Resource Requests Research Articles

Research on Load Balancing and Caching Strategy for Central Network

The central network architecture takes the information content as the processing object and can better meet the user access needs. However, the traditional network node load balancing and caching strategy has been difficult to adapt to the development of the central network architecture. Therefore, this article proposed a load balancing and cache management strategy based on the central network in order to provide a reference for the architecture and application of the central network. Based on the analysis of the existing network architecture, this article expounded the networking form and characteristics of the central network system. By analyzing the load scheduling requirements of network nodes, combined with the transmission characteristics of resource requests in the central network, a cache node load balancing method suitable for the requirements of the central network was proposed based on the existing load balancing algorithms. In addition, by analyzing the cache management strategy and its impact on the performance of the network system, a cache node information forwarding model with resource requests as the object in the central network environment was given. Finally, through experimental test and comparative analysis, the results showed that the method proposed in this article had certain feasibility and effectiveness. Compared with other typical strategies, the load balancing and caching strategy proposed in this article can better meet the user needs of the central network.

Optimization of Search Environments for Learning Contexts

This article proposes an improvement of search engines in a learning or training context. Indeed, the learner requests resources or learning content in a training or learning situation. The same goes for the trainer, who wishes to select the appropriate resources available to his learners. Unfortunately, existing search engines produce an enormous mass of content but sometimes do not match the learning context, thus causing an enormous loss of time for the learner or the teacher to find the appropriate resources among this important batch. Therefore, we suggest associating a complementary layer with search engines to extract the most relevant information related to learning or training situations from the engine results. For this purpose, an integrated filter eliminates irrelevant results to the current learning or training situation; and performs a weighted reclassification of these results based on Bloom’s taxonomy. In terms of the HMI, this layer allows having more informative result snippets. The experimentation of this environment is based on Google APIs. According to the Bloom hierarchy, the classification of the user question and the classification of the search results are carried out from Natural Language Processing based on Logistic Regression of Machine Learning Algorithms. The result obtained presents an intuitively favorable environment for education, leading to the implementation of a specific search engine capable of collecting, storing, and indexing educational concepts in the next stage of this project. A project to empirically evaluate the results obtained is currently underway.

An exploration of ethnic minorities' needs for multilingual information access of public digital cultural services

PurposeEthnic minorities (EMs), who make up a sizable proportion of multilingual users, are more likely to browse and search in their native language. It is helpful to identify multilingual users' information needs to provide public digital cultural services (PDCS) for making their life better.Design/methodology/approachThe in-context interview is an efficient way to explore EMs' information needs and evoke their daily experience with PDCS. The material from 31 one-on-one interviews with EMs in China was recorded and analyzed using thematic analysis.FindingsThe findings reveal that language proficiency is a critical factor influencing multilingual information access (MLIA) and multilingual users' information needs. Moreover, language ability, digital literacy and cultural literacy are important components of multilingual information literacy (MLIL), which is helpful for EMs to access PDCS. In light of Kochen's theory, the information needs of PDCS can be classified into the aroused need of resources, the recognized need of functions and services and expressed need. For the expressed need, it is necessary to develop a one-stop convergence platform of PDCS to process various requests of resources, functions and services in the future.Originality/valueThe findings will be valuable for governments, public institutions and social organizations in identifying, addressing and resolving these issues about PDCS.

An increase in spontaneous activity mediates visual habituation.

SUMMARYThe cerebral cortex is spontaneously active, but the function of this ongoing activity remains unclear. To test whether spontaneous activity encodes learned experiences, we measured the response of neuronal populations in mouse primary visual cortex with chronic two-photon calcium imaging during visual habituation to a specific oriented stimulus. We find that, during habituation, spontaneous activity increases in neurons across the full range of orientation selectivity, eventually matching that of evoked levels. This increase in spontaneous activity robustly correlates with the degree of habituation. Moreover, boosting spontaneous activity with two-photon optogenetic stimulation to the levels of visually evoked activity accelerates habituation. Our study shows that cortical spontaneous activity is linked to habituation, and we propose that habituation unfolds by minimizing the difference between spontaneous and stimulus-evoked activity levels. We conclude that baseline spontaneous activity could gate incoming sensory information to the cortex based on the learned experience of the animal.

COVID-19 pandemic: is it time for shared surgical guidelines? A systematic review of the literature.

