Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of Anthropogenic Activities and Climate Change on Forage Nutrition Storage in Tibetan Grasslands

Uncertainties about the impacts of anthropogenic activities and climate change on forage nutrition storage of grasslands can limit the adaptive management of grasslands across the whole Tibetan Plateau. The main objective was to investigate the impacts of anthropogenic activities and climate change on the forage nutrition storage of grasslands on the Tibetan Plateau. Based on random forest models, we quantified the responses of forage nutrition storage to anthropogenic activities and climate change across the whole Tibetan grasslands from 2000 to 2020. Warming and increased precipitation did not always increase forage nutrition storage, and cooling and decreased precipitation did not always reduce forage nutrition storage. Compared to temperature and precipitation changes, radiation change had stronger contributions to potential and actual forage nutrition storage. Humankind’s activities altered the impacts of climate change on forage nutrition storage. The impacts of anthropogenic activities on forage nutrition storage increased linearly with increasing mean annual temperature and decreasing elevation but showed quadratic relationships with longitude, mean annual precipitation and radiation. The change in the impacts of humankind’s activities on forage nutrition storage was more closely related to radiation change than temperature and precipitation changes. The findings observed by this study caution that the impacts of radiation change on forage nutrition forage should be taken seriously under global change. Both climate change and humankind activities cannot always increase forage nutrition storage but may cause the degradation of forage nutrition storage.
Classification Confidence in Exploratory Learning: A User's Guide

This paper investigates the post-hoc calibration of confidence for “exploratory” machine learning classification problems. The difficulty in these problems stems from the continuing desire to push the boundaries of which categories have enough examples to generalize from when curating datasets, and confusion regarding the validity of those categories. We argue that for such problems the “one-versus-all” approach (top-label calibration) must be used rather than the “calibrate-the-full-response-matrix” approach advocated elsewhere in the literature. We introduce and test four new algorithms designed to handle the idiosyncrasies of category-specific confidence estimation using only the test set and the final model. Chief among these methods is the use of kernel density ratios for confidence calibration including a novel algorithm for choosing the bandwidth. We test our claims and explore the limits of calibration on a bioinformatics application (PhANNs) as well as the classic MNIST benchmark. Finally, our analysis argues that post-hoc calibration should always be performed, may be performed using only the test dataset, and should be sanity-checked visually.
FIRST QUANTUM CORRECTION FOR THE MODULI SPACE OF STABLE BUNDLES OVER A RIEMANN SURFACE

We compute some Gromov-Witten invariants of the moduli space M� of odd degree rank two stable vector bundles over a Riemann surfaceof genus g ≥ 2. We thus find the first correction term for the quantum product of Mand hence get the two leading terms of the relations satisfied by the natural generators of the quantum cohomology of M�. Finally, we use this information to get a full description of the quantum cohomology of Mwhen the genus ofis g = 3.
Finite‐time synchronization and adaptive synchronization of memristive recurrent neural networks with delays

This paper solves the finite‐time synchronization and adaptive synchronization problems of drive‐response memristive recurrent neural networks with delays under two control methods. First, the state‐feedback control rule containing delays and the adaptive control rule are designed for realizing synchronization of drive‐response memristive recurrent neural networks in finite time. Then, on the basis of the Lyapunov stability theory, many algebraic sufficient conditions are obtained to guarantee finite‐time synchronization and adaptive synchronization of drive‐response memristive recurrent neural networks via two control methods, which are easily verified. In addition, the estimation of the upper bounds of the settling time of finite‐time synchronization is obtained. Lastly, to illustrate the effectiveness of the obtained theoretical results, two examples are given.
Modulation of Antioxidant Compounds in Fruits of Citrus reticulata Blanco Using Postharvest LED Irradiation

