Miracle-dz/UniFAR-FTUs huggingface dataset:
Carbon dioxide flux and net primary production of a boreal treed bog: Responses to warming and water-table-lowering simulations of climate change

Abstract. Midlatitude treed bogs represent significant carbon (C) stocks and are highly sensitive to global climate change. In a dry continental treed bog, we compared three sites: control, recent (1–3 years; experimental) and older drained (10–13 years), with water levels at 38, 74 and 120 cm below the surface, respectively. At each site we measured carbon dioxide (CO2) fluxes and estimated tree root respiration (Rr; across hummock–hollow microtopography of the forest floor) and net primary production (NPP) of trees during the growing seasons (May to October) of 2011–2013. The CO2–C balance was calculated by adding the net CO2 exchange of the forest floor (NEff-Rr) to the NPP of the trees. From cooler and wetter 2011 to the driest and the warmest 2013, the control site was a CO2–C sink of 92, 70 and 76 g m−2, the experimental site was a CO2–C source of 14, 57 and 135 g m−2, and the drained site was a progressively smaller source of 26, 23 and 13 g CO2–C m−2. The short-term drainage at the experimental site resulted in small changes in vegetation coverage and large net CO2 emissions at the microforms. In contrast, the longer-term drainage and deeper water level at the drained site resulted in the replacement of mosses with vascular plants (shrubs) on the hummocks and lichen in the hollows leading to the highest CO2 uptake at the drained hummocks and significant losses in the hollows. The tree NPP (including above- and below-ground growth and litter fall) in 2011 and 2012 was significantly higher at the drained site (92 and 83 g C m−2) than at the experimental (58 and 55 g C m−2) and control (52 and 46 g C m−2) sites. We also quantified the impact of climatic warming at all water table treatments by equipping additional plots with open-top chambers (OTCs) that caused a passive warming on average of ~ 1 °C and differential air warming of ~ 6 °C at midday full sun over the study years. Warming significantly enhanced shrub growth and the CO2 sink function of the drained hummocks (exceeding the cumulative respiration losses in hollows induced by the lowered water level × warming). There was an interaction of water level with warming across hummocks that resulted in the largest net CO2 uptake at the warmed drained hummocks. Thus in 2013, the warming treatment enhanced the sink function of the control site by 13 g m−2, reduced the source function of the experimental by 10 g m−2 and significantly enhanced the sink function of the drained site by 73 g m−2. Therefore, drying and warming in continental bogs is expected to initially accelerate CO2–C losses via ecosystem respiration, but persistent drought and warming is expected to restore the peatland's original CO2–C sink function as a result of the shifts in vegetation composition and productivity between the microforms and increased NPP of trees over time.
Self-regulated Learning in Students from Native Communities. Case: reading and reading comprehension

There is concern about the deficiency in reading in some students at the primary level in native communities and rural areas far from the city that are weak points in education, the main objective is to analyze the level of reading and comprehension of texts through self-regulated learning of students at the primary level. The research method is quantitative-descriptive. A virtual reading test was applied to measure the respective phases (anticipation, execution and self-reflection) and the level of learning in reading comprehension, and then a survey on self-regulated learning with its respective study dimensions was applied to 154 students of the sixth grade of primary education.
Modèles synthétiques des connaissances en hydrologie. Application à la régionalisation des crues en Europe alpine et méditerranéenne

Les modeles synthetiques en debits-durees-frequences, concept relativement nouveau en hydrologie, permettent de fournir une information representative du regime hydrologique en crue des bassins versants observes ou non. Grace a une bonne definition des variables hydrologiques qu'ils modelisent, a partir d'une forme mathematique souple et adimensionnelle, les modeles debits-durees-frequences dits qdf ont montre des proprietes regionales tout a fait interessantes (validation europeenne). Necessitant seulement deux descripteurs locaux, parametres d'entree, ils constituent d'un point de vue operationnel des outils hydrologiques performants, de mise en uvre aisee et conseillee en amenagement integre des cours d'eau et de leurs bassins versants
E-Chain: Lightweight and Secure BIoT Voting Mechanism on Variable Bandwidth Networks

