On the oscillatory instability of multiple-parameter systems

The postcritical oscillatory behaviour of an autonomous discrete system under the influence of two independent parameters is studied. Three distinct situations are identified and explored via the intrinsic harmonic balancing technique introduced earlier. In each case, the asymptotic equations of the behaviour surface in parameter-amplitude space are derived explicitly. It is observed that there exists an interesting analogy between this surface and the equilibrium surface associated with static instabilities. Indeed, the phenomenon analyzed here is akin to fold catastrophe. The family of limit cycles associated with the behaviour surface are also obtained in general terms. The results can be used very easily to analyze specific problems, and this has been demonstrated on an illustrative example.     

Process parameters for hydrocooling apricots, plums, and peaches

Freshly harvested apricots, plums, and peaches in crates containing 5–20kg were hydrocooled in flowing water at 1°C and 50mms^-1. The cooling parameters: lag factor, cooling coefficient, and time to half-cool, determined from the exponential decline in the dimensionless ratio of temperature differences between fruit centre and coolant temperature at time t and at zero time, varied somewhat with crate load. Increasing the load from 5 to 20kg increased the time to half-cool apricots by 17% and plums and peaches by 39%.     

Throughput enhanced scheduling (TES) scheme for ultra‐dense networks

This paper proposes two novel packet scheduling schemes, called as throughput enhanced scheduling (TES) and TES plus (TES+), for future ultra‐dense networks. These schemes introduce two novel parameters to the scheduling decision making and reformulate the parameters used by the state‐of‐the‐art schemes. The aim is to have a more balanced weight distribution between delay and throughput‐related parameters at scheduling decisions. Also include a new telecommunications related parameter into scheduling decision making that has not been studied by popular schedulers. The performance of novel schemes is compared with well‐known schemes—proportional fairness (PF), exponential/proportional fairness (EXP/PF), and M‐LWDF. For performance evaluation, five performance metrics—average spectral efficiency and delay, quality of service (QoS) violation ratio, jitter, and Jain's fairness index—are investigated. The simulation results show that proposed schemes can outperform all the compared scheduling schemes.     

BinEHO: a new binary variant based on elephant herding optimization algorithm

One of the new optimization techniques proposed in recent years is elephant herding optimization (EHO) algorithm. Despite its short history, EHO has been used to solve many engineering and real-world problems by attracting researcher attention with its advantages such as efficient global search ability, having fewer control parameters and ease of implementation. However, there is no remarkable binary variant of EHO algorithm in the literature. A new binary approach based on EHO algorithm is proposed in this study. The newer binary variant of EHO named as BinEHO is binarized with preserving the search ability of basic EHO. The main purpose of the study is to present a simple, efficient and robust binary variant which copes with different binary problems. Therefore, the proposed method is tested on three important binary optimization problems, 0–1 knapsack, uncapacitated facility location and wind turbine placement, in order to show its performance and accuracy. In addition, the BinEHO is compared with various binary variants on these problems. Experimental results and comparisons show that the BinEHO algorithm is a robust and efficient tool for binary optimization.

     

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern,dual tree complex wavelet transform and convolutional neural networks

In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelligence method, was used, and automatic classification of images was performed using convolutional neural networks (CNN). In the first training-test data set used in the study, there were 230 X-ray images, of which 150 were Covid-19 and 80 were non-Covid-19, while in the second training-test data set there were 476 X-ray images, of which 150 were Covid-19 and 326 were non-Covid-19. Thus, classification results have been provided for two data sets, containing predominantly Covid-19 images and predominantly non-Covid-19 images, respectively. In the study, a 23-layer CNN architecture and a 54-layer CNN architecture were developed. Within the scope of the study, the results were obtained using chest X-ray images directly in the training-test procedures and the sub-band images obtained by applying dual tree complex wavelet transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying local binary pattern (LBP) to the chest X-ray images. Within the scope of the study, four new result generation pipeline algorithms having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in this study, the training sessions were carried out using the k-fold cross validation method. Here the k value was chosen as 23 for the first and second training-test data sets. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy, F-1 score, and area under the receiver operating characteristic curve (AUC) for the first training-test data set were 0,9947, 0,9800, 0,9843, 0,9881 and 0,9990 respectively; while for the second training-test data set, they were 0,9920, 0,9939, 0,9891, 0,9828 and 0,9991; respectively. Within the scope of the study, finally, all the images were combined and the training and testing processes were repeated for a total of 556 X-ray images comprising 150 Covid-19 images and 406 non-Covid-19 images, by applying 2-fold cross. In this context, the average highest values of sensitivity, specificity, accuracy, F-1 score, and AUC for this last training-test data set were found to be 0,9760, 1,0000, 0,9906, 0,9823 and 0,9997; respectively.

