Miracle-dz/UniFAR-FTUs huggingface dataset:
A general Lagrangian tracking methodology for riverine flow and transport

This article presents a new public domain tool for generalized Lagrangian particle tracking in rivers. The approach can be applied with a variety of two‐ and three‐dimensional flow solvers. Particle advection by the flow is incorporated using flow fields from the chosen solver assuming particles follow the Reynolds‐averaged flow, although some other simple passive and active particle behaviors are also treated. Turbulence effects are treated using a random walk algorithm with spatial step lengths randomly chosen from Gaussian distributions characterized by the diffusivity from the flow solver. Our work extends this concept to a general framework that is solver and coordinate system independent to allow easy comparisons between differing flow treatments. To better treat problems where detailed information is required in specific regions, the approach includes novel cloning and colligation algorithms which enhance local resolution at modest computational expense. We also provide tools for computing local concentrations and total exposure over a user‐specified time interval. Several examples of predictions are provided to illustrate applications of the technique, including examination of the role of curvature‐driven secondary flows, storage in lateral separation eddies, treatment of larval drift, treatment of fuel spill dispersion, river‐floodplain connections, and sedimentation in floodplain ponds by tie channel connections. We also demonstrate that the model can reproduce analytically derived concentration profiles for simple diffusivities. These examples show that the Lagrangian particle tracking approach and the extensions proposed here are broadly applicable and viable for treating difficult river problems with multiple temporal and spatial scales. The examples also illustrate the utility of the cloning/colligation extensions and show how these can decrease the computational effort required on problems where high local resolution is required. Enhancement of the tools and even broader applicability can be achieved through the inclusion of multiple particle populations and particle–particle interactions.
Application of Artificial Neural Network in Predicting Farmers’ Response to Water Management Decisions on Wheat Yield

Water management usually involves decision-making with respect to allocation, scheduling and application of available water to different crops over an irrigation season so as to get maximum economic returns. A study was carried out in the Kaithal irrigation circle for prediction of farmers’ decisions regarding total depth of irrigation water, fraction of groundwater and delay in sowing on yield of wheat crop under varying conditions of groundwater and soil salinity using Artificial Neural Networks (ANN). Three ANN algorithms i.e. gradientdescent back propagation (BP), Levenberg-Marquardt (LM) and radial basis functions (RBF) with various architectures were used. It was found that radial basis function with a spread constant of 0.1 performed better in predicting wheat yield. Also, it was observed that ANN algorithm predicted wheat and rice yields better correlated to observed yields (r2=0.63 and 0.74) in comparison to regression model (r2=0.37 and 0.52)
Kidney Cancer Biomarker Selection Using Regularized Survival Models

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC showing a significant percentage of mortality. One of the priorities of kidney cancer research is to identify RCC-specific biomarkers for early detection and screening of the disease. With the development of high-throughput technology, it is now possible to measure the expression levels of thousands of genes in parallel and assess the molecular profile of individual tumors. Studying the relationship between gene expression and survival outcome has been widely used to find genes associated with cancer survival, providing new information for clinical decision-making. One of the challenges of using transcriptomics data is their high dimensionality which can lead to instability in the selection of gene signatures. Here we identify potential prognostic biomarkers correlated to the survival outcome of ccRCC patients using two network-based regularizers (EN and TCox) applied to Cox models. Some genes always selected by each method were found (COPS7B, DONSON, GTF2E2, HAUS8, PRH2, and ZNF18) with known roles in cancer formation and progression. Afterward, different lists of genes ranked based on distinct metrics (logFC of DEGs or β coefficients of regression) were analyzed using GSEA to try to find over- or under-represented mechanisms and pathways. Some ontologies were found in common between the gene sets tested, such as nuclear division, microtubule and tubulin binding, and plasma membrane and chromosome regions. Additionally, genes that were more involved in these ontologies and genes selected by the regularizers were used to create a new gene set where we applied the Cox regression model. With this smaller gene set, we were able to significantly split patients into high/low risk groups showing the importance of studying these genes as potential prognostic factors to help clinicians better identify and monitor patients with ccRCC.
Real-Life Experience on Directional Deep Brain Stimulation in Patients with Advanced Parkinson’s Disease

