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Background In recently years, the organic pollutants removal using semiconductor photocatalysis has attracted significant interest, however, the insufficient visible-light-harvesting ability and fast photogenerated charge recombination of photocatalysts vastly hinder their practical application. Methods In this work, a facile hydrothermal method was developed for the preparation of Ni doped ultrathin-layered and porous Bi2O2CO3, thus the enhanced light absorption and charge separation were simultaneously achieved. Significant Findings The experimental and characterization results show that the formation of ultrathin porous structure can promote multiple scattering of the light, thus enhancing light absorption. Moreover, the adjustment of electronic structure enhances the charge transfer rate greatly and suppresses the recombination of charge carriers. Consequently, the rhodamine B (RhB) photodegradation efficiency of Ni-Bi2O2CO3 has an obvious improvement, which is 8.94-fold higher than pristine Bi2O2CO3. And the primary active species are ·O2− and ·OH radicals in the photocatalytic reaction. Moreover, the degradation pathway of RhB is studied by LC-MS technology and a probable degradation pathway is presented. This work offers an efficacious strategy for synergistically modulating and optimizing photocatalysts and provides an efficient photocatalyst for wastewater treatment.

Multisensor fusion strategies have been widely applied in human activity recognition (HAR) in body sensor networks (BSNs). However, the sensory data collected by BSNs systems are often uncertain or even incomplete. Thus, designing a robust and intelligent sensor fusion strategy is necessary for high-quality activity recognition. In this article, Dezert–Smarandache theory (DSmT) is used to develop a novel sensor fusion strategy for HAR in BSNs, which can effectively improve the accuracy of recognition. Specifically, in the training stage, the kernel density estimation (KDE)-based models are first built and then precisely selected for each specific activity according to the proposed discriminative functions. After that, a structure of basic belief assignment (BBA) can be constructed, using the relationship between the test data of unknown class and the selected KDE models of all considered types of activities. In order to deal with the conflict between the obtained BBAs, proportional conflict redistribution-6 (PCR6) is applied to fuse the acquired BBAs. Moreover, the missing data of the involved sensors are addressed as ignorance in the framework of the DSmT without manual interpolation or intervention. Experimental studies on two real-world activity recognition datasets (The OPPORTUNITY dataset; Daily and Sports Activity Dataset (DSAD)) are conducted, and the results shows the superiority of our proposed method over some state-of-the-art approaches proposed in the literature.

Organic pollutants and cyanobacteria exist in water widely, which make significant impacts on human health so that appropriate methods are needed for their removal. In this work, Ni doped Bismuth oxychloride (BiOCl) photocatalysts were successfully synthesized by a simple hydrothermal method. The light absorption and charge carriers separation involved in superoxide (·O2−) generation can be optimized with the introduction of Ni element. And photocatalytic degradation experiments showed that the 9% Ni-BiOCl enhanced photodegradation activity of organic matter (Rh B and BPA) as well as M. aeruginosa. The degradation efficiency of Ni-BiOCl on the removal of Rh B and BPA were approximately 34.99% and 57% higher than that of pristine BiOCl. Furthermore, the algae inactivation was systematically studied by three-dimensional fluorescence spectrum. Results showed that ·O2− acted an irreplaceable role among the experiment of photocatalytic algae removal, and the details were described as ·O2− and h+ first destroyed the cell wall of M. aeruginosa, secondly inactivated the active fluorescent substances in the cell, and then catalyzed the oxidation of intracellular exudates such as chlorophyll and phycocyanin as inorganic substances. This study provides a multifunctional catalyst for controlling water pollution and environmental restoration.

In this paper, the transient electromagnetic–thermal simulation is performed for dispersive media using the discontinuous Galerkin time-domain (DGTD) method. Both the frequency-dependent and temperature-dependent properties of dispersive media are considered in the modeling. Instead of executing the complex convolution in the time domain, an auxiliary differential equation (ADE) method is employed to manipulate dispersive media, where the polarization current density is introduced as an auxiliary variable. While dealing with the thermal issue by the DGTD, the heat flux is utilized to construct the ADE for solving the thermal conduction equation. As the heat source in electromagnetic–thermal simulation, the transient power loss density for three typical dispersive media (Debye, Lorentz, and Drude) is derived by the electrodynamic approach and also explained with equivalent circuit models. The validity and accuracy of the proposed electromagnetic–thermal co-simulation method are demonstrated by the numerical examples.

A single-layer ± 45° dual-polarized directional array antenna based on phase control approach for millimeter-wave applications is proposed in this paper. Based on the series-fed dual circularly polarized array, the ± 45° polarized radiation is achieved by exciting the two ports of the array simultaneously and adjusting the feeding phase difference of the two ports. Since the two ports of the series-fed array are excited simultaneously, the beam direction of the array does not deflect with frequency changes. In addition, the entire antenna array is printed on a single-layer substrate. The array achieves dual-polarized radiation from 29.5 to 30.5 GHz.

