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Icosahedral B12 serves as a fundamental building block for a variety of boron allotropes and plays a crucial role in enhancing the stability of fullerene-like boron nanoclusters. However, progress in the development of two-dimensional (2D) functional assemblies made from icosahedral B12 has been limited. In this context, we propose a 2D semiconducting boron allotrope (h-B12) using icosahedral B12 as a building block. This structure features a stable honeycomb lattice, with its unit cell comprising two mirror-symmetric icosahedral B12 clusters. The results of first-principles calculations indicate that the band structure exhibits Dirac cones and a relatively high group velocity, similar to graphene. Additionally, it is found to be a material with a negative Poisson's ratio and a relatively low Young's modulus. Finally, our calculations reveal that the zigzag nanoribbon constructed from h-B12 displays prominent edge states at both edges, indicating that this structure represents a potential topological electronic material. Our findings shed light on the design and construction of nano-electronic materials via cluster assembly. [ABSTRACT FROM AUTHOR]

We first give a pre-order principle whose form is very general. Combining the pre-order principle and generalized Gerstewitz functions, we establish a general equilibrium version of set-valued Ekeland variational principle (denoted by EVP), where the objective function is a set-valued bimap defined on the product of quasi-metric spaces and taking values in a quasi-ordered linear space, and the perturbation consists of a subset of the ordering cone multiplied by the quasi-metric. From this, we obtain a number of new results which essentially improve the related results. Particularly, the earlier lower boundedness condition has been weakened. Finally, we apply the new EVPs to Psychology. [ABSTRACT FROM AUTHOR]

Various energy conversion devices have attracted much attention due to the rapid development of wearable microsensors and the increasing demand for continuous power supply. Among them, improving the performance of evaporation-based hydrovoltaic devices remains a meaningful endeavor. Here, we developed a conductive polymer modified flexible hydrovoltaic power generation device. By arranging the conducting polymer between the flexible substrate and the nanopore channel, the device can voltage output of 1 V and deliver more than 12 μA of current. Due to the durable attachment of the nanomaterials to the substrate, the device maintained over 60% of its electrical output capacity even after being immersed in water for 2 weeks. Improved substrate conductivity leads to an increase in over three times the current output. Furthermore, this device's output is influenced by changes in humidity, making it a viable respiratory monitoring sensor. The integration of polymer conductive materials has led to improved electrical output performance of hydrovoltaic devices, expanding their potential for applications. [ABSTRACT FROM AUTHOR]

When locating multiple magnetic targets, the interference between each magnetic dipole is often ignored, we propose a method to detect and locate multiple weak magnetic target signals under the interference of strong magnetic sources. First, under different burial depths, the influence of shallow magnetic sources and deep magnetic sources on each other was studied and the conclusion was drawn that shallow targets are less affected by deep targets, while deep targets have the opposite effect; and the normalized source strength (NSS) is introduced into the concept of magnetic inclination in the terrain field, this simultaneously utilizes the insensitivity of magnetic inclination to burial depth and the isotropy of NSS to amplify the signal characteristics of submerged deep targets, and initially obtains the horizontal position of the measured target; the idea of locating shallow targets first, removing the interference of shallow targets, and then locating deep targets is proposed, The results of positioning were fitted and optimized by moth Moth-Flame Optimization (MFO), and the accuracy of positioning was improved. In the simulation, compared with the total magnetic field intensity (TMI), the root mean square error (RMSE) of the positioning results of the three measured targets after using the proposed method is reduced from 0.2820, 0.1831 and 0.3972 to 0.0112, 0.0145 and 0.0326, the positioning error of deep target is greatly reduced, which proves the feasibility of the proposed method, in the actual experiment, the positioning accuracy of the deep measured magnetic source is also improved. [ABSTRACT FROM AUTHOR]

3D magnetic inversion is an important method for detecting underwater or underground magnetic objects, which can obtain the physical parameters and geometric features of the target. In order to solve the problem of smooth inversion results of L2 norm regularization, this paper proposes a three-dimensional inversion method of magnetic gradient data based on equivalent source weighting. First, the center position of the magnetic object is estimated using the correlation imaging method, and then the equivalent source weighting function is constructed based on the acquired center position. The weights are calculated according to the distance from the grid to the center of the magnetic object. The further away the grid is, the higher weight will be given. The Euclidean distance and Chebyshev distance are used for calculating the weights of grids. Finally, the equivalent source weighting function is added to the total objective function and solved by conjugate gradient method. Simulation experiments show that the equivalent source weighting function can reduce the root-mean-square error of the inversion results and improve the structural similarity. Compared with the Euclidean distance, the inversion result of the edges and corners of cubic magnetic body model is better when weighted with the Chebyshev distance. The proposed method does not require iterative solving and can avoid generating too smooth results, which improves the inversion accuracy. [ABSTRACT FROM AUTHOR]

When magnetic measuring instruments are used to measure the shape of ferromagnetic objects, the objects far away from the observation plane are likely to have blurred shape features because the magnetic field decays rapidly with distance. A bigger challenge is to measure multiple objects at the same time. When the relative positions of multiple measured objects and the observation plane are inappropriate, it is easy to have problems that the shape features of the deeper measured objects are not obvious and the magnetic signals of multiple measured objects are aliased, which usually leads to shape feature measurement failed. To address this issue, we propose an equalized shape feature enhancement method for multiple ferromagnetic objects. The method enhances shape features by evaluating the trends of the total horizontal derivative and vertical derivative of the magnetic field within the measurement area using the standard deviation. Meanwhile, the method combines the theory of ratio equalization and normalization to improve the shape features convergence of deeper objects and balance the signal aliasing interference between objects of different depths. Model simulation and experimental results show that the shape feature measurement results of the proposed method are clear and in good agreement with the ideal model. The method can effectively balance the magnetic anomaly amplitudes of the measured objects with different depths, and improve the accuracy and stability of shape feature measurement. We compare and analyze the processing effects of the proposed method and the traditional normalized standard deviation method (NSTD). It is calculated that the standard deviations of the results obtained by the proposed method and the NSTD method are 0.146 and 0.136, and the average peak-to-trough differences are 0.368 and 0.352, respectively. Therefore, the proposed method can better enhance the shape characteristics of ferromagnetic objects and has more practical application value. [ABSTRACT FROM AUTHOR]

