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In recent years, implementation of pay-for-performance (P4P) programmes in health care has become a worldwide initiative. However, most P4P programmes incorporate systems of Performance Indicators (PI) without accounting for trade-offs between indicators. This article has two objectives: (1) to develop a Data Envelopment Analysis (DEA) methodology for performance assessment of primary care providers; and (2) to propose an innovative methodology for P4P contracting based on the DEA assessment results. To achieve the first objective, we modify the standard DEA model to account for the necessary relations between the weights attributed to each PI and domain of performance and to account for the effect of the relevant environmental variables. To achieve the second objective, we combine relative and absolute performance assessments in the elaboration of several bases for reward, and then we link these assessments to a system of graduated rewards. A benchmarking programme is also proposed to contribute to the dissemination of best practices. This article contributes to the literature by proposing an enhanced methodology for performance assessment of primary care providers which can form the basis for P4P rewards planning. The applicability and advantages of the proposed methodology are illustrated with data from Portugal, but it can easily be adapted to different sets of PIs or domains, making it relevant for performance assessment and for P4P reward setting in other contexts and countries. info:eu-repo/semantics/publishedVersion

The performance assessment of strategic levels of an integrated management system (IMS) is critical to policy making phase of executive organizations. Because, the integrity of objectives and strategies in the overall system performance insures the integrity of activities and performance in the subsidiary departments and units. Unfortunately, this approach of assessment has been rarely focused. Here, the Fuzzy Cognitive Mapping-Relative Degree Analysis (FCM-RDA) technique was chosen to assess the performance trend of the health, safety, environment, and energy (HSEE)-IMS in a ministry during 2014-2019. There were 12 HSEE-IMS strategic sub-elements and 6 system sub-performances. The Gephi0.9.2 software was applied to simulate the FCM model. Accordingly, the sub-performance of policy and leadership was placed at the first priority. The development of HSEE management, and review (of policies and programs) were two HSEE-IMS sub-elements, categorized in the second priority. The results showed an ascending trend of the system performance during 2014-2018 and a descending trend during 2018-2019. It was found that the poor performance of HSEE-IMS sub-elements in the strategic and tactical levels resulted in the poor performance of system at the operational levels. There was found a strong relationship between the HSEE-IMS sub-elements and system sub-performances.

Interactive methods are useful decision-making tools for multiobjective optimization problems, because they allow a decision-maker to provide her/his preference information iteratively in a comfortable way at the same time as (s)he learns about all different aspects of the problem. A wide variety of interactive methods is nowadays available, and they differ from each other in both technical aspects and type of preference information employed. Therefore, assessing the performance of interactive methods can help users to choose the most appropriate one for a given problem. This is a challenging task, which has been tackled from different perspectives in the published literature. We present a bibliographic survey of papers where interactive multiobjective optimization methods have been assessed (either individually or compared to other methods). Besides other features, we collect information about the type of decision-maker involved (utility or value functions, artificial or human decision-maker), the type of preference information provided, and aspects of interactive methods that were somehow measured. Based on the survey and on our own experiences, we identify a series of desirable properties of interactive methods that we believe should be assessed.

Despite the widespread use of electronic devices and consumer products integrating flexible electronics, in industrial practice, the assembly process of film-based coverlays is still performed manually, and this, in turn, affects process accuracy in first place. Additionally, the related manual tasks can be troublesome for workers, causing stress and fatigue. As a result, such a process can represent a bottleneck in the fabrication of Flexible Printed Circuit Boards (FPCBs). In the present paper, the architecture of a gripper is proposed as the core device of a completely automated film-coverlay assembly process. The architecture of the gripper is vacuum-based and exploits the action of several independent valves, enabling the grip configuration according to the geometry of the component. The gripper operation is shown by implementing specific solutions related to the removal of the coverlay protective film and partial coverlay thermal bonding, thus providing also a detailed description of the phases concerning a feasible automated assembly process. The results of the tests concerning the evaluation of both solutions are reported and discussed in the paper. Finally, a simplified gripper version is prototyped to perform some of the tests, and it is also used to preliminarily assess the performance of the gripping strategy, with good results.

Performance assessments, in which human raters assess examinee performance in practical tasks, have attracted much attention in various assessment contexts involving measurement of higher-order abilities. However, difficulty persists in that ability measurement accuracy strongly depends on rater and task characteristics such as rater severity and task difficulty. To resolve this problem, various item response theory (IRT) models incorporating rater and task parameters, including many-facet Rasch models (MFRMs), have been proposed. When applying such IRT models to datasets comprising results of multiple performance tests administered to different examinees, test linking is needed to unify the scale for model parameters estimated from individual test results. In test linking, test administrators generally need to design multiple tests such that raters and tasks partially overlap. The accuracy of linking under this design is highly reliant on the numbers of common raters and tasks. However, the numbers of common raters and tasks required to ensure high accuracy in test linking remain unclear, making it difficult to determine appropriate test designs. We therefore empirically evaluate the accuracy of IRT-based performance-test linking under common rater and task designs. Concretely, we conduct evaluations through simulation experiments that examine linking accuracy based on a MFRM while changing numbers of common raters and tasks with various factors that possibly affect linking accuracy.

Performance assessment is a fundamental tool to successfully monitor and manage logistics and transport systems. In the field of Short Sea Shipping (SSS), the performance of the various maritime initiatives should be analyzed to assess the best way to achieve efficiency and guide related policies. This study proposes a quantitative methodology which can serve as a decision-support tool in the preliminary assessment and comparison of alternative SSS networks. The research is executed via a Mediterranean case study that compares a hypothetical Mediterranean ro-ro SSS network developed in the framework of a past Euro-Mediterranean cooperation project with the network of existing ro-ro liner services operating in the area. Performance benchmarking of the two networks is performed using a set of quantitative Key Performance Indicators (KPIs) and applying a factor-cluster analysis to produce homogeneous clusters of services based on the relevant variables while accounting for sample heterogeneity. Quantitative results mostly confirm the overall better performance of the prospective network and demonstrate that using KPIs and factor-cluster analysis to investigate the performance of maritime networks can provide policymakers with a preliminary wealth of knowledge that can help in setting targeted policy for SSS-oriented initiatives.

