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In this study, we synthesised metakaolin-based mesoporous geopolymer adsorbent and investigated the effect of alkaline activator modulus (molar ratios of SiO2/Na2O) on Pb2+ adsorption. The geopolymer prepared using 1.2 M alkaline activator performs excellent Pb2+ removal with a maximum adsorption capacity of 172.71 mg g-1. The pseudo-second-order model fit the adsorption kinetics satisfactorily, indicating that the adsorption process is dominated by chemical adsorption. The adsorption data appropriately fit the Langmuir isotherm model. The contributions of adsorption methods corresponding to the total quantity adsorbed declined in the following order: EDTA extraction (formation of Pb aluminium oxide and Pb-containing amorphous materials) > residual fraction (Pb stabilisation in the tetrahedral aluminosilicate network) > ion exchange. Overall, the alkaline activator modulus significantly influenced the Pb2+ adsorption characteristics of the geopolymer adsorbent.

Cities have specialised in particular urban functions throughout history, with consequential implications for urban and regional patterns of economic and social change. This specialisation takes place within overall national city size distributions and is manifest in different but often similarly variegated residential structures. Here we develop a novel and consistent methodological approach for measuring macro-scale city size and micro-scale residential differentiation using individual digital census records for the period 1881–1901. The use of family names and neighbourhood classification of dominant economic and social roles makes it possible to relate the changing city size distribution in Great Britain to patterns of urban growth and residential differentiation within urban areas. Together, we provide an integrated and consistent methodology that links the classification of all major urban area growth in Great Britain to attendant intra-urban geodemographic changes in urban residential structures. We suggest ways in which this manifests social and economic change across the settlement system for both new and long-established residents.

A datacenter that consists of hundreds or thousands of servers can provide virtualized environments to a large number of cloud applications and jobs that value the requirement of reliability very differently. Checkpointing a virtual machine (VM) is a proven technique to improve reliability. However, existing checkpoint scheduling techniques for enhancing reliability of distributed systems fails to achieve satisfactory results, either because they tend to offer the same, fixed reliability to all jobs, or because their solutions are tied up to specific applications and rely on centralized checkpoint control mechanisms. In this work, we first show that reliability can be significantly improved through contention-free scheduling of checkpoints. Then, inspired by the Carrier Sense Multiple Access (CSMA) protocol in wireless congestion control, we propose a novel framework for distributed and contention-free scheduling of VM checkpointing to provide reliability as a transparent, elastic service. We quantify reliability in closed form by studying system stationary behaviours, and maximize job reliability through utility optimization. Our design is validated via a proof-of-concept prototype that leverages readily available implementations in Xen hypervisors. The proposed checkpoint scheduling is shown to significantly reduce checkpointing interference and improve reliability by as much as one order of magnitude over contention-oblivious checkpoint schemes.

A thin film material, composed of Ni–Co alloy and photonic crystal, is presented in this paper. It not only has good magnetic properties but also shows bright structural color. Alternating current (AC) electrodeposition is used to prepare this composite film in a porous anodic alumina photonic crystal template. It was found that with the increase of the Co content in the deposition solution, the coercivity of the Ni–Co alloy nanorods prepared at the same deposition time first decreases and then increases, the maximum coercivity value is about 1370 Oe. The XRD results show that the Co (002) diffraction peak disappears and a new Co (100) diffraction peak appears as the Co content increases. PPMS results show that the magnetic properties of the Ni–Co alloy nanorods were enhanced due to the decrease of the pore diameter of the photonic crystal template. This happened because the aspect ratio became larger, which is consistent with the uniform rotation mechanism. The results also indicate that the pore size of the photonic crystal template is proportional to the immersion time in the deposition solution. Therefore, the structural color of the composite film has a red shift with the growth of the pore widening time. With good magnetic behavior and the stable structural color, this Ni–Co composite photonic crystal film can be applied in the field of anti-counterfeiting.

Organisms and other living things grow as fractals, helping to maximize space-filling ability and transmit flow efficiently. In recent years, many studies on fractals have been carried out, mostly ...

The performance of adaptive beamforming will degrade dramatically in practical application due to system errors including signal direction error, array geometry error, gain, and phase errors. Besides, the performance will further deteriorate when the desired signal is contained in the sample data. Therefore, a robust adaptive beamforming method based on the covariance matrix reconstruction with annular uncertainty set (AUS) and vector space projection (VSP) is proposed in this letter. By integrating the corresponding Capon spectrum over the surface of AUS, the interference-plus-noise covariance matrix (INCM) and the desired signal covariance matrix are reconstructed, respectively. Because the steering vector (SV) of desired signal lies in the intersection of the signal subspaces of the sample covariance matrix and the reconstructed desired signal covariance matrix, it can be estimated by the VSP method. Finally, the adaptive weight vector is calculated based on the reconstructed INCM and the estimated SV. Simulation and experiment results show that the proposed method can effectively suppress the interference and achieve excellent performance under system errors.

