Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-Term Temporal Scales of Hydrosphere Changes Observed by GPS Over Europe: A Comparison With GRACE and ENSO

Hydrogeodesy can benefit greatly from the use of global positioning system (GPS) displacements to analyze local changes in the hydrosphere, which the commonly used Gravity Recovery and Climate Experiment (GRACE) mission is unable to provide due to coarse spatial resolution. Hydrosphere changes recorded by GPS are unfortunately hidden among the other signals to which the system is also sensitive, so the sensitivity of GPS to changes in the hydrosphere on temporal scales from pluri-annual to decadal is questionable. We focus on hydrosphere signatures present on these long-term temporal scales as observed by GPS through the vertical displacement time series (DTS) of 122 permanent stations over Europe and compare them to the DTS derived from GRACE for GPS locations. Our methodology is based on the weighted Savitzky–Golay (S-G) filter, an underestimated filter in the field of geodetic time-series analysis. We show that the correspondence between GPS and GRACE on long-term temporal scales is generally strong, but decreases for coastal regions and regions where the coarse gridding of GRACE does not capture local hydrosphere effects. Furthermore, the negative correlation with El Niño Southern Oscillations (ENSO) is confirmed for Europe.
The diagnosis and management of vulval intraepithelial neoplasia

High‐risk human papilloma virus (HPV)‐associated vulval high‐grade squamous intraepithelial lesions and HPV‐independent differentiated vulval intraepithelial lesions represent distinct disease processes with different risks of progression to vulval squamous cell carcinoma. First‐line medical management is appropriate for women with biopsy‐confirmed vulval high‐grade squamous intraepithelial lesions who have been adequately counselled on the risk of occult invasive disease. Topical treatment should be undertaken in the context of a specialist clinic because it is not suitable for all patients. Surgical management is needed for differentiated vulval intraepithelial neoplasia.
Evaluating Methane Emissions From Decommissioned Unconventional Petroleum Wells in British Columbia, Canada

Hundreds of thousands of unconventional natural gas wells recently constructed across North America have transformed the global energy landscape and generated widespread concern relating to fugitive methane leakage. To date, no studies have evaluated the integrity of unconventional wells post‐abandonment. Here, we evaluated emissions at nine decommissioned unconventional wells within the Montney region of British Columbia, Canada and found two exhibited co‐emission of CH4 and CO2 from surrounding soils indicating integrity failure, releasing up to ∼2,000 kg of CO2‐eq/yr into the atmosphere. A further three wells exhibited statistically significant anomalous CO2 fluxes of ∼500 kg/year from surficial soils around the well, likely associated with minor integrity failure and derived from near total soil‐based aerobic oxidation of fugitive CH4. These findings suggest that more than half of decommissioned unconventional wells may generate emissions, however only relatively small contributions to GHG emissions result that are significantly mitigated through natural soils‐based CH4 oxidation.
ANTIMYCOTIC ACTIVITY OF THE ISOLATES OF LACTOBACTERIA FROM WA-TER AND MUSSELS OF BLACK SEA

Aim. Determination of the presence of antimycotic activity and its level in thirteen lactobacteria isolates from Black Sea water and mussels. Methods. Antimycotic activity of lactic acid bacteria (LAB) isolates was tested by agar diffusion method in 24-well plates. To determine character of action the plates were incubated for 7 days at 25 °C. Results. For the first time antimycotic activity of marine LAB from Odesa coast was estimated. The majority of the tested isolates of lactobateria exhibited antimycotic activity of high level completely inhibiting (in 100%) as mycelium growth as well as spore formation of Penicillium expansum UKM F-575 and Aspergillus niger UKM F-16706 on second day of the study. Four isolates (M.5.1, M.5.2, M.7.1, M.7.2) showed lower antimycotic activity (from 0 to 75%). Eight LAB isolates from seawater (W.1.1, W.1.2, W.1.3, W.1.4, W.1.5, W.1.дк, W.2.3, W.2.4) and one isolate from mussel liquor (M4.1) completely inhibited mycelial growth and sporulation of P. expansum UКМ F-575 and A. niger UКМ F-16706 even within seven days indicating fungicidal character of action. Conclusions. The most of tested LAB isolates from Black Sea exhibited high antimycotic activity against P. expansum UКМ F-575 and A. niger UКМ F-16706 indicator strains. The most active lactobacteria isolates were W.1. 1, W.1. 2, W.1. 3, W.1. 4, W.1. 5, W.1. дк, W.2. 3, W.2. 4 isolated from Black Sea water and M4.1 – from mussel liquor. The character of the revealed antimycotic activity was determined as fungicidal.
FPG-based transformer UAV tank target detection