Made available in DSpace on 2021-06-25T16:33:28Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-09-29 Background subtraction is a prerequisite for a wide range of applications, including video surveillance systems. A significant number of algorithms are often developed and published in different publication mediums in the area, such as workshops, symposiums, conferences, and journals. An important task in presenting a new background subtraction algorithms is to clearly show that its performance outperforms the performance of the state-of-the-art algorithms. In this paper, we present recommendations on how to evaluate the performance of background subtraction algorithms for surveillance systems. We identified, through a systematic mapping, the key steps and components of this evaluation process - procedures, methods, and tools - most used by the authors in each of these steps. Considering this statistical analysis, we perform a theoretical analysis of the most used key components to identify their pros and cons. Then, we define a set of recommendations that aim to standardize and clarify the performance evaluation process of a new background subtraction algorithm. Univ Tecnol Fed Parana, Cornelio Procopio, Brazil Univ Estadual Paulista, Bauru, SP, Brazil Simon Fraser Univ, Burnaby, BC, Canada Univ Sao Paulo, Elect Engn, Sao Paulo, Brazil Univ Estadual Paulista, Bauru, SP, Brazil

There is a significant growth in the utilization of renewable energy in the built environment. Due to the intermittent nature of most renewable energy sources, energy mismatch problems between on-site generation and demand both in hourly and seasonal levels is unavoidable. In addition, energy-pricing policies are leading to less or no Photovoltaic (PV) feed-in-tariffs in the near future and/or even providing incentives to uphold self-consumption. Therefore, it is essential to enhance the building designs in a way to improve the utilization of on-site generated energy and to decrease the dependency on the nearby energy grid. In this study, performance optimization of various residential building designs is carried out considering future policy scenarios. The objective is to minimize the dependency on the nearby energy grid and to maximize the self-consumption. To achieve this, a performance-based design support approach is proposed. A simulation framework is presented to conduct the performance assessment. The developed design support approach is demonstrated using a Dutch house that needs to be renovated considering homeowner as the primary decision-maker. The proposed approach can be used by designers and consultants in the design phase of a building to aid decision-makers in identifying future-proof building designs with low grid dependency.

Performance assessments, in which raters assess examinee performance for given tasks, have a persistent difficulty in that ability measurement accuracy depends on rater characteristics. To address this problem, various item response theory (IRT) models that incorporate rater characteristic parameters have been proposed. Conventional models partially consider three typical rater characteristics: severity, consistency, and range restriction. Each are important to improve model fitting and ability measurement accuracy, especially when the diversity of raters increases. However, no models capable of simultaneously representing each have been proposed. One obstacle for developing such a complex model is the difficulty of parameter estimation. Maximum likelihood estimation, which is used in most conventional models, generally leads to unstable and inaccurate parameter estimations in complex models. Bayesian estimation is expected to provide more robust estimations. Although it incurs high computational costs, recent increases in computational capabilities and the development of efficient Markov chain Monte Carlo (MCMC) algorithms make its use feasible. We thus propose a new IRT model that can represent all three typical rater characteristics. The model is formulated as a generalization of the many-facet Rasch model. We also develop a Bayesian estimation method for the proposed model using No-U-Turn Hamiltonian Monte Carlo, a state-of-the-art MCMC algorithm. We demonstrate the effectiveness of the proposed method through simulation and actual data experiments.

Molecular profiling from liquid biopsy, in particular cell-free DNA (cfDNA), represents an attractive alternative to tissue biopsies for the detection of actionable targets and tumor monitoring. In addition to PCR-based assays, Next Generation Sequencing (NGS)-based cfDNA assays are now commercially available and are being increasingly adopted in clinical practice. However, the validity of these products as well as the clinical utility of cfDNA in the management of patients with cancer has yet to be proven. Within framework of the Innovative Medicines Initiative (IMI) program CANCER-ID we evaluated the use of commercially available reference materials designed for ctDNA testing and cfDNA derived from Diagnostic Leukaphereses (DLA) for inter- and intra-assay as well as intra- and inter-laboratory comparisons. In three experimental setups, a broad range of assays including ddPCR, MassARRAY and various NGS-based assays were tested. We demonstrate that both reference materials with predetermined VAFs and DLA samples are extremely useful for the performance assessment of mutation analysis platforms. Moreover, our data indicate a substantial variability of NGS assays with respect to sensitivity and specificity highlighting the importance of extensive validation of the test performance before offering these tests in clinical routine practice.

Water supply and sewer networks are critical infrastructures that provide a basic service to society. However, these systems constantly age and degrade over time. In addition, since network infrastructures are so extensive in length, they require a significant investment in maintenance tasks. Hence, within the context of infrastructure asset management (IAM), accurately defining the most efficient investment planning possible is essential to ensure their long-term sustainability. This paper presents an original five-step comprehensive framework to successfully implement an infrastructure asset management strategy and plan long-term investments. Moreover, this methodology integrates innovative and relevant operational and convenience factors that, while provide the problem both with realism and practicality, have not been addressed so far. To illustrate the usefulness and applicability of this methodology, the case study of a large water company in Spain is presented.

Cloud Computing has assumed a relevant role in the ICT, profoundly influencing the life-cycle of modern applications in the manner they are designed, developed, and deployed and operated. In this article, we tackle the problem of supporting the design-time analysis of Cloud applications to identify a cost-optimized strategy for allocating components onto Cloud Virtual Machine infrastructural services, taking performance requirements into account. We present an approach and a tool, SPACE4Cloud, that supports users in modeling the architecture of an application, in defining performance requirements as well as deployment constraints, and then in mapping each architecture component into a corresponding VM service, minimizing total costs. An optimization algorithm supports the mapping and determines the Cloud configuration that minimizes the execution costs of the application over a daily time horizon. The benefits of this approach are demonstrated in the context of an industrial case study. Furthermore, we show that SPACE4Cloud leads to a cost reduction up to 60 percent, when compared to a first-principle technique based on utilization thresholds, like the ones typically used in practice, and that our solution is able to solve large problem instances within a time frame compatible with a fast-paced design process (less than half an hour in the worst case). Finally, we show that SPACE4Cloud is suitable to model even microservice-based applications and to compute the corresponding optimized deployment configuration which is compared with a state-of-the art meta-heuristic alternative method, achieving savings between 21 and 85 percent.

