Real-time Insights Research Articles

API management and cloud integration model for SMEs

In the rapidly evolving technological landscape, Small and Medium-sized Enterprises (SMEs) face significant challenges in maintaining efficient and scalable IT systems. This review outlines a strategic model for API management and cloud integration designed to enhance system performance and optimize cloud-native features. The proposed model focuses on the redesign and optimization of Application Programming Interfaces (APIs) to leverage cloud capabilities effectively, addressing common issues faced by SMEs in system integration and performance. The model begins with a comprehensive assessment of existing API architectures to identify inefficiencies and performance bottlenecks. It emphasizes the need for a cloud-native approach, which includes the adoption of microservices architecture and serverless computing. By redesigning APIs to align with cloud-native principles, the model aims to achieve greater scalability, flexibility, and responsiveness in handling varying workloads. Key components of the model include API versioning strategies, improved security protocols, and enhanced data management practices. The integration of cloud-native features such as containerization, orchestration, and automated scaling ensures that APIs are optimized for performance and reliability. This approach not only streamlines system operations but also reduces the overhead associated with traditional server-based architectures. Furthermore, the model incorporates best practices for continuous integration and continuous deployment (CI/CD), facilitating seamless updates and maintenance of APIs. It also includes monitoring and analytics tools to provide real-time insights into API performance and user interactions, enabling proactive management and optimization. By implementing this strategic model, SMEs can achieve significant improvements in system performance, reduce latency, and enhance overall cloud integration. The model supports the efficient utilization of cloud resources, leading to cost savings and improved operational efficiency. This approach positions SMEs to better compete in the digital economy by leveraging advanced technologies and optimizing their IT infrastructure.

Navigating the omics landscape in precision medicine: A bidirectional approach to patient care

This paper presents a novel bidirectional approach to precision medicine that combines proteomic, peptidomic and metabolomic analyzes with clinical data and genome-wide association studies (GWAS). This innovative strategy improves patient care by enabling a dynamic exchange from “patient to molecular pathway” and back, which significantly refines patient cohort stratification, improves diagnostic accuracy and personalizes treatment strategies. At the heart of this approach is the use of comprehensive multi-omics data to improve patient management by tailoring interventions to individual molecular profiles. This two-way flow not only optimizes treatment based on real-time insights from molecular pathways, but also improves the application of GWAS results in clinical scenarios. Advanced computational tools such as machine learning and network analysis are critical for navigating these complex data sets and translating intricate molecular data into actionable treatment plans. This integrated, adaptive framework promises to reshape the future of healthcare by tailoring treatments to patients' unique biological and genetic profiles.

A Comprehensive Review of SCADA-Based Wind Turbine Performance and Reliability Modeling with Machine Learning Approaches

The increasing reliance on wind energy to meet global energy demands has made wind turbine performance optimization and reliability a critical area of research. Supervisory Control and Data Acquisition (SCADA) data, which provides real-time operational insights into wind turbines, plays a pivotal role in predictive maintenance, fault detection, and energy output optimization. This review explores the current methodologies and advancements in wind turbine performance modeling and reliability analysis, with a particular emphasis on machine learning (ML) approaches. Existing studies that utilize SCADA data to implement various ML models, such as decision trees, neural networks, and ensemble learning techniques (bagging, boosting, stacking), are analyzed for their effectiveness in predicting turbine failures, improving energy efficiency, and optimizing maintenance schedules. Key findings from multiple studies are synthesized, highlighting the strengths, limitations, and real-world applications of these models. Challenges in data quality, model generalization, and the implementation of real-time ML-driven systems in wind farms are also addressed. This review aims to provide a comprehensive overview of the current state of SCADA-based wind turbine analysis and to offer a roadmap for future research that bridges the gap between data-driven models and their practical deployment in wind energy systems.

