Data Sources Research Articles

Air Quality Estimation and Forecasting via Data Fusion With Uncertainty Quantification: Theoretical Framework and Preliminary Results

AbstractIntegrating air quality information from models, satellites, and in situ monitors allows for both better estimation of air quality and better quantification of uncertainties in this estimation. Uncertainty quantification is important to appropriately convey confidence in these estimates and forecasts to users who will base decisions on these. Uncertainty quantification also allows tracing the value of information provided by different data sources. This can identify gaps in the monitoring network where additional data could further reduce uncertainties. This paper presents a framework for data fusion with uncertainty quantification, applicable to multiple air‐quality‐relevant pollutants. Testing of this framework in the context of nitrogen dioxide forecasting at sub‐city scales shows promising results, with confidence intervals typically encompassing the expected number of actual measurements during cross‐validation. The framework is now being implemented into an online tool to support local air quality management decision‐making. Future work will also include the incorporation of low‐cost air sensor data and the quantification of uncertainty at hyper‐local scales.

Implementasi Blue Economy terhadap Budi Daya Rumput Laut sebagai Keberlanjutan Ekonomi Masyarakat Pesisir Desa Tokorondo, Kabupaten Poso, Sulawesi Tengah

This study aims to assess the implementation of the blue economy concept in developing seaweed cultivation in Tokorondo Village, Poso Regency, Central Sulawesi. This study uses a descriptive research method with a qualitative approach, with data sources being primary data and secondary data. The study results indicate that the seaweed farming community has implemented all principles and indicators of the blue economy well and following the concept. Furthermore, the study results indicate that public awareness of implementing the blue economy concept significantly affects the sustainability of the community's economy in Tokorondo Village.

“Ensembled transfer learning approach for error reduction in landslide susceptibility mapping of the data scare region”

Landslide susceptibility map (LSM) plays an important role in providing the knowledge of slopes prone to future landslides. However, the applicability of LSM is often hindered due to high cost of data collection especially in mountainous region such as Himalayas. Therefore, this study proposes transfer learning approach (TL) to improve the performance of LSM by transferring the information from the data rich region (source) to data scare region (target). Two machine learning based model are trained in source area which is then transferred for prediction in target area and a source trained model combined with the knowledge of target is used for the prediction in the target area. The applicability of the proposed approach is tested considering Mandi district as source area and Kullu and Rudraprayag districts as target areas. Independent variables which included 11 landslide conducive factors from the source and target area, the independence was critically analysed using multi-collinearity test, while the geomorphic similarity was assessed using KL divergence method. Efficient machine learning method such as random forest (RF) and multi-layer perceptron (MLP) was used to train the models in both areas, statistical measure such as AUC-ROC, precision, recall, F-score, and accuracy were used to evaluate the performance of the LSMs. The results demonstrated the proposed approach for target area 1, the AUC value increased from 0.908 (Target trained on itself), to 0.942 (Target-Transfer Learned (TL)) and 0.959 (Target Combined) for RF and 0.896, 0.907 and 0.946 for MLP. Additionally, an increase in the precision was observed in RF while all other statistical measures increased to 0.023 for precision, 0.022 for recall, 0.023 for F-score and 0.066 for accuracy in the case of MLP. While on the other hand, in the case of target area 2, the AUC value for RF, ranged from 0.95 (Target area trained on itself), 0.80 (target TL) and 0.98 (target combined) and for MLP the AUC ranged from 0.82 for the source trained model while 0.84 for LSM obtained when both target and source data was used. Thus, the results revealed that proposed TL approach can be effective in improving the performance of LSM in data scare region in Himalayan region, thereby providing a promising approach in overcoming the issue of data limitation.

