Minimum Response Time Research Articles

Towards smart and secure batteries: Linking pressure and temperature profiles with electrochemical behavior through hybrid optical fiber sensors

A hybrid sensing configuration combining fiber Bragg grating (FBG) with a Fabry-Perot interferometer is proposed for highly sensitive detection of pressure and temperature inside a commercial lithium-ion battery (LiB). The battery is also instrumented externally with FBGs to monitor its surface temperature variations. The LiB is operated under several cycling tests at different C-rates and environmental temperatures. High pressure and temperature resolutions of ±0.4 mbar and ±0.5 °C, respectively, are attained for the developed hybrid sensor, along with a minimum response time of 1.7 s. When the LiB is subjected to higher environmental temperatures, reduced thermal variations and higher pressure variations are registered by the optical sensor. Overall, during the cycling tests, the external temperature sensors closely follows the internal sensors behavior. Incremental capacity analysis derivative curves were systematically applied throughout the battery cycling experiments to unveil relevant insights into the interplay between pressure and temperature variations and the LiB electrochemical behavior. It was found that the pressure fluctuations present a “breathing profile” directly linked with the volume electrode lithiation and delithiation processes.

Selecting facility location of Gendarmerie Search and Rescue (GSR) Units; An analysis of efficiency in disaster response

Disasters, referred to as events that result in physical, economic, and social losses for individuals and disrupt the daily activities of human communities, necessitate ongoing preparedness due to their unpredictable nature. Swift response during and after a disaster is crucial for preserving human life. Hence, it is imperative to initiate planning immediately following a disaster to ensure readiness for various tasks. Given these factors, search and rescue units must carefully select a base location that enables them to promptly reach affected areas.Disasters exhibit unique characteristics across different regions of Türkiye. While some regions are prone to earthquakes, others face the risks of landslides, avalanches, or floods. Consequently, the required measures for disaster management vary from region to region. Nevertheless, when the term “disaster” is mentioned in Türkiye, earthquakes often come to mind due to their frequent occurrence and significant impact. The Gendarmerie Search and Rescue (GSR) units have been actively responding to these earthquakes, renowned for their exemplary institutional discipline and working methods. This study aims to examine the operations and deployment locations of GSR units, which play a crucial role in mitigating the impact of frequent earthquakes in Türkiye, utilizing a SWOT analysis. Additionally, a Multi-Criteria Decision Making-based mathematical model will be employed to optimize task activities and to select the most suitable facility locations for GSR units. The use of mathematical modeling in this context ensures that GSR units are strategically positioned to maximize their operational effectiveness and minimize response times. The results will be evaluated through sensitivity analysis.

Job Scheduling in Hybrid Clouds With Privacy Constraints: A Deep Reinforcement Learning Approach

ABSTRACTWith the proliferation of cloud computing and the escalating demand for extensive data processing capabilities, an increasing number of enterprises are embracing hybrid cloud solutions. However, as more businesses move toward hybrid clouds, the need for effective solutions to privacy and security concerns becomes increasingly important. Although current scheduling approaches for cloud computing have addressed privacy protection to some extent, few have adequately considered the unique challenges posed by hybrid clouds. To address this gap, we propose a novel approach for scheduling jobs in hybrid clouds that prioritizes privacy protection. Our approach, called PH‐DRL, leverages Deep Reinforcement Learning (DRL) to intelligently allocate jobs to virtual machines, optimizing both privacy and Quality of Service (QoS), while minimizing response time. We present the detailed implementation of our approach and our experimental results demonstrate the superior performance of PH‐DRL in terms of privacy protection compared to existing methods.

