Improve Response Time Research Articles

Implementation and evaluation of digital twin framework for Internet of Things based healthcare systems

AbstractThe integration of digital twins (DTs) in healthcare is critical but remains limited in real‐time patient monitoring due to challenges in achieving low‐latency telemetry transmission and efficient resource management. This paper addresses these limitations by presenting a novel cloud‐based DT framework that optimises real‐time healthcare monitoring, providing a timely solution for critical healthcare needs. The framework incorporates a Pyomo‐based dynamic optimisation model, which reduces telemetry latency by 32% and improves response time by 52%, surpassing existing systems. Leveraging low‐cost, low‐latency multimodal sensors, the system continuously monitors critical physiological parameters, including SpO2, heart rate, and body temperature, enabling proactive health interventions. A DT definition language (Digital Twin Definition Language)‐based time series analysis and twin graph platform further enhance sensor connectivity and scalability. Additionally, the integration of machine learning (ML) strengthens predictive accuracy, achieving 98% real‐time accuracy and 99.58% under cross‐validation (cv = 20) using the XGBoost algorithm. Empirical results demonstrate substantial improvements in processing time, system stability, and learning capacity, with real‐time predictions completed in 17 ms. This framework represents a significant advancement in healthcare monitoring, offering a responsive and scalable solution to latency and resource constraints in real‐time applications. Future research could explore incorporating additional sensors and advanced ML models to further expand its impact in healthcare applications.

Smart Response: Leveraging AI Analytics for Enhanced Disaster Resilience

The emergence of artificial intelligence and cloud computing has transformed modern disaster response capabilities, particularly by developing sophisticated real-time analytics systems for natural disaster management. This article examines the integration of AI-powered analytics platforms that synthesize data from multiple sources, including satellite imagery, meteorological stations, and social media streams, to enable rapid response during crises. This article demonstrates that cloud-based real-time analytics substantially enhance early warning systems and optimize resource allocation during disaster events while significantly improving response times compared to traditional methods. However, these technological advances raise important ethical considerations regarding data privacy and equity in access to digital resources. The findings suggest that AI-driven disaster response systems offer unprecedented capabilities in protecting communities. However, their implementation must be guided by robust frameworks that ensure equitable access and maintain privacy standards while maximizing public safety benefits.

Automated Fire Sensing and Fire Extinguisher Module

The Hazard of fire and fire explosions poses significant risks to both property and human safety, necessitating immediate action to minimize damage. Conventional fire detection methods that rely on human observation may result in delays, particularly in unattended areas. To address these challenges, a specialized fire detection and extinguishing system is being developed with the capability to autonomously detect and extinguish fires. This system will also promptly notify individuals through messages and phone calls upon fire detection, improving response times and ensuring immediate awareness of fire incidents. Equipped with Arduino microcontrollers, the system integrates advanced sensors for precise detection of heat and smoke, allowing accurate pinpointing of fire locations. The use of Arduino technology provides adaptable functionality in various environments, ranging from residential to commercial settings. Effective communication of fire alerts is essential for promptly notifying firefighters and occupants, facilitating timely intervention and safe evacuations. The objective is to create an efficient system that harnesses modern technology to enhance fire safety measures, ultimately reducing fire related risks and effectively protecting lives.

Gwydion: Efficient auto-scaling for complex containerized applications in Kubernetes through Reinforcement Learning

