Scale Data Research Articles

Sanatorium-resort treatment of patients who have suffered a new coronavirus infection COVID-19 with neuropsychological disorders

Objective. Тo evaluate the clinical effectiveness of sanatorium-resort treatment of patients who have suffered a new coronavirus infection COVID-19 with neuropsychological disorders. Material and methods. The study included 72 patients who had suffered a new coronavirus infection COVID-19 with neuropsychological disorders (17 men and 55 women) aged from 34 to 63 years, who were treated at the Federal State Budgetary Institution United Sanatorium Podmoskovye of the Administration of the President of the Russian Federation in the early stages after an illness — up to 3 months after a new coronavirus infection COVID-19). The patients were divided into 2 groups (main and control). Each group included 36 people (main group — 27 women and 9 men; control group — 28 women and 8 men). Patients of both groups received basic sanatorium-resort treatment, including: nutritional therapy (diet option with a high protein content), climatotherapy in the middle climate zone to prevent adaptation failure, health path (dosed walking), exercise therapy with elements of breathing exercises. Patients of the main group, in addition to the basic sanatorium-resort treatment, received transcranial electrical stimulation from the Transair-05 apparatus according to the standard method (current strength up to 1.5 mA or more, duration 30 minutes), for a course of 10–12 procedures. In a comparative aspect, the obtained data from patients of the main and control groups before and after sanatorium-resort treatment were studied: complaints, biochemical blood test indicators and the results of the MFI-20 asthenia rating scale. Results. The use of an expanded program of sanatorium-resort treatment with the inclusion of transcranial electrical stimulation in patients who have suffered a new coronavirus infection COVID-19 with neuropsychological disorders contributes to a significant reduction in the number and severity of clinical symptoms (complaints), improvement of the cognitive sphere, psycho-emotional and functional state, which is confirmed by the assessment scale data asthenia and MFI-20 on the subscales «General asthenia», «Physical asthenia» and «Mental asthenia». Conclusion. The inclusion of transcranial electrical stimulation in the sanatorium-resort treatment program in patients who have suffered a new coronavirus infection COVID-19 with neuropsychological disorders increases the effectiveness of rehabilitation treatment after an acute infectious disease, which justifies the feasibility of using TES in widespread clinical practice.

Retraction notice: Semantic tracking and recommendation using fourfold similarity measure from large scale data using hadoop distributed framework in cloud

Retraction notice: Semantic tracking and recommendation using fourfold similarity measure from large scale data using hadoop distributed framework in cloud

MODEL KLASIFIKASI REGRESI LOGISTIK BINER UNTUK LAPORAN MASYARAKAT DI OMBUDSMAN REPUBLIK INDONESIA

A classification model is needed to predict data into the right class according to the pattern of previous data. Binary Logistic Regression can be used in building classification models, even though the independent variables are categorical scale data. Through binary logistic regression, it can also be seen which category of independent variables influences the response variable. Public complaint reports at the Ombudsman of the Republic of Indonesia are classified into reports that found maladministration and not. The Binary Logistic Regression model with several categorical independent variables related to the public complaint reports data applied resulted in a classification model with an overall classification accuracy of 66.08% and a sensitivity of 75.31% in estimating the presence of maladministration findings in the submitted public complaint reports. Based on the 95% confidence level of the model, it is known that the factors that influence the occurrence of maladministration are the Group of Reportees, the Substance of the Report, the Method of Submission, the Request for Confidentiality, and the Location of the Inspection Office. This model can be used as a reference to reduce the incidence of maladministration cases in public service providers by focusing socialization and education on categories that have a real influence on each of these factors

Right anterior insula ASL hypoperfusion as a diagnostic biomarker of prodromal and mild dementia with Lewy bodies: preliminary evidence using a Bayesian approach.

