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BackgroundMuscone, a key component of musk, exhibits anti-inflammatory properties. However, its therapeutic potential in inflammatory lung diseases, such as chronic obstructive pulmonary disease (COPD), remains largely unexplored. This study aimed to investigate whether Muscone could exert a protective effect in a mouse model of COPD in vivo.MethodsA COPD animal model was established by exposing mice to cigarette smoke (CS) and administering lipopolysaccharide (LPS) intranasally. After 4 weeks, mice were treated daily with dexamethasone (DEX) or different doses of Muscone for 3 weeks. Mouse body weight, lung function, and histopathology were determined. Serum levels of cytokines (IL-38, IL-1β, IL-17, TGF-β, IFN-γ) were measured using ELISA and qRT-PCR. Lung expression of CXCR3, IFN-γ, IL-17A, and RORγt was assessed by immunofluorescence.ResultsThe body weight of COPD mice was significantly lower than that of Muscone-treated COPD mice, consistent with decreased lung function, accompanied by reduced circulating and lung IL-38 levels. After Muscone administration, lung function was significantly improved, accompanied by upregulation of circulating and lung anti-inflammatory cytokines, including IL-38, in a dose-dependent manner, while the expression of pro-inflammatory cytokines was significantly reduced. Additionally, Muscone significantly inhibited the protein expression of CXCR3, IFN-γ, IL-17A, and RORγt in lung tissues of COPD mice.ConclusionThis study demonstrates that Muscone improves lung function in mice with COPD, potentially through a mechanism that may involve the modulation of cytokine expression, including the potential upregulation of anti-inflammatory cytokines such as IL-38. The precise underlying mechanisms of Muscone’s therapeutic effects in COPD remain to be fully elucidated. Further research is needed to investigate the correlation between COPD lung pathophysiology and the specific effects of Muscone treatment, including a more detailed analysis of the balance between pro- and anti-inflammatory mediators in COPD animal models, particularly utilizing IL-38 GKO mice to further investigate the role of IL-38 in mediating the therapeutic effects of Muscone.

Abstract Highly efficient separation of dispersed uranium is important for the sustainable development of nuclear industry, and adsorption is the most recognized approach. However, there are many coexisting interfering metal ions that compete with uranyl ion for the chelating ligands in the adsorbents and lead to low separation selectivity and efficiency. Herein, a coordination‐induced magnetism strategy is presented for the separation of uranium based on the conversion of diamagnetic cyanoferrocene (Fc‐CN) nanocrystals to uranium‐containing magnetic recoverable ferromagnetic aggregates. Different from previous adsorption strategies, this strategy combines the mechanisms of photocatalytic uranium enrichment and chemical uranium adsorption. Under light irradiation, electron of Fe(II) in Fc‐CN is excited and transfers to uranyl ion via the cyano group to form tight coordination bond between N atom in cyano group and uranium. This phenomenon is unique for uranyl ion, and thus, a high uranium removal rate of 97.98% is achieved in simulated nuclear wastewater with the presence of tremendous interfering ions, proving its highly selective and efficient uranium separation performance. The ability to form highly stable magnetic aggregates via photoinduced interaction between Fc‐CN and uranium enriches the understanding on the chemical properties of Fc‐CN and uranium.

Located at the easternmost sector of the Last Ice Area (LIA) with the oldest and thickest sea ice, the north of Greenland has witnessed some unprecedented polynyas since 2018, receiving considerable attention. Sea ice concentration (SIC) derived from passive microwave data can provide near daily observations of these polynyas. However, these SIC products are limited by the coarse spatial resolution of passive microwave data, with mixed pixels and large uncertainties at the ice–water divide. It is especially difficult in the case of newly formed polynyas in the form of leads with narrow openings, preventing accurate estimation of the regional energy budget and heat fluxes as well as the hindering causal analysis of the polynyas. In this paper, a novel state-of-the-art deep-learning-based super-resolution (SR) method is adopted, and passive microwave SR images, with spatial resolutions of up to 4 times the original, are generated and employed to derive super-resolved SIC (SR-SIC). Experimental results confirm the benefits of SR-SIC for finer monitoring of sea ice at both the polynya and lead scale compared to the original lower-resolution SIC, with an average 7.55% improvement in F1-Score for polynya extent and 31.17% for lead width. Using the SR-SIC, more accurate quantitative parameters of polynyas and leads are extracted to present more detailed spatial distributions at their different development stages. For example, SR-SIC with reduced mixed pixels can distinguish more SIC values below 40%, advantageous for detecting open water pixels within polynya areas and improving the accuracy of heat fluxes estimation and ice production calculation. Moreover, SR-SIC has the capability to identify leads with openings less than 3 km and to locate the finer boundaries of the polynyas. Finally, it is found that the lead development along the 2018 winter polynya is associated with regional wind, air temperature, and ice drift. The prominent summer polynya events in 2018, 2020, and 2021 may be in part caused by the thinning sea ice in the north of Greenland.

