Miracle-dz/UniFAR-FTUs huggingface dataset:
Heat flow observations on the Bermuda Rise and thermal models of midplate swells

The Bermuda Rise is a broad topographic swell which is apparent in both residual depth and geoid anomaly maps of the western North Atlantic. The magnitudes of the depth and geoid anomalies associated with the Bermuda Rise are similar to the anomalies associated with other swells surrounding recent volcanic islands (e.g., Hawaii), suggesting that despite the lack of recent volcanism on Bermuda, the rise has a similar origin to other midplate swells. Results are reported from 171 new heat flow measurements at seven carefully selected sites on the Bermuda Rise and the surrounding seafloor. Off the Bermuda Rise the basement depths are generally shallower and the heat flow higher than either the plate or boundary layer models predict, with the measured heat flow apparently reaching a uniform value of about 50 mW m−2 on 120 m.y. old crust. On the Bermuda Rise the heat flow is significantly higher (57.4±2.6 mW m−2) than off the swell (49.5±1.7 mW m−2). The magnitude of the anomalous heat flux (8–10 mW m−2) is comparable to that previously found along the older portion of the Hawaiian Swell near Midway. The existence of higher heat flow on both the Hawaiian Swell and Bermuda Rise indicates that these features fundamentally have a thermal origin. The differences in the shape, uplift, and subsidence histories of the Hawaiian Swell and Bermuda Rise can be quantitatively explained by the different absolute velocities of the Pacific and North American plates moving across a distributed heat source in the underlying mantle. Two-dimensional numerical convection models indicate that the observed depth, geoid, and heat flow anomalies are consistent with simple convection models in which the lower part of the thermally defined plate acts as the upper thermal boundary layer of the convection.
Antimicrobial Resistance and Virulence Determination of Enterococcus spp. isolated from Hospital Environment in Iran

Background: The role of the hospital environment as a source of pathogenic bacteria in recent studies has been poorly investigated. This study investigated the distribution of antimicrobial resistance genes and virulence determinants in Enterococcus species isolated from hospital environment in Sari, Iran. A total of 90 enterococci isolates were identified and species identification confirmed with specific primers. Seven resistance genes and two virulence associated genes were evaluated molecularly by multiplex polymerase chain reaction. Results: Of the 90 enterococcal isolates, 42 (46.66%), and 48 (53.33%) were identified as E. faecalis, and E. faecium, respectively. Also, 28 (66.6%) E. faecalis and 18 (37.5%) E. faecium isolates were multidrug-resistant (MDR). Among all 90 environmental isolates 54 (60%), 54 (60%), 8 (8.8%), 8 (8.8%), 60 (66.6%), 26 (28.8%), and 24 (26.6%) isolates contained tetM, tetL, vanA, vanB, ermB, aac (6´)-Ie-aph (2´´)-Ia, and aph (3´)-IIIa, respectively. Moreover, 88 (97.7%) and 16 (17.7%) isolates were detected as esp and ace positive ones, correspondingly. Conclusions: This report showed that the environmental isolates of Enterococcus are the major sources of antibiotic resistance genes that can transfer them to the clinical isolates of bacteria in hospital settings. An effective following strategy should be organized to clearance and stop emergence of these pathogenic bacteria.
Project Proposal: Generative Adversarial Networks

is capable of approximating any continuous function on R under certain weak conditions on σ, where σ : R→ R is a continuous univariate function and w, w, b ∈ R [3]. Note that this function describes a one-layer neural network. We can write a multilayer feedforward network generally as follows with the goal of determining the parameters w and b.  x1 = x x` = σ(w x`−1 + b ), ` = 1, . , L− 1 xL = wL ∗ xL−1 + bL
Progress in the Research on Branched Polymers with Emphasis on the Chinese Petrochemical Industry

Polymer flooding, one of the main methods for improving crude oil recovery using chemical flooding technology in China, is widely used for actual oil displacement. Partially hydrolyzed polyacrylamide (HPAM) is a commonly used linear polymer in polymer flooding, but it exhibits poor temperature and salt resistance due to its molecular structure. Therefore, branched polymers have been studied. This article provides a review of the specific synthetic methods and current practical applications in the petrochemical field of dendritic polymers and hyperbranched macromolecules. The advantages and disadvantages of each synthetic method for branched polymers are also elaborated. Finally, the application prospects of branched polymers are discussed. The feasibility of branched polymers in large quantities should be further verified through additional field tests, which should address concerns such as synthesis costs and reaction efficiency.
Research on Ultra-Wide Joint Bending Angle Detection Based on Transparent Tubing Structure

