Response Time Research Articles

Adaptive load balancing strategies in service composition for improved system performance

Service composition enables the integration of multiple services to create new functionalities, optimizing resource utilization and supporting diverse applications in critical domains such as safety-critical systems, telecommunications, and business operations. This paper addresses the challenges in comparing load-balancing algorithms within service composition environments and proposes a novel dynamic load-balancing algorithm designed specifically for these systems. The proposed algorithm aims to improve response times, enhance system efficiency, and optimize overall performance. Through a simulated service composition environment, the algorithm was validated, demonstrating its effectiveness in managing the computational load of a BMI calculator web service. This dynamic algorithm provides real-time monitoring of critical system parameters and supports system optimization. In future work, the algorithm will be refined and tested across a broader range of scenarios to further evaluate its scalability and adaptability. By bridging theoretical insights with practical applications, this research contributes to the advancement of dynamic load balancing in service composition, offering practical implications for high-tech system performance.

Implementing Enterprise-Scale iOS MDM : A Technical Deep Dive

As organizations increasingly adopt mobile technologies, the need for effective remote management of iOS devices has grown significantly. This technical article examines implementation strategies and real-world performance metrics from a study of Mobile Device Management (MDM) implementations across 12 diverse organizations, encompassing 2,500 enterprise devices during 2023-2024. The article reveals that optimized MDM solutions can achieve 99.99% uptime while reducing infrastructure costs by up to 45%. Key findings demonstrate that effective MDM implementation reduces device management costs by 64%, improves security compliance rates to 99.3%, and decreases incident response time by 73%. Through automated troubleshooting and optimization, organizations experienced a 67% reduction in security incidents and a 42% decrease in IT support overhead while achieving 85% reduction in deployment time and 82% acceleration in app deployment processes. The article comprehensively evaluates architecture design, deployment metrics, security implementation, performance analysis, and scalability considerations. Implementation of advanced security protocols resulted in a 98.7% prevention rate for common mobile security threats, while maintaining 99.99% system reliability. The article also highlights the importance of phased deployment strategies, which demonstrate 43% higher success rates compared to direct full-scale implementations, and provides strategic insights for future planning and infrastructure optimization.

PID control algorithm based on multistrategy enhanced dung beetle optimizer and back propagation neural network for DC motor control.

Traditional Proportional-Integral-Derivative (PID) control systems often encounter challenges related to nonlinearity and time-variability. Original dung beetle optimizer (DBO) offers fast convergence and strong local exploitation capabilities. However, they are limited by poor exploration capabilities, imbalance between exploration and exploitation phases, and insufficient precision in global search. This paper proposes a novel adaptive PID control algorithm based on enhanced dung beetle optimizer (EDBO) and back propagation neural network (BPNN). Firstly, the diversity of exploration is increased by incorporating a merit-oriented mechanism into the rolling behavior. Then, a sine learning factor is introduced to balance the global exploration and local exploitation capabilities. Additionally, a dynamic spiral search strategy and adaptive [Formula: see text]-distribution disturbance are presented to enhance search precision and global search capability. The BPNN is employed to fine-tune both PID and network parameters, leveraging its powerful generalization and learning ability to model nonlinear system dynamics. In the simplified motor experiments, the proposed controller achieved the lowest overshoot (0.5%) and the shortest response time (0.012s), with a settling time of 0.02s and a steady-state error of just 0.0010. In another set of experiments, the proposed controller recorded an overshoot and response time of 0.7% and 0.0010s, across five DC motor tests. These results demonstrate the proposed adaptive PID control algorithm has superior performance in optimizing control system parameters, as well as improving system robustness and stability.