Containers have reshaped application deployment and life-cycle management in recent cloud platforms. The paradigm shift from large monolithic applications to complex graphs of loosely-coupled microservices aims to increase deployment flexibility and operational efficiency. However, efficient allocation and scaling of microservice applications is challenging due to their intricate inter-dependencies. Existing works do not consider microservice dependencies, which could lead to the application’s performance degradation when service demand increases. As dependencies increase, communication between microservices becomes more complex and frequent, leading to slower response times and higher resource consumption, especially during high demand. In addition, performance issues in one microservice can also trigger a ripple effect across dependent services, exacerbating the performance degradation across the entire application. This paper studies the impact of microservice inter-dependencies in auto-scaling by proposing Gwydion, a novel framework that enables different auto-scaling goals through Reinforcement Learning (RL) algorithms. Gwydion has been developed based on the OpenAI Gym library and customized for the popular Kubernetes (K8s) platform to bridge the gap between RL and auto-scaling research by training RL algorithms on real cloud environments for two opposing reward strategies: cost-aware and latency-aware. Gwydion focuses on improving resource usage and reducing the application’s response time by considering microservice inter-dependencies when scaling horizontally. Experiments with microservice benchmark applications, such as Redis Cluster (RC) and Online Boutique (OB), show that RL agents can reduce deployment costs and the application’s response time compared to default scaling mechanisms, achieving up to 50% lower latency while avoiding performance degradation. For RC, cost-aware algorithms can reduce the number of deployed pods (2 to 4), resulting in slightly higher latency (300μs to 6 ms) but lower resource consumption. For OB, all RL algorithms exhibit a notable response time improvement by considering all microservices in the observation space, enabling the sequential triggering of actions across different deployments. This leads to nearly 30% cost savings while maintaining consistently lower latency throughout the experiment. Gwydion aims to advance auto-scaling research in a rapidly evolving dynamic cloud environment.

Adaptive load balancing strategies in service composition for improved system performance

Service composition enables the integration of multiple services to create new functionalities, optimizing resource utilization and supporting diverse applications in critical domains such as safety-critical systems, telecommunications, and business operations. This paper addresses the challenges in comparing load-balancing algorithms within service composition environments and proposes a novel dynamic load-balancing algorithm designed specifically for these systems. The proposed algorithm aims to improve response times, enhance system efficiency, and optimize overall performance. Through a simulated service composition environment, the algorithm was validated, demonstrating its effectiveness in managing the computational load of a BMI calculator web service. This dynamic algorithm provides real-time monitoring of critical system parameters and supports system optimization. In future work, the algorithm will be refined and tested across a broader range of scenarios to further evaluate its scalability and adaptability. By bridging theoretical insights with practical applications, this research contributes to the advancement of dynamic load balancing in service composition, offering practical implications for high-tech system performance.

Three-dimensional random-access confocal microscopy with 3D remote focusing system

Understanding biological activities in cells or deep tissues requires high-speed three-dimensional (3D) imaging. Substantial progress has been made with the emergence of 3D random-access microscopy. However, current solutions for fast 3D random-access imaging remain complex and costly. Herein we propose a simple, cost-effective, and fast 3D random-access confocal microscopy with remote focusing system. Our system shows isotropic response times across the x, y, and z axes, with a 34-fold improvement in axial response time over traditional piezo stages. We demonstrate its volumetric imaging performance with fluorescent particles and live cells. Furthermore, we validate the 3D random-access imaging capability of this system by continuously monitoring the signals in three different planes, showing a refresh rate of 500 Hz on two different positions in 3D. The simplicity, versatility, and affordability of our system promise widespread applications in research and industry.

“SafeSteps” A Flutter -Based Application to Provide Security to Women’s

With women’s safety being one of the most serious and pressing issues worldwide, rapid technological advancements have created new opportunities to enhance personal security. This paper presents an improved mobile application that prioritizes women’s safety through a SIM card-based location tracking approach instead of relying on GPS services that require an internet connection. This feature ensures consistent and reliable location tracking even in areas with weak or no internet access. Inspired by existing platforms like bSafe and My Safetipin, the app includes an emergency alert system that allows users to trigger SOS signals to authorities and nearby registered users by pressing the power button, volume button, or using specific gestures. The involvement of nearby users aims to provide immediate assistance before law enforcement arrives, potentially preventing harm or saving lives. The paper features a survey and technical analysis detailing the app’s architecture, usability, and the benefits of SIM based tracking for improved response times during emergencies. Key Words: Women’s safety, SIM card location tracking, emergency alert system, SOS message, offline tracking, mobile safety app, user assistance network.