Directional deep brain stimulation (dDBS) is preferred by patients with advanced Parkinson’s disease (PD) and by programming neurologists. However, real-life data of dDBS use is still scarce. We reviewed the clinical data of 53 PD patients with dDBS to 18 months of follow-up. Directional stimulation was favored in 70.5% of dDBS leads, and single segment activation (SSA) was used in 60% of dDBS leads. Current with SSA was significantly lower than with other stimulation types. During the 6-month follow-up, a 44% improvement in the Unified Parkinson’s Disease Rating Scale (UPDRS-III) points and a 43% decline in the levodopa equivalent daily dosage (LEDD) was observed. After 18 months of follow-up, a 35% LEDD decrease was still noted. The Hoehn and Yahr (H&Y) stages and scores on item no 30 “postural stability” in UPDRS-III remained lower throughout the follow-up compared to baseline. Additionally, dDBS relieved non-motor symptoms during the 6 months of follow-up. Patients with bilateral SSA had similar clinical outcomes to those with other stimulation types. Directional stimulation appears to effectively reduce both motor and non-motor symptoms in advanced PD with minimal adverse effects in real-life clinical care.
Load prediction of multi‐type electric vehicle charging stations based on secondary decomposition and feature selection

This study proposes a load prediction method for multi‐type electric vehicle charging stations based on secondary decomposition and feature selection. First, the original load sequence of an electric vehicle charging station is decomposed into relatively simple components using variational mode decomposition (VMD). The residual term after the decomposition carries rich information; therefore, this study introduces the idea of quadratic decomposition, where the residual term is decomposed by empirical mode decomposition (EMD). Second, an in‐depth investigation of the characteristics of ordinary, energy storage, and integrated charging stations for optical storage and charging establishes the corresponding influencing factor sets, and uses the maximum information coefficient to obtain the optimal feature set. Third, for the subsequent different frequencies, this study uses the long short‐term memory (LSTM) and back‐propagation neural network (BPNN) as basic learners to conduct the prediction. Finally, this study adopts the stacking ensemble learning strategy, takes the prediction result of the base learner and the optimal feature set as the input, and then obtains the final prediction result. Taking the real load data of three different types of charging stations in a city in Northwest China as an example, the model proposed in this study is evaluated and compared with other models. Meanwhile, the model's performance under the 2 to 6 h forecast periods was compared and analyzed. The results show that the model has good stability and representation, and can be used for load prediction of electric vehicle charging stations.
Leveraging Attitudes, Motivations, and Values from Farmers’ Market Managers in Florida: An Opportunity to Improve Nutritional Outcomes Through Extension

Increasingly, farmers’ markets in the United States offer nutrition incentives to help limited-resource shoppers afford fresh fruits and vegetables and support local farmers. Despite increased attention to these efforts, there has been less focus on the market managers and staff members responsible for implementing and administering incentive programs. Using qualitative data collected from semi-structured phone interviews, we explore the attitudes, motivations, and values of farmers’ market managers in relation to their efforts to administer and promote a nutrition incentive program at their respective markets in Florida. Results demonstrate that program adopting managers: 1) express concern for inclusivity, social justice, and equity; 2) sympathize with the economic pressures their produce grower-vendors face; 3) are motivated to provide locally grown foods to all shoppers; 4) take pride in ethical standards and transparency protocols; and 5) value loyalty between themselves, their vendors and their customers. We believe Extension professionals can leverage prevailing attitudes among market managers to improve manager engagement with nutrition incentive programs and ensure expanded nutritional access outcomes at farmers’ markets for limited-resource shoppers.
A photometric selection of White Dwarf candidates in SDSS DR10

We present a method which uses cuts in colour-colour and reduced proper motion-colour space to select white dwarfs without the recourse to spectroscopy while allowing an adjustable compromise between completeness and efficiency. Rather than just producing a list of white dwarf candidates, our method calculates a probability of being a white dwarf (Pwd) for any object with available multi band photometry and proper motion. We applied this method to all objects in the SDSS DR10 photometric footprint and to a few selected sources in DR7 which did not have reliable photometry in DR9 or DR10. This application results in a sample of 61969 DR10 and 3799 DR7 photometric sources with calculated Pwd from which it is possible to select a sample of ~23000 high-fidelity white dwarf candidates with Teff >~ 7000 K and <= 19. This sample contains over 14000 high confidence white dwarfs candidates which have not yet received spectroscopic follow-up. These numbers show that, to date, the spectroscopic coverage of white dwarfs in the SDSS photometric footprint is, on average, only ~40% complete. While we describe here in detail the application of our selection to the SDSS catalogue, the same method could easily be applied to other multi colour, large area surveys. We also publish a list of 8701 bright (<= 19) WDs with SDSS spectroscopy, of which 1781 are new identifications in DR9/10.
NEURAL NETWORK MODELS FOR DETECTION OF UNMANNED AERIAL VEHICLES BASED ON SPECTROGRAM ANALYSIS

The widespread use of unmanned aerial vehicles (UAVs) in both the national and military fields has made them the focus of industrial organizations. However, the use of UAVs has seriously affected the violation of the confidentiality of personal data, posed a threat to states, national institutions, nuclear power plants, historical places. One way to reduce this threat is to detect harmful UAVs. The article develops machine learning and deep learning methods based on sound signal analysis to detect harmful UAVs. Features were extracted from the sound signals and their ensemble was created. The created new data was transmitted to the input of neural network models in the form of vectors and drones were detected. The effectiveness of the proposed approach has been tested on a database open to scientific research.
A Study of Phase Equilibrium Of H2S in Several Physical Solvents Using Chemcad Simulator

