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This study analyzed the current status, hotspots, and emerging trends of global research on cognitive frailty, in order to provide new research ideas for researchers. Articles and reviews related to cognitive frailty, published from 2013 to 2021, were retrieved from the Web of Science Core Collection (WoSCC) database on 26 November 2021. CiteSpace 5.8.R3 was employed for data analyses. A total of 2077 publications were included. There has been a rapid growth of publications on cognitive frailty research since 2016. The United States, Italy, England, and Australia have been the leading research centers of cognitive frailty; however, China has also recently focused on this topic. The National Center for Geriatrics and Gerontology, and Shimada H. were found to be the most prolific institution and author, respectively. Co-citation analysis identified 16 clusters, of which the largest was cognitive frailty. The keywords which occurred most frequently were “older adult”, followed by “cognitive impairment”, “frailty”, “risk”, “dementia”, “prevalence”, “mortality”, “health”, and “Alzheimer’s disease”. Burst keyword detection revealed a rising interest in cognitive frailty models. By analyzing these publications from recent years, this study provides a comprehensive analysis of cognitive frailty research.

Accurate and up-to-date biodiversity forecasts enable robust planning for environmental management and conservation of landscapes under a wide range of uses. Future predictions of the species composition of ecological communities complement more frequently reported species richness estimates to better characterize the different dimensions of biodiversity. The models that make community composition forecasts are calibrated with data on species' geographic patterns for the present, which may not be good proxies for future patterns. The future establishment of novel communities represents data on species interactions unaccounted for by these models. However, detecting them in a systematic way presents challenges due to the lack of monitoring data for landscapes with high environmental turnover, where such communities are likely to establish. Here, we propose lightweight monitoring over both ecological and anthropogenic disturbance gradients using passive sensors (i.e., those that operate continuously without much human input) to detect novel communities with the aim of updating models that make community composition forecasts. Monitoring over these two gradients should maximize detection of novel communities and improve understanding of relationships between community composition and environmental change. Further, barriers regarding cost and effort are reduced by using relatively few sensors requiring minimal upkeep. Ongoing updates to community composition forecasts based on novel community data and better understanding of the associated uncertainty should improve future decision-making for both resource management and conservation efforts.

This paper describes the process of gathering, aggregating, harmonizing, and publishing epistolary metadata through collaboration with Finnish cultural heritage (CH) organizations in order to create an inclusive archive for bottom-up analyses of 19th-century epistolary culture in the Grand Duchy of Finland (1808/09-1917). The authors are working in the digital humanities consortium project Constellations of Correspondence (CoCo) [1]. The unified metadata collections are harmonized, linked, enriched, and published on a Linked Open Data (LOD) service, and as a semantic web portal. In Europe, there are several digital humanities projects using well-curated metadata (detailed information about senders, recipients, dates, and places) from edited letter collections - like CKCC [2], correspSearch [3, 4], the Early Modern Letters Online (EMLO) [5][6], Norkorr [7], and SKILLNET [8]. In our project, most of the data come from unpublished collections scattered around different Finnish CH organizations. Collaboration with these CH organizations is pivotal for the successful outcome of the project. It requires a dialogue with them throughout the whole project period in the form of seminars and site visits, as well as sharing blogs and newsletters, also after the organizations have provided their letter metadata. We have also already seen that some of the participating organizations are prepared to clean their metadata or catalogue previously uncatalogued archival material to provide better and more metadata for the project. We will discuss this two-way process using the Finnish National Gallery as a case study. An important challenge yet to be studied profoundly is, if and how the CoCo project will be able to deliver to the CH organizations their metadata in an enriched format. In the first phase of the project, we conducted a survey that was sent to over 100 CH organizations (extending from small local museums to official central archives). The paper describes how the information was collected and how the survey was constructed in order to provide us with detailed enough information regarding their 19th-century collections and metadata formats. At the same time, we had to keep the query succinct in order to make the answering as effortless as possible. As to the data processing, we began with more than 350 000 letters, from eight different sources, each in its own digital format. Although the received data is mostly structured, we needed to parse running text to retrieve metadata in nearly every collection. Moreover, we had to analyze each dataset and identify possible structural mistakes. Furthermore, some records required Natural Language Processing to get actor names (e.g. senders, recipients) in dictionary format. The most difficult task has been to process word files which contain correspondence metadata in a variety of formats, easily understandable to humans but difficult for computational processing. A harmonizing data model for epistolary metadata collections was developed, which builds on international standards like CIDOC CRM to promote interoperability. The most central classes are Letter, Place and Actor. Also, provenance and archival information are included. Finally, the actor data is enriched by linking it to external databases like Wikidata and the Finnish AcademySampo and BiographySampo. These external sources provide detailed biographical information, e.g., times and places of birth and death, name variations, occupations, or genealogical relationships. Information present in the letter metadata like actor names and times of sending and receiving is used for matching entities between our data and the external databases, and further to reconcile the actors between data sources. References [1] J. Tuominen, et al., Constellations of Correspondence: a linked data service and portal for studying large and small networks of epistolary exchange in the Grand Duchy of Finland, in: 6th Digital Humanities in Nordic and Baltic Countries Conference, 2022. URL: http://ceur-ws.org/Vol-3232/paper41.pdf. [2] C. van den Heuvel, Mapping knowledge exchange in Early Modern Europe: Intellectual and technological geographies and network representations, International Journal of Humanities and Arts Computing 9 (2015) 95–114. doi:10.3366/ijhac.2015.0140. [3] S. Dumont, S. Grabsch, J. Müller-Laackman, correspsearch – connect scholarly editions of correspondence (2.0.0) [web service], Berlin–Brandenburg Academy of Sciences and Humanities, 2021. URL: https://correspSearch.net. [4] S. Dumont, correspSearch – connecting scholarly editions of letters, Journal of the Text Encoding Initiative (2016). doi:10.4000/jtei.1742. [5] URL: http://emlo.bodleian.ox.ac.uk. [6] H. Hotson, T. Wallnig (Eds.), Reassembling the Republic of Letters in the Digital Age: Standards, Systems, Scholarship, Göttingen University Press, 2019. [7] A. Rockenberger, et al., Norwegian correspondences and linked open data, in: Proceedings of the Digital Humanities in the Nordic Countries 4th Conference, volume 2364 of CEUR Workshop Proceedings, 2019, pp. 365–375. URL: http://ceur-ws.org/Vol-2364/33_paper.pdf. [8] Sharing Knowledge in Learned and Literary Networks – The Republic of Letters as a Pan-European Knowledge Society (SKILLNET), URL: https://skillnet.nl.

The tutorial demonstrates a workflow that we developed for the text-based digital classification of an early modern advertisement newspaper which allows for subsequent and dynamic classification of the single ads according to the researcher’s interest. It also shows how this approach could be applied to similar publications, using our R-package.

The tutorial demonstrates a workflow that we developed for the text-based digital classification of an early modern advertisement newspaper which allows for subsequent and dynamic classification of the single ads according to the researcher’s interest. It also shows how this approach could be applied to similar publications, using our R-package. This is the presentation that accompanied the workshop. You can find the abstract of the workshop here. The project data and package are available at https://github.com/Avisblatt.

Motivation Flow cytometry (FCM) and transcription profiling are the two widely used assays in translational immunology research. However, there is no data integration pipeline for analyzing these two types of assays together with experiment variables for biomarker inference. Current FCM data analysis mainly relies on subjective manual gating analysis, which is difficult to be directly integrated with other automated computational methods. Existing deconvolutional analysis of bulk transcriptomics relies on predefined marker genes in the transcriptomics data, which are unavailable for novel cell types and does not utilize the FCM data that provide canonical phenotypic definitions of the cell types. Results We developed a novel analytics pipeline—FastMix—for computational immunology, which integrates flow cytometry, bulk transcriptomics and clinical covariates for identifying cell type-specific gene expression signatures and biomarker genes. FastMix addresses the ‘large p, small n’ problem in the gene expression and flow cytometry integration analysis via a linear mixed effects model (LMER) for both cross-sectional and longitudinal studies. Its novel moment-based estimator not only reduces bias in parameter estimation but also is more efficient than iterative optimization. The FastMix pipeline also includes a cutting-edge flow cytometry data analysis method—DAFi—for identifying cell populations of interest and their characteristics. Simulation studies showed that FastMix produced smaller type I/II errors than competing methods. Validation using real data of two vaccine studies showed that FastMix identified a consistent set of signature genes as in independent single-cell RNA-seq analysis, producing additional interesting findings. Availability and implementation Source code of FastMix is publicly available at https://github.com/terrysun0302/FastMix. Supplementary information Supplementary data are available at Bioinformatics online.

