Performance Assessment Strategy Research Articles

LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment.

Alzheimer's disease (AD) represents a significant global health issue, affecting over 55 million individuals worldwide, with a progressive impact on cognitive and functional abilities. Early detection, particularly of mild cognitive impairment (MCI) as an indicator of potential AD onset, is crucial yet challenging, given the limitations of current diagnostic biomarkers and the need for non-invasive, accessible tools. This study aims to address these gaps by exploring driving performance as a novel, non-invasive biomarker for MCI detection. Using the LiveDrive AI system, equipped with multimodal sensing (MMS) technology and a driving performance assessment strategy, the proposed work analyzes the predictive capacity of driving patterns in indicating cognitive decline. Machine learning models, trained on an expert-annotated in-house dataset, were employed to detect MCI status from driving performance. Key findings demonstrate the feasibility of using nuanced driving features, such as velocity and acceleration during turning, as indicators of cognitive decline. This approach holds promise for integration into smartphone or car applications, enabling real-time, continuous cognitive health monitoring. The implications of this work suggest a transformative step towards scalable, real-world solutions for early AD diagnosis, with the potential to improve patient outcomes and disease management.

Supervision, performance assessment, and recognition strategy (SPARS) - a multipronged strategy to strengthen medicines management in Nepal: pilot study methods and baseline results.

Nepal piloted a multipronged supervision, performance assessment, and recognition strategy (SPARS), to improve medicines management (MM) in public health facilities. This paper describes the SPARS pilot intervention and reports on MM performance at baseline. To build MM capacity at public sector health facilities, health workers were trained as MM supervisors to visit and supervise government health facilities, assess MM performance, and use the findings to provide support in MM practices. Performance was assessed using a SPARS indicator-based tool containing 25 indicators covering five MM domains: dispensing quality, prescribing quality, stock management, storage management, and ordering and reporting. From the middle of 2022, we trained 60 MM supervisors who began conducting supervisory visits at 284 health facilities in 12 randomly selected pilot districts located in three provinces. The intent, pending funding, is to continue supervision and expand SPARS to achieve national coverage. The overall MM baseline median score was 8.5 (34%) (maximum 25) with an inter-quartile range (IQR) of 6.4-10.7. Median facility domain scores (maximum 5) were: storage management, 2.0 (IQR 1.4-2.6); stock management 0.3 (IQR 0.0-1.0); ordering and reporting 2.5 (IQR 1.7-3.5); dispensing quality 1.3 (IQR 0.9-1.6); and prescribing quality 2.3 (IQR 1.3-3.3). The overall score did not differ significantly by level of care; however, hospitals had the lowest overall baseline score and individual domain scores. The baseline assessment using the SPARS tool provides valuable information on the current state of MM in Nepal, which was poor, assessed by the 25 SPARS indicators covering stock and storage management, ordering and reporting, prescribing, and dispensing. Assessing and building national capacity in MM is needed at health facilities at all levels of care.

A blueprint for integrating nursing history: Performance indicators for competency-based education

In 2021, the American Association of Colleges of Nursing published The Essentials: Core Competencies for Professional Nursing Education, a framework to educationally prepare individuals for the discipline of nursing. The framework includes 10 domains and specific expected competencies, sub-competencies, and concepts, with nursing history integrated in Domain 1: Knowledge for Nursing Practice. The delineation of specific competencies and sub-competencies encourages and supports competency-based education, holding students accountable to the mastery of critical areas of study. This article explores practical integration of nursing history into entry- and advanced-level nursing education curricula using a competency-based education approach. Specifically, it provides a blueprint for defining observable behaviors to assess competency associated with knowledge of nursing's history using the American Association for the History of Nursing (AAHN) nursing history framework. Clearly defined performance indicators for entry- and advanced-level nursing education allow students to demonstrate what they can do with what they know about nursing history and role expectations of the professional nurse. The nursing history performance indicators and assessment strategies suggested in this article can aid educators in prioritizing learning goals and measuring mastery for the nursing history-focused sub-competencies.

