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Intracellular pH is a key parameter related to various biological and pathological processes. In this study, a ratiometric pH fluorescent sensor ABTT was developed harnessing the amino-type excited-state intramolecular proton transfer (ESIPT) process. Relying on whether the ESIPT proceeds normally or not, ABTT exhibited the yellow fluorescence in acidic media, or cyan fluorescence in basic condition. According to the variation, ABTT behaved as a promising sensor which possessed fast and reversible response to pH change without interference from the biological substances, and exported a steady ratiometric signal (I478/I546). Moreover, due to the ESIPT effect, large Stokes shift and high quantum yield were also exhibited in ABTT. Furthermore, ABTT was applied for monitoring the pH changes in living cells and visualizing the pH fluctuations under oxidative stress successfully. These results elucidated great potential of ABTT in understanding pH-dependent physiological and pathological processes.

Novel Bi@BiOBr–Bi2MoO6 ternary heterostructure photocatalysts were successfully prepared by a facile two-step solvothermal strategy, which presents a uniform spherical structure with around 200 nm. The composition, morphology and light-harvesting properties of Bi@BiOBr–Bi2MoO6 were characterized by XRD, XPS, SEM, TEM and DRS. As expected, the as-prepared heterojunction exhibits extraordinary photocatalytic activity for nitrogen fixation in water without any sacrificial agent. The results reveal that the optimized Bi@BOB–BMO-2 sample has an excellent NH4+ yield rate of 167.2 μmol g−1 h−1, which is 2.5 and 2.1 times higher than that of pure Bi2MoO6 and Bi@BiOBr. The effect of an electron scavenger on the rate of NH4+ production and the stability of Bi@BOB–BMO-2 was also investigated, and the results were desirable. This study provides a promising strategy to design superior simulated solar-light driven heterojunction photocatalysts with distinguished nitrogen fixation performance under mild conditions.

This investigation was devoted to the saturated solubility of 2-acrylamide-2-methylpropanesulfonic acid in 14 pure organic solvents, namely, ethyl acetate, methanol, 1,4-dioxane, ethanol, isopropan...

This work was devoted to the equilibrium solubility of 2-nitrophenylacetic acid in 13 neat solvents ethylene glycol (EG), methanol, ethanol, acetonitrile, n-propanol, isopropanol, water, n-butanol, N,N-dimethylformamide (DMF), isobutanol, cyclohexane, ethyl acetate, and 1,4-dioxane ranging from 283.15 to 328.15 K. All determinations were made by the shake-flask technique at a pressure of p = 101.2 kPa. The mole fraction solubility magnitudes of 2-nitrophenylacetic acid increased gradually with the rising investigated temperature and presented a decreasing trend in the 13 neat solvents: DMF > (1,4-dioxoane, methanol) > ethanol (ethyl acetate) > n-propanol > n-butanol > isopropanol > EG > acetonitrile > isobutanol > water > cyclohexane. The method of linear solvation energy relationships was employed here to inspect the solvent–solvent and solute–solvent interactions. The solvent descriptors of the Hildebrand solubility parameter and polarizability/dipolarity presented great influence upon the solubility magnitudes of the solute 2-nitrophenylacetic acid. The obtained solubility values in mole fraction were correlated mathematically via four models/equations, namely, λh, non-random two-liquid, Apelblat, and Wilson. The maximum value of relative average deviation (RAD) was 3.68 × 10–2, and the maximum value of root-mean-square deviations was 116.78 × 10–4. The RAD values by using the Apelblat equation were smaller than that by using the other equations/models for a fixed solvent. Additionally, the dissolution properties, activity coefficient, and reduced excess enthalpy under the conditions of infinite dilution were obtained through the Wilson model.

Friction plug repair welding technology has been demonstrated to be effective to repair the glass fiber-reinforced polyamide 6 sheets in the present paper. Influences of repair hole geometries and parameters on joint morphology and mechanical performance were investigated. Results showed that defect-free repaired joints were produced with the utilization of tapered holes rather than cylindrical holes. Process parameters exerted significant influences on the cross-sectional profile and morphology of the joints. Defect-free repaired welds with larger stir zone thicknesses were produced with the proper increase of rotational speeds, but excessive rotational speeds caused the formation of cavities along the plug boundary and the reduction of stir zone thicknesses. The fluctuation of the wavy bottom interface increased under larger plunge rates and incomplete connections between stir zone and base material were observed under plunge rate of 25 mm/min. Extended dwell time led to larger stir zone thickness and improved joint morphology. Tensile tests showed that the strength of the repaired joints increased and then decreased with the enlargement of rotational speeds. Decreased plunge rate and extended dwell time led to promoted joint mechanical performance. Three failure modes were observed, which corresponded to low, middle, and high repaired weld strengths.

Solubility of 4-nitrophenylacetic acid in 13 neat solvents of n-propanol, methanol, ethylene glycol (EG), ethanol, isopropanol, cyclohexane, acetonitrile, N,N-dimethyl formamide (DMF), N,N-dimethyl...

In this contribution, the 5,7-dibromo-8-hydroxyquinoline solubility in neat N,N-dimethylaniline (DMA), n-butanol, methanol, N,N-dimethylformamide (DMF), ethanol, 2-butanone, n-propanol, ethyl aceta...

The 2-methoxy-4-nitroaniline solubilities in some monosolvents of n-propanol, methanol, ethyl acetate, ethanol, isopropanol, acetonitrile, ethylene glycol (EG), n-butanol, water, dimethyl sulfoxide...

