A novel distributed algorithm for complete targets coverage in energy harvesting wireless sensor networks

A fundamental problem in energy harvesting Wireless Sensor Networks (WSNs) is to maximize coverage, whereby the goal is to capture events of interest that occur in one or more target areas. To this end, this paper addresses the problem of maximizing network lifetime whilst ensuring all targets are monitored continuously by at least one sensor node. Specifically, we will address the Distributed Maximum Lifetime Coverage with Energy Harvesting (DMLC-EH) problem. The objective is to determine a distributed algorithm that allows sensor nodes to form a minimal set cover using local information whilst minimizing missed recharging opportunities. We propose an eligibility test that ensures the sensor nodes with higher energy volunteer to monitor targets. After that, we propose a Maximum Energy Protection (MEP) protocol that places an on-duty node with low energy to sleep while maintaining complete targets coverage. Our results show MEP increases network lifetime by 30% and has 10% less redundancy as compared to two similar algorithms developed for finite battery WSNs

THz Nanoscopy of Metal and Gallium Implanted Silicon

Drude model successfully quantifies the optical constants for bulk matter, but it is not suitable for subwavelength objects. In this paper, terahertz near-field optical microscopy and finite element simulation are used to study gold patches fabricated by Gallium etching. Electron transport is discovered in determining the optical signal strength. The signal from substrate is more complicated and still not fully understood. As the etching area decreases, near-field interaction is not dominated by doping concentration, and a higher signal is observed near connected metals. With the help of simulation, the abnormal enhancement phenomenon is discussed in detail, which lays the foundation for further experimental verification

Clinical Analysis of stereotactic body radiation therapy using extracranial gamma knife for patients with mainly bulky inoperable early stage non-small cell lung carcinoma

<p>Abstract</p> <p>Purpose</p> <p>To evaluate the clinical efficacy and toxicity of stereotactic body radiation therapy (SBRT) using extracranial gamma knife in patients with mainly bulky inoperable early stage non-small cell lung carcinoma (NSCLC).</p> <p>Materials and methods</p> <p>A total of 43 medically inoperable patients with mainly bulky Stage I/II NSCLC received SBRT using gamma knife were reviewed. The fraction dose and the total dose were determined by the radiation oncologist according to patients' general status, tumor location, tumor size and the relationship between tumor and nearby organ at risk (OAR). The total dose of 34~47.5 Gy was prescribed in 4~12 fractions, 3.5~10 Gy per fraction, one fraction per day or every other day. The therapeutic efficacy and toxicity were evaluated.</p> <p>Results</p> <p>The median follow-up was 22 months (range, 3-102 months). The local tumor response rate was 95.35%, with CR 18.60% (8/43) and PR 76.74% (33/43), respectively. The local control rates at 1, 2, 3, 5 years were 77.54%, 53.02%, 39.77%, and 15.46%, respectively, while the 1- and 2-year local control rates were 75% and 60% for tumor ≤3 cm; 84% and 71% for tumor sized 3~5 cm; 55% and 14.6% for tumor sized 5~7 cm; and 45%, 21% in those with tumor size of >7 cm. The overall survival rate at 1, 2, 3, 5 years were 92.04%, 78.04%, 62.76%, 42.61%, respectively. The toxicity of stereotactic radiation therapy was grade 1-2. Clinical stages were significantly important factor in local control of lung tumors (P = 0.000). Both clinical stages (P = 0.015) and chemotherapy (P = 0.042) were significantly important factors in overall survival of lung tumors.</p> <p>Conclusion</p> <p>SBRT is an effective and safe therapy for medically inoperable patients with early stage NSCLC. Clinical stage was the significant prognostic factors for both local tumor control and overall survival. The toxicity is mild. The overall local control for bulky tumors is poor. Tumor size is a poor prognostic factor, and the patients for adjuvant chemotherapy need to be carefully selected.</p

A Novel Two-Layer DAG-based Reactive Protocol for IoT Data Reliability in Metaverse

Many applications, e.g., digital twins, rely on sensing data from Internet of Things (IoT) networks, which is used to infer event(s) and initiate actions to affect an environment. This gives rise to concerns relating to data integrity and provenance. One possible solution to address these concerns is to employ blockchain. However, blockchain has high resource requirements, thereby making it unsuitable for use on resource-constrained IoT devices. To this end, this paper proposes a novel approach, called two-layer directed acyclic graph (2LDAG), whereby IoT devices only store a digital fingerprint of data generated by their neighbors. Further, it proposes a novel proof-of-path (PoP) protocol that allows an operator or digital twin to verify data in an on-demand manner. The simulation results show 2LDAG has storage and communication cost that is respectively two and three orders of magnitude lower than traditional blockchain and also blockchains that use a DAG structure. Moreover, 2LDAG achieves consensus even when 49\% of nodes are malicious