Drone base stations can help safeguard coverage and provide capacity relief when cellular networks are under stress. Examples of such stress scenarios are events with massive crowds or network outages. In this paper we focus on a disaster scenario with emergence of a traffic hotspot, where agile drone positioning and load management is a critical issue. In order to address this challenge, we propose and assess a data-driven algorithm which leverages real-time measurements to dynamically optimize the 3D position of the drone as well as a cell selection bias tuned for optimized load management. We compare the performance with three benchmark scenarios: i) no drone; ii) a drone positioned above the failing site; and iii) a drone with a statically optimized position and cell selection bias. The results demonstrate that the proposed algorithm significantly improves the call success rate and achieves close to optimal performance.

A cyber-physical system (CPS) represents the coupling network between a communication network and a power grid. Information flow (IF) as the medium of communication networks has an important influence on the performance assessment of this type of coupling network. However, traditional IF models cannot be applied to CPS directly because they do not take CPS distinctive coupling effect and associated operational mechanism into account. In this paper, we propose an information flow model (IFM) based on graph theory and traffic dynamics considering the functional architecture and transmission mode in realistic CPS to bridge the gap mentioned above. In the IFM, congestion rate and loss rate are employed to assess the performance of communication networks serving power grids under different transmission strengths and modes. Moreover, an auxiliary model about CPS topology is constructed, which can generate more realistic CPS topology. Case studies on an IEEE 39-bus system reveal the transmission performance of CPS in detail. The IFM is exploited to detect critical nodes and compare the effects of node enhancement on IF transmission of networks. In addition, IFM is also applied to compare the transmission performances of six network topologies, including three basic complex networks and three community-based networks. The results demonstrate the effectiveness of the proposed model, and some suggestions are provided on performance improvement of IF transmission for CPS.

This paper presents the actual status and future developments of a technological demonstrator of key enabling technologies of Industry 4.0. The final scope of the project is the realization of a platform where different solutions addressing specific companies needs can be analyzed and their performance compared. Available Industry 4.0 technologies allows the realization of various architectures, even though the most appropriate solution has to be found by the company, accounting for different aspects, as economy, security, specific skills to be maintained or given in outsourcing. The first core of the platform, already working, consists in a Control Loop Performance Monitoring (CLPM) system which operates in cloud as a single module able to supervise routine data coming from different plants. This is an attractive solution for many companies allowing to avoid costs of local installations, linked to monitoring systems, human resources and additional effort for maintenance and upgrading. Some technical details about the application on a pilot-scale plant are given to illustrate the status of the activities. The scope of the project is to add new features to the system as: CLPM extended to plants located in different sites, equipment condition monitoring and environmental data analyses. To this aim, future developments of the platform are discussed in terms of improved technologies, different protocols and architectures.

Goal: Simulators that incorporate haptic feedback for clinical skills training are increasingly used in medical education. This study addresses the neglected aspect of rendering simulated feedback for vascular palpation skills training by systematically examining the effect of haptic factors on performance. Methods: A simulator-based approach to examine palpation skill is presented. Novice participants with and without minimal previous palpation training performed a palpation task on a simulator that rendered controlled vibratory feedback under various conditions. Results: Five objective metrics were employed to analyze participants’ performance that yielded key findings in quantifying palpation performance. Participants’ palpation accuracy was influenced by all three haptic factors, ranging from moderate to statistically significant. Duration , Total Path Length and Ratio of Correct Movement also demonstrated utility for quantifying performance. Conclusions: We demonstrate that our affordable simulator is capable of rendering controlled haptic feedback suitable for skills training. Further, metrics presented in this study can be used for structured palpation skills assessment and training, potentially improving healthcare delivery.

Over the recent years, an increasing amount of research effort has been directed toward the development and optimization of optical cell stimulation (OCS) techniques, while electrical cell stimulation (ECS) has been perfected. This article investigates the existing ECS and OCS methods from a communication perspective and, after summarizing the main breakthroughs in both fields, introduces a novel system model that treats the stimulation as a communication process. Additionally, we discuss the main particularities of ECS and OCS models and conduct a comparative analysis that quantifies the performance of the aforementioned methods based on suitable engineering metrics such as average latency, power consumption, transmission distance, as well as stimulation frequency, pulse width, and amplitude. Finally, future research directions emerge from the provided insightful discussion.

Big Data or Data-Intensive applications (DIAs) seek to mine, manipulate, extract or otherwise exploit the potential intelligence hidden behind Big Data. However, several practitioner surveys remark that DIAs potential is still untapped because of very difficult and costly design, quality assessment and continuous refinement. To address the above shortcoming, we propose the use of a UML domain-specific modeling language or profile specifically tailored to support the design, assessment and continuous deployment of DIAs. This article illustrates our DIA-specific profile and outlines its usage in the context of DIA performance engineering and deployment. For DIA performance engineering, we rely on the Apache Hadoop technology, while for DIA deployment, we leverage the TOSCA language. We conclude that the proposed profile offers a powerful language for data-intensive software and systems modeling, quality evaluation and automated deployment of DIAs on private or public clouds.

Background The complex task of Electric Powered Wheelchairs (EPW) prescription relies mainly on personal experience and subjective observations despite standardized processes and protocols. The most informative measurements come from joystick monitoring, but recording direct joystick outputs require to disassemble the joystick. We propose a new solution called “SenseJoy” that is easy to plug on a joystick and is suitable to characterize the driver behavior by estimating the joystick command. Methods SenseJoy is a pluggable system embedded on EPW built with a 3D accelerometer and a 2D gyrometer placed within the joystick and another 3D accelerometer located at the basis of the joystick. Data is sampled at 39 Hz and processed offline. First, SenseJoy sensitivity is assessed on wheelchair driving tasks performed by a group of 8 drivers (31 ± 8 years old, including one driver with left hemiplegia, one with cerebral palsy) in a lab environment. Direct joystick measurements are compared with SenseJoy estimations in different driving exercises. A second group of 5 drivers is recorded in the ecological context of a rehabilitation center (41 ± 10 years old, with two tetraplegic drivers, one tetraplegic driver with cognitive disorder, one driver post-stroke, one driver with right hemiplegia). The measurements from all groups of drivers are evaluated with an unsupervised statistical analysis, to estimate driving profile clusters. Results The SenseJoy is able to measure the EPW joystick inclination angles with a resolution of 1.31% and 1.23% in backward/forward and left/right directions respectively. A statistical validation ensures that the classical joystick-based indicators are equivalent when acquired with the SenseJoy or with a direct joystick output connection. Using an unsupervised methodology, based on a similarity matrix between subjects, it is possible to characterize the driver profile from real data. Conclusion SenseJoy is a pluggable system for assessing the joystick controls during EPW driving tasks. This system can be plugged on any EPW equipped with a joystick control interface. We demonstrate that it correctly estimates the performance indicators and it is able to characterize driving profile. The system is suitable and efficient to assist therapists in their recommendation, by providing objective measures with a fast installation process.

