Reduce Response Time Research Articles

Transforming Cybersecurity into Critical Energy Infrastructure: A Study on the Effectiveness of Artificial Intelligence

This work explores the integration and effectiveness of artificial intelligence in improving the security of critical energy infrastructure, highlighting its potential to transform cybersecurity practices in the sector. The ability of artificial intelligence solutions to detect and respond to cyber threats in critical energy infrastructure environments was evaluated through a methodology that combines empirical analysis and artificial intelligence modeling. The results indicate a significant increase in the threat detection rate, reaching 98%, and a reduction in incident response time by more than 70%, demonstrating the effectiveness of artificial intelligence in identifying and mitigating cyber risks quickly and accurately. In addition, implementing machine learning algorithms has allowed for the early prediction of failures and cyber-attacks, significantly improving proactivity and security management in energy infrastructure. This study highlights the importance of integrating artificial intelligence into energy infrastructure security strategies, proposing a paradigmatic change in cybersecurity management that increases operational efficiency and strengthens the resilience and sustainability of the energy sector against cyber threats.

Trajectory tracking control of four-rotor UAV based on nonlinear extended state observer and model predictive control in wind disturbance environment

In this paper, a novel hybrid controller utilizing a nonlinear extended state observer (NLESO) is introduced. According to the Newton-Euler equation, the four-rotor UAV is modeled and divided into the position subsystem and attitude subsystem. The position subsystem is controlled using the Model Predictive Control (MPC) algorithm. At the same time, the attitude controller utilizes the Sliding Mode Control (SMC) algorithm, which provides an enhanced level of responsiveness, resulting in reduced response times. The Nonlinear Extended State Observer (NLESO) is used in each controller in order to counteract the effects of disturbances. The findings obtained from the simulation experiments demonstrate that the controller design exhibits favorable tracking performance and robust resistance against external disturbances.

Request Dispatching Over Distributed SDN Control Plane: A Multiagent Approach.

Software-defined networking (SDN) allows flexible and centralized control in cloud data centers. An elastic set of distributed SDN controllers is often required to provide sufficient yet cost-effective processing capacity. However, this introduces a new challenge: Request Dispatching among the controllers by SDN switches. It is essential to design a dispatching policy for each switch to guide the request distribution. Existing policies are designed under certain assumptions, including a single centralized agent, global network knowledge, and a fixed number of controllers, which often cannot be satisfied in practice. This article proposes MADRina, Multiagent Deep Reinforcement Learning for request dispatching, to design policies with high dispatching adaptability and performance. First, we design a multiagent system to address the limitation of using a centralized agent with global network knowledge. Second, we propose a Deep Neural Network-based adaptive policy to enable request dispatching over an elastic set of controllers. Third, we develop a new algorithm to train the adaptive policies in a multiagent context. We prototype MADRina and build a simulation tool to evaluate its performance using real-world network data and topology. The results show that MADRina can significantly reduce response time by up to 30% compared to existing approaches.

Autonomous Vehicle for Fire detecting and Extinguishing

— Our project presents an innovative solution for fire detection and extinguishing through an autonomous rover system. Leveraging fire sensors integrated with a node MCU, the system swiftly identifies fire outbreaks and transmits alerts to the rover via the ThingSpeak cloud platform. Upon receipt of the alert, the rover activates a buzzer for immediate local notification and dispatches alerts through a dedicated mobile application, ensuring rapid response from relevant personnel. Employing a line follower mechanism, the rover autonomously navigates towards the fire-affected area. Equipped with a water spraying system, the rover efficiently extinguishes flames, minimizing water wastage and enhancing firefighting effectiveness. This approach significantly reduces response time, enhancing safety across diverse environments. The system's ability to optimize resource utilization and automate firefighting procedures makes it adaptable to various settings, including industrial facilities, urban areas, and remote locations. Overall, our autonomous rover system represents a substantial advancement in firefighting technology, offering swift and efficient fire detection and extinguishing capabilities to mitigate fire hazards effectively. Keywords— NodeMCU, Rover, Flame Sensors, Servomotor, L293d motor drivers, Li-ion batteries

3D-Printed Fluorescent Hydrogel Consisting of Conjugated Polymer and Biomacromolecule for Fast and Sensitive Detection of Cr(VI) in Vegetables.

