In Situ Implanting ZrW2 O7 (OH)2 (H2 O)2 Nanorods into Hierarchical Functionalized Metal-Organic Framework via Solvent-Free Approach for Upgrading Catalytic Performance.

Host-guest catalyst provides new opportunities for targeted applications and the development of new strategies for preparing host-guest catalysts is highly desired. Herein, an in situ solvent-free approach is developed for implanting ZrW2 O7 (OH)2 (H2 O)2 nanorods (ZrW-NR) in nitro-functionalized UiO-66(Zr) (UiO-66(Zr)-NO2 ) with hierarchical porosity, and the encapsulation of ZrW-NR enables the as-prepared host-guest catalyst remarkably enhanced catalytic performance for both for oxidative desulfurization (ODS) and acetalization reactions. ZrW-NR@UiO-66(Zr)-NO2 can eliminate 500 ppm sulfur within 9 min at 40 °C in ODS, and can transform 5.6 mmol benzaldehyde after 3 min at room temperature in acetalization reaction. Its turnover frequencies reach 72.3 h-1 at 40 °C for ODS which is 33.4 times higher than UiO-66(Zr)-NO2 , and 28140 h-1 for acetalization which is the highest among previous reports. Density functional theory calculation result indicates that the W sites in ZrW-NR can decompose H2 O2 to WVI -peroxo intermediates that contribute to catalytic activity for the ODS reaction. This work opens a new solvent-free approach for preparing MOFs-based host-guest catalysts to upgrade their redox and acid performance.

An observed nocturnal ozone transport event in the Sichuan Basin, Southwestern China.

The ozone (O3) pollution in China drew lots of attention in recent years, and the Sichuan Basin (SCB) was one of the regions confronting worsening O3 pollution problem. Many previous studies have shown that regional transport is an important contributor to O3 pollution. However, very few features of the O3 profile during transport have been reported, especially in the border regions between different administrative divisions. In this study, we conducted tethered balloon soundings in SCB during the summer of 2020 and captured a nocturnal O3 transport event during the campaign. Vertically, the O3 transport occurred in the bottom of the residual layer, between 200 and 500 m above ground level. Horizontally, the transport pathway was directed from southeast to northwest based on the analysis of the wind field and air mass trajectories. The effect of transport in the residual layer on the surface O3 concentration was related to the spatial distribution of O3. For cities with high O3 concentrations in the upwind region, the transport process would bring clean air masses and abate pollution. For downwind lightly polluted cities, the transport process would slow down the decreasing or even increase the surface O3 concentration during the night. We provided observational facts on the profile features of a transboundary O3 transport event between two provincial administrative divisions, which implicated the importance of joint prevention and control measures. However, the sounding parameters were limited and the quantitative analysis was preliminary, more integrated, and thorough studies of this topic were called for in the future.

ACO:lossless quality score compression based on adaptive coding order.

BACKGROUND: With the rapid development of high-throughput sequencing technology, the cost of whole genome sequencing drops rapidly, which leads to an exponential growth of genome data. How to efficiently compress the DNA data generated by large-scale genome projects has become an important factor restricting the further development of the DNA sequencing industry. Although the compression of DNA bases has achieved significant improvement in recent years, the compression of quality score is still challenging. RESULTS: In this paper, by reinvestigating the inherent correlations between the quality score and the sequencing process, we propose a novel lossless quality score compressor based on adaptive coding order (ACO). The main objective of ACO is to traverse the quality score adaptively in the most correlative trajectory according to the sequencing process. By cooperating with the adaptive arithmetic coding and an improved in-context strategy, ACO achieves the state-of-the-art quality score compression performances with moderate complexity for the next-generation sequencing (NGS) data. CONCLUSIONS: The competence enables ACO to serve as a candidate tool for quality score compression, ACO has been employed by AVS(Audio Video coding Standard Workgroup of China) and is freely available at https://github.com/Yoniming/ACO.

Characteristics of ambient volatile organic compounds during spring O3 pollution episode in Chengdu, China.

