An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering

Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare bones particle swarm optimization for data clustering and investigated its clustering performance. In our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here, we extend this work applying a cluster-based approach tested on 13 UCI datasets. We compared with six multiobjective evolutionary clustering algorithms from the existing literature and ten from our previous work. The proposed technique was found to perform well on datasets highly overlapping clusters, such as CMC and Sonar. So far, we found only one work that used cluster-based MOEA for clustering data, the hierarchical topology multiobjective clustering algorithm. All other cluster-based MOEA found were used to solve other problems that are not data clustering problems. By clustering Pareto solutions and evaluating new candidates against the found cluster representatives, local search is introduced in the solution selection process within the objective space, which can be effective on datasets with highly overlapping clusters. This is an added layer of search control in the objective space. The results are found to be promising, prompting different areas of future research which are discussed, including the study of its effects with an increasing number of clusters as well as with other objective functions.

Study on Factors Affecting the Performance of a CRISPR/Cas-Assisted New Immunoassay: Detection of Salivary Insulin as an Example

The clustered regularly interspaced short palindromic repeat (CRISPR)/Cas is now playing a significant role in biosensing applications, especially when the trans-cleavage activity of several Cas effectors is discovered. Taking advantages of both CRISPR/Cas and the enzyme-linked immunosorbent assay (ELISA) in analytical and clinical investigations, CRISPR/Cas-powered ELISA has been successfully designed to detect a spectrum of analytes beyond nucleic acid. Herein, we developed a CRISPR/Cas12a-assisted new immunoassay (CANi) for detection of salivary insulin as an example. Specifically, factors (antibody selection, temperature, and assay time) affecting the CRISPR/Cas-based ELISA system’s performance were investigated. It was observed that the concentration of blocking solution, selection of the capture antibody pairs, and the sequences of triggering ssDNA and guiding RNA affected this immunoassay sensitivity. In contrast, the preincubation of CRISPR/Cas12a working solution and pre-mixture of detection antibody with anti-IgG–ssDNA did not show influence on the performance of CANi for the detection of insulin. Under optimized conditions, the sensitivity for detection of salivary insulin was 10 fg/ml with a linear range from 10 fg/ml to 1 ng/ml.

GenACO a multi-objective cached data offloading optimization based on genetic algorithm and ant colony optimization

Background Data exchange and management have been observed to be improving with the rapid growth of 5G technology, edge computing, and the Internet of Things (IoT). Moreover, edge computing is expected to quickly serve extensive and massive data requests despite its limited storage capacity. Such a situation needs data caching and offloading capabilities for proper distribution to users. These capabilities also need to be optimized due to the experience constraints, such as data priority determination, limited storage, and execution time. Methods We proposed a novel framework called Genetic and Ant Colony Optimization (GenACO) to improve the performance of the cached data optimization implemented in previous research by providing a more optimum objective function value. GenACO improves the solution selection probability mechanism to ensure a more reliable balancing of the exploration and exploitation process involved in finding solutions. Moreover, the GenACO has two modes: cyclic and non-cyclic, confirmed to have the ability to increase the optimal cached data solution, improve average solution quality, and reduce the total time consumption from the previous research results. Result The experimental results demonstrated that the proposed GenACO outperformed the previous work by minimizing the objective function of cached data optimization from 0.4374 to 0.4350 and reducing the time consumption by up to 47%.

A multi-objective sparse evolutionary framework for large-scale weapon target assignment based on a reward strategy

The asset-based weapon target assignment (ABWTA) problem is one of the important branches of the weapon target assignment (WTA) problem. Due to the current large-scale battlefield environment, the ABWTA problem is a multi-objective optimization problem (MOP) with strong constraints, large-scale and sparse properties. The novel model of the ABWTA problem with the operation error parameter is established. An evolutionary algorithm for large-scale sparse problems (SparseEA) is introduced as the main framework for solving large-scale sparse ABWTA problem. The proposed framework (SparseEA-ABWTA) mainly addresses the issue that problem-specific initialization method and genetic operators with a reward strategy can generate solutions efficiently considering the sparsity of variables and an improved non-dominated solution selection method is presented to handle the constraints. Under the premise of constructing large-scale cases by the specific case generator, two numerical experiments on four outstanding multi-objective evolutionary algorithms (MOEAs) show Runtime of SparseEA-ABWTA is faster nearly 50% than others under the same convergence and the gap between MOEAs improved by the mechanism of SparseEA-ABWTA and SparseEA-ABWTA is reduced to nearly 20% in the convergence and distribution.

