Imagination, Absorption and Subjective Time Estimation

The detection of early-warning signals of performance deterioration can help technical support teams in taking swift remedial actions, thus ensuring rigor in production support operations of large scale software systems. Performance anomalies or deterioration, if left unattended, often result in system slowness and unavailability. In this paper, we presents a simple, intuitive and low-overhead technique for recognizing the early warning signs in near real time before they impact the system The technique is based on the inverse relationship which exists between throughput and average response time in a closed system. Because of this relationship, a significant increase in the average system response time causes an abrupt fall in system throughput. To identify such occurrences automatically, Individuals and Moving Range (XmR) control charts are used. We also provide a case study from a real-world production system, in which the technique has been successfully used. The use of this technique has reduced the occurrence of performance related incidents significantly in our daily operations. The technique is tool agnostic and can also be easily implemented in popular system monitoring tools by building custom extensions.

We investigate optimal power and performance management for heterogeneous and arbitrary cloud servers in a data center. In particular, we study the problems of power-constrained performance optimization and performance-constrained power optimization in a data center with multiple heterogeneous and arbitrary servers. These problems are essential to find optimal server speeds, such that: 1) the average task response time is minimized, and that the total power consumption does not exceed certain power constraint or 2) the total power consumption is minimized, and that the average task response time does not exceed certain performance constraint. Each server is treated as a G/G/1 queuing system, whose task interarrival times and task execution requirements can have arbitrary probability distributions. Furthermore, these servers are entirely heterogeneous in terms of task interarrival time, task execution requirement, and power consumption models. The main contributions of this paper are summarized as follows: 1) we formulate the average task response time as well as the total power consumption in a data center with multiple heterogeneous and arbitrary servers as the functions of server speeds; 2) we define our optimization problems by finding optimal server speeds, since the server speeds determine both the average task response time and total power consumption; 3) we develop algorithms to find the optimal solutions and demonstrate numerical data; and 4) we also develop several closed-form heuristic solutions and compare their quality with that of the optimal solution. Our approach provides an analytical way of studying the power-performance tradeoff at the data center level.

In this letter, we analytically evaluate the average query response time of the Two-level Hierarchical Clustering based Hybrid-routing Protocol (THCHP) proposed recently for wireless sensor networks (WSNs). The accuracy of the average query response time analysis is verified via numerical simulations. Additionally, average query response time comparisons, between THCHP and a previously proposed Adaptive Periodic Threshold-sensitive Energy Efficient sensor Network (APTEEN) protocol, show that THCHP is better suited than APTEEN for delay sensitive WSN applications.

With the surge in data volume and dimensions, existing decision-making systems are difficult to capture nonlinear relationships and cope with the complexity and diversity of data, which leads to decision lag. To solve this problem, this article studies an intelligent decision-making system. The system integrates multi-layer feedforward neural networks and decision tree algorithms, and designs a distributed computing architecture based on the Apache Spark platform. Through the integration of the big data platform, the system integrates and processes multiple data sources; then, a three-layer feedforward neural network model is adopted, and a specific mechanism is used to prevent overfitting. After that, the decision tree algorithm is used to deeply explore the complex relationships in the data; Apache Spark is applied to accelerate the calculation process and efficiently query and analyze the stored data. The system also supports online learning strategies to continuously update the model to adapt to new data changes. The research results show that in the field of financial risk analysis, the average accuracy of the intelligent decision-making system in this article is 92.29 %, and the average response time is only 0.013 seconds; the average accuracy and response time of the traditional system are 82.54 % and 0.034 seconds respectively; the system in this article is significantly better than the traditional system. According to the study, an intelligent decision-making system not only increases decision-making's efficiency and accuracy but also offers solid base for more intelligent decisions in the future.

