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Carbon emission reduction is an important measure to mitigate the greenhouse effect, which has become a hotspot in global climate change research. To contribute to this, here, we fabricated two Co-based metal–organic frameworks (Co-MOFs), namely, {[Co3(NTB)2(bib)]·(DMA)2·(H2O)4}n(DZU-211) and {[Co3(NTB)2(bmip)]·(DMA)2}n(DZU-212) (H3NTB = 4,4′,4″-nitrilotribenzoic acid, bib = 1,4-bis(imidazol-1-yl)-butane, bmip = 1,3-bis(2-methyl-1H-imidazol-1-yl)propane) to realize efficient CO2/N2separation by dividing coordination spaces into suitable pores with narrow windows. DZU-211 reveals a 3D open porous framework, while DZU-212 exhibits a 3D double-fold interpenetrated structure. The two MOFs both possess large coordination spaces and small open pore sizes, via the bib ligand insertion and framework interpenetration, respectively. Comparatively, DZU-211 reveals superior selective CO2uptake properties due to its more suitable pore characteristics. Gas sorption experiments show that DZU-211 has a CO2uptake of 52.6 cm3g–1with a high simulated CO2/N2selectivity of 101.7 (298 K, 1 atm) and a moderate initial adsorption heat of 38.1 kJ mol–1. Moreover, dynamic breakthrough experiments confirm the potential application of DZU-211 as a CO2separation material from postcombustion flue gases.

Traditional granular hydrogels showed excellent injectivity, thermal integrity, and efficient remediation of heterogeneous reservoirs. However, granular hydrogels have demonstrated their inability to adapt to fractures due to the lack of sufficient interactions. Herein, we present new nanocomposite hydrogels consisting of cationic nanogelators and anionic granular hydrogels that can chemically in situ reform bulk hydrogels in the fractures. Interestingly, our granular hydrogels showed recross-linking independence on carrying fluids, contrary to prior reported fluid-dependent recross-linking granular hydrogels. The recross-linking of nanogelators and granular hydrogels can be accomplished from room temperature to 130 °C. The nanocomposite hydrogels displayed increased shear elastic moduli compared to pristine anionic granular hydrogels, probably due to the increased covalent cross-links formed by the homogeneous regenerative approach. We found that the granular hydrogels had high salinity tolerance even in the presence of 1000 ppm divalent ions of calcium (Ca2+) since Ca2+ions often act as the cross-linker for partially hydrolyzed acrylamide-based hydrogels. Overall, we obtained new regenerative nanocomposite hydrogels based on cationic nanogelators and anionic granular hydrogels for fracture treatments.

Light detection and ranging (LiDAR) has become one of the most important sensors in 3-D perception. With the advancement of sensor technology, the point cloud data generated by LiDAR have also become increasingly large. There are difficulties in processing them directly, such as hardware limitations, computational costs, storage constraints, algorithmic considerations, and other factors. One of the most common solutions is to sample the point clouds. Learning-based downsampling methods have been proven to be effective in point cloud classification, registration, and reconstruction. However, the integration of downsampling techniques with segmentation tasks remains inadequately investigated. This is mainly because, for segmentation tasks, to achieve higher segmentation accuracy, the sampled points need more detailed information and complete structural information. This will greatly increase the difficulty of sampling. This article proposes a learning-based sampling method for point cloud segmentation task. Our research analyzes the spatial relationships within point clouds using a simplification network to generate sampled points. A bidirectional chamfer distance (CD) is used to ensure that the original and sampled points have similar structural characteristics. The experimental results demonstrate that our network, SampleSegNet, outperforms alternative sampling methods.

