Data Integration Research Articles

"FLOODGUARD: An IoT-Powered Real-Time Flash Flood Monitoring and Forecasting System"

Abstract: In recent times, the surge in flooding incidents has become a pressing global issue, inflicting extensive damage on both lives and livelihoods. While floods remain an inevitable force of nature, their devastating impact can be alleviated. With the aid of cutting-edge technologies like the Internet of Things (IoT), proactive flood forecasting has become increasingly feasible. Through the seamless integration of IoT data, populations can receive early warnings and evacuate to safety, safeguarding both lives and valuables. A real-time solution is imperative, leveraging IoT data streams to deliver timely flood alerts. Our study presents an IoT-driven prototype designed to collect hydrological data from rivers, encompassing metrics such as water flow, level, and discharge, alongside meteorological parameters like temperature, humidity, wind speed, and direction. Employing a Long Short-Term Memory (LSTM) model, we analyze and classify collected data based on water discharge, level, rainfall, and temperature, predicting flood events as "no alert," "yellow alert," "orange alert," or "red alert." Given the dynamic nature of rivers, accurately measuring total water discharge poses challenges. To address this, we propose a novel methodology utilizing water flow, sectional area width, and average depth. Furthermore, we tackle the complexities associated with accurately measuring rainfall due to erratic weather conditions. Our system demonstrates robust performance in predicting flood event states, achieving high F1-scores of 97% for "no alert," 97% for "yellow alert," 96% for "orange alert," and 98% for "red alert.

Decision Making on a Software Upgrade or Decommission with Data Mining and Machine Learning Techniques in Information Technology Industry

The Organizations have been investing more in Technology and Infrastructure spends like software upgrades, software renewals, software replacements, platform migrations etc., apart from investment in Business, People, and Processes. In this context, it is not an easy task for stakeholders to decide whether to go for a software upgrade or to replace it with another software. There is no unified approach or solution to consolidate data and relationships of Information Technology Assets, Software Upgrades, Software costs, Software defects, Software Performance Metrics, Security issues, IT system versions, service level objectives etc. Due to this, the decision making of software upgrades and software decommissioning is a tedious process and takes more time and effort. There is a need to build a solution that can integrate and validate the information like software assets, software upgrade success and failure likelihoods, cost benefit analysis of Cloud Computing, software metrics for fault prediction, software maintainability prediction results, Digital Transformation readiness and other related factors. There is an opportunity to apply Machine Learning techniques in defining and deriving the success likelihoods on the following data: Systems and data integration, software assets compatibility, operational service level agreement breaches, quality assurance metrics, security issues, number of open defects, number of defect fixes, number of priority incidents, mean time to resolve critical incidents, expected cost increase in software maintenance, potential cost reduction with the software or hardware replacement etc. This Research Proposal outlines the above mentioned to build a recommendation system aka decision tree namely Software Upgrades or Decommissions Life Cycle.

Experiences, Lessons, and Challenges With Adapting REDCap for COVID-19 Laboratory Data Management in a Resource-Limited Country: Descriptive Study.