In order to analyze the inhibition mechanism of inhibitor in the process of low-temperature oxidation, nanometer aluminum hydroxide [Al(OH)3], nano-sized magnesium hydroxide [Mg(OH)2], and mixtures...

The solubility of 2,6-dichloro-4-nitroaniline (DCNA) in 12 pure solvents of n-butanol, n-propanol, methanol, ethylene glycol (EG), ethanol, isopropanol, acetonitrile, cyclohexane, 1,4-dioxane, ethyl acetate, water, and N-methyl pyrrolidone (NMP) was achieved through the shake-flask method at the temperatures from 278.15 to 323.15 K and ambient pressure p = 101.2 kPa. The mole fraction solubility of DCNA in the above solvents increased as the temperature increased and ranked as NMP > 1,4-dioxane > ethyl acetate > acetonitrile > n-butanol > n-propanol > ethanol > EG > isopropanol > methanol > cyclohexane > water. The solvent–solvent and solvent–solute interactions on the DCNA solubility were investigated using the linear solvation energy relationship on the basis of the solvent descriptors. The acquired solubility was correlated using the NRTL model, λh equation, modified Apelblat equation, and Wilson model. The obtained maximum relative average deviation and root-mean-square deviation were 6.53 × 10–2 and ...

Lithium batteries (LBs) have become increasingly important energy storage systems in our daily life. However, their practical applications are still severely plagued by the safety issues from liquid electrolyte, especially when the batteries are exposed to mechanical, thermal, or electrical abuse conditions. Gel polymer electrolytes (GPEs) are being considered as an effective solution to replace currently available organic liquid electrolyte for building safer LBs. This review provides recent advancements in GPEs applied for high-performance LBs. On the one hand, from the environmental and economic point of view, the skeletons of GPEs changed from traditional polymer to renewable and degradable polymer. On the other hand, in addition to being as a component with good electrochemical and physical characterizations, the GPEs also need to provide some functions for addressing the concerns of lithium (Li) dendrites, unstable cathode electrolyte interface, dissolution and migration of transition metal ions, “shuttle effect” of polysulfides, and so on. Finally, to synchronously meet the challenges from the advanced cathode and Li metal anode, the bio-based GPEs with multi-functionality are proposed to develop high-energy/power-density batteries in the future.

The solubilities of 3,5-dinitro-2-methylbenzoic acid in 13 solvents such as 1-propanol, 1-butanol, 1-pentanol, 1-hexanol, 1-heptanol, 2-propanol, ethyl acetate, 2-butanol, cyclohexane, toluene, eth...

With the development and construction of (high voltage direct current) HVDC system, more and more DC bias problems caused by monopole operation of HVDC are appeared in grid which leads to efficiency droop or potential risk of transformer. This paper proposes a new non-electrical method, Ground-state energy ratio (GER), based on Hilbert-Huang transform (HHT) characteristics of vibration signals which can reflect the DC bias accurately. Firstly, the transformer DC bias experiment platform is set up and the transformer vibration signals are measured. Meanwhile, the displacement signals are obtained by the quadratic integral. Secondly, the time-frequency spectra of the DC bias vibration signals are obtained by HHT analysis. According to the energy change of each mode in HHT, the GER method is proposed to quantify the DC bias of signal at each point. Lastly, Comparing the GER values of different parts of transformer, such as core, shell and winding, the vibration signals that can accurately reflect the degree of DC bias are confirmed. Results show that the HHT-GER values of vibration signals which on axial vibration of the iron core column and at the top of the transformer shell increase with the increase of DC bias. Therefore, the HHT-GER is an effective method to analyze the DC bias condition of transformer and diagnose the abnormal vibration of transformer core.

In this study, we have performed non-equilibrium molecular dynamics simulation (NEMD) to the interface thermal transport in silicene/stanene hetero-structures, which include the effect of structure size, temperature and defects on the thermal conductance G. The results show that G increases with the temperature significantly, while almost keep constant with the variable of the size of structure. The vacancy defects will lead to the decrease of saturated thermal conductance compared with the defect-free one, which is ascribed to the enhancement of phonon inversion process and the scattering concentration of the phonons. In particular, a singularity point of thermal conductance is found when a single vacancy defect is set in the interface of stanene with the vacancy defect moving. Our result indicates that the thermal conductance of silicene/stanene hetero-structures present remarkable properties (low size dependence and singularities exist as defects move) and are promising for applications of the low dimensional silicene/stanene nanostructure based thermal and nanoelectronic devices.

