Optimal Response Time Research Articles

Detection of the nonlinear response of vegetation to terrestrial water storage changes in central Asian endorheic basins

The terrestrial water storage anomaly (TWSA) is an important parameter for assessing the land water budget, and it interacts well with terrestrial ecosystems via complex hydrological processes. Recently, the decline in central Asian terrestrial water storage (TWS) has threatened the health of local ecosystems. Therefore, it is of great significance to adopt an efficient approach to explore and identify the nonlinear relationship between two important indicators, i.e., the TWSA and normalized difference vegetation index (NDVI) in the arid central Asian endorheic basins. In this study, we analysed the long-term trends of the TWSA and NDVI, and identified the lag month (1 month) as the optimal moving window of the nonlinear Granger causality test embedded in random forest to detect the nonlinear NDVI change response of NDVI changes in vegetation to the TWSA from 2003 to 2015. There are decreasing trends in TWSAs over approximately 81.7% of the study area and the NDVI generally decreased resulting in approximately 36% vegetation browning in the study area. The nonlinear Granger unidirectional causes of the TWSA were responsible for 97.9% of the NDVI variation in the study area considering the optimal response time for moving windows. The causes of vegetation browning in the central Asian Aral Sea basin and vegetation greening in the basins of Northwest China could be mostly explained by the changes in TWS. Our findings contribute to understanding the nonlinear causal linkages between vegetation and the TWSA in endorheic basins, and these findings provide insights for obtaining terrestrial water consumption patterns and water resource management under the joint influence of climate change and human activities.

Carbon ink printed flexible glove-based aptasensor for rapid and point of care detection of Chikungunya virus

Printed electronic technologies have sparked interest worldwide because of their capacity to transcend the limitations of existing high-cost electronics based on the fabrication of various devices deploying flexible substrates. Fabricating electrodes on stretchable, bendable, and soft substrates plays a vital role in flexible electronics. Hence this study demonstrates an electrochemical flexible substrate-based aptamer sensor for the Chikungunya virus (CHIKV) antigen over an extensive range of concentrations. In brief, ZnO nanorods were chemically synthesised and characterised by Field emission scanning electron microscopy (FESEM), UV-Vis spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, and X-Ray Diffraction (XRD), providing biocompatibility to the aptamer, a biorecognition element. The aptasensor showed a wide linearity (1 ng/ml-10 μg/ml), deploying a voltammetric identification in the presence of 10 mM methylene blue act as a redox-transition substance, with an estimated limit of detection of 1 ng/ml and 25 s as its optimal response time. The applicability of the sensor was further demonstrated by introducing CHIKV-Antigen into commercial serum samples. The built-in sensor has a shelf-life of approximately a month. CHIKV-Antigen can be detected rapidly on a portable platform. As a result, CHIKV can be diagnosed more quickly, allowing for more effective treatment choices.

Energy management of renewable based power grids using artificial intelligence: Digital twin of renewables

This paper proposes fog computing as a method of minimizing latency and maximizing performance by storing the information in the cloud to give a wide range of services to users using cloud computing. Also, consumer requests have been handled by several cloud Data Centers (DCs). Due to the fog's ability to attract more users and provide more services, load balancing has become increasingly crucial. It is therefore necessary to enhance the method that balances the fog's load. The study proposes a three-layer model comprising a cloud layer, a fog layer, and a user layer for optimal energy management in renewable power grids. The renewable energy sources are all modeled within the digital twin environment to make sure that very accurate monitoring is made. It is proposed for balancing the fog load using an artificial intelligence based optimization algorithm called the Whale optimization algorithm (WOA). Services are provided by fog servers in response to requests from users. In case of failure of the fog layer, all consumers' records are saved and services are provided to them through the cloud. Moreover, the service broker policies (SBP) have been applied to select the DCs efficiently. This paper compares the suggested algorithm to three previously developed algorithms including the particle swarm optimization (PSO), differential devolution (DE), and teaching–learning-based optimization (TLBO). It uses the three SBPs of closest data center, optimize response time (RT), reconfigure dynamically with load, and an improved SBP. It also minimizes RT and process time to improve the efficiency. The results show that there is an improvement in WOA's performance by approximately 5% compared to that of other algorithms.

