Search

Atmospheric rivers (ARs) are the primary mechanism for transporting water vapor from low latitudes to polar regions, playing a significant role in extreme weather in both the Arctic and Antarctica. With the rapidly growing interest in polar ARs during the past decade, it is imperative to establish an objective framework quantifying the strength and impact of these ARs for both scientific research and practical applications. The AR scale introduced by Ralph et al. (2019) ranks ARs based on the duration of AR conditions and the intensity of integrated water vapor transport (IVT). However, the thresholds of IVT used to rank ARs are selected based on the IVT climatology at middle latitudes. These thresholds are insufficient for polar regions due to the substantially lower temperature and moisture content. In this study, we analyze the IVT climatology in polar regions, focusing on the coasts of Antarctica and Greenland. Then we introduce an extended version of the AR scale tuned to polar regions by adding lower IVT thresholds of 100, 150, and 200 kg m−1 s−1 to the standard AR scale, which starts at 250 kg m−1 s−1. The polar AR scale is utilized to examine AR frequency, seasonality, trends, and associated precipitation and surface melt over Antarctica and Greenland. Our results show that the polar AR scale better characterizes the strength and impacts of ARs in the Antarctic and Arctic regions than the original AR scale and has the potential to enhance communication across observational, research, and forecasting communities in polar regions.

The Ross Ice Shelf, West Antarctica, experienced an extensive melt event in January 2016. We examine the representation of this event by the HIRHAM5 and MetUM high-resolution regional atmospheric models, as well as a sophisticated offline-coupled firn model forced with their outputs. The model results are compared with satellite-based estimates of melt days. The firn model estimates of the number of melt days are in good agreement with the observations over the eastern and central sectors of the ice shelf, while the HIRHAM5 and MetUM estimates based on their own surface schemes are considerably underestimated, possibly due to deficiencies in these schemes and an absence of spin-up. However, the firn model simulates sustained melting over the western sector of the ice shelf, in disagreement with the observations that show this region as being a melt-free area. This is attributed to deficiencies in the HIRHAM5 and MetUM output and particularly a likely overestimation of night-time net surface radiative flux. This occurs in response to an increase in night-time downwelling longwave flux from around 180–200 to 280 W m−2 over the course of a few days, leading to an excessive amount of energy at the surface available for melt. Satellite-based observations show that this change coincides with a transition from clear-sky to cloudy conditions, with clouds containing both liquid water and ice water. The models capture the initial clear-sky conditions but seemingly struggle to correctly represent cloud properties associated with the cloudy conditions, which we suggest is responsible for the radiative flux errors.

The use of remote sensing methods to accurately measure cloud properties and their spatiotemporal changes has been widely welcomed in many fields of atmospheric research. The Nanjing Joint Institute for Atmospheric Sciences (NJIAS) Himawari-8/9 Cloud Feature Dataset (HCFD) provides a comprehensive description of cloud features over the East Asia and west North Pacific regions for the 7-year period from April 2016 to December 2022. Multiple cloud variables, such as cloud mask, phase/type, top height, optical thickness, and particle effective radius, as well as snow, dust, and haze masks, were generated from the visible and infrared measurements of the Advanced Himawari Imager (AHI) on board the Japanese geostationary satellites Himawari-8 and Himawari-9 using a series of recently developed cloud retrieval algorithms. Verifications with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) 1 km cloud layer product and the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 cloud product (MYD06) demonstrate that the NJIAS HCFD gives higher skill scores than the Japanese Himawari-8/9 operational cloud product for all cloud variables except for cloud particle effective radius. The NJIAS HCFD even outperforms the MYD06 in nighttime cloud detection; cloud-top height, pressure, and temperature estimation; and infrared-only cloud-top phase determination. All evaluations are performed at the nominal 2 km scale, not including the effects of sub-pixel cloudiness or very thin cirrus. Two examples are presented to demonstrate applications of the NJIAS HCFD for climate and typhoon research. The NJIAS HCFD has been published in the Science Data Bank (https://doi.org/10.57760/sciencedb.09950, Zhuge 2023a; https://doi.org/10.57760/sciencedb.09953, Zhuge 2023b; https://doi.org/10.57760/sciencedb.09954, Zhuge 2023c; https://doi.org/10.57760/sciencedb.10158, Zhuge 2023d; https://doi.org/10.57760/sciencedb.09945, Zhuge 2023e).