Dynamically Balancing Load with Overload Control for Microservices

The microservices architecture simplifies application development by breaking monolithic applications into manageable microservices. However, this distributed microservice “service mesh” leads to new challenges due to the more complex application topology. Particularly, each service component scales up and down independently creating load imbalance problems on shared backend services accessed by multiple components. Traditional load balancing algorithms do not port over well to a distributed microservice architecture where load balancers are deployed client-side. In this article, we propose a self-managing load balancing system, BLOC, which provides consistent response times to users without using a centralized metadata store or explicit messaging between nodes. BLOC uses overload control approaches to provide feedback to the load balancers. We show that this performs significantly better in solving the incast problem in microservice architectures. A critical component of BLOC is the dynamic capacity estimation algorithm. We show that a well-tuned capacity estimate can outperform even join-the-shortest-queue, a nearly optimal algorithm, while a reasonable dynamic estimate still outperforms Least Connection, a distributed implementation of join-the-shortest-queue. Evaluating this framework, we found that BLOC improves the response time distribution range, between the 10th and 90th percentiles, by 2 –4 times and the tail, 99th percentile, latency by 2 times.

Underwater Vibration Sensor to Enable Automated and Contactless Voice Recognition

AbstractIndividuals suffering from voice disabilities have limited access to currently available automation technologies that operate through voice commands. To address this issue, an alternative voice recognition approach is essential without directly monitoring the audio signals generated from the vocal cord. In this work, the design of a chemically reactive and conductive sponge is reported to create an underwater vibration sensor with a fast response time and high sensitivity, through orthogonal modulation of conductivity (40–2150 kΩ), water repellence (0°–154°) and mechanical properties (0.32–2.63 MPa). This class of porous sponge sensors enables the identification of subtle water waves generated at the air–water interface and extends its utility to detecting a variety of locomotion (squatting, jumping, walking, etc.), as well as automated voice recognition using a deep learning model without direct contact with the human body. Overall, this underwater vibration sensor provides a novel basis for remote interaction with automated technologies, which finds use in medical diagnostics, human‐machine interfaces, and underwater communication systems.

Multimodal fusion-powered English speaking robot

IntroductionSpeech recognition and multimodal learning are two critical areas in machine learning. Current multimodal speech recognition systems often encounter challenges such as high computational demands and model complexity.MethodsTo overcome these issues, we propose a novel framework-EnglishAL-Net, a Multimodal Fusion-powered English Speaking Robot. This framework leverages the ALBEF model, optimizing it for real-time speech and multimodal interaction, and incorporates a newly designed text and image editor to fuse visual and textual information. The robot processes dynamic spoken input through the integration of Neural Machine Translation (NMT), enhancing its ability to understand and respond to spoken language.Results and discussionIn the experimental section, we constructed a dataset containing various scenarios and oral instructions for testing. The results show that compared to traditional unimodal processing methods, our model significantly improves both language understanding accuracy and response time. This research not only enhances the performance of multimodal interaction in robots but also opens up new possibilities for applications of robotic technology in education, rescue, customer service, and other fields, holding significant theoretical and practical value.

A General Strategy for Enhancing the Performance of Ga2O3-Based Self-Powered Solar-Blind Photodetectors through Band Structure Engineering

Abstract The spectral response of Ga2O3 almost perfectly covers the 200~280 nm solar-blind ultraviolet wavelength range, making it an ideal semiconductor material for fabricating solar-blind ultraviolet photodetectors. However, due to the considerably deep valence band energy of Ga2O3 the construction of heterojunctions typically induces a significant valence band offset (EV). Herein, we present a band engineering approach to improve the performance of Ga2O3 bases photodetectors. This pronounced valence band barrier can strongly influence the transport of photo-generated charge carriers, especially the extraction of holes in the depletion region. By introducing nitrogen (N) during the growth process, we elevated the valence band of Ga2O3 by 0.43 eV. The organic high-molecular-weight material of PEDOT:PSS has been utilized in conjunction with Ga2O3 to construct heterojunction photodetectors. The photodetectors exhibit excellent self-powered characteristics, with responsivity, detectivity, and response time being nearly ten times higher than those of Ga2O3 photodetectors before band structure modulation. The investigation into modulating the band structure of Ga2O3 carried out in this study will lay the theoretical foundation and provide technical solutions for developing satisfactory self-powered photodetectors.