Thermoplastic polyhydroxyurethanes (PHUs) were synthesized from cyclic carbonate aminolysis. Because of the hydroxyl groups in PHU, the choice of soft segment has a dramatic influence on nanophase separation in polyether-based PHUs. Use of a polyethylene glycol-based soft segment, which results in nanophase-separated thermoplastic polyurethane elastomers (TPUs), leads to single-phase PHUs that flow under the force of gravity. This PHU behavior is due to major phase mixing caused by hydrogen bonding of hard-segment hydroxyl groups to the soft-segment ether oxygen atoms. This hydrogen bonding can be suppressed by using polypropylene glycol-based or polytetramethylene oxide (PTMO)-based soft segments, which reduce hydrogen bonding by steric hindrance and dilution of oxygen atom content and result in nanophase-separated PHUs with robust, tunable mechanical properties. The PTMO-based PHUs exhibit reversible elastomeric response with hysteresis, like that of conventional TPUs. Because of nanophase separation with broad interphase regions possessing a wide range of local composition, the PTMO-based PHUs also demonstrate potential as novel broad-temperature-range acoustic and vibration damping materials, a function not observed with TPUs.

Parameter estimation with uncertainty quantification is essential in groundwater modeling to ensure model quality; however, parameter estimation, especially for non-Gaussian distributed parameters in highly heterogeneous aquifers, is still a great challenge. The ensemble smoother with multiple data assimilation (ES-MDA) is one of the most popular and effective ensemble-based data assimilation algorithms. However, it only works for multi-Gaussian fields, since two-point statistics are used to estimate the co-relation between parameters and state variables. The probability conditioning method (PCM) has the capability to integrate nonlinear flow data into facies simulation, but it has an assumption of homogeneity within each facies. Full characterization of facies and estimates of hydraulic conductivity within each facies are equally important. This work firstly modifies the original PCM, introducing a new probability assignment method, to consider within-facies heterogeneities, and then it is further combined with the ES-MDA to estimate non-Gaussian distributed hydraulic parameters in a groundwater model. The proposed method is evaluated using a two-facies case and a three-facies case in groundwater modeling. Both cases demonstrate that the modified PCM is effective for facies delineation, especially to identify high heterogeneities in each facies, as well as non-Gaussian characteristics with good connectivity within certain facies. The results also show that the performances of data reproduction and model prediction are of high accuracy and low uncertainty, which is attributed to the accurate characterization of the non-Gaussian parameters in the heterogeneous aquifers used.

With the growing deadline-sensitivity of cloud applications, adherence to specific deadlines is becoming increasingly crucial, particularly in shared clusters. A few slow tasks called stragglers can potentially adversely affect job execution times. Equally, inadequate slotting of data analytics applications could result in inappropriate resource deployment, ultimately damaging system performance. Against this backdrop, one effective way of tackling stragglers is by making extra attempts (or clones) 1 for every single straggler after the submission of a job. This paper proposes Shed $\pmb {+}$ , which is an optimization framework utilizing dynamic speculation that aims to maximize the jobs’ PoCD (Probability of Completion before Deadline) by making full use of available resources. Notably, our work encompasses a new online scheduler that dynamically recomputes and reallocates resources during the course of a job’s execution. According to our findings, Shed $\pmb {+}$ successfully leverages cloud resources and maximizes the percentage of jobs meeting their deadlines. In our experiments, we have seen this percentage for heavy load going up to 98% for Shed $\pmb {+}$ as opposed to nearly 68%, 40%, 35% and 37% for Shed, Dolly, Hopper and Hadoop with speculation enabled, respectively. 1 Cloning or extra attempts means task duplication.

Made available in DSpace on 2020-12-10T17:35:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-06-17 China Scholarship Council NSERC (DG) Poly-3-hexylthiophene (P3HT) is a benchmark semiconducting polymer in organic electronics. Ion-gated transistors (IGTs), making use of ionic gating media, are particularly interesting for flexible and printable& x2423;organic electronic applications. The molecular weight of P3HT is known to affect the morphology and structure of the corresponding films and, ultimately, the performance of devices based thereon. Here we report on IGTs based on films of P3HT with different molecular weights (similar to 20 kDa, 30-50 kDa and 80-90 kDa) and, as the gating medium, the well-investigated ionic liquid [EMIM][TFSI], to investigate the effects of the film morphological and structural properties on charge carrier transport and, eventually, IGT performance. P3HT films were deposited over rigid (SiO2/Si) and flexible (polyimide) substrates. All the P3HT IGTs could be operated at low voltage (about 1 V) and achieved a hole mobility larger than 0.1 cm(2) V-1 s(-1), pointing to the extremely favorable [EMIM][TFSI]/P3HT interface for IGT applications, for all the molecular weights investigated. We finally investigated the stability of flexible devices considering two different bending radii (R = 10 mm andR = 5 mm). Polytech Montreal, Engn Phys Dept, Montreal, PQ H3C 3A7, Canada Sao Paulo State Univ, Dept Fis Quim, BR-14800060 Sao Paulo, Brazil Sao Paulo State Univ, Dept Fis Quim, BR-14800060 Sao Paulo, Brazil

Analysis of changing patterns of ethnic residential segregation is usually framed by the coarse categorisations of ethnicity used in censuses and other large-scale public sector surveys and by the infrequent time intervals at which such surveys are conducted. In this paper, we use names-based classification of Consumer Registers to investigate changing degrees of segregation in England and Wales over the period 1997–2016 at annual resolution. We find that names-based ethnic classification of the individuals that make up Consumer Registers provides reliable estimates of the residential patterning of different ethnic groups and the degree to which they are segregated. Building upon this finding, we explore more detailed segregation patterns and trends of finer groups at annual resolutions and discover some unexpected trends that have hitherto remained unrecorded by Census-based studies. We conclude that appropriately processed Consumer Registers hold considerable potential to contribute to various domains of urban geography and policy.

