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With the rapid advancement of 5G technology, the Internet of Things (IoT) has entered a new phase of applications and is rapidly becoming a significant force in promoting economic development. Due to the vast amounts of data created by numerous 5G IoT devices, the Ethereum platform has become a tool for the storage and sharing of IoT device data, thanks to its open and tamper-resistant characteristics. So, Ethereum account security is necessary for the Internet of Things to grow quickly and improve people's lives. By modeling Ethereum transaction records as a transaction network, the account types are well identified by the Ethereum account classification system established based on Graph Neural Networks (GNNs). This work first investigates the Ethereum transaction network. Surprisingly, experimental metrics reveal that the Ethereum transaction network is neither optimal nor even satisfactory in terms of accurately representing transactions per account. This flaw may significantly impede the classification capability of GNNs, which is mostly governed by their attributes. This work proposes an Adaptive Multi-channel Bayesian Graph Attention Network (AMBGAT) for Ethereum account classification to address this difficulty. AMBGAT uses attention to enhance node features, estimate graph topology that conforms to the ground truth, and efficiently extract node features pertinent to downstream tasks. An extensive experiment with actual Ethereum transaction data demonstrates that AMBGAT obtains competitive performance in the classification of Ethereum accounts while accurately estimating the graph topology.

Abstract Catch bonds are a rare class of protein-protein interactions where the bond lifetime increases under an external pulling force. Here, we report how modification of anchor geometry generates catch bonding behavior for the mechanostable Dockerin G:Cohesin E (DocG:CohE) adhesion complex found on human gut bacteria. Using AFM single-molecule force spectroscopy in combination with bioorthogonal click chemistry, we mechanically dissociate the complex using five precisely controlled anchor geometries. When tension is applied between residue #13 on CohE and the N-terminus of DocG, the complex behaves as a two-state catch bond, while in all other tested pulling geometries, including the native configuration, it behaves as a slip bond. We use a kinetic Monte Carlo model with experimentally derived parameters to simulate rupture force and lifetime distributions, achieving strong agreement with experiments. Single-molecule FRET measurements further demonstrate that the complex does not exhibit dual binding mode behavior at equilibrium but unbinds along multiple pathways under force. Together, these results show how mechanical anisotropy and anchor point selection can be used to engineer artificial catch bonds.

In high-rise buildings, secondary water supply systems (SWSSs) are pivotal yet provide a conducive milieu for microbial proliferation due to intermittent flow, low disinfectant residual, and high specific pipe-surface area, raising concerns about tap water quality deterioration. Despite their ubiquity, a comprehensive understanding of bacterial community dynamics within SWSSs remains elusive. Here we show how intrinsic SWSS variables critically shape the tap water microbiome at distal ends. In an office setting, distinct from residential complexes, the diversity in piping materials instigates a noticeable bacterial community shift, exemplified by a transition from α-Proteobacteria to γ-Proteobacteria dominance, alongside an upsurge in bacterial diversity and microbial propagation potential. Extended water retention within SWSSs invariably escalates microbial regrowth propensities and modulates bacterial consortia, yet secondary disinfection emerges as a robust strategy for preserving water quality integrity. Additionally, the regularity of water usage modulates proximal flow dynamics, thereby influencing tap water's microbial landscape. Insights garnered from this investigation lay the groundwork for devising effective interventions aimed at safeguarding microbiological standards at the consumer's endpoint.

Abstract Heat conduction in solids is typically governed by the Fourier’s law describing a diffusion process due to the short wavelength and mean free path for phonons and electrons. Surface phonon polaritons couple thermal photons and optical phonons at the surface of polar dielectrics, possessing much longer wavelength and propagation length, representing an excellent candidate to support extraordinary heat transfer. Here, we realize clear observation of thermal conductivity mediated by surface phonon polaritons in SiO2 nanoribbon waveguides of 20-50 nm thick and 1-10 μm wide and also show non-Fourier behavior in over 50-100 μm distance at room and high temperature. This is enabled by rational design of the waveguide to control the mode size of the surface phonon polaritons and its efficient coupling to thermal reservoirs. Our work laid the foundation for manipulating heat conduction beyond the traditional limit via surface phonon polaritons waves in solids.

More and more attention has been paid to the waterflood development of carbonate reservoir in the Middle East with continuous expansion of the scale of carbonate reservoir development. Strong non-homogeneity of carbonate reservoirs results in serious conflicts between injection and production, causing low degree of water-driven control in the well network, and low degree of vertical mobilization. This article takes the main reservoir of an oil field in the Middle East region as an example, combining geological evaluation, dynamic analysis, and reservoir development strategies to study the effectiveness of water injection development in carbonate reservoirs. It shows that there is a correlation between pore type, oil-water relative permeability, and development method in the development of carbonate reservoirs. For reservoirs with matrix pores and micropores, water injection should be strengthened in the later stage of development; for reservoirs with dissolved pores and coarse pores, the iso-permeability point is relatively high, and the later development should focus on balanced water injection. Three different waterflooding models for formation development are designed to verify the feasibility of fine waterflooding schemes. The main differences are that one set of well patterns is combined injection and production, and three sets of well patterns are layered system waterflooding. The results show that the development effect in layered system waterflooding is the best. The daily production can be increased by about 16%, the cumulative oil production can be increased by about 8%, and the recovery factor can be increased by about 3%. Eventually, the stable oil and water control of carbonate reservoir can be realized.

