evaluation scheme
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Merrihan Badr Monir Mansour ◽  
Tamer Abdelkader ◽  
Mohammed Hashem AbdelAziz ◽  
El-Sayed Mohamed EI-Horbaty

Mobile edge computing (MEC) is a new computing paradigm that brings cloud services to the network edge. Despite its great need in terms of computational services in daily life, service users may have several concerns while selecting a suitable service provider to fulfil their computational requirements. Such concerns are: with whom they are dealing with, where will their private data migrate to, service provider processing performance quality. Therefore, this paper presents a trust evaluation scheme that evaluates the processing performance of a service provider in the MEC environment. Processing performance of service providers is evaluated in terms of average processing success rate and processing throughput, thus allocating a service provider in a relevant trust status. Service provider processing incompliance and user termination ratio are also computed during provider’s interactions with users. This is in an attempt to help future service users to be acknowledged of service provider’s past interactions prior dealing with it. Thus, eliminating the probability of existing compromised service providers and raising the security and success of future interactions between service providers and users. Simulations results show service providers processing performance degree, processing incompliance and user termination ratio. A service provider is allocated to a trust status according to the evaluated processing performance trust degree.

Biology ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1344
Soumaya Bourgou ◽  
Imtinen Ben Haj Jilani ◽  
Olfa Karous ◽  
Wided Megdiche-Ksouri ◽  
Zeineb Ghrabi-Gammar ◽  

Medicinal-aromatic plants (MAPs) are important sources for the development of new valuable products of interest to human and animal health, and are also used as ornamentals for the horticulture industry. However, the increased global demand and the uncontrolled exploitation of these plants constitute a threat to their sustainability. To date, few scientific investigations have focused on MAPs valorization and their domestication. The purpose of this study was to evaluate for the first time the medicinal-cosmetic potential of 399 local endemic Mediterranean plants confined to Crete (223 taxa), the Mediterranean coast-Rif of Morocco (94), and Tunisia (82). The new methodological scheme was developed by experts through three multidisciplinary co-creative workshops and was adjusted by end-users to point-scoring of nine attributes evaluating the potential of the targeted neglected and underutilized plants (NUPs) in the medicinal-cosmetic sector. The results were demonstrated as percentage of the maximum possible score. These assessments were further linked and discussed with respect to feasibility and readiness timescale evaluations for sustainable exploitation of the focal NUPs. A great diversity of local endemic NUPs (30 taxa, 11 families) were associated with interesting medicinal-cosmetic properties (>35% up to 94.44%). Among them, 8 taxa showed the highest medicinal-cosmetic potential (>55% of maximum possible score), half of which are threatened with extinction. Although ex-situ conservation efforts and applied research work are needed to safeguard and unlock the full potential of the local endemic NUPs evaluated herein, the proposed multifaceted evaluation scheme revealed that some local endemic NUPs of the studied regions can be sustainably exploited in short- or medium-term, following successful examples of Cretan NUPs e.g., Origanum dictramnus. The sustainable exploitation of high scored taxa of the studied regions can be fastened through targeted species-specific research bridging extant research gaps and facilitating conservation and stakeholder attraction.