The recent COrona Virus Disease 2019 (COVID-19) pandemic caused a massive disruption of surgical activity and after a year from its first outbreak surgeons still struggle to keep their regular activity coexisting with the virus exhausting requests of healthcare resources. The aim of this paper is to offer a comprehensive overview of the most important recommendations by the International Guidelines about general surgery, and possibly to invite building common shared guidelines to preserve the potential to provide surgical assistance despite the pandemic. This systematic review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. PubMed, Embase, Cochrane and Web of Science databases were searched. The searches revealed a total of 18579 articles published up to the end of February 2021. Five articles published between March and May 2020, were included in the present study: Guidelines from The European Society of Trauma and Emergency Surgery (ESTES), The Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) and The European Association for Endoscopic Surgeons (EAES), The Endoscopic and Laparoscopic Surgeons of Asia (ELSA), The European Hernia Society (EHS) and The International Organization for the Study of Inflammatory Bowel Disease (IOS-IBD). In the likely scenario that the SARS-CoV-2 pandemic will become an endemic chronic problem, we should not be forced to choose between COVID-19 or surgery in the future and find a way to make both coexisting.

Proactive Fault-Tolerance Technique to Enhance Reliability of Cloud Service in Cloud Federation Environment

Cloud federation is a new computing paradigm that has paved the way for cloud service providers (CSPs) to offer their unused resources (virtual machine) to other CSPs when their resource demands are low. Federation also allows CSPs to outsource their resource requests to other CSPs when their computing resources’ demands are high. Thus, in cloud federation environment reliability and availability of services offered by service providers increase as the CSPs are able to share their resources among themselves. Moreover, to maintain the reliability and availability of cloud services offered through federation, it is important that the computational environment of member CSPs within the federation is fault tolerant. Therefore, there is a need for fault tolerant system to guarantee cloud service reliability and availability in cloud federation environment. In this article, we propose a proactive fault tolerance system that preempts faults within the federation on the basis of CPU temperature. The fault tolerance system within the federation is modeled as a multi-objective optimization problem of maximizing profit and minimizing migration cost while redistributing resources (virtual machine) from faulty CSPs to non-faulty CSPs within the federation. To address this issue, we have also proposed an algorithm called Preference Based Fault Management (PBFM) to manage the federation in the event of faults. We perform extensive experiments to evaluate the effectiveness of our proposed mechanism and compare it with two other mechanisms MCAFM (Migration Cost Assured Fault Management) and PAFM (Profit Assured Fault Management). Results show that our proposed mechanism PBFM yields an optimized solution to the general problem of profit and migration cost trade-off in presence of faulty CSPs.

Virtual Simulation Resource Control Platform for Electric Power Training

Abstract Since virtual simulation needs multiple kinds of data as support, the construction of smooth simulation environment requires higher efficiency in data resource management and control. Based on this, a virtual simulation resource management and control platform for electric power training is proposed. On the basis of analyzing the architecture characteristics of virtual simulation engine, the platform structure of operation process simulation and optimization is designed based on three-layer BS mode. The virtual simulation platform is used as the exchange center to establish the data interface. It builds virtual simulation scenarios through mapping, and implements virtual protection tests through GOOSE messages for data management and protection. The control purpose is realized according to the operation process of the equipment and the inherent protection logic. In this paper, a communication module is designed to ensure smooth data transmission between modules. The test results show that the corresponding time of data resource request is mainly concentrated within 20ms, with good efficiency.

StigLD: Stigmergic Coordination in Linked Systems

While current Semantic Web technologies are well-suited for data publication and integration, the design and deployment of dynamic, autonomous and long-lived multi-agent systems (MAS) on the Web is still in its infancy. Following the vision of hypermedia MAS and Linked Systems, we propose to use a value-passing fragment of Milner’s Calculus to formally specify the generic hypermedia-driven behaviour of Linked Data agents and the Web as their embedding environment. We are specifically interested in agent coordination mechanisms based on stigmergic principles. When considering transient marker-based stigmergy, we identify the necessity of generating server-side effects during the handling of safe and idempotent agent-initiated resource requests. This design choice is oftentimes contested with an imprecise interpretation of HTTP semantics, or with rejecting environments as first-class abstractions in MAS. Based on our observations, we present a domain model and a SPARQL function library facilitating the design and implementation of stigmergic coordination between Linked Data agents on the Web. We demonstrate the efficacy our of modelling approach in a Make-to-Order fulfilment scenario involving transient stigmergy and negative feedback as well as by solving a problem instance from the (time constrained) Trucks World domain as presented in the fifth International Planning Competition.

