Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyses of infrequent (quasi‐decadal) large groundwater recharge events in the northern Great Basin: Their importance for groundwater availability, use, and management

There has been a considerable amount of research linking climatic variability to hydrologic responses in the western United States. Although much effort has been spent to assess and predict changes in surface water resources, little has been done to understand how climatic events and changes affect groundwater resources. This study focuses on characterizing and quantifying the effects of large, multiyear, quasi‐decadal groundwater recharge events in the northern Utah portion of the Great Basin for the period 1960–2013. Annual groundwater level data were analyzed with climatic data to characterize climatic conditions and frequency of these large recharge events. Using observed water‐level changes and multivariate analysis, five large groundwater recharge events were identified with a frequency of about 11–13 years. These events were generally characterized as having above‐average annual precipitation and snow water equivalent and below‐average seasonal temperatures, especially during the spring (April through June). Existing groundwater flow models for several basins within the study area were used to quantify changes in groundwater storage from these events. Simulated groundwater storage increases per basin from a single recharge event ranged from about 115 to 205 Mm3. Extrapolating these amounts over the entire northern Great Basin indicates that a single large quasi‐decadal recharge event could result in billions of cubic meters of groundwater storage. Understanding the role of these large quasi‐decadal recharge events in replenishing aquifers and sustaining water supplies is crucial for long‐term groundwater management.
Boron abundances and isotopic ratios of olivine grains on Itokawa returned by the Hayabusa spacecraft

We report the B abundances and isotopic ratios of two olivine grains from the S‐type asteroid Itokawa sampled by the Hayabusa spacecraft. Olivine grains from the Dar al Gani (DaG) 989 LL6 chondrite were used as a reference. Since we analyzed polished thin sections in both cases, we expect the contribution from the solar wind B (rich in 10B) to be minimal because the solar wind was implanted only within very thin layers of the grain surface. The Itokawa and DaG 989 olivine grains have homogeneous B abundances (~400 ppb) and 11B/10B ratios compatible with the terrestrial standard and bulk chondrites. The observed homogeneous B abundances and isotopic ratios of the Itokawa olivine grains are likely the result of thermal metamorphism which occurred in the parent asteroid of Itokawa, which had a similar composition as LL chondrites. The chondritic B isotopic ratios of the Itokawa samples suggest that they contain little cosmogenic B (from cosmic‐ray spallation reactions) rich in 10B. This observation is consistent with the short cosmic‐ray exposure ages of Itokawa samples inferred from the small concentrations of cosmogenic 21Ne. If other Itokawa samples have little cosmogenic B as well, the enrichment in 10B found previously on the surface of another Itokawa particle (as opposed to the bulk grain study here) may be attributed to implanted solar wind B.
Local ice formation via liquid water growth in slowly ascending humid aerosol/liquid water layers observed with ground‐based lidars and radiosondes

Observations with lidars at Wuhan (30.5°N, 114.4°E), China, from 2010 to 2013 captured nine cases of slowly ascending humid aerosol/liquid water layers that occurred at altitudes of ~2–4 km in winter. Each of them was almost transparent initially with the backscatter ratio far less than 7.0 and depolarization ratio less than 0.03. With a slow ascent, the layer developed into a nearly opaque liquid cloud layer and then ice crystals abruptly formed at the upper edge of the cloud layer with very high liquid water content. The ice crystals likely came from water drop freezing. The freezing temperatures estimated from radiosonde measurements were −3 to −8°C. For two available long‐lived (>16 h) cases, the layer was observed to always lie just below an inversion layer. The ice development on the layer was followed by rainfall.
Shoulder and arm pain: A “red herring” chief complaint in a smoker diagnosed with Pancoast-Tobías syndrome

