syedzayyan/paper_mill_benchmark huggingface dataset:
Analysis of the Effect of Music Therapy Interventions on College Students with Excessive Anxiety

In order to explore and analyze the effect of music therapy interventions on college students with excessive anxiety, this paper selected 240 year 2017- to year 2020-enrolled undergraduates from a comprehensive university in taiyuan city, shanxi province, in central china as research objects.These college students had been diagnosed as excessive anxiety and were randomly divided into two groups—intervention group and control group—with 120 students in each group.The control group received conventional mental health treatment for college students, while the intervention group received music therapy interventions on this basis with 3 times a week for 24 times.The instruments used in the music therapy include piano, percussion instruments, melodic instruments, and diffuse instruments; the specific implementation of each therapy is divided into five parts: warm-up, rhythm percussion, song singing, instrumental ensemble, and music appreciation.The study results show that before treatment, the excessive anxiety score of college student in the control group was 63-76 with an average score of 72.58 ± 5.27 ; after treatment, that was 45-64 with an average score of 54.46 ± 6.82 ; before treatment, the excessive anxiety score of college student in the intervention group was 62-78 with an average score of 72.56 ± 5.51 ; after treatment, that was 26-44 with an average score of 33.19 ± 5.51 .Before treatment, there was no significant difference in the excessive anxiety scores between the two groups of college students ( p &gt; 0.05 ); after treatment, the excessive anxiety scores of the two groups were lower than those before treatment, and the reduction degrees in the intervention group were bigger than those in the control group, with statistically significant difference ( p &lt; 0.05 ).Therefore, music therapy interventions can significantly reduce the excessive anxiety of college students; the analysis also shows that factors such as gender, grade, major, origin, repertoire type, therapy type, and anxiety type could affect the effect of music therapy interventions to some certain extent.For example, the effect of music therapy interventions on college students in psychology or related majors is better than that of students in other majors; the effect of receptive music therapy is better than that of creative and improvised music therapy; the effect of music therapy interventions on college students’ life event, romantic relationship, and social anxiety is better than that on college students’ test and job-hunting anxiety.

Journal: Occupational Therapy International (2023)
Publication types: Research Article;
Analysis and Research on the Rehabilitation Effect of Physical Exercise on College Students’ Mental Depression Based on Multidimensional Data Mining

In order to explore the physical exercise and psychological changes of college students, this study adopts the method of multidimensional data mining, taking 23,146 undergraduates from a university in guangzhou, guangdong province, as the research object.On the basis of summarizing and analyzing the previous research literature, this study expounds the development background, current situation, and future challenges of multidimensional data mining technology.This paper introduces the methods and principles of sample selection and multidimensional assessment of the physical and mental depression of college students, analyzes the physical health status of college students, summarizes the psychological changes of college students before and after the intervention, and discusses the relationship between physical exercise and mental health of college students.In addition, this paper analyzes the influencing factors and psychological changes of college students’ physical exercise and puts forward suggestions for improving the physical and mental health of college students.The results of this study show that with increasing age, college students have a lower risk of moderate anxiety and mild depression; girls are more prone to mild depression than boys; rural college students are more prone to mild, moderate anxiety; college students with nonmedical backgrounds were more likely to experience moderate anxiety than college students with medical backgrounds.During the intervention control period, continuous connection with others may encourage college students to actively use internal resources to actively cope with obstacles and setbacks, and as a protective factor, psychological resilience can appropriately weaken the association between risk factors in life and anxiety and relieve anxiety.The results of this study can provide a reference for further research on the physical exercise and psychological changes of college students.

Journal: Occupational Therapy International (2022)
Publication types: Research Article;
Application of Computer Simulation Optimization Algorithm in Waste Treatment of Drilling Engineering

The existing computer technology is used to conduct an in‐depth study and analysis of drilling waste treatment, and the results are analyzed by computer simulation optimization algorithms.Based on the system theory, we define the research system, combine the unique characteristics of the technological innovation mechanism of drilling waste treatment, and use the internal and external factors affecting the technological innovation dynamics of drilling waste treatment, such as drilling waste treatment capacity, from the current actual situation.On this basis, factor analysis is used to analyze the factors affecting technological innovation dynamics from both internal and external aspects of the system, establish a system model of technological innovation dynamics of drilling waste disposal, and give the initial values of the simulation model.This will reduce the pollution of the environment and enhance the competitiveness of enterprises.Drilling waste treatment technology is getting increased attention, but at this stage, there is no suitable technology innovation mechanism for drilling waste treatment.Through the simulation trend chart obtained by sensitivity analysis, the key factors in the system are found, and the innovation power mechanism of drilling waste treatment technology is constructed, which provides a basis for the formulation of enterprise technological innovation strategies and the development of technological innovation activities.Research on the dynamic mechanism of drilling waste treatment technology innovation explores a waste treatment mechanism suitable for the drilling industry, so as to promote the steady development of the drilling industry and at the same time provide theoretical support for the treatment of drilling engineering waste.