Improved Understanding of PEMFC Stack Durability Testing through Comprehensive In-Situ and Ex-Situ Characterization Techniques

Durability testing of Proton Exchange Membrane Fuel Cells for mobile applications remains a critical challenge. While most academic studies focus on single-cell testing, there are only few publications on stack testing of technical relevant sizes. This study utilizes comprehensive characterization techniques to enhance understanding of PEMFC stack durability, aiming to meet the currently targeted durability targets. A 5,500-hour long-term test, based on the Auto Stack Industry (ASI) test protocol, was performed on a 7-cell short stack of 240 cm² active area. In-situ techniques, such as spatially resolved current density measurement, cyclic voltammetry, and electrochemical impedance spectroscopy, provided real-time degradation insights, while ex-situ methods, including FIB-SEM analysis, infrared thermography or contact angle measurements provided information on material degradation mechanisms. Membrane degradation was found to be negligible. Slight thinning of the cathode indicates carbon corrosion of the catalyst support. Catalyst aging was observed in the form of Pt band formation and Co leaching. Gas diffusion layers showed hydrophobicity loss, and increased resistance in bipolar plates contributed to reduced performance. Our findings demonstrate that PEMFC stacks operated under automotive drive cycle protocols can meet the actual targeted durability objectives. Moreover, it illustrates the degradation phenomena that occur under realistic operating conditions and modes, and how they evolve over the duration of thousands of hours of operation.

Improved Understanding of PEMFC Stack Durability Testing through Comprehensive In-Situ and Ex-Situ Characterization Techniques

Durability testing of Proton Exchange Membrane Fuel Cells for mobile applications remains a critical challenge. While most academic studies focus on single-cell testing, there are only few publications on stack testing of technical relevant sizes. This study utilizes comprehensive characterization techniques to enhance understanding of PEMFC stack durability, aiming to meet the currently targeted durability targets. A 5,500-hour long-term test, based on the Auto Stack Industry (ASI) test protocol, was performed on a 7-cell short stack of 240 cm² active area. In-situ techniques, such as spatially resolved current density measurement, cyclic voltammetry, and electrochemical impedance spectroscopy, provided real-time degradation insights, while ex-situ methods, including FIB-SEM analysis, infrared thermography or contact angle measurements provided information on material degradation mechanisms. Membrane degradation was found to be negligible. Slight thinning of the cathode indicates carbon corrosion of the catalyst support. Catalyst aging was observed in the form of Pt band formation and Co leaching. Gas diffusion layers showed hydrophobicity loss, and increased resistance in bipolar plates contributed to reduced performance. Our findings demonstrate that PEMFC stacks operated under automotive drive cycle protocols can meet the actual targeted durability objectives. Moreover, it illustrates the degradation phenomena that occur under realistic operating conditions and modes, and how they evolve over the duration of thousands of hours of operation.

Application of cascaded neural network for prediction of voltage stability margin in a solar and wind integrated power system

Voltage stability is a paramount concern in the management of renewable-rich power systems. As the diffusion of renewable energy sources continues to increase, accurately estimating voltage stability margins becomes essential. This paper addresses the critical need for effective voltage stability margin estimation and presents a novel approach to predict the same on the solar and wind integrated power system. A new machine learning methodology is developed based on the cascaded neural network (CNN). The proposed CNN methodology effectively estimates the loadability margin in a renewable-rich power system under normal operating conditions, varied loading directions, and various contingency situations. The results of CNN are compared with different machine learning techniques and tested in the IEEE 30 bus system and IEEE 118 bus system under various operational circumstances. The results illustrate the efficiency of the suggested method for online assessment of a power system's Voltage Stability Margin. The proposed approach gives real-time insights into voltage stability margins by leveraging the power of data and powerful algorithms, allowing grid operators to proactively manage and optimize renewable energy integration while ensuring grid dependability and stability.

Kinetic study and modelling of drying of Chlorellavulgaris

Microalgae, a versatile source of biofuels, chemicals, and nutraceuticals, necessitates efficient drying for subsequent applications. Extensive studies have been done on the benefits and uses of microalgae, but very few are focusing on drying. This research focused on a specific microalga, Chlorella vulgaris, to analyze the drying kinetics involved in the moisture removal process. Data on drying behavior were collected using thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). As the temperature rose, the moisture content of the biomass rapidly decreased and peaked between 65 and 80 °C. From four widely used drying kinetics models, which are typically used to analyze the drying kinetics of agricultural goods, four non-isothermal drying models were derived. These models were assessed using the coefficient of determination (R2) and reduced chi-square (χ2). Page's model emerged as the best fit for describing drying kinetics. This study introduces a novel approach to characterize the intrinsic properties of freshly harvested Chlorella vulgaris by employing TGA and DSC. Unlike other studies focusing on conventional drying methods, our investigation provides real-time insights into the microalgae's thermal behavior during drying.

