Operating performance assessment of industrial process based on MIC-graph convolutional networks with local slow feature analysis.

In order to realize the operating performance assessment of multi-mode industrial production process with both qualitative and quantitative information, an online operating performance assessment method based on Rough Set (RS) is proposed. Operating performance assessment consists of offline modeling and online assessment. Firstly, the modeling data of different modes are collected to establish the mode rule base, and operating performance assessment rule base based on RS respectively; Then, the online operating performance assessment is implemented by mode identification of online sample data and rule matching; When the process is operating at a non-optimal performance, non-optimal cause identification is carried out through calculating the variable difference degree between the current non-optimal data and the rule in the optimal rule base, which offers guidance to the operators to bring the process back to the optimal performance.

This chapter looks at performance assessment in the context of the day-to-day functioning of irrigation and drainage systems, and shows how performance assessment can be integrated into the management processes of irrigation and drainage systems. An explanation of service delivery in irrigation and drainage is provided, followed by a brief discussion of the impact that different formulations of the physical infrastructure and management structure can have on service delivery. Approaches are then formulated for operational and strategic performance assessment of service delivery in these different contexts. A key focus is on performance-oriented management, the basic components of which are: (i) the specification of the services and the level of service provision by the irrigation service provider to the water users; (ii) agreement between the water users and the irrigation service provider on the rights and responsibilities of the water users, particularly in relation to payment for services received; (iii) procedures for monitoring the services provided and responsibilities fulfilled; and (iv) procedures for evaluating the services provided and the responsibilities fulfilled.

Local and global regularized sparse coding for data representation

Objective Simulator-Based Evaluation of Carotid Artery Stenting Proficiency (from Assessment of Operator Performance by the Carotid Stenting Simulator Study [ASSESS

Modeling basic freeway section level-of-service based on travel time and reliability

Modal frequencies are widely used as damage-sensitive features in vibration-based unsupervised anomaly detection for full-scale civil structures. However, environmental variations introduce nonlinear and non-Gaussian behaviors in modal frequencies, characterized by multicentricity and substantial variance differences. These behaviors inevitably mask damage-induced frequency shifts, leading to increased false-detection rates and degraded real-time performance. To address this, this paper presents a novel unsupervised framework for early-stage anomaly detection that integrates exponential slow feature analysis (SFA) with just-in-time learning-aided standard K -nearest neighbor metrics. First, nonlinear modal features with minimum temporal variation are extracted from modal frequencies via exponential SFA for dimensionality reduction. Afterward, under the standard measurement criteria, the Euclidean squared distances between each sample in the entire training set and its nearest neighbors are computed. The standard distance for each neighbor is then modeled, with the number of neighbors automatically selected through Bayesian optimization and cross-validation. The primary purpose of this step is to ensure that the nearest neighbor distances between samples under different environmental conditions and damage conditions including abrupt anomalies or gradual degradations, are measured on a consistent scale, thereby minimizing environmental variability. After that, a robust detection indicator is derived from the mean of squared nearest neighbor distances and combined with kernel density estimation to determine an online detection threshold for outliers. The results indicate the proposed method effectively mitigates strong environmental variability without prior environmental measurements and accurately identifies anomalies in bridge modal frequencies with negligible false-positive and false-negative errors.

Due to environmental fluctuations, the operating performance of complex industrial processes may deteriorate and affect economic benefits. In order to obtain maximal economic benefits, operating performance assessment is a novel focus. Therefore, this paper proposes a whole framework from operating performance assessment to nonoptimal cause identification based on partial-least-squares-based Granger causality analysis (PLS-GC) and Bayesian networks (BNs). The proposed method has three main contributions. First, a multiblock operating performance assessment model is established to correspondingly extract economic-related information and dynamic information. Then, a Bayesian network structure is established by PLS-GC that excludes the strong coupling of variables and simplifies the network structure. Lastly, nonoptimal root cause and and nonoptimal transmission path are identified by Bayesian inference. The effectiveness of the proposed method was verified on Benchmark Simulation Model 1.

