Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in the Structure of Assemblages of Three Liolaemus lizards (Iguania, Liolaemidae) in a Protected Area of South-Central Chile Affected by a Mixed-Severity Wildfire

The use of space by sympatric lizards was assessed in a protected area in south central Chile that was affected by a wildfire three years prior. Through systematic monitoring in plots with varying degrees of severity, the abundance of species was counted in each treatment (low severity, medium severity, high severity and unburned control), and were, in turn, subdivided into different microhabitats (ground, trunks and trees). The microhabitats were differentiated by the level of damage suffered by the vegetation as a consequence of the wildfire. Of the three species recorded, it was observed that the presence of Liolaemus tenuis was linked to sites with less fire damage, disappearing in high severity sites. In contrast, L. pictus displayed a more generalist use of microhabitats, with its presence being similar in the different treatments. On the other hand, L. lemniscatus showed a predominant use of the ground, which became more evident in the most severe sites. The pattern found are discussed, as well the influence of fire in reshaping environments.
A Real Time Image Processing Bird Repellent System Using Raspberry Pi

Rice is one of the most consumed foods in Nigeria, therefore it’s production should be on the high as to meet the demand for it. Unfortunately, the quantity of rice produced is being affected by pests such as birds on fields and sometimes in storage. Due to the activities of birds, an effective repellent system is required on rice fields. The proposed effective repellent system is made up of hardware components which are the raspberry pi for image processing, the servo motors for rotation of camera for better field of view controlled by Arduino connected to the raspberry pi, a speaker for generating predator sounds to scare birds away and software component consisting of python and Open Cv library for bird feature identification. The model was trained separately using haar features, HOG (Histogram of Oriented Gradients) and LBP (Local Binary Patterns). Haar features resulted in the highest accuracy of 76% while HOG and LBP were, 27% and 72% respectively. Haar trained model was tested with two recorded real time videos with birds, the false positives were fairly low, about 41%. This haar feature trained model can distinguish between birds and other moving objects unlike a motion detection system which detects all moving objects. This proposed system can be improved to have a higher accuracy with a larger data set of positive and negative images. Keywords—Electronic pest repeller Haar cascade classifier, ultrasonic
The Effects of Synesthesia on Recall Memory Abilities in Teenagers

Synesthesia is a rare phenomenon in which people associate an inducer, such as a sound, song, or sequence of time, with a specific concurrent, such as a color, shape, or physical sensation. Very little is known about what causes this phenomenon, although due to a recent influx of research, it has been recorded that synesthesia provides a wide array of cognitive benefits to those who experience it, with the most notable being memory. Children with synesthesia in group studies have not been shown to have advantageous memory, while adults with synesthesia in group studies have shown the opposite. However, a group study of this nature has never been conducted on teenagers with synesthesia, despite this being the age range where advantageous memory would seem likely to manifest. This study conducts an experimental analysis on the relationship between synesthesia and advantageous memory in teenagers for immediate, delayed, and matrix recall memory. The results of this study show that teenagers with synesthesia have a memory advantage in matrix recall memory, leading to the conclusion that, for some types of memory, synesthesia begins to provide a benefit in the years of adolescence.
Development of a chemical model to predict the doses of calcium sulfate and tartaric acid to acidify musts in Sherry area

Calcium sulfate is normally used as a complementary acidifier combined with tartaric acid. The doses corresponding to each one depend on the desired reduction of pH and on the composition of musts. However, considering that there are several interrelated chemical equilibria implied (tartaric acid dissociation, calcium tartrate and potassium bitartrate precipitation, etc.), it is not easy to predict the effect on pH of a mixed tartaric acid and calcium sulfate addition and to determine the necessary doses to reach the final pH required by the winemaker. In a model previously developed by the authors, the prediction of pH after an acidification was properly achieved. On the contrary, in the same model the prediction of the necessary doses of acidifiers to achieve a desired pH have higher errors due to some parameters, as pH and pK, are found as exponential functions in the equations. This work develops and verify the necessary corrections to the models so that appropriate predictions of the doses are obtained. With these corrections, prediction errors of less than 5% were obtained for all doses of acidifiers, confirming the good comprehension of the chemical equilibria involved in this practice.
Grazing and the Coupling of Biodiversity in Vascular Plant and Soil Microbial Communities