Adoption of Internet of Things (IoT) in Smart Farm Management: Implications for Sustainable Agriculture in Iraq

The Internet of Things (IoT) is revolutionizing agriculture by providing real-time data and insights to optimize farming processes. In Iraq, agriculture plays a critical role in the economy, yet the sector faces challenges such as inefficiency, water scarcity, and low productivity. Despite global advancements in IoT-based smart farming, adoption in Iraq remains limited. The agricultural sector in Iraq suffers from outdated practices, insufficient technological infrastructure, and a lack of awareness about modern farming techniques. Additionally, high costs of IoT implementation and limited expertise exacerbate the issue, preventing farmers from leveraging IoT solutions to enhance productivity and sustainability. This research explores the adoption of IoT technologies in Iraq’s agricultural sector. It aims to: 1-Analyze how IoT can address agricultural inefficiencies in Iraq. 2-Evaluate the potential benefits and challenges of implementing IoT in the country’s farms. 3-Offer actionable recommendations for integrating IoT into agricultural practices sustainably. The study reveals that IoT adoption can significantly improve resource management, reduce costs, and enhance yields. Specific benefits include optimized water usage, reduced pesticide application, and better disease management. However, barriers such as high initial costs, lack of expertise, and data privacy concerns hinder widespread adoption.

Enhancing IT Project Portfolio Management Through Dynamic Dashboard Visualization and Integrated Chatbot and Mathematical ML Model for Strategic Decision Making

This project paper presents an innovative approach to enhance IT Project Portfolio Management (IT PPM) using dynamic dashboard visualization and machine learning (ML) techniques. Effective IT PPM is essential for organizations to align IT projects with business objectives and optimize resource allocation. However, conventional methods often lack innovation and real-time visibility, leading to suboptimal decision-making and costly software investments. The objective of this project is to develop a dynamic dashboard visualization system integrated with a machine learning model derived from the Meta-Portfolio Method (MPM). The MPM leverages advanced analytics and historical project data to dynamically allocate resources and prioritize projects based on risk-return profiles. By incorporating the MPM into the dashboard, organizations gain real-time insights into project status, progress, and resource allocation, enabling informed decision-making and proactive portfolio optimization. The ML model derived from the MPM analyzes historical project data, including success rates, resource intensity, criticality, regulatory compliance, and portfolio diversification, to predict optimal project selection and resource allocation. By dynamically adapting to changing market conditions and project dynamics, the ML model facilitates strategic decision-making and enhances portfolio performance. Through the integration of dynamic dashboard visualization and ML-driven decision support, organizations can streamline IT PPM processes, improve project outcomes, and achieve strategic objectives effectively and efficiently. This innovative approach empowers organizations to navigate the complexities of IT project portfolios with agility, foresight, and confidence.

Optimizing the food supply chain through the integration of financial models and big data in procurement: A strategy for reducing food prices