2021 ◽  
Su Nguyen

<p>Scheduling is an important planning activity in manufacturing systems to help optimise the usage of scarce resources and improve the customer satisfaction. In the job shop manufacturing environment, scheduling problems are challenging due to the complexity of production flows and practical requirements such as dynamic changes, uncertainty, multiple objectives, and multiple scheduling decisions. Also, job shop scheduling (JSS) is very common in small manufacturing businesses and JSS is considered one of the most popular research topics in this domain due to its potential to dramatically decrease the costs and increase the throughput.  Practitioners and researchers have applied different computational techniques, from different fields such as operations research and computer science, to deal with JSS problems. Although optimisation methods usually show their dominance in the literature, applying optimisation techniques in practical situations is not straightforward because of the practical constraints and conditions in the shop. Dispatching rules are a very useful approach to dealing with these environments because they are easy to implement(by computers and shop floor operators) and can cope with dynamic changes. However, designing an effective dispatching rule is not a trivial task and requires extensive knowledge about the scheduling problem.   The overall goal of this thesis is to develop a genetic programming based hyper-heuristic (GPHH) approach for automatic heuristic design of reusable and competitive dispatching rules in job shop scheduling environments. This thesis focuses on incorporating special features of JSS in the representations and evolutionary search mechanisms of genetic programming(GP) to help enhance the quality of dispatching rules obtained.  This thesis shows that representations and evaluation schemes are the important factors that significantly influence the performance of GP for evolving dispatching rules. The thesis demonstrates that evolved rules which are trained to adapt their decisions based on the changes in shops are better than conventional rules. Moreover, by applying a new evaluation scheme, the evolved rules can effectively learn from the mistakes made in previous completed schedules to construct better scheduling decisions. The GP method using the newproposed evaluation scheme shows better performance than the GP method using the conventional scheme.  This thesis proposes a new multi-objective GPHH to evolve a Pareto front of non-dominated dispatching rules. Instead of evolving a single rule with assumed preferences over different objectives, the advantage of this GPHH method is to allow GP to evolve rules to handle multiple conflicting objectives simultaneously. The Pareto fronts obtained by the GPHH method can be used as an effective tool to help decision makers select appropriate rules based on their knowledge regarding possible trade-offs. The thesis shows that evolved rules can dominate well-known dispatching rules when a single objective and multiple objectives are considered. Also, the obtained Pareto fronts show that many evolved rules can lead to favourable trade-offs, which have not been explored in the literature.   This thesis tackles one of themost challenging issues in job shop scheduling, the interactions between different scheduling decisions. New GPHH methods have been proposed to help evolve scheduling policies containing multiple scheduling rules for multiple scheduling decisions. The two decisions examined in this thesis are sequencing and due date assignment. The experimental results show that the evolved scheduling rules are significantly better than scheduling policies in the literature. A cooperative coevolution approach has also been developed to reduce the complexity of evolving sophisticated scheduling policies. A new evolutionary search mechanisms and customised genetic operations are proposed in this approach to improve the diversity of the obtained Pareto fronts.</p>

2021 ◽  
Tim Kucera ◽  
Matteo Togninalli ◽  
Laetitia Meng-Papaxanthos

Motivation: Protein Design has become increasingly important for medical and biotechnological applications. Because of the complex mechanisms underlying protein formation, the creation of a novel protein requires tedious and time-consuming computational or experimental protocols. At the same time, Machine Learning has enabled to solve complex problems by leveraging the large amounts of available data, more recently with great improvements on the domain of generative modeling. Yet, generative models have mainly been applied to specific sub-problems of protein design. Results: Here we approach the problem of general purpose Protein Design conditioned on functional labels of the hierarchical Gene Ontology. Since a canonical way to evaluate generative models in this domain is missing, we devise an evaluation scheme of several biologically and statistically inspired metrics. We then develop the conditional generative adversarial network ProteoGAN and show that it outperforms several classic and more recent deep learning baselines for protein sequence generation. We further give insights into the model by analysing hyperparameters and ablation baselines. Lastly, we hypothesize that a functionally conditional model could create proteins with novel functions by combining labels and provide first steps into this direction of research.

2021 ◽  
Salum Azizi ◽  
Janneke Snetselaar ◽  
Robert Kaaya ◽  
Johnson Matowo ◽  
Hudson Onen ◽  

Abstract Background: To attain and sustain the universal Long-Lasting Insecticidal Nets (LLINs) coverage, cheap nets that provides equivalent or better protection than the standard LLINs, are required. While it is essential to follow the World Health Organization (WHO) guidelines for the evaluation of LLINs, adherence to the Good Laboratory Practice (GLP) is necessary to generate reliable and reproducible data that will facilitate efficient LLINs to be speedy registered. Adherence to GLP obviate the need to duplicate the assessment and ensures substandard LLINs are not reaching the market. This study aimed to evaluate efficacy of SafeNet NF® and SafeNet® LLIN in accordance to the WHO Pest Evaluation Scheme (WHOPES) and the GLP guidelines. Both candidate LLINs were manufactured with less fabrics to cut down manufacturing costs, motivated by the need for cheaper LLINs to achieve universal coverage. Materials & Methods: SafeNet NF® and SafeNet® LLIN, were assessed in experimental huts against wild, pyrethroid-resistant Anopheles arabiensis mosquitoes. Efficacy in terms of mosquito blood-feeding inhibition and mortality, was compared with Interceptor® LLIN and an untreated net. All nets were washed and artificially holed to simulate a used torn net. The GLP guidelines were followed throughout this study.Results: The mortality of mosquitoes exposed to SafeNet NF® and SafeNet® LLIN were equivalent to that of the reference net. Blood-feeding inhibition was only evident in Interceptor® LLIN. Adherence to GLP was observed throughout the study.Conclusions: Step-wise procedures to conduct LLIN evaluation in compliance to both WHOPES and GLP guidelines are elaborated in this study. SafeNet NF® and SafeNet® LLIN offers equivalent protection as Interceptor® LLIN and can facilitate universal LLIN coverage due to its low manufacturing cost. However, further research is needed to understand durability, acceptability and residual efficacy of these nets in field environments.

