Data Sources Research Articles

The Development of Interactive Learning Media for Islamic Religious Education in Elementary Schools in Indonesia

In today’s digital era, conventional teaching methods often fail to captivate students, making interactive media a promising solution. Interactive media combines visual, audio, and participatory elements, providing a more engaging and effective learning experience. The development process includes analyzing student needs and curriculum, selecting appropriate technology, creating interactive content, conducting trials, and evaluating effectiveness. This study uses a qualitative approach. Primary data sources in qualitative research are the words of the interview results obtained from primary data including respondents from teachers and students in SDMT Ponorogo. Data collection is carried out by means of snowball sampling. The data collection technique in this study is through observation, in-depth interviews and documentation. The data obtained during the study, both primary and secondary data are then processed and analyzed using Miles and Huberman model. The validity test of data in qualitative research includes tests, credibility, transferability, dependability, and confirmability. The article highlights the benefits of interactive media, including increased student motivation, improved understanding of abstract religious concepts, and the development of critical thinking skills. This approach not only modernizes religious education but also ensures that students remain interested and actively involved in learning. Through proper testing and implementation, interactive media can significantly enhance the quality of Islamic Religious Education at SDMT Ponorogo. The developed media, which consists of animated videos, interactive quizzes, and educational games, is able to make the learning process more engaging and increase student engagement.

Role of artificial intelligence in predicting neurological outcomes in post cardiac resuscitation

Being an extremely high mortality rate condition, cardiac arrest cases have rightfully been evaluated via various studies and scoring factors for effective resuscitative practices and neurological outcomes post resuscitation. This narrative review aims to explore the role of Artificial Intelligence (AI) in predicting neurological outcomes post cardiac resuscitation. Methodology involved detailed review of all relevant recent studies of AI, different machine learning algorithms, prediction tools and assessing their benefit in predicting neurological outcomes in post cardiac resuscitation cases as compared to more traditional prognostic scoring systems and tools. Previously, outcome determining clinical, blood and radiological factors were prone to other influencing factors like limited accuracy and time constraints. Studies conducted also emphasized that to predict poor neurological outcomes, a more multimodal approach helped adjust for confounding factors, interpret diverse datasets and provide a reliable prognosis which only demonstrates need for AI to help overcome challenges faced. Advanced machine learning algorithms like Artificial Neural Networks (ANN) using supervised learning by AI have improved accuracy of prognostic models outperforming conventional models. Several real-world cases of effective AI powered algorithm models have been cited here. Studies comparing machine learning tools like XGBoost, AI Watson, hyperspectral imaging, ChatGPT-4 and AI based gradient boosting have noted their beneficial uses. AI could help reduce workload, healthcare costs and help personalize care, process vast genetic and lifestyle data and help reduce side effects from treatments. Limitations of AI have been covered extensively in this article including data quality, bias, privacy issues and transparency. Our objectives should be to use more diverse data sources, use interpretable data output giving process explanation, validation method and implement policies to safeguard patient data. Despite the limitations, the advancements already made by AI and its potential in predicting neurological outcomes in post cardiac resuscitation cases has been quite promising and boosts a continually improving system, albeit requiring close human supervision with training and improving models, with plans to educate clinicians, the public and sharing collected data.

Leveraging Multiple Administrative Data Sources to Reduce Missing Race and Ethnicity Data: A Descriptive Epidemiology Cross-Sectional Study of COVID-19 Case Relative Rates.