Video anomaly detection has a wide range of applications in video monitoring-related scenarios. The existing image-domain-based anomaly detection algorithms usually require completely decoding the received videos, complex information extraction, and network structure, which makes them difficult to be implemented directly. In this article, we focus on anomaly detection directly for compressed videos. The compressed videos need not be fully decoded and auxiliary information can be obtained directly, which have low computational complexity. We propose a compressed video anomaly detection algorithm based on accurate abnormal region determination (ARD-VAD), which is suitable to be deployed on edge servers. First, to ensure the overall low complexity and save storage space, we sparsely sample the prior knowledge of I-frame representing the appearance information and motion vector (MV) representing the motion information from compressed videos. Based on the sampled information, a two-branch network structure, which consists of MV reconstruction branch and future I-frame prediction branch, is designed. Specifically, the two branches are connected by an attention network based on the MV residuals to guide the prediction network to focus on the abnormal regions. Furthermore, to emphasize the abnormal regions, we develop an adaptive sensing of abnormal regions determination module based on motion intensity represented by the second derivative of MV. This module can enhance the difference of the real anomaly region between the generated frame and the current frame. The experiments show that our algorithm can achieve a good balance between performance and complexity.

Off-screen handwriting recognition enriches the handwriting interaction paradigm for mobile devices. However, the existing approaches are only applicable to the specific environment and equipment conditions. In this article, we propose AcouWrite, a general, scalable and real-time handwriting recognition system based on active acoustic sensing. In detail, AcouWrite relies on active acoustic sensing using only a pair of microphones and speakers on the smartphone to capture real-time handwriting input. Particularly, we extract the short-time dCIR (st-dCIR) to monitor the changes in the acoustic transmission channel resulting from finger movement. Technically, we use a CNN-GRU classifier to complete the recognition task in AcouWrite. Moreover, we use data augmentation and spelling error correction methods to improve AcouWrite's robustness. To improve the generalization of our AcouWrite for new characters, we incorporate the transfer learning module into our AcouWrite. In various real-world environments, experiments demonstrate that AcouWrite achieves a mean recognition accuracy of 97.62%, a word accuracy (WA) of 96.4% and a character error rate (CER) of 1.5% for 100 common words, and an average response time of 94 milliseconds.

Transceiver in-phase/quadrature (IQ) skew due to device imperfections is one of the major factors restricting the transmission performance of coherent systems. Although two 4 × 4 real-valued multiple-in multiple-out equalizers can address the transceiver IQ skew impairments to some extent, it leads to high computational complexity in the receiver-side digital signal processing. Since the IQ skew is a relatively static impairment, the precise pre-compensation is an attractive alternative if the skew values are available. In this paper, a novel low-complexity and precise skew estimation method based on a specially designed training sequence is proposed, which can simultaneously estimate the transmitter side and receiver side IQ skew (Tx/Rx-skew) by a single-shot measurement. The training sequence exhibits the features of being concatenated by the pure real and imaginary sequences in the time domain and composed of four identical frequency blocks in the frequency domain. The former feature achieves the separation of the Tx-skew and Rx-skew since the pure real and imaginary signals pass through the I and Q channels individually at the transmitter side, while the latter one ensures a low-complexity acquirement for the skew value by the conjugate multiplication and sum operation between adjacent frequency blocks. The effectiveness of the proposed method is verified both in simulation and experiment, the results show that the estimation error is within ±0.2 ps, and the measurement range covers −2Ts to 2Ts, where Ts is the symbol period. It also exhibits robustness to other transceiver IQ imbalances, polarization variation, and optical signal-to-noise ratio.

By reducing the ratio of dark lines to the extreme, and improving the transmittance at the dark line with chiral liquid crystal, we have successfully developed novel chiral VA Mode, which is the world's first. The transmittance of 4K can be increased by 14%, and 8K can be increased by more.