Impact of Autoscaling on Application Performance in Cloud Environments

The current paper studies the impact of Autoscaling on application performance in Cloud computing environments. Cloud computing is one of the most encouraging innovations due to its vast applications. Predictive autoscaling is an advanced technique that aims to address the challenges in the autoscaling trends for large-scale systems. To account for the Quality of Service to the customer, features like load balancing according to the workload demand, understanding resource allocation and utilization, and dynamic decision-making are vital to any cloud computing application. This paper interprets these challenges and reviews a meta-reinforcement learning approach for predictive autoscaling in cloud environments. A novel RL-based predictive autoscaling approach on a popular large-scale digital payment platform system, Alipay, is compared with the existing models such as Autopilot and FIRM. The aim is to conduct a detailed analysis of performance metrics before and after autoscaling actions, aiming to identify optimal scaling strategies that minimize response time and maximize resource utilization without over- provisioning.

Research and Application of Emergency Logistics Resource Allocation Algorithm based on Supply Chain Network

The ”Fuzzy-Enhanced for Emergency Logistics Resource Allocation in Supply Chain Networks (FEM-ELRAS)” presents a novel approach to optimizing emergency logistics and resource allocation in supply chain networks, especially during critical disaster response scenarios. This research integrates fuzzy logic with the improve Multi Agent Genetic Algorithm (MAGA), creating a more adaptive and efficient framework capable of handling the uncertainties and complexities inherent in emergency situations. FEM-ELRAS employs fuzzy decision variables to represent ambiguous and fluctuating parameters like demand at disaster sites, supply availability, and variable transportation conditions. It incorporates a fuzzy inference system, utilizing expert-derived rules to guide the allocation process amidst uncertain and rapidly changing conditions. The algorithm’s evaluation mechanism is enhanced with fuzzy logic, offering a refined assessment of solution effectiveness, balancing multiple logistical objectives such as minimizing response time, optimizing costs, and maximizing resource utilization and delivery precision. Moreover, fuzzy logic principles are integrated into the genetic algorithm’s operators, enabling more context-sensitive and flexible solution adaptations. FEM-ELRAS is particularly designed to navigate the trade-offs between different logistical goals in emergency scenarios, making it a robust tool for decision-makers in disaster management. Its application promises significant improvements in emergency response efficiency, showcasing a step forward in the field of emergency logistics and supply chain management.

Artificial Intelligence Workload Allocation Method for Vehicular Edge Computing

Real-time applications such as smart transportation systems require minimum response time to increase performance. Incorporating edge computing, processing units near end devices, achieving fast response time. The collaboration between edge servers and cloud servers is beneficial in achieving the lowest response time by using edge servers and high computational resources by using cloud servers. The workload allocation between edge–cloud servers is challenging, especially in a highly dynamic system with multiple factors varying over time. In this paper, the workload allocation decisions among the edge servers and cloud are considered for autonomous vehicle systems. The autonomous vehicle system generates multiple tasks belonging to different AI applications running on the vehicles. The proposed method considers allocating the tasks to edge or cloud servers. The cloud servers can be reached through a cellular network or a wireless network. The proposed method is based on designing a neural network model and using a high number of features that contribute to the decision-making process. A huge dataset has also been generated for the implementation. The EdgeCloudSim is used as a simulator for implementation. The competitor's methods considered for the comparison are random, simple moving average (SMA) based, multi-armed bandit (MAB) theory-based, game theory-based, and machine learning-based workload allocation methods. The result shows an improvement in the average Quality of Experience (QoE), ranging from 8.33% to 28.57%, while the average failure rate achieved enhancement up to 50%.

Highly sensitive colorimetric detection of ascorbic acid using molecularly imprinted photonic crystal hydrogel sensor

Molecular imprinted photonic crystal hydrogel (MICPH) sensors have gained recognition as an efficient platform for selectively detecting analyte molecules. In the present work, we report the development and implementation of a colorimetric sensor based on MIPCH for the selective detection of Ascorbic acid (AA). The fabrication process of the MIPCH sensor involves the photo-polymerization of a hydrogel precursor solution containing AA molecules, which is encapsulated in the interstitial spaces of photonic crystal (PC) opal film. Following the encapsulation, these molecules are selectively removed from the hydrogel network, forming an AA-MIPCH sensor. The sensor exhibits a vivid structural color that redshifts upon rebinding with different concentrations of AA molecules. This alteration in structural coloration serves as a straightforward and visually discernible indicator of the sensor response and enables the quantitative measurement of the target molecule. The sensor exhibits remarkable sensitivity, featuring a low limit of detection (LoD) of 0.011 µM with high selectivity, minimal response time, and promising long-term stability. In addition, the sensor demonstrates its capability to detect the AA even from the complex matrix. A CNN-based deep learning algorithm in MATLAB® was employed to quantitatively predict the concentration of the target molecule. The integration with the machine learning algorithm yields a highly efficient and accurate smart sensor system that is well-suited for real-time sensing of ascorbic acid.