Optimizing the food supply chain through the integration of financial models and big data analytics presents a transformative strategy for reducing food prices. In an increasingly complex global market, managing procurement effectively is crucial for cost control and price stabilization. Financial models, such as cost-benefit analysis, financial forecasting, and risk assessment, provide valuable insights into procurement decisions, helping organizations anticipate price fluctuations and manage expenses. When combined with big data analytics, these models enable more precise predictions and strategic planning. Big data analytics harnesses vast amounts of information from various sources, including market trends, supplier performance, and consumer behavior. By analyzing this data, organizations can identify patterns, optimize inventory levels, and forecast demand with greater accuracy. This data-driven approach enhances decision-making in procurement, allowing for better negotiation with suppliers, efficient inventory management, and the reduction of wastage. The integration of financial models with big data provides a holistic view of the supply chain, enabling companies to implement dynamic pricing strategies and adjust procurement practices based on real-time insights. This integration helps in minimizing operational costs, improving supply chain efficiency, and ultimately reducing food prices for consumers. For example, predictive analytics can identify potential supply disruptions and adjust procurement strategies proactively, while financial models can evaluate the cost-effectiveness of various sourcing options. Challenges such as data integration, quality, and privacy must be addressed to fully leverage these technologies. Moreover, investment in advanced analytics tools and skilled personnel is essential for successful implementation. In conclusion, the synergy between financial models and big data analytics offers a strategic approach to optimizing the food supply chain. By enhancing procurement practices and leveraging real-time insights, this strategy contributes to reducing food prices and improving overall supply chain efficiency, benefiting both businesses and consumers. Keywords: Food Supply Chain, Financial Models, Big Data Analytics, Procurement Optimization, Cost Control, Price Reduction, Inventory Management, Predictive Analytics, Supply Chain Efficiency.

Circulating Tumor Cells: Origin, Role, Current Applications, and Future Perspectives for Personalized Medicine.

Circulating tumor cells (CTCs) currently represent a revolutionary tool offering unique insights for the evaluation of cancer progression, metastasis, and response to therapies. Indeed, CTCs, upon detachment from primary tumors, enter the bloodstream and acquire a great potential for their use for personalized cancer management. In this review, we describe the current understanding of and advances in the clinical employment of CTCs. Although considered rare and fleeting, CTCs are now recognized as key players favoring the development of cancer metastasis and disease recurrence, particularly in malignant melanoma, lung, breast, and colorectal cancer patients. To date, the advancements in technology and the development of several successful approaches, also including immunomagnetic enrichment allow for a reliable and reproducible detection and characterization of CTCs. Those innovative methodologies improved the isolation, quantification, and characterization of CTCs from the blood of cancer patients, providing extremely useful evidence and new insights into the nature of the tumor, its epithelial/mesenchymal profile, and its potential resistance to therapy. In fact, in addition to their prognostic and predictive value, CTCs could serve as a valuable instrument for real-time monitoring of treatment response and disease recurrence, facilitating timely interventions and thus improving patient outcomes. However, despite their potential, several challenges hinder the widespread clinical utility of CTCs: (i) CTCs' rarity and heterogeneity pose technical limitations in isolation and characterization, as well as significant hurdles in their clinical implementation; (ii) it is mandatory to standardize CTC detection methods, optimize the sample processing techniques, and integrate them with existing diagnostic modalities; and (iii) the need for the development of new techniques, such as single-cell analysis platforms, to enhance the sensitivity and specificity of CTC detection, thereby facilitating their integration into routine clinical practice. In conclusion, CTCs represent a potential extraordinary tool in cancer diagnostics and therapeutics, offering unprecedented opportunities for personalized medicine and precision oncology. Moreover, their ability to provide real-time insights into tumor biology, treatment response, and disease progression underlines a great potential for their clinical application to improve patients' outcomes and advance our understanding of cancer biology.

Sustainability and Capability Building in Organizations: A Focused Group Approach

This study investigates business sustainability and the development of comprehensive capabilities that are crucial for future-ready organizations. It delineates the sustainable business practices that are necessary to enhance the functional, operational, technological and strategic dimensions of an organization. Furthermore, we examine methods to augment a firm’s capacity to foster sustainable growth. The authors utilized a structured approach; four focused group discussions (FGDs) were conducted with experts from multinational firms to garner real-time industry insights in the context of the new normal. The discussions emphasized key practices such as change management, competency building, green business functions, regenerative strategies, empathetic leadership and the triple bottom line approach. This research underscores the real-time business practices implemented during and after the pandemic to address and mitigate the disruptions caused by the outbreak.