The dinitolmide solubility in twelve pure solvents such as methanol, ethanol, n-propanol, isopropanol, n-butanol, ethylene glycol (EG), ethyl acetate, dimethyl sulfoxide (DMSO), acetonitrile, cyclohexane, 1,4-dioxane and water and co-solvent mixtures of ethanol (1) + water (2), isopropanol (1) + water (2) and EG (1) + water (2) was studied by using a saturation shake-flask method over the temperatures from 283.15 K to 328.15 K under local atmospheric pressure p = 101.2 kPa. It was highest in solvent DMSO and lowest in solvent water. The achieved solubility data in monosolvents was mathematically described through the modified Apelblat equation; and in the binary mixtures, the Jouyban-Acree model. For the monosolvents, the maximum values of root-mean-square deviation and relative average deviation were 2.71 × 10−4 and 1.27%, respectively; and for the binary solvent mixtures, 0.276 × 10−4 and 1.46%. The solubility was correlated to KAT parameters and cavity term based on linear solvation energy relationships concept to investigate on the solvent effect. Results showed that the solubility in monosolvents depended significantly upon the Hildebrand parameter and nonspecific dipolarity/polarizability interactions. Quantitative values for the local mole fraction of ethanol (isopropanol or EG) and water around the dinitolmide were computed by using the Inverse Kirkwood–Buff integrals method. Dinitolmide was preferentially solvated by water in water-rich compositions; while in the intermediate and co-solvent-rich compositions for the aqueous mixtures of ethanol (isopropanol or EG), dinitolmide is preferentially solvated by the co-solvent. The preferential solvation magnitude of dinitolmide was higher in isopropanol mixtures than that in the other two solvent mixtures. Furthermore, transfer Gibbs free energy (ΔtrGo), enthalpy (ΔtrHo), and entropy (ΔtrSo) were deduced, demonstrating that the solubilization capacity was more favorable in the intermediate concentration of ethanol (isopropanol or EG).

As one of the more promising potential photovoltaic technologies, planar perovskite solar cells (PSCs) are arousing worldwide interest for their many advantages. Now, PSCs are being developed toward the direction of high-performance and longevity. However, the defects of the polycrystalline perovskite active layer limit further improvement of the device performance. Seeking simple and efficient strategies to reduce these trap states in the perovskite active layer is highly desirable. Here, a novel nonfunctionalized fullerene C60 o-quinodimethane bisadduct [C60(QM)2] was dissolved in chlorobenzene (CB) solvent and introduced into a CH3NH3PbI3 active layer by antisolvent dripping. Results showed that the introduced C60(QM)2 could effectively reduce the trap density of the MAPbI3 active layer, facilitating carrier extraction/injection from CH3NH3PbI3 to spiro-OMeTAD. As a result, a highest PCE of 18.4% for the PSC based on CH3NH3PbI3/C60(QM)2 was obtained, which increased by 10.1% compared with 16.7% for the...

Concept-free search, which embeds text and video signals in a joint space for retrieval, appears to be a new state-of-the-art. However, this new search paradigm suffers from two limitations. First, the search result is unpredictable and not interpretable. Second, the embedded features are in high-dimensional space hindering real-time indexing and search. In this paper, we present a new implementation of the Vireo video search system (Vireo-VSS), which employs a dual-task model to index each video segment with an embedding feature in a low dimension and a concept list for retrieval. The concept list serves as a reference to interpret its associated embedded feature. With these changes, a SQL-like querying interface is designed such that a user can specify the search content (subject, predicate, object) and constraint (logical condition) in a semi-structured way. The system will decompose the SQL-like query into multiple sub-queries depending on the constraint being specified. Each sub-query is translated into an embedding feature and a concept list for video retrieval. The search result is compiled by union or pruning of the search lists from multiple sub-queries. The SQL-like interface is also extended for temporal querying, by providing multiple SQL templates for users to specify the temporal evolution of a query.

International audience; Tarim Basin newly discovered the fault-karst carbonate reservoirs, which are formed by the large-scale tectonic fault activities and multiple-stage karstification. Four kinds of mediums coexist in the reservoirs, including the large cave, vug, tectonic fracture and matrix. The tectonic fractures interconnect with large caves in series to form the vertical beads-on-string structure, which is the most common connection pattern in reservoirs. To provide a well test method for evaluating this type of structure, this work firstly presents a multi-fracture-region multi-cave-region series connection physical model by simplifying vertical beads-on-string structure. We consider four kinds of mediums in the proposed physical model, including large caves, small vugs, high-angle tectonic fracture and rock matrix. The fracture regions mainly contain fracture, vug and matrix mediums. The cave regions contain cave medium. The corresponding mathematical model is also developed, in which the flow in fracture regions obeys the Darcy’s law, while the flow in cave regions is assumed to obey free flow. Furthermore, the gravity is taken into account because the flow is along the vertical direction. Then the typical flow regimes are analyzed and sensitivity analysis is conducted on crucial parameters. Results indicate that (a) the typical feature of vertical beads-on-string structure on type curves is that the cave storage regimes and linear flow regimes alternately appear; (b) the type curves will exhibit the cave storage regimes with unit-slope pressure derivative for the existence of large caves, which is different from the inter-porosity flow regimes for the existence of the vugs (slope ≠ 1); (c) the gravity effect could lead to unit-slope pressure and pressure derivative curves, which can be regarded as closed boundary in a peculiar sense; (d) gravity effect is difficult to be observed from well test curves with about 2-weeks test duration in real application. Finally, two cases from Shunbei Oilfield are interpreted to illustrate the practicability and feasibility of proposed method.

A single-machine infinite system (SMIS) simulation model with AVR, thyristor high gain exciter and PSS is established by using the mathematical mode and Simulink. Firstly, the dynamic performance simulation of the system is carried out under different disturbance conditions. Then analyze the dynamic performance of the system with different types of power system stabilizer ( PSS ) . The experimental results show that the system with PSS can effectively suppress the low-frequency oscillation and maintain the dynamic stability of the system. And the two-input PSS provides better robustness to the system than a single-input PSS.

