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The garnet aluminates RE3Al5O12(RE= Gd – Lu, Y) are an important class of optical materials with a range of applications. Typically, they do not tolerate large deviations from ideal stoichiometry, and their luminescence properties are controlled by dopant selection rather than modification of the host structure. Here, we use glass crystallization as a nonequilibrium synthesis route to a new family of highly nonstoichiometric gadolinium aluminate garnets, of formula Gd3+xAl5-xO12with x≤ 0.60. Remarkably, this range is much broader than the previously reported Y3+xAl5-xO12series (x≤ 0.4), despite the vast size contrast between Al3+and Gd3+, which are forced to share a crystallographic site in the nonstoichiometric materials: the endmember Gd3.6Al4.4O12lies halfway between ideal garnet and perovskite stoichiometries, with 30% of its octahedral Al3+sites substituted by Gd3+. In principle, this crystal chemistry should allow the synthesis of phosphor systems with rare-earth activators distributed over two different cation sublattices. To probe the response of luminescence properties to extreme nonstoichiometry in Gd3+xAl5-xO12, we synthesized three model phosphor systems by doping with Ce3+, Tb3+, or Tm3+/Yb3+and found that upconversion (Tm3+/Yb3+) phosphors have the most potential to be tuned by this approach. These results demonstrate that highly nonstoichiometric garnet aluminates are not limited to small rare-earth hosts such as YAG, opening new opportunities for development of different garnet-based optical and magnetic materials.

Breast lesion segmentation from ultrasound images is essential in computer-aided breast cancer diagnosis. To alleviate the problems of blurry lesion boundaries and irregular morphologies, common practices combine CNN and attention to integrate global and local information. However, previous methods use two independent modules to extract global and local features separately, such feature-wise inflexible integration ignores the semantic gap between them, resulting in representation redundancy/insufficiency and undesirable restrictions in clinic practices. Moreover, medical images are highly similar to each other due to the imaging methods and human tissues, but the captured global information by transformer-based methods in the medical domain is limited within images, the semantic relations and common knowledge across images are largely ignored. To alleviate the above problems, in the neighbor view, this paper develops a pixel neighbor representation learning method (NeighborNet) to flexibly integrate global and local context within and across images for lesion morphology and boundary modeling. Concretely, we design two neighbor layers to investigate two properties (i.e., number and distribution) of neighbors. The neighbor number for each pixel is not fixed but determined by itself. The neighbor distribution is extended from one image to all images in the datasets. With the two properties, for each pixel at each feature level, the proposed NeighborNet can evolve into the transformer or degenerate into the CNN for adaptive context representation learning to cope with the irregular lesion morphologies and blurry boundaries. The state-of-the-art performances on three ultrasound datasets prove the effectiveness of the proposed NeighborNet.

The electronic throttle system (ETS) is one of the important components of an automobile engine that adjust the engine air intake to directly affect the combustion power of the engine. In practical scenarios, proportional–integral–derivative (PID) is a common and effective method. However, the traditional PID is difficult to satisfy the ETS performance requirements, e.g., the system response overshoot is not allowed to exist. Moreover, the controller gains are also difficult to tune manually. To tackle the abovementioned issues, we propose a signal compensation control framework with an adaptive gain tuning strategy for the ETS, which consists of an upper level to tune controller gains and a lower level signal compensation controller to eliminate the nonlinear terms. The main feature of the signal compensation controller relies on two nonlinear compensators to remove the strong nonlinearity and uncertainty in the actual throttle system. Furthermore, we develop an adaptive gain tuning strategy based on deep deterministic policy gradient algorithm, and we analyze the corresponding stability and convergence properties of the proposed method. Finally, we illustrate the proposed algorithm on benchmarks and the tracking control problem using the real-world experimental platform of the ETS to show the effectiveness of the proposed algorithm.