Objective: Lower-limb exoskeleton systems are defined as gait training or walking-assisting devices for spinal cord injury or hemiplegic patients. Crutches, straps, and baffles are designed to protect subjects from falling. However, skin abrasions occur when the interaction forces are too large. In this study, the interaction forces between the human body and an exoskeleton system named the AIDER were measured to confirm whether the design was ergonomic. Background: The AIDER system is a wearable lower-limb exoskeleton. It secures a subject by binding on the waist, thighs, shanks, and feet. Method: Eight healthy subjects participated in the study. The interaction forces of the waist strap, thigh baffles, shank baffles, and crutch handles were measured by pressure sensors. Ten repetitions were completed in this study. After one repetition, custom comfort questionnaires were completed by the subjects. Results: Although a few of the peak values of the maximum intensities of pressure between the hands and crutch handles reached the minimum value of the pain–pressure threshold (PPT), the average pressure intensities were much smaller than the PPT value. Conclusions: The results indicated that the mechanical structure and control strategy of the AIDER must be improved to be more ergonomic in the future. [ABSTRACT FROM AUTHOR]

Lead halide perovskites have been regarded as promising materials for fabrication of high-performance photodetectors (PDs). However, spin-coated perovskite thin films generally have suffered from high-density grain boundaries and defect states, which severely deteriorated the performance and stability of resulting PDs. Fortunately, the above issues can be effectively solved via additive assisted metal ions doping. In this work, Co-based zeolite imidazole framework (ZIF-67) additive assisted Co ion doping has been proposed to prepare high-quality perovskite CH3NH3PbI3 (MAPbI3) thin films. Morphological, structural, optical, and electrical studies have shown that Co ion doping decreased grain boundaries as well as suppressed defect states, which greatly enhanced the performance and stability of MAPbI3/Si heterojunction PDs, including reducing dark current, increasing photocurrent, and increasing response speed. Finally, the mechanisms of suppression of defect states benefiting from Co ion doping were discussed based on density functional theory calculations. The results provided in this work will pave the way for development of high-performance and stable MAPbI3 PDs in the future. [ABSTRACT FROM AUTHOR]

Mammalian sterile 20-like (Ste20-like) protein kinase 3 (MST3) or serine/threonine-protein kinase 24 (STK24) is a serine/threonine protein kinase that belongs to the mammalian STE20-like protein kinase family. MST3 is a pleiotropic protein that plays a critical role in regulating a variety of events, including apoptosis, immune response, metabolism, hypertension, tumor progression, and development of the central nervous system. The MST3-mediated regulation is intricately related to protein activity, post-translational modification, and subcellular location. Here, we review the recent progress on the regulatory mechanisms against MST3 and its-mediated control of disease progression. [ABSTRACT FROM AUTHOR]

Discoveries of tetraquarks not only enrich the hadronic spectrum but also provide more platforms to understand quantum chromodynamics. We study the production processes of hidden-charm and open-charm tetraquarks in B decays by analyzing their topological amplitudes. Relations between different channels are found, which confront tests by experiments to probe the nature of the tetraquarks. Furthermore, promising channels to find more tetraquarks are proposed. [ABSTRACT FROM AUTHOR]

A high-precision three-dimensional reconstruction method of multiple magnetic targets is studied in this paper. The inversion space is divided into cubic grid cells, and various complex shapes of magnetic targets are simulated by the combination of grid cells. The forward calculation of the magnetic gradient tensor in the spatial domain is performed. Then, the correlation imaging method is used to calculate cross correlation between the observed magnetic gradient tensor and the theoretical magnetic gradient at each grid cell tensor due to a magnetic dipole. The spatial distribution and center position of magnetic targets are preliminarily estimated by the resultant correlation coefficients, and use them to construct the center weighting function as a constraint to the inversion equations. The solution is completed by using the conjugate gradient method to estimate the magnetic susceptibility of each grid cell, and the 3D outline of the magnetic target is depicted by using the visualization technique. The experimental results show that the proposed method can achieve the 3D reconstruction of multiple simple magnetic targets with high precision. [ABSTRACT FROM AUTHOR]

The study of ocean wave energy harvesting technology is of great importance in the field of distributed sensor energy supply. Aiming at the problems of insufficient output power, single direction of collecting vibration, restricted working frequency band and complex structure of traditional ocean wave energy harvesters of marine network nodes, a multi-directional extremely-low-frequency electromagnetic ocean wave energy harvester based on improved double pendulum structure is designed. The effect of different component parameters of the energy harvester on the output electrical performance is analyzed by simulation software and a constructed vibration test rig, and the relationship between output voltage and motion state is explored. After optimizing the structure, the ocean wave energy harvester has a minimum response frequency of about 0.25 Hz at a vertical wave height of 0.6 m and a minimum response flow speed of about 0.3 m/s in the horizontal direction. The peak output power is about 150 mW when the wave height is 0.6 m at a frequency of 1 Hz and about 110 mW when the horizontal speed is 0.9 m/s. Experimental results show that the energy harvester can better capture ocean wave energy and provide power for night lights and even IoT devices. [ABSTRACT FROM AUTHOR]

Amantadine is one of the most prescribed antiviral medications. Considering its abuse is life-threatening, monitoring amantadine residues is urgently needed. This study designed a dipstick assay based on lateral flow immunoassay (LFIA) platforms with an indicator range of 50–200 ng/mL. The dipstick consists of four coloured test lines that detect the concentration of amantadine semi-quantitatively in the range of 0–200 ng/mL. The amantadine concentration was categorised as 0–50, 50–100, 100–200, or > 200 ng/mL, depending on the response of the test lines. Furthermore, we measured the amantadine residues in commercial meat samples by this LFIA. The results were highly agreeable with those determined by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The LFIA shows potential for routine monitoring of amantadine in meat products, and the results of this study provide insight into the development of new LFIA devices for use in agricultural industries. [ABSTRACT FROM AUTHOR]

Neuroprotection in acute stroke has not been successfully translated from animals to humans. Animal research on promising agents continues largely in rats and mice which are commonly available to researchers. However, controversies continue on the most suitable species to model the human situation. Generally, putative agents seem less effective in mice as compared with rats. We hypothesized that this may be due to inter-species differences in stroke response and that this might be manifest at a genetic level. Here we used whole-genome microarrays to examine the differential gene regulation in the ischemic penumbra of mice and rats at 2 and 6 h after permanent middle cerebral artery occlusion (pMCAO; Raw microarray CEL data files are available in the GEO database with an accession number GSE163654). Differentially expressed genes (adj. p ≤ 0.05) were organized by hierarchical clustering, correlation plots, Venn diagrams and pathway analyses in each species and at each time-point. Emphasis was placed on genes already known to be associated with stroke, including validation by RT-PCR. Gene expression patterns in the ischemic penumbra differed strikingly between the species at both 2 h and 6 h. Nearly 90% of significantly regulated genes and most pathways modulated by ischemia differed between mice and rats. These differences were evident globally, among stroke-associated genes, immediate early genes, genes implicated in stress response, inflammation, neuroprotection, ion channels, and signal transduction. The findings of this study may have significant implications for the choice of species for screening putative stroke therapies. [ABSTRACT FROM AUTHOR]