Portable fluorescent film sensors offer a solution to the contamination issue in homogeneous sensor detection systems. However, their special structure leads to low sensitivity and a long response time, resulting in a significant scientific challenge limiting their development and application. In this work, we propose a dual design strategy to prepare highly sensitive film sensors for rapidly detecting Cr2O72-. Specifically, P(Fmoc-Osu)-SA hydrogel films were developed by integrating the biological macromolecule sodium alginate (SA) with the conjugated polymer poly(N-(9-Fluorenylmethoxycarbonyloxy)succinimide) (P(Fmoc-Osu)), using both mold and inkjet 3D printing methods. The "molecular wire effect" of the sensing unit P(Fmoc-Osu) and the water channel within the film substrate are responsible for the improved sensitivity and the reduced response time of this thin film sensor. P(Fmoc-Osu)-SA hydrogel films prepared by these two methods can rapidly detect Cr2O72- with limits of detection of 1.18 and 0.078 nM, respectively. Considering that 3D-printed hydrogel films can be tailored to different shapes according to detection needs, the P(Fmoc-Osu)-SA hydrogel films produced from this method were effectively applied in vegetable samples. This study provides an innovative and effective strategy for the development of biocompatible hydrogel sensors that offer the potential for determining trace amounts of Cr2O72- in agriculture.

Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid

In the Industry 4.0 era of smart grids, the real-world problem of blackouts and cascading failures due to cyberattacks is a significant concern and highly challenging because the existing Intrusion Detection System (IDS) falls behind in handling missing rates, response times, and detection accuracy. Addressing this problem with an early attack detection mechanism with a reduced missing rate and decreased response time is critical. The development of an Intelligent IDS is vital to the mission-critical infrastructure of a smart grid to prevent physical sabotage and processing downtime. This paper aims to develop a robust Anomaly-based IDS using a statistical approach with a machine learning classifier to discriminate cyberattacks from natural faults and man-made events to avoid blackouts and cascading failures. The novel mechanism of a statistical approach with a machine learning (SAML) classifier based on Neighborhood Component Analysis, ExtraTrees, and AdaBoost for feature extraction, bagging, and boosting, respectively, is proposed with optimal hyperparameter tuning for the early discrimination of cyberattacks from natural faults and man-made events. The proposed model is tested using the publicly available Industrial Control Systems Cyber Attack Power System (Triple Class) dataset with a three-bus/two-line transmission system from Mississippi State University and Oak Ridge National Laboratory. Furthermore, the proposed model is evaluated for scalability and generalization using the publicly accessible IEEE 14-bus and 57-bus system datasets of False Data Injection (FDI) attacks. The test results achieved higher detection accuracy, lower missing rates, decreased false alarm rates, and reduced response time compared to the existing approaches.

ETFC: Energy-efficient and deadline-aware task scheduling in fog computing

The Internet of Things (IoT) is constantly evolving and expanding. However, due to the limited IoT resources, it is intertwined with fog computing to use their resources to compensate for the limitations of IoT resources. On the other hand, fog devices face challenges, such as resource heterogeneity, high distribution, dynamism, and limitations, so an efficient task scheduling approach is needed to deploy fog computing resources effectively and improve the quality of service (QoS). This work mathematically formulates the task scheduling problem to minimize energy consumption and cost and improve QoS by reducing response time and deadline violation times of IoT tasks. Then, it proposes an Energy-efficient and deadline-Aware Task scheduling in Fog Computing (ETFC) method that predicts the traffic of fog nodes by a Support Vector Machine (SVM) and divides them into low-traffic and high-traffic groups. Next, the ETFC method schedules the low-traffic part with an algorithm based on reinforcement learning using the proposed ICLA-SOA, which is an algorithm based on irregular cellular learning automata and schedules the tasks of the high-traffic part with a metaheuristic algorithm using the proposed Non-dominated Sorting Genetic Algorithm (NSGA-III). The simulation results demonstrate that the ETFC method exhibits up to an 84 % enhancement in response time, up to a 33 % reduction in energy consumption, up to a 30 % decrease in costs, and up to a 28 % advancement in meeting task deadlines compared to other methods.