China stands out as a major contributor to anthropogenic methane (CH4) emissions, with coal mine methane (CMM) playing a crucial role. To control and reduce CH4 emissions, China has made a dedicated commitment and formulated an ambitious mitigation plan. To verify the progress made, the consistent acquisition of independent CH4 emission data is required. This paper aims to implement a wind-assigned anomaly method for the precise determination of regional-scale CMM emissions within the coal-rich Shanxi province. We use the TROPOspheric Monitoring Instrument (TROPOMI) CH4 observations from May 2018 to May 2023, coupled with ERA5 wind and a bottom-up inventory dataset based on the IPCC (Intergovernmental Panel on Climate Change) Tier 2 approach covering the Changzhi, Jincheng, and Yangquan regions of the Shanxi province. The derived emission strengths are 8.4 × 1026 molec. s−1 (0.706 Tg yr−1, ±25 %), 1.4 × 1027 molec. s−1 (1.176 Tg yr−1, ±20 %), and 4.9 × 1026 molec. s−1 (0.412 Tg yr−1, ±21 %), respectively. Our results exhibit biases of −18 %, 8 %, and 14 %, respectively, when compared to the IPCC Tier 2 bottom-up inventory. Larger discrepancies are found when comparing the estimates to the Copernicus Atmosphere Monitoring Service global anthropogenic emissions (CAMS-GLOB-ANT) and Emissions Database for Global Atmospheric Research (EDGARv7.0) inventories (64 %–176 %), suggesting that the two inventories may be overestimating CH4 emissions from the studied coal mining regions. Our estimates provide a comprehensive characterization of the regions within the Shanxi province, contribute to the validation of emission inventories, and provide additional insights into CMM emission mitigation.

Abstract Artificial neural networks are becoming an integral part of digital solutions to complex problems. However, employing neural networks on quantum processors faces challenges related to the implementation of non-linear functions using quantum circuits. In this paper, we use repeat-until-success circuits enabled by real-time control-flow feedback to realize quantum neurons with non-linear activation functions. These neurons constitute elementary building blocks that can be arranged in a variety of layouts to carry out deep learning tasks quantum coherently. As an example, we construct a minimal feedforward quantum neural network capable of learning all 2-to-1-bit Boolean functions by optimization of network activation parameters within the supervised-learning paradigm. This model is shown to perform non-linear classification and effectively learns from multiple copies of a single training state consisting of the maximal superposition of all inputs.

This paper introduces a unique multiyear dataset and the monitoring capability of the PANDA automatic weather station network, which includes 11 automatic weather stations (AWSs) across the Prydz Bay–Amery Ice Shelf–Dome A area from the coast to the summit of the East Antarctic Ice Sheet. The ∼ 1460 km transect from Zhongshan to Panda S follows roughly along ∼ 77∘ E longitude and covers all geographic units of East Antarctica. Initial inland observations, near the coast, started in the 1996/97 austral summer. All AWSs in this network measure air temperature, relative humidity, air pressure, wind speed and wind direction at 1 h intervals, and some of them can also measure firn temperature and shortwave/longwave radiation. Data are relayed in near real time via the Argos system. The data quality is generally very reliable, and the data have been used widely. In this paper, we firstly present a detailed overview of the AWSs, including the sensor characteristics, installation procedure, data quality control protocol and the basic analysis of each variable. We then give an example of a short-term atmospheric event that shows the monitoring capacity of the PANDA AWS network. This dataset, which is publicly available, is planned to be updated on a near-real-time basis and should be valuable for climate change estimation, extreme weather events diagnosis, data assimilation, weather forecasting, etc. The dataset is available at https://doi.org/10.11888/Atmos.tpdc.272721 (Ding et al., 2022b).