Understanding race and ethnicity (RE) differentials improves health outcomes. However, RE data are consistently missing from electronic laboratory reports, the primary source of COVID-19 case metrics. We addressed the missing RE differentials and compared vaccinated and unvaccinated cases from March 1, 2020, to May 30, 2023, in New York State (NYS), excluding New York City. This descriptive epidemiology cross-sectional study linked the NYS Electronic Clinical Laboratory Reporting System (ECLRS) with NYS Immunization Information System (NYSIIS) to address the missing RE data in the ECLRS system. The primary metric was the COVID-19 case relative risk (RR) for each RE relative to white individuals. There were 4,212,741 COVID-19 cases with 39% (1,624,818) missing RE data in ECLRS; missing RE data declined to 17% (726,023) after matching with NYSIIS. For those aged 65years or older (after matching), 42% were missing in 2020, which declined by 17% by 2023. In May 2021, COVID-19 RRs for vaccinated individuals were 1.09 (95% CI 0.90-1.32), 1.11 (95% CI 0.87-1.43), 1.13 (95% CI 0.93-1.39), and 1.89 (95% CI 1.01-3.52), and for unvaccinated individuals were 1.73 (95% CI 1.66-1.82), 0.84 (95% CI 0.78-0.92), 3.10 (95% CI 2.98-3.22), and 3.49 (95% CI 3.05-3.98) respectively for Hispanic, Asian/Pacific Islander, Black people, and American Indian/Alaska Native individuals. Matching case data with vaccine registries reduce missing RE data for COVID-19 cases. Disparity was lower in vaccinated than in unvaccinated individuals indicating that vaccination mitigated RE disparities early in the pandemic. This underscores the value of interoperable systems with automated matching for disparity analyses.

“Throw Dat Hat!”: Educational Experiences, Attainment, and Aspirations of Adolescent Female Trafficking Survivors in a Residential Facility

This study addresses the gap in research on the educational experiences of adolescent human trafficking survivors, with a focus on their past, present, and future educational experiences and goals. Its objective is to inform the best practices for educational programming within female adolescent residential care centers in the United States. Drawing on a subset of data from a broader mixed-methods case study conducted at the Allasso House residential facility, this research involved 11 current residents. The data sources included case files detailing their past educational experiences, assessments of their current educational achievements, and interviews exploring their future aspirations. The findings reveal key themes related to the residents past risk factors, current educational successes and barriers, and tensions in setting future goals. Most of the residents expressed a desire to attain high school equivalency, identifying financial stability as a primary motivation. They also aspired to form healthy families, while placing significant value on material success. Unlike studies that define success primarily through survival, sobriety, and the avoidance of re-trafficking, this research highlights the broader aspirations of these adolescents, emphasizing the critical role of education in general wellbeing, risk mitigation, and future success. This study underscores the importance of prioritizing educational attainment and long-term aspirations in future research and in the design of residential programs for adolescent survivors of trafficking.

Open Data-Driven 3D Building Models for Micro-Population Mapping in a Data-Limited Setting

Urban planning and management increasingly depend on accurate building and population data. However, many regions lack sufficient resources to acquire and maintain these data, creating challenges in data availability. Our methodology integrates multiple data sources, including aerial imagery, Points of Interest (POIs), and digital elevation models, employing Light Gradient Boosting Machine (LightGBM) and Gradient Boosting Decision Tree (GBDT) to classify building uses and morphological filtration to estimate heights. This research contributes to bridging the gap between data needs and availability in resource-constrained urban environments, offering a scalable solution for global application in urban planning and population mapping.

Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023)

