Multiple Data Research Articles

Improvement of the Surveillance Protocols After Experiencing a Pseudo-Epidemic After 2018 Central Sulawesi Earthquake in Indonesia.

Following a disaster, a pseudo-epidemic can occur due to redundant and duplicated data caused by infrastructure and information system disruptions. This study aims to investigate whether there have been improvements the post-disaster surveillance system in the comparison of diarrhea incidents between Central Sulawesi, Indonesia, and Cianjur, West Java, Indonesia. We conducted an analysis of the epidemic-prone disease diarrhea before and during disasters, comparing the data with secondary data from the Early Warning Alert and Response System (EWARS) and the District Health Information System V.02 (DHIS-2). In central Sulawesi in 2018 and Cianjur in 2022, we observed an upsurge in diarrhea cases in the first week after the disaster. Although diarrhea cases increased after the disaster, they remained within acceptable outbreak criteria. Multiplication and redundant data were detected in the DHIS-2 system in Central Sulawesi, likely leading to erroneous overreporting. Changes in surveillance officers and their personal experiences during the disaster contributed to data inconsistencies. As compared to Central Sulawesi, the DHIS-2 reporting form in Cianjur was simplified as an individual form to enhance efficiency and accuracy. Enhancing valid assessment and conducting thorough investigations are essential to improve surveillance protocols for epidemic-prone diseases following disasters.

Prediction of Individual Learning Experience in Terms of “Number Of Steps” as Solution Components of Arithmetic Tasks

The paper is devoted to the study of learning in solving arithmetic problems in the conditions of digital learning. Learning is considered as a functional system, the level of differentiation of which can be related to the number of steps in problem solving. The goal of the paper is to find out the model that could more accurately describe the relationships between the number of steps and task types on Addition data and predict the number of steps on Multiplication data. The hypothesis is that with a given similarity, there is a correlation between the number of steps in solution of addition and multiplication tasks. We have created two experimental courses, “Addition” and “Multiplication” to make participants learn optimal methods of calculation of arithmetic tasks. The courses have the same structure and belong to a common domain (arithmetic tasks). It is a condition for similarity of learning. We have found out significant positive correlation between the number of steps in solution of addition and multiplication tasks on average for the sample. We have used a regression-based classification. Few models have been built for each individual personally and trained on the Addition data, then applied on the Multiplication data. The best of these models correctly predict the number of steps in 33–40% of tasks (SD = 17–22%, max = 88%), in other tasks they give a prediction with a small error of 1–2 units, which indicates its medium predictive ability.

Ontology-based GraphQL server generation for data access and data integration

In a GraphQL Web API, a so-called GraphQL schema defines the types of data objects that can be queried, and so-called resolver functions are responsible for fetching the relevant data from underlying data sources. Thus, we can expect to use GraphQL not only for data access but also for data integration, if the GraphQL schema reflects the semantics of data from multiple data sources, and the resolver functions can obtain data from these data sources and structure the data according to the schema. However, there does not exist a semantics-aware approach to employ GraphQL for data integration. Furthermore, there are no formal methods for defining a GraphQL API based on an ontology. In this work, we introduce a framework for using GraphQL in which a global domain ontology informs the generation of a GraphQL server that answers requests by querying heterogeneous data sources. The core of this framework consists of an algorithm to generate a GraphQL schema based on an ontology and a generic resolver function based on semantic mappings. We provide a prototype, OBG-gen, of this framework, and we evaluate our approach over a real-world data integration scenario in the materials design domain and two synthetic benchmark scenarios (Linköping GraphQL Benchmark and GTFS-Madrid-Bench). The experimental results of our evaluation indicate that: (i) our approach is feasible to generate GraphQL servers for data access and integration over heterogeneous data sources, thus avoiding a manual construction of GraphQL servers, and (ii) our data access and integration approach is general and applicable to different domains where data is shared or queried via different ways.