The photovoltaic (PV) systems should operate at a maximum power point (MPP) to extract the maximum possible output power with high tracking efficiency under various operating conditions This paper discusses a new maximum power point tracking (MPPT) technique to extract the peak power from the PV panel/array during partial shaded conditions (PSCs). The proposed algorithm is based on bio-inspired Whale Optimization (WO) with reinitialization process when the PV system is subjected to change in shading pattern, and the algorithm tries to locate the global peak (GP) with a high convergence rate and high tracking efficiency. The proposed algorithm eliminates the computational burden faced by the hybrid MPPT algorithms as discussed in various literature and reduces the power oscillation during the change in operating conditions. The proposed technique is modeled and simulated under different test conditions using MATLAB/Simulink software. The performance of the proposed technique is compared with conventional perturb and observation (PO), Grey Wolf Optimization (GWO) and hybrid GWO (HGWO) techniques in terms of tracking time and tracking efficiency and the simulation result proves that WO technique displays high tracking efficiency (>95%) and less convergence time (

The research is aimed at evaluating the content validity and performance of an assessment instrument, based on futsal games in extracurricular activities. This is very important because of its ability to review objectively and also the application as a material for coach evaluation. In addition, the method used was research evaluation, with documents as the subject, and Delphi technique was applied in data collection by 5 experts. Furthermore analysis involved the use of formula Content Validity Ratio (CVR), which was developed by Lawshe, and the results showed the values obtained to be above 0.05, with indicators of Decision Making being 1.00, Skill = 1.00, Support = 0. 60 and Re-position = 1.00. In conclusion, the performance assessment instrument based on futsal game in extracurricular activities was observed to have high value validity contents.

A suitability assessment and performance optimisation is presented of narrowband Internet of Things (NB-IoT) cellular technology for use in smart energy distribution networks. The focus is on the reliable and timely delivery of outage restoration and management (ORM) messages at the event of a local or regional power outage. Both the cellular NB-IoT and the energy distribution networks are modelled in a system-level simulator, which is used to carry out an extensive sensitivity analysis of the ORM service performance w.r.t. various radio network configurations in different environments. In particular, different packet schedulers are proposed and analysed, addressing device prioritization and subcarrier allocation as essential mechanisms in optimizing the service performance. Furthermore, we consider all three possible NB-IoT spectral deployment modes: in-band, guard-band and stand-alone deployment. Results show that, with a proposed near-optimal radio network configuration, the reliability of the ORM message delivery is close to 100% for the majority of power outage scenarios, while the observed 95th transfer delay percentile for the ORM messages is within the acceptable limit of 20 s. The study concludes that indeed NB-IoT is a suitable technology for supporting ORM services in smart energy distribution networks. © 2019, The Author(s).

Graphical abstract, In [Scully, C.G., and Daluwatte, C., Evaluating performance of early warning indices to predict physiological instabilities. J Biomed Inform. 75 (2017) 14–21], a framework was presented to characterize the performance of warning indices to provide information on the 1) probability a critical health event will occur when a warning is given (analogous to positive predictive value) and 2) proportion of warned events to all events (analogous to sensitivity). This framework also provides information about the timeliness of the warnings with respect to event occurrence and the warning burden of the system. • In the current work, we provide information on how this framework can be used when cases without events are present in a dataset to examine the proportion of warned non-events to all non-events (analogous to false positive rate). • Information on steps to apply the method, software, data and results for the case study are also provided to enable implementation of the framework. • Application and extension of the framework is demonstrated and discussed by adding non-event records to our previous case study comparing two warning strategies to predict physiologic instabilities.

In this study, the performance of a triple redundant nine-phase permanent magnet-assisted synchronous reluctance motor with delta-connected windings is comprehensively assessed and compared with that of wye-connected winding under healthy and fault conditions, including open circuit, inter-turn short-circuit and terminal short circuit. The steady-state torque behaviour, loss, efficiency and temperature distribution of the two winding configurations are analysed and compared. It is shown that the delta-connected winding has higher output torque under one phase open-circuit fault and lower inter-turn short-circuit current with three-phase terminal short circuit.

Protein post-translational modification (PTM) is an important regulatory mechanism that plays a key role in both normal and disease states. Acetylation on lysine residues is one of the most potent PTMs owing to its critical role in cellular metabolism and regulatory processes. Identifying protein lysine acetylation (Kace) sites is a challenging task in bioinformatics. To date, several machine learning-based methods for the in silico identification of Kace sites have been developed. Of those, a few are prokaryotic species-specific. Despite their attractive advantages and performances, these methods have certain limitations. Therefore, this study proposes a novel predictor STALLION (STacking-based Predictor for ProkAryotic Lysine AcetyLatION), containing six prokaryotic species-specific models to identify Kace sites accurately. To extract crucial patterns around Kace sites, we employed 11 different encodings representing three different characteristics. Subsequently, a systematic and rigorous feature selection approach was employed to identify the optimal feature set independently for five tree-based ensemble algorithms and built their respective baseline model for each species. Finally, the predicted values from baseline models were utilized and trained with an appropriate classifier using the stacking strategy to develop STALLION. Comparative benchmarking experiments showed that STALLION significantly outperformed existing predictor on independent tests. To expedite direct accessibility to the STALLION models, a user-friendly online predictor was implemented, which is available at: http://thegleelab.org/STALLION.

