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Stray magnetic fields with a fixed frequency of 50/60 Hz are ubiquitous in buildings, factories, and power system equipment. Researchers are increasingly focusing on harvesting electrical energy from stray magnetic fields to provide sustainable energy for the Internet of Things (IoT) devices. Magneto‐mechano‐electric energy conversion is the most efficient way to convert low‐frequency stray magnetic fields into electricity. In this study, we proposed a hybrid piezo/triboelectric nanogenerator (HP/TENG) on the basis of a cantilever beam to use stray magnetic fields from the surrounding environment. The hybrid nanogenerator provided high output voltage/current and power of ∼176 V/375 μA and 4.7 mW (matched impedance of 20 kΩ) in a magnetic field environment of 4 Oe. The device provided a stable 3.6 V direct current output by incorporating energy management circuitry to sustainably drive power to commercial wireless temperature/humidity sensors. The HP/TENG has a significant application potential in IoT, which can use stray magnetic energy and a power wireless sensor system.

Tellurene has a phosphorene-like puckered configuration and exhibits great air stability. This characteristic makes it highly sensitive to some gases, and therefore, it has a huge potential in gas sensing applications. In this paper, the sensing performance of tellurene-based gas sensors toward SF6 decomposition products, including H2S, SO2, SO2F2, and SOF2, was systematically investigated. Three types of tellurene-based gas sensors were fabricated: single tellurene flake with ohmic contact (RCTF sensor), single tellurene flake with nonohmic contact (DCTF sensor), and drop-cast tellurene flakes with heat function (HTF sensor). The RCTF sensor exhibited good sensitivity and selectivity toward hydrogen sulfide but slow recovery speed like most two-dimensional materials. The DCTF sensor exhibited a significant improvement in sensitivity, selectivity, and recovery speed compared with the RCTF sensor. Finally, the HTF sensor demonstrated the fastest recovery speed of all tellurene-based sensors. This work showed that the nonohmic contact and heat pulse are easy and efficient methods to enhance the performance of tellurene-based gas sensors toward hydrogen sulfide, which supports the application potential of tellurene in gas sensors.

Organisms must make sense of a constant stream of sensory inputs from both internal and external sources which compete for attention by determining which ones are salient. The ability to detect and respond appropriately to potentially salient stimuli in the environment is critical to all organisms. However, the neural circuits that process salience are not fully understood. Here, we identify a population of glutamatergic neurons in the ventral pallidum (VP) that play a unique role in salience processing. Using cell-type-specific fiber photometry, we find that VP glutamatergic neurons are robustly activated by a variety of aversion- and reward-related stimuli, as well as novel social and non-social stimuli. Inhibition of the VP glutamatergic neurons reduces the ability to detect salient stimuli in the environment, such as aversive cue, novel conspecific and novel object. Besides, VP glutamatergic neurons project to both the lateral habenula (LHb) and the ventral tegmental area (VTA). Together, our findings demonstrate that the VP glutamatergic neurons participate in salience processing and therefore provide a new perspective on treating several neuropsychiatric disorders, including dementia and psychosis.

Wearable devices have become a research hotspot due to their prospective applications in wireless sensor networks and the Internet of Things. However, these technologies demand the generation of new power sources, which are efficient, flexible, sustainable, and stable. Triboelectric nanogenerators (TENGs), as a new type of power supply, have been widely studied for environmental energy harvesting and self-powered sensing; however, they have vastly limited stretchability, flexibility, and stability. For the first time, we report a single-electrode TENG based on hydrophobic ionic liquid gel, which is simultaneously transparent (average transmittance of 89% for visible light), stretchable (400%), and has super-stability-up to 3 months in various weather conditions (from -25 to +60 °C and humidity up to 80%). This TENG was used to power a vast range of flexible electronics, including 30 green light-emitting diodes (LEDs), an arch-shaped finger-bending sensor, and a transparent keyboard. This work provides a creative platform to access the next-generation sustainable wearable electronics.

The spectral computed tomography (CT) based on photon counting detectors can collect the incident photons with different energy ranges. However, due to the low photon counts in narrow energy bin and the unhomogeneous response problem of detector cells, there are severe noise and ring artifacts in reconstructed spectral CT images. We proposed an image denoising and ring artifacts removal method via improved Fully Convolutional Pyramid Residual Network (FCPRN). In our study, we scanned a mouse specimen with spectral CT based on photon counting detector, and reconstructed mouse CT images as data set. Then we use the data set to train our network for image denoising and ring artifacts removal. Experimental results demonstrated that the proposed method could reduce noise and suppress ring artifacts of spectral CT images concurrently in different energy ranges. And the performance of the FCPRN is better than that of some networks for CT image denoising.

