When Backpressure Meets Predictive Scheduling

Motivated by the increasing popularity of learning and predicting human user behavior in communication and computing systems, in this paper, we investigate the fundamental benefit of predictive scheduling, i.e., predicting and pre-serving arrivals, in controlled queueing systems. Based on a lookahead window prediction model, we first establish a novel equivalence between the predictive queueing system with a \emph{fully-efficient} scheduling scheme and an equivalent queueing system without prediction. This connection allows us to analytically demonstrate that predictive scheduling necessarily improves system delay performance and can drive it to zero with increasing prediction power. We then propose the \textsf{Predictive Backpressure (PBP)} algorithm for achieving optimal utility performance in such predictive systems. \textsf{PBP} efficiently incorporates prediction into stochastic system control and avoids the great complication due to the exponential state space growth in the prediction window size. We show that \textsf{PBP} can achieve a utility performance that is within $O(\epsilon)$ of the optimal, for any $\epsilon>0$, while guaranteeing that the system delay distribution is a \emph{shifted-to-the-left} version of that under the original Backpressure algorithm. Hence, the average packet delay under \textsf{PBP} is strictly better than that under Backpressure, and vanishes with increasing prediction window size. This implies that the resulting utility-delay tradeoff with predictive scheduling beats the known optimal $[O(\epsilon), O(\log(1/\epsilon))]$ tradeoff for systems without prediction

Approximate Sparse Regularized Hyperspectral Unmixing

Sparse regression based unmixing has been recently proposed to estimate the abundance of materials present in hyperspectral image pixel. In this paper, a novel sparse unmixing optimization model based on approximate sparsity, namely, approximate sparse unmixing (ASU), is firstly proposed to perform the unmixing task for hyperspectral remote sensing imagery. And then, a variable splitting and augmented Lagrangian algorithm is introduced to tackle the optimization problem. In ASU, approximate sparsity is used as a regularizer for sparse unmixing, which is sparser than l1 regularizer and much easier to be solved than l0 regularizer. Three simulated and one real hyperspectral images were used to evaluate the performance of the proposed algorithm in comparison to l1 regularizer. Experimental results demonstrate that the proposed algorithm is more effective and accurate for hyperspectral unmixing than state-of-the-art l1 regularizer

Porous Collagen Scaffold Reinforced with Surfaced Activated PLLA Nanoparticles

Porous collagen scaffold is integrated with surface activated PLLA nanoparticles fabricated by lyophilizing and crosslinking via EDC treatment. In order to prepare surface-modified PLLA nanoparticles, PLLA was firstly grafted with poly (acrylic acid) (PAA) through surface-initiated polymerization of acrylic acid. Nanoparticles of average diameter 316 nm and zeta potential −39.88 mV were obtained from the such-treated PLLA by dialysis method. Porous collagen scaffold were fabricated by mixing PLLA nanoparticles with collagen solution, freeze drying, and crosslinking with EDC. SEM observation revealed that nanoparticles were homogeneously dispersed in collagen matrix, forming interconnected porous structure with pore size ranging from 150 to 200 μm, irrespective of the amount of nanoparticles. The porosity of the scaffolds kept almost unchanged with the increment of the nanoparticles, whereas the mechanical property was obviously improved, and the degradation was effectively retarded. In vitro L929 mouse fibroblast cells seeding and culture studies revealed that cells infiltrated into the scaffolds and were distributed homogeneously. Compared with the pure collagen sponge, the number of cells in hybrid scaffolds greatly increased with the increment of incorporated nanoparticles. These results manifested that the surface-activated PLLA nanoparticles effectively reinforced the porous collagen scaffold and promoted the cells penetrating into the scaffold, and proliferation

Image Super-Resolution Reconstruction Based On L1/2 Sparsity

Based on image sparse representation in the shearlet domain, we proposed a L1/2 sparsity regularized unconvex variation model for image super-resolution. The L1/2 regularizer term constrains the underlying image to have a sparse representation in shearlet domain. The fidelity term restricts the consistency with the measured imaged in terms of the data degradation model. Then, the variable splitting algorithm is used to break down the model into a series of constrained optimization problems which can be solved by alternating direction method of multipliers. Experimental results demonstrate the effectiveness of the proposed method, both in its visual effects and in quantitative terms

Economic analysis and optimization of a renewable energy based power supply system with different energy storages for a remote island

This study investigates and compares the various combinations of renewable energies (solar, wind) and storage technologies (battery, pumped hydro storage, hybrid storage) for an off-grid power supply system. Four configurations (i.e., single RE source system, double RE source system, single storage, and double storage system) based on two scenarios (self-discharge equal to 0% and 1%) are considered, and their operational performance is compared and analyzed. The energy management strategy created for the hybrid pumped battery storage (HPBS) considers that batteries cover low energy surplus/shortages while pumped hydro storage (PHS) is the primary energy storage device for serving high-energy generations/deficits. The developed mathematical model is optimized using Particle Swarm Optimization and the performance and results of the optimizer are discussed in particular detail. The results evidence that self-discharge has a significant impact on the cost of energy (13%–50%) for all configurations due to the substantial increase in renewable energy (RE) generators size compared to the energy storage capacity. Even though solar-wind-PHS is the cost-optimal arrangement, it exhibits lower reliability when compared to solar-wind-HPBS. The study reveals the significance of HPBS in the off-grid RE environment, allowing more flexible energy management, enabling to guarantee a 100% power supply with minimum cost and reducing energy curtailment. Additionally, this study presents and discuss the results of a sensitivity analysis conducted by varying load demand and energy balance of all considered configurations is performed, which reveals the effectiveness of the supplementary functionality of both storages in hybrid mode. Overall, the role of energy storage in hybrid mode improved, and the total energy covered by hybrid storage increased (48%), which reduced the direct dependency on variable RE generation