In modern hot rolling process, operating performance assessment is of great practical significance for guiding the production adjustment for operators. From the perspective of classification, operating performance assessment is a multi-class classification problem. Since support vector data description (SVDD) is a one-class classifier, conventional methods usually construct an independent SVDD model for each class, which ignores the correlation among different classes. The hyperspheres of different classes may not be isolated but overlapped. If a test sample exists in the overlapping region, how to determine which class it belongs to is a knotty problem. Moreover, conventional methods treat all samples equally, but in practice, the sample number of different classes can be imbalanced, which will affect the classification performance of SVDD. In this study, an operating performance assessment method based on weighted multi-sphere SVDD (WMSVDD) is proposed for solving the aforementioned issues. WMSVDD considers the interactions among different classes in a unified way, optimizes the hyperspheres of different classes globally, and introduces a weight coefficient to the model for eliminating the affects of uneven class sizes. Simulation results on a real hot rolling process illustrate the effectiveness of the proposed method comparing to the traditional multi-class SVDD.

Inner-phase and inter-phase analysis based operating performance assessment and nonoptimal cause identification for multiphase batch processes

One of the competencies in Curriculum 2013 is having the skills to apply knowledge in doing scientific inquiry, while the skills are divided into two kinds: concrete and Achievement of these competencies can be determined by assessment. The information of student’s ability given by authentic assessment is believed to be more holistic and valid, one of them is performance assessment. It is conducted to assess how appropriate the students in completing their tasks in the form of action or deed. The aim of this research is to obtain a description of the practicality and percentage of agreement of performance assessment instruments on measurement topic for 10th grade secondary school. This research subject of trial as many as 19 students in 10th science class at SMA Muhammadiyah 2 Surabaya with trial design one group pre-test post-test. Data are collected by using observation and direct questionnaire methods. The results showed that the practicality of the instrument based on the implementation of the assessment and the results of student responses fulfills the practical criteria and agreement between observers on the implementation of product performance assessment and process performance assessment are well categorised.

A general and simple model for simulating the steady behavior of air-to-refrigerant fin-and-tube evaporators, which accounts for detailed flow state inside the tubes, is introduced. To account for the heat transfer between air and the working fluid, the evaporator is divided into a number of control volumes. Space dependent partial differential equations group is obtained from the mass, energy and momentum balances for each one. The corresponding discretized governing equations are solved afterwards. Empirical correlations are also required to estimate the void fraction, the internal and external heat transfer coefficients, as well as the pressure drops. According to the phase of refrigerating fluid, the evaporator can be divided into two distinct zones on the refrigerant-side: the vapor zone and the two-phase zone, while special emphasis is performed on the treatment of the two-phase zone. The distribution of flow pattern has been evaluated with the aim of improving the calculation accuracy. The model prediction is validated against experimental data for an evaporator using R22 as the working fluid, which shows a reasonable level of agreement: the cooling capacity is predicted within the error band of 3%. The developed model will have wide applications in operational optimization, performance assessment and pipeline design.

The fast-expanding electric vehicle market requires efficient high-voltage charging methods. The documentation introduces a new PV-based switched inductor-switched capacitor (SL-SC) high voltage gain boost converter that addresses high-voltage EV charging requirements. The proposed converter design offers high voltage gain performance together with increased efficiency and diminished stress on its components. Recursive circuit analysis includes modeling of the circuit design alongside operational principle research and performance assessment. The simulation together with experimental studies prove that the proposed circuit design outperforms traditional boost converters concerning operational effectiveness. Efficient power converters that provide maximum voltage gain and intelligent control designs must be established because high-voltage electric vehicle (EV) charging systems need more effective solutions. This research explores the implementation of a high voltage gain boost converter with PV-supplied SL-SC topology which utilizes ANN control for high-voltage EV charging systems. SL-SC networks integrated through the proposed topology produce both high voltage conversion ratios and minimal semiconductor component stress that improves system performance and efficiency. The controller based on ANN optimizes the converter operation across different solar irradiation and load ranges by providing rapid system responses alongside improved voltage regulation. The proposed system proves better than conventional boost and coupled inductor-based converters through comparative analysis of voltage gain along with efficiency and transient response performance. Testing results together with simulation data support that this proposed structure represents an effective solution for future high-voltage systems which enable EV charging sustainability.