Instinctive Detection of Accident Occurrence using Numerous Machine Learning Techniques with Comparative Study

Objectives: The research work aims to develop an automated detection system that uses video captures to identify roadside accidents or significant events. By alerting nearby hospitals and emergency services, the system reduces response times and potentially saves lives. The system’s integration with existing emergency response systems ensures prompt assistance to those in need. Methods: The video processing pipeline begins by converting video files into frames for analysis and feature extraction. These features serve as inputs for classification algorithms such as Random Forest, SVM, and KNN. The model’s performance is evaluated using a training set and unseen test data, with the predicted classifications compared against the ground truth labels. Findings: Among the tested classification algorithms, the Random Forest algorithm achieved the highest accuracy. Using 128 frames for analysis provided more comprehensive information, yielding a 96% accuracy rate. This combination proves to be a powerful tool in classification tasks, providing reliable and accurate outputs. Novelty: Machine learning algorithms are instrumental in automating accident detection from video captures. They analyse video footage to identify accidents and promptly alert relevant authorities. This technology can also dispatch emergency messages to nearby hospitals, ensuring quick assistance. The consideration of different frame counts in classification improves accuracy by capturing critical moments and patterns. Machine learning algorithms applied in this work significantly enhance emergency response, reduce response times, and potentially save lives. Keywords: ­ Emergency Services, Accidents, Feature Extraction, Random Forest, SVM, KNN

Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics.

IoT-based smart transportation monitors vehicles, cargo, and driver statuses for safe movement. Due to the limited computational capabilities of the sensors, the IoT devices require powerful remote servers to execute their tasks, and this phenomenon is called task offloading. Researchers have developed efficient task offloading and scheduling mechanisms for IoT devices to reduce energy consumption and response time. However, most research has not considered fault-tolerance-based job allocation for IoT logistics trucks, task and data-aware scheduling, priority-based task offloading, or multiple-parameter-based fog node selection. To overcome the limitations, we proposed a Multi-Objective Task-Aware Offloading and Scheduling Framework for IoT Logistics (MT-OSF). The proposed model prioritizes the tasks into delay-sensitive and computation-intensive tasks using a priority-based offloader and forwards the two lists to the Task-Aware Scheduler (TAS) for further processing on fog and cloud nodes. The Task-Aware Scheduler (TAS) uses a multi-criterion decision-making process, i.e., the analytical hierarchy process (AHP), to calculate the fog nodes' priority for task allocation and scheduling. The AHP decides the fog nodes' priority based on node energy, bandwidth, RAM, and MIPS power. Similarly, the TAS also calculates the shortest distance between the IoT-enabled vehicle and the fog node to which the IoT tasks are assigned for execution. A task-aware scheduler schedules delay-sensitive tasks on nearby fog nodes while allocating computation-intensive tasks to cloud data centers using the FCFS algorithm. Fault-tolerant manager is used to check task failure; if any task fails, the proposed system re-executes the tasks, and if any fog node fails, the proposed system allocates the tasks to another fog node to reduce the task failure ratio. The proposed model is simulated in iFogSim2 and demonstrates a 7% reduction in response time, 16% reduction in energy consumption, and 22% reduction in task failure ratio in comparison to Ant Colony Optimization and Round Robin.

LQTI boost pressure and EGR rate control of a diesel air charge system with eBoost assistance

Turbocharged diesel engines often suffer significant response delay due to so-called turbo-lag, especially engines with large displacement. For this reason, many technologies have been developed to reduce turbo-lag. This paper develops a coordinated control strategy for a diesel engine equipped with an eBoost (electrical compressor) system to significantly reduce turbo-lag. A multiple-input and multiple-output (MIMO) Linear Quadratic Tracking with Integral (LQTI) control strategy, along with its scheduling logic, is developed for the Ford 6.7 L 8-cylinder diesel engine equipped with a variable geometry turbocharger (VGT), exhaust gas recirculation (EGR), and added eBoost along with a bypass valve. Note that the existing production engine does not have an eBoost and bypass valve. Multiple model-based LQTI controllers were designed at different engine operational conditions based on the associated linearized models, and the control outputs are scheduled based upon the engine load condition and bypass valve position. The developed control strategy is validated in both simulation and experimental studies, and the test results show a reduction in engine response time by up to 81.36% in terms of reaching target intake manifold (boost) pressure following a load step-up, compared with the production configuration (without eBoost and bypass valve) with no significant impact on NOx emissions.