UAV reconnaissance has become an important means of battlefield reconnaissance and has received much attention in the military development of various countries. Therefore, UAV target detection accuracy is an important factor that restricts the effectiveness of military reconnaissance. How to improve the efficiency of UAV target detection and design high-precision neural network algorithms is the main direction of research in this paper. In this paper, for the current UAV target detection accuracy is not high, the FPG-based Transformer algorithm is proposed, which is mainly based on the Transformer, the Transformer Block is improved, and at the same time, the FPG pyramid structure is introduced in the downstream task, and the results show that, after the improvement of the Transformer, it improves the target detection accuracy, which is conducive to improving the accuracy of UAV reconnaissance.
Implementation of COIL in mental health education: Outcomes of a collaboration between Spain and the United States nursing students

Collaborative International Online Learning (COIL) provides students meaningful and valuable engagement with students in another country to reach the goals of academic programs at the participating institutions. The strategy has demonstrated benefits for nursing students, including the acquisition of self-awareness, empathy, cultural humility, and leadership skills. The aim of this study was to share the results of the partnership in a COIL project using digital technology between a university in the United States and a university in Spain. The goals of this collaboration were twofold: to expose students in the U.S. and Spain to an international experience on mental health and to provide a comparative view of their respective health care systems. The chosen technique focuses on perceptions of mental health and how mental health care is delivered in clinical settings. A total of 166 students took part in this experience. The virtual exchange allowed students and faculty to share best practices in caring for people with mental illness. Nursing students providing care to COVID-19 patients reflected on lessons learned during the pandemic and its impact on community mental health. Small group activities such as icebreakers, homework assignments, and reflections were used to share and compare experiences. Students completed COIL surveys, which demonstrated that students from both countries perceived learning gains using evidence-based best practices related to key nursing concepts in a mutually respectful virtual exchange. The results show a perceived increase in the comprehension of mental health-related content and enhancement of clinical practice. Therefore, the COIL methodology is a feasible option as it allows for an intercultural exchange with an in-depth examination of content from the learners and teaching perspectives. In addition, it prepares students to work in international teams virtually.
Exploring In Vitro the Combination of Cistus × incanus L. and Castanea sativa Mill. Extracts as Food Supplement Ingredients against H. pylori Infection

Cistus spp. have been traditionally used for inflammatory and infectious disorders, including gastrointestinal ailments, in the Mediterranean area. Among them, Cistus × incanus L. is one of the most frequently cited species in the literature for a variety of biological activities which include inflammatory diseases. Cistus spp. aerial parts are rich in polyphenols such as condensed and hydrolysable tannins, procyanidins, and flavonoids, which show gastroprotective activities. The purpose of the present study is to investigate the biological activities of a hydroalcoholic extract from Cistus × incanus L. aerial parts in gastric epithelial cells (GES-1) infected with H. pylori. The extracts inhibited IL-8 and NF-κB induced by H. pylori and showed antibacterial activity after simulated digestion. Since our previous paper reported interesting results on the ability of Castanea sativa Mill. leaf extract to decrease inflammatory conditions in H. pylori-infected gastric cells, the combination of Castanea sativa and Cistus × incanus extracts was also investigated, showing strong anti-inflammatory activity and inhibition of bacterial adhesion. This association of botanicals is proposed herein as a novel food supplement capable of counteracting gastric inflammatory conditions.
Exogenous abscisic acid and sugar induce a cascade of ripening events associated with anthocyanin accumulation in cultured Pinot Noir grape berries

