Silver Nanoflower Decorated Graphene Oxide Sponges for Highly Sensitive Variable Stiffness Stress Sensors

Soft conductive materials should enable large deformation while keeping high electrical conductivity and elasticity. The graphene oxide (GO)‐based sponge is a potential candidate to endow large deformation. However, it typically exhibits low conductivity and elasticity. Here, the highly conductive and elastic sponge composed of GO, flower‐shaped silver nanoparticles (AgNFs), and polyimide (GO‐AgNF‐PI sponge) are demonstrated. The average pore size and porosity are 114 µm and 94.7%, respectively. Ag NFs have thin petals (8–20 nm) protruding out of the surface of a spherical bud (300–350 nm) significantly enhancing the specific surface area (2.83 m² g^?1). The electrical conductivity (0.306 S m^?1 at 0% strain) of the GO‐AgNF‐PI sponge is increased by more than an order of magnitude with the addition of Ag NFs. A nearly perfect elasticity is obtained over a wide compressive strain range (0–90%). The strain‐dependent, nonlinear variation of Young's modulus of the sponge provides a unique opportunity as a variable stiffness stress sensor that operates over a wide stress range (0–10 kPa) with a high maximum sensitivity (0.572 kPa^?1). It allows grasping of a soft rose and a hard bottle, with the minimal object deformation, when attached on the finger of a robot gripper.     

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Identifying fruit disease manually is time-consuming, expert-required, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.     

WiMA: Towards a Multi-Criterion Association in Software Defined Wi-Fi Networks

Despite the planned installation and operations of the traditional IEEE 802.11 networks, they still experience degraded performance due to the number of inefficiencies. One of the main reasons is the received signal strength indicator (RSSI) association problem, in which the user remains connected to the access point (AP) unless the RSSI becomes too weak. In this paper, we propose a multi-criterion association (WiMA) scheme based on software defined networking (SDN) in Wi-Fi networks. An association solution based on multi-criterion such as AP load, RSSI, and channel occupancy is proposed to satisfy the quality of service (QoS). SDN having an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput performance. To implement WiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network simulator. The performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30% and enhances the throughput by 20%–50%, hence maintaining user fairness and accommodating more wireless devices and traffic load in the network, when compared to traditional client-driven (CD) approach and state of the art Wi-Balance approach.     

Localization of nervonic acid beta-oxidation in human and rodent peroxisomes: impaired oxidation in Zellweger syndrome and X-linked adrenoleukodystrophy

Studies with purified subcellular organelles from rat liver indicate that nervonic acid (C24:1) is beta-oxidized preferentially in peroxisomes. Lack of effect by etomoxir, inhibitor of mitochondrial beta-oxidation, on beta-oxidation of lignoceric acid (C24:0), a peroxisomal function, and that of nervonic acid (24:1) compared to the inhibition of palmitic acid (16:0) oxidation, a mitochondrial function, supports the conclusion that nervonic acid is oxidized in peroxisomes. Moreover, the oxidation of nervonic and lignoceric acids was deficient in fibroblasts from patients with defects in peroxisomal beta-oxidation [Zellweger syndrome (ZS) and X-linked adrenoleukodystrophy (X-ALD)]. Similar to lignoceric acid, the activation and beta-oxidation of nervonic acid was deficient in peroxisomes isolated from X-ALD fibroblasts. Transfection of X-ALD fibroblasts with human cDNA encoding for ALDP (X-ALD gene product) restored the oxidation of both nervonic and lignoceric acids, demonstrating that the same molecular defect may be responsible for the abnormality in the oxidation of nervonic as well as lignoceric acid. Moreover, immunoprecipitation of activities for acyl-CoA ligase for both lignoceric acid and nervonic acid indicate that saturated and monoenoic very long chain (VLC) fatty acids may be activated by the same enzyme. These results clearly demonstrate that similar to saturated VLC fatty acids (e.g., lignoceric acid), VLC monounsaturated fatty acids (e.g., nervonic acid) are oxidized preferentially in peroxisomes and that this activity is impaired in X-ALD. In view of the fact that the oxidation of unsaturated VLC fatty acids is defective in X-ALD patients, the efficacy of dietary monoene therapy, "Lorenzo's oil," in X-ALD needs to be evaluated.     

Transient liquid phase diffusion bonding and associated recrystallization phenomenon when joining ODS ferritic superalloys

Oxide dispersion strengthened (ODS) ferritic superalloys attribute their excellent intermediate and high temperature creep resistant properties to the distribution of an inert oxide, Y₂O₃ within highly directional and elongated grains. Careful selection of joining techniques is, therefore, of utmost importance so that the parent metal microstructure is not disrupted and is continuous across the bond line. Transient liquid phase (TLP) bonding is a suitable technique which has been used to join the ferritic superalloys MA957, MA956 and PM2000 using an amorphous foil based on an Fe-B-Si composition. To further minimize disruption to the parent alloy microstructure at the bond line, thin sputter coats based on the Fe-B-Si composition have also been used successfully for TLP bonding. Results have shown a boron-induced secondary recrystallized zone at the bond line in MA957 which acts as a barrier to further grain growth across the bond line on subsequent zone annealing. Differential scanning calorimetry shows that this recrystallization is triggered at 200 °C below the usual recrystallization temperature during heat treatment and occurs only when the metal filler melts and there is a free flux of boron into the base metal. Texture measurements show that the boron-induced recrystallization is of the same nature as the recrystallization produced by heat treatment but possesses a stronger directional 110 fibre texture. In contrast, grain growth across the bond line could be achieved in TLP bonds produced in MA956. However, a similar heat treatment for PM2000 produced simultaneous but independent secondary recrystallization both at the joint and in the bulk alloy. This difference in behaviour between these two similar alloys is attributed to differences in their thermomechanical processing.     