In the field of cognitive radio (CR), cooperative spectrum sensing (CSS) utilizes the spatial diversity of each secondary user (SU) to accurately detect spectrum holes. Although the information exchange and decision fusion in CSS improves sensing accuracy, it also brings additional communication overhead and energy consumption. Since the energy of SUs is limited, it is necessary to improve the sensing performance while reducing the energy consumption, i.e., maximizing the energy efficiency (EE). In this article, two effective bitwise differential voting rules (DVRs) are proposed to reduce the number of bits reported without compromising sensing performance. Meanwhile, this article derives theoretical performance expressions for the proposed bitwise DVRs and proposes a differential mechanism-based quantized data fusion (QDF) to achieve optimal EE. The proposed QDF optimizes the quantization interval and the global decision threshold to achieve the highest EE and regulates the number of quantization bits through the differential mechanism. Finally, the numerical simulation results confirm the effectiveness and robustness of the proposed bitwise DVRs. Moreover, the proposed energy-efficient differential mechanism-based QDF shows superiority over other energy-efficient QDF techniques that only apply DVR in terms of EE.

Flight safety is a key issue in the aviation industry. Recently, with the prevalence of flight data recording systems, some deep learning-based studies have been devoted to predicting safety incidents based on flight data. However, these studies, although they exhibit higher prediction accuracy, have largely neglected the interpretability analysis of safety incidents which is of great concern to airlines and pilots. To address this issue, we define flight safety prediction as a multiscale time series classification problem and propose an interpretable model named IMTCN to provide both accurate predictions and high interpretability of flight safety. First, multiple temporal convolutional networks (TCNs) are utilized to capture local representations and long effective histories from multivariate flight data. Because different flight parameters are collected with diverse sampling frequencies, multiple TCNs are used to handle these parameters separately. Then, we creatively adapt the class activation mapping (CAM) method, which has been used for interpretation in image classification, and combine it with the TCN to provide flight data interpretability. The established model can pinpoint key flight parameters and corresponding moments that contribute most to safety incidents. Experimental results on a real-world dataset with 37,943 Airbus A320 aircraft flights show that our model outperforms the baselines on the task of exceedance classification and prediction 2 seconds and 4 seconds in advance, and case studies demonstrate its superb interpretability for flight safety analysis.

Coordination polymer {[Co(BTIA)(H2O)0.5]·1.5CH3OH}n(Co-1) was synthesized from Co(NO3)2·6H2O and multidentate ligand 4,6-bis-(4H-1,2,4-triazol-1-yl)-isophthalic acid(H2BTIA) under solvothermal conditions. Crystal analysis found that Co-1was a (44·62)2(416·612) 3D framework with one dimensional channels, which gives it good adsorption performance for greenhouse gas CO2and anionic dyes CR2−and TY−. In particular for the large-size CR2−, it behaves a very fast removal rate. In addition, magnetic analysis discloses that antiferromagnetic(AF) coupling exists in binuclear cobalt unit.

A viscosity jump of one to two orders of magnitude in the lower mantle of Earth at 800–1,200-km depth is inferred from geoid inversions and slab-subducting speeds. This jump is known as the mid-mantle viscosity jump1,2. The mid-mantle viscosity jump is a key component of lower-mantle dynamics and evolution because it decelerates slab subduction3, accelerates plume ascent4and inhibits chemical mixing5. However, because phase transitions of the main lower-mantle minerals do not occur at this depth, the origin of the viscosity jump remains unknown. Here we show that bridgmanite-enriched rocks in the deep lower mantle have a grain size that is more than one order of magnitude larger and a viscosity that is at least one order of magnitude higher than those of the overlying pyrolitic rocks. This contrast is sufficient to explain the mid-mantle viscosity jump1,2. The rapid growth in bridgmanite-enriched rocks at the early stage of the history of Earth and the resulting high viscosity account for their preservation against mantle convection5–7. The high Mg:Si ratio of the upper mantle relative to chondrites8, the anomalous 142Nd:144Nd, 182W:184W and 3He:4He isotopic ratios in hot-spot magmas9,10, the plume deflection4and slab stagnation in the mid-mantle3as well as the sparse observations of seismic anisotropy11,12can be explained by the long-term preservation of bridgmanite-enriched rocks in the deep lower mantle as promoted by their fast grain growth.

