3D-SDAR modeling of hERG potassium channel affinity: A case study in model design and toxicophore identification

A dataset of 237 human Ether-à-go-go Related Gene (hERG) potassium channel inhibitors (180 of which were used for model building and validation, whereas 57 constituted the “true” external prediction set) collected from 22 literature sources was modeled by 3D-SDAR. To produce reliable and reproducible classification models for hERG blocking, the initial set of 180 chemicals was split into two subsets: a balanced modeling set consisting of 118 compounds and an unbalanced validation set comprised of 62 compounds. A PLS bagging-like algorithm written in Matlab was used to process the data and assign each compound to one of the two (hERG+ or hERG-) activity classes. The best predictive model evaluated on the basis of a fully randomized hold-out test set (comprising 20% of the modeling set) used 4 latent variables and a grid of 6 ppm × 6 ppm × 1 Å in the C-C region, 6 ppm × 30 ppm × 1 Å in the C-N region, and 30 ppm × 30 ppm × 1 Å in the N-N region. An overall accuracy of 0.84 was obtained for both the hold-out test set and the validation set. Further, an external prediction set consisting of 57 drugs and drug derivatives was used to estimate the true predictive power of the reported 3D-SDAR model – a slight reduction of the overall accuracy down to 0.77 was observed. 3D-SDAR map of the most frequently occurring bins and their projection on the standard coordinate space of the chemical structures allowed identification of a three-center toxicophore composed of two aromatic rings and an amino group. A U test along the distance axis of the most frequently occurring 3D-SDAR bins was used to set the distance limits of the toxicophore. This toxicophore was found to be similar to an earlier reported phospholipidosis (PLD) toxicophore.     

Pragmatic correlation analysis for probabilistic ranking over relational data

It is widely recognized that effective ranking methods for relational data (e.g., tuples) enable users to overcome the limitations of the traditional Boolean retrieval model and the hardness of structured query writing. To determine the rank of a tuple, term frequency-based methods, such as tf × idf (term frequency × inverse document frequency) schemes, have been commonly adopted in the literature by simply considering a tuple as a single document. However, in many cases, we have noted that tf × idf schemes may not produce effective rankings or specific orderings for relational data with categorical attributes, which is pervasive today. To support fundamental aspects of relational data, we apply the notions of correlation analysis to estimate the extent of relationships between queries and data. This paper proposes a probabilistic ranking model to exploit statistical relationships that exist in relational data of categorical attributes. Given a set of query terms, information on correlative attribute values to the query terms is used to estimate the relevance of the tuple to the query. To quantify the information, we compute the extent of the dependency between correlative attribute values on a Bayesian network. Moreover, we avoid the prohibitive cost of computing insignificant ranking features based on a limited assumption of node independence. Our probabilistic ranking model is domain-independent and leverages only data statistics without any prior knowledge such as user query logs. Experimental results show that our work improves the effectiveness of rankings for real-world datasets and has a reasonable query processing efficiency compared to related work.     

Tuning and hybrid parallelization of a genetic-based multi-point statistics simulation code

One of the main difficulties using multi-point statistical (MPS) simulation based on annealing techniques or genetic algorithms concerns the excessive amount of time and memory that must be spent in order to achieve convergence. In this work we propose code optimizations and parallelization schemes over a genetic-based MPS code with the aim of speeding up the execution time. The code optimizations involve the reduction of cache misses in the array accesses, avoid branching instructions and increase the locality of the accessed data. The hybrid parallelization scheme involves a fine-grain parallelization of loops using a shared-memory programming model (OpenMP) and a coarse-grain distribution of load among several computational nodes using a distributed-memory programming model (MPI). Convergence, execution time and speed-up results are presented using 2D training images of sizes 100 × 100 × 1 and 1000 × 1000 × 1 on a distributed-shared memory supercomputing facility.     