There is a need for molecular-structure-based predictive models that guide the molecular design of materials with desired properties. Herein, we developed a general model based on group-contributio...

Transparent polymer electrolytes such as poly(vinyl alcohol)-based H+, Li+, K+, and Na+ gels have been widely used as both an electrolyte and a separator for flexible transparent supercapacitors (FTSCs). However, these gels sandwiched between the electrodes in FTSCs are easily compressed under bending and compression due to their viscous flow behavior, resulting in the deformation of electrode spacing and the unstable capacitance performance. To resolve this issue, herein, we introduce monodispersed polystyrene (PS) microspheres into PVA-LiCl polymer gel electrolytes as spacers to precisely control the electrode spacing during the assembly of FTSCs using single-walled carbon nanotubes/indium tin oxide-polyethylene terephthalate (ITO-PET) or MnO2/multiwalled carbon nanotubes/ITO-PET as transparent electrodes. The electrode spacing could be tuned by varying the diameter of PS microspheres, for example, 20, 40, and 80 μm. More importantly, the PS microsphere spacers protect the gel electrolyte from the squeeze when bending takes place, allowing the stable performance output by FTSCs under a bending state. After repeating bending tests, the capacitance remains 95.6%, indicating the high stability and flexibility of the devices with the assistance of PS microsphere spacers.

This paper analyzes the influence of harmonic frequencies on mechanical parts of wind turbines. Pitch system and drive train behavior of both doubly fed induction generator (DFIG) and full converter (FC) based wind turbines will be analyzed in detail.

The redundant convolutional encoder-decoder network has been proven useful in speech enhancement tasks. This network can capture the localized time-frequency details of speech signals through the fully convolutional network structure and the feature selection capability that results from the encoder-decoder mechanism. However, extracting informative features, which we regard as important for the representational capability of speech enhancement models, is not considered explicitly. To solve this problem, we introduce the attention mechanism into the convolutional encoder-decoder model to explicitly emphasize useful information from three aspects, namely, channel, space, and concurrent space-and-channel. Furthermore, the attention operation is specifically achieved through the squeeze-and-excitation mechanism and its variants. The model can adaptively emphasize valuable information and suppress useless ones by assigning weights from different perspectives according to global information, thereby improving its representational capability. Experimental results show that the proposed attention mechanisms can employ a small fraction of parameters to effectively improve the performance of CNN-based models compared with their normal versions, and generalize well to unseen noises, signal-to-noise ratios (SNR) and speakers. Among these mechanisms, the concurrent space-channel-wise attention exhibits the most significant improvement. And when comparing with the state-of-the-art, they can produce comparable or better results. We also integrate the proposed attention mechanisms with other convolutional neural network (CNN)-based models and gain performance. Moreover, we visualize the enhancement results to show the effect of the attention mechanisms more clearly.

Computer-aided diagnosis systems with deep learning frameworks have been used to identify benign and malignant pulmonary nodules in lung cancer diagnosis. It's commonly known that a premise of training complex deep neural nets is the large-scale labeled datasets. However, the abundance of labeled datasets is usually unavailable in many medical image domains. This factor can lead to the poor generalization performance of deep learning models. In this paper, we propose a novel multi-discriminator generative adversarial network model combined with an encoder for the classification of benign and malignant pulmonary nodules. To the best of our knowledge, we are the first to apply unsupervised learning to identify benign and malignant lung nodules. Firstly, we use a multi-discriminator generative adversarial network to build a generative model trained with unlabeled benign lung nodule images. Then an encoder is combined with the trained generative model to establish a mapping of benign pulmonary nodule images to the latent space. The benign and malignant lung nodules are scored by calculating the GAN discriminator feature loss and image reconstruction loss. The model yields high anomaly scores on malignant images and low anomaly scores on benign images. Experimental results show that our method with only a small number of unlabeled datasets could achieve more competitive results compared with other supervised deep learning approaches.

The auditory selection framework with attention and memory (ASAM), which has an attention mechanism, embedding generator, generated embedding array, and life-long memory, is used to deal with mixed speech. When ASAM is applied to speech enhancement, the discrepancy between the voice and noise feature memories is huge and the separability of noise and voice is increased. However, ASAM cannot achieve desirable performance in terms of speech enhancement because it fails to utilize the time-frequency dependence of the embedding vectors to generate a corresponding mask unit. This work proposes a novel embedding encoder-decoder (EED), and a convolutional neural network (CNN) is used as decoder. The CNN structure is good at detecting local patterns, which can be exploited to extract correlation embedding data from the embedding array to generate the target spectrogram. This work evaluates a similar ASAM, EED with an LSTM encoder and a CNN decoder (RC-EED), RC-EED with an attention mechanism (RC-AEED), other similar EED structures and baseline models. Experiment results show that RC-EED and RC-AEED networks have good performance on speech enhancement task at low signal-to-noise ratio conditions. In addition, RC-AEED exhibits superior speech enhancement performance over ASAM and achieves better speech quality than do deep recurrent network and convolutional recurrent network.