Identifying and validating a biomarker with high specificity in early-stage dementia with Lewy bodies (DLB) using a feasible method is crucial to enhance the current suboptimal diagnostic procedure. Previous research revealed abnormalities, including hypoperfusion in the right anterior insular cortex at group level, in prodromal DLB. Exploring hypoperfusion of the right anterior insula, at an individual-level and assessing its relevance as a potential imaging biomarker in early DLB, has, to our knowledge, not been investigated. Our preliminary study aims to assess the feasibility of the technique and to provide a methodological framework for further investigation. We assessed the feasibility and accuracy of the hypoperfusion of the right anterior insula per arterial spin labelling magnetic resonance imaging (ASL-MRI) as a diagnostic biomarker in early DLB and provided rough estimates of its sensitivity and specificity. Defining the region of interest based on previous research, we established the biomarker as the hypoperfusion of the right anterior insula. Discriminative and analytical performances were assessed in comparison to a control group of treatment-resistant depression patients. Bayesian diagnostic reasoning was employed to assess the biomarker diagnostic usability in early DLB in two scenarios: healthy elderly controls and mild cognitive impairment. Additionally, we updated probabilities by integrating data from the Mayo-clinic cognitive fluctuations scale and real-time quaking-induced conversion (RT-QuIC) α-synuclein data. Lastly, a whole-brain perfusion analysis of DLB patients was conducted to identify further brain regions with discriminative abilities. We successfully replicated the right anterior insular hypoperfusion (RAI-Hypo) in all DLB patients at the individual level. The overall sensitivity of the biomarker was 96%, and the specificity was 92%. Bayesian testing revealed the biomarker's highest performance in early-stage DLB with cognitive fluctuations, showcasing a diagnostic potential associated with a high precision and moderate accuracy. In a cognitively non-impaired population, the RAI-Hypo showed a limited usability and lacked in selectivity to qualify as a screening tool. The exploratory whole-brain analysis revealed perfect discriminative capacities in the bilateral anterior insulae and the left inferior parietal lobule. Further studies are needed to confirm our preliminary results. If performance is maintained in subsequent studies and is compared to a more suitable control population, the proposed biomarker may be eventually sufficient to discriminate early-stage DLB from non-DLB.

Effectiveness of Tendo-Muscular Massage for Pain Reduction and ROM Improvement Post-Injury Knee Joint

This study aims to determine the effectiveness of tendo-muscular massage. The method used in the research is descriptive quantitative. The sample in this study amounted to 30 people who had mild to moderate knee injuries during sports and other physical activities. The order of data collection, namely starting with the pretest measurement, then given a one-time tendo-muscullar massage treatment which is carried out for 8 minutes and ends with a posttest measurement. The research data that has been obtained is then analysed with descriptive quantitative. Interval and ratio scale data that met the normality test were analysed using the paired sample t-test parametric statistical test, while data that did not meet the normality test using the wilcoxon non-parametric statistical tests. The results of measurements with the Visual Analogue Scale (VAS) experienced an average pain reduction of 2.5 and a significance of 0.000 <0.05, while the range of motion measured with a goniometer experienced an average increase of 6 degrees, T count of 6,819 and Significance 0.000 in flexion movements and an average increase of 0.88 degrees, T count of 7,848 and Significance 0.000 in extension movements, as well as an increase in joint motion function of 28.83%. So it can be concluded that Tendo-muscular massage has a significant effect on reducing pain and increasing the range of motion (ROM) of the knee joint. Keyword. Effectiveness, Tendo-Muscular Massage, Pain Reduction, ROM Improvement, Knee

Integrated effects of pavement simulation models and scale differences on the thermal environment of tropical cities: physical and numerical modeling experiments

Simulation methods attempt to explain what happens in full-scale environments. However, as simplification procedures, they also have their limitations and opportunities. One of the applications is to use the output data of a physical model to calibrate numerical simulation, or even to use outputs of numerical simulations to analyze urban scale studies. But it is uncertain the error in the interaction between these models. This study aims to analyze the impact of scale analysis and pavements simulation model modification on ambient and surface temperature of asphalt pavement in a physical model of a tropical city street canyons. Therefore, a scaled outdoor experiment was conducted, and a numerical simulation model, using ENVI-met software, was used to investigate the spatiotemporal variation of air and pavement surface temperature, in urban (1:1) and reduced (1:15) scales. For studies on the surface temperature of pavements, within the temperature range of 12 ºC to 37 ºC, it is recommended to calibrate physical models using as input, data derived from numerical simulation models, yielding a mean absolute percentage error (MAPE) of 4.9%. For estimating data in real-world urban scale, within the air temperature range of 15 ºC to 37 ºC, it is proposed to use output data from simulated models in ENVI-met, that presented a mean absolute error (MAE) of ± 0.59 or physical models (MAE = ± 0.66). These results would be useful for the development of urban surface temperatures parametrizations.

