Online Teaching Wireless Video Stream Resource Dynamic Allocation Method considering Node Ability

At present, wireless network technology is advancing rapidly, and intelligent equipment is gradually popularized, which rapidly developed the mobile streaming media business. All kinds of mobile video applications have enriched people’s lives by carrying huge traffic randomly. Wireless networks (WNs) are facing an unprecedented burden, which allocates very important wireless video resources. Similarly, in WNs, the network status is dynamic and the terminal is heterogeneous, which causes the traditional video transmission system to fail to meet the needs of users. Hence, Scalable Video Coding (SVC) has been introduced in the video transmission system to achieve bit rate adaptation. However, in a strictly hierarchical traditional computer network, the wireless resource allocation strategy usually takes throughput as the only way to optimize the target, and it is terrible to make more optimizations for scalable video transmission. This article proposed a cross-layer design to enable information to be transmitted between the wireless base station and the video server to achieve joint optimization. To improve users’ satisfaction with video services, the wireless resource allocation problem and the video stream scheduling problem are jointly considered, which keep the optimization space larger. Based on the proposed architecture, we further study the design of wireless resource allocation algorithms and rate-adaptive algorithms for the scenario of multiuser transmission of scalable video in the Long-Term Evolution (LTE) downlink. Experimental outcomes have shown substantial performance enhancement of the proposed work.

A Combinatorial Optimization Model for Emergency Resource Allocation after Disasters

Disasters occur over a short or long period of time and cause large-scale harm to humans, infrastructure, as well as the ecosystem every year. Immediate response after a disaster helps minimize its impact on life and property. Therefore, it is crucial to have an emergency response system ready to handle any emergency that may come up after a disaster. In this paper, a model is proposed to optimize the distribution of emergency services at disaster-struck points. Due to the NP-hardness of the problem, two metaheuristic algorithms, Particle Swarm Optimization and Cuckoo Search Optimization have been used to dynamically allocate the available resources based on the given situation. The proposed model uses the distance between the emergency location and the emergency service provider, and the severity of the emergency as the main metrics for scoring any considered solution. The conducted experiments demonstrate that the model provides effective, efficient, and dynamic allocation service at emergency locations in simulated disaster situations.

Ropinirole hydrochloride for amyotrophic lateral sclerosis: A single-center, randomized feasibility, double-blind, placebo-controlled trial

ABSTRACTBackgroundWe previously used an induced pluripotent stem cell-based drug repurposing approach to demonstrate that ropinirole hydrochloride (ropinirole) attenuated amyotrophic lateral sclerosis (ALS)-specific pathological phenotypes. Here, we assessed the safety and feasibility of ropinirole in ALS patients to verify its efficacy.MethodsWe conducted a randomized feasibility trial of ALS. Twenty participants with ALSFRS-R scores greater than 2 points were randomly assigned using dynamic allocation to receive ropinirole or placebo for 24 weeks in the double-blind period. Upon completion, participants could choose to participate in the following 24-week open-label active extension period. The primary outcomes were safety and tolerability. The secondary outcomes for the feasibility trial objective were the change in the ALS Functional Rating Scale-Revised (ALSFRS-R) score, composite functional endpoint, combined assessment of function and survival, event-free survival, and time to ≤50% forced vital capacity (blinded outcome assessment). This study is registered with the UMIN Clinical Trials Registry, UMIN000034954.FindingsTwenty-one participants were randomized into two groups (ropinirole group; n=14) and received ropinirole (n=13) or placebo (n=7) and the data of all participants were analysed using mixed-effects models for repeated measures together. Overall, the incidences of adverse events, most of which had been reported previously, were similar within both groups. Notably, the incidence of gastrointestinal disorders (mainly, temporary mild nausea and diarrhoea) was high at 76·9% in the ropinirole group (14·3% in the placebo group). Regarding the feasibility of verifying efficacy, there were no significant differences in the ALSFRS-R score and combined assessment of function and survival scores during the double-blind period for 6 months, while the participants in the ropinirole group had lived an additional 27·9 weeks without disease progression events compared with the placebo group (log-rank test, 95% confidence interval, 4·3–37·4) at 12 months (secondary outcome).InterpretationRopinirole is safe and tolerable for patients with ALS and this trial indicates feasibility for a subsequent large-scale trial.FundingThis study was funded by The Japan Agency for Medical Research and Development and K Pharma Inc.

A Research on Routing and Spectrum Allocation Algorithm for Elastic Optical Networks Based on Deep Learning

Dynamic allocation request and spectrum release will lead to spectrum fragmentation, which will affect the allocation of subsequent services and spectrum resource utilization of elastic optical network. This paper proposes a new routing and spectrum allocation algorithm based on deep learning, which will find the best routing and spectrum allocation method for a specific network, so as to improve the overall network performance. Simulation results show that compared with the traditional resource allocation strategy, the neural network model used in this paper can improve the degree of spectrum fragmentation and reduce the network blocking probability.