ObjectiveExperimental animal models have been used for decades to study the development and progression of chronic obstructive pulmonary disease (COPD). However, there is a lack of methods for constructing animal models of COPD for optimal modelling. This systematic literature review (SLR) aimed to assess the various methods used to establish COPD animal models, highlight their advantages and limitations, and explore more optimized approaches for establishing such models.MethodsA systematic search was performed in four English databases (PubMed, Embase, Web of Science, and the Cochrane Library) and four Chinese databases (Chinese Biomedical Literature Database, China National Knowledge Infrastructure, China Science and Technology Journal Database, and Wanfang Database). Of the 8,015 retrieved full-text manuscripts, 453 were selected.ResultsSmoking (n = 140), smoking combined with lipopolysaccharide (LPS) (n = 275), smoking combined with protease drip (PPE) (n = 10), smoking combined with bacteria (n = 23), and smoking combined with particulate matter (PM2.5) (n = 5) were the most used methods for establishing animal models of COPD. Rats and mice were the most frequently selected experimental animals, with male animals accounting for 79.47% of the total. A total of 92.49 and 29.14% of the articles reviewed considered lung pathology of experimental animals only and lung pathology and lung function tests, respectively.ConclusionOur review suggests that the best way to establish an animal model of COPD is to combine smoking with LPS. Although findings from animal models of COPD cannot be directly extrapolated to human COPD, they could provide useful tools for further investigation into human COPD disease.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023407555, Identifier PROSPERO CRD42023407555.

Abstract This is the protocol for an updated Campbell systematic review. The objectives are as follows: To evaluate the effect of behavioral interventions on smoking cessation among homeless individuals.

ABSTRACTIt is critical to understand the mechanisms and impacts of global climate change in cryosphere and polar environment. Here we describe the design and progress of the remote sensing products we have developed to monitor these changes. We obtain datasets by combining multi-sensor remote sensing systems with field validation systems, and then develop a methodological system that combines multiple sources and calibration technologies to address the challenges of data heterogeneity in dynamically monitoring key parameters. This global change and response special project supported by the National Key Research and Development Program of China, named ‘observation and inversion of critical parameters of changes in the cryosphere and polar environment’, providing comprehensive records of essential elements of the cryosphere through long-term satellite records. The multi-team is completing the project and collecting data from multi-sensor to create remote sensing data products that include records of changes in the cryosphere, such as permafrost, glaciers, ice sheet mass balance, seasonal snow accumulation, sea ice, and albedo changes. Finally, the existing ground- and space-based observing system can be improved to provide dynamic monitoring of key parameters in the cryosphere and changes in the polar environment.

Abstract We present data from the Heart Rate Variability and Emotion Regulation (HRV-ER) randomized clinical trial testing effects of HRV biofeedback. Younger (N = 121) and older (N = 72) participants completed baseline magnetic resonance imaging (MRI) including T1-weighted, resting and emotion regulation task functional MRI (fMRI), pulsed continuous arterial spin labeling (PCASL), and proton magnetic resonance spectroscopy (1H MRS). During fMRI scans, physiological measures (blood pressure, pulse, respiration, and end-tidal CO2) were continuously acquired. Participants were randomized to either increase heart rate oscillations or decrease heart rate oscillations during daily sessions. After 5 weeks of HRV biofeedback, they repeated the baseline measurements in addition to new measures (ultimatum game fMRI, training mimicking during blood oxygen level dependent (BOLD) and PCASL fMRI). Participants also wore a wristband sensor to estimate sleep time. Psychological assessment comprised three cognitive tests and ten questionnaires related to emotional well-being. A subset (N = 104) provided plasma samples pre- and post-intervention that were assayed for amyloid and tau. Data is publicly available via the OpenNeuro data sharing platform.

The greenhouse effect of atmospheric pollution is globally concerning. China is transitioning to market-driven emission reduction from policy-driven efforts. In 2021, key power industry emitters were included in the national carbon trading market. However, many companies lack willingness and understanding of carbon assets, hindering progress. Research on power companies in Beijing, a political and carbon market pilot region, is valuable. This study obtained data on the participation of Beijing’s power generation companies in the carbon market and the construction of their carbon management systems during the first compliance period through the distribution of surveys. The findings revealed that the implementation and preparation of carbon inventory, Chinese Certified Emission Reduction (CCER) development, the allocation of carbon management personnel, and training are key factors influencing the actual effectiveness of carbon management within companies. Based on the survey results and the impact pathways, this study outlines the preparatory work, system content, and construction steps for power companies to build a carbon management system. It summarizes five key areas of work for power companies in managing carbon assets: carbon inventory, carbon management personnel and mechanisms, carbon trading, carbon emission reduction, and carbon finance. This provides guidance to help power companies fulfill their obligations smoothly, add value to their carbon assets, and achieve low-carbon development goals. Additionally, it offers a reference for other industry enterprises that are about to enter carbon trading.