Within the 9th European Framework programme, since 2021 EUROfusion is operating five tokamaks under the auspices of a single Task Force called ‘Tokamak Exploitation’. The goal is to benefit from the complementary capabilities of each machine in a coordinated way and help in developing a scientific output scalable to future largre machines. The programme of this Task Force ensures that ASDEX Upgrade, MAST-U, TCV, WEST and JET (since 2022) work together to achieve the objectives of Missions 1 and 2 of the EUROfusion Roadmap: i) demonstrate plasma scenarios that increase the success margin of ITER and satisfy the requirements of DEMO and, ii) demonstrate an integrated approach that can handle the large power leaving ITER and DEMO plasmas. The Tokamak Exploitation task force has therefore organized experiments on these two missions with the goal to strengthen the physics and operational basis for the ITER baseline scenario and for exploiting the recent plasma exhaust enhancements in all four devices (PEX: Plasma EXhaust) for exploring the solution for handling heat and particle exhaust in ITER and develop the conceptual solutions for DEMO. The ITER Baseline scenario has been developed in a similar way in ASDEX Upgrade, TCV and JET. Key risks for ITER such as disruptions and run-aways have been also investigated in TCV, ASDEX Upgrade and JET. Experiments have explored successfully different divertor configurations (standard, super-X, snowflakes) in MAST-U and TCV and studied tungsten melting in WEST and ASDEX Upgrade. The input from the smaller devices to JET has also been proven successful to set-up novel control schemes on disruption avoidance and detachment.

In this paper, we propose a Leader-Follower cooperative localization system for AGV group, which equipped with camera/IMU/UWB anchors as the Leader and IMU/UWB tags as the Follower. Firstly, we introduce the plane constraint with SE(2) into VIO to suppress drift in 2D motion; Secondly, the Follower receives the Leader's VIO pose and calculates the relative pose through the multiple groups of UWB ranging. The algorithm of Leader and Follower is decoupled, which means Leader can choose different self_localization technology to adapt to different scenario, such as LiDAR-Inertial Odometry(LIO), GNSS, etc. In the Follower, a tightly coupled graph optimization based algorithm to fuse UWB ranging and IMU is proposed to estimate its relative pose to the Leader. Lastly, extensive field experiment shows the system can realize omni-directional cooperative positioning in indoor and outdoor environments and the accuracy of collaborative positioning reaches centimeter level.

Abstract The preparation of thermal equilibrium states is important for the simulation of condensed matter and cosmology systems using a quantum computer. We present a method to prepare such mixed states with unitary operators and demonstrate this technique experimentally using a gate-based quantum processor. Our method targets the generation of thermofield double states using a hybrid quantum-classical variational approach motivated by quantum-approximate optimization algorithms, without prior calculation of optimal variational parameters by numerical simulation. The fidelity of generated states to the thermal-equilibrium state smoothly varies from 99 to 75% between infinite and near-zero simulated temperature, in quantitative agreement with numerical simulations of the noisy quantum processor with error parameters drawn from experiment.

Mobile laser scanning systems (MLS) have been widely used in collecting three-dimensional point clouds for many applications, such as 3D mapping, road facilities inventory and high definition map. Although MLS is calibrated accurately to obtain precise locations of point clouds, it is still challenging to obtain precise locations of point clouds in the areas of GPS signal denied or narrow streets with high dense buildings, resulting in uneven position deviations of point clouds between the overlapping trajectory areas. In this paper, a marker-free calibration method is proposed to solve the above problems. The proposed method firstly partitions the trajectory into segments according to the error distribution while collecting the point clouds. Secondly, the features in each overlapped area are extracted and a kind of Locally Aggregated Descriptors are built for the matching. Thirdly, a coarse-to-fine pairwise point clouds alignment is applied on the overlapping areas. Finally, the global alignment of point clouds is fulfilled with minimizing the position deviations between the overlapping areas and the adjacent segments. The proposed method has been used to correct the point clouds from several different MLSs. Experiments show that this method automatically locates and corrects the uneven position deviations in terms of good robustness and efficiencies. Besides, it proves that the proposed method is also an easy-to-use tool for the automatic correction of MLS point clouds position and boresights.