Dynamic Beam Hopping Time Slots Allocation Based on Genetic Algorithm of Satellite Communication under Time-Varying Rain Attenuation

Beam hopping technology is considered to provide a high level of flexible resource allocation to manage uneven traffic requests in multi-beam high throughput satellite systems. Conventional beam hopping resource allocation methods assume constant rainfall attenuation. Different from conventional methods, by employing genetic algorithm this paper studies dynamic beam hopping time slots allocation under the effect of time-varying rain attenuation. Firstly, a beam hopping system model as well as rain attenuation time series based on Dirac lognormal distribution are provided. On this basis, the dynamic allocation method by employing genetic algorithm is proposed to obtain both quantity and arrangement of time slots allocated for each beam. Simulation results show that, compared with conventional methods, the proposed algorithm can dynamically adjust time slots allocation to meet the non-uniform traffic requirements of each beam under the effect of time-varying rain attenuation and effectively improve system performance.

Latency Reduction in Vehicular Sensing Applications by Dynamic 5G User Plane Function Allocation with Session Continuity

Vehicle automation is driving the integration of advanced sensors and new applications that demand high-quality information, such as collaborative sensing for enhanced situational awareness. In this work, we considered a vehicular sensing scenario supported by 5G communications, in which vehicle sensor data need to be sent to edge computing resources with stringent latency constraints. To ensure low latency with the resources available, we propose an optimization framework that deploys User Plane Functions (UPFs) dynamically at the edge to minimize the number of network hops between the vehicles and them. The proposed framework relies on a practical Software-Defined-Networking (SDN)-based mechanism that allows seamless re-assignment of vehicles to UPFs while maintaining session and service continuity. We propose and evaluate different UPF allocation algorithms that reduce communications latency compared to static, random, and centralized deployment baselines. Our results demonstrated that the dynamic allocation of UPFs can support latency-critical applications that would be unfeasible otherwise.

Towards Grading Chest X-rays of COVID-19 Patients Using a Dynamic Radial Basis Function Network Classifier

The high volume of COVID-19 Chest X-rays and less number of radiologists to interpret those is a challenge for the highly populous developing nations. Moreover, correct grading of the COVID-19 stage by interpreting the Chest X-rays manually is time-taking and could be biased. It often delays the treatment. Given the scenario, the purpose of this study is to develop a deep learning classifier for multiple classifications (e.g., mild, moderate, and severe grade of involvement) of COVID-19 Chest X-rays for faster and accurate diagnosis. To accomplish the goal, the raw images are denoised with a Gaussian filter during pre-processing followed by the Regions of Interest, and Edge Features are identified using Canny’s edge detector algorithm. Standardized Edge Features become the training inputs to a Dynamic Radial Basis Function Network classifier, developed from scratch. Results show that the developed classifier is 88% precise and 86% accurate in classifying the grade of illness with a much faster processing speed. The contribution lies in the dynamic allocation of the (i) number of Input and Hidden nodes as per the shape and size of the image, (ii) Learning rate, (iii) Centroid, (iv) Spread, and (v) Weight values during squared error minimization; (vi) image size reduction (37% on average) by standardization, instead of dimensionality reduction to prevent data loss; and (vii) reducing the time complexity of the classifier by 26% on average. Such a classifier could be a reliable assistive tool to human doctors in screening and grading COVID-19 patients and in turn, would help faster management of the patients as per the stages of COVID-19.

Utility Optimization of Federated Learning with Differential Privacy

Secure and trusted cross-platform knowledge sharing is significant for modern intelligent data analysis. To address the trade-off problems between privacy and utility in complex federated learning, a novel differentially private federated learning framework is proposed. First, the impact of data heterogeneity of participants on global model accuracy is analyzed quantitatively based on 1-Wasserstein distance. Then, we design a multilevel and multiparticipant dynamic allocation method of privacy budget to reduce the injected noise, and the utility can be improved efficiently. Finally, they are integrated, and a novel adaptive differentially private federated learning algorithm (A-DPFL) is designed. Comprehensive experiments on redefined non-I.I.D MNIST and CIFAR-10 datasets are conducted, and the results demonstrate the superiority of model accuracy, convergence, and robustness.

Metagenomics-informed soil biogeochemical models projected less carbon loss in tropical soils in response to climate warming

A major challenge of quantifying feedback between microbial communities and climate is the vast diversity of microbial communities and the intricacy of soil biogeochemical processes they mediate. We overcome this challenge by simplifying the representation of diverse enzyme functions from metagenomics data. We developed a dynamic allocation scheme for enzyme functional classes (EFCs) based on the premise that microbial communities act to maximize acquisition of limiting resources while minimizing energy expenditure for acquiring unlimited resources. We incorporated this scheme into a biogeochemical model to explicitly represent microbial functional diversity and simulate responses of microbially-mediated soil biogeochemical processes to varying environmental and nutrient conditions. Representing microbial functional diversity and environmental acclimation improved predictions of the stoichiometry of microbial biomass and mitigated the sensitivity of soil organic carbon to warming in nutrient-deficient regions. Our results indicate the importance of microbial functional diversity and environmental acclimation for projecting climate feedbacks of nutrient-limited soils.