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Any computing architecture cannot be designed with complete confidentiality. As a result, at any point, it may leak the information. So, it is important to decide leakage threshold in any computing architecture. To prevent leakage more than the predefined threshold, quantitative analysis is helpful. This paper aims to provide a method to quantify information leakage in service-oriented architecture (SOA)-based Web services.

To visualize the dynamic binding of SOA components, first, the orchestration of components is modeled. The modeling helps to information-theoretically quantify information leakage in SOA-based Web services. Then, the paper considers the non-interference policy in a global way to quantify information leakage. It considers not only variables which interfere with security sensitive content but also other architectural parameters to quantify leakage in Web services. To illustrate the attacker’s ability, a strong threat model has been proposed in the paper.

The paper finds that information leakage can be quantified in SOA-based Web services by considering parameters that interfere with security sensitive content and information theory. A hypothetical case study scenario of flight ticket booking Web services has been considered in the present paper in which leakage of 18.89 per cent information is calculated.

The paper shows that it is practically possible to quantify information leakage in SOA-based Web services. While modeling the SOA-based Web services, it will be of help to architects to identify parameters which may cause the leakage of secret contents.

To design, control and evaluate photovoltaic (PV) systems, an accurate model is required. Accuracy of PV models depends on model parameters. This study aims to use a new algorithm called improved social spider algorithm (ISSA) to detect model parameters.

To improve performance of social spider algorithm (SSA), an elimination period is added. In addition, at the beginning of each period, a certain number of the worst solutions are replaced by new solutions in the search space. This allows the particles to find new paths to get the best solution.

In this paper, ISSA is used to estimate parameters of single-diode and double-diode models. In addition, effect of irradiation and temperature on I–V curves of PV modules is studied. For this purpose, two different modules called multi-crystalline (KC200GT) module and polycrystalline (SW255) are used. It should be noted that to challenge the performance of the proposed algorithm, it has been used to identify the parameters of a type of widely used module of fuel cell called proton exchange membrane fuel cell. Finally, comparing and analyzing of ISSA results with other similar methods shows the superiority of the presented method.

Changes in the spider’s movement process in the SSA toward the desired response have improved the algorithm’s performance. Higher accuracy and convergence rate, skipping local minimums, global search ability and search in a limited space can be mentioned as some advantages of this modified method compared to classic SSA.

This purpose of this paper is to present a methodology for optimally planning long‐haul road transport activities through proper aggregation of customer orders in separate full‐truckload or less‐than‐truckload shipments in order to minimize total transportation costs.

The model is applied to a specific Italian multi‐plant firm operating in the plastic film for packaging sector. The method, given the order quantities to be shipped and the location of customers, aggregates shipments in subgroups of compatible orders resorting to a heuristic procedure and successively consolidates them in optimized full truck load and less than truck load shipments resorting to a Genetic Algorithm in order to minimize total shipping costs respecting delivery due dates and proper geographical and truck capacity constraints.

The paper demonstrates that evolutionary computation techniques may be effective in tactical planning of transportation activities. The model shows that substantial savings on overall transportation cost may be achieved adopting the proposed methodology in a real life scenario.

The main limitation of this optimisation methodology is that an heuristic procedure is utilized instead of an enumerative approach in order to at first aggregate shipments in compatible sets before the optimisation algorithm carries out the assignments of customer orders to separate truckloads. Even if this implies that the solution could be sub‐optimal, it has demonstrated a very satisfactory performance and enables the problem to become manageable in real life settings.

The proposed methodology enables to rapidly choose if a customer order should be shipped via a FTL or a LTL transport and performs the aggregation of different orders in separate shipments in order to minimize total transportation costs. As a consequence, the task of logistics managers is greatly simplified and consistently better performances respect manual planning can be obtained.

The described methodology is original in both the kind of approach adopted to solve the problem of optimising orders shipping in long‐haul direct shipping distribution logistics, and in the solution technique adopted which integrates heuristic algorithm and an original formulation of a GA optimisation problem. Moreover, the methodology solves both the truckload assignment problem and the choice of LTL vs FTL shipment thus representing an useful tool for logistics managers.

An association — Geomail — has been formed consisting of private electronic mail system operators, designed to provide a full international service.

Memory plays a vital role in designing embedded systems. A larger memory can accommodate more and larger applications but increases cost area, as well as energy requirements. Hence, the purpose of this paper is to propose code compression techniques to solve this issue by minimizing the code size of the application program by compressing the instructions with higher static frequency.

The idea is based on the static and dynamic frequency-based algorithm combined with bit mask and dictionary-based algorithm for MIPS32 processor, in order to minimize the code size and improves compression ratio.

The experimental result shows that the proposed system achieves up to 67 percent compression efficiency.

The paper presents enhanced versions of the code compression technique.

In our contemporary living environment, most people want, not only to maintain privacy and security, but also to avoid losing the keys or passwords for their doors at home. Additionally, many would like to own a fast, convenient and cost-effective exclusive door lock controller to establish some peace of mind. For this paper, the author has designed a smart door lock device which has a non-contact pattern recognition controller to recognize a tiny pattern that can be displayed by mobile devices. The paper aims to discuss this issue.