Costs of the Supervision, Performance Assessment and Recognition Strategy (SPARS) for improving medicines management in Nepal.

Nepal implemented a pilot of the Supervision, Performance Assessment, and Recognition Strategy (SPARS) program aimed to increase health workers' ability to manage medicines through on- the-job training and support from a new cadre of Medicines Management Supervisors (MMS). This study aims to assess the implementation costs. Data from the SPARS central database and facilities was analysed to assess the total cost of the SPARS pilot including 293 public health facilities from 12 districts of 3 provinces, from May 2022 until July 2023. We estimated the number of health facilities to achieve a successful performance status, defined as a SPARS score ≥ 18.75 out of 25 (75%) and estimated the cost per facility to reach a successful score. In total, 293 facilities received 838 visits, performed by 48 MMS for an estimated total cost of $226,531. 124 facilities (44.3%) reached a score of 18.75 points after two or more visits. On average, it costs $1827 USD per facility to reach a successful SPARS score. This study assesses the costs of implementing SPARS in 12 districts in Nepal. These findings can provide insights into further scaling up SPARS in Nepal or in other countries.

Measurement Model of Full-Width Roughness Considering Longitudinal Profile Weighting

This study proposes and establishes a roadway longitudinal profile weighting model and innovatively develops a process and method for evaluating road surface roughness. Initially, the Gaussian model is employed to accurately fit the distribution frequency of vehicle centerlines recorded in British Standard BS 5400-10, and a generalized lateral distribution model of wheel trajectories is further derived. Corresponding model parameters are suggested for different types of lanes in this study. Subsequently, based on the proposed distribution model, a longitudinal profile weighting model for lanes is constructed. After adjusting the elevation of the cross-section, the equivalent longitudinal elevation of the roadway is calculated. Furthermore, this study presents a new indicator and method for assessing the roughness of the entire road surface, which comprehensively considers the elevations of all longitudinal profiles within the lane. To validate the effectiveness of the proposed new method and indicator, a comparative test was conducted using a vehicle-mounted profiler and a three-dimensional measurement system. The experimental results demonstrate significant improvements in measurement repeatability and scientific rigor, offering a new perspective and evaluation strategy for road performance assessment.

Business Management Goal Setting and Performance Appraisal Strategies of Development

The purpose of this paper is to explore the issue of management optimization in construction engineering cost consulting enterprises, and to provide scientific and reasonable management methods and strategies for enterprises to improve their management level and overall competitiveness by studying and analyzing the formulation of goal setting and performance evaluation strategies. This paper adopts the method of combining literature review and case analysis to conduct research. Firstly, through reviewing relevant literature, we understand the relevant theories and practical experience of goal setting and performance evaluation; then, combined with the actual situation of engineering cost consulting enterprises, we conduct an in-depth analysis and research on the goal setting and performance evaluation strategy of enterprises; finally, through the method of case study, we conduct an empirical research on the management optimization of enterprises, and verify the effectiveness and feasibility. The findings of this paper include the following: 1. Key elements of goal-setting and core indicators of performance assessment for engineering cost consulting firms were summarized; 2. Based on the results of the questionnaire survey and the case study, a performance appraisal strategy for different positions and levels is proposed; 3. Based on the results of the empirical study, the effectiveness and feasibility of the goal-setting and performance assessment strategies were verified. Through researching and analyzing the management optimization problems of construction engineering cost consulting enterprises.

Blending Efficiency and Resilience in the Performance Assessment of Urban Intersections: A Novel Heuristic Informed by Literature Review

Urban mobility underscores the vital importance of ensuring traffic efficiency on road segments, intersections, and transportation networks, especially in challenging circumstances. In this perspective, the essential approach to improving urban intersection efficiency should involve understanding critical factors for maintaining operational performance in the face of disruptions such as storms. This paper, inspired by a systematic literature review, presents a novel heuristic for evaluating urban intersection efficiency, with resilience as its guiding principle. The methodological path was designed to address the fundamental question: How can urban intersections be designed and managed to ensure efficiency and resilience in the face of disruptions? Drawing inspiration from the Highway Capacity Manual procedure, the methodological approach encompasses both pre-storm and post-storm scenarios, comparing delay times at roundabouts and signalized intersections before and after a storm. The results reveal significant changes in delay times for traffic signals, although the choice between roundabouts and signalized intersections should be context-specific, considering factors like traffic conditions, resilience requirements, and associated trade-offs. By shedding light on the interplay between intersection design, control strategies, and urban resilience, this research provides valuable insights into integrating resilience considerations into intersection performance assessment and management strategies. It also underscores how particular intersection designs can impact efficiency and recovery, essential considerations when assessing whether a road or intersection project is resilient.