Introduction: A Pancoast tumor is a rare apical lung cancer that can lead to a constellation of signs and symptoms known as Pancoast-Tobías syndrome, which involves ipsilateral shoulder and arm pain, Horner’s syndrome, and atrophy of the intrinsic hand muscles. As a Pancoast tumor progresses, it first invades the brachial plexus and causes unrelenting shoulder and arm pain in the C8-T2 nerve distributions. However, when evaluating this presenting symptom, it is often considered benign, especially in aging patients. Consequently, Pancoast-Tobías syndrome is easily misdiagnosed or delayed in diagnosis, resulting in poor prognosis, particularly for patients with advanced disease. Case Report: We present the case of an elderly Afro-Caribbean woman with a 24-pack-year smoking history who presented with unrelenting pain to the right shoulder and arm for two months. During that time, she visited multiple family physicians. Onset of pain coincided with a wasp sting that was complicated by a large local reaction. Despite clinical resolution of the inflammatory reaction, the pain persisted even with multi-modal oral analgesics. Detailed physical examination later revealed ipsilateral atrophy of the intrinsic hand muscles and focal chest findings concerning for an underlying consolidative lung process. This prompted diagnostic chest imaging, which identified a right-sided Pancoast tumor aggressively invading adjacent structures. She soon developed Horner’s syndrome, resulting in clinicoradiologic confirmation of Pancoast-Tobías syndrome. Conclusion: It is important to consider Pancoast-Tobías syndrome as an important differential of unrelenting shoulder and arm pain, even in the absence of pulmonary symptoms, in patients with risk factors for lung cancer, such as chronic smokers.
How to effectively obtain metadata from remote sensing big data

What can be considered big data when dealing with remote sensing imagery? In general terms, big data is defined as data requiring high management capabilities characterized by 3 V’s: Volume, Velocity and Variety. In the past, (e.g. 1975), considering the computational and databases resources available, a series of Landsat-1 imagery from the same region could be considered big data. Nowadays, several satellites are available, and they produce massive amounts of data. Certainly, an image data set obtained by a single satellite, for a specific region and along time, fills the 3 V’s requirements to be considered big data as well. In order to deal with remote sensing big data, we propose to explore the generation of metadata based on the detection of simple features. Besides the intrinsic geographic information on every remote sensing scene, no additional metadata is usually considered. We propose basic image processing algorithms to detect basic well-known patterns, and include them as tags, such as cloud, shadow, stadium, vegetation, and water, according to what is detectable at each spatial resolution. In this work we show preliminary results using imagery from RapidEye sensor, with 5 meter spatial resolution, composed by two full coverages of Brazil with RapidEye multispectral imagery (around 40k scenes).
GRDL: A New Global Reservoir Area‐Storage‐Depth Data Set Derived Through Deep Learning‐Based Bathymetry Reconstruction

Reservoirs play a critical role in the global water cycle by regulating the flow of water from the environment into human systems. Accurate estimation of the area‐storage‐depth relationships for global reservoirs is essential for effective hydrological modeling and reservoir storage monitoring. Bathymetry reconstruction presents a promising approach to derive this information. Current bathymetry methods either rely on simple approximations or are constrained by dependence on altimetry data or field survey data. To overcome these limitations, this study presents a pioneering approach involving training a deep learning model to reconstruct bathymetry and establish precise area‐storage‐depth relationships. We trained the deep learning model with approximately 160,000 simulated reservoirs derived from Shuttle Radar Topography Mission (SRTM) and fine‐tuned the model based on hundreds of reservoirs with bathymetry data. By employing the trained model and SRTM, the bathymetry of 7,250 reservoirs in the Global Reservoir and Dam Database were subsequently reconstructed. The method is validated against comprehensive reference data sets, including 54 test reservoirs with bathymetry data, 118 satellite altimetry‐based reservoirs, and 68 LiDAR‐based reservoirs. The reconstructed bathymetry achieves a mean absolute error of 7.87 m and a mean error of +2.05 m for the test reservoirs. The validation against satellite altimetry and LiDAR‐based references significantly outperforms previous geometric approximation techniques, with median normalized root mean square error (NRMSE) values of 20.6% for area‐storage and 22.1% for area‐level curves. Additionally, the reservoir storage variations are estimated with precision, outperforming previous methods. The proposed deep learning‐based approach presents a robust solution for accurate reservoir bathymetry estimation and establishes more reliable area‐storage‐depth relationships for reservoirs worldwide.
The Effect of Forest Tenure on Forest Composition in a Miao Area of Guizhou, China