The COVID-19 pandemic brought challenges requiring timely health data sharing to inform accurate decision-making at national levels. In Botswana, we adapted and integrated the Research Electronic Data Capture (REDCap) and the District Health Information System version 2 (DHIS2) platforms to support timely collection and reporting of COVID-19 cases. We focused on establishing an effective COVID-19 data flow at the national public health laboratory, being guided by the needs of health care professionals at the National Health Laboratory (NHL). This integration contributed to automated centralized reporting of COVID-19 results at the Ministry of Health (MOH). This paper reports the experiences, challenges, and lessons learned while designing, adapting, and implementing the REDCap and DHIS2 platforms to support COVID-19 data management at the NHL in Botswana. A participatory design approach was adopted to guide the design, customization, and implementation of the REDCap platform in support of COVID-19 data management at the NHL. Study participants included 29 NHL and 4 MOH personnel, and the study was conducted from March 2, 2020, to June 30, 2020. Participants' requirements for an ideal COVID-19 data management system were established. NVivo 11 software supported thematic analysis of the challenges and resolutions identified during this study. These were categorized according to the 4 themes of infrastructure, capacity development, platform constraints, and interoperability. Overall, REDCap supported the majority of perceived technical and nontechnical requirements for an ideal COVID-19 data management system at the NHL. Although some implementation challenges were identified, each had mitigation strategies such as procurement of mobile Internet routers, engagement of senior management to resolve conflicting policies, continuous REDCap training, and the development of a third-party web application to enhance REDCap's capabilities. Lessons learned informed next steps and further refinement of the REDCap platform. Implementation of REDCap at the NHL to streamline COVID-19 data collection and integration with the DHIS2 platform was feasible despite the urgency of implementation during the pandemic. By implementing the REDCap platform at the NHL, we demonstrated the possibility of achieving a centralized reporting system of COVID-19 cases, hence enabling timely and informed decision-making at a national level. Challenges faced presented lessons learned to inform sustainable implementation of digital health innovations in Botswana and similar resource-limited countries.

Digital Marketing Strategies to Improve Customer Experience and Engagement

The study explores the different digital marketing strategies that companies can use in diverse industries to improve customer experience and engagement. The study involves a combination of literature review, survey, interviews, and case study which uncovers the critical findings on efficient ways of implementing customer satisfaction, loyalty, and trust. The research results showed that personalization is the most fundamental issue it was highlighted by nearly 80% of respondents as an important aspect of digital marketing campaigns. The expert panel notes that social media and interactive content play a critical role in engaging customers and creating brand ambassadorship. Such pieces of work represent the omnichannel marketing campaigns that have been successfully implemented and showcase the need for smooth integration across marketing channels. The study acknowledges the existence of barriers like organizational silos and data integration but also points out the possibilities of using upcoming technologies to create interactive customer experiences.

Shared genetic aetiology of Alzheimer’s disease and age-related macular degeneration by APOC1 and APOE genes

BackgroundAlzheimer’s disease (AD) and age-related macular degeneration (AMD) share similar pathological features, suggesting common genetic aetiologies between the two. Investigating gene associations between AD and AMD may provide useful insights...

Integrating Multi-omics Data for Alzheimer's Disease to Explore Its Biomarkers Via the Hypergraph-Regularized Joint Deep Semi-Non-Negative Matrix Factorization Algorithm.

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder. Its etiology may be associated with genetic, environmental, and lifestyle factors. With the advancement of technology, the integration of genomics, transcriptomics, and imaging data related to AD allows simultaneous exploration of molecular information at different levels and their interaction within the organism. This paper proposes a hypergraph-regularized joint deep semi-non-negative matrix factorization (HR-JDSNMF) algorithm to integrate positron emission tomography (PET), single-nucleotide polymorphism (SNP), and gene expression data for AD. The method employs matrix factorization techniques to nonlinearly decompose the original data at multiple layers, extracting deep features from different omics data, and utilizes hypergraph mining to uncover high-order correlations among the three types of data. Experimental results demonstrate that this approach outperforms several matrix factorization-based algorithms and effectively identifies multi-omics biomarkers for AD. Additionally, single-cell RNA sequencing (scRNA-seq) data for AD were collected, and genes within significant modules were used to categorize different types of cell clusters into high and low-risk cell groups. Finally, the study extensively explores the differences in differentiation and communication between these two cell types. The multi-omics biomarkers unearthed in this study can serve as valuable references for the clinical diagnosis and drug target discovery for AD. The realization of the algorithm in this paper code is available at https://github.com/ShubingKong/HR-JDSNMF .

