Real-time Response Research Articles

Extensive DNA Damage and Loss of Cell Viability Occur Synergistically With the Combination of Recombinant Methioninase and Paclitaxel on Pancreatic Cancer Cells which Report DNA-Damage Response in Real Time.

Methionine restriction selectively arrests cancer cells during the S-phase of the cell cycle. We hypothesized that DNA damage may occur in S-phase in cancer cells during methionine restriction. To determine if this occurs, we used MiaPaCa-2Tet-On 53BP1-green fluorescent protein (GFP) pancreatic cancer cells, which report GFP fluorescence in real time after DNA-damage response (DDR) in these cells. We also determined whether a chemotherapy drug in combination with methionine restriction increases the rate of DNA damage. MiaPaCa-2Tet-On 53BP1-GFP cells were used for in vitro experiments. The 25% and 50% inhibitory concentrations (IC25 and IC50, respectively) of recombinant methioninase (rMETase) and paclitaxel on MiaPaCa-2Tet-On 53BP1-GFP pancreatic cancer cells were determined. Cell viability and DDR with rMETase alone, paclitaxel alone, and their combination were measured in MiaPaCa-2Tet-On 53BP1-GFP cells. The IC25 of rMETase on MiaPaCa-2Tet-On 53BP1-GFP cells was 1.66 U/ml. The IC25 for paclitaxel on MiaPaCa-2Tet-On 53BP1-GFP cells was 3.31 nM. The combination of rMETase and paclitaxel synergistically reduced the viability of MiaPaCa-2Tet-On 53BP1-GFP cells. The IC50 of paclitacel on MiaPaCa-2Tet-On 53BP1-GFP cells was 5.1 nM. The IC50 of rMETase on MiaPaCa-2Tet-On 53BP1-GFP cells was 2.3 U/ml. The combination of rMETase (IC50) plus paclitaxel (IC50) on MiaPaCa-2Tet-On 53BP1-GFP cells also caused more DNA damage than either agent alone. The present study suggests the synergy of methionine restriction and chemotherapy is due, at least in part, to DNA damage of cancer cells.

1.L. Scientific session: Public Health data in support of healthy ageing policies

Abstract As Europe grapples with an ageing population, the imperative of health policies that promote healthy ageing is increasingly critical. This workshop aims to explore the significant role of public health data in shaping and supporting such policies. By integrating insights from administrative records to sophisticated research infrastructures, we will highlight the multifaceted uses of data in public health, particularly in the realms of non-communicable diseases and health-related behaviours in older populations. The session will open by examining how administrative records have been utilised within Lombardy’s regional health system to understand the epidemiology and access to care for people living with dementia. The discussion will then cover the advantages and challenges associated with integrating health data from diverse sources, emphasising the importance of quality, privacy, and the potential for real-time surveillance and response systems in healthcare. Following this, the focus will shift to the determinants of good health among older adults, presenting findings from a comprehensive cross-sectional study that identifies key factors contributing to what can be termed as “superhero” health status: seniors who maintain excellent physical and mental health. This analysis will delve into socioeconomic, behavioural, and environmental factors that influence health outcomes in the elderly, providing evidence-based recommendations for public health interventions and highlighting the potentialities of ad hoc surveys in examining health outcomes during emergencies. Thirdly, the workshop will also address the impact of significant life transitions on health behaviours, specifically the transition to retirement and its effects on alcohol consumption. Leveraging longitudinal data from a pan-European multidisciplinary research infrastructure, it will be demonstrated how retirement affects daily habits and lifestyles, including changes in alcohol consumption patterns. This segment will explore the implications of these findings for public health planning and policy formulation. In conclusion, there will be a discussion about healthcare system policies that support healthy ageing and the global impact of ageing on economics and health economics. These discussions will draw from the presented data sources and studies to suggest ways in which public health policies can be designed to reduce disparities and improve overall health outcomes among the elderly. This workshop is designed to provide a comprehensive overview of how data-driven insights can inform effective public health strategies and interventions to support an ageing society. By exploring various aspects of health data application, from individual behaviour to systemic policy adjustments, the session aims to highlight the critical role of data in promoting sustainable health and well-being in older adults, especially in the context of increasing life expectancy and prolonged retirement periods. Key messages • Harnessing data from diverse sources is crucial for understanding and enhancing healthy ageing processes. • Utilising longitudinal studies and robust data to design informed public health policies is essential for effective ageing interventions.