Simple Summary The study evaluated the effect of different light quality regimes applied during postharvest storage on the regulation of phenolic compounds, carotenoids, and antioxidant activity in the peel and pulp of Phlegrean mandarin fruits. After seven days of red–blue (RB) light regime exposure, the increase of antioxidants in both fruit components confirmed the effectiveness of the light quality modulation in enhancing plant phytochemical content. The boosted production of bioactive compounds under RB light may also be employed for delaying senescence and improving the shelf-life of fruits. Abstract Phlegrean mandarin fruits are already known for health-promoting properties due to the high concentration of phytochemicals in peel, pulp, and seed. Biotic and abiotic factors, including light, may modulate their biosynthesis, metabolism, and accumulation. In this context, light-emitting diodes (LED) have recently been applied to control nutritional traits, ripening process, senescence, fruit shelf-life, and pathogenic microbial spoilage of fruits. This study investigated the effect of the seven-day exposure of Phlegrean mandarin fruits to two LED regimes, white (W) and red–blue (RB), to test the possibility that the storage under specific light wavelengths may be used as green preservation technology that enhances fruit phytochemical properties. To pursue this aim, the antioxidant activity and polyphenolic profile of the pulp and peel of mandarins under W and RB light regimes were evaluated and compared with Control fruits not exposed to LED treatment. Our results indicated that storage under W and RB treatments modulates the antioxidant content in pulp and peel differently. Compared to W, the RB regime increases the ascorbic acid, flavonoid, anthocyanin, and carotenoid concentrations, while the polyphenol profile analysis reveals that the number of important phytochemicals, i.e., quercetin rutinoside, chlorogenic acid, sinensetin, and rutin, are higher under W. The overall data demonstrated that postharvest LED irradiation is a valid tool for modifying fruit phytochemical properties, which also boosts specific bioactive compounds.
3A-ReRAM: Adaptive Activation Accumulation in ReRAM-Based CNN Accelerator

ReRAM-based computing is good at accelerating convolutional neural network (CNN) inference due to its high computing parallelism, but its rigid crossbar structure may become less efficient in the face of the random data sparsity abundant in CNNs. In this study, we propose <inline-formula> <tex-math notation="LaTeX">$3A$ </tex-math></inline-formula>-ReRAM, a novel crossbar architecture that can dynamically predict the accumulated results to enable adaptive activation accumulation, so that both zero and small values in feature map can be exploited in each matrix-vector multiplication (MVM) operation for speedup. To dynamically predict the results, we propose an efficient parallel predictor to find larger adapted boxes for increased computing parallelism without hurting accuracy. For a better scheduling between the dynamic predictions, we propose an efficient input window management with light-weight hardware support. With dynamic prediction and calculation, <inline-formula> <tex-math notation="LaTeX">$3A$ </tex-math></inline-formula>-ReRAM architecture naturally fits the ReRAM crossbar structure but enables a totally different way to dynamically exploit the sparsity and small values in feature maps. It greatly improves the performance by increasing the computing parallelism and saves energy consumption by much less analog-digital conversions. The evaluation results show that <inline-formula> <tex-math notation="LaTeX">$3A$ </tex-math></inline-formula>-ReRAM architecture can increase the performance by up to <inline-formula> <tex-math notation="LaTeX">$13.03\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$16.31\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$2.46\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$2.58\times $ </tex-math></inline-formula> compared to ReRAM-based CNN accelerators ISAAC, PUMA (sparsity-unaware) and SRE, FORMS (sparsity-aware), and the total energy can be reduced by <inline-formula> <tex-math notation="LaTeX">$8.93\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$10.07\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$2.97\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$4.58\times $ </tex-math></inline-formula>, respectively.
Recombinant ADAMTS-13 Improves Survival of Mice Subjected to Endotoxemia