Abstract. Snapshots derived from the World Soil Information Service (WoSIS) are served freely to the international community. These static datasets provide quality-assessed and standardised soil profile data that can be used to support digital soil mapping and environmental applications at broad scale levels. Since the release of the preceding snapshot in 2019, refactored ETL (extract, transform and load) procedures for screening, ingesting and standardising disparate source data have been developed. In conjunction with this, the WoSIS data model was overhauled, making it compatible with the ISO 28258 and Observations and Measurements (O&M) domain models. Additional procedures for querying, serving and downloading the publicly available standardised data have been implemented using open software (e.g. GraphQL API). Following up on a short discussion of these methodological developments we discuss the structure and content of the “WoSIS 2023 snapshot”. A range of new soil datasets was shared with us, registered in the ISRIC World Data Centre for Soils (WDC-Soils) data repository and subsequently processed in accordance with the licences specified by the data providers. An important effort has been the processing of forest soil data collated in the framework of the EU-HoliSoils project. We paid special attention to the standardisation of soil property definitions, description of the soil analytical procedures and standardisation of the units of measurement. The 2023 snapshot considers soil chemical properties (total carbon, organic carbon, inorganic carbon (total carbonate equivalent), total nitrogen, phosphorus (extractable P, total P and P retention), soil pH, cation exchange capacity and electrical conductivity) and physical properties (soil texture (sand, silt and clay), bulk density, coarse fragments and water retention), grouped according to analytical procedures that are operationally comparable. Method options are defined for each analytical procedure (e.g. pH measured in water, KCl or CaCl2 solution, molarity of the solution, and soil / solution ratio). For each profile we also provide the original soil classification (i.e. FAO, WRB and USDA system with their version) and pedological horizon designations as far as these have been specified in the source databases. Three measures for “fitness for intended use” are provided to facilitate informed data use: (a) positional uncertainty of the profile's site location, (b) possible uncertainty associated with the operationally defined analytical procedures and (c) date of sampling. The most recent (i.e. dynamic) dataset, called wosis_latest, is freely accessible via various web services. To permit consistent referencing and citation, we also provide a static snapshot (in this case, December 2023). This snapshot comprises quality-assessed and standardised data for 228 000 geo-referenced profiles. The data come from 174 countries and represent more than 900 000 soil layers (or horizons) and over 6 million records. The number of measurements for each soil property varies (greatly) between profiles and with depth, this generally depending on the objectives of the initial soil sampling programmes. In the coming years, we aim to gradually fill gaps in the geographic distribution of the profiles, as well as in the soil observations themselves, this subject to the sharing of a wider selection of “public” soil data by prospective data contributors; possible solutions for this are discussed. The WoSIS 2023 snapshot is archived and freely available at https://doi.org/10.17027/isric-wdcsoils-20231130 (Calisto et al., 2023).

The Fuse Platform: Integrating data from IoT and other Sensors into an Industrial Spatial Digital Twin

Abstract. Digital Twins as virtual representations of industrial assets are being used to assimilate varied sources of data for improved awareness and decision making in operations and process optimisation. This paper explores the integration of IoT sensors into a spatial digital twin called Fuse that Woodside Energy has been building for the assets it operates. We describe the Fuse platform and its knowledge graph data core that is used to organise and inter-relate data for presentation within 3D visualisations, domain-specific contexts and immersive augmented reality presentations. The key contribution here is the use of a knowledge graph to link diverse data sources so as to contextualise sensor data for actionable insights. One area of significant innovation has been the development and use of new Internet of Things (IoT) devices which have been enabled by advances in sensor technology, connectivity, and cloud computing. These new tailored data sources are complimenting existing plant and resource planning data for improved asset monitoring, predictive maintenance and process automation.

Invisible favelas

This article presents the first part of the results of a study on the presence and/ or absence of Rio de Janeiro’s favelas in the city’s cartography. Different data sources were used: research in the municipal archives—including the Rio’s first aerial photographs ever made, cartographic records and infrastructure maps from the 1920s and 1930s; research in building codes; and research on digital cartographic platforms. The aim of the article is to investigate when and how the urban phenomenon of favelas officially appears in the city’s records and to reflect on how this late and inconsistent recognition may have affected the existence of the phenomenon itself, since its lack of official recognition created bureaucratic barriers to public administration actions. The argument of this article is that cartographic production has kept the favela an invisible part of the city and how, especially in recent decades, this invisibility has been a political choice. The article concludes that the choice for invisibility has contributed to these large territories becoming deeply embedded in the city’s urban fabric.

