Hydrogeology of the Pearl River Delta, southern China

The study describes the hydrogeological setting of the Pearl River Delta, a sub-tropical area of southern China encompassing the metropolises of Guangzhou, Shenzhen, Hong Kong and Macau. In the last 40 years, a booming economy and a population of about 60 million has increased water demand satisfied by a huge system of dams and reservoirs. Aquifers in the studied area are underutilized and only a few recent studies have addressed hydrogeological characterization at a local scale. Understanding groundwater dynamics of the Pearl River Delta is important for developing additional water supplies, understanding and mitigating groundwater pollution, and for implementing ‘Sponge City' concepts. Via a collection of data from literature and field surveys, the hydrogeological setting of the area is synthetized and represented through thematic maps, cross sections and a hydro-stratigraphic column. Hydrogeological conceptual models are developed that describe the groundwater dynamics in urban and rural areas within the Pearl River Delta

Mind's Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language Models

Large language models (LLMs) have achieved remarkable advancements in the field of natural language processing. However, the sheer scale and computational demands of these models present formidable challenges when considering their practical deployment in resource-constrained contexts. While techniques such as chain-of-thought (CoT) distillation have displayed promise in distilling LLMs into small language models (SLMs), there is a risk that distilled SLMs may still carry over flawed reasoning or hallucinations inherited from their LLM counterparts. To address these issues, we propose a twofold methodology: First, we introduce a novel method for distilling the self-evaluation capability inherent in LLMs into SLMs, which aims to mitigate the adverse effects of erroneous reasoning and reduce hallucinations. Second, we advocate for a comprehensive distillation process that incorporates multiple distinct chain-of-thought and self-evaluation paradigms and ensures a more holistic and robust knowledge transfer into SLMs. Experiments on three NLP benchmarks demonstrate that our method significantly improves the performance of distilled SLMs and sheds light on the path towards developing smaller models closely aligned with human cognition.Comment: 13 pages, 5 figure

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte

Calibration and Estimation of Attitude Errors for a Rotating Fan-Beam Scatterometer Using Calibration Ground Stations

9 pages, 9 figures, 1 table.-- © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe rotating fan-beam scatterometer (RFSCAT) onboard Chinese-French Oceanic SATellite (CFOSAT) due to launch in 2018 is a new type of radar scatterometer system for ocean surface wind vector measurement. It can give observations with more azimuth and incidence angles for a single wind vector cell (WVC) than other available scatterometers. This has been proved effective in bettering the retrieved wind quality by the simulation approach. However, its innovative observing geometry is challenging for the coming in-orbit external calibration. In this paper, CFOSAT attitude errors are estimated, and its antenna gain pattern is monitored and verified based on the external calibration strategy of a Ku-band scatterometer employing calibration ground stations (CGSs). The effects of satellite attitude errors on the measurements are also analyzed, together with simulation results for the external calibration. It is shown that a gain pattern with accuracy of 0.08 dB and attitude errors within 0.025° are achieved. © 2008-2012 IEEEPeer Reviewe

Provably Secure Group Key Management Approach Based upon Hyper-sphere

Abstract. Secure group communication systems have become increasingly important for many emerging network applications. An efficient and robust group key management approach is indispensable to a secure group communication system. Motivated by the theory of hyper-sphere, this paper presents a new group key management approach with a group controller GC. In our new design, a hypersphere is constructed for a group and each member in the group corresponds to a point on the hyper sphere, which is called the member’s private point. The GC computes the central point of the hyper-sphere, intuitively, whose “distance ” from each member’s private point is identical. The central point is published such that each member can compute a common group key, using a function by taking each member’s private point and the central point of the hyper-sphere as the input. This approach is provably secure under the pseudo-random function (PRF) assumption. Compared with other similar schemes, by both theoretical analysis and experiments, our scheme (1) has significantly reduced memory and computation load for each group member; (2) can efficiently deal with massive membership change with only two re-keying messages, i.e., the central point of the hypersphere and a random number; and (3) is efficient and very scalable for large-size groups

Towards carbon-neutral sustainable development of China

As a major economy with large amounts of greenhouse gas (GHG) emissions and ecosystem carbon sink, China’s commitment and pathway towards carbon neutrality is of global importance. Faced with the dual challenges of sustained economic growth and environmental protection, there is pressing need to integrate scientific knowledge from multiple disciplines to support policymaking on emission mitigation and carbon sink enhancement. This focus issue, with a companion workshop with the same theme, offers an opportunity to meet such need. With a total of 21 published papers, the focus issue provides more solid evidence of intensifying weather extremes caused by anthropogenic emissions, evaluates the potential of exploitation of terrestrial carbon sink which is in turn under the threat of warming, and reveals the challenges and opportunities of anthropogenic emission mitigation from perspectives of GHG types, economic sectors, environmental co-benefits, and disproportional impacts across the stakeholders. A comprehensive framework to combine data and models from related disciplines is a crucial next step to form integrated information much needed for climate action

Preparation and Optical Properties of Infrared Transparent 3Y-TZP Ceramics

In the present study, a tough tetragonal zirconia polycrystalline (Y-TZP) material was developed for use in high-speed infrared windows and domes. The influence of the preparation procedure and the microstructure on the material’s optical properties was evaluated by SEM and FT-IR spectroscopy. It was revealed that a high transmittance up to 77% in the three- to five-micrometer IR region could be obtained when the sample was pre-sintered at 1225 °C and subjected to hot isostatic pressing (HIP) at 1275 °C for two hours. The infrared transmittance and emittance at elevated temperature were also examined. The in-line transmittance remained stable as the temperature increased to 427 °C, with degradation being observed only near the infrared cutoff edge. Additionally, the emittance property of 3Y-TZP ceramic at high temperature was found to be superior to those of sapphire and spinel. Overall, the results indicate that Y-TZP ceramic is a potential candidate for high-speed infrared windows and domes