A Smart Tourism Resource Information Management Platform Based on Multi-Source Data Fusion

Leveraging multiple data sources to enhance tourism resource management and visitor behavior analysis has become a key challenge in the context of the booming smart tourism industry. In this study, we explore how to integrate and optimize multiple data sources including social media activities, user reviews, tourism statistics, and geographic information to build a comprehensive information management platform for smart tourism resources. Given the limitations inherent in isolated and decentralized data processing approaches in the smart tourism domain, we propose a new approach using deep learning autoencoders for efficient extraction and fusion of meaningful features from heterogeneous datasets. Our methodology encompasses a rigorous data collection and preprocessing phase, ensuring data quality and consistency, followed by the application of autoencoders to learn high-level feature representations conducive to data integration. The fused data facilitate the development of strategies for the optimal allocation of tourism resources and nuanced analysis of visitor behavior patterns. Experimental evaluations demonstrate the model’s proficiency in capturing intricate data relationships, significantly enhancing the predictive accuracy for tourism demand forecasting, and enabling personalized visitor recommendations. The results underscore the potential of our approach to revolutionize smart tourism management practices by providing actionable insights into resource optimization and visitor engagement strategies, thereby contributing to the sustainable growth of the tourism sector.

Supervised multiple kernel learning approaches for multi-omics data integration

BackgroundAdvances in high-throughput technologies have originated an ever-increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics. Multiple kernel learning (MKL) has shown to be a flexible and valid approach to consider the diverse nature of multi-omics inputs, despite being an underused tool in genomic data mining.ResultsWe provide novel MKL approaches based on different kernel fusion strategies. To learn from the meta-kernel of input kernels, we adapted unsupervised integration algorithms for supervised tasks with support vector machines. We also tested deep learning architectures for kernel fusion and classification. The results show that MKL-based models can outperform more complex, state-of-the-art, supervised multi-omics integrative approaches.ConclusionMultiple kernel learning offers a natural framework for predictive models in multi-omics data. It proved to provide a fast and reliable solution that can compete with and outperform more complex architectures. Our results offer a direction for bio-data mining research, biomarker discovery and further development of methods for heterogeneous data integration.

The Innovation of Teaching based on Technology: Improving Educational Quality in Digital Era

To complete the research answer on the topic of producing digital learning to improve the quality of student learning in the era of the technological revolution 4.0 at STITNU Sakinah Dharmasraya, researchers have collected various materials that answer research questions. Likewise, to collect primary data, researchers interviewed 30 academics (students and lecturers) of STITNU Sakinah Dharmasraya to find out their perspectives and experiences in developing digital-based teaching to improve student learning outcomes during regular education and throughout education. Therefore, a review of different data sets was carried out, both in the form of secondary source data from various publications related to technology and teaching as well as data that researchers asked academics to find answers to. Researchers can simplify the development of digital teaching to improve the quality of student learning in the Revolution 4.0 era at STITNU Sakinah Dharmasraya. This has resulted in several new insights, such as technology has innovated the learning process because digital applications have helped academics solve learning challenges and provide more benefits. They hope that these findings can provide new insights into learning in higher education and their use in policy making

Evidence from Mendelian randomization analysis combined with meta-analysis for the causal validation of the relationship between 91 inflammatory factors and lumbar disc herniation

Lumbar disc herniation (LDH) is a common spinal disease. In recent years, an increasing number of observational studies have reported the impact of inflammatory factors on LDH. By conducting Mendelian randomization (MR) analysis on 91 inflammatory factors, it is possible to reveal their causal relationship with LDH, providing new insights for prevention and treatment strategies. In this study, a two-sample MR analysis was performed, using 91 inflammatory factors as exposure data, and LDH data from 2 different sources as outcome data. Subsequently, the most significant results from the inverse-variance weighted analysis were subjected to meta-analysis, with multiple corrections applied to the thresholds to ensure result accuracy. Finally, reverse causality MR analysis was conducted to validate the causal relationship between the identified positive inflammatory factors and LDH. Ninety-one cytokines were analyzed in relation to LDH using MR with data from the Finngen and UK Biobank databases. The inverse-variance weighted results from both analyses were then meta-analyzed, and multiple corrections were applied to the significance threshold of the meta-analysis results. Ultimately, only 1 cytokine, tumor necrosis factor-beta levels (genome-wide association study ID: GCST90274840), showed a significant association after the combined MR analysis and multiple corrections, with an odds ratio of 1.073 (95% confidence interval: 1.034–1.113, P = .0154). Furthermore, this positive cytokine did not display any reverse causality with LDH from either data source. Tumor necrosis factor-beta levels are a risk factor for LDH, potentially increasing the risk of developing the condition and exacerbating its symptoms.