     

A comparison of clustering-based privacy-preserving collaborative filtering schemes

Privacy-preserving collaborative filtering (PPCF) methods designate extremely beneficial filtering skills without deeply jeopardizing privacy. However, they mostly suffer from scalability, sparsity, and accuracy problems. First, applying privacy measures introduces additional costs making scalability worse. Second, due to randomness for preserving privacy, quality of predictions diminishes. Third, with increasing number of products, sparsity becomes an issue for both CF and PPCF schemes.In this study, we first propose a content-based profiling (CBP) of users to overcome sparsity issues while performing clustering because the very sparse nature of rating profiles sometimes do not allow strong discrimination. To cope with scalability and accuracy problems of PPCF schemes, we then show how to apply k-means clustering (KMC), fuzzy c-means method (FCM), and self-organizing map (SOM) clustering to CF schemes while preserving users’ confidentiality. After presenting an evaluation of clustering-based methods in terms of privacy and supplementary costs, we carry out real data-based experiments to compare the clustering algorithms within and against traditional CF and PPCF approaches in terms of accuracy. Our empirical outcomes demonstrate that FCM achieves the best low cost performance compared to other methods due to its approximation-based model. The results also show that our privacy-preserving methods are able to offer precise predictions.     

News from the field: Visual planning and urbanism in the mid‐twentieth century conference,Newcastle, UK, 11–13 September 2007

Gjirokastra, a century-old small city in southern Albania (now a UNESCO World Heritage site), provides an outstanding example of a Classical and Late Ottoman urban centre. At the same time, it is a special example of urban and architectural design based on self-defence by individual family units. Through an excursion of Gjirokastra's residential neighbourhoods, this article discusses how defence concerns guided its urban morphology and building typology until the collapse of the Ottoman Empire. The idea of war, feuds, and fear has pervaded the residents' self-identity for centuries. The military character of their houses owes much to the local ‘warrior’ mentality that prevailed in times of war and peace.     

Synthesis and metal ions sorption properties of styrene‐based polymers

Atactic polystyrene (PS) was chemically modified with maleic (MAH), succinic (SAH), and phtalic (PhAH) anhydrides. Styrene was copolymerized with acrylic (AA) and methacrylic acids (MA). Amount of carboxyl groups (%) bound to polymers was determined in the range of 6.9–25.9. Different modified polystyrenes (MPS) and styrene copolymers were used in the experiments to study Cu(II) and Zn(II) ions adsorption probability and their comparison. Sorption capacity of the polymers for the metal ions were investigated in aqueous media containing different amounts of these ions (5–40 mg/L) and at different pH values (2.0–6.0). Adsorption behavior of heavy metal ions could be modeled using both the Langmuir and Freundlich isotherms. It was found that the adsorption capacity is highest at pH value of 6, whereas it decreases as the pH value decreases at temperature 25°C ± 1°C for 240 min. The results obtained from the adsorption capacity experiments for Cu(II) and Zn(II) ions were 3.47–5.45 and 5.42–6.85 mg/g, respectively. The affinity order of polymers for both metal ions was observed as follows: SMAC > SAAC > MPS with MAH > MPS with SAH > MPS with PhAH. The maximum adsorption capacities of SMAC were 6.85mg/g for Zn(II) and 5.45 mg/g for Cu(II). © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009