As sessile organisms, plants must directly deal with an often complex and adverse environment in which hyperosmotic stress is one of the most serious abiotic factors, challenging cellular physiology and integrity. The plasma membrane (PM) is the hydrophobic barrier between the inside and outside environments of cells and is considered a central compartment in cellular adaptation to diverse stress conditions through dynamic PM remodeling. Endocytosis is a powerful method for rapid remodeling of the PM. In animal cells, different endocytic pathways are activated in response to osmotic stress, while only a few reports are related to the endocytosis response pathway and involve a mechanism in plant cells upon hyperosmotic stress. In this study, using different endocytosis inhibitors, the microdomain-specific dye di-4-ANEPPDHQ, variable-angle total internal reflection fluorescence microscopy (VA-TIRFM), and confocal microscopy, we discovered that internalized Clathrin Light Chain-Green Fluorescent Protein (CLC-GFP) increased under hyperosmotic conditions, accompanied by decreased fluorescence intensity of CLC-GFP at the PM. CLC-GFP tended to have higher diffusion coefficients and a fraction of CLC-GFP molecules underwent slower diffusion upon hyperosmotic stress. Meanwhile, an increased motion range of CLC-GFP was found under hyperosmotic treatment compared with the control. In addition, the order of the PM decreased, but the order of the endosome increased when cells were in hyperosmotic conditions. Hence, our results demonstrated that clathrin-mediated endocytosis and membrane microdomain-associated endocytosis both participate in the adaptation to hyperosmotic stress. These findings will help to further understand the role and the regulatory mechanism involved in plant endocytosis in helping plants adapt to osmotic stress.

In the process of installation and debugging of the plenoptic sensor, due to the existence of uncertain factors, assembly errors exist among the components, which adversely affect the distortion wavefront detection of the plenoptic sensor. In this paper, a numerical simulation model of the plenoptic sensor is established, the Zernike polynomial method is used to simulate the distorted wavefront of different turbulence intensities, and the phase reconstruction error RMS of the plenoptic sensor is used as an evaluation index to simulate and analyze the assembly errors that may occur during installation and commissioning. Through simulation research, it is found that the assembly error of the plenoptic sensor in the installation and debugging process will reduce the wavefront detection accuracy of the plenoptic sensor, and the degree of influence of the assembly error on the wavefront detection accuracy has a certain relationship with the turbulence intensity. The influence law of assembly errors with different turbulence intensities, different error types and different error ranges on the wavefront detection of the plenoptic sensor is summarized, which has guiding significance for the installation and debugging of the plenoptic sensor and the analysis of the wavefront detection error.

Chemiresistive sensor devices using two-dimensional (2D) materials have been extensively studied. However, so far no single material has all the desirable attributes. For example, graphene and MXenes lack a band gap, transition metal dichalcogenides (such as MoS2) lack high sensitivity, while phosphorene exhibits poor air-stability. Here we report that tellurene, a new emergent single-element 2D material, excels as a sensor for NO2 detection. Devices based on tellurene exhibit a detection limit of ∼25 ppb, large detection range (from 25 ppb to 5 ppm), baseline noise as low as ∼0.5%, and excellent “selectivity” to NO2 even in the presence of cross-contaminants such as CO, SO2, H2S, acetone and ethanol. Density functional theory calculations indicate that large adsorption energy and intense charge redistribution are the reasons for the high sensitivity and selectivity of tellurene for NO2 detection. Further, we find that “heat-pulsing” enables fast sensor response and recovery time, without damaging the tellurene sheets. Most importantly, tellurene exhibits outstanding air-stability, making it a promising candidate for next-generation chemical sensors.

The turbulence effect, thermal blooming effect, laser beam aberration, platform jitter, and other effects in the process of high energy laser propagation in the atmosphere will cause serious degradation of laser beam quality, which will have a negative impact on the actual application of laser propagation engineering. It is important in the engineering application of high-energy laser propagation to evaluate the far-field intensity distribution quickly. Based on the optical transfer function (OTF) theory of imaging system, the propagation process of high-energy lasers is modeled as the imaging process of point source. By using the convolution of point spread function (PSF) of jitter, turbulence, thermal blooming, and aberration of emission system, fast calculation of the far-field intensity distribution of high energy laser is realized. The calculation results are compared with those obtained by the 4D wave optics simulation program in different propagation scenarios. The results show that the calculated facula distribution and encircled energy of this method are in good agreement with the simulation results of wave optics, which can realize the fast and accurate evaluation of the far-field intensity distribution of high-energy laser propagation and provide a reference for practical engineering application.