Using dissolved O2as the cathodic co-reactant of three-dimensional (3D) g-C3N4is a convenient method to improve the electrochemiluminescence (ECL) signal, but it still suffers the disadvantages of limited luminous efficiency of 3D g-C3N4and low content, low reactivity, and instability of dissolved O2. Here, N vacancy with high density was first introduced into the structure of 3D g-C3N4(3D g-C3N4-NV), which could conveniently realize multipath ECL improvement by simultaneously solving the above shortcomings effectively. Specifically, N vacancy could change the electronic structure of 3D g-C3N4to broaden its band gap, increase fluorescence (FL) lifetime, and accelerate electron transfer rate, obviously improving the luminous efficiency of 3D g-C3N4. Meanwhile, N vacancy made the excitation potential of 3D g-C3N4-NV to shift from −1.3 to −0.6 V, effectively weakening the electrode passivation. Moreover, the adsorption capacity of 3D g-C3N4-NV was obviously enhanced, which could make the dissolved O2enrich around 3D g-C3N4-NV. And massive active NV sites of 3D g-C3N4-NV could promote O2to more efficiently convert to reactive oxygen species (ROS) that were key intermediates in ECL generation. Using the newly proposed 3D g-C3N4-NV-dissolved O2system as an ECL emitter, an ultrasensitive target conversion biosensor was constructed for miRNA-222 detection. The fabricated ECL biosensor exhibited satisfactory analytical performance for miRNA-222 with a detection limit of 16.6 aM. The strategy achieved multipath ECL improvement by introducing high-density N vacancy simply in the 3D structure of g-C3N4and could open a new horizon for developing a high-performance ECL system.

Preparing high-efficiency ECL gold nanoclusters (Au NCs) still faces a serious challenge due to the poor stability of co-reactant radicals in aqueous media. Herein, we report a ligand-based shielding effect induced record near-infrared (λmax= 786 nm) ECL efficiency of β-cyclodextrin-protected Au NCs (β-CD-Au NCs) with triethylamine (TEA) as co-reactant. The ligand of β-CD-Au NCs with a matched hydrophobic cavity could encapsulate TEA driven by host–guest chemistry, which not only allows the generation of TEA•in the cavity to diminish environmental exposure, thus reducing the quenching by dissolved oxygen, water, etc., but also shortens the charge transfer pathway without extensive chemical modification. Density functional theory, 1H NMR spectra, electron paramagnetic resonance, and differential pulse voltammetry studies revealed that the β-CD ligand-based shielding effect significantly increased the reactivity efficiency of TEA. More importantly, in stark contrast to those of traditional ligand-protected Au NCs, the ECL efficiency of β-CD-Au NCs enhanced 321-fold versus BSA-Au NCs, 153-fold versus ATT-Au NCs, and 19-fold versus GSH-Au NCs with 1 mM TEA. Therefore, this work provides an in-depth understanding of the crucial role of ligands in enhancing the active co-reactant radical stability for high-efficiency ECL metal NCs to immensely stimulate their promising applications. Using the β-CD-Au NCs as emitters, a “signal off” ECL sensing platform was constructed to detect noradrenaline as a model target with a lower detection limit of 0.91 nM.

Herein, dihydrolipoic acid (DHLA)-stabilized copper nanoclusters (Cu NCs) with high aggregation-induced electrochemiluminescence (AIECL) in polymer hydrogel were prepared to construct an ECL biosensor for detection of microRNA-21. DHLA, a small molecule ligand with two sulfhydryl groups, was used as a protective agent to synthesize Cu NCs, which improved the ECL stability and intensity of Cu NCs. Furthermore, the Cu NCs were loaded into the (PVP–PVA)hydrogel to form the DHLA-Cu NCs@(PVP–PVA)hydrogel composite, which showed effective AIECL performance. The confinement of Cu NCs into the hydrogel increased the local concentration of Cu NCs, which could not only prevent oxides from entering the copper core, but also limit the vibration to reduce non-radiative transitions of Cu NCs, leading to a distinct AIECL emission. Then, combined with the self-priming clip trigger isothermal amplification (SCTIA) technology, an ECL biosensor was constructed to realize the sensitive detection of miRNA-21. Interestingly, SCTIA technology was a simple and efficient strategy that realized multiple-cycle amplified processes to acquire a mass of output DNA, achieving remarkable signal amplification. Therefore, this strategy provided an efficient approach in the preparation of Cu NCs with high AIECL emission and target amplification technology, which might have promising potential in clinical application.

