Scheduling for batch processes based on clustering approximated timed reachability graphs.

To solve some scheduling problems of batch processes based on timed Petri net models, timed extended reachability graphs (TERGs) and approximated TERGs can be used. Such graphs abstract temporal specifications and represent parts of timed languages. By exploring the feasible trajectories in a TERG, optimal schedules can be obtained with respect to the makespans of batch processes that are modeled by timed Petri nets.

The role of neutrophil percentage to albumin ratio in predicting 1-year mortality in elderly patients with hip fracture and external validation.

Objectives: This study aimed to investigate the association between the neutrophil percentage to albumin ratio (NPAR) on the day of admission and mortality 1 year after surgery in elderly patients with hip fractures.

Methods: Clinical characteristics and blood markers of inflammation were retrospectively collected from October 2016 to January 2022 in elderly patients with hip fractures at two different regional tertiary medical centers. It is divided into a training set and an external validation set.

Identification of aging-related biomarkers and immune infiltration characteristics in osteoarthritis based on bioinformatics analysis and machine learning.

Background: Osteoarthritis (OA) is a degenerative disease closely related to aging. Nevertheless, the role and mechanisms of aging in osteoarthritis remain unclear. This study aims to identify potential aging-related biomarkers in OA and to explore the role and mechanisms of aging-related genes and the immune microenvironment in OA synovial tissue.

Current-state opacity verification in discrete event systems using an observer net.

Due to the proliferation of contemporary computer-integrated systems and communication networks, there is more concern than ever regarding privacy, given the potential for sensitive data exploitation. A recent cyber-security research trend is to focus on security principles and develop the foundations for designing safety-critical systems. In this work, we investigated the problem of verifying current-state opacity in discrete event systems using labeled Petri nets.

Comparative Study on Noise-Estimation-Based Fuzzy C-Means Clustering for Image Segmentation.

Since a noisy image has inferior characteristics, the direct use of Fuzzy C -Means (FCM) to segment it often produces poor image segmentation results. Intuitively, using its ideal value (noise-free image) benefits FCM's robustness enhancement. Therefore, the realization of accurate noise estimation in FCM is a new and important task.

Low oxygen levels with high redox heterogeneity in the late Ediacaran shallow ocean: Constraints from I/(Ca + Mg) and Ce/Ce* of the Dengying Formation, South China.

Most previous studies focused on the redox state of the deep water, leading to an incomplete understanding of the spatiotemporal evolution of the redox-stratified ocean during the Ediacaran-Cambrian transition. In order to decode the redox condition of shallow marine environments during the late Ediacaran, this study presents I/(Ca + Mg), carbon and oxygen isotope, major, trace, and rare earth element data of subtidal to peritidal dolomite from the Dengying Formation at Yangba, South China. In combination with the reported radiometric and biostratigraphic data, the Dengying Formation and coeval successions worldwide are subdivided into a positive δ C excursion (up to ~6‰) in the lower part (~551-547 Ma) and a stable δ C plateau (generally between 0‰ and 3‰) in the middle-upper part (~547-541 Ma).

Uncertainty quantification of PM concentrations using a hybrid model based on characteristic decomposition and fuzzy granulation.

The prediction of air pollution plays an important role in reducing the emission of air pollutants and guiding people to carry out early warning and control, so it attracts many scholars to conduct modeling and research on it. However, most of the current researches fail to quantify the uncertainty in prediction and only use traditional fuzzy information granulation to process data, resulting in the loss of much detail information. Therefore, this paper proposes a hybrid model based on decomposition and granular fuzzy information to solve these problems.

Selection of a stealthy and harmful attack function in discrete event systems.

In this paper we consider the problem of joint state estimation under attack in partially-observed discrete event systems. An operator observes the evolution of the plant to evaluate its current states. The attacker may tamper with the sensor readings received by the operator inserting dummy events or erasing real events that have occurred in the plant with the goal of preventing the operator from computing the correct state estimation.