An enhanced round robin using dynamic time quantum for real-time asymmetric burst length processes in cloud computing environment.

Cloud computing is a popular, flexible, scalable, and cost-effective technology in the modern world that provides on-demand services dynamically. The dynamic execution of user requests and resource-sharing facilities require proper task scheduling among the available virtual machines, which is a significant issue and plays a crucial role in developing an optimal cloud computing environment. Round Robin is a prevalent scheduling algorithm for fair distribution of resources with a balanced contribution in minimized response time and turnaround time. This paper introduced a new enhanced round-robin approach for task scheduling in cloud computing systems. The proposed algorithm generates and keeps updating a dynamic quantum time for process execution, considering the available number of process in the system and their burst length. Since our method dynamically runs processes, it is appropriate for a real-time environment like cloud computing. The notable part of this approach is the capability of scheduling tasks with asymmetric distribution of burst time, avoiding the convoy effect. The experimental result indicates that the proposed algorithm has outperformed the existing improved round-robin task scheduling approaches in terms of minimized average waiting time, average turnaround time, and number of context switches. Comparing the method against five other enhanced round robin approaches, it reduced average waiting times by 15.77% and context switching by 20.68% on average. After executing the experiment and comparative study, it can be concluded that the proposed enhanced round-robin scheduling algorithm is optimal, acceptable, and relatively better suited for cloud computing environments.

Optimizing the fleet of emergency medical service vehicles

This study addresses the problem of optimizing composition and distribution of an ambulance fleet within an existing emergency medical system. The primary objective is to formulate a strategy aimed at minimizing response times and enhancing the system's responsiveness, particularly to critically ill patients. The optimal spatial distribution of various types of vehicles is proposed using a hierarchical p-median model. The effects of these strategies are evaluated through a detailed computer simulation model. Experimental studies have shown that optimizing station locations contributes more significantly than altering the fleet composition and associated dispatching protocols. In the context of the Slovak Republic, the proportion of high-priority incidents responded to within 8 minutes could be increased by 8.7% in urban areas and 10.5% in rural areas, solely through optimal deployment of currently active ambulances. An additional improvement of nearly 2% could be achieved by introducing 13 patient transport ambulances, optimally distributed throughout the country.

A single-coil-driven tristable electromagnetic mini valve with multiple working states

Electromagnetic mini valves have attracted great interest in medical devices for their excellent performance and high integration. In state-of-the-art research, most mini valves have only two working states, while certain ones featuring three to four working states necessitate multiple driving sources. To solve these problems, a novel tristable electromagnetic mini valve with three stable working states achieved by a single coil was proposed. The actuation mechanism of this mini valve mainly comprises a cylindrical magnet (CM) and a ring magnet (RM) coaxially arranged. In the absence of current through the coil, the stability of the magnets is upheld by their mutual repulsion and the attraction of the iron core. Upon modifying both the magnitude and orientation of the current, one of the magnets traverses from one terminus to another each time. The two magnets are either stationary at the start point or the endpoint. Thereby, four position relationships are formed, three of which are stable. The minimum response time and energy consumption are 3.75 ms and 0.013 J, respectively. The working pressure of the valve ranges from 0 to 200 kPa. The maximum flow rate and back pressure reach 10.5 L/min and 180 kPa, respectively. A tristable design contributes to lower energy consumption, and the three-working-state function actuated by a single coil facilitates a diminution in the size of the mini valve. Consequently, a diminished quantity of valves is realized for multiple degree-of-freedom pneumatic actuator control, which promotes the miniaturization of the whole system. The dimensions and performance of the proposed mini valve substantiate its potential in pneumatic medical devices.

