Miracle-dz/UniFAR-FTUs huggingface dataset:
iKnowFirst: An Efficient DPU-Assisted Compaction for LSM-Tree-Based Key-Value Stores

In scenarios with write-intensive workloads, LSM-tree-based key-value stores, such as RocksDB, suffer from compaction-induced performance degradation. RocksDB provides configurable compaction options to mitigate the severe read/write amplification problems associated with compaction. The advent of the Data Processing Unit (DPU) allows us to better utilize the configurable options of RocksDB to guide the key-value store system in choosing a suitable compaction strategy with prior knowledge of the workload characteristics. This paper proposes iKnowFirst, an efficient DPU-assisted key-value store. iKnowFirst (1) sets a data buffer on the DPU and separates hot-cold data to relieve the pressure of subsequent LSM-tree compaction, (2) senses the characteristics of the workloads in advance, and dynamically guides RocksDB to choose different compaction modes or enable/disable compaction when the workloads change, to cope with the scenario of write outbreak, and (3) implements an auto-selecting interface for compaction strategies selection. Our prototype implementation and experimental results show that iKnowFirst achieves 3.2× improvement compared to the original RocksDB on write-intensive and highly skewed workloads while showing acceptable performance under read-intensive workloads.
Playas and Lunettes on the Southern High Plains: Morphometric and Spatial Relationships

The surface of the Southern High Plains is dotted with thousands of small (<5 km2), circular, seasonally dry depressions, or playas, some of which are accompanied by a crescentic dune ridge, or lunette. The plays are inset into the sheets of eolian sediment that blanket the region (Blackwater Draw Formation) and the lunettes rest on the eolian mantle. Approximately 25,000 small playas, 1,100 of which have a lunette, were counted from all 540 topographic quadrangles covering the region (verified using data from soil surveys). Statistical analyses of the physical and spatial relationships of playas and lunettes were undertaken to better understand the origins and development of playas, and their relationship to lunettes. Data were collected from a subset of 40 topographic maps, digitized on a GIS, and analyzed using a nonparametric statistical procedure. Variation in texture of the Blackwater Draw Formation exerts considerable control on the distribution and size of playas. Playas are most numerous in the coarsest (sandy), most easily deflated substrate, followed by the finest substrate which produce erodible, sand‐size aggregates. The deepest playas and those with largest area also are in the finest, easily erodible substrate. These data support the hypothesis of playa development by erosion rather than by dissolution and subsidence. Lunettes most often are associated with deeper and wider playas. Presence or absence of lunettes probably is related to the nature of the playa fill and is not a function of substrate texture. Lunettes form from deflation of calcareous playa fill which is more common in larger playas. Otherwise, the controls on the distribution of this fill are unknown.
Testing for a decline in diversity prior to extinction: Languedocian (latest mid‐Cambrian) distribution of cinctans (Echinodermata) in the Iberian Chains, NE Spain

Abstract: The middle Cambrian strata of the Iberian Chains (north‐eastern Spain) and the Montagne Noire (southern France) record an adaptative radiation of cinctans and trilobites, which spanned the Leonian–early Languedocian interval. A diachronous diversity peak was reached by both benthic groups when favourable palaeoenvironmental conditions (clayey vs. silty substrates) were established. The acme in diversity was followed by a gradual decline and a barren interval associated with the onset of the mid‐Languedocian regression, well constrained throughout the western Mediterranean region. For trilobites, the aftermath of the regression is characterized by a late‐Languedocian major faunal turnover of families, followed by a renewed Furongian–early Tremadocian radiation related to the stepwise immigration of trilobite invaders from northern and eastern Gondwana, under persistent transgressive conditions. In contrast, the cinctans reappeared only patchily in late‐Languedocian monospecific coquinas and finally disappeared before the Furongian. Thus, the late Languedocian is a crucial interval in which to analyse the decline in diversity and final extinction of cinctans in the aftermath of the mid‐Languedocian regression.
The Uneven Response of Different Snow Measures to Human-Induced Climate Warming

AbstractThe effect of human-induced climate warming on different snow measures in the western United States is compared by calculating the time required to achieve a statistically significant linear trend in the different measures, using time series derived from regionally downscaled global climate models. The measures examined include the water content of the spring snowpack, total cold-season snowfall, fraction of winter precipitation that falls as snow, length of the snow season, and fraction of cold-season precipitation retained in the spring snowpack, as well as temperature and precipitation. Various stakeholders may be interested in different sets of these variables. It is found that temperature and the fraction of winter precipitation that falls as snow exhibit significant trends first, followed in 5–10 years by the fraction of cold-season precipitation retained in the spring snowpack, and later still by the water content of the spring snowpack. Change in total cold-season snowfall is least detecta.
Feature Extraction Techniques in Sentiment Analysis Using Deep Learning: A Review