Journal: Complexity (2021)
Publication types: Research Article;
Analysis of the Correlation between Emerging Industry Development and University Students’ Entrepreneurship Based on Big Data

With the strong support of local governments for strategic emerging industries such as high‐end equipment manufacturing, new materials, and new energy, strategic emerging industries are playing an increasingly important role in the economy and society.With the increasing enthusiasm of college graduates for independent entrepreneurship, college students’ entrepreneurship is constantly integrated with the development of strategic emerging industries.Based on this background, aiming at the practical problems of the development of strategic emerging industries, this study innovatively puts forward the method of using big data technology and gm model to realize the dynamic model analysis of the development of strategic emerging industries and college students’ entrepreneurial behavior.This article analyzes the correlation between dynamic big data such as industrial scale, industrial market, and industrial direction of local strategic emerging industries and university entrepreneurship, so as to provide theoretical support for the development strategy of strategic emerging industries.Through the neural network algorithm, this article evaluates the entrepreneurship of college students, so as to provide a digital basis for the layout of strategic emerging industries to attract talents and entrepreneurship.Experiments show that the big data integration system established by gm correlation analysis and ant colony elman regression artificial neural network has high accuracy and can well identify the priority relevance of the industrial direction of strategic emerging industries to college students’ entrepreneurship.It provides theoretical support for regional policy makers to better formulate college students’ entrepreneurship strategy and the development plan of emerging industries.

Journal: Discrete Dynamics in Nature and Society (2022)
Publication types: Research Article;
An Empirical Analysis of Mental Health and Social Support in the Elderly of Different Age Groups: A Survey and Analysis of Data from China

To explore the effects of social support on the mental health of older adults in different age groups in china, and to provide reference for the development of social support policies to promote the mental health of older adults in china.This paper uses a random sampling method to conduct a questionnaire survey of 3,000 older adults aged 60 years or older in nanjing and a dichotomous logistic regression model for empirical analysis.The two dimensions of mental health and social adjustment were used to measure the mental health status of the elderly.The results showed that the overall mental health status of the elderly in nanjing city was more optimistic, and the nonempty nesters with male gender, higher education level, and partners were generally healthier; objective and subjective support had significant positive effects on the mental health of middle and high aged elderly, while the utilization of support had more significant effects on the health of low-aged elderly.This paper proposes that government departments should develop a localized elderly support system and social support policies adapted to the actual situation in nanjing according to the quantitative distribution and demand characteristics of the mental health of elderly people in different age groups.The study is structured as follows: the second part contains data sources and statistical methods.The third part is the analysis of empirical results.The fourth part is the discussion and conclusion part of the article.

Journal: Occupational Therapy International (2022)
Publication types: Research Article;
Artificial Intelligence-Based Interactive Art Design under Neural Network Vision Valve

Interactive art design (iad) is an organic integration of art and technology.From the perspective of the development of ai machines from ancient times to the present, it has gone through the stages of command interface, graphical interface, and multimedia interface.The development of interactive art has been around for some years.As an art form, it not only brings optimization and enjoyment to people’s quality of life but also meets the needs of human-computer interaction, improves the efficiency of art design, and realizes human-human interaction.The purpose is to bring different feelings and experiences to the works and people’s psychology.In particular, the application of ai in iad has not only brought great changes to designers.It also derives the interaction of behavioral limbs, which brings a greater experience and interaction to the audience, thereby creating a better interactive effect.Therefore, this paper completes the following work: (1) the research progress of ai in iad at home and abroad is introduced.(2) the combination of ai and iad is proposed, and the basic principle of rbf neural network and intelligent optimization algorithm are introduced, and the evaluation index of iad is constructed.(3) using the constructed dataset to test two intelligent optimization algorithms, the results show that the pso-rbf model is more excellent in evaluating the quality of iad.The trained model is used for experiments, and the output of the model is compared with the expert evaluation results, and the error is very small.Comparing the quality indicators of iad before and after the integration of ai, the results show that ai has an excellent improvement effect on iad.

