Selective chaotic maps Tiki-Taka algorithm for the S-box generation and optimization

Neural Computing and Applications - Cryptography often involves substituting (and converting) the secret information into dummy data so that it could reach the desired destination without leakage....     

A systematic review on emperor penguin optimizer

Neural Computing and Applications - Emperor Penguin Optimizer (EPO) is a recently developed metaheuristic algorithm to solve general optimization problems. The main strength of EPO is twofold....     

Increased Chondrocyte Apoptosis Is Associated with Progression of Osteoarthritis in Spontaneous Guinea Pig Models of the Disease

Osteoarthritis (OA) is the most common joint disease characterised by degradation of articular cartilage and bone remodelling. For almost a decade chondrocyte apoptosis has been investigated as a possible mechanism of cartilage damage in OA, but its precise role in initiation and/or progression of OA remains to the determined. The aim of this study is to determine the role of chondrocyte apoptosis in spontaneous animal models of OA. Right tibias from six male Dunkin Hartley (DH) and Bristol Strain 2 (BS2) guinea pigs were collected at 10, 16, 24 and 30 weeks of age. Fresh-frozen sections of tibial epiphysis were microscopically scored for OA, and immunostained with caspase-3 and TUNEL for apoptotic chondrocytes. The DH strain had more pronounced cartilage damage than BS2, especially at 30 weeks. At this time point, the apoptotic chondrocytes were largely confined to the deep zone of articular cartilage (AC) with a greater percentage in the medial side of DH than BS2 (DH: 5.7%, 95% CI: 4.2–7.2), BS2: 4.8%, 95% CI: 3.8–5.8), p > 0.05). DH had a significant progression of chondrocyte death between 24 to 30 weeks during which time significant changes were observed in AC fibrillation, proteoglycan depletion and overall microscopic OA score. A strong correlation (p ≤ 0.01) was found between chondrocyte apoptosis and AC fibrillation (r = 0.3), cellularity (r = 0.4) and overall microscopic OA scores (r = 0.4). Overall, the rate of progression in OA and apoptosis over the study period was greater in the DH (versus BS2) and the medial AC (versus lateral). Chondrocyte apoptosis was higher at the later stage of OA development when the cartilage matrix was hypocellular and highly fibrillated, suggesting that chondrocyte apoptosis is a late event in OA.     

MC‐MIPOG: A Parallel t‐Way Test Generation Strategy for Multicore Systems

Combinatorial testing has been an active research area in recent years. One challenge in this area is dealing with the combinatorial explosion problem, which typically requires a very expensive computational process to find a good test set that covers all the combinations for a given interaction strength (t). Parallelization can be an effective approach to manage this computational cost, that is, by taking advantage of the recent advancement of multicore architectures. In line with such alluring prospects, this paper presents a new deterministic strategy, called multicore modified input parameter order (MC‐MIPOG) based on an earlier strategy, input parameter order generalized (IPOG). Unlike its predecessor strategy, MC‐MIPOG adopts a novel approach by removing control and data dependency to permit the harnessing of multicore systems. Experiments are undertaken to demonstrate speedup gain and to compare the proposed strategy with other strategies, including IPOG. The overall results demonstrate that MC‐MIPOG outperforms most existing strategies (IPOG, IPOF, IPOF2, IPOG‐D, ITCH, TConfig, Jenny, and TVG) in terms of test size within acceptable execution time. Unlike most strategies, MC‐MIPOG is also capable of supporting high interaction strengths of t > 6.     

Subchondral Bone Plate Changes More Rapidly than Trabecular Bone in Osteoarthritis

Osteoarthritis (OA) is the most common joint disorder, characterised by focal loss of cartilage and increased subchondral bone remodelling at early OA stages of the disease. We have investigated the temporal and the spatial relationship between bone remodelling in subchondral bone plate (Sbp) and trabecular bone (Tb) in Dunkin Hartley (DH, develop OA early) and the Bristol Strain 2 (BS2, control which develop OA late) guinea pigs. Right tibias were dissected from six male animals of each strain, at 10, 16, 24 and 30 weeks of age. Micro-computed tomography was used to quantify the growth plate thickness (GpTh), subchondral bone plate thickness (SbpTh) and trabecular bone thickness (TbTh), and bone mineral density (BMD) in both Sbp and Tb. The rate of change was calculated for 10–16 weeks, 16–24 weeks and 24–30 weeks. The rate of changes in Sbp and Tb thickness at the earliest time interval (10–16 weeks) were significantly greater in DH guinea pigs than in the growth-matched control strain (BS2). The magnitude of these differences was greater in the medial side than the lateral side (DH: 22.7 and 14.75 µm/week, BS2: 5.63 and 6.67 µm/week, respectively). Similarly, changes in the BMD at the earliest time interval was greater in the DH strain than the BS2, again more pronounced in the disease prone medial compartment (DH: 0.0698 and 0.0372 g/cm³/week, BS2: 0.00457 and 0.00772 g/cm³/week, respectively). These changes observed preceded microscopic and cellular signs of disease as previously reported. The rapid early changes in SbpTh, TbTh, Sbp BMD and Tb BMD in the disease prone DH guinea pigs compared with the BS2 control strain suggest a link to early OA pathology. This is corroborated by the greater relative changes in subchondral bone in the medial compared with the lateral compartment.     