Experimental analysis of extreme wave loads on a containership

Model-scale experiments were conducted to investigate the non-linearities associated with the Vertical Bending Moment (VBM) of a rigid container vessel in irregular waves. Long-duration runs ( ≃ 25 hours of data in full scale) were performed to allow the calculation of statistically meaningful extreme values of hogging and sagging on five different sea-states (from Hs = 6 m up to Hs = 17 m).The model tests are presented in detail, and sampling errors are estimated. It is found that the ratio between linear and non-linear VBM (the so-called non-linear factor) can be modeled as a function of the linear value only. For a given linear value, the non-linear correction is the same, whatever the sea state. This observation, new in the fully non-linear case, is very valuable as it opens the door to efficient design assessment methodologies such as increased design sea state or design waves.

Scalable data management in global health crises: Leveraging blockchain technology

Scalable data management in global health crises: Leveraging blockchain technology

Predicting Plant Growth Stages Using Random Forest Classifier: A Machine Learning Approach

The optimization of plant growth through predictive modelling is a crucial aspect of modern agricultural practices. This study investigates the application of a Random Forest Classifier to predict plant growth stages based on various environmental and management factors. The dataset, sourced from Kaggle, includes variables such as soil type, sunlight hours, water frequency, fertilizer type, temperature, and humidity. The research involves extensive data pre-processing, including encoding categorical variables, scaling data, and splitting it into training (80%) and testing (20%) sets. The Random Forest Classifier is implemented with 5-fold cross-validation, and its performance is evaluated using accuracy, precision, recall, and F1-score metrics. The model exhibits robust performance with an average accuracy of 84.27%, precision of 85.59%, recall of 84.27%, and F1-score of 83.98%. Visualization techniques such as correlation heatmaps, PCA plots, t-SNE plots, and violin plots are used to provide insights into the data structure and feature relationships. The results confirm the hypothesis that machine learning can effectively predict plant growth stages, offering significant implications for precision agriculture. By accurately identifying growth stages, farmers and greenhouse managers can optimize resource allocation and management practices, leading to enhanced crop yields and sustainability. The study's limitations include the specificity of the dataset and the sole use of the Random Forest Classifier. Future research should explore additional machine learning models and incorporate more diverse datasets to improve generalizability. The findings contribute to the growing body of knowledge on the application of machine learning in agriculture and suggest practical applications for improving agricultural productivity

Scaling and Merging Time-Resolved Laue Data with Variational Inference.

Time-resolved X-ray crystallography (TR-X) at synchrotrons and free electron lasers is a promising technique for recording dynamics of molecules at atomic resolution. While experimental methods for TR-X have proliferated and matured, data analysis is often difficult. Extracting small, time-dependent changes in signal is frequently a bottleneck for practitioners. Recent work demonstrated this challenge can be addressed when merging redundant observations by a statistical technique known as variational inference (VI). However, the variational approach to time-resolved data analysis requires identification of successful hyperparameters in order to optimally extract signal. In this case study, we present a successful application of VI to time-resolved changes in an enzyme, DJ-1, upon mixing with a substrate molecule, methylglyoxal. We present a strategy to extract high signal-to-noise changes in electron density from these data. Furthermore, we conduct an ablation study, in which we systematically remove one hyperparameter at a time to demonstrate the impact of each hyperparameter choice on the success of our model. We expect this case study will serve as a practical example for how others may deploy VI in order to analyze their time-resolved diffraction data.

A scalable and transparent data pipeline for AI-enabled health data ecosystems.