Implementation of PID controller and enhanced red deer algorithm in optimal path planning of substation inspection robots

AbstractIn contemporary power transmission systems, substation monitoring stands as a vital but challenging task. While robotics offers promise in this regard, its potential is still nascent, struggling to replicate human intelligence. This article's core aim was to optimize robot path planning (RPP). Employing the enhanced red deer algorithm (ERDA), we sought to bolster RPP for more efficient substation inspections. The key methods used seem to be modeling, experimentation, comparative analysis, and some elements of data benchmarking to systematically evaluate and validate their proposed technique and models both in simulation and the real world. Research aims to enhance substation inspection effectiveness and bolster the safety of power usage in society. Proposed hybrid approach, combining proportional–integral–derivative (PID) with ERDA (PID–ERDA), underpins an Intelligent Intelligent RPP framework tailored to substation inspections. Examining the PID–ERDA model's performance, it significantly improved path length by 18%–29% and reduced response times by 14%–26% compared with PID or ERDA alone. PID–ERDA consistently achieved optimal solutions in 40–60 trials out of 85, while PID and ERDA managed 20–40 trials with inconsistent optimization. Additionally, it reduced average response times to 17–20 s from 21 to 27 s observed when using PID and ERDA separately. PID–ERDA also demonstrated superior path accuracy, surpassing methods like improved adaptive control algorithm‐feedforward neural network, enhanced unified algorithm‐susceptible‐infected‐removed, and bounded behavior‐particle swarm optimization by 7%–13%. The study affirms that the PID–ERDA model significantly enhances path planning for substation inspections, representing a milestone in RPP for power station inspections within modern power transmission systems. The primary contribution of this research is the significant improvement it brings to RPP for power station inspections, especially in substation monitoring within modern power transmission systems.

Vehicle Accident Detection System

Road traffic accidents pose a serious threat to public safety and cause a large number of victims every year. Of course, in order to reduce these risks, the demand for traffic accidents must increase. This brief presents a new solution using technology to improve road safety. The collision detection system is designed to detect and alert police and emergency services in the event of a collision. The system uses a combination of sensors, cameras, and machine learning algorithms to accurately identify accidents, analyze their severity, and send important information to first responders; thus, reducing response time and potentially saving lives. Key Words: public safety, emergency services, real-time, sensors

Enhanced piezoelectricity of MAPbI3 by the introduction of MXene and its utilization in boosting high-performance photodetectors.

Recently, perovskite photodetectors (PDs) have been risen to prominence due to substantial research interest. Beyond merely tweaking the composition of materials, a cutting-edge advancement lies in leveraging the innate piezoelectric polarization properties of perovskites themselves. Here, the investigation shows utilizing Ti3C2Tx, a typical MXene, as an intermediate layer for significantly boosting the piezoelectric property of MAPbI3 thin films. This improvement is primarily attributed to the enhanced polarization of the methylammonium (MA+) groups within MAPbI3, induced by the OH groups present in Ti3C2Tx. A flexible PD based on the MAPbI3/MXene heterostructure was then fabricated. The new device is sensitive to a wide range of wavelengths, displays greatly enhanced performances owing to the piezo-phototronic coupling. Moreover, the device is endowed with a greatly reduced response time, down to millisecond level, through the pyro-phototronic effect. The characterization shows applying a -1.2% compressive strain on the PD leads to a remarkable 102% increase of the common photocurrent, and a 76% increase for the pyro-phototronic current. The present work reveals how the emerging piezo-phototronic and pyro-phototronic effects can be employed to design high-performance flexible perovskite PDs. This article is protected by copyright. All rights reserved.