Text mining and information retrieval are fundamentally dependent on the selection of text feature items. Features must be created manually for use in traditional feature extraction methods. While it may take a lot of effort to manually build an effective feature, deep learning makes it possible to automatically learn new representations of successful features from training data in the context of new applications. Text mining has made a lot of progress thanks to deep learning, which is a new way to find features. Deep learning is different from more traditional methods because it automatically learns features from large datasets, while the latter adopts handcrafted features, which rely heavily on the prior knowledge of designers and thus make it nearly impossible to take advantage of large datasets. Feature representation in deep learning may be automatically taught using big data, which typically consists of millions of parameters. This thesis starts with a brief overview of the most common ways to extract text features. It then talks about the widespread use of deep learning methods in text feature extraction and their applications. Finally, it makes some predictions about the future of deep learning's role in feature extraction.
Effects of sustainable soil management on fertility of agricultural soils

Intensive agriculture and several anthropogenic factor led gradually to decrease soil organic matter and microbial biomass, thereby to a consequent loss of quality and fertility of soil. The sustainability of agricultural practices represents an aspect that could be not anymore negligible if environment protection and defense are the target. In the present thesis chemical and biochemical properties and humic substances characterization of agricultural soils, under greenhouse, amended with a mixture of compost from municipal solid waste enriched with poplar scraps in two different ratio and doses, were studied through a multidisciplinary approach. All parameters studied in a previous one year-experiment were followed for two years more, after yearly supply of organic amendment. This repeated treatment determined a strong improvement in most chemical and biochemical parameters. By increasing organic carbon, humic fraction, especially in the plot with higher C/N ratio and amendment dose supplied, increased to indicate the enhance of organic matter and the slow mineralization of organic mixture due to wood scraps that kept the organic matter input stable over time. The activity of the main enzymes involved in the biogeochemical cycles of nutrients markedly increased after amendments and then, after a visible decrease, remained stable over time without reaching the initial values, before the first amendment. A second study was carried out to assess the fertility of a soil under organic farming in comparison to another under conventional management, both cultivated with processing tomato (Solanum lycopersicum). Besides chemical and biological properties, investigated similarly to the previous study, soil microbiologic characterization and functional gene assess as Phytase from Bacillus sp. were performed. Organic farming favoured the enhance of soil fertility in term of organic carbon, total nitrogen, enzyme activities and in particular in microbial population. In fact soil biomass underwent an improvement of the presence and expression of functional gen Phy. The analyzed sustainable agricultural system improved the fertility of intensive arable soil and positively affected microbial biomass causing change and functional properties of microbial population.
Sequence Stratigraphy, Facies Geometries, and Depositional History of the Mississippi Fan, Gulf of Mexico (1)

The Mississippi Fan is a large, mud-dominated submarine fan over 4 km thick that was deposited in the deep Gulf of Mexico during the late Pliocene and Pleistocene. Analysis of 19000 km of multifold seismic data across the fan defined 17 seismic sequences, each characterized by a series of channel, levee, and associated overbank deposits, along with other mass transport deposits. At the base of nine sequences are a series of seismic facies consisting of mounded, hummocky, chaotic, and subparallel reflections, which constitute 10-20% of the sediments in the sequence. These facies are externally mounded in cross section and occur in two general regions of the fan. In the upper and middle fan, they occur below channels and are elongated in shape, mimicking the channel's distr bution. In the middle to lower fan, they have a fan-shaped distribution, increasing in width downfan. These facies are interpreted to have formed as disorganized slides, debris flows, and turbidites, and are informally called mass transport complexes. Overlying this basal interval and characteristic of all sequences are well-developed channel-levee systems, which constitute 80-90% of the fan's sediments. Channels consist of high-amplitude, subparallel reflections. Levee sediments have subparallel reflections that have moderate to high amplitudes at the base changing upward to low amplitude. The vertical change in amplitude may reflect a decrease in the grain size and bed thickness of the levee sediments. Overbank sediments consist of interbedded subparallel to hummocky and mounded reflections, suggesting both turbidites derived from the channel, as well as slides and debris flows derived from the slope. Pliocene-Pleistocene eustatic cycles are interpreted to have been the major factor controlling the timing and style of sedimentation in the fan. Mass transport complexes are interpreted to have formed during a lowering of sea level, and reflect sediments derived from retrogressive slumping during the formation of submarine canyons in the upper slope and outer shelf. Channel-levee systems were deposited when sea level was near its lowest position and sediment derived from deltas was transported into the deep basin via submarine canyons. During highstands in sea level, a thin layer of hemipelagic sediment was deposited on the fan surface. The Mississippi Fan serves as an exploration model for mud-dominated submarine fans and has four prospective reservoir facies: channel sands with linear trends, unchannelized sands beyond the downdip terminus of the channel (possible lobes), potentially sand-prone levees immediately adjacent to initial channels deposited in some sequences, and limited parts of mass transport complexes.
Sampling hidden microbial eukaryotic biodiversity in the tropics: new insights from the Paramecium aurelia complex (Ciliophora, Protozoa)