Recalculation of the Human Development Index via Multiplicative Data Envelopment Analysis

Recalculation of the Human Development Index via Multiplicative Data Envelopment Analysis

Long-Run Behavior Estimation of Temporal Boolean Networks With Multiple Data Losses.

This brief devotes to investigating the long-run behavior estimation of temporal Boolean networks (TBNs) with multiple data losses, especially the asymptotical stability. The information transmission is modeled by Bernoulli variables, based on which an augmented system is constructed to facilitate the analysis. A theorem guarantees that the asymptotical stability of the original system can be converted to that of the augmented system. Subsequently, one necessary and sufficient condition is obtained for asymptotical stability. Furthermore, an auxiliary system is derived to study the synchronization issue of the ideal TBNs with normal data transmission and TBNs with multiple data losses, as well as an effective criterion for verifying synchronization. Finally, numerical examples are given to illustrate the validity of the theoretical results.

Identification of Mitophagy-Related Genes in Sepsis

Background: Numerous studies have shown that mitochondrial damage induces inflammation and activates inflammatory cells, leading to sepsis, while sepsis, a systemic inflammatory response syndrome, also exacerbates mitochondrial damage and hyperactivation. Mitochondrial autophagy eliminates aged, abnormal or damaged mitochondria to reduce intracellular mitochondrial stress and the release of mitochondria-associated molecules, thereby reducing the inflammatory response and cellular damage caused by sepsis. In addition, mitochondrial autophagy may also influence the onset and progression of sepsis, but the exact mechanisms are unclear. Methods: In this study, we mined the available publicly available microarray data in the GEO database (Home - GEO - NCBI (nih.gov)) with the aim of identifying key genes associated with mitochondrial autophagy in sepsis. Results: We identified four mitophagy-related genes in sepsis, TOMM20, TOMM22, TOMM40, and MFN1. Conclusion: This study provides preliminary evidence for the treatment of sepsis and may provide a solid foundation for subsequent biological studies.

A characterization method for reprocessed spent nuclear fuel through neutron and gamma-ray multiplicity counting

It is difficult to measure neutrons and gamma rays coming directly from uranium and plutonium atoms in pyroprocessed ingot, which is of great interest to nuclear safeguards. Most neutrons of the ingot are emitted from 244Cm isotope and gamma rays from its fission products. Therefore, it is necessary to identify spent fuel characteristics such as initial enrichment, burnup and cooling time from measurement and estimate plutonium mass from calibration curve or burnup calculations.In this study, we explored the possibility of characterizing pyroprocessed ingots using neutron and gamma-ray multiplicity counting. We particularly focused on finding the possible correlation between neutron and gamma-ray multiplicity properties (singles, doubles, and triples) and spent fuel properties for U/TRU/RE preprocessed ingot. We examined neutron and gamma-ray correlation for spent fuel, using the Cascading Gamma-ray Multiplicity with Fission (CGMF) and the Fission Reaction Event Yield Algorithm (FREYA) library in MCNP6.2. Due to high spontaneous fission rate, neutron and gamma-ray multiplicity parameters did not strongly correlate with multiplication factor from the point model but was proportional to 244Cm mass. The results based on the CGMF and FREYA fission library showed the similar trends for neutron and gamma-ray cases. The sum of two multiplicity data yielded best linearity with the 244Cm effective mass. Although total neutron counting (S) alone was sufficient for effectively characterizing spent fuel due to its strong linearity, this feasibility study confirmed that, in principle, it will be more effective to calculate the sum of neutron and gamma-ray multiplicity properties to directly estimate the mass of 244Cm, rather than relying on the conventional method of calculating the multiplication factor.

Contrastive Disentanglement for Quantitative Ultrasound Muscle Atrophy Evaluation.

