Analysis of Lake Stratification and Mixing and Its Influencing Factors over High Elevation Large and Small Lakes on the Tibetan Plateau

Lake stratification and mixing processes can influence gas and energy transport in the water column and water–atmosphere interactions, thus impacting limnology and local climate. Featuring the largest high-elevation inland lake zone in the world, comprehensive and comparative studies on the evolution of lake stratification and mixing and their driving forces are still quite limited. Here, using valuable temperature chain measurements in four large lakes (Nam Co, Dagze Co, Bangong Co, and Paiku Co) and a “small lake” adjacent to Nam Co, our objectives are to investigate the seasonal and diurnal variations of epilimnion depth (Ep, the most important layer in stratification and mixing process) and to analyze the driving force differences between “small lake” and Nam Co. Results indicate that Ep estimated by the methods of the absolute density difference (&lt;0.1 kg m−3) from the surface and the Lake-Analyzer were quite similar, with the former being more reliable and widely applicable. The stratification and mixing in the four large lakes showed a dimictic pattern, with obvious spring and autumn turnovers. Additionally, the stratification form during heat storage periods, with Ep quickly locating at depths of approximately 10–15 m, and, after that, increasing gradually to the lake bottom. Additionally, the diurnal variation in Ep can be evidenced both in the large and small lakes when temperature measurements above 3 m depth are included. For Nam Co, the dominant influencing factors for the seasonal variation of Ep were the heat budget components (turbulent heat fluxes and radiation components), while wind speed only had a relatively weak positive correlation (r = 0.23). In the “small lake”, radiation components and wind speed show high negative (r = −0.43 to −0.59) and positive (r = 0.46) correlation, with rare correlations for turbulent heat flux. These reported characteristics have significance for lake process modeling and evaluation in these high-elevation lakes.</p

Ion Exchange Membranes for Electrodialysis: A Comprehensive Review of Recent Advances

Electrodialysis related processes are effectively applied in desalination of sea and brackish water, waste water treatment, chemical process industry, and food and pharmaceutical industry. In this process, fundamental component is the ion exchange membrane (IEM), which allows the selective transport of ions. The evolvement of an IEM not only makes the process cleaner and energy-efficient but also recovers useful effluents that are now going to wastes. However ion-exchange membranes with better selectivity, less electrical resistance, good chemical, mechanical and thermal stability are appropriate for these processes. For the development of new IEMs, a lot of tactics have been applied in the last two decades. The intention of this paper is to briefly review synthetic aspects in the development of new ion-exchange membranes and their applications for electrodialysis related processes

Retrieval of All-Sky Land Surface Temperature Considering Penetration Effect Using Spaceborne Thermal and Microwave Radiometry

Thermal infrared (TIR) remote sensing (RS) has been widely adopted for monitoring land surface temperature (LST). However, its application has been limited to cloud-free conditions, resulting in a need for LST retrieval methods that combine microwave (MW) and TIR channels. This is especially crucial in areas frequently covered by clouds. One limitation of the current LST retrieval methods is the absence of considering the penetration effect (PE) of MW, which leads to great uncertainty in barren and sparsely vegetated areas. To address this issue, this study proposes a new perspective that considers the PE to merge the LST retrieved from MW and TIR channels. The soil temperature integral equation is simplified based on the soil temperature and water content profiles. Consequently, a PE-based model is developed to convert the effective soil temperature into LST and merge the LST estimated from passive MW observations with those from moderate resolution imaging spectroradiometer (MODIS) LST products. The model considering PE performs better than the method that does not consider PE, as demonstrated by higher $R$ and lower root-mean-square error (RMSE) values. The PE-based model is then applied to Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) data, and the estimated LST is found to fit well with the MODIS LST product ( $R$ = 0.91). Using this model, an all-sky LST is retrieved by merging passive MW observations and MODIS LST products. Validation of the model at eight ground-based stations over the Tibetan Plateau (TP) demonstrates its reasonable accuracy in both clear-sky and cloudy conditions.</p

Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation

Multimodal machine translation (MMT) aims to improve translation quality by incorporating information from other modalities, such as vision. Previous MMT systems mainly focus on better access and use of visual information and tend to validate their methods on image-related datasets. These studies face two challenges. First, they can only utilize triple data (bilingual texts with images), which is scarce; second, current benchmarks are relatively restricted and do not correspond to realistic scenarios. Therefore, this paper correspondingly establishes new methods and new datasets for MMT. First, we propose a framework 2/3-Triplet with two new approaches to enhance MMT by utilizing large-scale non-triple data: monolingual image-text data and parallel text-only data. Second, we construct an English-Chinese {e}-commercial {m}ulti{m}odal {t}ranslation dataset (including training and testing), named EMMT, where its test set is carefully selected as some words are ambiguous and shall be translated mistakenly without the help of images. Experiments show that our method is more suitable for real-world scenarios and can significantly improve translation performance by using more non-triple data. In addition, our model also rivals various SOTA models in conventional multimodal translation benchmarks.Comment: 8 pages, ACL 2023 Findin

Channel Tracking for RIS-aided mmWave Communications Under High Mobility Scenarios

The emerging reconfigurable intelligent surface (RIS) technology is promising for applications in the millimeter wave (mmWave) communication systems to effectively compensate for propagation loss or tackle the blockage issue. Considering the high mobility of users in realistic scenarios, it is essential to adjust the phase shifts in real time to align the beam towards the mobile users, which requires to frequently estimate the channel state information. Hence, it is imperative to design efficient channel tracking schemes to avoid the complex channel estimation procedure. In this paper, we develop a novel channel tracking scheme with two advantages over conventional schemes. First, our tracking scheme is based on the cascaded angles at the RIS instead of the accurate angle values, which is more practical. Second, it can be employed under a more general setting where the noise can be non-Gaussian. Simulation results show the high tracking accuracy of our proposed scheme, and validate the superiority to the existing EKF-based tracking scheme.Comment: 5 pages, 4 figures, Submitted to IEE

Texygen: A Benchmarking Platform for Text Generation Models

We introduce Texygen, a benchmarking platform to support research on open-domain text generation models. Texygen has not only implemented a majority of text generation models, but also covered a set of metrics that evaluate the diversity, the quality and the consistency of the generated texts. The Texygen platform could help standardize the research on text generation and facilitate the sharing of fine-tuned open-source implementations among researchers for their work. As a consequence, this would help in improving the reproductivity and reliability of future research work in text generation.Comment: 4 page

Learning-based real-time imaging through dynamic scattering media

Imaging through dynamic scattering media is one of the most challenging yet fascinating problems in optics, with applications spanning from biological detection to remote sensing. In this study, we propose a comprehensive learning-based technique that facilitates real-time, non-invasive, incoherent imaging of real-world objects through dense and dynamic scattering media. We conduct extensive experiments, demonstrating the capability of our technique to see through turbid water and natural fog. The experimental results indicate that the proposed technique surpasses existing approaches in numerous aspects and holds significant potential for imaging applications across a broad spectrum of disciplines

Winter Daytime Warming and Shift in Summer Monsoon Increase Plant Cover and Net CO2 Uptake in a Central Tibetan Alpine Steppe Ecosystem

Over the past decades, human-induced climate change has led to a widespread wetting and warming of the Tibetan Plateau (TP), affecting both ecosystems and the carbon cycling therein. Whether the previously observed climate changes stimulate carbon uptake via enhanced photosynthesis or carbon loss via enhanced soil respiration remains unclear. Here we present 14 years of observations of carbon fluxes, meteorological variables and remotely sensed plant cover estimations from a central Tibetan alpine steppe ecosystem at Nam Co, the third largest lake on the TP. Using modified Mann-Kendall trend tests, we found a significant increasing daily net carbon uptake of 0.5 g C m−2 decade−1, which can be explained by a widespread greening at the southern shore of lake Nam Co. The Plateau-wide changes in temperature and precipitation are locally expressed as an increasing diurnal temperature range during winter, higher water availability during spring, higher cloud cover during early summer and less water availability during late summer. While these changes differ over the course of the year, they tend to stimulate plant growth more than microbial respiration, leading to an increased carbon uptake during all seasons. This study indicates that during the 14 years study period, a higher amplitude in winter temperatures and an earlier summer monsoon promote carbon uptake in a central Tibetan alpine steppe ecosystem