Recommender Systems Applications: Data Sources, Features, and Challenges

In recent years, there has been growing interest in recommendation systems, which is matched by their widespread adoption across various sectors. This can be attributed to their effectiveness in reducing an avalanche of data into individualized information that is meaningful, relevant, and can easily be absorbed by a single person. Several studies have recently navigated the landscape of recommendation systems, attending to their approaches, challenges, and applications, as well as the evaluation metrics necessary for effective implementation. This systematic review investigates the understudied aspects of recommendation systems, including the data input into the systems and their features or outputs. The data in (input) and data out (features) are both diverse and vary significantly from not just one application domain to another, but also from one application use case to another, which is a distinction that has not been thoroughly addressed in the past. In addition, this study explores several application domains, providing a comprehensive breakdown of the categorical data consumed by these systems and the features, or outputs, of these systems. Without focusing on any particular journals or their rankings, this study collects and reviews articles on recommendation systems published from 2018 to April 2024, in four top-tier research repositories, including IEEE Xplore Digital Library, Springer Link, ACM Digital Library, and Google Scholar.

Ecological change increases malaria risk in the Brazilian Amazon

Ecological change in the Brazilian Amazon is closely linked to human mobility and health. Mining, agriculture, logging, and other activities alter highly diverse ecological and demographic contexts and subsequent exposure to diseases such as malaria. Studies that have attempted to quantify the impact of deforestation on malaria in the Brazilian Amazon have produced conflicting results. However, they varied in methodology and data sources. Most importantly, all studies used annual data, neglecting the subannual seasonal dynamics of malaria. Here, we fill the knowledge gap on the subannual relationship between ecological change in the Brazilian Amazon and malaria transmission. Using the highest spatiotemporal resolution available, we estimated the effect of deforestation on malaria cases between 2003 and 2022 using a stratified Bayesian spatiotemporal hierarchical zero-inflated Poisson model fitted with the Integrated Nested Laplace Approximation. The model was also stratified by state. We found that a 1% increase in 1-mo lagged deforestation increased malaria cases in a given month and municipality by 6.3% [95% credible interval (Crl): 6.2, 6.5%]. Based on an interaction term included in the model, the effect of deforestation on malaria was even larger in areas with higher forest cover. We found that the coefficients for deforestation and mobility were highly variable when stratified by state. Our results provide detailed evidence that, on average, deforestation increases malaria transmission, but that the relationship is not spatiotemporally uniform. These results have implications for stratifying malaria control interventions based on ecological dynamics to help Brazil achieve its goal of malaria elimination by 2035.

Comprehensive Evaluation of the Cardiovascular Protective Effects of SGLT2 Inhibitors in Patients with Advanced Chronic Kidney Disease: A Real-World Evidence.

Introduction Diabetes, kidney disease, and cardiovascular disease have complex interactions and coexistences that significantly worsen a patient's overall health. Previous research results have shown that SGLT2i hypoglycemic drugs can not only effectively control blood sugar in diabetic patients, but also protect the kidneys and heart. This study further focuses on diabetic patients with kidney disease to explore the effectiveness of using SGLT2i hypoglycemic drugs in avoiding heart-related complications or death. Methods This is a multi-center retrospective cohort study using the Taipei Medical University Clinical Research Database (TMUCRD) as the data source. This study selected patients who suffered from both type 2 diabetes and chronic kidney disease from 2008/01/01 to 2020/12/31 as the research team. Integrated or separate 4P-MACE (4-point major adverse cardiovascular events) and mortality were the outcomes of this study. The Kaplan Meier curves method and Cox proportional hazard regression analysis were used to explore the association between each influencing factor and the outcome. Results A total of 5,005 patients with type 2 diabetes and CKD were included in this study, of which 524 patients were stably treated with SGLT2i, 3,952 patients were treated with DPP4i, and 529 patients were treated with TZD. The results showed that the SGLT2i user group had a significantly lower risk of 4P-MACE compared with the SGLT2i non-user group (HR: 0.68, 95% CI [0.49, 0.95], p=0.024). The SGLT2i group had a significantly lower risk of cardiovascular mortality compared with the DPP4i and TZD groups (HR: 0.37, 95% CI [0.21, 0.65], p<0.001; HR: 0.42, 95% CI [0.20, 0.90], p=0.025). Conclusion This study found that for patients with both diabetes and kidney disease, SGLT2i is a better option than other oral hypoglycemic medications because it can significantly avoid the occurrence of heart-related complications. The results of this study can be used as a reference for clinical medication selection practice.

