Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulated impacts of irrigation on evapotranspiration in a strongly exploited region: a case study of the Haihe River basin, China

Irrigation is the major water supply for crop production in water‐limited regions. However, this important water component is usually neglected or simplified in hydrological modelling primarily because information concerning irrigation is notably difficult to collect. To assess real effects of irrigation on the simulation of evapotranspiration (ET) in water‐limited region, the Community Land Model version 4 was established over a typical semi‐humid agricultural basin in the northern China – the Haihe River basin. In the irrigated cropland, incorporating an irrigation scheme can enhance the simulated ET and improve the simulation of spatial variability of soil moisture content. We found that different configurations in the irrigation scheme do not cause significant differences in the simulated annual ET. However, simulated ET with simulated irrigation differs clearly from that with observed irrigation in mean annual magnitude, long‐term trend and spatial distribution. Once the irrigation scheme is well‐calibrated against observations, it reasonably reproduces the interannual variability of annual irrigation, when irrigation water management is relatively stable. More importantly, parameter calibration should be consistent with the configuration of the source of irrigation water. However, an irrigation scheme with a constant parameter value cannot capture the trend in the annual irrigation amount caused by abrupt changes in agricultural water management. Compared with different remotely sensed ET products, the enhancement in the simulated ET by irrigation is smaller than the differences among these products, and the trend in simulated ET with the observed irrigation cannot be captured correctly by the remotely sensed ET. Copyright © 2014 John Wiley & Sons, Ltd.
Dissipative observer design for singular neutral systems

The dissipative observer design for a singular neutral system is studied in the article. The rigorous dissipative analysis of a singular neutral system is carried out. And the observer-based dissipative control problem is discussed through the use of linear matrix inequality (LMI). The method of observer design is given. Finally, the proposed method's effectiveness and universality are demonstrated by a final example.
SAR image ATR using SVM with a low dimensional combined feature

In this paper, a grayscale wavelet moment and entropy combined feature, which can well represent the images with much lower dimensions, is proposed for the MSTAR targets' classification, also a discriminative feature selection and evaluation method for multi-class targets is presented. By introducing SVM as the classifier to some simulation tests, it can be shown that the grayscale wavelet moment and entropy combined feature has good capability for translation, scaling and rotation transformation in both local information and global information conditions, by the reasons of wavelet moments' multi-resolution analysis, moment invariant quality and entropy's statistic quality for image disorder. The test also confirms that this feature has a significant improvement on classificatory accuracy using SVM with a lower feature dimension than the other features such as the single wavelet moment, Hu's moment and PCA.
Research on improving TransUnet network for feature classification

To address the low accuracy of TransUent network in small-area feature recognition, two feature classification networks based on improved TransUent network are proposed: DS-TransUnet network and DS-A-TransUnet network.DS-TransUnet network introduces a residual module in the bottom layer of the Convolutional Neural Network (CNN) module of the hybrid encoder of TransUent network to deepen the network and uses convolutional operation instead of maximum pooling in the hybrid encoder to reduce the loss of feature information. The DS-TransUnet network introduces a residual module at the bottom of the Convolutional Neural Networks (CNN) module of the TransUent network to deepen the network, and uses convolutional operations instead of maximum pooling in the hybrid encoder to reduce the loss of feature information, and uses depth-separable convolution instead of regular convolution to reduce the residual blocks and the large number of parameters caused by convolutional operations; the DS-A-TransUnet network On the basis of DS-TransUnet network, a void space pyramidal pooling module is introduced, and the classification accuracy is further improved by using void convolution with different expansion rates in parallel to obtain different size of perceptual fields and fully extract multi-scale features. The experimental results show that the recognition accuracy of DS-TransUnet network is 3.06% and 2.85% higher than that of TransUent network for arable land and water bodies, respectively, and the overall recognition accuracy reaches 89.14%; the recognition accuracy of DS-A-TransUnet network is 3.17% and 4.79% higher than that of TransUent network for arable land and water bodies, respectively, and the overall recognition accuracy reaches 90.53%. The overall recognition accuracy reached 90.53%.
Research on evaluation model of new power system information communication digitalization ability

This paper focuses on the research of the evaluation model of information communication and digitization capability in the new power system. Through the analysis of information communication network and digital technology in the power system, a method for evaluating the information communication and digitization capability of the power system is proposed, and the effectiveness of the proposed method is verified through experiments. Specifically, this paper first introduces the current situation and trend of information communication and digitization technology in the power system, and then elaborates on the importance and difficulties of evaluating the information communication and digitization capability of the power system and proposes an evaluation model based on the fuzzy hierarchy analysis method and entropy weighting method. Next, this paper details the design and implementation process of the model, including the construction of the index system, the determination of index weights, data collection, and processing methods. Finally, this paper verifies the effectiveness and feasibility of the proposed evaluation model through experiments. The results show that the model can accurately evaluate the information communication and digitization capability of the power system, providing a scientific basis for the construction and management of the power system.
A control method for a bidirectional vehicular platoon via a disturbance observer