Using a deconvolution algorithm, it is shown that resonant inelastic X-ray scattering (RIXS) spectra can be reliably estimated from measurements of photoelectrons created through absorption of RIXS photons in a chosen material. The method works on previously reported data with ∼0.5 eV resolution, and simulations show its potential for accurately estimating spectral features with hundreds of meV or smaller width., Resonant inelastic X-ray scattering (RIXS) has become an important scientific tool. Nonetheless, conventional high-resolution (few hundred meV or less) RIXS measurements, especially in the soft X-ray range, require low-throughput grating spectrometers, which limits measurement accuracy. Here, the performance of a different method for measuring RIXS, i.e. photoelectron spectrometry for analysis of X-rays (PAX), is computationally investigated. This method transforms the X-ray measurement problem of RIXS to an electron measurement problem, enabling use of high-throughput, compact electron spectrometers. X-rays to be measured are incident on a converter material and the energy distribution of the resultant photoelectrons, the PAX spectrum, is measured with an electron spectrometer. A deconvolution algorithm for analysis of such PAX data is proposed. It is shown that the deconvolution algorithm works well on data recorded with ∼0.5 eV resolution. Additional simulations show the potential of PAX for estimation of RIXS features with smaller widths. For simulations using the 3d levels of Ag as a converter material, and with 105 simulated detected electrons, it is estimated that features with a few hundred meV width can be accurately estimated in a model RIXS spectrum. For simulations using a sharp Fermi edge to encode RIXS spectra, it is estimated that one can accurately distinguish 100 meV FWHM peaks separated by 45 meV with 105 simulated detected electrons that were photoemitted from within 0.4 eV of the Fermi level.

Background Smartphones and their built-in sensors allow for measuring functions in disease-related domains through mobile tests. This could improve disease characterization and monitoring, and could potentially support treatment decisions for multiple sclerosis (MS), a multifaceted chronic neurological disease with highly variable clinical manifestations. Practice effects can complicate the interpretation of both improvement over time by potentially exaggerating treatment effects and stability by masking deterioration. Objective The aim of this study is to identify short-term learning and long-term practice effects in 6 active tests for cognition, dexterity, and mobility in user-scheduled, high-frequency smartphone-based testing. Methods We analyzed data from 264 people with self-declared MS with a minimum of 5 weeks of follow-up and at least 5 repetitions per test in the Floodlight Open study, a self-enrollment study accessible by smartphone owners from 16 countries. The collected data are openly available to scientists. Using regression and bounded growth mixed models, we characterized practice effects for the following tests: electronic Symbol Digit Modalities Test (e-SDMT) for cognition; Finger Pinching and Draw a Shape for dexterity; and Two Minute Walk, U-Turn, and Static Balance for mobility. Results Strong practice effects were found for e-SDMT (n=4824 trials), Finger Pinching (n=19,650), and Draw a Shape (n=19,019) with modeled boundary improvements of 40.8% (39.9%-41.6%), 86.2% (83.6%-88.7%), and 23.1% (20.9%-25.2%) over baseline, respectively. Half of the practice effect was reached after 11 repetitions for e-SDMT, 28 repetitions for Finger Pinching, and 17 repetitions for Draw a Shape; 90% was reached after 35, 94, and 56 repetitions, respectively. Although baseline performance levels were highly variable across participants, no significant differences between the short-term learning effects in low performers (5th and 25th percentile), median performers, and high performers (75th and 95th percentile) were found for e-SDMT up to the fifth trial (β=1.50-2.00). Only small differences were observed for Finger Pinching (β=1.25-2.5). For U-Turn (n=15,051) and Static Balance (n=16,797), only short-term learning effects could be observed, which ceased after a maximum of 5 trials. For Two Minute Walk (n=14,393), neither short-term learning nor long-term practice effects were observed. Conclusions Smartphone-based tests are promising for monitoring the disease trajectories of MS and other chronic neurological diseases. Our findings suggest that strong long-term practice effects in cognitive and dexterity functions have to be accounted for to identify disease-related changes in these domains, especially in the context of personalized health and in studies without a comparator arm. In contrast, changes in mobility may be more easily interpreted because of the absence of long-term practice effects, even though short-term learning effects might have to be considered.

Background Smartphone location data can be used for observational health studies (to determine participant exposure or behavior) or to deliver a location-based health intervention. However, missing location data are more common when using smartphones compared to when using research-grade location trackers. Missing location data can affect study validity and intervention safety. Objective The objective of this study was to investigate the distribution of missing location data and its predictors to inform design, analysis, and interpretation of future smartphone (observational and interventional) studies. Methods We analyzed hourly smartphone location data collected from 9665 research participants on 488,400 participant days in a national smartphone study investigating the association between weather conditions and chronic pain in the United Kingdom. We used a generalized mixed-effects linear model with logistic regression to identify whether a successfully recorded geolocation was associated with the time of day, participants’ time in study, operating system, time since previous survey completion, participant age, sex, and weather sensitivity. Results For most participants, the app collected a median of 2 out of a maximum of 24 locations (1760/9665, 18.2% of participants), no location data (1664/9665, 17.2%), or complete location data (1575/9665, 16.3%). The median locations per day differed by the operating system: participants with an Android phone most often had complete data (a median of 24/24 locations) whereas iPhone users most often had a median of 2 out of 24 locations. The odds of a successfully recorded location for Android phones were 22.91 times higher than those for iPhones (95% CI 19.53-26.87). The odds of a successfully recorded location were lower during weekends (odds ratio [OR] 0.94, 95% CI 0.94-0.95) and nights (OR 0.37, 95% CI 0.37-0.38), if time in study was longer (OR 0.99 per additional day in study, 95% CI 0.99-1.00), and if a participant had not used the app recently (OR 0.96 per additional day since last survey entry, 95% CI 0.96-0.96). Participant age and sex did not predict missing location data. Conclusions The predictors of missing location data reported in our study could inform app settings and user instructions for future smartphone (observational and interventional) studies. These predictors have implications for analysis methods to deal with missing location data, such as imputation of missing values or case-only analysis. Health studies using smartphones for data collection should assess context-specific consequences of high missing data, especially among iPhone users, during the night and for disengaged participants.