Wireless sensor networks are an emerging technology that is used to monitor points or objects of interest in an area. Despite its many applications, this kind of network is often limited by the fact that it is difficult to provide energy to the nodes continuously, forcing the use of batteries, which restricts its operations. Network density may also lead to other problems. Sparse networks require stronger transmissions and have little redundancy while dense networks increase the chances of overhearing and interference. To address these problems, many novel medium access control (MAC) protocols have been developed through the years. The objective of this study is to assess the effectiveness of the T-MAC, B-MAC, and RI-MAC protocols in a variable density network used to collect data inside freight trucks carrying fruits that perish quickly. This article is part of the PrunusPós project, which aims to increase the efficiency of peach and cherry farming in Portugal. The comparison was done using the OMNET++ simulation framework. Our analysis covers the behavior and energetic properties of these protocols as the density of the network increases and shows that RI-MAC is more adaptable and consumes less energy than the alternatives., This study is within the activities of project “PrunusPós - Optimization of processes for the storage, cold conservation, active and/or intelligent packaging and food quality traceability in post-harvested fruit products”, project n. º PDR2020-101-031695, Partnership n.º 87, initiative n.º 175, promoted by PDR 2020 and co-funded by FEADER within Portugal 2020. P.D.G. thanks the support of Fundação para a Ciência e Tecnologia (FCT) and C-MAST - Centre for Mechanical and Aerospace Science and Technologies, under project UIDB/00151/2020. V. N. G. J. S. and J. M. L. P. C. acknowledge that this work is funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/50008/2020. The authors would also like to acknowledge the company InspiringSci, Lda for its interest and valuable contribution to the successful development of this work.

Correct monitoring and communication of performance throughout the system design life-cycle is of paramount importance in complex safety and security projects. To this end a combination of a NATO Architectural Framework Based Dashboard and an advanced performance assessment model has been proposed. This paper presents the model proposed within the framework of a persistent maritime surveillance project. Specifically, the modelling approach inherited from multi-criteria decision making makes use of knowledge acquisition methods to elicit quantitative weights assigned to Key Performance Indicators. The analysis highlights strengths and weaknesses of two alternative elicitation approaches to be further exploited to improve the overall performance assessment.

Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various prediction algorithms and tools have been developed for predicting antiviral peptides (AVPs). The current COVID-19 pandemic has underscored the need to develop more efficient and accurate machine learning (ML)-based prediction algorithms for the rapid identification of therapeutic peptides against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Several peptide-based ML approaches, including anti-coronavirus peptides (ACVPs), IL-6 inducing epitopes and other epitopes targeting SARS-CoV-2, have been implemented in COVID-19 therapeutics. Owing to the growing interest in the COVID-19 field, it is crucial to systematically compare the existing ML algorithms based on their performances. Accordingly, we comprehensively evaluated the state-of-the-art IL-6 and AVP predictors against coronaviruses in terms of core algorithms, feature encoding schemes, performance evaluation metrics and software usability. A comprehensive performance assessment was then conducted to evaluate the robustness and scalability of the existing predictors using well-constructed independent validation datasets. Additionally, we discussed the advantages and disadvantages of the existing methods, providing useful insights into the development of novel computational tools for characterizing and identifying epitopes or ACVPs. The insights gained from this review are anticipated to provide critical guidance to the scientific community in the rapid design and development of accurate and efficient next-generation in silico tools against SARS-CoV-2.

Blockchain technology plays a pivotal role in the undergoing fourth industrial revolution or Industry 4.0. It is considered a tremendous boost to company digitalization, thus, considerable investments in blockchain are being made. However, there is no single blockchain technology, but various solutions exist, and they cannot interoperate with one each other. The ecosystem envisioned by the Industry 4.0 does not have centralized management or leading organization, so a single blockchain solution cannot be imposed. The various organizations hold their own blockchains, which must interoperate seamlessly. Despite some solutions for blockchain interoperability being proposed, the problem is still open. This paper aims to devise a secure solution for blockchain interoperability. The proposed approach consists of a relay scheme based on Trusted Execution Environment to provide higher security guarantees than the current literature. In particular, the proposed solution adopts an off-chain secure computation element invoked by a smart contract on a blockchain to securely communicate with its peered counterpart. A prototype has been implemented and used for the performance assessment, e.g., to measure the latency increase due to cross-blockchain interactions. The achieved and reported experimental results show that the proposed security solution introduces an additional latency that is entirely tolerable for transactions. At the same time, the usage of the Trusted Execution Environment imposes a negligible overhead.

Purpose Decision-making and dexterity, features that become increasingly relevant in (robot-assisted) minimally invasive surgery, are considered key components in improving the surgical accuracy. Recently, DROP-IN gamma probes were introduced to facilitate radioguided robotic surgery. We now studied if robotic DROP-IN radioguidance can be further improved using tethered Click-On designs that integrate gamma detection onto the robotic instruments themselves. Methods Using computer-assisted drawing software, 3D printing and precision machining, we created a Click-On probe containing two press-fit connections and an additional grasping moiety for a ProGrasp instrument combined with fiducials that could be video tracked using the Firefly laparoscope. Using a dexterity phantom, the duration of the specific tasks and the path traveled could be compared between use of the Click-On or DROP-IN probe. To study the impact on surgical decision-making, we performed a blinded study, in porcine models, wherein surgeons had to identify a hidden 57Co-source using either palpation or Click-On radioguidance. Results When assembled onto a ProGrasp instrument, while preserving grasping function and rotational freedom, the fully functional prototype could be inserted through a 12-mm trocar. In dexterity assessments, the Click-On provided a 40% reduction in movements compared to the DROP-IN, which converted into a reduction in time, path length, and increase in straightness index. Radioguidance also improved decision-making; task-completion rate increased by 60%, procedural time was reduced, and movements became more focused. Conclusion The Click-On gamma probe provides a step toward full integration of radioguidance in minimal invasive surgery. The value of this concept was underlined by its impact on surgical dexterity and decision-making.

The use of technologies still under development (Silicon PhotoMultipliers and Time-to-Digital Converter) for the upgrade of our time-resolved optical mammograph highlighted the crucial need for a thorough and progressive instrument characterization (from extensive laboratory tests on phantoms to in vivo measurements), potentially of general interest. Despite satisfying results on phantoms, in vivo tests urged an instrumental change, with the adoption of a just released high-throughput TDC to obtain high-quality breast scans (much wider scan area, better day-by-day reproducibility, improved signal quality, simpler set-up). A clinical study on monitoring of neoadjuvant chemotherapy is now ongoing at the San Raffaele Hospital, Milan.

