Data Sources Research Articles

A Prerequisite for need of Harmonized Nutrient Reference Values & its Standardized Terminology for Better access of Nutritional Supplements Globally

Abstract: To reduce the risk of insufficient nutrient intake, nutrient reference values for humans are set at the average nutrient need level without a safety margin. The vast majority of nations and areas in the globe set nutritional intake guidelines for their citizens. Currently, no harmonized nutrient reference value has been published, as well as the development of standards cannot be achieved through the use of formalized frameworks or terminology. The study's objective was to create recommendations for appropriate and secure micronutrient intake ranges that could be used to inform dietary advice. As relevant to the study, we examined micronutrient data from food intake surveys from representative nations. The comparison of national standards illustrates that the level of reference values for nutrient for individual countries is dependent on variables such as methodology, data sources, physical activity exemptions, standard body weights, and age range. In the current study, iron, calcium, magnesium, vitamin A, B12, D and folate were identified as the most commonly under-consumed micronutrients. In this article, the level of six countries' standards are compared to the current reference values for nutrients. Further, numerous words for different dietary standards components are discussed, and a final set of harmonized reference nutrient intake for the dietary advice is provided.

Exploring the Relationship between Consumer Protection and Product Liability: Civil and Islamic Perspectives

Consumer protection is an aspect which aims to ensure that consumers are always given adequate protection from a legal perspective and also to increase the level of consumers’ trust in the quality of goods and services, which will, in turn, contribute to the economic growth of a nation. However, today, issues surrounding product liability cases are no longer uncommon, where consumers or third parties face the risk of suffering injury or property damage due to the existence of defects in a particular product. Thus, the main objective of this study is to analyze the relationship between consumer protection and product liability from both civil and Islamic perspectives. The type of research being carried out is doctrinal legal research. The data sources, including case law and statutory provision, are analyzed using content and critical analysis methods. The results show that according to civil and Islamic perspectives, consumer protection in the context of product liability plays a crucial role in conferring legal protection to consumers who are adversely affected by the use of particular products that pose a risk to health and safety. Further, the authors expect that the results of this study shall contribute new and significant knowledge by proving that the aspect of consumer protection is not only related to product liability issues from a civil perspective, but it is also can be evaluated from the Islamic perspective, which the earlier researchers have rarely discussed.

Antenatal care coverage and early childhood mortality in Zimbabwe: new interpretations from nationally representative household surveys.

Zimbabwe has implemented universal antenatal care (ANC) policies since 1980 that have significantly contributed to improvements in ANC access and early childhood mortality rates. However, Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), two of Zimbabwe's main sources of health data and evidence, often provide seemingly different estimates of ANC coverage and under-five mortality rates. This creates confusion that can result in disparate policies and practices, with potential negative impacts on mother and child health in Zimbabwe. We conducted a comparability analysis of multiple DHS and MICS datasets to enhance the understanding of point estimates, temporal changes, rural-urban differences and reliability of estimates of ANC coverage and neonatal, infant and under-five mortality rates (NMR, IMR and U5MR, separately) from 2009 to 2019 in Zimbabwe. Our two samples z-tests revealed that both DHS and MICS indicated significant increases in ANC coverage and declines in IMR and U5MR but only from 2009 to 2015. NMR neither increased nor declined from 2009 to 2019. Rural-urban differences were significant for ANC coverage (2009-15 only) but not for NMR, IMR and U5MR. We found that there is a need for more precise DHS and MICS estimates of urban ANC coverage and all estimates of NMR, IMR and U5MR, and that shorter recall periods provide more reliable estimates of ANC coverage in Zimbabwe. Our findings represent new interpretations and clearer insights into progress and gaps around ANC coverage and under-five mortality rates that can inform the development, implementation, monitoring and evaluation of policy and practice responses and further research in Zimbabwe.

Oculomics: A Crusade Against the Four Horsemen of Chronic Disease.