Transparency and traceability are essential for establishing trustworthy artificial intelligence (AI). The lack of transparency in the data preparation process is a significant obstacle in developing reliable AI systems which can lead to issues related to reproducibility, debugging AI models, bias and fairness, and compliance and regulation. We introduce a formal data preparation pipeline specification to improve upon the manual and error-prone data extraction processes used in AI and data analytics applications, with a focus on traceability. We propose a declarative language to define the extraction of AI-ready datasets from health data adhering to a common data model, particularly those conforming to HL7 Fast Healthcare Interoperability Resources (FHIR). We utilize the FHIR profiling to develop a common data model tailored to an AI use case to enable the explicit declaration of the needed information such as phenotype and AI feature definitions. In our pipeline model, we convert complex, high-dimensional electronic health records data represented with irregular time series sampling to a flat structure by defining a target population, feature groups and final datasets. Our design considers the requirements of various AI use cases from different projects which lead to implementation of many feature types exhibiting intricate temporal relations. We implement a scalable and high-performant feature repository to execute the data preparation pipeline definitions. This software not only ensures reliable, fault-tolerant distributed processing to produce AI-ready datasets and their metadata including many statistics alongside, but also serve as a pluggable component of a decision support application based on a trained AI model during online prediction to automatically prepare feature values of individual entities. We deployed and tested the proposed methodology and the implementation in three different research projects. We present the developed FHIR profiles as a common data model, feature group definitions and feature definitions within a data preparation pipeline while training an AI model for "predicting complications after cardiac surgeries". Through the implementation across various pilot use cases, it has been demonstrated that our framework possesses the necessary breadth and flexibility to define a diverse array of features, each tailored to specific temporal and contextual criteria.

Sistem Akuntansi Penjualan Kredit dan Pengendalian Intern Piutang Tak Tertagih Pada Koperasi Di SMA 12 Kota Padang

A company in order to run well and achieve goals must exercise internal control. Internal control of bad debts is an activity carried out by the company that aims to protect company assets, produce accurate information and ensure compliance with policies made by management so as to prevent bad debts. This study aims to determine the effect of the credit sales accounting system on the internal control of bad debts in cooperatives at SMA N 12 Padang. The type of research is quantitative, the population of this study is all employees and active members in the SMA N 12 Padang cooperative as many as 42 people using total sampling techniques. Research measuring instruments using Likert scale questionnaires and data analysis using simple linear regression tests. Data processing using the SPSS program. The results showed that the regression coefficient value of the credit sales accounting system was 0.616 and the >calculated value of the ttable was 6.909>2.021, meaning Ha was accepted and H0 was rejected. The conclusion is that there is an influence of the credit sales accounting system on the internal control of bad debts in cooperatives at SMA N 12 Padang.

EAR-Q outcomes in healthy adults: determining normative data

BackgroundPatient reported outcome measures (PROMs) are gaining importance in research and as a clinical tool to measure pre- and post-surgical patient satisfaction. The EAR-Q, an example of a clinically used PROM, focuses on patient satisfaction regarding their ears. However, the absence of EAR-Q scores in adults without external ear conditions causes challenging interpretation of the results in patients with ear conditions. Therefore, this study aims to find normative data for scales of the EAR-Q in healthy adults.MethodsThis cross-sectional, single centre study included 515 healthy adults without conditions of the external ears. The participants filled in an online questionnaire with questions from the EAR-Q scales. A Rasch score between 0 and 100 was calculated, in which higher scores correlate with more satisfaction of that particular scale.ResultsMean scores for the EAR-Q ± standard deviation were: appearance 74 ± 20, adverse effects 94 ± 9, appearance distress 78 ± 14, psychological 64 ± 15 and social 66 ± 13. There was no difference in appearance scale between males and females; however, females scored lower on the appearance distress scale and psychological scale. Participants aged 30 and older scored higher on the appearance scale and appearance distress scale.ConclusionsThis is the first study to generate normative data for the EAR-Q in an adult population without ear conditions. The presented findings and normal scores support future research and clinical implementation of the EAR-Q. Clinicians can guide decision-making with these normative data as a reference point. Level of Evidence: Not gradable

Harnessing Data Analytics for Enhanced Understanding and Management of Depression Disorders in Mental Health Care

Depression is a widespread and incapacitating mental health condition that affects millions of people around the world.The integration of data analytics into mental health care presents a transformative opportunity to enhance the understanding, diagnosis, and management of depression. This paper explores the application of data analytics in identifying patterns and trends from diverse data sources such as electronic health records (EHRs) and social media. Through advanced techniques including machine learning, natural language processing (NLP), and predictive modeling, data analytics facilitates early detection of depressive symptoms, the development of personalized treatment plans, and continuous patient monitoring. Additionally, the aggregation and analysis of large- scale data provide valuable insights for public health strategies aimed at reducing the prevalence and impact of depression. This paper highlights the critical role of interdisciplinary collaboration in leveraging data analytics to improve mental health outcomes and underscores the importance of robust safeguards to ensure patient confidentiality and trust. Keywords Data Analytics, Depression, Mental Health, Predictive Modeling, Machine Learning, Natural Language Processing, Electronic Health Records

