Average Response Time Research Articles

The Impact of Afterpulsing Effects in Single-Photon Detectors on the Performance Metrics of Single-Photon Detection Systems

A single-photon detection system based on InGaAs SPADs is a high-precision optical measurement system capable of detecting quantum-level optical signals. However, the afterpulsing effect when using InGaAs SPADs severely limits their practical utility. The impact of afterpulsing effects on the performance of systems based on this type of detector can no longer be ignored. Therefore, this paper provides a detailed analysis of the measurement errors induced by afterpulsing effects and proposes a correction method based on a power-law model. This method analyzes the probability distribution of afterpulsing effects using the power-law model and improves the expressions for the system’s average count rate and signal-to-noise ratio by calculating the average number of afterpulses within the average response time. The influence of afterpulse probability and dead time on the system’s average count rate is also analyzed. This afterpulse correction method mitigates the measurement errors caused by afterpulsing effects, thereby enhancing the system’s measurement accuracy.

Using AI for Dynamic Resource Allocation and Performance Optimization in Software Systems

This research work aims at evaluating the possibilities of using artificial intelligence in dynamic resource management and optimization of software system performance. In today’s complex world of application usage, normal methods of resource management are unable to cater to these dynamic needs and fulfill its usage potential. In this work, an assessment of three mainstream AI techniques – reinforcement learning, neural network, and genetic algorithm – is performed based on performance indicators such as resource utilization and consumption, average response time, throughput, costs, prediction capability, stability, and time taken to converge. The results show that the neural networks have the best resource acquisition performance as well as response rates, while the reinforcement learning has the best cost management and flexibility rates. As it has been pointed out, genetic algorithms are quite useful in finding optimization solutions, however real-time responsiveness is lack. Thus, the results provide significant understating of how to choose the proper AI technique depending on the specific application needs which in turn will be useful for organizations willing to improve their resource management using AI-based solutions.

Operational Performance Evaluation Model for Food Processing Machinery Industry Chain

This study aims to create a performance evaluation model for the food processing machinery industry. The goal is to help food processing plants improve both process quality and competitiveness. Additionally, component failures may disrupt the continuous operation of the food processing machine, potentially resulting in insufficient production and delays in delivery, which in turn leads to cost losses. For the sold food processing machinery, decreases in the average number of failures within a unit of time, the average repair response time when a failure occurs, and the average repair duration are three crucial factors in minimizing the total expected loss due to machine failures. Based on these three important factors, this study established the following evaluation indices: (1) the processing performance index, (2) the repair reporting performance index, and (3) the maintenance performance index. These indices serve as tools for assessing the performance of the three key operational aspects. This study employed a radar chart to construct the evaluation model, which can directly compare the critical values with the point estimates of three indices. Consequently, this approach can judge whether the operational performance has achieved the required level. This can maintain the simplicity and usability of point estimates while reducing the risk of misjudgment due to sampling errors.

Design and Implementation of Virtualization Cloud Computing System Intelligent Terminal Application Layer

Cloud task scheduling has become a trend, and the shortcomings of traditional scheduling algorithms can be optimized through mathematical models of other cloud task scheduling scenarios. In order to improve the virtualization data processing effect of intelligent terminal application layer, this paper proposes an improved krill swarm optimization algorithm based on adaptive weight. The optimization of cluster load balancing and task average response time ratio are used to improve the convergence and accuracy of task scheduling algorithm. Moreover, this paper uses CloudSim simulation tool to conduct experiments to verify the effectiveness of the proposed model. In addition, this paper proposes an application-based virtualization method, which virtualizes the application programs inside the host machine into the virtualization software inside the virtual machine, so that the virtual machine can access it. Finally, this paper verifies the reliability of the proposed method with experiments, thus providing a theoretical reference for the subsequent design of intelligent terminal application layer virtualization cloud computing system. Compared with the traditional way of using physical hardware, using virtual machine hardware is more flexible, efficient and safe, which brings great convenience to the development and deployment of applications.