ObjectiveThis study aimed to establish an accurate and efficient scientific calculation model for the nutritional composition of catering food to estimate energy and nutrient content of catering food.MethodsWe constructed a scientific raw material classification database based on the Chinese food composition table by calculating the representative values of each food raw material type. Using China’s common cooking methods, we cooked 150 dishes including grains, meat, poultry, fish, eggs, and vegetables and established a database showing the raw and cooked ratios of various food materials by calculating the ratio of raw to cooked and the China Total Diet Research database. The effects of various cooking methods on the nutritional composition of catering food were analyzed to determine correction factors for such methods on the nutritional components. Finally, we linked the raw material classification, raw and cooked ratio, and nutritional component correction factor databases to establish a model for calculating the nutritional components of catering food. The model was verified with nine representative Chinese dishes.ResultsWe have completed the construction of an accurate and efficient scientific calculation model for the nutritional composition of catering food, which improves the accuracy of nutrition composition calculation.ConclusionThe model constructed in this study was scientific, accurate, and efficient, thereby promising in facilitating the accurate calculation and correct labeling of nutritional components in catering food.

Abstract Chromatin replication involves the assembly and activity of the replisome within the nucleosomal landscape. At the core of the replisome is the Mcm2-7 complex (MCM), which is loaded onto DNA after binding to the Origin Recognition Complex (ORC). In yeast, ORC is a dynamic protein that diffuses rapidly along DNA, unless halted by origin recognition sequences. However, less is known about the dynamics of ORC proteins in the presence of nucleosomes and attendant consequences for MCM loading. To address this, we harnessed an in vitro single-molecule approach to interrogate a chromatinized origin of replication. We find that ORC binds the origin of replication with similar efficiency independently of whether the origin is chromatinized, despite ORC mobility being reduced by the presence of nucleosomes. Recruitment of MCM also proceeds efficiently on a chromatinized origin, but subsequent movement of MCM away from the origin is severely constrained. These findings suggest that chromatinized origins in yeast are essential for the local retention of MCM, which may facilitate subsequent assembly of the replisome.

Nitrogen (N) has received attention as an indicator of water quality and pollution. However, there is still a lack of systematic research on the influence of temperature. An experiment was conducted with five tanks containing sediments from the Pearl River Delta region of China and distilled water to assess the release of N from sediments under controlled conditions; temperatures from 10 to 30 °C were assessed. Results show that the effect of temperature on N in the water column and sediment is nonlinear. NO3-N was affected at temperatures between 20 and 25 °C in shallow sediments (1–3 cm below the sediment–water interface) with rapid increase concentration, while NH4-N concentration in water column was decreased significantly with increased temperature between 10 and 15 °C. NO3-N was dominant at temperatures from 25 to 30 °C. However, the presence of NH4-N in a water body can inhibit its release, thus the relationship between the diffusive flux with temperature is not linear. The relationship between N diffusive flux at the sediment–water interface was described by Polynomial2D and Lorentz2D models. HIGHLIGHTS General relationship for nitrogen concentration in the water column.; Distribution of nitrogen in water column and sediment with temperature.; Nitrogen flux at the sediment–water interface at different temperatures.; Effects of dissolved oxygen, pH, on nitrogen release from sediments.; Nitrogen pollution control measures in different seasons.;

ABSTRACTRemote sensing image segmentation plays an important role in many industrial-grade image processing applications. However, the problem of uncertainty caused by intraclass heterogeneity and interclass blurring is prevalent in high-resolution remote sensing images. Moreover, the complexity of information in high-resolution remote sensing images leads to a large amount of background information around objects. To solve this problem, a new fuzzy convolutional neural network is proposed in this paper. This network resolves the ambiguity and uncertainty of feature information by introducing a fuzzy neighbourhood module in the deep learning network structure. In addition, it adds a multi-attention gating module to highlight small object features and separate them from the complex background information to achieve fine segmentation of high-resolution remote sensing images. Experimental results on three different segmentation datasets suggest that the proposed method has higher segmentation accuracy and better performance than other deep learning networks, especially for complicated shadow information. Code will be provided in (https://github.com/tingtingqu/code).

Abstract The development of the Internet and big data has led to the emergence of graphs as an important data representation structure in various real-world scenarios. However, as data size increases, computational complexity and memory requirements pose significant challenges for graph embedding. To address this challenge, this paper proposes a multilevel embedding refinement framework (MERIT) based on large-scale graphs, using spectral distance-constrained graph coarsening algorithms and an improved graph convolutional neural network model that addresses the over-smoothing problem by incorporating initial values and identity mapping. Experimental results on large-scale datasets demonstrate the effectiveness of MERIT, with an average AUROC score 8% higher than other baseline methods. Moreover, in a node classification task on a large-scale graph with 126,825 nodes and 22,412,658 edges, the framework improves embedding quality while enhancing the runtime by 25 times. The experimental findings highlight the superior efficiency and accuracy of the proposed approach compared to other graph embedding methods.

