Queue Wait Research Articles

Opportunities and Challenges of Implementing a Jigsaw Type Cooperative Learning Model on Two Variable Linear Equation System Material

This study aims to describe the opportunities and challenges of implementing Jigsaw-type cooperative learning on the material of Two Variable Linear Equation Systems. The subjects of this study were all 27 grade VIII students of Yedam Abepura Junior High School. Data collection techniques use data triangulation, namely by observation techniques, student worksheet data and interviews. The results of this study showed that the opportunity to apply Jigsaw-type cooperative learning to the material of the two-variable linear equation system received a positive response from all students, enthusiastic and enthusiastic students in doing tests on worksheets based on observations. The implementation process went well and smoothly and was completed on time. For satisfaction, teachers and students are quite satisfied with the results obtained. The challenge in implementing this Jigsaw type cooperative learning model is that there are differences in students' basic academic abilities or in other words, students' basic abilities, especially Mathematics learning, vary so that when learning the Jigsaw model cooperative model, students who do not understand need to get extra explanations with additional long time compared to students who already have a good academic ability foundation finish faster and wait queue of friends who have not moved from another group during explaining the material. Then the odd number of students also causes difficulty dividing in groups evenly.

Fashion Recommendation System and Virtual Trial Room

The fashion industry has recently witnessed massive growth in the clothing and textile industries. In e-commerce marketplaces, where there are many options accessible, relevant product material or information can be filtered, arranged, and efficiently communicated to users. As the standard of living increased, people's attention began to shift towards fashion, which is considered to be a common form of aesthetic expression. The development of the fashion business over time has been influenced by this human tendency. The abundance of clothing alternatives on e-commerce sites, however, has made it more difficult for customers to select the ideal outfit. In this article, we describe a personalised fashion recommender system that generates suggestions for the user based on input. In order to enhance the consumer shopping experience, we developed a software application that combines a recommender system with the OpenCV and TensorFlow technologies. This will enable customers to virtually try on clothing without having to wait in a large queue. The application uses OpenCV to map the clothes onto the customer’s body, the customer can then have a look at it and decide whether to buy the clothes or not. According to the results, there is no need to visit trial rooms because the mapping is done precisely onto the user's body. Thus, our app offers a quick, simple, and accurate method of trying on clothing, and we think that will help shape the retail sector.

Predicting accurate batch queue wait times on production supercomputers by combining machine learning techniques

AbstractThe ability to accurately predict when a job on a supercomputer will leave the queue and start to run is not only beneficial for providing insights to users, but can also help enable non‐traditional HPC workloads that are not necessarily suited to the batch queue style‐approach that is ubiquitous on production HPC machines. However there are numerous challenges in achieving such a prediction with high accuracy, not least because the queue's state can change rapidly and depend upon many factors. In this work, we explore a novel machine learning approach for predicting queue wait times, hypothesising that such a model can capture the complex behavior resulting from the queue policy and other interactions to generate accurate job start times. For ARCHER2 (HPE Cray EX), Cirrus (HPE 8600), and 4‐cabinet (HPE Cray EX) we explore how different machine learning approaches and techniques improve the accuracy of our predictions, comparing against the estimation generated by Slurm. By combining categorization and regression models, we demonstrate that our approach delivers the most accurate predictions across our machines of interest, with the result of this work being the ability to predict job start times within 1 min of the actual start time for around 65% of jobs on ARCHER2 and 4‐cabinet, and 76% of jobs on Cirrus. When compared against what Slurm can deliver, via the backfill plugin, this represents around 3.8 times better accuracy on ARCHER2 and 18 times better for Cirrus. Furthermore our approach can accurately predicting the start time for three quarters of all job within 10 min of the actual start time on ARCHER2 and 4‐cabinet, and for 90% of jobs on Cirrus. Whilst the initial driver of this work was to better facilitate non‐traditional, interactive and urgent, workloads on HPC machines, the insights gained can also be used to provide wider benefits to users, enrich existing batch queue systems, and inform supercomputing center policy also.