Outright fit resource allocation approach for advance reservation requests in elastic optical networks

Elastic optical networks (EONs) are a promising technology for the next-generation optical network, and advanced reservation (AR) applications have been growing expeditiously over the past few years. Thus, the routing and spectrum allocation problem for AR requests has become a significant concern. This paper proposes an approach named outright fit advanced resource reservation to improve the spectral resource allocation efficiency for AR connection requests. Our proposed approach works in two stages: The proposed resource reservation strategy first uses an outright fit resource allocation strategy to reserve the spectral resource for a connection request within the absolute best service window and leaves adequate spectral resources for subsequent requests. If a connection request cannot get resources in the first stage, the proposed optimal rescheduling technique then is applied to serve the request. Our proposed optimal rescheduling technique uses spectrum shifting and reallocation without altering the routing paths, and its optimal characteristic decreases the number of rescheduling operations, thereby decreasing the rescheduling computational complexity and cost, which improves the network’s quality of service. Moreover, this rescheduling approach increases the opportunities for successful rescheduling of the requests without inducing many spectrum fragments, significantly reducing the blocking probability. The simulation results illustrate that the proposed approach can considerably enhance the blocking performance and obtains high spectrum utilization.

Intelligent Cruise Guidance and Vehicle Resource Management With Deep Reinforcement Learning

The emergence of new business and technological models for urban-related transportation has revealed the need for transportation network companies (TNCs). Most research works on TNCs optimize the interests of drivers and passengers, and the operator assuming vehicle resources remain unchanged, but ignore the optimization of resource utilization and satisfaction from the perspective of flexible and controllable vehicle resources. In fact, the load of the scene is variable in time, which necessitates the flexible control of resources. Drivers wish to effectively utilize their vehicle resources to maximize profits. Passengers desire to spend minimum time waiting and the platform cares about the commission they can accrue from successful trips. In this article, we propose an adaptive intelligent cruise guidance and vehicle resource management model to balance vehicle resource utilization and request success rate, while improving platform revenue. We propose an advanced deep reinforcement learning (DRL) method to autonomously learn the statuses and guide the vehicles to hotspot areas where they can pick orders. We assume the number of online vehicles in the scene is flexible and the learning agent can autonomously change the number of online vehicles in the system according to the real-time load to improve effective vehicle resource utilization. An adaptive reward mechanism is enforced to control the importance of vehicle resource utilization and request success rate at decision steps. The simulation results and analysis reveal that our proposed DRL-based scheme balances vehicle resource utilization and request success rate at acceptable levels while improving the platform revenue, compared with other baseline algorithms.

Decoupled Design of Fault-Tolerant Control for Dual-Three-Phase IPMSM With Improved Memory Efficiency and Reduced Current RMS

Fault tolerance is critical to real-time control of the dual-three-phase interior permanent magnet synchronous machine (IPMSM). This article proposes an efficient decoupled design for fault-tolerant control (FTC) of dual-three-phase IPMSMs to improve the average torque and minimize the current root mean square (rms) for torque ripple and loss reduction under the open-phase fault. Specifically, the FTC design is divided into two subtasks to derive the FTC strategy with a simplified design, in which the two subtasks are the fundamental current design and the harmonic current design. The optimal current solution to FTC is derived in a memory- and computation-efficient way. The proposed solution can achieve better transient performance and reduce the current rms for FTC, which is critical to practical applications with dynamic changing loads. In comparison with existing methods, the proposed FTC can effectively reduce the request of memory and computation resources from the drive system. Extensive experiments and comparisons are conducted to evaluate the proposed FTC on a laboratory dual-three-phase IPMSM.

Providing Context for Identifying Effective Introductory Mechanics Courses

Research-based assessments (RBAs) measure how well a course achieves discipline-specific outcomes. Educators can use outcomes from RBAs to guide instructional choices and to request resources to implement and sustain instructional transformations. One challenge for using RBAs, however, is a lack of comparative data, particularly given the skew in the research literature toward calculus-based courses at highly selective institutions. In this article, we provide a large-scale dataset and several tools educators in introductory physics courses can use to inform how well their courses foster student conceptual understanding of Newtonian physics. The supplemental materials include this dataset and these tools. Educators and administrators will often target courses with high drop, withdrawal, and failure rates for transformations to student-centered instructional strategies. RBAs and the comparative tools presented herein allow educators to address critiques that the course transformations made the courses “easier” by showing that the transformed course supported physics learning compared to similar courses at other institutions. Educators can also use the tools to track course efficacy over time.

Path Integral Molecular Dynamics of Liquid Water in a Mean-Field Particle Reservoir.