[Introduction] To achieve the "carbon peak and neutrality" goals, the ways for enterprise energy conservation and emission reduction need to fully explored, and the green and low-carbon policy tools should be fully exploited. Green hydrogen, as a clean energy, has become the key to social low-carbon transition thanks to its full link zero carbon emission. Carbon emission rights trading market is a key policy tool to control and reduce carbon emissions by making use of the market-oriented mechanism. Building a carbon trading center hydrogen energy industry trading mechanism to achieve the coupling of carbon trading and hydrogen energy will greatly promote the achieving of the "carbon peak and neutrality" goals. [Method] Based on this, a coupling mechanism of "green hydrogen market - national carbon trading market - electricity market" was built in this paper. The "green hydrogen market - national carbon trading market - electricity market" was modeled and simulated by further utilizing the system dynamics method, and the interaction of multiple markets was studied. [Result] The results show that: Firstly, the coupling among the green hydrogen market, carbon trading market and electricity market can be realized through green hydrogen certification and carbon quota trading, and it is found that the coupling model is feasible through simulation. Secondly, it is found through the simulation of the basic scenario that the coupling of multiple markets can stimulate the increase of carbon price and control the increase of thermal power generation and green hydrogen production. Finally, increasing the proportion of green hydrogen certification and improving the obsolete unit elimination mechanism and carbon quota auction mechanism will help form a carbon pricing mechanism. [Conclusion] On the one hand, this study diversifies the green hydrogen trading models, promotes the participation of green hydrogen in carbon trading to obtain certain benefits, and reduces the hydrogen energy cost. On the other hand, it makes use of the multi-market linkage mechanism to bring into play the forced effect of carbon price and promote the reduction of the proportion of traditional fossil hydrogen production and thermal power generation.

Accurate ice flow velocity data are essential for studying the mass balance of the Antarctic ice sheet. However, there is a lack of ice velocity maps of 1960s–80s in basin-wide regions or the entire ice sheet. In this study, an enhanced hierarchical network densification approach is developed for basin-wide Antarctic velocity mapping using historical ARGON and Landsat images. The produced multiple historical velocity maps from 1963 to 1989 in the region of the Fimbul and Jelbart ice shelves, East Antarctica, achieved an accuracy better than 29 m a−1. They revealed that the ice flow velocity had no significant changes over the period. Combining the surface mass balance estimate with the ice discharge estimated from our historical velocity maps and recently published velocity maps, we estimated a positive mass balance of 8.6 ± 3.9 Gt a−1 in the study area from 1963 and 2015. Our results indicate that the region's positive mass balance, as estimated in recently published studies, has been maintained since the 1960s. It is also in concordance with the low level of mass balance from 1992 to 2017 in East Antarctica. This suggests that the study area has been stable since the 1960s.

Passive microwave data is an important data source for the continuous monitoring of Arctic-wide sea ice concentration (SIC). However, its coarse spatial resolution leads to blurring effects at the ice-water divides, resulting in the great challenges of fine-scale and accurate SIC estimation, especially for regions with low SIC. Besides, the SIC derived by operational algorithms using high-frequency passive microwave observations has great uncertainties in open water or marginal ice zones due to atmospheric effects. In this article, a novel framework is proposed to achieve accurately SIC estimation with improved spatial details from original low-resolution Advanced Microwave Scanning Radiometer 2 (AMSR2) images, with joint the super-resolution (SR) and SIC estimation network. Based on the SR network, the spatial resolution of original AMSR2 images can be improved by four times, benefiting to construct AMSR2 SR features with more high-frequency information for SIC estimation. The SIC network with an encoder–decoder structure and atrous convolution, is employed to accurately perform the SIC retrieval by considering the characteristics of passive microwave images in the Arctic sea ice region. Experimental results show that the proposed SR-Aided SIC estimation approach can generate accurate SIC with more detailed sea ice textures and much sharper sea ice edges. With respect to MODIS SIC products distributed in Arctic scale, the proposed model achieves a root-mean-square error (RMSE) of 5.94% and mean absolute error (MAE) of 3.04%, whereas the Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) SIC results have three and two times greater values of RMSE and MAE.

Urbanization has accelerated in China, resulting in a noticeable conflict between urban expansion and land use/land cover (LULC). This issue can exacerbate ecological and developmental problems in urban neighborhoods, so it is crucial to explore the relationship between long-term urban expansion and LULC spatial and temporal transitions. We first defined urban built-up areas in terms of human economic activity and then used spatial measures to examine urban expansion and spatio-temporal changes in 25 LULC classes by analyzing 369 cities across China. Our findings indicate that China’s urban built-up areas expanded 9.1 times between 1995 and 2020, with the fastest growth rate occurring between 1995 and 2005 at about 16%. We observed a negative association between China’s urban built-up area and dry farming land, forests, and grassland in that order, with grassland exhibiting the weakest correlation. The negative correlation between urban built-up areas and paddy fields became positive after 2010, while the positive correlation with reservoirs and canals became negative after 2015. This study utilized spatial measures to demonstrate the spatial heterogeneity of urban expansion in China and explored the extent to which different LULC area changes explained urban expansion. We provide a reference for decision-making efforts on land management and sustainable use in urban areas.

