A survey on search results diversification techniques

The quantity of information placed on the web has been greater than before and is increasing rapidly day by day. Searching through the huge amount of data and finding the most relevant and useful result set involves searching, ranking, and presenting the results. Most of the users probe into the top few results and neglect the rest. In order to increase user’s satisfaction, the presented result set should not only be relevant to the search topic, but should also present a variety of perspectives, that is, the results should be different from one another. The effectiveness of web search and the satisfaction of users can be enhanced through providing various results of a search query in a certain order of relevance and concern. The technique used to avoid presenting similar, though relevant, results to the user is known as a diversification of search results. This article presents a survey of the approaches used for search result diversification. To this end, this article not only provides a technical survey of existing diversification techniques, but also presents a taxonomy of diversification algorithms with respect to the types of search queries.     

Carob Kibble: A Bioactive‐Rich Food Ingredient

Carob (Ceratonia siliqua L.) is well known for its valuable locust bean gum obtained from the carob seeds. Separation of seeds from the pod leaves behind the carob kibble which is a good source of dietary fiber, sugars, and a range of bioactive compounds such as polyphenols and pinitol. Bioactive compounds present in carob kibble have been found to be beneficial in the control of many health problems such as diabetes, heart diseases, and colon cancer due to their antidiabetic, antioxidant, and anti‐inflammatory activities. Carob kibble has substantial potential to be used as a food ingredient. This article focuses on the composition, health benefits, and food applications of carob kibble.     

Detection of COVID-19 from chest X-ray images: Boosting the performance with convolutional neural network and transfer learning

Coronavirus disease (COVID-19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID-19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID-19 can protect people from severe infection. Unfortunately, COVID-19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID-19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID-19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID-19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID-19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre-trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X-rays (1200 cases of COVID-19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X-ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID-19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1-score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID-19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.     

Multidisciplinary wing design optimization considering global sensitivity and uncertainty of approximation models

In recent years, high-fidelity analysis tools, such as computational fluid dynamics and finite element method, have been widely used in multidisciplinary design optimization (MDO) to enhance the accuracy of design results. However, complex MDO problems have many design variables and require long computation times. Global sensitivity analysis (GSA) is proposed to assuage the complexity of design problems by reducing dimensionality where variables that have low impact on the objective function are neglected. This avoids wasting computational effort and time on low-priority variables. Additionally, uncertainty introduced by the fidelity of the analysis tools is considered in design optimization to increase the reliability of design results. Reliability-based design optimization (RBDO) and possibility-based design optimization (PBDO) methods are proposed to handle uncertainty in design optimization. In this paper, the extended Fourier amplitude sensitivity test was used for GSA, whereas a collaborative optimization-based framework with RBDO and PBDO was used to consider uncertainty introduced by approximation models. The proposed method was applied to an aero-structural design optimization of an aircraft wing to demonstrate the feasibility and efficiency of the developed method. The objective function was to maximize the lift-to-drag ratio. The proposed process reduced calculation efforts by reducing the number of design variables and achieved the target probability of failure when it considered uncertainty. Moreover, this work evaluated previous research in RBDO with MDO for the wing design by comparing it with the PBDO result.     

Scheduling flowshops with condition-based maintenance constraint to minimize expected makespan

Flexible job-shop scheduling problem (FJSP) is an extended traditional job-shop scheduling problem, which more approximates to practical scheduling problems. This paper presents a multi-objective genetic algorithm (MOGA) based on immune and entropy principle to solve the multi-objective FJSP. In this improved MOGA, the fitness scheme based on Pareto-optimality is applied, and the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Efficient crossover and mutation operators are proposed to adapt to the special chromosome structure. The proposed algorithm is evaluated on some representative instances, and the comparison with other approaches in the latest papers validates the effectiveness of the proposed algorithm.     

Viscoelastic properties of architected foams based on the Schoen IWP triply periodic minimal surface

Abstract

In this article, we studied the viscoelastic properties of an architected foam based on the mathematically-known Schoen IWP triply periodic minimal surface (TPMS) under both time and frequency domains. IWP-based architectures possess unique multifunctional attributes when used as a three-dimensional (3D) reinforcement in composites. The 3?D representative volume elements (RVEs) of different relative densities (i.e., the ratio of the foam’s density to the density of its solid counterpart) were generated and studied using the finite element method in order to predict the effective uniaxial, shear, and bulk viscoelastic responses of IWP-foams as a function of relative density and/or frequency. The principle of time-temperature superposition principle was used to create the master curve of the observed relative-density dependent mechanical responses (loss tangent, storage and loss moduli) in frequency domains. Reduced uniaxial, bulk, and shear stiffness-loss map results suggested that the IWP-foam possesses strongest uniaxial viscoelastic response while highest damping can be achieved under shear responses. Relaxation behavior of IWP-foam was compared with other six different types of open-cell periodic foams. It was found that IWP-foam uniaxial response is similar to simple cubic foam, bulk relaxation response is similar to primitive-foam while shear response follows the behavior of body centered cubic foam. Among these foams, we found that IWP-foam is the best candidate to use as a damper under uniaxial and hydrostatic loading conditions.     

Production Lot-Sizing Under Learning Effects: An Efficient Solution Technique

In this paper, we consider a production-inventory model which assumes that learning occurs as a function of the number of units produced. We analyze two cases: the first case allows for no forgetting between production runs and the second case (a generalization of the first case) allows for some given degree of forgetting between production runs. In the first case, we show that learning only has an impact on initial lot-sizes for large order quantities and that steady state lot-sizes will approach the traditional EOQ amount. In addition, we show that succeeding lot-sizes are always nonincreasing. Applying these results to the second case when forgetting occurs, we develop efficient heuristic algorithms with complexity O(N logN) to determine order quantities. Results from our algorithms are compared to optimal solutions; these comparisons indicate that our algorithms usually provide solutions within one percent of the optimal cost.     

Combined deadbeat control of a series-parallel convertercombination used as a universal power filter

This paper presents the notion of combined control of a system of interconnected power electronic converters. The concept is demonstrated using a three-phase series-parallel active power filter as an example. The described active power filter consists of a series-parallel combination of two full bridge VSIs capable of arbitrarily controlling the input current and output voltage. The proposed control scheme treats the converter combination as a single unit and uses the inverse system model to generate deadbeat control response for both input current and output voltage. A full-order predictive state observer is used to reduce the number of sensors. Simulation results show better disturbance rejection characteristics of the proposed control when compared to the separately controlled converter scheme