This paper investigates a bidirectional vehicular platoon control problem where a disturbance observer is designed. A useful distributed Sliding-Mode Control (SMC) law is designed with a disturbance estimation for the following vehicles, which can communicate with its immediate predecessor and successor. By applying a coupled sliding-mode surface approach and Lyapunov stability theory, the spacing error of each following vehicles can be driven to the proposed sliding-mode surface in finite-time and converge to zero ultimately. Furthermore, the string stability is also demonstrated analytically by choosing a suitable coupled coefficient. Finally, simulation results show the effectiveness of the presented control law in addressing disturbance.
Abiogenic methane in deep‐seated mid‐ocean ridge environments: Insights from stable isotope analyses

In this paper we examine geochemical processes that control volatile chemistry at depth in mid-ocean ridge environments by focusing on CO2-CH4-H2O-H2 fluids entrapped in plutonic rocks from the Southwest Indian Ridge (SWIR), Ocean Drilling Program Hole 735B. Compositional and isotopic analyses of CO2-CH4-H2O and CH4-H2O-H2 fluids show that methane production involved two phases of magma-hydrothermal activity, which spanned supersolidus to greenschist facies metamorphic conditions. The first phase of methane generation is characterized by fluid inclusions that contain up to 30–50 mol % CO2 and 43 mol % CH4. Isotopic analyses of CO2, CH4, and H2O released at >900°C yields δ13C(CO2) values of −24‰ to −2‰, δ13C(CH4) values of −30‰ to −19‰, δD(CH4) values of −244‰ to −128‰, and average δD(H2O) values of −43±6‰. Phase equilibria and isotopic data strongly indicate that the CO2-CH4-H2O fluids reflect Rayleigh distillation of evolved magmatic CO2, subsequent closed-system respeciation, and attendant graphite precipitation at temperatures of ∼500–800°C, and at fO2 from −3 log units below, to close to quartz-fayalite-magnetite oxygen fugacity (QFM) conditions. The second phase of CH4 production involves CH4-H2O±H2±C-fluids that contain >40 mol % CH4. Phase equilibria indicate that the CH4-H2O fluids were trapped under equilibrium conditions at 400°C, very near to QFM conditions. Our study suggests that in the absence of CO2 as a stable fluid component, extensive distillation fractionation or alteration processes are required to form this later generation of methane. The mean δ13C values of methane extracted at 500°C from the gabbros (−25±4.4‰) are remarkably similar to the range of light carbon observed in studies of mantle rocks. We conclude that the presence of reduced carbon species in oceanic gabbros and mantle peridotites is a potential source of carbon in hydrothermal fluids and that serpentinization processes play a key role in the production of methane at greenschist facies conditions. Although total methane concentrations are low (0.3–0.6 mmol/kg) in the SWIR samples, on a global scale, plutonic layer 3 comprises ∼60% of the oceanic crust and thus represents a potentially immense reservoir (∼1019 gCH4) for abiogenic methane in mid-ocean ridge hydrothermal systems. Production of methane and hydrocarbon species should be a common process in mid-ocean ridge systems where high-temperature fluids interact with mafic mineral phases. This is particularly significant because carbon-bearing fluids may provide sustenance to subsurface-and vent-associated microbial communities and therefore represent an important link between deep-seated hydrothermal systems and more shallow crustal environments.
The binary classification of CT images of pneumonia realized by machine learning

In the light of the COVID-19 outbreak in late 2019, numerous researchers have attempted to employ machine learning techniques to identify CT images of pneumonia patients. However, the approach may be marred by limitations such as low accuracy, deficient datasets and inadequate model utilization. This paper is committed to using machine learning to build multiple models and improve the accuracy. Infected and uninfected lung CT images are classified in the dataset containing more than 4,000 CT images, with the objective of aiding medical practitioners in diagnosing the disease. The method can be explained as firstly training with five basic machine learning models, among which the random forest model achieves a relatively high accuracy of 97.79%; secondly, using the multi-layer back propagation neural network and convolutional neural network to predict the training set respectively. Of the two models, the convolutional neural network demonstrates superior efficacy, achieving an accuracy rate of 94.27%. Finally, the transfer learning method is used to pretrain six different models, ultimately attaining a maximum accuracy rate of 90.73% on the Xception model.
OptiRing: Low-Resolution Optical Sensing for Subtle Thumb-to-Index Micro-Interactions