The convergence of Blockchain and Internet of Things (BIoT) is fully considered as a paradigm for mitigating threats related to the trust, security, and privacy of Internet of Things (IoT) data. However, because the bandwidth across nodes and time varies in practical IoT networks, it is difficult for existing BIoT mechanisms guarantee blockchain consensus performances. The consensus time could become long owing to low-bandwidth nodes taking longer to download blocks than high-bandwidth nodes. Conventional wisdom holds that removing low-bandwidth nodes can decrease the consensus time, but the nodes could have high-bandwidth at another time owing to bandwidth variability; thus, kicking which nodes out of the consensus is a great challenge. In this article, a novel lightweight BIoT convergence (namely, E-Chain) is proposed to overcome bandwidth variability. The E-Chain first decouples the blockchain into on-chain validating and off-chain voting components. In the off-chain voting part, each node incurs a one-bit communication overhead for voting on a block based on a reputation index. This voting component does not need to download the full content of the block, and is therefore not affected by bandwidth variability. The reputation index was formulated using a rating algorithm with multidimensional IoT network metrics. In addition, the voting mechanism is secure and can still reach the correct consensus when suffering from byzantine attacks. By contrast, a block is validated and stored in a dispersed manner in the on-chain validating part. The E-Chain performances were then evaluated and compared with state-of-the-art mechanisms. Experimental results show that the E-Chain mechanism can significantly decrease both the consensus time and memory resources, and incur an acceptable memory overhead for resource-constrained IoT nodes.
Towards Industry Data Governance: Construction of An Industrial Data Decentralized Distributed Symbiotic Sharing Space Based on Tensor

One of the focuses of smart manufacturing technology is data management and processing. However, there are issues which is difficult to integrate the data within the enterprise, share industry chain data and obtain open data in the management and processing of existing industrial data. To address this challenge, this paper proposes a construction method of industrial data decentralized distributed symbiotic sharing space (iDS3) based on tensor. The proposed method consists of three core steps. First, metadata is extracted from multi-source heterogeneous raw data; Second, a knowledge graph is constructed using industrial mechanism to enrich the metadata; Finally, tensors are used to store metadata and its corresponding unique identifier ID. A metadata encoding rule based on industrial mechanisms is adopted in this work, which can support tensor queries in different application scenarios, thereby achieving ubiquitous storage of data by solving the distributed symbiotic sharing of multi-source heterogeneous raw data. At present, the industrial data governance platform (iDGP) developed based on iDS3 has been applied to the data integration of industrial enterprises such as ships, integrated circuits and new energy vehicles, the integration development with various the third-party data processing tools, and application services of industry dataspaces and so on, which shows that iDS3 has been a successful solution for the development of technology-driven Industry 4.0 to value-driven Industry 5.0.
Maternal vitamin D in pregnancy and infant's gut microbiota: a systematic review

Background An infant's gut microbiome plays a vital role in their health, and various factors can impact their gut microbiota composition. This review aimed to summarize the current knowledge regarding the associations between maternal prenatal supplementation with vitamin D and the composition of infants' gut microbiota. Method A comprehensive systematic search was done on Web of Science, Scopus, PubMed, ScienceDirect, and Google Scholar databases without date restrictions until December 2022 using relevant keywords. All relevant original articles in English were eligible for the present review. Results Eight articles (two mice, three randomized clinical trials, and three cohort studies) were included in this review. The included mice studies reported that maternal prenatal vitamin D supplementation significantly affects the offspring's gut microbiome composition (such as enhancing the abundance of colonic Bacteroides). Moreover, the included cohort studies revealed a significant association between maternal supplementation with vitamin D during pregnancy and the infant's gut microbiome. However, one-third of clinical trials indicated that vitamin D levels in utero could influence the colonization of the microbial community in the infant's gut. Conclusion The findings of this review revealed that maternal vitamin D supplementation during pregnancy was linked to an infant's gut microbiome and could impact their gut microbiota composition. However, more studies are warranted to confirm these results.
Optimization and Representation of Non-Slicing VLSI Floorplanning

The recent advances in the semiconductor nano technologies increase the complexity of very large-scale integration circuits. With the fabrication technology entering the deep Nanoscale era more demand at greater huge complication of integrated chip design arises. The development of eccentric floor plan without over-lapping is the intention of VLSI floor planning. The major objective of VLSI is concerned with floor plan area minimization and wire length optimization. Floor planning is the important step that creates an optimal layout solution for the VLSI circuits. This article attempted to collect the necessary information in reducing the floor plan layout by means of reducing the wire length, white space area with the employment of computer aided metaheuristic algorithms. In order to ease the process of execution the best local search algorithms like a Particle Swarm Optimization and Simulated Annealing algorithms is compared.
Advances in finding benefits for patients with diabetes

The etiology of diabetes is complex, the course of the disease is long, patients are easy to appear negative emotions, leading to a decline in the quality of life, with the rise of positive psychology, the positive psychology of diabetes patients has become a research hotspot of scholars,benefit finding is one of the hallmarks of positive psychology.This paper reviews the concepts, assessment tools and influencing factors of benefit discovery in patients with diabetes, aiming to provide reference for promoting the physical and mental health of patients with diabetes.
Construct Validation of the Multiple-Choice Items of the English Subtest of the Advanced Subjects Test in Taiwa

