattern analysis (XRD), FTIR analysis, scanning electron microscope (SEM), and transmission electron microscope (TEM) as all indicate the surface morphology and successful ball milling synthesis of Ag/MgO nanocomposites. The UV visible spectroscopy wavelength range of AgNPs and MgONPs is 330 nm and 470 nm, respectively. FTIR analysis revealed that different functional groups of modified biochar nanocomposites such as O-H group are 3728 cm-1 and for C-H bond is 932 cm-1, C-O group is 1420 cm-1, and C=O is 1785 cm-1, respectively. Adsorption tests showed that 1.0 gL-1 dosage with 60% phosphate removal, an ion, and 0.2 gL-1 of dosages that had 85% methylene blue decomposition, a charged synthetic dye, were the lowest absorption levels. This research suggests that ball milling offers the advantages of stabilization and chemical adaptability for customized remediation of different atmospheric contaminants. Ball milling is a facile and feasible process to fabricate carbon-metal-oxide nanomaterials.
An Evaluation Method for the Construction of Double-Qualified Teachers in Vocational Schools Based on ID3 Algorithm and Neural Networking
Authors: Wu Henan , Gui Peng
Published: September 2022

In vocational schools, the traditional evaluation approach of forming a double-qualified teaching team has the issue of an imperfect evaluation standard, resulting in low accuracy. Based on the ID3 algorithm, this research develops an evaluation technique for double-qualified teacher team construction in vocational schools. The characteristics of dual-teacher education are gathered, customized training plans are produced, and the selection mechanism for vocational school teachers is maximised based on the requirements of students’ vocational ability training. By calculating the information gain of each attribute, the ID3 algorithm is used to formulate teacher evaluation standards, and dimensionless post-data are comprehensively processed to design the evaluation mode of teacher team construction. Experimental results: the average accuracy of the evaluation method for the construction of double-qualified teachers in vocational schools based on association rules, as well as the other two evaluation methods, was 69.959%, 58.851 percent, and 58.744 percent, respectively, indicating that the evaluation method for the construction of double-qualified teachers in vocational schools integrated with the ID3 algorithm was more accurate.
Metaheuristics for a Large-Scale Vehicle Routing Problem of Same-Day Delivery in E-Commerce Logistics System
Authors: Tao Yi , Lin Changhui , Wei Lijun
Published: September 2022

In this paper, we introduce a new variant of large-scale vehicle routing problem that arises in the goods distribution of city e-commerce logistics, the multi-depot vehicle routing problem with order split and allocation (MD-CVRP-OSA), which incorporates the issue of split and allocation of online orders into traditional VRP. A mathematical formulation is constructed for the MD-CVRP-OSA, and an efficient metaheuristics algorithm based on a variable neighborhood search (VNS) solution framework is designed to solve it. The proposed method is tested on a large family of instances, including real-world data collected from JD.com, and the effectiveness of the VNS algorithm and also the algorithm components are analyzed.