Structured illumination microscopy (SIM) is one of the most widely applied wide field super resolution imaging techniques with high temporal resolution and low phototoxicity. The spatial resolution of SIM is typically limited to two times of the diffraction limit and the depth of field is small. In this work, we propose and experimentally demonstrate a low cost, easy to implement, novel technique called speckle structured illumination endoscopy (SSIE) to enhance the resolution of a wide field endoscope with large depth of field. Here, speckle patterns are used to excite objects on the sample which is then followed by a blind-SIM algorithm for super resolution image reconstruction. Our approach is insensitive to the 3D morphology of the specimen, or the deformation of illuminations used. It greatly simplifies the experimental setup as there are no calibration protocols and no stringent control of illumination patterns nor focusing optics. We demonstrate that the SSIE can enhance the resolution 2–4.5 times that of a standard white light endoscopic (WLE) system. The SSIE presents a unique route to super resolution in endoscopic imaging at wide field of view and depth of field, which might be beneficial to the practice of clinical endoscopy.

Abstract The rapid development of blockchain technology has garnered increasing attention, particularly in the field of edge computing. It has become a significant subject of research in this area due to its ability to protect the privacy of data. Despite the advantages that blockchain technology offers, there are also security threats that must be addressed. Attackers may manipulate certain nodes in the blockchain network, which can result in tampering with transaction records or other malicious activities. Moreover, the creation of a large number of false nodes can be utilized to gain control and manipulate transaction records of the blockchain network, which can compromise the reliability and security of edge computing. This paper proposes a blockchain node detection method named $$T^2A2vec$$ T 2 A 2 v e c that provides a more secure, credible, and reliable solution to address these challenges. In order to achieve $$T^2A2vec$$ T 2 A 2 v e c , a transaction dataset that is evenly distributed in both space and time was collected. The transaction dataset is constructed as a transaction graph, where nodes represent accounts and edges describe transactions. BP neural network is used to extract account features, and a random walk strategy based on transaction time, type, and amount is used to extract transaction features. The obtained account features and transaction features are fused to obtain account representation. Finally, the obtained node representation is fed into different classifiers to identify malicious nodes.

Abstract Graph representation learning has made significant strides in various fields, including sociology and biology, in recent years. However, the majority of research has focused on static graphs, neglecting the temporality and continuity of edges in dynamic graphs. Furthermore, dynamic data are vulnerable to various security threats, such as data privacy breaches and confidentiality attacks. To tackle this issue, the present paper proposes a data security detection method based on a continuous-time graph embedding framework (CTDGE). The framework models temporal dependencies and embeds data using a graph representation learning method. A machine learning algorithm is then employed to classify and predict the embedded data to detect if it is secure or not. Experimental results show that this method performs well in data security detection, surpassing several dynamic graph embedding methods by 5% in terms of AUC metrics. Furthermore, the proposed framework outperforms other dynamic baseline methods in the node classification task of large-scale graphs containing 4321477 temporal information edges, resulting in a 10% improvement in the F1 score metric. The framework is also robust and scalable for application in various data security domains. This work is important for promoting the use of continuous-time graph embedding framework in the field of data security.

Aquatic ecosystem degradation is a considerable challenge of global interest. Although substantial efforts have been accomplished to restore phytoplankton-dominated shallow lakes to a clear state with high coverage of submerged macrophytes, the phenomenon, driving mechanism, and threshold range of such nonlinear regime shifts remain unclear. Here, we constructed an aquatic ecological model based on PCLake, simulated and predicted the aquatic ecological changes before and after treatments of Xinglong Lake, a subtropical artificial shallow lake that had been completed ecological restoration in Chengdu, China. The model included the future prediction of TN, TP, Chl-a, and aquatic vegetation biomass, as well as the threshold of regime shifts using bifurcation analysis. We also quantified the impact of some common management measures (point and non-point source pollution control, water level regulation) on the stability of the lake's aquatic ecosystem. The results showed that the thresholds of Xinglong Lake from a clear state to a turbid state were 1.405 mg/m2/day and vice versa 0.68 mg/m2/day, indicating its initial turbidity and transitional state after treatment. After treatment, phosphorus loads still exceeded the threshold for a clear-water state, while the non-point source pollution of Xinglong Lake accounted for more than 80%. To prevent the aquatic ecosystem from collapsing in the future, it is necessary to combine upstream water quality improvement, runoff and air pollution control to reduce total pollution by 60%. Reasonable water level regulation, such as adjusting the water level downwards by 0.5 m in spring, is beneficial in maintaining the health and stability of aquatic ecosystems. The present study can be used to evaluate the effectiveness of the management measures of Xinglong Lake as well as to provide scientific support for the management of shallow lakes.