Online Bridge Structural Condition Assessment Based on the Gaussian Process: A Representative Data Selection and Performance Warning Strategy

Data-driven methods have now been widely used in structural health monitoring of civil infrastructures thanks to the rapid development of sensor technologies with massive structural and operational condition data. One main issue of data-driven methods is that the computational time increases with the number of monitoring data used, which limits their applications for online structural condition assessment. Focusing on bridge structural health monitoring, this paper proposes a representative data selection strategy for online performance assessment based on Gaussian process models. The proposed method can effectively reduce the required monitoring data size for training, allowing the bridge performance assessment to be conducted in a real-time manner. The method is developed in a probabilistic manner, allowing associated uncertainty of bridge monitoring data to be rigorously considered. A probabilistic warning index is proposed for bridge condition assessment and anomaly detection. The proposed method is validated using synthetic data and applied to structural condition assessment of two full-scale bridges, illustrating the feasibility for real implementations.

Algal-Based Hollow Fiber Membrane Bioreactors for Efficient Wastewater Treatment: A Comprehensive Review

The treatment of living organisms is a critical aspect of various environmental and industrial applications, ranging from wastewater treatment to aquaculture. In recent years, algal-based hollow fiber membrane bioreactors (AHFMBRs) have emerged as a promising technology for the sustainable and efficient treatment of living organisms. This review provides a comprehensive examination of AHFMBRs, exploring their integration with algae and hollow fiber membrane systems for diverse applications. It also examines the applications of AHFMBRs in various areas, such as nutrient removal, wastewater treatment, bioremediation, and removal of pharmaceuticals and personal care products. The paper discusses the advantages and challenges associated with AHFMBRs, highlights their performance assessment and optimization strategies, and investigates their environmental impacts and sustainability considerations. The study emphasizes the potential of AHFMBRs in achieving enhanced nutrient removal, bioremediation, and pharmaceutical removal while also addressing important considerations such as energy consumption, resource efficiency, and ecological implications. Additionally, it identifies key challenges and offers insights into future research directions. Through a systematic analysis of relevant studies, this review aims to contribute to the understanding and advancement of algal-based hollow fiber membrane bioreactors as a viable solution for the treatment of living organisms.

Development and initial validation of a video-based peroral endoscopic myotomy assessment tool

Video analysis has emerged as a potential strategy for performance assessment and improvement. We aimed to develop a video-based skill assessment tool for peroral endoscopic myotomy (POEM). POEM was deconstructed into basic procedural components through video analysis by an expert panel. A modified Delphi approach and 2 validation exercises were conducted to refine the POEM assessment tool (POEMAT). Twelve assessors used the final POEMAT version to grade 10 videos. Fully crossed generalizability (G) studies investigated the contributions of assessors, endoscopists' performance, and technical elements to reliability. G coefficients below .5 were considered unreliable, between .5 and .7 as modestly reliable, and above .7 as indicative of satisfactory reliability. After task deconstruction, discussions, and the modified Delphi process, the final POEMAT comprised 9 technical elements. G analysis showed low variance for endoscopist performance (.8%-24.9%) and high interrater variability (range, 63.2%-90.1%). The G score was moderately reliable (≥.60) for "submucosal tunneling" and "myotomy" and satisfactorily reliable (≥.70) for "active hemostasis" and "mucosal closure." We developed and established initial content and response process validity evidence for the POEMAT. Future steps include appraisal of the tool using a wider range of POEM videos to establish and improve the discriminative validity of this tool.