Journal: Journal of Sensors (2022)
Publication types: Research Article;
A Weighted Cluster Head Selection Algorithm for Energy Efficient Wireless Sensor Networks

The wireless sensor network’s (wsns) lifetime is mainly dependent on the re of the sensor nodes (se n).In recent years, energy minimization in a wsn has been a prominent research topic, and numerous solutions have been proposed.This research focuses on the energy minimization of the se ns where firstly, k-medoid clustering algorithm is applied to create clusters.Second, a weighted cluster head selection technique is used to choose a cluster head (ch) by integrating three independent weights associated with an se n: energy, distance from the centroid, and distance from the sink node (sn).According to the energy level and distance from the sn and cluster’s centre, each node is assigned a constant weight.The simulation results are compared to existing methodologies, and the results show that the suggested network’s lifetime enhances.

Journal: Journal of Sensors (2022)
Publication types: Research Article;
A Study of Language Use Impact in Radio Broadcasting: A Linguistic and Big Data Integration Approach

Broadcasting more culturally educating and language-reviving contents are ways radio stations can help revitalize indigenous languages in delta north in nigeria.The challenges faced in communicating through indigenous dialects on radio stations are majorly caused by the lack of indigenous language professionals and linguists in the broadcast stations.The absence of these professionals is a major constraint to the development of the community.The broadcast media can help manage multilingualism through the introduction of new words which would give little or no room for lexicon dearth but would expand the language lexicon.Using these dialects during broadcast gives relevance to all dialects, reduces language dearth, and keeps people connected to their culture.Programmes anchored in indigenous dialects enhance the vocabulary, comprehension, and language vitality of the language.The study examined the impact of local language used in radio broadcasting using a descriptive big data survey research design.The study’s population comprises of all the inhabitants of delta north from which a sample of 10 broadcast staff and 120 radio listeners in delta north senatorial district in nigeria was drawn using a stratified random sampling technique.The instrument of data collection was a structured questionnaire with closed questions and a self-structured interview.The sample employed frequency distribution tables, percentages, and charts in the presentation and analysis of data.The results revealed that majority of the respondents in delta north listened to radio broadcast indicating that the use of indigenous dialects/language can have massive impact on the people.The study also found that majority of the respondents use indigenous languages in their day-to-day activities, with english being used majorly only in schools.The study recommends, among others, that the national broadcasting commission review their policy on the allocated time of broadcast in indigenous languages and that more indigenous language experts and linguists should be incorporated into the broadcast system.

Journal: Journal of Sensors (2022)
Publication types: Research Article;
A Review of Detection of Antibiotic Residues in Food by Surface-Enhanced Raman Spectroscopy

Antibiotics, as veterinary drugs, have made extremely important contributions to disease prevention and treatment in the animal breeding industry.However, the accumulation of antibiotics in animal food due to their overuse during animal feeding is a frequent occurrence, which in turn would cause serious harm to public health when they are consumed by humans.Antibiotic residues in food have become one of the central issues in global food safety.As a safety measure, rapid and effective analytical approaches for detecting these residues must be implemented to prevent contaminated products from reaching the consumers.Traditional analytical methods, such as liquid chromatography, liquid chromatography mass spectrometry, and capillary electrophoresis, involve time-consuming sample preparation and complicated operation and require expensive instrumentation.By comparison, surface-enhanced raman spectroscopy (sers) has excellent sensitivity and remarkably enhanced target recognition.Thus, sers has become a promising alternative analytical method for detecting antibiotic residues, as it can provide an ultrasensitive fingerprint spectrum for the rapid and noninvasive detection of trace analytes.In this study, we comprehensively review the recent progress and advances that have been achieved in the use of sers in antibiotic residue detection.We introduce and discuss the basic principles of sers.We then present the prospects and challenges in the use of sers in the detection of antibiotics in food.Finally, we summarize and discuss the current problems and future trends in the detection of antibiotics in food.