Konsep Ellected Official Sebagai Syarat Pencalonanan Presiden dan Wakil Presiden Perspektif Maslahah

Constitutional Court Decision Number: 90/PUU-XXI/2023 is a decision relating to the requirements for presidential and vice presidential candidates. The basis for filing the lawsuit is Law Number 7 of 2017 concerning General Elections. The results of this decision state that the age limit for presidential and vice presidential candidates is 40 years or that they are currently occupying positions elected through general elections (Elected Official). Literature research with researchers utilizing the Constitutional Court Decision as a primary source, and secondary data sources using books and journals. Research shows that the concept of Elected Official in the Constitutional Court decision refers to the judge's view. According to Constitutional Justices Enny Nurbaningsih and Daniel Yusmic P. Foekh, what is meant by officials is elected through elections (Elected Official), here the governor is the regional head. M. Guntur Hamzah mentioned that the officials elected through elections (Elected Official) are the President, Vice President, DPR, DPD, DPRD, Governor/Deputy Governor, Regent/Deputy Regent, Mayor/Deputy Mayor. The Maslahah perspective views the concept of Ellected Official as being categorized as Maslahah Tahsiniyyah. Maslahah Tahsiniyyah to complete human life. The benefits of Tahsiniyyah need to be fulfilled to provide peaceKeyword : Constitutional Court Decision, Elected Official, Maslahah

Automated Nutrient Deficiency Detection and Recommendation Systems Using Deep Learning in Nutrition Science

Nutrient deficiencies affect millions globally, contributing to severe health issues and reduced quality of life. Traditional methods of diagnosing these deficiencies and recommending dietary adjustments are often time-intensive, prone to error, and lack personalization. The advent of deep learning has revolutionized nutrition science, offering automated, accurate, and scalable solutions. This paper delves into the development and application of automated nutrient deficiency detection and recommendation systems powered by deep learning. Key components of such systems include advanced data processing techniques that analyze multimodal datasets, such as biomarkers, dietary records, and food images. Convolutional Neural Networks (CNNs) excel in recognizing and quantifying nutrients from food images, while Recurrent Neural Networks (RNNs) handle time-series dietary data. Generative Adversarial Networks (GANs) and Natural Language Processing (NLP) facilitate data augmentation and textual analysis of dietary logs, respectively. These systems enable precise detection of deficiencies and generate tailored dietary plans based on individual needs, considering demographic and lifestyle factors. This article highlights case studies and practical implementations of deep learning models in real-world applications, such as AI-powered nutrition apps and biomarker-based deficiency prediction. It also addresses significant challenges, including data quality, algorithmic bias, and ethical concerns related to privacy and equity. Furthermore, the study explores future opportunities, such as integrating explainable AI, leveraging multi-modal data sources, and enhancing IoT-based tracking devices to improve recommendation systems. By bridging the gap between AI technology and nutrition science, these systems hold the potential to revolutionize global dietary health, offering scalable, personalized, and efficient solutions to combat nutrient deficiencies.

Using the German National Medication Plan for Clinical Studies in Practice-Based Research Networks.

The German National Medication Plan (GNMP) can be a valuable and interoperable data source for clinical studies, due to its digital specification and mandatory provisioning for chronically ill patients. Digital transfer of a patients current GNMP from the Patient Data Management System (PDMS) into electronic case report forms would avoid error prone manual data capturing. It is also essential for studies in practice-based research networks (PBRN), where data capturing must have as little impact as possible on everyday practice. The following issues are currently preventing seamless digital integration: There is no standardized interoperable export of the GNMP from PDMS. In the current form, pharmaceutical catalogs are needed to decode the contained pharmaceutical registration numbers. As accessibility to the pharmaceutical catalogs is restricted, there is no generic access to the actual information needed for study data evaluation. In order to conduct studies, feasible workarounds for these issues had to be implemented in the standard operating procedures, tools and participating GP practices. To overcome the GNMP's current lack of digital interoperability, the proposed solution combines semi-automated data export from PDMS at the GP practice and manual database search at the study center with a semi-automated processing pipeline to balance workload between GP practices, study management and evaluation.

Accurately Identifying Language Disorder in School-Age Children Using Dynamic Assessment of Narrative Language.