Surface ozone (O3) has become a critical pollutant impeding air quality improvement in many Chinese megacities. Chengdu is a megacity located in Sichuan Basin in southwest China, where O3 pollution occurs frequently in both spring and summer. In order to understand the elevated O3 during spring in Chengdu, we conducted sampling campaign at three sites during O3 pollution episodes in April. Volatile organic compounds (VOCs) compositions at each site were similar, and oxygenated VOCs (OVOCs) concentrations accounted for the highest proportion (35%-45%), followed by alkanes, alkens (including acetylene), halohydrocarbons, and aromatics. The sensitivity of O3 to its precursors was analyzed using an observation based box model. The relative incremental reactivity of OVOCs was larger than other precursors, suggesting that they also played the dominant role in O3 formation. Furthermore, the positive matrix factorization model was used to identify the dominant emission sources and to evaluate their contribution to VOCs in the city. The main sources of VOCs in spring were from combustion (27.75%), industrial manufacturing (24.17%), vehicle exhaust (20.35%), and solvent utilization (18.35%). Discussions on VOCs and NOx reduction schemes suggested that Chengdu was typical in the VOC-limited regime, and VOC emission reduction would help to prevent and control O3. The analysis of emission reduction scenarios based on VOCs sources showed that the emission reduction ratio of VOCs to NO2 needs to reach more than 3 in order to achieve O3 prevention. Emission reduction from vehicular exhaust source and solvent utilization source may be more effective.

On microphone array beamforming and insights into the underlying signal models in the short-time-Fourier-transform domain.

This paper studies signal models for microphone array beamforming in the short-time-Fourier-transform (STFT) domain with long acoustic impulse responses. The major contributions are as follows. First, the signal modeling problem is investigated in the STFT domain and a general decomposition is proposed for the convolved source signal. Second, new insights into the array manifold are presented: the STFT of the windowed acoustic impulse response from the source to the sensors. Third, the structure of the reference signal is analyzed: it can be viewed as the output of a beamformer without considering the noise in the observation signal. Fourth, based on the new perspectives and decomposition, a signal model is derived based on the use of the superdirective beamformer. Finally, three performance measures are defined, based on which three optimal/suboptimal signal models are derived and their performance is assessed under different acoustic environments and analysis window lengths. The performance of the well-known minimum variance distortionless response (MVDR) beamformer is evaluated, which justifies the properties of the developed signal models.

Temporal and spatial distribution characteristics and source origins of volatile organic compounds in a megacity of Sichuan Basin, China.

As important pollution gases and represented precursors of both ozone and second organic aerosol (SOA), the component characteristics, source origins, environmental health and emission control of volatile organic compounds (VOCs), are gaining more and more attention in Chinese megacities. In order to understand the concentration, composition and temporal and spatial distribution characteristics of VOCs in the atmosphere of Chengdu, a megacity located in Sichuan basin in southwest China, the offline sampling measurements of VOCs were carried out at 28 different field sites covering all the districts and counties of Chengdu during special periods from May 2016 to January 2017. Speciated VOCs measurement was performed by the GC-FID/MS, and 99 species were identified. The averaged total VOC mixing ratios of each sampling site were in the range from 35.03 to 180.57 ppbv. Based on these observational data, the distribution characteristics of VOCs in different months and different regions of Chengdu were clarified. The VOCs data were used to estimate the potential amount of ozone, secondary aerosol formation and health risk assessment in Chengdu. Furthermore, the positive matrix factorization (PMF) model was used to identify the dominant emission sources and evaluate their contribution to VOCs in the city. The two main sources of VOCs in Chengdu were motor vehicle exhaust and solvent utilization. These accounted for 43% of all emission sources. In the summertime, due to higher temperatures and stronger sunlight, the contribution of natural sources and secondary emissions were also relatively high, which were supported by the regional emission inventories. Finally, the controlling direction of VOCs and O3 pollution in Chengdu was discussed, and the VOCs pollution control strategy was proposed for the near future.

A Convex Optimization Algorithm for Compressed Sensing in a Complex Domain: The Complex-Valued Split Bregman Method.