Advancements in Deep Learning for Autonomous Driving in Indian Road Conditions

Recent advancements in deep learning techniques tailored for autonomous driving in Indian road conditions are crucial for revolutionizing transportation systems. Indian roads present unique challenges, including unpredictable traffic patterns, diverse road infrastructures, and challenging weather conditions. Deep learning is pivotal in addressing these challenges, focusing on perception, decision-making, and control. Various architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Reinforcement Learning (DRL), are analyzed for their efficacy in autonomous driving. Sensor fusion techniques enhance perception capabilities, such as lidar, radar, and camera data integration. Additionally, advancements in semantic segmentation and object detection algorithms improve scene understanding and obstacle recognition. Collaborative efforts between academia, industry, and government agencies are essential for accelerating the deployment of autonomous driving technology in Indian road conditions. In conclusion, leveraging cutting-edge deep learning methodologies enables safe and efficient navigation of autonomous vehicles, ushering in a transformative future in transportation systems.

Exposure to Molybdate Results in Metabolic Disorder: An Integrated Study of the Urine Elementome and Serum Metabolome in Mice

The increasing use of molybdate has raised concerns about its potential toxicity in humans. However, the potential toxicity of molybdate under the current level of human exposure remains largely unknown. Endogenous metabolic alterations that are caused in humans by environmental exposure to pollutants are associated with the occurrence and progression of many diseases. This study exposed eight-week-old male C57 mice to sodium molybdate at doses relevant to humans (0.01 and 1 mg/kg/day) for eight weeks. Inductively coupled plasma mass spectrometry (ICP-MS) and ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS) were utilized to assess changes in urine element levels and serum metabolites in mice, respectively. A total of 838 subjects from the NHANES 2017–2018 population database were also included in our study to verify the associations between molybdenum and cadmium found in mice. Analysis of the metabolome in mice revealed that four metabolites in blood serum exhibited significant changes, including 5-aminolevulinic acid, glycolic acid, l-acetylcarnitine, and 2,3-dihydroxypropyl octanoate. Analysis of the elementome revealed a significant increase in urine levels of cadmium after molybdate exposure in mice. Notably, molybdenum also showed a positive correlation with cadmium in humans from the NHANES database. Further analysis identified a positive correlation between cadmium and 2,3-dihydroxypropyl octanoate in mice. In conclusion, these findings suggest that molybdate exposure disrupted amino acid and lipid metabolism, which may be partially mediated by molybdate-altered cadmium levels. The integration of elementome and metabolome data provides sensitive information on molybdate-induced metabolic disorders and associated toxicities at levels relevant to human exposure.

Enhancing Safety in Cargo Handling Units Using Thermal and Motion IoT Sensors

To improve supply chain efficiency by establishing real-time communication via Internet of Things (IoT) devices between cargo units and Logistics Control Systems (LCS). The method makes it possible to continuously monitor characteristics like location, temperature, humidity, and handling affects by carefully placing IoT sensors within cargo containers. These Internet of Things (IoT) devices work together to create a network that sends data in real time to a centralized LCS platform. This data gives logistics operators fast insights for proactive disruption response, risk mitigation, and optimized route planning. The research uses encryption techniques, redundancy plans, and flexible software interfaces to address issues with data security, device dependability, and integration into current infrastructure. Furthermore, by integrating machine learning and predictive analytics, the LCS can anticipate potential problems, which helps promotes increased customer happiness, decreased losses, and greater operational performance in contemporary logistics and freight transportation.

Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data.

Existing methods for gene regulatory network (GRN) inference rely on gene expression data alone or on lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited independent data points still presents a daunting challenge. Here we present LINGER (Lifelong neural network for gene regulation), a machine-learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor motifs as a manifold regularization. LINGER achieves a fourfold to sevenfold relative increase in accuracy over existing methods and reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Following the GRN inference from reference single-cell multiome data, LINGER enables the estimation of transcription factor activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies.

Highly structured populations of copepods at risk to deep-sea mining: Integration of genomic data with demogenetic and biophysical modelling.