Tight sandstone gas in coal-bearing strata has become an important unconventional gas worldwide. This research aims at understanding the impacts of coal evolution on diagenesis and reservoir quality of sandstones by means of thin section, cathode luminescence (CL), scanning electron microscope (SEM), stable isotope analysis, and fluid inclusions. The results show that carbonate cement was the main type of cement developed in tight sandstone reservoirs, of which the carbonate ions were jointly provided by coal layers and reaction in adjacent mudstones. For sandstones close to coal layers, their carbonate ions were mainly controlled by coal evolution and there was various carbonate cement developed due to cations supplied by seawater and the intense dissolution of volcanic fragments and feldspars. Combined with coal thermal simulation and burial history, it implies that at different maturity stages of coal evolution, its impact on sandstone diagenesis was different. In the early stage, the main product was CO 2 , which caused the dissolution of early calcite and promoted intense compaction. In the mature stage, CO 2 and other hydrocarbon gases were released, resulting in more dissolution and silica cementation. In the over-mature stage, the main product was methane, and CO 2 that did not spill out preserved by forming carbonate cement. The coal evolution had a strong impact on the reservoir quality of sandstone with large grain sizes, but little effect on medium- and fine-grained sandstones. High porosity and low permeability reservoirs were usually formed in small conglomerates and coarse-grained sandstones away from coal layers. While low porosity and high permeability reservoirs were distributed close to coal layers. This study aids in understanding the formation mechanism of tight sandstone reservoirs in coal measures and provides theoretic support for further exploration.

While remote sensing images could convey essential information of surface water environment, the low spatial resolution limits their application. This study carried out a series of experiment tests of thermal discharge from a coastal power plant and constructed an image dataset HY_IRS, representing the transport and diffusion of discharged heated water in tidal waters. Two image super-resolution (SR) reconstruction models based on deep learning (DL), ESPCN and ESRGAN, were trained based on this dataset and then used to reconstruct high-resolution remote sensing images. It shows that the two DL models can markedly improve the spatial resolution of the surface diffusion image of thermal discharging, with the PSNR improved by 8.3% on average. The trained two models were successfully used to improve the spatial resolution of thermal infrared remote sensing SST images from Landsat8 TIRS, indicating that the SR model based on DL has a good effect and a crucial application prospect in the field of improving the resolution of pollutant diffusion remote sensing images.

Study region: This study was conducted in Xiangjiaba Reservoir, located in the Jinsha River, China's upper reach of the Yangtze River. Study focus: Large channel-type reservoirs are characterized by significant runoff, deep water, and narrow-and-long channel shape, creating unique hydrodynamic and thermodynamic conditions for their eco-environmental systems. However, the hydro-thermal processes of this type reservoir are not fully known. A large channel-type reservoir was selected to investigate the macroscopic processes through a field survey and employing a three-dimensional (3D) hydro-thermodynamic model. New hydrological insights for the region: The reservoir experienced a seasonal varying thermal stratification, with a vertical temperature difference of ∼9 °C. Meanwhile, a conjoint flow pattern was detected, consisting of an upper-layer warm water flow and a lower-layer circulation, which was very different from the lakes' wind-induced pattern. When the reservoir was stratified, the inflow warm water on the thermocline could produce a shear effect, promoting vertical mixing and further contributing to hypolimnion shrinking. The reservoir experienced a water age increase during the non-stratification period, with the near-dam age being ∼20 days. The trap effect of the lower-layer circulation retained the cold water in the hypolimnion during the stratification period, and the water age reached 115 days. The presented varying flow pattern and transport timescale have essential effects on the eco-environmental systems, providing practical insights into managing water quality and aquatic environment in reservoirs.

The authors demonstrate a label-free superresolution imaging method by using a hyperbolic material as a substrate for tailored light-matter interactions. The hyperbolic material enhanced scattering, combined with dark-field detection, result in 5.5-fold resolution improvement beyond the diffraction limit.

Abstract There is growing evidence that individual bacteria sense and respond to changes in mechanical loading. However, the subtle responses of multispecies biofilms to dynamic fluid shear stress are not well documented because experiments often fail to disentangle any beneficial effects of shear stress from those delivered by convective transport of vital nutrients. We observed the development of biofilms with lognormally distributed microcolony sizes in drinking water on the walls of flow channels underflow regimes of increasing complexity. First, where regular vortices induced oscillating wall shear and simultaneously enhanced mass transport, which produced the thickest most extensive biofilms. Second, where unsteady uniform flow imposed an oscillating wall shear, with no enhanced transport, and where the biomass and coverage were only 20% smaller. Finally, for uniform steady flows with constant wall shear where the extent, thickness, and density of the biofilms were on average 60% smaller. Thus, the dynamics of shear stress played a significant role in promoting biofilm development, over and above its magnitude or mass transfer effects, and therefore, mechanosensing may prevail in complex multispecies biofilms which could open up new ways of controlling biofilm structure.