Since accurate thermal analysis plays a critical role in the thermal design and management of the 3-D system-level integration, in this paper, a discontinuous Galerkin time-domain (DGTD) algorithm is proposed to achieve this purpose. Such as the parabolic partial differential equation (PDE), the transient thermal equation cannot be directly solved by the DGTD method. To address this issue, the heat flux, as an auxiliary variable, is introduced to reduce the Laplace operator to a divergence operator. The resulting PDE is hyperbolic, which can be further written into a conservative form. By properly choosing the definition of the numerical flux used for the information exchange between neighboring elements, the hyperbolic thermal PDE can be solved by the DGTD together with the auxiliary differential equation. The proposed algorithm is a kind of element-level domain decomposition method, which is suitable to deal with multiscale geometries in 3-D integrated systems. To verify the accuracy and robustness of the developed DGTD algorithm, several representative examples are benchmarked.

Ultrathin 2D Bi3O4Cl nanosheets are promising photocatalysts for photocatalytic organic pollutions degradation, and tailoring the electronic structure by non-metal element doping of Bi3O4Cl is an important strategy to increase its photocatalytic activity. However, the role of doped non-metal atoms on charge carriers separation and light absorption has not been understood in depth. Here, the B-doped Bi3O4Cl ultrathin nanosheets are fabricated via a solvothermal way, which increase solar absorption and electron-hole separation of Bi3O4Cl. The products are characterized by FE-SEM, TEM, AFM, indicating that B-doped Bi3O4Cl are 3.87 nm thick nanosheets. And UV–Vis-DRS, XPS, PL and density functional theory show that the doped B atoms play multiple roles in facilitating photocatalytic performance: (1) inducing midgap states to immensely expand the light response region up from 450 nm to 557 nm; (2) acting as the electron capture centers to accelerate charge carries separation. The ESR technology shows that B-doped Bi3O4Cl can produce more O2– and OH radicals. As a result, the B-doped sample achieves a high-efficient photocatalytic bisphenol A and ciprofloxacin degradation, 3-fold and 2.1-fold higher than pure Bi3O4Cl, respectively. This work presents new opinions into the design of photocatalyst and confirms the role of electronic structure modulation on tuning catalytic activity.

Bio-carbon derived from agricultural waste is promising as electrode material of supercapacitor. However, it is still challenging to obtain excellent capacitance from raw biomass with an inherently dense and hard structure. In this work, a facile and scalable route is developed to fabricate N-doped hierarchical porous bio-carbon from cherry stones (CSs). Through the pretreatment of lignin dissolution-reprecipitation, the dense CSs were successfully transformed into porous precursor. With the promotion of mass transfer inside the material, the as-obtained carbon can be well-manipulated by N-doping and molten salt activation. A hierarchical and porous structure was formed in the bio-carbon with a specific surface area of up to 1681 m2 g−1. The material possessed an excellent specific capacitance of 370.5 F g−1 in 6 M KOH at a current density of 0.5 A g−1 and a prominent rate performance with a capacitance remaining 225.1 F g−1 at 20.0 A g−1. After 5000 cycles, an initial capacitance of 99.1% was maintained. All the results ensured that the bio-carbon derived from CSs could serve as renewable, low-cost and high-performance electrode materials for supercapacitors, and the proposed pretreatment method could effectively enhance energy storage capability of biomass-derived activated carbon.

International audience; Multi-source information fusion strategies in target recognition have been widely applied. Generally, each source is defined and modelled over a common frame composed of the hypotheses to discern. However, in practice, the independent sources of evidence can refer to distinct frames of discernment in terms of the hypotheses they consider. Under this condition, the classical combination process cannot be applied directly. Working with distinct frames of discernment for information fusion is a problem often encountered in the development of recognition systems which requires a particular attention. In order to combine such sources, this paper presents a new combination method which splits the process of fusion into two steps: construction of granular structure, calculation of belief mass, followed by the fusion process. Our simulations results show that the proposed method can effectively solve the problem of fusion of sources defined on distinct frames.

International audience; The classifier based on rough sets is widely used in pattern recognition. However, in the implementation of rough set-based classifiers, there always exist the problems of uncertainty. Generally, information decision table in Rough Set Theory (RST) always contains many attributes, and the classification performance of each attribute is different. It is necessary to determine which attribute needs to be used according to the specific problem. In RST, such problem is regarded as attribute reduction problems which aims to select proper candidates. Therefore, the uncertainty problem occurs for the classification caused by the choice of attributes. In addition, the voting strategy is usually adopted to determine the category of target concept in the final decision making. However, some classes of targets cannot be determined when multiple categories cannot be easily distinguished (for example, the number of votes of different classes is the same). Thus, the uncertainty occurs for the classification caused by the choice of classes. In this paper, we use the theory of belief functions to solve two above mentioned uncertainties in rough set classification and rough set classifier based on Dezert-Smarandache Theory (DSmT) is proposed. It can be experimentally verified that our proposed approach can deal efficiently with the uncertainty in rough set classifiers.