Copepoda is the most abundant taxon in deep-sea hydrothermal vents, where hard substrate is available. Despite the increasing interest in seafloor massive sulphides exploitation, there have been no population genomic studies conducted on vent meiofauna, which are known to contribute over 50% to metazoan biodiversity at vents. To bridge this knowledge gap, restriction-site-associated DNA sequencing, specifically 2b-RADseq, was used to retrieve thousands of genome-wide single-nucleotide polymorphisms (SNPs) from abundant populations of the vent-obligate copepod Stygiopontius lauensis from the Lau Basin. SNPs were used to investigate population structure, demographic histories and genotype-environment associations at a basin scale. Genetic analyses also helped to evaluate the suitability of tailored larval dispersal models and the parameterization of life-history traits that better fit the population patterns observed in the genomic dataset for the target organism. Highly structured populations were observed on both spatial and temporal scales, with divergence of populations between the north, mid, and south of the basin estimated to have occurred after the creation of the major transform fault dividing the Australian and the Niuafo'ou tectonic plate (350 kya), with relatively recent secondary contact events (<20 kya). Larval dispersal models were able to predict the high levels of structure and the highly asymmetric northward low-level gene flow observed in the genomic data. These results differ from most studies conducted on megafauna in the region, elucidating the need to incorporate smaller size when considering site prospecting for deep-sea exploitation of seafloor massive sulphides, and the creation of area-based management tools to protect areas at risk of local extinction, should mining occur.

Data distribution tailoring revisited: cost-efficient integration of representative data

Data distribution tailoring revisited: cost-efficient integration of representative data

Research on the Exposure Risk Analysis of Wildfires with a Spatiotemporal Knowledge Graph

This study focuses on constructions that are vulnerable to fire hazards during wildfire events, and these constructions are known as ‘exposures’, which are an increasingly significant area of disaster research. A key challenge lies in estimating dynamically and comprehensively the risk that individuals are exposed to during wildfire spread. Here, ‘exposure risk’ denotes the potential threat to exposed constructions from fires within a future timeframe. This paper introduces a novel method that integrates a spatiotemporal knowledge graph with wildfire spread data and an exposure risk analysis model to address this issue. This approach enables the semantic integration of varied and heterogeneous spatiotemporal data, capturing the dynamic nature of wildfire propagation for precise risk analysis. Empirical tests are employed for the study area of Xichang, Sichuan Province, using real-world data to validate the method’s efficacy in merging multiple data sources and enhancing the accuracy of exposure risk analysis. Notably, this approach also reduces the time complexity from O (m×n×p) to O (m×n).

Nonlinear elasticity with the Shifted Boundary Method

We propose a new unfitted/immersed computational framework for nonlinear solid mechanics, which bypasses the complexities associated with the generation of CAD representations and subsequent body-fitted meshing. This approach allows to speed up the cycle of design and analysis in complex geometry and requires relatively simple computer graphics representations of the surface geometries to be simulated, such as the Standard Tessellation Language (STL format). Complex data structures and integration on cut elements are avoided by means of an approximate boundary representation and a modification (shifting) of the boundary conditions to maintain optimal accuracy. An extensive set of computational experiments in two and three dimensions is included.

Exploring the relationship between Treg-mediated risk in COPD and lung cancer through Mendelian randomization analysis and scRNA-seq data integration

