Design and implementation of a new tuned hybrid intelligent model to predict the uniaxial compressive strength of the rock using SFS-ANFIS

This study proposes a novel design to systematically optimize the parameters for the adaptive neuro-fuzzy inference system (ANFIS) model using stochastic fractal search (SFS) algorithm. To affirm the efficiency of the proposed SFS-ANFIS model, the predicting results were compared with ANFIS and three hybrid methodologies based on ANFIS combined with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Accurate prediction of uniaxial compressive strength (UCS) is of great significance for all geotechnical projects such as tunnels and dams. Hence, this study proposes the use of SFS-ANFIS, GA-ANFIS, DE-ANFIS, PSO-ANFIS, and ANFIS models to predict UCS. In this regard, the fresh water tunnel of Pahang–Selangor located in Malaysia was considered and the requirement data samples were collected. Different statistical metrics such as coefficient of determination (R²) and mean absolute error were used to evaluate the models. Referring to the efficiency results of SFS-ANFIS, it can be found that the SFS-ANFIS (with the R² of 0.981) has higher ability than PSO-ANFIS, DE-ANFIS, GA-ANFIS, and ANFIS models in predicting the UCS.

     

Multi-indicator Active Queue Management Method

A considerable number of applications are running over IP networks. This increased the contention on the network resource, which ultimately results in congestion. Active queue management (AQM) aims to reduce the serious consequences of network congestion in the router buffer and its negative effects on network performance. AQM methods implement different techniques in accordance with congestion indicators, such as queue length and average queue length. The performance of the network is evaluated using delay, loss, and throughput. The gap between congestion indicators and network performance measurements leads to the decline in network performance. In this study, delay and loss predictions are used as congestion indicators in a novel stochastic approach for AQM. The proposed method estimates the congestion in the router buffer and then uses the indicators to calculate the dropping probability, which is responsible for managing the router buffer. The experimental results, based on two sets of experiments, have shown that the proposed method outperformed the existing benchmark algorithms including RED, ERED and BLUE algorithms. For instance, in the first experiment, the proposed method resides in the third-place in terms of delay when compared to the benchmark algorithms. In addition, the proposed method outperformed the benchmark algorithms in terms of packet loss, packet dropping, and packet retransmission. Overall, the proposed method outperformed the benchmark algorithms because it preserves packet loss while maintaining reasonable queuing delay.     

Customized trapping of magnetic particles

This paper presents an efficient technique for trapping of magnetic particles in confined spatial locations using customized designs of micro-coils (MCs). Large magnetic field gradients of up to 20 T/mm and large magnetic forces in the range of 10⁻⁸ Newton on magnetic particles with diameter of 1 μm have been achieved using MCs with several planar geometrical configurations. A large magnetic field gradient is generated and enhanced by two structural parameters: the small width and high aspect ratio of each single conductor and the ferromagnetic pillars positioned at high-flux density locations. This arrangement creates very steep magnetic potential wells, in particular at the vicinity of the pillars. The system allowed capturing of suspended magnetic particles as far as 1,000 μm from the center of the device. Magnetic particles/cells have been trapped and confined in single and in arrays of deep magnetic potential wells corresponding to the MCs configuration.     

Fractional Order Nonlinear Bone Remodeling Dynamics Using the Supervised Neural Network

This study aims to solve the nonlinear fractional-order mathematical model (FOMM) by using the normal and dysregulated bone remodeling of the myeloma bone disease (MBD). For the more precise performance of the model, fractional-order derivatives have been used to solve the disease model numerically. The FOMM is preliminarily designed to focus on the critical interactions between bone resorption or osteoclasts (OC) and bone formation or osteoblasts (OB). The connections of OC and OB are represented by a nonlinear differential system based on the cellular components, which depict stable fluctuation in the usual bone case and unstable fluctuation through the MBD. Untreated myeloma causes by increasing the OC and reducing the osteoblasts, resulting in net bone waste the tumor growth. The solutions of the FOMM will be provided by using the stochastic framework based on the Levenberg-Marquardt backpropagation (LVMBP) neural networks (NN), i.e., LVMBPNN. The mathematical performances of three variations of the fractional-order derivative based on the nonlinear disease model using the LVMPNN. The static structural performances are 82% for investigation and 9% for both learning and certification. The performances of the LVMBPNN are authenticated by using the results of the Adams-Bashforth-Moulton mechanism. To accomplish the capability, steadiness, accuracy, and ability of the LVMBPNN, the performances of the error histograms (EHs), mean square error (MSE), recurrence, and state transitions (STs) will be provided.     