Chronic, non-communicable diseases present a major barrier to living a long and healthy life. In many cases, early diagnosis can facilitate prevention, monitoring, and treatment efforts, improving patient outcomes. There is therefore a critical need to make screening techniques as accessible, unintimidating, and cost-effective as possible. The association between ocular biomarkers and systemic health and disease (oculomics) presents an attractive opportunity for detection of systemic diseases, as ophthalmic techniques are often relatively low-cost, fast, and non-invasive. In this review, we highlight the key associations between structural biomarkers in the eye and the four globally leading causes of morbidity and mortality: cardiovascular disease, cancer, neurodegenerative disease, and metabolic disease. We observe that neurodegenerative disease is a particularly promising target for oculomics, with biomarkers detected in multiple ocular structures. Cardiovascular disease biomarkers are present in the choroid, retinal vasculature, and retinal nerve fiber layer, and metabolic disease biomarkers are present in the eyelid, tear fluid, lens, and retinal vasculature. In contrast, only the tear fluid emerged as a promising ocular target for the detection of cancer. The retina is a rich source of oculomics data, the analysis of which has been enhanced by artificial intelligence-based tools. Although not all biomarkers are disease-specific, limiting their current diagnostic utility, future oculomics research will likely benefit from combining data from various structures to improve specificity, as well as active design, development, and optimization of instruments that target specific disease signatures, thus facilitating differential diagnoses.

Consumption prediction with privacy concern: Application and evaluation of Federated Learning

In this paper, we propose privacy-friendly electricity consumption prediction models based on Federated Learning (FL). Federated Learning provides a novel framework for Artificial Neural Network training. It decouples data storage from model training from decentralized data sources. A central server does not need to collect raw training data from individual clients but only the model parameters, like the gradients or weights updates. The sensitive raw data can be safely stored and used by end-users themselves. We improve the accuracy of the FL model by first clustering households, and training a personalized model for each cluster. We also analyze the Deep Leakage from Gradients (DLG) attack in our study case with three scenarios. Our simulations suggest that the DLG attack can barely succeed in consumption prediction. In this way, the FL model can guarantee clients’ privacy by design. We use a large-scale dataset that contains 3590 households’ 1.5 years of consumption to test the FL model’s performance. Several clustering algorithms are tested for the following experiments. To comprehensively test the FL model’s performance, we propose several popular neural network models: the simple Deep Neural Network, Long Short-Term Memory, Convolutional Neural Network, and WaveNet. These models are both trained under the centralized and federated framework. The federated trained models slightly sacrifice the model’s accuracy while guaranteeing the client’s data privacy. Meanwhile, the federated model shows remarkable scalability. Under the FL framework, new clients can obtain their prediction 6 times faster. Moreover, the federated model has strong robustness against missing or damaged training data. With a certain percentage of missing data in the training set, the centralized model’s accuracy gets 27% worse, while the federated model’s accuracy only gets 2% worse.

A study of rainfall thresholds for landslides in Badung Regency using satellite-derived rainfall grid datasets

Integrating field rainfall data with satellite data improves data accuracy and overcomes rainfall data limitations for rain thresholds. Integration can involve field rainfall data, satellite rainfall data, or a different satellite dataset. Merging these rainfall data sources provides more spatial coverage of satellite data. To determine how well rainfall thresholds predict rainfall-triggered landslides, the threshold model must be validated. This study will evaluate satellite rainfall data before and after integration in developing a rainfall threshold model for landslide prediction in Badung Regency. To do so, the study used a cumulative rainfall threshold over 3, 7, 15, and 30 days and two rainfall satellite products (integrated merged multi-satellite retrievals (IMERG) and precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)). Median, first, and third quartiles were used to set thresholds. The area under the curve (AUC) was calculated to validate rainfall threshold outcomes using receiver operating characteristic (ROC) curves. Analysis showed that integrating satellite rainfall data into the rainfall threshold model for landslide prediction yields better results than other methods. An AUC value of 0.903 (90.3%) for the 30-day cumulative rainfall thresholds supports this claim. This model could be a good input for a landslide early warning system in Badung Regency.