BackgroundEvidence from observational studies suggests an association between chronic obstructive pulmonary disease (COPD) and lung cancer. The potential interactions between the immune system and the lungs may play a causative role in COPD and lung cancer and offer therapeutic prospects. However, the causal association and the immune-mediated mechanisms between COPD and lung cancer remain to be determined.MethodsWe employed a two-sample Mendelian randomization (MR) approach to investigate the causal association between COPD and lung cancer. Additionally, we examined whether immune cell signals were causally related to lung cancer, as well as whether COPD was causally associated with immune cell signals. Furthermore, through two-step Mendelian randomization, we investigated the mediating effects of immune cell signals in the causal association between COPD and lung cancer. Leveraging publicly available genetic data, our analysis included 468,475 individuals of European ancestry with COPD, 492,803 individuals of European ancestry with lung cancer, and 731 immune cell signatures of European ancestry. Additionally, we conducted single-cell transcriptome sequencing analysis on COPD, lung cancer, and control samples to validate our findings.FindingsWe found a causal association between COPD and lung cancer (odds ratio [OR] = 1.63, 95% confidence interval [CI] = 1.31–2.02, P-value < 0.001). We also observed a causal association between COPD and regulatory T cells (odds ratio [OR] = 1.19, 95% confidence interval [CI] = 1.01–1.40, P-value < 0.05), as well as a causal association between regulatory T cells and lung cancer (odds ratio [OR] = 1.02, 95% confidence interval [CI] = 1.002–1.045, P-value < 0.05). Furthermore, our two-step Mendelian randomization analysis demonstrated that COPD is associated with lung cancer through the mediation of regulatory T cells. These findings were further validated through single-cell sequencing analysis, confirming the mediating role of regulatory T cells in the association between COPD and lung cancer.InterpretationAs far as we are aware, we are the first to combine single-celled immune cell data with two-sample Mendelian randomization. Our analysis indicates a causal association between COPD and lung cancer, with regulatory T cells playing an intermediary role.

A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology.

Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact: anirban@klyuniv.ac.in.

SMART DRILLING TECHNOLOGIES: HARNESSING AI FOR PRECISION AND SAFETY IN OIL AND GAS WELL CONSTRUCTION

This paper explores the integration of AI in smart drilling technologies, examining its applications, benefits, challenges, and future prospects. By harnessing the power of AI, smart drilling technologies enable proactive decision-making, automation, and optimization throughout the drilling lifecycle. From well planning and design to real-time monitoring and control, AI-driven systems improve operational performance, reduce risks, and maximize resource recovery. Despite facing challenges such as data integration, technology adoption, and regulatory compliance, the potential benefits of smart drilling technologies are substantial. Enhanced precision, improved safety, increased efficiency, and sustainable practices are among the key benefits offered by these technologies. Looking towards the future, opportunities for further innovation and advancement abound, including the development of advanced AI algorithms, integration with IoT and big data analytics, and a focus on environmental sustainability. By embracing innovation, collaboration, and a commitment to sustainability, the oil and gas industry can unlock new opportunities for growth and resilience in the evolving landscape of oil and gas well construction. Smart drilling technologies hold the promise of reshaping the future of well construction, paving the way for safer, more efficient, and sustainable drilling operations in the oil and gas industry. Smart drilling technologies are revolutionizing the oil and gas industry, offering unprecedented levels of precision and safety in well construction. By integrating artificial intelligence (AI) into drilling processes, these technologies optimize drilling parameters, reduce risks, and maximize resource recovery.. Enhanced precision, improved safety, increased efficiency, and sustainable practices are among the key benefits offered by these technologies. Looking towards the future, opportunities for further innovation and advancement abound, including the development of advanced AI algorithms, integration with IoT and big data analytics, and a focus on environmental sustainability.&#x0D; Keywords: Smart drilling, Artificial intelligence (AI), Oil and gas industry Efficiency, Safety, Sustainability.

Application and progress of water colour remote sensing technology in monitoring chlorophyll concentration changes in seawater

This paper provides an in-depth discussion on the application and progress of water colour remote sensing technology in monitoring changes in chlorophyll concentration in seawater. The hydrochromatic remote sensing technique uses spectral data acquired by remote sensing satellites and airborne platforms to monitor and analyse the distribution of marine chlorophyll, which is of great significance for understanding and protecting marine ecosystems. The article first introduces the basic principles and development history of this technology, and then discusses in detail its applications in global ocean monitoring, including resource management, environmental assessment, and ecological protection. In the discussion, we highlight the advantages of water colour remote sensing technology in providing large-scale and efficient ocean monitoring, while also pointing out the challenges it faces in terms of data accuracy, atmospheric disturbance handling and algorithm development. In particular, these challenges are gradually being overcome with the application of advanced computational techniques, such as machine learning and artificial intelligence, resulting in significant improvements in monitoring accuracy and efficiency. Finally, this paper looks at the future direction of water colour remote sensing technology, including technological innovation, data integration and interdisciplinary collaboration, highlighting the potential value of this technology in addressing global climate change and marine environmental protection. Through this comprehensive analysis, we have gained a deeper understanding and appreciation of the important role of water colour remote sensing technology in global ocean monitoring and management.