Abstract Background Members of the transient receptor potential canonical (TRPC) protein family are widely distributed in the hippocampus of mammals and exert respective and cooperative influences on the functions of neurons. The relationship between specific TRPC subtypes and neuroinflammation is receiving increasing attention. Methods Using Cx3cr1CreERIL-10−/− transgenic mice and their littermates to study the relationship between TRPC channels and memory impairment. Results We demonstrated that Cx3cr1CreERIL-10−/− mice displayed spatial memory deficits in object location recognition (OLR) and Morris water maze (MWM) tasks. The decreased levels of TRPC4 and TRPC5 in the hippocampal regions were verified via reverse transcription polymerase chain reaction, western blotting, and immunofluorescence tests. The expression of postsynaptic density protein 95 (PSD95) and synaptophysin in the hippocampus decreased with an imbalance in the local inflammatory environment in the hippocampus. The number of cells positive for ionized calcium-binding adaptor molecule 1 (Iba1), a glial fibrillary acidic protein (GFAP), increased with the high expression of interleukin 6 (IL-6) in Cx3cr1CreERIL-10−/− mice. The nod-like receptor protein 3 (NLRP3) inflammasome was also involved in this process, and the cytokines IL-1β and IL-18 activated by NLRP3 were also elevated by western blotting. The co-localization of TRPC5 and calmodulin-dependent protein kinase IIα (CaMKIIα) significantly decreased TRPC5 expression in excitatory neurons. AAV9-CaMKIIα-TRPC5 was used to upregulate TRPC5 in excitatory neurons in the hippocampus. Conclusions The results showed that the upregulation of TRPC5 improved the memory performance of Cx3cr1CreERIL-10−/− mice related to inhibiting NLRP3 inflammasome-associated neuroinflammation. Graphical Abstract

Abstract Spectral clustering is an unsupervised clustering algorithm, and is widely used in the field of pattern recognition and computer vision due to its good clustering performance. However, the traditional spectral clustering algorithm is not suitable for large‐scale data classification, such as hyperspectral remote sensing image, because of its high computational complexity, and it is difficult to characterize the inherent uncertainty of the hyperspectral remote sensing image. This paper uses fuzzy anchors to process hyperspectral image classification and proposes a novel spectral clustering algorithm based on fuzzy similarity measure. The proposed algorithm utilizes the fuzzy similarity measure to obtain the similarity between the data points and the anchors, and then gets the similarity matrix. Finally, spectral clustering is performed on the similarity matrix to compute the classification results. The experimental results on the hyperspectral remote sensing image data sets have demonstrated the effectiveness of the proposed algorithm, and the introduction of fuzzy similarity measure gives rise to a more robust similarity matrix. Compared with existing methods, the proposed algorithm has a better classification result on the hyperspectral remote sensing image, and the kappa coefficient obtained by the proposed algorithm is 2% higher than the traditional algorithms.

Abstract Background Within the last decade, genetic engineering and synthetic biology have revolutionized society´s ability to mass-produce complex biological products within genetically-modified microorganisms containing elegantly designed genetic circuitry. However, many challenges still exist in developing bioproduction processes involving genetically modified microorganisms with complex or multiple gene circuits. These challenges include the development of external gene expression regulation methods with the following characteristics: spatial–temporal control and scalability, while inducing minimal permanent or irreversible system-wide conditions. Different stimuli have been used to control gene expression and mitigate these challenges, and they can be characterized by the effect they produce in the culture media conditions. Invasive stimuli that cause permanent, irreversible changes (pH and chemical inducers), non-invasive stimuli that cause partially reversible changes (temperature), and non-invasive stimuli that cause reversible changes in the media conditions (ultrasound, magnetic fields, and light). Methods Opto-control of gene expression is a non-invasive external trigger that complies with most of the desired characteristics of an external control system. However, the disadvantage relies on the design of the biological photoreceptors and the necessity to design them to respond to a different wavelength for every bioprocess needed to be controlled or regulated in the microorganism. Therefore, this work proposes using biocompatible metallic nanoparticles as external controllers of gene expression, based on their ability to convert light into heat and the capacity of nanotechnology to easily design a wide array of nanostructures capable of absorbing light at different wavelengths and inducing plasmonic photothermal heating. Results Here, we designed a nanobiosystem that can be opto-thermally triggered using LED light. The nanobiosystem is composed of biocompatible gold nanoparticles and a genetically modified E. coli with a plasmid that allows mCherry fluorescent protein production at 37 °C in response to an RNA thermometer. Conclusions The LED-triggered photothermal protein production system here designed offers a new, cheaper, scalable switchable method, non-destructive for living organisms, and contribute toward the evolution of bioprocess production systems.

Convolutional neural-network-based autoencoders, which can integrate the spatial correlation between pixels well, have been broadly used for hyperspectral unmixing and obtained excellent performance. Nevertheless, these methods are hindered in their performance by the fact that they treat all spectral bands and spatial information equally in the unmixing procedure. In this article, we propose an adaptive spectral–spatial attention autoencoder network, called SSANet, to solve the mixing pixel problem of the hyperspectral image. First, we design an adaptive spectral–spatial attention module, which refines spectral–spatial features by sequentially superimposing the spectral attention module and spatial attention module. The spectral attention module is built to select useful spectral bands, and the spatial attention module is designed to filter spatial information. Second, SSANet exploits the geometric properties of endmembers in the hyperspectral image while considering abundance sparsity. We significantly improve the endmember and abundance results by introducing minimum volume and sparsity regularization terms into the loss function. We evaluate the proposed SSANet on one synthetic dataset and four real hyperspectral scenes, i.e., Samson, Jasper Ridge, Houston, and Urban. The results indicate that the proposed SSANet achieved competitive unmixing results compared with several conventional and advanced unmixing approaches with respect to the root mean square error and spectral angle distance.