Integration of overlapping sequences emerges with consolidation through medial prefrontal cortex neural ensembles and hippocampal-cortical connectivity.

Systems consolidation theories propose two mechanisms that enable the behavioral integration of related memories: coordinated reactivation between hippocampus and cortex, and the emergence of cortical traces that reflect overlap across memories. However, there is limited empirical evidence that links these mechanisms to the emergence of behavioral integration over time. In two experiments, participants implicitly encoded sequences of objects with overlapping structure. Assessment of behavioral integration showed that response times during a recognition task reflected behavioral priming between objects that never occurred together in time but belonged to overlapping sequences. This priming was consolidation-dependent and only emerged for sequences learned 24 hr prior to the test. Critically, behavioral integration was related to changes in neural pattern similarity in the medial prefrontal cortex and increases in post-learning rest connectivity between the posterior hippocampus and lateral occipital cortex. These findings suggest that memories with a shared predictive structure become behaviorally integrated through a consolidation-related restructuring of the learned sequences, providing insight into the relationship between different consolidation mechanisms that support behavioral integration.

The design of intelligent highway transportation system in smart city based on the internet of things.

The design of intelligent expressway transportation system based on the Internet of Things is studied to improve the safety, travel experience, and operation management of expressway. The characteristics of the Internet of Things and cloud computing technology and its application on the expressway are analyzed, and the system design requirements of expressway intelligent transportation are understood. Besides, the overall architecture of the system is studied and designed. The IaaS layer, PaaS layer, and SaaS layer of the cloud platform are designed and deployed. The intelligent information system can make expressway highly informative. The simulation experiments reveal that the system only needs 120 milliseconds in accident processing time, which is far lower than the intelligent transportation system that only uses edge computing technology (201 milliseconds) and the intelligent transportation system that only uses cloud computing technology (443 milliseconds). Meanwhile, the accident response time is only 12s, which is also superior to other models. In terms of cost-effectiveness, the monthly cost of the system is 7004 yuan, with a CPU utilization rate of 53%, demonstrating good cost-effectiveness and resource utilization efficiency. In addition, compared with the existing system, the average traffic congestion time has been reduced by 25%, the traffic accident rate has been reduced by 18%, and the accident rate has been reduced by 27%. The intelligent traffic system design of expressway, expressway safety, travel service, and operation management is effectively improved by researching the intelligent traffic system design of expressway.

Ag@WO3 core-shell nanocomposite for wide range photo detection.

This study successfully synthesized high-performance photodetectors based on Ag-WO3 core-shell heterostructures using a simple and economical two-step pulsed laser ablation in water method and has investigated the electrical characteristics of the Ag@WO3 nanocomposite heterojunction. The Hall effect tests indicate that the synthesized Ag@WO3 exhibits n-type conduction with a Hall mobility of 1.25 × 103 cm2V-1S-1. Dark current-voltage properties indicated that the created heterojunctions displayed rectification capabilities, with the highest rectification factor of around 1.71 seen at a 5V bias. A photodetector's responsivity reveals the existence of two response peaks, which are situated in the ultraviolet and visible region. The photodetector demonstrates a rapid response time of less than 100ms. The detectivity values for wavelengths of 350nm and 490nm were 35 × 1013 Jones and 28 × 1013 Jones, respectively. The n-Ag-WO3/n-Si photodetector achieved a maximum EQE of 11.5% in the ultraviolet wavelength when subjected to 3V and illuminated with 350nm (26 mW/cm2) light. The devices demonstrate rapid switching behavior with a rise time of 0.32s and a fall time of 0.33s. The time-dependent light response of a photodetector under illumination at 26 mW/cm2 is seen at a bias of 3V. The light exhibits a rise and decay duration of 15s, while the photocurrent gain is measured at 9µA. The photocurrent of devices exhibited a positive correlation with the incoming light intensity, suggesting that the junction has the potential to function as a photo detector.

