Improve Response Time Research Articles

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.

Serverless Computing in the Edge-Cloud Continuum : Challenges, Opportunities, and a Novel Framework

This article explores the integration of serverless computing across the edge-cloud continuum, addressing the growing demand for low-latency, data-intensive applications in the era of the Internet of Things (IoT). We present EdgeServe, a novel framework that extends the serverless paradigm to encompass edge devices and cloud resources, overcoming the limitations of traditional cloud-centric approaches. Through comprehensive simulations and real-world case studies, including a smart city traffic management system, an industrial IoT predictive maintenance application, and a mobile augmented reality gaming platform, we evaluate the performance, scalability, and cost-effectiveness of our proposed framework. Our results demonstrate significant improvements in application response times, with latency reductions of up to 82% in time-critical functions, and an average cost reduction of 43% compared to cloud-only serverless deployments. We also observe a 28% decrease in overall energy consumption in certain scenarios. The article addresses key challenges in resource management, data consistency, adaptive function placement, and security in distributed environments. Our findings have important implications for application architects and cloud service providers, paving the way for a new generation of edge-native serverless platforms. This research contributes to the growing body of knowledge on distributed systems and offers insights into the future evolution of serverless computing in heterogeneous computing environments.

Cloud Replica Management-Based Hybrid Optimization

This research addresses the challenges in cloud-based replica management by proposing a novel strategy employing a Genetically Implied Greywolf with Oppositional Learning (GIGOL) hybrid optimization technique. This approach optimizes multi-objectives such as response time, load balancing, availability, replication cost, and energy consumption, ensuring cost-effectiveness and energy efficiency. The GIGOL model integrates Genetic Algorithm, opposition learning, and Grey Wolf Optimization, aiming to achieve optimal replica placement. The study emphasizes resolving overhead issues through machine learning techniques for efficient cloud-based replica management. Performance evaluation showcases improvements in response time, load balancing, availability, replication cost, and energy consumption, highlighting the effectiveness of the proposed approach within budget constraints and management policies.

Comparison of Virtual Reality Exergames and Nature Videos on Attentional Performance: A Single-Session Study.

This study aimed to investigate the acute effects of a single session of a VR exergame (Beat Saber) and a VR nature video (Ireland 4K) on attentional performance, using the Flanker and Attentional Blink (AB) tasks. The objective was to assess whether these VR interventions could enhance attentional control, as measured by improvements in response times and accuracy. A total of 39 psychology students, aged 19-25, were randomly assigned to one of three groups: VR exergame, VR nature video, or control. Participants completed the Flanker and AB tasks before and after the intervention. A repeated measures design was employed to analyze changes in response times and accuracy across pre- and post-test sessions. The study revealed significant improvements in response times and accuracy across all groups in the post-test measures, indicating a strong training effect. In the AB task, shorter stimulus onset asynchrony (SOA) led to decreased accuracy and slower response times, emphasizing the difficulty in processing closely spaced targets. The interaction between Type and Group in response times for target stimuli suggested that the intervention types differentially influenced processing speed in specific conditions. The findings suggest that while brief VR interventions did not produce significant differences between groups, the training effect observed highlights the influence of task-specific factors such as SOA and target presence. Further research is needed to explore whether longer or repeated VR sessions, as well as the optimization of task-specific parameters, might lead to more pronounced cognitive benefits.

A Framework for Automated Big Data Analytics in Cybersecurity Threat Detection

This research presents a novel framework designed to enhance cybersecurity through the integration of Big Data analytics, addressing the critical need for scalable and real-time threat detection in large-scale environments. Utilizing technologies such as Apache Kafka for efficient data ingestion, Apache Flink for stream processing, and advanced machine learning models like LSTM and Autoencoders, the framework offers robust anomaly detection capabilities. It also includes automated response mechanisms using SOAR and XDR systems, significantly improving response times and accuracy in threat mitigation. The proposed solution not only addresses current challenges in handling vast and complex data but also paves the way for future advancements, such as the integration of more sophisticated AI techniques and application across various domains, including IoT and cloud security. This research contributes to the field by providing a comprehensive, adaptive, and scalable framework that meets the demands of modern cybersecurity landscapes.