Experiments are essential to understand the behaviour and performance of energy storage systems. In this field, a considerable amount of experimental data is generated and data processing is a tedious task. To date, research teams working in the field of energy storage tend to focus on developing their own analysis tools rather than using existing open source software. This strategy can be detrimental to the quality and reproducibility of the research. This paper presents DATTES, a free and open source software for analysing experimental battery data. The software provides a comprehensive and customizable toolkit for extracting, analysing and visualizing experimental data. It also creates gateways to other open software and tools. In this way, DATTES enables users to get the most out of their experimental data and engage in open and reproducible science., {"references":["S. Hettrick, 2014:Software in research survey (2018). doi:10.5281/zenodo.1183562.","M. Hassini, E. Redondo-Iglesias, P. Venet, Lithium-ion battery data: From production to prediction, Batteries (in press, 2023).","P. Herring, C. B. Gopal, M. Aykol, J. H. Montoya, A. Anapolsky, P. M. Attia, W. Gent, J. S. Hummelshøj, L. Hung, H.-K. Kwon, et al., Beep: A python library for battery evaluation and early prediction, SoftwareX 11 (2020) 100506. doi:10.1016/j.softx.2020.100506.","V. Sulzer, S. G. Marquis, R. Timms, M. Robinson, S. J. Chapman, Python battery mathematical modelling (pybamm), Journal of Open Research Software 9 (1) (2021). doi:10.5334/JORS.309.","M. D. Murbach, B. Gerwe, N. Dawson-Elli, L.-k. Tsui, impedance. py: A python package for electrochemical impedance analysis, Journal of Open Source Software 5 (52) (2020) 2349. doi: 10.21105/joss.02349.","VEHLIB. URL: https://gitlab.univ-eiffel.fr/eco7/vehlib","M. Hassini, E. Redondo-Iglesias, P. Venet, S. Gillet, Y. Zitouni, Second Life Batteries in a Mobile Charging Station: Model Based Performance Assessment, in: EVS35, 35nd International Electric Vehicle Symposium & Exhibition, Oslo, Norway, 2022. URL: https://hal.science/hal-03708744","M. Hassini, E. Redondo-Iglesias, P. Venet, S. Gillet, Y. Zitouni, Second Life Batteries in a Mobile Charging Station: Experimental Performance Assessment, working paper or preprint (2022). URL: https://hal.science/hal-03713844v1","M. Hassini, E. Redondo-Iglesias, P. Venet, Second-life batteries modeling for performance tracking in a mobile charging station, World Electric Vehicle Journal 14 (4) (2023) 94. doi:10.3390/ wevj14040094","DATTES : Data Analysis Tools for Tests on Energy Storage. URL: https://gitlab.com/dattes/dattes/","R. C. Jiménez, M. Kuzak, M. Alhamdoosh, M. Barker, B. Batut, M. Borg, S. Capella-Gutierrez, N. C. Hong, M. Cook, M. Corpas, et al., Four simple recommendations to encourage best practices in research software, F1000Research 6 (2017). doi:10.12688/f1000research.11407.1"]}

Chat technologies have gained significant attention in recent years due to their ability to automatically generate charts and graphs from raw data. These frameworks use manufactured insights (AI) and machine learning (ML) calculations to convert complex datasets into outwardly engaging visual representations. This paper provides a comprehensive review of the state-of-the-art in chatbot technologies, discussing their underlying techniques, applications, strengths, limitations, and future directions. Furthermore, we give a comparative analysis of a number of well-known Chatbot systems, emphasizing their salient characteristics and performance indicators. The findings of this review paper contribute to a better understanding of chatbot technologies and provide guidance for researchers, developers and end-users in this rapidly evolving field.

With the development of big data concept and technology, big data has an important impact on human development. This paper studies the relationship between the consumption pattern and mental health of enterprise employees under the normalization of epidemic prevention and control. Starting from the consumption structure and behavior of enterprise employees, it defines the meaning of enterprise employees’ consumption and the connotation of enterprise employees’ health psychology and analyzes the relationship between consumption behavior and consumption psychology and the elements of enterprise employees’ health psychology. Based on the change of employees’ income structure and consumption patterns, this paper speculates the relationship between employees’ consumption patterns and mental health, analyzes the correlation between employees’ consumption patterns and mental health through a questionnaire survey, and calculates the Correlation Clustering statistical results. It plays an important role in building a good enterprise staff consumption culture under the normalization of epidemic prevention and control and effectively realizes the significance of purifying the social consumption environment.

Thermo-mechanical Pulping (TMP) is one of the most energy-intensive industries where most of the electrical energy is consumed in the refining process. This paper proposes the energy-saving refining optimization strategy by integrating the machine learning algorithm and heuristic optimization method. First, refining specific energy consumption (RSEC) and pulp quality identification models are developed using Artificial Neural Networks. In the second step, the developed identification models are incorporated with the Genetic algorithm to minimize the total refining specific energy consumption while maintaining the same pulp quality. Simulation results prove that a deep multilayer perceptron neural network is a powerful tool for creating refining energy and quality identification models with the model correlation coefficients of 0.97, 0.94, 0.92, and 0.67 for the first-stage RSEC, second-stage RSEC, final pulp fiber length, and freeness prediction, respectively. Findings confirm that the average total RSEC reduction of 14 % is achievable by utilizing the proposed optimization method.

With the economic development, smart communities have been widely studied and applied. However, the system in this field is not perfect, and there are still a series of problems, such as high construction cost, low level of intelligence, mutual independence of different systems, difficulty in unified management, and so on. To solve the above problems, this paper proposes the smart community big data dynamic analysis model based on logistic regression model. First, this paper constructs the big data research architecture of smart community based on IOT technology, including IAAs, DAAS, PAAS, and SaaS layers and the virtual service layer of resource scheduling of spatiotemporal information cloud platform optimized by spatiotemporal law. And the IoT platform is designed to collect data to lay the foundation for research. Second, this paper is oriented to the big data application requirements with distribution and mobility as the main technical characteristics. Based on the distributed data flow, this paper designs mining operator to provide technical support for the data mining algorithm; at the same time, this paper constructs a high-dimensional random matrix model for measuring big data and then deduces its abnormal data detection theory and method to detect high-dimensional abnormal data. Finally, this paper uses logistic regression model to predict the development trend of smart community and provide guarantee for smart community service. The simulation results show the efficiency and accuracy of prediction can be improved based on logistic regression model. Furthermore, it effectively avoid repeated construction and waste of resources in the community and form a new community management model based on intelligent and information-based social management and service.

Global cities play a significant role in world economy as they serve as key hubs of economic activity and trade. These cities are centers of innovation, finance, culture, and commerce, attracting businesses and entrepreneurs from all over the world. They are characterized by their openness, diversity, and their ability to attract and retain talent. This paper includes a bibliometric analysis of the structure of global cities through examining the literature on global cities, including the document type, country/territory distribution, institution distribution, geographical distribution of authors, specially most active authors and their interests or research areas, relationships between principal authors and more relevant journals, and the research hot spots. The input data consists of journal articles archived by the Web of Science from 1991 to 2023, and the analysis is performed using SciMAT and VOS Viewer. The result of this paper would provide valuable insights into the state of research on this topic, including who is conducting research, where it is being conducted, what types of publications are being produced, and which themes are having the most impact on the field. Such an analysis would be useful for researchers, policymakers, and other stakeholders interested in understanding the role of global cities in the world economy. Refereed/Peer-reviewed

My technical report focuses on the design and implementation of a virtual platform to test the NGS Shipboard. During my internship, I was part of the navigation team with a primary focus on enhancing the navigation and combat systems of naval gunfire ships. Our primary goal was to establish an effective platform for testing the navigation and combat capabilities of various naval ships. Our team successfully designed and developed a virtual shipboard model that recorded data from different ships. Its application extended to our project, where we utilized virtual reality to model and test ship designs. The Navy's decision to adopt virtual reality in ship design stemmed from the potential timesaving benefits it offered and the emerging advancements in virtual reality and how it is evolving. My STS research paper analyzes how businesses can enhance their understanding of how to create effective customer interactions that fulfill customer needs and expectations by examining the impact of technology and emerging trends. The widespread adoption of digital technologies has revolutionized customer-business interactions, leading to a shift in dynamics. Online experiences prioritize personalization, convenience, and speed and customer behavior and expectations are influenced by various factors, including cultural norms, social contexts, and individual preferences. This STS research paper highlights the rise of online shopping, saving customers time and making it easier to purchase products online over in-person interactions. The synthetic connection between the technical report and the STS research paper is the emergence of virtual reality. Augmented reality is a form of virtual reality, and it is used widely in e-commerce sales and enhances shopping experiences by providing interactive experiences. With online shopping becoming a more sociotechnical phenomena, and the navy utilizing virtual reality to provide interactive experiences to test ships, we can analyze the advancements in technology and how technology is saving us time.