With the development of global clean energy and the implementation of carbon emission reduction policies, the direct purchase of electricity by large consumers has been increasingly promoted as a special form of electricity trading. Therefore, on the basis of the completion of low-carbon emission reduction targets in each country, how to rationalize the electricity purchase by large consumers in the electricity market so as to reduce their electricity purchase costs has become the main target of attention in each country. Currently, there are fewer studies in existing research on the direct electricity purchase strategy of large consumers under the consideration of the weight of consumption responsibility and risk. Based on this, this paper constructs a dual-game model for direct electricity purchase by large consumers based on the Stackelberg game and non-cooperative game theory. The concept of value at risk is further introduced, and the optimal strategy of direct electricity purchase by large consumers is proposed. The results of this study show that when market players make decisions on the purchase and sale of electricity, power suppliers will increase their biddings to obtain the highest returns, and large consumers can reduce the transaction costs by combining the medium- and long-term market with the spot market to purchase electricity. In the choice of electricity purchase market, with the increasing risk factor, large consumers shift from the risky spot market to the less risky medium- and long-term market and option market. This paper provides a reference for the issues of power suppliers’ contract bidding and large consumers’ electricity purchase strategy in the medium- and long-term contract transactions.

Measurement(s) Stress • Burnout • Affect • Depression • Sleep • Physical Activity Measurement • Alcohol Use History • Frequency Any Tobacco Use • Personality • Social Support • Intragroup Conflict • Challenge and Hindrance Stressors • Demographics • Context and Atypical Events • Daily Stressors • Most Stressful Event • Work Context • Job Performance • Job Satisfaction • Stressors at Work • Charting at Home • Coworker Trust • Social Networks at Work • Socialization Outside of Work • Use of Wellness Resources • Heart Rate • Step Count • Acoustic Features • Team Interactions • Proximity to Key Objects • Cell Phone Use • Hospital Contextual Data • Coping with Stress • Productivity at Work • Pride at Work • Teamwork • Support System Technology Type(s) Perceived Stress Scale - 14 Questionnaire • Survey • Patient Health Questionnaire - 9 Item • Pittsburgh Sleep Quality Index • FitBit • International Physical Activity Questionnaire (August 2002) Short Last 7 Days Self-Administered Format • Unihertz Atom Phone • Minew E8- TILES Interaction Sensors • Minew E8- Eddystone Beach • Rescuetime • Evaluations • Patient Census • Interview Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location Los Angeles County and University of Southern California Medical Center

Summary: Detection of radioactive iodide ions (I–) is important for protecting human beings from the hazards of radioactive pollution. Herein, we present a protocol for detecting I– using a zirconium(IV)-enhanced strategy. We describe steps for optimizing the I– detection approach, establishing standard curves, and finally applying the approach. The use of zirconium(IV) greatly improves the detection performance and endows this approach with an ultralow detection limit of 0.176 nM together with wide applicability in various aqueous environments.For complete details on the use and execution of this protocol, please refer to Feng et al. (2022).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Advances in cancer treatment have increased patient survival rates, shifting clinical focus towards minimizing treatment-related morbidity, including cardiovascular issues. Since echocardiography allows for a comprehensive non-invasive assessment at all cancer stages, it is well suited to monitor cardiovascular disease secondary to oncology treatment. This has earned it significant attention in the study of cardiac tumors and treatment-induced cardiac alterations. Ultrasound methods—ranging from transthoracic and transesophageal echocardiography to ultrasound diagnostic techniques including myocardial strain imaging, myocardial work indices, three-dimensional cardiac imaging—offer a holistic view of both the tumor and its treatment impact cardiac function. Stress echocardiography, myocardial contrast echocardiography, and myocardial acoustic angiography further augment this capability. Together, these echocardiographic techniques provide clinicians with early detection opportunities for cardiac damage, enabling timely interventions. As such, echocardiography continues to be instrumental in monitoring and managing the cardiovascular health of oncology patients, complementing efforts to optimize their overall treatment and survival outcomes.

Abstract Synthesis of framework materials possessing specific spatial structures or containing functional ligands has attracted tremendous attention. Herein, a halogen hydrogen-bonded organic framework (XHOF) is fabricated by using Cl− ions as central connection nodes to connect organic ligands, 7,7,8,8-tetraaminoquinodimethane (TAQ), by forming a Cl−···H3 hydrogen bond structure. Unlike metallic node-linked MOFs, covalent bond-linked COFs, and intermolecular hydrogen bond-linked HOFs, XHOFs represent a different kind of crystalline framework. The electron-withdrawing effect of Cl− combined with the electron-rich property of the organic ligand TAQ strengthens the hydrogen bonds and endows XHOF-TAQ with high stability. Due to the production of excited electrons by TAQ under light irradiation, XHOF-TAQ can efficiently catalyze the reduction of soluble U(VI) to insoluble U(IV) with a capacity of 1708 mg-U g−1-material. This study fabricates a material for uranium immobilization for the sustainability of the environment and opens up a new direction for synthesizing crystalline framework materials.