Fruit quality is dependent on various factors including flavour, texture and colour. These factors are determined by the ripening process, either climacteric or non-climacteric. In grape berry, which is non-climacteric, the process is signalled by a complex set of hormone changes. Abscisic acid (ABA) is one of the key hormones involved in ripening, while sugar availability also plays a significant role in certain ripening aspects such as anthocyanin production. To understand the relative influence of hormone and sugar signalling in situ can prove problematic due to the physiological and environmental (abiotic and biotic) factors at play in vineyards. Here we report on the use of in vitro detached berry culture to investigate the comparative significance of ABA and sugar in the regulation of Pinot noir berry anthocyanin production under controlled conditions. Using a factorial experimental design, pre-véraison berries were cultured on media with various concentrations of sucrose and ABA. After 15 days of in vitro culture, the berries were analysed for changes in metabolites, hormones and gene expression. Results illustrated a stimulatory effect of sucrose and ABA on enhancing berry colour and a corresponding increase in anthocyanins. Increased ABA concentration was able to boost anthocyanin production in berries when sucrose supply was low. The sucrose and ABA effects on berry anthocyanins were primarily manifested through the up-regulation of transcription factors and other genes in the phenylpropanoid pathway, while in other parts of the pathway a down-regulation of key proanthocyanindin transcription factors and genes corresponded to sharp reduction in berry proanthocyanidins, irrespective of sucrose supply. Similarly, increased ABA was correlated with a significant reduction in berry malic acid and associated regulatory genes. These findings suggest a predominance of berry ABA over berry sugar in coordinating the physiological and genetic regulation of anthocyanins and proanthocyanins in Pinot noir grape berries.
Electrical resistance tomography on two-phase flow based on compressed sensing theory

Considering the problem that the imaging accuracy by traditional algorithms for two-phase flow is not ideal, this paper produces the electrical resistance tomography (ERT) algorithms based on compressed sensing (CS) theory, which focuses on three key issues of signal sparse representation, observation matrix optimization and signal reconstruction. Specifically, in order to improve the signal sparsity, Haar wavelets are adopted as the sparse basis, and the pixel index prior information is introduced. To make the sensitivity matrix in ERT better meet the CS theory requirement, SVD optimization is conducted. As for the imaging algorithm, the objective function based on the regularization items of L1 or L0 is selectively used according to the two-phase flow pattern. The experimental results indicate that compared with the commonly used image reconstruction algorithms such as Tikhonov algorithm, Landweber algorithm, and CG algorithm, the algorithm proposed in this paper has more comprehensive and balanced performance, and can be used as an efficient image reconstruction solution of ERT for two-phase flow.
Nonintrusive Load Identification Considering Unknown Load Based on Bimodal Fusion and One-Class Classification

Nonintrusive load monitoring (NILM) enables the real-time monitoring and data analysis of energy consumption, providing more accurate, efficient, and intelligent data support for energy management. However, traditional NILM models can only identify known load (KL) presented in the training dataset; they cannot perform unknown load (UKL) detection, and they have problems with feature selection, including information loss and limitations in capturing load signal information, which make them less suitable for the user side. In this article, a new method based on bimodal fusion and one-class (OC) classification is proposed for NILM. The bimodal features include 2-D image features and multidimensional sequence features. To enrich the feature representation, the bimodal features are fused by long short-term memory (LSTM) network. To address UKL detection, a simple and lightweight OC model based on deep support vector data description (DSVDD) is innovatively proposed. The proposed model is also validated on multiple publicly available datasets, demonstrating its significant advantages and applicability both on KL identification and UKL detection.
A Vector Routing Protocol for Computing and Network Convergence in Digital Grid Scenario