A Bayesian hierarchical small area population model accounting for data source specific methodologies from American Community Survey, Population Estimates Program, and Decennial census data

A Bayesian hierarchical small area population model accounting for data source specific methodologies from American Community Survey, Population Estimates Program, and Decennial census data

Analyzing electroencephalograph signals for early Alzheimer’s disease detection: deep learning vs. traditional machine learning approaches

Alzheimer’s disease (AD) stands as a progressive neurodegenerative disorder with a significant global public health impact. It is imperative to establish early and accurate diagnoses of AD to facilitate effective interventions and treatments. Recent years have witnessed the emergence of machine learning (ML) and deep learning (DL) techniques, displaying promise in various medical domains, including AD diagnosis. This study undertakes a comprehensive contrast between conventional machine learning methods and advanced deep learning strategies for early AD diagnosis. Conventional ML algorithms like support vector machines, decision trees, and K nearest neighbor have been extensively employed for AD diagnosis through relevant feature extraction from heterogeneous data sources. Conversely, deep learning techniques such as multilayer perceptron (MLP) and convolutional neural networks (CNNs) have demonstrated exceptional aptitude in autonomously uncovering intricate patterns and representations from unprocessed data like EEG data. The findings reveal that while traditional ML methods may perform adequately with limited data, deep learning techniques excel when ample data is available, showcasing their potential for early and precise AD diagnosis. In conclusion, this research paper contributes to the ongoing discourse surrounding the choice of appropriate methodologies for early Alzheimer’s disease diagnosis.

Proactive System Maintenance: Machine Learning Models for Predicting and Preventing Potential System Failures

Abstract: In modern industrial systems, the prevention of failures and downtime is of paramount importance for ensuring efficiency and productivity. Proactive system maintenance approaches leverage machine learning (ML) models to predict potential failures before they occur, enabling pre-emptive actions to be taken [1]. In this paper, we present a comprehensive review of existing research on proactive system maintenance, focusing on the development and application of ML algorithms for fault prediction and prevention. We discuss various machine learning techniques, data sources, feature engineering methods, and evaluation metrics employed in this domain [8]. Furthermore, we propose novel algorithms and strategies for enhancing the effectiveness of proactive maintenance systems. Through experimentation and case studies, we demonstrate the feasibility and benefits of utilizing machine learning for proactive maintenance in diverse industrial settings.

A Comprehensive Analysis of various Covid-19 Detection and Pandemic Prevention Systems and Methods Using Data Science

Abstract: The paper proposes a technique for early Covid-19 test prediction using AI and data science. The study analyzes various data sources to understand the patterns and trends of the pandemic and the impact of various interventions on the spread of the virus. The proposed system integrates data from multiple sources to identify potential cases and can also track the spread of the virus in real-time. The system has high accuracy in predicting positive cases and has the potential to improve our ability to detect and predict Covid-19 cases, ultimately saving lives and reducing the economic impact of the pandemic. The proposed technique for early Covid-19 test prediction using AI and data science can be a game- changer in our fight against the pandemic. With the integration of multiple data sources, including individual health records, social media, and geographic data, the system can identify potential cases even before symptoms appear. This early detection can help individuals get timely medical attention and prevent the spread of the virus to others.

Exploring Sentiment Analysis across Text, Audio, and Video: A Comprehensive Approach and Future Directions

Abstract: This study presents a comprehensive exploration of sentiment analysis techniques across text, audio, and video modalities. Leveraging natural language processing (NLP), speech recognition, and computer vision algorithms, the research demonstrates the versatility and adaptability of sentiment analysis across diverse data sources. The necessity of such an approach lies in its ability to provide deeper insights into user emotions and opinions expressed in various mediums, including written text, spoken language, and visual content. Moreover, the study highlights the importance of sentiment analysis in understanding customer feedback, market trends, social media sentiments, and sentiment-aware recommendation systems. Future directions include advancing algorithmic accuracy and efficiency, integrating multimodal fusion techniques, and exploring applications in diverse domains, thereby paving the way for enhanced sentiment analysis capabilities and broader realworld applications

A Retrospective Database Analysis to Investigate if Electrolytes in Venous Blood are Equivalent to the Levels in Arterial Blood.