The design of a bending angle sensor consisting of plastic optical fibers (POFs) embedded in transparent tubing is proposed for the detection of the angle of human joints. Highly sensitive sensing is achieved by the bending of the transparent tubing between the two fiber end faces which is functioning as a hollow-core optical fiber with the air inside the tubing as the core and the tubing as the cladding. Simulation results based on the ray tracing method show that when the transparent tubing was bent, both end-face coupling and internal reflection mechanisms coexisted in the beam transmission process, with reflection in the tubing being the main mechanism at a large bending angle. A motor-driven angle measurement system is constructed to achieve accurate control of the bending angle. Experimental results show that the sensor can achieve ultra-wide-angle detection ranging from 0° to 180° with a high sensitivity of 0.136 dB/° and a linear regression coefficient ( $\text{R}^{{2}}{)}$ of 0.997. Finally, the dynamic response characteristics of the sensor are tested on both motor-controlled bending and joint bending, and the results show fast response, good repeatability, and stability of the proposed sensor.
A Novel Technique for Operational Band Extension of SIW-Based Resonant Slot Array Antennas for 5G

A method for resonant slot array antenna matching is proposed in this contribution. It is based on an accurate analysis of frequency dependent equivalent characteristics of radiating slots of a series fed waveguide slot antennas. E/m simulation workflow for obtaining and tuning the normalized admittance of a longitudinal slot is shown. For experimental validation of the method, two SIW-based slot array antenna prototypes of 27.5-29.5 GHz range were manufactured. The antennas are considered prospective for 5G applications and include two slot arrays of 4 and 5 slots each. Measurement results have proved that the operational bandwidth of the slot array antennas that were matched in accordance with the technique is increased twice, from 3.7% to 7.1% relative bandwidth.
Combining CNN and Transformer for Noise-Robust Image Compressed Sensing

Compressed sensing (CS), as a signal acquisition and reconstruction method, benefits from deep learning technology and can quickly and accurately reconstruct signals from a small number of observations. Emerging Transformer networks bring significant gains to CS while also leaving unresolved issues. One obvious issue is that the reconstruction performance of CS methods drops sharply in the presence of noise. To achieve accurate CS reconstruction in noisy scenes, this paper proposes to improve the latest Transformer-based CS method with a CNN correction module, resulting in a joint scheme for noise-robust image CS recovery. The scheme adopts a combination of a deep learning reconstruction module and a denoising module. Specifically, it consists of a reconstruction module with a sampling subnet for a trainable perceptual matrix strategy, an initial reconstruction subnet, a hybrid reconstruction subnet, and a trainable denoising module. Extensive experiments show that the scheme can effectively improve the image quality and noise robustness of compressed sensing reconstruction.
Simulated hydrological effects of grooming and snowmaking in a ski resort on the local water balance

Abstract. The presence of a ski resort modifies the snow cover at the local scale, due to snow management practices on ski pistes, especially grooming and snowmaking. Snow management exerts 2-fold effects on the local hydrological cycle, through (i) abstraction and transfer of water used for snowmaking, and (ii) changes in water runoff due to added snow mass through snowmaking and/or delayed melting of the snowpack due to snow grooming. This induces a local pressure on water resources, which has seldom been addressed in scientific studies hitherto. Here we introduce a method to compute the hydrological effects of snow management on ski pistes and we apply and illustrate its results for the case study of the La Plagne ski resort in the Northern French Alps. The approach mainly relies on snow cover modelling using the Crocus snow cover driven by SAFRAN reanalysis and climate projections. Model results are evaluated against in-situ hydrological observations and show that the modelling approach, although very simplified for many hydrological processes, provides relevant information and insights in terms of the influence of snow-related processes on water resources. Our study shows a visible impact of grooming, virtually eliminating snowmelt in winter, thus delaying the onset of snowmelt. This results is a lower snowmelt flux during the wintertime, low flow period, on the order of −10 % to −20 %, compensated by higher amounts when snow melts. While about 10 % of the water used for snowmaking is estimated to be lost by evaporation through the ice formation process from the liquid water droplets, we find that, in the case studied, the annual scale alteration of water resources is limited and estimated to be on the order of 1 % to 2 %. This is due to the fact that the amount of water used for snowmaking on ski pistes represents a fraction of 10 % to 20 % of total annual precipitation, that ski pistes cover typically 10 % of the surface area of catchments within which ski resorts are located, and that snowmaking equipment covers, in the case of La Plagne, 40 % of the surface area of ski pistes. Therefore, in this case, snowmaking mainly leads to a moderate shift in snow cover formation and snowmelt processes and plays, for example, a smaller role than the influence of future climate change on mountain hydrology. This study provides an initial overview of the influence of grooming and snowmaking on river flows in a mountain catchment, which can inform future studies on water management and climate change adaptation in areas with ski tourism facilities. This study does not discuss long-term sustainability challenges of ski tourism and other aspects of the local environmental impacts (landscape, biodiversity) of snow management, such as the construction and use of mountain water reservoirs and other earthworks in ski resorts.
Temporal and Spatial Characteristics of Hard X-Ray Sources in a Flare Model with a Vertical Current Sheet