Waste Management has become a challenging topic for fast-growing areas in many countries. The situation became worse when more and more countries have banned the import of different types of wastes. In this paper, we showcase a real-life example of an intelligent way of managing waste using Smart Bins over the Internet of Things and an integration with a dashboard system for decision-makers. We examined if there is any correlation between the smart bins' fullness and the date or month. [ABSTRACT FROM AUTHOR]

In the context of global science and technology, all countries pay more and more attention to the text analysis of emotional intonation, and the emotional intonation text analysis and policy orientation of enterprise management in major international and domestic enterprises have also changed from shallow to deep. In the twenty-first century, with the rapid development of human society, people's demand for living standards and material needs increases rapidly, and employees' awareness and needs for work are constantly changing. At present, there is the problem of emotional intonation text analysis error in the management of the enterprise, and the task and emotional transmission command are not clear and thorough. It is necessary to reasonably use deep-learning-related algorithms, especially convolutional neural network and other algorithms, to study the emotional intonation text analysis and policy guidance of the enterprise management. Aiming at the forefront of deep learning development, the latest deep learning technologies are constantly introduced. The research field of emotional intonation text analysis and policy orientation of enterprise management is focused. Through simulation experiment, the characteristics of emotional intonation text analysis and policy orientation research of different enterprise management are compared and analyzed, so as to further improve the emotional intonation text analysis and policy orientation of deep learning for enterprise management. [ABSTRACT FROM AUTHOR]

Summary: Lithium‐ion batteries are used in a wide range of applications due to their cleanliness and stability, and the health management of lithium‐ion batteries has become a necessity. The most important aspect of health management is the prediction of the remaining useful life (RUL) of the battery. Therefore, a RUL estimation model based on the aging factor of the charging process and an improved multi‐kernel relevance vector machine is proposed in order to achieve high accuracy estimation of the RUL of lithium‐ion batteries. First, eight aging features highly correlated with lithium‐ion batteries capacity degradation are extracted based on charging current, voltage, and temperature data, then, their correlation is proved using gray relation analysis. Secondly, the improved gray wolf constrained optimization algorithm is used to determine the kernel function combination coefficients of the multi‐kernel relevance vector machine, and the RUL prediction model of the improved multi‐kernel relevance vector machine is established. Finally, using the battery dataset from NASA, aging data of three datasets, 24°C, 43°C, and 4°C, with a total of 11 batteries, were selected for validation. The validation results show that the improved multi‐kernel relevance vector machine prediction model has higher prediction accuracy and more robust long‐term prediction capability, with RUL prediction error less than 10 cycles and MAE less than 0.05, both of which are better than that of the single‐kernel relevance vector machine model and other multi‐kernel relevance vector machine models. [ABSTRACT FROM AUTHOR]

With electric vehicles getting increasingly popular, there has been a lot of interest in encouraging future electric vehicle development in terms of greater charging efficiency, greater benefits to the environment, and greater reliability. One of the most prominent research is on the scheduling of electric vehicle charging and navigation. High-quality navigation and charging scheduling strategy must depend on the state of the coupled network between the transportation and power networks, while the outcomes of scheduling can also have a significant impact on these networks. In this paper, we propose a novel dynamic navigation and charging strategy that fully considers the coupled traffic and power distribution network. Firstly, electric vehicles with similar travel and charging requirements are classified. Secondly, an elastic scheme including fixed route navigation and flexible charging scheduling at charging stations is provided. We also propose an information integration framework for the implementation of electric vehicle routing and charging scheduling, information interaction between sectors including the status of heterogeneous electric vehicles, day-ahead power scheduling, and base load demands. The novel strategy aims at optimal electric vehicle navigation and charging at the system level achieving a lower overall travel cost, charging cost, carbon emission, and load variance of the power distribution network. Classification algorithms, stochastic dynamic programming, and queuing theory are used for mathematical modeling. Numerical results demonstrate the effectiveness of the proposed novel strategy for the navigation and charging scheduling of electric vehicles. • A novel navigation and charging strategy for electric vehicles is proposed. • A framework is proposed to integrate optimized information. • Electric vehicles with similar travel and charging requirements can be classified. • The elastic navigation and charging scheme are customized. • The proposed strategy effectively navigates electric vehicle grouping. [ABSTRACT FROM AUTHOR]

• After treatment, abnormal activity in some brain regions returned to normal. • The rTMS plus antidepressants may improve the symptoms of MDD patients. • The rTMS plus antidepressants improved neuronal activity on the right ACC. • The rTMS plus antidepressants improved neuronal activity on the right OFC. • ALFF may predict the response of MDD patients to rTMS plus antidepressants. We investigated the neuroimaging changes and clinical efficacy of repetitive transcranial magnetic stimulation (rTMS) combined with antidepressants in major depressive disorder (MDD) patients. We scanned 35 patients with MDD and 27 healthy controls (HC) with resting-state functional magnetic resonance imaging (fMRI) before and after treatment. We analyzed amplitude of low-frequency fluctuation (ALFF) and the correlation with clinical variables. The rate of significant efficacy after treatment was higher in the combination treatment group than in the antidepressant group, although not statistically significant. At baseline, ALFF increased in the left middle temporal, brain stem, and left cerebellum and decreased in the right anterior cingulate (ACC), right orbital frontal cortex (OFC), and right caudate. ALFF increased in the left fusiform and decreased in the right lingual gyrus, left middle occipital gyrus, and left superior occipital gyrus after antidepressants. ALFF increased in the right ACC, right OFC, and right rectus after combination treatment. ALFF changes in the right ACC/OFC were negatively correlated with HAMD changes. After treatment, abnormal activity in some brain regions normalized, but these regions differed between the two treatment groups. rTMS combined with antidepressants therapy may improve MDD symptoms by improving neuronal activity levels in the right ACC and right OFC. [ABSTRACT FROM AUTHOR]