The patterns recognized by the exclusively designed security device are created using a two-dimensional text and graphics method that can be stored by mobile devices. Therefore, when key-owners come near their doors’ control panels, the established two-dimensional text and graphics cyphers can be displayed for verification purposes on a mobile device’s screen. The author used the MT19937 (32 bit) virtual random number generator as the encryption technology.

The recognized pattern is built using a two-dimensional text and graphics method that can be stored by owners’ mobile devices. Therefore, when a key-owner comes near a door’s control panel, the established two-dimensional text and graphics cypher can be displayed.

Use of either wireless or wired internet causes momentary delays to lock control signals.

Upon each occasion of a code verification, doors are opened and short message service (SMS) notifications are sent to the key-owners simultaneously. The technology described in this paper can be used at any civilian installations that require door lock controls. The application range is very wide and can be customized readily.

The technology discussed here could also be applied widely and easily in various fields.

The lock has a two-phase design for secure opening: i.e. mask-bit enabling and simultaneous SMS notification. The contactless smart door lock system combines three parts that are linked via the internet, which are a cloud server and database, a personal mobile device, and the device’s fixtures and fittings. The fixtures and fittings include a complementary metal oxide silicon sensor, a limiting switch, an electromagnetic switch, a microcontroller unit, and an RJ-45 internet port. The results of this study could be applied at any civilian installation that requires door lock control.

Layout optimization of structures aims to find the optimal topology and member sizes in an integrated manner. For this purpose, the most successful attempts have addressed the outstanding features of the genetic algorithms.

This paper utilizes a direct index coding (DIC) in a way that the optimization algorithm can simultaneously integrate topology and size in a minimal length chromosome in order to seek the true optimum in an efficient and reasonable manner. Proper genetic operators are adopted for this special kind of encoding together with some modifications in the topological mutation aiming to improve the convergence of the algorithm.

The present DIC, has the following features: enforcing one‐to‐one correspondence between discrete genotype space and the problems' phenotype space; avoiding any out‐of‐bound parameter addressing and limiting the GA search only to necessary genotypes; reduction in the size of genotype search space to increase the algorithm convergence and the possibility of leading to the global optimum; dealing with direct genetic operators so that the GA parameters can be purely controlled to tune the desired balance between convergence and escaping from local optima.

Employing direct index chromosome makes it possible to eliminate the additional topological bits in treated examples.

This paper aims to utilize an evolutionary algorithm as an optimization technique to solve design optimization of a pivoted‐pad journal bearing performance characteristics considering objective functions which are contradictory to each other. These functions include fluid film thickness, film temperature, and power loss along with design variables vector, which consists of pad axial length to journal diameter ratio, pad arc length, bearing radial clearance, pad offset factor, and pad preload factor.

In order to improve performance characteristics of the pivoted‐pad journal bearing, the genetic algorithm (GA) was employed and the modeling of the bearing was represented with the finite element method. Also, a numerical method, global criterion, was conducted to validate the efficiency and applicability of the proposed method.

This paper utilizes the GA to solve multi‐objective optimization of the pivoted‐pad journal bearing performance governed by design variables such as pad axial length to journal diameter ratio, pad arc length, bearing radial clearance, pad offset factor, and pad preload factor for a range of rotational speed. The global criterion method (GCM) proves that the proposed method is very efficient and useful for the optimal design of the pivoted‐pad journal bearings. An acceptable results agreement between the GA and the GCM has been obtained. The GA is well suited to find the optimum solutions with a high probability since it starts with an initial set of solutions rather than a single solution. The GA explores different areas of search space in parallel and, it is good at exploitation.

With the help of the proposed method, it will be possible to have an idea about bearing geometric parameters on a more rational basis, and to better predict the dynamic performance of rotor systems supported on the pivoted‐pad journal bearings.

Investigates how a particular work team interprets and comes to understand quality management initiatives centered around customer service. The study set out to add to the understanding of how work team members interpret and learn as a part of a functional work based team operating within a quality management work environment. Data sources, including field notes, an extensive reflective journal, strategic plans, annual reports, e‐mail messages and office memos, provided rich, in‐depth information. The study argues that, contrary to much of the management‐based learning literature, learning is used as a way to mold and shape attitudes of workers and to control them.

The purpose of this paper is to build a compact and accurate classifier for high-dimensional classification.

A classification approach based on class-dependent feature subspace (CFS) is proposed. CFS is a class-dependent integration of a support vector machine (SVM) classifier and associated discriminative features. For each class, our genetic algorithm (GA)-based approach evolves the best subset of discriminative features and SVM classifier simultaneously. To guarantee convergence and efficiency, the authors customize the GA in terms of encoding strategy, fitness evaluation, and genetic operators.

Experimental studies demonstrated that the proposed CFS-based approach is superior to other state-of-the-art classification algorithms on UCI data sets in terms of both concise interpretation and predictive power for high-dimensional data.

UCI data sets rather than real industrial data are used to evaluate the proposed approach. In addition, only single-label classification is addressed in the study.

The proposed method not only constructs an accurate classification model but also obtains a compact combination of discriminative features. It is helpful for business makers to get a concise understanding of the high-dimensional data.

The authors propose a compact and effective classification approach for high-dimensional data. Instead of the same feature subset for all the classes, the proposed CFS-based approach obtains the optimal subset of discriminative feature and SVM classifier for each class. The proposed approach enhances both interpretability and predictive power for high-dimensional data.