Doping heteroatoms in photocatalyst is an effective strategy to signally enhance the photocatalytic activity. Herein, we have favorably fabricated the carbonate doped Bi2MoO6 via a facile one-pot solvothermal method, which was verified by structure and constituent characterization analysis. In addition, the NO removal efficiency of carbonate-intercalated Bi2MoO6 is ~34%, far-exceeding that of the pure Bi2MoO6 (~13%), whilst exhibits a good stability and durability, owing to that the dopants could modulate the electron states of the Bi2MoO6, thus stimulating charge separation and migration, incenting transformation of reactive oxygen species and facilitating reactants activation, which are synthetically investigated by experimental characterization coupled with DFT calculation. Significantly, the in situ DRIFTS measurement was employed to dynamic monitor the NO oxidation process and clarify the photocatalytic mechanism under visible light irradiation. This work provides an efficient strategy to design photocatalysts with tunable motivating charge conversion and reactants activation towards NO photooxidation.

A Data Processing Network (DPN) streams massive volumes of data collected and stored by the network to multiple processing units to compute desired results in a timely fashion. Due to ever-increasing traffic, distributed cache nodes can be deployed to store hot data and rapidly deliver them for consumption. However, prior work on caching policies has primarily focused on the potential gains in network performance, e.g., cache hit ratio and download latency, while neglecting the impact of cache on data processing and consumption. In this paper, we propose a novel framework, DeepChunk, which leverages deep Q-learning for chunk-based caching in wireless DPN. We show that cache policies must be optimized for both network performance during data delivery and processing efficiency during data consumption. Specifically, DeepChunk utilizes a model-free approach by jointly learning limited network, data streaming, and processing statistics at runtime and making cache update decisions under the guidance of deep Q-learning. It enables a joint optimization of multiple objectives including chunk hit ratio, processing stall time, and object download time while being self-adaptive under the time-varying workload and network conditions. We build a prototype implementation of DeepChunk with Ceph, a popular distributed object storage system. Based on real-world Wifi and 4G traces, our extensive experiments and evaluation demonstrate significant improvement, i.e., 52% increase in total reward and 68% decrease in processing stall time, over a number of baseline caching policies.

The evaluation of eco-toxicity of ionic liquids (ILs) in the aquatic environment is essential for their safe utilization and QSTR approach plays an important role in obtaining the eco-toxicity data of ILs with diverse structures. Usually, the descriptors used to build QSTR model were made up of anion and cation descriptors, and their interactions were often neglected to some extent. In this work, based on the optimization of the ILs structure, a new set of descriptors were proposed to describe the interaction between anions and cations, and some new atomic distribution matrices were constructed to calculate norm descriptors of ILs, anion and cation. A norm index-based QSTR model was built to predict the eco-toxicity of ILs toward Leukemia rat cell line (IPC-81). This model has satisfactory statistical results with the R2 of 0.954 and RMSE of 0.241, respectively. Furthermore, leave-one-out cross-validation and applicability domain results showed good stability and predictability of this model. This approach showed that the interaction between cations and anions could be reflected by optimizing the whole structure of ILs which might play an important role for describing the eco-toxicity of ILs. Therefore, it is further suggested that the norm descriptors would be applicable to predict the eco-toxicity of ILs towards IPC-81.

Preparation of two-dimensional hybrid nanoarrays with porous structure and multiple active components is considered as an effective strategy to improve the electrochemical performance of supercapacitors. Herein, etched CoAl LDO/MnO2 was in-situ prepared on Ni foam by a simple hydrothermal process and subsequent alkali etching treatment. The morphologies and structures of synthetic nanocomposites were examined by XRD, SEM, TEM, EDS, BET and XPS. The nanocomposites present unique hybrid nanoarrays in which etched CoAl LDO nanoarrays form a core and worm-like MnO2 nanosheets create a shell. The electrochemical properties of etched CoAl LDO/MnO2 electrodes were examined by CV, CC and cycling tests. The results exhibit an excellent areal specific capacitance of 991 mF cm−2, desirable rate capability and outstanding cycling stability of 79.87% retention over 10000 cycles. The asymmetric supercapacitor using etched CoAl LDO/MnO2//AG achieves the maximum energy density and power density of 16.6 Wh kg−1 and 5000 W kg−1, respectively. The excellent electrochemical performance of the as-prepared material suggests its potential application for clean energy storage.