IMPORTANCE: Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler characterization. OBJECTIVE: To deploy DASSi to patients with no AS or with mild or moderate AS at baseline to identify AS development and progression. DESIGN, SETTING, AND PARTICIPANTS: This is a cohort study that examined 2 cohorts of patients without severe AS undergoing echocardiography in the Yale New Haven Health System (YNHHS; 2015-2021) and Cedars-Sinai Medical Center (CSMC; 2018-2019). A novel computational pipeline for the cross-modal translation of DASSi into cardiac magnetic resonance (CMR) imaging was further developed in the UK Biobank. Analyses were performed between August 2023 and February 2024. EXPOSURE: DASSi (range, 0-1) derived from AI applied to echocardiography and CMR videos. MAIN OUTCOMES AND MEASURES: Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR). RESULTS: A total of 12 599 participants were included in the echocardiographic study (YNHHS: n = 8798; median [IQR] age, 71 [60-80] years; 4250 [48.3%] women; median [IQR] follow-up, 4.1 [2.4-5.4] years; and CSMC: n = 3801; median [IQR] age, 67 [54-78] years; 1685 [44.3%] women; median [IQR] follow-up, 3.4 [2.8-3.9] years). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increment: YNHHS, 0.033 m/s per year [95% CI, 0.028-0.038] among 5483 participants; CSMC, 0.082 m/s per year [95% CI, 0.053-0.111] among 1292 participants), with values of 0.2 or greater associated with a 4- to 5-fold higher AVR risk than values less than 0.2 (YNHHS: 715 events; adjusted hazard ratio [HR], 4.97 [95% CI, 2.71-5.82]; CSMC: 56 events; adjusted HR, 4.04 [95% CI, 0.92-17.70]), independent of age, sex, race, ethnicity, ejection fraction, and AV-Vmax. This was reproduced across 45 474 participants (median [IQR] age, 65 [59-71] years; 23 559 [51.8%] women; median [IQR] follow-up, 2.5 [1.6-3.9] years) undergoing CMR imaging in the UK Biobank (for participants with DASSi ≥0.2 vs those with DASSi <.02, adjusted HR, 11.38 [95% CI, 2.56-50.57]). Saliency maps and phenome-wide association studies supported associations with cardiac structure and function and traditional cardiovascular risk factors. CONCLUSIONS AND RELEVANCE: In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker was independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.

By employing ssNMR techniques on PVCN-SPE, a cross-linked SPE, two types of Li ions associated with different polymer segments are identified. The VC to EO unit ratio in the network affects crystallization prevention, enhancing ion mobility.

Applying the serverless paradigm to edge computing improves edge resource utilization while bringing the benefits of flexible scaling and pay-as-you-go to latency-sensitive applications. This extends the boundaries of serverless computing and improves the quality of service for Function-as-a-Service users. However, as an emerging cloud computing paradigm, serverless edge computing faces pressing challenges, with one of the biggest obstacles being delay caused by excessively long container cold starts. Cold start delay is defined as the time between when a serverless function is triggered and when it begins to execute, and its existence seriously impacts resource utilization and Quality of Service (QoS). In this article, we study how to minimize the total system cost by caching function containers and selecting routes for neighboring functions via edge or public clouds. We prove that the proposed problem is NP-hard even in the special case where the user request contains only one function, and that the unpredictability of user requests and the impact between adjacent time decisions require that the problem to be solved in an online fashion. We then design the Online Lazy Caching algorithm, an online algorithm with a worst-case competitive ratio using a randomized dependent rounding algorithm to solve the problem. Extensive simulation results show that the proposed online algorithm can achieve close-to-optimal performance in terms of both total cost and cold start cost compared to other existing algorithms, with average improvements of 31.6$\%$% and 51.7$\%$%.

Fermentation substrates of rice with different milling degrees (MDs) were prepared and fermented with human feces to compare their fermentation properties and effects on gut microbiota. MD 0s, MD 5s and MD 60s represented brown rice, moderately-milled rice and white rice, respectively. After in vitrofermentation, the MD 5s group showed higher starch utilization, compared with the MD 0s and 60s groups evaluated by Fourier transform infrared spectrometer, and confocal laser scanning microscope. Effects of fermentation substrates of rice with different MDs on gut microbiota were evaluated by 16S rDNA sequencing. All the sample groups reduced the pH and produced short-chain fatty acids (SCFAs) and branched-chain fatty acids. The MD 5s group exhibited higher α-diversity than the MD 0s and 60s groups. Abundances of Phascolarctobacterium, Blautiaand norank_f_Ruminococcaceae were higher in the MD 0s and 5s groups, compared with the MD 60s group. These bacteria were also positively correlated with the SCFAs production via Spearman correlation analysis. In vitroculture assay revealed that fermentation substrates of MD 0s and 5s promoted the growth of two probiotics (Akkermansia muciniphilaand Bifidobacterium adolescentis). Our results showed that moderate milling might be an appropriate way to produce rice products with richer nutrients and better fermentation properties.

