A Search Strategy of Level-Based Flooding for the Internet of Things

This paper deals with the query problem in the Internet of Things (IoT). Flooding is an important query strategy. However, original flooding is prone to cause heavy network loads. To address this problem, we propose a variant of flooding, called Level-Based Flooding (LBF). With LBF, the whole network is divided into several levels according to the distances (i.e., hops) between the sensor nodes and the sink node. The sink node knows the level information of each node. Query packets are broadcast in the network according to the levels of nodes. Upon receiving a query packet, sensor nodes decide how to process it according to the percentage of neighbors that have processed it. When the target node receives the query packet, it sends its data back to the sink node via random walk. We show by extensive simulations that the performance of LBF in terms of cost and latency is much better than that of original flooding, and LBF can be used in IoT of different scales

Predictive algorithm for run-in value of warp knitting based on weave matrix

To predict the run-in values of single-needle-bar warp-knitted fabrics, three-dimensional weave matrixes have been established by considering main parameters of shogging movement, take-up density and total bar number. Length of a stitch has been deduced from the parameters in weave matrixes, and a new predictive algorithm model is developed. Moreover, to validate the accuracy of the proposed predictive algorithm, 30 samples with different parameters are knitted on HKS4-EL warp-knitting machine, and the predicted run-in values and measured run-in values of the samples are compared. It can be deduced from the comparison that the predictive algorithm model can provide high prediction accuracy with a relative error of < 4.26%

Strongly Nonlinear Topological Phases of Cascaded Topoelectrical Circuits

Circuits provide ideal platforms of topological phases and matter, yet the study of topological circuits in the strongly nonlinear regime, has been lacking. We propose and experimentally demonstrate strongly nonlinear topological phases and transitions in one-dimensional electrical circuits composed of nonlinear capacitors. Nonlinear topological interface modes arise on domain walls of the circuit lattices, whose topological phases are controlled by the amplitudes of nonlinear voltage waves. Experimentally measured topological transition amplitudes are in good agreement with those derived from nonlinear topological band theory. Our prototype paves the way towards flexible metamaterials with amplitude-controlled rich topological phases and is readily extendable to two and three-dimensional systems that allow novel applications.Comment: accepted by Frontiers of Physics, 18+9 pages, 4+3 figure

Preparation and Performances of Warp-Knitted Hernia Repair Mesh Fabricated with Chitosan Fiber

In this paper, warp-knitted knitted fabrics with chitosan fibers for ventral hernia repair were fabricated with three kinds of structures. The properties of chitosan fiber, yarns, and fabrics were tested. The results demonstrated that the properties of a mesh fabricated with 1-0/1-2/2-3/2-1// structure were slightly better than those of other fabrics. The mechanical properties of the three produced fabrics were weak. However, the results demonstrated that chitosan meshes have many advantages, such as excellent hygroscopicity, and thermal and antimicrobial properties, which makes them one of the best materials for ventral hernia repair. The findings have theoretical and practical significance for the industrial uses of chitosan in ventral hernia repair

Determining the prognosis of Lung cancer from mutated genes using a deep learning survival model: a large multi-center study

Abstract Background Gene status has become the focus of prognosis prediction. Furthermore, deep learning has frequently been implemented in medical imaging to diagnose, prognosticate, and evaluate treatment responses in patients with cancer. However, few deep learning survival (DLS) models based on mutational genes that are directly associated with patient prognosis in terms of progression-free survival (PFS) or overall survival (OS) have been reported. Additionally, DLS models have not been applied to determine IO-related prognosis based on mutational genes. Herein, we developed a deep learning method to predict the prognosis of patients with lung cancer treated with or without immunotherapy (IO). Methods Samples from 6542 patients from different centers were subjected to genome sequencing. A DLS model based on multi-panels of somatic mutations was trained and validated to predict OS in patients treated without IO and PFS in patients treated with IO. Results In patients treated without IO, the DLS model (low vs. high DLS) was trained using the training MSK-MET cohort (HR = 0.241 [0.213–0.273], P < 0.001) and tested in the inter-validation MSK-MET cohort (HR = 0.175 [0.148–0.206], P < 0.001). The DLS model was then validated with the OncoSG, MSK-CSC, and TCGA-LUAD cohorts (HR = 0.420 [0.272–0.649], P < 0.001; HR = 0.550 [0.424–0.714], P < 0.001; HR = 0.215 [0.159–0.291], P < 0.001, respectively). Subsequently, it was fine-tuned and retrained in patients treated with IO. The DLS model (low vs. high DLS) could predict PFS and OS in the MIND, MSKCC, and POPLAR/OAK cohorts (P < 0.001, respectively). Compared with tumor-node-metastasis staging, the COX model, tumor mutational burden, and programmed death-ligand 1 expression, the DLS model had the highest C-index in patients treated with or without IO. Conclusions The DLS model based on mutational genes can robustly predict the prognosis of patients with lung cancer treated with or without IO

Seamless Weft Knit Vest with Integrated Needle Sensing Zone for Monitoring Shoulder Movement: A First Methodological Study

The integration of textile-based flexible sensors and electronic devices has accelerated the development of wearable textiles for posture monitoring. The complexity of the processes required to create a complete monitoring product is currently reflected in three main areas. The first is the sensor production process, which is complex. Second, the integration of the sensor into the garment requires gluing or stitching. Finally, the production of the base garment requires cutting and sewing. These processes deteriorate the user experience and hinder the commercial mass production of wearable textiles. In this paper, we knitted a one-piece seamless knitted vest (OSKV) utilizing the one-piece seamless knitting technique and positioned an embedded needle sensing zone (EHSZ) with good textile properties and electrical performance for monitoring human shoulder activity. The EHSZ was knitted together with the OSKV, eliminating the need for an integration process. The EHSZ exhibited good sensitivity (GF = 2.23), low hysteresis (0.29 s), a large stretch range (200%), and excellent stability (over 300 cycles), satisfying the requirement to capture a wide range of deformation signals caused by human shoulder movements. The OSKV described the common vest process structure without the stitching process. Furthermore, OSKV fulfilled the demand for seamless and trace-free monitoring while effortlessly and aesthetically satisfying the knitting efficiency of commercial garments

Predictive algorithm for run-in value of warp knitting based on weave matrix

237-241To predict the run-in values of single-needle-bar warp-knitted fabrics, three-dimensional weave matrixes have been established by considering main parameters of shogging movement, take-up density and total bar number. Length of a stitch has been deduced from the parameters in weave matrixes, and a new predictive algorithm model is developed. Moreover, to validate the accuracy of the proposed predictive algorithm, 30 samples with different parameters are knitted on HKS4-EL warp-knitting machine, and the predicted run-in values and measured run-in values of the samples are compared. It can be deduced from the comparison that the predictive algorithm model can provide high prediction accuracy with a relative error of< 4.26%