Design and Implementation of a Scalable RFID-Based Attendance System with an Intelligent Scheduling Technique

Radio Frequency Identification (RFID) technology; a convenient and flexible technology which is well suited for fully automated systems, is directing human lifestyle towards automation and reality. Integrating RFID into attendance management systems makes the tasks of both users and administrators easy, smart, convenient, and practical. Earlier implementations of RFID-based attendance systems involve different approaches and facilities. Different intertwined characteristics (i.e., scalability, and automation) are suggested in evaluating the performance of these systems. The coverage of these characteristics appears to be adequate in achieving a good systems implementation. Some of the existing systems present high performance; however, these systems still have gaps in some of the suggested characteristics. In order to mind these gaps, a scalable RFID-based attendance system with an intelligent scheduling technique; called Intelligent and fully Automated Attendance System (IAAS), has been designed and implemented. Finally, the performance of IAAS has been evaluated through a comparison with existing attendance systems.     

Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

Neural Computing and Applications - This paper discusses a new variant of Henry Gas Solubility Optimization (HGSO) Algorithm, called Hybrid HGSO (HHGSO). Unlike its predecessor, HHGSO allows...     

Modified Recursive Least Squares algorithm to train the Hybrid Multilayered Perceptron (HMLP) network

In this paper, a new learning algorithm, called the Modified Recursive Least Square (MRLS), is introduced for the Hybrid Multilayered Perceptron (HMLP) network. Adopting the Recursive Least Square (RLS) algorithm as its basis, the MRLS algorithm differs from RLS in the way that the weight of the linear connections for the HMLP network is estimated. The convergence rate of the MRLS algorithm is further improved by varying the forgetting factor, optimizing the way the momentum and learning rate are assigned. To investigate its applicability, the MRLS algorithm is demonstrated on the HMLP network using six benchmark data sets obtained from the UCI repository. The classification performance of the HMLP network trained with the MRLS algorithm is compared with those of the HMLP network trained with the Modified Recursive Prediction Error (MRPE) algorithm and the MLP trained with the standard RLS algorithm as well as with other commonly adopted machine learning classifiers. The comparison results indicated that the proposed MRLS trained HMLP network provides significant improvement over RLS trained MLP network, MRPE trained HMLP network, and other machine learning classifiers in terms of accuracy, convergence rate and mean square error (MSE).     

Design and implementation of a t-way test data generation strategy with automated execution tool support

To ensure an acceptable level of quality and reliability of a typical software product, it is desirable to test every possible combination of input data under various configurations. However, due to the combinatorial explosion problem, exhaustive testing is practically impossible. Resource constraints, cost factors, and strict time-to-market deadlines are some of the main factors that inhibit such a consideration. Earlier research has suggested that a sampling strategy (i.e., one that is based on a t-way parameter interaction) can be effective. As a result, many helpful t-way sampling strategies have been developed and can be found in the literature.Several advances have been achieved in the last 15 years, which have, in particular, served to facilitate the test planning process by systematically minimizing the test size required (based on certain t-way parameter interactions). Despite this significant progress, the integration and automation of strategies (from planning process to execution) are still lacking. Additionally, strategizing to sample (and construct) a minimum test set from the exhaustive test space is an NP-complete problem; that is, it is often unlikely that an efficient strategy exists that could regularly generate an optimal test set. Motivated by these challenges, this paper discusses the design, implementation, and validation of an efficient strategy for t-way testing, the GTWay strategy. The main contribution of GTWay is the integration of t-way test data generation with automated (concurrent) execution as part of its tool implementation. Unlike most previous methods, GTWay addresses the generation of test data for a high coverage strength (t > 6).