Achieving efficient and secure accesses to real-time information from the designated IoT node is the fundamental key requirement for the applications of the Internet of Things. However, IoT nodes are prone to physical attacks, public channels reveal the sensitive information, and gateways that manage the IoT nodes suffer from the single-point failure, thereby causing the security and privacy problems. In this paper, a blockchain-based user remote authentication scheme using physical unclonable functions (PUFs) is proposed to overcome these problems. The PUFs provide physically secure identities for the IoT nodes and the blockchain acts as a distributed database to manage the key materials reliably for gateways. The security analysis is conducted and shows that our scheme realizes reliable security features and resists various attacks. Furthermore, a prototype was implemented to prove our scheme is efficient, scalable, and suitable for IoT scenarios.

With the development of the Internet of Things technology, the use of smart devices is ubiquitous, thereby making the smart home become an innovative IoT application to change our lives. However, the application of smart home should serve thousands of families and the number of edge servers and terminal devices s huge. Thus, it is difficult for a single central server or cluster to manage such large-scale devices with high efficiency. In addition, the structure of IoT is flexible and the edge or terminal devices can join a certain IoT system without strict restriction. The system may suffer from unauthorized access from malicious devices. In this paper, a physically secure authentication scheme for smart home based on a hybrid blockchain is proposed. A novel structure of the blockchain is leveraged to relieve the stress of the central server and storage device’s information permanently. Physical unclonable functions are used to provide identities for authorized devices. The security and evaluation analysis shows that our scheme has reliable security features and is the potential to improve the efficiency of the smart home system.

Facial biometrics have the advantages of high reliability, strong distinguishability and easily acquired for authentication. Therefore, it is becoming wildly used in identity authentication filed. However, there are stability, security and privacy issues in generating face key, which brings great challenges to face biometric authentication. In this paper, we propose a biometric key generation scheme based on face image. On the one hand, a deep neural network model for feature extraction is used to improve the stability of identity authentication. On the other hand, a key generation mechanism is designed to generate random biometric key while hiding original facial biometrics to enhance security and privacy of user authentication. The results show the FAR reach to 0.53% and the FRR reach to 0.57% in LFW face database, which achieves the better performance of biometric identification, and the proposed method is able to realize randomness of the generated biometric keys by NIST statistical test suite.

The traditional speaker verification method has inefficient performance in confirming the speaker identity and the text contents. In addition, there is an urgent need for the authentication system to understand the text contents concisely and comprehensively. To address above problems, a multi-fusion acoustic feature generation method is proposed for the Speaker identity Verification system. First, a Bidirectional Long Short Term Memory (BLSTM) network is trained as an acoustic feature extractor to extract spliced MFCC - TANDEM acoustic features, and then a multi-fusion BLSTM network is further trained to obtain more robust multi-fusion deep feature, and the Probabilistic Linear Discriminant Analysis (PLDA) probability is employed as a back-end classifier to deal with this feature, then all these acoustic features are trained in the Gaussian Mixture Model-Universal Background Model (GMM - UBM) framework. This experiment was evaluated on the RSR2015 data corpus. And the results show that the system using the multi-fusion acoustic feature has about a 35% reduction in the EER compared to the system using the spliced MFCC-TANDEM features. This shows that the multi-fusion acoustic features are more discrimination. Moreover, the system that used the multi-fusion acoustic features shows significant performance improvements over the baseline system, with Equal Error Rate (EER) reductions of about 40%.

A water-soluble polysaccharide, designated as LBP-3, was isolated and purified from Lycium barbarum. Chemical analysis indicated that LBP-3 was composed of arabinose and galactose at a molar ratio of 1.00:1.56. The average molecular weight of LBP-3 was 6.74 × 104 Da. The structural features of LBP-3 were investigated by Fourier-transform infrared spectroscopy (FT-IR), methylation, and nuclear magnetic resonance (NMR). LBP-3 is a highly branched polysaccharide with a backbone of 1, 3-linked β-Galp, which is partially substituted at C-6. The branches contain 1, 5-linked α-Araf, 1, 6-linked β-Galp, 1, 3-linked α-Araf, and 1, 4-linked α-Araf. In vitro studies revealed that LBP-3 induced a concentration-dependent decrease in the levels of Aβ42/Aβ40 in N2a/APP695 cells. Proteomic analysis was conducted to investigate the potential molecular mechanism underlying the neuroprotective effect of LBP-3, and the results suggested that LBP-3 might have the potential for the treatment of AD.

Answering query with semantic concepts has long been the mainstream approach for video search. Until recently, its performance is surpassed by concept-free approach, which embeds queries in a joint space as videos. Nevertheless, the embedded features as well as search results are not interpretable, hindering subsequent steps in video browsing and query reformulation. This paper integrates feature embedding and concept interpretation into a neural network for unified dual-task learning. In this way, an embedding is associated with a list of semantic concepts as an interpretation of video content. This paper empirically demonstrates that, by using either the embedding features or concepts, considerable search improvement is attainable on TRECVid benchmarked datasets. Concepts are not only effective in pruning false positive videos, but also highly complementary to concept-free search, leading to large margin of improvement compared to state-of-the-art approaches.

Video summarization has unprecedented importance to help us overview current ever-growing amount of video collections. In this paper, we propose a novel dynamic video summarization model based on deep learning architecture. We are the first to solve the imbalanced class distribution problem in video summarization. The over-sampling algorithm is used to balance the class distribution on training data. The novel two-stream deep architecture with the cost-sensitive learning is proposed to handle the class imbalance problem in feature learning. In the spatial stream, RGB images are used to represent the appearance of video frames, and in the temporal stream, multi-frame motion vectors with deep learning framework is firstly introduced to represent and extract temporal information of the input video. The proposed method is evaluated on two standard video summarization datasets and a standard emotional dataset. Empirical validations for video summarization demonstrate that our model achieves performance improvement over the existing and state-of-the-art methods. Moreover, the proposed method is able to highlight the video content with the active level of arousal in affective computing task. In addition, the proposed frame-based model has another advantage. It can automatically preserve the connection between consecutive frames. Although the summary is constructed based on the frame level, the final summary is comprised of informative and continuous segments instead of individual separate frames.