Despite thousands of sex-biased genes being found in chickens, the genetic control of sexually dimorphic and left-right asymmetry during gonadal differentiation is not yet completely understood. This study aimed to identify microRNAs (miRNAs), long noncoding RNAs (lncRNAs), messenger RNAs (mRNAs), and signaling pathways during gonadal differentiation in chick embryos (day 6/stage 29). The left and right gonads were collected for RNA sequencing. Sex-biased, side-biased miRNAs, lncRNAs, mRNAs, and shared differentially expressed miRNAs (DEmiRNA)–differentially expressed mRNAs (DEmRNA)–differentially expressed lncRNAs (DElncRNA) interaction networks were performed. A total of 8 DEmiRNAs, 183 DElncRNAs, and 123 DEmRNAs were identified for the sex-biased genes, and 7 DEmiRNAs, 189 DElncRNAs, and 183 DEmRNAs for the side-biased genes. The results of quantitative real-time PCR were generally consistent with the RNA-sequencing results. The study suggested that miRNAs and lncRNAs regulation were novel gene-specific dosage compensation mechanism and they could contribute to left-right asymmetry of chicken, but sex-biased and side-biased miRNAs, lncRNAs, and mRNAs were independent of each other. The competing endogenous RNA (ceRNA) networks showed that 17 target pairs including miR-7b (CYP19A1, FSHR, GREB1, STK31, CORIN, and TDRD9), miR-211 (FSHR, GREB1, STK31, CORIN, and TDRD9), miR-204 (FSHR, GREB1, CORIN, and TDRD9), and miR-302b-5p (CYP19A1 and TDRD9) may play crucial roles in ovarian development. These analyses provide new clues to uncover molecular mechanisms and signaling networks of ovarian development.

Abstract We describe the realization and characterization of a compact, autonomous fiber laser system that produces the optical frequencies required for laser cooling, trapping, manipulation, and detection of 87Rb atoms - a typical atomic species for emerging quantum technologies. This device, a customized laser system from the Muquans company, is designed for use in the challenging operating environment of the Laboratoire Souterrain à Bas Bruit (LSBB) in France, where a new large scale atom interferometer is being constructed underground - the MIGA antenna. The mobile bench comprises four frequency-agile C-band Telecom diode lasers that are frequency doubled to 780 nm after passing through high-power fiber amplifiers. The first laser is frequency stabilized on a saturated absorption signal via lock-in amplification, which serves as an optical frequency reference for the other three lasers via optical phase-locked loops. Power and polarization stability are maintained through a series of custom, flexible micro-optic splitter/combiners that contain polarization optics, acousto-optic modulators, and shutters. Here, we show how the laser system is designed, showcasing qualities such as reliability, stability, remote control, and flexibility, while maintaining the qualities of laboratory equipment. We characterize the laser system by measuring the power, polarization, and frequency stability. We conclude with a demonstration using a cold atom source from the MIGA project and show that this laser system fulfills all requirements for the realization of the antenna.

Satellite data assimilation of hyperspectral Cross-track Infrared Sounder (CrIS) clear channels in regions with but not affected by low-level clouds requires a set of height-dependent cloud emission and scattering indices (CESIs) that are sensitive to cloud top pressures. In this study, three CESIs are generated by pairing CrIS longwave and shortwave channels at three altitudes around 212 (CESI-5), 452 (CESI-9) and 1085 hPa (CESI-19). The training dataset for CESIs is the European Centre for Medium-Range Weather Forecasting analysis. CESI-5 is strongly (weakly) affected by those ice clouds with cloud-top pressures (CTPs) above (below) ∼300 hPa; CESI-9 is strongly (weakly) affected by those ice clouds with CTPs above (below) ∼600 hPa; and CESI-19 is highly correlated with the ice cloud optical depth (ICOD) obtained from the Atmospheric Infrared Sounder (AIRS) retrieval product. CESIs have latitudinal and scan-dependent biases that need removal. Verified with AIRS ICOD data, CESI-19 is greater than 5 K (6.5 K) if an ice cloud is present. This 5-K threshold gives a probability of correct typing of 85.3% (86.5%), a false alarm rate of 9.5% (5.1%) and a leakage rate of 4.5% (5.0%) during the daytime (nighttime). CESI-5 (CESI-9) is greater than −3 K (−1 K) if an ice cloud is present above 200 hPa (between 200 and 300 hPa). The case of Typhoon Maria (2018) demonstrates the potential of using height-dependent CESIs to identify above-cloud CrIS clear channels.

The Upper Indus River Basin (UIB) has developed the largest midlatitude mountain glaciers worldwide. Ice thickness and volume distribution are important prerequisites for glaciological and hydrological investigations. In this paper, we presented detailed estimates of ice thickness in UIB region. Using ground penetrating radar, we measured glacier ice thickness on six typical glaciers; we obtained the parameters of the GlabTOP2 from these measurements and analyzed its uncertainty. Using the verified GlabTOP2 model, we simulated glacier ice thickness and volume in UIB subcatchments. The simulated results indicated that the UIB glacier thickness distribution was not uniform, ranging from 0 to 488 m, with an average thickness of 78 m. Total volume was defined as 1269.70 km3 in 2000, which corresponded to 1142.73 km3 water volume. According to the calculated discharge data from the Besham hydrological station, the total glacier volume of UIB generally can supply water resources for the downstream area for at least 15 years. And the glacier surface elevations generally decreased from 2000 to 2016 in UIB subbasins, although there was significant spatial heterogeneity in the seven subcatchments. The annual glacier surface elevation change rate of the Hindu Kush area was the smallest, followed by that of the Karakoram. The greatest glacier elevation change rate was observed in the Western Himalaya, indicating rapid glacial melting.