Numerical Investigation of a Solar-Heating System with Solar-Tower Receiver and Seasonal Storage in Northern China: Dynamic Performance Assessment and Operation Strategy Analysis

Solar-heating technology is a promising solution to help China achieve the “3060 double carbon” target as soon as possible. Seasonal thermal storage (STS) can effectively solve the mismatch problem of solar-heating systems between the supply and demand of thermal energy. Due to the instability of solar radiation resources and the heat demand, it is necessary to analyze the dynamic response characteristics and operation strategy optimization of the system in different operation stages. Yet, related studies are still scarce. The aim of this paper is to study the switching mechanism of the operation modes and the transitive relation of the system energy in different operation stages based on a pilot solar-heating system with STS in Huangdicheng, northern China. The impacts of different heating strategies on the system performance were also analyzed with a dynamic simulated method in TRNSYS. The results showed that the solar fraction of the system reached 89.4% in the third year, which was 3.6% higher than that in the first year. The quality–quantity heating operation strategies are effective ways to improve the discharge efficiency of the STS and the system performance without a heat pump. The electricity consumption of the pump on the heating side could be significantly reduced by 44.6% compared with the quality control. Ultimately, the findings in this paper are valuable for the optimization of the operation of solar-heating systems.

Weapon performance and contest assessment strategies of the cavitating snaps in snapping shrimp

Abstract Animals compete in contests over limited resources. Contestants forfeit once they ascertain that their opponent has greater resource‐holding potential (RHP; mutual assessment) or once they reach a threshold of costs (self‐assessment). Functional scaling studies of contest behaviour performance can inform how assessment signals, offensive capacity and endurance scale with RHP and thereby elucidate the mechanisms through which each of these assessment types operates. Here, we performed behavioural contest analyses to determine the assessment strategies used in snapping shrimp (Alpheus heterochaelis) contests. Then, we used biomechanical measurements of a common contest behaviour to inform how assessment might operate. We were specifically interested in the snapping behaviour during which snapping shrimp fire imploding cavitation bubbles—hereafter, ‘snaps’—at their opponents. We showed that A. heterochaelis use mutual assessment early in contests. Then, when they fire snaps, they switch to cumulative assessment—a type of self‐assessment where contestants endure costs from their own behaviours (e.g. energy) and their opponent's (e.g. injury). Because larger individuals tend to win contests, we then tested how the maximum performance and endurance of snaps scaled with size. We measured the average angular velocity of the snapping dactyl, cavitation bubble duration and pressure of snaps as metrics of performance. We measured 10 snaps per individual (n = 76 individuals). From this series of 10 snaps, we calculated the maximum of each metric as the maximum performance and the attrition of each metric over the course of 10 snaps as a measure of endurance. Maximum performance increased with size, but endurance did not. This suggests that cumulative assessment in snapping shrimp is driven by opponent‐imposed costs. Our results are not consistent with self‐assessment based on endurance; however, the experiment could not fully replicate the quick succession of snaps fired in real contests. Future experiments should better replicate the rapid firing of snaps to test if endurance matters in a more ecologically relevant context. Our framework of integrating biomechanics and behavioural ecology provides a pathway to identify precise mechanisms of contest assessment and animal behaviour more broadly. Read the free Plain Language Summary for this article on the Journal blog.

Performance assessment method of dynamic process based on SFA-GPR

A dynamic process performance assessment method based on slow feature analysis and Gaussian process regression (SFA-GPR) is proposed to capture more complete feature information from industrial process data with dynamic characteristics, thus improving the accuracy of the assessment model. First, different binary number performance labels are used to categorize data setsin terms of performance grades, and the process feature subspace is extracted using the slow feature analysis algorithm, followed by the establishment of the Gaussian process regression offline model between the feature subspace and the performance labels. During the online assessment, in conjunction with the proposed performance assessment strategy, performance grades and the current process’s operating status are determined based on the performance label of sample data obtained by the assessment model. Finally, the method is used to assess the online performance of the debutanizer and cracking furnace in the ethylene plant. The findings indicate that the method has a higher level of assessment accuracy.