On-chip circuits based on plasmonic systems are a promising potential technology. Here the authors present efficient, on-chip, localized plasmonic excitation based on resonant inelastic electron tunneling with metallic quantum well junction.

A method was preposed for mining conditional preferences and learning CP-nets on the data stream based on the inverted matrix structure. The transaction layout using the inverted matrix reduces the number of times the database is scanned, and through random access, frequent preference items can be found in less than one full scan. In addition, through the establishment of frequent preference tree FP-Tree, the generation of candidates is reduced. Experimental results show that, compared with other methods of learning CP-nets structure, this method can obtain accurate CP-nets faster, show better performance in large transaction databases, and reduce memory requirements.

Structured illumination microscopy is usually limited to 2 times spatial resolution improvement over the diffraction limit. Here, the authors introduce a metamaterial structure to generate speckle-like sub-diffraction limit illumination patterns in the near field, and achieve a 7-fold resolution improvement down to 40 nm.

Riser recoil control is a complicated nonlinear control problem. An intelligent control methodology is proposed to control the complicated riser recoil response based on fuzzy control theory. A dynamic nonlinear model of drilling riser/tensioner coupling system is established for riser recoil analysis. The riser system is divided into several elements and the coupling effect between the riser and discharged mud is considered in the dynamic model. A fuzzy controller for the opening of the recoil control valve is designed for the riser recoil control. A comprehensive analysis method is proposed for riser recoil control simulation. Simulation results show that the designed fuzzy controller can effectively control the riser recoil response. The adaptability of the designed controller is also proved by the simulation results. The application of the designed controller can reduce the risk of accidents during riser recoil process.

Understanding bacterial adhesion is important in a number of different areas of study. Here using a range of simulations and experimental methods, the authors, report on the molecular mechanism behind the binding of bacteria to cellulose fibers at high shear force in the human gut.

Abstract Resolution capability of the linear structured illumination microscopy (SIM) plays a key role in its applications in physics, medicine, biology, and life science. Many advanced methodologies have been developed to extend the resolution of structured illumination by using subdiffraction‐limited optical excitation patterns. However, obtaining SIM images with a resolution beyond 40 nm at visible frequency remains as an insurmountable obstacle due to the intrinsic limitation of spatial frequency bandwidth of the involved materials and the complexity of the illumination system. Here, a low‐loss natural organic hyperbolic material (OHM) that can support record high spatial‐frequency modes beyond 50k0, i.e., effective refractive index larger than 50, at visible frequencies is reported. OHM‐based speckle structured illumination microscopy demonstrates imaging resolution at 30 nm scales with enhanced fluorophore photostability, biocompatibility, easy to use and low cost. This study will open up a new route in super‐resolution microscopy by utilizing OHM films for various applications including bioimaging and sensing.

The invasions of marine organisms into the intake of nuclear power plants threaten the normal operation of such plants. Most published numerical models assumed that marine organisms passively follow the current, but such models neglected their biological swimming ability. In this work, adopting a hydrodynamic mathematical model to replicate the flow around the Daya Bay nuclear power base, the invasion characteristics of Acetes were explored by considering the behavior of biological movement. A concept of biological residual current was introduced to describe biological movements that were dominated by both tidal current and biological swimming ability. The biological residual currents near the nuclear power plant were obtained for cases with different nocturnal migration periods (12 h, 13 h, 14 h, 15 h, and 16 h). Using the Lagrangian particle-tracking method, the primary invasion paths of Acetes were obtained, as well as the travel time of Acetes to the intake, based on the biological residual current along monitoring points. The results showed that the invading time for Acetes reaching the water intake of the nuclear power base was significantly decreased when biological migration behavior was considered. When the nocturnal active period was over 13 h, it took only 10 days for Acetes to enter the western waters of Daya Bay from the southwest of Da Lajia Island and then continue migrating to the water intake in the nuclear power base. When the nocturnal active period was less than 13 h, it took more than 20 days for Acetes to travel the same distance. The present work provides a new methodology for the simulation and prediction of the migration of marine organisms.

Speech mental imagery is a quasi-perceptual experience that occurs in the absence of real speech stimulation. How imagined speech with higher-order structures such as words, phrases and sentences is rapidly organized and internally constructed remains elusive. To address this issue, subjects were tasked with imagining and perceiving poems along with a sequence of reference sounds with a presentation rate of 4 Hz while magnetoencephalography (MEG) recording was conducted. Giving that a sentence in a traditional Chinese poem is five syllables, a sentential rhythm was generated at a distinctive frequency of 0.8 Hz. Using the frequency tagging we concurrently tracked the neural processing timescale to the top-down generation of rhythmic constructs embedded in speech mental imagery and the bottom-up sensory-driven activity that were precisely tagged at the sentence-level rate of 0.8 Hz and a stimulus-level rate of 4 Hz, respectively. We found similar neural responses induced by the internal construction of sentences from syllables with both imagined and perceived poems and further revealed shared and distinct cohorts of cortical areas corresponding to the sentence-level rhythm in imagery and perception. This study supports the view of a common mechanism between imagery and perception by illustrating the neural representations of higher-order rhythmic structures embedded in imagined and perceived speech.