Skin lesion segmentation is a fundamental procedure in computer-aided melanoma diagnosis. However, due to the diverse shape, variable size, blurry boundary, and noise interference of lesion regions, existing methods may struggle with the challenge of inconsistency within classes and indiscrimination between classes. In view of this, we propose a novel method to learn and model inter-pixel correlations from both global and local aspects, which can increase inter-class variances and intra-class similarities. Specifically, under the encoder-decoder architecture, we first design a pyramid transformer inter-pixel correlations (PTIC) module, aiming at capturing the non-local context information of different levels and further exploring the global pixel-level relationship to deal with the large variance of shape and size. Further, we devise a local neighborhood metric learning (LNML) module to strengthen the local semantic correlations learning capability and increase the separability between classes in the feature space. These two modules can complementarily strengthen the feature representation capability via exploiting the inter-pixel semantic correlations, thus further improving intra-class consistency and inter-class variance. Comprehensive experiments are performed on public skin lesion segmentation datasets: ISIC 2018, ISIC2016, and PH2, and experimental results demonstrate that the proposed method achieves better segmentation performance than other state-of-the-art methods.

In this study, cost-efficient atmospheric pressure chemical vapor deposition has been successfully developed to produce well-aligned high-quality monocrystalline Bi2S3nanowires. By virtue of surface strain-induced energy band reconstruction, the Bi2S3photodetectors demonstrate a broadband photoresponse across 370.6 to 1310 nm. Upon a gate voltage of 30 V, the responsivity, external quantum efficiency, and detectivity reach 23 760 A W−1, 5.55 × 106%, and 3.68 × 1013Jones, respectively. The outstanding photosensitivity is ascribed to the high-efficiency spacial separation of photocarriers, enabled by synergy of the axial built-in electric field and type-II band alignment, as well as the pronounced photogating effect. Moreover, a polarization-discriminating photoresponse has been unveiled. For the first time, the correlation between quantum confinement and dichroic ratio is systematically explored. The optoelectronic dichroism is established to be negatively correlated with the cross dimension (i.e., width and height) of the channel. Specifically, upon 405 nm illumination, the optimized dichroic ratio reaches 2.4, the highest value among the reported Bi2S3photodetectors. In the end, proof-of-concept multiplexing optical communications and broadband lensless polarimetric imaging have been implemented by exploiting the Bi2S3nanowire photodetectors as light-sensing functional units. This study develops a quantum tailoring strategy for tailoring the polarization properties of (quasi-)1D material photodetectors whilst depicting new horizons for the next-generation opto-electronics industry.

A 3-D lumped parameter circuit model based on the nodal analysis to simulate signal propagation and position response characteristics of capacitive division image readout (C-DIR) is proposed. The current pulses, charge collection efficiency, and position reconstruction patterns are calculated for different electrical parameters (charge division capacitor $C_{c}$ , perimeter capacitor $C_{p}$ , diagonal capacitor $C_{d}$ , electrode parasitic capacitor $C_{s}$ , and the sheet resistance of the resistive layer $R_{\mathrm {Ge}}$ ), and their influence on imaging linearity is investigated. The simulation results show that $R_{\mathrm {Ge}}$ affects the amplitude, pulsewidth, and polarity of the current pulse in the C-DIR. The sheet resistance of the resistive layer needs to be larger than 10 $\text{M}\Omega $ for the charge to be efficiently collected by the readout electronics. Several capacitance ratios ( $C_{c}/C_{p}$ , $C_{c}/C_{s}$ , and $C_{c}/C_{d})$ mainly affect the imaging linearity. It has been found that to obtain good detector imaging performance, $C_{c}/C_{p}$ should be less than 0.01, $C_{c}/C_{s}$ should be larger than 100, and $C_{c}/C_{d}$ should be larger than 10, and when $R_{\mathrm {Ge}}$ is larger than 10 $\text{M}\Omega $ , the imaging nonlinearity (rms) can be less than 1%. The reliability of the simulation results was verified by experimental measurements of a prototype C-DIR detector designed by ourselves. An optimized imaging performance with imaging nonlinearity (rms) of 2.18% was achieved.

