Miracle-dz/UniFAR-FTUs huggingface dataset:
Phosphorous Supply to a Eutrophic Artificial Lake: Sedimentary versus Groundwater Sources

The eutrophic Lake Eichbaumsee, a ~1 km long and 280 m wide (maximum water depth 16 m) dredging lake southeast of Hamburg (Germany), has been treated for water quality improvements using various techniques (i.e., aeration plants, removal of dissolved phosphorous by aluminum phosphorous precipitation, and by Bentophos® (Phoslock Environmental Technologies, Sydney, Australia), adsorption) during the past ~15 years. Despite these treatments, no long-term improvement of the water quality has been observed and the lake water phosphorous content has continued to increase by e.g., ~670 kg phosphorous between autumn 2014 and autumn 2019. As no creeks or rivers drain into the lake and hydrological groundwater models do not suggest any major groundwater discharge into the lake, sources of phosphorous (and other nutrients) are unknown. We investigated the phosphorous fluxes from sediment pore water and from groundwater in the water body of the lake. Sediment pore water was extracted from sediment cores recovered by divers in August 2018 and February 2019. Diffusive phosphorous fluxes from pore water were calculated based on phosphorus gradients. Stable water isotopes (δ2H, δ18O) were measured in the lake water, in interstitial waters in the banks surrounding the lake, in the Elbe River, and in three groundwater wells close to the lake. Stable isotope (δ2H, δ18O) water mass balance models were used to compute water inflow/outflow to/from the lake. Our results revealed pore-water borne phosphorous fluxes between 0.2 mg/m2/d and 1.9 mg/m2/d. Assuming that the measured phosphorous fluxes are temporarily and spatially representative for the whole lake, about 11 kg/a to 110 kg/a of phosphorous is released from sediments. This amount is lower than the observed lake water phosphorous increase of ~344 kg between April 2018 and November 2018. Water stable isotope (δ2H, δ18O) compositions indicate a water exchange between an aquifer and the lake water. Based on stable isotope mass balances we estimated an inflow of phosphorous from the aquifer to the lake of between ~150 kg/a and ~390 kg/a. This result suggests that groundwater-borne phosphorous is a significant phosphorous source for the Eichbaumsee and highlights the importance of groundwater for lake water phosphorous balances.
Event‐triggered control of singularly perturbed linear system with DoS attacks

This paper presents the event-triggered control problem of linear singularly perturbed systems in the presence of denial-of-service attacks. To save the communication and computing resources, event-triggered mechanisms in two transmission channels are used to guarantee the ultimately bounded stability of the closed-loop system. Absolute event-triggering conditions are proposed in the sensor and the controller nodes, where the controller node utilises only the state of the slow model of the singularly perturbed system. Positive lower bounds of inter-event times are given to eliminate Zeno behaviour in the presence and absence of DoS attacks. Finally, two examples are provided to illustrate the efﬁciency and feasibility of the obtained results.
A systematic literature review of AI in the sharing economy

ABSTRACT Although artificial intelligence (AI) has been adopted in sharing economy platforms, few studies have investigated this phenomenon in this context. Consequently, there is no thorough overview of how AI has been used in the sharing economy. To address this research gap, a systematic literature review was performed for this paper. This method can be useful for the exploration of new and emerging trends within disciplines and allows boundaries to be mapped on what is known thereby identifying gaps on what is yet to be known. After screening, 28 English journal articles were selected in a qualitative synthesis. Results show AI can help the sharing economy platforms by enhancing trust, matching assets, and understanding participants’ preferences and attitudes. Based on these findings, potential directions are established. The current study will contribute to both the sharing economy and AI literature, and the results may help practitioners and academia to achieve a greater understanding of this topic.
Rural Life in Late Socialism

Late socialist countries are transforming faster than ever. Across China, Laos and Vietnam, where market economies coexist with socialist political rhetoric and the Communist party state’s rule, sweeping processes of change open up new vistas of imaginaries of the future alongside uncertainty and anxiety. These countries are three of very few living examples that combine capitalist economics with party state politics. Consequently, societal transformations in these contexts are subject to pressures and agendas not found elsewhere, and yet they are no less subject to global forces than elsewhere. As all three countries maintain substantial rural populations, and because those rural areas are themselves places of change, how rural people across these changing contexts undertake future making is a timely and significant question. The contributions in the issue address this question by engaging with lived experiences and government agendas across Laos, China and Vietnam, showing a politics of development in which desire and hope are entangled with the contradictions and struggles of late socialism.
Chunking by social relationship in working memory