Background: Misoprostol is an analogue of prostaglandin (PGE1) and the first registered PGN used for the treatment of peptic ulcer disease. Misoprostol should not be used in pregnant women but it can be used in termination of intrauterine fetal death (IUFD). It has been used for the induction of abortion in the first and second trimester of pregnancy. Misoprostol has already been proved to be effective and efficient for second trimester since 1987. There is insufficient information available from clinical trials to support their use to induce labor in third trimester of pregnancy. Aim of the study: The aim of this study was to describe the effect of misoprostol in third trimester of pregnancy. Methods: This retrospective study was carried out in two large hospitals in Dhaka city, which is the capital of a South Asian country, Bangladesh. The record of the studied subject was taken from the Department of Obstetrics and Gynecology of Alpha Hospital and the maternity ward of Beta Hospital from June 2018 to August 2019. The actual names of the two hospitals are pseudonym for privacy. A detailed history of each patient was taken and physical and obstetric examination were done. In total 52 patients were investigated and data were analyzed by SPSS version 23.0. Results: In this study, the primi gravida response time was found as the highest. On an average 20.34 hours was needed for the primi gravida category. In single, 2 and multi-parity cases the average pain response times were found as 16.3125, 10.3375 and 14 hours respectively. In Primi, 2nd, 3rd and 5th gravida cases the average delivery times were found 13.0714, 12.5938, 9.0769 and 3 hours respectively. Against single, two and three doses of misoprostol, the average pain response times were found 7.6875, 11.5375 and 19.25 hours respectively. Conclusion: In this study, it had been found that, most of the patient of IUFD was ‘primigravid’ (30.8%) of total respondents having complication with medical and endocrinal disease. Pre- eclampsia is more common in prim gravid than multi. There is history of thyroid dysfunction. In some cases, now a day, GDM is the cause behind IUFD.

The emergence of computation intensive automotive applications poses significant challenges on computation capacity of automotive electronic systems, thus vehicular edge computing (VEC) is introduced as a new computing paradigm into the internet of vehicle (IoV) to improve its data processing capability. However, as the computation capacity is limited in VEC servers, efficient task offloading algorithms need to be proposed. This paper first proposes a multi-task offloading model and gives the related task response time analysis method, and then, both a mixed-integer linear programming (MILP)-based algorithm and a simulated annealing-based heuristic algorithm are proposed to minimize the task response time. By comparing with a baseline algorithm, the MILP-based offloading algorithm can reduce the average task response time by 91.45%, and the heuristic offloading algorithm can reduce the average task response time by 70%.

Developing a new scheduling algorithm and conducting the performance analysis to recognize its effect in practice can be a laborious task. CPU scheduling is crucial in achieving the operating system's (OS) design goals. There exists a variety of scheduling algorithms in the field and in this paper, a performance comparison of different existing scheduling algorithms by simulating the same bundle of tasks is carried out. A variety of algorithms under batch OS and time-sharing OS are considered. Upon the analysis, a novel task scheduling algorithm incorporating the merits of existing algorithms is proposed for a single CPU system. The performance of various algorithms is compared with the proposed algorithm for parameters viz., throughput, CPU utilization, average turnaround time, waiting time, and response time. Extensive simulation analysis for the various bundle of tasks is conducted and the proposed algorithm is found to outperform the other algorithms in terms of guaranteed reduced average response time. Thus, an efficient CPU scheduler is proposed to accommodate varying workloads at run-time making the best use of the CPU in a particular execution scenario.

Cloudlet is an emerging technology in mobile cloud computing. However users may be far away from cloudlets due to the mobility of mobile users, which leads to a poor network connectivity, thus, user experience will be poor. While a user moves across multiple cloudlets areas, issues of service scheduling between cloudlets to better support user experience become important. In this paper, we consider a latency-sensitive and stateful service scheduling problem in cloudlets. We propose a novel cloudlet service model and formulate the problem with the goal of finding the optimal service running sequence which minimizes the average service response time during the whole running process of the service for a user. To solve this problem, we propose an algorithm called Mobility Prediction-based Markov Decision Process (MPMDP). The proposed algorithm takes user’s mobility prediction into account, and makes an decision based on Markov Decision Process to decide on which cloudlet the service should run for a user each time. Finally, we evaluate the effectiveness of the proposed MPMDP algorithm by simulations with real-world users’ traces. The simulation result shows our algorithm achieves a lower average response time compared with previous schemes.