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.

Ion‐Selective Mobility Differential Amplifier: Enhancing Pressure‐Induced Voltage Response in Hydrogels

Piezoionics is an emerging mechanical‐electrical energy conversion paradigm that enables self‐powered sensing systems for next‐generation intelligent wearable electronics. However, there are currently no rational design approaches to enhance the stimulus response of piezoionic devices. Here, we present a strategy using crown ether as ion‐selective mobility differential amplifiers for enhancing the pressured‐induced voltage response in ionic polyvinyl alcohol (PVA) hydrogels. The crown ether grafted PVA (PVA‐CE) hydrogel prototype achieves a 30‐fold amplified piezoionic coefficient of 1490 nV Pa−1 within 0 – 1 kPa, compared to 49 nV Pa−1 of the unmodified PVA. The PVA‐CE exhibits an ultra‐low pressure detection limit of 0.2 Pa with a fast response time of 18.1 ms. Leveraging these properties, we further demonstrate a human somatosensory network analogous PVA‐CE piezoionic skin using arrayed pressure sensing. These capabilities hold great promise for emerging healthcare applications such as synthetic biology, soft robotics, and beyond.

Olfactory response and food preference of Gromphadorhina portentosa (Blattodea: blaberidae) to agro-industrial waste for biomass production

The Madagascar cockroach (Gromphadorhina portentosa) is a species widely used in insect farming, including in animal and human diets. Because of these potential applications, we are looking for effective ways to breed them in captivity, through alternatives that reduce production costs. One solution to this problem would be to use agro-industrial waste to feed these insects. Therefore, this study aimed to evaluate the olfactory behavior of the Madagascar cockroach when exposed to different agro-industrial residues. The experiments were conducted at the Entomoculture Laboratory of the Federal Rural University of Pernambuco (UFRPE) using agro-industrial waste from sugarcane bagasse (Saccharum spp.), forage palm (Opuntia ficus), moringa (Moringa oleifera L.), and wheat bran (Triticum vulgare). The waste was distributed randomly in the evaluation arena. There were 20 repetitions of both sexes with an evaluation period of 20 min each. The bioassays were carried out in sequence: response time of the cockroaches, first olfactory choice, and food preference. Males and females had the highest average consumption in the 20-min evaluation period, and the first olfactory choice was for moringa; however, food preference was concentrated on wheat bran, where females had an average consumption of 1.3g of wheat bran and males 0.85g. Therefore, using wheat bran could make large-scale production of Madagascar cockroaches feasible and potentially make it possible to reuse and sustainably use this waste.

Factors affecting emergency medical dispatchers decision making in stroke calls - a qualitative study.

Emergency Medical Communication Centers (EMCC) have a key role in the prehospital chain-of-stroke-survival by recognizing stroke patients and reducing prehospital delay. However, studies on EMCC stroke recognition report both substantial undertriage and overtriage. Since mis-triage at the EMCC challenges the whole chain-of-stroke-survival, by occupying limited resources for non-stroke patients or failing to recognize the true stroke patients, there is a need to achieve a more comprehensive understanding of the dispatchers' routines and experiences. The aim of this study was to explore factors affecting EMCC dispatcher's decision-making in stroke calls. A qualitative exploratory study, based on individual semi-structured interviews of 15 medical dispatchers from EMCC Oslo, Norway. Interviews were conducted during August and October 2022 and analyzed using the principles of thematic analysis. We identified four themes: [1] Pronounced stroke symptoms are easy to identify [2]. Non-specific neurological symptoms raise suspicion of acute stroke but are difficult to differentiate from other medical conditions [3]. Consistent use of the Criteria Based Dispatch (CBD) protocol may increase EMCC overtriage [4]. Contextual conditions at EMCC can affect dispatchers' decision-making process and the ability for experiential learning. Medical dispatchers at the EMCC perceive vague and non-specific stroke symptoms, such as dizziness, confusion or altered behaviour, challenging to differentiate from symptoms of other less time-critical medical conditions. They also perceive the current CBD protocol in use as less supportive in assessing such symptoms. High workload and strict EMCC response time interval requirements hinder the gathering of essential patient information and the ability to seek guidance in cases of doubt, potentially exacerbating both EMCC undertriage and overtriage. The absence of feedback loops and other strategies for experiential learning in the EMCC hampers the medical dispatcher's ability to evaluate their own assessments and improve dispatch accuracy.