ABSTRACT Working memory (WM) uses knowledge and relations to organize and store multiple items in fewer structured units, or chunks. We investigated: (a) whether a crowd that exceeds the WM capacity is retained better if individuals can be grouped in social chunks; and (b) what counts as a social chunk: two individuals involved in a meaningful interaction or just spatially close and face-to-face. In a delayed change-detection task, participants were more accurate in reporting changes in arrays involving facing (vs. non-facing) dyads whether they depicted meaningful interactions or not (Experiments 1, 2 and 4). This advantage survived a secondary task that increased WM load, only when facing dyads formed meaningful interactions (Experiment 3). Thus, WM uses representation of interaction to chunk crowds in social groups. The mere face-to-face positioning is sufficient to trigger social chunking, although without a semantic anchor this process is fainter and more susceptible to interference.
A novel, density-independent and FTIR-compatible approach for the rapid extraction of microplastics from aquatic sediments

Microplastics have been detected in aquatic sediments around the world, highlighting the propensity of this matrix to serve as a sink for these structural pollutants. More reliable and reproducible extraction protocols for microplastics would facilitate comparisons across studies. A number of different extraction techniques are currently used to separate microplastics from sediment and almost exclusively employ density-based separations, which take advantage of the inherent densities of plastic particles. Some of these techniques are cost-effective but fail to fully recover all plastic types. Other techniques may recover most plastic types, but are more costly and/or hazardous to human or environmental health. We present here a novel, cost-effective oil extraction protocol (OEP) that provides an alternative to density-based approaches by taking advantage of the oleophilic properties of microplastics. Using this technique, we counted microplastic particles in spiked sediment samples using light microscopy and observed 96.1% ± 7.4 recovery for total microplastics, with recovery rates of 92.7% ± 4.3 for fibers and 99% ± 1.4 for particles. Subsequent analysis with Fourier-Transform Infrared Spectrometry (FTIR) revealed that the oil interfered with the FTIR spectrum of microplastics, but that an additional, post-extraction clean-up step using ethyl alcohol (90%) removed residual traces of oil and eliminated the FTIR spectral interference. The application of this new technique to shoreline sediment samples collected from sites in urban Vancouver, British Columbia, Canada, and a remote beach on Vancouver Island, as well as bulk seawater, demonstrated that the oil extraction protocol is effective for environmental samples. This novel OEP represents a cost-effective and reliable alternative to leading density-based techniques.
Nutrition treatment in pediatric burns patients

Burn trauma causes obvious pathophysiological disorders that potentially affect the organ systems. For this reason, there are specific nutrient needs that require aggressive food intervention after burns. Nutritional support in burn patients differs from general intensive care patients. The nutritional support to be applied during the hospitalization process is changed and even changed according to the developments during the disease period. Therefore, a multidisciplinary approach must be provided for patient nutritional support planning. Nutritional support of pediatric burn patient is one of the important issues in the continuity of care. Early nutrition support is widely accepted as a standard of care. Estimating calorie needs remains a poorly defined science, and direct measurement is often impractical. Enteral nutrition, both safety concerns and care, are the preferred route for the benefits of intestinal mucosal integrity. Additional substances and pharmacological agents have been advocated by some people, but there is no consensus on their routine use. The clinician should still pay attention to reasonable judgment and the overall progress of the patient to maximize results. With the use of aggressive resuscitation, nutritional support, infection control, surgical treatment and early rehabilitation, as well as multidisciplinary collaboration, better psychological and physical outcomes can be achieved for burn children.
New penalized Bayesian adaptive lasso binary regression

The scale mixture of normal mixing with Rayleigh as representation of Laplace prior of β has introduced by Flaih et al[1]. We employed this new scale mixture for the adaptive lasso Binary regression. New hierarchical model is considering ,as well the Gibbs sampler algorithm in introduced . We considering the new penalized Bayesian adaptive lasso in Binary regression as variable selection method in case of presenting they high dimensional data . The new proposed model can overcame the multicollinearity problem in predictor variables. We conducting simulation analysis, as well as real data application to show the performance of the proposed method.
8-16-4 graphyne: Square-lattice two-dimensional nodal line semimetal with a nontrivial topological Zak index

An unprecedented graphyne allotrope with square symmetry and nodal line semimetallic behavior has been proposed in the two-dimensional (2D) realm. The emergence of the Dirac loop around the high-symmetry points in the presence of both the inversion and time-reversal symmetries is a predominant feature of the electronic band structure of this system. Besides, the structural stability in terms of the dynamic, thermal, and mechanical properties has been critically established for the system. Following the exact analytical model based on the real-space renormalization group scheme and tight-binding approach, we have inferred that the family of 2D nodal line semimetals with square symmetry can be reduced to a universal four-level system in the low-energy limit. This renormalized lattice indeed explains the underlying mechanism responsible for the fascinating emergence of 2D square nodal line semimetals. Besides, the analytical form of the generic dispersion relation of these systems is well supported by our density-functional theory results. Finally, the nontrivial topological properties have been explored for the predicted system without breaking the inversion and time-reversal symmetry of the lattice. We have obtained that the edge states are protected by the nonvanishing topological index, i.e., Zak phase.
The Association of Arable Weeds with Modern Wild Cereal Habitats: Implications for Reconstructing the Origins of Plant Cultivation in the Levant