Revealing the Impact of Composition on Oil Monitoring Performance of Ni-Ti-Cu Coated Optical Fiber

Shape Memory Alloy-coated optical fiber provides an economical, Smart, and real-time monitoring technological solution for Industry 4.0. In this work, the compositional influence of Ni-Ti-Cu coating on optical signal actuation has been studied. Morphological studies showed improvement in the grain size, surface roughness, and a better shape memory effect. Thermal characteristics revealed an increase in the phase transformation temperature of NiTiCu with higher copper content. Experimental analysis of optical fiber sensors in Mineral Oil resulted in a gradual shift in the actuation temperature range. A sensitivity of ∼52 mV/°C was recorded in the actuation window, showcasing the improved response time for copper-rich composition. A comparison of coated-uncoated optical fiber performance in oil has also been presented, and delamination studies were conducted by continuously exposing the samples to hot oil. The work can be referred for requirement-based composition selection for real-time and smart monitoring capability in the Oil industries.

Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study

The increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, heterogeneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments.

Automated Surgical Wound Classification for Intelligent Emergency Care Applications

Developing AI-powered solutions for emergency care is critical to improving patient outcomes. This study presents an advanced AI model designed to accurately classify surgical wounds to facilitate prompt and appropriate emergency response. We used two state-of-the-art image classification models, ResNeXt and Vision Transformer (ViT), to evaluate their effectiveness in classifying wound images. These models were selected based on preliminary evaluations of several popular models, and their superior performance metrics justified their final selection. The models were trained on a combined dataset of approximately 1,000 images from the Medetec and AZH(Advancing the Zenith of Healthcare) datasets. To improve classification accuracy, the dataset was preprocessed, including image resizing, flipping, normalization, and augmentation. The ViT model demonstrated superior performance, achieving an accuracy of 92.78%, precision of 94.89%, recall of 91.87%, and an F1 score of 92.44%. These results surpass those of existing studies such as Shenoy et al. (85.1% accuracy), Rostami et al. (68.7% accuracy), and Gao et al. (76.6% accuracy). Our proposed AI system not only accelerates life-saving first aid by providing timely and accurate wound classification but also enhances the skills of emergency responders through continuous learning and real-time feedback. By integrating AI into emergency service protocols, we aim to improve response times and collaboration among medical personnel, ultimately contributing to better patient survival rates.

Priority based inter-twin communication in vehicular digital twin networks

ABSTRACT With the advancement and boom of autonomous vehicles, vehicular digital twins (VDTs) have become an emerging research area. VDT can solve the issues related to autonomous vehicles and provide improved and enhanced services to users. Recent studies have demonstrated the potential of using priorities in acquiring improved response time. However, since VDT is comprised of intra-twin and inter-twin communication, it leads to a reduced response time as traffic congestion grows, which causes issues in the form of accidents. It would be encouraging if priorities could be used in inter-twin communication of VDT for data sharing and processing tasks. Moreover, it would also be effective for managing the communication overhead on the digital twin layer of the cloud. This paper proposes a novel priority-based inter-twin communication in VDT to address this issue. We formulate the problem for priorities of digital twins and applications according to their categories. In addition, we describe the priority-based inter-twin communication in VDT in detail, and algorithms for priority communication for intra-twin and inter-twin are designed, respectively. Finally, experiments on different priority tasks are conducted and compared with two existing algorithms, demonstrating our proposed algorithm's effectiveness and efficiency.