Ciliates, including the genus Paramecium, are among the most thoroughly researched groups of free-living microbial eukaryotes. However, our knowledge of their biodiversity appears to be restricted. Therefore, more data is required for tropical regions, to generate a more accurate picture of the distribution of the cryptic Paramecium species. In the current paper, recent data on the tropical biodiversity of the Paramecium aurelia species complex is presented. We believe that the COI mtDNA fragment allows for an evaluation of the geographic variation of particular cryptic species within the Paramecium aurelia complex, while also being sufficient for species identification. The obtained data indicates that the examined tropical populations may be very variable (with more than 50% previously unknown COI haplotypes discovered). Consequently, it is reasonable to assume that tropical environments reveal a high biodiversity of Paramecium ciliates.
A Microstrip Patch Antenna Immersed in Water – A Preliminary Investigation

Microstrip patch antennas (MPAs) are versatile in their utility and they are frequently used as sensors for permittivity measurements. This short paper is focused on a rectangular configuration designed to resonate at 2.5 GHz. The performance of the antenna is analysed while it is radiating both in air and in water. This is required in order to assess the feasibility of using the antenna as a sensor for permittivity measurements in water. Analytical equations for designing the rectangular patch are used in combination with several simulations using CST Studio Suite in order to optimise the antenna through a trial and error procedure. A significant decrease in the magnitude of the resonant peak of the antenna and an increase in the quality factor is observed as the volume of water above the antenna is increased.
A CNN Deep Learning Technique for Botnet Attack Detection for IoT Application

The Internet of Things (IoT) is a significant technology that enables us to develop many helpful online applications. The Internet of Things allows remote operating items using preexisting network infrastructure. Services that are based on information technology, computers, and other technologies are included in the cyber world. The cyber-world sees many protocols and technological advancements that lead to improvements. When it comes to cyber-based services, security is an extremely significant issue. Classification methods based on machine learning (ML) and deep learning (DL) can accurately forecast the most effective model for a dataset. A cyber assault on a wide scale carried out by devices infected with malware and managed remotely is known as a botnet attack. Under the context of IoT applications, an effective method for detecting botnet attacks is presented in this research.
Direct Model Predictive Control with Constrained Power Losses for Grid-Tied Converters with LCL Filters

This paper proposes a direct model predictive control (MPC) method that improves the efficiency of grid-tied converters with LCL filters, while minimizing the grid current distortions. To do so, the MPC problem not only accounts for the grid current reference tracking minimization, but it also limits the converter power losses by introducing explicit constraints on them. To this aim, different problem formulations are examined to limit the losses. Specifically, the FCS-MPC problem is designed such that it either limits the losses per semiconductor device or the three-phase converter losses. The presented results verify the effectiveness of the proposed strategy and demonstrate the performance benefits compared to conventional control and modulation methods, such as voltage-oriented control (VOC) with space vector modulation (SVM) or discontinuous pulse width modulation (DPWM).
Analytical derivations for the behavior and fabric evolution of a linear orthotropic ice polycrystal

The fabric of polar ice exhibiting orthotropic symmetry (i.e., three orthogonal planes of reflexional symmetry) is described by a continuous orientation distribution function (ODF) which represents the density of grains whose c axes have a given orientation. For a given ODF, the constitutive law of such anisotropic ice is obtained by a homogenization procedure based on the assumptions that the ice single crystal behaves as a linear transversely isotropic material and that the grains experience a uniform state of stress. Under loading conditions which respect the symmetries of orthotropy, the evolution of the macroscopic behavior of a polycrystal of ice, as well as the evolution of its fabric, are fully determined by analytical expressions. The solution for a transversely isotropic polycrystal is deduced as a reduction from the orthotropic case. In this special configuration, the results from the present model are compared to fabric measurements made by Thorsteinsson et al. [1997] on the GRIP ice core.
How useful is expert opinion for predicting the distribution of a species within and beyond the region of expertise? A case study using brush-tailed rock-wallabies Petrogale penicillata