Plantation forests, cultivated through artificial seeding and planting methods, are of great significance to human society. However, most experimental sites for these forests are located in remote areas. Therefore, in-depth studies on remote forest management and off-site experiments can better meet the experimental and management needs of researchers. Based on an experimental plantation forest of Triploid Populus Tomentosa, this paper proposes a digital twin architecture for a virtual poplar plantation forest system. The framework includes the modeling of virtual plantation and data analysis. Regarding this system architecture, this paper theoretically analyzes the three main entities of the physical world, digital world, and researchers contained in it, as well as their interaction mechanisms. For virtual plantation modeling, a tree modeling method based on LiDAR point cloud data was adopted. The transitional particle flow method was proposed to combine with AdTree method for tree construction, followed by integration with other models and optimization. For plantation data analysis, a database based on forest monitoring data was established. Tree growth equations were derived by fitting the tree diameter at breast height data, which were then used to predict and simulate trends in diameter-related data that are difficult to measure. The experimental result shows that a preliminary digital twin-oriented poplar plantation system can be constructed based on the proposed framework. The system consists of 2160 trees and simulations of 10 types of monitored or predicted data, which provides a new practical basis for the application of digital twin technology in the forestry field. The optimized tree model consumes over 67% less memory, while the R2 of the tree growth equation with more than 100 data items could reach more than 87%, which greatly improves the performance and accuracy of the system. Thus, utilizing forestry information networking and digitization to support plantation forest experimentation and management contributes to advancing the digital transformation of forestry and the realization of a smart management model for forests.

This paper provides an in-depth introduction to research methods and discusses numerous aspects related to the research process. It begins with an overview of research—what it is, why it is important, what forms it might take, and what its fundamental components are—before shifting its attention to the design of research and the benefits and drawbacks of various approaches. In addition, the article details the purpose of and procedures for a literature review. Data collection, its varieties, technique selection, validity, and reliability are then addressed. Ethics in research and guidelines for research are also discussed, along with the various types of data analysis, and how results should be obtained. In addition, the paper covers topics such as reference and citation management. This paper takes a narrative approach to summarising the scholarly resources useful for introductory-level coursework in research method. Both research and writing are never-ending processes, with endless opportunities for improvement. However, the compilation of essential academic resources might help university students, researchers and research method instructors., Cite this item: Toyon, M. A. S. (2023). Introduction to research: Mastering the basics. Scholars Journal of Research in Social Science, 3(1), 1-24., {"references":["Caruana, E. J., Roman, M., Hernández-Sánchez, J., & Solli, P. (2015). Longitudinal Studies. Journal of Thoracic Disease, 7(11), 537-540.","Ciesielska, M., Boström, K. W., & Öhlander, M. (2018). Observation Methods. In M. Ciesielska, & D. Jemielniak, Qualitative Methodologies in Organization Studies- Volume II: Methods and Possibilities (pp. 33-52). Cham: Palgrave Macmillan. https://doi.org/10.1007/978-3-319-65442-3_2","Creswell, J. W. (2012). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research. Boston: Pearson.","Crotty, M. (1998). The Foundations of Social Research: Meaning and Perspective in the Research Process. SAGE: London.","Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to Conduct a Bibliometric Analysis: An Overview and Guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070","Drost, E. A. (2011). Validity and Reliability in Social Science Research. Education Research and Perspectives, 38(1), 105-124.","Eriksson, P., & Kovalainen, A. (2008). Qualitative Methods in Business Research. SAGE: London. https://doi.org/10.4135/9780857028044","Ferrari, R. (2015). Writing Narrative Style Literature Reviews. Medical Writing, 24(4), 230-235. https://doi.org/10.1179/2047480615Z.000000000329","Field, A. (2009). Discovering Statistics Using SPSS (and sex and drugs and rock 'n' roll). London: SAGE Publications Ltd.","Gray, D. E. (2014). Doing Research in the Real World. London: SAGE.","Greenwood, D. J., & Levin, M. (2006). Introduction to Action Research: Social Research for Social Change. Thousand Oaks: SAGE.","Gregory, A. T., & Denniss, A. R. (2018). An Introduction to Writing Narrative and Systematic Reviews - Tasks, Tips and Traps for Aspiring Authors. Heart, Lung and Circulation, 27, 893-898. https://doi.org/10.1016/j.hlc.2018.03.027","Guest, G., MacQueen, K. M., & Namey, E. E. (2012). Applied Thematic Analysis. London: SAGE.https://doi.org/10.4135/9781483384436","Kumar, V., & Garg, M. L. (2018). Predictive Analytics: A Review of Trends and Techniques. International Journal of Computer Applications, 182(1), 31-37. https://doi.org/10.5120/ijca2018917434","Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive Analytics: Literature Review and Research Challenges. International Journal of Information Management, 50, 57-70. https://doi.org/10.1016/j.ijinfomgt.2019.04.003","Lind, D., Marchal, W., & Wathen, S. (2015). Statistical Techniques in Business and Economics. 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Research in Nursing & Health, 18(2), 179-183. https://doi.org/10.1002/nur.4770180211","Saunders, M. N., Lewis, P., & Thornhill, A. (2007). Research Methods for Business Students. Essex: Pearson.","Shelby, L. B., & Vaske, J. J. (2008). Understanding Meta-Analysis: A Review of the Methodological Literature. Leisure Sciences, 30(2), 96-110. https://doi.org/10.1080/01490400701881366","Stebbins, R. A. (2001). Exploratory Research in the Social Sciences. London: SAGE. https://doi.org/10.4135/9781412984249","Toyon, M. (2021). Explanatory Sequential Design of Mixed Methods Research: Phases and Challenges. International Journal of Research in Business and Social Science, 10(5), 253-260. https://doi.org/10.20525/ijrbs.v10i5.1262","Vasileiou, K., Barnett, J., Thorpe, S., & Young, T. (2018). Characterising and Justifying Sample Size Sufficiency in Interview-Based Studies: Systematic Analysis of Qualitative Health Research over a 15-year period. 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This paper constructs a platform framework for extensive data analysis of college students’ psychological quality with the help of the thinking mode of big data and related technologies and proposes the construction principles, data sources, data processing methods, data platform construction, and platform application of big data analysis platform for college students’ psychological quality assessment. This paper combines the application methods of big data technology, collects the management data related to the psychological quality assessment of college students, saves them into the system database with certain storage logic, and realizes the function of psychological quality assessment through the design of selected psychological quality assessment data, data management and data resource management and other parts based on the data results of extensive data analysis. This study provides some insights into the psychological quality assessment of college students. The strength of association between the variables of psychological quality assessment of college students changes over time, but the overall psychological structure is more stable. This stable psychological structure characteristic is conducive to constructing the policy of constant psychological education in large universities.

As a class of randomized learner model, stochastic configuration networks (SCNs) have been successfully applied in a few data analytics tasks. Given the industrial big data modeling tasks, however, the original SCNs potentially lead to excessive training time. To this end, this paper extends SCNs to a hybrid parallel version, termed HPSCNs. In the hybrid parallel learning algorithm, two SCNs are synchronously constructed. The difference between the two of them is that one employs a point-incremental algorithm, and another one adopts a block-incremental algorithm. Moreover, a data parallel method is established based on a dynamic block strategy to accelerate the establishment of candidate node pool for each SCN. Additionally, this paper proposes an adaptive hyperparameter adjustment method, which allows the hyperparameters in the supervisory mechanism to be automatically adjusted along with the learning process. Comparative experiments are first conducted through four large-scale benchmark datasets, followed by the fully discussion on the algorithm parameters. Finally, a practical industrial application case is made, where a grinding particle size soft sensor is developed based on HPSCN, showing the effectiveness of the proposed algorithm.

Evaluating air transport service quality is fundamen-tal to ensure acceptable quality standards for users and improve the services offered to passengers and tourists. In the transportation literature there is a wide range of studies about the evaluation of public transport service quality based on passengers’ perceptions; however, more recently, the evaluation of air transport service quality is becoming a relevant issue. Evaluating service quality in air transport sector represents a more stimulating chal-lenge, given the complexity of air transport system in re-gards to the other systems; in fact, air transport service is characterised by a great variety of service aspects relat-ing to services offered by the airlines and provided by the companies managing airports. The complexity of such a service requires a deep investigation on the methods adopted for collecting and analysing the data regarding passengers’ perceptions. We propose this paper just for treating these interesting aspects and to provide an ex-haustive literature review of the studies analysing ser-vice quality from the passengers’ point of view, where the opinions of the passengers are collected by the Customer Satisfaction Surveys (CSS). We decided to select papers published within the last decade (2010–2020) in journals indexed in important databases such as Scopus and WoS.