When stimulated by proinflammatory mediators, endothelial cells release ultra-large von Willebrand factor (ULVWF) multimers that are hyperactive in activating and aggregating platelets. These ULVWF multimers can accumulate in the circulation and on the inflamed endothelium because they are insufficiently cleaved by the metalloprotease ADAMTS-13, which becomes moderately deficient under conditions of systemic inflammation. This moderate ADAMTS-13 deficiency may lead to thrombotic complications that contribute to ischemic tissue injury and organ failure that are associated with severe infections. To test this hypothesis, we investigated whether recombinant ADAMTS-13 improves the pathological course of endotoxemia in lipopolysaccharide (LPS)-treated mice. C57BL/J6 mice received a bolus infusion of either 5 µg/mouse of ADAMTS-13 or vehicle control 30 min after LPS challenge and were monitored for seven-day survival. During the monitoring period, platelet counts, VWF antigen, and ADAMTS-13 activity were measured. Thrombosis was also examined by the immunohistochemistry in the liver. We found that ADAMTS-13 reduced mortality from 66% to 34.9%. The improved survival was associated with a greater recovery from thrombocytopenia, higher plasma ADAMTS-13 activity, and less thrombotic vascular occlusion. These results suggest that systemic inflammation could result in deficient ULVWF proteolysis by ADAMTS-13 and that ADAMTS-13 improves the outcomes of endotoxemia-induced inflammation.
Heavy metals and sulfate removal from acid mine drainage using steel mill slag in a rotational mixing device

The steel mill slag (SMS) production in converter furnaces is one of the main by-products generated during steel production. This waste material is usually composed of different metal oxides that include basic cations such Ca, Mn, and Mg. These cations can favor alkalinity and a heavy metals removal when they are partially dissolved in a contaminant solution such as acid mine drainage (AMD). This work aims to study the potential use of SMS produced in converter furnaces in the mining-steel region of Boyacá- Colombia as an alternative to pre-treat AMD produced by coal mining in the zone. The SMS were separated into two particle sizes by screening, and these were put in contact with AMD samples in a rotational mixing system for 36h. The pH, total Fe (Fe2+, 3+), SO42-, and heavy metals concentration were analyzed at 0h, 12h, 24h, and 36h time intervals. The final results show that SMS produced in the region is an optimal low-cost alternative to treat AMD, allowing complete heavy metals removal in the firsts 12h of treatment. Furthermore, Fe and SO42- removal were observed, reaching 99.9% and 85%, respectively, at 36h. The application of rotational mixing allows complete mixing between AMD and SMS, reducing the possible precipitates adhesion on the surface, limiting the reactivity losses. This mixing treatment device favors a continuous use, avoiding the application of cleaning stages that could be required in the future for optimal functioning.
A Systematic Review and Meta-Analysis of Systemic and Local Antibiotic Therapy in the Surgical Treatment of Peri-Implantitis

(1) Background: Given the existing controversy regarding the use of antibiotics in the treatment of peri-implantitis, this systematic review and meta-analysis aim to ascertain how beneficial the role of systemic and local antibiotics is in peri-implant surgical therapy, considering the harmful effects that they represent and the abuse of antibiotics in terms of global health. (2) Methods: To determine the therapeutic efficacy of the administration of antibiotics in the surgical treatment of peri-implantitis in terms of probing pocket depth (PPD) and bleeding on probing (BoP), electronic and manual bibliographic searches were carried out in the Embase and PubMed databases, collecting data that related to before and after treatment. (3) Results: The adjunctive use of local antibiotics provides significant improvements in PPD (MD = 1.29; 95% CI: 0.56 to 2.02; p ≤ 0.0006; I2 = 0%) when compared with surgical treatment alone. No significant differences were found in the other subgroup; that is, the use of systemic antibiotics did not significantly improve PPD changes in the surgical treatment of peri-implantitis (MD = 0.40; 95% CI: −0.15 to 0.95; p = 0.15; I2 = 0). (4) Conclusions: The use of local antibiotics in the surgical treatment of peri-implantitis seems to offer treatment improvements in terms of PPD and BoP, unlike that observed with the use of systemic antibiotics. However, these results should be taken with caution as they also depend on the type of surgical technique used, whether regenerative or resective. More research is needed on this topic to understand the role of local and systemic antibiotics in the treatment of peri-implantitis.
Magnetic Resonance Imaging in the Diagnosis of Female Adnexal Masses: Comparison With Histopathological Examination