Fabrication of biomimetic cell microenvironment closely resembling the native tissue is critical in regenerative medicine. This paper presents a novel meniscus tissue scaffold fabricated by a hybrid additive manufacturing technology to closely resemble the natural topology of the extracellular matrix. A skeletal scaffold was 3D printed and a layer of random or aligned polycaprolactone and collagen nanofibers were embedded between two frames. A compression test was performed to study the mechanical properties of the system. Human osteosarcoma cells were cultured in the scaffold for 7 days to evaluate the effect of scaffold microstructure on cell growth. With reinforced nanofibers, the hybrid scaffold showed superior compression strength compared to 3D printed scaffold without nanofibers. The hybrid scaffold induced the cells to organize into an aligned structure. The study shows the potential of hybrid bio fabrication process to be developed as a scalable platform for biomimetic scaffolds with patterned fibrous microstructure. It will facilitate the future development of clinical solutions for musculoskeletal tissue regeneration.

With the proliferation of video data, video summarization is an ideal tool for users to browse video content rapidly. In this paper, we propose a novel foveated convolutional neural networks for dynamic video summarization. We are the first to integrate gaze information into a deep learning network for video summarization. Foveated images are constructed based on subjects’ eye movements to represent the spatial information of the input video. Multi-frame motion vectors are stacked across several adjacent frames to convey the motion clues. To evaluate the proposed method, experiments are conducted on two video summarization benchmark datasets. The experimental results validate the effectiveness of the gaze information for video summarization despite the fact that the eye movements are collected from different subjects from those who generated summaries. Empirical validations also demonstrate that our proposed foveated convolutional neural networks for video summarization can achieve state-of-the-art performances on these benchmark datasets.

The published model coefficients of Zhang and co-workers [J. Chem. Thermodynamics 94 (2016) 1–6] were discussed for mathematically describing the solubility of rivaroxaban (form I) by using the Jouyban-Acree model in binary mixture of ethyl acetate with N,N-dimethylformamide and Buchowski (λh) equation in the three solvent mixtures. The published model coefficients fail to describe the mole fraction solubility as stated in the published paper. The model coefficients are re-analyzed by using the Jouyban-Acree model and Buchowski (λh) equation based on the authors’ experimental solubility data.

Errors are discovered regarding the published equation coefficients of Fan and co-workers [J. Chem. Thermodynamics 58 (2013) 288–291] for mathematically describing the solubility behavior of evodiamine in neat solvents using the modified Apelblat equation. The back-calculated values using the published equation coefficients are not the mole fraction solubility as stated in the published paper. What’s more, the parameters in the modified Apelblat equation were re-analysed according to the experimental solubility data.

This study proposes a three-index flow-based mixed integer formulation to solve a two-echelon location routing problem with simultaneous pickup and delivery. In this formulation, pickup and delivery demands can be addressed using the same vehicle in each echelon of the network to reduce costs and increase logistics efficiency. We solve such NP-hard problem by developing a multistart hybrid heuristic with path relinking (MHH-PR) which is composed of local search and a variable neighbourhood descent algorithm. In the algorithm, three constructive heuristics are applied to generate diversified initial solutions, and path relinking is introduced for intensification and postoptimisation. Results indicate that MHH-PR can reduce the gap between the near optimal and global optimal solutions by 1%-2%. The proposed algorithm significantly improves computational efficiency by reducing the computational time of more than 10 min for existing cases involving 20 nodes to less than 10 s.

With the growth of complexity and extent, large scale interconnected network systems, e.g. transportation networks or infrastructure networks, become more vulnerable towards external disruptions. Hence, managing potential disruptive events during the design, operating, and recovery phase of an engineered system therefore improving the system's resilience is an important yet challenging task. In order to ensure system resilience after the occurrence of failure events, this study proposes a mixed-integer linear programming (MILP) based restoration framework using heterogeneous dispatchable agents. Scenario-based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from nature. Moreover, different from conventional SO using deterministic equivalent formulations, additional risk measure is implemented for this study because of the temporal sparsity of the decision making in applications such as the recovery from extreme events. The resulting restoration framework involves a large-scale MILP problem and thus an adequate decomposition technique, i.e. modified Lagrangian dual decomposition, is also employed in order to achieve tractable computational complexity. Case study results based on the IEEE 37-bus test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations.

In this paper, we present the features implemented in the 4th version of the VIREO Video Search System (VIREO-VSS). In this version, we propose a sketch-based retrieval model, which allows the user to specify a video scene with objects and their basic properties, including color, size, and location. We further utilize the temporal relation between video frames to strengthen this retrieval model. For text-based retrieval module, we supply speech and on-screen text for free-text search and upgrade the concept bank for concept search. The search interface is also re-designed targeting the novice user. With the introduced system, we expect that the VIREO-VSS can be a competitive participant in the Video Browser Showdown (VBS) 2020.