ABSTRACT Reconstructing the origins of plant cultivation in southwest Asia is crucial for understanding associated processes such as the emergence of sedentary communities and domesticated crops. Among the criteria archaeobotanists developed for identifying the earliest plant cultivation, the presence of potential arable weeds found in association with wild cereal and legume remains has been used as a basis for supporting models of prolonged wild plant cultivation before domesticated crops appear. However, the proposed weed floras mainly consist of genus-level identifications that do not differentiate between arable weeds and related species that characterise non-arable habitats. Here we test, for the first time, whether the potential arable weed taxa widely used to identify wild plant cultivation also occur in non-cultivated wild cereal populations. Based on modern survey data from the southern Levant we show that the proposed weed taxa characterise both grasslands and fields. Our findings, therefore, do not support the use of these taxa for reconstructing early cultivation. Instead, for future studies we suggest an approach based on the analysis of plant functional traits related to major agroecological variables such as fertility and disturbance, which has the potential to overcome some of the methodological problems.
The global significance of biodiversity science in China: an overview

Abstract Biodiversity science in China has seen rapid growth over recent decades, ranging from baseline biodiversity studies to understanding the processes behind evolution across dynamic regions such as the Qinghai-Tibetan Plateau. We review research, including species catalogues; biodiversity monitoring; the origins, distributions, maintenance and threats to biodiversity; biodiversity-related ecosystem function and services; and species and ecosystems’ responses to global change. Next, we identify priority topics and offer suggestions and priorities for future biodiversity research in China. These priorities include (i) the ecology and biogeography of the Qinghai-Tibetan Plateau and surrounding mountains, and that of subtropical and tropical forests across China; (ii) marine and inland aquatic biodiversity; and (iii) effective conservation and management to identify and maintain synergies between biodiversity and socio-economic development to fulfil China's vision for becoming an ecological civilization. In addition, we propose three future strategies: (i) translate advanced biodiversity science into practice for biodiversity conservation; (ii) strengthen capacity building and application of advanced technologies, including high-throughput sequencing, genomics and remote sensing; and (iii) strengthen and expand international collaborations. Based on the recent rapid progress of biodiversity research, China is well positioned to become a global leader in biodiversity research in the near future.
Detectando linhas de pesquisa emergentes com análise de redes via algoritmo de agrupamento hierárquico

Este trabalho tem como objetivo a aplicação de algoritmos de clusterização hierárquico para detectar linhas de pesquisa emergentes, que buscam mensurar eficiência utilizando o método Data Envelopment Analysis DEA. O processo básico para analisar as fontes de pesquisa emergentes em DEA, utiliza algoritmos de agrupamentos. Este trabalho procura analisar o algoritmo de agrupamento topológico, o qual é baseado na ideia de modularidade, aplicado a uma base de dados de citação. Uma abordagem alternativa a este método de agrupamento é a proposta de utilização do algoritmo aglomerativo hierárquico o qual é baseado na fusão de agrupamentos com base em medidas de proximidade. As etapas para a aplicação dos algoritmos de clusterização são: coleta de dados de citação na plataforma de dados bibliográficos Web of Science; construção de redes de citação para cada ano; exclusão dos artigos que não citam outros artigos da rede, mantendo apenas o componente principal da rede; divisão da rede em grupos via método de agrupamento; determinação do peso de cada artigo em seu grupo e nos demais grupos da rede; e, por fim, analisar o conteúdo de cada grupo via análise de Natural Language Processing.
Hydrodynamics, Sediment Transport and Morphological Features at the Confluence Between the Yangtze River and the Poyang Lake

Confluences act as critical nodes in a river network as they affect flow, sediment transport, water quality, and ecological patterns. A complete knowledge about hydro‐morpho‐sedimentary processes at river confluences is still incompleted and it has been usually accepted that secondary flows are weak because of the significant role of form roughness in large rivers. In this study, two field surveys were conducted on the flow structure, suspended sediment transport and morphology of the confluence between the Yangtze River (the largest river in China) and the Poyang Lake (the largest freshwater lake in China). Dual counter‐rotating cells were observed during high flow conditions and a single secondary cell appeared in low flow conditions. These helical cells restricted the core size of high sediment concentration and downwelling flows acted as a barrier hindering the exchange of sediment between the two rivers. Furthermore, the observed large scour hole was likely related to the downwelling and upwelling flows caused by helical motions. In low flow conditions, the scour hole looked like a deep channel, which was likely related to a long‐surviving helical cell. The scour hole disappeared further downstream, when either the helical motion got weak during low flow conditions, or when a reverse helical cell occurred during high flow conditions. Hydrodynamics, suspended sediment transport and morphological features observed at such a large confluence demonstrated that river planform geometry and discharge ratio affected the flow structure, especially the helical motion. This in turn affected sediment transport as well as the local bed morphology.
Development of an Integrated IoT-Based Greenhouse Control Three-Device Robotic System