The pericytes (PCs) surrounding capillaries are vital regulators of capillary constriction. Persistent PC contraction results in the increased capillary constriction, therefore leading to the impaired cerebral blood flow (CBF) recovery after reperfusion and worsening the clinical outcomes in stroke patients. However, the potential determinants of PC functions during ischemia/reperfusion are poorly understood. Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit Delta (PIK3CD/PI3Kδ) is a crucial factor involved with neuronflammation during ischemic stroke. PI3Kδ has shown the expression in PCs, while its effect on PC functions has not been explored yet. In this study, a rodent ischemia/reperfusion model was established in C57BL/6 mice via transient middle cerebral artery occlusion and reperfusion (MCAO/R). The PI3Kδ expression in ischemic penumbra was remarkably upregulated following MCAO/R induction. PI3Kδ inhibitor CAL-101 improved the CBF recovery, ischemic brain injury, and suppressed capillary constriction in MCAO/R mice. Besides, the production of tumor necrosis factor alpha (TNF-α), an inducer for tissue injury, and the expression of transient receptor potential vanilloid type 2 (TRPV2), a channel protein permitting calcium (Ca2+) uptake, were significantly reduced in ischemic penumbra after CAL-101 treatment. In vitro, oxygen-glucose deprivation and reoxygenation (OGD/R) enhanced the expression of PI3Kδ and TRPV2 in primary mouse PCs. CAL-101 suppressed the TNF-α-induced TRPV2 expression in OGD/R-treated PCs, thus inhibiting the Ca2+ uptake and PC contraction. Collectively, this study suggests that PI3Kδ is a critical regulator of PC function during ischemic stroke. • PI3Kδ was upregulated in ischemic penumbra of MCAO/R mice. • PI3Kδ inhibitor (CAL-101) alleviated the ischemic brain injury and improved cerebral capillary constriction in MCAO/R mice. • CAL-101 inhibited OGD/R-induced pericyte (PC) contraction and Ca2+ uptake in OGD/R-induced PCs in a TNF-α-dependent manner. • PI3Kδ is a critical regulator of PC function during ischemic stroke. [ABSTRACT FROM AUTHOR]

Regarding the continuing increase of anthropogenic carbon emissions in the power system with growing energy consumption, researchers have focused on managing the demand side to achieve low-carbon transition. However, such a low-carbon transition cannot be achieved effectively without the proper incentive scheme. Moreover, it is desired to improve the computation quality of carbon tracing tools and extend their applications by state-of-art techniques. To fulfil these existing research gaps, this paper proposes a low-carbon optimal scheduling model with demand response (DR) based carbon intensity control. It aims to reduce the dependence of customers on energy sources with high carbon intensities while decreasing reasonable energy consumption. Likewise, a data-driven approach conducted with the Bayesian interfere regression is proposed to carry out the carbon emission flow (CEF) model to cope with the drawbacks of the conventional emission calculation. Moreover, the storage emission and relevant dynamic impacts to the system are fully considered. The proposed data-driven approach is tested on a series of IEEE systems with different sizes, its high accuracy and efficiency are proved by the simulation results. Further, simulation results indicate that the proposed scheduling model can effectively achieve carbon emission mitigation. [ABSTRACT FROM AUTHOR]

In this study, the effects of postharvest treatment with 1% pullulan, 2% calcium chloride, and 1% chitosan on the physiological and biochemical properties, enzymatic activities and the differentially expressed genes (DEGs) with sucrose metabolism of Annona squamosa L. fruits stored at 15 ± 1°C for 12 days were investigated. Exogenous substances treatment retained a higher level of firmness, surface color, total soluble solids, organic acids, and tvolatile flavor substances compared to the control fruit. Exogenous substances treatments, particularly calcium chloride, enhanced sucrose phosphate synthase activity, whereas it inhibited acid invertase (AI) and neutral invertase (NI) activities. These changes in enzyme activities together resulted in a significantly higher sucrose content and lower glucose and fructose contents. The number (1550 genes), proportion (6.46%), and expression levels of DEGs in the calcium chloride‐treated fruit, which was enriched in the starch and sucrose metabolism pathways, were all significantly higher than in chitosan treatment. The findings indicated that exogenous calcium chloride might alter sucrose metabolism during fruit ripening by regulating the enzymatic activities and metabolism pathway. This will provide a theoretical basis for further research on the regulation of sugar metabolism by exogenous substances treatment to solve the problem of quality degradation in the storage of A. squamosa. Novelty impact statement: This study indicated that the application of chitosan, pullulan, or calcium chloride, especially calcium chloride treatment, can effectively maintain the quality of Annona squamosa fruit during storage. It was found that exogenous calcium chloride might alter sucrose metabolism during fruit ripening by regulating the enzymatic activities and metabolism pathway. The results provided a theoretical basis for exploring the mechanism of exogenous substances for maintaining the postharvest quality and enhancing the shelf life of A. squamosa fruit in further research. [ABSTRACT FROM AUTHOR]

A virtual power plant (VPP) is typically a collection of distributed energy resources (DERs) aggregated by an energy service provider (ESP). However, recruiting DER owners to participate in a VPP is challenging. Therefore, we propose a profitable and flexible VPP recruitment-participation approach that incorporates both long-term regular recruitment and short-term casual recruitment. Casual recruitment caters to ambitious DER participants, consisting of fair and bet-on modes. The latter establishes a set of pre-determined payoff conditions, the fulfillment or non-fulfillment of which confers the participants a contractual right to get compensation from the ESP. To ensure the success of the proposed recruitment approach, we address two key problems. First, we introduce a new index, unconventional arbitrage opportunity (UAO), for evaluating future profits and propose a conditional time series generative adversarial network to predict UAO with weather conditions. Second, we introduce a payoff allocation method that combines fairness and incentives to motivate casual DER participants. The incentive coefficients are optimized using an improved deep reinforcement learning algorithm. Case studies are conducted to verify the proposed recruitment-participation approach, the effectiveness of the UAO prediction model, and the optimized incentive coefficients. • CTSGAN-based UAO prediction method helps ESP anticipate future UAO. • Flexible and profitable VPP recruitment approach integrates regular and casual modes. • Eliminates the prerequisite for DERs and the ESP to reach a consensus before VPP formation. • Optimize the incentive coefficient with CEPR-TD3 algorithm. [ABSTRACT FROM AUTHOR]