Human activity recognition with smartphone sensors using deep learning neural networks

Human activities are inherently translation invariant and hierarchical. Human activity recognition (HAR), a field that has garnered a lot of attention in recent years due to its high demand in various application domains, makes use of time-series sensor data to infer activities. In this paper, a deep convolutional neural network (convnet) is proposed to perform efficient and effective HAR using smartphone sensors by exploiting the inherent characteristics of activities and 1D time-series signals, at the same time providing a way to automatically and data-adaptively extract robust features from raw data. Experiments show that convnets indeed derive relevant and more complex features with every additional layer, although difference of feature complexity level decreases with every additional layer. A wider time span of temporal local correlation can be exploited (1 × 9–1 × 14) and a low pooling size (1 × 2–1 × 3) is shown to be beneficial. Convnets also achieved an almost perfect classification on moving activities, especially very similar ones which were previously perceived to be very difficult to classify. Lastly, convnets outperform other state-of-the-art data mining techniques in HAR for the benchmark dataset collected from 30 volunteer subjects, achieving an overall performance of 94.79% on the test set with raw sensor data, and 95.75% with additional information of temporal fast Fourier transform of the HAR data set.     

Simultaneous determination of epinephrine and ascorbic acid at the electrochemical sensor of triazole SAM modified gold electrode

The electrochemical sensor of triazole (TA) self-assembled monolayer (SAM) modified gold electrode (TA SAM/Au) was fabricated. The electrochemical behaviors of epinephrine (EP) at TA SAM/Au have been studied. The TA SAM/Au shows an excellent electrocatalytic activity for the oxidation of EP and accelerates electron transfer rate. The diffusion coefficient is 1.135 × 10⁻⁶ cm² s⁻¹. Under the optimum experiment conditions (i.e. 0.1 mol L⁻¹, pH 4.4, sodium borate buffer, accumulation time: 180 s, accumulation potential: 0.6 V, scan rate: 0.1 Vs⁻¹), the cathodic peak current of EP versus its concentration has a good linear relation in the ranges of 1.0 × 10⁻⁷ to 1.0 × 10⁻⁵ mol L⁻¹ and 1.0 × 10⁻⁵ to 6.0 × 10⁻⁴ mol L⁻¹ by square wave adsorptive stripping voltammetry (SWASV), with the correlation coefficient of 0.9985 and 0.9996, respectively. Detection limit is down to 1.0 × 10⁻⁸ mol L⁻¹. The TA SAM/Au can be used for the determination of EP in practical injection. Meantime, the oxidative peak potentials of EP and ascorbic acid (AA) are well separated about 200 ± 10 mV at TA SAM/Au, the oxidation peak current increases approximately linearly with increasing concentration of both EP and AA in the concentration range of 2.0 × 10⁻⁵ to 1.6 × 10⁻⁴ mol L⁻¹. It can be used for simultaneous determination of EP and AA.     

Aviation emission inventory development and analysis

An up to date and accurate aviation emission inventory is a prerequisite for any detailed analysis of aviation emission impact on greenhouse gases and local air quality around airports. In this paper we present an aviation emission inventory using real time air traffic trajectory data. The reported inventory is in the form of a 4D database which provides resolution of 1° × 1° × 1000 ft for temporal and spatial emission analysis. The inventory is for an ongoing period of six months starting from October 2008 for Australian Airspace.In this study we show 6 months of data, with 492,936 flights (inbound, outbound and over flying). These flights used about 2515.83 kt of fuel and emitted 114.59 kt of HC, 200.95 kt of CO, 45.92 kt of NO_x, 7929.89 kt of CO₂, and 2.11 kt of SO_x. From the spatial analysis of emissions data, we found that the CO₂ concentration in some parts of Australia is much higher than other parts, especially in some major cities. The emission results also show that NO_x emission of aviation may have a significant impact on the ozone layer in the upper troposphere, but not in the stratosphere.It is expected that with the availability of this real time aviation emission database, environmental analysts and aviation experts will have an indispensable source of information for making timely decisions regarding expansion of runways, building new airports, applying route charges based on environmentally congested airways, and restructuring air traffic flow to achieve sustainable air traffic growth.     