In this paper, the transient electromagnetic-thermal simulation via the discontinuous Galerkin time domain (DGTD) method is performed for the Lorentz medium, which is a kind of dispersive material with frequency-dependent permittivity. To avoid doing the convolution in the time domain, the auxiliary differential equation (ADE) method is adopted to manipulate the dispersive relation. The transient dielectric loss of Lorentz media is derived according to the electrodynamic approach and is treated as the heat source for transient thermal simulation. The accuracy and validity of the proposed method are demonstrated by the numerical example.

In many applications involving epistemic uncertainties usually modeled by belief functions, it is often necessary to approximate general (non-Bayesian) basic belief assignments (BBAs) to subjective probabilities (called Bayesian BBAs). This necessity occurs if one needs to embed the fusion result in a system based on the probabilistic framework and Bayesian inference (e.g. tracking systems), or if one needs to make a decision in the decision making problems. In this paper, we present a new fast combination method, called modified rigid coarsening (MRC), to obtain the final Bayesian BBAs based on hierarchical decomposition (coarsening) of the frame of discernment. Regarding this method, focal elements with probabilities are coarsened efficiently to reduce computational complexity in the process of combination by using disagreement vector and a simple dichotomous approach. In order to prove the practicality of our approach, this new approach is applied to combine users' soft preferences in recommender systems (RSs). Additionally, in order to make a comprehensive performance comparison, the proportional conflict redistribution rule #6 (PCR6) is regarded as a baseline in a range of experiments. According to the results of experiments, MRC is more effective in accuracy of recommendations compared to original Rigid Coarsening (RC) method and comparable in computational time.

A discontinuous Galerkin time-domain (DGTD) algorithm is proposed in this paper for transient thermal simulation of system integration. In order to reduce the complexity in the modeling of package structures, the equivalent thermal parameters of TSV interposer and bump layers are extracted properly. Then, by introducing the heat flux as an intermediate variable, the Laplace operator in the heat conduction equation is degraded to be a divergence operator. By this means, the resulting equations are written in the form of conservation and solved by the standard DGTD method. Due to the local operation of DGTD, the finalized matrix equations can be solved in the element-level form with the explicit time-marching scheme, which is rather suitable for large-scale problems. To validate the proposed method, a representative example on the thermal analysis of system integration is illustrated.

In many applications involving epistemic uncertainties usually modeled by belief functions, it is often necessary to approximate general (non-Bayesian) basic belief assignments (BBAs) to subjective probabilities (called Bayesian BBAs). This necessity occurs if one needs to embed the fusion result in a system based on the probabilistic framework and Bayesian inference (e.g. tracking systems), or if one wants to use classical decision theory to make a decision. There exists already several methods (probabilistic transforms) to approximate any general BBA to a Bayesian BBA. From a fusion standpoint, two approaches are usually adopted: 1) one can approximate at first each BBA in subjective probabilities and use Bayes fusion rule to get the final Bayesian BBA, or 2) one can fuse all the BBAs with a fusion rule, typically Dempster-Shafer's, or PCR6 rules (which is very costly in computations), and convert the combined BBA in a subjective probability measure. The former method is the simplest method but it generates a high loss of information included in original BBAs, whereas the latter is intractable for high dimension problems. This paper presents a new method to achieve this task based on hierarchical decomposition (coarsening) of the frame of discernment, which can be seen as an intermediary approach between the two aforementioned methods. After the presentation of this new method, we show through simulations how its performs with respect to other methods.

The study of alternative probabilistic transformation (PT) in DS theory has emerged recently as an interesting topic, especially in decision making applications. These recent studies have mainly focused on investigating various schemes for assigning both the mass of compound focal elements to each singleton in order to obtain Bayesian belief function for realworld decision making problems. In this paper, work by us also takes inspiration from both Bayesian transformation camps, with a novel evolutionary-based probabilistic transformation (EPT) to select the qualified Bayesian belief function with the maximum value of probabilistic information content (PIC) benefiting from the global optimizing capabilities of evolutionary algorithms. Verification of EPT is carried out by testing it on a set of numerical examples on 4D frames. On each problem instance, comparisons are made between the novel method and those existing approaches, which illustrate the superiority of the proposed method in this paper. Moreover, a simple constraint-handling strategy with EPT is proposed to tackle target type tracking (TTT) problem, simulation results of the constrained EPT on TTT problem prove the rationality of this modification.