Abstract This study sought to ascertain how different grazing management protocols affect the coupling between soil microbial and vascular plant communities. Changes in microbial and plant communities were observed over a grazing season (Spring 2013–Spring 2014) at 2 previously ungrazed agricultural sites—a moist lowland (Longfield Farm) and a drier upland (Normanskill Farm)—near Albany, NY. Each landscape was divided into 6 fenced enclosures (paddocks). One paddock at each farm was managed by a managementintensive grazing (MIG) protocol, which employs high stock density and frequent rotations. A second paddock was managed by continuous grazing (CG) at lower (conventional) stock density. A third paddock at each site was left ungrazed (U). Three “simulation” paddocks were used to explore the underlying dynamics of grazer-microbe-plant interactions. By spring 2014, plant species richness (S) was significantly higher (t-tests: P < 0.05) in the MIG paddocks at both farms. At Normanskill Farm, S was correlated with both microbial diversity and biomass, while at Longfield Farm, S was independent of microbial diversity and biomass but varied directly with soil moisture. Our findings suggest that while MIG leads to increased S relative to CG, different forcing factors may be responsible for the enhancements in upland and lowland systems.
Discovery of a new micro-pagurid fauna (Crustacea: Decapoda: Paguridae) in the Lesser Antilles, Caribbean Sea

ABSTRACT Six new monotypic genera and eight new species of hermit crabs of the family Paguridae Latreille, 1802 are fully described based on specimens obtained during two separate, intensive biodiversity studies on the islands of Guadeloupe, Curaçao, and Dominica in the Lesser Antilles, Caribbean Sea. The study in Guadeloupe utilized a broad variety of sampling techniques, including dredging, baited traps, and a SCUBA-operated vacuum device; in Curaçao and Dominica, a manned submersible was used. The high number of new taxa discovered is surprising given they were obtained in a relatively small area of the Antilles in habitats ranging in depth from shallow water (c. 1 m) to deep reefs (250 m). Tissue samples were extracted from most specimens for CO1 barcoding and other genetic analyses for future phylogenetic investigations. Gene sequences are reported to complement the morphological descriptions. The new species are unusual in their minute sizes, ranging 0.4–1.5 mm in shield length; and aspects of their morphology, in particular gill number and characteristics of male sexual tubes, which could not be clearly matched to any of the existing genera of Paguridae. The term “micro-pagurid” is introduced for this diverse, diminutive fauna of Paguridae (adult shield length ≤ 1.5 mm), discovered living in cryptic habitats and complex deep reef structures of these three islands. A brief, preliminary discussion of the possible significance of this micro-pagurid diversity, specialized morphology, and biogeography is included.
Are Regulators Effective at Unraveling Accounting Manipulation? Evidence from Public Utility Commissions

Prior research proposes that a monopolist with private information inflates its reported costs under rate regulation to extract an informational rent. Using a sample of U.S. electric utilities from 1990–2011, we first confirm an unexpected increase in operating expense during rate review periods, then decompose operating expense into its cash and accrual components, and find the cash component accounts for 89% of this increase. The observed pattern is consistent with some combination of real activities management and utility managers misrepresenting transitory expense shocks as permanent. We then focus on identifying regulators’ effectiveness at unraveling this manipulation and minimizing the rent. We estimate that, on average, regulators allow 17¢ out of every dollar of abnormal cash expense to be recovered in future annual revenue, a statistically significant amount. Next, we study the effects of regulators’ ability (proxied by experience) and motivation (proxied by whether they were elected) to unravel accounting manipulation. We find that whereas inexperienced and politically appointed regulators allow a significant portion of abnormal cash expense to be recovered (41¢ and 24¢ out of every dollar, respectively), experienced and elected regulators do not (although the difference between appointed and elected regulators is not statistically significant). Our findings suggest that regulators differ in their ability to identify manipulation—with experience enhancing this ability—and that, on average, state regulators effectively unravel most of the effect of accounting manipulation. This paper was accepted by Suraj Srinivasan, accounting.
A deep learning mask analysis toolset using mask SEM digital twins

Sub-nanometer accuracy attainable with electron micrograph SEM images is the only way to “see” well enough for the mask analysis needed in EUV mask production. Because SEM images are pixel dose maps, deep learning (DL) offers an attractive alternative to the tedious and error-prone mask analysis performed by the operators and expert field application engineers in today’s mask shops. However, production demands preclude collecting a large enough variety and number of real SEM images to effectively train deep learning models. We have found that digital twins that can mimic the SEM images derived from CAD data provide an exceptional way to synthesize ample data to train effective DL models. Previous studies [1, 2, 3, 4] have shown how deep learning can be used to create digital twins. However, it was unclear if SEM images generated with digital twins would have sufficient quality to train a deep learning network to classify real SEM images. This paper shows how we built three DL tools for SEM-based mask analysis. The first tool automatically filters good quality SEM images, particularly for test chips, using a DL-based binary classifier. A second tool uses another DL model to align CAD and SEM images for applications where it is important that features on both the images are properly aligned. A third tool uses a DL multi-class classifier to categorize various types of VSB mask writer defects. In developing the three tools, we trained state-of-the-art deep neural networks on SEM images generated using digital twins to achieve accurate results on real SEM images. Furthermore, we validated the results of trained deep learning models through model visualization and accuracy-metric evaluation.
Two new species of the tribe Meconematini (Orthoptera: Tettigoniidae: Meconematinae) from China and male song characters of Pseudocosmetura yaoluopingensis sp. nov.