In an industrial Internet with clear hierarchical division like China’s digital grid, real-time and fast intelligent decision-making for power production control at the local field level can be supported by cloud-edge computing. In the cloud-edge fusion within this scenario, there will be a large amount of information interaction and data transmission between entities in different locations, which also involves differentiated routing decisions for computing tasks. Therefore, it is of great significance to comprehensively consider the computing resources and network resources in the digital grid to schedule the computing tasks in the above scenario. Distributed routing is a routing method relative to centralized control routing (such as SDN, software-defined network). Each individual distributed router calculates the routing table itself and then determines the forwarding strategy based on the previous calculation results. Such as EIGRP (Enhanced Interior Gateway Routing Protocol), DSDV (Destination Distance Vector Protocol), etc. Based on the distributed vector routing technology, this paper proposes a distributed routing protocol that can comprehensively consider the existing computing resources and network resources (delay, bandwidth, etc.) in the network for calculation, and designs a simulation test system. The system can virtualize a small-scale network including several routing nodes, and complete processes such as neighbor discovery, route notification, route computation, and data forwarding, thereby verifying the availability of routing protocols.
Face and Iris Recognition Using Neural Network: A Review

The data is used for the recognition of facial features including database and sensor images but a problem occurs with the difficult facial looks, lighting effects, and the intricacy of an image background. The most prevalent and practical application for biometric technology and image processing is “Face Recognition”. This paper offers algorithms for recognizing faces and irises using“Artificial Neural Networks (ANNs)”, that technology utilized for preprocessing, extracting features and image classification. Furthermore, this study will discuss how ANN is useful for biometric identification in unimodal and multimodal systems. Several ANN-suggested approaches that provide an overview of face recognition using ANN are available. Because of this, a thorough overview of different ANN approaches and algorithms for the recognition of faces is presented in this paper. In addition to highlighting the advantages and disadvantages of these systems and literature studies, the research study also evaluates the performance of various ANN techniques and algorithms.
Deep Q-Learning Based Online Beam Tracking for Hybrid Beamforming Systems

This paper investigates an online beam tracking problem in multi-user downlink hybrid beamforming systems. A base station (BS) equipped with a hybrid beamforming antenna system tracks multiple users with mobility by controlling its analog beamforming precoder. To ensure stable wireless services for mobile users, a codebook-based online deep Q-learning (DQL) is proposed, operating through feedback of signal-to-interference-plus-noise ratio (SINR). Specifically, to maximize the achievable sum rate, the DQL agent deployed at the BS intelligently adjusts the analog beamforming precoder based on a beamforming codebook with uniformly quantized angles in the first phase, guided by the SINR feedback from the users. Subsequently, after the analog precoder is fixed, the digital precoder is designed using zero-forcing (ZF) to simultaneously support multiple users. Numerical results demonstrate that the proposed online beam tracking can efficiently adjust its analog precoder according to the users’ mobility, leading to a significant improvement in the sum rate performance.
MIFRL: A Model for Automatic Diagnosis of Laryngeal Diseases Based on Multi-Modal Information Fusion

Insufficient clinical experience and heavy workload may lead to misdiagnosis or omission when manually analyzing laryngoscope images and diagnosing diseases. Based on deep learning, automatic diagnosis and generation of endoscopic reports are of great importance. Therefore, based on both text and image modalities, we propose the multimodal internal-related feature representation learning (MIFRL) network model for multi-attribute recognition and endoscopic reports generation. Firstly, text and image encoding modules are used to extract feature, which simplifies the process of manually labeling images. Next, internal-related feature mining module is designed for multimodal information fusion and class imbalance problem. Finally, a multi-branch structure is designed to output the multi-attribute recognition of the lesions and generate endoscopic reports. In addition, asymmetric loss (ASL) is introduced to further mitigate the class imbalance problem. Based on our dataset, the average accuracy of endoscopic reports and attributes classification were 85.5% and 90.7%, respectively. In the recognition of the smallest number of leukoplakia images, mean average precision (mAP) was improved from 81.4% to 86.7%. Compared with other models, our model also achieved better results.
Detection and mapping abandoned areas of artisanal and small-scale gold mining (ASGM) using multi-sensor data on Google Earth Engine: A case study of Kuantan Singingi, Riau