Violence Detection Using Wi-Fi and 5G/6G Sensing Technologies: A Review

Violence, a pervasive societal concern, demands innovative approaches for its early detection and prevention. This review paper explores the intersection of violence detection and wireless fidelity (Wi-Fi), alongside fifth-generation (5G) and sixth-generation (6G) mobile technologies. Wi-Fi sensing, initially employed for human activity detection, has also demonstrated versatility across a number of other important applications. The significance of leveraging Wi-Fi sensing for violence detection is investigated, underscoring its ability to enhance security protocols and minimise response time. Moreover, through the development and use of machine learning algorithms to analyse and interpret intricate channel state information (CSI) features, the accuracy of violence detection can be improved. Furthermore, this investigation delves into the rapidly developing domain of mobile sensing, examining its contribution to the advancement of violence detection functionalities. The potential convergence of 5G and forthcoming 6G sensing technologies increases the effectiveness of violence detection. Through an analysis of Wi-Fi and mobile sensing technologies, this review paper highlights the transformative capacity that their integration may have on approaches to violence prevention and response.

Drift-based task management in support of pervasive edge applications

The Internet of Things (IoT) creates a sprawling network where multiple devices can interact, enabling a variety of devices to communicate, collect information, and carry out tasks in support of services that end users may enjoy. Edge Computing, known for its lower latency compared to the Cloud, has sparked interest in managing the execution of tasks within a huge ecosystem that acts as a cover upon the IoT infrastructure. The primary challenge lies in maximizing the use of limited edge resources while minimizing response time, prompting numerous research endeavors. However, existing efforts often overlook shifts in the collected data that may affect the execution of tasks and the production of knowledge. This paper focuses on developing a mechanism that considers data and concept drifts to optimize the management of tasks. The ultimate goal is to maximize the accuracy levels while optimizing resource utilization, through a tasks’ offloading scheme that accounts for distribution-based similarity, offering substantial benefits in managing these constrained resources efficiently. The shifts in data can help determine if the execution of a task is efficient in a specific node and become part of a reasoning model that guides the offloading decisions. Finally, we evaluate our model using a large set of experimental scenarios.

High-precision high-speed nanopore ping-pong control system based on field programmable gate array.

"Molecular ping-pong," emerging as a control strategy in solid-state nanopore technology, presents a highly promising approach for repetitive measurements of single biomolecules, such as DNA. This paper introduces a high-precision, high-speed nanopore molecular ping-pong control system consisting of a home-built trans-impedance amplifier (TIA), a control system based on a Field Programmable Gate Array (FPGA), and a LabVIEW program operating on the host personal computer. Through feedback compensation and post-stage boosting, the TIA achieves a high bandwidth of about 200kHz with a gain of 100 MΩ, along with low input-referred current noise of 1.6 × 10-4pA2/Hz at 1kHz and 1.1 × 10-3pA2/Hz at 100kHz. The FPGA-based control system demonstrates a minimum overall response time (tdelay) of 6.5 μs from the analog input current signal trigger to the subsequent reversal of the analog output drive voltage signal, with a control precision of 1 μs. Additionally, a LabVIEW program has been developed to facilitate rapid data exchange and communication with the FPGA program, enabling real-time signal monitoring, parameter adjustment, and data storage. Successful recapture of individual DNA molecules at various tdelay, resulting in an improvement in capture rate by up to 2 orders of magnitude, has been demonstrated. With unprecedented control precision and capture efficiency, this system provides robust technical support and opens novel research avenues for nanopore single-molecule sensing and manipulation.