This paper describes two new species of the tribe Meconematini from China, Acosmetura longielatasp. nov. and Pseudocosmetura yaoluopingensissp. nov. Data on the male song characters of Pseudocosmetura yaoluopingensissp. nov. are also provided. The type specimens of all new species are preserved in the Museum of Hebei University.
Developing extruded seed pellets to overcome soil hydrophobicity and seedling emergence barriers

Handlingeditor:DrFlorenciaYannelli Abstract 1. Globally, soil water repellency is a major constraint to plant establishment, restrictingwater infiltration andmoisture retention in the seed zonewhich results in poor germination and seedling emergence. 2. To address this problem within an ecosystem restoration context, we investigated the use of a surfactant in extruded seed pellets to improve native plant recruitment in water-repellent topsoils of two proteaceous woodland species, Banksia menziesii R.Br (glasshouse trial) and Lambertia inermis R.Br (field trial). In this two-part study, we first examined B. menziesii seedling performance in detail under glasshouse conditions for differences in survival between the extruded pelleting formulations after an induced drought at 12weeks. 3.We demonstrated that therewas no difference in seedling emergence amongst control seed and pellet treatments in B. menziesii. Initially, B. menziesii seedlings emerged faster in the control treatment (non-pelleted control seeds) and had greater initial plant growth (leaf and root production), however by Week 12, seedlings generated from pellets were not significantly different from the control seeds and pellets + surfactant had the greatest number of leaf establishment. 4. Survival after drought of B. menziesii seedlings ranged from 14 to 31 days with pellet + surfactant surviving approximately 2.6 days (11.8%) longer than the control seeds. For the second species, L. inermis, seedling emergenceunder field conditionswas approximately 24% greater in seedlings derived from extruded pellets; however, there was no difference in overall survival due to post-emergence predation. 5. This study provides a proof of concept that seedling emergence in water-repellent soils can be enhancedwith extrudedpellets containing surfactants.Our demonstration under in situ and ex situ conditions confirms the prospective use of seed enhancement technologies with future development and field-testing warranted.
Far infrared imaging, an effective way to screen maize seedling mutants for drought stress response

Abstract Water shortage is a global challenge and affects crop growth and development seriously. Stoma is the main channel of plant water transpiration. Water transpiration through stomata is an endothermic process and affects temperature of plants, especially leaf surface. In this study, we established a method for screening maize seedling mutant with abnormal leaf temperature by far infrared imaging. We found that seedling mutants with abnormal leaf temperature manifested different drought tolerance. Mutants with lower leaf temperature demonstrated faster moisture loss rate, poorer drought tolerance, higher osmotic potential, lower leaf relative water content, more accumulation of hydrogen peroxide, more serious cell membrane damage and more robust root systems and biomass under drought treatment, which are opposite in maize seedling mutants with higher leaf surface temperature compared with the corresponding control. Taken together, the method we established is an effective way to screen maize mutants with abnormal drought response.
Effects of Diets Containing Different Types of Carbohydrates on Hepatic Metabolism

The study of diets rich in different quantities of simple and complex carbohydrates is essential as an aid inÂ the prevention of several types of organic damage. Objectives: To assess the effects of hyperglycidic, isocaloricÂ diets with variations in carbohydrate type (simple or complex) on the metabolism of rats. Methodology:Â Forty Wistar rats were divided into 4 groups: control group (57.96% starch and 12.04% saccharoseÂ - CG), carbohydrate mixture group (35% saccharose, 35% starch - MG), simple-carbohydrate-group (70%Â saccharose - SG), and complex-carbohydrate-group (70% starch - CCG). The animals were allowed to habituateÂ to the diets and then received them for 28 days, with free access to water. Results: Ration weight andÂ consumption did not differ between groups. The amount of hepatic fat was found to be greater in SG comparedÂ to CG. Hepatic malondialdehyde (MDA) determination revealed that SG presented the lowest value,Â although this result was accompanied by the lowest vitamin E value, demonstrating that the consumptionÂ of this antioxidant was higher in SG. Reduced glutathione values did not differ between groups, raising theÂ hypothesis that in this case vitamin E was the first antioxidant barrier to be utilized. Blood glucose levelsÂ differed between CG, MG and SG in relation to CCG only on day 14; however, the oral glucose toleranceÂ test performed at the end of the experiment did not demonstrate a difference between groups. Serum fructosamineÂ differed significantly between MG and CG and CCG and also between SG and CG and CCG, withÂ the values for MG and SG being higher than those for the other groups. Conclusion : The consumption ofÂ simple carbohydrates led to hepatic steatosis and altered the antioxidant system even within a short periodÂ of time, in addition to modifying the fructosamine values, revealing an increase in glycated serum proteinsÂ that may cause damage over a longer period of time. Â
Development of the national feral swine map, 1982–2016