The reverse flow of seawater causes salinity in inland waterways and threatens water resources of the coastal population. In the Pearl River Delta, saltwater intrusion has resulted in a water crisis. In this study, we proposed a tailored approach to chlorinity prediction at complex delta systems like the Pearl River Delta. We identified the delayed predictors prior to optimization based on the maximal information coefficient (MIC) and Pearson's correlation coefficient (r). To achieve an ensemble simulation, a Bayesian model averaging (BMA) method was applied to integrate temporally sensitive empirical model predictions given by random forest (RF), support vector machine (SVM), and Elman neural network (ENN). The results showed that: (a) The ENN performed the worst among the three; (b) The BMA approach outperformed the individual models (i.e., RF, ENN, and SVM) in terms of Nash–Sutcliffe efficiency (NSE), and the percentage of bias (Pbias). The BMA weights reflect the model performance and the correlation of the predictions given by its ensemble models. (c) Our variable selection method resulted in a stronger model with greater interpretability.

Accurately determining the network energy consumption of each software principal when multiple ones are active is the key to mobile energy optimization. Tail energy accounting, which attributes tail energy to individual software principals, remains an open problem. Besides, tail energy has also become a major energy drain, especially in mobile ad modules that generate frequent, intermittent network traffics by on-demand ad downloading. In this paper, we propose a systematic framework for mobile ad prefetching and energy optimization, based on a novel tail energy accounting policy using cooperative game theory. In particular, we maximize the sum of deadline- and energy-aware ad utility, by jointly determining apps’ aggressiveness in ad prefetching. The proposed tail energy accounting not only characterizes the energy profile of each app's ad module, a crucial input in energy optimization, but also enables an efficient solution by decoupling decision making of individual apps. The proposed framework is implemented on Android with negligible performance/network overhead. Using real-world apps and usage traces, we demonstrate a significant reduction in mobile network energy consumption by up to 45 percent compared with existing approaches. To the best of our knowledge, it is the first fully implemented ad management system transparent to apps and ad ecosystem.

Understanding the mechanical properties of nanoconfined polymers is essential in the design of nanostructured soft materials. Here, we investigate the mechanical properties of free-standing polymer...

The barriers to social integration posed by ethnic residential segregation are currently receiving renewed attention in Great Britain. A common characteristic of past studies of ethnic segregation ...

Cloud-as-the-center computing paradigms face multiple challenges in the 5G and Internet of Things scenarios, where the service requests are usually initiated by the end-user devices located at network edge and have rigid time constraints. Therefore, Fog computing, or mobile edge computing, is introduced as a promising solution to the service provision in the tiered IoT infrastructure to compensate the shortage of traditional cloud-only architecture. In this cloud-to-things continuum, several cloudlet or mobile edge server entities are placed at the access network to handle the task offloading and processing problems at the network edge. This raises the resource scheduling problem in this tiered system, which is vital for the promotion of the system efficiency. Therefore, in this paper, a scheduling mechanism for the cloudlets or fog nodes are presented, which takes the mobile tasks' deadline and resources requirements at the same time while promoting the overall profit of the system. First, the problem at the cloudlet, to which IoT devices offload their tasks, is formulated as a multi-dimensional 0–1 knapsack problem. Second, based on ant colony optimization, a scheduling algorithm is presented which treat this problem as a subset selection problem. Third, to promote the performance of the system in the dynamic environments, a churn-refined algorithm is further put forward. A series of simulation experiments have shown that out proposal outperforms many state-of-the-art algorithms in both profit and guarantee ratio.

Methyl caprylate is expected to be one of the main components of biodiesel due to its high cetane number and good oxidation stability. Details of the experimental speeds of sound in methyl caprylat...

Schematic maps are popular for the representation of transport networks. Many automated methods have been developed to generate such maps. In these methods, optimization techniques work with variou...

Silicone additives are often added to coating formulations for improved slip and mar performance. However, the relationship between the chemical structure of silicone slip/mar additives and their performances has not been well understood. In the current study, six different silicone slip/mar additives made of silicone gum dispersions or silicone polyethers were investigated. Micron-sized silicone gum dispersions (G1 and G2) result in the lowest coefficient of friction and the highest mar resistance in a waterborne acrylic coating; however, they also lead to the highest surface roughness and the lowest gloss. The submicron-sized silicone gum dispersion (G3) results in a smaller improvement in the slip/mar performance and a much smaller detrimental impact on the surface roughness and gloss. All of the silicone polyethers (P1, P2, and P3) contribute to smaller improvement in slip/mar performance compared to silicone gum dispersions, but they have minimum impact on the gloss and roughness of coatings. Studies by AFM and XPS demonstrate coatings with micron-sized silicone gum dispersions have the highest surface segregation of silicone whereas coatings with silicone polyethers have the lowest surface segregation of silicone. A strong correlation between the surface segregation of silicone and the slip/mar performances of coatings is observed.

Enthalpy of vaporization (∆Hv) is an important physicochemical property and has been widely used in chemical industry and process design. In this work, based on the concept of norm index proposed by our group, a QSPR model was developed to predict ∆Hv at the boiling point for 13 kinds of organic compounds including chain and cyclic hydrocarbons, alcohols, ketones, carboxylic acids and amines etc. The results showed that this model had satisfactory predictive performance with the squared correlation coefficient R2 of 0.9503, the Fisher's criterion F of 378.84 and the average absolute relative deviation of 3.17%. The internal validation, external validation and applicability domain analysis results indicated the robustness and reliability of this model. Moreover, comparison results demonstrated that this model performed well both in accuracy and stability. The satisfactory results further suggested that the norm index can be applied to the calculation of ∆Hv for organic compounds.