In order to accelerate the construction of a clean, low-carbon, safe and efficient energy system, China set the provincial weight of responsibility for renewable energy power consumption and established a renewable energy power absorption guarantee mechanism in 2019. As a market incentive policy, it has enduring effect on the low-carbon transformation of the power industry. Firstly, the operation mechanism of the renewable energy consumption guarantee mechanism is analyzed. The general framework, core elements and supporting measures are clarified. Secondly, a stock-flow diagram is constructed based on the system dynamics method. It contains the green electricity market sub-module, the green-certificate market sub-module and the excess power absorption market sub-module. Finally, multiple scenarios are set up to simulate the impact of the green-certificate market and excess power absorption market improvements on the installed capacity and tariff of China’s green power market. The renewable energy guarantee mechanism is an effective means to promote the consumption of green electricity in China. In addition, in the short term the cost of electricity for users has increased, but in the long term the cost of electricity shows a fluctuating downward trend. This study provides theoretical references for the formulation of clean and low-carbon policy objectives in the power industry and the optimization of market mechanisms.

Studies have indicated that the decrease in the extent of Arctic sea ice in recent years has had a significant impact on the Arctic ecosystem and global climate. In order to understand the evolution of sea ice, it is becoming increasingly imperative to have continuous observations of Arctic-wide sea ice with high spatial resolution. Passive microwave sensors have the benefit of being less susceptible to weather, wider coverage, and higher temporal resolution. However, it is challenging to retrieve accurate parameters of sea ice due to the low spatial resolution of passive microwave images. Therefore, improving the spatial resolution of passive microwave images is beneficial for reducing the uncertainty of sea ice parameters. In this paper, four competitive multi-image super-resolution (MISR) networks are selected to explore the applicability of the networks on multi-frequency Advanced Microwave Scanning Radiometer 2 (AMSR2) images of Arctic sea ice. The upsampling factor is set to 4 in the experiment. Firstly, the optimal input lengths of the image sequence for the four MISR networks are found, and then the best network on different frequency band images is further identified. Furthermore, some factors, including seasons, sea ice motion, and polarization mode of images, that may affect the super-resolution (SR) results are analyzed. The experimental results indicate that utilizing images from winter yields superior SR results. Conversely, SR results are the worst during summer across all four MISR networks, exhibiting the largest difference in PSNR of 4.48 dB. Additionally, the SR performance is observed to be better for images with smaller magnitudes of sea ice motion compared to those with larger motions, with the maximum PSNR difference of 2.04 dB. Finally, the SR results for vertically polarized images surpass those for horizontally polarized images, showcasing an average advantage of 4.02 dB in PSNR and 0.0061 in SSIM. In summary, valuable suggestions for selecting MISR models for passive microwave images of Arctic sea ice at different frequency bands are offered in this paper. Additionally, the quantification of the various impact factors on SR performance is also discussed in this paper, which provides insights into optimizing MISR algorithms for passive microwave sea ice imagery.

Abstract Night shift workers are often associated with circadian misalignment and physical discomfort, which may lead to burnout and decreased work performance. Moreover, the irregular work hours can lead to significant negative health outcomes such as poor eating habits, smoking, and being sedentary more often. This paper uses commercial wearable sensors to explore correlates and differences in the level of physical activity, sleep, and circadian misalignment indicators among day shift nurses and night shift nurses. We identify which self-reported assessments of affect, life satisfaction, and sleep quality, are associated with physiological and behavioral signals captured by wearable sensors. The results using data collected from 113 nurses in a large hospital setting, over a period of 10 weeks, indicate that night shift nurses are more sedentary, and report lower levels of life satisfaction than day-shift nurses. Moreover, night shift nurses report poorer sleep quality, which may be correlated with challenges in their attempts to fall asleep on off-days.

Accurate 3D pathfinding for multiple locomotion types is crucial for indoor applications. Many of the existing methods rely on the correct topology or the rich semantics of the indoor space, which restricts their usage in unstructured and semantic-less environments. Though space-decomposition methods, such as voxelization, have been applied to support 3D pathfinding, two major defects remain: the first is that voxelization of a coarse resolution brings about discretization artifacts, which is detrimental to the accessibility of wheeled platforms. The second is that the movement behaviors of different locomotion types were not modeled and integrated with a consistent world representation. This paper employs the octree to reconstruct the indoor spaces in multiresolution, especially to differentiate between slopes and steps. A height-related spatial weighting is proposed for modeling the 3D movement cost. The behaviors of typical locomotion types are modeled by the variable-length search in continuous coordinate space and compose a unified hybrid A* pathfinding algorithm. Experiments show this method is robust and can be applied to both structured and unstructured building models. The results are accurate 3D paths conforming to the movement behaviors of multiple locomotion types.