Video-blogs (Vlogs)-based Project: A Meta Analysis

This study aims to analyze the relationship between using video-blogs (vlogs)-based projects on student academic achievement through project-based learning. This study applied a meta-analysis through comprehensive sources, either from the international or the Indonesian index by considering certain criteria. The data sources in this research were obtained from twenty-seven studies that had met the inclusion criteria. The inclusion criteria used are 1) research published in 2020-2024; 2) research must be obtained from the Google Scholar database; ScienceDirect; ERIC; Wiley and IEEE; 3) Publications indexed by SINTA, Scopus, and Web of Science; 4) publications must be related to Vlog-based project; 5) the research has a value (R); (t) or (f) and 6) sample size (N) ≥ 30 students. The study showed that the Vlog-based project significantly influenced students' academic achievement (z = 8,042; p < 0.001; CI 95 % [ 0.526; 0.962]. The effect size in this study is a high effect size category (rRE = 1,024). In this meta-analysis, the data presented is accurate, valid, and representative, free from publication bias. This finding explains that V-logs-based project-based learning positively affects students' academic achievement. This study shows that using Vlogs-based projects has a positive impact on students' academic achievement, and it encourages teachers to enhance students' academic performance. Furthermore, the Vlogs-based project motivates students to engage more actively and creatively with the learning materials.

Analysis of Machine Learning-Based NLOS Signal Identification Algorithm for UWB Indoor Localization Using CIR Waveform Features

Abstract. Ultra-wideband (UWB) technology stands out among numerous indoor positioning techniques due to its high operating frequency, low interception capability, resistance to multipath effects, and strong penetration. The UWB uses the time-of-arrival (TOA) to estimate the distance between the transmitter and receiver anchors in centimeter accuracy. However, in complex indoor positioning environments, obstacles such as walls, glass windows, metal plates, and wooden doors may block and reflect signals, inevitably causing non-line-of-sight (NLOS) errors that significantly affect positioning accuracy. The NLOS signal has lower signal energy due to the reflections. Thus, the channel impulse responses (CIR) from NLOS and LOS are different. To address the NLOS signal identification issue in UWB positioning, we utilize UWB CIR data collected from various positioning scenarios as the data source. CIR waveform input features are provided for the NLOS signal recognition model, and four machine learning models—Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), K-Nearest Neighbors (KNN), and XGBoost—are trained and optimized for NLOS signal recognition. The aim of the study is to analyze the performance of different machine learning algorithms for NLOS signal recognition in UWB indoor localization using these features. Experimental results indicate that machine learning-based NLOS signal recognition algorithms can achieve an accuracy of approximately 77.46%, precision of 80.46%, and an F1 score of 0.81. Among the four models, the XGBoost model demonstrates generally better recognition performance.

Program Evaluation of Pancasila Student Profile Strengthening Project Using the CSE-UCLA Model

The research aims to evaluate the program of the Pancasila student profile strengthening project in elementary schools in Semarang Regency, Indonesia. This research method used the CSE-UCLA evaluation model with a qualitative approach that included the aspects of program assessment, program planning, program implementation, program improvement, and program certification. The data sources came from the school principal/curriculum teachers, classroom teachers, students, and the parents of students at Pangudi Luhur Ambarawa Elementary School and Tlompakan 1 Tuntang Elementary School in Semarang Regency. Data collection was through interviews, document studies, and observations. The data analysis technique used qualitative descriptive methods. The research results showed that in the Assessment program, P5 was implemented in accordance with the vision, mission, and objectives. The theme and project options were chosen based on environmental issues and the needs of the students, and the project was also supported by the parents; Program planning, both schools had goals to be achieved in P5 so that the project ran with a clear direction and purpose; Program implementation, teachers have good strategies in carrying out P5 but improvements were needed in the socialization of the P5 program; Program improvement, there needs to be enhancements in the documentation of monitoring and supervision results; Certification program, the goals of the P5 program were achieved and all parties were satisfied with the existence of the P5 program, which had a positive impact on the students. The P5 program was running well, and all stakeholders were satisfied. However, there was a need for improvement in documentation and communication with students' parents. In addition, it is necessary to broaden project options, support teachers, and encourage them to prioritize character development over tangible results.