From Numeric to Granular Models: A Quest for Error and Performance Analysis.

In this study, we establish a new design methodology of granular models realized by augmenting the existing numeric models through analyzing and modeling their associated prediction error. Several novel approaches to the construction of granular architectures through augmenting existing numeric models by incorporating modeling errors are proposed in order to improve and quantify the numeric models' prediction abilities. The resulting construct arises as a granular model that produces granular outcomes generated as a result of the aggregation of the outputs produced by the numeric model (or its granular counterpart) and the corresponding error terms.

Solving Biobjective Distributed Flow-Shop Scheduling Problems With Lot-Streaming Using an Improved Jaya Algorithm.

A distributed flow-shop scheduling problem with lot-streaming that considers completion time and total energy consumption is addressed. It requires to optimally assign jobs to multiple distributed factories and, at the same time, sequence them. A biobjective mathematic model is first developed to describe the considered problem.

An efficient density peak cluster algorithm for improving policy evaluation performance.

In recent years, the XACML (eXtensible Access Control Markup Language) is widely used in a variety of research fields, especially in access control. However, when policy sets defined by the XACML become large and complex, the policy evaluation time increases significantly. In order to improve policy evaluation performance, we propose an optimization algorithm based on the DPCA (Density Peak Cluster Algorithm) to improve the clustering effect on large-scale complex policy sets.

Language-based Opacity Verification and Enforcement in the Framework of Labeled Petri Nets.

This work deals with the language-based opacity verification and enforcement problems in discrete event systems modeled with labeled Petri nets. Opacity is a security property that relates to privacy protection by hiding secret information of a system from an external observer called an "intruder". A secret can be a subset of a system's language.

A Design of Granular Classifier Based on Granular Data Descriptors.

Designing effective and efficient classifiers is a challenging task given the facts that data may exhibit different geometric structures and complex intrarelationships may exist within data. As a fundamental component of granular computing, information granules play a key role in human cognition. Therefore, it is of great interest to develop classifiers based on information granules such that highly interpretable human-centric models with higher accuracy can be constructed.

Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles.

Due to the limitations of data transfer technologies, existing studies on urban traffic control mainly focused on isolated dimension control such as traffic signal control or vehicle route guidance to alleviate traffic congestion. However, in real traffic, the distribution of traffic flow is the result of multiple dimensions whose future state is influenced by each dimension's decisions. Presently, the development of the Internet of Vehicles enables an integrated intelligent transportation system.

Performance Evaluation of Combining with Medical and Old-Age Care in Pension Institutions of China: A Two-Stage Data Envelopment Analysis.

Purpose: The aim of this paper was to build a performance evaluation index system for the combination of medical and old-age care services in pension institutions of China.

Methods: A two-stage data envelopment analysis (DEA) is used to evaluate the performance of 30 pension institutions in China.

Results: The results show that the two-stage DEA accounted for a relatively high affiance of medical and nursing care services, but resource allocation still needs to be further optimized.

Kullback-Leibler Divergence-Based Fuzzy C-Means Clustering Incorporating Morphological Reconstruction and Wavelet Frames for Image Segmentation.

In this article, we elaborate on a Kullback-Leibler (KL) divergence-based Fuzzy C -Means (FCM) algorithm by incorporating a tight wavelet frame transform and morphological reconstruction (MR). To make membership degrees of each image pixel closer to those of its neighbors, a KL divergence term on the partition matrix is introduced as a part of FCM, thus resulting in KL divergence-based FCM. To make the proposed FCM robust, a filtered term is augmented in its objective function, where MR is used for image filtering.

Model abstraction for discrete-event systems by binary linear programming with applications to manufacturing systems.

Model abstraction for finite state automata is helpful for decreasing computational complexity and improving comprehensibility for the verification and control synthesis of discrete-event systems (DES). Supremal quasi-congruence equivalence is an effective method for reducing the state space of DES and its effective algorithms based on graph theory have been developed. In this paper, a new method is proposed to convert the supremal quasi-congruence computation into a binary linear programming problem which can be solved by many powerful integer linear programming and satisfiability (SAT) solvers.