Artisanal and small-scale gold mining (ASGM) activities in Kuantan Singingi, Riau have been operating over a decade without proper permits and using unsafe procedures for the environment. Mercury releases and degraded land have been the leading factors in the decreased environmental functions. ASGM activities are nomadic and secluded, posing a considerable challenge in detecting their location and extent. The aims of this study are to provide a method for detecting and mapping ASGM footprints utilizing multi-sensor data on cloud computing platforms. The detection method is performed using a supervised random forest algorithm. The result successfully mapped an ASGM footprints, estimating an area of 10,044.38 ha with 89.23% accuracy through Sentinel-1 data and an area of 12,308.57 ha with 87.25% accuracy through Sentinel-2 data. The spatial distribution of ASGM footprints is scattered over the streams and tributaries across all regions. These maps are pivotal in establishing regulatory measures for environmental restoration and preventing further expansion of degraded land.
Valuing protected areas through contingent valuation : a case study of Chitwan National Park, Nepal

Nepal is identified by the United Nations as one of the world's least developed countries, yet the conservation of its protected areas is praised globally. Despite succes in some regions, sustainable funding sources are scarce and a lack of adequate funds can jeopardize conservation efforts. Using the contingent valuation method, this research assesses the potential to capture value from foreign tourists traveling in the country. Tourists were asked their willingness to pay for conservation and environmental efforts at Chitwan National Park, World Heritage Site, Nepal. The results show that 71.5% of those who responded were willing to pay more than the current entrance fee of $7 USD (sample mean $21.94 USD). This suggests that the current price of the park entrance fee could be increased with minimal negative results on tourist numbers and that management has the potential to capture the value tourists already hold for Chitwan National Park.
Fake News Detection Based on Machine Learning Algorithm

The widespread spread of false information via media outlets has raised awareness among researchers of the significance of fake news identification. The unregulated dissemination of incorrect information online cannot be stopped in today's world of globalization. Because of the detrimental effects it has on society, the propagation of fake news must be stopped. Additionally, this will cause more significant problems and a potential threat, like misunderstandings, slander, and the encouragement of users to disseminate aggressive lies based on fake news on their social media. In nations like India, where fake news travels swiftly through social media platforms, it is imperative to take strong measures to stop the dissemination of misleading information among the people. This research paper deals with one of the solutions to the above problem. It also aims to compare the technology used in the project and various other technologies that have been developed till now.
LE-YOLO: a lightweight and efficient cucumber pruning target detection and recognition method based on improved YOLOv5s

Firstly, the current cucumber pruning operation is carried out manually. Secondly, the existing target detection algorithm is not accurate enough to efficiently detect and identify the cucumber pruning target. In response to the above issues, this article proposes the LW-YOLO algorithm based on improved YOLOv5s-7.0. Initially, FCSP is constructed to replace the C3 module in the baseline model so as to reduce parameters, speed up detection, and improve detection accuracy. Then MCACB is designed to realize downsampling, reduce parameters, effectively aggregate feature information, and improve detection performance. Finally, the EW K-means clustering algorithm is proposed to estimate the prior boxes, which further improves the detection accuracy and robustness of the model. The experimental results show that the proposed algorithm has the smallest model size, only 8.39MB; the detection accuracy is the highest, with map@. 5% reaching 97.1; less computing resources are needed, and GFLOPs is only 10.0 GB; and it runs faster, with 294.1 FPS. The various indicators of the LW-YOLO algorithm are not only better than the baseline model YOLOv5s-7.0 but also better than the latest models YOLOv6, YOLOv7, YOLOv7-tiny, and YOLOv8, which meet the detection and recognition requirements of cucumber pruning robots and can be deployed.