Muscleatrophy reduces the quality of life and increases morbidity and mortality from other diseases. The development of non-invasive muscle atrophy evaluation method is of great practical value. The lack of gold standard for pathological grading usually allows only the duration of weightlessness as a criterion for the degree of atrophy. However, the adaptive reductive remodeling of muscle physiology and structure shows a trend of nonlinear changes in time. Consequently, using weightlessness time as a benchmark for the degree of atrophy is inaccurate. This paper proposes a new ultrasound imaging-based method for quantifying muscle atrophy that utilizes weakly supervised information between multiple data partitions with controlled variance components, overcoming the limitations of using the weightlessness time as a criterion. We introduce a group-supervised contrastive disentanglement network (GCDNet) to disentangle the individual variances, muscle growth and atrophy components of ultrasound images, and quantify the degree of atrophy using the disentangled atrophy component. The feasibility of GCDNet is validated by the separability, independence, and representativeness of the disentangled components. To simplify the application of GCDNet, a muscle atrophy scoring network requiring no reference images is developed by distilling the GCDNet's knowledge of muscle atrophy quantization. The strength of the proposed methodology allows us, for the first time to our knowledge, to study the muscle growth attribute during hind-limb unloading and the spatial distribution of muscle atrophy.

Synergistic evaluation of energy security and environmental sustainability in BRICS geo-political entities: An integrated index framework

Research background: The increasing demand for energy, driven by economic growth and population expansion, is a critical driver of societal progress. However, the predominant reliance on fossil fuels to meet this demand presents significant challenges, particularly in the rapidly developing BRICS nations (Brazil, Russia, India, China, and South Africa). These countries are faced with a complex interplay of energy security and environmental sustainability issues, stemming from their substantial fossil fuel reserves and the associated environmental consequences. The challenges manifest as inequalities in access to clean energy, environmental degradation, and heightened vulnerability to the impacts of climate change. Addressing these multifaceted issues requires a comprehensive approach. Metrics-based strategies, which employ aggregated indices derived from a diverse set of energy and environmental indicators, have the potential to provide valuable insights into these complexities. However, the development of a universally applicable energy sustainability index is complicated by the heterogeneity of metrics, disparities between countries, and methodological challenges, emphasizing the need for an innovative and holistic analytical framework. Purpose of the article: This study aims to develop a tailored Energy Security and Environmental Sustainability Index for BRICS economies to evaluate the robustness of their energy systems and the viability of their ecological practices. The index serves as an instrument to assess the progress of these nations in the Energy and Environment domain and identify areas that require targeted interventions and improvements. Methods: The construction of the composite ESESI involves the selection of relevant parameters and the application of a Multi-Criteria Decision Analysis (MCDA) framework in conjunction with the Weighted Product Method (WPM). To ensure objectivity in the determination of optimal and least favorable weights for each indicator, the study employs the Multiplicative Data Envelopment Analysis (MDEA) model. Findings & value added: The ESESI analysis reveals disparities in the progress made by BRICS nations in enhancing energy security, promoting renewable energy deployment, and mitigating environmental impacts. While some countries demonstrate substantial advancements, others face challenges in improving energy efficiency and reducing carbon emissions. The study underscores the necessity for tailored policies and targeted infrastructural enhancements that align with the unique challenges and strengths of each nation. Harnessing the abundant renewable energy potential through advanced energy trade mechanisms and fostering cross-border investments are identified as crucial strategies for ensuring environmental sustainability and long-term energy in the BRICS region. The ESESI provides a valuable tool for policymakers and researchers to evaluate the progress of BRICS nations in achieving sustainable energy goals and to inform evidence-based decision-making processes. By offering a comprehensive and scientifically rigorous assessment framework, this study contributes to the ongoing discourse on sustainable energy transitions and environmental stewardship in the context of rapidly developing economies.

Graph-Collaborated Auto-Encoder Hashing for Multiview Binary Clustering.