A Case Study On Customer Satisfaction Towards Online Banking In Bangladesh

Today's technological era has made all daily tasks much easier than they were in the past. Everything has been digitized thanks to technology. A further one among them is banking. Technology has resulted in many improvements in the banking industry. Every task in the banking sector is digitized, from account opening to money transfers through online banking, eliminating the need for customers to visit banks and wait in long queues. This makes work for both customers and banking institutions easy, efficient, and quick. Online banking also encourages competition among banks to offer their customers better services and draw in a growing number of customers. to check the level of customer satisfaction. 500 randomly chosen and conveniently placed individuals are surveyed to achieve this. The result of the research shows that Privacy & Security, Online Bill Pay, Fund Transfer, any time anywhere use, Update information, delivers services and save time and energy are directly related to Customer Satisfaction towards Online Banking in Bangladesh.

So curious that I want to buy it: The positive effect of queue wait on consumers' purchase intentions

AbstractQueue wait is a prevalent daily experience, with most prior research documenting its negative consequences on consumer outcomes. Recent research has suggested otherwise, revealing potential positive effects of queue wait. However, studies on the positive queue effects are rather limited and have largely neglected the role of consumers' affective experience in driving potential favorable consumer outcomes. Expanding this stream of research, in this article, we build on the queue wait literature and the curiosity literature to propose that the presence of queue waits generates feelings of curiosity and more importantly such curious feelings influence consumers' decision‐making in fashions that enhance their purchase intentions. The boundary condition for this effect was also examined. Five studies provided empirical evidence: the presence versus absence of queue wait enhances participants' purchase intentions that carry real consequences (Study 1); this effect is mediated by participants' feelings of curiosity after accounting for quality (Study 2) or after controlling for quality (Studies 1, 3–5), and is stronger among participants with hedonic versus utilitarian consumption motivation (Studies 4 and 5). By documenting the curiosity account for the positive effect of queue wait on consumers' purchase intentions, this research adds to extant research on positive queue effects and expands the curiosity literature into the queue wait context. The observed moderating effect of hedonic versus utilitarian consumption motivation echoes with the curiosity account. Practical implications for marketers were discussed.

Highly Efficient Traffic Planning for Autonomous Vehicles to Cross Intersections Without a Stop

Waiting in a long queue at traffic lights not only wastes valuable time but also pollutes the environment. With the advances in autonomous vehicles and 5G networks, the previous jamming scenarios at intersections may be turned into non-stop weaving traffic flows. Toward this vision, we propose a highly efficient traffic planning system, namely DASHX, which enables connected autonomous vehicles to cross multi-way intersections without a stop. Specifically, DASHX has a comprehensive model to represent intersections and vehicle status. It can constantly process large volumes of vehicle information, resolve scheduling conflicts, and generate optimal travel plans for all vehicles coming toward the intersection in real time. Unlike existing works that are limited to certain types of intersections and lack considerations of practicability, DASHX is universal for any type of 3D intersection and yields the near-maximum throughput while still ensuring riding comfort. To better evaluate the effectiveness of traffic scheduling systems in real-world scenarios, we developed a sophisticated open source 3D traffic simulation platform (DASHX-SIM) that can handle complicated 3D road layouts and simulate vehicles’ networking and decision-making processes. We have conducted extensive experiments, and the experimental results demonstrate the practicality, effectiveness, and efficiency of the DASHX system and the simulator.