Paper-Based Electrodes Decorated with Silver and Zinc Oxide Nanocomposite for Electro-Chemical Sensing of Methamphetamine

We present the development of an electrochemical paper-based analytical device (ePAD) for the detection of methamphetamine. Methamphetamine is a stimulant that young people use as an addictive narcotic, and it must be detected quickly since it may be hazardous. The suggested ePAD has the advantages of being simple, affordable, and recyclable. This ePAD was developed by immobilizing a methamphetamine-binding aptamer onto Ag-ZnO nanocomposite electrodes. The Ag-ZnO nanocomposites were synthesized via a chemical method and were further characterized via scanning electron microscopy, Fourier transform infrared spectroscopy, and UV-vis spectrometry in terms of their size, shape, and colloidal activity. The developed sensor showed a limit of detection of about 0.1 μg/mL, with an optimum response time of about 25 s, and its extensive linear range was between 0.01 and 6 μg/mL. The application of the sensor was recognized by spiking different beverages with methamphetamine. The developed sensor has a shelf life of about 30 days. This cost-effective and portable platform might prove to be highly successful in forensic diagnostic applications and will benefit those who cannot afford expensive medical tests.

Evaluating Nurses' Time to Response by Severity and Cancer Stage in a Remote Symptom Monitoring Program for Patients With Breast Cancer.

Remote symptom monitoring (RSM) using electronic patient-reported outcomes enables patients with cancer to communicate symptoms between in-person visits. A better understanding of key RSM implementation outcomes is crucial to optimize efficiency and guide implementation efforts. This analysis evaluated the association between the severity of patient-reported symptom alerts and time to response by the health care team. This secondary analysis included women with stage I-IV breast cancer who received care at a large academic medical center in the Southeastern United States (October 2020-September 2022). Symptom surveys with at least one severe symptom alert were categorized as severe. Response time was categorized as optimal if the alert was closed by a health care team member within 48 hours. Odds ratios (ORs), predicted probabilities, and 95% CIs were estimated using a patient-nested logistic regression model. Of 178 patients with breast cancer included in this analysis, 63% of patients identified as White and 85% of patients had a stage I-III or early-stage cancer. The median age at diagnosis was 55 years (IQR, 42-65). Of 1,087 surveys included, 36% reported at least one severe symptom alert and 77% had an optimal response time by the health care team. When compared with surveys that had no severe symptom alerts, surveys with at least one severe symptom alert had similar odds of having an optimal response time (OR, 0.97; 95% CI, 0.68 to 1.38). The results were similar when stratified by cancer stage. Response times to symptom alerts were similar for alerts with at least one severe symptom compared with alerts with no severe symptoms. This suggests that alert management is being incorporated into routine workflows and not prioritized based on disease or symptom alert severity.

P‐92: A Fast Response Time FFS‐LCD with Novel Electrode Design

In this paper, an FFS LCD is developed which contains ITO3 electrode other than the ITO2 pixel electrode, and the ITO3 electrode has a separate timing signal. This configuration can optimize response time by increasing the electric field strength of LC cell. In addition, the transmittance for the LCD using novel electrode design is also improved compared to conventional LCD. This electrode design will have opportunities for applications in the field of gaming and virtual reality displays.

Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones.

Global warming and climate change are responsible for many disasters. Floods pose a serious risk and require immediate management and strategies for optimal response times. Technology can respond in place of humans in emergencies by providing information. As one of these emerging artificial intelligence (AI) technologies, drones are controlled in their amended systems by unmanned aerial vehicles (UAVs). In this study, we propose a secure method of flood detection in Saudi Arabia using a Flood Detection Secure System (FDSS) based on deep active learning (DeepAL) based classification model in federated learning to minimize communication costs and maximize global learning accuracy. We use blockchain-based federated learning and partially homomorphic encryption (PHE) for privacy protection and stochastic gradient descent (SGD) to share optimal solutions. InterPlanetary File System (IPFS) addresses issues with limited block storage and issues posed by high gradients of information transmitted in blockchains. In addition to enhancing security, FDSS can prevent malicious users from compromising or altering data. Utilizing images and IoT data, FDSS can train local models that detect and monitor floods. A homomorphic encryption technique is used to encrypt each locally trained model and gradient to achieve ciphertext-level model aggregation and model filtering, which ensures that the local models can be verified while maintaining privacy. The proposed FDSS enabled us to estimate the flooded areas and track the rapid changes in dam water levels to gauge the flood threat. The proposed methodology is straightforward, easily adaptable, and offers recommendations for Saudi Arabian decision-makers and local administrators to address the growing danger of flooding. This study concludes with a discussion of the proposed method and its challenges in managing floods in remote regions using artificial intelligence and blockchain technology.