We present OptiRing, a ring-based wearable device that enables subtle single-handed micro-interactions using low-resolution camera-based sensing. We demonstrate that our approach can work with ultra-low image resolutions (e.g., 5 × 5 pixels), which is instrumental in addressing privacy concerns and reducing computational needs. Using a miniature camera, OptiRing supports thumb-to-index finger gestures, such as stateful pinch and left/right swipes, as well as continuous 1-DOF input. We present a modeling approach that uses heuristic-based methods to identify interactions and machine learning for input gating, generalizing recognition across users and sessions. We assess this technique’s capabilities, accuracy, and limitations through a user study with 15 participants. OptiRing achieved 93.1% accuracy in gesture recognition, 99.8% accuracy for stateful pinch gestures, and a minimal number of false positives. Further, we validate OptiRing’s ability to handle continuous 1D input in a Fitts’ law study. We discuss these findings, the tradeoff between resolution and interaction accuracy, and the potential of this technology, which can help address challenges associated with optical sensing for input.
Impact of strategies to mitigate misinformation in diverse settings and populations: a protocol for a living evidence synthesis

Introduction Misinformation refers to inadvertent misleading information that the public may be exposed and share without intent to cause harm, and can delay or prevent effective care, affect mental health, lead to misallocation of health resources and/or create or exacerbate public-health crises. There are many strategies to address misinformation, but there is a need to evaluate their effects. Our objective is to synthesise and routinely update evidence to assess the impact of strategies to mitigate health-related misinformation in diverse settings, and populations. Methods and analysis We will search seven databases in May 2023 with planned updates at 6 and 9 months, which will be supplemented with searches for grey literature and reference lists of included studies and contacting experts. Two reviewers will independently screen all search results for studies that evaluate one or more approaches to addressing health-related misinformation. One researcher will conduct data extraction and risk of bias assessments, which will be reviewed by a second reviewer for accuracy. We will include experimental, quasi-experimental and observational studies for any populations, settings and diseases without language or publication restrictions. We will conduct quantitative analysis if meta-analytical pooling is possible. If pooling is not possible, we will synthesise quantitative data according to outcomes and interventions addressed, and present a narrative summary of findings disaggregated by sex and/or gender, irrespective of whether differences were found. Ethics and dissemination There are no individuals or protected health information involved and no safety issues identified. Results will be published through the Global Commission on Evidence and COVID-END websites, in a peer-reviewed journal, as well as through plain-language materials. PROSPERO registration number CRD42023421149.
Community and Facility Health Information System Integration in Malawi: A Comparison of Machine Learning and Probabilistic Record Linkage Methods

Accurate and efficient record linkage methods are essential to link patients between community health worker digital health apps and an EMR system, facilitating information flow and improving coordination of care. This study presents the eTrace workflow as an illustrative example, highlighting the benefits of enhanced coordination of care for patients in antiretroviral and non-communicable disease programs in rural Neno district, Malawi. This research focuses on the following major contributions: (1) development of a machine learning-based record linkage model for electronic health information systems, (2) comparison between the machine learning-based and probabilistic approaches to record linkage and (3) a concrete evaluation of our approach on real data for the eTrace workflow. A review of the standard record linkage architecture and its application to health information exchange systems is also presented. An empirical comparison conducted of logistic regression and the Fellegi-Sunter algorithms for this use case reveals comparable results. Both classifiers demonstrate an average precision of 0.86, while logistic regression achieves a higher recall at a fixed 0.90 precision of 0.74.
Cross-equatorial Geostrophic Adjustment

Abstract A geostrophic adjustment model is used to find out how water can cross the equator, and how far it can reach, while conserving its potential vorticity, in the context of geostrophic adjustment. A series of problems is considered; all but the last permit variation north–south only. The first problem discusses the equatorial version of the classic midlatitude adjustment problem of a one-layer, reduced gravity fluid in the Southern Hemisphere which is suddenly permitted to slump away from its initially uniform height distribution. Fluid which crosses the equator reaches farther northward than it began south of the equator. The configuration in which fluid reaches the farthest north requires fluid starting as far south as is possible subject to water actually crossing the equator. Particles move north a distance of at most 2.32 deformation radii. This problem is then extended in turn to a one-layer fluid occupying all space, whose depth changes abruptly from one value to another, and to the linearize.
Prognostic prediction models for oropharyngeal squamous cell carcinoma (OPSCC): a protocol for systematic review, critical appraisal and meta-analysis