Bivariate quality control using two-stage intelligent monitoring scheme

In manufacturing industries, it is well known that process variation is a major source of poor quality products. As such, monitoring and diagnosis of variation is essential towards continuous quality improvement. This becomes more challenging when involving two correlated variables (bivariate), whereby selection of statistical process control (SPC) scheme becomes more critical. Nevertheless, the existing traditional SPC schemes for bivariate quality control (BQC) were mainly designed for rapid detection of unnatural variation with limited capability in avoiding false alarm, that is, imbalanced monitoring performance. Another issue is the difficulty in identifying the source of unnatural variation, that is, lack of diagnosis, especially when dealing with small shifts. In this research, a scheme to address balanced monitoring and accurate diagnosis was investigated. Design consideration involved extensive simulation experiments to select input representation based on raw data and statistical features, artificial neural network recognizer design based on synergistic model, and monitoring–diagnosis approach based on two-stage technique. The study focused on bivariate process for cross correlation function, ρ = 0.1–0.9 and mean shifts, μ = ±0.75–3.00 standard deviations. The proposed two-stage intelligent monitoring scheme (2S-IMS) gave superior performance, namely, average run length, ARL₁ = 3.18–16.75 (for out-of-control process), ARL₀ = 335.01–543.93 (for in-control process) and recognition accuracy, RA = 89.5–98.5%. This scheme was validated in manufacturing of audio video device component. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis in BQC.     

Perchlorate-selective polymeric membrane electrode based on a cobaloxime as a suitable carrier

A cobaloxime ([chlorobis(dimethylglyoximeato)(triphenylphosphine)] cobalt (III), [Co(dmgH)₂pph₃Cl]) incorporated in a plasticized poly(vinyl chloride) membrane was used to develop a perchlorate-selective electrode. The influence of membrane composition on the electrode response was studied. The electrode exhibits a Nernstian response over the perchlorate concentration range 1.0 × 10⁻⁶ to 1 × 10⁻¹ mol l⁻¹ with a slope of −56.8 ± 0.7 mV per decade of concentration, a detection limit of 8.3 × 10⁻⁷, a wide working pH range (3–10) and a fast response time (<15 s). The electrode shows excellent selectivity towards perchlorate with respect to many common anions. The electrode was used to determine perchlorate in water and human urine.     

Tuning the push–pull configuration for efficient second-order nonlinear optical properties in some chalcone derivatives

Using the density functional theory methods, we effectively tune the second-order nonlinear optical (NLO) properties in some chalcone derivatives. Various unique push–pull configurations are used to efficiently enhance the intramolecular charge transfer process over the designed derivatives, which result in significantly larger amplitudes of the first hyperpolarizability as compared to their parent molecule. The ground state molecular geometries have been optimized using B3LYP/6-311G** level of theory. A variety of methods including B3LYP, CAM-B3LYP, PBE0, M06, BHandHLYP and MP2 are tested with 6-311G** basis set to calculate the first hyperpolarizability of parent system 1. The results of M06 are found closer to highly correlated MP2 method, which has been selected to calculate static and frequency dependent first hyperpolarizability amplitudes of all selected systems. At M06/6-311G** level of theory, the permanent electronic dipole moment (μ_tot), polarizability (α₀) and static first hyperpolarizability (β_tot) amplitudes for parent system 1 are found to be 5.139 Debye, 274 a. u. and 24.22 × 10⁻³⁰ esu, respectively. These amplitudes have been significantly enhanced in designed derivatives 2 and 3. More importantly, the (β_tot) amplitudes of systems 2 and 3 mount to 75.78 × 10⁻³⁰ and 128.51 × 10⁻³⁰ esu, respectively, which are about 3 times and 5 times larger than that of their parent system 1. Additionally, we have extended the structure-NLO property relationship to several newly synthesized chalcone derivatives. Interestingly, the amplitudes of dynamic frequency dependent hyperpolarizability μβ_ω (SHG) are also significantly larger having values of 366.72 × 10⁻⁴⁸, 856.32 × 10⁻⁴⁸ and 1913.46 × 10⁻⁴⁸ esu for systems 1–3, respectively, at 1400 nm of incident laser wavelength. The dispersion behavior over a wide range of change in wavelength has also been studied adopting a range of wavelength from 1907 to 544 nm. Thus, the present work realizes the potential of designed derivatives as efficient NLO-phores for modern NLO applications.     