Guizhou Province is one of the most expansive and important forest regions in China. Traditional Miao people have inhabited the mountains and managed the forest resources of this region for more than 1000 years. In the 1980s, the Chinese government transferred many collectively owned forests to individual household control. Since then, there have been 3 forest tenure types in Miao areas: state forests, collective forests, and household forests. The Collective Forest Tenure Reform was implemented nationwide starting in 2005, and in Guizhou in 2007, to develop and stabilize the forest tenure institutions. To investigate the effect of tenure type on the floristic composition and size structure of local forests, we conducted inventories in replicate forests under each type of tenure in 3 traditional Miao villages in Leishan County. Results showed that tree species richness and diversity were significantly higher in state and collective forests than in household forests; no significant differences were detected among villages. Cunninghamia lanceolata, an important local timber species, was most abundant in household forests, while higher proportions of associated broadleaf and pine species were recorded in state and collective forests. The lack of significant differences between state and collective forests for most measurement variables suggests that the inherent similarities between these 2 tenure types created by long-term use and management by the Miao have largely overshadowed the effects of more recent management efforts by the state. Each tenure regime offers different benefits, and a portfolio including all 3 tenure types would best provide the ecosystem services and economic opportunities required by forest-dependent communities.
Pharmacological effects of Ginkgo biloba and its active ingredients in the treatment of cardiovascular diseases

Cardiovascular diseases (CVDs) are the leading cause of deaths among adult population in China, whose incidence and mortality rates continue rising with a trend towards younger ages. Although numerous drugs are used to prevent and treat CVDs, the long-term prognosis of patients with CVDs is still not very satisfactory. Therefore, there is an urgent need to explore new safe and effective drugs for CVDs in clinical practice. As a traditional medicine in China, Ginkgo biloba has a history of more than 2,000 years in clinical practice experience, meanwhile its therapeutic role in CVDs has been revealed in more and more clinical studies. As a living fossil of traditional Chinese medicine, Ginkgo biloba , a deciduous tree plant of the Ginkgo family and genus, contains active medicinal ingredients in its leaves, kernels, outer skins and roots. Ginkgo biloba leaves are the main medicinal part, which are mild in nature, sweet, bitter and astringent in taste. Ginkgo biloba activates the circulation of blood, relieves pains, and lowers lipids, which is mainly used to treat chest obstruction and heartaches, stroke hemiplegia, and asthma, as well as hyperlipidemia, etc. This paper aims to review the medicinal properties of Ginkgo biloba and its active ingredients in the treatment of CVDs, with emphasis on the antioxidant, apoptosis-inhibiting, anti-inflammatory mechanism and other mechanisms, so as to provide a scientific basis for their clinical application as well as further development and utilization.
Lychee peel extract attenuates depression-like behavior in a rat model of chronic restraint stress

The utilization of vegetable or fruit by-products as functional foods to protect against several diseases, including depression, has gained great interest. Litchi chinensis Sonn. (lychee; LC) peel is considered an important source of polyphenols, which could be beneficial for mental health. We investigated the antidepressant effect and possible mechanisms of LC peel extract in a rat model of chronic restraint stress (CRS). Five groups of male rats were used: naïve control, vehicle (distilled water) + stress, fluoxetine 20 mg/kg + stress, LC peel extract 150 mg/kg + stress, and LC peel extract 300 mg/kg + stress. All groups except the naïve control were exposed to CRS (6 hours/day for 56 days) to induce depression-like activity. Only the high dose of LC peel extract exerted an antidepressant effect on the rat’s behavior patterns in both the tail suspension ( p < 0.001) and forced swimming ( p < 0.001) tests, restored brain serotonin levels ( p < 0.001), decreased lipid peroxidation ( p < 0.05), improved superoxide dismutase activity ( p < 0.001), and reduced plasma corticosterone (CORT) ( p < 0.05). This study provides the first evidence that an ethanolic extract of LC peel possesses antidepressant activity mediated by its antioxidant effect, regulating the CORT hormone and normalizing serotonin concentrations.
A novel approach for enhanced abnormal action recognition via coarse and precise detection stage