In-depth profiling of transmembrane proteins in the brain leads to the identification of GPCR regulators in a disease model., Transmembrane proteins play vital roles in mediating synaptic transmission, plasticity, and homeostasis in the brain. However, these proteins, especially the G protein–coupled receptors (GPCRs), are underrepresented in most large-scale proteomic surveys. Here, we present a new proteomic approach aided by deep learning models for comprehensive profiling of transmembrane protein families in multiple mouse brain regions. Our multiregional proteome profiling highlights the considerable discrepancy between messenger RNA and protein distribution, especially for region-enriched GPCRs, and predicts an endogenous GPCR interaction network in the brain. Furthermore, our new approach reveals the transmembrane proteome remodeling landscape in the brain of a mouse depression model, which led to the identification of two previously unknown GPCR regulators of depressive-like behaviors. Our study provides an enabling technology and rich data resource to expand the understanding of transmembrane proteome organization and dynamics in the brain and accelerate the discovery of potential therapeutic targets for depression treatment.

Data naming is the most critical construct of Named Data Networking (NDN). The way a piece of content is named has profound impacts on content discovery, routing of user requests, data retrieval, and security. Besides, the naming of individual pieces of content seriously affects how the network behaves. While names are ubiquitous in NDN, the design choices for content names and how they affect the network have largely been overlooked. NDN applications and protocols usually name content to fit their particular application scenarios, often derived from existing naming conventions. However, these ad-hoc naming schemes often ignore the impact of these names on the network and the applications themselves. Drawing upon our experience in applying NDN to multiple science domains, we point out different exiting naming schemes in scientific communities, how we translated these names into NDN names, and the effect of naming on the network. Based on these observations, we provide a set of naming guidelines for future scientific applications and network operators supporting those applications.

Caching is integral to Named Data Networking (NDN). Routers in NDN networks are encouraged to cache content and serve later requests from their caches. As NDN has evolved, researchers have realized that different caching schemes work better for different types of content and patterns of content requests. From a measurement perspective, this means that being able to determine the caching schemes in use within an NDN network can be essential to understanding the network's performance. In this paper, we investigate the feasibility of detecting NDN caching schemes via active measurement (i.e., by sending requests into the network and measuring responses) from edge systems (e.g., by users). We show it is possible to determine what algorithms routers are using to decide what content to cache. Furthermore, for stochastic caching schemes with fixed caching probabilities, we show it is possible to infer the caching probability. Finally, while we do not seek to understand routers' cache replacement policies (which we leave to future work), we find that the methods for determining the caching algorithm are robust to cross traffic that may impact the content of a router's cache.

Interest Flooding Attack (IFA) is one of the main security threats for the Named Data Networking (NDN). Most of its existing countermeasures enable intermediate routers near the attackers to independently detect the attack and consider the typical attack scenario in which attackers directly send malicious Interests at a constant and relatively high rate after the attack starts. Moreover, they may also throttle legitimate Interests when enforcing the existing defense measures at intermediate routers as it is still difficult for them to distinguish the Interests issued by attackers from those issued by legitimate consumers. Instead, this work aims at a more sophisticated attack scenario in which attackers start the attack at a relatively lower rate but gradually speed up to keep the Pending Interest Tables (PITs) of the victims increasing to finally deplete the PIT resources for legitimate consumers. It is relatively difficult for intermediate routers to independently and timely detect such a sophisticated IFA. To solve this problem, we propose a mechanism to detect and mitigate the sophisticated IFA from the network-wide view, dubbed as DMNWV. In DMNWV, a central controller monitors the network and makes a comprehensive and prompt decision on whether there is an ongoing IFA based on the overall state of the whole network collected from the abnormity information reports sent by the first-hop routers of attackers. Attack sources can be directly located after an IFA is determined, and then the routers directly connected to attackers (i.e., access routers) can take targeted measures based on the located attackers to prevent malicious Interest from entering the network without throttling legitimate Interests. We conduct an experimental study to evaluate the performance of DMNWV, explore the parameter settings of the attack detection algorithm at access routers, and measure the communication overhead of the central controller. The experimental results validate that DMNWV can timely detect and mitigate the sophisticated IFA without throttling requests from legitimate consumers with significantly low communication overhead of the central controller, which will not bring about too much burden to the network.