Validation of optimised intracranial spectroscopic probe for instantaneous in-situ monitoring and classification of traumatic brain injury

The development of an optical interface to directly distinguish the brain tissue's biochemistry is the next step in understanding traumatic brain injury (TBI) pathophysiology and the best and most appropriate treatment in cases where in-hospital intracranial access is required. Despite TBI being a globally leading cause of morbidity and mortality in patients under 40, there is still a lack of objective diagnostical tools. Further, given its pathophysiological complexity the majority of treatments provided are purely symptomatic without standardized therapeutic targets. Our tailor-engineered prototype of the intracranial Raman spectroscopy probe (Intra-RSP) is designed to bridge the gap and provide real-time spectroscopic insights to monitor TBI and its evolution as well as identify patient-specific molecular targets for timely intervention. Raman spectroscopy being rapid, label-free and non-destructive, renders it an ideal portable diagnostics tool. In combination with our in-house developed software, using machine learning algorithms for multivariate analysis, the Intra-RSP is shown to accurately differentiate simulated TBI conditions in rat brains from the healthy controls, directly from the brain surface as well as through the rat's skull. Using clinically pre-established methods of cranial entry, the Intra-RSP can be inserted into a 2-piece optimised cranial bolt with integrated focussing and correctly identify a sample in real-life conditions with an accuracy >80 %. To further validate the Intra-RSP's efficiency as a TBI monitoring device, rat brains mildly damaged from inflicted spinal cord injury were found to be correctly classified with 94.5 % accuracy. Through optimization and rigorous in-vivo validation, the Intra-RSP prototype is envisioned to seamlessly integrate into existing standards of neurological care, serving as a minimally invasive, in-situ neuromonitoring tool. This transformative approach has the potential to revolutionize the landscape of neurological care by providing clinicians with unprecedented insights into the nature of brain injuries and fostering targeted, timely and effective therapeutic interventions.

Global traumatic brain injury intracranial pressure: from monitoring to surgical decision.

Traumatic brain injury (TBI) is a significant global public health issue, heavily impacting human health, especially in low-and middle-income areas. Despite numerous guidelines and consensus statements, TBI fatality rates remain high. The pathogenesis of severe TBI is closely linked to rising intracranial pressure (ICP). Elevated intracranial pressure can lead to cerebral herniation, resulting in respiratory and circulatory collapse, and ultimately, death. Managing intracranial pressure (ICP) is crucial in neuro-intensive care. Timely diagnosis and precise treatment of elevated ICP are essential. ICP monitoring provides real-time insights into a patient's condition, offering invaluable guidance for comprehensive management. ICP monitoring and standardization can effectively reduce secondary nerve damage, lowering morbidity and mortality rates. Accurately assessing and using true ICP values to manage TBI patients still depends on doctors' clinical experience. This review discusses: (a) Epidemiological disparities of traumatic brain injuries across countries with different income levels worldwide; (b) The significance and function of ICP monitoring; (c) Current status and challenges of ICP monitoring; (d) The impact of decompressive craniectomy on reducing intracranial pressure; and (e) Management of TBI in diverse income countries. We suggest a thorough evaluation of ICP monitoring, head CT findings, and GCS scores before deciding on decompressive craniectomy. Personalized treatment should be emphasized to assess the need for surgical decompression in TBI patients, offering crucial insights for clinical decision-making.

Construction of supply chain management information system based on networked web service composition technology

Web service composition is crucial for creating valuable services by integrating pre-existing ones. With their service-oriented architecture (SOA), which can be used for any system design, web services can increase flexibility. Fusing Web services architecture with Semantic Web services can better assist supply chain coordination in a distributive, autonomous, and ever-changing corporate environment than current information technology. Decisions must be made quickly and with enough information many systems fail to provide real-time supply chain insight. Forecasting, inventory management, and decision-making may all be impacted by poor data quality. Modifying preexisting systems according to unique organizational needs may be challenging and expensive. Hence, this paper proposes a semantic web service-based supply chain management framework (SWS-SCMF) to analyze the web services in supply chains and examine how they interact using Web Ontology Language (OWL)-S, including automated discovery, construction, and invocation. The suggested method for improving supply chain data integration uses an ontology-based multiple-agent decision support system. Different accessibility tools, data formats, management information systems, semantic web, and databases are integrated across the five interconnected levels of the system. Businesses may find the proposed approach useful for data and information sharing when dealing with complex supply chain management. The suggested SWS-SCMF is an adaptable, accurate, and effective method for bidirectional chaining composition that uses mediators to enable the automated composition of Semantic Web services. The numerical results show that our proposed method enhances the overall performance ratio by 94%, accuracy ratio by 98%, and supply chain management ratio by 91% compared to other methods.