Administered in each July in Taiwan, the Advanced Subjects Test (AST) is a high-stakes college entrance test, of which an English subtest (AST-E) is an integral part. The AST-E score affects considerably which universities test takers are qualified to be admitted to. To them, the major concern is: what English ability does the AST-E really measure? This study was intended to investigate the construct validity of the multiple-choice (MC) items of the AST-E through confirmatory factor analysis (CFA) using data on test takers’ responses to the MC items from 2015 to 2016. In the first part of this study, the MC items were classified into several language components by five experienced raters based on the two classification frameworks of Purpura (1999, 2004). The chosen components represented an incipient model for describing the relationship between the un-observed components and the data. In the second part, a series of CFAs were performed, first on the incipient model and then on some alternative models, to look for a model that offers a relatively good fit to the data. The CFA results showed that, although the incipient model involved multiple components/factors, a one-factor model was found to best portray the characteristics of test takers’ responses to the MC items for 2015 and 2016, suggesting that the MC items appeared to tap simply their general English reading ability rather than a set of their divisible English reading skills. Finally, this study concluded with some pedagogical and practical implications for Taiwan high school English teachers and AST-E test constructors.
Fast VIE-DDM for 3-D Electromagnetic Scattering of Complicated Anomalies in Layered Media Using Adaptive Cross Approximation

Simulations of 3-D electromagnetic (EM) scattering from complicated anomalies in a layered medium are important in geophysical applications. The volume integral equation (VIE) method, specifically its fast solvers such as fast Fourier transform (FFT)-based methods, has been widely applied to tackle these problems. Recently, we proposed to combine the domain decomposition method (DDM) with VIE to solve the problems with multiple objects in a layered medium (VIE-DDM). This method leveraged 3-D and 2-D FFTs to accelerate the self- and mutual-coupling matrix-vector multiplications (MVMs) in the stabilized-biconjugate gradient (BCGS) method when solving the linear system derived from the VIE-DDM. Though significantly extending the applicability and improving the efficiency of VIE in some scenarios, the BCGS-FFT-DDM (or VIE-DDM-FFT) has the limitations of requiring conformal and uniform meshes in the $xy$ plane and not allowing dense meshes in the $z$ -direction. It turned out both the limitations come from the mutual-coupling matrices. In this article, we propose to exploit the rank-deficient property of the mutual-coupling matrices relating well-separated objects, apply the adaptive cross approximation (ACA) method to find the low-rank factors of these matrices, and then achieve the MVMs using the low-rank factors. The ACA is purely algebraic and only requires a few rows and columns from the original matrix, which can be easily obtained by computing Green’s functions, to find the low-rank factor matrices. With this new method, both the storage and computation complexities become linear. These linear complexities successfully avoid the bottleneck of dense $z$ -direction meshes in the BCGS-FFT-DDM. Furthermore, the new method does not strictly require conformal and uniform meshes in any direction for different objects, making it more flexible in treating multiscale problems. We tested the performance of this method in two typical scenarios and compared the results with the BCGS-FFT-DDM and commercial platforms. The results show that the new method achieves significant improvement in memory and CPU time cost and has more flexibility in treating multiscale problems.
Cloud Parameterizations in SUNYA Regional Climate Model for the East Asia Summer Monsoon Simulations

East Asia summer monsoon (EASM) simulations are conducted to evaluate three schemes which determine cloud properties used in the radiation calculation of the State University of New York at Albany (SUNYA) regional climate model (RCM). Scheme-Ⅰ uses diagnostic cloud cover and cloud water while Scheme-Ⅱ uses prognostic cloud water along with overcast sky; both schemes are commonly employed in RCMs. In Scheme-Ⅲ, cloud cover is determined by diagnostic formula, but the cloud water is calculated by the weighted means of its diagnosed and prognosed values. Therefore, Scheme-Ⅲ considers the subgrid-scale clouds as Scheme-Ⅰ and maintains consistent cloud properties in radiation calculation and microphysical processes as Scheme-Ⅱ. Cloud radiative forcing (CRF) which provides a quantification of the cloud-radiation-climate interaction is adopted to compare the three schemes in simulating the 1991 EASM, characterized by large amounts of cloud and persistent rainfall over Yangtze-Huai River valley. With these three cloud schemes, the SUNYA RCM is capable of simulating the intra-seasonal variations of observed cloud cover and longwave CRF. The transition of shortwave CRF is not properly simulated due to the constantly presented low-level clouds. Mostly the magnitude of CRF is overestimated by 13-22 W m^(-2) for shortwave CRF and by 12-16 W m^(-2) for longwave CRF. It is also found that the surface temperature biases are highly correlated (with correlation coefficient greater than 0.8) to the shortwave CRF biases. Therefore, Scheme-Ⅲ resulting in less low-level cloud water and the least shortwave CRF biases simulates surface temperatures in better agreement with observations. Analyses of surface energy balance components indicate that the CRF changes dominate the surface temperature responses and the consequent surface latent heat and sensible heat flux feedbacks significantly offset them. Finally, comparisons of the diurnal variations of simulated cloud water among the three schemes show that Scheme-Ⅲ provides consistency between cloud microphysics and radiation.
Applying RIS in Multi-User SWIPT-WPCN Systems: A Robust and Environmentally-Friendly Design