As an educational concept based on learning output, OBE (Outcome-Based Education) is student-centered and emphasizes students’ personal progress and learning achievement. Based on BD (big data) analysis, this paper proposes a new talent training mode for the electronic information science and technology specialty. This model employs BD analysis technology to examine the correlation index between social demand and talent cultivation, based on the employment situation of students in the country in previous years. The teaching reform was carried out under the OBE concept, and a new training scheme was formed. Training objectives, graduation requirements, curriculum system, and continuous improvement mechanism were all determined. This paper proposes an algorithm for determining the degree of similarity between the knowledge required by the organization and the knowledge held by its employees. The person with the highest similarity is identified as the training candidate by the algorithm, and the training candidate is then trained according to the knowledge that the organization requires. Talent training in the field of electronic information science and technology has yielded positive results.

The increasingly expanding genomic databases generate the need for new tools for their processing and further use. In the paper, a bioinformatics tool, which is a search engine of microsatellite elements—trinucleotide repeat sequences (TRS) in files of FASTA type—is presented. An innovative approach was applied in the tool, which consists of connecting—within one search engine—both mapping of TRS motifs and extracting sequences that are found between the mapped TRS motifs. Accordingly, we present hereby the tool called TRS-omix, which comprises a new engine for searching information on genomes and enables generation of sets of sequences and their number, providing the basis for making comparisons between genomes. In our paper, we showed one of the possibilities of using the software. Using TRS-omix and other IT tools, we showed that we were able to extract sets of DNA sequences that can be assigned only to the genomes of the extraintestinal pathogenic Escherichia coli strains or to the genomes of the intestinal pathogenic Escherichia coli strains, as well as providing the basis for differentiation of the genomes/strains belonging to each of these clinically essential pathotypes.

In today's world, women's safety is a significant and crucial problem. Even in these modern times, where technology has improved considerably, women's safety remains a worry. Incidences of rape, stalking, taunting, sexual assault, molestation, harassment, domestic abuse and other forms of violence are progressing daily. Many rules and regulations have been enacted to prevent these heinous acts. Despite this, the rate of crime is rapidly increasing. The Government has developed a number of devices and applications; however they do not make use of advanced technologies like Machine Learning, Data Science etc. The paper highlights various research papers in the context of women safety, women awareness and machine learning process. Machine learning has the power to predict and analyze the data in an effective and efficient way. As the quality of data plays a vital role during the analysis, so data preparation is a crucial step of machine learning process because the raw data may be noisy, incomplete or inconsistent. The collected data from women respondents is prepared and pre-processed to transform the raw data into a structured and understandable format. Lastly, the data is analyzed using principal component analysis, KMO and Bartlett's Test to predict the important and correlated parameters that influence the women safety.

It is generally understood that administrative data at the level of the individual, family and wider population is fundamental to delivering client centred services which aim to support families and respond to, and reduce child abuse. They are valuable to policy makers and practitioners and play an important role in research. The focus of this paper is the potential use of administrative data from statutory family support and child protection and welfare services in Ireland for policy, practice and research. In the context of an evolving legislative and policy framework in Ireland, we provide an overview of the statutory family support and child protection services provided by Tusla Child and Family Agency. We suggest that this context provides an exceptional opportunity for developing administrative data sets in child protection and welfare and in family support. The benefits and challenges of developing administrative data sets are discussed. The paper concludes with recommendation for developing and linking administrative data sets to better understand and respond to the needs of children and families presenting to the services

Introduction: In today's rapid development of science and technology, digital network data mining technology is developing as fast as the expansion of the frontiers of science and technology allows, with a very broad application level, covering most of the civilized environment. However, there is still much to explore in the application of sports training. Objective: Analyze the feasibility of data mining based on the digital network of sports training, maximizing athletes’ training. Methods: This paper uses the experimental analysis of human FFT, combined with BP artificial intelligence network and deep data mining technology, to design a new sports training environment. The controlled test of this model was designed to compare advanced athletic training modalities with traditional modalities, comparing the athletes’ explosive power, endurance, and fitness. Results: After 30 days of physical training, the athletic strength of athletes with advanced fitness increased by 15.33%, endurance increased by 15.85%, and fitness increased by 14.23%. Conclusion: The algorithm designed in this paper positively impacts maximizing athletes’ training. It may have a favorable impact on training outcomes, as well as increase the athlete's interest in the sport. Level of evidence II; Therapeutic studies - investigating treatment outcomes.

This paper presents a method for the digital history of a discipline (social psychology in this application) through the analysis of scientific publications. The titles of a comprehensive set of papers published in the Journal of Personality and Social Psychology (1965–2021) were collected, yielding a total of 10,222 items. The corpus thus constructed underwent several stages of preprocessing until the final conversion into a terms x time-points matrix, where terms are stemmed words and multi-words. After normalizing frequencies via a chi square-like transformation, clusters of words portraying similar temporal patterns were identified by functional (textual) data analysis and distance-based curve clustering. Among the best candidates in terms of the number of clusters, the solutions with six, nine and thirteen clusters (from lower to higher resolution) have been chosen and the nesting relationship demonstrated. They reveal—at different levels of granularity—increasing, decreasing, and stable keywords trends, highlighting methods, theories, and application domains that have become more popular in recent years, lost popularity, or have remained in common use. Moreover, this method allows to highlight historical issues (such as crises in the discipline or debates over the use of terms). The results highlight the core topics of social psychology in the past and today, underlying the crucial contribution of this method for the digital history of a discipline.

Stars form and live in hierarchical structures such as star clusters on the smallest scales up to groups of galaxies on the largest scales. The role of young and massive star clusters in the evolution of the host galaxies is one of the most active fields of research in modern astronomy. The most massive stars are able to inject into the interstellar medium large amounts of energy and momentum through their radiation, their stellar winds and ultimately by exploding as supernovae. Also because of their clustered configuration, massive stars have an impact on the host galaxy with their energetic output, which is often referred to as a whole with the term feedback. Feedback processes are invoked by numerical simulations in order to reproduce galaxies with realistic properties. In the most extreme scenarios stellar feedback can launch galactic winds that may suppress the star formation and mix the gas content not only within the galaxy but also with the intergalactic medium. In this thesis I describe the scientific background of my doctoral studies and present my research project on the local starburst galaxy Haro 11 (z=0.021). Together with my supervisors, we use multi-band photometry from the Hubble Space Telescope for resolving the nuclear starburst into tens of star clusters with masses between 105 and 107 solar masses and ages younger than 20 Myr. We observe that star-formation is propagating across the galaxy from the eastern side to the western side. Additionally, we model aperture-matched ultraviolet and optical spectroscopy and derive the physical properties of the stellar populations in the three starburst knots (A, B, C). This also allow us to estimate the following stellar feedback quantities: photo-ionisation rate, energy of stellar winds and supernovae, and their power. We compare the above measurements with the information on the kinematics of the ionised gas within the three knots, extracted from optical spectroscopy. Combining our analysis with previous works, we find a multi-phase medium with maximum outflow velocities up to 400 km/s. The three knots have at different times all hosted powerful outflows, whose energetics is consistent with the quantified stellar feedback. This indicates that the stellar winds and SN explosions have driven the detected outflows. Our work is important not only for shedding light on the physics of feedback, but also because it combines and compares different methodologies (i.e. photometry and spectroscopy, ultraviolet and optical wavelengths). I conclude with an outlook on the upcoming projects of my doctoral studies, which will exploit the methodology that I developed during the first two years of my PhD and extend it to a whole sample of star-forming nearby galaxies. Such projects also connect with the science that will be enabled by the future astronomical observatories. The work on Haro 11 is the subject of a paper recently submitted to the journal MNRAS. A copy of the paper can be found at the end of this thesis.