RNA chaperones, such as the hepatitic C virus (HCV) core protein, are proteins that aid in the folding of nucleic acids. Here authors use single‐molecule spectroscopy and simulation to show that the HCV core protein acts as a flexible macromolecular counterion which facilitates nucleic acid folding.

Core, well logging and seismic data were used to investigate sandbody architectural characteristics within Lower Member of Minghuazhen Formation in Neogene, Bohai BZ25 Oilfield, and to analyze the sedimentary microfacies, distribution and internal architecture characteristics of the bar finger within shoal water delta front. The branched sand body within shoal water delta front is the bar finger, consisting of the mouth bar, distributary channel over bar, and levee. The distributary channel cuts through the mouth bar, and the thin levee covers the mouth bar which is located at both sides of distributary channel. The bar finger is commonly sinuous and its sinuosity increases basinward. The distributary channel changes from deeply incising the mouth bar to shallowly incising top of the mouth bar. The aspect ratio ranges from 25 to 50 and there is a double logarithmic linear positive relationship between the width and thickness for the bar finger, which is controlled by base-level changing in study area. For the bar finger, injection and production in the same distributary channel should be avoided during water flooding development. In addition, middle–upper distributary channel and undrilled mouth bar are focus of tapping remaining oil. Key Words: shoal water delta, bar finger, sandbody architecture, Bohai BZ25 Oilfield, Neogene Minghuazhen Formation, remaining oil

Bed shear stress is an important measure of benthic habitats since it is related to many ecological processes. In this study, we focused on the fluctuating characteristics of shear stress in rough-bed open-channel flows. The roughness element method was adopted to mimic natural rough beds and the Improved Delayed Detached Eddy Simulation (IDDES) model was used to obtain comprehensive information about shear stress near the rough bed. Three arrangement patterns of the roughness elements were simulated to compare their effects on flow structure and shear stresses. The arrangements of the roughness elements altered the Reynold stress and turbulent kinetic energy characteristics, due to the variance of blockage in lateral directions that led to flow detachment and changes in the flow directions. Quadrant analysis revealed the spatial variations of the instantaneous shear stress burst events at different locations in the wake. By using spectrum analysis, the accumulation of shear-stress energy from small to large vortex scales was estimated, which revealed that the instantaneous effect of the shear stress was significantly stronger than the effect of the time-averaged shear stress, especially on small scales. The results of this study suggest the significance of the fluctuation part of shear stress in further studies on ecological processes.

The combination of Global Positioning System-Real Time Kinematic (GPS-RTK) and depth sounder is an important technical method of modern underwater topographic survey. In this paper, the combined technology was integrated with unmanned surface vehicle (USV) technology to construct an unmanned survey system suitable for underwater topographic survey in stilling basin, and it was applied to survey the underwater topography of the stilling basin of Tingzikou hydrojunction project. Based on the surveying and mapping data and 3D model, the trend of elevation change of the stilling basin including apron, end sill, antiscour section, and river convergence section was analyzed. The results show that, for the surface outlet stilling basin of Tingzikou, after flood, the structure is complete, the boundaries are clear, the water quality is good, and there is little sediment on the surface of apron from overflow dam section to end sill. Furthermore, no obvious sediment or structural abnormality has been found in the still basin. Affected by the bedrock and cofferdam cobble gravel sediment, the elevation of the antiscour section and the downstream convergence section of the surface outlet stilling basin increased significantly. For the bottom outlet stilling basin of Tingzikou, there is large sediment within a range of 20 m∼40 m from the bottom sill and the maximum height of it reaches 2.04 m, while there is no obvious sediment or structural abnormality in the remaining region of basin. The critical sedimentation height can effectively judge the safety grade of the sedimentation height in stilling basin and provide a reliable support for assessing the overall safety of stilling basin. The application of unmanned survey technology improves the accuracy and timeliness of underwater topography and sedimentation distribution of stilling basin, which has significant application research value and promotion significance.

Single-molecule force spectroscopy with the atomic force microscope provides molecular level insights into protein function, allowing researchers to reconstruct energy landscapes and understand functional mechanisms in biology. With steadily advancing methods, this technique has greatly accelerated our understanding of force transduction, mechanical deformation, and mechanostability within single- and multi-domain polyproteins, and receptor-ligand complexes. In this focused review, we summarize the state of the art in terms of methodology and highlight recent methodological improvements for AFM-SMFS experiments, including developments in surface chemistry, considerations for protein engineering, as well as theory and algorithms for data analysis. We hope that by condensing and disseminating these methods, they can assist the community in improving data yield, reliability, and throughput and thereby enhance the information that researchers can extract from such experiments. These leading edge methods for AFM-SMFS will serve as a groundwork for researchers cognizant of its current limitations who seek to improve the technique in the future for in-depth studies of molecular biomechanics.

