Decision Model for COTS Component Procurement Based on Case-based Retrieval and Goal Programming

Compared with traditional information system development methodology, COTS-based information system has the following advantages: Avoid expensive development and maintenance; frequent upgrades often anticipate organization’s need; rich functionality; mature technologies; tracks technology trends, etc. However, how to select appropriate COTS components is a complex problem. For improving the accuracy of decision-making in COTS component procurement, a two-period model is put forward. In the first period, the procurement requirement of each COTS component is compared with a COTS component case base by case-based retrieval (CBR) and the initial candidates are selected. In the second period, a (0-1) integer goal programming model is created to optimize cost and time of the whole COTS-based system, and help decision makers to decide the final candidates. Case shows that the two-period method declines the complexity of computation and increases the rationality of decisio

An incremental points-to analysis with CFL-reachability

Abstract. Developing scalable and precise points-to analyses is increasingly important for analysing and optimising object-oriented programs where pointers are used pervasively. An incremental analysis for a program updates the existing analysis information after program changes to avoid reanalysing it from scratch. This can be efficiently deployed in software development environments where code changes are often small and frequent. This paper presents an incremental approach for demand-driven context-sensitive points-to analyses based on Context-Free Language (CFL) reachability. By tracing the CFL-reachable paths traversed in computing points-to sets, we can precisely identify and recompute on demand only the points-to sets affected by the program changes made. Combined with a flexible policy for controlling the granularity of traces, our analysis achieves significant speedups with little space overhead over reanalysis from scratch when evaluated with a null dereferencing client using 14 Java benchmarks.

Adjacent Slice Feature Guided 2.5D Network for Pulmonary Nodule Segmentation

More and more attention has been paid to the segmentation of pulmonary nodules. Among the current methods based on deep learning, 3D segmentation methods directly input 3D images, which takes up a lot of memory and brings huge computation. However, most of the 2D segmentation methods with less parameters and calculation have the problem of lacking spatial relations between slices, resulting in poor segmentation performance. In order to solve these problems, we propose an adjacent slice feature guided 2.5D network. In this paper, we design an adjacent slice feature fusion model to introduce information from adjacent slices. To further improve the model performance, we construct a multi-scale fusion module to capture more context information, in addition, we design an edge-constrained loss function to optimize the segmentation results in the edge region. Fully experiments show that our method performs better than other existing methods in pulmonary nodule segmentation task

In Situ Focused Ion Beam Scanning Electron Microscope Study of Microstructural Evolution of Single Tin Particle Anode for Li-Ion Batteries

Tin (Sn) is a potential anode material for highenergy density Li-ion batteries because of its high capacity, safety, abundance and low cost. However, Sn suffers from large volume change during cycling, leading to fast degradation of the electrode. For the first time, the microstructural evolution of micrometer-sized single Sn particle was monitored by focused-ion beam (FIB) polishing and scanning electron microscopy (SEM) imaging during electrochemical cycling by in situ FIB-SEM. Our results show the formation and evolution of cracks during lithiation, evolution of porous structure during delithiation and volume expansion/contraction during cycling. The electrochemical performance and the microstructural evolution of the Sn microparticle during cycling are directly correlated, which provides insights for understanding Sn-based electrode materials

Review of Energy Transition Policies in Singapore, London, and California

The paper contains the online supplementary materials for "Data-Driven Prediction and Evaluation on Future Impact of Energy Transition Policies in Smart Regions". We review the renewable energy development and policies in the three metropolitan cities/regions over recent decades. Depending on the geographic variations in the types and quantities of renewable energy resources and the levels of policymakers' commitment to carbon neutrality, we classify Singapore, London, and California as case studies at the primary, intermediate, and advanced stages of the renewable energy transition, respectively

Selenium Nanocomposite Cathode with Long Cycle Life for Rechargeable Li-Se Batteries

Selenium (Se) is a potential cathode material for high energy density rechargeable lithium batteries. In this study, a binder‐free Se‐carbon nanotube (CNT) composite electrode has been prepared by a facile chemical method. At initial state, Se is present in the form of branched nanowires with a diameter of <150 nm and a length of 1–2 μm, interwoven with CNTs. After discharge and re‐charge, the Se nanowires are converted to nanoparticles embedded in the CNT network. This synthesis method provides a path for fabricating the Se cathodes with controllable mass loading and thickness. By studying the composite electrodes with different Se loading and thickness, we found that the electrode thickness has a critical impact on the distribution of Se during repeated cycling. Promising cycling performance was achieved in thin electrodes with high Se loading. The composite electrode with 23 μm thickness and 60 % Se loading shows a high initial capacity of 537 mAh g−1 and stable cycling performance with a capacity of 401 mAh g−1 after 500 cycles at 1 C rate. This study reports a synthesis strategy to obtain Se/CNT composite cathode with long cycle life for rechargeable Li−Se batteries