Optimizing Web-Based Survey Applications with Laravel and Cloud Computing

This study aims to optimize the performance of a web-based survey application through the implementation of the Laravel framework and cloud computing technology. Survey applications often face challenges regarding response speed and scalability as the number of users increases. In this research, performance testing of the application was conducted both before and after optimization. The test results show that the application's response time significantly decreased after the implementation of Laravel and cloud technology. Before optimization, the average response time reached 4.5 seconds at 1,000 users, while after optimization, it dropped to 2.2 seconds. This performance improvement was achieved through efficient caching implementation, database query optimization using Eloquent ORM, and balanced load distribution via load balancers. Additionally, the application's availability increased to 99.9% thanks to cloud features such as auto-scaling and data replication. The conclusion of this study indicates that the combination of Laravel and cloud computing technology is effective in enhancing the performance and reliability of web-based survey applications, providing practical guidance for other web application developers facing similar challenges.

SBAC-SDN: A Scalable Blockchain-based Access Control in Northbound Interface for Multi-Controller SDN with Load Balancing Mechanism

Abstract The rapid adoption of Software-Defined Networking (SDN) has not only revolutionized network management but also presented challenges in scalability and security, particularly in multi-controller environments. This paper introduces a load balancing technique aimed at enhancing the scalability of the Northbound interface, a critical component often susceptible to performance bottlenecks. The experimental setup utilized Mininet and Ryu to create a simulated multi-controller SDN environment, where SBAC-SDN was thoroughly evaluated based on CPU and memory usage, response times, and error rates. The results showed substantial improvements in critical performance indicators, confirming the effectiveness of the load balancing technique in addressing scalability challenges. The findings indicate that the system can be scaled without excessive resource consumption, as CPU usage peaked at 60.3% and memory usage remained below 8 GB even with eight controllers. The stable average response times were consistently below 1 s, and the high throughput, ranging from 4.34 to 4.67 requests per second, further demonstrates the efficiency of the system. The error rate remained 0.0% across all configurations, highlighting the robustness of the implemented security measures. This study contributes to ongoing efforts to improve the scalability and overall robustness of SDNs, aligning with the broader goals of reliability and efficiency in network management.

Optimizing Cloud Computing Performance by Integrating the Novel PSBR Service Broker Policy and Load Balancing Algorithms

As cloud computing advances, organizations' IT infrastructure and application deployment processes are moving to the cloud because cloud computing provides everything as a service over the Internet. The performance of a cloud-based application is based on proper datacenter selection and workload distribution within the selected datacenter. Service broker policies are used for suitable datacenter selection, and load balancing algorithms(LBA) are applied to distribute workloads. This paper is to evaluate the effect of a proposed service broker policy (PSBR) on the performance of cloud-based applications with LBA. To achieve the objective, the behavior of the TikTok application was modeled using the worldwide users’ statistics on the cloud simulation framework, namely CloudAnalyst. As a result, the average response time and data center processing time are measured. Next, the PSBR provides better results than the existing service proximity-based policy. This paper supports cloud service providers' benefits, from coordination between data center configuration, data center selection, and workload distribution to cloud users' identification of the appropriate procedures for their organization or application. PSBR with Active Monitoring had the best average response time of 75.1 ms, while SPR consistently exhibited higher average times across all algorithms, with the highest being 84.5 ms for Round Robin. Under the PSBR policy, Throttled had the lowest average processing time (4.67), while Round Robin had the highest (5.72). Similarly, under the SPR policy, Throttled maintained its efficiency with the lowest average (4.8), while Round Robin showed the highest (5.79).