The subjective inner experience of mental imagery is among the most ubiquitous human experiences in daily life. Elucidating the neural implementation underpinning the dynamic construction of mental imagery is critical to understanding high-order cognitive function in the human brain. Here, we applied a frequency-tagging method to isolate the top-down process of speech mental imagery from bottom-up sensory-driven activities and concurrently tracked the neural processing time scales corresponding to the two processes in human subjects. Notably, by estimating the source of the magnetoencephalography (MEG) signals, we identified isolated brain networks activated at the imagery-rate frequency. In contrast, more extensive brain regions in the auditory temporal cortex were activated at the stimulus-rate frequency. Furthermore, intracranial stereotactic electroencephalogram (sEEG) evidence confirmed the participation of the inferior frontal gyrus in generating speech mental imagery. Our results indicate that a disassociated neural network underlies the dynamic construction of speech mental imagery independent of auditory perception.

Conditional preference networks (CP-nets) are used as intuitive graphical tools to represent conditional preference statements regarding the values of a set of attributes. Making use of CP-nets to solve certain learning problems has attracted growing attention from scholars and become a hot topic in the field of artificial intelligence. Therefore, many methods have been proposed to solve these learning problems. However, these approaches suffer from two main disadvantages: the amount of time required and their lack in concrete structures. To overcome these limitations, in this paper, we first provide theoretical support for the use of a conditional independent test for learning the structure of CP-nets. Second, we propose the dependent degree to calculate the dependency relationship among attributes. Finally, we present an algorithm to obtain the structures of CP-nets. Beyond that, a number of database samples have been reduced by filtering out insignificant or noise data, and a concrete structure of learned CP-nets with 18 attributes is given. The experiments show that our approach can obtain a better structure of CP-nets without materially increasing the time required for the process and put forward contrast to methods presented antecedently.

With the sharp increase of digital data emerging at present, the data in new applications are generated fast. Continuous cumulative data have gradually become massive and difficult to be handled due to limited workspace and limited amount of time. The conventional learning conditional preference networks' algorithm cannot successfully process the data streams. In this paper, we introduce the model of learning CP-nets from preference data streams and formalize the question. Then, an incremental approach is presented through which we can learn the CP-nets with gradually increasing data streams. The proposed method is verified on simulated data and real data, and it is also compared with other works.

When plasmonic structures reach the nanoscale, quantum size effects become important for their optical properties. Here, Qian et al. find a giant third-order nonlinear Kerr response from nanometre thick gold quantum wells, which they attribute to quantum size effects.

The purpose of this research is to propose a real-time, inexpensive, and portable method for measuring water surface velocities, in which a particle tracking velocimetry (PTV) system is developed by using a smartphone device. The system uses the mobile camera to capture the tracer floating on the river surface, whose movement is expected to represent the velocity of the water surface. The recorded tracer images are identified from the background by using the normalized RGB color model. The magnification ratio, which transforms the distance in pixel to the real distance, is estimated by using the pre-specified mobile height from the water surface, or the characteristic length of the tracer. The proposed system is built based on the iPhone 6s device. The system is tested in two cases. One case is the artificially generated images recording the movement of table tennis, and the relative error does not exceed 3%. The other is the comparison to the velocity measured by acoustic doppler current profilers (ADCP) in the river, and the relative error is not more than 15%. Finally, the limitation of the proposed method is discussed.

Algal blooms have been frequently found at the upper reaches of the Tanglang River, which is downstream from the eutrophic Dianchi Lake. The eutrophic lake upstream is considered to be a potential source of phytoplankton, which contributes to the development of harmful algal blooms in the river downstream and can cause many serious problems for the river ecology. However, few studies focused on these kinds of rivers. Therefore, a field observation and laboratory analysis were conducted in this study. The results showed that the Tanglang River was obviously spatially heterogeneous due to the eutrophic Dianchi Lake upstream. The toxic Microcystis from the Dianchi Lake dominated the phytoplankton at the upper reaches, but these were gradually, rather than immediately, replaced by centric diatoms and chlorococalean green algae in the middle and lower reaches. The results of correlation analysis indicated that the changes in hydrodynamic conditions and underwater light intensity accounted for the spatial variations. The differences in the adaptability of different algae to changing aquatic environments explained the spatial variations of phytoplankton abundance. The dominant algae, most of which was from the Dianchi Lake upstream, determined the characteristics of the total abundance at the Tanglang River.

This paper presents a preliminary design methodology for small unmanned battery powered tailsitters. Subsystem models, including takeoff weight, power and energy consumption models, and battery discharge model, were investigated, respectively. Feasible design space was given by simulation with mission and weight constraints, while the influences of wing loading and battery ratio were analyzed. Case study was carried out according to the design process, and the results were validated by previous designs. The design methodology can be used to determine key parameters and make necessary preparations for detailed design and vehicle realization of small battery powered tailsitters.