An Approach to the Assessment of Detectability of Subsurface Targets in Polar Ice From Satellite Radar Sounders

A satellite mission onboard a radar sounder for the observation of the earth’s polar regions can greatly support the monitoring of the cryosphere and climate change analyses. Several studies are in progress proposing the design and demonstrating the performance of such an earth-orbiting radar sounder (EORS). However, one critical aspect of the cryospheric targets that are often ignored and simplified in these studies is the complex geoelectrical nature of the polar ice. In this article, we present a performance assessment of the polar ice target detectability by focusing on their realistic representation. This is obtained by simulating the orbital radargrams corresponding to different regions of the polar cryosphere by leveraging the data available from airborne campaigns in Antarctica and Greenland. We propose novel performance metrics to analyze the detectability of the internal reflecting horizons (IRHs), the basal interface, and to analyze the nature of the basal interface. This performance assessment strategy can be applied to guide the design of the signal-to-noise ratio (SNR) budget at the surface, which can further support the selection of the main orbital instrument parameters, such as the transmitted power, the two-way antenna gain, and the processing gains.

An operating performance assessment strategy with multiple modes based on least squares support vector machines for drilling process

Rate of penetration (ROP) is one of the crucial drilling condition monitoring parameters due to its vital role in real-time assessing drilling operating performance. Operators often adjust operating parameters to meet higher performance requirements. Therefore, drilling operating performance assessment is critical for controlling and optimizing of the drilling process. An operating performance assessment strategy with multiple modes based on least square support vector machines for drilling process is presented in this paper. First, the process capability index is to be taken as the indicator of the ROP and defines the drilling operating performance. Next, the K-means clustering algorithm is used to identify the operating modes. Then, for each mode, an individual drilling operating performance assessment model is established by the method of least squares support vector machines. Finally, drilling operating performance grade is obtained, and actual data of drill well are used for experiments. Further comparative analyses were performed with other state-of-the-art methods, including the Decision tree, Support vector machines (SVM), Least squares support vector machines (LS-SVM), Principal component analysis (PCA), and Partial least squares (PLS). Simulations revealed that the proposed method results in the accurate assessment of operating performance in the drilling process with the accuracy of 87%, the precision of 85.3%, the recall of 88.2%, and the F-Score of 87.6%. In particular, the assessment accuracy was improved by 18.6%, 11.3%, 5.2%, 9.68%, 8.32% in comparison to Decision Tree, SVM, LS-SVM, PCA, and PLS. Performance comparisons reflect the superiority of our model that can ensure high accuracy about operating performance in a drilling process.

Ensemble intelligent systems for predicting water network condition index

Aging infrastructure and funding problems continue to plague water supply networks worldwide. The failure risk of water mains is posing a considerable threat to drinking water security in urban centers, necessitating performance assessment strategies. The prediction of networks' condition index has seldom been studied, while a novel automated method can be developed to reasonably address this issue, thus promoting sustainability-based strategies. This paper presents an integrated framework for the assessment and quantification of the water main condition index. The Arequipa region in Peru consists of eight provinces; this region is chosen to exemplify the proposed framework due to the rapid pace of urbanization, making water infrastructure even more crucial. The novelty of this study includes innovation of research concept as it restructures the water network condition assessment by exploiting the hybridization technique of two potent intelligent systems; namely, the adaptive neuro-fuzzy inference system and the fuzzy inference system. These systems are employed sequentially to accomplish computational simulations and reasoning consolidations to generate automatically one condition index. Thus, making up for the lack of integrated research in water piping system. This research also gives prominence to enhancing water sustainability via incorporating the physical factors (pipe attributes) together with operational and environmental factors, represented by the impact of leaching and disinfection byproducts on people's health. The neuro-fuzzy processor is designed to predict each province's condition index via the grid partitioning and hybrid algorithm. The hybrid algorithm uses a combination of backpropagation and least-squares regression to optimize and tune the fuzzy parameters. The fuzzy consolidator indicated that the region's network condition index is 63.1, which reveals a medium condition for the Arequipa region water networks. The integrated framework is validated by conducting a comparative analysis with the multiple linear regression model. The results reveal better performance of the proposed framework since it demonstrates higher R2 of 0.9145. This study promotes multidimensional applications in urban sustainability, environmental sustainability, and urban water management systems.

