Dynamic Evaluation Framework Research Articles

Assessment of green development level performance in G20 countries: A dynamic evaluation framework

To achieve sustainable development goals and foster future prosperity globally, the promotion of green development is essential. Nevertheless, persistent regional disparities in green development among the G20 nations stem from differing economic and environmental conditions in dynamic contexts. In light of these conditions, it is essential for governments to evaluate their country's current green development performance in comparison with higher-performing nations and to learn from their experiences. This study introduces a new dynamic evaluation framework that integrates the importance of evaluation indicators using the dynamic entropy weighting method, a Dynamic Technique for Order Preference by Similarity to Ideal Solution method, and principal component analysis. This framework serves as a tool for G20 countries to assess their green development levels between 2015 and 2021. A key contribution of this study is the facilitation of the G20's ability to implement relevant reform measures and monitor green development progress in a dynamic environment. This framework can serve as a model for similar evaluations in other regions globally. The study's main findings indicate that G20 countries with high levels of green development possess strong economic foundations and prioritise the balanced development of their economies, societies, environments, and innovation.

A Universal Fairness Evaluation Framework for Resource Allocation in Cloud Computing

Abstract: Machine learning is nowadays ubiquitous, providing mechanisms for supporting decision making that leverages big data analytics. However, this recent rise in importance of machine learning also raises societal concerns about the dependability and trustworthiness of systems which depend on such automated predictions. In cloud computing, fairness is one of the most significant indicators to evaluate resource allocation algorithms, which reveals whether each user is allocated as much as that of all other users having the same bottleneck. However, how fair an allocation algorithm is remains an urgent issue. In this paper, we propose Dynamic Evaluation Framework for Fairness (DEFF), a framework to evaluate the fairness of an resource allocation algorithm. In our framework, two sub-models, Dynamic Demand Model (DDM) and Dynamic Node Model (DNM), are proposed to describe the dynamic characteristics of resource demand and the computing node number under cloud computing environment. Combining Fairness on Dominant Shares and the two sub-models above, we finally obtain DEFF. In our experiment, we adopt several typical resource allocation algorithms to prove the effectiveness on fairness evaluation by using the DEFF framework.

Evaluation of education and training impacts for the unemployed: Challenges of new data

This study utilises an exceptionally rich English administrative dataset, to estimate employment impacts from training voluntarily initiated by unemployed individuals. A Coarsened Exact Matching approach is adopted, in a dynamic evaluation framework, to estimate impacts up to 5 years from training start. We identify economically and statistically significant impacts, estimated separately for (i) all training starters, (ii) the partially, and (iii) fully treated. Investigation of possible endogenous selection into partial/full treatment, using distance to training provider as an instrumental variable, suggests inclusion of extensive employment and learning histories in a matching framework, justifies invocation of the conditional independence assumption for comparisons of full/partial treatment. The partially treated secure a return that is, on average, 2 percentage points lower than full treatment. Thus, an `intention to treat' approach would not alter conclusions on the efficacy of training; but using the partially treated to estimate counterfactual outcomes risks understating returns.

Evaluation of resources and environment carrying capacity from the perspective of resource cross-regional mobility: A dynamic evaluation framework and practice

Evaluation of resources and environment carrying capacity from the perspective of resource cross-regional mobility: A dynamic evaluation framework and practice

Transmission Reliability Evaluation for Wireless Sensor Networks

Network reliability of Wireless Sensor Networks (WSNs) is difficult to be evaluated because of its complexity, multistates, and dynamic characteristics. To satisfy the user's need of reliability evaluation for network transmission we propose some evaluation models and a dynamical evaluation framework. The evaluation models are mission-oriented and based on transmission paths (uplink and downlink). The dynamic evaluation framework can be on-demand customized and it will be autoupdated once the communication environment changes or the nodes fail. Finally, we simulated clustered and mesh WSNs with NS-2. Simulation result shows that the proposed evaluation framework was effective and accurate.