Some structures or components of coastal buildings are often in an unsaturated state between drying and wetting conditions, and their performance deterioration is extremely serious. This paper mainly studied Cl−diffusion of coral aggregate concrete under the coupled erosion of drying-wetting cycles, sulfate and chloride salts. Based on the microscopic morphology of corroded concrete, the erosion mechanism of the concrete under the coupled erosion of drying-wetting cycles sulfate and chloride salt was elaborated. The results indicated that, under the coupling erosion of sulfate and chloride salts, a large amount of Na2SO4crystals will precipitate on the surface of concrete. The addition of mineral admixtures can not only improve the relative dynamic elastic modulus and relative compressive strength of coral aggregate concrete, but also improve its resistance to Cl−corrosion. SO42−reacts with cement hydration products to produce ettringite and gypsum, which hinder further Cl−corrosion during the early stage of erosion. However, as the erosion time increases, large numbers of erosion products lead to continuous expansion of cracks and accelerate Cl−transport. Finally, based on the transport mechanism of Cl−in the concrete, machine learning prediction models for Cl−content were established using CART, SVR, and XGBoost, respectively. It was found that the predicted values of the XGBoost model were in good agreement with the measured values, and the goodness of fit of the test set was R2 = 0.98, RMSE = 0.021%, MAE = 0.013%. This work can provide a new method for predicting chloride concentration in concrete under the coupling effect of multiple factors.

Diabetic wound is one of the serious complications of diabetes, and the wound is persistent and easily recurring, which seriously endangers the health and life of patients. How to effectively promote the healing of diabetic wounds has been a hot spot and difficult area of clinical research. Some previous studies have shown that dihydromyricetin has the effects of regulating blood glucose, controlling the severity, and inhibiting scarring. In the present study, we used polylactic-co-glycolic acid nanoparticles as a carrier to load dihydromyricetin to make drug-loaded nanoparticles and applied them dropwise (200 µL) to diabetic mice wounds by topical application to observe the healing and scar formation of diabetic wounds. We found that the healing rate of the diabetic mice was faster and the scar formation was less obvious. In addition, the elevated blood glucose level and weight loss of the mice in the treatment group were also reduced. Therefore, nanoparticle-mediated dihydromyricetin may be an effective treatment for diabetic wounds.

In this paper, the A356-B2O3-KBF4system was designed, and (AlB2 + Al2O3)/A356 in situ dual-phase reinforced particle composites with mass fractions of 2 wt%, 4 wt%, 6 wt% and 8 wt% were prepared by the DMR method. The acoustic-magnetic coupling control was carried out during the preparation of 6 wt% (AlB2 + Al2O3)/A356 composite. XRD, PC, SEM, EDS, and dry friction tests studied the composites' microstructure and high-temperature wear properties. The results show that the matrix α-Al grains of the composite are refined under the action of the coupling field, with a grain size of: 94.3 um. The AlB2particles are rod-shaped with a radial size of 11 um, and the Al2O3particles are nano-sized flakes. The wear mechanism of (AlB2 + Al2O3)/A356 dual-phase in situ aluminum matrix composites is high-temperature oxidation abrasive wear, which differs from the adhesive wear of the matrix alloy. Due to the anti-friction, lubrication and anti-wear effects of the dual-phase in situ particles (AlB2 + Al2O3), the wear resistance of the composites is improved, and the coefficient of friction is reduced by 13.4% compared to the A356 matrix alloy. With the increase in temperature, the wear resistance of 6 wt% (AlB2 + Al2O3)/A356-1 kW-10 Hz composite was substantially better than that of the A356 matrix alloy. The wear rate of the composite at different temperatures was reduced by 8.82% (100 °C), 11.29% (200 °C), and 13.69% (300 °C), respectively, compared with that of the matrix A356.