Ultra-light, ultra-resilient and ultra-flexible, multifunctional composite carbon nanofiber aerogel for physiological signal monitoring and hazard warning in extreme environments

The emergence of multimodal wearable flexible self-powered electronic devices undoubtedly plays a positive role in areas such as healthcare and energy. However, the preparation of three-dimensional elastic bodies suitable for humidity, temperature, and pressure sensing often involves complex fabrication processes, internal network discontinuities, and difficulties in achieving applications in extreme environments. In this paper, a one-step preparation of three-dimensional fluffy and interlayer porous nanofiber precursors is achieved using direct electrostatic spinning technology. Subsequently, high-temperature calcination and in situ polymerization processes were used to obtain multifunctional composite carbon nanofiber aerogels (MCCNA), which are characterized by ultra-lightness, super elasticity, three-dimensional fluffiness, and interlayer porousness, and are suitable for extreme environments. The ultra-light (53.67 mg cm−3) MCCNA can accurately detect humidity signals over an extremely wide range (10 %RH to 95 %RH), with a minimum temperature response time of 0.5 K. Its pressure response time is only 20 ms, and even after 5000 cycles of pressure loading, it still maintains a stable electrical signal response. Most notably, through demonstration, MCCNA can monitor the actions of firefighters in high-temperature and complex situations such as fires. Furthermore, it can monitor and provide high-temperature warnings for the temperature thresholds in the microenvironment of firefighting suits. The developed self-powered flexible wearable electronic device holds outstanding prospects for applications in healthcare, energy, and extreme conditions.

AdaBoost‐powered cloud of things framework for low‐latency, energy‐efficient chronic kidney disease prediction

AbstractThe United Nations' sustainable development agenda has set an ambitious goal of reducing premature mortality from non‐communicable diseases by 33% by 2030. Among these diseases, chronic kidney disease (CKD) is a significant contributor to both morbidity and mortality. Integrating the Internet of Things (IoT) and cloud computing in healthcare has gained momentum, particularly in remote patient monitoring. However, it is essential to acknowledge that cloud computing has limitations, particularly in handling vast volumes of Big Data, mainly due to scalability and latency concerns. This article proposes a novel framework, AdaBoostCoTCKD, to mitigate latency issues, minimize response times, reduce power consumption, and optimize network resources for predicting CKD. The framework leverages the synergy between the AdaBoost machine learning technique and fog computing paradigms to enhance the precision and efficiency of CKD prediction methods. In addition, it introduces an auxiliary cloud‐based database, enriching the pool of future insights and facilitating prospective database infrastructure expansions. This augmentation is expected to impact predictive accuracy positively. We conducted comprehensive experiments to demonstrate the effectiveness of our approach. Our model achieved an impressive training accuracy of 99.928% and testing accuracy of 99.975%, while the fog environment reduced latency by 31% and energy consumption by 75% compared to traditional cloud‐based solutions. Our proposed system enables early CKD detection and offers advantages over cloud‐only solutions, providing a robust and efficient platform for healthcare IoT applications with significant clinical value. These promising results underscore the potential of combining fog computing and the AdaBoost machine learning technique to advance healthcare by addressing latency, response time, power consumption, and network resource optimization challenges in CKD prediction.

Advance Emergency and Traffic Management System

Urban Alert is an innovative Android application that addresses urban emergencies, traffic management, and safety concerns. The system has a strong accident or emergency feature that allows users to quickly capture and upload incident details with location information. The traffic management component uses advanced algorithms such as YOLO and CNN to monitor and analyze real- time traffic conditions through IoT-enabled cameras. The application optimizes ambulance routes for zero traffic, minimizing response times during emergencies. Furthermore, safety options allow users to send alerts to relevant departments based on their specific needs. This paper examines the technical implementation, results, and evaluation of each feature, demonstrating how they can impact urban safety and response efficiency. Urban Alert is a comprehensive solution that enhances emergency response, traffic management, and public safety in urban areas. Keywords – Emergency Response, Traffic Management, IoT and AI Integration, Ambulance Routing, Safety Options, Real-time Incident Monitoring.