Same same but different: The graded influence of vowel quality and prosodic prominence on letter detection.

This study investigates the impact of phonetic realisation and prosodic prominence on visual letter identification, focusing on the letter <e> in German bisyllabic words. Building upon previous research, a computerised letter search task was conducted with 78 skilled adult readers. Words featuring different phonetic realisations of <e > (/eː, ɛ, e, l̩, n̩, ɐ/) in stressed and unstressed first and second syllables were systematically included. Analyses of error rates and response times revealed a graded pattern in the detection of <e>, with the closed long (/eː/) and closed short (/e/) realisations being easiest to detect, open (/ɛ/) and near-open central (/ɐ/) vowels becoming incrementally harder, and silent vowels in syllabic consonants (/n̩/) being the most challenging. Results divided by position and stress of the syllable containing the target letter further indicated influences of prosodic prominence. The findings contribute to understanding the intricate interplay of grapheme-phoneme correspondences and prosodic structure in skilled readers' visual letter recognition.

Prediction of automotive response using supervised machine learning in antilock braking systems and comparison with different models for improved vehicle safety

Abstract Previous research on predicting automotive response in antilock braking systems faced several common drawbacks. Limited availability of high-quality datasets and computational constraints hindered the models' ability to generalize and perform effectively in real-time. Additionally, integrating these models into existing vehicle systems is challenging, often requiring extensive calibration and testing to ensure reliability and safety. This study focuses on enhancing automotive safety through the integration of machine learning with advanced braking systems. An antilock braking system model is developed using MATLAB, accounting for key parameters such as speed, mass, wheel radius, and moment of inertia. Various probability ranges are simulated to evaluate the system's real-time response in dynamic environments. A supervised machine learning approach is applied, with lasso regression used for model validation. Braking response was evaluated at three speeds: 25 km/h, 45 km/h, and 65 km/h. To assess model accuracy, error analysis was conducted using metrics such as mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE). Results showed a significant improvement in prediction accuracy, with MAPE reduced to 3.2%, MAE to 0.15 seconds, RMSE to 0.25 seconds, and MSE to 0.06 seconds.A comparative analysis is done using the Python Jupyter Framework, further validating the findings. The machine learning model successfully forecasts braking response times, and validates its prediction against random datasets.

A novel approach to enhancing smart stiffness of soft robotic gripper fingers for wider grasping capability

AbstractThis paper presents a proposed design of soft gripper fingers with adjustable stiffness that could be employed in the applications requiring adaptable and stable grasping. The main idea is to combine the under-actuated cable driven mechanism of a soft gripper finger with particle and layer jamming mechanisms to create a new grasping function with variable stiffness for different manipulation requirements. The movement of the soft gripper finger is produced by a cable-driven mechanism. However, particle and layer jamming chambers were embodied as a variable stiffness mechanism for the variable stiffness function. A single soft gripper finger module was developed and tested with particle and layer jamming chamber attached to it. The stiffness and response time of the soft gripper finger were measured in three distinct configurations: single finger module, particle jamming chamber attached to the finger, and layer jamming chamber attached to the finger. The comparison reveals that combining a soft finger with particle jamming increased performance by 20% compared to using the soft finger alone, while combining it with layer jamming led to an 80% increase. Additionally, layer jamming combined with a soft finger showed a 28% increase compared to particle jamming combined with a soft finger. Furthermore, simulation of the soft finger was conducted to estimate the deflection of the soft gripper finger under various applied forces. Moreover, proposed closed loop smart stiffness mechanism for the soft gripper was modeled and simulated by evaluating both soft and hard objects and simulation results were obtained for different cases. The findings indicated that the stiffness of the soft gripper finger can be adjusted for different grasping requirements.

