Deep Reinforcement Learning for Resource Management in Network Slicing

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201

Evolutionary dynamics of rabies viruses highlights the importance of China rabies transmission in Asia

AbstractRabies in Asia is emerging as a serious public health issue. To explore the possible origin, phylogenetic relationships, and evolutionary dynamics of Asian Rabies viruses (RABV), we examined 200 complete nucleoprotein (N) gene sequences from RABV isolates in the region. Phylogeny supported the classification of Asian RABVs into five distinct clusters in lyssavirus genotype 1. Our geospatial and temporal analyses demonstrated that China appears to be the prime source of Asian RABVs. Understanding of rabies transmission and associated human activities, such as dog translocation, can help rabies control and elimination in Asia through collaborative efforts or programs

A mixed integer linear programming model for minimum backbone grid

Developing a minimum backbone grid in the power system planning is beneficial to improve the power system’s resilience. To obtain a minimum backbone grid, a mixed integer linear programming (MILP) model with network connectivity constraints for a minimum backbone grid is proposed. In the model, some constraints are presented to consider the practical application requirements. Especially, to avoid islands in the minimum backbone grid, a set of linear constraints based on single-commodity flow formulations is proposed to ensure connectivity of the backbone grid. The simulations on the IEEE-39 bus system and the French 1888 bus system show that the proposed model can be solved with higher computational efficiency in only about 30 min for such a large system and the minimum backbone grid has a small scale only 52% of the original grid. Compared with the improved fireworks method, the minimum backbone grid from the proposed method has fewer lines and generators

Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals

As lower-limb exoskeleton and prostheses are developed to become smarter and to deploy man–machine collaboration, accurate gait recognition is crucial, as it contributes to the realization of real-time control. Many researchers choose surface electromyogram (sEMG) signals to recognize the gait and control the lower-limb exoskeleton (or prostheses). However, several factors still affect its applicability, of which variation in the loads is an essential one. This study aims to (1) investigate the effect of load variation on gait recognition; and to (2) discuss whether a lower-limb exoskeleton control system trained by sEMG from different loads works well in multi-load applications. In our experiment, 10 male college students were selected to walk on a treadmill at three different speeds (V3 = 3 km/h, V5 = 5 km/h, and V7 = 7 km/h) with four different loads (L0 = 0, L20 = 20%, L30 = 30%, L40 = 40% of body weight, respectively), and 50 gait cycles were performed. Back propagation neural networks (BPNNs) were used for gait recognition, and a support vector machine (SVM) and k-nearest neighbor (k-NN) were used for comparison. The result showed that (1) load variation has significant effects on the accuracy of gait recognition (p < 0.05) under the three speeds when the loads range in L0, L20, L30, or L40, but no significant impact is found when the loads range in L0, L20, or L30. The least significant difference (LSD) post hoc, which can explore all possible pair-wise comparisons of means that comprise a factor using the equivalent of multiple t-tests, reveals that there is a significant difference between the L40 load and the other three loads (L0, L20, L30), but no significant difference was found among the L0, L20, and L30 loads. The total mean accuracy of gait recognition of the intra-loads and inter-loads was 91.81%, and 69.42%, respectively. (2) When the training data was taken from more types of loads, a higher accuracy in gait recognition was obtained at each speed, and the statistical analysis shows that there was a substantial influence for the kinds of loads in the training set on the gait recognition accuracy (p < 0.001). It can be concluded that an exoskeleton (or prosthesis) control system that is trained in a single load or the parts of loads is insufficient in the face of multi-load applications

Optimal surgical procedure for treating early-stage adenoid cystic carcinoma of the breast

Abstract To explore the superiority of breast conservation surgery (BCS) to mastectomy in treating early-stage adenoid cystic carcinoma of the breast (BACC). Patients with surgically treated stage I/II BACC were enrolled between 2000 and 2019 in the SEER database; they were divided into the BCS and mastectomy groups. Overall survival (OS) and disease-specific survival (DSS) were compared between the two groups, and Cox hazard regression models were used to determine the independent predictors. Of the 583 patients in the study, 386 were included in the BCS group. The 10-year OS rates for the BCS and mastectomy groups were 78% (95% CI: 74–82%) and 76% (95% CI: 70–82%), respectively, but the difference was not statistically significant (p = 0.968). The 10-year DSS rates for the BCS and mastectomy groups were 95% (95% CI: 93–97%) and 89% (95% CI: 85–93%), respectively, and the difference was statistically significant (p = 0.002). Pathological examination of regional lymph nodes and adjuvant treatment were not associated with improved OS or DSS, but age, disease grade, and lymph node metastasis were independent prognostic factors. For stage I/II BACC, BCS can achieve more satisfactory 10-year OS and DSS than mastectomy

Evaporation of ethanol/water mixture droplets on a pillar-like PDMS surface

Evaporation of about 6.0 mu L ethanol/water droplets with different ethanol concentrations on both planar and pillar-like polydimethylsiloxane (PDMS) surfaces was studied. A small fraction of the as-fabricated pillar-like PDMS surface is Wenzel-wetted while other part Cassie-Baxter-wetted. When ethanol/water droplet evaporates on a planar PDMS surface, the contact radius continuously decreases with time, and the contact angle first increases to the maximum, then keeps unchanged and finally decreases. However, when a mixture droplet evaporates on a pillar-like PDMS surface, the droplet first experiences a relatively long constant contact radius (CCR) stage without or with the addition of ethanol. There is a spreading process in the final stage of a mixture droplet with an initial ethanol concentration of 25%. A spontaneous spreading is also found at the end of the CCR stage when a 50% ethanol/water mixture droplet evaporates on a pillar-like PDMS surface. To better understand the spreading behavior, a KH-8700 microscopy was used to record the process from top view and it was found that the solid-liquid area developed to an octagon. The average spreading speed was measured to be 0.3-0.9 mu m/s. Using Taylor's expansion theorem, excess free energy was calculated and it is concluded that evaporating droplet will spontaneously spread on a pillar-like PDMS surface when the excess energy becomes larger than the energy barrier