For the purpose of developing high-quality metal/polymer joints without defects to satisfy the strict requirement of biomedical application, 3D printed TC4 alloy was joined with non-polar ultra-high molecular weight polyethylene (UHMWPE) via friction spot joining (FSpJ). In this study, five sets of experiments with different joining temperatures were designed to figure out the formation mechanisms of defects. The joining temperature (TJ) histories were measured with K-type thermocouple inserted in the lap interface. Macro−/micro-scale defects in TC4/UHMWPE joints were analyzed. Numerical simulation was carried out to study the temperature distribution during joining and gap formation during cooling. Micro−/nano-scale interfacial bonding was characterized using scanning electron microscope and transmission electron microscope. Then, the formation mechanisms of defects were better understood. Macro-scale defects were shown as insufficient filling and bubbles. Insufficient filling is linearly related to the TJ which can be alleviated by applying higher TJ. However, when the TJ is >385 °C, large bubbles appear due to excessive and fast thermal degradation of UHMWPE. Meanwhile, micro-scale defect, appearing as continuous gaps caused by the shrinkage of polymer can be avoided by enhancing the micro-scale interfacial bonding.

As a result of the drastic reduction in shallow mineral resources, the exploitable potential and reserves of proven mines are insufficient, and the mineral resources in deep ground need to be more explored in a more refined way. There are some disadvantages of the existing instruments, such as few channels and slow sampling rate. Therefore, a multiparameter transient electromagnetic instrument with synchronous receiving has been developed and tested. The instrument is composed of two controllers – embedded controller and programmable logic controller – which can provide a diversified information combination for follow-up information processing. Under the grounding electrode source emission mode, the real-time synchronous transient electromagnetic acquisition system of six channels is achieved with a sampling rate of 128 000 samples per second (SPS). The data acquired by the six channels is recorded in the full-time range of the time domain. Furthermore, experiments were carried out in the laboratory, open areas, and actual mine. Through data analysis, the measured data curves of the mining area are highly consistent with the existing geochemical exploration curves and geological profile.

In order to accomplish the automatic mobile mapping task in a small area of interest, a low cost UAV system is proposed in this paper. Multiple sensors including a global shutter camera and an inertial measurement unit are calibrated and synchronized to collect data from the area of interest. First the images are matched by the chronological order and the SfM is utilized. Then the origin SfM result is integrated with the IMU data by adding the IMU constraints into the bundle adjustment. At last the photogrammetry point clouds are generated using PMVS according to the extrinsic parameters. Experiments are undertaken in a typical scene with photogrammetry point clouds generated. The trajectory estimated by the proposed integration method are compared with the method that relies on image only, showing that the proposed method has better performance.

Ecosystems in alpine mountainous regions are vulnerable and easily disturbed by global environmental change. Alpine swamp meadow, a unique grassland type in the eastern Tibetan Plateau that provides important ecosystem services to the upstream and downstream regions of international rivers of Asia and other parts of the world, is undergoing severe degradation, which can dramatically alter soil hydraulic properties and water cycling processes. However, the effects of alpine swamp meadow degradation on soil hydraulic properties and the corresponding influencing mechanisms are still poorly understood. In this study, soil moisture content (SMC), field capacity (FC) and saturated hydraulic conductivity (Ks) together with several basic soil properties under lightly degraded (LD), moderately degraded (MD) and severely degraded (SD) alpine swamp meadow were investigated; the variations in SMC, FC and Ks with alpine swamp meadow degradation and their dominant influencing factors were analysed. The results showed that SMC and FC decreased consistently from LD to SD, while Ks decreased from LD to MD and then increased from MD to SD, following the order of LD > SD > MD. Significant differences in soil hydraulic properties between degradation degrees were found in the upper soil layers (0–20 cm), indicating that the influences of degradation were most pronounced in the topsoils. FC was positively correlated with capillary porosity, water-stable aggregates, soil organic carbon, and silt and clay content; Ks was positively correlated with non-capillary porosity (NCP). Relative to other soil properties, soil porosity is the dominant factor influencing FC and Ks. Capillary porosity explained 91.1 % of total variance in FC, and NCP explained 97.3 % of total variance in Ks. The combined effect of disappearing root activities and increasing sand content was responsible for the inconsistent patterns of NCP and Ks. Our findings suggest that alpine swamp meadow degradation would inevitably lead to reduced water holding capacity and rainfall infiltration. This study provides a more comprehensive understanding of the soil hydrological effects of vegetation degradation. Further hydrological modelling studies in the Tibetan Plateau and similar regions are recommended to understand the effects of degraded alpine swamp meadows on soil hydraulic properties.