Due to the rise of distributed energy resources (DERs), virtual power plants (VPPs) have gained intensive academic and industrial attention. While current centralized VPPs benefit from their large aggregated capacity in the wholesale market, they lack effective incentivizing participants for asset investment and market participation. Conversely, the peer-to-peer (P2P) market offers better profits for prosumers through direct trading; however, transactions hinge on approval from associated authorities, such as electricity retailers, who read their power meters. Such an approval procedure becomes complex, even impossible, when more retailers are involved. Hosting the P2P market in a VPP environment can conveniently resolve this issue. This paper proposes a novel holistic P2P (H-P2P) market model hosted in a VPP environment to provide the VPP participants with financial incentives and optimize the network and market operations simultaneously. The proposed new market structure and trade principle allow the proposed H-P2P market to trade active and reactive energies for both financial benefits and network constraints. Using the novel transactive voltage/VAR control (TVVC) method proposed in this paper, the reactive energy trading through the P2P market can support effective voltage regulation and incentivize the VPP participants to provide such voltage control ancillary services actively. Finally, a novel multi-stage P2P negotiation process is introduced to divide the VPP regulatory, active and reactive energy convergence stages, which allows faster P2P decision-making with improved computing efficiency than the conventional multi-period alternating direction method of multipliers (ADMM). The case study validates that the proposed H-P2P market can increase the financial incentives of the prosumers who install PVs and batteries by a large extent and the pure load units by a relatively smaller percentage. [Display omitted] • A novel synergistic model integrating a holistic P2P market in an internal centralized VPP environment. • Simultaneous active and reactive energy trading in the holistic P2P market regulated by the VPP coordinator. • A novel transactive voltage/VAR control concept with P2P reactive energy trading for the internal VPP voltage control. • An innovative multi-stage P2P negotiation mechanism to converge the active and reactive energy transaction decisions. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are pivotal in the progression toward sustainable transportation. However, a growing body of recent studies has challenged the often-assumed notion that electric vehicles are often considered to be net-zero emissions. These studies suggest that electric vehicles could potentially generate substantial emissions due to their reliance on fossil fuels to generate electricity, especially when receiving fast charging services. This phenomenon has been mitigated with the use of renewable energy sources in the power system. However, due to the intermittent nature of renewable energy sources, additional requirements have arisen. To address this situation, this paper presents an interdisciplinary study of collaborative fast-charging stations (FCS) and distributed renewable energy planning based on the interactions between traffic and power networks in order to increase the adoption rate of electric vehicles and reduce emissions caused by traffic and power systems. The locations and sizing of fast-charging stations, distributed photovoltaic generation, and renewable energy power supply for electric vehicles are determined in the proposed strategy through multi-objective integer programming. The proposed planning strategy considers heterogeneous electric vehicle driving range constraints, the operation security of the power distribution system, the reliability and expansion of the current power distribution, as well as different types of air pollutant classifications resulting from the charging behaviors. To guarantee the feasibility and accuracy of the planning results, an interactive algorithm is applied to solve the multi-objective integer model. Several cases are conducted to validate the proposed approach. The results demonstrate that the proposed planning strategy has good performance in the planning of fast-charging stations and distributed renewable energy integration. • A joint planning approach is proposed. • Optimize the locations and sizing of the fast-charging stations and distributed renewable energy. • Quantify different types of air pollutant emissions during charging services to promote the adoption of EVs and renewable energy. • An integer multi-objective model with an interactive algorithm is proposed. • The results demonstrate that the proposed method confers significant advantages to the application of EVs and renewable energy. [ABSTRACT FROM AUTHOR]

To investigate the identification method of magnetic moving targets and provide theoretical explanations, a magnetic target motion model was established based on the magnetic dipole model, incorporating the target moving velocity, direction angle, and close proximity approach (CPA), and magnetic signature waveforms were obtained under different conditions. The control variable method was used to study the correlation between the magnetic signature waveforms and the target moving speed, direction angle, and CPA, as the basis for realizing the recognition of the magnetic moving target. The highly sensitive tunneling magnetoresistive (TMR) sensor was used for experiments, and the results are in agreement with simulations. The waveform width and peak value have an obvious corresponding relationship with the above three motion states, which provides an efficient method for extracting the motion state of a moving target. [ABSTRACT FROM AUTHOR]

Firmware formats vary from vendor to vendor, making it difficult to track which vendor or device the firmware belongs to, or to identify the firmware used in an embedded device. Current firmware analysis tools mainly distinguish firmware by static signatures in the firmware binary code. However, the extraction of a signature often requires careful analysis by professionals to obtain it and requires a significant investment of time and effort. In this paper, we use Doc2Vec to extract and process the character information in firmware, combine the file size, file entropy, and the arithmetic mean of bytes as firmware features, and implement the firmware classifier by combining the Extra Trees model. The evaluation is performed on 1,190 firmware files from 5 router vendors. The accuracy of the classifier is 97.18%, which is higher than that of current approaches. The results show that the proposed approach is feasible and effective. [ABSTRACT FROM AUTHOR]

The penetration of photovoltaics (PVs) has been increasing in active distribution networks (ADN), which leads to severe voltage violation problems. PV inverters can provide fast and flexible reactive power support and are now allowed to participate in the voltage regulation process. This paper proposes a real-time combined central and local Volt/Var control (VVC) strategy to mitigate voltage violation problems while minimizing the network power loss. Based on the historical PV and load data, the load flow and optimal power flow are performed in the centralized controller (CC) to obtain multiple voltage and optimal power settings for each PV system. The local controller (LC) then generates voltage control curves with these optimal scatters. To improve the voltage control effect, a novel 3-Dimension voltage control curve is proposed considering both the measurements of node voltage and PV generation. Moreover, a data-driven deep convolution neural network is designed and trained to generate optimal local voltage control curves without prior knowledge of specific curve functions. The proposed approach is tested on the IEEE 33-bus distribution system and simulation results verify the effectiveness in voltage control compared with the optimal Q(V) and Q(P) method. [ABSTRACT FROM AUTHOR]

In recent years, the number of vehicles in the city is increasing. However, the increase in the number of cars caused a series of traffic problems, such as road congestion, traffic accidents, environmental pollution, and so on. To handle the problems, Internet of Vehicles (IoV) is emerging. As we know, deep learning has acquired significant success in many fields, and it is also applied in IoV. However, some studies show that deep learning is vulnerable to the crafted samples formed by adding small perturbations to original samples. Thus, the vulnerability of deep learning may pose a huge security threat to IoV. To ensure the security of deep learning, we conduct experiments to investigate whether there are adversarial samples in the IoV field. We try to generate adversarial examples by GPS data. The models we attack are trajectory mode detection models. Since both GPS trajectory data and image data are continuous data, we adopt the algorithm of Computer Vision to generate adversarial examples. We utilize white-box attack algorithms (Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) algorithms) and black-box attack algorithm (One Pixel Attack) to generate adversarial examples. We adopt Dynamic Time Warping (DTW) to measure the similarity between adversarial examples and original trajectory data. Experimental results show that a small perturbation can successfully fool deep neural networks with high confidence. [ABSTRACT FROM AUTHOR]