With the proliferation of urban video surveillance systems, the abundance of surveillance video data has emerged as a pivotal asset for enhancing public safety. Within these video archives, the identification of abnormal human actions carries profound implications for security incidents. Nevertheless, existing surveillance systems primarily rely on conventional algorithms, leading to both missed incidents and false alarms. To address the challenge of automating multi-object surveillance video analysis, this study introduces a comprehensive method for the detection and recognition of multi-object abnormal actions. This study comprises a two-stage framework: the coarse detection stage employs an enhanced YOWOv2E model for spatio-temporal action detection, while the precise detection stage utilizes a two-stream network for precise action classification. In parallel, this paper presents the PSA-Dataset to address the current limitations in the field of abnormal action detection. Experimental results, collected from both public datasets and a self-built dataset, illustrate the effectiveness of the proposed method in identifying a wide spectrum of abnormal actions. This work offers valuable insights for automating the analysis of human actions in videos pertaining to public security.
Interactive Control of Lower Limb Exoskeleton Robots: A Review

The interactive control of lower limb exoskeleton robots (LLERs) is important to achieve compliance and safety. Significant challenges in the interactive control of LLERs include how to accurately recognize human motion intention, how to accurately recognize the motion environment, and how to achieve natural, stable, and accurate interactive control in accordance with the human motion intention and the environment. This article presents a detailed classification of motion intention recognition, interactive control based on motion intention, and interactive control based on the LLER’s motion environment and summarizes the methodologies in each category. In addition, the advantages and disadvantages of motion intention recognition, motion environment recognition, and interactive control based on the motion intention and motion environment are analyzed from a macroscopic perspective, and current trends are discussed. Finally, this article explores which requirements should be met in future LLER applications to improve the naturalness, stability, and accuracy of interactive control.
Organoplatinum Chemistry Related to Alkane Oxidation: The Effect of a Nitro Substituent in Ligands Having an Appended Phenol Group

The organoplatinum chemistry of the ligands 2-C5H4N-CH2-NH-C6H3-2-OH-5-X (L1, X = H; L3, X = NO2) and 2-C5H4N-CH=N-C6H3-2-OH-5-X (L2, X = H; L4, X = NO2), which contain an appended phenol substituent, is described. Comparisons are made between the ligands with amine or imine groups (L1, L3 vs. L2, L4) and ligands with X = H or NO2 (L1, L2 vs. L3, L4), and major differences are observed. Thus, on reaction with the cycloneophylplatinum(II) complex [{Pt(CH2CMe2C6H4)(μ-SMe2)}2], ligands L1, L2 and L4 give the corresponding platinum(II) complexes [Pt(CH2CMe2C6H4)(κ2-N,N′-L)], containing a Pt··HO hydrogen bond, whereas L3 gives a mixture of isomeric platinum(IV) hydride complexes [PtH(CH2CMe2C6H4)(κ3-N,N′,O-L3-H)], which are formed by oxidative addition of the phenol O-H bond and which react further with oxygen to give [Pt(OH)(CH2CMe2C6H4)(κ3-N,N′,O-L3-H)]. The differences in reactivity are proposed to be due to the greater acidity of the nitro-substituted phenol groups in L3 and L4 and to the greater ability of the deprotonated amine ligand L3 over L4 to stabilize platinum(IV) by adopting the fac-κ3-N,N′,O-L3-H coordination mode.
Geometric methods in quantum information and entanglement variational principle