CHEMCAD process simulator was used for the analysis of the literature experimental phase equilibrium data of H2S with three physical solvents (Sulfolane, Propylene Carbonate (PC), and N-Methyl-2-Pyrrolidone (NMP)) at different temperatures ( 298.15 oK , 323.15 oK, and 373.15 oK). Two thermodynamic models, Peng-Robinson (PR) and Soave-Redlich-Kwong (SRK), were used. The equilibrium data of H2S- Sulfolane and H2S- Propylene Carbonate (PC) systems were successfully correlated using SRK thermodynamic model. The deviation was noticed only for H2S-Sulfolane system as the temperature increases to 373 oK, to give a deviation of less than 5% . The thermodynamic models used in present study, PR, and SRK , built in CHEMCAD simulator and used as such without modification, fail to correlate the equilibrium data of H2S- NMP (N-Methyl-2-Pyrrolidone) system for all temperature range used. Modifications of the thermodynamic models were performed by editing user defined binary interaction parameters (BIP) of the systems used, gives good results.
Autistic preschoolers’ engagement and language use in gross motor versus symbolic play settings

Background and aims: Although adjustment of the environment is recommended as a support strategy in evidence-based interventions for children with autism, the impact of doing so (and the how and why) is not well understood. One essential environmental factor to consider when providing supports for preschool-aged autistic children is the play setting, specifically, the materials available in the child's play context. The aim of this study was to compare engagement states and number of utterances produced by preschool-aged autistic children within symbolic vs. gross motor play settings. Examining the relationship between gross motor play settings and children's social engagement and spoken language use is particularly important to explore for autistic children given differences in their sensory processing, motor skill development, and choice of and interaction with toys relative to neurotypical peers. Methods: Seventy autistic children aged 25-57 months were videotaped during natural play interactions with a parent. Children's social engagement and number of spoken utterances were examined in five minutes each of play with symbolic toys and play with gross motor toys. Continuous time-tagged video coding of the child-caregiver engagement states was conducted, and the child's frequency of spoken language was identified using language sample analysis. The specific variables examined were; (a) engagement with caregiver, (b) engagement with objects only, (c) unengaged (no evident engagement with objects or people), and (d) total number of spoken utterances. The relationship between play setting (symbolic vs gross motor) and child language and engagement state variables was examined with linear mixed effects modelling. Results: Significant main effects were revealed for the interaction between play setting and autistic children's engagement. Young autistic children were more likely to engage with caregivers in play environments with gross motor toys (moderate effect) and also were more likely to have periods of unengaged time (not overtly directing their attention to objects or people; small effect) in this setting. Further, when in a setting with symbolic toys, autistic children were more likely to spend their time focusing attention solely on objects (large effect). No interaction was found between play setting and total number of utterances spoken by autistic children. Conclusions and implications: This study confirmed the importance of continued research focused on understanding the relationship between children's play settings and their social engagement and language use. Although preliminary, findings support the idea that there is an interaction between preschool-aged autistic children's social engagement and their play settings. Further, our results suggest that there can be value in clinicians differentiating children's play settings (i.e., gross motor vs symbolic) when assessing and supporting social engagement capacities of young autistic children.
Transcriptome Analysis Identifies Key Metabolic Changes in the Brain of Takifugu rubripes in Response to Chronic Hypoxia

The brain is considered to be an extremely sensitive tissue to hypoxia, and the brain of fish plays an important role in regulating growth and adapting to environmental changes. As an important aquatic organism in northern China, the economic yield of Takifugu rubripes is deeply influenced by the oxygen content of seawater. In this regard, we performed RNA-seq analysis of T. rubripes brains under hypoxia and normoxia to reveal the expression patterns of genes involved in the hypoxic response and their enrichment of metabolic pathways. Studies have shown that carbohydrate, lipid and amino acid metabolism are significant pathways for the enrichment of differentially expressed genes (DEGs) and that DEGs are significantly upregulated in those pathways. In addition, some biological processes such as the immune system and signal transduction, where enrichment is not significant but important, are also discussed. Interestingly, the DEGs associated with those pathways were significantly downregulated or inhibited. The present study reveals the mechanism of hypoxia tolerance in T. rubripes at the transcriptional level and provides a useful resource for studying the energy metabolism mechanism of hypoxia response in this species.
Comparison of Nozzle-Based and Nozzle-Free Electrospinning for Preparation of Fast-Dissolving Nanofibers Loaded with Ciprofloxacin