The penetration of photovoltaics (PVs) and electric vehicles (EVs) is increasing in active distribution networks (ADN), which may lead to severe voltage violation problems. This paper proposes a two-stage real-time Volt/Var control method to mitigate fast voltage violations. In the first stage, hourly on-load tap changer (OLTC) and capacitor banks (CBs) are scheduled based on the optimal power flow method. The optimization problem is formulated as a mixed-integer second-order cone programming (MISOCP) which can be effectively solved. In the second stage, a data-driven network partition method is proposed to select critical bus and assess the voltage violation risk of each control area, followed by the intra-day dispatch of EVs. Based on the partition results, reactive power of PVs and EVs is controlled in real-time to mitigate voltage violations. Considering the suddenly active power drop of PVs, a rule-based control strategy coordinating PVs and CBs is proposed to improve the reactive power reserve in ADN. The proposed approach is tested on the IEEE 33-bus and 123-bus distribution networks and simulation results verify the effectiveness both in network partition and real-time voltage control. [ABSTRACT FROM AUTHOR]

The acceleration of distributed energy resources and carbon pricing policies have compelled utilities to act and to prioritize carbon-constrained infrastructure augmentation in their capital programs. To implement various carbon emission reduction policies, power system transmission planning has become more challenging. The existing energy system will face massive retirement of coal-fired power plants (CFPPs), large scale integration of renewable energy and network expansion. In this paper, an electricity network planning and transformation roadmap, which has two milestones, is put forward. In the first stage, a mathematical model is proposed based on the average cost of carbon emission reduction to realize the cooperation of CFPPs retirement and renewable energy investment. It can help the network carry out the transition from a fossil-fuel dominated system to a low-carbon oriented system. Because of the promising prospect of power-to-gas (P2G) technology, in the second milestone, a method based on carbon emission flow (CEF) is employed to help the power-to-gas stations (P2GSes) to select the construction site and capacity. The gas network constraints are modeled to guarantee that P2GSes can work smoothly without energy flow congestion in both electricity and gas networks. According to the simulation results in case studies, our method can reach the emission reduction target more economically and effectively, and the P2GSes can produce and absorb clean energy. [ABSTRACT FROM AUTHOR]

An $(r, \delta)$ -locally repairable code ($(r, \delta)$ -LRC for short) was introduced by Prakash et al. for tolerating multiple failed nodes in distributed storage systems, which was a generalization of the concept of $r$ -LRCs produced by Gopalan et al.. An $(r, \delta)$ -LRC is said to be optimal if it achieves the Singleton-like bound. Recently, Chen et al. generalized the construction of cyclic $r$ -LRCs proposed by Tamo et al. , and constructed several classes of optimal $(r, \delta)$ -LRCs of length $n$ for $n\, |\, (q-1)$ or $n\,|\, (q+1)$ , respectively in terms of a union of the set of zeros controlling the minimum distance and the set of zeros ensuring the locality. Following the work of , , this paper first characterizes $(r, \delta)$ -locality of a cyclic code via its zeros. Then we construct several classes of optimal cyclic $(r, \delta)$ -LRCs of length $n$ for $n\, |\, (q-1)$ or $n\,|\, (q+1)$ , respectively from the product of two sets of zeros. Our constructions include all optimal cyclic $(r,\delta)$ -LRCs proposed in , , and our method seems more convenient to obtain optimal cyclic $(r, \delta)$ -LRCs with flexible parameters. Moreover, many optimal cyclic $(r,\delta)$ -LRCs of length $n$ for $n\, |\, (q-1)$ or $n\,|\, (q+1)$ , respectively with $(r+\delta -1)\nmid n$ can be obtained from our method. [ABSTRACT FROM AUTHOR]

• Nutrient changes in Z. bungeanum fruit during development studied. • Fruit enlargement and seed development critical for high nutrient requirements. • Nutrient accumulation in mature fruit: Ca > K > N > Mg > P > Fe > Mn > Zn > Cu. • Increased nutrient absorption by seeds during development. • Higher nutrient content and accumulation in seeds than other parts. Learn about the dynamic changes of mineral nutrients in fruit is important to make effective fertilization measures during the fruit development. The content and accumulation of mineral nutrients (included N, P, K, Ca, Mg, Fe, Mn, Cu and Zn) in fruit (included pericarp, seed and whole fruit) of Zanthoxylum bungeanum 'Hanyuan' were measured during the development to explore nutrient requirements and allocation. The changes of nutrient contents and accumulation in fruit of Z. bungeanum 'Hanyuan' showed a similar trend. A temporary increase was observed in the content of N, P, Mg, Mn and Cu after blooming (AF). Subsequently, all nutrient contents declined rapidly during the fruit enlargement stage and a slight decrease during the seed formation stage, while the nutrient contents remained relatively stable in the other stages. The nutrient accumulation increased the most rapidly during the fruit enlargement and seed development stages, and there was a highly significant linear correlation between the fruit nutrient accumulation and dry weight (P < 0.05). Except for N and Mn, the content and accumulation of other nutrients were higher in the seed than that in the pericarp. Furthermore, the nutrient accumulation in fruit at maturity (115 d AF) ranked as Ca > K > N > Mg > P > Fe > Mn > Zn > Cu. Both the fruit enlargement (15–39 d AF) and seed development (57–86 d AF) stages are crucial periods for nutrient requirements. The nutrient absorption by the seed was increased, and nutrient accumulation in the pericarp tended to slow down or remained relatively stable during the seed development stage. [ABSTRACT FROM AUTHOR]

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) can induce substantial cytotoxicity in tumor cells but rarely exert cytotoxic activity on non-transformed cells. In the present study, we therefore evaluated interactions between TRAIL and IER3 via co-immunoprecipitation and immunofluorescence analyses, leading us to determine that these two proteins were able to drive the apoptotic death of hepatocellular carcinoma (HCC) cells and to disrupt their proliferative and migratory abilities both in vitro and in vivo. From a mechanistic perspective, we determined that TRAIL and IER3 were capable of inhibiting Wnt/β-catenin signaling. Together, these results indicate that TRAIL can control the pathogenesis of HCC at least in part via interacting with IER3 to inhibit Wnt/β-catenin signaling, thus indicating that this TRAIL/IER3/β-catenin axis may be a viable therapeutic target in HCC patients. [ABSTRACT FROM AUTHOR]