We analyzed changes in the height of the coronal hard X-ray (HXR) source for flares SOL2013-05-13T01:50 and SOL2013-05-13T15:51. Analysis of the Reuven Ramaty High Energy Solar Spectroscopic Imager data revealed the downward motion of the HXR source and the separation of the sources by energy and height. In the early stages of the flares, a negative correlation was found between the HXR source area in the corona and HXR flux. For the SOL2013-05-13T15:51 event, an increasing trend in the time delay spectra at the footpoints was obtained. For both events, the spectra of the time delays in the coronal HXR source showed a decreasing trend with energy in certain flare phases. To interpret the observed phenomena, we considered a flare model of collapsing traps and calculated the distribution functions of accelerated electrons along the magnetic loop using a nonstationary relativistic kinetic equation. This approach considers betatron and Fermi first-order acceleration mechanisms. The increasing trend of the time delay spectra at the footpoints was explained by the high mirror ratio in the magnetic loop and betatron acceleration mechanism. The observed features in the spatial and temporal behavior of the HXR sources, such as the negative correlation between the HXR source area and HXR flux, can be interpreted by the collapsing trap model.
Greywater Treatment in combined Biofilter/Constructed Wetlands in Cold Climate

In Norway systems consisting of an aerobic biofilter followed by a subsurface horizontal flow constructed wetland has been very successful in reducing organic matter, indicator bacteria, nitrogen and phosphorus in greywater. Because of phosphate free detergents are used in Norway and no toilet waste is included, the average influent total phosphorus concentrations (measured as septic tank effluent-STE) are about 1mg P/l and the average influent total nitrogen concentrations are in the range 8-10 mg N/l. The aerobic biofilter prior to the wetland is essential to remove BOD in a climate where the plants are dormant during the cold season. When combined with a horizontal flow constructed wetland the concentrations of indicator bacteria in the effluent meets European standards for swimming water quality. The effluent concentrations for phosphorus are generally < 0.2 mg P/l and for nitrogen < 5mg N/l. The combined biofilter /constructed wetland systems require 1-3m 2 surface area per person. The
Organic matter decomposition in subtropical South America aquatic environments: a systematic and scientometric review

Abstract: Aim To evaluate the general situation of studies on the decomposition of organic matter in aquatic environments in the subtropical region of South America. Methods We compiled publications (1990-2018) with a combination of words referring to decomposition, debris, aquatic environments and South American countries with subtropical climate. The characteristics of the publications were analysed over time. Results A total of 1,042 publications were found, with 106 specifically addressing the purpose of this study. For subtropical climates in South America, this research topic is incipient. Most studies were carried out in streams (39.6%) and studied the decomposition process and associated invertebrates (42.5%). There is a great deficiency of studies in lentic environments (e.g. lakes, 10%). Conclusion Studies on the decomposition of organic detritus in aquatic ecosystems in the subtropical region of South America are still emerging. While the knowledge about the role of shredders is more or less understood, the importance of microorganisms on the decomposition process still needs to be better evaluated. Finally, our study demonstrated that scientific collaboration boosted publication on the topic.
Relationships between calibrated ages and depth in stratigraphical sequences: an estimation procedure by mixed-effect regression

We present a procedure for estimating age-depth relationships in stratigraphical sequences by means of a generalized mixed-effect regression using an ancillary function for the partitioning of the fixed effect and the random effect corresponding to the degree of representativity of the individual calibrated dates for a particular section of the sedimentary sequence. The procedure uses mid-point estimates of the calibrated ages in combination with the central distributional range as the basis for estimating the fixed relationship between age and depth. Further, it combines the variability of the calibrated age at individual layers (within-object variance) with estimation of the variability of the calibrated age distribution as a whole between the layers standardized by the fixed effect (between-object variance). These components of random variability can be considered independent, and hence the uncertainty of the estimated fixed relationship between age and depth can be estimated by combining the two random variables. The procedure follows the logic of mixed-effects models, but with prior information about the expected variance within each dated object.
Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning

As one of the most fundamental topics in reinforcement learning (RL), sample efficiency is essential to the deployment of deep RL algorithms. Unlike most existing exploration methods that sample an action from different types of posterior distributions, we focus on the policy sampling process and propose an efficient selective sampling approach to improve sample efficiency by modeling the internal hierarchy of the environment. Specifically, we first employ clustering methods in the policy sampling process to generate an action candidate set. Then we introduce a clustering buffer for modeling the internal hierarchy, which consists of on-policy data, off-policy data, and expert data to evaluate actions from the clusters in the action candidate set in the exploration stage. In this way, our approach is able to take advantage of the supervision information in the expert demonstration data. Experiments on six different continuous locomotion environments demonstrate superior reinforcement learning performance and faster convergence of selective sampling. In particular, on the LGSVL task, our method can reduce the number of convergence steps by 46.7% and the convergence time by 28.5%. Furthermore, our code is open-source for reproducibility. The code is available at https://github.com/Shihwin/SelectiveSampling.
Distributed under Creative Commons Cc-by 4.0 the Use of Gene Interaction Networks to Improve the Identification of Cancer Driver Genes

Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses. A number of genomic libraries are available in R. DriverNet and DawnRank employ pathway-based methods that use gene interaction graphs in matrix form. We investigated the benefit of combining data from 3 different sources on the prediction outcome of cancer driver genes by DriverNet and DawnRank. An enriched dataset was derived comprising 13,862 genes with 372,250 interactions, which increased its accuracy by 17% and 28%, respectively, compared to their original networks. The study identified 33 new candidate driver genes. Our study highlights the potential of combining networks and weighting edges to provide greater accuracy in the identification of cancer driver genes.
Scaling a Machine Learning Approach to many kinds of Malicious Behavior for Cybersecurity

Monitoring processes and system resources to detect malware or malicious behavior at runtime is an enduring problem. Static signature detection offers little utility as malware easily bypasses signature detection with code polymorphism or obfuscation techniques. Software verification offers strong assurances for behavior but has limited scope and scale. Still, systems continue to routinely execute un-certified software components, suggesting that the benefits to do so outweigh the additional costs for all but limited niche applications requiring extra security. Therefore run-time monitoring remains the defacto and universally applicable modality for malcode detection. run-time monitors, enabled by Machine Learning (ML) models, can detect subtle behavior changes even with single shot training, and represent the current state of the art; however little has been done to specify the requirements and unique challenges of designing monitor software so it can be used to train and robustly detect malware in systems at run-time. The continuation of threats posed by Ransomware warrants prompt detection and mitigation of malicious processes at run-time. The re-design of system monitors to enable the current state of the art is overdue. Here we address the unique challenges of monitoring systems while training and deploying behavior-based ML models. We specify the software engineering requirements of each task individually and the trade offs for system integration. Our contributions include specifying the desired properties for monitor software and showing how they correspond to mathematical properties that ease ML behavior model development and enhance ML behavior model deployment.
Distributed under Creative Commons Cc-by 4.0 Differences in Stability of Seed-associated Microbial Assemblages in Response to Invasion by Phytopathogenic Microorganisms

Seeds are involved in the vertical transmission of microorganisms from one plant generation to another and consequently act as reservoirs for the plant microbiota. However, little is known about the structure of seed-associated microbial assemblages and the regulators of assemblage structure. In this work, we have assessed the response of seed-associated microbial assemblages of Raphanus sativus to invading phytopathogenic agents, the bacterial strain Xanthomonas campestris pv. campestris (Xcc) 8004 and the fungal strain Alternaria brassicicola Abra43. According to the indicators of bacterial (16S rRNA gene and gyrB sequences) and fungal (ITS1) diversity employed in this study, seed transmission of the bacterial strain Xcc 8004 did not change the overall composition of resident microbial assemblages. In contrast seed transmission of Abra43 strongly modified the richness and structure of fungal assemblages without affecting bacterial assemblages. The sensitivity of seed-associated fungal assemblage to Abra43 is mostly related to changes in relative abundance of closely related fungal species that belong to the Alternaria genus. Variation in stability of the seed microbiota in response to Xcc and Abra43 invasions could be explained by differences in seed transmission pathways employed by these microorganisms , which ultimately results in divergence in spatio-temporal colonization of the seed habitat.
Performance Optimization for Ferroelectric HfZrOx on a Ge Substrate by Modifying the Deposition Temperature

Hafnia ferroelectric-based Fe-FET devices have attracted increasing interest due to their CMOS compatibility, high scalability and low power consumption. Ge-based Fe-FETs could help overcome the shortcomings of Si-based Fe-FETs caused by the interface layer. In this work, the effect of the deposition temperature on the TiN/Hf0.5Zr0. 5O2/Ge device performance is systematically studied, which is helpful for finding the optimal conditions for Ge-based Fe-FETs. By adjusting the deposition temperature, the endurance property of the MFS capacitor is successfully enhanced to 2×105 cycles with a large minimum remnant polarization (2Pr ≈ 46 μC/cm2). The observed phenomenon could be well understood by the effects of the deposition temperature and impurity concentration on the grain size.