Survey on MapReduce Scheduler Algorithms in Hadoop Framework

Hadoop serves as a robust framework tailored for the storage and processing of vast data volumes across clusters. Its foundation lies in the Hadoop Distributed File System (HDFS) for data storage, complemented by the MapReduce paradigm for data processing. MapReduce, functioning as a parallel computing framework, operates adeptly on distributed clusters to manage large-scale data processing tasks. Within this framework, scheduling emerges as a pivotal aspect, influencing the overall system's performance and efficiency. The essence of scheduling in MapReduce lies in enhancing performance, minimizing response times, and optimizing resource allocation. This paper undertakes a systematic exploration of existing scheduling algorithms, offering a fresh classification and detailed examination of each category. Furthermore, the analysis delves into the core principles, objectives, as well as the strengths and weaknesses of these scheduling algorithms.

A Study Comparing Waiting Times in Global and Local Queuing Systems with Heterogeneous Workers

A virtual marketplace or service-providing system must ensure minimal task response times. Varying working rates among the human workers in the system can lead to longer delays for certain tasks. The waiting time in the queue is crucially affected by the queueing architecture used in the system, whether global or local. Studies generally favor global queue systems over local ones, assuming similar processing rates. However, system behavior changes when workers are heterogeneous. In this research, we used simulation to compare the waiting times of tasks assigned to three categories of processing rates in both architectures and with various routing policies in local queues. We found that when using random tie-breaking, there was a correlation between waiting time duration and the proportion of tie-breaking events. Performance is improved when controlling these events using scheduling awareness of the workers’ processing rates. The global queue outperforms local queues when the workers are homogeneous. However, the push mechanisms that control the assignment processes and heterogeneity-aware algorithms improve local queue system waiting times and load balance. It is better than global queues when tasks are assigned to medium and fast workers, but it also enables specific slow workers’ assignments.

A novel segmented random search based batch scheduling algorithm in fog computing

In fog computing, batch scheduling is an important and challenging task aiming at reducing the response latency. Response time includes the scheduling time, execution time and other factors such as network latency. However, the majority of the existing batch scheduling algorithms primarily focus on minimizing the execution time of tasks for IoT devices, ignoring the algorithms' scheduling time. This contributes to suboptimal overall response time, which is a critical quality-of-service (QoS) indicator and significantly impacts performance. Optimizing both execution and scheduling time is therefore crucial for achieving minimum response time, improving QoS, and alleviating load-balancing issues. This work introduces a novel approach for the load-balancing difference variable and uses dimensionality reduction techniques for dynamic task organization. The main contribution comprises two distinctive algorithms: the Pre-allocation Minimum Completion Time (PMT) algorithm and the Segmented Random Search Fog Computing Batch Scheduling Algorithm (SRS). These algorithms are designed to address the inherent characteristics of the problem. To evaluate the performance and applicability of batch scheduling algorithms, we establish a model incorporating compliance and application performance indicators. We introduce the Logical Recursive Indirect Comparison Analysis method to assess and evaluate batch scheduling algorithms. Simulation results demonstrate the exceptional effectiveness of the SRS algorithm. It exhibits a remarkable improvement in comprehensive performance indices, ranging from 139.10% to 261.18%, and optimization quality enhancement rates of 64.25%–154.13% compared to classical standard optimization algorithms. Compared with advanced algorithms, the SRS algorithm also outperforms, with optimization quality improvement rates of 22.74% and 71.11% compared to AEOSSA and CHMPAD.

Microflow sensing and control using an in-channel birefringent biomembrane.

This study describes the function, optimization, and demonstration of a new class of passive, low-cost microfluidic flow meters based on birefringent chitosan biomembranes analyzed by polarized microscopy. We subjected the membrane to dynamic flow conditions while monitoring the real-time response of its optical properties. We obtained figures of merit, including the linear response operating range (0 to 65 μL min-1), minimum response time (250 ms), sensitivity (2.03% × 10-3 μL-1 min), and minimum sensor longevity (1 week). In addition, possible sources of interference were identified. Finally, we demonstrate the membrane as a low-cost flow rate measurement device for the close loop control of a commercial pressure-driven pump. Preliminary experiments using a basic PID controller with the membrane-based flow rate measurement device showed that stable control could be achieved and the system could reach steady-state behavior in less than 15 seconds. Analysis of fundamental limits to sensor response time indicate the potential for faster steady-state behaviour.