LEVERAGING DATA-DRIVEN TECHNIQUES FOR EFFICIENT DATA MINING IN CLOUD COMPUTING ENVIRONMENTS

The capacity to efficiently use big data and analytics is becoming a critical differentiator for company growth in today's data-driven environment. Using important trends, obstacles, and best practices as a framework, this article investigates how to promote company growth via the use of big data and analytics. An important issue in cloud computing is deciding on an acceptable amount and location of data. Decisions about resource management are based on data aspects and operations in data-driven infrastructure management (DDIM), a novel solution to this problem. It is critical to have a unified system that can manage various forms of big data and the analysis of that data, as well as common knowledge management functions. The approach stated in this research is DD-DM-CCE, or Data-Driven Methods for Efficient Data Mining in Cloud Computing Environments. Improving data using derived information from maximum frequent correlated pattern mining is the main focus of the work. By considering the centrality factor, the DD-DM-CCE method may help choose the best locations to store data in order to reduce access latency. In order to gain a competitive edge, this study offers a cloud-based conceptual framework that can analyze large data in real time and improve decision making. Efficient big data processing is possible with cloud computing infrastructures that can store and analyze massive amounts of data, as this reduces the upfront cost of the massively parallel computer infrastructure needed for big data analytics. According to simulations run on cloud computing, the DD-DM-CCE approach does better than the status quo regarding hit ratio, effective network utilization, and average response time. According to this study, data mining methods are valuable and successful in predicting how consumers will utilize cloud services.

The development and characterisation of 3D-printed multi-material thermistor

This paper introduces a multi-material, low-cost, and highly sensitive thermistor concept developed for body temperature monitoring. The designed thermistor employs poly(3,4-ethylenedioxythophene):poly(4-styrenesulfonate) (PEDOT:PSS) for the temperature sensing layer, silver (Ag) for the contact electrodes, and polycarbonate (PC) as the substrate. In contrast to traditional printed electronics substrates used in thermistor development, the utilisation of Material Extrusion via Fused Deposition Modelling (FDM) for the manufacture of PC substrates is the distinct feature of the work. For the manufacture of multi-material thermistors two different Additive Manufacturing (AM) methods: FDM for substrates and micro dispensing for PEDOT:PSS sensing layer and Ag electrodes, were utilised. Two thermistors with varying sensing areas, namely D1 and D2, were designed and fabricated. The thermistors demonstrated a high degree of linearity and repeatability within the temperature range of 25–45°C with a viable hysteresis maximum around 3%. The measurement sensitivity of thermistors was assessed based on Temperature Coefficient of Resistance (TCR) values, which were –0.68 ±0.057% and –0.49 ±0.078% per °C for the D1 design and –0.53 ±0.078% and –0.40 ±0.069% per °C for the D2 design, during heating and cooling, respectively. Comparable average response and recovery times to the literature were also acquired, which were 31.47 ±1.02 and 54.42 ±0.70seconds for D1 and 27.38 ±0.96 and 48.45 ±1.69seconds for D2, during heating and cooling, respectively. It was evident that the sensing area had an impact on thermistor TCR as well as response and recovery times, where higher TCR and faster response and recovery times were recorded with the small sensing area. It was also observed that humidity had a significant impact on thermistor reliability, exhibiting a normalised resistance change of ∼12% and ∼9% of the initial measurement at 50 and 75% RH, respectively. When compared to PEDOT:PSS-based thermistors reported in the literature, the obtained results in this research have demonstrated that our multi-material thermistor concept is a promising candidate for the low-cost and highly sensitive multi-material thermistor that can be manufactured in a fully additive manner using AM technologies.

Research and Application of Leakage Monitoring Techniques for Gas Field Water Pipelines

Abstract To study the effectiveness and application effects of leakage monitoring techniques for gas field water pipelines in the Sichuan-Chongqing region, seven kinds of flow limitation holes with different diameters were used to simulate the leakage of the gas field water pipeline to carry out a field test. The monitoring conditions of two leakage monitoring techniques, namely dynamic pressure wave compound method and negative pressure wave and transmission balance method, were compared and analyzed under different working conditions. The test results showed that: (1) When the pipeline was full, the leakage with apertures being smaller than 2 mm could not be effectively monitored. A single-end alarm could be achieved using the dynamic pressure wave compound method, but the positioning was ineffective. An alarm could not be effectively achieved using the transmission balance method. (2) When the pipeline was full, for the leakage with apertures being 2 mm and above, early warning could be well achieved using the both methods, and the monitoring effects were nearly the same, with the average positioning error being about 48 m and the average alarm response time being about 58 s. (3) When there were empty pipe slug flows in the pipeline, only apertures being 5 mm and above could be alarmed; however, positioning could not be effectively achieved, and system technical indicators could not be reached. (4) When there were large elevation differences in the gas field water pipeline and empty pipe slug flows could not be eliminated, the both techniques were not applicable.