Journal: Bioinorganic Chemistry and Applications (2021)
Publication types: Review Article;
A Pan-Cancer Analysis of the Role of PBRM1 in Human Tumors

To understand common but distinctive systems to drive oncogenic stages in human tumors is critical for understanding disease programme and developing novel therapeutic strategies.Pbrm1 is a critical gene in oncogenesis.We found that pbrm1 is upregulated in multiple cancer genes.Prognostic analyses indicated that higher pbrm1 showed better disease outcomes of head and neck squamous cell carcinoma (hnsc), kirc, and ucec, while poorer outcomes in kich, skin cutaneous melanoma (skcm), and esophageal carcinoma (esca).Pbrm1 mutation was most frequent in renal cell carcinoma and showed better disease outcomes of pan-cancer.We also discovered that pbrm1 performance was associated with endothelial cell invasion status in coad, hnsc, kirc, luad, lusc, ov, and paad, and cancer-related fibroblast invasion was observed in coad, hnsc, kirc, lusc, meso, ov, and paad.We also make the comparison of pbrm1’s phosphorylation between normal and basic tumor systems as well as explore potential systems with distinctive functions in pbrm1-mediated oncogenesis.The analysis of pan-cancer offers us an outline of pbrm1’s functions in various human cancers, which could promote a comprehensive understanding of pbrm1 in tumorigenesis.

Journal: Stem Cells International (2022)
Publication types: Research Article;
A Novel Approach to Automate Complex Software Modularization Using a Fact Extraction System

Complex software systems that support organizations are updated regularly, which can erode system architectures.Moreover, documentation is rarely synchronized with the changes to the software system.This creates a slew of issues for future software maintenance.To this goal, information extraction tools use exact approaches to extract entities and their corresponding relationships from source code.Such exact approaches extract all features, including those that are less prominent and may not be significant for modularization.In order to resolve the issue, this work proposes an enhanced approximate information extraction approach, namely, fact extractor system for java applications (fesja) that aims to automate software modularization using a fact extraction system.The proposed fesja technique extracts all the entities along with their corresponding more dominant formal and informal relationships from a java source code.Results demonstrate the improved performance of fesja, by extracting 74 (classes), 43 (interfaces), and 31 (enumeration), in comparison with eminent information extraction techniques.

Journal: Journal of Mathematics (2022)
Publication types: Research Article;
A Mathematical Queuing Model Analysis Using Secure Data Authentication Framework for Modern Healthcare Applications

Healthcare application is one of the most promising developments to provide on-time demand services to the end users, vehicles, and other road side units (rsus) in the urban environment.In recent years, several application interfaces have been developed to connect, communicate, and share the required services from one source to another.However, the urban environment holds a complex entity of both homogenous and heterogeneous devices to which the communication/sensing range between the devices leads to connectivity breakage, lack of needed service in time, and other environmental constraints.Also, security plays a vital role in allowing everyone in the urban area to access/request services according to their needs.Again, this leads to a massive breakthrough in providing reliable service to authentic users or a catastrophic failure of service denial involving unauthorized user access.This paper proposes a novel topological architecture, secure authentication relay-based urban network (s-arun), designed for healthcare and other smart city applications for registered transportation stakeholders.The registered stakeholders hold a built-in data security framework with three subsystems connected to the s-arun topology: (1) authentication subsystem: the stakeholder must identify themselves to the source responder as part of the authentication subsystem before transmitting the actual data service request; (2) connectivity subsystem: to periodically check the connection state of stakeholders as they travel along with the road pattern; and (3) service subsystem: each source responder will keep a separate queue for collecting data service requests, processing them quickly, and sending the results to the appropriate stakeholder.The kerberos authentication method is used in working with s-arun’s model to connect the stakeholders securely and legitimately.The performance of the proposed s-arun is assessed, and the performance metric toward key generation and other data security-related metrics is tested with existing schemes.

Journal: Journal of Sensors (2022)
Publication types: Research Article;
A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream

In medical filed, predicting the occurrence of heart diseases is a significant piece of work.Millions of healthcare-related complexities that have remained unsolved up until now can be greatly simplified with the help of machine learning.The proposed study is concerned with the cardiac disease diagnosis decision support system.An open ml repository data stream with 1 million instances of heart disease and 14 features is used for this study.After applying to preprocess and feature engineering techniques, machine learning approaches like random forest, decision trees, gradient boosted trees, linear support vector classifier, logistic regression, one-vs-rest, and multilayer perceptron are used to perform binary and multiclassification on the data stream.When combined with the max abs scaler technique, the multilayer perceptron performed satisfactorily in both binary (accuracy 94.8%) and multiclassification (accuracy 88.2%).Compared to the other binary classification algorithms, the gbt delivered the right outcome (accuracy of 95.8%).Multilayer perceptrons, however, did well in multiple classifications.Techniques such as oversampling and undersampling have a negative impact on disease prediction.Machine learning methods like multilayer perceptrons and ensembles can be helpful for diagnosing cardiac conditions.For this kind of unbalanced data stream, sampling techniques like oversampling and undersampling are not practical.

Journal: Journal of Sensors (2022)
Publication types: Research Article;