Information and Communication Technologies, to a long extent, have a major influence on our social life, economy as well as on worldwide security. Holistically, computer networks embrace the Information Technology. Although the world is never free from people having malicious intents i.e. cyber criminals, network intruders etc. To counter this, Intrusion Detection System (IDS) plays a very significant role in identifying the network intrusions by performing various data analysis tasks. In order to develop robust IDS with accuracy in intrusion detection, various papers have been published over the years using different classification techniques of Data Mining (DM) and Machine Learning (ML) based hybrid approach. The present paper is an in-depth analysis of two focal aspects of Network Intrusion Detection System that includes various pre-processing methods in the form of dimensionality reduction and an assortment of classification techniques. This paper also includes comparative algorithmic analysis of DM and ML techniques, which applied to design an intelligent IDS. An experiment al comparative analysis has been carried out in support the verdicts of this work using ‘Python’ language on ‘kddcup99’ dataset as benchmark . Experimental analysis had been done in which we had found more impact on dimensionality reduction and MLP performed well in the true classification to establish secure network. The motive behind this effort is to detect different kinds of malware as early as possible with accuracy, to provide enhanced observant among various existing techniques that may help the fascinated researchers for future potential works.

Purpose The prediction of by-product gas is an important guarantee for the full utilization of resources. The purpose of this research is to predict gas consumption to provide a basis for gas dispatch and reduce the production cost of enterprises. Design/methodology/approach In this paper, a new method using the ensemble empirical mode decomposition (EEMD) and the back propagation neural network is proposed. Unfortunately, this method does not achieve the ideal prediction. Further, using the advantages of long short-term memory (LSTM) neural network for long-term dependence, a prediction method based on EEMD and LSTM is proposed. In this model, the gas consumption series is decomposed into several intrinsic mode functions and a residual term (r(t)) by EEMD. Second, each component is predicted by LSTM. The predicted values of all components are added together to get the final prediction result. Findings The results show that the root mean square error is reduced to 0.35%, the average absolute error is reduced to 1.852 and the R-squared is reached to 0.963. Originality/value A new gas consumption prediction method is proposed in this paper. The production data collected in the actual production process is non-linear, unstable and contains a lot of noise. But the EEMD method has the unique superiority in the analysis data aspect and may solve these questions well. The prediction of gas consumption is the result of long-term training and needs a lot of prior knowledge. Relying on LSTM can solve the problem of long-term dependence.

Growing demand for forest machines that cost less to operate than current compared to traditional hydraulic and mechanical ones, along with regulatory pressures for lower emissions, is increasing manufacturers’ interest in developing electric and hybrid drives. While purely electric drives of forest machines meet a lot of bottlenecks (costs of the electric components, battery durability, duration of charging, access to the electrical grid, size of batteries that can ensure enough energy for 8 h working time), electric hybrid drives offer a favorable solution for the propulsion of forestry machinery in terms of lower fuel consumption and improved efficiency. Among all forest vehicles, specialized forest tractors (skidders), so far, have not been considered for forest vehicles with hybrid drive capabilities. A skidder is a forest-articulated self-propelled vehicle for pulling trees or parts of trees. In most countries in southern Europe, the use of skidders equipped with forest winches is the most common technique for timber extraction. The first goal of the research is to develop methods for measuring the energy consumption of skidders at different operating tasks and under different field conditions. Research was performed on the skidder Ecotrac 140V (from Croatian producer Hittner Ltd.) during timber extraction in mountainous terrains in Lika–Senj County. The skidder was equipped with a measuring device WIGO-E (Telematic Data collector) gateway with an integrated GPS system, which ensured data were collected from sensors and motor and stored in a computer via CANBUS and data transfer with GSM to Web platforms. Additionally, a fuel-flow meter was installed on the skidder. Data on fuel consumption (mL), position (traveling route), detection of winch work, engine rpm (min−1), engine torque (% of max), throttle position (%), and engine temperature were measured with a sampling frequency of 5 s. Furthermore, skidder load volumes per cycles and slopes of tractor paths were constantly measured. The paper shows the skidder’s energy consumption per day, work cycle, and individual work procedure with regard to the size of the load, the slope of the tractor path, and the direction of movement based on overlapping and merging all measurement data. Using mathematical and simulation models of the drive with defined operating cycles obtained by measurement, the possibilities of the hybrid drive and the dimensions of the elements of the hybrid drive (internal combustion engine, electric motor, batteries, control unit) were determined and are presented in this paper.

This paper proposes the epidemic propagation model SEAIHR to elucidate the propagation mechanism of the Corona Virus Disease of 2019 (COVID-19). Based on the analysis of the propagation characteristics of COVID-19, the hospitalization isolation state and recessive healing state are introduced. The home morbidity state is introduced to consider the self-healing of asymptomatic infected populations, the early isolation of close contractors, and the impact of epidemic prevention and control measures. In this paper, by using the real epidemic data combined with the changes in parameters in different epidemic stages, multiple model simulation comparative tests were conducted. The experimental results showed that the fitting and prediction accuracy of the SEAIHR model was significantly better than the classical epidemic propagation model, and the fitting error was 34.4–72.8% lower than that of the classical model in the early and middle stages of the epidemic.

The goal of data analysis is to obtain information which is not easy to interpret, but will help to use it correctly. The paper describes a new approach to the use of intelligent technologies for certain business decisions, namely to research the pricing policy of the cost of houses based on their size. The object of the research is data analysis about available houses for sale, their size and value. The subject of the research are methods, algorithms and means of intelligent data analysis. The article considers the most simple and understandable, at the same time, widely used method of mathematical programming for solving problems of this kind – the gradient descent. The work uses the RMSE loss function. The derivative of this function shows the computation of the gradient for linear regression. The work uses linear regression i.e. a model of the dependence of variables (cost and area of the house) with a linear function of dependence. Data for analysis were downloaded from several Internet resources for the sale and rental of housing. The paper presents a visualization of the gradient descent method on the error function with two parameters. In addition to 3D plots, contour plots are presented. A contour plot is a method of representing a three-dimensional image in a two-dimensional plane, which well shows the step-by-step operation of the gradient descent method. As a result, a model was designed, where you can observe how the model adapts to the training data and is ready to perform its task. After testing the proposed model, you can determine the approximate cost of the house depending on its size. The error function is minimized and satisfies the requirements of the given task. In the future, it is planned to increase the amount of input data for analysis, indicating the location, the number of rooms, and the size of the surrounding area., Мета аналізу даних – отримувати інформацію, яку непросто витлумачити, але яка, якщо її зрозуміти, допоможе правильно її використати. У статті описано новий підхід до використання інтелектуальних технологій для певних бізнес-рішень, а саме для дослідження цінової політики вартості будинків залежно від їх розмірів. Об’єктом дослідження є аналіз даних інформації про наявні в продажу будинки, їх розміри і вартість. Предметом дослідження є методи, алгоритми та засоби інтелектуального аналізу даних. У статті розглядається найбільш простий і зрозумілий, водночас часто використовуваний метод математичного програмування для вирішення завдань такого роду – метод градієнтного спуску (gradient descent). У роботі використана найбільш поширена функція втрат середньоквадратичної помилки. Похідна цієї функції показує обчислення градієнта для лінійної регресії. Використано лінійну регресію (Linear regression) – модель залежності змінних (вартості та площі будинку) з лінійною функцією залежності. Дані для аналізу були завантажені з декількох інтернет-ресурсів продажу та оренди житла. В статті представлено візуалізацію методу градієнтного спуску по функції помилки за двома параметрами. Крім 3D-графіків, у роботі представлені контурні графіки. Контурний графік – це метод представлення тривимірного зображення у двомірній площині, що добре представляє покрокову роботу методу градієнтного спуску. В результаті було спроєктовано модель, де можна побачити, як модель підлаштовується під тренувальні дані і готова виконувати своє завдання. Провівши тестування запропонованої моделі, можна виявити приблизну вартість будинку залежно від його розмірів. Функція помилки мінімізована і відповідає вимогам поставленого завдання. В подальшому планується збільшити кількість вхідних даних для аналізу, вказавши місце розташування, кількість кімнат, величину прилеглої території.

The expatriates’ (expats) vote in the 2022 Lebanese parliamentary elections has been a frequently discussed topic, with respect to its impact on the results and the role it played in helping the Change Movement and independent groups secure additional seats. As part of a series of in-depth research papers on the 2022 parliamentary elections, the Electoral Lab at IFI drew an analysis of the voting trends of the Lebanese Expats or the Out of Country Vote (OCV) and developed important findings and conclusions, detailed in this paper.