In this study, we investigated the structure–absorption relationship of common surface modifiers of chitosan (CTS), polyvinyl acetate (PVA), and titanium dioxide (TiO2) with α-quartz surface using molecular dynamics simulation. And the orientations and combinations derived from structures between modified α-quartz and ZSM-5 crystallites were also investigated. The results show that PVA is a non-linear organic macromolecule with a large amount of hydroxyl groups on its surface, which easily adhere to the surface of the substrate and agglomerate. CTS is a straight-chain structure containing a large number of hydroxyl and amino groups, which easily accumulate and spread on the surface of the substrate. TiO2 not only forms hydrogen bonds and complexes with the substrate but also interacts with each other to form a dense modifier layer. We observed that a large number of stable Ti–O–Si chemical bonds formed between the modified layer of inorganic small-molecule TiO2 and the surface of α-quartz, which compacted and stabilized the attached ZSM-5 film. Moreover, the orientation angle of the ZSM-5 nanocrystalline nucleus on the modified α-quartz was computed, which confirmed that the b-axis orientation of the ZSM-5 nanocrystalline nucleus was the highest on the surface of the substrate modified by TiO2. We discussed the influence of the modified temperature of modifiers in the constructed materials, and we have observed the adsorption state differences of TiO2 at different modified temperatures. We also discussed the catalytic properties of the materials prepared by the corresponding methods in conversion of methanol-to-aromatics (MTA) reaction. These results agree with our previous experimental data. By employing molecular dynamics simulation, we have obtained more precise conclusive information of the b-oriented growth of ZSM-5 crystallites, which highly depends on the surface modifiers.

With the development of the times, our life and e-commerce links even more closely. The medical and health field has also seen the emergence of medical e-commerce, which makes it extremely easy for people to buy medicines and other activities, but the medical distribution problems that come with it are becoming more and more prominent. In order to solve the problem of the current high cost of medical distribution and circulation, this paper selects the most basic medical and health institution in China—the community is the starting point, and proposes the path planning of vehicle distribution based on the integration of community drug supply and demand information. The planned path can achieve the goal of reducing the total cost of distribution and improving the efficiency of medical logistics distribution.

Multi-video summarization, which tries to generate a single summary for a collection of video, is an important task in dealing with ever-growing video data. In this paper, we are the first to propose a graph convolutional network for multi-video summarization. The novel network measures the importance and relevance of each video shot in its own video as well as in the whole video collection. The important node sampling method is proposed to emphasize the effective features which are more possible to be selected as the final video summary. Two strategies are proposed to integrate into the network to solve the inherent class imbalance problem in the task of video summarization. The loss regularization for diversity is used to encourage a diverse summary to be generated. Extensive experiments are carried out, and in comparison with traditional and recent graph models and the state-of-the-art video summarization methods, our proposed model is effective in generating a representative summary for multiple videos with good diversity. It also achieves state-of-the-art performance on two standard video summarization datasets.

Nanoscale zeolitic imidazolate framework-8 coating with certain density can significantly improve the osteogenic activity of titanium implants both in vitro and in vivo. In this study, we loaded di...

Managing potential disruptive events at the operating phase of an engineered system therefore improving the system's failure resilience is an importance yet challenging task in engineering design. The resilience of an engineered system can be improved by enhancing the failure restoration capability of the system with appropriate system control strategies. Therefore, control-guided failure restoration is an essential step in engineering design for resilience. Considering different characteristics of disruptive events and their impacts to the performance of a system, effective control strategies for the failure restoration must be selected correspondingly. However, the challenge is to develop generally applicable guiding principles for selecting effective control strategies, thus implementing the control-guided failure restorations. In this paper, a comparison of three commonly used control strategies for dynamic system control is conducted with the focus on the effectiveness of restoring system performance after the system has undergone different major disruptive events. A case study of an electricity transmission system is used to demonstrate the dynamic system modeling and the comparison of three control strategies for disruption management.

For full description of images’ semantic information, image retrieval tasks are increasingly using deep convolution features trained by neural networks. However, to form a compact feature representation, the obtained convolutional features must be further aggregated in image retrieval. The quality of aggregation affects retrieval performance. In order to obtain better image descriptors for image retrieval, we propose two modules in our method. The first module is named generalized regional maximum activation of convolutions (GR-MAC), which pays more attention to global information at multiple scales. The second module is called saliency joint weighting, which uses nonparametric saliency weighting and channel weighting to focus feature maps more on the salient region without discarding overall information. Finally, we fuse the two modules to obtain more representative image feature descriptors that not only consider the global information of the feature map but also highlight the salient region. We conducted experiments on multiple widely used retrieval data sets such as roxford5k to verify the effectiveness of our method. The experimental results prove that our method is more accurate than the state-of-the-art methods.

Static video summarization is recognized as an effective way for users to quickly browse and comprehend large numbers of videos. In this paper, we formulate static video summarization as a clustering problem. Inspired by the idea from high density peaks search clustering algorithm, we propose an effective clustering algorithm by integrating important properties of video to gather similar frames into clusters. Finally, all clusters' center will be collected as static video summarization. Compared with existing clustering-based video summarization approaches, our work can detect frames which are highly relevant and generate representative clusters automatically. We evaluate our proposed work by comparing it with several state-of-the-art clustering-based video summarization methods and some classical clustering algorithms. The experimental results evidence that our proposed method has better performance and efficiency.

