Research on Product Elements’ Safety-Critical Degree

According to system safety practice, the safety of a system is a designed characteristic. Therefore, the role of safety in the lifecycle of the system is important to consider during the design phase. There are many factors affecting product safety, and each of them plays a part in determining a product’s overall safety. It’s important to locate isolate key elements that influence a product’s overall safety, and design with those factors in mind, as well as the time, cost and effectiveness of their influence on the design phase. The concept of “safety-critical” has long been a part of the system safety field, and scholars from different countries have studied safety-critical items in a system; unfortunately, the concept can be somewhat subjective and, often, disagreement occurs. These judgments can be affected by many factors and can cause inaccurate assumptions about which parts are critical, as well as encourages deviation in product development and maintenance that can cause serious consequences to product safety

Analysis of labor resources wastage in China’s real estate brokerage: from the perspective of opportunity costs

Real estate brokerage has experienced the rapid growth over the past two decades in China, with a significant increase of employees. In particular, in the megacities like Beijing, the growth of employees exceeds the growth of real estate transaction volume. This may lead to the wastage of labor resources. In this regard, the optimal employee size (OES) in China’s real estate brokerage is proposed from the perspective of opportunity costs, which include both under-size and over-size costs. In the proposed OES models, a real estate brokerage firm makes the optimal decisions of number of employees by minimizing expected opportunity costs. In addition, an iterative algorithm is employed to obtain the optimal employee size in different scenarios. The result reveals that high profit gained from the business does attract more employees than what is needed. By addressing various scenarios based on the game model, it is found that asymmetric competition, the increase of market participants, and demand fluctuations also contribute to the labor resources wastage in real estate brokerage industry. The theoretical analysis results are verified by taking Beijing as the case study. Finally, suggestions for reducing labor resources wastage in real estate brokerage of China are provided

Bibliometric analysis of electroencephalogram research in mild cognitive impairment from 2005 to 2022

BackgroundElectroencephalogram (EEG), one of the most commonly used non-invasive neurophysiological examination techniques, advanced rapidly between 2005 and 2022, particularly when it was used for the diagnosis and prognosis of mild cognitive impairment (MCI). This study used a bibliometric approach to synthesize the knowledge structure and cutting-edge hotspots of EEG application in the MCI.MethodsRelated publications in the Web of Science Core Collection (WosCC) were retrieved from inception to 30 September 2022. CiteSpace, VOSviewer, and HistCite software were employed to perform bibliographic and visualization analyses.ResultsBetween 2005 and 2022, 2,905 studies related to the application of EEG in MCI were investigated. The United States had the highest number of publications and was at the top of the list of international collaborations. In terms of total number of articles, IRCCS San Raffaele Pisana ranked first among institutions. The Clinical Neurophysiology published the greatest number of articles. The author with the highest citations was Babiloni C. In descending order of frequency, keywords with the highest frequency were “EEG,” “mild cognitive impairment,” and “Alzheimer’s disease”.ConclusionThe application of EEG in MCI was investigated using bibliographic analysis. The research emphasis has shifted from examining local brain lesions with EEG to neural network mechanisms. The paradigm of big data and intelligent analysis is becoming more relevant in EEG analytical methods. The use of EEG to link MCI to other related neurological disorders, and to evaluate new targets for diagnosis and treatment, has become a new research trend. The above-mentioned findings have implications in the future research on the application of EEG in MCI

Discrepancies in resistant starch and starch physicochemical properties between rice mutants similar in high amylose content

The content of resistant starch (RS) was considered positively correlated with the apparent amylose content (AAC). Here, we analyzed two Indica rice mutants, RS111 and Zhedagaozhi 1B, similar in high AAC and found that their RS content differed remarkably. RS111 had higher RS3 content but lower RS2 content than Zhedagaozhi 1B; correspondingly, cooked RS111 showed slower digestibility. RS111 had smaller irregular and oval starch granules when compared with Zhedagaozhi 1B and the wild type. Zhedagaozhi 1B showed a B-type starch pattern, different from RS111 and the wild type, which showed A-type starch. Meantime, RS111 had more fa and fb1 but less fb3 than Zhedagaozhi 1B. Both mutants showed decreased viscosity and swelling power when compared with the parents. RS111 had the lowest viscosity, and Zhedagaozhi 1B had the smallest swelling power. The different fine structures of amylopectin between RS111 and Zhedagaozhi 1B led to different starch types, gelatinization properties, paste viscosity, and digestibility. In addition to enhancing amylose content, modifications on amylopectin structure showed great potent in breeding rice with different RS2 and RS3 content, which could meet the increasing needs for various rice germplasms

Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer’s Disease

This paper seeks to advance the state-of-the-art in analysing fMRI data to detect onset of Alzheimer’s disease and identify stages in the disease progression. We employ methods of network neuroscience to represent correlation across fMRI data arrays, and introduce novel techniques for network construction and analysis. In network construction, we vary thresholds in establishing BOLD time series correlation between nodes, yielding variations in topological and other network characteristics. For network analysis, we employ methods developed for modelling statistical ensembles of virtual particles in thermal systems. The microcanonical ensemble and the canonical ensemble are analogous to two different fMRI network representations. In the former case, there is zero variance in the number of edges in each network, while in the latter case the set of networks have a variance in the number of edges. Ensemble methods describe the macroscopic properties of a network by considering the underlying microscopic characterisations which are in turn closely related to the degree configuration and network entropy. When applied to fMRI data in populations of Alzheimer’s patients and controls, our methods demonstrated levels of sensitivity adequate for clinical purposes in both identifying brain regions undergoing pathological changes and in revealing the dynamics of such changes

PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions

Graph node embedding aims at learning a vector representation for all nodes given a graph. It is a central problem in many machine learning tasks (e.g., node classification, recommendation, community detection). The key problem in graph node embedding lies in how to define the dependence to neighbors. Existing approaches specify (either explicitly or implicitly) certain dependencies on neighbors, which may lead to loss of subtle but important structural information within the graph and other dependencies among neighbors. This intrigues us to ask the question: can we design a model to give the maximal flexibility of dependencies to each node's neighborhood. In this paper, we propose a novel graph node embedding (named PINE) via a novel notion of partial permutation invariant set function, to capture any possible dependence. Our method 1) can learn an arbitrary form of the representation function from the neighborhood, withour losing any potential dependence structures, and 2) is applicable to both homogeneous and heterogeneous graph embedding, the latter of which is challenged by the diversity of node types. Furthermore, we provide theoretical guarantee for the representation capability of our method for general homogeneous and heterogeneous graphs. Empirical evaluation results on benchmark data sets show that our proposed PINE method outperforms the state-of-the-art approaches on producing node vectors for various learning tasks of both homogeneous and heterogeneous graphs.Comment: 24 pages, 4 figures, 3 tables. arXiv admin note: text overlap with arXiv:1805.1118