In a critically ill patient, when an arterial blood sample is processed on an arterial blood gas (ABG) analyzer, it also measures electrolytes apart from analyzing the blood gases. The turnaround time for ABG analysis is way too less compared to the conventional electrolyte analysis with a serum sample. This study intends to investigate whether values of electrolytes estimated in arterial blood can substitute the routinely practiced method. This is a retrospective cross-sectional study. The source of data is patients' reports of serum electrolytes and ABG analysis from the Clinical Biochemistry laboratory, CIMS Teaching Hospital, Chamarajanagar between January and June 2021. The electrolytes report of 200 patients from whom both arterial and venous blood samples were sent to the Clinical Biochemistry laboratory on the same day and at the same time for analysis were selected. The data was compiled, compared, and correlated using a suitable statistical tool. The mean and standard deviation of sodium (135.62 ± 5.20 in venous vs 134.08 ± 8.49 in arterial blood), potassium (4.20 ± 0.64 vs 3.80 ± 0.75), and chloride (102.28 ± 4.99 vs 96.33 ± 8.11) were observed. However, when the concordance correlation coefficient and Bland-Altman plot analysis were made there was no agreement between electrolytes analyzed on serum in an autoanalyzer with that of ABG analyzer. We conclude that the electrolytes measured by a conventional autoanalyzer on a serum sample cannot be replaced by values analyzed on a blood gas analyzer. Devaki RN, Kasargod P, Roopa Urs AN, Chandrika N. A Retrospective Database Analysis to Investigate if Electrolytes in Venous Blood are Equivalent to the Levels in Arterial Blood. Indian J Crit Care Med 2024;28(5):442-446.

Culture and local wisdom of the lampung indigenous people on the Pesisir Barat in the utilization of the Repong Damar Agroforestry System

Forest resources have a very important role in people's lives, both directly and indirectly. Agroforestry can be an interesting example in the production of timber and non-timber forest products. Agroforestry is an intensive land management system that combines forestry plants and agricultural plants to achieve optimal results, while still paying attention to aspects of land conservation and carrying out cultivation in accordance with the needs of local communities. Indigenous communities on the Pesisir Barat inherit the values of Repong Damar through unique management, which is based on local wisdom of the local area. This research was conducted using a qualitative approach using the research desk method. This study approach seeks to explore data from various sources as secondary data so that the main source of research data is obtained using the research desk method or Secondary Data Analysis (SDA) with qualitative data. Indigenous communities in Pesisir Barat Regency manage Repong Damar by applying traditional values, namely regulated through customary law, inheritance of Repong Damar, and values in managing Repong Damar as a guideline for not destroying forests and continuing to manage them properly. Therefore, granting utilization permits, collaborative implementation, partnerships, and cooperation in empowering indigenous communities through local culture and wisdom in utilizing agroforestry systems carried out around the Repong Damar customary forest can help maintain sustainability and the ecosystem within it.

The Effectiveness of Hammock Positioning in Regulating Behavioral Patterns in Preterm: A Systematic Review

Background- Preterm newborns benefit from supportive developmental techniques such as hammock placement, which give them a sense of being in the womb that promotes calm, relaxation, and deep restful sleep. Objective- To identify and synthesize the available evidence on the effect of hammock positioning in regulating behavioral patterns and sleep in preterm infants admitted to the NICU Data Sources: We searched the Google Scholar, Pubmed, and Scopus databases for reports of primary research studies using a three-step strategy. We also searched for and reviewed the reference lists of retrieved articles. Methods: We included data from randomized controlled trials, Quasi-experimental and Comparative study measuring the effects of hammock positioning on physiological stress outcomes, defined as oxygen saturation, body temperature, heart rate and respiratory rate, Sleep-wakefulness, and behavioral pattern. The PRISMA model was used to conduct data extraction. We performed a narrative synthesis of all studies when data were available from multiple studies. Results: Five studies were eligible for inclusion. Infants who received hammock positioning has shown to have a higher mean heart rate, oxygen saturation and temperature, Sleep-wakefulness and improved behavioral patterns. Conclusions: Current evidence suggests that hammock positioning in the neonatal intensive care unit setting is a safe method that may significantly affect some of the physiological parameters of stress, improved behavioral patterns and Sleep-wakefulness in preterm infants. However, due to clinical heterogeneity, further studies are needed to assess the effects of physiological stress in the neonatal intensive care unit on the development of preterm infants.