Performance Analysis of Modified SRPT in Multiple-Processor Multitask Scheduling

In this paper we study the multiple-processor multitask scheduling problem in both deterministic and stochastic models, where each job have several tasks and is complete only when all its tasks are finished. We consider and analyze Modified Shortest Remaining Processing Time (M-SRPT) scheduling algorithm, a simple modification of SRPT, which always schedules jobs according to SRPT whenever possible, while satisfying the non-preemptive constraint. The M-SRPT algorithm is proved to achieve a competitive ratio of θ(log α + β) for minimizing response time, where α denotes the ratio between maximum job workload and minimum job workload, β represents the ratio between maximum non-preemptive task workload and minimum job workload. In addition, the competitive ratio achieved is shown to be optimal (up to a constant factor), when there are constant number of machines. We further consider the problem under Poisson arrival and general workload distribution (i. e., M/GI/N system), and show that M-SRPT achieves asymptotic optimal mean response time when the traffic intensity ρ approaches 1, if job size distribution has finite support. Beyond finite job workload, the asymptotic optimality of M-SRPT also holds for infinite job size distributions with certain probabilistic assumptions, for example, M/M/N system with finite task workload.

Workflow aware analytical model to predict performance and cost of serverless execution

SummaryServerless computing has emerged as a powerful deployment model based on the Function‐as‐a‐Service (FaaS) paradigm, where applications are orchestrated through a set of independent functions. The function orchestration within an application can be represented through a serverless workflow, which defines the overall execution plan of the application. To ensure the quality of service for serverless computing platforms, it is essential to develop performance and cost models that can predict the service quality that can be obtained from deploying and executing applications in the cloud platform. While several analytical models have been developed for various cloud deployment frameworks in recent years, there has been a lack of performance and cost analysis models for serverless computing platforms. The existing performance and cost monitoring tools available in serverless frameworks face several challenges, such as complexity, lack of transparency, and incomplete monitoring data. In this paper, we fill the gap by proposing an efficient workflow‐based analytical model that can estimate the end‐to‐end response time and cost of the serverless execution plan. The proposed model can handle complex structures like loop, cycles, self‐loop, and parallel substructures that exist in serverless workflows. Additionally, we propose a heuristic optimization algorithm to identify the optimal resource configuration to achieve the optimal response time under a given budget constraint. We evaluated the effectiveness of the proposed model by considering seven serverless applications in both AWS Lambda and Microsoft Azure platforms. We compared the accuracy of the proposed model with the real values of response time and cost obtained in AWS Lambda and Microsoft Azure serverless platforms. The proposed performance and cost model in the AWS Lambda platform has been observed to have an average accuracy of 99.2% and 98.7% respectively. In the Microsoft Azure platform, the average accuracy of the performance and cost model has been observed to be 98.6% and 98.2% respectively.

A value of prediction model to estimate optimal response time to threats for accident prevention

A value of prediction model to estimate optimal response time to threats for accident prevention

Optical bi-enzyme-titania biosensor system: A new way to detect lactose

Lactose concentration is a key parameter for assessing the quality of raw and processed milk and identifying abnormal milk. Current methods for lactose determination are laborious, time-consuming and oftentimes require skilled lab workers, expensive equipment, and special reagents. In this study, an optical biosensor system based on nanosized titanium dioxide (TiO2) is presented for routine measurement of lactose in milk without sample preparation. TiO2 was obtained via sol-gel synthesis and characterized using dynamic light scattering, microelectrophoresis, X-ray diffraction, transmission electron microscopy, and nitrogen adsorption-desorption analysis. The system, whose colorimetric response was based on the chromogen-free TiO2-based detection of hydrogen peroxide (H2O2), used β-galactosidase and/or glucose oxidase as the biorecognition elements. The selectivity and sensitivity of the biosensors for lactose and glucose was investigated by diffuse reflectance spectroscopy using standard aqueous lactose and glucose solutions and whole milk samples. The results showed that the proposed biosensors had high optical signal reproducibility (RSD = 5–6%), high sensitivity (LOD = 0.005 wt%), and high selectivity. The linear dynamic ranges of lactose and glucose detection were from 0.005 wt% to 0.100 wt%, and the optimal response time of the biosensor was 25 min. The listed features and advantages of label-free detection make the biosensor attractive for simple, inexpensive, sensitive, and selective determination of lactose in milk.