A universal fairness evaluation framework for resource allocation in cloud computing

In cloud computing, fairness is one of the most significant indicators to evaluate resource allocation algorithms, which reveals whether each user is allocated as much as that of all other users having the same bottleneck. However, how fair an allocation algorithm is remains an urgent issue. In this paper, we propose Dynamic Evaluation Framework for Fairness (DEFF), a framework to evaluate the fairness of an resource allocation algorithm. In our framework, two sub-models, Dynamic Demand Model (DDM) and Dynamic Node Model (DNM), are proposed to describe the dynamic characteristics of resource demand and the computing node number under cloud computing environment. Combining Fairness on Dominant Shares and the two sub-models above, we finally obtain DEFF. In our experiment, we adopt several typical resource allocation algorithms to prove the effectiveness on fairness evaluation by using the DEFF framework.

Advisory Professorship Model as a Tool for Practice-Based Regional University–Industry Cooperation

The growing importance of “practice-based innovation models” has challenged the current consensus on the role of universities as main drivers of regional innovation systems. New models are needed to ensure the efficiency of co-operation between the region and universities. In this article, we present and analyse the effects of one practice-based innovation tool, the advisory professorship model, developed in the Lahti region, Finland. The Lahti region is relatively big by Finnish standards, but has no university of its own, which makes new types of knowledge transfer necessary. The empirical analysis is based on interviews of the companies that participated in the materials technology advisory professorship programme, developed and operated by Tampere University of Technology. In the analysis, we utilize the conceptual framework and analytical matrix developed by Tura et al. [(2008) Breaking inside the black box: Towards a dynamic evaluation framework of regional innovative capability, Science and Public Policy, 35(10), pp. 733–744] in the measuring of regional innovation capability. The study approach is based on regional and industrial viewpoints. In the light of the study, the programme had positive impacts on the innovation capability and innovation processes of companies. The advisory professorship model can be considered a useful practice-based innovation tool for regional university–industry co-operation with some limitations.

Performance of Serum Biomarkers for the Early Detection of Invasive Aspergillosis in Febrile, Neutropenic Patients: A Multi-State Model

BackgroundThe performance of serum biomarkers for the early detection of invasive aspergillosis expectedly depends on the timing of test results relative to the empirical administration of antifungal therapy during neutropenia, although a dynamic evaluation framework is lacking.MethodsWe developed a multi-state model describing simultaneously the likelihood of empirical antifungal therapy and the risk of invasive aspergillosis during neutropenia. We evaluated whether the first positive test result with a biomarker is an independent predictor of invasive aspergillosis when both diagnostic information used to treat and risk factors of developing invasive aspergillosis are taken into account over time. We applied the multi-state model to a homogeneous cohort of 185 high-risk patients with acute myeloid leukemia. Patients were prospectively screened for galactomannan antigenemia twice a week for immediate treatment decision; 2,214 serum samples were collected on the same days and blindly assessed for (1->3)- β-D-glucan antigenemia and a quantitative PCR assay targeting a mitochondrial locus.ResultsThe usual evaluation framework of biomarker performance was unable to distinguish clinical benefits of β-glucan or PCR assays. The multi-state model evidenced that the risk of invasive aspergillosis is a complex time function of neutropenia duration and risk management. The quantitative PCR assay accelerated the early detection of invasive aspergillosis (P = .010), independently of other diagnostic information used to treat, while β-glucan assay did not (P = .53).ConclusionsThe performance of serum biomarkers for the early detection of invasive aspergillosis is better apprehended by the evaluation of time-varying predictors in a multi-state model. Our results provide strong rationale for prospective studies testing a preemptive antifungal therapy, guided by clinical, radiological, and bi-weekly blood screening with galactomannan antigenemia and a standardized quantitative PCR assay.