Multispectral Lidar System can emit simultaneous laser pulses at the different wavelengths. The reflected multispectral energy is captured through a receiver of the sensor, and the return signal together with the position and orientation information of sensor is recorded. These recorded data are solved with GNSS/IMU data for further post-processing, forming high density multispectral 3D point clouds. As the first commercial multispectral airborne Lidar sensor, Optech Titan system is capable of collecting point clouds data from all three channels at 532nm visible (Green), at 1064 nm near infrared (NIR) and at 1550nm intermediate infrared (IR). It has become a new source of data for 3D land cover classification. The paper presents an Object Based Image Analysis (OBIA) approach to only use multispectral Lidar point clouds datasets for 3D land cover classification. The approach consists of three steps. Firstly, multispectral intensity images are segmented into image objects on the basis of multi-resolution segmentation integrating different scale parameters. Secondly, intensity objects are classified into nine categories by using the customized features of classification indexes and a combination the multispectral reflectance with the vertical distribution of object features. Finally, accuracy assessment is conducted via comparing random reference samples points from google imagery tiles with the classification results. The classification results show higher overall accuracy for most of the land cover types. Over 90% of overall accuracy is achieved via using multispectral Lidar point clouds for 3D land cover classification.

With the rapid developments of the sensor technology, high spatial resolution imagery and airborne Lidar point clouds can be captured nowadays, which make classification, extraction, evaluation and analysis of a broad range of object features available. High resolution imagery, Lidar dataset and parcel map can be widely used for classification as information carriers. Therefore, refinement of objects classification is made possible for the urban land cover. The paper presents an approach to object based image analysis (OBIA) combing high spatial resolution imagery and airborne Lidar point clouds. The advanced workflow for urban land cover is designed with four components. Firstly, colour-infrared TrueOrtho photo and laser point clouds were pre-processed to derive the parcel map of water bodies and nDSM respectively. Secondly, image objects are created via multi-resolution image segmentation integrating scale parameter, the colour and shape properties with compactness criterion. Image can be subdivided into separate object regions. Thirdly, image objects classification is performed on the basis of segmentation and a rule set of knowledge decision tree. These objects imagery are classified into six classes such as water bodies, low vegetation/grass, tree, low building, high building and road. Finally, in order to assess the validity of the classification results for six classes, accuracy assessment is performed through comparing randomly distributed reference points of TrueOrtho imagery with the classification results, forming the confusion matrix and calculating overall accuracy and Kappa coefficient. The study area focuses on test site Vaihingen/Enz and a patch of test datasets comes from the benchmark of ISPRS WG III/4 test project. The classification results show higher overall accuracy for most types of urban land cover. Overall accuracy is 89.5% and Kappa coefficient equals to 0.865. The OBIA approach provides an effective and convenient way to combine high resolution imagery and Lidar ancillary data for classification of urban land cover.

In order to solve the automation of 3D indoor mapping task, a low cost multi-sensor robot laser scanning system is proposed in this paper. The multiple-sensor robot laser scanning system includes a panorama camera, a laser scanner, and an inertial measurement unit and etc., which are calibrated and synchronized together to achieve simultaneously collection of 3D indoor data. Experiments are undertaken in a typical indoor scene and the data generated by the proposed system are compared with ground truth data collected by a TLS scanner showing an accuracy of 99.2% below 0.25 meter, which explains the applicability and precision of the system in indoor mapping applications.