Raised blood pressure affects reactivity to expressive music features

Abstract Background Music has been shown to lower hypertensive patients’ blood pressure in the long-term, but predominant approaches overlook the wide range of complex real-time responses. Mapping the acute dynamics of music features can help discern blood pressure (BP) reactivity. Understanding the mechanisms of music BP effects can broaden the scope for precision medicine. Purpose The aims of this study are to determine: (1) if reshaped music features alter BP reactivity; and, (2) whether BP reactivity differs for individuals with high and normal BP. Methods Participants listened to music with computationally altered tempo and loudness played back on a Disklavier piano. We compared continuous BP measurements in 40 participants (23 females, mean age of 41.3 y (95%CI: 37.2-45.4)), 20 with baseline BP measurement above 140/90 mmHg (H-bBP) and 20 with bBP below (N-bBP), paired by playlist. Continuous BP was recorded while listening to a playlist of 9 reshaped versions of 8 distinct pieces (≈40 min) after a 5-minute baseline silence. Results The mean increase in systolic and diastolic BP during music (reactivity) was significantly higher for N-bBP than H-bBP (mean (95%CI); mmHg): systolic 8.6 (6.2-11.1) vs 5.0 (2.7-7.3); p=0.031; diastolic 7.7 (5.9-9.5) vs 4.6 (2.9-6.3); p=0.0046. No significant difference in pulse pressure (PP) reactivity was found; however, the N-bBP subjects had significantly lower mean PP during music than H-bBP subjects (45.2 (40.2-44.8) vs 54.2 (51.7-56.7) ; p<0.001). Based on mean tempos, N-bBP subjects had significantly lower average PP than H-bBP subjects during slow (<60bpm; 44.6 (40.6-48.6) vs 52.6 (48.9-56.3); p=0.016) and fast (>120bpm; 43.9 (39.9-47.9) vs 57.2 (52.9-61.5); p<0.001) music, though not medium pace music. Similarly, N-bBP participants had significantly lower PP during quiet (<13 dB; 44.0 (39.6-48.4) vs 55.7 (50.7-60.7); p<0.001) and loud (>17 dB; 44.0 (39.6-48.4) vs 55.5 (51.1-59.9); p<0.001) music, but not during moderately loud music. H-bBP and N-bBP participants’ PP measures are more similar during moderately fast and moderately loud music. In addition, N-bBP’s baseline-normalised mean PP was significantly lower only during fast music (3.3 (-2.3-8.6) vs 12.9 (6.9-18.9); p = 0.04). Separating responses to unaltered vs reshaped music––i.e. music made faster and/or louder, in the case of one piece, slower and/or quieter––significant differences are found between N-bBP and H-bBP groups only for reshaped music in baseline-normalised diastolic BP (6.8 (4.7-8.9) vs 3.9 (2.1-5.7); p=0.02) and PP (45.0 (42.2-47.8) vs 54.8 (51.8-57.8); p<0.001). Conclusions H-bBP subjects had less BP reactivity to music, especially in resting vascular tone PP. Reshaping of music can modulate the level of differences between groups, particularly for immoderate tempo and loudness. Music can induce significant changes in BP profile, which indicates that it could potentially be utilised as a tailored anti-hypertensive therapy.Differences between music and baselineDifferences in tempos and loudness cat.

Optimizing Ammonium Polyphosphate–Acrylic Intumescent Coatings with Sustainable Fillers for Naval Fire Safety

This study explores the potential of natural and recycled materials to enhance the fire behavior of eco-friendly intumescent coatings, compared to a traditional ammonium polyphosphate (APP)-based one. To achieve this, cork, halloysite clay, and recycled glass were evaluated as natural fillers and sustainable components within the coating formulation. The aim was to reduce the reliance on synthetic materials and minimize the environmental impact while maintaining fire performance. Fire exposure tests were conducted to assess the in situ char formation and its relationship to the heat source and char foaming process. The results highlighted that all functionalized coatings exhibited suitable intumescent behavior. The best results were evidenced by cork-filled coating that evidenced an intumescent capacity about 40% higher than the traditional ammonium polyphosphate (APP)-based one. This provided valuable insights into the coating’s real-time response to fire, determining its suitability for various fire-resistant applications.