Как удовлетворённость жизнью зависит от типа населённого пункта?

Life satisfaction is a multidimensional indicator that is influenced by many factors. Studies of national life satisfaction factors provide adequate estimates when countries are homogeneous “macroregions” with common social, cultural, and institutional foundations. However, within large and heterogeneous national states, of which Russia is one, internal regional differences in living conditions can be significant, which means that assessments of happiness levels are dominated by various factors, including the type of human settlement and its inherent characteristics. The conducted research is deepening the understanding of the factors that have a significant impact on subjective life satisfaction in various types of settlements in Russia - cities, towns, and villages. The Russia Longitudinal Monitoring Survey - Higher School of Economics (RLMS-HSE) was used as the data source. Econometric modeling is based on an ordered logit model with random effects using panel data. We discovered the high significance of subjective evaluation of wealth satisfaction, worried needs, and availability of paid vacations for all Russians, regardless of the type of settlement. The main differences in the set and degree of influence of factors lie along the line separating the values of collectivism and individualism, which are dominant in cities and villages, respectively. There is less concern about health and the possibility of job loss among rural residents reliant on the support of neighbors and family, in which men do not associate life satisfaction with the duration of the working day, and women do not associate life satisfaction with the amount of wage and maternity. In contrast, individualistically oriented residents of cities where nuclear families predominate and connections among people are weak tend to derive life satisfaction from self-realization through earnings, leisure, children, and health care.

Sequential Bayesian Phylogenetic Inference.

The ideal approach to Bayesian phylogenetic inference is to estimate all parameters of interest jointly in a single hierarchical model. However, this is often not feasible in practice due to the high computational cost. Instead, phylogenetic pipelines generally consist of sequential analyses, whereby a single point estimate from a given analysis is used as input for the next analysis (e.g., a single multiple sequence alignment is used to estimate a gene tree). In this framework, uncertainty is not propagated from step to step, which can lead to inaccurate or spuriously confident results. Here, we formally develop and test a sequential inference approach for Bayesian phylogenetic inference, which uses importance sampling to generate observations for the next step of an analysis pipeline from the posterior distribution produced in the previous step. Our sequential inference approach presented here not only accounts for uncertainty between analysis steps, but also allows for greater flexibility in software choice (and hence model availability) and can be computationally more efficient than the traditional joint inference approach when multiple models are being tested. We show that our sequential inference approach is identical in practice to the joint inference approach only if sufficient information in the data is present (a narrow posterior distribution) and/or sufficiently many importance samples are used. Conversely, we show that the common practice of using a single point estimate can be biased, e.g., a single phylogeny estimate to transform an unrooted phylogeny into a time-calibrated phylogeny. We demonstrate the theory of sequential Bayesian inference using both a toy example and an empirical case study of divergence-time estimation in insects using a relaxed clock model from transcriptome data. In the empirical example, we estimate three posterior distributions of branch lengths from the same data (DNA character matrix with a GTR+Γ+I substitution model, an amino acid data matrix with empirical substitution models, and an amino acid data matrix with the PhyloBayes CAT-GTR model). Finally, we apply three different node-calibration strategies and show that divergence-time estimates are affected by both the data source and underlying substitution process to estimate branch lengths as well as the node-calibration strategies. Thus, our new sequential Bayesian phylogenetic inference provides the opportunity to efficiently test different approaches for divergence time estimation, including branch-length estimation from other software.

Standardizing Fidelity to Evidence-Based Practice in the Early Psychosis Intervention Network.

This study investigated variations in the measurement of fidelity to coordinated specialty care (CSC) within the Early Psychosis Intervention Network (EPINET), a learning health system that consists of 101 CSC programs within eight hubs. The study investigated the degree to which five fidelity scales could be mapped onto a standard scale. The investigators identified six fidelity scales in use by EPINET participants; examined their item content, scoring, and data sources; and mapped five scales onto the First Episode Psychosis Services Fidelity Scale (FEPS-FS), which is the most widely used scale. Mapping five fidelity scales onto the FEPS-FS showed that the percentage of FEPS-FS components successfully mapped ranged from 42% to 81%. Mapping five scales onto one that uses dichotomous scoring identified the degree of variation in measures and reduced the amount and quality of usable fidelity data. Identifying variations in fidelity measurement is a core function of a learning health system.