Nano-copper suspension filling based fabrication and application of FBG temperature sensor with fast response function

The fast response is a significant index for measuring the performance of temperature sensors. In this paper, a copper capillary temperature sensor is developed utilizing fiber Bragg grating (FBG) as the sensing element and nano-copper suspension as the filler. Nano-copper suspension with high thermal conductivity is meaningful to improve the response speed of this sensor. The relationship between the preparation parameters of nano-copper suspension and the thermal conductivity is accurately established to obtain the optimum response time of the temperature sensor. The suspension with small particle size and high concentration significantly improves the sensor's response speed, which is close to the bare FBG. In addition, the temperature sensor encapsulated with a copper capillary tube has high sensitivity, which is three times that of the bare FBG. This temperature sensor and bare FBG are simultaneously applied to the motor with a winding inter-turn short circuit as the initial fault. Both this sensor and the bare FBG capture the coincident temperature field, which implies that the sensor has no apparent response lag compared with the bare FBG in practical applications. Results demonstrated here may afford some valuable references for obtaining temperature filed in small electromechanical components.

Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing.

Data centers are producing a lot of data as cloud-based smart grids replace traditional grids. The number of automated systems has increased rapidly, which in turn necessitates the rise of cloud computing. Cloud computing helps enterprises offer services cheaply and efficiently. Despite the challenges of managing resources, longer response plus processing time, and higher energy consumption, more people are using cloud computing. Fog computing extends cloud computing. It adds cloud services that minimize traffic, increase security, and speed up processes. Cloud and fog computing help smart grids save energy by aggregating and distributing the submitted requests. The paper discusses a load-balancing approach in Smart Grid using Rock Hyrax Optimization (RHO) to optimize response time and energy consumption. The proposed algorithm assigns tasks to virtual machines for execution and shuts off unused virtual machines, reducing the energy consumed by virtual machines. The proposed model is implemented on the CloudAnalyst simulator, and the results demonstrate that the proposed method has a better and quicker response time with lower energy requirements as compared with both static and dynamic algorithms. The suggested algorithm reduces processing time by 26%, response time by 15%, energy consumption by 29%, cost by 6%, and delay by 14%.

Optimization of the Wire Diameter Based on the Analytical Model of the Mean Magnetic Field for a Magnetically Driven Actuator

A magnetic field induced by an electromagnetic coil is the key variable that determines the performance of a magnetically driven actuator. The applicability of the empirical models of the coil turns, static resistance, and inductance were discussed. Then, the model of the mean magnetic field induced by the coil was established analytically. Based on the proposed model, the sinusoidal response and square-wave response were calculated with the wire diameter as the decision variable. The amplitude and phase lag of the sinusoidal response, the time-domain response, steady-state value, and the response time of the square-wave response were discussed under different wire diameters. From the experimental and computational results, the model was verified as the relative errors were acceptably low in computing various responses and characteristic variables. Additionally, the optimization on the wire diameter was carried out for the optimal amplitude and response time. The proposed model will be helpful for the analytical analysis of the mean magnetic field, and the optimization result of the wire diameter under limited space can be employed to improve the performance of a magnetically driven actuator.

Agent-based modeling of mass shooting case with the counterforce of policemen

AbstractMass shooting cases have caused large casualties worldwide. The counterforce, such as the policemen, is of great significance to reducing casualties, which is the core issue of social safety governance. Therefore, we model both the killing force and counterforce, to explore the crowd dynamics under the shooting. Taking the “Borderline” shooting in 2018 as the target case, the agent-based modeling is applied to back-calculate this dynamic process and explore key behavior rules of individuals. The real death tolls of three classes of agents (civilians, policemen, & killers) are as the real function, based on which we calculate the gaps between real target case and simulations. Eventually, we obtain three optimal solutions, which achieve the least gap or highest matching degree. Besides, we make counterfactual inferences under the optimal solutions, to explore the strategic interactions between policemen and killers. For strategies of killers, we explore different sizes, positions, and moving patterns of the killers. The optimal size of policemen is four to five, for each one killer. For strategies of policemen, we explore the size, locations, and the response time. It indicates that optimal response time of policemen is thirty to forty shots of the killer, and the death of civilians and policemen can be minimized, and the death probability of the killer can be maximized. These findings help to improve public safety governance for our cities. To effectively deal with sudden shooting terrorist cases, patrol routes, reasonable settings, and swift dispatches of the police (stations) should be considered.

Joint Optimization of Response Time and Deployment Cost in Next-Gen IoT Applications

The irruption of the Internet of Things (IoT) has attracted the interest of both the industry and academia for their application in intensive domains, such as healthcare. The strict Quality of Service (QoS) requirements of the next generation of intensive IoT applications require the QoS to be optimized considering the interplay of three key dimensions: 1) computing; 2) networking; and 3) application. This optimization requirement motivates the use of paradigms that provide virtualization, flexibility, and programmability to IoT applications. In the computing dimension, paradigms, such as edge or fog computing, software-defined networks in the networking dimension, along with micro-services architectures for the application dimension, are suitable for QoS-strict IoT scenarios. In this work, we present a framework, named Next-gen IoT Optimization (NIoTO), that considers these three dimensions and their interplay to place microservices and networking resources over an infrastructure, optimizing the deployment in terms of average response time and deployment cost. The evaluation of NIoTO in a healthcare case study reveals a response time speed up of up to 5.11 and a reduction in cost of up to 9% with respect to other state-of-the-art techniques.