Unsupervised hashing methods have attracted widespread attention with the explosive growth of large-scale data, which can greatly reduce storage and computation by learning compact binary codes. Existing unsupervised hashing methods attempt to exploit the valuable information from samples, which fails to take the local geometric structure of unlabeled samples into consideration. Moreover, hashing based on auto-encoders aims to minimize the reconstruction loss between the input data and binary codes, which ignores the potential consistency and complementarity of multiple sources data. To address the above issues, we propose a hashing algorithm based on auto-encoders for multiview binary clustering, which dynamically learns affinity graphs with low-rank constraints and adopts collaboratively learning between auto-encoders and affinity graphs to learn a unified binary code, called graph-collaborated auto-encoder (GCAE) hashing for multiview binary clustering. Specifically, we propose a multiview affinity graphs' learning model with low-rank constraint, which can mine the underlying geometric information from multiview data. Then, we design an encoder-decoder paradigm to collaborate the multiple affinity graphs, which can learn a unified binary code effectively. Notably, we impose the decorrelation and code balance constraints on binary codes to reduce the quantization errors. Finally, we use an alternating iterative optimization scheme to obtain the multiview clustering results. Extensive experimental results on five public datasets are provided to reveal the effectiveness of the algorithm and its superior performance over other state-of-the-art alternatives.

Are children with disabilities more likely to be malnourished than children without disabilities? Evidence from the Multiple Indicator Cluster Surveys in 30 countries

IntroductionA key Sustainable Development Goal target is to eliminate all forms of malnutrition. Existing evidence suggests children with disabilities are at greater risks of malnutrition, exclusion from nutrition programmes and...

A Survey of Visual Transformers.

Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP). Inspired by such significant achievements, some pioneering works have recently been done on employing Transformer-liked architectures in the computer vision (CV) field, which have demonstrated their effectiveness on three fundamental CV tasks (classification, detection, and segmentation) as well as multiple sensory data stream (images, point clouds, and vision-language data). Because of their competitive modeling capabilities, the visual Transformers have achieved impressive performance improvements over multiple benchmarks as compared with modern convolution neural networks (CNNs). In this survey, we have reviewed over 100 of different visual Transformers comprehensively according to three fundamental CV tasks and different data stream types, where taxonomy is proposed to organize the representative methods according to their motivations, structures, and application scenarios. Because of their differences on training settings and dedicated vision tasks, we have also evaluated and compared all these existing visual Transformers under different configurations. Furthermore, we have revealed a series of essential but unexploited aspects that may empower such visual Transformers to stand out from numerous architectures, e.g., slack high-level semantic embeddings to bridge the gap between the visual Transformers and the sequential ones. Finally, two promising research directions are suggested for future investment. We will continue to update the latest articles and their released source codes at https://github.com/liuyang-ict/awesome-visual-transformers.

Multimodal Image Classification by Multiview Latent Pattern Extraction, Selection, and Correlation.

The large amount of data available in the modern big data era opens new opportunities to expand our knowledge by integrating information from heterogeneous sources. Multiview learning has recently achieved tremendous success in deriving complementary information from multiple data modalities. This article proposes a framework called multiview latent space projection (MVLSP) to integrate features extracted from multiple sources in a discriminative way to facilitate binary and multiclass classifications. Our approach is associated with three innovations. First, most existing multiview learning algorithms promote pairwise consistency between two views and do not have a natural extension to applications with more than two views. MVLSP finds optimum mappings from a common latent space to match the feature space in each of the views. As the matching is performed on a view-by-view basis, the framework can be readily extended to multiview applications. Second, feature selection in the common latent space can be readily achieved by adding a class view, which matches the latent space representations of training samples with their corresponding labels. Then, high-order view correlations are extracted by considering feature-label correlations. Third, a technique is proposed to optimize the integration of different latent patterns based on their correlations. The experimental results on the prostate image dataset demonstrate the effectiveness of the proposed method.

Robust Representation Learning for Power System Short-Term Voltage Stability Assessment Under Diverse Data Loss Conditions.