Spatial Analysis of Land Use Land Cover Dynamics in the Madurai District Using Sentinel-2 Data and Supervised Learning Algorithms

Abstract. LULC, or Land Use and Land Cover, refers to the classification and description of different types of land and its usage patterns, including urban areas, forests, agricultural land, etc. In remote sensing, satellite imagery for LULC mapping is becoming more widespread. Numerous studies examine various approaches to improve mapping efficiency and accuracy, highlighting the significance of various data sources, machine learning algorithms, and categorization techniques. This study employs machine learning classifiers, namely Random Forest (RF), Support Vector Machine (SVM), Gradient Boosted Trees (GTB), Classification and Regression Trees (CART), and K-Nearest Neighbors (KNN) for land use and land cover (LULC) classification of Madurai district utilizing Google Earth Engine. The findings reveal the impressive performance of Random Forest, boasting an overall accuracy of 99.01 percent coupled with a commendable Kappa coefficient of 98.68. Conversely. However, amidst these commendable achievements, it’s noteworthy to highlight the nuanced variations observed between the accuracy of training and validation sets. This discrepancy is attributed to the intrinsic intricacies of the learning processes inherent within the algorithms, underscoring the nuanced nature of algorithmic methodologies and their implications on accuracy assessment within spatial analysis frameworks. The generated land use and land cover (LULC) map allows for a comparison between the ground truth data and the surveys conducted to assess issues such as water scarcity and the drying of natural and man-made water bodies.

Agricultural Strategies of Young Farmers on Small-Scale Land in Sriharjo, Bantul

Abstract: The agricultural situation in the Special Region of Yogyakarta is becoming increasingly concerning. The number of farmers is declining, and young people are showing less interest in agriculture. This has led to problems with production output and a food security crisis. A solution to these challenges is to empower youth to engage in farming. The aim of this study is to identify groups of young farmers and the efforts made to address current challenges. The unit of analysis in this study is youth groups involved in farming activities on limited land. The research was conducted with a youth farming group called Taruna Tani Hijaunya Cinta in Sriharjo Village, Bantul. A case study design was employed, using in-depth interviews conducted both in person and via telephone. The research was carried out from January to June 2022, with limited observations and interviews due to the COVID-19 pandemic. To supplement the data, document and photo collection from the farmer group was also conducted. Thematic analysis was used to analyze various data sources. The findings of this study indicate that the majority of the group's members are children of farmers aged 17-35, with educational backgrounds ranging from secondary school to university students. The results show that the use of technology in farming, the development of social media for marketing, and the creation of agro-tourism have enhanced the competitiveness of young people in the agricultural sector, created new business opportunities, and stimulated local economic growth. For long-term sustainability, it is recommended that the government and educational institutions strengthen training and education programs for youth to enable them to continue innovating and adapting to market changes. Keywords: Limited Land Agriculture, Young Farmers, Youth

Semi-supervised comparative learning compensation method for chemical gas sensor drift.

The gradual and unpredictable variation in chemo-sensory signal responses when exposed to the same analyte under identical conditions, commonly referred to as sensor drift, has long been recognized as one of the most serious challenges faced by chemical sensors. The traditional drift compensation method is both labor-intensive and expensive, as it requires frequent collection and labeling of gas samples for recalibration. Introducing a small number of meaningful drift calibration samples can be an attractive strategy to reduce the computational load and improve the performance of the updated classifier. However, under the influence of drift, new challenges arise due to the difference in the distribution of source and target domain data. This paper proposes a novel algorithm framework called semi-supervised contrastive learning drift compensation (SSCLDC). The framework automatically extracts high-level abstract features based on a multilayer perceptron to better represent the structure of the source data. In addition, to address the issue of data distribution differences caused by drift between the source and target domains. We add a small number of reference sample pairs into the training for semi-supervised learning. Combining a contrastive loss function that can represent the matching degree of paired samples effectively overcomes the problem of sensor drift. The Kennard-Stone sequential algorithm is used to select the representative reference sample from the set of candidate reference samples. Experiments conducted on a widely used long-term chemical gas sensor drift dataset demonstrate that the proposed method outperforms several classic drift compensation techniques, highlighting its effectiveness and practical applicability.