Most plant diseases show visible symptoms, and the technique which is accepted today is that an experienced plant pathologist diagnoses the disease through optical observation of infected plant leaves. The fact that the disease diagnosis process is slow to perform manually and another fact that the success of the diagnosis is proportional to the pathologist's capabilities makes this problem an excellent application area for computer-aided diagnostic systems. Instead of classical machine learning methods, in which manual feature extraction should be flawless to achieve successful results, there is a need for a model that does not need pre-processing and can perform a successful classification. In this study, EfficientNet deep learning architecture was proposed in plant leaf disease classification and the performance of this model was compared with other state-of-the-art deep learning models. The PlantVillage dataset was used to train models. All the models were trained with original and augmented datasets having 55,448 and 61,486 images, respectively. EfficientNet architecture and other deep learning models were trained using transfer learning approach. In the transfer learning, all layers of the models were set to be trainable. The results obtained in the test dataset showed that B5 and B4 models of EfficientNet architecture achieved the highest values compared to other deep learning models in original and augmented datasets with 99.91% and 99.97% respectively for accuracy and 98.42% and 99.39% respectively for precision.

Performance assessment and standardization are indispensable for instruments of clinical relevance in general and clinical instrumentation based on photon migration/diffuse optics in particular. In this direction, a multi-laboratory exercise was initiated with the aim of assessing and comparing their performances. 29 diffuse optical instruments belonging to 11 partner institutions of a European level Marie Curie Consortium BitMap1 were considered for this exercise. The enrolled instruments covered different approaches (continuous wave, CW; frequency domain, FD; time domain, TD and spatial frequency domain imaging, SFDI) and applications (e.g. mammography, oximetry, functional imaging, tissue spectroscopy). 10 different tests from 3 well-accepted protocols, namely, the MEDPHOT2 , the BIP3 , and the nEUROPt4 protocols were chosen for the exercise and the necessary phantoms kits were circulated across labs and institutions enrolled in the study. A brief outline of the methodology of the exercise is presented here. Mainly, the design of some of the synthetic descriptors, (single numeric values used to summarize the result of a test and facilitate comparison between instruments) for some of the tests will be discussed.. Future actions of the exercise aim at deploying these measurements onto an open data repository and investigating common analysis tools for the whole dataset.

In urban areas, the Global Navigation Satellite System (GNSS) can lead to position errors of tens of meters due to signal obstruction and severe multipath effects. In cases of 3D-positioning, the vertical coordinate is estimated less accurately than are the horizontal coordinates. Multisensor systems can enhance navigation performance in terms of accuracy, availability, continuity and integrity. However, the addition of multiple sensors increases the system cost, and thereby the applicability to low-cost applications is limited. By using the concept of receiver clock modelling (RCM), the position estimation can be made more robust; the use of high-sensitivity (HS) GNSS receivers can improve the system availability and continuity. This paper investigates the integration of a low-cost HS GNSS receiver with an external clock in urban conditions; subsequently, the gain in the navigation performance is evaluated. GNSS kinematic data is recorded in an urban environment with multiple geodetic-grade and HS receivers. The external clock stability information is incorporated through the process noise matrix in a Kalman filter when estimating the position, velocity and time states. Results shows that the improvement in the precision of the height component and vertical velocity with both receivers is about 70% with RCM compared with the estimates obtained without applying RCM. Pertaining accuracy, the improvement in height with RCM is found to be about 70% and 50% with geodetic and HS receivers, respectively. In terms of availability, the HS receiver delivers an 100% output compared with a geodetic receiver, which provides an output 99⋅4% of the total experiment duration. Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation..

Large-scale assessments in education (2021) 9, 25 S., International large-scale assessments such as PISA or PIAAC have started to provide public or scientific use files for log data; that is, events, event-related attributes and timestamps of test-takers' interactions with the assessment system. Log data and the process indicators derived from it can be used for many purposes. However, the intended uses and interpretations of process indicators require validation, which here means a theoretical and/or empirical justification that inferences about (latent) attributes of the test-taker's work process are valid. This article reviews and synthesizes measurement concepts from various areas, including the standard assessment paradigm, the continuous assessment approach, the evidence-centered design (ECD) framework, and test validation. Based on this synthesis, we address the questions of how to ensure the valid interpretation of process indicators by means of an evidence-centered design of the task situation, and how to empirically challenge the intended interpretation of process indicators by developing and implementing correlational and/or experimental validation strategies. For this purpose, we explicate the process of reasoning from log data to low-level features and process indicators as the outcome of evidence identification. In this process, contextualizing information from log data is essential in order to reduce interpretative ambiguities regarding the derived process indicators. Finally, we show that empirical validation strategies can be adapted from classical approaches investigating the nomothetic span and construct representation. Two worked examples illustrate possible validation strategies for the design phase of measurements and their empirical evaluation. (DIPF/Orig.)

One of the most challenging applications targeted by evolving (beyond-)5G technology is virtual reality (VR). Particularly, 'Social VR' applications provide a fully immersive experience and sense of togetherness to users residing at different locations. To support such applications the network must deal with huge traffic demands, while keeping end-to-end latencies low. Moreover, the radio access network must deal with the volatility and vulnerability of mmWave radio channels, where even small movements of the users may have substantial effects on the Quality of Experience. We present an integral modelling framework for feasibility assessment and performance optimization of the radio access network for Social VR applications in indoor office scenarios. Using the presented modelling approach, we conduct an extensive simulation-based assessment to determine the performance impact of head motion, the frequency band (3.5 GHz, 26 GHz) and radio network configurations, and derive the required carrier bandwidth for a range of 'Social VR' scenarios. Insights into these issues are a prerequisite for setting up guidelines for network deployment and configuration as well as for the development of (AI/ML-based) methods for dynamic resource management to optimally support Social VR applications.