Rapid Guessing in Low-Stakes Assessments: Finding the Optimal Response Time Threshold with Random Search and Genetic Algorithm

Rapid guessing is an aberrant response behavior that commonly occurs in low-stakes assessments with little to no formal consequences for students. Recently, the availability of response time (RT) information in computer-based assessments has motivated researchers to develop various methods to detect rapidly guessed responses systematically. These methods often require researchers to identify an RT threshold subjectively for each item that could distinguish rapid guessing behavior from solution behavior. In this study, we propose a data-driven approach based on random search and genetic algorithm to search for the optimal RT threshold within a predefined search space. We used response data from a low-stakes math assessment administered to over 5000 students in 658 schools across the United States. As we demonstrated how to use our data-driven approach, we also compared its performance with those of the existing threshold-setting methods. The results show that the proposed method could produce viable RT thresholds for detecting rapid guessing in low-stakes assessments. Moreover, compared with the other threshold-setting methods, the proposed method yielded more liberal RT thresholds, flagging a larger number of responses. Implications for practice and directions for future research were discussed.

Response time optimization for vulnerability management system by combining the benchmarking and scenario planning models

<span>The growth of information and communication technology has made the internet network have many users. On the other side, this increases cybercrime and its risks. One of the main attack targets is network weakness. Therefore, cyber security is required, which first does a network scan to stop the attack. Points of vulnerability on the network can be discovered using scanning techniques. Furthermore, mitigation or recovery measures can be implemented. However, it needs a short response time and high accuracy while scanning to reduce the level of damage caused by cyber-attacks. In this paper, the proposed method improves the performance of a vulnerability management system based on network and port scanning by combining the benchmarking and scenario planning models. On a network scanning to discover open ports on a subnet, Masscan can achieve response times of less than 2 seconds, and on scenario planning for detection on a single host by Nmap can reach less than 4 seconds. It was combining both models obtained an adequate optimization response time. The total response time is less than 6 seconds.</span>

Performance Evaluation of Load-Balancing Algorithms with Different Service Broker Policies for Cloud Computing

Cloud computing has seen a major boom during the past few years. Many people have switched to cloud computing because traditional systems require complex resource distribution and cloud solutions are less expensive. Load balancing (LB) is one of the essential challenges in cloud computing used to balance the workload of cloud services. This research paper presents a performance evaluation of the existing load-balancing algorithms which are particle swarm optimization (PSO), round robin (RR), equally spread current execution (ESCE), and throttled load balancing. This study offers a detailed performance evaluation of various load-balancing algorithms by employing a cloud analyst platform. Efficiency concerning various service broker policy configurations for load-balancing algorithms’ virtual machine load balance was also calculated using metrics such as optimized response time (ORT), data center processing time (DCPT), virtual machine costs, data transfer costs, and total cost for different workloads and user bases. Many of the past papers that were mentioned in the literature worked on round robin and equally spread current execution, and throttled load-balancing algorithms were based on efficiency and response time in virtual machines without recognizing the relation between the task and the virtual machines, and the practical significance of the application. A comparison of specific load-balancing algorithms has been investigated. Different service broker policy (SBP) tests have been conducted to illustrate the load-balancing algorithm capabilities.

A New Algorithm for Real-Time Scheduling and Resource Mapping for Robot Operating Systems (ROS)

A new version of a robot operating system (ROS-2) has been developed to address the real-time and fault constraints of distributed robotics applications. However, current implementations lack strong real-time scheduling and the optimization of response time for various tasks and applications. This may lead to inconsistent system behavior and may affect system performance. This article presents a new and efficient heuristic scheduling algorithm for the ROS-2 framework to improve resource mapping and system scheduling for robotics applications. The proposed scheduling design includes grouping callbacks into executors, assigning executor priority, and sequencing callbacks inside executors. The timing constraints and functional properties are expressed in the formal design model, and real-time scheduling is performed with respect to the timing constraints. The benefits of the proposed solution are demonstrated by a set of experimental results. Using this algorithm, it is shown that the number of executors needed to schedule a set of callbacks is minimized and the system schedulability is maximized for loaded and overloaded cases.