Abstract Marine biofouling is one of the most significant challenges hindering practical uranium extraction from seawater. Single atoms have been widely used in catalytic applications because of their remarkable redox property, implying that the single atom is highly capable of catalyzing the generation of reactive oxygen species (ROS) and acts as an anti‐biofouling substance for controlling biofouling. In this study, the Co single atom loaded polyacrylamidoxime (PAO) material, PAO‐Co, is fabricated based on the binding ability of the amidoxime group to uranyl and cobalt ions. Nitrogen and oxygen atoms from the amidoxime group stabilize the Co single atom. The fabricated PAO‐Co exhibits a broad range of antimicrobial activity against diverse marine microorganisms by producing ROS, with an inhibition rate up to 93.4%. The present study is the first to apply the single atom for controlling biofouling. The adsorbent achieves an ultrahigh uranium adsorption capacity of 9.7 mg g−1 in biofouling‐containing natural seawater, which decreased only by 11% compared with that in biofouling‐removed natural seawater. These findings indicate that applying single atoms would be a promising strategy for designing biofouling‐resistant adsorbents for uranium extraction from seawater.

Touch as a modality in social communication has been getting more attention with recent developments in wearable technology and an increase in awareness of how limited physical contact can lead to touch starvation and feelings of depression. Although several mediated touch methods have been developed for conveying emotional support, the transfer of emotion through mediated touch has not been widely studied. This work addresses this need by exploring emotional communication through a novel wearable haptic system. The system records physical touch patterns through an array of force sensors, processes the recordings using novel gesture-based algorithms to create actuator control signals, and generates mediated social touch through an array of voice coil actuators. We conducted a human subject study (N = 20) to understand the perception and emotional components of this mediated social touch for common social touch gestures, including poking, patting, massaging, squeezing, and stroking. Our results show that the speed of the virtual gesture significantly alters the participants' ratings of valence, arousal, realism, and comfort of these gestures with increased speed producing negative emotions and decreased realism. The findings from the study will allow us to better recognize generic patterns from human mediated touch perception and determine how mediated social touch can be used to convey emotion. Our system design, signal processing methods, and results can provide guidance in future mediated social touch design.

In multi-robot simultaneous localization and mapping (SLAM) systems, the system must create a consistent global map with multiple local maps and loop closures between robot poses. However, false-positive loop closures caused by perceptual aliasing can severely distort the global map, especially in GNSS-denied areas, where a good prior of relative poses between robots is unavailable. In addition, the performance of the consistency metric in existing map fusion methods relies on accurate odometry from each robot. However, in practice, cumulative noise is inevitably present in robot trajectories, which leads to poor map fusion with existing methods. Thus, in this paper, we propose a robust consistency-based inter-robot and intra-robot loop closure selection algorithm for map fusion. We consider both pairwise-loop consistency and loop-odometry consistency to improve robustness against false-positive loop closures and accumulative noise in the odometry. Specifically, we select a reliable inter-robot loop closure measurement with a consistency-based strategy to provide an initial prior of relative pose between two robot trajectories and update the pose variables of the robot trajectories. The loop closure selection problem is formulated as a maximum edge weight clique problem in graph theory. A performance evaluation of the proposed method was conducted on the ManhattanOlson3500, modified CSAIL and Bicocca datasets, and the experimental results demonstrate that the proposed method outperforms the pairwise consistency measurement set maximization method (PCM) under severe accumulative noise and can be integrated with M-estimation methods.

The enzyme 1-acylglycerol-3-phosphate O-acyltransferase 2 (AGPAT2) is an intermediate enzyme in triglyceride synthesis. The aim was to study the regulatory mechanism of AGPAT2 on Nile tilapia, Oreochromis niloticus. In this study, antisense RNA technology was used to knock-down AGPAT2 in Nile tilapia. Compared with the control groups (transfected with ultrapure water or the blank expression vector), the AGPAT2 knock-down group showed a significantly higher weight gain rate, special growth rate, visceral somatic index, and hepatopancreas somatic index; and significantly increased the total cholesterol, triglycerides, glucose, low-density lipoprotein cholesterol, and insulin levels in serum. In addition, the contents of total cholesterol and triglycerides and the abundance of superoxide dismutase, catalase, and glutathione peroxidase in the liver significantly increased, while the malondialdehyde content significantly decreased. The liver cells became severely vacuolated and accumulated lipids in the AGPAT2 knock-down group. Comparative transcriptome analyses (AGPAT2 knock-down vs. control group) revealed 1789 differentially expressed genes (DEGs), including 472 upregulated genes and 1313 downregulated genes in the AGPAT2 knock-down group. Functional analysis showed that the main pathway of differentially expressed genes enrichment was lipid metabolism and oxidative stress, such as steroid biosynthesis, unsaturated fatty acid biosynthesis, the PPAR signaling pathway, and the P53 pathway. We used qRT-PCR to verify the mRNA expression changes of 13 downstream differential genes in related signaling pathways. These findings demonstrate that knock-down of AGPAT2 in tilapia leads to abnormal lipid metabolism and oxidative stress.

