Supply Chain Collaboration and its relevant factors’ contribution to OEM

Supply chain has become a key business issue for many companies and it is based on the integration of all activities that add value to customers, from product design through to delivery. Building an effective and efficient manufacturing supply chain can become a core or even a distinctive competency. However, building a close relationship with suppliers is not easy, and it would require companies develop high level of trust, appropriate communication, enhanced information technology, proper cross-functional coordination, and many other aspects which are not involved in this project due to the length of this dissertation and the time for doing it. This dissertation is go to firstly explain the relevant theories of supply chain, such as relevant factors which effect on supply chain collaboration, and analyze the relationship among them. Latterly, it is going to prove this relationship by specific cases and identify how particular factors benefit OEM and the contribution of these factors to successful supply chain collaboration

Research on Warnings with New Thought of Neuro-IE

AbstractSafety production is a seriously stubborn problem in modern industry engineering. Warnings, as the most fundamental and important measure used in safety management, especially in Mine Exploitation, have played a vital role in risk cognition, behaviors guide and accidents prevention. However, traditional researches are so subjective that it's hard to deeply explore the inner mechanism and process, which has been hidden behind the outer behaviors. As a result, the effectiveness of Warnings is much discounted. In this paper, we make use of neuroscience methods to study Warnings from the basically cognitive levels and have acquired preliminary achievements, which provide new evidence, discussion and introductions for former researches

Glutamate Excitotoxicity Inflicts Paranodal Myelin Splitting and Retraction.

Paranodal myelin damage is observed in white matter injury. However the culprit for such damage remains unknown. By coherent anti-Stokes Raman scattering imaging of myelin sheath in fresh tissues with sub-micron resolution, we observed significant paranodal myelin splitting and retraction following glutamate application both ex vivo and in vivo. Multimodal multiphoton imaging further showed that glutamate application broke axo-glial junctions and exposed juxtaparanodal K+ channels, resulting in axonal conduction deficit that was demonstrated by compound action potential measurements. The use of 4-aminopyridine, a broad-spectrum K+ channel blocker, effectively recovered both the amplitude and width of compound action potentials. Using CARS imaging as a quantitative readout of nodal length to diameter ratio, the same kind of paranodal myelin retraction was observed with applications of Ca2+ ionophore A23187. Moreover, exclusion of Ca2+ from the medium or application of calpain inhibitor abolished paranodal myelin retraction during glutamate exposure. Examinations of glutamate receptor agonists and antagonists further showed that the paranodal myelin damage was mediated by NMDA and kainate receptors. These results suggest that an increased level of glutamate in diseased white matter could impair paranodal myelin through receptor-mediated Ca2+ overloading and subsequent calpain activation

Verifying Safety of Neural Networks from Topological Perspectives

Neural networks (NNs) are increasingly applied in safety-critical systems such as autonomous vehicles. However, they are fragile and are often ill-behaved. Consequently, their behaviors should undergo rigorous guarantees before deployment in practice. In this paper, we propose a set-boundary reachability method to investigate the safety verification problem of NNs from a topological perspective. Given an NN with an input set and a safe set, the safety verification problem is to determine whether all outputs of the NN resulting from the input set fall within the safe set. In our method, the homeomorphism property and the open map property of NNs are mainly exploited, which establish rigorous guarantees between the boundaries of the input set and the boundaries of the output set. The exploitation of these two properties facilitates reachability computations via extracting subsets of the input set rather than the entire input set, thus controlling the wrapping effect in reachability analysis and facilitating the reduction of computation burdens for safety verification. The homeomorphism property exists in some widely used NNs such as invertible residual networks (i-ResNets) and Neural ordinary differential equations (Neural ODEs), and the open map is a less strict property and easier to satisfy compared with the homeomorphism property. For NNs establishing either of these properties, our set-boundary reachability method only needs to perform reachability analysis on the boundary of the input set. Moreover, for NNs that do not feature these properties with respect to the input set, we explore subsets of the input set for establishing the local homeomorphism property and then abandon these subsets for reachability computations. Finally, some examples demonstrate the performance of the proposed method.Comment: 25 pages, 11 figures. arXiv admin note: substantial text overlap with arXiv:2210.0417

Safety Verification for Neural Networks Based on Set-boundary Analysis

Neural networks (NNs) are increasingly applied in safety-critical systems such as autonomous vehicles. However, they are fragile and are often ill-behaved. Consequently, their behaviors should undergo rigorous guarantees before deployment in practice. In this paper we propose a set-boundary reachability method to investigate the safety verification problem of NNs from a topological perspective. Given an NN with an input set and a safe set, the safety verification problem is to determine whether all outputs of the NN resulting from the input set fall within the safe set. In our method, the homeomorphism property of NNs is mainly exploited, which establishes a relationship mapping boundaries to boundaries. The exploitation of this property facilitates reachability computations via extracting subsets of the input set rather than the entire input set, thus controlling the wrapping effect in reachability analysis and facilitating the reduction of computation burdens for safety verification. The homeomorphism property exists in some widely used NNs such as invertible NNs. Notable representations are invertible residual networks (i-ResNets) and Neural ordinary differential equations (Neural ODEs). For these NNs, our set-boundary reachability method only needs to perform reachability analysis on the boundary of the input set. For NNs which do not feature this property with respect to the input set, we explore subsets of the input set for establishing the local homeomorphism property, and then abandon these subsets for reachability computations. Finally, some examples demonstrate the performance of the proposed method.Comment: 19 pages, 7 figure

UR4NNV: Neural Network Verification, Under-approximation Reachability Works!