Real-Time Control of Thermal Synchronous Generators for Cyber-Physical Security: Addressing Oscillations with ANFIS

This paper introduces a novel real-time ANFIS controller, specifically designed for thermal synchronous generators, to mitigate the risks associated with cyber-physical attacks on power systems. The controller integrates the dynamic model of the turbine’s thermomechanical components, such as the boiler and heat transfer processes, within the synchronous generator. In contrast to previous studies, this model is designed for practical implementation and addresses often-overlooked areas, including the interaction between electrical and thermomechanical components, real-time control responses to cyber-physical attacks, and the incorporation of economic considerations alongside technical performance. This study takes a comprehensive approach to filling these gaps. Under normal conditions, the proposed controller significantly improves the management of industrial turbines and governors, optimizing existing control systems with a particular focus on minimizing generation costs. However, its primary innovation is its ability to respond dynamically to local and inter-area power oscillations triggered by cyber-physical attacks. In such events, the controller efficiently manages the turbines and governors of synchronous generators, ensuring the stability and reliability of power systems. This approach introduces a cutting-edge thermo-electrical control strategy that integrates both electrical and thermomechanical dynamics of thermal synchronous generators. The novelty lies in its real-time control capability to counteract the effects of cyber-physical attacks, as well as its simultaneous consideration of economic optimization and technical performance for power system stability. Unlike traditional methods, this work offers an adaptive control system using ANFIS (Adaptive NeuroFuzzy Inference System), ensuring robust performance under dynamic conditions, including interarea oscillations and voltage deviations. To validate its effectiveness, the controller undergoes extensive simulation testing in MATLAB/Simulink, with performance comparisons against previous state-of-the-art methods. Benchmarking is also conducted using IEEE standard test systems, including the IEEE 9-bus and IEEE 39-bus networks, to highlight its superiority in protecting power systems.

Mitigating Maritime Cybersecurity Risks Using AI-Based Intrusion Detection Systems and Network Automation During Extreme Environmental Conditions

The maritime industry is increasingly confronted with a myriad of cybersecurity challenges exacerbated by extreme environmental conditions, technological advancements, and heightened reliance on automation. This review paper discusses the intersection of these factors, focusing on the adoption of artificial intelligence (AI)-based intrusion detection systems (IDS) and network automation as vital strategies for mitigating cybersecurity risks. The paper begins by outlining the unique cybersecurity threats faced by the maritime sector, which include data breaches, phishing attacks, and malware threats, all amplified by adverse weather and geographical isolation. In light of these challenges, the rationale for integrating AI-driven solutions into maritime operations is discussed. AI-based IDS can enhance threat detection capabilities through advanced machine learning algorithms that adapt to evolving cyber threats while minimizing false positives. Additionally, network automation can improve connectivity and data security, facilitating real-time monitoring and response to incidents. The review also addresses the critical need for collaboration between maritime and technology industries, emphasizing how partnerships can foster innovation and provide tailored solutions to the sector’s specific needs. Furthermore, the paper examines current implementations and case studies that illustrate successful applications of AI and automation in adverse maritime conditions. While recognizing the potential benefits, the review highlights the technical and operational challenges inherent in these implementations, including data integration, regulatory compliance, and cultural differences between sectors. Ultimately, this paper aims to provide a comprehensive overview of the state of maritime cybersecurity and the pivotal role of AI and automation in shaping a resilient, secure maritime future. The findings underscore the importance of ongoing research and development, collaborative efforts, and the necessity of adaptable strategies to safeguard maritime operations against the evolving landscape of cyber threats.

A comparative analysis of intelligent energy modelling approaches for a grid-tied PVT system application