Optimized Tiny Machine Learning and Explainable AI for Trustable and Energy-Efficient Fog-Enabled Healthcare Decision Support System

The Internet of things (IoT)-based healthcare decision support system plays a crucial role in modern medicine, especially with the rise in chronic illnesses and an aging population necessitating continuous remote health monitoring. Current healthcare decision support systems struggle to deliver timely and accurate decisions with minimal latency due to limited real-time healthcare data and inefficient computational resources. There is a critical need for an optimized, energy-efficient machine learning model that reliably supports remote health monitoring within IoT and fog computing environments. Our study proposes an Optimized Tiny Machine Learning (TinyML) and Explainable AI (XAI) binary classification model for a trustable and energy-efficient healthcare decision support system, leveraging fog computing to optimize performance. The fog-based approach improves response times and enhances bandwidth usage, addressing critical needs such as reduced latency, higher bandwidth utilization, and decreased packet loss. To further improve efficiency, we incorporate the innovative mLZW data compression technique, significantly enhancing data communication efficiency and reducing response time to critical health alerts. However, limited real-time healthcare data records challenge machine learning classification performance. By implementing a TinyML algorithm, our system demonstrates superior performance to other machine learning models. The proposed optimized TinyML model achieves an impressive F1 score of 0.93 for health abnormalities detection, emphasizing its robustness and effectiveness. This paper highlights the potential of TinyML and XAI in delivering robust, trustworthy, and energy-aware healthcare solutions, making significant contributions toward effective remote health monitoring and decision support in fog-enabled IoT networks.Graphical abstract

Leveraging AI and Machine Learning for Performance Optimization in Web Applications

The rapid evolution of web technologies has placed an unprecedented demand on web applications to deliver high performance, scalability, and responsiveness. In this context, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative tools that can significantly optimize the performance of web applications. This paper explores the integration of AI and ML techniques in enhancing various aspects of web application performance, including load balancing, caching, resource management, and user experience personalization. The deployment of AI-driven algorithms enables real-time monitoring and predictive analytics, allowing for proactive adjustments to prevent bottlenecks and ensure smooth operation under varying loads. One of the primary areas where AI and ML contribute is in load balancing. Traditional load balancing techniques often rely on static or rule-based methods, which may not adapt well to dynamic web environments. AI-powered load balancers, however, can learn from traffic patterns and predict surges in demand, enabling more efficient distribution of traffic across servers. This adaptive approach not only improves response times but also reduces the risk of server overloads, enhancing the overall reliability of web applications.

Effects of Psychological Training on Competitive Fencing Outcomes

This study assesses how psychological training affects Fencing competition results, with a particular emphasis on improving cognitive skills and overall performance. Psychological techniques like stress reduction, mindfulness, and visualization have been shown to help with decision-making, focus, and emotional regulation in a variety of sports, including Fencing. Twenty fencers, ages 17 to 20, participated in the study and completed a six-month psychological training course. The findings show that psychological training greatly improves competitive performance as measured by improved response times, win/loss ratios, and psychological evaluations. These results support earlier studies and imply that regular athletic preparation can greatly benefit from the inclusion of psychological training. The brief training period and small sample size are limitations that call for additional research on long-term effects and a variety of participant groups.

Fast-response liquid crystal display device with trapezoidal pixel electrodes

ABSTRACT A fast response liquid crystal display (LCD) with trapezoidal pixel electrodes is proposed. The trapezoidal shape of the pixel electrode generates a slanted electric field, eliminating the need for a non-zero pre-tilt angle to prevent random domain formation in the LC. Additionally, the absence of a pre-tilt angle enhances viewing angles. During operation, LC molecules can form virtual walls, increasing anchoring energy and improving response time. At 38% transmittance, the response time is 5.1 ms, while at 60% transmittance, it increases to 8.3 ms. With a circular polariser, the device achieves a contrast ratio of 200:1 and a viewing cone of approximately 18°. Consequently, the trapezoidal pixel electrode FFS cell is suitable for fast LCDs used in automotive displays and digital signage. Its narrow viewing angles with a circular polariser also make it ideal for personal privacy displays and certain mobile displays.