Flash floods are among the most devastating natural hazards but the analyses of flash floods are hindered by the lack of hydro-meteorological data. In particular, data issues are critical in ungauged basins. This paper presents an investigation of flash flood modeling using coupled hydrological and hydrodynamic models. Specifically, the TOPography-based hydrological MODEL (TOPMODEL) is employed to obtain the flood hydrograph and the coupled 1D-2D hydrodynamic model MIKEFLOOD is used to simulate floodplain inundation. The methods are tested in the Pajiang River, a mountainous basin in South China. The results show that the TOPMODEL effectively simulates the flood hydrograph and the Nash–Sutcliffe coefficients of the flow rate are larger than 0.80 for both calibration and validation. Also, MIKEFLOOD effectively simulates the flood inundation extent and flooding depth and the BIAS for simulated water level is −0.004 m. Further, using socioeconomic and historical flood disaster datasets, areas with high flash flood risk are identified. A flash flood risk assessment shows a clear trend of change in the flood hazard and the spatial heterogeneity of the flood vulnerability is significant. A sensitivity analysis of the flood vulnerability to the socioeconomic indicators indicates that cultivated land is the most sensitive indicators, and the population and the construction areas have similar influences. Overall, the coupled hydrological and hydrodynamic models prove effective and can be used in practical applications for flash flood risk mapping.

Allometry originally referred to the scaling relationship between the size of a body part and the size of the whole body when an organism grows. Gradually, researchers introduced it into urban stud...

© 1963-2012 IEEE. Efficient 2-D full-waveform forward and inverse electromagnetic scattering solvers are presented in this communication. The combined field volume integral equation (CFVIE) is formulated in a layered medium. Different from the previous work, the mixed-order basis function is adopted in the discretization of the 2-D CFVIE. Meanwhile, the permittivity and permeability are simultaneously reconstructed in the inversion through the variational Born iteration method, which is enhanced with a self-adaptive regularization factor and applied to the multifrequency data. The proposed forward solver is validated by comparisons with the finite-element simulations.

This paper researches the sub-synchronous harmonic impedance of doubly-fed induction generator (DFIG) wind turbine. For analyzing the harmonic impedances, additional harmonic voltage source is applied over the sub-synchronous frequency range and different operating conditions of the DFIG wind turbine are considered. Also, different mechanical parameter of the DFIG are changed for analyzing their influence on the harmonic impedances.

Binary code analysis is crucial in various software engineering tasks, such as malware detection, code refactoring, and plagiarism detection. With the rapid growth of software complexity and the increasing number of heterogeneous computing platforms, binary analysis is particularly critical and more important than ever. Traditionally adopted techniques for binary code analysis are facing multiple challenges, such as the need for cross-platform analysis, high scalability and speed, and improved fidelity, to name a few. To meet these challenges, machine learning-based binary code analysis frameworks attract substantial attention due to their automated feature extraction and drastically reduced efforts needed on large-scale programs. In this paper, we provide the taxonomy of machine learning-based binary code analysis, describe the recent advances and key findings on the topic, and discuss the key challenges and opportunities. Finally, we present our thoughts for future directions on this topic.

Metalenses provide new solutions to the shortcomings of traditional lenses, such as bulky volume and low resolution. In this paper, an ultrathin planar metalens based on the nanohole structure in a gold film was designed, which had only one variable (the diameter of the nanohole) and showed the ability to focus at the designed wavelength of 980 nm. Then, further improvements were made by adding a finite array of circular surface corrugations, which could enhance the resolution and energy at the focal spot because of the excitation of surface plasmons (SPs). The central symmetry of the metalens makes it polarization insensitive. Besides, the metalens with surface corrugations had a relatively high numerical aperture (NA = 0.848). This method of adding surface corrugations could also be adopted for other metal surface plasmon metalens to enhance the focusing effect in the far-field.

The phenomenon of efficiency droop is comprehensively investigated in an asymmetric GaN-based laser diode (LD). Numerical simulations and experiments are both conducted. It is found that with the introduction of a sandwiched GaN/InAlN/GaN lower quantum barrier (LQB) instead of the bulk GaN LQB in an asymmetric GaN-based high-power blue LD, the efficiency droop is effectively mitigated to only 9.7% under continuous-wave operation with no degradation in the crystal quality. Compared with the conventional asymmetric GaN-based LD, the wall-plug efficiency is successfully increased by 24.7% from 15.8% to 19.7% at high injection current of 2000 mA, promoting the increase of slope efficiency and output power, while the threshold current is reduced by 15.2%. These improvements are primarily attributed to the inserted high-quality InAlN thin barrier layer, which could form an extra potential barrier beneath the first InGaN quantum well to remarkably alleviate the hole overflow from the active region and enhance the radiative recombination rate.

Opinion targets and sentiment terms extraction is a key task in aspect-level sentiment analysis. Existing researches have shown that using the dependency structures of reviews helps to complete the task. In this paper, we present a novel opinion targets and sentiment terms extraction model based on self-attention. The proposed model learns the contextual feature of each token in a review through a LSTM network, and a self-attention mechanism is used to directly capture the relations between any two tokens in a review, thus the global dependencies and internal structure of reviews can be modeled better. Experiments results on two benchmark datasets show that the proposed model achieves a better performance than current state-of-the-art in opinion targets and sentiment terms extraction task.