Operando electron spin probes for the study of battery processes

Operando electron spin probes, namely magnetometry and electron paramagnetic resonance (EPR), provide real-time insights into the electrochemical processes occurring in battery materials and devices. In this work, we describe the design criteria and outline the development of operando magnetometry and EPR electrochemical cells. Notably, we show that a clamping mechanism, or springs, are needed to achieve sufficient compression of the battery stack and an electrochemical performance on par with that of a standard Swagelok-type cell. The tandem use of operando EPR and magnetometry allows us to identify five distinct and reversible redox processes taking place on charge and discharge of the intercalation-type LiNi0.5Mn0.5O2 Li-ion cathode. While redox processes in conversion-type electrodes are notoriously difficult to investigate using standard characterization methods (e.g. X-ray based) and/or post mortem analysis, due to the formation of poorly crystalline and metastable reaction intermediates and products during cycling, we show that operando magnetometry provides unique insight into the kinetics and reversibility of Fe nanoparticle formation in the Na3FeF6 electrode for Na-based batteries. Step increases in the cell magnetization upon extended cycling indicate the build-up of Fe nanoparticles in the system, hinting at only partially reversible charge–discharge processes. The broad applicability of the tools developed herein to a range of electrode chemistries and structures, from intercalation to conversion electrodes, and from crystalline to amorphous systems, makes them particularly promising for the development of electrochemical energy storage technologies and beyond.

463 Cyber-Physical Systems with robots and AI for precision dairy farming

Abstract The emergence of precision livestock farming (PLF) offers a transformative lens through which to prioritize animal welfare. At the heart of this transformation lies the rumen, a complex microbial ecosystem within dairy cows responsible for converting feed into nutrients and impacting cow health, productivity, and environmental footprint. Despite its pivotal role, our understanding of the inner workings of the rumen remains limited due to a lack of real-time monitoring tools. My research leverages AI and robotics in PLF to address this gap, focusing on developing a closed-loop system for continuous, in-vivo rumen monitoring. This system employs cyber-physical systems (CPS) embedded with modular sensors and adaptive algorithms. These sensor modules, powered by AI-based data tracking, autonomously monitor rumen temperature, humidity, pressure, and methane concentration, enabling early detection of imbalances and prompt interventions to maintain optimal rumen health. A key innovation is the self-powered in-vivo robot capable of traversing the rumen’s stratified layers. Guided by AI-powered localization, this robot unlocks unparalleled access to new data, aiding in identifying specific regions associated with conditions like subacute ruminal acidosis (SARA) and methane production. This non-invasive approach eliminates the need for traditional cannulation, minimizing stress on the animal and opening new avenues for animal scientists. The impact of this research extends beyond individual cow health. By optimizing rumen function, we can improve feed efficiency, reduce methane emissions, and promote overall sustainability in dairy farming. Additionally, real-time data insights pave the way for precision nutrition, tailored interventions, and improved disease prevention, directly enhancing animal welfare. This novel approach to rumen monitoring represents a significant step towards realizing the full potential of PLF. By integrating AI and robotics, we can usher in a future where animal welfare and environmental sustainability go hand-in-hand, ultimately contributing to a more responsible and ethical dairy industry.