The Split Bregman method (SBM), a popular and universal CS reconstruction algorithm for inverse problems with both l1-norm and TV-norm regularization, has been extensively applied in complex domains through the complex-to-real transforming technique, e.g., MRI imaging and radar. However, SBM still has great potential in complex applications due to the following two points; Bregman Iteration (BI), employed in SBM, may not make good use of the phase information for complex variables. In addition, the converting technique may consume more time. To address that, this paper presents the complex-valued Split Bregman method (CV-SBM), which theoretically generalizes the original SBM into the complex domain. The complex-valued Bregman distance (CV-BD) is first defined by replacing the corresponding regularization in the inverse problem. Then, we propose the complex-valued Bregman Iteration (CV-BI) to solve this new problem. How well-defined and the convergence of CV-BI are analyzed in detail according to the complex-valued calculation rules and optimization theory. These properties prove that CV-BI is able to solve inverse problems if the regularization is convex. Nevertheless, CV-BI needs the help of other algorithms for various kinds of regularization. To avoid the dependence on extra algorithms and simplify the iteration process simultaneously, we adopt the variable separation technique and propose CV-SBM for resolving convex inverse problems. Simulation results on complex-valued l1-norm problems illustrate the effectiveness of the proposed CV-SBM. CV-SBM exhibits remarkable superiority compared with SBM in the complex-to-real transforming technique. Specifically, in the case of large signal scale n = 512, CV-SBM yields 18.2%, 17.6%, and 26.7% lower mean square error (MSE) as well as takes 28.8%, 25.6%, and 23.6% less time cost than the original SBM in 10 dB, 15 dB, and 20 dB SNR situations, respectively.

Dual-Channel Reconstruction Network for Image Compressive Sensing.

The existing compressive sensing (CS) reconstruction algorithms require enormous computation and reconstruction quality that is not satisfying. In this paper, we propose a novel Dual-Channel Reconstruction Network (DC-Net) module to build two CS reconstruction networks: the first one recovers an image from its traditional random under-sampling measurements (RDC-Net); the second one recovers an image from its CS measurements acquired by a fully connected measurement matrix (FDC-Net). Especially, the fully connected under-sampling method makes CS measurements represent original images more effectively. For the two proposed networks, we use a fully connected layer to recover a preliminary reconstructed image, which is a linear mapping from CS measurements to the preliminary reconstructed image. The DC-Net module is used to further improve the preliminary reconstructed image quality. In the DC-Net module, a residual block channel can improve reconstruction quality and dense block channel can expedite calculation, whose fusion can improve the reconstruction performance and reduce runtime simultaneously. Extensive experiments manifest that the two proposed networks outperform state-of-the-art CS reconstruction methods in PSNR and have excellent visual reconstruction effects.

Bacillomycin D Produced by Bacillus amyloliquefaciens Is Involved in the Antagonistic Interaction with the Plant-Pathogenic Fungus Fusarium graminearum.

Fusarium graminearum (teleomorph: Ascomycota, Hypocreales, Gibberella, Gibberella zeae) is a destructive fungal pathogen that threatens the production and quality of wheat and barley worldwide. Controlling this toxin-producing pathogen is a significant challenge. In the present study, the commercially available strain Bacillus amyloliquefaciens (Bacteria, Firmicutes, Bacillales, Bacillus) FZB42 showed strong activity against F. graminearum The lipopeptide bacillomycin D, produced by FZB42, was shown to contribute to the antifungal activity. Purified bacillomycin D showed strong activity against F. graminearum, and its 50% effective concentration was determined to be approximately 30 µg/ml. Analyses using scanning and transmission electron microscopy revealed that bacillomycin D caused morphological changes in the plasma membranes and cell walls of F. graminearum hyphae and conidia. Fluorescence microscopy combined with different dyes showed that bacillomycin D induced the accumulation of reactive oxygen species and caused cell death in F. graminearum hyphae and conidia. F. graminearum secondary metabolism also responded to bacillomycin D challenge, by increasing the production of deoxynivalenol. Biological control experiments demonstrated that bacillomycin D exerted good control of F. graminearum on corn silks, wheat seedlings, and wheat heads. In response to bacillomycin D, F. graminearum genes involved in scavenging reactive oxygen species were downregulated, whereas genes involved in the synthesis of deoxynivalenol were upregulated. Phosphorylation of MGV1 and HOG1, the mitogen-activated protein kinases of F. graminearum, was increased in response to bacillomycin D. Taken together, these findings reveal the mechanism of the antifungal action of bacillomycin D.IMPORTANCE Biological control of plant disease caused by Fusarium graminearum is desirable. Bacillus amyloliquefaciens FZB42 is a representative of the biocontrol bacterial strains. In this work, the lipopeptide bacillomycin D, produced by FZB42, showed strong fungicidal activity against F. graminearum Bacillomycin D caused morphological changes in the plasma membrane and cell wall of F. graminearum, induced accumulation of reactive oxygen species, and ultimately caused cell death in F. graminearum Interestingly, when F. graminearum was challenged with bacillomycin D, the deoxynivalenol production, gene expression, mitogen-activated protein kinase phosphorylation, and pathogenicity of F. graminearum were significantly altered. These findings clarified the mechanisms of the activity of bacillomycin D against F. graminearum and highlighted the potential of B. amyloliquefaciens FZB42 as a biocontrol agent against F. graminearum.