Integrating CRISPR–Cas12a sensors with a portable glucose meter (PGM) was developed based on the target-induced activation of the collateral cleavage activity of Cas12a. Considering the portability, low cost and facile incorporation of the PGM system with suitable Cas12a sensors to recognize many targets, the CRISPR/Cas12a-PGM system demonstrated here paves a way to further broaden the POC applications of CRISPR-based diagnostics.

A combination of mobile edge computing (MEC) and cloud computing paradigms has the potential to greatly alleviate the challenges facing Internet of Things (IoT). We consider a tiered IoT infrastructure in which data generated by an IoT sensor/device is delivered to a data center for processing through an intermediate MEC server. The MEC server can either directly transmit the data to the data center or pre-process the data and then transmit it to the data center over a shared channel. The goal is to maintain the freshness of the data delivered to the data center. In this paper, we assume a probabilistic model for pre-processing by the MEC server. Sensor data is assumed to be generated as a Poisson process and the transmission times over the two paths are assumed to have general distributions.We use Age of Information (AoI) as a measure of data freshness at the data center. We perform stationary distribution analysis in this system and obtain closed form expressions for average AoI and average peak AoI. We focus on selecting the offloading probabilities in conjunction with the mean service times for each server for optimal operation determined by average AoI and peak AoI. Our numerical results show the effect of path diversity in the selection of best offloading probability and service times.

Direction-of-arrival (DOA) estimation in a spatially isotropic white noise background has been widely researched for decades. However, in practice, such as underwater acoustic ambient noise in shallow water, the ambient noise can be spatially colored, which may severely degrade the performance of DOA estimation. To solve this problem, this paper proposes a DOA estimation method based on sparse Bayesian learning with the modified noise model using acoustic vector hydrophone arrays. Firstly, an applicable linear noise model is established by using the prolate spheroidal wave functions (PSWFs) to characterize spatially colored noise and exploiting the excellent performance of the PSWFs in extrapolating band-limited signals to the space domain. Then, using the proposed noise model, an iterative method for sparse spectrum reconstruction is developed under a sparse Bayesian learning (SBL) framework to fit the actual noise field received by the acoustic vector hydrophone array. Finally, a DOA estimation algorithm under the modified noise model is also presented, which has a superior performance under spatially colored noise. Numerical results validate the effectiveness of the proposed method.

The nineteenth century saw rapid urbanization and dramatic social change in Great Britain, some of which can now be viewed at national scales for the first time through linkage of georeferenced digital historical data to contemporary and historical framework data. Here, we attempt to georeference every individual address record from the 1881, 1891, and 1901 censuses for Great Britain and to define the fast-growing historical street networks and residential geographies of every urban settlement. We next devise a scale-free historical geodemographic classification using variables common to these three censuses and assign cluster group characteristics to every urban street segment. We also link the evolution of the urban street morphology with changes in residential differentiation and the geodemographic assignments over the twenty-year study period. The results of this intensive data processing make it possible to chart the development of urban residential areas across Great Britain and bring focus to the changing social structures of the cities. We examine these changes with examples drawn from the entire British urban settlement system. Our conclusions discuss the implications of this extensive analysis for improved understanding of the evolution of Great Britain’s urban system.

Home testing is an attractive emerging strategy to combat the COVID-19 pandemic and prevent overloading of healthcare resources through at-home isolation, screening and monitoring of symptoms. However, current diagnostic technologies of SARS-CoV-2 still suffer from some drawbacks because of the tradeoffs between sensitivity, usability and costs, making the test unaffordable to most users at home. To address these limitations, taking advantage of clustered regularly interspaced short palindromic repeats (CRISPRs) and a portable glucose meter (PGM), we present a proof-of-concept demonstration of a target-responsive CRISPR-PGM system for translating SARS-CoV-2 detection into a glucose test. Using this system, a specific N gene, N protein, and pseudo-viruses of SARS-CoV-2 have been detected quantitatively with a PGM. Given the facile integration of various bioreceptors into the CRISPR-PGM system, the proposed method provides a starting point to provide patients with a single-device solution that can quantitatively monitor multiple COVID-19 biomarkers at home., COVID-19 glucose test: translating SARS-CoV-2 detection into a glucose test is achieved by incorporating target-responsive rolling circle amplification and a CRISPR-based collateral cleavage module with a portable glucose meter.

