Examining Drivers and Impacts of Informatization in Shanghai Manufacturing Firms

With careful theoretical development and empirical data examination, this paper investigates several key factors that influence the IT usage in Shanghai firms: technology resource, human resource and environment resource. On the basis of the resource-based view and the process model, the study imports government regulation policies, as well as e-government actions, as environmental resource to affect firms’ IT usage. By surveying 398 manufacturing firms in Shanghai and statistically analyzing the field data using structural equation modeling technique, the study contributes several insights to the IT usage in Chinese firms. First of all, this study sheds lights on the value creation process of firms’ informatization in Shanghai manufacturing industry and validates the route from IT investment to value realization. Second, the findings suggest that government promotion policies have significant impacts on manufacturing firms’ technology infrastructure and IT management decision. However, there is no evidence showing the government impact on firms’ IT usage level

Silver-Mediated Trifluoromethoxylation of Aryl Stannanes and Arylboronic Acids

A silver-mediated cross-coupling of trifluoromethoxide with aryl stannanes and arylboronic acids to give aryl trifluoromethyl ethers is reported. This is the first report of a transition-metal-mediated $C_{aryl}–OCF_3$ bond formation.Chemistry and Chemical Biolog

PSYCHOSOCIAL FACTORS LEAD TO DELINQUENCY ITENTION ON ONLINE PEER-TO-PEER LENDING PLATFORM: A SURVEY EVIDENCE

In recent years, online P2P lending grows remarkably. Past studies mainly used direct second-hand data from P2P platforms to conclude many factors are related to people\u27s delinquency and default behaviours, lacking further exploration on how people\u27s social and psychological status could impact their behaviour during the borrowing and repayment process. On foundation of general strain theory (GST) and the model of frame selection (MFS), we used survey method and collected data from more than 700 Chinese subjects. A two-stage structural equation model was proposed. In the first stage, we investigated how people\u27s psychosocial factors (e.g. economic capacity, sense of fairness and sociability etc.) could shape their individual feelings and attitudes in social context (e.g. life satisfaction and self-esteem) as well as morality. In the second stage, we tested the relationship between life satisfaction, self-esteem, moral norm and people\u27s delinquency intention on P2P lending platform. The empirical results suggest that higher psychosocial status will be conductive to better individual feelings of life satisfaction and self-esteem. Moreover, better psychosocial factors will mostly lead to a higher moral norm of people. Therefore, these favourable feelings and morality further contribute to less delinquency intention on P2P lending platform. Our research has both academic and practical implications

PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference

The rapid growth and deployment of deep learning (DL) has witnessed emerging privacy and security concerns. To mitigate these issues, secure multi-party computation (MPC) has been discussed, to enable the privacy-preserving DL computation. In practice, they often come at very high computation and communication overhead, and potentially prohibit their popularity in large scale systems. Two orthogonal research trends have attracted enormous interests in addressing the energy efficiency in secure deep learning, i.e., overhead reduction of MPC comparison protocol, and hardware acceleration. However, they either achieve a low reduction ratio and suffer from high latency due to limited computation and communication saving, or are power-hungry as existing works mainly focus on general computing platforms such as CPUs and GPUs. In this work, as the first attempt, we develop a systematic framework, PolyMPCNet, of joint overhead reduction of MPC comparison protocol and hardware acceleration, by integrating hardware latency of the cryptographic building block into the DNN loss function to achieve high energy efficiency, accuracy, and security guarantee. Instead of heuristically checking the model sensitivity after a DNN is well-trained (through deleting or dropping some non-polynomial operators), our key design principle is to em enforce exactly what is assumed in the DNN design -- training a DNN that is both hardware efficient and secure, while escaping the local minima and saddle points and maintaining high accuracy. More specifically, we propose a straight through polynomial activation initialization method for cryptographic hardware friendly trainable polynomial activation function to replace the expensive 2P-ReLU operator. We develop a cryptographic hardware scheduler and the corresponding performance model for Field Programmable Gate Arrays (FPGA) platform

AutoReP: Automatic ReLU Replacement for Fast Private Network Inference

The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients' data privacy and security issues. Private inference (PI) techniques using cryptographic primitives offer a solution but often have high computation and communication costs, particularly with non-linear operators like ReLU. Many attempts to reduce ReLU operations exist, but they may need heuristic threshold selection or cause substantial accuracy loss. This work introduces AutoReP, a gradient-based approach to lessen non-linear operators and alleviate these issues. It automates the selection of ReLU and polynomial functions to speed up PI applications and introduces distribution-aware polynomial approximation (DaPa) to maintain model expressivity while accurately approximating ReLUs. Our experimental results demonstrate significant accuracy improvements of 6.12% (94.31%, 12.9K ReLU budget, CIFAR-10), 8.39% (74.92%, 12.9K ReLU budget, CIFAR-100), and 9.45% (63.69%, 55K ReLU budget, Tiny-ImageNet) over current state-of-the-art methods, e.g., SNL. Morever, AutoReP is applied to EfficientNet-B2 on ImageNet dataset, and achieved 75.55% accuracy with 176.1 times ReLU budget reduction.Comment: ICCV 2023 accepeted publicatio

Multifunctional Ag@Fe(2)O(3) yolk-shell nanoparticles for simultaneous capture, kill, and removal of pathogen