Several studies have demonstrated that dynamic assessment can be a less biased, valid approach for the identification of language disorder among diverse school-age children. However, all prior studies have included a relatively small number of participants, which is generally not adequate for psychometric research. This is the first large-scale study to (a) examine whether a dynamic assessment of narrative language yields indifferent outcomes regardless of several demographic variables including age, race/ethnicity, multilingualism, or gender; (b) examine the sensitivity and specificity of the dynamic assessment of language among a large sample of students with and without language disorder; and (c) identify specific cut-points by grade to provide clinically useful data. Participants included 634 diverse first- through fifth-grade students with and without language learning disorder. Students were confirmed as having a language disorder using a triangulation technique involving several sources of data. A dynamic assessment of narrative language, which took approximately 10 min, was administered to all students. Results indicated that the dynamic assessment had excellent (> 90%) sensitivity and specificity and that modifiability scores were not meaningfully different across any of the demographic variables. The dynamic assessment of narrative language accurately identified language disorder across all student demographic groups. These findings suggest that dynamic assessment may provide less biased classification than traditional, static forms of assessment.

The association between fruit and vegetable intake and gastrointestinal cancers risk from Mendelian randomization analysis

Recent research has demonstrated a relationship between fruit and vegetable intake and gastrointestinal cancers, but the causality of these associations remains uncertain. This investigation sought to elucidate whether there is a potential causal relationship between the intake of fruits and vegetables and the risk of gastrointestinal cancers. Employing two-sample Mendelian randomization (MR), this research explored the causative influence of consuming fruits (fresh and dried) and vegetables (salads/raw and cooked) on the susceptibility to gastrointestinal cancers. The UK Medical Research Council-Integrative Epidemiology Unit (MRC-IEU) provided the summary statistics for these exposure variables, while the summary statistics for outcome data came from four other data sources. Univariable and multivariable MR were performed using inverse variance-weighted (IVW), MR-Egger, weighted media (WM), and Lasso model methods. Besides, multiple methods were employed for sensitivity analyses to guarantee the robustness of the findings, including MR-Egger intercept, Cochran’s Q test, and MR-PRESSO. Furthermore, the Phenoscanner V2 database was employed to identify possible confounders. The main analysis of univariable MR found that dried fruit consumption provided protection against pancreatic and oral cavity/pharyngeal cancers. However, this correlation becomes non-significant when potential confounders such as smoking, drinking, and body mass index (BMI) are accounted for. Furthermore, neither univariate nor multivariate MR analyses revealed enough data to prove a causal relationship between the intake of fresh fruit, vegetables (including salad/raw and cooked), and gastrointestinal cancers. There is insufficient evidence for a causal association between the consumption of fruits and vegetables and the occurrence of gastrointestinal cancers. Further empirical research is needed to corroborate these dietary factors’ role in the etiology of gastrointestinal cancers.

Psychometric development and validation of the Hospital Resilience Index

BackgroundThroughout the past decades, the United States healthcare system has seen increased efforts to promote continuity of operations in response to external or internal stressors. There is no marker to characterize this facet of health system resilience. The two databases which may contain the most relevant data are the American Hospital Association (AHA) annual survey, which gathers over 1000 data points from more than 6000 hospitals, and the RAND corporation, which provides information on the financial health and viability of hospitals and health care systems. In that sense, AHA focuses more on organizational and practice issues, while RAND will cover more economic factors. Our study aimed to develop and psychometrically validate a new Hospital Resilience Index (HRI).MethodsWe took the AHA and RAND databases as primary data sources, and defined hospital closure as the main negative outcome indicating failed resilience. We performed descriptive statistics, and regression analysis of the databases.ResultsOur findings show that a combination of eleven variables is strongly associated with the likelihood of hospital closure. These factors mirror smaller hospital size, lack of ancillary functions, staffing structure of the hospital, size of facilities, number of surgeries performed, Medicare discharges, operating expenses, and medical school affiliation as a teaching hospital. We further classified hospitals with a low HRI (< 25) or high HRI (> 25). In this setup, we found that both hospitals with a low HRI and hospitals that would subsequently close were characterized by smaller patient census, smaller numbers of surgeries, fewer beds, a smaller staff, and a lower operating margin.ConclusionsTogether, these factors would point to a higher resilience to external stressors in larger, more expanded hospitals and healthcare systems, offering a broad range of services, and having a higher operating margin. The higher tiers of the healthcare system therefore seem to have more resilience, but the recent Covid-19 pandemic exhibited how much all tiers in the system are needed to respond to extraordinary crises such as pandemics or large natural disasters. Future research should seek to determine whether tracking the HRI over time may be a tool to identify hospitals at risk of closure.