Handling missing values and large-dimensional features are crucial requirements for data-driven fault diagnosis systems. However, most intelligent data-driven diagnostic systems are not able to handle missing data. The presence of high-dimensional feature sets can also further complicate the process of fault diagnosis. This paper aims to devise a missing data imputation unit along with a dimensionality reduction unit in the pre-processing module of the diagnostic system. This paper proposes a novel pooling strategy for missing data imputation (PSMI). This strategy can simplify complex patterns of missingness and incrementally update the pool. The pre-processing module receives incomplete observations, PSMI estimates missing values, and, then, the dimensionality reduction unit transforms completed observations onto a lower-dimensional feature space. These transformed observations are then fed as inputs to the fault classification module for decision making and diagnosis. This diagnostic scheme makes use of various state-of-the-art missing data imputation, dimensionality reduction and classification algorithms. This enables a comprehensive comparison and allows to find the best techniques for the sake of diagnosing faults in the Tennessee Eastman process. The obtained results show the effectiveness of the proposed pooling strategy and indicate that principal component analysis imputation and heteroscedastic discriminant analysis approaches outperform other imputation and dimensionality reduction techniques in this diagnostic application.

Big data analytics involve systematic approach to find hidden patterns to help the organization grow from large volume and variety of data. In recent years big data analytics is widely used in the agricultural domain to improve yield. Viticulture (the cultivation of grapes) is one of the most lucrative farming in India. It is a subdivision of horticulture and is the study of wine growing. The demand for Indian Wine is increasing at about 27% each year since the 21st century and thus more and more ways are being developed to improve the quality and quantity of the wine products. In this paper, we focus on a specific agricultural practice as viticulture. Weather forecasting and disease detection are the two main research areas in precision viticulture. Leaf disease detection as a part of plant pathology is the key research area in this paper. It can be applied on vineyards of India where farmers are bereft of the latest technologies. Proposed system architecture comprises four modules: Data collection, data preprocessing, classification and visualization. Database module involve grape leaf dataset, consists of healthy images combined with disease leaves such as Black measles, Black rot, and Leaf blight. Models have been implemented on Apache Hadoop using map reduce programming framework. It apply feature extraction to extract various features of the live images and classification algorithm with reduced computational complexity. Gray Level Co-occurrence Matrix (GLCM) followed by K-Nearest Neighborhood (KNN) algorithm. System also recommends the necessary steps and remedies that the viticulturists can take to assure that the grapes can be salvaged at the right time and in the right manner based on classification results. Overall system will help Indian viticulturists to improve the harvesting process. Accuracy of the model is 72% and it can be increased as a future work by including deep learning with time series grape leaf images.

The massive number of sensors deployed in the Internet of Things (IoT) produce gigantic amounts of data for facilitating a wide range of applications. Deep Learning (DL) would undoubtedly play a role in generating valuable inferences from this massive volume of data and hence will assist in creating smarter IoT. In this regard, exploring the potential of DL for IoT data analytics becomes highly crucial. This paper begins with a concise discussion on the Deep Neural Network (DNN) and its different architectures. The potential benefits that DL will bring to the IoT are also discussed. Then, a detailed review of DL-driven IoT use-cases is presented. Moreover, this paper formulates a DL-based model for Human Activity Recognition (HAR). It carries out a performance comparison of the proposed model with other machine learning techniques to delineate the superiority of the DL model over other techniques. Apart from enlightening the potential of DL in IoT applications, this paper will serve as an impetus to encourage advanced research in the realm of DL-driven IoT applications.

Data quality has become an area of increasing concern in marketing research. Methods of collecting data, types of data analyzed, and data analytics techniques have changed substantially in recent years. It is important, therefore, to examine the current state of marketing research, and particularly self-administered questionnaires. This paper provides researchers important advice and rules of thumb for crafting high quality research in light of the contemporary changes occuring in modern marketing data collection practices. This is accomplished by a proposed six-step research design process that ensures data quality, and ultimately research integrity, are established and maintained throughout the research process—from the earliest conceptualization and design phases, through data collection, and ultimately the reporting of results. This paper provides a framework, which if followed, will result in reduced headaches for researchers and more robust results for decision makers.

Beran Instruments Limited have been monitoring induction slip motors for over 20 years in high electrical noise environments. Due to the high-temperature environment of certain customer installations, integrated electronics piezo-electric (IEPE) sensors are not employed, and charge sensors are installed to monitor the condition of the motors. Over the past 20 years, Beran have used a variety of techniques, including the development of specialist isolated analogue signal conditioning amplifiers, to enable the true vibration signals that occur around the drive frequency (which is coupled to, and therefore affected by, the mains frequency) to be visualised.Beran Instruments Limited undertook a research and development programme, Machinery Health State Detection (mSTATE) supporting Future Net Zero Energy Generation. The goal of the programme was to develop a new capability to subtract electrical noise from sensor signals and was conducted in partnership with the University of Bristol and EDF Energy Torness Nuclear Power Station, with funding from the United Kingdom’s innovation agency, Innovate UK. The mSTATE project delivered a novel algorithm that allows complete subtraction of 50 Hz mains frequencies from the charge sensor signals allowing, for the first time, our end users, and future customers the ability to visualise the true vibration signals and the ability to trend machine health over time without the use of specialist and expensive analogue signal conditioning systems.This paper outlines the history and development of condition monitoring of induction slip motors at Beran Instruments, and how the mSTATE project resulted in a new approach to subtract 50 Hz noise from accelerometer signals using the power of modern signal processing and data architectures.

International audience; Context. The Calern Asteroid Polarimetric Survey (CAPS), a collaboration between the INAF Astrophysical Observatory of Torino (Italy) and the Observatoire de la Côte d’Azur (Nice, France), has produced new asteroid polarimetric data for a number of years, and is one of the most important, currently active projects of asteroid polarimetry. Aims. The purpose of this paper is to make public the CAPS data collected thus far, to explain the adopted techniques of data reduction and computation of phase-polarisation curves for the measured objects, and explain, by means of some examples, the importance of the CAPS database. Methods. The pipeline of data reduction has been recently updated and made as automatic as possible, using numerical algorithms developed specifically for the purposes of CAPS. The derivation of phase-polarisation curves for the observed asteroids is done using established criteria and algorithms that have recently been slightly improved, and are also summarised in this paper. Results. The CAPS catalogue is a steadily growing source of information which can be exploited for different purposes, including, but not limited to, an updated calibration of the relations existing between different polarimetric parameters and the geometric albedo of the objects, and a study of classes of objects that can be most easily identified by means of their polarimetric properties. These subjects will be more specifically discussed in separate papers. Conclusions. Asteroid polarimetry data nicely complement the results of other more commonly used techniques, including visible and IR photometry and spectroscopy. CAPS contains a lot of much-desired information about physical properties, which can hardly be inferred by means of other techniques.

Due to the configuration process of a complex product scheme, a design structure often has the characteristics of multi-level, multi-attribute, creativity, and complexity; in order to improve the efficiency and quality of product scheme design, it has important research value to reasonably organize, reason, and reuse design knowledge. In this paper, the extension modeling problem under the extension design mode of complex product scheme is studied, the multitype design knowledge element modeling expression model of complex product scheme design is given, and the extension process model and the implication process model of requirement analysis of complex product scheme design is established. A new demand element weight assignment method based on extension distance is proposed to obtain accurate demand analysis index weight from the perspective of combined qualitative and quantitative analysis. On the basis of constructing the extension correlation degree of demand primitives, this paper puts forward the implementation method of the extension design pattern for the demand analysis of a complex product scheme design and gives the specific implementation algorithm. Finally, an example of product design is given to illustrate the method, and the results show the effectiveness and operability of the method.