The aim of this study is to describe and evaluate the impact of the Linking to Improve Nursing Care and Knowledge (LINK) project on increasing nurse-led clinical research. Nurse-generated research is the cornerstone of evidence-based practice and continues to be a marker of nursing excellence. However, the dearth of PhD-prepared nurses creates a challenge for creating an environment to promote clinical nursing research. We evaluated the LINK project, an academic-clinical partnership, to assess its impact and feasibility, for fostering nurse-led clinical research. The LINK project created a formal command and control structure bringing together existing academic resources, including a PhD-prepared nurse researcher, a biostatistician, and a development of a formal research consultation request process. Measures tracked over a 12-month period included average response time, request volume, client satisfaction, institutional review board (IRB)-submitted protocols, and work products. All measures exceeded expectations with an average 1-day request response time, 35 requests, 98% client satisfaction, a 367% increase in nurse-led IRB approved protocols from the previous 12-month period, and 2 publications in peer-reviewed journals. The process and outcome measures indicate that the LINK project is feasible, sustainable, and reproducible. We were able to meet and, in many cases, exceed measurement goals. In addition, implementation science literature indicates that the most valid measure of a successful project rollout is user satisfaction and usefulness. The LINK project received consistently positive feedback.

BackgroundIn collaboration with the Mobile Monitoring of Cognitive Change (M2C2; 1‐U2C‐AG060408‐01) Project, our team developed a tablet‐based electronic Stroop (eStroop) task. Initial development explored direct translation of the paper‐Stroop (pStroop) often used in clinical settings, and found that this resulted in ceiling effects. Herein we report on initial validation of the eStroop task from the iPACES v3 clinical trial.MethodInitial validation examined eStroop data (n = 59 sessions) serially collected from ten MCI and caregiver participants in the iPACES v3 clinical trial (mean age = 70.2, mean years of education = 16.9). Included in analyses were eStroop data for both traditional metrics (i.e., overall accuracy by block condition), response time, as well as typically constructed difference/ratio scores (i.e., AC ratio). After a baseline tele‐neuropsychological evaluation in which paper Stroop was administered, at predetermined study intervals (typically weekly), participants were automatically prompted to complete the eStroop on their iPACES Android tablet. In the eStroop, participants were presented with four blocks of 40 trials: (1) color words in black font, (2) congruent word and font color; (3) incongruent words with font color; (4) mixed congruent and incongruent trials.ResultThe paper Stroop AC Ratio (total seconds to complete paper A, divided by time to complete paper C), was moderately negatively correlated (r = ‐0.52, p < .001) with average response time on block three of eStroop (incongruent words with font color). The pStroop AC Ratio was also significantly negatively correlated with number of errors on block one (r = ‐0.42, p < .001), two (r = ‐0.26, p = 0.04), three (r = ‐0.39, p = 0.002), and four (r = ‐0.26, p = .049). A weak, significant positive correlation (r = 0.44, p < .001) was observed between the AC ratio from pStroop and eStroop. Very weak non‐significant correlations between paper Stroop AC Ratio and average response time on blocks one, two, and four of eStroop were observed.ConclusionThese pilot data provide preliminary evidence that the eStroop can serve as a remotely and self‐administered digital test for serial assessment of executive function and will be further evaluated in our current iPACES study.

A three-dimnensional vision system for on-line operation that aids a collision avoidance system for an industrial robot is developed. Because of the real-time requirement, the process that locates and describes the obstacles must be fast. To satisfy the safety requirement, the obstacle model should always contain the physical obstacle entirely. This condition leads to the bounding box description of the obstacle, which is simple for the computer to process. The image processing is performed by a Machine Intelligence Corporation VS-100 machine vision system. The control and object perception is performed by the developed software on a host Digital Equipment Corporation VAX 11/780 Computer. The resultant system outputs a file of the locations and bounding descriptions for each object found. When the system is properly calibrated, the bounding descriptions always completely envelop the obstacle. The response time is data-dependent. When using two cameras and processed on UNIX time sharing mode, the average response time will be less than 2 s if eight or fewer objects are present. When using all three cameras, the average response time will be less than 4 s if eight or fewer objects are present.