We combined silver and iron oxide nanoparticles to make unique Ag@Fe(2)O(3) yolk-shell multifunctional nanoparticles by the Kirkendall effect. After the surface functionalization using glucose, the Ag@Fe(2)O(3)-Glu conjugates exhibited both high capture efficiency of bacteria and potent antibacterial activity. The Ag@Fe(2)O(3) yolk-shell nanostructures may offer a unique multifunctional platform for simultaneous rapid detection and capture of bacteria and safe detoxification treatment.National Science Foundation of China[21021061, 81000662]; Fundamental Research Funds for the Central Universities[2010121012]; Program for New Century Excellent Talents in University[NCET-10-0709

Synthesis of magnetic, fluorescent and mesoporous core-shell-structured nanoparticles for imaging, targeting and photodynamic therapy

A synthetic method to prepare novel multifunctional core-shell-structured mesoporous silica nanoparticles for simultaneous magnetic resonance (MR) and fluorescence imaging, cell targeting and photosensitization treatment has been developed. Superparamagnetic magnetite nanoparticles and fluorescent dyes are co-encapsulated inside nonporous silica nanoparticles as the core to provide dual-imaging capabilities (MR and optical). The photosensitizer molecules, tetra-substituted carboxyl aluminum phthalocyanine (AlC(4)Pc), are covalently linked to the mesoporous silica shell and exhibit excellent photo-oxidation efficiency. The surface modification of the core-shell silica nanoparticles with folic acid enhances the delivery of photosensitizers to the targeting cancer cells that overexpress the folate receptor, and thereby decreases their toxicity to the surrounding normal tissues. These unique advantages make the prepared multifunctional core-shell silica nanoparticles promising for cancer diagnosis and therapy.NSFC[21021061, 20925103, 20871100]; Fok Ying Tung Education Foundation[121011]; NSF of Fujian Province[2009J06005]; Fundamental Research Funds for the Central Universities[2010121015]; Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministr

The relations between metabolic variations and genetic evolution of different species

Metabonomics has been applied in many bio-related scientific fields. Nevertheless, some animal research works are shown to fail when they are extended to humans. Therefore, it is essential to figure out suitable animal modeling to mimic human metabolism so that animal findings can serve humans. In this study, two kinds of commonly selected body fluids, serum and urine, from humans and various experimental animals were characterized by integration of nuclear magnetic resonance (NMR) spectroscopy with multivariate statistical analysis to identify the interspecies metabolic differences and similarities at a baseline physiological status. Our results highlight that the dairy cow and pig may be an optimal choice for transportation and biodistribution studies of drugs and that the Kunming (KM) mouse model may be the most effective for excretion studies of drugs, whereas the Sprague-Dawley (SD) rat could be the most suitable candidate for animal modeling under overall considerations. The biochemical pathways analyses further provide an interconnection between genetic evolution and metabolic variations, where species evolution most strongly affects microbial biodiversity and, consequently, has effects on the species-specific biological substances of biosynthesis and corresponding biological activities. Knowledge of the metabolic effects from species difference will enable the construction of better models for disease diagnosis, drug metabolism, and toxicology research. (C) 2015 Elsevier Inc. All rights reserved.Metabonomics has been applied in many bio-related scientific fields. Nevertheless, some animal research works are shown to fail when they are extended to humans. Therefore, it is essential to figure out suitable animal modeling to mimic human metabolism so that animal findings can serve humans. In this study, two kinds of commonly selected body fluids, serum and urine, from humans and various experimental animals were characterized by integration of nuclear magnetic resonance (NMR) spectroscopy with multivariate statistical analysis to identify the interspecies metabolic differences and similarities at a baseline physiological status. Our results highlight that the dairy cow and pig may be an optimal choice for transportation and biodistribution studies of drugs and that the Kunming (KM) mouse model may be the most effective for excretion studies of drugs, whereas the Sprague-Dawley (SD) rat could be the most suitable candidate for animal modeling under overall considerations. The biochemical pathways analyses further provide an interconnection between genetic evolution and metabolic variations, where species evolution most strongly affects microbial biodiversity and, consequently, has effects on the species-specific biological substances of biosynthesis and corresponding biological activities. Knowledge of the metabolic effects from species difference will enable the construction of better models for disease diagnosis, drug metabolism, and toxicology research. (C) 2015 Elsevier Inc. All rights reserved

Synthesis of magnetic, fluorescent and mesoporous core-shell-structured nanoparticles for imaging, targeting and photodynamic therapy

通讯作者地址: Chen, XL (通讯作者),Xiamen Univ, Coll Chem & Chem Engn, State Key Lab Phys Chem Solid Surfaces, Xiamen 361005, Peoples R ChinaA synthetic method to prepare novel multifunctional core-shell-structured mesoporous silica nanoparticles for simultaneous magnetic resonance (MR) and fluorescence imaging, cell targeting and photosensitization treatment has been developed. Superparamagnetic magnetite nanoparticles and fluorescent dyes are co-encapsulated inside nonporous silica nanoparticles as the core to provide dual-imaging capabilities (MR and optical). The photosensitizer molecules, tetra-substituted carboxyl aluminum phthalocyanine (AlC(4)Pc), are covalently linked to the mesoporous silica shell and exhibit excellent photo-oxidation efficiency. The surface modification of the core-shell silica nanoparticles with folic acid enhances the delivery of photosensitizers to the targeting cancer cells that overexpress the folate receptor, and thereby decreases their toxicity to the surrounding normal tissues. These unique advantages make the prepared multifunctional core-shell silica nanoparticles promising for cancer diagnosis and therapy.NSFC21021061,20925103,20871100,Fok Ying Tung Education Foundation 121011 NSF of Fujian Province 2009J06005 Fundamental Research Funds for the Central Universities 2010121015 Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministr