Search

A biomass-based catalyst, CuxOy@CS-400, was employed as an excellent recyclable heterogeneous catalyst to realize the sulfonylation reaction of aniline derivatives with sodium sulfinates. Various substrates were compatible, giving the desired products moderate to good yields at room temperature. In addition, this heterogeneous copper catalyst was also easy to recover and was recyclable up to five times without considerably deteriorating in catalytic efficiency. Importantly, these sulfonylation products were readily converted to the corresponding 4-sulfonyl anilines via a hydrolysis step. The method offers a unique strategy for synthesizing arylsulfones and has the potential to create new possibilities for developing heterogeneous copper-catalyzed C-H functionalizations. [ABSTRACT FROM AUTHOR]

Finite-time consensus problem of hybrid-order heterogeneous multi-agent systems under a signed digraph topology is investigated in this paper. For heterogeneous multi-agent systems composed of first-order and second-order agents, a novel pinning event-triggered control protocol is devised to facilitate the attainment of the desired consensus state within a finite time. This control method overcomes communication barriers between first-order and second-order multi-agent systems, achieving effective control performance while reducing controller update frequency and communication costs. Based on graph theory and the Lyapunov stability method, several novel matrices are defined to address the finite-time consensus problem in hybrid-order multi-agent systems, and these matrices also facilitate the theoretical derivation process. Furthermore, it is demonstrated that the control protocol designed for hybrid-order systems is devoid of Zeno behavior. Finally, a detailed numerical example is supplied to illustrate the validity of the theoretical analysis. [ABSTRACT FROM AUTHOR]

The High Intensity Muon Beams (HIMB) project at the Paul Scherrer Institute (PSI) will deliver muon beams with unprecedented intensities of up to 10 10 muons / s for next-generation particle physics and material science experiments. This represents a hundredfold increase over the current state-of-the-art muon intensities, also provided by PSI. We performed beam dynamics optimisations and studies for the design of the HIMB beamlines MUH2 and MUH3 using Graphics Transport, Graphics Turtle, and G4beamline, the latter incorporating PSI's own measured π + cross-sections and variance reduction. We initially performed large-scale beamline optimisations using asynchronous Bayesian optimisation with DeepHyper. We are now developing an island-based evolutionary optimisation code glyfada based on the Paradiseo framework, where we implemented Message Passing Interface (MPI) islands with OpenMP parallelisation within each island. Furthermore, we implemented an island model that is also suitable for high-throughput computing (HTC) environments with asynchronous communication via a Redis database. The code interfaces with the codes COSY INFINITY and G4beamline. The code glyfada will provide heterogeneous island model optimisation using evolutionary optimisation and local search methods, as well as part-wise optimisation of the beamline with automatic advancement through stages. We will use the glyfada for a future large-scale optimisation of the HIMB beamlines. [ABSTRACT FROM AUTHOR]

The concentration of anthocyanins in common beans indicates their nutritional value. Understanding this concentration makes it possible to identify the functional compounds present. Previous studies have presented color characterization as two-dimensional histograms, based on the probability mass function. In this work, we proposed a new type of color characterization represented by three two-dimensional histograms that consider chromaticity and luminosity channels in order to verify the robustness of the information. Using a neuroevolutionary approach, we also found a convolutional neural network (CNN) for the regression task. The results demonstrate that using three two-dimensional histograms increases the accuracy compared to the color characterization represented by one two-dimensional histogram. As a result, the precision was 93.00 ± 5.26 for the HSI color space and 94.30 ± 8.61 for CIE L*a*b*. Our procedure is suitable for estimating anthocyanins in homogeneous and heterogeneous colored bean landraces. [ABSTRACT FROM AUTHOR]

In this paper, based on the spatial panel data of 30 provinces in China from 2008 to 2019, the relationship between environmental regulation (ER), environmental decentralization (ED) and the green total factor productivity of the construction industry (hereafter GTFPCI) was investigated through the spatial Dobbin model, and the heterogeneity of the relationship between three environmental regulations and the GTFPCI under different environmental decentralization intensities was studied through a threshold panel model. The results showed that the three environmental regulations had different degrees of impact on the GTFPCI, among which market-based environmental regulations (MERs) played the most obvious role. Environmental regulation and environmental decentralization have spillover effects on the GTFPCI in surrounding provinces. On the whole, with the increasing intensity of environmental decentralization, environmental regulation plays a stronger positive role in promoting the GTFPCI. However, through a threshold analysis, it was seen that the regulatory impact of different environmental regulations on the GTFPCI are heterogeneous in different ranges of environmental decentralization intensity. [ABSTRACT FROM AUTHOR]

Iron (Fe) is considered to be one of the most significant elements due to its wide applications. Recent years have witnessed a burgeoning interest in Fe catalysis as a sustainable and cost-effective alternative to noble metal catalysis in organic synthesis. The abundance and low toxicity of Fe, coupled with its competitive reactivity and selectivity, underscore its appeal for sustainable synthesis. A lot of catalytic reactions have been performed using heterogeneous catalysts of Fe oxide hybridized with support systems like aluminosilicates, clays, carbonized materials, metal oxides or polymeric matrices. This review provides a comprehensive overview of the latest advancements in Fe-catalyzed organic transformation reactions. Highlighted areas include cross-coupling reactions, C–H activation, asymmetric catalysis, and cascade processes, showcasing the versatility of Fe across a spectrum of synthetic methodologies. Emphasis is placed on mechanistic insights, elucidating the underlying principles governing iron-catalyzed reactions. Challenges and opportunities in the field are discussed, providing a roadmap for future research endeavors. Overall, this review illuminates the transformative potential of Fe catalysis in driving innovation and sustainability in organic chemistry, with implications for drug discovery, materials science, and beyond. [ABSTRACT FROM AUTHOR]

The rapid and accurate forecasting of two-phase flow in porous media is a critical challenge in oil field development, exerting a substantial impact on optimization and decision-making processes. Although the Convolutional Long Short-Term Memory (ConvLSTM) network effectively captures spatiotemporal dynamics, its generalization in predicting complex engineering problems remains limited. Similarly, although the Fourier Neural Operator (FNO) demonstrates adeptness at learning operators for solving partial differential equations (PDEs), it struggles with three-dimensional, long-term prediction. In response to these limitations, we introduce an innovative hybrid model, the Convolutional Long Short-Term Memory-Fourier Neural Operator (CL-FNO), specifically designed for the long-term prediction of three-dimensional two-phase flows. This model integrates a 3D convolutional encoder–decoder structure to extract and generate hierarchical spatial features of the flow fields. It incorporates physical constraints to enhance the model's forecasts with robustness through the infusion of prior knowledge. Additionally, a temporal function, constructed using gated memory-forgetting mechanisms, augments the model's capacity to analyze time series data. The efficacy and practicality of the CL-FNO model are validated using a synthetic three-dimensional case study and application to an actual reservoir model. [ABSTRACT FROM AUTHOR]

A space–air–ground–sea integrated network (SAGSIN) is a promising heterogeneous network framework for the next generation mobile communications. Moreover, federated learning (FL), as a widely used distributed intelligence approach, can improve advanced network performance. In view of the combination and cooperation of SAGSINs and FL, an FL-based SAGSIN framework faces a number of unprecedented challenges, not only from the communication aspect but also on the security and privacy side. Motivated by these observations, in this article, we first give a detailed state-of-the-art review of recent progress and ongoing research works on FL-based SAGSINs. Then, the challenges of FL-based SAGSINs are discussed. After that, for different service demands, basic applications are introduced with their benefits and functions. In addition, two case studies are proposed, in order to improve SAGSINs' communication efficiency under a significant communication latency difference and to protect user-level privacy for SAGSIN participants, respectively. Simulation results show the effectiveness of the proposed algorithms. Moreover, future trends of FL-based SAGSINs are discussed. [ABSTRACT FROM AUTHOR]

Single-board computers (SBCs) are emerging as an efficient and economical solution for fog and edge computing, providing localized big data processing with lower energy consumption. Newer and faster SBCs deliver improved performance while still maintaining a compact form factor and cost-effectiveness. In recent times, researchers have addressed scheduling issues in Hadoop-based SBC clusters. Despite their potential, traditional Hadoop configurations struggle to optimize performance in heterogeneous SBC clusters due to disparities in computing resources. Consequently, we propose modifications to the scheduling mechanism to address these challenges. In this paper, we leverage the use of node labels introduced in Hadoop 3+ and define a Frugality Index that categorizes and labels SBC nodes based on their physical capabilities, such as CPU, memory, disk space, etc. Next, an adaptive configuration policy modifies the native fair scheduling policy by dynamically adjusting resource allocation in response to workload and cluster conditions. Furthermore, the proposed frugal configuration policy considers prioritizing the reduced tasks based on the Frugality Index to maximize parallelism. To evaluate our proposal, we construct a 13-node SBC cluster and conduct empirical evaluation using the Hadoop CPU and IO intensive microbenchmarks. The results demonstrate significant performance improvements compared to native Hadoop FIFO and capacity schedulers, with execution times 56% and 22% faster than the best_cap and best_fifo scenarios. Our findings underscore the effectiveness of our approach in managing the heterogeneous nature of SBC clusters and optimizing performance across various hardware configurations. [ABSTRACT FROM AUTHOR]

H-Beta zeolite is a solid acid catalyst commonly utilized in the catalytic conversion of biomass resources. In this study, H-Beta zeolite was calcined at different temperatures (350, 550, 750, and 1000 °C) to explore the effects of high temperature-induced dealumination on its physicochemical properties and its catalytic ability to convert glucose into 5-hydroxymethylfurfural (HMF). It was shown that as the calcination temperature increased, the Si-O-Al bond of H-Beta zeolite was broken and its dealumination effect was enhanced. Dealumination led to the collapse of the framework of H-Beta zeolite and a reduction in the number of acid sites, which in turn reduced its catalytic performance and the efficiency of HMF formation from glucose. Furthermore, H-Beta zeolite exhibited an extraordinary catalytic ability for the production of HMF from carbohydrates. Using glucose and cellulose as substrates, superior HMF yields of 91% and 46%, respectively, were achieved under optimal reaction conditions. Further, calcination removes carbon deposits in the recovered H-Beta zeolite, but it affects the cycling stability of the catalyst. Meanwhile, the by-products formed during the synthesis of HMF from glucose catalyzed by H-Beta zeolite catalyst were also clearly detected. [ABSTRACT FROM AUTHOR]

This study focuses on optimizing federated learning in heterogeneous data environments. We implement the FedProx and a baseline algorithm (i.e., the FedAvg) with advanced optimization strategies to tackle non-IID data issues in distributed learning. Model freezing and pruning techniques are explored to showcase the effective operations of deep learning models on resource-constrained edge devices. Experimental results show that at a pruning rate of 10%, the FedProx with structured pruning in the MIT-BIH and ST databases achieved the best F1 scores, reaching 96.01% and 77.81%, respectively, which achieves a good balance between system efficiency and model accuracy compared to those of the FedProx with the original configuration, reaching F1 scores of 66.12% and 89.90%, respectively. Similarly, with layer freezing technique, unstructured pruning method, and a pruning rate of 20%, the FedAvg algorithm effectively balances classification performance and degradation of pruned model accuracy, achieving F1 scores of 88.75% and 72.75%, respectively, compared to those of the FedAvg with the original configuration, reaching 56.82% and 85.80%, respectively. By adopting model optimization strategies, a practical solution is developed for deploying complex models in edge federated learning, vital for its efficient implementation. [ABSTRACT FROM AUTHOR]

The purpose of this study was to explain how the heterogeneous elements embedded in the Jimei Peninsula affect the transformation of the production landscape into a consumption landscape and the connection between urban governance and economic transformation. The study took a qualitative approach, utilizing historical literature analysis, a field investigation, and in-depth interviews to explore the driving forces and impacts of coastal-zone functional transformation. A total of 26 residents were interviewed individually or collectively, the current situation in the coastal zone with a length of about 16.1 km was recorded in detail, and all the collected elements were divided into six landscape categories for analysis. The results indicate that urban positioning, economic development, policies, and residents are the main factors driving the continuous advancement of the Jimei Peninsula zone. The coast has completed the functional transformation from meeting the residents' survival needs to tourists' sightseeing needs. The traditional fishing culture in this area is slowly disappearing with the tide of time, and navigation technology is being passed down through the Jimei School Village. This study reveals the dynamic process of the transformation of coastal functions in representative coastal tourism cities in China, bringing attention to coastal ecology and local fishing culture, and raising people's awareness of cautious coastal development and sustainable blue-economy development. [ABSTRACT FROM AUTHOR]

Recently, heterostructured photocatalysts have gained significant attention in the field of photocatalysis due to their superior properties compared to single photocatalysts. One of the key advantages of heterostructured photocatalysts is their ability to enhance charge separation and broaden the absorption spectrum, thereby improving photocatalytic efficiency. Zinc oxide is a widely used n-type semiconductor with a proper photoelectrochemical activity. In this study, zinc oxide nanorod arrays were synthesized, and then the surfaces of ZnO nanorods were modified with the p-type semiconductor Co3O4 to create a p–n junction heterostructure. A significant increase in the photocurrent for the ZnO/Co3O4 composite, of 4.3 times, was found compared to pure ZnO. The dependence of the photocurrent on the morphology of the ZnO/Co3O4 composite allows for optimization of the morphology of the ZnO nanorod array to achieve improved photoelectrochemical performance. The results showed that the ZnO/Co3O4 heterostructure exhibited a photocurrent density of 3.46 mA/cm2, while bare ZnO demonstrated a photocurrent density of 0.8 mA/cm2 at 1.23 V. The results of this study provide a better understanding of the mechanism of charge separation and transfer in the heterostructural ZnO/Co3O4 photocatalytic system. Furthermore, the results will be useful for the design and optimization of photocatalytic systems for water splitting and other applications. [ABSTRACT FROM AUTHOR]

The centrifugal force field in a hydrocyclone was affected by the concave-wall curvature radius R0, and the mechanism underlying droplet deformation was closely related to the mass transfer efficiency. Numerical simulation and experimental data were collected to reveal the deformation characteristics and mechanism of a single droplet crossing concave-wall jet. Normalized interfacial energy γ and stretching performance were provided to investigate the droplet deformation process. The results showed that the droplet was stretched along the streamwise direction and shrank along the spanwise direction in the concave-wall jet. The droplet interfacial energy and deformation were the largest when the droplet crossed the jet boundary at t = 0.20 s. The maximum γ value increased with the increase in R0 by 57.3% to 71.4%, and the distance between the droplet and concave wall increased with R0. The Q-criterion was exported to show the increase in the vortex strength with the decrease in R0 at the jet boundary. The pressure distribution inside the droplet showed that the pressure decreased as R0 increased, while the pressure difference increased along the streamwise and wall-normal directions. This study suggested that the droplet breakup was more difficult for a smaller R0, which was beneficial for liquid–liquid heterogeneous separation. [ABSTRACT FROM AUTHOR]

Federated learning is a distributed machine learning algorithm that enables collaborative training among multiple clients without sharing sensitive information. Unlike centralized learning, it emphasizes the distinctive benefits of safeguarding data privacy. However, two challenging issues, namely heterogeneity and backdoor attacks, pose severe challenges to standardizing federated learning algorithms. Data heterogeneity affects model accuracy, target heterogeneity fragments model applicability, and model heterogeneity compromises model individuality. Backdoor attacks inject trigger patterns into data to deceive the model during training, thereby undermining the performance of federated learning. In this work, we propose an advanced federated learning paradigm called Federated Mutual Distillation Learning (FMDL). FMDL allows clients to collaboratively train a global model while independently training their private models, subject to server requirements. Continuous bidirectional knowledge transfer is performed between local models and private models to achieve model personalization. FMDL utilizes the technique of attention distillation, conducting mutual distillation during the local update phase and fine-tuning on clean data subsets to effectively erase the backdoor triggers. Our experiments demonstrate that FMDL benefits clients from different data, tasks, and models, effectively defends against six types of backdoor attacks, and validates the effectiveness and efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

The evaluation of rock porosity and the mercury injection capillary pressure (MICP) curve is fundamental for oil and gas exploration and production. Digital rock (DR) technology, incorporating 3D micro-CT imaging and numerical methods, has been widely employed to predict these properties. However, analyzing the pore structure of heterogeneous rocks, such as fractured rocks or glutenite, solely through single-scale DR analysis poses challenges. Existing upscaling methods have limitations in fully representing the complete range of pore structures at different scales, with limited comparison to experimental data. To address this, we propose a novel method that upscales porosity and simulates the MICP curve from nano-scale to core scale by merging results from micro-CT (at resolutions of 35 μm and 2 μm) and SEM (at resolutions of 6.5 nm and 65 nm). We validate the developed DR model by applying it to sandstones, glutenite, and igneous rocks, and achieve excellent agreement between the experimental data and the multi-scale DR model across 67 samples. The results demonstrate that the multi-scale model effectively captures the porosity and pore structures across the entire range. In contrast, the single digital rock (DR) model underestimates the porosity measurements for both homogeneous sandstones and heterogeneous cores. While the MICP model based on a single DR proves suitable for homogeneous rock samples, it introduces noticeable discrepancies when applied to heterogeneous rock samples. The developed multi-scale method significantly enhances the confidence in using DR to assess the pore structure of complex rocks. [ABSTRACT FROM AUTHOR]

The permeability coefficient of construction materials plays a crucial role in engineering quality and durability. In this study, a microstructure model based on real aggregate shape and digital image technology is proposed to predict the permeability coefficient of concrete. A two-dimensional, three-component finite element model of cement concrete was established considering the interfacial transition zone (ITZ) between aggregate and mortar. The permeability coefficient prediction model was developed by the finite element method. The accuracy of the model was verified by experimental data, and the influence of the water−cement ratio on the permeability coefficient of concrete was analyzed. The results show that this method has good prediction accuracy with a relative error of 1.73%. According to the verified model, the influences of aggregate content, aggregate characteristics, aggregate location, ITZ thickness, and other factors on the permeability of concrete were explored. The higher the water−cement ratio, the higher the permeability coefficient. With the increase in aggregate content, the permeability coefficient decreases. Aggregate permeability has a significant influence on the effective permeability coefficient of concrete within a certain range. The greater the roundness of aggregate, the greater the permeability of concrete. On the contrary, the larger aggregate size causes lower permeability. The permeability coefficient of concrete with segregation is lower than that with uniform distribution. At the same time, the permeability increases with the increase of ITZ thickness. [ABSTRACT FROM AUTHOR]

Detecting fraudulent users in social networks could reduce online fraud and telecommunication fraud cases, which is essential to protect the lives and properties of internet users and maintain social harmony and stability. We study how to detect fraudulent users by using heterogeneous graph representation learning and propose a heterogeneous graph representation learning algorithm to learn user node embeddings to reduce human intervention. The experimental results show promising results. This article investigates how to use better heterogeneous graph representation learning to detect fraudulent users in social networks and improve detection accuracy. [ABSTRACT FROM AUTHOR]

fac-Re(2,2'-bipyridine)(CO)3Cl] (denoted as ReCC) is an efficient molecule-catalyst with high selectivity in the photoreduction of CO2 to CO in a homogeneous system. However, the two major drawbacks of Re(I) complexes in the homogeneous system, easy degradation and difficult separation, seriously hinder its development in the field of industrial applications. In this paper, we designed and prepared two different Re-complex fixation systems (denoted as ReCC@TiO2-5 wt% and ReCC-TiO2-5 wt%) based on TiO2 gel via the sensitization method and sol–gel method, respectively. Compared with a pure ReCC complex, both of them exhibited excellent photocatalytic reduction activity. In particular, the sol–gel hybrid system (ReCC-TiO2-5 wt%) displayed outstanding positive synergistic effects on the photocatalytic activity and the long durability of the photocatalytic process. A series of characterizations were carried out to explore the probable photocatalytic reduction process mechanism, which provides the theoretical basis and technical support for the Re complex fixation method. [ABSTRACT FROM AUTHOR]

Due to the advancement of sensor gadgets and telecommunication technology, wireless sensor networks (WSNs) have drawn a lot of observations in the recent period. Inaccessible terrain, disaster zones, or polluted conditions are typically where they are deployed at random, making battery replacement or recharge challenging or even impossible. Network lifespan is therefore extremely important to a WSN. An abundance of power-effective strategies in a diverse wireless sensor network are surveyed in this paper. We first provide an overview of the fundamental network radio representation and how it may be utilized to analyze different trade-offs between network deployment costs and an energy-efficient clustering approach. We also highlight a few protocols that can be utilized in heterogeneous networks that are energy efficient. [ABSTRACT FROM AUTHOR]

This paper investigates the observability of discrete-time two-time-scale multi-agent systems with heterogeneous features under leader–follower architecture. First, a singular perturbation difference model for the discussed system is established based on consensus agreement. Second, to eliminate the numerical ill-posed problem that may arise from the singularly perturbed small parameter that distinguishes different time scales in the observability analysis, the order of the system model is reduced using the boundary layer theory of the singular perturbation system to obtain a slow-time-scale subsystem and a fast-time-scale subsystem. Then, based on the matrix theory, some algebraic and graphical features that guarantee the observability of the system are obtained. Finally, the validity of the theoretical results is verified by a numerical example. [ABSTRACT FROM AUTHOR]

With the rapid development of industry and the increasing demand for transportation, traditional sources of energy have been excessively consumed. Biodiesel as an alternative energy source has become a research focus. The most common method for biodiesel production is transesterification, in which lipid and low carbon alcohol are commonly used as raw materials, in the presence of a catalyst. In the process of transesterification, the performance of the catalyst is the key factor of the biodiesel yield. This paper reviews the recent research progress on homogeneous and heterogeneous catalysts in biodiesel production. The advantages and disadvantages of current homogeneous acid catalysts and homogeneous base catalysts are discussed, and heteropolyacid heterogeneous catalysts and biomass-derived base catalysts are described. The applications of the homogeneous and heterogeneous catalyst derivatives ionic liquids/deep eutectic solvents and nanocatalysts/magnetic catalysts in biodiesel production are reviewed. The mechanism and economic cost of current homogeneous acid catalysts and homogeneous base catalysts are also analyzed. The unique advantages of each type of catalyst are compared to better understand the microscopic details behind biodiesel. Finally, some challenges of current biodiesel catalysts are summarized, and future research directions are presented. This review will provide general and in-depth knowledge on the achievements, directions, and research priorities in developing novel homogeneous/heterogeneous catalysts for the green and cost-effective production of biodiesel. [ABSTRACT FROM AUTHOR]

The exploration of inexpensive and stable heterogeneous catalysts for C–S coupling reactions remains a challenging issue. Herein, we successfully prepared a new biomass-derived copper catalyst and applied it to the selective C(sp3)–H-directed sulfonylation of ketone hydrazones with commercial sodium sulfinates. The catalyst was characterized using different spectroscopic and microscopic techniques. Importantly, the prepared biomass-supported Cu catalyst catalyzed the C–S coupling reaction with considerably high activity. A variety of aryl and alkyl sulfinates were converted to the corresponding sulfones with good yields. In particular, the heterogeneous catalyst could be recovered easily after the synthesis and consecutively used at least five times with no appreciable decrease in the catalytic activity. Lastly, the copper catalyst is expected to have further applications in organic reactions catalyzed by Cu/CuO2. [ABSTRACT FROM AUTHOR]

Simple Summary: Glioblastoma (GBM) is the most aggressive type of astrocytic glioma. GBMs are diffuse infiltrating tumours that present with extensive hypoxia and genetic heterogeneity amongst other features that have rendered treatment strategies ineffectual, despite recent advances in multimodality therapy regimens. The prognosis remains poor and median survival is less than 17 months using adjuvant chemotherapy and X-ray external radiotherapy. Therefore, strategies should be investigated to target complications associated with this malignancy. A targeted approach with high linear energy transfer particles could address infiltration and cellular aggressiveness (e.g., heterogeneity, hypoxia, and intrinsic radiosensitivity) issues, respectively. Boron neutron capture therapy (BNCT), a biochemically-targeted modality, proposes an attractive solution for GBM. A hybrid computational framework was previously developed by our group to quantify the efficacy of BNCT at its current status of development for a simplified GBM model. This work has expanded the framework to a semi-realistic GBM model with heterogeneous radiosensitivity and anisotropic microscopic extensions; moreover, the neutron beam model and neutron transport components have undergone substantial improvements. Background: Glioblastomas (GBMs) are notorious for their aggressive features, e.g., intrinsic radioresistance, extensive heterogeneity, hypoxia, and highly infiltrative behaviours. The prognosis has remained poor despite recent advances in systemic and modern X-ray radiotherapy. Boron neutron capture therapy (BNCT) represents an alternative radiotherapy technique for GBM. Previously, a Geant4 BNCT modelling framework was developed for a simplified model of GBM. Purpose: The current work expands on the previous model by applying a more realistic in silico GBM model with heterogeneous radiosensitivity and anisotropic microscopic extensions (ME). Methods: Each cell within the GBM model was assigned an α / β value associated with different GBM cell lines and a 10 B concentration. Dosimetry matrices corresponding to various MEs were calculated and combined to evaluate cell survival fractions (SF) using clinical target volume (CTV) margins of 2.0 & 2.5 cm. SFs for the BNCT simulation were compared with external X-ray radiotherapy (EBRT) SFs. Results: The SFs within the beam region decreased by more than two times compared to EBRT. It was demonstrated that BNCT results in markedly reduced SFs for both CTV margins compared to EBRT. However, the SF reduction as a result of the CTV margin extension using BNCT was significantly lower than using X-ray EBRT for one MEP distribution, while it remained similar for the other two MEP models. Conclusions: Although the efficiency of BNCT in terms of cell kill is superior to EBRT, the extension of the CTV margin by 0.5 cm may not increase the BNCT treatment outcome significantly. [ABSTRACT FROM AUTHOR]

The Mishrif Formation in X Oilfield in Iraq is heterogeneous and has prominent development contradictions, and the development plan required urgent adjustment. Based on data regarding the core, cast thin sections, physical property, mercury injection experiments, and development performance, the main geological factors causing the unbalanced development of the Mishrif Formation are identified, and the corresponding development strategy is proposed. The results show that the High Flow Zones (HFZs) are the main geological factors causing unbalanced production in the thick bioclastic limestone reservoir. There are three kinds of HFZs in MA, MB1, and MB2 intervals, namely, the point shoal type, the tidal channel type, and the platform margin shoal type. All HFZs have different scales and distribution patterns. HFZs have ultra-high permeability and large permeability differences with the surrounding reservoir. During development, the oil mainly comes from HFZs, and the considerable reserves in the low permeability reservoir surrounding the HFZs are difficult to develop. The size of the pore throat of the HFZs greatly varies, and permeability is mainly dominated by the mega-pore throat (>10 μm) and the macro-pore throat (2.5~10 μm). In water flood development, the injected water rapidly advances along the mega-pore throat and the macro-pore throat, and the oil in the micro-pore or medium-pore throats are difficult to be displace. It can be concluded that the Mishrif Formation is vertically heterogeneous. The connectivity of HFZs in different intervals greatly varies. As a result, the Mishrif Formation is divided into three development units, MA, MB1, and MB2 + MC, and production wells are deployed in HFZs. The MA adopts a reverse nine-point injection-production pattern, for which the well spacing is 900 m using a vertical well, and the injection well should avoid the HFZs near the faults. The MB1 adopts an irregular five-point injection-production pattern using a vertical well, and the injection wells are deployed at the edge of the tidal channel or in the lagoon. MB2_1 deploys horizontal production wells, for which the well spacing is 900 m. Horizontal production wells, for which the well spacing is 300 m, are deployed in the lower MB2, and the lateral horizontal production wells are converted into injection wells after water breakthrough, and the horizontal wells deployed in the lower part of MC should moderately inject water. [ABSTRACT FROM AUTHOR]

The reduction of nitroarenes to arylamines is a synthetically important transformation both in the laboratory and in industry. Herein, Palladium (Pd) nanoparticles were synthesized via incorporation with mesoporous fumed silica material by doping technique. Water was used as a solvent and the as-synthetized material was reduced by using NaBH4 to ensure the total transformation of PdO into Pd nanoparticles. The synthesized sample was characterized by using inductively coupled plasma (ICP) elemental analysis, X-ray powder diffraction (XRD), N2 sorption measurement, scanning electron microscope (SEM), energy-dispersive spectroscopy (EDX), and transmission electron microscopy (TEM). Data showed that the Pd nanoparticles were successfully synthesized and supported on the mesoporous silica with an average size in the ranges of 10–20 nm, with an irregular shape. The purity of the synthesized sample was confirmed by EDX analysis which exhibits the presence of Si, O, and Pd. The catalytic activity of the prepared sample was evaluated in the heterogeneous reduction of nitroarenes to aromatic amines. Reduction reaction was monitored by Shimadzu GC-17A gas chromatography (GC, Japan) equipped with flam ionization detector and RTX-5 column, 30 m × 0.25 mm, 1-μm film thickness. Helium was used as carrier gas at flow rate 0.6 mL/min. Interestingly, the green hydrogenation of nitroarenes to primary amine compounds was achieved in an aqueous solution with high efficiency and in a short time; moreover, the reusability of heterogeneous Pd-SiO2 was performed for four repeated cycles with more than 88% of efficiency at the fourth run. Finally, the heterogeneity of catalysis with high reliability and eco-friendly processes is a super new trend of nitroarenes reduction in the industry and economic scales. [ABSTRACT FROM AUTHOR]

Conversion rule is a key element for a cellular automata (CA) model, and it is a significant and challenging issue for both domestic and international experts. Traditional research regarding CA models often constructs a single conversion rule for the entire study area, without differentiating it on the basis of the unique growth features of each location. On the basis of this, a partitioned and heterogeneous land-use simulation model (PHLUS) is constructed by integrating a CA and Markov model: (1) A general conversion rule is constructed for the entire study area. By establishing a land development potential evaluation index system, the conversion rule is refined and differentiated; (2) By coupling a CA model with a Markov model, PHLUS can realize land-use simulation both in micro and macro scales. A simulation study is conducted for the Pearl River Delta region. The results show that: (1) By transforming the CA model rules to further distinguish zones, the accuracy is improved. Compared with the traditional CA-Markov model, the simulation accuracies for 2010 and 2020 are improved by 11.55% and 7.14%, respectively. For built-up land simulation, the PHLUS simulation errors for 2010 and 2020 are only 0.7% and 0.57%, respectively; and (2) Under land-use simulation for 2030, cultivated land and forest land will transfer to built-up land. The built-up land area will reach 10,919 km2. Guangzhou and Shenzhen have the greatest potential for land development, and the built-up land area for the two cities will reach 2727 km2. [ABSTRACT FROM AUTHOR]

Phenolic pollutants in industrial wastewater are considered to be harmful aromatic compounds. With the development of industry and pharmaceuticals, phenolic pollutants and their derivatives have gradually started to affect people's daily lives. Therefore, it is necessary to strictly control the content of phenolic pollutants in industrial wastewater, not only for the natural environment but also for human life. The research optimized the existing treatment methods for classified pollutants, and successfully prepared a heterogeneous photo-Fenton catalyst Fe3O4@B-rGO (9.3%). The characterization results of the catalyst showed that the synthesis of the catalyst was successful, and its specific surface area was 11.28 (m2/g), and the pore volume area was 0.137 (m3/g), respectively, which were larger than those of the other two comparative catalysts. In addition, the research conclusion also showed that the catalyst prepared during the research had good catalytic activity, the treatment efficiency of Fe3O4@B-rGO (9.3%) to bisphenol A could reach 100%, and the mineralization rate could reach 67.4%. In the reaction, the main active radicals are generated, and catalyst Fe3O4@B-rGO (9.3%) can produce more active free radicals compared with Fe3O4 and Fe3O4@rGO. [ABSTRACT FROM AUTHOR]

In a typical ultra-deep high-temperature and high-pressure heterogeneous reservoir in Xinjiang, gas channeling quickly occurs during gas injection because of the heterogeneity of the reservoir, the low viscosity of gas injection, and the high gas-oil fluidity ratio. The identification and prediction methods of gas channeling in gas injection development were studied. First, gas channeling discrimination parameters were determined by the numerical simulation method. According to the ratio of gas to oil produced and the composition of oil and gas produced, the flow stages of formation fluid were divided into five regions: gas phase zone, two-phase zone, miscible zone, dissolved gas and oil zone, and original oil zone. The basis for gas channeling identification (namely, the field characterization parameters for gas channeling discrimination) was discovered through analysis and the knowledge of the operability of field monitoring data as the following two parameters: (1) the C1 content rising again on the previous platform when the trailing edge of the two-phase zone is produced and (2) the continuous rise of the gas-oil ratio in production. Then, considering the original high-pressure characteristics of the reservoir, the field characterization parameters of gas channeling under different formation pressures in the exploitation process (namely, C1 content and gas-oil ratio) were simulated and determined. Thus, a gas channeling discrimination method was established for gas injection development in ultra-deep high-temperature and high-pressure heterogeneous reservoirs. According to this gas injection approach, a gas channeling discrimination method was developed, and the field gas channeling judgment was carried out for a gas injection effective D1 well. The results of gas tracer detection were compared to verify the accuracy of this method, leading to strong support for this method in slowing down the gas channeling. [ABSTRACT FROM AUTHOR]

Four-dimensional seismic analysis is an effective reservoir surveillance tool to track the changes of fluid and pressure phases in the oil and gas reservoirs over time of the baseline and monitoring seismic acquisition. In practice, the 4D seismic signal associated with such changes may be negligible, especially in heterogeneous carbonate reservoirs. Therefore, 4D seismic analysis is a model for integrating various disciplines in the oil and gas industry, such as seismic, petrophysics, reservoir engineering, and production engineering. In this study, we started the 4D seismic workflow with a 1D well-based 4D feasibility study to detect the likelihood of 4D signals before performing 4D seismic co-processing of the baseline and monitoring surveys starting from the seismic field data of both datasets. As part of a full 4D seismic co-processing of the baseline and monitor surveys, 4D seismic metric attributes were analyzed over the survey area to measure the improvement in seismic acquisition repeatability for the scarce 1994 baseline seismic and the 2014 monitor seismic survey. For the monitor survey, a 4D time-trace shift was performed using the baseline survey as a reference to measure the time shifts between the baseline and monitor surveys at 20-year intervals. The 4DFour-dimensional dynamic trace warping was followed by a 4D seismic inversion to compare the 4D difference in the seismic inverted data with the difference in seismic amplitude. The seismic inversion helped overcome noise, multiple contaminations, and differences in dynamic amplitude range between the baseline and monitor seismic surveys. We then examined the relationship between well logs and seismic volumes by predicting a volume of log properties at the well locations of the seismic volume. In this method, we computed a possibly nonlinear operator that can predict well logs based on the properties of adjacent seismic data. We then tested a Deep Feed Forward Neural Network (DFNN) on six wells to adequately train and validate the machine learning approach using the baseline and monitoring seismic inverted data. The objective of trying such a deep machine learning approach was to predict the density and porosity of both the baseline and the monitoring seismic data to validate the accuracy of the 4D seismic inversion and evaluate the changes in reservoir properties over a time-lapse of 20 years of production from 1994 to 2014. Finally, we matched the 4D seismic signal with changes in reservoir production properties, investigating the mechanism underlying the observed 4D signal. It was found that the detectability of 4D signals is primarily related to changes in fluid saturation and pressure changes in the reservoir, which increased from 1994 to 2014. This innovative closed-loop research proved that the low repeatability of seismic acquisition can be compensated by optimal 4D seismic co-processing with a complete integration workflow to assess the reliability of the 4D seismic observed signal. [ABSTRACT FROM AUTHOR]

This estimation method operates by integrating the input values that are redundantly collected from heterogeneous devices through the selection of a representative value and estimating missing values by using a multimodal RNN. Users use a heterogeneous healthcare platform mainly in a mobile environment. Users who pay a relatively large amount of attention to healthcare possess various types of healthcare devices and collect data through their mobile devices. The collected data may be duplicated depending on the types of these devices. This data duplication causes an ambiguity issue in that it is difficult to determine which value among multiple data should be taken as the user's actual value. Accordingly, it is necessary to create a neural network structure that considers the data value at the time previous to the current time. RNNs are appropriate for handling data with a time series characteristic. To learn an RNN-based neural network, learning data that have the same time step are required. Therefore, an RNN in which one variable becomes single-modal was designed for each learning run. In the RNN, a cell is a gated recurrent unit (GRU) cell that presents sufficient accuracy in the small resource environment of mobile devices. The RNNs that are learned according to the variables can each operate without additional learning, even if the situation of the user's mobile device changes. In a heterogeneous environment, missing values are generated by various types of errors, including errors caused by battery charge and discharge, sensor failure, equipment exchange, and near-field communication errors. The higher the missing value ratio, the greater the number of errors that are likely to occur. For this reason, to achieve a more stable heterogeneous health platform, missing values must be considered. In this study, a missing value was estimated by means of multimodal deep learning; that is, a multimodal deep learning method was designed with one neural network that was connected with each learned single-modal RNN using a fully connected network (FCN). Each RNN input value delivers mutual influence through the weights of the FCN, and thereby, it is possible to estimate an output value even if any one of the input values is missing. According to the evaluation in terms of representative value selection, when a representative value was selected by using the mean or median, the most stable service was achieved. As a result of the evaluation according to the estimation method, the accuracy of the RNN-based multimodal deep learning method is 3.91%p higher than that of the SVD method. [ABSTRACT FROM AUTHOR]

Cloud computing seems to be the result of advancements in distributed computing, parallel computing, and network computing. The management and allocation of cloud resources have emerged as a central research direction. An intelligent resource allocation system can significantly minimize the costs and wasting of resources. In this paper, we present a task scheduling technique based on the advanced Phasmatodea Population Evolution (APPE) algorithm in a heterogeneous cloud environment. The algorithm accelerates up the time taken for finding solutions by improving the convergent evolution of the nearest optimal solutions. It then adds a restart strategy to prevent the algorithm from entering local optimization and balance its exploration and development capabilities. Furthermore, the evaluation function is meant to find the best solutions by considering the makespan, resource cost, and load balancing degree. The results of the APPE algorithm being tested on 30 benchmark functions show that it outperforms similar algorithms. Simultaneously, the algorithm solves the task scheduling problem in the cloud computing environment. This method has a faster convergence time and greater resource usage when compared to other algorithms. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) continue to provide essential services for various applications such as surveillance, data gathering, and data transmission from hazardous environments to safer destinations. This has been enhanced by the energy-efficient routing protocols that are mostly designed for such purposes. Gateway-based Energy-Aware Multi-hop Routing protocol (MGEAR) is one of the homogenous routing schemes that was recently designed to more efficiently reduce the energy consumption of distant nodes. However, it has been found that the protocol has a high energy consumption rate, lower stability period, and poorer data transmission to the Base station (BS) when it was deployed for a longer period of time. In this paper, an enhanced Heterogeneous Gateway-based Energy-Aware multi-hop routing protocol (HMGEAR) is proposed. The proposed routing scheme is based on the introduction of heterogeneous nodes in the existing scheme, selection of the head based on the residual energy, introduction of multi-hop communication strategy in all the regions of the network, and implementation of energy hole elimination technique. All these strategies are aiming at reducing energy consumption and extend the life of the network. Results show that the proposed routing scheme outperforms two existing ones in terms of stability period, throughputs, residual energy, and the lifetime of the network. [ABSTRACT FROM AUTHOR]

From a basic refrigerator to a self-driving car, emerging technologies are increasingly involving various facets of our daily lives. These bring together many regularly used devices, each with its own characteristics, to communicate and collaborate within the same system. Computer network experts regard this so-called structure as a heterogeneous network made up of several connected objects that do not speak the same language. Communication is therefore ensured by additional types of nodes, such as gateways or converters. In this case, we can detect an increased complexity and a decreased level of security. And thus, the need to adopt a common slang for these kinds of networks has been brought to life. In this work, we compare two different routing protocols: optimized link-state routing (OLSR) and the multipath heterogeneous ad hoc network OLSR (MHAR-OLSR). The latter is an OLSR extension with new functionalities: nodes identification, paths calculation, paths classification, and paths choice that we designed for heterogeneous ad hoc networks composed of MANET, VANET, and FANET devices; it ensures direct communication between these diverse components. We verify and explain all the elements of our solution using colored Petri nets. We also present a global evaluation of Packet Delivery Ratio (PDR), End-To-End Delay, and energy consumption as QoS measures with different numbers of nodes in a heterogeneous scenario. To do this, we use NS-3 and BonnMotion as a tool-set of simulation. Experimental results show improvement in performance when compared to the classical routing protocol. [ABSTRACT FROM AUTHOR]

Featured Application: Heterogeneous electro-Fenton-like processes based on catalyst and electrocatalyst using transition metals for 2-phenylphenol degradation. The hunt for efficient and environmentally friendly degradation processes has positioned the heterogeneous advanced oxidation processes as an alternative more interesting and economical rather than homogenous processes. Hence, the current study lies in investigating the efficiency of different heterogeneous catalysts using transition metals in order to prevent the generation of iron sludge and to extend the catalogue of possible catalysts to be used in advanced oxidation processes. In this study, nickel and zinc were tested and the ability for radical-generation degradation capacity of both ions as homogeneous was evaluated in the electro-Fenton-like degradation of 2-phenylphenol. In both cases, the degradation profiles followed a first-order kinetic model with the highest degradation rate for nickel (1 mM) with 2-phenylphenol removal level of 90.12% and a total organic reduction near 70% in 2 h. To synthesise the heterogeneous nickel catalyst, this transition metal was fixed on perlite by hydrothermal treatment and in a biochar or carbon nanofibers by adsorption. From the removal results using the three synthesized catalysts, it is concluded that the best catalysts were obtained by inclusion of nickel on biochar or nanofibers achieving in both with removal around 80% before 1 h. Thus, to synthetize a nickel electrocatalyst, nickel doped nanofibers were included on carbon felt. To do this, the amount of carbon black, nickel nanofibers and polytetrafluoroethylene to add on the carbon felt was optimized by Taguchi design. The obtained results revealed that under the optimised conditions, a near-complete removal was achieved after 2 h with high stability of the nickel electrocatalyst that open the applicability of this heterogeneous system to operate in flow systems. [ABSTRACT FROM AUTHOR]

In this paper, a model is proposed for the optimal operation of multi-energy microgrids (MEMGs) in the presence of solar photovoltaics (PV), heterogeneous energy storage (HES) and integrated demand response (IDR), considering technical and economic ties among the resources. Uncertainty of solar power as well as the flexibility of electrical, cooling and heat load demand are taken into account. A p-efficient point method is applied to compute PV power at different confidence levels based on historical data. This method converts the uncertain PV energy from stochastic to deterministic to be included in the optimization model. The concept of demand response is extended and mathematically modeled using a linear function based on the quantized flexibility interval of multi-energy load demand. As a result, the overall model is formulated as a mixed-integer linear program, which can be effectively solved by the commercial solvers. The proposed model is implemented on two typical summer and winter days for various cases. Results of case studies show the important benefits for maximum PV utilization, energy efficiency and economic system operation. Moreover, the influence of the different confidence levels of PV power and effectiveness of IDR in the stochastic circumstances are addressed in the optimization-based operation. [ABSTRACT FROM AUTHOR]

Graphics processing units (GPUs) facilitate massive parallelism and high-capacity storage, and thus are suitable for the iterative reconstruction of ultrahigh-resolution micro computed tomography (CT) scans by on-the-fly system matrix (OTFSM) calculation using ordered subsets expectation maximization (OSEM). We propose a finite state automaton (FSA) method that facilitates iterative reconstruction using a heterogeneous multi-GPU platform through parallelizing the matrix calculations derived from a ray tracing system of ordered subsets. The FSAs perform flow control for parallel threading of the heterogeneous GPUs, which minimizes the latency of launching ordered-subsets tasks, reduces the data transfer between the main system memory and local GPU memory, and solves the memory-bound of a single GPU. In the experiments, we compared the operation efficiency of OS-MLTR for three reconstruction environments. The heterogeneous multiple GPUs with job queues for high throughput calculation speed is up to five times faster than the single GPU environment, and that speed up is nine times faster than the heterogeneous multiple GPUs with the FIFO queues of the device scheduling control. Eventually, we proposed an event-triggered FSA method for iterative reconstruction using multiple heterogeneous GPUs that solves the memory-bound issue of a single GPU at ultrahigh resolutions, and the routines of the proposed method were successfully executed on each GPU simultaneously.

Various information systems adopt different information category standards and description methods even for the same battlefield space. This leads to the heterogeneity of information distributed in different systems, which hinders the information sharing among different systems. In this paper, we adopt the idea of schema mapping and design a framework of realizing heterogeneous information sharing based on content. We design a concept-logic tree model to organize the battlefield situation information and realize the mapping from local concept models in different systems to a global unified concept model. By constructing a unified information space, we realize the centralized organization, storage, and management of entity description information. Then, the information broadcasting mechanism and content based information query mechanism are designed to realize information sharing. Theoretical analysis and experiment results verify the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Recently, heterostructured photocatalysts have gained significant attention in the field of photocatalysis due to their superior properties compared to single photocatalysts. One of the key advantages of heterostructured photocatalysts is their ability to enhance charge separation and broaden the absorption spectrum, thereby improving photocatalytic efficiency. Zinc oxide is a widely used n-type semiconductor with a proper photoelectrochemical activity. In this study, zinc oxide nanorod arrays were synthesized, and then the surfaces of ZnO nanorods were modified with the p-type semiconductor Co 3 O 4 to create a p-n junction heterostructure. A significant increase in the photocurrent for the ZnO/Co 3 O 4 composite, of 4.3 times, was found compared to pure ZnO. The dependence of the photocurrent on the morphology of the ZnO/Co 3 O 4 composite allows for optimization of the morphology of the ZnO nanorod array to achieve improved photoelectrochemical performance. The results showed that the ZnO/Co 3 O 4 heterostructure exhibited a photocurrent density of 3.46 mA/cm 2 , while bare ZnO demonstrated a photocurrent density of 0.8 mA/cm 2 at 1.23 V. The results of this study provide a better understanding of the mechanism of charge separation and transfer in the heterostructural ZnO/Co 3 O 4 photocatalytic system. Furthermore, the results will be useful for the design and optimization of photocatalytic systems for water splitting and other applications.

Computer vision has been developed greatly in the past several years, and many useful and interesting technologies have been presented and widely applied. Image matching is an important technology based on similarity measurement. In this paper, we propose a novel feature describing model based on scale space and local principle component analysis for heterogeneous image matching. The traditional uniform eight-direction statistics is updated by a task-related k-direction statistics based on prior information of the keypoints. In addition, the k directions are determined by an approximately solution of a Min-Max problem. The principle component analysis is introduced to compute the main directions of local patches based on the gradient field. In addition, the describing vector is formed by then implementing PCA on each sub-patch of a 4 × 4 mesh. Experimental results show the accuracy and efficiency of proposed method. [ABSTRACT FROM AUTHOR]

Accurately measuring regional evapotranspiration (ET) is of great significance for studying global climate change, regional hydrological cycles, and surface energy balance. However, estimating regional ET from mixed vegetation types is still challenging. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) and the Surface Energy Balance System (SEBS) models were applied to estimate surface ET in a small agricultural watershed. Landsat8 satellite images were used as input data to the single-source models. The two models were validated at single point and ecosystem scales. The results showed that both models overestimated ET observations in paddy fields and orange groves but underestimated them in dry farmland. The error was mainly caused by the heterogeneity of the mixed pixels. The linear spectral mixture model and a set of equations were introduced to reduce the simulation error. The revised results showed that the relative precision of SEBAL was improved by 9.87% and 10.06%, respectively. This research is expected to provide new ideas for future development of accurate remote-sensing ET estimations on heterogeneous surfaces. [ABSTRACT FROM AUTHOR]

Chlorobenzene (CB), as a typical Volatile Organic Contaminants (VOC), is toxic, highly persistent and easily migrates in water, posing a significant risk to human health and subsurface ecosystems. Therefore, exploring effective approaches to remediate groundwater contaminated by CB is essential. As an enhanced micro-electrolysis system for CB-contaminated groundwater remediation, this study attempted to couple the iron-copper bimetal with biochar. Two series of columns using sands with different grain diameters were used, consisting of iron, copper and biochar fillings as the permeable reactive barriers (PRBs), to simulate the remediation of CB-contaminated groundwater in homogeneous and heterogeneous aquifers. Regardless of the presence of homogeneous or heterogeneous porous media, the CB concentrations in the effluent from the PRB columns were significantly lower than the natural sandy columns, suggesting that the iron and copper powders coupled with biochar particles could have a significant removal effect compared to the natural sand porous media in the first columns. CB was transported relatively quickly in the heterogeneous porous media, likely due to the fact that the contaminant residence time is proportional to the infiltration velocities in the different types of porous media. The average effluent CB concentrations from the heterogeneous porous media were lower than those from homogeneous porous media. The heterogeneity retarded the vertical infiltration of CB, leading to its extended lateral distribution. During the treatment process, benzene and phenol were observed as the products of CB degradation. The ultimate CB removal efficiency was 61.4% and 68.1%, demonstrating that the simulated PRB system with the mixture of iron, copper and biochar was effective at removing CB from homogeneous and heterogeneous aquifers. [ABSTRACT FROM AUTHOR]

Convolution is the most computationally intensive task of the Convolutional Neural Network (CNN). It requires a lot of memory storage and computational power. There are different approaches to compute the solution of convolution and reduce its computational complexity. In this paper, a matrix multiplication-based convolution (ConvMM) approach is fully parallelized using concurrent resources of GPU (Graphics Processing Unit) and optimized, considerably improving the performance of the image classifiers and making them applicable to real-time embedded applications. The flow of this CUDA (Compute Unified Device Architecture)-based scheme is optimized using unified memory and hardware-dependent acceleration of matrix multiplication. Proposed flow is evaluated on two different embedded platforms: first on an Nvidia Jetson TX1 embedded board and then on a Tegra K1 GPU of an Nvidia Shield K1 Tablet. The performance of this optimized and accelerated convolutional layer is compared with its sequential and heterogeneous versions. Results show that the proposed scheme significantly improves the overall results including energy efficiency, storage requirement and inference performance. In particular, the proposed scheme on embedded GPUs is hundreds of times faster than the sequential version and delivers tens of times higher performance than the heterogeneous approach. [ABSTRACT FROM AUTHOR]

Clustering is an effective technique used to reduce energy consumption and extend the lifetime of wireless sensor network (WSN). The characteristic of energy heterogeneity of WSNs should be considered when designing clustering protocols. We propose and evaluate a novel distributed energy-efficient clustering protocol called DCE for heterogeneous wireless sensor networks, based on a Double-phase Cluster-head Election scheme. In DCE, the procedure of cluster head election is divided into two phases. In the first phase, tentative cluster heads are elected with the probabilities which are decided by the relative levels of initial and residual energy. Then, in the second phase, the tentative cluster heads are replaced by their cluster members to form the final set of cluster heads if any member in their cluster has more residual energy. Employing two phases for cluster-head election ensures that the nodes with more energy have a higher chance to be cluster heads. Energy consumption is well-distributed in the proposed protocol, and the simulation results show that DCE achieves longer stability periods than other typical clustering protocols in heterogeneous scenarios. [ABSTRACT FROM AUTHOR]

This paper investigates the capacity problem of heterogeneous wireless networks in mobility scenarios. A heterogeneous network model which consists of n normal nodes and m helping nodes is proposed. Moreover, we propose a D-delay transmission strategy to ensure that every packet can be delivered to its destination nodes with limited delay. Different from most existing network schemes, our network model has a novel two-tier architecture. The existence of helping nodes greatly improves the network capacity. Four types of mobile networks are studied in this paper: i.i.d. fast mobility model and slow mobility model in two-dimensional space, i.i.d. fast mobility model and slow mobility model in three-dimensional space. Using the virtual channel model, we present an intuitive analysis of the capacity of two-dimensional mobile networks and three-dimensional mobile networks, respectively. Given a delay constraint D, we derive the asymptotic expressions for the capacity of the four types of mobile networks. Furthermore, the impact of D and m to the capacity of the whole network is analyzed. Our findings provide great guidance for the future design of the next generation of networks. [ABSTRACT FROM AUTHOR]

Durian shell, a biomass waste, was simply burned and then could serve as a heterogeneous catalyst for the transesterification reaction of palm oil with methanol at room temperature. The chemical composition, structure, and morphology of the catalyst were well-characterized by XRD, BET, SEM, TEM, EDS, TGA, FT-IR, and XPS measurement. With the preparation temperature rising to 350 °C, the maximum yield of the biodiesel could reach 94.1% at room temperature, and the optimum reaction conditions were 8 wt.% catalyst, 8:1 methanol/oil molar ratio, ad 2.5 h reaction time. The characterizations results indicated that K 2 O and K 2 CO 3 existed on the surface of catalyst, and a moderate amount of carbon, which acts as a carrier, attributed to the activity of the catalyst. After repeating five times, the catalyst prepared at 350 °C showed better stability than other catalysts. This might be because the incomplete combustion of the remaining carbon slowed down the loss of K to some extent.

To evaluate and improve the quality of land surface albedo products, validation with ground measurements of albedo is crucial over the spatially and temporally heterogeneous land surface. One of the essential steps for satellite albedo product validation is coarse scale observation technique development with long time ground-based measurements. In this paper, the optimal nodes were selected from the wireless sensor network (WSN) to perform observation at large scale and in longer time series for validation of albedo products. The relative difference is used to analyze the spatiotemporal representativeness of each node. The random combination method is used to assess the number of required sites (NRS) and then to identify the most representative combination (MRC). On this basis, an upscaling transform function with different weights for each node in the MRC, which are calculated with the ordinary least squares (OLS) linear regression method, is used to upscale WSN node albedo from point scale to the field scale. This method is illustrated by selecting the optimal nodes and upscaling surface albedo from point observation to the field scale in the Heihe River basin, China. Primary findings are: (a) The method of reducing the number of observations without significant loss of information about surface albedo at field scale is feasible and effective; (b) When only few sensors are available, the most representative locations in time and space should be the first priority; when a number of sensors are available in the heterogeneous land surface, it is preferable to install them in different land surface, rather than the most representative locations; (c) The most representative combination (MRC) combined with the upscaling weight coefficients can give a robust estimate of the field mean surface albedo. These efforts based on ground albedo observations promote the chance to use point information for validation of coarse scale albedo products. Moreover, a preliminary validation of the MODIS (Moderate Resolution Imaging Spectroradiometer) albedo product was performed as the tentative application for upscaling predictions. [ABSTRACT FROM AUTHOR]

Inspired by the high adhesiveness of the electrospun fiber, we propose a method to fabricate multi-scale heterogeneous hump-on-string fiber via the adsorption of nanoparticles, the NPCTi which is the hydrolysate of titanium tetrachloride (TiCl4) and the nanoparticles containing Al (NPCAl) which is produced by the hydrolysis of Trimethylaluminium (TMA, Al(CH3)3). The water collection efficiency of the fibers can be easily controlled via changing not only the size of the beads but also the ratio of the Ti and Al. In addition, we introduce a computational fluid dynamics (CFD) simulation to show the pressure distribution of on the surface of the fibers, which gives another explanation regarding the high water collection efficiency. [ABSTRACT FROM AUTHOR]

Organic-inorganic hybrid polyoxometalate (POM) compounds are a subset of materials with unique structures and physical/chemical properties. The combination of metal-organic coordination complexes with classical POMs not only provides a powerful way to gain multifarious new compounds but also affords a new method to modify and functionalize POMs. In parallel with the many reports on the synthesis and structure of new hybrid POM compounds, the application of these compounds for heterogeneous catalysis has also attracted considerable attention. The hybrid POM compounds show noteworthy catalytic performance in acid, oxidation, and even in asymmetric catalytic reactions. This review summarizes the design and synthesis of organic-inorganic hybrid POM compounds and particularly highlights their recent progress in heterogeneous catalysis. [ABSTRACT FROM AUTHOR]