A Pd composite catalyst with a micro-mesoporous structure was successfully prepared by hydrothermal crystallization and applied to the hydrodesulfurization (HDS) of coal tar. The mesoporous Pd/γ-Al2O3 and microporous Pd/ZSM-5 catalysts were compared to study the effects of the catalyst pore structure on the HDS activity. A tetrahydronaphthalene solution with 2 wt% dibenzothiophene (DBT) was used as the model compound for coal tar. The reaction products were detected via gas chromatography/mass spectrometry, while the reaction path and mechanism of HDS on the catalyst were speculated. The results show that the mesopores were sulfur poisoned after irreversible adsorption with SO42− species and had high deactivation rates in HDS reactions due to a weak carbon deposition resistance. However, the micro-mesoporous composite structure has anti-coking deposition and sulfur resistance due to its high mass transfer efficiency, suitable acid distribution, and Pd dispersion. This special material with the micro-mesoporous structure played a dual role, including a strong sulfur (especially H2S) resistance in its micropores and desulfurization capacity in its mesopores. And the DBT conversion over FC reached 85.5% in the HDS reaction. After 10 h of the FC catalyst used in the HDS reaction, Pb loss rate and acid loss were not less than 5.64% and 2.98%, respectively. At the same time, direct desulfurization is the primary route of HDS reactions in the micro-mesoporous structure catalyst. The free diffusion of overflow hydrogen on the micro-mesoporous interface and the desorption of H2S have important effects to maintain a constant number of active centres for the catalyst.

To prevent coal spontaneous combustion, it is critical to accurately simulate and predict the low-temperature oxidation process of coal. In this study, an experiment system was constructed to investigate the temperature and gas concentration of two typical coals during low temperature oxidation. The back propagation neural network (BPNN) was proposed to simulate this process under six factors, including activation energy, void fraction, moisture content, air flow rate, stacking time and location of measuring point. The average relative error of oxygen concentration, temperature and CO concentration are 1.34 %, 1.09 % and 1.15 %, respectively. Besides, the statistical performance indicators are precise. Most importantly, the predicted temperature and gas concentration fields can be utilized to analyze coal spontaneous combustion process which allows us to track down the crucial factors on spontaneous combustion. By applying our BPNN modeling method, the odds of coal spontaneous combustion can be lowered.

The aim of this study was to explore the interactive effect of Illuminance (E) and Correlated Colour Temperature (CCT) on the visual appreciation of the colour rendition of lighting (termed as colour preference), as well as on a newly defined visual attribute termed the degree of white light sensation, which describes the Chinese understanding on a loosely-defined but widely-used concept: ‘white light’. Thirty E−CCT combinations, that included six E values from 50 lx to 1800 lx and five CCT values from 2500 K to 6500 K, were used to illuminate a pair of highly coloured Jiaozhi artefacts, as well as the neutral space of an empty light booth. Thirty observers, fifteen males and fifteen females, were invited to take part in the visual experiments to rate the colour preference and the white light sensation. The results indicated that both E and CCT significantly influenced both visual attributes. For colour preference, the impact of E was greater while for the degree of white light sensation, stronger influence of CCT was observed. Based on the psychophysical results, the existing metrics that are used to evaluate the colour preference of E−CCT combinations were validated and a new model was proposed to characterize the degree of white light sensation. In addition, gender difference was investigated and it was found that male and female observers behaved similarly in assessing white light but unlike male observer, female observers did not prefer light sources with the highest degree of white light sensation.

Biothiols, including cysteine (Cys), homocysteine (Hcy) and glutathione (GSH), play pivotal roles in numerous physiological events, however, due to the similar structures and reactivities, differentiation of these biothiols remains a challenging task. Herein, we reported a maleimide-appended fluorescent probe ABTT-MA capable of specific sensing of Cys over other structure-alike biothiols with an ultralow detection limit (9.4 nM). Via a series of investigations into the mechanism, the high selectivity toward Cys is ascribed to a Michael addition/S,N-intramolecular rearrangement cascade reaction, in which the involvement of the mercapto and the adjacent amino groups is necessary to proceed the sensing process. Given this presupposition, Cys could be kinetically discriminated from Hcy and GSH. Upon the addition of Cys, a strong fluorescence appeared and the intensity at 503 nm was correlated with Cys concentration linearly over a wide range (0–10 μM). Thanks to the aggregation-induced emission (AIE) and excited-state intramolecular proton transfer (ESIPT) activity, the selective detection of Cys was realized not only in aqueous media but in the paper-based point-of-care test (POCT). Significantly, the practicability of ABTT-MA in exogenous and endogenous Cys imaging in living cells was elucidated as well.

Multi-video summarization is an effective tool for users to browse multiple videos. In this paper, multi-video summarization is formulated as a graph analysis problem and a dynamic graph convolutional network is proposed to measure the importance and relevance of each video shot in its own video as well as in the whole video collection. Two strategies are proposed to solve the inherent class imbalance problem of video summarization task. Moreover, we propose a diversity regularization to encourage the model to generate a diverse summary. Extensive experiments are conducted, and the comparisons are carried out with the state-of-the-art video summarization methods, the traditional and novel graph models. Our method achieves state-of-the-art performances on two standard video summarization datasets. The results demonstrate the effectiveness of our proposed model in generating a representative summary for multiple videos with good diversity.