Integrating simultaneous wireless information and power transfer (SWIPT) and wireless-powered communication network (WPCN) has been regarded as one of the energy-efficient paradigms, which can complement each other in uplink and downlink transmission. However, severe path loss and imprecise channel estimation hinder the system performance in energy harvesting (EH) and information transmission. To break this gridlock, in this paper, reconfigurable intelligent surface (RIS) is applied to SWIPT-WPCN systems for ameliorating transmission efficiency, and system robustness is considered under imperfect channel state information. Specifically, aiming at minimizing system energy consumption, a robust resource allocation optimization problem is formulated by jointly optimizing the beamforming vector of the base station, the phase shifts of the RIS, power-splitting factors, EH time, and the transmit power of each user, where the non-linearity of EH and channel uncertainties are taken into account. To deal with this intractable non-convex problem, the S-procedure approach is applied to transform the original problem into a deterministic one, and a block coordinate descent-based iterative algorithm with the successive convex approximation and the penalty-based method is proposed to obtain sub-optimal solutions. Simulation results demonstrate that the proposed algorithm outperforms the benchmark algorithms regarding energy saving and lower outage probability.
EVAPOTRANSPIRATION MEASUREMENTS AND MODELING FOR THREE WETLAND SYSTEMS IN SOUTH FLORIDA 1

ABSTRACT: At the Everglades Nutrient Removal project in south Florida, three lysimeters were installed to measure daily evapotranspiration (ET) rates from cattails (Typha domingensis), mixed marsh vegetation, and an open water/algae system. The cattail lysimeter began operation in February 1993. The mixed marsh vegetation lysimeter began operation in January 1994, and the open water lysimeter with occasional algae cover began operation in December 1993. The mean measured ET rate was 3.6 mm, 3.5 mm, and 3.7 mm per day for the cattail, mixed marsh vegetation, and open water/algae system, respectively. High resolution weather data were continuously measured at the site. Six models were applied to estimate daily ET rates of the three systems. The Penman‐Monteith equation best estimated ET of cattail and mixed marsh vegetation, and the Penman Combination equation was most suitable for the open water/algae system. Empirical equations based on solar radiation and maximum temperature produced estimates of daily ET from the three systems that are comparable to models that require many more parameters. In cases where limited data is available, the calibrated simple models can be used to estimate ET from wetlands in south Florida.
TIF: Threshold Interception and Fusion for Compact and Fine-Grained Visual Attribution

The blackbox nature of deep models has prompted a growing interest in explaining their inner workings and decision-making processes. Although backpropagation (BP)-based attribution methods are popular for visual interpretation, existing methods frequently yield implausible outcomes. For example, gradient-based attribution methods tend to highlight irrelevant regions and generate noise, while CAM-based attributions suffer from low resolution and blurry results. These limitations undermine their ability to correctly identify the target objects and fail to provide the desired justification to guarantee the credibility of the model's decision-making. In this article, we analyze plausibility issues in the frequency domain and point out that the plausibility issues correspond to frequency-domain incompleteness, i.e., the frequency-domain representation of explanations lacks low- or high-frequency components. Then, we propose a straightforward yet effective approach, threshold interception and fusion (TIF), to address this issue by fusing multilayer attributions. Our strategy involves collecting attribution results for all neurons and dividing the attribution map into a concept region that represents the current neuron and background regions based on a given threshold value $\alpha$. We then fuse these concept regions with the neuron weights in each layer and upsample the layer attributions to match the input size. Finally, we obtain the overall attribution by summing the layer attributions pixelwise. Our experiments demonstrate TIF efficacy by consistently enhancing visual performance across a variety of gradient-based attributions. To further demonstrate the ability to provide compact and fine-grained target objects, we directly employ TIF for the weakly supervised semantic segmentation task. Our results illustrate that TIF significantly outperforms existing methods without additional supervision or architectural modifications. We also observe an overall TIF improvement in the fidelity metric, suggesting that compactness and fine-graininess are not only plausibility issues but also fidelity issues.