1. Species' distribution modelling relies on adequate data sets to build reliable statistical models with high predictive ability. However, the money spent collecting empirical data might be better spent on management. A less expensive source of species' distribution information is expert opinion. This study evaluates expert knowledge and its source. In particular, we determine whether models built on expert knowledge apply over multiple regions or only within the region where the knowledge was derived. 2. The case study focuses on the distribution of the brush-tailed rock-wallaby Petrogale penicillata in eastern Australia. We brought together from two biogeographically different regions substantial and well-designed field data and knowledge from nine experts. We used a novel elicitation tool within a geographical information system to systematically collect expert opinions. The tool utilized an indirect approach to elicitation, asking experts simpler questions about observable rather than abstract quantities, with measures in place to identify uncertainty and offer feedback. Bayesian analysis was used to combine field data and expert knowledge in each region to determine: (i) how expert opinion affected models based on field data and (ii) how similar expert-informed models were within regions and across regions. 3. The elicitation tool effectively captured the experts' opinions and their uncertainties. Experts were comfortable with the map-based elicitation approach used, especially with graphical feedback. Experts tended to predict lower values of species occurrence compared with field data. 4. Across experts, consensus on effect sizes occurred for several habitat variables. Expert opinion generally influenced predictions from field data. However, south-east Queensland and north-east New South Wales experts had different opinions on the influence of elevation and geology, with these differences attributable to geological differences between these regions. 5. Synthesis and applications. When formulated as priors in Bayesian analysis, expert opinion is useful for modifying or strengthening patterns exhibited by empirical data sets that are limited in size or scope. Nevertheless, the ability of an expert to extrapolate beyond their region of knowledge may be poor. Hence there is significant merit in obtaining information from local experts when compiling species' distribution models across several regions.
North-South Connections

According to the bipolar seesaw hypothesis, climate warming in Greenland was accompanied by simultaneous climate cooling in Antarctica, and vice versa, during the rapid climatic changes of the last deglaciation. Morganet al. have found, however, that at least one cooling episode in Antartica preceded Greenland warming by 500 years. In his Perspective, Stocker discusses the implications of these results for the bipolar seesaw hypothesis.
Two methods for firn-area and mass-balance monitoring of Svalbard glaciers with SAR satellite images

Abstract This paper presents two methods for glacier monitoring on Svalbard using synthetic aperture radar (SAR) satellite images. Both methods were developed on glaciers in the Kongsfjorden area. The first method monitors the firn area extent and the firn line over time by thresholding and filtering the SAR image. Manual detection of the threshold is preferable, but using a constant threshold for all images also gives adequate results. A retreat of the firn-line position is visible, especially on Kongsvegen, corresponding to consecutive years of negative mass balance. The second method applies a k-means classification to three clusters on the glacier surface. The areal extent of the resulting class on the upper part of the glacier correlates remarkably well with the independently measured mass balance of Kongsvegen, having a correlation coefficient of around 0.89 for the various glaciers. This is because the snow from the accumulation area influences the k-means classification. Thus, on glaciers where mass-balance values are available, new mass-balance values can be predicted solely from SAR images. For glaciers where no mass balance is available, the area change cannot be calibrated to absolute mass-balance values, but relative changes can be predicted.
Malaria Incidence Prediction Using Leaky Integrate and Fire Neuron

A malaria parasite is transmitted to humans with female anopheles bites. Malaria is a common disease in rural and urban areas of most countries. Malaria affects public health and flourishes mainly in sub-tropical countries and tropical countries. The impact of the disease is high, whereas the prevention control system facilities are limited. A prominent prediction model is needed to overcome the effects of malaria disease and prevent infected humans from it. In this study, we focus on determining the malaria-abundant regions with the help of environmental factors and utilizing spiking neural networks as a classification/prediction model for the Ponda region in Goa, India. Groundwork findings of malaria-abundant areas are collected using clinical data from a renowned regional hospital. Leaky integrate and fire neuron models exhibited interesting spiking patterns, which are further used for classifying the malaria-prone zones. This model also demonstrated the best prediction accuracy of 98.47% and 90.87% for village Boma using the MalariaDataset2 and Curti using the MalariaDataset1, respectively. This study tries to contribute a step forward in making India a malaria-free country, a target set by World Health Organization.
A New Tracer Advection Scheme for Bryan and Cox Type Ocean General Circulation Models

In regions with large tracer gradients and/or velocities the advection scheme that is used in many ocean models leads to significant under- and overshoot of the tracer values. In the case of the U.K. Fine Resolution Antarctic Model (FRAM), this lead to unphysical negative surface temperatures in some regions and overheating in others. In this paper, a new advection scheme is proposed and tested in the ocean model context using a limited-area model centered on a region where problems occurred in FRAM.