A liquid rate gyroscope using electro-conjugate fluid

We propose a novel liquid rate gyroscope using an electro-conjugate fluid (ECF). The electro-conjugate fluid is a dielectric fluid that works as a smart fluid, generating a powerful jet flow (ECF jet) when subjected to a high DC voltage. In this study, we introduce this functional fluid into gyroscopes. Although the sensing principle for angular rate is based on that of a conventional gas rate sensor, the proposed gyroscope has a much higher sensitivity because the density of the liquid is generally higher than that of a gas. In addition, the gyroscope is small in size because the ECF jet is generated only with a pair of tiny electrodes. In other words, the pumping part of the proposed gyroscope does not need mechanical moving parts, resulting in an ECF gyroscope more suitable for micro-applications than a gas rate sensor, which requires a pumping mechanism inside. We fabricated a prototype of the liquid rate gyroscope (40 mm × 60 mm × t7 mm) and confirmed its characteristics by experiments. The experimental results confirm the effectiveness of the proposed liquid rate gyroscope. The prototype has a scale factor of ?29 mV/(°/s) with an applied voltage of 4.5 kV, which is 2.2 times more sensitive than the conventional gas rate gyroscope.     

An efficient data processing framework for mining the massive trajectory of moving objects

Recently, there has been increasing development of positioning technology, which enables us to collect large scale trajectory data for moving objects. Efficient processing and analysis of massive trajectory data has thus become an emerging and challenging task for both researchers and practitioners. Therefore, in this paper, we propose an efficient data processing framework for mining massive trajectory data. This framework includes three modules: (1) a data distribution module, (2) a data transformation module, and (3) a high performance I/O module. Specifically, we first design a two-step consistent hashing algorithm, which takes into account load balancing, data locality, and scalability, for a data distribution module. In the data transformation module, we present a parallel strategy of a linear referencing algorithm with reduced subtask coupling, easy-implemented parallelization, and low communication cost. Moreover, we propose a compression-aware I/O module to improve the processing efficiency. Finally, we conduct a comprehensive performance evaluation on a synthetic dataset (1.114 TB) and a real world taxi GPS dataset (578 GB). The experimental results demonstrate the advantages of our proposed framework.     

FPGA implementation of reversible watermarking in digital images using reversible contrast mapping

Reversible contrast mapping (RCM) and its various modified versions are used extensively in reversible watermarking (RW) to embed secret information into the digital contents. RCM based RW accomplishes a simple integer transform applied on pair of pixels and their least significant bits (LSB) are used for data embedding. It is perfectly invertible even if the LSBs of the transformed pixels are lost during data embedding. RCM offers high embedding rate at relatively low visual distortion (embedding distortion). Moreover, low computation cost and ease of hardware realization make it attractive for real-time implementation. To this aim, this paper proposes a field programmable gate array (FPGA) based very large scale integration (VLSI) architecture of RCM-RW algorithm for digital images that can serve the purpose of media authentication in real-time environment. Two architectures, one for block size (8 × 8) and the other one for (32 × 32) block are developed. The proposed architecture allows a 6-stage pipelining technique to speed up the circuit operation. For a cover image of block size (32 × 32), the proposed architecture requires 9881 slices, 9347 slice flip-flops, 11291 number 4-input LUTs, 3 BRAMs and a data rate of 1.0395 Mbps at an operating frequency as high as 98.76 MHz.     

