Combining DRSA decision-rules with FCA-based DANP evaluation for financial performance improvements

This study proposes a combined method to integrate soft computing techniques and multiple criteria decision making (MCDM) methods to guide semiconductor companies to improve financial performance (FP) – based on logical reasoning. The complex and imprecise patterns of FP changes are explored by dominance-based rough set approach (DRSA) to find decision rules associated with FP changes. Companies may identify its underperformed criterion (gap) to conduct formal concept analysis (FCA) – by implication rules – to explore the source criteria regarding the underperformed gap. The source criteria are analysed by decision making trial and evaluation laboratory (DEMATEL) technique to explore the cause-effect relationship among the source criteria for guiding improvements; in the next, DEMATEL-based analytical network process (DANP) can provide the influential weights to form an evaluation model, to select or rank improvement plans. To illustrate the proposed method, the financial data of a real semiconductor company is used as an example to show the involved processes: from performance gaps identification to the selection of five assumed improvement plans. Moreover, the obtained implication rules can integrate with DEMATEL analysis to explore directional influences among the critical criteria, which may provide rich insights and managerial implications in practice. First published online: 17 Sep 201

On Two Apriori-Based Rule Generators: Apriori in Prolog and Apriori in SQL

This paper focuses on two Apriori-based rule generators. The first is the rule generator in Prolog and C, and the second is the one in SQL. They are named Apriori in Prolog and Apriori in SQL, respectively. Each rule generator is based on the Apriori algorithm. However, each rule generator has its own properties. Apriori in Prolog employs the equivalence classes defined by table data sets and follows the framework of rough sets. On the other hand, Apriori in SQL employs a search for rule generation and does not make use of equivalence classes. This paper clarifies the properties of these two rule generators and considers effective applications of each to existing data sets

NIS-Apriori Algorithm with a Target Descriptor for Handling Rules Supported by Minor Instances

For each implication τ: Condition_part⇒ Decision_part defined in table data sets, we see τ is a rule if τ satisfies appropriate constraints, i.e., support(τ)≥α and accuracy(τ)≥β for two threshold values α and β (0<α,β≤1 ). If τ is a rule for relatively high α , we say τ is supported by major instances. On the other hand, if τ is a rule for lower α , we say τ is supported by minor instances. This paper focuses on rules supported by minor instances, and clarifies some problems. Then, the NIS-Apriori algorithm, which was proposed for handling rules supported by major instances from tables with information incompleteness, is extended to the NIS-Apriori algorithm with a target descriptor. The effectiveness of the new algorithm is examined by some experiments.The seventh International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2019), 27 - 29 March, 2019, Nara, Japa

Updated discussions on ‘Hybrid multiple criteria decisionmaking methods: a review of applications for sustainability issues’

A recent review discussed a variety of hybrid multiple criteria decision-making (H.M.C.D.M.) methods on the subject of sustainability issues. Some soft computing techniques, such as the fuzzy set, have contributed significantly to H.M.C.D.M. studies, emulating the imprecise or uncertain judgments of experts/decision makers in a complex environment. Nevertheless, a new rising trend in H.M.C.D.M., known as multiple rule-based decision-making (M.R.D.M.), which has the advantage of revealing understandable knowledge for supporting systematic improvements based on influential network relation maps (I.N.R.M.), was not discussed in the review. This study therefore attempts to extend the review by introducing recent developments and the associated work on M.R.D.M. for solving practical problems, updating the discussion

An adjusted Apriori algorithm to itemsets defined by tables and an improved rule generator with three-way decisions

The NIS-Apriori algorithm, which is extended from the Apriori algorithm, was proposed for rule generation from non-deterministic information systems and implemented in SQL. The realized system handles the concept of certainty, possibility, and three-way decisions. This paper newly focuses on such a characteristic of table data sets that there is usually a fixed decision attribute. Therefore, it is enough for us to handle itemsets with one decision attribute, and we can see that one frequent itemset defines one implication. We make use of these characteristics and reduce the unnecessary itemsets for improving the performance of execution. Some experiments by the implemented software tool in Python clarify the improved performance.International Joint Conference on Rough Sets, IJCRS 2020, June 29 – July 3, 2020, Havana, Cuba (COVID-19の感染拡大によるオンライン開催に変更

Clinical Outcome of Mycobacterium abscessus Infection and Antimicrobial Susceptibility Testing