Dual Control Strategy for Non-Minimum Phase Behavior Mitigation in DC-DC Boost Converters Using Finite Control Set Model Predictive Control and Proportional–Integral Controllers

Model Predictive Control (MPC) has emerged as a promising alternative for controlling power converters, offering benefits such as flexibility, simplicity, and rapid control response, particularly when short-horizon algorithms are employed. This paper introduces a system using a short-horizon Finite Control Set MPC (FCS-MPC) strategy to specifically address the challenge of non-minimum phase behavior in boost converters. The non-minimum phase issue, which complicates the control process by introducing an initial inverse response, is effectively mitigated by the proposed method. A Proportional–Integral (PI) controller is integrated to dynamically adjust the reference current based on the output voltage error, thereby enhancing overall system stability and performance. Unlike conventional PI-MPC methods, where the PI controller has an influence on the system dynamics, the PI controller in this approach is solely used for tuning the reference current needed for the FCS-MPC controller. The PI controller addresses small deviations in output voltage, primarily due to model prediction inaccuracies, ensuring steady-state accuracy, while the FCS-MPC handles fast dynamic responses to adapt the controller’s behavior based on load conditions. This dual control strategy effectively balances the need for precise voltage regulation and rapid adaptation to varying load conditions. The proposed method’s effectiveness is validated through a multi-stage simulation test, demonstrating significant improvements in response time and stability compared to traditional control methods. Hardware-in-the-loop testing further confirms the system’s robustness and potential for real-time applications in power electronics.

A framework for data-driven decision making in advanced manufacturing systems: Development and implementation

Integration of sophisticated technologies such as Internet of Things, cyber physical systems and big data analytics have revolutionized the advanced manufacturing systems (AMS). However, implementation of data-driven decision making in AMS still remains challenging due to data heterogeneity, real-time processing demands, and integration complexities. This paper overcomes this challenge by presenting a novel framework for adoption of DDDM in AMS to enhance its decision-making capabilities. This framework consists of six stages: manufacturing stage, sensing stage, data stage, knowledge stage, decision stage, and application stage. The proposed framework leverages big data analytics to extract actionable insights from diverse datasets, integrates CPS to create a seamless interaction between physical and digital systems, and employs IoT technologies for real-time data acquisition and monitoring. The framework is validated through a comprehensive case study involving a CNC milling machine dataset, demonstrating significant improvements in operational efficiency, decision accuracy, and response time. The case study involves detailed data collection steps, preprocessing, and analysis, showcasing the framework’s practical implementation and effectiveness. The results show that the proposed framework addresses existing challenges and provides a scalable solution for DDDM implementation in AMS.

Quantum Marine Predator Algorithm: A Quantum Leap in Photovoltaic Efficiency Under Dynamic Conditions

The Quantum Marine Predator Algorithm (QMPA) presents a groundbreaking solution to the inherent limitations of conventional Maximum Power Point Tracking (MPPT) techniques in photovoltaic systems. These limitations, such as sluggish response times and inadequate adaptability to environmental fluctuations, are particularly pronounced in regions with challenging weather patterns like Sunderland. QMPA emerges as a formidable contender by seamlessly integrating the sophisticated hunting tactics of marine predators with the principles of quantum mechanics. This amalgamation not only enhances operational efficiency but also addresses the need for real-time adaptability. One of the most striking advantages of QMPA is its remarkable improvement in response time and adaptability. Compared to traditional MPPT methods, which often struggle to keep pace with rapidly changing environmental factors, QMPA demonstrates a significant reduction in response time, resulting in up to a 30% increase in efficiency under fluctuating irradiance conditions for a resistive load of 100 Ω. These findings are derived from extensive experimentation using NASA’s worldwide power prediction data. Through a detailed comparative analysis with existing MPPT methodologies, QMPA consistently outperforms its counterparts, exhibiting superior operational efficiency and stability across varying environmental scenarios. By substantiating its claims with concrete data and measurable improvements, this research transcends generic assertions and establishes QMPA as a tangible advancement in MPPT technology.

Visual Implicit Pre-Cueing Improves Response Time in Decision-Making in Dyslexic Children.

This study aimed to examine the effect of visual pre-cueing presented at different time intervals in the response time of dyslexic and non-dyslexic children. Fifteen dyslexic and 15 non-dyslexic children performed a computerised four-choice reaction time task across four conditions: no pre-cue and a 43-ms time interval (or duration) of a centralised dot appearing in the stimulus circle at 43, 86 or 129 ms prior to the stimulus. Each condition was repeated eight times, totaling 32 trials, and presented in a random order. Response correctness and response times were recorded for each trial, and z-scores were obtained by standardising performance in the three pre-cued conditions relative to the no pre-cue condition. Dyslexic children took longer to respond in the task than non-dyslexic children. Both dyslexic and non-dyslexic children had faster response times in the pre-cued conditions than in the conditions without the pre-cue. These lower response times were inversely correlated with the length of the pre-cueing interval. These results suggest that dyslexic children use visual pre-cueing to improve decision-making. The ability of dyslexic children to use pre-cues may offer an interesting avenue for the exploration of interventions aimed at minimising behavioural and cognitive difficulties resulting from dyslexia.