With the rapid development of the Internet, malware traffic is seriously endangering the security of cyberspace. Convolutional neural networks (CNNs)-based malware traffic classification can automatically learn features from raw traffic, avoiding the inaccuracy of hand-design traffic features. Through the experiments and comparisons of LeNet, AlexNet, VGGNet, and ResNet, it is found that LeNet has good and stable classification ability for malware traffic and normal traffic. Then, a field programmable gate array (FPGA) accelerator for CNNs-based malware traffic classification is designed, which consists of a parameterized hardware accelerator and a fully automatic software framework. By fully exploring the parallelism between CNN layers, parallel computation and pipeline optimization are used in the hardware design to achieve high performance. Simultaneously, runtime reconfigurability is implemented by using a global register list. By encapsulating the underlying driver, a three-layer software framework is provided for users to deploy their pre-trained models. Finally, a typical CNNs-based malware traffic classification model was selected to test and verify the hardware accelerator. The typical application system can classify each traffic image from the test dataset in 18.97 μs with the accuracy of 99.77%, and the throughput of the system is 411.83 Mbps.

The solubility of 5,7-dibromo-8-hydroxyquinoline in four co-solvent mixtures of isopropanol + water, N,N-dimethylformamide (DMF) + water, N-methyl-2-pyrrolidone (NMP) + water and 1,4-dioxane + water was acquired through the saturation shake-flask method at temperatures from 288.15 to 333.15 K. The solubility magnitudes were maximum in the neat solvents of DMF (isopropanol, NMP or 1,4-dioxane) for the four co-solvent mixtures. The 5,7-dibromo-8-hydroxyquinoline solubility was correlated very well with the help of the Jouyban-Acree model and van’t Hoff-Jouyban-Acree model, obtaining relative average deviations less than 6.07% and root-mean-square deviations less than 4.81 × 10−4. The local mole fractions of isopropanol (DMF, NMP or 1,4-dioxane) and water nearby the 5,7-dibromo-8-hydroxyquinoline were quantitatively evaluated through the method of Inverse Kirkwood–Buff integrals. 5,7-Dibromo-8-hydroxyquinoline was preferentially solvated by water for these mixtures in water-rich compositions; while within intermediate and co-solvent-rich compositions, 5,7-dibromo-8-hydroxyquinoline was preferentially solvated by isopropanol (DMF, NMP or 1,4-dioxane). Additionally, the Kamlet and Taft linear solvation energy relationships were used to rationalize the solvent effect on the solubility. Results indicated that the variation of solubility was mainly governed by the change in cavity formation energy in all the aqueous mixtures.

Low-grade waste heat, which exhibits widely in industrial production and daily life, shows great promise for smart electronics and sensors, but we always ignore this energy. Recently, developing thermoelectric conversion devices becomes a popular subject. Unfortunately, the relatively low Seebeck coefficient of current devices limits their further applications. Thus, it should give priority to matching suitable electrolyte and constructing high-rate system. Here, we employ a series of common neutral electrolytes to optimize the energy storage behavior towards high capacitance and rate capability. As a result, the commercial carbon electrode displays a high specific capacitance of 220.8 F g−1 at a current density of 1 A g−1 and a good retention of 61.2% even at a high rate of 20 A g−1 in 1.0 mol L−1 potassium nitrate electrolyte. Meanwhile, the assembled symmetric supercapacitor can output the maximum energy/power density of 13.1 Wh kg−1/15.7 kW kg−1 based on the active material, together with an excellent durability (~109% capacitance retention over 10,000 cycles at 5 A g−1). Besides, a thermally chargeable supercapacitor exhibits a satisfying output potential of 1.21 mV K−1 under optimized cases. Significantly, the potential value is highly determined by temperature gradient.

The magnetic properties of nanocrystalline Fe77Si10B9Cu1Nb3-xAlx (x = 0 and 1) alloys have been investigated and their structural and electromagnetic parameters have been quantitatively studied by X-ray diffraction, transmission electron microscopy and Mossbauer spectra under one-step and two-step annealing processes. The nanocrystalline structure consists of single α-Fe(Si) phase embedded in a residual amorphous phase. Both saturation magnetic flux density (Bs) and permeability (μ) of the nanocrystalline alloys are increased by substituting 1 at% Al for Nb and one-step annealing, from Bs = 1.41 T to 1.47 T and from μ = 18,000 to 23,000 at 1 kHz, respectively. The two-step annealing has little effect on the Bs, coercivity (Hc) and grain size of the nanocrystalline alloys, but greatly improves the μ of the Al-doped alloy, reaching up to 28,000 at 1 kHz. The improved μ can be attributed to the increased magnetic moment and exchange stiffness constant, homogeneous chemical structure and reduced magnetostriction. The Al-doped nanocrystalline alloy with high Bs, high μ, low Hc and good frequency stability are good candidates for magnetic shielding pieces of wireless charging.

Coarse-grained molecular dynamics simulations are used to investigate the conformations of semiflexible ring-linear blends in ellipsoidal confinement. Ordered structures of semiflexible ring polymers (SRPs) in ring-linear blends rely strongly on the number density of blends (ρ), the chain length (Nlinear) and the bending energy (Kb,linear) of semiflexible linear polymers, as well as the curvature of confinement. For a low number density ρ, SRPs are immersed randomly in the matrix of linear polymers in ellipsoid confinement. However, for a high number density ρ, a complete separation of SRPs from flexible linear polymers occurs, and a highly ordered aggregation structure of SRPs at the equatorial plane is formed in the mixture of semiflexible linear polymers with large Kb,linear. These explicit ordered aggregations of SRPs in ellipsoidal confinement are induced by a delicate competition between the entropic excluded volume (depletion) effects and bending contributions. This investigation can help us understand the complicated conformations of ring-linear blends in ellipsoidal confinement.