Sıla Öcalan-Özel ◽  
Patrick Llerena

This paper explores the relationship between the industry collaborations of grant applicant teams and the outcomes of a multistage grant evaluation process. We studied this relationship by focusing on two possible channels of impact of industry engagement—team diversity (or the diversity effect) and prior collaboration experience (or the experience effect)—and examined their influence on the evaluators' decision by using the proxies of direct industry engagement (i.e., the involvement of a company-affiliated researcher in the grant applicant team) and indirect industry engagement (i.e., joint publications with a company-affiliated researcher prior to the grant application), respectively. We analyzed data extracted from the application and reviewed materials of a multidisciplinary, pan-European research funding scheme—European Collaborative Research (EUROCORES)—for the period 2002–2010 and conducted an empirical investigation of its three consecutive grant evaluation stages at the team level. We found that teams presenting an indirect engagement were more likely to pass the first stage of selection, whereas no significant relationships were found at any of the three evaluation stages for teams presenting a direct engagement. Our findings point to the heterogeneity of the decision-making process within a multistage grant evaluation scheme and suggest that the policy objective of fostering university–industry collaboration does not significantly impact the funding process.

2021 ◽  
Vol 22 (1) ◽  
Rogia Kpanou ◽  
Mazid Abiodoun Osseni ◽  
Prudencio Tossou ◽  
Francois Laviolette ◽  
Jacques Corbeil

Abstract Background Deep learning methods are a proven commodity in many fields and endeavors. One of these endeavors is predicting the presence of adverse drug–drug interactions (DDIs). The models generated can predict, with reasonable accuracy, the phenotypes arising from the drug interactions using their molecular structures. Nevertheless, this task requires improvement to be truly useful. Given the complexity of the predictive task, an extensive benchmarking on structure-based models for DDIs prediction was performed to evaluate their drawbacks and advantages. Results We rigorously tested various structure-based models that predict drug interactions using different splitting strategies to simulate different real-world scenarios. In addition to the effects of different training and testing setups on the robustness and generalizability of the models, we then explore the contribution of traditional approaches such as multitask learning and data augmentation. Conclusion Structure-based models tend to generalize poorly to unseen drugs despite their ability to identify new DDIs among drugs seen during training accurately. Indeed, they efficiently propagate information between known drugs and could be valuable for discovering new DDIs in a database. However, these models will most probably fail when exposed to unknown drugs. While multitask learning does not help in our case to solve the problem, the use of data augmentation does at least mitigate it. Therefore, researchers must be cautious of the bias of the random evaluation scheme, especially if their goal is to discover new DDIs.

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Ahmed Eweda ◽  
Abobakr Al-Sakkaf ◽  
Tarek Zayed ◽  
Sabah Alkass

PurposeThe purpose of this study is to develop a condition assessment (CA) model for a building's indoor 21 environments and to improve the building's asset management process.Design/methodology/approachThe methodology is based on dividing the building into spaces, which are the principal evaluated elements based on the building's indoor environmental quality (IEQ). An evaluation scheme was prepared for the identified factors and the analytical hierarchy process (AHP) technique was used to calculate the relative weight of each space inside the building as well as the contribution of each IEQ factors (IEQFs) in the overall environmental condition of each space inside the building. The multi-attribute utility theory (MAUT) was then applied to assess the environmental conditions of the building as a whole and its spaces. An educational building in Canada was evaluated using the developed model.FindingsEach space type was found to have its own IEQFs weights, which confirms the hypothesis that the importance and allocation of each IEQF are dependent on the function and tasks carried out in each space. A similar indoor environmental assessment score was calculated using the developed model and the building CA conducted by the facility management team; “89%” was calculated, using K-mean clustering, for the physical and environmental conditions.Originality/valueIEQ affects occupants' assessment of their quality of life (QOL). Despite the existence of IEQ evaluation models that correlate the building's IEQ and the occupants' perceived indoor assessments, some limitations have led to the necessity of developing a comprehensive model that integrates all factors and their sub-criteria in an assessment scheme that converts all the indoor environmental factors into objective metrics.

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