Long noncoding RNAs (lncRNAs) play a role in the emergence and progression of several human tumors, including luminal B breast cancer (BC). The biological functions and potential mechanisms of lncRNA myocardial infarction-associated transcripts (MIAT) in luminal B BC, on the contrary, are unknown. In this work, we used UALCAN database analysis to find high expression of lncRNA MIAT in luminal BC tissues and also confirmed high levels of lncRNA MIAT expression in luminal B BC tissues and cells. In vitro knockdown of MIAT inhibited the proliferation, migration, and invasion of BT474 cells. In addition, we found that miR-150-5p levels were significantly reduced in luminal B BC specimens and cells, and miR-150-5p levels were significantly increased when MIAT was knocked down. And TIMER database analysis showed that MIAT was positively associated with PDL1. Through bioinformatic tools and in vitro experiments, lncRNA MIAT could function as a competitive endogenous RNA (CeRNA) to further regulate programmed cell death ligand 1 (PDL1) expression by directly sponging miR-150-5p. In conclusion, our data suggest that MIAT, an oncogene, may sponge miR-150-5p to regulate PDL1 expression and affect proliferation, migration, and invasion in luminal B BC in vitro.

Carbon dioxide (CO2) reduction is a possible way to solve the greenhouse effect. Conventional catalytic CO2reduction faces some problems, such as high free energy barriers, limitation of cheap and effective catalysts. Here, we develop a highly efficient and highly selective CO2reduction under mild conditions by laser reduction in liquid (LRL). A high CO yield of 12.3 mmol h−1with near 100% selectivity and 13.3% energy conversion efficiency of laser to CO is achieved. In LRL, abundant active species are created immediately by laser-induced plasma in small bubbles along with transient extremely high temperature. Thermodynamically, CO2reduction proceeds quickly with the interaction of active species at high temperature. Kinetically, the rapid quenching process inhibits reverse reaction and keeps products at initial stage. The synergy of thermodynamics and kinetics results in high yield and high selectivity. These findings provide an alternative approach for CO2reduction under ambient conditions.

Exploring the spatial pattern and its influencing mechanism of cultural heritage is of great significance for the systematic protection, inheritance and utilization of cultural heritage. In this study, the national-level cultural heritage of China as the research object, and the spatial patterns of the cultural heritage (CH), tangible cultural heritage (TCH) and intangible cultural heritage (ICH) are analyzed are summarized using the multiple geospatial analysis method, and their spatial distribution differences. Then, the optimal parameter geodetector model was further used to analyze the influencing and interaction effect of natural environment (NE) factors, socioeconomic and cultural environment (SCE) factors on the spatial distribution of CH, TCH and ICH, and the interaction mechanism was deeply discussed. The results show that high-density region of CH are mainly located in the southeastern Heihe-Tengchong Line of China, while low-density region are mostly located in the northwestern Heihe-Tengchong Line. In terms of local spatial differences, the spatial distribution of material cultural heritage is different in different river basins, different terrains and different climates. The spatial distribution of ICH mainly shows significant differences in terrain and climate, and does not show significant differences in river basins. In terms of influencing factors, sociocultural environment, education level, historical settlement environment, road traffic, climate, economy, population and terrain factors all have a significant impact on the spatial distribution of CH and TCH. Sociocultural environment, education level, historical settlement environment, road traffic, climate, economy and population have a significant impact on the spatial distribution of ICH, while topographic factors and water system factors have no significant impact on the spatial distribution of ICH. Compared with ICH, TCH is more likely to be directly affected by topographic conditions (altitude, relief of terrain) and hydrological conditions (production and living water sources and flood risk) during its formation stage. At the same time, topographic and hydrological conditions can also indirectly affect the spatial distribution of TCH by directly affecting human agricultural activities, economic activities and cultural activities.