Teachers' and Student's Perception on Shadow Education in Mathematics Learning

The title of this study was Teachers and Students Perceptions of Shadow Education in Mathematics learning. This study aims to cover the perceptions of teachers and students regarding shadow education. A qualitative research design specifically using narrative interviews, was employed in this study. The main tools of this study were interview and focus group discussion. The primary data source of this study were two community schools and mathematics teachers. Two students and one Teacher from each of the two schools were selected based on purposive sampling . After collecting the data themes were generated through coding. Both positive and negative perception were observed among teachers and students. A positive expectation was that coaching class would enhance students achievements while negative expectation was that regular class attendance might decrease. An improvements in teachers’ economics situations was a positive aspects. Shadow education was also seen to increase students self confidence and enhance use of materials when solving mathematical problem, which were considered beneficial aspects.

Enhancing keratoconus detection with transformer technology and multi-source integration

Keratoconus is a progressive eye disease characterized by the thinning and conical distortion of the cornea, leading to visual impairment. Early and accurate detection is essential for effective management and treatment. Traditional diagnostic methods, relying primarily on corneal topography, often fail to detect early-stage keratoconus due to their subjective nature and limited scope. In this research, we present a novel multi-source detection approach utilizing transformer technology to predict keratoconus progression more accurately. By integrating and analyzing diverse data sources, including corneal topography, aberrometry, pachymetry, and biomechanical properties, our method captures subtle changes indicative of disease progression. Transformer networks, known for their capability to model complex dependencies in data, are employed to handle the multimodal datasets effectively. Experimental results demonstrate that our approach significantly outperforms existing methods, such as SVM-based, Random Forests-based, and CNN-based models, in terms of accuracy, precision, recall, and F-score. Moreover, the proposed system exhibits lower execution times, highlighting its efficiency in clinical settings. This innovative methodology holds the potential to revolutionize keratoconus management by enabling earlier and more precise interventions, ultimately enhancing patient outcomes and contributing significantly to both the medical and machine learning communities.

Occupational risks of COVID-19: a case-cohort study using health insurance claims data in Germany

BackgroundStudies on occupation and COVID-19 infection that cover a range of occupational groups and adjust for important confounders are lacking. This study aimed to estimate occupational risks of hospitalization with COVID-19 by taking into account sociodemographic factors and previous comorbidities.MethodsWe applied a case-cohort design using workers insured with one of Germany’s largest statutory health insurers as a data source for occupational and demographical information as well as for information on comorbidities. Cox regression models with denominator weights for cases and controls assessed relative risks of hospitalization with COVID-19 in 2020.ResultsThe study consisted of 11,202 COVID-19 cases and 249,707 non-cases. After adjusting for age, sex, number of pre-existing comorbidities, and socioeconomic status, we found at least doubled risks for occupations in theology and church work (HR = 3.05; 95% CI 1.93–4.82), occupations in healthcare (HR = 2.74; 95% CI 2.46–3.05), for bus and tram divers (HR = 2.46; 95% CI 2.04–2.97), occupations in meat processing (HR = 2.16; 95% CI 1.57–2.98), and professional drivers in passenger transport (e.g. taxi drivers) (HR = 2.00; 95% CI 1.59–2.51). In addition, occupations in property marketing and management, social workers, laboratory workers, occupations in personal care (e.g. hairdressers), occupations in housekeeping and occupations in gastronomy all had statistically significantly increased risks compared to the reference population (administrative workers).ConclusionsWe identified occupations with increased risks for hospitalization with COVID-19. For those having a doubled risk it can be assumed that COVID-19 diseases are predominantly occupationally related. By identifying high-risk occupations in non-healthcare professions, effective measures to prevent infections in the workplace can be developed, also in case of a future pandemic.

Economic Impact of Bariatric Surgery in Australia: 16-Year Results from the 45 and Up Study with Linked Health Data.