Monitoring and Predicting Air Quality with IoT Devices

The growing concern about air quality and its influence on human health has prompted the development of sophisticated monitoring and forecast systems. This article gives a thorough investigation into forecasting the air quality index (AQI) with an Internet of Things (IoT) device that analyzes temperature, humidity, PM10, and PM2.5 levels. The dataset used for this analysis comprises 5869 data points across six critical parameters essential for accurate air quality prediction. The data from these sensors is sent to the ThingSpeak cloud platform for storage and preliminary analysis. The system forecasts AQI using a TensorFlow-based regression model, delivering real-time insights. The combination of IoT technology and machine learning improves the accuracy and responsiveness of air quality monitoring systems, making it a useful tool for environmental management and public health protection. This work presents comparatively the effectiveness of feedforward neural network models trained with the ‘adam’ and ‘RMSprop’ optimizers over different epochs, as well as the machine learning algorithm random forest with varying numbers of estimators to forecast AQI. The models were trained using both types of regression analysis: linear regression and random forest regression. The findings show that the model achieves a high degree of accuracy, with the predictions closely aligning with the actual AQI values, thus having the potential to significantly reduce the negative health impact associated with poor air quality, protecting public health and alerting users when pollution levels are higher than allowed. Specifically, the random forest model with 100 estimators delivers the best overall performance for both AQI 10 and AQI 2.5, achieving the lowest Mean Absolute Error (MAE) of 0.2785 for AQI 10 and 0.2483 for AQI 2.5. This integration of IoT technology and advanced predictive analysis addresses the significant worldwide issue of air pollution by identifying the pollution hotspots and allowing decision-makers for quick reactions, and the development of effective strategies to reduce pollution sources.

Innovative digital twin with artificial neural networks for real-time monitoring of structural response: A port structure case study

Port structures face the constant threat of deterioration due to exposure to dynamic and seismic forces, as well as harsh environmental condition prevalent in a maritime setting. These challenges lead to induced stress concentrations within the structures, resulting in potential catastrophic failures. Despite significant effort, expenses, and time consumption associated with traditional structural health monitoring systems (SHM), regular inspections remain the norm. This study introduces innovative methods to replace traditional approaches, leveraging Artificial Neural Networks (ANNs) as surrogates for real-time finite element (FE) modelling. The goal is to develop FE-based real-time digital twins for port structure by using sensor data, visualizing structural behaviour, and enabling a comprehensive monitoring of its structural response. An ANN model is trained using a validated FE model of the structure, generating immediate deformations based on sensor readings. By implementing this approach, stress variations are efficiently obtained and visualized throughout the structure. Unlike traditional methods that follow an inverse approach in estimating the entire structural response based on sensor values, the ANNs demonstrate high efficiency in addressing ill-conditioning issues inherent in such processes. This integrated methodology showcases the effectiveness of ANNs in providing real-time insights into the structural response of port infrastructure, offering a viable addition to the conventional SHM practices. The trained ANN model generates results in a high accuracy where the testing error is in the order of 10−5 and it generates data within 15 milli seconds demonstrating the near real-time conditions. The development of digital twins, facilitated by ANNs, demonstrates a promising solution for continuous monitoring, predictive maintenance, and risk mitigation in the face of dynamic operational and environmental challenges. The study contributes to the advancement of smart infrastructure by harnessing the capabilities of artificial intelligence and digital twin technology.

A two-variant model of SARS-COV-2 transmission: estimating the characteristics of a newly emerging strain

BackgroundThe Covid-19 pandemic has been characterized by the emergence of novel SARS-CoV-2 variants, each with distinct properties influencing transmission dynamics, immune escape, and virulence, which, in turn, influence their impact on local populations. Swift analysis of the properties of newly emerged variants is essential in the initial days and weeks to enhance readiness and facilitate the scaling of clinical and public health system responses.MethodsThis paper introduces a two-variant metapopulation compartmental model of disease transmission to simulate the dynamics of disease transmission during a period of transition to a newly dominant strain. Leveraging novel S-gene dropout analysis data and genomic sequencing data, combined with confirmed Covid-19 case data, we estimate the epidemiological characteristics of the Omicron variant, which replaced the Delta variant in late 2021 in Philadelphia, PA. We utilized a grid-search method to identify plausible combinations of model parameters, followed by an ensemble adjustment Kalman filter for parameter inference.ResultsThe model successfully estimated key epidemiological parameters; we estimated the ascertainment rate of 0.22 (95% credible interval 0.15–0.29) and transmission rate of 5.0 (95% CI 2.4–6.6) for the Omicron variant.ConclusionsThe study demonstrates the potential for this model-inference framework to provide real-time insights during the emergence of novel variants, aiding in timely public health responses.