Microfluidic continuous magnetophoretic protein separation using nanoparticle aggregates

We demonstrate a microfluidic continuous-flow protein separation process in which silica-coated superparamagnetic nanoparticles interact preferentially with hemoglobin in a mixture with bovine serum albumin, and the resulting hemoglobin-nanoparticle aggregates are recovered online using magnetophoresis. We present detailed modeling and analysis of this process yielding quantitative estimates of the recovery of both proteins, validated by experiments. While several previous studies utilize an average particle size in modeling magnetophoretic particle trajectories or process design, in this study we emphasize the importance of accounting for particle size distributions in calculating particle recovery, and therefore in estimating separation efficiency. We combine experimentally measured size distributions of protein-nanoparticle aggregates with simulations of particle trajectories and provide a simple analytical method to calculate the efficiency of separation at various flow speeds, which fully accounts for heterogeneity in particle sizes. Our method can potentially be used for affinity based biomolecular separations at both analytical and preparative scales by exploiting well-established techniques to functionalize nanoparticle surfaces with selective ligands. Further, the modeling methodology presented here may be applied to provide better estimates of particle recovery in a broad range of magnetophoretic separation processes involving heterogeneity in particle sizes.     

Determining malicious executable distinguishing attributes and low-complexity detection

Detection of rapidly evolving malware requires classification techniques that can effectively and efficiently detect zero-day attacks. Such detection is based on a robust model of benign behavior and deviations from that model are used to detect malicious behavior. In this paper we propose a low-complexity host-based technique that uses deviations in static file attributes to detect malicious executables. We first develop simple statistical models of static file attributes derived from the empirical data of thousands of benign executables. Deviations among the attribute models of benign and malware executables are then quantified using information-theoretic (Kullback-Leibler-based) divergence measures. This quantification reveals distinguishing attributes that are considerably divergent between benign and malware executables and therefore can be used for detection. We use the benign models of divergent attributes in cross-correlation and log-likelihood frameworks to classify malicious executables. Our results, using over 4,000 malicious file samples, indicate that the proposed detector provides reasonably high detection accuracy, while having significantly lower complexity than existing detectors.     

Attention,polychronicity, and expertise in prospective memory performance: Programmers' vulnerability to habit intrusion error in multitasking

The paper examines prospective memory (ProM) in programmer multitasking and reports administration of a naturalistic atypical action. The study emphasizes on how attention, time orientation, and expertise affect ProM performance in multitasking among a group of computer science and engineering students (N=108). The results suggest that attention play a crucial role in multitasking and ProM performance with respect to whether or not a word display requires more attention to be devoted in monitoring and identifying it for an appropriate action. Polychrons exhibit lesser degree of ProM performance failure than monochrons whereas expertise does not have an effect. Finally, results show that out of overall ProM performance failure, habit intrusion errors comprise of 16.22% occurring 1.75 times in every 10 valid click responses of ProM task. Moreover, experts demonstrate a superior performance over novices in programming.     

Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation

With growing interest in extending GIS to support multi-criteria decision-making (MCDM) methods, enhancing GIS-based MCDM with sensitivity analysis (SA) procedures is crucial to understand the model behavior and its limitations. This paper presents a novel approach of examining multi-criteria weight sensitivity of a GIS-based MCDM model. It explores the dependency of model output on the weights of input parameters, identifying criteria that are especially sensitive to weight changes and to show the impacts of changing criteria weights on the model outcomes in spatial dimension. A methodology was developed to perform simulations where the weights associated with all criteria used for suitability modelling were varied one-at-a-time (OAT) to investigate their relative impacts on the final evaluation results. A tool which incorporates the OAT method with the Analytical Hierarchy Process (AHP) within the ArcGIS environment was implemented. It permits a range of user defined simulations to be performed to quantitatively evaluate model dynamic changes, measures the stability of results with respect to the variation of different parameter weights, and displays spatial change dynamics. A case study of irrigated cropland suitability assessment addressing the application of the new GIS-based AHP-SA tool is described. It demonstrates that the tool is spatial, simple and flexible.     

MTPSO algorithm for solving planar graph coloring problem

In this paper, we proposed a modified turbulent particle swarm optimization (named MTPSO) model for solving planar graph coloring problem based on particle swarm optimization. The proposed model is consisting of the walking one strategy, assessment strategy and turbulent strategy. The proposed MTPSO model can solve the planar graph coloring problem using four-colors more efficiently and accurately. Compared to the results shown in Cui et al. (2008), not only the experimental results of the proposed model can get smaller average iterations but can get higher correction coloring rate when the number of nodes is greater than 30.