Maximum a posteriori-based depth sensing with a single-shot maze pattern.

This study addressed the general problem of correspondence retrieval for single-shot depth sensing where the coded features cannot be detected perfectly. The traditional correspondence retrieval technique can be regarded as maximum likelihood estimation with a uniform distribution prior assumption, which may lead to mismatches for two types of insignificant features: 1) incomplete features that cannot be detected completely because of edges, tiny objects, and many depth variations, etc.; and 2) distorted features disturbed by environmental noise. To overcome the drawback of the uniform distribution assumption, we propose a maximum a posteriori estimation-based correspondence retrieval method that uses the significant features as priors to estimate the weak or missing features. We also propose a novel monochromatic maze-like pattern, which is more robust to ambient illumination and the colors in scenes than the traditional patterns. Our experimental results demonstrate that the proposed system performs better than the popular RGB-D cameras and traditional single-shot techniques in terms of accuracy and robustness, especially with challenging scenes.

Single-Shot Dense Depth Sensing with Color Sequence Coded Fringe Pattern.

A single-shot structured light method is widely used to acquire dense and accurate depth maps for dynamic scenes. In this paper, we propose a color sequence coded fringe depth sensing method. To overcome the phase unwrapping problem encountered in phase-based methods, the color-coded sequence information is embedded into the phase information. We adopt the color-encoded De Bruijn sequence to denote the period of the phase information and assign the sequence into two channels of the pattern, while the third channel is used to code the phase information. Benefiting from this coding strategy, the phase information distributed in multiple channels can improve the quality of the phase intensity by channel overlay, which results in precise phase estimation. Meanwhile, the wrapped phase period assists the sequence decoding to obtain a precise period order. To evaluate the performance of the proposed method, an experimental platform is established. Quantitative and qualitative experiments demonstrate that the proposed method generates a higher precision depth, as compared to a Kinect and larger resolution ToF (Time of Flight) camera.

Pollution control costs of a transboundary river basin: Empirical tests of the fairness and stability of cost allocation mechanisms using game theory.

With rapid economic growth, transboundary river basin pollution in China has become a very serious problem. Based on practical experience in other countries, cooperation among regions is an economic way to control the emission of pollutants. This study develops a game theoretic simulation model to analyze the cost effectiveness of reducing water pollutant emissions in four regions of the Jialu River basin while considering the stability and fairness of four cost allocation schemes. Different schemes (the nucleolus, the weak nucleolus, the Shapley value and the Separable Cost Remaining Benefit (SCRB) principle) are used to allocate regionally agreed-upon water pollutant abatement costs. The main results show that the fully cooperative coalition yielded the highest incremental gain for regions willing to cooperate if each region agreed to negotiate by transferring part of the incremental gain obtained from the cooperation to cover the losses of other regions. In addition, these allocation schemes produce different outcomes in terms of their fairness to the players and in terms of their derived stability, as measured by the Shapley-Shubik Power Index and the Propensity to Disrupt. Although the Shapley value and the SCRB principle exhibit superior fairness and stabilization to the other methods, only the SCRB principle may maintains full cooperation among regions over the long term. The results provide clear empirical evidence that regional gain allocation may affect the sustainability of cooperation. Therefore, it is implied that not only the cost-effectiveness but also the long-term sustainability should be considered while formulating and implementing environmental policies.