With the help of neural network-based representation learning, significant progress has been recently made in data-driven online dynamic stability assessment (DSA) of complex electric power systems. However, without sufficient attention to diverse data loss conditions in practice, the existing data-driven DSA solutions' performance could be largely degraded due to practical defective input data. To address this problem, this work develops a robust representation learning approach to enhance DSA performance against multiple input data loss conditions in practice. Specifically, focusing on the short-term voltage stability (SVS) issue, an ensemble representation learning scheme (ERLS) is carefully designed to achieve data loss-tolerant online SVS assessment: 1) based on an efficient data masking technique, various missing data conditions are handled and augmented in a unified manner for lossy learning dataset preparation; 2) the emerging spatial-temporal graph convolutional network (STGCN) is leveraged to derive multiple diversified base learners with strong capability in SVS feature learning and representation; and 3) with massive SVS scenarios deeply grouped into a number of clusters, these STGCN-enabled base learners are distinctly assembled for each cluster via multilinear regression (MLR) to realize ensemble SVS assessment. Such a divide-and-conquer ensemble strategy results in highly robust SVS assessment performance when faced with various severe data loss conditions. Numerical tests on the benchmark Nordic test system illustrate the efficacy of the proposed approach.

No genetic causality between obesity and benign paroxysmal vertigo: A two-sample Mendelian randomization study.

We applied Mendelian randomization to explore the causal relationship between obesity and benign paroxysmal vertigo (BPV). We chose two types of obesity diseases. Obesity due to excessive calories and other or unspecified obesity from the FinnGen database. We used genomic significance (p < 5 × 10-8) to obtain independent single nucleotide polymorphisms (SNPs) as instrumental variables. Similarly, genome-wide association study data for the disease BPV were selected from the FinnGen database. R was then used to test the data for multiplicity and heterogeneity, as well as to detect the effect of individual SNPs on the results. Random effects inverse variance weighting was used as the main statistical analysis. First, by analyzing, we found an outlier in obesity due to excessive calories (rs12956821). Outliers were then removed, and the statistical results were analyzed without heterogeneity (p > 0.05) and horizontal pleiotropy (p > 0.05), as well as individual SNPs having no effect on the results. Meanwhile, random-effects IVW results showed obesity due to excessive calories (p = 0.481; OR = 0.941), and other or unspecified obesity (p = 0.640; OR = 0.964). The present study did not find a causal relationship between the above two obesity types and BPV at the genetic level.

FedCAE: A New Federated Learning Framework for Edge-Cloud Collaboration Based Machine Fault Diagnosis

With the coming of the industrial big data era, data-driven fault diagnosis models emerge recently and show potential results in many studies. However, it is impractical to collect massive high-quality data in actual industrial applications, which strongly reduces the performance of fault diagnosis models. Therefore, multiple data owners are motivated to share their own data for training a brilliant model with the aggregated dataset. Notwithstanding, it is unrealistic to share data due to the potential conflict of interest. For addressing the issue of data island, a new federated framework namely FedCAE is proposed for fault diagnosis. In this framework, each client is equipped with a convolutional autoencoder (CAE) trained by its corresponding local data. Then, all CAEs are uploaded to a server and aggregated to a global CAE according to an adaptive weighted selection mechanism. Afterwards, the global CAE is downloaded to all clients for extracting low-level features from local datasets by its encoder and uploading these features to train a global fault diagnosis classifier in the server. At last, the classifier is downloaded to all clients for completing their own diagnosis tasks. Experiment results on two bearing datasets suggest FedCAE offers a promising solution on confidential decentralized learning.