Shot Optimization in Quantum Machine Learning Architectures to Accelerate Training

Quantum Machine Learning (QML) has recently emerged as a rapidly growing domain as an intersection of Quantum Computing (QC) and Machine Learning (ML) fields. Hybrid quantum-classical models have demonstrated exponential speedups in various machine learning tasks compared to their classical counterparts. On one hand, training of QML models on real hardware remains a challenge due to long wait queue and the access cost. On the other hand, simulation-based training is not scalable to large QML models due to exponentially growing simulation time. Since the measurement operation converts quantum information to classical binary data, the quantum circuit is executed multiple times (called shots) to obtain the basis state probabilities or qubit expectation values. Higher number of shots worsen the training time of QML models on real hardware and the access cost. Higher number of shots also increase the simulation-based training time. In this paper, we propose shot optimization method for QML models at the expense of minimal impact on model performance. We use classification task as a test case for MNIST and FMNIST datasets using a hybrid quantum-classical QML model. First, we sweep the number of shots for short and full versions of the dataset. We observe that training the full version provides 5-6% higher testing accuracy than short version of dataset with up to 10X higher number of shots for training. Therefore, one can reduce the dataset size to accelerate the training time. Next, we propose adaptive shot allocation on short version dataset to optimize the number of shots over training epochs and evaluate the impact on classification accuracy. We use a (a) linear function where the number of shots reduce linearly with epochs, and (b) step function where the number of shots reduce in step with epochs. We note around 0.01 increase in loss and ~4% (1%) reduction in testing accuracy for reduction in shots by up to 100X (10X) for linear (step) shot function compared to conventional constant shot function for MNIST dataset, and 0.05 increase in loss and ~5-7% (5-7%) reduction in testing accuracy with similar reduction in shots using linear (step) shot function on FMNIST dataset. For comparison, we also use the proposed shot optimization methods to perform ground state energy estimation of different molecules and observe that step function gives the best and most stable ground state energy prediction at 1000X less number of shots.

GROWTH OF DIGITAL PAYMENTS IN INDIA-: A COMPARATIVE STUDY- PRE AND DURING COVID-19

The main purpose of the study is to measure the progress, reasons for shifting from cash payment to digital payment in pre and during the Covid-19. NEFT, RTGS, UPI, Internet Banking, debit card and credit card have been included in digital transactions. Three variables like impact of lockdown, social distancing, digital and financial literacy have been taken in the study. To collect the primary data a well structured questionnaire was designed and circulated through emails and social platforms to 500 respondents.360 respondents returned the questionnaire after giving information. Data has been interpreted using the Chi-Square test. In the study it is found that discussed factors have a significant impact on growth of digital payments. Due to government law, consumers' perceptions, change in buying behavior, health safety issues and dire need of time in a pandemic, digital payment has grown sharply. Customers have shifted towards digital payments. During Covid -19 small vendors like vegetable seller, kirana store were using digital devices to accept payment. On the other hand, It is observed that there is a sharp decline in the holding of Cash. Carrying of cash attract various issues like chances of theft, wait in queue for withdrawal of cash from ATM, non availability of cash in the ATM, Unnecessary Carrying of ATM card.

Smart Village Model In Improving Service Quality

A smart village concept can be used as a solution to overcome various problems that occur in the administration of village governance. The purpose of this research is to build a smart village system and find out the benefits of this system. The type of research in this research is descriptive qualitative and the data collection method used in this research is interviews and field observations. From interviews and field observations, demographic data and village economic potential were obtained. The result of this research is the establishment of a smart village model system to improve service quality. Several village problems have been resolved by the Berekah village government, namely up-to-date village demographic data, community economic potential, and other types of services. With this smart village application, the community is well served, without having to wait in long and long queues. The novelty of this smart village model is that it is easy for the community to make population files and other services, as well as demographic data and community economic potential that can be developed.