We introduce rQUIC, an integration of the QUIC protocol and a coding module. rQUIC has been designed to feature different coding/decoding schemes and is implemented in go language. We conducted an extensive measurement campaign to provide a thorough characterization of the proposed solution. We compared the performance of rQUIC with that of the original QUIC protocol for different underlying network conditions as well as different traffic patterns. Our results show that rQUIC not only yields a relevant performance gain (shorter delays), especially when network conditions worsen, but also ensures a more predictable behavior. For bulk transfer (long flows), the delay reduction almost reached 70% when the frame error rate was 5%, while under similar conditions, the gain for short flows (web navigation) was approximately 55%. In the case of video streaming, the QoE gain (p1203 metric) was, approximately, 50%. This work was supported in part by the Basque Government through the Elkartek Program under the Hodei-x Project under Agreement KK-2021/00049; in part by the Spanish Government through the Ministerio de Economía y Competitividad, Fondo Europeo de Desarrollo Regional (FEDER) through the Future Internet Enabled Resilient smart CitiEs (FIERCE) under Grant RTI2018-093475-AI00; and in part by the Industrial Doctorates Program of the University of Cantabria under Grant Call 2019.

The use of drone base stations offers an agile mechanism to safeguard coverage and provide capacity relief when cellular networks are under stress. Such stress conditions can occur for example in case of special events with massive crowds or network outages. In this paper we focus on a disaster scenario with emergence of a hotspot, and analyze the impact of the drone position (altitude, horizontal position) and selection bias on the network performance. We determine the optimal settings of these control parameters as a function of the hotspot location, and demonstrate that the optimized values can drastically reduce the fraction of failed calls.

We present a simulation-based assessment of the performance potential of distributed MIMO (D-MIMO), multi-user MIMO (MU-MIMO) and particularly the combined D/MU-MIMO operation, for which we extend previously published scheduling and beamforming principles. The assessment study reveals that, when optimizing average user throughput performance, D-MIMO, while fruitless when used in isolation, is very effective when intelligently combined with MU-MIMO. Alternatively, when optimizing the cell edge performance, MU-MIMO, while also shown to be ineffective when used in isolation, is in fact very valuable when accompanied by a suitable configuration of D-MIMO. As an illustrative example, when using a jointly optimised configuration of D/MU-MIMO in a highly loaded urban deployment scenario, a 121% cell edge performance gain can be attained over a scenario using only D-MIMO, and even a demonstrated 153% gain over a scenario where only the MU-MIMO feature is available.

Declining costs for high-performance batteries are leading to a global increased use of storage systems in residential buildings. Especially in conjunction with reduced photovoltaic (PV) feed-in tariffs, a large market has been developed for PV battery systems to increase self-sufficiency. They differ in the type of coupling between PV and battery, the nominal capacities of their components, and their degree of integration. High system performance is particularly important to achieve profitability for the operator. This paper presents and evaluates methods for a uniform determination of PV battery system performance. Already the requirement analysis reveals that a performance comparison of PV battery systems must cover the efficiency and effectiveness during system operation. A method based on a derivation of key performance indicators (KPIs) for these two criteria through an application test is proposed. It is evaluated by comparison to other methods, such as the System Performance Index (SPI) and aggregation of conversion and storage efficiency. These methods are applied with five systems in a laboratory test bench to identify their advantages and drawbacks. Here, a particular focus is on compliance with the initially formulated requirements in terms of both test procedures and KPI derivations. Analysis revealed that the proposed method addresses these requirements well, and is beneficial in terms of result comprehensibility and KPI validity.

This article presents a new, holistic model for the air traffic management system, built during the Vista project. The model is an agent-driven simulator, featuring various stakeholders such as the Network Manager and airlines. It is a microscopic model based on individual passenger itineraries in Europe during one day of operations. The article focuses on the technical description of the model, including data and calibration issues, and presents selected key results for 2035 and 2050. In particular, we show clear trends regarding emissions, delay reduction, uncertainty, and increasing airline schedule buffers.

Enhancing students’ critical thinking (CT) skills is an essential goal of higher education. This article presents a systematic approach to conceptualizing and measuring CT. CT generally comprises the following mental processes: identifying, evaluating, and analyzing a problem; interpreting information; synthesizing evidence; and reporting a conclusion. We further posit that CT also involves dealing with dilemmas involving ambiguity or conflicts among principles and contradictory information. We argue that performance assessment provides the most realistic—and most credible—approach to measuring CT. From this conceptualization and construct definition, we describe one possible framework for building performance assessments of CT with particular attention to extended performance tasks within the assessment system. The framework is a product of an ongoing, collaborative effort, the International Performance Assessment of Learning (iPAL). The framework comprises four main ingredients: (1) The storyline describes a carefully curated version of a complex, real-world situation. (2) A portfolio of documents in a range of formats, drawn from multiple sources chosen to have specific characteristics. (3) The challenge frames the task to be accomplished. (4) The scoring rubric which comprises a set of scales each linked to a facet of the construct. We discuss a number of use cases, as well as the challenges that arise with the use and valid interpretation of performance assessments. The final section presents elements of the iPAL research program that involve various refinements and extensions of the assessment framework, a number of empirical studies, along with linkages to current work in online reading and information processing.

Construction projects are struggling to reach satisfactory performance in a context full of various and many risks. Despite the long-lasting study of performance in construction no universal approaches have emerged from numerous proposals. A mainstream in dealing with this issue is benchmarking methods with the use of appropriate indicators. This research work adopts such a group of indicators that address more aspects than the usual cost, schedule, and quality considerations. Following suggestions for proper adjustments and clarifications for the appropriate use of this group of indicators, this research proceeds by introducing the risk aspect to the calculation of project performance. Identified risks are modeled through fuzzy numbers to deal with data incompleteness and then they are appropriately added to compose risk coefficients for the performance dimensions expressed through the respective indicators. Then, the overall project&rsquo, s performance is calculated addressing a risk-informed performance assessment of construction projects. The whole process is tested for verification with a theoretical example. The development of a simple yet inclusive and adjustable methodological framework for risk-informed performance assessment is expected to provide practitioners with a relatively easy approach for integrating both the risk and performance management in construction.

The measurement of power performance is an important procedure in the design verification and ongoing health monitoring of a tidal turbine. Standardised methods state that the performance should be measured relative to two independently located flow sensors, the arrangement of which is often non-trivial and necessitates additional cost. Recent interest in the usage of flow sensors mounted on the turbine has demonstrated their capabilities in profiling the rotor approach flow, but this instrument configuration is not recognised in the performance assessment standard. This study evaluates the merits of the turbine mounted configuration by measuring the performance of a tidal turbine relative to this reference and to a conventional seabed placed instrument. The turbine mounted sensor is found to provide a better reference of the free-stream conditions, evident from an improved agreement with theoretical predictions of device performance and a reduced amount of variation in the results. This new method could reduce both the costs and uncertainty associated with existing performance assessment best practices.