WuMKG: a Chinese painting and calligraphy multimodal knowledge graph

Chinese Painting and Calligraphy (ChP&C) holds significant cultural value, representing integral aspects of both Chinese culture and global art. A considerable amount of ChP&C works are dispersed worldwide. With the emergence of digital humanities, a vast collection of cultural artifacts data is now available online. However, the online databases of these artifacts remain decentralized and diverse, posing significant challenges to their effective organization and utilization. Addressing this, our paper focuses on the Wu Men School of Painting and proposes a framework for constructing a multimodal knowledge graph for the ChP&C domain. We construct the domain ontology by analyzing the ChP&C knowledge schema. Then, we acquire knowledge from diverse data sources, including textual and visual information. To enhance data collection around collecting historical context and subject matter, we propose methods for seal extraction and subject extraction specific to ChP&C. We validate the effectiveness of these methods on the constructed dataset. Finally, we construct the Wu Men Multimodal Knowledge Graph (WuMKG) and implement applications such as cross-modal retrieval, knowledge-based question-answering and visualization.

The future of academic librarianship: the imperatives of embedded librarianship practice and implementation

PurposeThe present study discusses the future of academic librarianship, emphasizing the essential aspects of embedded librarianship practice and implementation. In addition, it also explores the changing role of academic librarians, addressing challenges and opportunities in various contexts and highlighting the significance of embedded librarianship practice.Design/methodology/approachThe study employs a literature review to understand the significance of embedded librarianship and its implementation in academic libraries. The researchers searched a range of electronic data sources, including Emerald Insight, Taylor and Francis, Science Direct, Web of Science, EBSCOhost, Scopus, Information Science and Technology Abstracts (LISTA), Library and Information Science Abstracts (LISA), etc. Moreover, only peer-reviewed articles were considered for the review.FindingsThe study identified five major competencies, i.e. communication and interpersonal skills, teaching, research, collaboration and coordination and technological expertise, that are essential for implementing embedded librarianship in the academic library. However, in developing countries, there are various challenges faced by academic librarians such as inadequate ICT infrastructure, resource accessibility, cooperations, cultural diversity and a lack of financial support from the authority were major barriers to the successful implementation of embedded librarianship.Practical implicationsThe study findings are of considerable value to academic librarians, providing them with the opportunity to assess and evaluate their competencies and acquire the essential skills for implementing embedded librarianship. Further, the study also recommended some practical implications of embedded librarianship practices in the academic environment.Originality/valueThe findings and insights from this investigation provide a valuable perspective on the future of academic librarianship, shedding light on the critical role that embedded librarianship plays in shaping its direction.

Spatio‐temporal data integration for species distribution modelling in R‐INLA

Abstract Species distribution modelling is a highly used tool for understanding and predicting biodiversity change, and recent work has emphasised the importance of understanding how species distributions change over both time and space. Spatio‐temporal models require large amounts of data spread over time and space, and as such are clear candidates to benefit from model‐based integration of different data sources. However, spatio‐temporal models are highly computationally intensive and integrating different data sources can make this approach even more unfeasible to ecologists. Here we demonstrate how the R‐INLA methodology can be used for model‐based data integration for spatio‐temporally explicit modelling of species distribution change. We demonstrate that this method can be applied to both point and areal data with two contrasting case studies, one using the SPDE approach for modelling spatio‐temporal change in the Gatekeeper butterfly (Pyronia tithonus) across Great Britain and the second using a spatio‐temporal areal model to describe change in caddisfly (Trichoptera) populations across the River Thames catchment. We show that in the caddisfly case study integrating together different data sources led to greater understanding of the change in abundance across the River Thames both seasonally and over 5 years of data. However, in the butterfly case study moving to a spatio‐temporal context exacerbated differences between the data sources and resulted in no greater ecological insight into change in the Gatekeeper population. Our work provides a computationally feasible framework for spatio‐temporally explicit integration of data within SDMs and demonstrates both the potential benefits and the challenges in applying this methodology to real ecological data.