Current genetic diagnostics in inborn errors of immunity

New technologies in genetic diagnostics have revolutionized the understanding and management of rare diseases. This review highlights the significant advances and latest developments in genetic diagnostics in inborn errors of immunity (IEI), which encompass a diverse group of disorders characterized by defects in the immune system, leading to increased susceptibility to infections, autoimmunity, autoinflammatory diseases, allergies, and malignancies. Various diagnostic approaches, including targeted gene sequencing panels, whole exome sequencing, whole genome sequencing, RNA sequencing, or proteomics, have enabled the identification of causative genetic variants of rare diseases. These technologies not only facilitated the accurate diagnosis of IEI but also provided valuable insights into the underlying molecular mechanisms. Emerging technologies, currently mainly used in research, such as optical genome mapping, single cell sequencing or the application of artificial intelligence will allow even more insights in the aetiology of hereditary immune defects in the near future. The integration of genetic diagnostics into clinical practice significantly impacts patient care. Genetic testing enables early diagnosis, facilitating timely interventions and personalized treatment strategies. Additionally, establishing a genetic diagnosis is necessary for genetic counselling and prognostic assessments. Identifying specific genetic variants associated with inborn errors of immunity also paved the way for the development of targeted therapies and novel therapeutic approaches. This review emphasizes the challenges related with genetic diagnosis of rare diseases and provides future directions, specifically focusing on IEI. Despite the tremendous progress achieved over the last years, several obstacles remain or have become even more important due to the increasing amount of genetic data produced for each patient. This includes, first and foremost, the interpretation of variants of unknown significance (VUS) in known IEI genes and of variants in genes of unknown significance (GUS). Although genetic diagnostics have significantly contributed to the understanding and management of IEI and other rare diseases, further research, exchange between experts from different clinical disciplines, data integration and the establishment of comprehensive guidelines are crucial to tackle the remaining challenges and maximize the potential of genetic diagnostics in the field of rare diseases, such as IEI.

Physiologically Based Biopharmaceutics Modeling (PBBM): Best Practices for Drug Product Quality, Regulatory and Industry Perspectives: 2023 Workshop Summary Report.

Physiologically based biopharmaceutics modeling (PBBM) is used to elevate drug product quality by providing a more accurate and holistic understanding of how drugs interact with the human body. These models are based on the integration of physiological, pharmacological, and pharmaceutical data to simulate and predict drug behavior in vivo. Effective utilization of PBBM requires a consistent approach to model development, verification, validation, and application. Currently, only one country has a draft guidance document for PBBM, whereas other major regulatory authorities have had limited experience with the review of PBBM. To address this gap, industry submitted confidential PBBM case studies to be reviewed by the regulatory agencies; software companies committed to training. PBBM cases were independently and collaboratively discussed by regulators, and academic colleagues participated in some of the discussions. Successful bioequivalence "safe space" industry case examples are also presented. Overall, six regulatory agencies were involved in the case study exercises, including ANVISA, FDA, Health Canada, MHRA, PMDA, and EMA (experts from Belgium, Germany, Norway, Portugal, Spain, and Sweden), and we believe this is the first time such a collaboration has taken place. The outcomes were presented at this workshop, together with a participant survey on the utility and experience with PBBM submissions, to discuss the best scientific practices for developing, validating, and applying PBBMs. The PBBM case studies enabled industry to receive constructive feedback from global regulators and highlighted clear direction for future PBBM submissions for regulatory consideration.