Testing behaviour change with an artificial intelligence chatbot in a randomized controlled study

Chatbots can effect large-scale behaviour change because they are accessible through social media, flexible, scalable, and gather data automatically. Yet research on the feasibility and effectiveness of chatbot-administered behaviour change interventions is sparse. The effectiveness of established behaviour change interventions when implemented in chatbots is not guaranteed, given the unique human–machine interaction dynamics. We pilot-tested chatbot-based behaviour change through information provision and embedded animations. We evaluated whether the chatbot could increase understanding and intentions to adopt protective behaviours during the pandemic. Fifty-nine culturally and linguistically diverse participants received a compassion intervention, an exponential growth intervention, or no intervention. We measured participants’ COVID-19 testing intentions and measured their staying-home attitudes before and after their chatbot interaction. We found reduced uncertainty about protective behaviours. The exponential growth intervention increased participants’ testing intentions. This study provides preliminary evidence that chatbots can spark behaviour change, with applications in diverse and underrepresented groups.

Verification of grip strength as an evaluation tool for locomotive syndrome in rheumatoid arthritis

ObjectivesLocomotive syndrome (LS) leads to reduced physical function and a high risk of becoming bedridden. Grip strength serves as an indicator of upper limb and overall physical function. Rheumatoid arthritis (RA) patients with reduced grip strength frequently show finger and wrist joint inflammation. The purpose of this study was to verify grip strength as an evaluation tool for physical function and LS in RA patients. MethodsAs part of an ongoing multicenter observational study, 591 consecutive RA patients whose background information was available, including data for the 25-question Geriatric Locomotive Function Scale (GLFS-25) and grip strength, were examined. LS was defined as a GLFS-25 score ≥ 16 points. Finger and wrist joint inflammation were defined as tender or swollen joints. ResultsAmong the 591 patients, 244 (41.3%) patients had LS, and 167 (28.3%) were male. Receiver operating characteristic curve analysis yielded cut-off values of grip strength for LS of 24 kg (specificity 72.2%; sensitivity 62.7%) for males and 17 kg (specificity 65.7%; sensitivity 67.6%) for females. Multivariable logistic regression analysis revealed a significant association of grip strength with LS, even after adjusting for finger and wrist joint inflammation. ConclusionsLS was significantly associated with grip strength, even after adjusting for the presence of finger and wrist joint inflammation. We recommend adopting grip strength measurement as a screening tool for evaluating LS and guiding interventions.

The Impact of Cloud Computing, Quantum Computing, and Blockchain on Data Security and Business Efficiency

This study explores the impact of cloud computing, quantum computing, and blockchain on data security and business efficiency. The primary objective is to qualitatively analyze the literature to understand how these emerging technologies contribute to enhancing security measures and operational efficiency within businesses. The research employs a qualitative literature review methodology, synthesizing findings from academic articles, industry reports, case studies, and empirical studies to provide a comprehensive overview of current knowledge in this field. The literature review methodology involves systematically collecting and analyzing scholarly sources that discuss various aspects of cloud computing, quantum computing, and blockchain. The study categorizes the literature into key themes, such as the benefits of cloud computing in providing scalable and flexible data storage solutions, the potential of quantum computing to revolutionize encryption and problem-solving capabilities, and the role of blockchain in ensuring transparency and immutability of data transactions. Thematic analysis is used to identify patterns and trends in how these technologies influence data security and business efficiency. The findings indicate that cloud computing enhances business efficiency by enabling seamless data access, reducing IT costs, and improving collaboration. Quantum computing holds promise for significantly advancing data security through quantum encryption, which offers higher levels of security compared to classical encryption methods. Blockchain technology ensures data integrity and transparency, reducing the risk of fraud and enhancing trust in digital transactions.

