Modeling Decision Systems via Uncertain Programming

By uncertain programming we mean the optimization theory in generally uncertain (random, fuzzy, rough, fuzzy random, etc.) environments. The main purpose of this paper is to present a brief review on uncertain programming models, and classify them into three broad classes: expected value model, chanceconstrained programming and dependent-chance programming. This presentation is based on the book: B. Liu, Theory and Practice of Uncertain Programming, PhisicaVerlag, Heidelberg, 200

Stochastic Multilevel Programming with a Hybrid Intelligent Algorithm

A framework of stochastic multilevel programming is proposed for modelling decentralized decision-making problem in stochastic environment. According to different decision criteria, the stochastic decentralized decision-making problem is formulated as expected value multilevel programming, and chanceconstrained multilevel programming. In order to solve the proposed stochastic multilevel programming models for the Stackelberg-Nash equilibriums, genetic algorithms, neural networks and stochastic simulation are integrated to produce a hybrid intelligent algorithm. Finally, two numerical examples are provided to illustrate the effectiveness of the hybrid intelligent algorithm

A survey on Uncertainty Theory (Probability Symposium)

本発表では，Liu[1]により構築されたUncertainty Theoryについて紹介する．Uncertainty Theoryは確率論の枠組みでは記述することが困難な現象，特に人間の意思決定の不確実性を記述するために考案された理論である．Zadeh[3]により提案されたファジィ理論は，広く普及し，数学，工学，経済，OR様々な分野で広く発展を遂げてきた．Uncertainty Theoryは確率論の議論と並行する形で展開される

Promoting Cardiac Repair through Simple Engineering of Nanoparticles with Exclusive Targeting Capability toward Myocardial Reperfusion Injury by Thermal Resistant Microfluidic Platform

Nanoparticle (NP)-based intravenous administration represents the most convenient cardiac targeting delivery routine, yet, there are still therapeutic issues due to the lack of targeting efficiency and specificity. Active targeting methods using functionalization of ligands onto the NPs' surface may be limited by trivial modification procedures and reduced targeting yield in vivo. Here, a microfluidics assisted single step, green synthesis method is introduced for producing targeting ligands free heart homing NPs in a tailored manner. The generated beta-glucan-based NPs exhibit precise and efficient targeting capability toward Dectin-1(+) monocytes/macrophages, which are confirmed as main pathogenesis mediators for cardiac ischemic/reperfusion (I/R) injury, with a sequentially enhanced cardiac NP accumulation, and this targeting strategy is exclusively suitable for cardiac I/R but not for other cardiovascular diseases, as confirmed both in murine and human model. Comparing to FDA-approved nano-micelles formulation, beta-glucan NPs loaded with NACHT, LRR, and PYD domains-containing protein 3 (NLRP3) inflammasome inhibitor (CY-09) exhibit better efficiency in ameliorating myocardial injury and heart failure induced by surgically induced I/R. These findings indicate a simple production of targeting-ligand free NPs, and demonstrate their potential therapeutic applications for preclinical I/R-induced cardiac injury amelioration.Peer reviewe

Promoting Cardiac Repair through Simple Engineering of Nanoparticles with Exclusive Targeting Capability toward Myocardial Reperfusion Injury by Thermal Resistant Microfluidic Platform

Nanoparticle (NP)-based intravenous administration represents the most convenient cardiac targeting delivery routine, yet, there are still therapeutic issues due to the lack of targeting efficiency and specificity. Active targeting methods using functionalization of ligands onto the NPs' surface may be limited by trivial modification procedures and reduced targeting yield in vivo. Here, a microfluidics assisted single step, green synthesis method is introduced for producing targeting ligands free heart homing NPs in a tailored manner. The generated beta-glucan-based NPs exhibit precise and efficient targeting capability toward Dectin-1(+) monocytes/macrophages, which are confirmed as main pathogenesis mediators for cardiac ischemic/reperfusion (I/R) injury, with a sequentially enhanced cardiac NP accumulation, and this targeting strategy is exclusively suitable for cardiac I/R but not for other cardiovascular diseases, as confirmed both in murine and human model. Comparing to FDA-approved nano-micelles formulation, beta-glucan NPs loaded with NACHT, LRR, and PYD domains-containing protein 3 (NLRP3) inflammasome inhibitor (CY-09) exhibit better efficiency in ameliorating myocardial injury and heart failure induced by surgically induced I/R. These findings indicate a simple production of targeting-ligand free NPs, and demonstrate their potential therapeutic applications for preclinical I/R-induced cardiac injury amelioration.Peer reviewe

Data-Driven Induction of Shadowed Sets Based on Grade of Fuzziness

We propose a procedure devoted to the induction of a shadowed set through the post-processing of a fuzzy set, which in turn is learned from labeled data. More precisely, the fuzzy set is inferred using a modified support vector clustering algorithm, enriched in order to optimize the fuzziness grade. Finally, the fuzzy set is transformed into a shadowed set through application of an optimal alpha-cut. The procedure is tested on synthetic and real-world datasets

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001