The algorithm to retrieve the ice water path (IWP) from Microwave Humidity Sounder (MHS) measurements of two window channels at 89 and 157 GHz was already developed, and the IWP retrieval products have been made operationally available since NOAA-15. An adaptation of the same algorithm to the Advanced Technology Microwave Sounder (ATMS) is not straightforward due to differences of central frequencies and fields-of-view (FOVs) of the window channels between ATMS and MHS. In this study, ATMS and MHS orbit data from May 2016 to April 2017 are first collocated using the Simultaneous Nadir Overpass (SNO) method. Then two linear relationships between ATMS and MHS window channels are established using the SNO data: one between MHS channel 1 (89.0 GHz, 15-km nadir resolution) and ATMS channel 16 (88.2 GHz, 32-km nadir resolution), and the other between MHS channel 2 (157.0 GHz, 15-km nadir resolution) and ATMS channel 17 (165.5 GHz, 16-km nadir resolution). Since MHS IWPs are calculated differently when the scattering parameters are small (0.19, and a linear interpolation within the interval of . The ATMS IWP is then derived using the modified algorithm by obtaining equivalent MHS brightness temperatures according to the regression relationships, while the MHS IWP is derived directly using the modified algorithm. Also, the ATMS results are collocated with MHS orbits for comparison purposes. The spatial distributions of ATMS- and MHS-derived IWPs are consistent. A quantitative analysis shows that the two results are approximately same with deviations smaller than about 0.02 kg m−2.

In order to evaluate the impact of human activities (mainly dam building) on the Changjiang River sediment discharging into the sea, the spatial–temporal variations in the sediment load of different tributaries of the river were analyzed to reveal the quantity, grain size and composition patterns of the sediment entering the sea. The results show that the timing of reduction in the sediment load of the main stream of the Changjiang was different from those associated with downstream and upstream sections, indicating the influences of the sub-catchments. Four stepwise reduction periods were identified, i.e., 1956–1969, 1970–1985, 1986–2002, and 2003–2010. The proportion of the sediment load originating from the Jinsha River continuously increased before 2003; after 2003, channel erosion in the main stream provided a major source of the sediment discharging into the sea. In addition, in response to dam construction, although mean grain size of the suspended sediment entering the sea did not change greatly with these different periods, the inter-annual variability for sediment composition or the relative contributions from the various tributaries changed considerably. Before 2003, the clay, silt and sand fractions of the river load were supplied directly by the upstream parts of the Changjiang; after 2003, although the clay component may still be originating mainly from the upstream areas, the source of the silt and sand components have been shifted to a large extent to the river bed erosion of the middle reach of the river. These observations imply that the load, grain size and sediment composition deposited over the coastal and shelf water adjacent to the river mouth may have changed rapidly recently, in response to the catchment changes.

This article develops a systematic methodology to consider the impacts of contaminants on regeneration network when synthesizing the integrated water network for batch processes. The method is based on the improved state-space superstructure where full configurations of the network are incorporated and concentrate streams of two outflows regeneration modules can be reused and further purified. To bridge the gap to industrial applications, regeneration modules with variable removal efficiencies for different types of contaminants and the inhibitory impact of toxic substances on regeneration network are considered. On these basis, an overall mixed-integer nonlinear programming (MINLP) model is formulated. The results of the case study show that the further reuse of concentrate streams decreases the fresh water consumption and cost- optimal network can be achieved when considering the effects of contaminants. In addition, the toxic substances in wastewater for regeneration also lead to the integration of different regeneration technologies.

Because polymer is a mixture composed of many repeat units, the properties of polymer with same repeat unit vary in different molecular weight distribution (MWD). MWD, in a sense, could be regarded as the formulation of polymer, producing tailored polyethylene (PE) with specified quality targets contains not only process design, but also formulated product design. Product design and process design problems were always solved separately in previous work, which led to a suboptimal solution. In this work, an integrated method for ethylene polymerization in solution with Ziegler-Natta catalyst is proposed to obtain optimal feed conditions and volume of reactor when polymer properties such as glass transition temperature and melt index are specified. In the proposed framework, MWD is found to be the key variable in the interplay between product design and process design. This optimization model is solved through nonlinear programming by maximizing profit which considers product revenue, material and reactor cost, and an illustrative example is presented to demonstrate the validity and advantages of the proposed approach.