Bariatric surgery is the most effective long-term therapy for severe obesity; however, empirical investigation of its economic impacts has been based on limited samples, short-term costs, and a narrow range of cost categories. This study aimed to evaluate the economic impacts of bariatric surgery in a large cohort of Australians aged ≥ 45years. N = 1157 operated (surgery) and 1157 non-operated participants were selected from the 45 and Up Study. Data sources included the baseline and follow-up surveys along with linked state and national administrative health datasets. Linear mixed-effects regression predicted the cost trajectory 8years pre- and post-bariatric surgery, and the difference-in-differences approach evaluated its economic impact. Sensitivity analyses included an approximation of indirect costs and subgroup analysis by surgery type. The matched cohort composed 77% female, had an average age of 58.1 ± 5.8years. Direct healthcare costs increased over time in both groups. Costs for the operated group peaked ($15,884) during the surgery year and became up to 23.8% lower than those for the non-operated group from the second year post-surgery when including indirect costs. Surgery's economic benefits increased over longer horizons, with a maximum annual cost-saving of $3196 per person in the eighth post-surgery year. However, even after accounting for indirect costs, cumulative cost-savings were not achieved. Subgroup analysis revealed sleeve gastrectomy as the least costly surgical option. Higher short-term costs in the surgery year primarily drove inter-group cost differences. The economic value of bariatric surgery lies in the long-term benefits, particularly when considering indirect costs.

Modern Sources of Controls in Case-Control Studies

Modern Sources of Controls in Case-Control Studies

Implementing high-performance work systems: dynamics of professionalisation in a promoter-driven organisation

Purpose Recent literature on high-performance work systems (HPWS) has questioned the universal success of HPWS and highlighted the need to examine contextual factors and micro-level employee experiences. To address this issue, this study aimed to explore the dynamics of HPWS implementation. Using the case of an Indian public-limited pharmaceutical company transitioning towards professionalisation, it examined the inconsistencies between the intended, enacted and perceived HPWS practices. Design/methodology/approach This study used a case study approach, drawing on multiple data sources, including surveys, interviews with various stakeholders, observations, documents and reports. Quantitative data was gathered through job satisfaction and organisational culture surveys completed by 888 employees. The qualitative interview data was analysed deductively, with themes derived from the existing HPWS literature. Findings The analysis revealed the organisation’s struggle in integrating professionally oriented HPWS practices within its promoter-driven bureaucratic culture. Employees reported a lack of autonomy, poor communication and inequitable opportunities for training and promotion. These cultural misalignments undermined the effectiveness of HPWS, leading to perceptions of exploitation, decreased job satisfaction and reduced trust in management. The findings highlight that HPWS practices cannot be implemented in parts without holistically considering the organisational context and culture. Practical implications The study offers practical guidance to organisations on implementing HPWS effectively. It emphasises the importance of continuously integrating employee feedback into HPWS practices to ensure sustainable performance improvements. Originality/value This study addresses a critical gap in HPWS literature by offering a post-implementation analysis focused on employee experiences within a traditional family firm. It highlights the systemic challenges that hinder the effectiveness of HPWS in practice.

Early publications on the stratigraphy of the northern Pennines at the turn of the 19 th century and the first long stratigraphic sections

Detailed stratigraphic sections recorded by the mining industry in Great Britain were first published during the 18 th century. In the northern Pennines, Forster presented the first representative long composite stratigraphic section in 1809. It encompassed the entire known Carboniferous (as it was later named) succession, with the portion representing the lead mining district summarising ‘ a sequence worked out during many centuries of lead mining .’ Three other shorter sections, also compiled from mining industry data and dating to between 1785 and 1800, had been published previously, with limited circulation. While the lateral continuity of the geology results in a broad similarity across all of the early sections, same-scale comparison combined with contemporary accounts constrains locations of the source data. A recent assertion that an unpublished composite section presented by Walton in 1793 to the Literary and Philosophical Society of Newcastle-on-Tyne was the first ‘ stratigraphical section of the lead mining districts ’ with ‘ Hutchinson, Millar and then Forster later using his work without acknowledgement ’ is shown to be incorrect. The interest of Sir Joseph Banks in the emergence of stratigraphic geology in this period suggests that he may have been the enigmatic ‘A Constant Reader’ who corresponded on the topic of these early sections in the Philosophical Magazine. Forster's 1809 publication was the first concise synthesis of the Pennines Carboniferous, making ‘the miners’ succession’ more widely accessible and providing a basis for later work.