Polyethyleneimine‐modified magnetic multi‐walled carbon nanotubes were developed to extract 10 mycotoxins. Simple modification of polyethyleneimine was achieved on the magnetic substrate by using an epoxy‐containing silane agent as a linker. The resultant magnetic adsorbent was integrated with reverse phase and anion exchange interaction sites. Under optimal extraction conditions, only 20.0 mg adsorbent was used to extract the mycotoxins from 50.0 mL loading solution. The maximum adsorption capacities of the adsorbent toward the mycotoxins ranged from 4.9 to 10.2 mg/g. Adsorption and desorption were completed within 3.0 and 2.0 min, respectively. The adsorbent could be used for six repeated runs without evident change in extraction performance. The adsorbent combined with liquid chromatography–tandem mass spectrometry was applied further to analyze the mycotoxins in milk. The absolute recoveries of the 10 mycotoxins ranged from 88.3 to 103.5% with relative standard deviations that ranged from 2.4 to 6.5%, and their limits of detection were 0.003 to 0.442 μg/kg. The proposed adsorbent has great potential in the routine analysis of mycotoxins in ordinary analytical chemistry laboratory. [ABSTRACT FROM AUTHOR]

As exoskeleton robots are more frequently applied to impaired people to regain mobility, detection and recognition of human gait motions is important to prepare suitable control modes for exoskeletons. This paper proposes to explore the potential of the ensemble empirical mode decomposition (EEMD) method to help analyze and recognize gait motions for human subjects who wear the exoskeleton to walk. The intrinsic mode functions (IMFs) extracted from the original gait signals by EEMD are utilized to act as inputs for classification algorithms. Evident correlations are found between some IMFs and original gait kinematic sequences. Experimental results on gait phase recognition performance on 14 able-bodied subjects are shown. The performance of the composing signals extracted from the original signals as IMF 1 ∼ IMF 8 is investigated, which indicates that IMF 8 might be helpful when wearing exoskeleton and IMF 5 might be helpful when walking without exoskeleton on gait recognition. And the similarity of joint synergy between wearing and without wearing exoskeleton is analyzed, and the result shows that the joint synergy might change between with and without wearing exoskeleton. The quantitative results show that based on some IMFs of the same orders, these machine learning algorithms can achieve promising performances. [ABSTRACT FROM AUTHOR]

An amphiphilic block copolymer consisting of poly(N-acryloyl-glucosamine) (PAGA) and poly(tert-butyl methacrylate) (PtBMA) was designed and grafted on magnetic multi-walled carbon nanotubes (Fe3O4MWCNTs). The resultant Fe3O4MWCNTs@copolymer was proposed as QuEChERS adsorbent for determination of 15 mycotoxins and 25 pesticides in grains via liquid chromatography tandem mass spectrometry. The adsorbent was characterized by a transmission electron microscope, scanning electron microscope, elemental analysis, and other techniques. The common matrix interferences were efficiently removed by the proposed adsorbent, such as pigment, fatty acids, and the saccharide. PAGA segment played an important role in removing the hydrophilic interferences through hydrogen bonding due to the high density of hydroxyl groups. PtBMA segment removed the fatty residues through its strong hydrophobic carbon moiety. In comparison with the commercially available QuEChERS adsorbents, the proposed adsorbent had higher adsorption capacities towards the typical matrix interferences. To achieve satisfactory recoveries of analytes, various parameters in the QuEChERS procedure were comprehensively investigated. Under the optimal conditions, 95.0% of the analytes showed satisfactory recoveries in the range 70.0–120% as well as negligible matrix effects. The limits of detection (LOD) were in the range 0.00015–1.3 μg kg−1. Compared with previously reported QuEChERS methods, the proposed method had improved sensitivity and benefited from low matrix effects. The recoveries of analytes in various grains were in the range 60.8–108% with relative standard deviations (RSD) less than 13%. Moreover, the Fe3O4MWCNTs@copolymer exhibited good synthetic reproducibility and rapid magnetic separation (less than 10 s). The research provides a versatile platform to develop multi-functional QuEChERS adsorbents based on the amphiphilic block copolymer. [ABSTRACT FROM AUTHOR]

By using a pre-order principle in [Qiu JH. A pre-order principle and set-valued Ekeland variational principle. J Math Anal Appl. 2014;419:904–937], we establish a general equilibrium version of set-valued Ekeland variational principle (denoted by EVP), where the objective function is a set-valued bimap defined on the product of left-complete quasi-metric spaces and taking values in a quasi-ordered linear space, and the perturbation consists of a cone-convex subset of the ordering cone multiplied by the quasi-metric. Moreover, we obtain an equilibrium EVP, where the perturbation contains a σ-convex subset and the quasi-metric. From the above two general EVPs, we deduce several interesting corollaries, which extend and improve the related known results. Several examples show that the obtained set-valued EVPs are new. Finally, applying the above EVPs to organizational behavior sciences, we obtain some interesting results on organizational change and development with leadership. In particular, we show that the existence of robust organizational traps. [ABSTRACT FROM AUTHOR]

In order to achieve sustainable development goals, modern power systems need to achieve a low-carbon transition. Retirement and replacement of aging coal-fired power plants (CFPP) is the major part of electricity network transition. However, the sudden retirement of large aging CFPP could lead to power supply or reserve shortages and generally, it takes several years to complete the replacement process. This paper proposes a low-carbon electricity network transition model, helping to plan aging CFPP retirement and renewable power plants installation as well as network augmentation. The trade-off decisions are found among three conflicting objectives including cost, risk and carbon emission. Moreover, the carbon emission flow (CEF) model is applied in the planning process to calculate carbon emissions from the demand side, in order to assess the efficacy of the low-carbon transition. A relatively new multi-objective natural aggregation algorithm (MONAA) is introduced and applied to find the optimal solution. The proposed model is verified on a modified IEEE 24-bus RTS system and a modified IEEE 118-bus system. According to the numerical results, the proposed model can achieve carbon mitigation, cost and risk benefits and provide a roadmap to guide the energy network transformation towards a low-carbon economy. [ABSTRACT FROM AUTHOR]

Carbon financing policies such as emission trading have been used to assist in emission mitigation worldwide. As energy end-users/consumers are the underlying driver of emissions, it would be difficult to effectively mitigate carbon emissions by creating an emission trading market without active end-users’ involvement. In electricity markets, demand side management (DSM) in the smart grid can manage demands in response to power supply conditions and influence end-users to contribute to improving both network efficiency and economic efficiency. However, it is a relatively new topic to study the environmental benefits of DSM. This paper proposes a two-stage scheduling model to comprehensively investigate the environmental benefits of consumers participating in both electricity and carbon emission trading markets through active DSM. A developed zero sum gains-data envelopment analysis (ZSG-DEA) model based multi-criteria allocation scheme for emission allocation is employed. Meanwhile, the carbon emission flow model (CEF) is applied to track the “virtual” carbon flow accompanying power flow. According to case studies on the IEEE 24-bus system and IEEE 118-bus system, the proposed model can effectively achieve carbon emission mitigation and provide consumers extra environmental benefits in some scenarios. This model can be an important guide for governments to establish emission trading schemes. [ABSTRACT FROM AUTHOR]