Renewable energy resources are a long-term answer to the current chronic energy dilemma. Solar photovoltaic (PV) systems are the most often used form of a practical solution for power supply and energy management in buildings. A PV-thermal (PVT) system is essential to lowering the demand for grid energy by capturing electrical and thermal energy from sunlight, optimising energy usage and encouraging sustainable energy practices. The most significant difficulty faced by developing countries, such as South Africa, is a lack of power supply to meet demand while limiting grid overload. An alternative source of energy is a critical component. This research compares two intelligent energy modelling methodologies: optimal control scheme-based open loop and model predictive control (MPC) in a PVT application. The primary goal of the modelling is to limit the electricity required from the grid while stressing the system’s energy efficiency and cost-effectiveness. The open loop approach uses mathematical optimisation techniques to establish the best control strategy for the PVT system. The ideal set-points for various system parameters are found by presenting the problem as an optimisation task to reduce grid power usage. The optimal control technique delivers a global optimisation solution based on the system dynamics and constraints. The MPC technique employs a predictive control technique of the PVT system to create optimal control actions in a receding horizon manner. The MPC algorithm anticipates future system behaviour and generates control actions that minimise grid power drawn by iteratively solving an optimisation problem at each time step. This robust online control scheme enables real-time modifications and responses to system disruptions that effectively and dynamically coordinate system behaviour.

Smart Green Cotton Textiles with Hierarchically Responsive Conductive Network for Personal Healthcare and Thermal Management.

Whereas cotton as an abundant natural cellulose has been widely used for sustainable and skin-friendly textiles and clothes, developing cotton fabrics with smart functions that could respond to various stimuli is still eagerly desired while remaining a great challenge. Herein, smart multiresponsive cotton fabric with hierarchically copper nanowire interwoven MXene conductive networks that are seamlessly assembled along a 3D woven fabric template for efficient personal healthcare and thermal comfort regulation is successfully developed. The robust hierarchically interwoven conductive network was "glued" and protected by organic conductive polymer poly(3,4-ethylenedioxythiophene) along a 3D interconnected fabric template to enhance interfacial adherent and environmental stability. Benefiting from the robust multiresponsive hierarchically interwoven conductive network, smart cotton fabric exhibits real-time response to various external stimuli (light/electrical/heat/temperature/stress), and the details of human activities can be accurately recognized and monitored. Furthermore, the porous structure of 3D smart fabric induced strong capillary force and confinement to phase change materials PEG, which exhibits a wide range of phase transition temperatures for efficient thermal comfort regulation. After further encapsulation with transparent fluorosilicone resin, the smart cotton fabric exhibits excellent self-cleaning performance with water/oil repellent. The smart multiresponsive cotton fabrics hold great promise in next-generation wearable systems for efficient personal healthcare and thermal management.

Marketing affordance of social media for product innovation: the role of organizational structures

PurposeDespite substantial research attention on the business use of social media in innovation literature, the action-based mechanisms through which social media affect firms' product innovation remain unclear. We address this gap by drawing on the affordance theory and examining the effects of social media applications on product innovations.Design/methodology/approachThe study was based on analyzing data collected through a questionnaire survey of Chinese firms. Partial least squares structural equation modeling was applied to test the hypotheses.FindingsThe results show that social media applications have a positive effect on marketing affordances (including customer behavior pattern discovery, real-time market response and marketing ambidexterity), in turn positively influencing product innovation performance. Organizational structures shape the actualization of the marketing affordance of social media; while organizational formalization positively moderates the relationships between social media application and marketing affordances, organizational centrality weakens these relationships.Practical implicationsManagers will benefit from understanding how social media can be applied to help their firms achieve product innovation. When manufacturing firms engage in social media for product innovation, they may find it worthwhile to focus on marketing actions such as customer behavior pattern discovery, real-time market response and marketing ambidexterity.Originality/valueThis study elucidates the action-based mechanism of marketing affordance by which social media usage improves product innovation performance. This study also highlights the design of organizational structure as an important approach to realizing the potential benefits of social media in product innovation.

Cyber Security Threat Detection Using Machine Learning

The present review delves into the development and effectiveness of machine learning (ML) methods in the context of cybersecurity threat identification. More sophisticated and adaptable solutions are required as cyber threats increase in number and complexity beyond what can be achieved with standard security techniques. This study provides a thorough assessment of the several machine learning (ML) techniques that have been used to identify and categorize cyber risks. These algorithms include supervised, unsupervised, and deep learning approaches. We assess these methods' efficacy in identifying malware, phishing, and network intrusions while emphasizing their advantages and disadvantages. While ML offers considerable gains over traditional approaches, comparative study shows that issues like algorithmic bias, data quality, and adaptation to changing threats still exist. In an effort to improve predictive capabilities and real-time threat response, the evaluation also highlights trends and future directions in the integration of machine learning with cybersecurity. In order to implement ML-driven security systems, academics and practitioners may use this paper as a thorough guide. Keywords—Cybersecurity, Machine Learning, Algorithms, Threats, Security.