The national carbon emission trading mechanism is an important policy tool for the Chinese government to control and reduce greenhouse gas emissions by using the market mechanism. The Beijing–Tianjin–Hebei power market is the focus of energy conservation and consumption reduction in China. Problems have already existed in the synergic development of the Beijing–Tianjin–Hebei power market and carbon trading market. In this article, the development status of the Beijing–Tianjin–Hebei power market is analyzed and the coupling mechanism between the carbon market and power market is combed out to build a synergism model of the carbon market and the Beijing–Tianjin–Hebei power market based on the system dynamics. From the research results, firstly, the Beijing–Tianjin–Hebei power market comes with a high energy consumption intensity and a high proportion of carbon emissions. The coupling of carbon market and power market forces the power industry to reduce carbon emissions through the effective transmission of carbon costs to power prices. Secondly, carbon price shows an upward trend in the context of the current policy scenario, which can give play to the role of price signal in the future. The revenue of thermal power plants, which are the carbon emission right sellers, with new technologies, has increased significantly, while the revenue of carbon emission right buyers, which are the manufacturers of undeveloped units, has increased less. Finally, the technical progress of thermal power plants, the introduction of auction mechanism, the increase in initial carbon price settings and the direct transmission of carbon costs are all factors that promote the effectiveness of carbon trading policy tools in the Beijing–Tianjin–Hebei power market. This study provides theoretical guidance for the synergic development of the “power-carbon” market.

Subglacial water bodies are critical components in analyzing the instability of the Antarctic ice sheet. Their detection and identification normally rely on geophysical and remote sensing methods such as airborne radar echo sounding (RES), ground seismic, and satellite/airborne altimetry and gravity surveys. In particular, RES surveys are able to detect basal terrain with a relatively high accuracy that can assist with the mapping of subglacial hydrology systems. Traditional RES processing methods for the identification of subglacial water bodies mostly rely on their brightness in radargrams and hydraulic flatness. In this study, we propose an automatic method with the main objective to differentiate the basal materials by quantitatively evaluating the shape of the A-scope waveform near the basal interface in RES radargrams, which has been seldom investigated. We develop an automatic algorithm mainly based on the traditional short-time Fourier transform (STFT) to quantify the shape of the A-scope waveform in radargrams. Specifically, with an appropriate window width applied on the main peak of each A-scope waveform in the RES radargram, STFT shows distinct and contrasting frequency responses at the ice-water interface and ice-rock interface, which is largely dependent upon their different reflection characteristics at the basal interface. We apply this method on 882 RES radargrams collected in the Antarctic’s Gamburtsev Province (AGAP) in East Antarctica. There are 8822 identified A-scopes with the calculated detection value larger than the set threshold, out of the overall 1,515,065 valid A-scopes in these 882 RES radargrams. Although these identified A-scopes only takes 0.58% of the overall A-scope population, they show exceptionally continuous distribution to represent the subglacial water bodies. Through a comprehensive comparison with existing inventories of subglacial lakes, we successfully verify the validity and advantages of our method in identifying subglacial water bodies using the detection probability for other basal materials of theoretically the highest along-track resolution. The frequency signature obtained by the proposed joint time–frequency analysis provides a new corridor of investigation that can be further expanded to multivariable deep learning approaches for subglacial and englacial material characterization, as well as subglacial hydrology mapping.

Recently, discriminative correlation filters (DCF) based trackers have gained much attention and obtained remarkable achievements for their high efficiency and outstanding performance. However, undesirable boundary effects occur when the DCF-based trackers suffer from challenging situations, such as occlusion, background clutters, fast motion, and so on. To address these problems, this work proposes a novel adaptive spatial regularization and temporal-aware correlation filters (ASTCF) model to deal with the boundary effects which occur in the correlation filters tracking. Firstly, our ASTCF model learns a more robust correlation filter template by introducing spatial regularization and temporal-aware components into the objective function. The adaptive spatial regularization provides a more robust appearance model to handle the large appearance changes at different times; meanwhile, the temporal-aware constraint can enhance the time continuity and consistency of this model. They make correlation filters model more discriminating, and also reduce the influence of the boundary effects during the tracking process. Secondly, the objective function can be transformed into three sub-problems with closed-form solutions and effectively solved via the alternating direction method of multipliers (ADMM). Finally, we compare our tracker with some representative methods and evaluate using three different benchmarks, including OTB2015, VOT2018 and LaSOT datasets, where the experimental results demonstrate the superiority of our tracker on most of the performance criteria compared with the existing trackers.

A novel multiview inverse synthetic aperture radar (ISAR) imaging method is proposed to simulate high-resolution and identifiable ISAR image of complex targets by handling large-angle and wide-bandwidth scattering data. The scattering data are simulated with the shooting and bouncing ray (SBR) method. The bidirectional ray-tracing algorithm is developed to reduce the computation time. Simulation results indicate that the improved method is efficient and reliable to calculate electromagnetic (EM) scattering of electrically large targets. To implement the multiview ISAR imaging method after data simulation, we divide the large-angle and wide-bandwidth scattering data into subaperture data and conduct ISAR image processing for each subaperture datum locally. Transforming each subaperture ISAR image to the global coordinate system and summing them together will produce the high-resolution ISAR image that is meaningful for the database set up for synthetic aperture radar automatic target recognition (SAR ATR). The final simulation ISAR images further validate the great performance of our scattering calculation algorithm and ISAR imaging method.