Enhancement of second-order nonlinear optical response in boron nitride nanocone: Li-doped effect

The unusual properties of Li-doped boron nitride nanomaterials have been paid further attention due to their wide applications in many promising fields. Here, density functional theory (DFT) calculations have been carried out to investigate the second-order nonlinear optical (NLO) properties of boron nitride nanocone (BNNC) and its Li-doped BNNC derivatives. The natural bond orbital charge, electron location function, localized orbital locator and frontier molecular orbital analysis offer further insights into the electron density of the Li-doped BNNC derivatives. The electron density is effectively bounded by the Li atom and its neighboring B atoms. The Li-doped BNNC molecules exhibit large static first hyperpolarizabilities (β_tot) up to 1.19 × 10³ a.u. for Li@2N-BNNC, 5.05 × 10³ a.u. for Li@2B-BNNC, and 1.08 × 10³ a.u. for Li@BN-BNNC, which are significantly larger than that of the non-doped BNNC (1.07 × 10² a.u.). The further investigations show that there are clearly dependencies of the first hyperpolarizabilities on the transition energies and oscillator strengths. Moreover, time-dependent DFT results show that the charge transfer from BNNC to Li atom becomes more pronounced as doping the Li atom to BNNC. It is also found that the frequency-dependent effect on the first hyperpolarizabilities is weak, which may be beneficial to experimentalists for designing Li-doped BNNC molecules with large NLO responses.     

Computer-based analysis of explicit approximations to the implicit Colebrook–White equation in turbulent flow friction factor calculation

The implicit Colebrook–White equation has been widely used to estimate the friction factor for turbulent fluid-flow in rough-pipes. In this paper, the state-of-the-art review for the most currently available explicit alternatives to the Colebrook–White equation, is presented. An extensive comparison test was established on the 20 × 500 grid, for a wide range of relative roughness (ε/D) and Reynolds number (R) values (1 × 10^?6 ? ε/D ? 5 × 10^?2; 4 × 10³ ? R ? 10⁸), covering a large portion of turbulent flow zone in Moody’s diagram. Based on the comprehensive error analysis, the magnitude points in which the maximum absolute and the maximum relative error are occurred at the pair of ε/D and R values, are observed. A limiting case of the most of these approximations provided friction factor estimates that are characterized by a mean absolute error of 5 × 10^?4, a maximum absolute error of 4 × 10^?3 whereas, a mean relative error of 1.3% and a maximum relative error of 5.8%, over the entire range of ε/D and R values, respectively. For practical purposes, the complete results for the maximum and the mean relative errors versus the 20 sets of ε/D value, are also indicated in two comparative figures. The examination results for error properties of these approximations gives one an opportunity to practically evaluate the most accurate formula among of all the previous explicit models; and showing in this way its great flexibility for estimating turbulent flow friction factor. Comparative analysis for the mean relative error profile revealed, the classification for the best-fitted six equations examined was in a good agreement with those of the best model selection criterion claimed in the recent literature, for all performed simulations.     

Flow-injection determination of iron(III) in soil by biamperometry using two independent redox couples

A flow-injection biamperometric method for the determination of iron(III) has been described. The detector consists of two chambers separated by a salt bridge, and one platinum wire working electrode is embedded in each chamber, respectively. When iron(III) solution and hydrogen peroxide solution simultaneously flow through two chambers, the reduction of iron(III) at one platinum electrode is associated with the oxidation of hydrogen peroxide at the other platinum electrode, forming such a system as similar to a reversible couple one. The biamperometric system can perform the determination of iron(III) without any external potential difference. The linear relationship is obtained from 1.0 × 10⁻⁶ to 1.0 × 10⁻⁴ mol l⁻¹ with a detection limit of 6.0 × 10⁻⁷ mol l⁻¹. The proposed method exhibits the satisfactory reproducibility with a relative standard derivation (R.S.D.) of 1.4% for 17 successive determinations of 2.0 × 10⁻⁵ mol l⁻¹ iron(III) and is applied to the determination of iron(III) in soil.     