Assessing the harmonization of structured electronic health record data to reference terminologies and data completeness through data provenance

AbstractIntroduction(1) Assess the harmonization of structured electronic health record data (laboratory results and medications) to reference terminologies and characterize the severity of issues. (2) Identify issues of data completeness by comparing complementary data domains, stratifying by time, care setting, and provenance.MethodsQueries were distributed to 3 Data Partners (DP). Using harmonization queries, we examined the top 200 laboratory results and medications by volume, identifying outliers and computing summary statistics. The completeness queries looked at 4 conditions of interest and related clinical concepts. Counts were generated for each condition, stratified by year, encounter type, and provenance. We analyzed trends over time within and across DPs.ResultsWe found that the median number of codes associated with a given laboratory/medication name (and vice versa) generally met expectations, though there were DP‐specific issues that resulted in outliers. In addition, there were drastic differences in the percentage of patients with a given concept depending on provenance.ConclusionsThe harmonization queries surfaced several mapping errors, as well as issues with overly specific codes and records with “null” codes. The completeness queries demonstrated having access to multiple types of data provenance provides more robust results compared with any single provenance type. Harmonization errors between source data and reference terminologies may not be widespread but do exist within CDMs, affecting tens of thousands or even millions of records. Provenance information can help identify potential completeness issues with EHR data, but only if it is represented in the CDM and then populated by DPs.

Fuzzy Method for Preliminary Classification of the Micro-Electromechanical Systems Data

Data of micro-electromechanical systems (sensors) are widely used in modern multipurpose intellectual environments, autonomous mobile robots and automated systems possessing intellectual components. Since the amount of data delivered by the sensors is rather considerable and their significance varies we propose a fuzzy method of the data preliminary classification aimed at selection of the data important for the planning and control decisions while meaningless data or data that do not contain new information are discarded. As a result the amount of the resource intensive intellectual processing and the workload in the network infrastructure could be reduced significantly. The paper presents the analysis for the preliminary classification problem, the abstract model of a sensor and classification method. The method is based on the delay which is controlled by multiplicative decrease additive increase rule and expresses the expectation of the context uncertainty. The delay decreases if the variables measured by the sensor presumably change and increases if it keep its value. Numerical examples are presented as well. They consider the set of mount chest accelerometer uncalibrated measurements. We compare source data set and set of the selected values formed by an algorithm implementing fuzzy preliminary classification. Several metrics were used to compare the selected values with the source data and with CMA filter as well.

Respiratory sarcopenia: aspects of pathogenesis, approaches to diagnosis

The severe course of infectious and inflammatory diseases of the respiratory system, high rates of morbidity and mortality among the elderly and senile aged people may be associated with respiratory sarcopenia, a new nosological form that describes age-related changes in the strength and structure of the respiratory muscles. The aim of the review is to update the data of open access scientific sources on aspects of pathogenesis and diagnostic approaches to respiratory sarcopenia. Foreign and domestic scientific publications on this topic were analyzed from 2019 to the present. The mechanisms of respiratory sarcopenia pahogenesis were assessed in the light of its relationship with whole body sarcopenia, age-related mechanisms of aging of the respiratory system as a whole, risk factors and lifestyle aspects of the elderly. The currently proposed diagnostic algorithm for respiratory sarcopenia is considered in detail. Conclusion. Age-related changes in muscle tissue also affect the respiratory muscles. Whole-body sarcopenia, according to some authors, may be accompanied by respiratory sarcopenia. The proposed terminology and algorithm for diagnosing this pathology require additional data accumulation and further discussion.