Reactor network synthesis plays an important role in finding green and sustainable chemical production flowsheet. Previous researches were usually optimized under specific reaction path, leading to a suboptimal network structure, and less attention has been paid to heat integration between reactors. However, simultaneously optimizing these two problems with multiple reaction paths involved inevitably leads to a combinational explosion of computational complexities. Therefore, a hierarchical optimization approach for the heat-integrated complex reactor network is proposed in this work. In the first step, the reaction paths tree and its relationship with reactor networks are discussed by embedding different reaction pathways into reactor network superstructure. The corresponding mathematical model is formulated as a mixed integer non-linear programming (MINLP) problem and a number of alternative reactor networks with specified catalysts arrangement are obtained by the interactive iteration solving strategy. In the second step, heat integration between reactors is achieved by the synthesis of heat exchanger network, which leads to a significant decrease in the total energy consumption. Ultimately, a case study for p-xylene production is introduced to demonstrate the effectiveness of the proposed method.

The launch of the Japanese Advanced Himawari Imager (AHI) on 7 October 2014 represents a new era of geostationary operational environmental satellite (GOES) imagers, providing many more channels than any previously launched GOES imagers for the first time. In this study, we compare the impacts of assimilating all AHI versus GOES-like infrared channels radiances on regional forecasts over Eastern China. The National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and Advanced Research Weather Research and Forecast model are employed. Positive impacts are obtained on quantitative precipitation forecasts (QPFs) associated with a typical summer precipitation case over eastern China in both set-ups, i.e. one assimilating all 10 AHI infrared channels (AHIA) and the other assimilating only four GOES-like AHI channels (AHIG). It is found that a southwest to northeast oriented band of the atmosphere with high water vapor content that was formed and moved inland with time under the influence of a subtropical high and an eastward-propagating middle-latitude trough was responsible for the persistent precipitation in the eastern China of the selected case. The AHIA experiment generated the largest improvement on QPFs due to it generating a wetter atmosphere in the middle and low troposphere over the ocean off the southeast coast of China than the AHIG experiment and a control experiment without assimilating any AHI channel.

A new synthesis methodology is developed to optimize process scheduling and direct heat integration schemes for multipurpose batch plants simultaneously. Firstly, the concept of associated task is introduced to describe the heat transfer requirements of production tasks and an improved State-Tasks-Network representation is adopted to capture all streams-streams, units-streams and units-units heat integration opportunities. Besides, the detailed design of heat transfer schemes and time sharing mechanism of heat exchangers are also involved in the proposed framework by considering heat transfer tasks-units allocation constraints. Then, a mixed integer linear programming (MILP) model is formulated to maximize profit. It should be noted that the trade-off between utility consumption and equipment cost has been taken into account. At last, an illustrative example is presented to demonstrate the validity and advantages of the proposed approach.

The existence of numerous water treatment/regeneration technologies capable of increasing the reuse opportunities for water–using network of batch processes calls for a systematic methodology to choose between them. Hence, this article aims to propose a new framework for the synthesis of total water network in batch plants when varieties of treatment units are available. First, a state space superstructure incorporating all feasible configurations for water reuse, recycle and treatment is proposed, which also accounts for the selection of different wastewater treatment technologies. Then, a mixed-integer nonlinear programming (MINLP) model embedded with the scheduling formulation of treatment subsystemis formulated based on the proposed superstructure. Furthermore, trade-off between treatment cost and fresh water consumptionis also taken into consideration in the formulation. In addition, five rules are developed in the pre-processing procedure to eliminate redundant water treatment technologies to simplify the topological structure of the synthesis problem. Two cases are presented to compare different categories of treatment units in total water network and demonstrate the effectiveness of the proposed methodology.

Here we compare human monoclonal antibody (MAb) production from mouse strains that carry disruptions of their endogenous mouse IgH/Igκ loci and harbor human IgM + Igκ (BABκ) or human IgM + Igκ + Igλ transloci (BABκ,λ). We found that whereas both strains proved effective for the isolation of antigen-specific IgM antibodies, many of the IgM MAbs elicited from BABκ comprise human μ chains that are associated with mouse λ chains. In contrast, BABκ,λ mice gave rise to fully functional, polymeric human IgM antibodies comprising both human IgH and human IgL chains. Therefore, the inclusion of a human Igλ translocus (in addition to the human IgH + Igκ transloci) not only diminishes problems of endogenous mouse Igλ expression but also provides a strain of mice that yields fully human MAbs to a wide range of antigens, as witnessed by the isolation of MAbs to human blood cells, tumor cell lines, and an immunoglobulin idiotype.