Traffic engineering (TE) in multi-region networks is a challenging problem due to the requirement that each region must independently compute its routing decisions based on local observations, yet with the goal of optimizing global TE objectives. Traditional approaches often lack the agility to adapt to changing traffic patterns and thus may suffer hefty performance loss under highly dynamic traffic demands. In this paper, we propose a data-driven framework for multi-region TE problems, which makes novel use of multi-agent deep reinforcement learning. In particular, we propose two reinforcement learning agents for each region, namely T-agents and O-agents, to control the terminal traffic and outgoing traffic, respectively. These distributed agents collect local link utilization statistics within their regions, optimize local routing decisions, and observe the resulting congestion-related reward. To facilitate these agents for optimizing global TE objectives, we tailor the agent design carefully including input, output, and reward functions. The proposed framework is evaluated extensively using real-world network topologies (e.g., Telstra and Google Cloud) and synthetic traffic patterns (e.g., the Gravity model). Numerical results show that comparing with existing protocols and single-agent learning algorithms, our solution can significantly reduce congestion and achieve nearly-optimal performance with both superior scalability and robustness. Throughout our simulations, over 90% of tests limit congestion within 1.2 times the global optimal solution.1

Recently, the direction of arrival estimation with co-prime arrays has gradually been applied in underwater scenarios because of its significant advantages over traditional uniform linear arrays. Despite the advantages of co-prime arrays, the spatial spectra obtained directly from conventional beamforming can be degraded by grating lobes due to the sparse spatial sampling in passive sensing applications, which will seriously deteriorate the estimation performance. In this paper, capon beamforming is applied to a co-prime sensor array as a pretreatment before high-resolution direction of arrival (DOA) estimation methods. The amplitudes extracted from the beam-domain outputs of two subarrays and the phases extracted from the cross-spectrum of the spatial spectrum are exploited to suppress the spurious peaks in beam patterns and eliminate ambiguities. Consequently, interference can be further mitigated, and the performance of high-resolution DOA methods will be guaranteed. Simulations show that the method proposed can improve the reliability and accuracy of DOA estimation with great value in practice.

We develop a theory of Sobolev orthogonal polynomials on the Sierpinski gasket ( $$SG$$ ), which is a fractal set that can be viewed as a limit of a sequence of finite graphs. These orthogonal polynomials arise through the Gram–Schmidt orthogonalisation process applied on the set of monomials on $$SG$$ using several notions of a Sobolev inner products. After establishing some recurrence relations for these orthogonal polynomials, we give estimates for their $$L^2$$ , $$L^\infty $$ , and Sobolev norms, and study their asymptotic behavior. Finally, we study the properties of zero sets of polynomials and develop fast computational tools to explore applications to quadrature and interpolation.

In recent years, many new trends, such as large-scale renewable generations, electric cars and demand response, appear in power systems. Grid uncertainties are greatly increased due to the new trends. Thus, it is necessary to consider the impact of grid uncertainties in the decision-making process of optimal transmission switching. In this paper, a stochastic programming based approach is proposed for optimal transmission switching problems. Grid uncertainties are represented by different scenarios and are taken into consideration in the proposed approach. This new approach is based on the ACOPF and the scenario reduction technique is adopted to preserve only the key scenarios in the stochastic programming formulation. The corresponding scheme is designed for scheduling and online operation. The proposed scheme is validated on the South Carolina-500 bus synthetic system.

The rapid growth of computing capabilities at network edge calls for efficient management frameworks that not only considers placing hot data on edge storage for best accessibility and performance, but also makes optimal utilization of edge storage space. In this paper, we solve a joint optimization problem by exploiting both data popularity (for optimal data access performance) and data similarity (for optimal storage space efficiency). We show that the proposed optimization is NP- hard and develop a 2⌈2Γ⌉ - 1 + e-approximation algorithm by (i) making novel use of δ-similarity graph to capture pairwise data similarity and (ii) leveraging the k-MST algorithm to solve a Prize Collecting Steiner Tree problem on the graph. The proposed algorithm is prototyped using an open-source distributed storage system, Cassandra. We evaluate its performance extensively on a real-world testbed and with respect to real-world IoT datasets. The algorithm is shown to achieve over 55% higher edge service rate and reduces request response time by about 30%.

The thermally activated blistering and exfoliation of GaAs wafers and SiO2/Si3N4/GaAs wafers after H+ and He+ implantation is systematically investigated. Surface morphologies and microscopic defects are detected and analyzed by various measurements, such as optical microscopy (OM), atomic force microscopy (AFM), and transmission electron microscopy (TEM). Blistering and exfoliation are obtained on the surfaces of the GaAs and SiO2/Si3N4/GaAs wafers by either the exclusive implantation of 5 &times, 1016 He+/cm2 alone or by co-implantation of 0.5 &times, 1016 He+/cm2 and 4 &times, 1016 H+/cm2. Our experimental results show that the blistering and exfoliation of the SiO2/Si3N4/GaAs layer occurred when the concentration of He+ was relatively low, where fewer dislocations and nanocavities were created near the interface between the Si3N4 and GaAs layers.

The redundant convolutional encoder-decoder network has proven useful in speech enhancement tasks. It can capture localized time-frequency details of speech signals through both the fully convolutional network structure and feature selection capability resulting from the encoder-decoder mechanism. However, it does not explicitly consider the signal filtering mechanism, which we regard as important for speech enhancement models. In this study, we introduce an attention mechanism into the convolutional encoderdecoder model. This mechanism adaptively filters channelwise feature responses by explicitly modeling attentions (on speech versus noise signals) between channels. Experimental results show that the proposed attention model is effective in capturing speech signals from background noise, and performs especially better in unseen noise conditions compared to other state-of-the-art models.