LOAD BALANCING IN CLOUD COMPUTING VIA MAYFLY OPTIMIZATION ALGORITHM

Cloud computing is a new technology that enables users to store and retrieve data via the Internet on demand rather than using their hardware. Cloud computing comprises distinct data centers (servers) and clients (users). Load unbalancing is a multi-variant, multi-constraint issue that lowers the efficacy and performance of system resources. Therefore, a load scheduling technique is needed to distribute work among the right VMs and preserve the trade-off between them. To achieve better performance, this paper presents a novel mayfly optimization algorithm for load balancing (MFO-LB), which utilizes mayfly flight behavior and mating dynamics. The proposed technique balances the load in the cloud by managing the incoming loads by allocating resources according to user requests. The proposed work intends to increase performance by uniformly dividing the workload among the virtual machines, which will decrease utilization and reaction time. The proposed MFO-LB approach is beneficial for maintaining system stability, reducing response time (RT), and maximizing resource productivity in cloud environments. Finally, the effectiveness of the proposed technique is assessed by employing several metrics, including execution cost, RT, execution time, and makespan. The proposed method achieves up to 23.4 % low RT, a 24 %decrease in makespan, and a 31.5 % decrease in completion time, respectively.

Online Ambulance Booking System

Abstract: The Online Ambulance Booking Service is a vital healthcare innovation designed to address the critical need for efficient and timely emergency medical transportation. In the modern world, access to medical care is of utmost importance, and this service aims to bridge the gap between patients and emergency medical services. This abstract provides an overview of the key features and functionalities of the system. The Online Ambulance Booking Service leverages technology to create a userfriendly platform that enables individuals to request ambulance services with ease. Users can access the service through a web application or mobile app, making it accessible to a wide range of people. The Online Ambulance Booking Service is a solution that enhances emergency medical response, saving valuable time in critical situations. It fosters a sense of security and reliability in healthcare services, benefiting both the general public and healthcare providers. This abstract highlight the importance of such a service in the healthcare industry, emphasizing its role in improving patient outcomes and reducing response times during emergencies.

Designing, development and validation of an app to reduce the response time of the emergency medical services.

Delays in prehospital care attributable to the call-taking process can often be traced back to miscommunication, including uncertainty around the call location. Geolocation applications have the potential to streamline the call-taking process by accurately identifying the caller's location. To develop and validate an application to geolocate emergency calls and compare the response time of calls made via the application with those of conventional calls made to the Brazilian Medical Emergency System (Serviço de Atendimento Médico de Urgência-SAMU). This study was conducted in two stages. First, a geolocating application for SAMU emergency calls (CHAMU192) was developed using a mixed methods approach based on design thinking and subsequently validated using the System Usability Scale (SUS). In the second stage, sending time of the geolocation information of the app was compared with the time taken to process information through conventional calls. For this, a hypothetical case control study was conducted with SAMU in the Maringá, Paraná, Brazil. A control group of 350 audio recordings of emergency calls from 2019 was compared to a set of test calls made through the CHAMU192 app. The CHAMU192 group consisted of 201 test calls in Maringá. In test calls, the location was obtained by GPS and sent to the SAMU communication system. Comparative analysis between groups was performed using the Mann-Whitney test. CHAMU192 had a SUS score of 90, corresponding to a "best imaginable" usability rating. The control group had a median response time of 35.67 seconds (26.00-48.12). The response time of the CHAMU192 group was 0.20 (0.15-0.24). The use of the CHAMU192 app by emergency medical services could significantly reduce response time. The results demonstrate the potential of app improving the quality and patient outcomes related to the prehospital emergency care services.