Chromium(III)-selective sensor based on tri-o-thymotide in PVC matrix

Tri-o-thymotide (I) has been used as an electroactive material in PVC (poly(vinyl chloride)) matrix for fabrication of chromium(III)-selective sensor. The membrane containing tri-o-thymotide, sodium tetraphenyl borate (NaTPB), dibutyl phthalate (DBP) and PVC in the optimum ratio 5:1:75:100 (w/w) exhibits a working concentration range of 4.0 × 10⁻⁶ to 1.0 × 10⁻¹ M with a Nernstian slope of 20.0 ± 0.1 mV/decade of activity in the pH range of 2.8–5.1. The detection limit of this sensor is 2.0 × 10⁻⁷ M. The electrode exhibits a fast response time of 15 s, shows good selectivity towards Cr³⁺ over a number of mono-, bi- and trivalent cations and can also be used in partially non-aqueous medium (up to 15%, v/v) also. The assembly has been successfully used as an indicator electrode in the potentiometric titration of chromium(III) against EDTA and also to determine Cr(III) quantitatively in electroplating industry waste samples.     

A new discrete circuit for readout of resistive sensor arrays

In this paper, we present a method that simplifies the interconnect complexity of N × M resistive sensor arrays from N × M to N + M. In this method, we propose to use two sets of interconnection lines in row–column fashion with all the sensor elements having one of their ends connected to a row line and other end to a column line. This interconnection overloading results in crosstalk among all the elements. This crosstalk causes the spreading of information over the whole array. The proposed circuit in this method takes care of this effect by minimizing the crosstalk. The circuit makes use of the concept of virtual same potential at the inputs of an operational amplifier in negative feedback to obtain a sufficient isolation among various elements. We theoretically present the suitability of the method for small/moderate sized sensor arrays and experimentally verify the predicted behavior by lock-in-amplifier based measurements on a light dependent resistor (LDR) in a 4 × 4 resistor array. Finally, we present a successful implementation of this method on a 16 × 16 imaging array of LDR.     

Comparison of satellite-derived and in-situ observations of ice and snow surface temperatures over Greenland

The most practical way to get spatially broad and continuous measurements of the surface temperature in the data-sparse cryosphere is by satellite remote sensing. The uncertainties in satellite-derived LSTs must be understood to develop internally-consistent decade-scale land surface temperature (LST) records needed for climate studies. In this work we assess satellite-derived “clear-sky” LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and LSTs derived from the Enhanced Thematic Mapper Plus (ETM+) over snow and ice on Greenland. When possible, we compare satellite-derived LSTs with in-situ air temperature observations from Greenland Climate Network (GC-Net) automatic weather stations (AWS). We find that MODIS, ASTER and ETM+ provide reliable and consistent LSTs under clear-sky conditions and relatively-flat terrain over snow and ice targets over a range of temperatures from ? 40 to 0 °C. The satellite-derived LSTs agree within a relative RMS uncertainty of ~ 0.5 °C. The good agreement among the LSTs derived from the various satellite instruments is especially notable since different spectral channels and different retrieval algorithms are used to calculate LST from the raw satellite data. The AWS record in-situ data at a “point” while the satellite instruments record data over an area varying in size from: 57 × 57 m (ETM+), 90 × 90 m (ASTER), or to 1 × 1 km (MODIS). Surface topography and other factors contribute to variability of LST within a pixel, thus the AWS measurements may not be representative of the LST of the pixel. Without more information on the local spatial patterns of LST, the AWS LST cannot be considered valid ground truth for the satellite measurements, with RMS uncertainty ~ 2 °C. Despite the relatively large AWS-derived uncertainty, we find LST data are characterized by high accuracy but have uncertain absolute precision.