A method which can be used to compensate for a distorted orbital angular momentum and wavefront of a beam in atmospheric turbulence, simultaneously, has been proposed. To confirm the validity of the method, an experimental setup for up-link propagation of a vortex beam in a turbulent atmosphere has been simulated. Simulation results show that both of the distorted orbital angular momentum and the distorted wavefront of a beam due to turbulence can be compensated by an adaptive optics system with the help of a cooperative beacon at satellite. However, when the number of the lenslet of wavefront sensor (WFS) and the actuators of the deform mirror (DM) is small, satisfactory results cannot be obtained.

As a promising future Internet architecture, named data networking (NDN) supports name-based routing and caching for content retrieval throughout the network, which enables fast, reliable, and, more importantly, energy-efficient content dissemination in smart cities. However, NDN’s vulnerability against the content poisoning attack is considered to decelerate the process of applying NDN to energy management in smart cities. The content poisoning isolates valid content from the network by injecting a poisoned content with a legitimate name into in-network caches. The caching, delivery, and signature verification of poisoned content diminish the advantage of NDN in energy efficiency when it disseminates content in smart cities. This paper focuses on content poisoning mitigation for energy management in smart cities and first analyzes the state of the art and the challenges of content poisoning mitigation. We then propose a light-weight mitigation mechanism by enhancing NDN with a name-key-based forwarding and multipath forwarding-based inband probe. Name-key-based forwarding forwards interests toward content sources trusted by consumers to reduce the injection of poisoned content. If there is still on-path content poisoning, the multipath forwarding of a reissued interest that excludes poisoned content acts as inband probes and invokes on-demand signature verifications at intermediate routers. This purges poisoned content from caches as soon as possible, delivers legitimate content to the present consumer, and restores legitimate content retrieval for future Interests without requiring any out-of-band communications. Our experimental results demonstrate that our proposed content poisoning mitigation mechanism restores legitimate content retrieval pretty soon with relatively small verification overhead at intermediate routers and is well adapted to diverse network settings, which would accelerate the deployment of NDN in smart cities to disseminate content in an energy-efficient way.

Interest Flooding Attack (IFA) is one of the main security threats for the Named Data Networking (NDN). Most of its existing countermeasures enable intermediate routers near the attackers to independently detect the attack and consider the typical attack scenario in which attackers directly send malicious Interests at a constant and relatively high rate. Moreover, they may also throttle legitimate Interests when enforcing the existing defence measures at intermediate routers as it is still difficult for them to distinguish the Interests issued by attackers from those issued by legitimate consumers. Instead, this work aims at a more sophisticated attack scenario in which attackers start the attack at a relatively lower rate but gradually speed up to keep the Pending Interest Tables (PITs) of the victims increasing to finally deplete the PIT resources for legitimate consumers. It is relatively difficult for intermediate routers to independently and timely detect such a sophisticated IFA. To solve this problem, we propose a mechanism to detect the sophisticated IFA from the network-wide view. A central controller monitors the network and makes a comprehensive and prompt decision on whether there is an ongoing IFA based on the overall state of the whole network collected from the abnormity information reports sent by the first-hop routers of attackers. Attack sources can be directly located after an IFA is determined and then the malicious Interests can be prevented from entering the network without throttling legitimate Interests. We conduct an experimental study to evaluate the performance of the proposed mechanism and explore the parameter settings of the attack detection algorithm at access routers. The experimental results validate that our mechanism can timely detect and mitigate the sophisticated IFA without throttling requests from legitimate consumers.

The analytical expressions of radial and the longitudinal components of scintillation index are derived in weak oceanic turbulence. The effects of off-axis distance, propagation distance, and three oceanic parameters (i.e., the ratio of temperature to salinity contribution to the refractive index spectrum w , the rate of dissipation of the mean squared temperature χ T and the rate of dissipation of the turbulent kinetic energy e ) on radial component of scintillation index are examined. The influences of propagation distance and three oceanic parameters on the longitudinal component of scintillation index are investigated. It is shown that the radial component of scintillation increases as off-axis distance increases. Both radial and longitudinal components of scintillation increase as propagation distance, w and χ T increase while decreases as e increases. Besides, the longitudinal component of scintillation increases more drastically for plane wave than others, which indicates the plane wave is affected the most at the fixed turbulent strength. The longest weak turbulence distance for a plane wave is shorter than that for a Gaussian or spherical wave.