State-of-the-Art and Opportunities for Forward Osmosis in Sewage Concentration and Wastewater Treatment

The application of membrane technologies for wastewater treatment to recover water and nutrients from different types of wastewater can be an effective strategy to mitigate the water shortage and provide resource recovery for sustainable development of industrialisation and urbanisation. Forward osmosis (FO), driven by the osmotic pressure difference between solutions divided by a semi-permeable membrane, has been recognised as a potential energy-efficient filtration process with a low tendency for fouling and a strong ability to filtrate highly polluted wastewater. The application of FO for wastewater treatment has received significant attention in research and attracted technological effort in recent years. In this review, we review the state-of-the-art application of FO technology for sewage concentration and wastewater treatment both as an independent treatment process and in combination with other treatment processes. We also provide an outlook of the future prospects and recommendations for the improvement of membrane performance, fouling control and system optimisation from the perspectives of membrane materials, operating condition optimisation, draw solution selection, and multiple technologies combination.

Solution Selection from a Pareto Optimal Set of Multi-Objective Reservoir Operation via Clustering Operation Processes and Objective Values

Multi-objective evolutionary algorithms (MOEAs) are widely used to optimize multi-purpose reservoir operations. Considering that most outcomes of MOEAs are Pareto optimal sets with a large number of incomparable solutions, it is not a trivial task for decision-makers (DMs) to select a compromise solution for application purposes. Due to the increasing popularity of data-driven decision-making, we introduce a clustering-based decision-making method into the multi-objective reservoir operation optimization problem. Traditionally, solution selection has been conducted based on trade-off ranking in objective space, and solution characteristics in decision space have been ignored. In our work, reservoir operation processes were innovatively clustered into groups with unique properties in decision space, and the trade-off surfaces were analyzed via clustering in objective space. To attain a suitable performance, a new similarity measure, referred to as the Mei–Wang fluctuation similarity measure (MWFSM), was tailored to reservoir operation processes. This method describes time series in terms of both their shape and quantitative variation. Then, a compromise solution was selected via the joint use of two clustering results. A case study of the Three Gorges cascade reservoirs system under small and medium floods was investigated to verify the applicability of the proposed method. The results revealed that the MWFSM effectively distinguishes reservoir operation processes. Two more operation patterns with similar positions but different shapes were identified via MWFSM when compared with Euclidean distance and the dynamic time warping method. Furthermore, the proposed method decreased the selection range from the whole Pareto optimal set to a set containing relatively few solutions. Finally, a compromise solution was selected.