Pharmacist-facilitated Patient Reported Outcome Measure (PROM) monitoring: developing an EHR SmartForm© to monitor side effects of oral oncolytics during routine telehealth encounters.

Patient reported outcome measures (PROMs) are increasingly used in oncology care, but pharmacists providing direct patient care have been overlooked. We engaged pharmacists and adults receiving oral oncolytics (chemotherapy medication taken by mouth) to develop a SmartForm© in the electronic health record (EHR) for PROM monitoring. Pharmacists verbally ask the patient side effect questions during routine telehealth encounters and enter responses in real time. Our development process was guided by the Knowledge to Action Framework. In phase 1 (Knowledge Inquiry), we prioritized side effects to assess in the EHR SmartForm© via interviews with patients and a Delphi panel with pharmacists. Adults receiving oral oncolytics for breast (n = 12), thoracic (n = 12), or hematological (n = 12) cancer were interviewed, with purposeful sampling for adults who were aged 65 + years or Black. Interviews were coded with content analysis. We conducted three Delphi rounds, with 11/19, 13/19, and 19/19 pharmacists, respectively. In phase 2 (Knowledge Synthesis), PROM items were selected and the EHR SmartForm© programmed. In phase 3 (Knowledge Tailoring), we conducted usability testing with pharmacists. Pharmacists and patients were consistent in prioritizing side effects of oral oncolytics and 10 were retained. Patients advocated asking whether they can do their usual activities, while pharmacists added medication adherence. Usability testing yielded suggestions to simplify the SmartForm©. By presenting screenshots of our SmartForm©, our findings are useful to other healthcare systems looking for a PROM solution integrated in the EHR, with a reasonable pharmacist/clinician workload, and no requirement for patients to have internet access/comfort.

Rational Design of Regenerable Amino-Functionalized Fluorescent Covalent Organic Framework for the Exclusive Detection of Mercury(II).

Goal-oriented development of novel covalent organic frameworks (COFs) to construct a sensing platform for highly toxic mercury (II, Hg2+) is of tremendous significance. Recently, numerous COFs with sulfur-based ligands were developed for Hg2+ monitoring; however, strong binding of Hg2+ by sulfur makes their regeneration very tough. Herein, we designed and developed an amino-functionalized fluorescent COF (COF-NH2) through facile postmodification for Hg2+ detection in which the π-conjugation skeleton is the signal reader and the nitrogen-based side is the highly selective Hg2+ receptor. More importantly, this nitrogen-based receptor permits the reversible binding of Hg2+. As a sensing platform, the outstanding performance of COF-NH2 for Hg2+ detection was reached with respect to high sensitivity with an ultralow detection of 15.3 nM, real-time response with rapid signal change of 10 s, and facile visualization with significant fluorescence color change. Expectedly, COF-NH2 obtained facile recycling which still shows excellent response performance toward Hg2+ after six cycles based on the reversible interaction between amino groups and Hg2+. Our work not only shows an attractive foreground of fluorescent COF for Hg2+ detection but also emphasizes the easy construction of novel COF materials via the rational introduction of metal ligands for the recognition of other metal ions.

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

Neural network-enabled, all-electronic control of non-Newtonian fluid flow.

Real-time, all-electronic control of non-Newtonian fluid flow through a microscale channel is crucial for various applications in manufacturing and healthcare. However, existing methods lack the sensitivity required for accurate measurement and the real-time responsiveness necessary for effective adjustment. Here, we demonstrate an all-electronic system that enables closed-loop, real-time, high-sensitivity control of various waveforms of non-Newtonian fluid flow (0.76 μl min-1) through a micro-sized outlet. Our approach combines a contactless, cuff-like flow sensor with a neural-network control program. This system offers a simple, miniaturized, versatile, yet high-performance solution for non-Newtonian fluid flow control, easily integrated into existing setups.