Spirotetramat has been utilized extensively in the world for its long-lasting effects. However, since form I has been reported with the disadvantages of low solubility and poor pesticide release rate in the literature, systematic crystal form screening is urgently required. In this study, a new polymorph (form II) was discovered by melt crystallization, and its single crystal grew in melt microdroplets. In comparison to form I, form II is a metastable crystal form with a lower melting point and enthalpy as well as higher lattice energy. Its solubility is higher than that of form I. The increased solubility facilitates the release of pesticides at an accelerated rate. Furthermore, a series of stability experiments have shown that form II can maintain stability at room temperature and ambient humidity for more than 3 months. The form II crystals retain their stability even after grinding or heating. Meanwhile, the phase transformations among three solid forms were presented. Finally, a solution crystallization method was proposed to prepare form II crystals with uniform particle size distribution.

The two-dimensional–three-dimensional (2D–3D) heterostructured perovskite solar cells (PSCs) have drawn widespread interest, wherein the organic spacer plays a significant role in the photovoltaic performance. Herein, a novel π-conjugated organic spacer with the aggregation-induced emission (AIE) property, (Z)-2-([1,1′-biphenyl]-4-yl)-3-(5-(4-(3-aminopropoxy)phenyl)thiophen-2-yl)acrylonitrile (BPCSA-S), is designed and synthesized, which is successfully applied for the in situ construction of 2D–3D heterostructured PSCs via the two-step solution method. By virtue of the functional groups (i.e., cyano, thiophene, and amino) in BPCSA-S, the BPCSA-S organic spacer can trigger the in situ growth of 2D perovskites, which will serve as the template for the heteroepitaxial growth of 3D perovskites, thus obtaining a 2D–3D heterostructured film with high-quality and few defects. More pleasingly, benefiting from the AIE property and delocalized π-electrons in the π-conjugated BPCSA-S organic spacer, excellent photosensitization process and carrier transport can be achieved. Consequently, the resultant 2D–3D heterostructured PSCs yield a pleasing PCE of 22.07%, accompanied by mitigatory hysteresis, as well as enhanced stability. Our research shows a hopeful multifunctional organic spacer approach using the novel π-conjugated AIE organic spacer for high-performance PSCs.

Accurately acquiring crucial data on the ambient surroundings and physiological processes delivered via subtle temperature fluctuation is vital for advancing artificial intelligence and personal healthcare techniques but is still challenging. Here, we introduce an electrically induced cation injection mechanism based on thermal-mediated ion migration dynamics in an asymmetrical polymer bilayer (APB) composed of nonionic polymer and polyelectrolyte layers, enabling the development of ultrasensitive flexible temperature sensors. The resulting optimized sensor achieves ultrahigh sensitivity, with a thermal index surpassing 10,000 K–1, which allows identifying temperature differences as small as 10 mK with a sensitivity that exceeds 1.5 mK. The mechanism also enables APB sensors to possess good insensitivity to various mechanical deformations─features essential for practical applications. As a proof of concept, we demonstrate the potential impact of APB sensors in various conceptual applications, such as mental tension evaluation, biomimetic thermal tactile, and thermal radiation detection.

An emerging environmental contaminant, bis(2-ethylhexyl)-2,3,4,5-tetrabromophthalate (TBPH), can bioaccumulate in the liver and affect hepatic lipid metabolism. However, the in-depth mechanism has yet to be comprehensively explored. In this study, we utilized transgenic zebrafish Tg (Apo14: GFP)to image the interference of TBPH on zebrafish liver development and lipid metabolism at the early development stage. Using integrated lipidomic and transcriptomic analyses to profile the lipid remodeling effect, we uncovered the potential effects of TBPH on lipophagy-related signaling pathways in zebrafish larvae. Decreased lipid contents accompanied by enhanced lipophagy were confirmed by the measurements of Oil Red O staining and transmission electron microscopy in liver tissues. Particularly, the regulatory role of the foxo1factor was validated via its transcriptional inhibitor. Double immunofluorescence staining integrated with biochemical analysis indicated that the enhanced lipophagy and mitochondrial fatty acid oxidation induced by TBPH were reversed by the foxo1inhibitor. To summarize, our study reveals, for the first time, the essential role of foxo1-mediated lipophagy in TBPH-induced lipid metabolic disorders and hepatoxicity, providing new insights for metabolic disease studies and ecological health risk assessment of TBPH.