Background/PurposeMycobacterium abscessus is the most resistant and rapidly growing mycobacterium and causes a wide range of clinical infectious diseases. The relationship between antimicrobial susceptibility and clinical outcome needs to be further evaluated.MethodsForty M. abscessus isolates were obtained from clinical specimens of 40 patients at the Taichung Veterans General Hospital from January 2006 to December 2008. Antimicrobial susceptibility testing was performed using the broth microdilution method according to the recommendations of the National Committee for Clinical Laboratory Standards. The clinical manifestations and outcomes were reviewed from medical records.ResultsTwenty-two patients were diagnosed with M. abscessus infection. Cough (86.3%), hemoptysis (31.8%) and fever (18.1%) were the most common symptoms. The radiographic findings included reticulonodular opacities (50.0%), consolidation (31.8%) and cavitary lesions (18.1%). The 40 isolates were susceptible to amikacin (95.0%), cefoxitin (32.5%), ciprofloxacin (10.0%), clarithromycin (92.5%), doxycycline (7.5%), imipenem (12.5%), moxifloxacin (22.5%), sulfamethoxazole (7.5%) and tigecycline (100%). The rate of treatment failure was 27.3% at the end of the 12th month after the start of treatment, although these patients were treated with a combination of clarithromycin and other antimicrobial agents.ConclusionM. abscessus is naturally susceptible to clarithromycin and amikacin, variably susceptible to cefoxitin and imipenem, and resistant to most other antimicrobial drugs. Combination therapy with clarithromycin, amikacin and other active antimicrobial agents may lead to clinical improvement; however, the rate of treatment failure is still high

Doping Dependence of the Chemical Potential in Cuprate High-TcT_{\rm c} Superconductors I: La2−x_{2-x}Srx_xCuO4_4

The names of the authors, which were inadvertedly lacking in the tex-file submitted two days ago, have been added.Comment: 10 pages, figures on request. Revtex, version 2, Materials Science Center Internal Report Number VSGD.94.6.

Exploration of the proteomic landscape of small extracellular vesicles in serum as biomarkers for early detection of colorectal neoplasia

[[abstract]]Background: Patient participation in colorectal cancer (CRC) screening via a stool test and colonoscopy is suboptimal, but participation can be improved by the development of a blood test. However, the suboptimal detection abilities of blood tests for advanced neoplasia, including advanced adenoma (AA) and CRC, limit their application. We aimed to investigate the proteomic landscape of small extracellular vesicles (sEVs) from the serum of patients with colorectal neoplasia and identify specific sEV proteins that could serve as biomarkers for early diagnosis. Materials and Methods: We enrolled 100 patients including 13 healthy subjects, 12 non-AAs, 13 AAs, and 16 stage-I, 15 stage-II, 16 stage-III, and 15 stage-IV CRCs. These patients were classified as normal control, early neoplasia, and advanced neoplasia. The sEV proteome was explored by liquid chromatography-tandem mass spectrometry. Generalized association plots were used to integrate the clustering methods, visualize the data matrix, and analyze the relationship. The specific sEV biomarkers were identified by a decision tree via Orange3 software. Functional enrichment analysis was conducted by using the Ingenuity Pathway Analysis platform. Results: The sEV protein matrix was identified from the serum of 100 patients and contained 3353 proteins, of which 1921 proteins from 98 patients were finally analyzed. Compared with the normal control, subjects with early and advanced neoplasia exhibited a distinct proteomic distribution in the data matrix plot. Six sEV proteins were identified, namely, GCLM, KEL, APOF, CFB, PDE5A, and ATIC, which properly distinguished normal control, early neoplasia, and advanced neoplasia patients from each other. Functional enrichment analysis revealed that APOF+ and CFB+ sEV associated with clathrin-mediated endocytosis signaling and the complement system, which have critical implications for CRC carcinogenesis. Conclusion: Patients with colorectal neoplasia had a distinct sEV proteome expression pattern in serum compared with those patients who were healthy and did not have neoplasms. Moreover, the six identified specific sEV proteins had the potential to discriminate colorectal neoplasia between early-stage and advanced neoplasia. Collectively, our study provided a six-sEV protein biomarker panel for CRC diagnosis at early or advanced stages. Furthermore, the implication of the sEV proteome in CRC carcinogenesis via specific signaling pathways was explored.[[notice]]補正完

PDZ-RhoGEF ubiquitination by Cullin3–KLHL20 controls neurotrophin-induced neurite outgrowth

Ubiquitin-dependent degradation of PDZ-RhoGEF restricts RhoA activity to facilitate neurite outgrowth

Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latency

Recently, image enhancement and restoration have become important applications on mobile devices, such as super-resolution and image deblurring. However, most state-of-the-art networks present extremely high computational complexity. This makes them difficult to be deployed on mobile devices with acceptable latency. Moreover, when deploying to different mobile devices, there is a large latency variation due to the difference and limitation of deep learning accelerators on mobile devices. In this paper, we conduct a search of portable network architectures for better quality-latency trade-off across mobile devices. We further present the effectiveness of widely used network optimizations for image deblurring task. This paper provides comprehensive experiments and comparisons to uncover the in-depth analysis for both latency and image quality. Through all the above works, we demonstrate the successful deployment of image deblurring application on mobile devices with the acceleration of deep learning accelerators. To the best of our knowledge, this is the first paper that addresses all the deployment issues of image deblurring task across mobile devices. This paper provides practical deployment-guidelines, and is adopted by the championship-winning team in NTIRE 2020 Image Deblurring Challenge on Smartphone Track.Comment: CVPR 2020 Workshop on New Trends in Image Restoration and Enhancement (NTIRE