By using generalized nonconvex separation functionals and a pre-order principle in Qiu [J Math Anal Appl. 2014;419:904–937], we establish a general set-valued Ekeland variational principle (briefly, denoted by EVP), where the objective function is a set-valued map taking values in a real vector space quasi-ordered by a convex cone K and the perturbation consists of a cone-convex subset H of K multiplied by the distance function. Here, the assumption on lower semi-continuity of the objective function is replaced by a weaker one: sequentially lower monotony. And the assumption on lower boundedness of the objective function is taken to be the weakest of several different kinds. From the general set-valued EVP, we deduce a number of particular versions of set-valued EVP, which extend and improve the related results in the literature. In particular, we give several EVPs for approximately efficient solutions in set-valued optimization, which not only extend the related results from vector-valued objective functions into set-valued objective functions, but also improve the related results by removing a usual assumption for K-boundedness (by scalarization) of the objective function's range. [ABSTRACT FROM AUTHOR]

Picking first arrivals is a fundamental but not easy task in the oil seismic exploration, especially in situations where the signal-to-noise ratios are very low. The paper proposed a new picking algorithm with the emerging new graph signal processing technology in the signal processing field. Fundamentally, first arrival picking is actually as a problem to recognize direct seismic waves among background noises. It is apparently that background noises and the direct waves are from different sources, and therefore, there is no connectivity among them, which formulates the fundamentals of the proposed method. With the help of graph signal processing theory, such non-connectivity is fully explored and cast into an optimization problem for picking the first arrivals in oil exploration. From simulations and real measurements results, the proposed method is validated for first arrival picking with good performances, especially in situations with low signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

Four truxene-based conjugated microporous polymers (Tr-CMPs) were prepared via different synthetic methods and their structure-property relationships were studied. The polymer networks have high Brunauer-Emmett-Teller (BET) specific surface areas ranging from 554 m2·g−1 to 1024 m2·g−1. Pore sizes of the CMPs with different linkers are mainly located between 0.60 and 1.96 nm. Among all the Tr-CMPs, Tr-CMP4 has the highest BET surface area of 1024 m2·g−1 and exhibits the highest H2 uptake of 0.88 wt%. Tr-CMP2 prepared by Suzuki-Miyaura coupling reaction has the highest photoluminescence quantum yields (PLQYs) of 13.06% and CO2 uptake of 6.25 wt%. [ABSTRACT FROM AUTHOR]

In oil seismic exploration, automatic picking of first arrivals is very important but difficult for seismic data with low signal-to-noise ratios. In the paper, we proposed a new method based on mutual information in information theory. Mutual information between signals and noises is zero theoretically; thus, random noises have fewer effects on first arrivals pickup with the criterion of mutual information. The paper presents a detailed description of the method based on mutual information. Tests and verification with simulation data and field measurements are carried out. When compared with other well-known methods, the first break picking accuracy and efficiency of the proposed algorithm are verified. [ABSTRACT FROM AUTHOR]

Due to the rapid development of distributed renewable generation, an effective risk assessment and early warning mechanism for active distribution networks is of great significance to maintain the system reliability and enhance energy grid resilience. In this paper, a novel risk assessment model is proposed to assess the probability of potential disturbances to the grid and provide accurate advice for trading prosumers’ renewable energy. The model can compute node failure probability (FP) for transmission networks as well as the area FP for distribution networks, while combining the two perspectives by topology analysis. A weather threshold value is first derived to define the extreme weather condition. Then the FP of transmission lines is calculated by joint probability models under four instances of extreme climate. For distribution networks, the weather influence is obtained by applying the Rare Events Logistic Regression model initially. Then the equipment fault related to the geographical feature is captured using feeder taxonomy and hierarchical clustering. Furthermore, the accidental factor as a new parameter is introduced to evaluate the vandalism, vegetation, and operating fault to the grid. Finally, the warning information and advice for customers will be presented after fault chain analysis. The FP for a specific area in Australia is analyzed in case studies to verify the proposed model. [ABSTRACT FROM AUTHOR]

• A high order all-Mach number solver for isentropic Euler equations is presented. • It is based on finite difference in space and IMEX in time. • Material waves are treated explicitly, while acoustic waves are treated implicitly. • The schemes are shown to be asymptotic preserving with incompressible limit. • Several tests in one and two space dimensions show the effectiveness of the schemes. In this paper, new high order schemes are constructed and analyzed, for the numerical solution of Euler equations of isentropic gas dynamics. Material waves are treated explicitly, while acoustic waves are treated implicitly, thus avoiding severe CFL restrictions for low Mach flows. High order accuracy in space is obtained by finite difference WENO schemes; while high order in time is obtained by IMEX methods with semi-implicit linearization treatment. The schemes are proven to be asymptotic preserving and asymptotic accurate as the Mach number vanishes. Several tests in one and two space dimensions illustrate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

Recently, ozone pollution has gradually replaced PM2.5 as the main pollutant affecting air pollution. In this study, we synthesized a series of Mn 3 O 4 catalysts by hydrothermal method changing the precursors and tested their activities at different relative humidity, gas volume space velocity of 150,000 h−1 and 5 ppm ozone. Remarkably, Mn 3 O 4 -SO 4 prepared with MnSO 4 as precursor showed excellent catalytic ozone decomposition activity, almost completely converting 5 ppm of ozone at different relative humidity ranges. Finally, the most active Mn 3 O 4 -SO 4 catalyst was tested for its usability limit at RH= 90%, after 28 h of testing under high humidity conditions, it had retained successfully the complete decomposition of low concentrations of ozone. The catalysts were characterized by XRD, Raman, HRTEM, XPS, BET, H 2 O-TPD and in situ IR NH 3 adsorption. The characterization analysis revealed that the Mn 3 O 4 -SO 4 surface could exposed a highly active (101) crystalline surface with high specific surface area, excellent hydrophobicity as well as ozone adsorption capacity, which were highly favorable for ozone decomposition under high humidity conditions. In this work, Mn 3 O 4 exhibits good catalytic activity, which provides an additional option for future studies of manganese oxides. [Display omitted] • Excellent catalytic activity of Mn 3 O 4 prepared from MnSO 4 and (NH 4) 2 S 2 O 8. • The (101) crystalline surface of Mn 3 O 4 has a high chemical activity. • The application of Mn 3 O 4 to the removal of surface ozone in living conditions. [ABSTRACT FROM AUTHOR]