The control of large greenhouse installations, especially those with hydroponics crops, is based on the analysis and use of data recorded by many sensors. At the same time, the size of such installations does not allow for their effective terrestrial surveillance, to detect problems promptly. In recent years, there has been an interest in the development of autonomous agbots equipped with agricultural sensors. Several ground-based AGV (automated guided vehicles) and UAV (unmanned aerial vehicles) systems have been developed for use in open-air plots. A key feature of all these innovative systems is spectroscopy, the development of which has been assisted by the surveillance capabilities and speed of modern-day UAVs (drones). However, there is a lag in the use of spectroscopy inside greenhouses since UAVs do not move freely indoors. In this paper, we propose as a solution a three-device (3DS) system.
Mixing and sink effects of air purifiers on indoor PM2.5 concentrations: A pilot study of eight residential homes in Fresno, California

ABSTRACT We measured real-time and integrated PM2.5 inside eight occupied single-family homes in Fresno, California to evaluate how turbulent air mixing and pollutant removal caused by a filter-based air purifier influences the levels of fine particles in everyday indoor environments. In each home, we used a real-time monitor to log PM2.5 levels every 5 min over 12 weeks during which air purifiers were operating, except for a designed 3-day shutdown period for baseline measurements. We assessed how the operation of air purifiers changed the patterns of the frequency distributions for short-term (5 min) concentrations, which included spikes produced by sporadic indoor activities or emissions. This allowed us to examine the reduction effectiveness of air purifiers on concentrations of both recently emitted and well-mixed background aerosols. We observed a systematic change in the 5-min PM2.5 distributions in different homes—while air purifiers reduced 96% of the 5-min concentrations, they increased the magnitudes of the top 4%, representing transient concentration peaks. This phenomenon is consistent with what would be theoretically expected based on passive scalar turbulence in fluid physics. We also collected gravimetric filter samples for PM2.5 composition, finding mean reductions of the long-term (2–5 days) concentrations of 29%–37% for indoor PM2.5 and endotoxin. A less significant reduction (19%–26%) was seen for Pb (Lead). © 2016 American Association for Aerosol Research
EDC-Net: Edge Detection Capsule Network for 3D Point Clouds

Edge features in point clouds are prominent due to the capability of describing an abstract shape of a set of points. Point clouds obtained by 3D scanner devices are often immense in terms of size. Edges are essential features in large scale point clouds since they are capable of describing the shapes in down-sampled point clouds while maintaining the principal information. In this paper, we tackle challenges of edge detection tasks in 3D point clouds. To this end, we propose a novel technique to detect edges of point clouds based on a capsule network architecture. In this approach, we define the edge detection task of point clouds as a semantic segmentation problem. We built a classifier through the capsules to predict edge and non-edge points in 3D point clouds. We applied a weakly-supervised learning approach in order to improve the performance of our proposed method and built in the capability of testing the technique in wider range of shapes. We provide several quantitative and qualitative experimental results to demonstrate the robustness of our proposed EDC-Net for edge detection in 3D point clouds. We performed a statistical analysis over the ABC and ShapeNet datasets. Our numerical results demonstrate the robust and efficient performance of EDC-Net.
Functional Modeling of High-Dimensional Data: A Manifold Learning Approach

This paper introduces stringing via Manifold Learning (ML-stringing), an alternative to the original stringing based on Unidimensional Scaling (UDS). Our proposal is framed within a wider class of methods that map high-dimensional observations to the infinite space of functions, allowing the use of Functional Data Analysis (FDA). Stringing handles general high-dimensional data as scrambled realizations of an unknown stochastic process. Therefore, the essential feature of the method is a rearrangement of the observed values. Motivated by the linear nature of UDS and the increasing number of applications to biosciences (e.g., functional modeling of gene expression arrays and single nucleotide polymorphisms, or the classification of neuroimages) we aim to recover more complex relations between predictors through ML. In simulation studies, it is shown that ML-stringing achieves higher-quality orderings and that, in general, this leads to improvements in the functional representation and modeling of the data. The versatility of our method is also illustrated with an application to a colon cancer study that deals with high-dimensional gene expression arrays. This paper shows that ML-stringing is a feasible alternative to the UDS-based version. Also, it opens a window to new contributions to the field of FDA and the study of high-dimensional data.