Differential Analysis of Emergency Vehicle Detection in Urban Traffic : A Systematic Review

The differential analysis of emergency vehicle detection in urban traffic is crucial for improving response times and reducing traffic-related delays during emergencies. This systematic review aims to analyze various methods and technologies, such as acoustic, visual, and sensor-based systems, used for detecting emergency vehicles in complex urban environments. The motivation behind this study is the growing need for more efficient traffic management systems that prioritize emergency vehicles, minimizing delays caused by congestion. However, limitations include the inconsistent performance of detection systems in varying weather, lighting, and noise conditions, as well as integration challenges with existing infrastructure. The objective is to evaluate current detection methods, identify their limitations, and propose potential improvements to enhance the accuracy and reliability of emergency vehicle detection in urban traffic systems.

IoT based smart emergency response system (SERS) for monitoring vehicle, home and health status

This paper presents an innovative Internet of Things (IoT)-based smart emergency response system that aims to improve safety and efficiency across vehicles, homes and healthcare settings. The proposed system combines advanced sensors, communication technologies, and intelligent algorithms to provide a comprehensive solution for real-time data monitoring and emergency management. Here, the vehicle device is equipped with temperature, gas, flame, GPS, piezo, and accelerometer sensor and the system can detect accidents, hazardous conditions, precise location automatically notifying emergency services. The home devices incorporate Flame Sensor, humidity sensor, DHT11, MQ-2 gas sensor, MQ-4 Gas Sensor, MQ-135 gas sensor, and intrusion detection to ensure immediate alerts and response to potential security threats. Lastly the health device is equipped with temperature, humidity, stress levels, heart rate, oxygen saturation, and fetal heart rate sensor which can monitor vital signs and detect anomalies, triggering alerts for medical emergencies and facilitating timely intervention. The novelty of the proposed study is it incorporates the three devices, processes the data and automatically notifies the emergency responders for further emergency assistance. Moreover, the system utilizes a cloud-based platform to collect, analyze, and disseminate data, enabling seamless coordination between emergency responders such as ambulance for health and accident emergency, fire brigade for fire emergency and affected individuals. We have also developed a MySQL-PHP-Laravel based live dashboard and emergency response system where both the user and relevant stakeholder will have the access which could mitigate the emergency situation in the quickest amount of time without human intervention. The findings of our study reveals that incorporation of IoT and smart infrastructure offers significant improvement of server response time 3 ms, accuracy > 99% and overall effectiveness of emergency management. This integrated approach not only enhances individual safety but also contributes to broader public safety goals, showcasing the potential of IoT technologies in creating smarter, safer environments.

From Monitoring to Observability: A Paradigm Shift in IT Operations

This article explores the paradigm shift from traditional monitoring to observability in IT operations, driven by the increasing complexity of modern distributed systems, data overload, evolving cybersecurity threats, and the need for proactive problem-solving. Observability platforms, built on the four pillars of events, metrics, traces, and logs, provide comprehensive insights into system behavior and performance. Adopting observability practices offers numerous benefits, including automated discovery of system components, dependency mapping, topology visualization, correlative intelligence, faster root cause diagnosis, and more accurate anomaly detection through auto-baselining. Organizations that embrace observability report significant improvements in incident response times, system reliability, and overall operational efficiency. The article guides successfully transitioning to an observability-focused approach by embracing automation, fostering a culture of observability, implementing the right tools, and leveraging AI and machine learning for advanced analytics and insights.

Enhanced NO2 Gas Sensing in Nanocrystalline MoS2 via Swift Heavy Ion Irradiation: An Experimental and DFT Study.