High-performance GaN based one-chip direct coupled field-effect-transistor logic (DCFL) circuits were demonstrated, in which enhancement-mode (E-mode) GaN high electron mobility transistors (HEMTs) were formed simultaneously with depletion-mode (D-mode) components through the selective-area growth of p-NiO gates at room temperature by sputtering. The process boasts advantages such as a low thermal budget cost, eliminating the need for high-temperature regrowth of p-GaN, and preventing dry-etch damage. The E-mode HEMT showcases a high current density of 1.3 A/mm, a positive threshold voltage of 0.83 V, and an ON-OFF current ratio of ${7}.{24}\times {10} ^{{8}}$ , which enable input/output logic level matching with a low drive/load ratio of 1.0. The E/D-mode inverter exhibits substantial logic-low and logic-high noise margins of 2.09 V and 2.45 V, respectively, a logic voltage swing of 4.78 V, a switching threshold of 2.45 V and a voltage gain of 42 at a supply voltage of 5.0 V. With the demonstrated capability to drive power switches, this architecture provides an elegant solution for high-frequency power switching applications.

This work presents a fully printed paper-based humidity sensor fabricated by an office inkjet printer. The original black ink serves as the electrode material, while a saturated sodium chloride solution as the functional ink for preparing humidity sensors. The sensor performances are regulated by adjusting the printed cycles of the sensing layers. Furthermore, the potential applications of this paper-based humidity sensor in real-time respiratory monitoring and continuous skin moisture monitoring are demonstrated. This low-cost (each humidity sensor coats about 0.125 cents), environmentally friendly, and facile fabrication method provides a viable solution for the simplified and quantity production of disposable electronics.

Recent researchers have proposed adaptive inference methods with an early-exiting mechanism, which stops the inference procedure of input if the prediction is with high confidence, leading to a fine-grained resource allocation based on the complexity of inputs. However, the adaptive inference strategy can only be applied for image classification tasks, such a system for object detection is still under-investigated since it lacks an appropriate way to measure the uncertainty of detection results for multiple objects in an image. To address this issue, in this article, we propose a novel adaptive object detection system (AdaDet) based on early-exit neural networks. By using stochastic variables to represent localization predictions, our method can measure the uncertainty of the detection result for an object, based on which we further develop an entropy-based criterion to estimate the reliability of the whole detection results for an input image. With the proposed method, samples that achieve high-confidence detection results will exit early from the detection model, leading to low computational costs for inferring these samples. While only complex images need to finish the whole computational graph to achieve better detection results during inference. We experimentally demonstrate that the proposed AdaDet enhances detection performance under anytime prediction and adaptive inference settings on the most common data set (MS COCO) used in this field.

Recent years have witnessed a growing interest in compressed video action recognition due to the rapid growth of online videos. It remarkably reduces the storage by replacing raw videos with sparsely sampled RGB frames and other compressed motion cues (motion vectors and residuals). However, existing compressed video action recognition methods face two main issues: First, the inefficiency caused by the usage of coarse-level information under full resolution, and second, the disturbing due to the noisy dynamics in motion vectors. To address the two issues, this paper proposes a dynamic spatial focus method for efficient compressed video action recognition (CoViFocus). Specifically, we first use a light-weighted two-stream architecture to localize the task-relevant patches for both the RGB frames and motion vectors. Then the selected patch pair will be processed by a high-capacity two-stream deep model for the final prediction. Such a patch selection strategy crops out the irrelevant motion noise in motion vectors, as well as reduces the spatial redundancy of the inputs, leading to the high efficiency of our method in the compressed domain. Moreover, we found that the motion vectors can help our method to address the possibly happened static-issue, which means that the focus patches get stuck at some regions related to static objects rather than target actions, which further improves our method. Extensive results on both the HMDB-51 and UCF-101 datasets demonstrate the effectiveness and efficiency of our method in compressed video action recognition tasks.