Automatic tuning of whole applications using direct search and a performance-based transformation system

In many cases, simple analytical models used by traditional compilers are no longer able to yield effectively optimized code for complex programs because of the enormous complexity of processor architectures. A promising alternative approach for optimizing applications effectively has been the use of search-based empirical methods. The success of empirically tuned library generators such as ATLAS has shown that this strategy can be effective for domain-specific programs. However, to date there has been no general-purpose tool for effective empirical optimization of whole programs. The main obstacle to this approach has been the need for evaluating a prohibitively large number of alternative program variants. To address this problem, we have developed a prototype tool for automatic application tuning that uses loop-level performance feedback and a direct search strategy to guide search for the best set of optimization parameters. Experiments on four different architectures show that direct search can be an effective technique for finding good values for transformation parameters in a reasonable time. This material is based on work supported by the Department of Energy under Contract Nos. 03891-001-99-4G, 74837-001-03 49, 86192-001-04 49, and/or 12783-001-05 49 from the Los Alamos National Laboratory.     

A new removable resist for high aspect ratio applications

A variety of different photo resists are used for fabrication of MEMS. Presently good results were reported for SU-8, a chemically amplified negative tone photoresist. But SU-8 has a disadvantage for some applications in LIGA technique, especially in the X-ray mask fabrication. After processing the finished resist pattern are hardly soluble from the substrate. This paper will briefly describe the current status of the development of the new negative tone photoresist CAR 44 whose big advantage is the easy removableness of the cross linked pattern. This work widely uses the contents of the presentation “A New Removable Resist for High Aspect Ratio Applications” to the High Aspect Ratio Micro Structure Technology workshop HARMST 2005 held in Gyeongyu (Republic of Korea), June 10–13, 2005.     

Noninvasive estimation of baroreflex sensitivity using pressurepulse amplification

Studies using peripheral pressure measurements have facilitated studies in identifying particular gain characteristics of baroreflex control loops. However, the baroreceptors are located in central regions of the arterial tree (aortic arch and carotid arteries) and are subjected to peak pressures that are different to those measured in peripheral locations (e.g., finger). The analysis presented shows that if the known transfer function properties between peripheral and central pressures are used to derive central pressure from peripheral measurements, the baroreflex gain characteristics can be calculated in terms of central aortic pressure changes. The results obtained by this method are markedly different than those calculated using peripheral pressure     

GC–MS metabolomics revealed protocatechuic acid as a cytotoxic and apoptosis-inducing compound from black rice brans

Food Science and Biotechnology - GC–MS metabolomics was used to discriminate the phytochemicals profile of Indonesian white, red, and black rice brans, and Japanese white rice brans. This...     

Lambda Calculus with Explicit Recursion

This paper is concerned with the study ofλ-calculus with explicit recursion, namely of cyclicλ-graphs. The starting point is to treat aλ-graph as a system of recursion equations involvingλ-terms and to manipulate such systems in an unrestricted manner, using equational logic, just as is possible for first-order term rewriting. Surprisingly, now the confluence property breaks down in an essential way. Confluence can be restored by introducing a restraining mechanism on the substitution operation. This leads to a family ofλ-graph calculi, which can be seen as an extension of the family ofλσ-calculi (λ-calculi with explicit substitution). While theλσ-calculi treat the let-construct as a first-class citizen, our calculi support the letrec, a feature that is essential to reason about time and space behavior of functional languages and also about compilation and optimizations of programs