Introduction Adnexal masses present a special diagnostic challenge because it is difficult to differentiate between benign and malignant lesions clinically. The diagnosis of malignancy is vital, and imaging is the most important part of the evaluation of adnexal masses. This study was conducted with the goal of comparing the accuracy of magnetic resonance imaging (MRI) in diagnosing female adnexal masses in comparison with histopathology examination (HPE). A total of 70 female patients with suspected adnexal lesions were selected for the study. After obtaining informed consent from the patients, an MRI was performed with a subsequent histopathological examination of the lesion. Results The study revealed that MRI demonstrated 27% non-neoplastic, 47% benign, and 26% malignant lesions. HPE, the gold standard for identifying and classifying pathological masses, positively identified the lesions and classified them as non-neoplastic, surface epithelial-stromal, germ cell, and sex cord-stromal tumors. The present study of 70 cases with adnexal masses showed a strong positive correlation between MRI and HPE findings. Conclusion MRI provides the added advantage of visualization of the tumor matrix with differential identification of the fatty and cystic tissue through heterogeneous signals and enhancement indicating aggressiveness and local spread. MRI has greater diagnostic accuracy when compared to ultrasonography (USG), with HPE as the gold standard for discriminating between benign and malignant adnexal masses.
Connections between the Madden–Julian Oscillation and surface temperatures in winter 2018 over eastern North America

From January to March 2018, one of the strongest Madden–Julian Oscillation (MJO) events of the last 45 years progressed eastward along the equator from the Indian Ocean to the Pacific Ocean then back to the Indian Ocean. In response to strong tropospheric heating in the MJO's active convective envelope, several pronounced Rossby wave trains developed and extended from the equatorial tropics, across the extratropical Pacific and North America, and into the extratropical Atlantic. The origins of these Rossby wave trains evolved eastward with time, generally following the eastward progression of the MJO, but preferentially clustered in subtropical India and Southeast Asia and in two locations in the subtropical Pacific Ocean: along 160°E and 170°W. Over eastern North America, surface and lower‐tropospheric temperatures rose to more than 12 °C above normal when the MJO convective envelope was over the Indian Ocean (in mid‐January) and Western Hemisphere (in late February). In between those warm periods, temperatures cooled to below normal while the MJO convection was over the western Pacific. These temperature anomalies evolved in time with the pronounced Rossby wave trains that linked eastern North America with the Tropics in the Eastern Hemisphere: warm temperatures occurred when ridging was present over eastern North America and cooler temperatures occurred when troughing was present. This variability is discussed and placed in context of recent work showing the MJO's role in modulating temperature and circulation.
Association of Dietary Intake of Zinc and Selenium with Breast Cancer Risk: A Case-Control Study in Chinese Women

As major nonenzymatic antioxidant components in the body, dietary Zinc (Zn) and Selenium (Se) may have an impact on breast cancer development. This study aimed to investigate the relationship between dietary Zn, Se intake and breast cancer risk in Chinese women. The case-control study included 1591 cases and 1622 age-frequency matched controls. Dietary intake was collected using a validated food frequency questionnaire. Dietary Zn and Se were divided into four categories: Zn/Se from plants, Zn/Se from meat, Zn/Se from red meat, and Zn/Se from white meat. Unconditional logistic regression models and restricted cubic spline analyses were performed to identify potential associations. Zn from white meat intake was linearly and inversely associated with breast cancer risk, and Se from red meat intake was linearly and positively associated with breast cancer risk, with adjusted odds ratio and 95% confidence interval of 0.76 (0.61–0.95) and 1.36 (1.04–1.77), respectively. Non-linear relationships were found between total dietary Zn, Zn from meat, Zn from red meat intake and breast cancer risk (pnon-linearity < 0.05). In conclusion, dietary Zn and Se intake were associated with breast cancer risk in Chinese women, and the optimal intake of Zn may be beneficial for breast cancer prevention.
Two-dimensional system of strongly interacting electrons in silicon (100) structures