Wireless video offloading in mobile-edge-computing (MEC)-enabled Industrial Internet of Things imposes a risk of exposing users' private data to eavesdroppers. It is difficult for existing secure video offloading schemes to simultaneously guarantee security, reduce latency and energy consumption in privacy-sensitive multicell scenarios where users are unwilling to offload data to other cells. In this article, a secure video offloading scheme based on multicell federated (MCF) deep reinforcement learning (DRL) is proposed to facilitate a secure, real-time, and efficient MEC network by efficient orchestration of limited resources. We formulate a collaborative optimization problem of video frame resolution and resources to minimize latency and energy consumption while maximizing the security rate subject to analytic accuracy and limited resources. To solve the formulated NP-hard problem, a MCF DRL algorithm based on the frameworks of multicell horizontal federated learning (FL) and hierarchical reward function-based twin delayed deep deterministic policy gradient (TD3) is proposed. First of all, hierarchical reward function-based TD3 is employed to solve the collaborative optimization NP-hard problem formulated for each single cell, where the optimal solution can be efficiently approached by the agent under the guidance of the innovatively designed hierarchical reward function. Then, multicell horizontal FL is applied on TD3 to obtain a model with higher model quality by averagely aggregating multiple individual TD3 models. Simulation results reveal that the proposed algorithm outperforms comparison algorithms in terms of utility, cost, latency, energy consumption, and security rate.

Sheet piles play a crucial role in cofferdams, significantly influencing safety during excavation. The integration of optical frequency domain reflectometry (OFDR) technology enables sheet piles to autonomously monitor stress and deformation throughout the construction process. The feasibility of this method was demonstrated in an engineering case study. Furthermore, a layout approach was proposed for glass-carbon fiber composite-based optical cables, demonstrating its suitability for the distributed monitoring of sheet piles within cofferdams. The wavelet theory and optical smooth spline algorithm were used for data analysis. The findings indicated that the OFDR technology successfully captured the strain, bending moment, and deflection distribution on sheet piles during various excavation stages. These findings support the promotion of OFDR-based monitoring of sheet piles as an innovative cofferdam monitoring technology.

Individual trees are fundamental to urban ecosystems as they play an important role in energy transfer, pollutant removal, and habitat formation. Leaf area (LA) is an important factor to quantify the effect of individual trees on urban ecosystems. Terrestrial laser scanners (TLSs) are widely recognized as the most accurate devices for tree structural measurements. However, they face challenges in estimating LA from LA index (LAI) for individual trees primarily due to arbitrary and confusing horizontal projection areas. Occlusion and clumping effects further hinder the objective and accurate LA measurements of individual trees. Therefore, we developed the slant leaf area index-based method (SLAIM) to estimate the LA of individual trees from single-scan TLS data by introducing the concept of slant leaf area index (SLAI). SLAI quantifies the amount of leaves along the view direction, and it can be retrieved at given view zeniths using gap probability. Subsequently, LA can be accumulated by SLAI across the whole crown. Tests with simulated and field-measured TLS point clouds demonstrate SLAIM’s accuracy, with the relative errors (REs) in LA below 10% in most cases. Stratified LA validation reveals an $R^{2}$ exceeding 0.77 across all realistic crowns, along with a root-mean-square error (RMSE) under 2 m2. SLAIM’s advantages include compatibility with single-scan point clouds, effective correction of clumping effects, and consideration of variations in leaf projection coefficients at different zeniths. SLAIM proves more efficient and practical for actual LA measurements, showcasing its potential for advanced urban ecosystem research.