An Approach to the Performance of Passport Services through Workload and Work Environment: Study at the Wonosobo Immigration Office

This study aims to identify the effect of workload and work environment on the performance of passport issuance services at the Wonosobo Immigration Office. Workload is defined as a series of tasks that must be carried out by a work unit within an organization or by an individual who serves in a position within a certain period of time. Meanwhile, the work environment is defined as a place where a worker completes his duties in accordance with the goals to be achieved. There is a mismatch in the number of human resources in passport services compared to the high level of passport applications. The research method used in this research is quantitative method. Data collection was carried out through observation and distribution of questionnaires. Sampling using total sampling techniques, and data analysis was carried out using a Likert scale. The data analysis technique applied in this study is using multiple linear regression data analysis techniques. The results of data collection show the significance of workload of 0.32 and work environment of 0.21 greater than 0.05, which means that there is an influence of workload and work environment on employee performance in passport services. This study recommends increasing the number of human resources and improving the work environment to improve the performance of passport issuance services.

Semi-automated computer vision-based tracking of multiple industrial entities: a framework and dataset creation approach

This contribution presents the TOMIE framework (Tracking Of Multiple Industrial Entities), a framework for the continuous tracking of industrial entities (e.g., pallets, crates, barrels) over a network of, in this example, six RGB cameras. This framework makes use of multiple sensors, data pipelines, and data annotation procedures, and is described in detail in this contribution. With the vision of a fully automated tracking system for industrial entities in mind, it enables researchers to efficiently capture high-quality data in an industrial setting. Using this framework, an image dataset, the TOMIE dataset, is created, which at the same time is used to gauge the framework’s validity. This dataset contains annotation files for 112,860 frames and 640,936 entity instances that are captured from a set of six cameras that perceive a large indoor space. This dataset out-scales comparable datasets by a factor of four and is made up of scenarios, drawn from industrial applications from the sector of warehousing. Three tracking algorithms, namely ByteTrack, Bot-Sort, and SiamMOT, are applied to this dataset, serving as a proof-of-concept and providing tracking results that are comparable to the state of the art.

Fuzzy convolutional neural network model applied to classification problems

This paper presents an approach incorporating fuzzy logic techniques inside a convolutional neural network to manage uncertainty present in the multiple data sources that the model handles when training. The implementation considers the use of information and filters in the fuzzy spectrum, as well as the creation of a new layer to replace the traditional convolution layer with a fuzzy convolutional layer. The aim is to design artificial intelligence algorithms that combine the potential of deep convolutional neural networks and fuzzy logic to create robust systems that allow modeling the uncertainty present in the sources of data and that are applied to classification problems. The fuzzification process is developed using three membership functions, including the Triangular, Gaussian, and S functions. The work was tested in databases oriented to traffic signs, due to the complexity of the different circumstances and factors in which a traffic sign can be found.

A Self-consistent Data-driven Model for Determining Stellar Parameters from Optical and Near-infrared Spectra

Data-driven models, which apply machine learning to infer physical properties from large quantities of data, have become increasingly important for extracting stellar properties from spectra. In general, these methods have been applied to data in one wavelength regime or another. For example, APOGEE Net has been applied to near-IR spectra from the Sloan Digital Sky Survey (SDSS)–V APOGEE survey to predict stellar parameters (T eff, log g, and [Fe/H]) for all stars with T eff from 3000 to 50,000 K, including pre-main-sequence stars, OB stars, main-sequence dwarfs, and red giants. The increasing number of large surveys across multiple wavelength regimes provides the opportunity to improve data-driven models through learning from multiple data sets at once. In SDSS-V, a number of spectra of stars will be observed not just with APOGEE in the near-IR, but also with BOSS in the optical regime. Here, we aim to develop a complementary model, BOSS Net, that will replicate the performance of APOGEE Net in these optical data through label transfer. We further improve the model by extending it to brown dwarfs, as well as white dwarfs, resulting in a comprehensive coverage between 1700 < T eff < 100,000 K and 0 < logg < 10, to ensure BOSS Net can reliably measure parameters of most of the commonly observed objects within this parameter space. We also update APOGEE Net to achieve a comparable performance in the near-IR regime. The resulting models provide a robust tool for measuring stellar evolutionary states, and, in turn, enable characterization of the star-forming history of the Galaxy.