Studies of various experimental groups that explore the properties of a two-dimensional electron gas in silicon semiconductor systems ((100) Si-MOSFET and (100) SiGe/Si/SiGe quantum wells) in the vicinity of the metal–insulator transition are described and critically analyzed. Results are identified that are common to all research: (i) the effective mass of electrons measured at the Fermi level in the metallic region increases as the electron density decreases and, if extrapolated, tends to diverge; (ii) the behavior of the energy-averaged mass in the metallic region is quite different in the two systems: in Si-MOSFETs, it also exhibits a tendency to diverge, while in the SiGe/Si/SiGe quantum wells it saturates in the limit of low electron densities; (iii) there is a small number (depending on the sample quality) of localized electrons in the metallic phase; (iv) the properties that the electron system exhibits in the insulating phase in the vicinity of the metal–insulator transition are typical of amorphous media with a strong coupling between particles.
Introduction to Dislocations

In materials science, dislocations are irregularities within the crystal structure or atomic scale of engineering materials, such as metals, semi-conductors, polymers, and composites. Discussing this specific aspect of materials science and engineering, Introduction to Dislocations is a key resource for students. The book provides students and practitioners with the fundamental principles required to understand dislocations. Comprised of 10 chapters, the text includes advanced computer modeling and very high-resolution electron microscopy to help readers better understand the structure of atoms close to the core of dislocations. It shows that atomic arrangement has a significant effect on the formation of dislocations and thereby on the properties of solids. The first two chapters of the book present an overview of dislocations. The crystal structures and the various defects and dislocations are discussed, and methods of observation and diagnosis of dislocations are covered. Chapters 3 to 5 discuss the behavior of dislocations and explain how changes in the structure and arrangement of atoms can affect the behavior of dislocations. The three chapters also discuss the mechanical properties of dislocations. The remaining chapters offer a detailed discussion of the mechanisms of dislocations and the mechanical strength of crystalline solids. The book is written for undergraduate- and graduate-level students in both materials science and mechanical engineering. Non-experts and novices working on mechanical properties, mechanisms of deformation and fracture, and properties of materials, as well as industrial and academic researchers, will find this book invaluable. * Long-established academic reference by an expert author team, highly regarded for their contributions to the field. * Uses minimal mathematics to present theory and applications in a detailed yet easy-to-read manner, making this an understandable introduction to a complex topic. * Unlike the main competition, this new edition includes recent developments in the subject and up-to-date references to further reading and research sources.
Detection of COVID-19 Cases from X-ray Images Using Capsule-based Network

Coronavirus (Covid-19) disease has spread abruptly all over the world since the end of 2019. Computed Tomography (CT) scans and X-ray images are used to detect this disease. Different Deep Neural Network (DNN)-based diagnosis solutions has been developed, mainly based on Convolutional Neural Networks (CNNs), to accelerate identification of covid-19 cases. however, CNNs lose important information in intermediate layers and require large datasets. In this paper, capsule network (CAPSNET) is used. Capsule network performs better than CNNs for small datasets. an accuracy of 0.9885, F1-score of 0.9883, precision of 0.9859, recall of 0.9908 and area under the curve (AUC) of 0.9948 are achieved on the capsule-based framework with hyperparameter tuning. Moreover, different dropout rates are investigated to decrease the overfitting. Accordingly, a dropout rate of 0.1 shows best results. finally, we remove one convolution layer and decrease the number of trainable parameters to 146,752, which is a promising result.