A Decision Framework for Enhancing Adhoc Network Stability and Security

A Decision Framework for Enhancing Network Stability and Security. In the modern digital era, the growing complexity of networks and increasing frequency of cyber threats demand robust strategies for ensuring network stability and security. This paper proposes a decision framework that integrates real-time analytics, proactive monitoring, and adaptive response mechanisms to enhance network resilience against failures, attacks, and performance degradation. The framework leverages advanced machine learning algorithms, network flow analysis, and security protocols to dynamically adjust to network conditions and mitigate risks .The decision framework operates in two key dimensions: Stability: It focuses on ensuring uninterrupted network performance by optimizing resource allocation, traffic management, and fault tolerance mechanisms. By analyzing network traffic patterns and identifying potential bottlenecks or vulnerabilities, the framework makes proactive decisions to reroute traffic, adjust bandwidth, and prioritize critical data flows.Security: The framework enhances security by detecting potential threats, such as Distributed Denial of Service (DDoS) attacks, unauthorized access, or malware propagation. Using a combination of intrusion detection systems (IDS), firewalls, and behavioral anomaly detection, it identifies threats in real-time and implements automatic countermeasures, such as isolating affected network segments, patching vulnerabilities, or blocking malicious traffic.A key innovation of this framework is its use of multi-criteria decision-making (MCDM) techniques to balance trade-offs between network performance and security in real time. The model continuously evaluates factors such as latency, throughput, and risk exposure to make informed, optimal decisions that ensure both stability and protection. Furthermore, the framework adapts to evolving network conditions using reinforcement learning, allowing it to learn from past incidents and improve its decision-making over time.Simulation results demonstrate that the proposed framework significantly reduces network downtime, improves threat detection response times, and mitigates the impact of security breaches. This decision framework presents a scalable solution for modern, dynamic networks, offering enhanced protection while maintaining high performance in the face of complex challenges.

A digital twin modeling and application for gear rack drilling rigs lifting system

A comprehensive digital transformation has been undergone by the oil and gas industry, wherein digital twins are leveraged to enable real-time data analysis, providing predictive and diagnostic engineering insights. The potential for developing intelligent oil and gas fields is substantial with the implementation of digital twins. A digital twin framework for gear rack drilling rigs is proposed, built upon an understanding of the digital twin composition and characteristics of the gear rack drilling rig lifting system. The framework encompasses descriptions of digital twin characteristics specific to drilling rigs, the application environment, and behavioral rules. The modeling approach integrates mechanism modeling, real-time performance response, instantaneous data transmission, and data visualization. To illustrate this framework, exemplary case studies involving the transmission unit and support unit of the lifting system are presented. Mechanism models are constructed to analyze dynamic gear performance and support unit response. Real-time data transmission is facilitated through sensor-based monitoring, enhancing the prediction speed and accuracy of dynamic performance through a synergy of mechanism modeling, machine learning, and real-time data analysis. The digital twin of the lifting system is visualized utilizing the Unity3D platform. Furthermore, functionalities on data acquisition, processing, and visualization across diverse application scenarios are encapsulated into modular components, streamlining the creation of high-fidelity digital twins. The frameworks and modeling methodologies presented herein can serve as a foundational and methodological guide for the exploration and implementation of digital twin technology within the oil and gas industry, ultimately fostering its advancement in this sector.

Identifying the spatial patterns of population displacement during wildfires in Valparaíso, Chile

ABSTRACT We present a data visualisation approach to support the rapid humanitarian response during the 2024 Valparaíso wildfires in Chile. Combining Meta user location data for population displacement, NASA FIRMS satellite imagery for wildfire locations, and census data, we identify key origins and destinations of displaced people during recent wildfires in Valparaíso, Chile. Our choropleth maps reveal spatial patterns of movement and socioeconomic factors, demonstrating the value of integrating diverse data sources for near real-time crisis response.

Hysteresis response of carbon release and nitrate reduction in polymer denitrification systems

