Terminal sliding mode control strategy design for second-order nonlinear system

This study mainly focuses on the terminal sliding mode control (TSMC) strategy design, including an adaptive terminal sliding mode control (ATSMC) and an exact-estimator-based terminal sliding mode control (ETSMC) for second-order nonlinear dynamical systems. In the ATSMC system, an adaptive bound estimation for the lump uncertainty is proposed to ensure the system stability. On the other hand, an exact estimator is designed for exact estimating system uncertainties to solve the trouble of chattering phenomena caused by a sign function in ATSMC law in despite of the utilization of a fixed value or an adaptive tuning algorithm for the lumped uncertainty bound. The effectiveness of the proposed control schemes can be verified in numerical simulations.<br /

Towards Assumption-free Bias Mitigation

Despite the impressive prediction ability, machine learning models show discrimination towards certain demographics and suffer from unfair prediction behaviors. To alleviate the discrimination, extensive studies focus on eliminating the unequal distribution of sensitive attributes via multiple approaches. However, due to privacy concerns, sensitive attributes are often either unavailable or missing in real-world scenarios. Therefore, several existing works alleviate the bias without sensitive attributes. Those studies face challenges, either in inaccurate predictions of sensitive attributes or the need to mitigate unequal distribution of manually defined non-sensitive attributes related to bias. The latter requires strong assumptions about the correlation between sensitive and non-sensitive attributes. As data distribution and task goals vary, the strong assumption on non-sensitive attributes may not be valid and require domain expertise. In this work, we propose an assumption-free framework to detect the related attributes automatically by modeling feature interaction for bias mitigation. The proposed framework aims to mitigate the unfair impact of identified biased feature interactions. Experimental results on four real-world datasets demonstrate that our proposed framework can significantly alleviate unfair prediction behaviors by considering biased feature interactions

DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research

The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-based search engines often fall short in assisting users who may not be familiar with specific terminologies. To address this, we present a knowledge graph-based paper search engine for biomedical research to enhance the user experience in discovering relevant queries and articles. The system, dubbed DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS) tagging to extract terminologies and relationships from article abstracts to create a KG. To reduce information overload, DiscoverPath presents users with a focused subgraph containing the queried entity and its neighboring nodes and incorporates a query recommendation system, enabling users to iteratively refine their queries. The system is equipped with an accessible Graphical User Interface that provides an intuitive visualization of the KG, query recommendations, and detailed article information, enabling efficient article retrieval, thus fostering interdisciplinary knowledge exploration. DiscoverPath is open-sourced at https://github.com/ynchuang/DiscoverPath

Sintering Temperature-Dependence on Radiopacity of Bi(2−x) ZrxO(3+x/2) Powders Prepared by Sol-Gel Process

Bismuth oxide (Bi2O3) is an effective additive used to enhance radiography resolution for dental materials. However, there are potential concerns regarding its biocompatibility and connection to tissue discoloration. In the present study, we modified the radiopacity properties of Bi2O3 with zirconium oxide (ZrO2) using a sol-gel process and investigated the composition, as well as the effects of heat treatment temperature using Thermogravimetry analysis (TGA), differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), and X-ray diffraction (XRD). The harvested Bi2&minus;xZrxO3+x/2 particles showed that the dominant phase transferred from &alpha;-Bi2O3 to &beta;-Bi7.38Zr0.62O12.31 after a heat treatment of over 750 &deg;C for 2 h. As the x values of Bi2&minus;xZrxO3+x/2 increased from 0.2 to 1.0, more zirconium oxide precipitated onto the particle surface, thus enhancing the surface roughness of particles. For sol-gel Bi1.8Zr0.2O3.1 powders (x = 0.2), the radiopacity values became 4.90 &plusmn; 0.23 and 5.83 &plusmn; 0.22 mmAl after a heat treatment of 500 &deg;C and 750 &deg;C, respectively

Pediatric renal transplantation: Results and prognostic factors

As renal transplantation may increase survival rates and improve quality of life for children with end-stage renal disease, we investigated the long-term outcomes and prognostic factors of pediatric renal transplantation. A retrospective study was conducted to review 25 pediatric renal transplantations, either from live or deceased donors, in our hospital from 1995 to 2008. The cumulative graft survival rate was calculated using the Kaplan-Meier method. Log rank tests were employed to identify categorical prognostic factors for graft survival of the pediatric renal transplantations, and Cox regression analysis for numeric factors. The mean age of our study subjects was 11.63 ± 3.76 years, and the mean follow-up period was 49.24 ± 33.72 months. The 12-month and 36-month graft survival rates were 92% and 82.14%, respectively. The rejection-free survival rates were 88% and 72.88% in the first and third years, respectively. All of the patients were alive during the follow-up period. Acute rejection (p = 0.0175) and male sex (p = 0.0384) were found to be significant factors for graft survival. For pediatric patients, we found that renal transplantation is now a safe and effective surgical procedure for children with end-stage renal disease. Acute rejection and male gender were identified as prognostic factors for poor graft survival

Pediatric renal transplantation: Results and prognostic factors

Background/Objective: As renal transplantation may increase survival rates and improve quality of life for children with end-stage renal disease, we investigated the long-term outcomes and prognostic factors of pediatric renal transplantation. Methods: A retrospective study was conducted to review 25 pediatric renal transplantations, either from live or deceased donors, in our hospital from 1995 to 2008. The cumulative graft survival rate was calculated using the Kaplan-Meier method. Log rank tests were employed to identify categorical prognostic factors for graft survival of the pediatric renal transplantations, and Cox regression analysis for numeric factors. Results: The mean age of our study subjects was 11.63 ± 3.76 years, and the mean follow-up period was 49.24 ± 33.72 months. The 12-month and 36-month graft survival rates were 92% and 82.14%, respectively. The rejection-free survival rates were 88% and 72.88% in the first and third years, respectively. All of the patients were alive during the follow-up period. Acute rejection (p = 0.0175) and male sex (p = 0.0384) were found to be significant factors for graft survival. Conclusion: For pediatric patients, we found that renal transplantation is now a safe and effective surgical procedure for children with end-stage renal disease. Acute rejection and male gender were identified as prognostic factors for poor graft survival