Rapid prototyping is an automated manufacturing process that quickly builds physical models from CAD files of three-dimensional prototypes. It dramatically speeds up design and manufacturing processes and substantially reduces the production cost. With the advanced Web technologies, rapid prototyping manufacturing systems can be further integrated and more effectively utilized. We have designed a distributed web platform where users can design prototypes, simulate manufacturing, and issue ordering requests for real manufacturing through browser interfaces. However, an improper scheduling approach may degrade system utilization and throughput significantly. To solve this problem, we propose a real-time scheduling approach to maximize system utilization and minimize the average response time with the best effort. Using this approach, new requests can be accepted dynamically and all accepted requests can be finished before deadlines. We show that the scheduling overhead can be neglected and the average response time is very close to the optimal solution. With the proposed scheduling approach, the Web rapid prototyping platform is able to fully utilize Web resources for rapid prototyping manufacturing and the quality of service is significantly improved.

Based on the system architecture and software structure of GMLC (Gateway Mobile Location Center) in 3G (third generation), a new dynamic load-balancing algorithm is proposed. It bases on dynamic feedback and imports the increment for admitting new request into the load forecast. It dynamically adjusts the dispatching probability according to the remainder process capability of each node. Experiments on the performance of algorithm have been carried out in GMLC and the algorithm is compared with Pick-KX algorithm and DFB (Dynamic FeedBack) algorithm in average throughput and average response time. Experiments results show that the average throughput of the proposed algorithm is about five percents higher than that of the other two algorithms and the average response time is four percents higher under high system loading condition.

The wireless fluid level indicator comprises a sensing and transmitting circuit for detecting and transmitting the levels of fluids from a container to the receiving circuit which receives and displays the amount of the measured volume. This has been achieved using an ultrasonic sensor (HC-SR04), a firmware (PIC16F648A/PIC16F876A microcontroller programmed in C/assembly languages), an encoder (HT12E) and a radio frequency (RF) transmitter transmitting at a bandwidth of 433MHz to an RF receiver interfaced with a decoder (HT12D), liquid crystal display (LCD) and a Buzzer. The device when tested measured water levels and transmitted same over a distance of up to 98.5 meters in open areas and up to 50 meters in highly obstructed areas. The average response time was also estimated to be 0.1 second. As a contact-less depth detection system, the device finds useful applications in measurement of fluid levels in homes and industries where concentrates, table water, transmission oils are produced, without affecting the chemical or physical properties of such substances-an advantage over the contact methods used in fluid level detection.
 Aims: To design and implement a wireless microcontroller based fluid level display system using ultrasonic sensing method.
 Study Design: By encoding the Transmitter module to sense and detect the amount of water level, while the decoder module decodes the received data and display the result.
 Place and Duration of Study: Department of Physics, University of Agriculture Makurdi, Benue State, Nigeria, between November 2018 and April 2019.
 Methodology: An RF transceiver, encoder/decoder (HT12E/HT12D), a 2x16 Liquid display module, 4MHz crystal oscillator, a buzzer and other peripherals were interfaced to two 8-bit controllers; PIC16F648A and PIC16F876A. The controllers were programmed in C/Assembly languages to enable effective communication of the system. The system was then calibrated to read and transmit water level values from a 30-centimeter deep tank over a distance of 1.0 - 98.5 meters.
 Results: The system was tested to have maximum sensitivity over a distance of 98.5 meters in non-obstructed areas and 50 meters in obstructed areas. The average response time was also estimated to be 0.5 seconds.
 Conclusion: The design and implementation of a wireless fluid level display system using ultrasonic sensing technique has been successfully done. The system workability is suitable for deployment and usage in domestic and industrial purposes.

Statistical modeling of emergency medical services’ response and rescue times to road traffic crashes in the Kingdom of Saudi Arabia