Background Performing physiotherapy exercises in front of a physiotherapist yields qualitative assessment notes and immediate feedback. However, practicing the exercises at home lacks feedback on how well patients are performing the prescribed tasks. The absence of proper feedback might result in patients performing the exercises incorrectly, which could worsen their condition. We present an approach to generate performance scores to enable tracking the progress by both the patient at home and the physiotherapist in the clinic. Objective This study aims to propose the use of 2 machine learning algorithms, dynamic time warping (DTW) and hidden Markov model (HMM), to quantitatively assess the patient’s performance with respect to a reference. Methods Movement data were recorded using a motion sensor (Kinect V2), capable of detecting 25 joints in the human skeleton model, and were compared with those of a reference. A total of 16 participants were recruited to perform 4 different exercises: shoulder abduction, hip abduction, lunge, and sit-to-stand exercises. Their performance was compared with that of a physiotherapist as a reference. Results Both algorithms showed a similar trend in assessing participant performance. However, their sensitivity levels were different. Although DTW was more sensitive to small changes, HMM captured a general view of the performance, being less sensitive to the details. Conclusions The chosen algorithms demonstrated their capacity to objectively assess the performance of physical therapy. HMM may be more suitable in the early stages of a physiotherapy program to capture and report general performance, whereas DTW could be used later to focus on the details. The scores enable the patient to monitor their daily performance. They can also be reported back to the physiotherapist to track and assess patient progress, provide feedback, and adjust the exercise program if needed.

Nowadays, the evaluation of physiological characteristics and training load quantification in road cycling is frequently performed through power meter data analyses, but the scientific evidence behind this tool is scarce and often contradictory. The aim of this paper is to review the literature related to power profiling, functional threshold testing, and performance assessment based on power meter data. A literature search was conducted following preferred reporting items for review statement (PRISMA) on the topic of {“cyclist” OR “cycling” AND “functional threshold” OR “power meter”}. The reviewed evidence provided important insights regarding power meter-based training: a) functional threshold testing is closely related to laboratory markers of steady state; b) the 20-min protocol represents the most researched option for functional threshold testing, although shorter durations may be used if verified on an individual basis; c) power profiling obtained through the recovery of recorded power outputs allows the categorization and assessment of the cyclist’s fitness level; and d) power meters represent an alternative to laboratory tests for the assessment of the relationship between power output and cadence. This review elucidates the increasing amount of studies related to power profiling, functional threshold testing, and performance assessment based on power meter data, highlighting the opportunity for the expanding knowledge that power meters have brought in the road cycling field.

This paper presents an approach for creating online assessment power curves by calculating the variations between the baseline and actual power curves. The actual power curve is divided into two regions based on the operation rules of a wind turbine, and the regions are individually assessed. The raw data are filtered using the control command, and outliers are detected using the density-based spatial clustering of applications with noise clustering method. The probabilistic area metric is applied to quantify the variations of the two power curves in the two regions. Based on this result, the variation in the power curves can be calculated, and the results can be used to dynamically evaluate the power performance of a wind turbine. The proposed method is verified against the derivation of secondary principal component method and traditional statistical methods. The potential applications of the proposed method in wind turbine maintenance activities are discussed.

Comparative Judgement (CJ) aims to improve the quality of performance-based assessments by letting multiple assessors judge pairs of performances. CJ is generally associated with high levels of reliability, but there is also a large variation in reliability between assessments. This study investigates which assessment characteristics influence the level of reliability. A meta-analysis was performed on the results of 49 CJ assessments. Results show that there was an effect of the number of comparisons on the level of reliability. In addition, the probability of reaching an asymptote in the reliability, i.e., the point where large effort is needed to only slightly increase the reliability, was larger for experts and peers than for novices. For reliability levels of .70 between 10 and 14 comparisons per performance are needed. This rises to 26 to 37 comparisons for a reliability of .90.

To deal with the inconsistency of the minimum variance (MV) benchmark in evaluating non-Gaussian disturbance systems, this paper proposed a new benchmark, which combined entropy with output mean value. For a cascade control system, the new benchmark was constructed by analyzing the weakness of the MV benchmark and the pure Renyi entropy benchmark. In order to estimate the more accurate performance of the unknown system, an improved estimation of distribution algorithm based on entropy criterion is given. It can identify the disturbance distribution and calculate the new index evaluation value. Finally, different disturbance distributions were used to verify the consistency of the new index. The experimental results show that the proposed index and algorithms are consistent and effective in evaluating the performance of the unknown systems with non-Gaussian disturbance.

Neuromorphic computing systems are an emerging field that takes its inspiration from the biological neural architectures and computations inside the mammalian nervous system. The spiking neural networks (SNNs) mimic real biological neural networks by conveying information through the communication of short pulses between neurons. Since each neuron in these networks is connected to thousands of others, high bandwidth is required. Moreover, since the spike times are used to encode information in SNN, very low communication latency is also necessary. On the other hand, the combination of Two-dimensional Networks-on-Chip (2D-NoC) and Three-dimensional Integrated Circuits (3D-ICs) can provide a scalable interconnection fabric in large-scale parallel SNN systems. Although the SNNs have some intrinsic fault-tolerance properties, they are still susceptible to a significant amount of faults; especially, when we talk about integrating the large-scale SNN models in hardware. Consequently, the need for efficient solutions capable of avoiding any malfunctions or inaccuracies, as well as early fault-tolerance assessment, is becoming increasingly necessary for the design of future large-scale reliable neuromorphic systems. This paper first presents an analytical model to assess the effect of faulty connections on the performance of a 3D-NoC-based spiking neural network under different neural network topologies. Second, we present a fault-tolerant shortest-path k-means-based multicast routing algorithm (FTSP-KMCR) and architecture for spike routing in 3D-NoC of spiking neurons (3DFT-SNN). Evaluation results show that the proposed SP-KMCR algorithm reduces the average latency by 12.2% when compared to the previously proposed algorithm. In addition, the proposed fault-tolerant methodology enables the system to sustain correct traffic communication with a fault rate up to 20%, while only suffering 16.23% longer latency and 5.49% extra area cost when compared to the baseline architecture.