Introduction Oropharyngeal squamous cell carcinoma (OPSCC) is increasingly prevalent and has significantly heterogeneous risks of survival for diagnosed individuals due to the inter-related risk factors. Precise prediction of the risk of survival for an individual patient with OPSCC presents a useful adjunct to therapeutic decision-making regarding the management of OPSCC. The aim of this systematic review, critical appraisal and meta-analysis is to assess prognostic prediction models for OPSCC and lay a foundation for future research programmes to develop and validate prognostic prediction models for OPSCC. Methods and analysis This protocol will follow the Preferred Reporting Items for Systematic Review and Meta-Analyses Protocol statement. Based on predefined criteria, electronic databases including MEDLINE, Embase, Web of Science, the Cochrane Library and China National Knowledge Infrastructure (CNKI) will be searched for relevant studies without language restrictions from inception of databases to present. This study will systematically review published prognostic prediction models for survival outcomes in patients with OPSCC, describe their characteristics, compare performance and assess risk of bias and real-world clinical utility. Selection of eligible studies, data extraction and critical appraisal will be conducted independently by two reviewers. A third reviewer will resolve any disagreements. Included studies will be systematically summarised using appropriate tools designed for prognostic prediction modelling studies. Risk of bias and quality of studies will be assessed using the Prediction Model Risk of Bias Assessment Tool and the Transparent Reporting of a multivariable prediction model for individual prognosis or diagnosis. Performance measures of these models will be pooled and analysed with meta-analyses if feasible. Ethics and dissemination This review will be conducted completely based on published data, so approval from an ethics committee or written consent is not required. The results will be disseminated through a peer-reviewed publication. PROSPERO registration number CRD42023400272.
Volumetric Conversion Of Allocations:A Means To An End Or An Important Process InIts Own Right?

Volumetric allocation of irrigation water is government policy in South Australia. All newly prescribed water resources are allocated volumetrically. Irrigation licences existing prior to implementation of the policy are being converted to volumetric allocations. The desired outcomes of a successful allocation conversion process are a good matching between resource availability and allocated licence volumes, and minimal legal challenges to the new system. Achieving these dual outcomes can be extremely challenging, but is critical to the sustainability of irrigation in the region. The Limestone Coast volumetric conversion process encapsulated around 2,500 water licences, covering about 6,000 groundwater bores and an allocated equivalent volume of over 630,000 megalitres from two regional aquifers. The conversion process employed a range of activities, and took over 4 years to develop. The community response to the conversion process strongly suggests that the considerable effort and resources expended to do this job thoroughly and equitably was well worthwhile, and further, augers well for the ongoing sustainability of irrigation in this region.
Semi-synthetic nanobody-ligand conjugates exhibit tunable signaling properties and enhanced transcriptional outputs at neurokinin receptor-1

Antibodies have proven highly valuable for therapeutic development; however, they are typically poor candidates for applications that require activation of G protein-coupled receptors (GPCRs), the largest collection of targets for clinically approved drugs. Nanobodies (Nbs), the smallest antibody fragments retaining full antigen-binding capacity, have emerged as promising tools for pharmacologic applications, including GPCR modulation. Past work has shown that conjugation of Nbs with ligands can provide GPCR agonists that exhibit improved activity and selectivity compared to their parent ligands. The neurokinin-1 receptor (NK1R), a GPCR targeted for the treatment of pain, is activated by peptide agonists such as Substance P (SP) and neurokinin A (NKA), which induce signaling through multiple pathways (Gs, Gq and β-arrestin). In this study, we investigated whether conjugating NK1R ligands with Nbs that bind to a separate location on the receptor would provide chimeric compounds with distinctive signaling properties. We employed sortase A-mediated ligation to generate several conjugates consisting of Nbs linked to NK1R ligands. Many of these conjugates exhibited divergent and unexpected signaling properties and transcriptional outputs. For example, some Nb-NKA conjugates showed enhanced receptor binding capacity, high potency partial agonism, prolonged cAMP production, and an increase in transcriptional output associated with Gs signaling; whereas other conjugates were virtually inactive. Nanobody conjugation caused only minor alterations in ligand-induced upstream Gq signaling with unexpected enhancements in transcriptional (downstream) responses. Our findings underscore the potential of nanobody conjugation for providing compounds with advantageous properties such as biased agonism, prolonged duration of action, and enhanced transcriptional responses. These compounds hold promise not only for facilitating fundamental research on GPCR signal transduction mechanisms but also for the development of more potent and enduring therapeutics.