Intelligent Resource Monitoring and Control Method in Vehicular Ad-hoc Networks for Electric Vehicle Enabled Microgrids

Software-Defined Networks (SDN) and Cloud Radio Access Networks (CRANs) are added to vehicle ad hoc networks (VANETs). The goals include making data transfer and resource sharing more efficient, achieving the shortest possible wait and response times, and ensuring the network is reliable even when conditions change. The usual ways of handling and grouping loads in Electric Vehicles (EVs), which are made for stable environments, need to be changed to work with VANETs, which are constantly evolving. To regularly meet these high service levels, focus on more adaptable and durable solutions. This study shows a software-defined EV fog computing design that improves VANETs' resource sharing. The suggested design uses smart controls placed strategically in the network to make the flow of data and use of resources as efficient as possible. The system uses parallel processing to split up computing tasks among EV stations. This makes the network more mobile and lessens the chance of jams. Simulations and real-world tests of the model show that it makes the network much more efficient. The study found that compared to traditional methods, the average response time went up by 29%, network delay went down by 23%, and it took 27% less time to get to the best assets spread.

Evaluation Of the Emergency Severity Index Triage For Improving Emergency Room Services Quality In Indonesian Hospital

Background:Effective prioritization is crucial in delivering high-quality emergency medical services, although it is often lacking. Objective:This study aims to evaluate the application of triage based on the Emergency Severity Index (ESI) in improving the quality of emergency departments in hospitals in Indonesia. Methods:An accidental sample of 100 emergency room patients was selected. Patient satisfaction was measured using questionnaires, while infrastructure, support facilities, and the triage process were assessed through direct observation. Results:The evaluation found that nurse staffing levels did not meet standards and were inadequate to meet patient needs. Triage facilities and infrastructure at both hospitals met established standards. The triage process, conducted on 100 respondents by 30 nurses, revealed that the majority of patients were categorized as ESI level 3, comprising 82 (82%) respondents. There was an excessive use of triage resources beyond projections, and patient placement did not always adhere to standards, although the average response time was within the accepted range. Patient satisfaction was evaluated across five dimensions; the most satisfying dimensions were assurance and empathy, with 90% of respondents rating thempositively, while the tangibles dimension received the lowest satisfaction rating at 84%. The correlation between the application of ESI triage and patient satisfaction was not statistically significant (p-value = 0.101, higher than 0.05). Conclusion:It is recommended that hospitals increase the number and competence of nursing staff in emergency departments to ensure response times and patient placement align with ESI triage standards, thereby enhancing the effectiveness of triage implementation. Keywords:emergency severity index triage; quality of care; emergency department

Magazine Identification on SS1-V1 Assault Rifle using Web-based HX711 Load Cell Sensor

The SS1-V1 is an assault rifle model equipped with a magazine as one of its main components. The magazine plays a crucial role in storing and loading ammunition. However, magazines must be stored separately from the weapon as their integrated storage can pose a risk to a country’s security. Therefore, this research proposes a web-based system capable of identifying the presence of magazines in weapons in real-time. This system is supported by various hardware components, including a load cell sensor, HX711 sensor module, Arduino UNO R3, and an Ethernet shield for network connectivity. In addition, API is used for data management, which is then stored in the database. The results of this research indicate that the average response time for each rack within a cabinet is between 2.7s to 3.3s, while for racks serving as slaves, it ranges from 14.16s to 15.01s. Based on the results of the weight-based weapon identification testing, there is a weight difference of 0.1kg to 0.2kg. These results state that all tests were successfully identified by the web system according to the conditions of the weapons on the rack.