Introduction: The emergence of the concepts like Big Data, Data Science, Machine Learning (ML), and the Internet of Things (IoT) in recent years has added the potential of research in today's world. The continuous use of IoT devices, sensors, etc. that collect data continuously is putting tremendous pressure on the existing IoT network. Materials and Methods: This resource-constrained IoT environment is flooded with data acquired from millions of IoT nodes deployed at the device level. The limited resources of the IoT Network have driven the researchers towards data Management. This paper focuses on data classification at the device level, edge/fog level, and cloud level using machine learning techniques. Results: The data coming from different devices is vast and is of variety. Therefore, it becomes essential to choose the right approach for classification and analysis. This will help in optimizing the data at the device, edge/fog level for better performance of the network in the future. Conclusion: This paper presents data classification, machine learning approaches, and a proposed mathematical model for the IoT environment.

This special issue of the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS) contains 11 papers both from the extended outcomes of the Multitemp 2019 presented papers, and from the submissions by following a general Call-for-Papers of this special issue. These papers focus on the interesting and relevant topics in the multitemporal data analysis.

The rapid development of Internet technology has promoted the vigorous development of the multimedia. As one of the most classic instruments, the violin has been fully developed in its creation, education, and performance. In the face of more and more violin performances, the effective organization and retrieval of these musical works is an urgent problem to be solved, while it is common to classify and organize music based on the emotional properties of the performance. Deep learning is a model based on feature hierarchy and unsupervised feature learning, which has strong learning ability and adaptability. Based on the recurrent neural network (RNN) method, long short-term memory (LSTM) is one of the classic models of deep learning that can effectively learn the characteristics of time series data and achieve effective predictions. Therefore, based on the classical Hevner emotion classification model, this paper proposes an emotion recognition method for dynamic violin performances based on LSTM, which selects acoustic features and classifies the audio acoustic signals contained in the violin performances. In order to verify the effectiveness of this method, this paper carries out data labeled, feature selection, and model test on the actual violin music data by turn. The results show that the proposed method can greatly reduce the training time and improve the prediction accuracy, which reached 83%, higher than the existing methods. Meanwhile, the accuracy and iteration times of violin playing music of different emotional categories are also counted. Moreover, the method is robust to the genre, timbre, and noise changes, and the emotion recognition effect is superior.

In the COVID-19 era, people face situations that they have never experienced before, which alerted the importance of the ESG. Investors also consider ESG indexes as an essential factor for their investments, and some research yielded that the return on sustainable funds is more significant than on non-sustainable ones. Nevertheless, a deficiency in research exists about analyzing ESG through artificial intelligence algorithms due to adversity in collecting ESG-related datasets. Therefore, this paper suggests integrated AI approaches to the ESG datasets with the five different experiments. We also focus on analyzing the governance and social datasets through NLP algorithms and propose a straightforward method for predicting a specific firm’s ESG rankings. Results were evaluated through accuracy score, RMSE, and MAE, and every experiment conducted relevant scores that achieved our aim. From the results, it could be concluded that this paper successfully analyzes ESG data with various algorithms. Unlike previous related research, this paper also emphasizes the importance of the adversarial attacks on the ESG datasets and suggests methods to detect them effectively. Furthermore, this paper proposes a simple way to predict ESG rankings, which would be helpful for small businesses. Even though it is our limitation that we only use restricted datasets, our research proposes the possibility of applying the AI algorithms to the ESG datasets in an integrated approach.

With the continuous rise of my country’s international status, the cultivation of students’ cross-cultural thinking ability has become an important goal of college English teaching. Since the twentieth century, theories about critical thinking ability have been abundant, and how to improve students’ critical thinking ability is also a hotspot of reform. In foreign countries, there are rich teaching models, but in China, it is still a major problem to be solved to cultivate students’ speculative ability through specific teaching methods. How teachers’ classroom teaching is related to the cultivation of speculative ability. In addition to having direct application significance for teachers to realize the cultivation of speculative ability in routine teaching, the research on these issues is conducive to opening the “black box” of classroom instruction. The most crucial tasks in teaching speculative reading and cross-cultural education in today’s college English curriculum are the only means of enhancing students’ critical thinking and cross-cultural communication skills. This paper analyses, researches, and applies the diagnostic evaluation model using data gathered during system usage. In order to enhance the diagnostic evaluation function of the college English diagnostic practice system and the associated test paper generation algorithm, the author then designs and implements two test recommendation algorithms in accordance with the diagnostic evaluation model.

Ovaj rad je opisao koncept okruženja Interneta stvari i važnosti kibernetičke sigurnosti u modernom svijetu. Dan je pregled osnovnih elemenata umjetne inteligencije u kontekstu neuronskih mreža koje su zatim korištene kako bi se pokušao izgraditi osnovni element nadzornog alata koji bi detektirao kibernetičke napade u IoT mrežama. Rad je obradio tri pristupa ovakvom alatu, jedan koristeći isključivo analizu metrika dostupnih na rubnim uređajima, te dva različita pristupa s gledišta umjetne inteligencije kojima se isti alat pokušao realizirati uz pomoć analize mrežnog prometa rubnih uređaja. Posljednja poglavlja ovog rada prilažu rezultate i prijedloge za buduća istraživanja. This paper describes the concept of an IoT network and the importance of cybersecurity in the modern world. An overview of basic elements used in the field of artificial intelligence was given. Specifically, an overview of neural networks which were then used as a foundation upon which a cyberattack detection system would be built in order to detect such hazards in an IoT network. The paper presents three approaches to creating such software, one of which exclusively analyses the telemetry data collected on the edge devices themselves, whilst the other two employ different artificial intelligence strategies in order to build software which would analyze the network traffic in the IoT edge layer. The final chapters present the final results and provide suggestions for future research.

In this paper, a CNN model for color element data analysis of the urban spatial environment is constructed through an in-depth study of color element data analysis. This paper investigates a high-order structure formed by a few nodes; it proposes a motif-based graph autoencoder MODEL, combining redefined first- and second-order similarities and perfectly integrating motif structure and autoencoder. The algorithm first proposes an efficient graph transformation method to add the influence of central nodes. It then offers a primary awareness mechanism to aggregate the information of noncentral neighbors. Cen GCN_D and Cen GCN_E outperform the latest algorithms in node classification, link prediction, node clustering, and network visualization. As the number of network layers increases, the advantages of these two variants become progressively more prominent. This paper uses a support vector machine to implement classification validation based on CNN. The experimental results show that when 450 images are randomly selected as training data, the classification accuracy obtained by using the features of different CNN output layers is distributed between 91.4% and 95.2%. When the training set of the experiment reaches more than 300, the accuracy can exceed 90%, and the experimental results corresponding to different training sets a more stable trend. Finally, the trained classifier model is obtained in this thesis, which achieves the purpose of fast classification prediction based on CNN for color element data analysis of urban spatial environments.

With the rapid development of computer science, there are more and more kinds of discrete dynamic systems. Computer integrated system CIMS, network communication database administrator system, and human behavior analysis system are all discrete dynamic systems. At present, many researchers have studied by adding human behavior data to discrete dynamic systems. This paper aims to study the behavior data of English learners by using the discrete dynamic modeling technology of complex systems and the discrete dynamic system modeling method of Petri nets. By adding the behavior data of learners to the discrete dynamic system of fuzzy Petri nets, the system is diagnosed and optimized. The experimental results show that the complex discrete dynamic system in this paper has achieved good experimental results according to the performance indicators selected in theory. Based on the combination of the above technologies and systems, the fuzzy Petri net discrete dynamic system studied in this paper improves the processing speed of English learners' behavior data.

The development of light detection and ranging (lidar) technology began in the 1960s, following the invention of the laser, which represents the central component of this system, integrating laser scanning with an inertial measurement unit (IMU) and Global Positioning System (GPS). Lidar technology is spreading to many different areas of application, from those in autonomous vehicles for road detection and object recognition, to those in the maritime sector, including object detection for autonomous navigation, monitoring ocean ecosystems, mapping coastal areas, and other diverse applications. This paper presents lidar system technology and reviews its application in the modern road transportation and maritime sector. Some of the better-known lidar systems for practical applications, on which current commercial models are based, are presented, and their advantages and disadvantages are described and analyzed. Moreover, current challenges and future trends of application are discussed. This paper also provides a systematic review of recent scientific research on the application of lidar system technology and the corresponding computational algorithms for data analysis, mainly focusing on deep learning algorithms, in the modern road transportation and maritime sector, based on an extensive analysis of the available scientific literature.