Recently, formal verification of deep neural networks (DNNs) has garnered considerable attention, and over-approximation based methods have become popular due to their effectiveness and efficiency. However, these strategies face challenges in addressing the "unknown dilemma" concerning whether the exact output region or the introduced approximation error violates the property in question. To address this, this paper introduces the UR4NNV verification framework, which utilizes under-approximation reachability analysis for DNN verification for the first time. UR4NNV focuses on DNNs with Rectified Linear Unit (ReLU) activations and employs a binary tree branch-based under-approximation algorithm. In each epoch, UR4NNV under-approximates a sub-polytope of the reachable set and verifies this polytope against the given property. Through a trial-and-error approach, UR4NNV effectively falsifies DNN properties while providing confidence levels when reaching verification epoch bounds and failing falsifying properties. Experimental comparisons with existing verification methods demonstrate the effectiveness and efficiency of UR4NNV, significantly reducing the impact of the "unknown dilemma".Comment: 11 pages, 4 figure

Perspective of key healthcare professionals on antimicrobial resistance and stewardship programs: A multicenter cross-sectional study from Pakistan

Copyright © 2020 Hayat, Rosenthal, Gillani, Chang, Ji, Yang, Jiang, Zhao and Fang. Background: Antimicrobial resistance (AMR) is an increasing global threat, and hospital-based antimicrobial stewardship programs (ASPs) are one of the effective approaches to tackle AMR globally. This study was intended to determine the attitude of key healthcare professionals (HCPs), including physicians, nurses, and hospital pharmacists, towards AMR and hospital ASPs. Methods: A cross-sectional study design was used to collect data from HCPs employed in public teaching hospitals of Punjab, Pakistan, from January 2019 to March 2019. A cluster-stratified sampling method was applied. Descriptive statistics, Mann Whitney and Kruskal Wallis tests were used for analysis. Results: A response rate of 81.3% (881/1083) for the surveys was obtained. The majority of the physicians (247/410, 60.2%) perceived AMR to be a serious problem in Pakistani hospitals (p \u3c 0.001). Most of the HCPs considered improving antimicrobial prescribing (580/881, 65.8%; p \u3c 0.001) accompanied by the introduction of prospective audit with feedback (301/881, 75.8%; p \u3c 0.001), formulary restriction (227/881, 57.2%; p = 0.004) and regular educational activities (300/881, 75.6%; p = 0.015) as effective ASP methods to implement hospital ASPs in Pakistan. A significant association was found between median AMR and ASP scores with age, years of experience, and types of HCPs (p \u3c 0.05). Conclusions: The attitude of most of the HCPs was observed to be positive towards hospital-based ASPs regardless of their poor awareness about ASPs. The important strategies, including prospective audit with feedback and regular educational sessions proposed by HCPs, will support the initiation and development of local ASPs for Pakistani hospitals

Perspective of Pakistani physicians towards hospital antimicrobial stewardship programs: A multisite exploratory qualitative study

© 2019 Raha Orfali et al. Plicosepalus is an important genus of the Loranthaceae family, and it is a semiparasitic plant grown in Saudi Arabia, traditionally used as a cure for diabetes and cancer in human and for increasing lactation in cattle. A flavonoid quercetin (P1), (-)-catechin (P2), and a flavane gallate 2S,3R-3,3′,4′,5,7-pentahydroxyflavane-5-O-gallate (P3) were isolated from the methanol extract of the aerial parts of P. curviflorus (PCME). The PCME and the isolated compounds were subjected to pharmacological assays to estimate peroxisome proliferator-activated receptors PPARα and PPARγ agonistic, anti-inflammatory, cytotoxic, and antimicrobial activities. Results proved for the first time the dual PPAR activation effect of the PCME and catechin (P2), in addition to the promising anti-inflammatory activity of the flavonoid quercetin (P1). Interestingly, both PCME and isolated compounds showed potent antioxidant activities while no antimicrobial effect against certain microbial strains had been reported from the extract and the isolated compounds. Based on the pharmacological importance of these compounds, an HPTLC validated method was developed for the simultaneous estimation of these compounds in PCME. It was found to furnish a compact and sharp band of compounds P1, P2, and P3 at Rf = 0.34, 0.47, and 0.65, respectively, using dichloromethane, methanol, and formic acid (90: 9.5: 0.5, (v/v/v)) as the mobile phase. Compounds P1, P2, and P3 were found to be 11.06, 10.9, 6.96 μg/mg, respectively, in PCME. The proposed HPTLC method offers a sensitive, precise, and specific analytical tool for the quantification of quercetin, catechin, and flavane gallates in P. curviflorus