Polymer denitrification has received much attention in the field of advanced wastewater treatment. It can release carbon source stably during long-term operation, which can be used as electron donor for denitrification. However, the response of the polymer denitrification system to the transient changes of nitrate is not sufficiently disclosed yet. In this study, the response of a polymer denitrification system to nitrate was comprehensively investigated through a series of experiments. Therefore, real-time response and hysteresis response phenomena were identified. The time dependence of microorganisms in the system and the recovery of the hysteresis response were elucidated. The experimental results revealed distinct response patterns before and after the hysteresis tipping point. The denitrifying microorganisms, which showed a high adaptive capacity, exhibited a real-time response over a range of low nitrate concentration variations (∼20–30 mg/L). In contrast, microbial recovery is poor over a range of high nitrate concentration variation (∼35–40 mg/L), which is referred to as a hysteresis response. Finally, the hysteresis response mechanism was revealed by monitoring the recovery of denitrification enzymes, gene and microbial communities. The results showed that transient shocks of high nitrate loads affect microbial community structure stability, denitrifying enzyme activity and gene expression. Meanwhile, the abundance of Microbacterium associated with carbon release was reduced. The combination of these factors leads to a hysteresis response in denitrification and carbon release. This work contributes to a deeper understanding of the hysteresis behavior in polymer denitrification systems, offering critical insights for optimizing system performance and improving nitrogen removal efficiency.

7056 Can ChatGPT Educate Multicultural Patients About Metabolic Syndrome?

Abstract Disclosure: G. Wu: None. E. Cheng: None. V.V. Toram: None. K. Rosen: None. A. Wong: None. W. Zhao: None. M. Del Buono: None. Background: Metabolic Syndrome, which affects Asians, is a major risk factor for diabetes. According to the National Institute of Health (NIH), 1 in 3 Americans have Metabolic Syndrome. Diabetes affects 30 million Americans, and 8 million more are undiagnosed. ChatGPT and Bard are Artificial Intelligence (AI) mediated LLMs launched on 11/30/2022 and 3/21/2023, respectively, that generate real-time conversational responses. Purpose: Can ChatGPT educate our multicultural patients about metabolic syndrome? Methods: 1. User prompt into ChatGPT and Bard: What is metabolic syndrome? I am a South Asian male, 40 years old, with a cholesterol level of 250 and a fasting blood sugar level of 120. The exact prompt was used with an Asian male as a substitute. 2. The prompt was translated into Telugu, Hindi, Simplified Chinese, and Traditional Chinese via Google Translate. 3. Flesch-Kincade (FK) method of ascertaining reading level was used. FK is used by the US Department of Education. Results: Flesch-Kincaid reading ease: ChatGPT: English=29.2, Telugu=60.1, Hindi=33.4, Chinese (Trad.)=14.5, Chinese (Simp.)=22.6 Bard: English=35.3, Telugu=52, Hindi=51.7, Chinese (Trad.)=52, Chinese (Simp.)=36.8 Language Group Comparison to English: ChatGPT: Indian Languages p=0.004; Chinese Languages p=0.081. Bard: Indian Languages p=0.007; Chinese Languages p=0.136. Of note, the Telegu response has one incorrect response. Conclusions: In conclusion, ChatGPT and Bard can provide factual responses based on English prompts. Foreign prompts can yield inaccurate information. Physicians and their teams still need to supervise patient usage. Presentation: 6/2/2024

Enhancing consumer trust in financial services: the role of technological security innovations

The financial services industry increasingly relies on technological advancements to maintain and enhance consumer trust. Consumer trust is a critical component influencing customer loyalty, financial inclusion, and overall economic stability. This paper explores the impact of technological security innovations on consumer trust in financial services. Fundamental innovations such as blockchain, advanced encryption, biometric authentication, artificial intelligence, and cloud security play significant roles in addressing security challenges. Blockchain technology provides a decentralized, tamper-proof method for recording transactions, enhancing transparency and reducing fraud risks. Advanced encryption techniques ensure the confidentiality and integrity of financial data, which is critical for protecting sensitive information. Biometric authentication methods offer secure and user-friendly solutions for identity verification, reducing reliance on traditional passwords and enhancing security. Artificial intelligence and machine learning improve fraud detection capabilities by providing real-time monitoring and response. Cloud security solutions offer scalable protection for financial data, ensuring compliance with regulatory standards. Through empirical analysis and case studies, this study demonstrates that these technologies significantly enhance consumer confidence by providing greater transparency, security, and reliability in financial transactions. The findings offer valuable insights for policymakers, financial institutions, and technology developers aiming to foster a more resilient and trust-based financial ecosystem. This research contributes to the existing literature by providing evidence of the direct impact of technological security innovations on consumer trust. Keywords: Consumer Trust, Financial Services, Technological Innovations, Blockchain, Encryption, Biometric Authentication, Artificial Intelligence, Cloud Security.