Ovarian cancer (OC) is one of the most common reproductive tumors in women, whereas current treatment options are limited. β-lactamase-like-protein 2 (LACTB2) has been observed to be associated with various cancers, but its function in OC is unknown. Therefore, we evaluate the prognostic value and the underlying function of LACTB2 in OC. In this study, high expression of LACTB2 was observed in OC compared with normal controls. Kaplan–Meier Plotter analysis revealed that overexpressed LACTB2 is strongly correlated with poor prognosis. We conducted GO/KEGG analysis to investigate the potential biological function of LACTB2 in OC. GESA analysis showed that LACTB2 was closely related to immune-related pathways. Subsequently, we explored the relationship between LACTB2 and 24 types of immune cells in OC. The results suggested that LACTB2 was positively associated with multiple tumor-infiltrating immune cells. Importantly, LACTB2 may modulate immune cell infiltration in OC to influence prognosis. In conclusion, LACTB2 can be used as a promising prognostic biomarker and immunotherapy target for OC.

With the rapid growth of internet of thing (IoT) devices, cooperative spectrum sensing (CSS) has emerged as a promising solution to leverage the spatial diversity of multiple secondary IoT sensing nodes (SNs) for spectrum availability. However, the cooperative paradigm also incurs increased cooperative costs between each SN and the fusion center (FC), leading to decreased cooperative efficiency and achievable throughput, especially in large‐scale cognitive IoT (CIoT). To address these challenges, we present a sequential detection with feedback information (SD‐FI) approach in this paper. To achieve this objective, we propose a two‐way CSS model that formulates an optimization problem of Bayes cost in a quickest detection framework with feedback. To solve this optimization problem, we derive the structure of the optimal local decision rule from the local decision function and determine the optimal detection threshold in conjunction with the cost function. Following the optimal threshold pair, we implement the optimal SD‐FI and theoretically demonstrate the uniqueness of the optimal threshold and optimal sensing time. Simulation results demonstrate superiority of SD‐FI in terms of cooperative performance (i.e., detection performance and Bayes cost) and sample size. Notably, even with limited sensing time, our proposed SD‐FI exhibits high throughput, highlighting its effectiveness in enhancing spectrum availability and utilization in CIoT. A two‐way cooperative spectrum sensing model among two internet of things and the fusion center is formulated. With the help of sequential idea and the feedback information from the fusion center at each sensing, we derive Bayes cost as the objective function and optimize the proposed sequential detection with feedback information by the optimal thresholds.

Cooperative spectrum sensing (CSS) has emerged as a promising strategy for identifying available spectrum resources by leveraging spatially distributed sensors in cognitive wireless sensor networks (CWSNs). Nevertheless, this open collaborative approach is susceptible to security threats posed by malicious sensors, specifically Byzantine attack, which can significantly undermine CSS accuracy. Moreover, in extensive CWSNs, the CSS process imposes substantial communication overhead on the reporting channel, thereby considerably diminishing cooperative efficiency. To tackle these challenges, this article introduces a refined CSS approach, termed weighted sequential detection (WSD). This method incorporates channel state information to validate the global decision made by the fusion center and assess the trust value of sensors. The trust value based weight is assigned to sensing samples, which are then integrated into a sequential detection framework within a defined time window. This sequential approach prioritizes samples based on descending trust values. Numerical simulation results reveal that the proposed WSD outperforms conventional fusion rules in terms of the error probability, sample size, achievable throughput, and latency, even under varying degrees of Byzantine attack. This innovation signifies a substantial advancement in enhancing the reliability and efficiency of CSS.

This study aims to evaluate the interaction of flavonoid–flavonoid by inhibiting the function of P-glycoprotein (P-gp). The cellular uptake of seven substrates and eleven co-incubated inhibitors was measured in KB/MDR cells. The effect of galangin or morin on the absorption of silibinin or wogonin was carried out in the rat everted gut sacs. Docking was performed to evaluate the interactions between inhibitors and P-gp. Most substrates were greatly enhanced by at least five co-incubated inhibitors. Conversely, the increased uptake of substrates coincided with a decrease or without affecting the uptake of inhibitors, implying a competitive/non-competitive inhibition on P-gp. The enhancement effect by galangin or morin on the transport of silibinin or wogonin was verified in everted gut sacs. Docking explained the inhibition of flavonoids on P-gp by competitively binding to the ATP site. These results provide a strategy for increasing the absorption of flavonoids by co-administration.