Breaking inside the black box: towards a dynamic evaluation framework for regional innovative capability

Alongside theories of innovation, different measures of innovativeness and innovative capability have been developed. However, there are problems with the existing evaluation tools, especially when considering the dynamics of innovation processes. The measurement of the innovativeness of regions is particularly challenging. This paper describes recent changes in regional innovation policy models. It is argued that these policy models demand new tools for evaluating regional innovative capability. This paper tries to overcome some of the problems of the current evaluation tools. It outlines a framework for the network-based innovative capability evaluation matrix for evaluating and developing regional innovation policies. Copyright , Beech Tree Publishing.

Does the Order and Timing of Active Labor Market Programs Matter?

This paper extends the traditional focus of active labor market policy evaluation from a static comparison of participation in a program versus nonparticipation (or participation in another program) to the evaluation of the effects of program sequences, i.e. multiple participation or timing of such programs. We use a dynamic evaluation framework that explicitly allows for dynamic selection into different stages of such sequences based on past intermediate outcomes to analyze multiple programs, the timing of programs, and the order of programs. The analysis is based on exceptionally comprehensive data on the Austrian labor force. Our findings suggest that (i) active job search programs are more effective after a qualification program compared to the reverse order, that (ii) multiple participations in qualification measures dominates single participation, and that (iii) the effectiveness of specific labor market programs deteriorates the later they start during an unemployment spell.

Evaluating the Dynamic Behavior of Database Applications

This paper explores the effect that changing access patterns has on the performance of database management systems. Changes in access patterns play an important role in determining the efficiency of key performance optimization techniques, such as dynamic clustering, prefetching, and buffer replacement. However, all existing benchmarks or evaluation frameworks produce static access patterns in which objects are always accessed in the same order repeatedly. Hence, we have proposed the Dynamic Evaluation Framework (DEF) that simulates access pattern changes using configurable styles of change. DEF has been designed to be open and fully extensible (e.g., new access pattern change models can be added easily). In this paper, we instantiate DEF into the Dynamic Object Evaluation Framework (DoEF) which is designed for object databases, that is, object-oriented or object-relational databases, such as multimedia databases or most XML databases. The capabilities of DoEF have been evaluated by simulating the execution of four different dynamic clustering algorithms. The results confirm our analysis that flexible conservative reclustering is the key in determining a clustering algorithm’s ability to adapt to changes in access pattern. These results show the effectiveness of DoEF at determining the adaptability of each dynamic clustering algorithm to changes in access pattern in a simulation environment. In a second set of experiments, we have used DoEF to compare the performance of two real-life object stores: Platypus and SHORE. DoEF has helped to reveal the poor swapping performance of Platypus.

Evaluation Framework for Dynamic Vehicle Routing Strategies Under Real-Time Information

Dynamic vehicle dispatching and routing strategies are important aspects of commercial vehicle operations applications of intelligent transportation systems technologies. One possible operational benefit of real-time information is the ability to reroute commercial vehicles efficiently according to current traffic conditions. A dynamic evaluation framework in which dispatching and routing strategies for individual vehicles can be tested in a realistic traffic simulation environment is developed. The simulation-assignment model, DYNASMART, is applied to evaluate such strategies in a traffic network. Numerical experiments conducted in the Taichung network are presented to investigate dynamic vehicle dispatching and routing strategies under real-time information supply strategies and to assess the effectiveness of such strategies in a dynamic perspective.

Dynamic evaluation approach for virtual conflict decision training

Conflict decision is a continuous decision process in a dynamic conflict environment. The evaluation of the dynamic conflict situation is the basis of making proper decisions at different stages of the conflict process. Traditional evaluation approaches are static and do not consider enough impact factors in real conflict environment, so cannot reflect the real conflict situation and its evolution. This paper presents a dynamic evaluation framework for supporting continuous decisions in dynamic conflict environments. The framework includes the timed evaluation models for conflict force, conflict situation, and psychological pressure, as well as a graphical trend analysis approach. The framework has been applied to a virtual military conflict decision training system. A set of dynamic curves is used to intuitively reflect the dynamic evaluation result. Decision makers can know the current conflict situation and its evolution process through these dynamic curves at any stage of the conflict process so can make more evidential decisions.