Compressive spectral imaging using variable number of measurements.

The compressive spectral imaging method always cuts down on the number of images for obtaining the spectral data cube of a scene. Our method cuts down on the number of sensors on the imaging plane, so as to fit some practical constraints, (e.g., size, weight, battery capacity, memory space, transmission bandwidth). Moreover, only a few of sensors on the imaging plane are needed, while more prior knowledge about the object in the scene has been achieved. The proposed method is based on the concept of coded dispersion, by which many pixels of spectral data are caught by one pixel on the imaging plane. Its measurement matrix is modified so that the number of measurements can be variable under different circumstances to save the transmission bandwidth. We demonstrate the validity of the proposed method, that with prior knowledge of scenes available, it offers a way to acquire spectral images using a variable number of measurements.

Dual-camera design for coded aperture snapshot spectral imaging.

Coded aperture snapshot spectral imaging (CASSI) provides an efficient mechanism for recovering 3D spectral data from a single 2D measurement. However, since the reconstruction problem is severely underdetermined, the quality of recovered spectral data is usually limited. In this paper we propose a novel dual-camera design to improve the performance of CASSI while maintaining its snapshot advantage. Specifically, a beam splitter is placed in front of the objective lens of CASSI, which allows the same scene to be simultaneously captured by a grayscale camera. This uncoded grayscale measurement, in conjunction with the coded CASSI measurement, greatly eases the reconstruction problem and yields high-quality 3D spectral data. Both simulation and experimental results demonstrate the effectiveness of the proposed method.

Compressive SAR imaging with joint sparsity and local similarity exploitation.

Compressive sensing-based synthetic aperture radar (SAR) imaging has shown its superior capability in high-resolution image formation. However, most of those works focus on the scenes that can be sparsely represented in fixed spaces. When dealing with complicated scenes, these fixed spaces lack adaptivity in characterizing varied image contents. To solve this problem, a new compressive sensing-based radar imaging approach with adaptive sparse representation is proposed. Specifically, an autoregressive model is introduced to adaptively exploit the structural sparsity of an image. In addition, similarity among pixels is integrated into the autoregressive model to further promote the capability and thus an adaptive sparse representation facilitated by a weighted autoregressive model is derived. Since the weighted autoregressive model is inherently determined by the unknown image, we propose a joint optimization scheme by iterative SAR imaging and updating of the weighted autoregressive model to solve this problem. Eventually, experimental results demonstrated the validity and generality of the proposed approach.

One-shot depth acquisition with a random binary pattern.

In this paper, we propose a new spatial encoding method that integrates the random binary pattern and the improved phase-difference-matching method to acquire a dense and precise depth map. The adopted binary pattern can simplify pattern projecting devices compared with the patterns that use color. The density of speckles in the pattern is periodic and the positions of them are random. Based on these two properties, we propose an improved phase-difference corresponding method, which is divided into two steps: the coarse matching step to estimate the approximate coordinates of pixels in the pattern via analyzing the phase values of the image, and the fine matching step to compensate errors of the coarse matching results and to achieve subpixel accuracy. This matching method does not require an extra optimization method with high computational complexity. In the experiment, we show the effectiveness of the proposed method. We also evaluate this method in actual experiments. The results show that this method has advantages over the time-of-flight camera and Kinect, particularly in terms of precision.

A new optical surface measurement method with iterative sparsity-constrained threshold phase retrieval algorithm.

Due to its low complexity and acceptable accuracy, phase retrieval technique has been proposed as an alternative to solve the classic optical surface measurement task. However, to capture the overall wave field, phase retrieval based optical surface measurement (PROSM) system has to moderate the CCD position during the multiple-sampling procedure. The mechanical modules of CCD movement may bring about unexpectable deviation to the final results. To overcome this drawback, we propose a new PROSM method based on spatial light modulator (SLM). The mechanical CCD movement can be replaced by an electrical moderation of SLM patterns; thus the deviation can be significantly suppressed in the new PROSM method. In addition, to further improve the performance, we propose a new iterative threshold phase retrieval algorithm with sparsity-constraint to effectively reconstruct the phase of wave field. Experimental results show that the new method provides a more simple and robust solution for the optical surface measurement than the traditional techniques and achieves higher accuracy.