Image Super-Resolution Reconstruction Based on L1/2 Sparsity

Based on image sparse representation in the shearlet domain, we proposed a L1/2 sparsity regularized unconvex variation model for image super-resolution. The L1/2 regularizer term constrains the underlying image to have a sparse representation in shearlet domain. The fidelity term restricts the consistency with the measured imaged in terms of the data degradation model. Then, the variable splitting algorithm is used to break down the model into a series of constrained optimization problems which can be solved by alternating direction method of multipliers. Experimental results demonstrate the effectiveness of the proposed method, both in its visual effects and in quantitative terms

HOXA-AS2 Promotes Proliferation and Induces Epithelial-Mesenchymal Transition via the miR-520c-3p/GPC3 Axis in Hepatocellular Carcinoma

Background/Aims: Previous studies have demonstrated that long non-coding RNAs (lncRNAs) may play critical roles in cancer biology, including Hepatocellular carcinoma (HCC). The HOXA cluster antisense RNA2 (HOXA-AS2) lncRNA plays an important role in carcinogenesis, however, the underlying role of HOXA-AS2 in HCC remains unknown. The present study examined the effects of HOXA-AS2 on the progression of HCC, and explored the underlying molecular mechanisms. Methods: Quantitative real-time PCR was used to detect HOXA-AS2 expression in HCC tissues and cell lines. Furthermore, the effects of HOXA-AS2 silencing and overexpression on cell proliferation, cell cycle, apoptosis, migration, and invasion were assessed in HCC in vitro and in vivo. Furthermore, bioinformatics online programs predicted and luciferase reporter assay were used to validate the association of HOXA-AS2 and miR-520c-3p in HCC cells. Results: We observed that HOXA-AS2 was up-regulated in HCC tissues and cell lines. In vitro experiments revealed that HOXA-AS2 knockdown significantly inhibited HCC cells proliferation by causing G1 arrest and promoting apoptosis, whereas HOXA-AS2 overexpression promoted cell growth. Further functional assays indicated that HOXA-AS2 significantly promoted HCC cell migration and invasion by promoting EMT. Bioinformatics online programs predicted that HOXA-AS2 sponge miR-520c-3p at 3’-UTR with complementary binding sites, which was validated using luciferase reporter assay. HOXA-AS2 could negatively regulate the expression of miR-520c-3p in HCC cells. MiR-520c-3p was down-regulated and inversely correlated with HOXA-AS2 expression in HCC tissues. miR-520c-3p suppressed cell proliferation, invasion and migration in HCC cells, and enforced expression of miR-520c-3p attenuated the oncogenic effects of HOXA-AS2 in HCC cells. By bioinformatic analysis and dual-luciferase reporter assay, we found that miR-223-3p directly targeted the 3’-untranslated region (UTR) of Glypican-3 (GPC3), one of the key players in HCC. GPC3 was up-regulated in HCC tissues, and was negatively correlated with miR-520c-3p expression and positively correlated with HOXA-AS2 expression. Conclusion: In summary, our results suggested that the HOXA-AS2/miR-520c-3p/GPC3 axis may play an important role in the regulation of PTC progression, which could serve as a biomarker and therapeutic target for HCC

Train Scheduling Optimization for an Urban Rail Transit Line: A Simulated-Annealing Algorithm Using a Large Neighborhood Search Metaheuristic

This paper describes an optimization model for an irregular train schedule. The aim is to optimize both the maximum train loading rate and the average deviation of departure intervals under time-varying passenger transport demand for an urban rail transit line in consideration of practical train operation constraints, i.e., headway, running time between stations, dwell time, and capacity. A heuristic simulated-annealing algorithm is designed to solve the optimization model, and a case study of an urban rail transit line is performed to assess its efficacy. The results show that, compared with the current regular train schedule, the total train dwell time under the optimized irregular schedule is reduced from 900 s to 848 s, and the reduction ratio for the maximum train loading rate is from 1.2% to 3.6% for different stations. When the average train departure interval is allowed to vary from 120 to 170 s, the optimized irregular schedule decreases the maximum train loading rate of the collinear and noncollinear sections by 3.21%–4.82% and 2.52%–3.64%, respectively. Sensitivity analysis is performed for a nonnegative weight coefficient, average train departure interval, and proportion of full-length and short-turn routings. The proposed approach can be used to support capacity improvement and schedule optimization for urban rail transit lines