The Southeastern Cooperative Wildlife Disease Study developed the first national feral swine (Sus scrofa) map for the United States in 1982 in collaboration with state wildlife resources agencies. National feral swine distribution maps were completed in 1982, 1988, and 2004 as individual maps, and then annually beginning in 2008 through the National Feral Swine Mapping System (NFSMS). The NFSMS is an internet-based data-collection website used in collaboration with state wildlife resources agencies and the U.S. Department of Agriculture, Animal and Plant Health Inspection Service. These ongoing collaborations have provided for a long-term database that documents the expansion of established feral swine populations in the United States. The number of states reporting established populations was 18 in 1982 and 35 in 2016. The geographic area included in the distribution of feral swine increased from 544,854 km2 in 1982 to 1,675,618 km2 in 2016. We describe the development of the national feral swine maps including details on how these data were collected; provide feral swine maps for select years from 1982 to 2016; and note some of the uses of these maps. These maps detail the expansion of feral swine since 1982, which can be used in the future to monitor increases or decreases in the distribution of feral swine as natural reproduction of feral swine continues and feral swine control programs are implemented. © 2017 The Wildlife Society.
Deep Clustering and Representation Learning with Geometric Structure Preservation

In this paper, we propose a novel Deep Clustering and Representation Learning(DCRL) framework for learning effective representation with local and global structure preservation and partitioning data into clusters where each cluster contains data points from a compact manifold. In our framework, the latent space is manipulated to separate data points from different manifolds with Clustering Loss as guidance. Motivated by the observation that the clustering-oriented loss may corrupt the geometric structure of the latent space, two structure-oriented losses Isometric Loss and Ranking Loss are proposed to preserve the intra-manifold local structure and inter-manifold global structure, respectively. Our experimental results on various datasets show that DCRL achieves performance comparable to current state-of-the-art deep clustering algorithms and exhibits far superior performance in downstream tasks, demonstrating the importance and effectiveness of preserving geometric structure both locally and globally.
Impact of the Charge-Regulation Effect on Bacterial Growth and Bioavailability of Sorbed Growth Substrates

2 as a function of separation distance between the surfaces. This is the so-called chargeregulation effect and it can be modeled as a function of the dissociation constants and site density of the surface functional groups. This study is based on the hypothesis linking cellular bioenergetics, where energy is stored as proton gradient across the bacterial cytoplasmic membrane, and the chargeregulation effect, where the cell surface proton concentration is altered during adhesion. Previous studies have shown that bioenergetics is affected by the charge-regulation process and that the effect is a direct function of the acidic and basic functional groups on the adhering surface. These studies were performed under non-growth conditions, and demonstrate that activity increases for acidic surfaces and decreases for basic surface, in agreement with the hypothesis. The purpose of this study was to build upon the prior studies and examine how charge-regulated surfaces can affect the growth of bacteria. First, we developed and demonstrated a simple approach for characterizing chargeregulated surfaces using zeta potential – pH titration data. The purpose was to model the surface electrostatic potential and charge density as a function of pH. This allowed modeling of the materials used in this study and interpretation of the experimental results. Having knowledge of surface properties, preliminary biodegradation experiments were conducted using regular sand (acidic functionality) and iron-coated sand (basic functionality). The results demonstrated that attached bacterial growth was directly affected by the charge-regulation effect. The iron-coated sand carrying more positive
Semantic Segmentation via Enhancing Context Information by Fusing Multiple High-Level Features

Semantic segmentation has been one of the most important research areas in computer vision. In semantic segmentation, both the context information and spatial information are important in semantic segmentation performance of a con-volutional neural network (CNN) model. This study focuses on methods to optimize the context information. We proposed a structure consisting of multiple upsampling blocks and concatenated Dense Block(DB), enhancing the context information on the decoder by the fusion of multiple high-level features. Furthermore, we use Blur Pooling [1], which is a better downsampling method proposed by Richard Zhang, making the CNN more shift-invariant to an input image. Finally, we determined the drawback of using the cross entropy, which can result in poor small-object-detection performance, on semantic segmentation tasks. Furthermore, we boosted the performance of our model by applying the soft-IoU loss function. Finally, we test our model's performance using the CamVid dataset established. The experimental results show that our proposed approach can provide a better ability of integrating the context feature in the upsampling path. The mIoU result of our proposed model was 70.534% on the Camvid dataset, outperforming the 65.8% mIoU result of FC-DenseNet67, which is our baseline model, obtained from the study in [2].