Experienced and Prospective Wait in Queues: A Behavioral Investigation

Experienced and Prospective Wait in Queues: A Behavioral Investigation

Shuttle-Exploiting Attacks and Their Defenses in Trapped-Ion Quantum Computers

Trapped-ion (TI) quantum bits are a front-runner technology for quantum computing. TI systems with multiple interconnected traps can overcome the hardware connectivity issue inherent in superconducting qubits and can solve practical problems at scale. With a sufficient number of qubits on the horizon, the multi-programming model for Quantum Computers (QC) has been proposed where multiple users share the same QC for their computing. Multi-programming is enticing for quantum cloud providers as it can maximize device utilization, throughput, and profit for clouds. Users can also benefit from the short wait queue. However, shared access to quantum computers can create new security issues. This paper presents one such vulnerability in shared TI systems that require <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">shuttle</i> operations for communication among traps. Repeated shuttle operations increase quantum bit energy and degrade the reliability of computations (fidelity). We show adversarial program design approaches requiring numerous shuttles. We propose a random and systematic methodology for adversary program generation. Our analysis shows shuttle-exploiting attacks can substantially degrade the fidelities of victim programs by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 2 \times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 63 \times $ </tex-math></inline-formula> . Finally, we present several countermeasures such as adopting a hybrid initial mapping policy, padding victim programs with dummy qubits, and capping maximum shuttles.

Information System Design of Online Motorcycle and Car Repair Shop Using Dijkstra Method

Now more people are using motorized vehicles. In addition, the use of technology is also increasing, people are increasingly experiencing fast-paced services. However, in the midst of busy society and online services, some services still have to be done manually, one of which is register for a motorcycle or car repair shop. There are still many people difficult to find the right, closest, and comfortable with the needs of their vehicle. With the existing problems, a vehicle service ordering system is needed that can serve the community quickly and practically, which can be accessed by many people, especially in Tangerang City. With a vehicle service ordering system for motorcycles or car repairshop, people can easily find a repair shop that is the closest to their location and can order without having to wait in long queues. The design of the system for the closest repair shop locations uses the Dijkstra method. The workings of Dijkstra's Algorithm is to create a path to one optimal node at each step. Dijkstra's algorithm has the property to find the point whose distance from the starting point is the shortest. To find out whether the system has been accepted and has met the requirements, the system is tested using the User Acceptance Test (UAT) method, and from the test results, 85.1% of users are satisfied with the system.

SELF MEDICATION: A STUDY OF ATTITUDES, REASONS &amp; INDICATIONS

The practice of self-care may change from locality and region. It is more in younger and educated people because they have no time to go to doctor and no patience to wait. So, there was ample scope to pursue a research on self-medication. Self-medication has number of reasons, for example - use for self-care feeling sympathy towards family members, no time to go to doctor and wait in long queue. There is an increased trend of self-medication particularly among educated people. A quantitative evaluative research approach is used in the study to evaluate the effectiveness of health teaching programme on knowledge and attitude regarding self-medication.

Vaccine and the Need To Be Heard: Considerations for COVID-19 Immunization in ESKD.

Vaccine and the Need To Be Heard: Considerations for COVID-19 Immunization in ESKD.

WITHDRAWN: Link delay based efficient search method for clustered peer to peer networks

Due to self-organizing and autonomy nature of the Peer to Peer networks they are considered to be an important model for a wide range of application. The main challenge is to organize the peer nodes and perform search for a required data among the peer nodes. The clustering approach helps to efficiently organize and manage the network infrastructure. This paper proposed an approach to perform searching efficiently in a clustered network. The clustering can be implemented in a distributed fashion depending on the neighbouring nodes. Hierarchical clustering approach is adopted and each cluster is generated based on the link delay which reduces the transmission time between the cluster head or super node and the peer nodes in the network. In the clustered topology the congestion in the network especially in Cluster Controller (CC) is controlled by monitoring the state of the nodes including its wait queue length and processing capacity. An efficient searching method is proposed and compared with the flooding approach followed in the Gnutella network. The proposed approaches are most suitable for a file sharing network in which the bandwidth consumption while searching for a required data is reduced and hence the overall download time and the performance of the network is increased.