The study aimed to prepare ciprofloxacin-loaded polyvinylpyrrolidone electrospun nanofibers for oral drug delivery, using a conventional nozzle-based and a lab-built nozzle-free electrospinning equipment. To produce nanofibers, electrospinning is the process most often used. However, from the industry’s point of view, conventional electrospinning does not have sufficiently high productivity. By omitting the nozzle, productivity can be increased, and so the development of nozzle-free processes is worthwhile. In this study, a solution of ciprofloxacin and polyvinylpyrrolidone was electrospun under similar conditions, using both single-nozzle and nozzle-free methods. The two electrospinning methods were compared by investigating the morphological and physicochemical properties, homogeneity, in vitro drug release, and cytotoxicity. The stability of the nanofibers was monitored from different aspects in a 26 month stability study. The results showed that the use of the nozzle-free electrospinning was preferable due to a higher throughput, improved homogeneity, and the enhanced stability of nanofiber mats, compared to the nozzle-based method. Nevertheless, fast dissolving nanofibers loaded with poorly water-soluble ciprofloxacin were produced by both electrospinning methods. The beneficial properties of these nanofibers can be exploited in innovative drug development; e.g., nanofibers can be formulated into orodispersible films or per os tablets.
Diachronic stability and typology

Traditionally, historical linguists have been concerned about tracking how items consisting of formfunction pairings change between the different stages of a linguistic lineage. The form of an item can change, as when a sound change applies to a segment in a word, or the function can change, as when a word undergoes semantic change. Sound change and semantic change can be studied independently of one another. But for the purpose of the traditional comparative method form and function are inseparable, form-function pairings constituting the essential comparanda. Since at least some aspects of form-function pairings must be ignored for unrelated languages to be at all comparable, language typologists have looked more abstractly at languages. This is probably the reason why the idea of studying the historical behavior of generalized (abstract) structural features came from typologically oriented linguists such as Joseph Greenberg and Johanna Nichols. Historical typologists are not “particularistic” (Greenberg 1978: 64). That is, they do not investigate the histories of individual items in a particular language, but rather the diachronic behavior of linguistic features in a more abstract sense. Introducing a level of abstraction entails a movement from finding facts to observing tendencies, and this, in turn, introduces a need for seeking statistical validation for different claims. This paper focusses on differences among structural features of phonology, morphology, and syntax with regard to their temporal stabilities. I will provide a critical review of work in this area and discuss possible explanations for major findings.
Accountability as Constructive Dialogue: Can NGOs Persuade States to Conserve Biodiversity?

Abstract State-to-state accountability has greatly failed to improve compliance with multilateral environmental agreements. As this is also the case in the Convention on Biological Diversity (CBD), this article explores how and with what effect nongovernmental organizations (NGOs) persuade states to fulfill their commitments to conserve biodiversity. The article conceptualizes accountability as learning-enabling dialogue with the potential to influence state behavior through the provision of constructive criticism. The underlying argument is that NGOs can contribute to overcoming implementation challenges by engaging in constructive dialogue with states. The triangulation of interviews with NGOs, CBD documents, and gray literature suggests that NGOs can challenge or even prevent states’ inertia by establishing critical but cooperative multilevel partnerships with states to advance implementation. Reconceptualizing accountability as constructive dialogue may contribute to realizing the transformative potential of accountability. However, more evidence is needed to understand the roles of NGOs in fostering learning and the impact of learning on improving implementation, compliance, and environmental outcomes.
Integrated Evolving Spiking Neural Network and Feature Extraction Methods for Scoliosis Classification

: Adolescent Idiopathic Scoliosis (AIS) is a deformity of the spine that affects teenagers. The current method for detecting AIS is based on radiographic images which may increase the risk of cancer growth due to radiation. Photogrammetry is another alternative used to identify AIS by distinguishing the curves of the spine from the surface of a human’s back. Currently, detecting the curve of the spine is manually performed, making it a time-consuming task. To overcome this issue, it is crucial to develop a better model that automatically detects the curve of the spine and classify the types of AIS. This research proposes a new integration of ESNN and Feature Extraction (FE) methods and explores the architecture of ESNN for the AIS classification model. This research identifies the optimal Feature Extraction (FE) methods to reduce computational complexity.
Reinforced Likelihood Box Particle Filter

This letter is concerned with the development of a general scheme for box particle filtering. It is based on the likelihood computation, the most crucial step of the estimation strategy. The proposed filter takes advantages from strong aspects of various existing box particle filters and adds an interesting reinforced likelihood computation method that enhances the estimation results. An overview on Box Particle Filters and discussions from assumptions used in the literature to the filters performance evaluation approach are presented. Also, comparative study of the obtained results by performing several scenarios on the illustration example is provided to highlight the efficiency of the proposed estimation strategy.