A unitary ESPRIT scheme of joint angle estimation for MOTS MIMO radar.

The transmit array of multi-overlapped-transmit-subarray configured bistatic multiple-input multiple-output (MOTS MIMO) radar is partitioned into a number of overlapped subarrays, which is different from the traditional bistatic MIMO radar. In this paper, a new unitary ESPRIT scheme for joint estimation of the direction of departure (DOD) and the direction of arrival (DOA) for MOTS MIMO radar is proposed. In our method, each overlapped-transmit-subarray (OTS) with the identical effective aperture is regarded as a transmit element and the characteristics that the phase delays between the two OTSs is utilized. First, the measurements corresponding to all the OTSs are partitioned into two groups which have a rotational invariance relationship with each other. Then, the properties of centro-Hermitian matrices and real-valued rotational invariance factors are exploited to double the measurement samples and reduce computational complexity. Finally, the close-formed solution of automatically paired DOAs and DODs of targets is derived in a new manner. The proposed scheme provides increased estimation accuracy with the combination of inherent advantages of MOTS MIMO radar with unitary ESPRIT. Simulation results are presented to demonstrate the effectiveness and advantage of the proposed scheme.

Superimposed Semantic Communication for IoT-Based Real-Time ECG Monitoring.

Real-time electrocardiogram (ECG) monitoring and diagnosis through Internet of Things (IoT) are crucial for addressing the severity and timely treatment of cardiovascular diseases, enabling timely intervention and preventing life-threatening complications. However, current ECG monitoring research predominantly focuses on individual aspects such as signal compression, diagnostic analysis, or secure transmission, lacking joint optimization of various modules in IoT scenarios. To address this gap, this work proposes a novel framework based on superimposed semantic communication for real-time ECG monitoring in IoT. The framework comprises three hierarchical levels: the edge level for data collection and processing, the relay level for signal compression and coding, and the cloud level for data analysis and reconstruction. The proposed framework offers several unique advantages. By employing semantic encoding guided by ECG classification tasks, it selectively extracts crucial features within and between signals, improving compression ratio and adaptability to channel noise. The superimposed semantic encoding achieves content encryption without requiring any additional operations. Moreover, the framework utilizes lightweight anomaly detection neural networks, reducing edge device power consumption and conserving communication resources. Simulation and real experimental results demonstrate that the proposed method achieves real-time encoding and transmission of ECG signals with a compression ratio of 0.019 on the MIT-BIH dataset. Furthermore, it attains a heartbeat classification accuracy of 0.988 and a reconstruction error of 0.061.

TransVQA: Transferable Vector Quantization Alignment for Unsupervised Domain Adaptation.

Unsupervised Domain adaptation (UDA) aims to transfer knowledge from the labeled source domain to the unlabeled target domain. Most existing domain adaptation methods are based on convolutional neural networks (CNNs) to learn cross-domain invariant features. Inspired by the success of transformer architectures and their superiority to CNNs, we propose to combine the transformer with UDA to improve their generalization properties. In this paper, we present a novel model named Trans ferable V ector Q uantization A lignment for Unsupervised Domain Adaptation (TransVQA), which integrates the Transferable transformer-based feature extractor (Trans), vector quantization domain alignment (VQA), and mutual information weighted maximization confusion matrix (MIMC) of intra-class discrimination into a unified domain adaptation framework. First, TransVQA uses the transformer to extract more accurate features in different domains for classification. Second, TransVQA, based on the vector quantization alignment module, uses a two-step alignment method to align the extracted cross-domain features and solve the domain shift problem. The two-step alignment includes global alignment via vector quantization and intra-class local alignment via pseudo-labels. Third, for intra-class feature discrimination problem caused by the fuzzy alignment of different domains, we use the MIMC module to constrain the target domain output and increase the accuracy of pseudo-labels. The experiments on several datasets of domain adaptation show that TransVQA can achieve excellent performance and outperform existing state-of-the-art methods.