Geometrical methods in quantum information are very promising for both providing technical tools and intuition into difficult control or optimization problems. Moreover, they are of fundamental importance in connecting pure geometrical theories, like GR, to quantum mechanics, like in the AdS/CFT correspondence. In this paper, we first make a survey of the most important settings in which geometrical methods have proven useful to quantum information theory. Then, we lay down a general framework for an action principle for quantum resources like entanglement, coherence, and anti-flatness. We discuss the case of a two-qubit system.
DSCA-PSPNet: Dynamic spatial-channel attention pyramid scene parsing network for sugarcane field segmentation in satellite imagery

Sugarcane plays a vital role in many global economies, and its efficient cultivation is critical for sustainable development. A central challenge in sugarcane yield prediction and cultivation management is the precise segmentation of sugarcane fields from satellite imagery. This task is complicated by numerous factors, including varying environmental conditions, scale variability, and spectral similarities between crops and non-crop elements. To address these segmentation challenges, we introduce DSCA-PSPNet, a novel deep learning model with a unique architecture that combines a modified ResNet34 backbone, the Pyramid Scene Parsing Network (PSPNet), and newly proposed Dynamic Squeeze-and-Excitation Context (D-scSE) blocks. Our model effectively adapts to discern the importance of both spatial and channel-wise information, providing superior feature representation for sugarcane fields. We have also created a comprehensive high-resolution satellite imagery dataset from Guangxi’s Fusui County, captured on December 17, 2017, which encompasses a broad spectrum of sugarcane field characteristics and environmental conditions. In comparative studies, DSCA-PSPNet outperforms other state-of-the-art models, achieving an Intersection over Union (IoU) of 87.58%, an accuracy of 92.34%, a precision of 93.80%, a recall of 93.21%, and an F1-Score of 92.38%. Application tests on an RTX 3090 GPU, with input image resolutions of 512 × 512, yielded a prediction time of 4.57ms, a parameter size of 22.57MB, GFLOPs of 11.41, and a memory size of 84.47MB. An ablation study emphasized the vital role of the D-scSE module in enhancing DSCA-PSPNet’s performance. Our contributions in dataset generation and model development open new avenues for tackling the complexities of sugarcane field segmentation, thus contributing to advances in precision agriculture. The source code and dataset will be available on the GitHub repository https://github.com/JulioYuan/DSCA-PSPNet/tree/main.
Developing a computational ontology to understand the relational aspects of audience formation [version 1; peer review: 2 approved]

In this article, we discuss an innovative audience research methodology developed for the AHRC-funded ‘ Beyond the Multiplex: Audiences for Specialised Film in English Regions ’ project (BtM). The project combines a computational ontology with a mixed-methods approach drawn from both the social sciences and the humanities, enabling research to be conducted both at scale and in depth, producing complex relational analyses of audiences. BtM aims to understand how we might enable a wide range of audiences to participate in a more diverse film culture, and embrace the wealth of films beyond the mainstream in order to optimise the cultural value of engaging with less familiar films. BtM collects data through a three-wave survey of film audience members’ practices, semi-structured interviews and film-elicitation groups with audience members alongside interviews with policy and industry experts, and analyses of key policy and industry documents. Bringing each of these datasets together within our ontology enables us to map relationships
Quantitative assessment of the potential for soil improvement by planting Miscanthus on saline- alkaline soil and the underlying microbial mechanism

Phytoremediation using Miscanthus is a valuable technique for the restoration of saline- alkaline soil, but the potential is not quantified. To assess this potential, a 5 years field experiment was conducted on saline- alkaline soils with three genotypes of Miscanthus and an unplanted control. The bulk soil (0– 20- cm depth) physicochemical properties were measured annually from 2015 to 2018, while the bacterial and fungal community structures of the rhizosphere soil were investigated by high-throughput sequencing after 5 years under Miscanthus (2018). The results showed that Miscanthus cultivation on saline-