Real-time monitoring and optimization of machine learning intelligent control system in power data modeling technology

In response to the problems of insufficient model accuracy and poor real-time performance in real-time monitoring and optimization of data models in traditional power systems, this paper explored them based on machine learning. It used methods such as box plots and Kalman filters to preprocess power data and used Fourier transform and long short-term memory (LSTM) network to extract data features. Based on long-term and short-term memory networks, this paper constructed a deep neural network model to process data. Recursive feature elimination method can be used for feature selection in the model, and multi-model integration method can be used for feature fusion. The experiment trained and tested the model on two datasets, IEEE 39-Bus and Pecan Street. The experimental results show that the accuracy of the paper's model in both the training and testing sets is higher than that of the convolutional neural network, decision tree, and support vector machine models in the comparative experiments, reaching 93 % and 94 %, respectively. The average response times in three different testing methods were 139.8 ms, 151 ms, and 140.6 ms, respectively. The lowest daily failure rate for 30 consecutive days of work was 0 %, which reflects the stability of the model; in the satisfaction survey of practitioners, the proportion of high recognition answers to each question was higher than 80 %. The experimental results show that the machine learning intelligent control system has achieved good application in power data modeling technology, providing a reference for similar research in the future.

Pengembangan Sistem Seleksi Proposal Penelitian Berbasis Web Service Menggunakan REST API

Web service-based application development has become a vital component of the modern in-formation technology ecosystem. The implementation of web services facilitates interoperability among diverse systems and platforms. REST API (REpresentational State Transfer Application Programming Interface) is one of the most popular architectures for creating efficient and scalable web services. This study examines the implementation of REST API in the development of web service-based applications. The case study focuses on the creation of a Web service-based Research Proposal Selection System. The REST API implementation method in this study adheres to the system development life cycle (SDLC) to ensure that the developed API meets business needs while being efficient, secure, and reliable. The analysis phase is conducted to gather both functional and non-functional REST API requirements. During the design phase, the REST API architecture is designed in detail, including how data will be transferred and processed, and how the API will interact with databases and other systems. Furthermore, in the implementation phase, the REST API is developed, ensuring that each endpoint operates correctly. In developing the Proposal Selection System, the implementation of web services integrates data from various existing sys-tems, including employee, academic, and research data. The system encompasses various features, such as proposal import, proposal mapping, proposal review, proposal assessment, proposal se-lection, and reporting. The research findings indicate that implementing the REST API simplifies the development of web service applications. The test results reveal that the average response time is relatively stable, measuring 31 ms without iterations and 29 ms with five iterations.

Popularity-Aware Service Provisioning Framework in Cloud Environment

To balance the tradeoff between quality of service (QoS) and operating expenditure (OPEX), the service provider should request the appropriate amount of resources to the cloud operator based on the estimated variation of service requests. This paper proposes a popularity-aware service provisioning framework (PASPF), which leverages the network data analytics function (NWDAF) to obtain analytics on service popularity variations. These analytics estimate the congestion level and list of top services contributing most of the traffic change. Based on the analytics, PASPF enables the service provider to request the appropriate amount of resources for each service for the next time period to the cloud operator. To minimize the OPEX of the service provider while keeping the average response time of the services below their requirements, we formulate a constrained Markov decision process (CMDP) problem. The optimal stochastic policy can be obtained by converting the CMDP model into a linear programming (LP) model. Evaluation results demonstrate that the PASPF can achieve less than 50% OPEX of the service provider compared to a popularity-non-aware scheme while keeping the average response time of the services below the requirement.