In low-complexity long-range (LoRa) systems, the prevailing decoding technique is based on hard-decision information due to the computationally intensive nature of the conventional maximum a posteriori probability in log-domain (Max-Log-MAP) soft demapper. In this letter, we propose a sorting-based soft-demapping (SSD) algorithm for LoRa systems. Our proposed algorithm reduces the complexity order of the soft-demapper from $\mathcal {O}(M\log _{2} M)$ to $\mathcal {O}(M)$ , where $M=2^{\text {SF}}$ and SF represents the spreading factor, thus matching the complexity of hard decision. Importantly, this complexity reduction does not lead to any substantial performance degradation when compared to the Max-Log-MAP demapper. As a result, soft decoding can be employed in low-complexity LoRa systems to effectively enhance receiver sensitivity.

LiDAR and camera are two critical sensors that can provide complementary information for accurate 3D object detection. Most works are devoted to improving the detection performance of fusion models on the clean and well-collected datasets. However, the collected point clouds and images in real scenarios may be corrupted to various degrees due to potential sensor malfunctions, which greatly affects the robustness of the fusion model and poses a threat to safe deployment. In this paper, we first analyze the shortcomings of most fusion detectors, which rely mainly on the LiDAR branch, and the potential of the bird’s eye-view (BEV) paradigm in dealing with partial sensor failures. Based on that, we present a robust LiDAR-camera fusion pipeline in unified BEV space with two novel designs under four typical LiDAR-camera malfunction cases. Specifically, a mutual deformable attention is proposed to dynamically model the spatial feature relationship and reduce the interference caused by the corrupted modality, and a temporal aggregation module is devised to fully utilize the rich information in the temporal domain. Together with the decoupled feature extraction for each modality and holistic BEV space fusion, the proposed detector, termed RobBEV, can work stably regardless of single-modality data corruption. Extensive experiments on the large-scale nuScenes dataset under robust settings demonstrate the effectiveness of our approach.

Look-up table (LUT) enabled digital pre-distortion (DPD) is an effective means of nonlinear compensation. However, considering the storage requirement and feasibility of training process, the memory length is commonly limited to 3 or 5, which limits the nonlinear compensation performance of LUT-DPD. With the growing demands of high baud rate transmission, the system is more sensitive to nonlinear impairments, and LUT-DPD with short memory length is insufficient to meet the requirement on nonlinear compensation performance. Neural network (NN) has been proposed as a pre-distorter, while it requires precise modeling of the transceiver or complex multiplication operation. Following the training process of LUT, NN-based pre-distorter can also be trained on received samples, which is simpler to be implemented. Besides, giving scope to the learning ability of NN, a small number of samples are enough for training process. Moreover, the memory length can be easily expanded in NN-based method. Therefore, in this paper, we propose a sample-based NN-DPD which extends the memory length to 9 and even 13, with relatively low complexity. We experimentally demonstrate a transmission of beyond 100 Gbit/s PAM-6/PAM-8 signal in an intensity modulation and direct detection (IM/DD) system. For 40 Gbaud PAM-6 signal, a maximum receiver sensitivity gain of 1.5 dB is obtained at the KP4-FEC threshold when memory length is increased from 3 to 13. Compared to Volterra nonlinear equalizer (VNLE), the computational complexity of NN-DPD is reduced by nearly 60% when memory length is 9. The NN-DPD is a promising solution to reducing the influence of nonlinearity for future high-order modulation signal.

Clear cell renal cell carcinoma (ccRCC) is a complex disease with remarkable immune and metabolic heterogeneity. Here we perform genomic, transcriptomic, proteomic, metabolomic and spatial transcriptomic and metabolomic analyses on 100 patients with ccRCC from the Tongji Hospital RCC (TJ-RCC) cohort. Our analysis identifies four ccRCC subtypes including De-clear cell differentiated (DCCD)-ccRCC, a subtype with distinctive metabolic features. DCCD cancer cells are characterized by fewer lipid droplets, reduced metabolic activity, enhanced nutrient uptake capability and a high proliferation rate, leading to poor prognosis. Using single-cell and spatial trajectory analysis, we demonstrate that DCCD is a common mode of ccRCC progression. Even among stage I patients, DCCD is associated with worse outcomes and higher recurrence rate, suggesting that it cannot be cured by nephrectomy alone. Our study also suggests a treatment strategy based on subtype-specific immune cell infiltration that could guide the clinical management of ccRCC.