Purpose: Persistent underrepresentation of racially, ethnically, and socioeconomically diverse populations, coupled with protracted enrollment timelines, continues to compromise the generalizability, external validity, and equity of clinical research. Blood sampling, among the most frequently mandated in-person procedures, presents logistical barriers that inadvertently impede participation, retention, and biospecimen quality. Travel requirements, scheduling constraints, and inconsistent specimen handling disproportionately burden older adults, rural populations, and participants with limited temporal or transportation flexibility. We describe a novel, AI-integrated patient recruitment paradigm operationally coupled with decentralized clinical research phlebotomy and supported by digital chain-of-custody documentation and quality oversight. We present key operational outcomes associated with this integrated approach. Design: We conducted a comprehensive operational evaluation of an AI-enabled recruitment workflow integrated with mobile and community-based clinical research phlebotomy across multiple trial sites. Outcomes were compared descriptively against historical controls utilizing conventional, site-centric recruitment and phlebotomy workflows, facilitating assessment of enrollment efficiency, demographic representativeness, and operational performance. Result: The AI-enabled recruitment system identified 2,350 potential candidates and facilitated enrollment of 320 participants, representing 64% of the total study population (N = 500). Compared with historical controls, time to complete enrollment decreased from 12 months to 8 months 33% reduction. Concurrently, the proportion of enrolled participants from underrepresented populations increased from 22% to 38%, demonstrating substantial enhancement in trial representativeness without compromising operational feasibility. An AI-integrated recruitment strategy coupled with decentralized clinical research phlebotomy can substantially accelerate enrollment while enhancing demographic representativeness. When combined with standardized digital workflows and rigorous chain-of-custody oversight, this model preserves biospecimen integrity and data reliability, positioning phlebotomy as a strategic enabler of equitable, efficient, and high-quality clinical research.

Massive amounts of building operational data are collected and stored in modern buildings, which provide rich information for in-depth investigation and assessment of actual building operational performance. However, the current utilization of big building operational data is far from being effective due to the gaps between building engineering and advanced big data analytics. Data mining is a promising technology for extracting previously unknown yet potentially useful insights from big data. This paper aims to explore the potential application of advanced data mining techniques for effective utilization of big building operational data. A case study of mining the operational data of an educational building for performance improvement is presented. Decision tree, clustering analysis, and association rule mining are adopted to analyze the operational data. The results show that useful knowledge can be extracted for identifying typical building operation patterns, detecting operation deficiencies, and spotting energy conservation opportunities. Practical application:The current utilization of big building operational data in the building industry is rather limited due to the lack in experience of using advanced big data analytics. This study presents a data mining-based method for analyzing massive building operational data. The case study results validate the efficiency and effectiveness of the method proposed. It can help building professionals to discover valuable insights into building operation patterns and thereby developing strategies for improving building energy efficiency. The method can be fully realized using the open-source software R, which provides great flexibilities in its integration with building automation systems.

The present study details the implementation of an improvement process for a gamification activity from a Calculus course for undergraduate students aimed to develop mathematical modeling competencies. The improvement process was studied taking into account three types of data: the results of the students' performance assessment, the meta-evaluation of the educational and proactive effects and gamification costs, and data on the level of satisfaction achieved on the educational activity. The results of 50 performance assessments were analyzed, as well as the results from the meta-evaluation process carried out by the course's teacher. The study concludes that while it is true that gamification is a strategy that introduces a high level of innovation and brings the type of motivation and emotion that encourages learning, its educational intent can be further strengthened by including performance assessment and meta-evaluation processes to better understand its function and make adjustments to its design in a timely manner.