Nanostructured transition metal dichalcogenides (TMDs) like MoS2 hold promise for gas sensing applications due to their exceptional properties. However, limitations exist in maximizing sensor performance, such as limited active sites for gas interaction and sluggish response/recovery times. This study explores swift heavy ion (SHI) irradiation as a strategy to address these challenges in MoS2-based NO2 gas sensors. MoS2 nanoflakes were fabricated and subsequently irradiated with 120 MeV silver (Ag) ions to induce structural and morphological modifications. Characterization techniques confirmed the formation of Mo and S vacancies within the MoS2 lattice due to irradiation. Significantly, SHI irradiation resulted in a remarkable enhancement of approximately 3 times improvement in sensing response compared to pristine MoS2 sensors. Additionally, the irradiated sensors exhibit substantial improvements in both response and recovery times for NO2 detection. SHI irradiation resulted in the formation of self-affine nanostructures and increased grain fragmentation as fluence rises. This enhanced surface area is hypothesized to promote gas-sensor response. To gain deeper insights into the underlying mechanism, first-principles calculations were employed. These calculations suggest that electron transfer occurs from the MoS2 surface to the NO2 molecule during interaction. Furthermore, the irradiation-induced vacancies facilitate stronger NO2 adsorption on the MoS2 surface compared to the pristine sample. This work demonstrates the effectiveness of SHI irradiation in engineering defects within MoS2 nanoflakes, leading to significantly improved NO2 gas-sensing performance. This approach offers a promising avenue for developing next-generation TMD-based gas sensors with enhanced sensitivity, response times, and stability.

Cost-Utility Analysis of Teledermatology Units in Primary Care Centers Versus Face-to-Face Dermatology Consultations in the Hospital.

To perform an economic evaluation to determine whether or not teledermatology (TD) units in primary care (PC) centers offer an alternative in terms of cost-utility and cost per quality-adjusted life years (QALYs) to conventional dermatology consultations (face-to-face dermatology [F-F/D]) at the hospital from the perspective of the Public Health System (PHS) and the patients. This is a randomized, controlled, nonblinded, and multicenter study. During 6 months, data from 450 patients (TD: 225 vs. F-F/D: 225) were collected. From both perspectives, costs, quality of life, and costs per QALYs were analyzed. The QALY scores were estimated from the EuroQol-5D-5L (EQ5D-5L) questionnaire responses. From the perspective of the PHS, the cost per patient was 53.04% lower in the TD group (p < 0.001). Hospital visits decreased by 72.43% in the TD group (p < 0.001). From the patients' perspective, TD reduced costs per patient by 77.59% (p < 0.001). The cost per QALY was 63.34% higher in the F-F/D group (p < 0.001). The TD group's total costs were 56.34% lower (p < 0.001). Furthermore, patients in the TD group gained 0.05 QALYs more than those in the F-F/D group (p = 0.004). This study shows that TD units in PC represent a significant cost-effective alternative to conventional hospital follow-up. To enhance TD in PC, it is important to introduce remote consultation platforms incorporating artificial intelligence for prediagnosis. This will enable general practitioners and nurses to make more accurate initial assessments. It is also crucial to provide thorough training to healthcare personnel using these technologies to ensure more efficient and personalized care. Public health nurses will benefit from gaining new skills in managing digital tools, which will help in the early identification of dermatological diseases and reduce unnecessary referrals to specialists. This will optimize resources and improve response times for patients.

Fault tolerant & priority basis task offloading and scheduling model for IoT logistics

The Internet of Things (IoT) has connected millions of devices to the internet for communication and computation purposes. Due to constraints such as limited battery power, processing capabilities, and storage capacity, IoT devices often rely on remote fog nodes for task execution, a practice known as task offloading. The NP-hard nature of offloading and scheduling IoT tasks onto fog nodes poses significant challenges. Current IoT task offloading often relies on single parameters like device MIPS, RAM, or battery power. Similarly, task allocation on fog nodes frequently prioritizes VM MIPS. To address energy consumption and task failures in IoT systems, robust fault tolerance, efficient task offloading, and effective scheduling are crucial. This paper introduces a novel Fault-Tolerant, Priority-Based Task Offloading and Scheduling Model (FP-TOSM) for IoT logistics. Utilizing the Analytic Hierarchy Process (AHP) for multi-criteria decision-making, IoT tasks are prioritized. Tasks below a specified threshold are executed locally, while those within a defined range are offloaded to a fog node. Tasks exceeding this range are delegated to the cloud. The Euclidean formula is used to determine the proximity between the IoT logistics vehicle and fog nodes for offloading decisions. Dynamic re-clustering fault tolerance mechanisms are utilized to manage task failures after offloading. Successful offloaded tasks are allocated to suitable VMs in the cloud and fog nodes. The proposed strategy selects the most suitable VM for offloaded tasks using a multi-criteria decision-making process to reduce energy consumption, SLA violations, and execution costs. The model's performance is evaluated through simulations in iFogSim2. Results demonstrate reductions in energy consumption by up to 5.8 % and 10.4 %, decreases in SLA violations by up to 25.2 %, and a 16.28 % improvement in response time compared to an ACO-based model with a response time of 17.8 %. Additionally, the task failure ratio shows a significant reduction of up to 22.1 %. These findings highlight the effectiveness of FP-TOSM in enhancing efficiency and reliability within fog computing environments.