Tube-Based Output Feedback Robust MPC for LPV Systems With Scaled Terminal Constraint Sets.

This article provides a solution to tube-based output feedback robust model predictive control (RMPC) for discrete-time linear parameter varying (LPV) systems with bounded disturbances and noises. The proposed approach synthesizes an offline optimization problem to design a look-up table and an online tube-based output feedback RMPC with tightened constraints and scaled terminal constraint sets. In the offline optimization problem, a sequence of nested robust positively invariant (RPI) sets and robust control invariant (RCI) sets, respectively, for estimation errors and control errors is optimized and stored in the look-up table.

A Granular Approach to Interval Output Estimation for Rule-Based Fuzzy Models.

Rule-based fuzzy models play a dominant role in fuzzy modeling and come with extensive applications in the system modeling area. Due to the presence of system modeling error, it is impossible to construct a model that fits exactly the experimental evidence and, at the same time, exhibits high generalization capabilities. To alleviate these problems, in this study, we elaborate on a realization of granular outputs for rule-based fuzzy models with the aim of effectively quantifying the associated modeling errors.

Optimization of Granulation-Degranulation Mechanism Through Neurocomputing.

Information granulation and degranulation play a fundamental role in granular computing (GrC). Given a collection of information granules (referred to as reference information granules), the essence of the granulation process (encoding) is to represent each data (either numeric or granular) in terms of these reference information granules. The degranulation process (decoding) that realizes the reconstruction of original data is associated with a certain level of reconstruction error.

A Two-Stage Approach for Constructing Type-2 Information Granules.

In this article, we are concerned with the formation of type-2 information granules in a two-stage approach. We present a comprehensive algorithmic framework which gives rise to information granules of a higher type (type-2, to be specific) such that the key structure of the local granular data, their topologies, and their diversities become fully reflected and quantified. In contrast to traditional collaborative clustering where local structures (information granules) are obtained by running algorithms on the local datasets and communicating findings across sites, we propose a way of characterizing granular data (formed) by forming a suite of higher type information granules to reveal an overall structure of a collection of locally available datasets.

Improving social functioning in community-dwelling patients with schizophrenia: a randomized controlled computer cognitive remediation therapy trial with six months follow-up.

Computerized cognitive remediation therapy (CCRT) has been found to generally improve cognition among patients with schizophrenia, but its effect on functioning has not been extensively studied. This study addressed this gap in the literature by investigating the effect of CCRT and its long-term efficacy among community-dwelling patients with schizophrenia. 157 Chinese patients with schizophrenia were recruited from communities and randomized to CCRT (n = 78) or treatment as usual (TAU; n = 79) groups for 12 weeks with 4-5 sessions per week.

A Weighted Fidelity and Regularization-Based Method for Mixed or Unknown Noise Removal from Images on Graphs.

Image denoising technologies in a Euclidean domain have achieved good results and are becoming mature. However, in recent years, many real-world applications encountered in computer vision and geometric modeling involve image data defined in irregular domains modeled by huge graphs, which results in the problem on how to solve image denoising problems defined on graphs. In this paper, we propose a novel model for removing mixed or unknown noise in images on graphs.

Development and Analysis of Neural Networks Realized in the Presence of Granular Data.

In this article, we propose a design and evaluation framework of granular neural networks realized in the presence of information granules. Neural networks realized in this manner are able to process both nonnumerical data, such as information granules as well as numerical data. Information granules are meaningful and semantically sound entities formed by organizing existing knowledge and available experimental data.

Wavelet Frame-Based Fuzzy C-Means Clustering for Segmenting Images on Graphs.

In recent years, image processing in a Euclidean domain has been well studied. Practical problems in computer vision and geometric modeling involve image data defined in irregular domains, which can be modeled by huge graphs. In this paper, a wavelet frame-based fuzzy C -means (FCM) algorithm for segmenting images on graphs is presented.