Linking Delay Announcements, Abandonment, and Service Time

Using call center field data, we examine the reference-dependent impact of delay announcements on both customer abandonment behavior and time spent in service. Delay announcements induce a reference point telling customers how long they must wait for service. Customers behave differently before and after this reference point. Our novel contributions include demonstrating the effects of delay announcements on service time, as well as simultaneously considering both reference-dependent abandonment behavior and reference- dependent service times. One additional minute spent waiting after the reference point increases the hazard rate of abandonment by up to 52%, a much larger effect than the hazard rate decrease from having one additional minute to go before the reference point. For those customers who do not abandon, we also observe reference-dependent behavior during service. Conditional on the same total wait in queue, one additional minute spent waiting after the reference point triggers up to 21 extra seconds in service. While longer total waiting time in queue also leads to longer service time, its effect is much smaller than this reference-dependent effect. Reference-dependent behaviors in abandonment and service impact system-level congestion in opposite directions. Therefore, managers should consider both effects when considering operational decisions, including staffing and customer prioritization.

A Queue Waiting Cost-Aware Control Model for Large Scale Heterogeneous Cloud Datacenter

Optimal load distribution and speed scaling in a heterogeneous data center that takes account of the tradeoff between the maintenance and operation costs and system performance are crucial issues of cloud computing. Due to the changing states of cloud centers and diversity of the task arrival rate, a static control model is infeasible for cloud computing. In this article, we aim to provide a novel multi-server control model with dynamic feedback to acquire dynamic states of the cloud system, and with queue waiting cost-awareness to optimize the queue wait time and load distribution in task assignment and server configuration management. Using the technology of speed scaling, each server in a data center is configured as an <inline-formula><tex-math notation="LaTeX">$M/M/1$</tex-math></inline-formula> queue system with variable service rate, and the service rate is a function of the length of the task queue. We formulate two optimization problems, the optimal load distribution problem and the optimal service rate controlling problem, and provide algorithms to solve these problems, facilitating load distributions and service rate adjustments. We also present numerical simulations to validate our model. The results show our model to be efficient in multi-server dynamic configurations and task assignments according to the feedback information for the tradeoff between the system cost and performance.

Intelligent Medical Information System

Now a days, meeting doctors is quite too frequent. People always meet doctors in order to get treatment for their symptoms. Existing clinic registration systems have to make an appointment with a doctor only when they visit the clinic or hospital and wait in queues for long hours in order to get treatment and everything was tedious too because everything was done manually. So many mistakes could have been made. Therefore, many online appointment systems have been developed in order to book appointments with doctors through mobile application. No priority based appointments and an emergency situation for appointments with doctors is considered. All these aspects are taken into consideration and an online appointment booking system with doctors is especially designed for the Madurai city, Tamil Nadu, India. We will design an android application that tracks the location of a user using particle swarm optimization algorithm and also it displays the details of all the hospitals and clinics in the Madurai district and the available specialists in each stream. This system uses java programming language as interface between the user and doctor and MySQL as the database.

A hybrid approach for scheduling applications in cloud computing environment

Cloud computing plays an important role in our daily life. It has direct and positive impact on share and update data, knowledge, storage and scientific resources between various regions. Cloud computing performance heavily based on job scheduling algorithms that are utilized for queue waiting in modern scientific applications. The researchers are considered cloud computing a popular platform for new enforcements. These scheduling algorithms help in design efficient queue lists in cloud as well as they play vital role in reducing waiting for processing time in cloud computing. A novel job scheduling is proposed in this paper to enhance performance of cloud computing and reduce delay time in queue waiting for jobs. The proposed algorithm tries to avoid some significant challenges that throttle from developing applications of cloud computing. However, a smart scheduling technique is proposed in our paper to improve performance processing in cloud applications. Our experimental result of the proposed job scheduling algorithm shows that the proposed schemes possess outstanding enhancing rates with a reduction in waiting time for jobs in queue list.

Effective Servicing of a Busy Base Station using Queuing Models in Cognitive Radio Networks

This paper aims the effective utilization of spectrum using queuing models in cognitive radio. The data from various service providers are obtained and considered for our analysis. Analytical mathematical models based on queuing theory are developed for effectively servicing the arrival calls and improving the service rate. Based on the following parameters, we design a model to service the calls effectively: Average number of customers in the system, Average number of customers in the queue, Average wait in the system and Average wait in queue.