A data driven performance assessment strategy for centralized chiller systems using data mining techniques and domain knowledge

Chillers are among the major energy consumers in building heating, ventilation and air conditioning systems and appropriate performance assessment of chiller systems is essential to ensuring their operational optimality while delivering satisfactory indoor thermal comfort. This paper presents a data driven performance assessment strategy for centralized chiller systems using multiple data mining and advanced visualization techniques. The energy consumption patterns of the chiller system were quantitatively and qualitatively analyzed by using the Conditional Inference Tree (CIT) and Agglomerative Hierarchical Clustering (AHC), and Association Rule Mining (ARM), respectively. A performance indicator of Coefficient of Performance (COP) Destruction (%) was introduced to represent the quality of the achieved COP. The performance of this strategy was evaluated using one-year operating data of a centralized chiller system installed in a commercial building. The results showed that the data mining techniques can be effectively used for performance assessment of chiller systems. The results from the quantitative and qualitative analysis showed that the chiller performance was strongly influenced by the temperature difference across the evaporator. The system studied generally showed good performance when the part load ratio was above 45% and the chiller power ratio was above 50%, and it showed relatively poor performance when the temperature difference across the evaporator was below 3.1 °C.

Investigating energy performance of large-scale seasonal storage in the district heating system of chifeng city: Measurements and model-based analysis of operation strategies

This paper presents a modeling and simulation method that supports energy performance assessment and operation strategy investigation of borehole thermal energy storage in the Chifeng district heating (DH) system. A living laboratory in Chifeng, China that integrates a 0.5 million m3 borehole thermal energy storage system, an on-site solar thermal plant and excess heat from a copper plant is presented. The research adopts Modelica models from open source libraries to evaluate the system. The validity of the borehole thermal energy storage model is evaluated through an inter-model comparison study and an empirical validation test. We used the validated model to investigate three operation strategies. We conclude that the time-scheduled combined operation strategy is more beneficial for the studied system regarding CO2 emission reduction.

NhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.

Lysine crotonylation (Kcr) is a newly discovered type of protein post-translational modification and has been reported to be involved in various pathophysiological processes. High-resolution mass spectrometry is the primary approach for identification of Kcr sites. However, experimental approaches for identifying Kcr sites are often time-consuming and expensive when compared with computational approaches. To date, several predictors for Kcr site prediction have been developed, most of which are capable of predicting crotonylation sites on either histones alone or mixed histone and nonhistone proteins together. These methods exhibit high diversity in their algorithms, encoding schemes, feature selection techniques and performance assessment strategies. However, none of them were designed for predicting Kcr sites on nonhistone proteins. Therefore, it is desirable to develop an effective predictor for identifying Kcr sites from the large amount of nonhistone sequence data. For this purpose, we first provide a comprehensive review on six methods for predicting crotonylation sites. Second, we develop a novel deep learning-based computational framework termed as CNNrgb for Kcr site prediction on nonhistone proteins by integrating different types of features. We benchmark its performance against multiple commonly used machine learning classifiers (including random forest, logitboost, naïve Bayes and logistic regression) by performing both 10-fold cross-validation and independent test. The results show that the proposed CNNrgb framework achieves the best performance with high computational efficiency on large datasets. Moreover, to facilitate users' efforts to investigate Kcr sites on human nonhistone proteins, we implement an online server called nhKcr and compare it with other existing tools to illustrate the utility and robustness of our method. The nhKcr web server and all the datasets utilized in this study are freely accessible at http://nhKcr.erc.monash.edu/.

Design Performance Assessment System and Strategy Based on Balanced Scorecard Concept

The concept of performance assessment system design and strategy with a Balanced Scorecard model is the concept of performance assessment in a contemporary because it combines the perspective of financial, customer, internal business process, and integrated learning and growth.