Supervised classification is the commonly used method for extracting ground information from images. However, for supervised classification, the selection and labelling of training samples is an expensive and time-consuming task. Recently, automatic information indexes have achieved satisfactory results for indicating different land-cover classes, which makes it possible to develop an automatic method for labelling the training samples instead of manual interpretation. In this paper, we propose a method for the automatic selection and labelling of training samples for high-resolution image classification. In this way, the initial candidate training samples can be provided by the information indexes and open-source geographical information system (GIS) data, referring to the representative land-cover classes: buildings, roads, soil, water, shadow, and vegetation. Several operations are then applied to refine the initial samples, including removing overlaps, removing borders, and semantic constraints. The proposed sampling method is evaluated on a series of high-resolution remote sensing images over urban areas, and is compared to classification with manually labeled training samples. It is found that the proposed method is able to provide and label a large number of reliable samples, and can achieve satisfactory results for different classifiers. In addition, our experiments show that active learning can further enhance the classification performance, as active learning is used to choose the most informative samples from the automatically labeled samples.

The high-definition map (HD-map) of road structures is crucial for the safe planning and control of autonomous vehicles. However, generating and updating such maps requires intensive manual work. Simultaneous localization and mapping (SLAM) is able to automatically build and update a map of the environment. Nevertheless, there is still a lack of SLAM method for generating vector-based road structure maps. In this paper, we propose a vector-based SLAM method for the road structure mapping using vehicle-mounted multibeam LiDAR. We propose using polylines as the primary mapping element instead of grid maps or point clouds because the vector-based representation is lightweight and precise. We explored the following: (1) the extraction and vectorization of road structures based on multiframe probabilistic fusion; (2) the efficient vector-based matching between frames of road structures; (3) the loop closure and optimization based on the pose-graph; and (4) the global reconstruction of the vector map. One specific road structure, the road boundary, is taken as an example. We applied the proposed mapping method to three road scenes, ranging from hundreds of meters to over ten kilometers and the results are automatically generated vector-based road boundary maps. The average absolute pose error of the trajectory in the mapping is 1.83 m without the aid of high-precision GPS.

Hybrid scheme combining shooting and bouncing ray with semi-deterministic facet model is proposed to analyze composite scattering from ship-ocean scene in this study. This model can deal with complex electromagnetic interaction between ship and sea surface. Thus, scattering properties of composite ship-ocean scenes with influence of various parameters (such as incident angle and wind speed) can be studied and analyzed efficiently. Studying such properties is of significance for target detection and high-resolution radar imaging in sea environments. Accuracy and performance of this method are validated and evaluated by comparing with multilevel fast multipole method of FEKO for electrically small objects. All simulation results indicate that the proposed method is suitable for providing preliminary radar cross section prediction of electrically large composite model.

Landslides are one of the most destructive geo-hazards that can bring about great threats to both human lives and infrastructures. Landslide monitoring has been always a research hotspot. In particular, landslide simulation experimentation is an effective tool in landslide research to obtain critical parameters that help understand the mechanism and evaluate the triggering and controlling factors of slope failure. Compared with other traditional geotechnical monitoring approaches, the close-range photogrammetry technique shows potential in tracking and recording the 3D surface deformation and failure processes. In such cases, image matching usually plays a critical role in stereo image processing for the 3D geometric reconstruction. However, the complex imaging conditions such as rainfall, mass movement, illumination, and ponding will reduce the texture quality of the stereo images, bringing about difficulties in the image matching process and resulting in very sparse matches. To address this problem, this paper presents a multiple-constraints based robust image matching approach for poor-texture close-range images particularly useful in monitoring a simulated landslide. The Scale Invariant Feature Transform (SIFT) algorithm was first applied to the stereo images for generation of scale-invariate feature points, followed by a two-step matching process: feature-based image matching and area-based image matching. In the first feature-based matching step, the triangulation process was performed based on the SIFT matches filtered by the Fundamental Matrix (FM) and a robust checking procedure, to serve as the basic constraints for feature-based iterated matching of all the non-matched SIFT-derived feature points inside each triangle. In the following area-based image-matching step, the corresponding points of the non-matched features in each triangle of the master image were predicted in the homologous triangle of the searching image by using geometric constraints, followed by a refinement course with similarity constraint and robust checking. A series of temporal Single-Lens Reflex (SLR) and High-Speed Camera (HSC) stereo images captured during the simulated landslide experiment performed on the campus of Tongji University, Shanghai, were employed to illustrate the proposed method, and the dense and reliable image matching results were obtained. Finally, a series of temporal Digital Surface Models (DSM) in the landslide process were constructed using the close-range photogrammetry technique, followed by the discussion of the landslide volume changes and surface elevation changes during the simulation experiment.