Railway Accident Prevention Using Sensor

Rail accidents pose a significant threat to both human life and infrastructure. This abstract presents a comprehensive overview of the railway accident prevention system that incorporates various sensors for effective monitoring and early detection of potential threats. The proposed system includes sensors such as ultrasonic Sensor and Vibration Sensor. These sensors work in tandem to collect real-time data on various parameters, including train location, Accident, obstacles on the track. Obstacles on the right of way of the train can cause derailment, collision, injuries to train passengers and loss of properties, Railway accident prevention and protection are a key part of a wider picture of transport safety. so, there is a need to look at various ways to prevent or reduce the frequency and severity of these accidents by using Arduino based safety system to mitigate these accidents. The aim of this paper is to simulate a program in Proteus to detect obstacles on the right of way of trains. Arduino code is written to detect obstacle on the track in the train. By implementing these features in real time application, we can avoid accidents up to a very significant margin. The railway accident prevention system aims to minimize the occurrence of accidents, reduce response time, and enhance overall safety in railway operations through the use of advanced sensor technologies.

Leveraging electronic interactions in poly (O-toluidine)/ cadmium sulfide nanocomposite for improved nitrogen dioxide sensing

Detecting nitrogen dioxide (NO2) at room temperature remains challenging for current gas sensor technologies. This study aims to address these limitations by developing chemiresistive sensors based on poly (O-toluidine) (POT)/Cadmium sulfide (CdS) nanocomposites. The combination of POT and CdS nanoparticles creates a specialized architecture to enable swift and sensitive NO2 detection at room temperature. Gas sensing evaluations demonstrated significant performance enhancements including 104/73 s response/recovery times and 73% sensitivity at 60 ppm NO2. The sensors also showed repeatability over cycles and stability over 60 days. Comparative analysis confirmed that integrating POT with CdS amplifies sensitivity and reduces response times compared to using POT alone. This establishes a materials-by-design strategy to surpass existing gas sensing limitations.

Clinical decision support tools useful for identifying sepsis risk.

Evaluate the effectiveness of the clinical decision support tools (CDSTs), POC Advisor (POCA), and Modified Early Warning System (MEWS) in identifying sepsis risk and influencing time to treatment for inpatients, comparing their respective alert mechanisms. This study was conducted at two academic university medical center hospitals. Data from adult inpatients in medical-surgical and telemetry units were analyzed from January 1, 2020, to December 31, 2020. Criteria included sepsis-related ICD-10 codes, antibiotic administration, and ordered sepsis labs. Subsequent statistical analyses utilized Fisher's exact test and Wilcoxon Rank Sum test, focusing on mortality differences by age, sex, and race/ethnicity. Among 744 patients, 143 sepsis events were identified, with 83% already receiving treatment upon CDST alert. Group 1 (POCA alert) showed reduced response time compared with MEWS, while Group 3 (MEWS) experienced longer time to treatment. Group 4 included sepsis events missed by both systems. Mortality differences were not significant among the groups. While CDSTs play a role, nursing assessment and clinical judgment are crucial. This study recognized the potential for alarm fatigue due to a high number of CDST-driven alerts, while emphasizing the importance of a collaborative approach for prompt sepsis treatment and potential reduction in sepsis-related mortality.

Towards providing a priority-based vital sign offloading in healthcare with serverless computing and a fog-cloud architecture

Smart cities and healthcare services have been gaining much attention in recent years, as the benefits offered by this field of research are significant and improve quality of life. Systems can proactively detect health problems by monitoring a person’s vital signs and make automated decisions to prevent these problems from worsening. With this in mind, we highlight two essential requirements that smart city architectures should consider to achieve elevated quality of experience in the healthcare domain. The first is to process high-priority vital signs with short response times, so people with comorbidities can have health problems identified as soon as possible. The second is to employ scalability techniques to deal with high usage peaks resulting from people concentrating in specific city neighborhoods. This paper contributes to this field of research by proposing SmartVSO - a computational model of a hierarchic, scalable, tree-based, fog-cloud architecture that executes healthcare services with reduced response time for urgent vital signs. Fog computing is employed to reduce response times, and cloud computing is used to provide virtually infinite computing resources. Serverless computing is the main technology we consider for deploying and running healthcare services because this allows authorized companies to implement their own services in a distributed and pluggable approach, without recompiling the proposed modules and reducing scalability concerns for engineers. An experiment with 80,000 vital signs indicates that our solution processes 60% of the very critical ones in no more than 5.3 s, while an architecture without fog computing and without prioritization takes up to 231 min (around 3 h and 51 min) to process 60% of very critical vital signs.