We present a simulation scheme for path integral simulation of molecular liquids where a small open region is embedded in a large reservoir of non interacting point‐particles. The scheme is based on the latest development of the adaptive resolution technique AdResS and allows for the space‐dependent change of molecular resolution from a path integral representation with 120 degrees of freedom to a point particle that does not interact with other molecules and vice versa. The method is applied to liquid water and implies a sizable gain regarding the request of computational resources compared to full path integral simulations. Given the role of water as universal solvent with a specific hydrogen bonding network, the path integral treatment of water molecules is important to describe the quantum effects of hydrogen atoms’ delocalization in space on the hydrogen bonding network. The method presented here implies feasible computational efforts compared to full path integral simulations of liquid water which, on large scales, are often prohibitive.

Assessment of Various Scheduling and Load Balancing Algorithms in Integrated Cloud-Fog Environment

Background: It is required to design a suitable scheduling algorithm that enhances the timely execution of goals such as load distribution, cost monitoring, and minimal time lag to react, increased security awareness, optimized energy usage, dependability, and so on. In order to attain these criteria, a variety of scheduling strategies based on hybrid, heuristic, and meta-heuristic techniques are under consideration. Objective: IoT devices and a variety of network resources make up the integrated cloud-fog environment. Every fog node has devices that release or request resources. A good scheduling algorithm is required in order to maintain the requests for resources made by various IoT devices. Method: This research focuses on analysis of numerous scheduling challenges and techniques employed in a cloud-fog context. This work evaluates and analyses the most important fog computing scheduling algorithms. Results: The survey of simulation tools used by the researchers is done. From the compared results, the highest percentage in the literature has 60% of scheduling algorithm which is related to task scheduling and 37% of the researchers have used iFogSim simulation tool for the implementation of the proposed algorithm defined in their research paper. Conclusion:: The findings in the paper provide a roadmap of the proposed efficient scheduling algorithms and can help researches to develop and choose algorithms close to their case studies.

Long-term, infection-acquired immunity against the SARS-CoV-2 Delta variant in a hamster model.

The emergence of the SARS-CoV-2 Delta variant (B.1.617.2) raises concerns about potential reduced sensitivity of the virus to antibody neutralization and subsequent vaccine breakthrough infections. Here, we use a live virus neutralization assay with sera from Pfizer- and Moderna-vaccinated individuals to examine neutralizing antibody titers against SARS-CoV-2 and observe a 3.9- and 2.7-fold reduction, respectively, in neutralizing antibody titers against the Delta variant compared with an early isolate bearing only a D614G substitution in its spike protein. We observe similar reduced sensitivity with sera from hamsters that were previously infected with an early isolate of SARS-CoV-2. Despite this reduction in neutralizing antibody titers against the Delta variant, hamsters previously infected (up to 15 months earlier) with an early isolate are protected from infection with the Delta variant, suggesting that the immune response to the first infection is sufficient to provide protection against subsequent infection with the Delta variant.

C-V2X Centralized Resource Allocation with Spectrum Re-Partitioning in Highway Scenario

Cellular Vehicle-to-Everything communication is an important scenario of 5G technologies. Modes 3 and 4 of the wireless systems introduced in Release 14 of 3GPP standards are intended to support vehicular communication with and without cellular infrastructure. In the case of Mode 3, dynamic resource selection and semi-persistent resource scheduling algorithms result in a signalling cost problem between vehicles and infrastructure, therefore, we propose a means to decrease it. This paper employs Re-selection Counter in centralized resource allocation as a decremental counter of new resource requests. Furthermore, two new spectrum re-partitioning and frequency reuse techniques in Roadside Units (RSUs) are considered to avoid resource collisions and diminish high interference impact via increasing the frequency reuse distance. The two techniques, full and partial frequency reuse, partition the bandwidth into two sub-bands. Two adjacent RSUs apply these sub-bands with the Full Frequency Reuse (FFR) technique. In the Partial Frequency Reuse (PFR) technique, the sub-bands are further re-partitioned among vehicles located in the central and edge parts of the RSU coverage. The sub-bands assignment in the nearest RSUs using the same sub-bands is inverted concerning the current RSU to increase the frequency reuse distance. The PFR technique shows promising results compared with the FFR technique. Both techniques are compared with the single band system for different vehicle densities.