IEA Wind Task 36 – International Collaboration on Forecast Improvements

<p>The International Energy Agency (IEA) Wind Task 36 on Wind Power Forecasting organises international collaboration, among national weather centres with an interest and/or large projects on wind forecast improvements (NOAA, DWD, ...), forecast vendors and forecast users to facilitate scientific exchange to be prepared for future challenges.</p><p>The talk discusses the general setup of the Task, and the latest developments. Among those are decision making under uncertainty. To this aim, a series of forecasting experiments are being developed and one initial experiment was tested by a wide audience. The forecasting experiments took the form of a game, during which the participants could experience the benefit of probabilistic information on their decisions to trade. <br>Other results include an information portal for meteorological data, and the IEA Recommended Practice for Forecast Solution Selection which is divided into 3 parts:  (1) "Forecast Solution Selection Process", (2) "Designing and Executing Forecasting Benchmarks and Trials", and  (3) "Evaluation of Forecasts and Forecast Solutions". The Recommended Practice guideline encourages forecast users to establish a framework of metrics that help identify, whether the user's forecast performance criteria effectively incentivize the forecast provider to optimize towards the forecast target variable that has the most value for the user's application(s). For this year, we intend to update the guideline in the light of the experiences throughout the industry in its initial application, and after collecting this experience at 3 Open Space workshops.</p><p>Collaboration is open to IEA Wind member states; 12 countries are already actively collaborating.</p><p>The Task is divided in three work packages: Work Package (WP) 1 is a collaboration on the improvement of the scientific basis for the wind predictions themselves. This includes numerical weather prediction (NWP) model physics, but also widely distributed information on accessible datasets. This WP also currently organises a benchmark for NWP models, based on the Wind Forecast Improvement Project 2 (WFIP2) datasets. WP2 deals with the power conversion from the wind speed forecasts and the associated vendor issues. Amongst other things, WP2 published the IEA Recommended Practice on how to select an optimal wind power forecast solution for a specific application. The focus of WP3 is on the engagement of end users to disseminate the best practice in the use of wind power predictions, especially probabilistic forecasts and also what kind of measurements are required in real-time environments</p><p>A major activity of the Task is the organisation of workshops and special sessions at conferences, like this one. Previous workshops on e.g. forecasting on the minute scale including lidars, a workshop on the value of forecasts, or special sessions on the Wind Energy Science Conference, the Wind Integration Workshop, the ESIG Meteorology and Market Design for Grid Services workshops are still visible online from the IEAWindForecasting YouTube channel.</p>

Evaluation of Groundwater Salinization Risk Following Application of Anti-Dust Emission Solutions on Unpaved Roads in Arid and Semiarid Regions

Unpaved roads could be a significant source of dust emission. A common and effective practice to suppress this emission is the application of brine solution on these roads. However, this application could increase the risk of water source salinization in arid and semiarid regions, such as Israel. The general objective of the present study was to investigate the potential effects of treated wastewater (TWW), fresh water (FW), and brine applications as anti-dust emission solutions on water source salinization in these regions. A rainfall simulator experiment and a mass balance model were used for this goal. The TWW loaded the highest amounts of Cl, Na, and Ca+Mg on the unpaved roads, while the brine loaded higher amounts of Cl and Ca+Mg than the FW, and ~0 Na. In the rainfall experiment, runoff was not formed, and ~100% of the loaded amounts were leached downwards by rain, indicating a negligible salinization risk to surface water. We estimated that the average increases in the Cl concentrations in the modeled aquifer, following TWW, brine, and FW applications, were low: 1.2–1.6, 0.58–0.8, and 0.32–0.4 mg L−1, respectively. Thus, the solution selection for preventing dust emission should be based on the total cost of the solution application.

Evaluation of Suppressed Mite Reproduction (SMR) Reveals Potential for Varroa Resistance in European Honey Bees (Apis mellifera L.)

In the fight against the Varroa destructor mite, selective breeding of honey bee (Apis mellifera L.) populations that are resistant to the parasitic mite stands as a sustainable solution. Selection initiatives indicate that using the suppressed mite reproduction (SMR) trait as a selection criterion is a suitable tool to breed such resistant bee populations. We conducted a large European experiment to evaluate the SMR trait in different populations of honey bees spread over 13 different countries, and representing different honey bee genotypes with their local mite parasites. The first goal was to standardize and validate the SMR evaluation method, and then to compare the SMR trait between the different populations. Simulation results indicate that it is necessary to examine at least 35 single-infested cells to reliably estimate the SMR score of any given colony. Several colonies from our dataset display high SMR scores indicating that this trait is present within the European honey bee populations. The trait is highly variable between colonies and some countries, but no major differences could be identified between countries for a given genotype, or between genotypes in different countries. This study shows the potential to increase selective breeding efforts of V. destructor resistant populations.