Enhancing the performance of hydrogel strain/pressure sensors via gradient-entanglement-induced surface wrinkling patterns

Due to the advantages of high mechanical compliance and good biocompatibility, conductive hydrogels exhibit great application potential in flexible electronics. As the requirements in high sensitivity and high mechanical performance are increasing, the construction of patterned surfaces has been proven an effective approach, yet most of the common methods are complex and high-cost. Herein, we proposed a facile strategy to construct gradient entanglement dual network (GEDN) hydrogel with spontaneously patterned surface and consequent high surface area, which greatly enhances the sensitivity of hydrogel sensors. The strategy of high entanglement as well as the dual network in hydrogel achieved greatly improved mechanical performance. The prepared polyvinyl alcohol (PVA)/gradient entangled polyacrylamide (PAAm) DN and surface-patterned hydrogel possesses the advantages of excellent stretchability (681 %), high tensile strength (1.01 MPa), toughness (4.23 MJ/m3), and relatively low mechanical hysteresis. Meanwhile, it showed high sensitivity, wide detection range, and rapid response both as strain sensor (GFmax = 10.96, detection range = 681 %, an average response time of 170 ms) and pressure sensor (Smax = 0.17 kPa−1, detection range = 225 kPa, an average response time of 230 ms). In summary, it provides a new strategy for design of the multifunctional high-performance flexible devices, exhibiting great application prospects.

An Autoscalable Approach to Optimize Energy Consumption using Smart Meters data in Serverless Computing

Serverless computing has evolved as a prominent paradigm within cloud computing, providing on-demand resource provisioning and capabilities crucial to Science and Technology for Energy Transition (STET) applications. Despite the efficiency of auto-scalable approaches in optimizing performance and cost in distributed systems, their potential remains underutilized in serverless computing due to the lack of comprehensive approaches. So an auto-scalable approach has been designed using Q-learning, which enables optimal resource scaling decisions. This approach proves useful for adjusting resources dynamically to maximize resource utilization by automatically scaling up or down resources as needed. Further, the proposed approach has been validated using AWS Lambda with key performance metrics such as probability of cold start, average response time, idle instance count, energy consumption etc. The experimental results demonstrate that the proposed approach performs better than the existing approach by considering the above parameters. Finally, the proposed approach has also been validated to optimize the energy consumption of smart meters data

DDS-P: Stochastic models based performance of IoT disaster detection systems across multiple geographic areas

Effective management of catastrophic events in high-risk zones necessitates a holistic technological approach to protect ecosystems, biodiversity, and native populations. Limitations in sensor range and connectivity hamper real-time data gathering in secluded areas, while financial and technical hurdles hinder the creation of cost-effective, automated systems. This study presents stochastic models, the LoRaW protocol, and cloud technology to enhance sensor deployment simulations. Wireless Sensor Networks and LoRa technology are crucial for extensive monitoring and communication infrastructures. Stochastic Petri Net models optimize system components by assessing crucial performance indicators, such as average response time and system utilization, thus improving disaster response and supporting research hypotheses.

EETS: An energy-efficient task scheduler in cloud computing based on improved DQN algorithm

The huge energy consumption of data centers in cloud computing leads to increased operating costs and high carbon emissions to the environment. Deep Reinforcement Learning (DRL) technology combines of deep learning and reinforcement learning, which has an obvious advantage in solving complex task scheduling problems. Deep Q Network(DQN)-based task scheduling has been employed for objective optimization. However, training the DQN algorithm may result in value overestimation, which can negatively impact the learning effectiveness. The replay buffer technique, while increasing sample utilization, does not distinguish between sample importance, resulting in limited utilization of valuable samples. This study proposes an enhanced task scheduling algorithm based on the DQN framework, which utilizes a more optimized Dueling-network architecture as well as Double DQN strategy to alleviate the overestimation bias and address the shortcomings of DQN. It also incorporates a prioritized experience replay technique to achieve importance sampling of experience data, which overcomes the problem of low utilization due to uniform sampling from replay memory. Based on these improved techniques, we developed an energy-efficient task scheduling algorithm called EETS (Energy-Efficient Task Scheduling). This algorithm automatically learns the optimal scheduling policy from historical data while interacting with the environment. Experimental results demonstrate that EETS exhibits faster convergence rates and higher rewards compared to both DQN and DDQN. In scheduling performance, EETS outperforms other baseline algorithms in key metrics, including energy consumption, average task response time, and average machine working time. Particularly, it has a significant advantage when handling large batches of tasks.