On the localization of reward effects in overlapping dual tasks.

In dual-task (DT) situations, performance deteriorates compared with single-task situations. Such performance decrements are frequently explained with the serial scheduling of the response selection stages constituting a bottleneck. Proof of this assumption stems from the observation that response times for the second task (task 2; RT 2) increase with decreasing stimulus-onset asynchrony (SOA).In this study, we investigated how the reward prospect for task 1 performance affects task 1 and task 2 processing. For that purpose, we relied on the psychological refractory period paradigm (PRP) as a chronometric tool, to determine the locus of the reward effect in the processing chain of both tasks.We obtained improved task 1 and task 2 performance; as indicated by reduced RTs in the reward compared to the no reward condition of task 1 and task 2. Furthermore, the reward effect propagated at short SOA from task 1 onto task 2, suggesting that the locus of the reward effect can be pinpointed before or at the bottleneck of task 1. Importantly, the mean reward effect on task 1 was increased compared to task 2, thus indicating that parts of the reward effect were not propagated onto task 2, therefore affecting task 1 motor processes.In Experiment 2, we tested for the locus of the effect propagation to task 2. Therefore, we implemented a difficulty manipulation of the response selection of task 2. The results indicate that the reward effect is propagated from task 1 onto the response selection stage of task 2.

Ion-Selective Mobility Differential Amplifier: Enhancing Pressure-Induced Voltage Response in Hydrogels.

Piezoionics is an emerging mechanical-electrical energy conversion paradigm that enables self-powered sensing systems for next-generation intelligent wearable electronics. However, there are currently no rational design approaches to enhance the stimulus response of piezoionic devices. Here, we present a strategy using crown ether as ion-selective mobility differential amplifiers for enhancing the pressured-induced voltage response in ionic polyvinyl alcohol (PVA) hydrogels. The crown ether grafted PVA (PVA-CE) hydrogel prototype achieves a 30-fold amplified piezoionic coefficient of 1490 nV Pa-1 within 0 - 1 kPa, compared to 49 nV Pa-1 of the unmodified PVA. The PVA-CE exhibits an ultra-low pressure detection limit of 0.2 Pa with a fast response time of 18.1 ms. Leveraging these properties, we further demonstrate a human somatosensory network analogous PVA-CE piezoionic skin using arrayed pressure sensing. These capabilities hold great promise for emerging healthcare applications such as synthetic biology, soft robotics, and beyond.

Analysis of the Application of Emergency Severity Index Toward Patient Triage Response Time In the Emergency Department: Literature Review

Introduction: Triage is the process of determining the condition of patients at risk of death, potential disability, and worsening patient conditions. Response time is crucial in treating patients quickly and accurately. Objective This literature study aims to analyze the application of the Emergency Severity Index (ESI) Triage to the Triage Response Time in the Emergency Department. Methods: This research employs a literature review study with an analytic method approach that meets the inclusion and exclusion criteria. The search for articles or journals utilizes keywords and Boolean operators to refine the search and select relevant literature. The keywords used include ESI, Triage, Response time," and "Emergency Department. Results: The study reveals the effectiveness of utilizing the Emergency Severity Index (ESI) Triage on Triage Response Time in the Emergency Department. . Conclusion The speed at which nurses respond and determine patient triage can be enhanced through the use of ESI (Emergency Severity Index). To support this, nurses must possess knowledge and skills related to the ESI to minimize the mortality rate of patients.