Understanding Users’ Behavior towards Applications Privacy Policies

Recently, smartphone usage has increased tremendously, and smartphones are being used as a requirement of daily life, equally by all age groups. Smartphone operating systems such as Android and iOS have made it possible for anyone with development skills to create apps for smartphones. This has enabled smartphone users to download and install applications from stores such as Google Play, App Store, and several other third-party sites. During installation, these applications request resource access permissions from users. The resources include hardware and software like contact, memory, location, managing phone calls, device state, messages, camera, etc. As per Google’s permission policy, it is the responsibility of the user to allow or deny any permissions requested by an app. This leads to serious privacy violation issues when an app gets illegal permission granted by a user (e.g., an app might request for granted map permission and there is no need for map permission in the app, and someone can thereby access your location by this app). This study investigates the behavior of the user when it comes to safeguarding their privacy while installing apps from Google Play. In this research, first, seven different applications with irrelevant permission requests were developed and uploaded to two different Play Store accounts. The apps were live for more than 12 months and data were collected through Play Store analytics as well as the apps’ policy page. The preliminary data analysis shows that only 20% of users showed concern regarding their privacy and security either through interaction with the development team through email exchange or through commenting on the platform and other means accordingly.

Virtualized High Throughput Satellite Gateway with a Global Bandwidth Management Method

With the development of new satellite payload technology, in order to improve the utilization of system resources, research is based on software-defined network (SDN) and network function virtualization (NFV) gateway architecture. Based on this architecture, the system realizes global resource management and overall data distribution, which can solve the problem of resource allocation and maximum/minimum rate guarantee between different VNO terminals under different beams, different gateways, and different satellites. For this, a global bandwidth management method can be used which is mainly a process of management to control the traffic on a communication link. The proposed global resource management and control method can be based on the rate guarantee value of the VNO/terminal configured in the system as the basic limiting condition and reallocate the rate guarantee value limiting parameter according to the resource application status of the online terminal. The method can maximize the resource utilization of the entire satellite communication system and satisfy the resource request of the user terminal as much as possible.

Selfish Routing Game-Based Multi-Resource Allocation and Fair Scheduling of Indivisible Jobs in Edge Environments

Distributed and heterogeneous edge computing environments require efficient allocation and scheduling of multiple users’ applications. This paper presents a game-theoretic solution to model the competition between time-sensitive internet of things (IoT) applications with indivisible loads to be allocated and scheduled to edge servers. We model the allocation problem as a selfish routing game such that no job is unsatisfied with its allocation. Also, the allocated jobs are scheduled using a weighted time-sharing approach, which assigns resources to jobs proportional to their demands and deadlines. We proved the existence of pure Nash equilibrium in the introduced game and presented a greedy and polynomial time algorithm to obtain the solution called SAFSA. Using an extended version of the CloudSim simulator, we assess our approach in different situations by employing a real-world dataset of the Google cluster. Since the proposed algorithm considers jobs’ deadlines beside resource requests in allocation and scheduling decisions, it can simultaneously minimize average response time and maximize the number of jobs completed within their deadlines.The simulation results confirm the ability of SAFSA to efficiently execute the jobs within their deadlines, especially when there are many IoT devices and edge servers in the system, which is the main characteristic of IoT environments.

Efficient Reinforcement Learning-Based Transmission Control for Mitigating Channel Congestion in 5G V2X Sidelink

Channel congestion has been an open challenge for vehicular networks due to the limited resource of communication channels. Explosion of channel access requests from a massive number of transmitter vehicles can exhaust bandwidth and then degrade transmission quality. The rapid drop of messages (because of the high bit error rate in the transmission congestion condition) can threaten the safety of connected vehicles. Maintaining congestion-free communications is then essential to improve the reliability for vehicular networks, including Cellular-V2X (C-V2X)-based cooperative intelligent transport systems and road-safety applications. In this work, we present a novel intelligent transmission control model, namely DEEPCUT, to automatically adjust the message broadcasting rate of a transmitter vehicle. DEEPCUT works based on a Double Deep Q-learning Networks with Prioritized Experience Relay framework. DEEPCUT encourages the transmitter vehicle to (1) reduce its broadcasting rate if the vehicle is maintaining a safe distance from its neighbors and (2) increase the rate if the vehicle is approaching the others at a high-risk distance, all done by using reward/punish strategies. The evaluation results show that DEEPCUT can cut up 16% redundant data while increasing 22% packet reception rate compared with baseline models, particularly in crowd vehicular communications. Our risk-based transmission control can be an excellent complement to address the congestion when the channel cannot satisfy every vehicle’s resource requests. At best, the risk assessment-based approach in our congestion control method can provide a novel material to enhance Decentralized Congestion Control (DCC) for 5G V2X sidelink in the coming specifications.