ABSTRCTThe α-fetoprotein (AFP) and soluble intercellular adhesion molecule-1 (sICAM-1) have certain diagnostic value, but their potential value in prognosis prediction, especially immunotherapy response prediction, remains unclear in liver cancer. Through the tumor-free survival (TFS) and overall survival (OS) rates analyses of serum AFP and sICAM-1 levels in 87 patients with primary hepatocellular carcinoma (HCC), the patients whose AFP and sICAM-1 levels were normal (AFP < 20 μg/L or sICAM-1 < 1000 μg/L) before surgery or recovered to normal after surgery exhibited a lower tumor recurrence rate and better OS than patients with elevated serum levels of the two markers. Combined analysis showed that patients with synchronously elevated levels of AFP and sICAM-1 showed the lowest TFS and OS. In addition, the RNA-seq data and clinical information of The Cancer Genome Atlas Liver Hepatocellular Carcinoma were collected to analyze the predictive values of AFP and ICAM-1 in the diagnosis, prognosis and immunotherapy of HCC. The results indicated that the combined application of the two indicators had higher accuracy in both the diagnosis and prognostic prediction of HCC by receiver operating characteristic curves. AFP and ICAM-1 were significantly correlated with multiple immune cells in HCC samples but not in normal samples. The patients with low expression of the two indicators were most likely to benefit from the immune checkpoint blockade therapy. In conclusion, AFP and ICAM-1 play vital roles in the diagnosis, prognostic prediction, and immunotherapy of HCC, suggesting that they are considered as prognostic predictors in clinical practice.

Carbon nanomaterials are ideal fillers in composites for electrothermal applications due to their ultrahigh electrical conductivity, thermal conductivity, and excellent mechanical property. In this study, high-performance electrothermal laminate films were prepared by a layer-by-layer process, which used tannic acid-functionalized multiwalled carbon nanotubes as filler and thermoplastic polyurethane as substrate. The prepared electrothermal laminate films exhibit excellent mechanical property, electrical heating property, and high electrothermal performance, which can reach steady-state temperature in about 120 s. Additionally, the maximum steady-state temperature can reach ∼150 °C at 20 V. During 10 different cycles of experiments and 5 h aging time, no significant change in temperature was observed. Moreover, from the results, it is clear that the performance of the samples is related to the amount of filler added and the applied voltage, and we can precisely control the sample performance by regulating the amount of filler addition and applied voltage. We believe that these electrothermal laminate films could be widely used in wearable heating cotton fabric, controllable electric heating module, floor heating, and deicing devices.

Unmanned aerial vehicles (UAVs) are becoming a popular research topic in applications that do not require human intervention. A variety of applications and devices coexist in the environment where UAVsoperate, resulting in a serious spectrum shortage. Therefore, cognitive radio (CR) is a promising solution for opportunistic access to underutilized spectrum bands by the primary user (PU) through cooperative spectrum sensing (CSS) technique. However, the flexible location of UAVsmakes CSSinefficient and even difficult to be implemented. In view of this, a cognitive UAVnetwork model consisting of a pair of UAVswhich follows a circular flight trajectory to participate in CSSis proposed in a spectrum sensing frame structure. According to the local energy detection, we further propose an optimization problem about the stopping time in a quickest detection paradigm and conduct out Bayesian detection method with feedback to minimize the sensing delay and the false alarm probability by optimizing the stopping time. Moreover, we theoretically derive the optimal threshold pair and prove the optimal stopping time by means of Markov process. At last, a series of numerical simulations are shown to corroborate the proposed Bayesian detection method with feedback, in terms of the false alarm probability, the sensing delay, and achievable throughput. In contrast to the classic Neyman‐Pearsonand Bayesian detection methods, the advantage of Bayesian detection method with feedback sensing is presented. The cognitive UAV network following a circular flight trajectory.