H-VFI: Hierarchical Frame Interpolation for Videos with Large Motions

Capitalizing on the rapid development of neural networks, recent video frame interpolation (VFI) methods have achieved notable improvements. However, they still fall short for real-world videos containing large motions. Complex deformation and/or occlusion caused by large motions make it an extremely difficult problem in video frame interpolation. In this paper, we propose a simple yet effective solution, H-VFI, to deal with large motions in video frame interpolation. H-VFI contributes a hierarchical video interpolation transformer (HVIT) to learn a deformable kernel in a coarse-to-fine strategy in multiple scales. The learnt deformable kernel is then utilized in convolving the input frames for predicting the interpolated frame. Starting from the smallest scale, H-VFI updates the deformable kernel by a residual in succession based on former predicted kernels, intermediate interpolated results and hierarchical features from transformer. Bias and masks to refine the final outputs are then predicted by a transformer block based on interpolated results. The advantage of such a progressive approximation is that the large motion frame interpolation problem can be decomposed into several relatively simpler sub-tasks, which enables a very accurate prediction in the final results. Another noteworthy contribution of our paper consists of a large-scale high-quality dataset, YouTube200K, which contains videos depicting a great variety of scenarios captured at high resolution and high frame rate. Extensive experiments on multiple frame interpolation benchmarks validate that H-VFI outperforms existing state-of-the-art methods especially for videos with large motions

Master–slave game-based optimal scheduling of community-integrated energy system by considering incentives for peak-shaving and ladder-type carbon trading

To alleviate the challenges posed by high energy consumption, significant carbon emissions, and conflicting interests among multiple parties in a community-level microgrid, the authors of this study propose a master–slave game-based optimal scheduling strategy for a community-integrated energy system (CIES). First, we analyze the decision variables and revenue-related objectives of each stakeholder in the CIES, and use the results to construct a framework of implementation. Second, we develop a model to incentivize peak regulation and a ladder-type carbon trading model that consider the correlation between the load owing to residential consumers, the load on the regional grid, and the sources of carbon emissions. Third, we propose a master–slave game-based mechanism of interaction and a decision-making model for each party to the game, and show that it has a Stackelberg equilibrium solution by combining genetic algorithms and quadratic programming. The results of evaluations showed that compared with an optimization strategy that considers only the master–slave game, the proposed strategy increased the consumption surplus of the user aggregator by 13.65%, the revenue of the community energy operator by 7.95%, increased the revenue of the energy storage operator, reduced CO2 emissions by 6.10%, and adequately responded to peak-cutting and valley-filling by the power grid company

KMT2A promotes melanoma cell growth by targeting hTERT signaling pathway.

Melanoma is an aggressive cutaneous malignancy, illuminating the exact mechanisms and finding novel therapeutic targets are urgently needed. In this study, we identified KMT2A as a potential target, which promoted the growth of human melanoma cells. KMT2A knockdown significantly inhibited cell viability and cell migration and induced apoptosis, whereas KMT2A overexpression effectively promoted cell proliferation in various melanoma cell lines. Further study showed that KMT2A regulated melanoma cell growth by targeting the hTERT-dependent signal pathway. Knockdown of KMT2A markedly inhibited the promoter activity and expression of hTERT, and hTERT overexpression rescued the viability inhibition caused by KMT2A knockdown. Moreover, KMT2A knockdown suppressed tumorsphere formation and the expression of cancer stem cell markers, which was also reversed by hTERT overexpression. In addition, the results from a xenograft mouse model confirmed that KMT2A promoted melanoma growth via hTERT signaling. Finally, analyses of clinical samples demonstrated that the expression of KMT2A and hTERT were positively correlated in melanoma tumor tissues, and KMT2A high expression predicted poor prognosis in melanoma patients. Collectively, our results indicate that KMT2A promotes melanoma growth by activating the hTERT signaling, suggesting that the KMT2A/hTERT signaling pathway may be a potential therapeutic target for melanoma

Spontaneous rotational symmetry breaking in KTaO3_3 interface superconductors

Strongly correlated electrons could display intriguing spontaneous broken symmetries in the ground state. Understanding these symmetry breaking states is fundamental to elucidate the various exotic quantum phases in condensed matter physics. Here, we report an experimental observation of spontaneous rotational symmetry breaking of the superconductivity at the interface of YAlO$_3$/KTaO$_3$ (111) with a superconducting transition temperature of 1.86 K. Both the magnetoresistance and upper critical field in an in-plane field manifest striking twofold symmetric oscillations deep inside the superconducting state, whereas the anisotropy vanishes in the normal state, demonstrating that it is an intrinsic property of the superconducting phase. We attribute this behavior to the mixed-parity superconducting state, which is an admixture of $s$-wave and $p$-wave pairing components induced by strong spin-orbit coupling. Our work demonstrates an unconventional nature of the pairing interaction in the KTaO$_3$ interface superconductor, and provides a new platform to clarify a delicate interplay of electron correlation and spin-orbit coupling.Comment: 7 pages, 4 figure

Site-Mutation of Hydrophobic Core Residues Synchronically Poise Super Interleukin 2 for Signaling: Identifying Distant Structural Effects through Affordable Computations

A superkine variant of interleukin-2 with six site mutations away from the binding interface developed from the yeast display technique has been previously characterized as undergoing a distal structure alteration which is responsible for its super-potency and provides an elegant case study with which to get insight about how to utilize allosteric effect to achieve desirable protein functions. By examining the dynamic network and the allosteric pathways related to those mutated residues using various computational approaches, we found that nanosecond time scale all-atom molecular dynamics simulations can identify the dynamic network as efficient as an ensemble algorithm. The differentiated pathways for the six core residues form a dynamic network that outlines the area of structure alteration. The results offer potentials of using affordable computing power to predict allosteric structure of mutants in knowledge-based mutagenesis.This work was supported by the Natural Science Foundation of China (No. 20475019 & 21473065 to Yanfang Cui), and Wuhan Science and Technology R&D Program (No. 201060623259 & No. 200860423220 to Yanfang Cui)