Revolutionizing Ocean Cleanup: A Portuguese Case Study with Unmanned Vehicles Fighting Spills

It is of the utmost importance for every country to monitor and control maritime pollution within its exclusive economic zone (EEZ). The European Maritime Safety Agency (EMSA) has developed and implemented the CleanSeaNet (CSN) satellite monitoring system to aid in the surveillance and control of hydrocarbon and hazardous substance spills in the ocean. This system’s primary objective is to alert European Union (EU) coastal states to potential spills within their EEZs, enabling them to take the necessary legal and operational actions. To reduce operational costs and increase response capability, the feasibility of implementing a national network (NN) of unmanned vehicles (UVs), both surface and aerial, was explored using a Portuguese case study. The following approach and analysis can be easily generalized to other case studies, bringing essential knowledge to the field. Analyzing oil spill alert events in the Portuguese EEZ between 2017 and 2021 and performing a strengths, weaknesses, opportunities, and threats (SWOT) analysis, essential information has been proposed for the optimal location of an NN of UVs. The study results demonstrate that integrating spill alerts at sea with UVs may significantly improve response time, costs, and personnel involvement, making maritime pollution combat actions more effective.

Analysis and comparison of encryption and verification algorithms applied to a Fog Computing Architecture

ABSTRACT Due to the Internet of Things (IoT) widespread adoption, data traffic to cloud servers for analysis has surged, impacting IoT system latency. Fog Computing emerged as a solution, bringing processing capabilities closer to data generation points, reducing data sent to the cloud, and improving response times. However, it intensifies security concerns due to the proximity of sensitive data to users and limited node computational resources. This research aims to study encryption and data verification algorithms using various datasets and the cryptography software OpenSSL. Testing is performed on Raspberry Pi hardware, facilitating the implementation of Fog nodes. Metrics such as CPU, memory, and energy consumption are evaluated using scripts. Results show that data type does not influence algorithm choice, and patterns related to data size behaviour are identified, aiding in defining security strategies.

Implicit motor sequence learning using three-dimensional reaching movements with the non-dominant left arm

Interlimb differences in reach control could impact the learning of a motor sequence that requires whole-arm movements. The purpose of this study was to investigate the learning of an implicit, 3-dimensional whole-arm sequence task with the non-dominant left arm compared to the dominant right arm. Thirty-one right-hand dominant adults completed two consecutive days of practice of a motor sequence task presented in a virtual environment with either their dominant right or non-dominant left arm. Targets were presented one-at-a-time alternating between Random and Repeated sequences. Task performance was indicated by the time to complete the sequence (response time), and kinematic measures (hand path distance, peak velocity) were used to examine how movements changed over time. While the Left Arm group was slower than the Right Arm group at baseline, both groups significantly improved response time with practice with the Left Arm group demonstrating greater gains. The Left Arm group improved performance by decreasing hand path distance (straighter path to targets) while the Right Arm group improved performance through a smaller decrease in hand path distance combined with increasing peak velocity. Gains made during practice on Day 1 were retained on Day 2 for both groups. Overall, individuals reaching with the non-dominant left arm learned the whole-arm motor sequence task but did so through a different strategy than individuals reaching with the dominant right arm. The strategy adopted for the learning of movement sequences that require whole-arm movements may be impacted by differences in reach control between the nondominant and dominant arms.