Implementation of Peer Instruction Method on Teaching of Acids and Bases to 12th Grade Students: An Action Research

This study aims to investigate the applicability and effectiveness of the peer instruction method on teaching the subject of acids and bases at the 12th grade level. In addition, it is aimed to determine the effect of peer instruction on students’ attitudes towards chemistry and in-class discussion, and to examine opinions of students to peer instruction after implementation. The sample of the study consists of 21 12th grade students in a private high school. During the research process, the unit of acids and bases was covered by the researcher with the peer teaching method and implementation was completed in 5 weeks. In this study, which was designed as an action research, qualitative and quantitative data were used together. Quantitative data were collected through acids-bases concept test (ABCT), chemistry attitude scale (CAS), argumentativeness scale (AS), concept questions, and qualitative data were collected through method opinion scale (MOS), semi-structured interview, and observation. The analysis of the data was carried out using quantitative and qualitative methods. The results showed that there was a notable increase in the academic achievement of the students after the implementation. Furthermore, the results obtained from ABCT and semi-structured interviews indicated that peer instruction improved students’ conceptual learning, and also it is effective in eliminating misconceptions. Although the pretest-posttest scores of CAS and AS did not demonstrate a considerable statistical difference, observation and semi-structured interview data pointed out that students’ attitudes towards chemistry and in-class discussion increased positively. At the end of the implementation, it was observed that students’ attitudes towards the peer instruction method are positive and students found it very useful and effective.

Characterization of Wood, Leaves, Barks, and pod wastes from Prosopis africana biomass for biofuel production

One of the approaches for increasing the contribution of biomass to the renewable energy mix is the valorization of biomass to bioenergy. Evaluating the potential of unconventional biomass sources could significantly accelerate the assessment for suitability as feedstock for bioenergy production as a sustainable solution. The study aimed to characterize the Prosopis africana biomass of wood, barks, leaves, and pods towards providing valuable data for scaling up and incorporating these materials into the bioenergy crop database. Characterizations of wood, leaves, barks, and pod wastes from Prosopis africana biomass were investigated based on the proximate, ultimate, and compositional analysis of pulverized samples of the PA biomass to determine their physical, thermal, and chemical properties towards assessing their potential for valorization to bioenergy. The lignocellulosic materials were characterized by scanning electron microscopy, energy dispersive X-ray, Fourier transform infrared spectroscopy, thermogravimetric analysis, and X-ray diffraction. The results show that the pulverized sample wastes have porous structures with varying degrees of crystallinity (wood: 89.20 %, bark: 23.90 %, leaves: 32.48 %, pods: 23.08 %), suggesting different susceptibilities to conversion processes. Notably, the wood sample had the lowest moisture content (3.13 %), and the pod sample had the highest volatile matter content (75.83 %), indicating a high potential for biofuel production. The higher heating values (HHV) and lower heating values (LHV) of the samples ranged from 15.23 to 20.49 MJ/kg and 13.83 to 18.79 MJ/kg, respectively. These calorific values are competitive with established lignocellulosic bioenergy feedstocks, positioning PA biomass as promising candidates for solid biofuel applications.

CUPID: An efficient spatio-temporal data engine

In the IoT era, abundant spatio-temporal data is generated from various devices thanks to the prevalence of positioning techniques. Due to a lack of effective systems to manipulate the data, advanced scalable and efficient data management systems are necessary to support more and more urban applications.We propose Cupid, which is powered by scholars from Chongqing University and Guangzhou Urban Planning and Design Survey Research Institute, an efficient spatio-temporal Data engine. It extends JUST by introducing many functionalities to make it more applicable to deployment, and can efficiently manage large-scale spatio-temporal data. In Cupid, Apache HBase is utilized as the storage, GeoMesa serves as the spatio-temporal data indexing tool, and Apache Spark acts as the execution engine. We introduce many optimizations to ensure usability and reduce computational overhead. To make Cupid easy to use, we design and implement a SQL-like query language. Furthermore, to deploy the system as a PaaS while speeding up the execution, we leverage Apache Livy, a service that enables easy interaction with a Spark cluster over REST interfaces, together with gRPC to effectively collect results. Extensive experiments illustrate that Cupid outperforms other state-of-the-art spatio-temporal big data management systems in terms of efficiency and scalability.