To improve spectrum utilization, cognitive radio (CR) enables unauthorized internet of thing (IoT) devices to opportunistically access the channel underutilized by the primary user (PU) in a cognitive IoT (CIoT). To this end, cooperative spectrum sensing (CSS) plays a crucial role in CR technology, which leverages the spatial diversity of corroborative IoT devices to accurately detect the PU signal. However, this open cooperative paradigm may suffer from spectrum sensing data falsification (SSDF) attack in which malicious IoT devices intentionally mislead the fusion center (FC) by providing false sensing results to make an incorrect global decision regarding the PU status. To effectively characterize the attack behavior of malicious IoT devices, we propose a massive SSDF attack model described by the attack cycle and malicious ratio within a sensing period to characterize the malicious behaviors. Additionally, we introduce a delivery evaluation mechanism and propose a dynamic sliding window‐CSS (DSW‐CSS) to mitigate the impact of massive SSDF attack. Moreover, we introduce a sequential reporting mechanism to further reduce the number of samples required by the global decision‐making of the FC. Finally, simulation results show the flexibility and aggressiveness of the proposed attack model and demonstrate the correctness and effectiveness of DSW‐CSS.

The new 8-layer twinned hexagonal solid solution Ba8Cr4–xTa4+0.6xO24(x= 0.0–3.0) was isolated through the aliovalent substitution of Ta5+for Cr3+in Ba2CrTaO6, showing the widest B-site vacancy content range among the 8-layer twinned hexagonal perovskites. Ba8Cr4–xTa4+0.6xO24forms a simple 8-layer hexagonal perovskite structure within 0.0 ≤ x< 2.4 and a tripled 8-layer hexagonal perovskite superstructure within 2.4 ≤ x≤ 3.0. The latter shows expanded aand baxes by 3times in comparison to the simple 8-layer hexagonal perovskite structure owing to the partial face-sharing octahedral (FSO) B cation ordering along the abplane. The B-cation and vacancy distributions in the tripled superstructure were characterized by neutron and X-ray powder diffraction and further confirmed by a scanning transmission electron microscopy–high angle annular dark field imaging and intensity profile analysis. The formation of 8-layer twinned hexagonal perovskites Ba8Cr4–xTa4+0.6xO24in an extended solid solution range can be attributed to the presence of both covalent B–B and B–O–B bonding and B-site vacancies in the FSO sites. This work provides an effective way of combining covalent B–B and B–O–B bonding and vacancy creation as well as the cationic ordering in the FSO sites to reduce electrostatic repulsion, which could further enable the stabilization of new hexagonal perovskite compounds.

Access to safe abortion is essential to satisfy women’s need for healthcare and control over their life choices. Modern regulation of abortion appears to be female friendly because it abandons the old oppressive appearance and wears a liberal face. By examining the images of “glorious mothers” and “rational women” constructed by the Chinese and English regulatory models of abortion through a feminist lens, this Article aims to offer a more critical response to the two jurisdictions. Furthermore, by comparing the two models of abortion law, the Article provides feminists and reformers with a chance to learn about each other’s experiences and different regulatory strategies. As a result, they will be able to view the merits and shortcomings of their national abortion law from a fresh angle. Finally, this comparative study offers a basis for feasible law reform proposals that would empower women in decision making concerning abortion.

As wireless communication technology advances rapidly, spectrum resource scarcity has become a major challenge for wireless devices and applications. To address the issue of spectrum resource scarcity, cognitive radio (CR) technology was developed. Its objective is to utilize spectrum sensing to detect the state of the authorized/primary user (PU), allowing the unauthorized/secondary users (SUs) to dynamically access the spectrum resources. Furthermore, cooperative spectrum sensing (CSS) is designed to increase the PU signal's detection accuracy but also suffers from Byzantine attack from malicious users (MUs). To acquire a more profound insight of the detrimental effects of a Byzantine attack on CSS, we establish a probabilistic model that characterizes malicious actions in an infrastructure‐based cognitive radio network (CRN). Building upon this new perspective of MUs, we conduct a comprehensive analytical study to investigate the achievable throughput, energy consumption (EC) of spectrum sensing and data transmission in the CSS process. Moreover, the close‐formed expressions of energy efficiency (EE) with respect to the attack parameters are derived and the impact analysis of Byzantine attack on EE are also provided under the four scenarios. Finally, a series of numerical simulation results demonstrate that provides a guideline for a more secure and robust Byzantine defense strategy in the future.