Miracle-dz/UniFAR-FTUs huggingface dataset:
Observation of the Hanle effect in giant magnetoresistance measurements

We report the observation of the Hanle effect in the giant magnetoresistance measurement of metallic lateral nanostructures. By applying magnetic fields perpendicularly to the magnetization of the electrodes, we obtain resistance values lower than that of the parallel magnetization state, due to the extra decoherence provided by the random propagation time of the carriers. An analytical model is developed, enabling us to extract the characteristic transport parameters of CoFe, Al, and Cu from the fitting of the experimental results. We compare the obtained parameters to that of standard non-local Hanle measurements in lateral spin-valves, with the interest that this measurement configuration gives much larger signal variation.
Improving Task Scheduling on The Cloud Through Low Latency and Cost Effective Technique

Offloading is emerging as a promising idea to allow handheld devices to access intensive applications without incurring performance or power costs. This is especially useful for businesses that want to run line-of-business applications on handhelds. However, developing applications using cloud computing resources is a challenge because supporting the low-latency and scalability needs of applications requires highly dynamic orchestration of heterogeneous resources at different levels of the network hierarchy. It Is difficult. To reduce this complexity, this application provides a programming model that provides simplified programming abstractions and supports applications that scale dynamically at runtime. The goal of offloading is to decide for or against offloading. Code offload can make decisions in a number of ways. Much of the research on code swapping focuses on more sophisticated if-else conditions. Code offloading machine learning is currently an important research topic. Machine learning methods are employed to significantly improve the currently recommended methods and to perform robustly when tackling a wide range of learning tasks
Sociodemographic Disparities in Mercury Exposure from United States Coal-Fired Power Plants

Hazardous air pollutants emitted by United States (U.S) coal-fired power plants have been controlled by the Mercury and Air Toxics Standards (MATS) since 2012. Sociodemographic disparities in traditional air pollutant exposures from U.S. power plants are known to occur but have not been evaluated for mercury (Hg), a neurotoxicant that bioaccumulates in food webs. Atmospheric Hg deposition from domestic power plants decreased by 91% across the contiguous U.S. from 6.4 Mg in 2010 to 0.55 Mg in 2020. Prior to MATS, populations living within 5 km of power plants (n = 507) included greater proportions of frequent fish consumers, individuals with low annual income and less than a high school education, and limited English-proficiency households compared to the US general population. These results reinforce a lack of distributional justice in plant siting found in prior work. Significantly greater proportions of low-income individuals lived within 5 km of active facilities in 2020 (n = 277) compared to plants that retired after 2010, suggesting that socioeconomic status may have played a role in retirement. Despite large deposition declines, an end-member scenario for remaining exposures from the largest active power plants for individuals consuming self-caught fish suggests they could still exceed the U.S. Environmental Protection Agency reference dose for methylmercury.
Collaborative Computation Offloading for Photovoltaic Power Prediction in Energy Internet: A Similarity-Aware Stable Matching Approach

The advances of communication technology and edge intelligence are deriving new computation offloading modes in the energy Internet by integrating computing capabilities of cloud servers, edge gateways (EGs), and terminal nodes into forecasting the renewable energy generation. However, the largely dispersed data generated by abundant photovoltaic (PV) stations and limited transmission capacity will degrade the collaboration of clouds, edges, and end nodes and, as a result, fail to satisfy the delay requirements of tasks. Owing to the similarity of power data generated by PV stations with akin geographical positions and weather conditions, we can reuse and offload the selected and representative power data so as to reduce transmission costs and overloads. In this article, we propose a similarity-aware stable matching approach (SASMA) to efficiently offload prediction tasks to EGs or cloud platforms with reusing the computing results. Specifically, we analyze task similarity and build the reuse strategy for the power data, and propose a similarity graph algorithm (SGA) to select representative PV stations and derive reuse relations. We also propose a similarity-based Gale–Shapley algorithm to match reused PV stations, computing nodes with prediction models. The objective is to maximize the prediction accuracy with a stable match. Simulation results show the effectiveness of the proposed approach while examining the tradeoff between the prediction accuracy and the system delay.
Is gastrulation the most important time in your life?

Gastrulation is considered a fundamental process of metazoan embryogenesis, as the layers that give place to all body parts are laid down by extensive cell movements and cell differentiation. It is commonly thought that gastrulation establishes the entire body axis. The discovery of neuromesodermal progenitors is challenging this view. These cells keep their pluripotency after gastrulation and they are able to differentiate into ectoderm (neural tube) and mesoderm derivatives (paraxial mesoderm, notochord) directly, without passing through a germ layer intermediate. They contribute to the elongation of the body axis and could play a key role in the evolution of the chordate body plan.
Mean square stabilization of discrete‐time switching Markov jump linear systems

This paper considers a special class of hybrid system called switching Markov jump linear system. The system transition is governed by two rules. One is Markov chain and the other is a deterministic rule. Furthermore, the transition probability of the Markov chain is not only piecewise but also orchestrated by a deterministic switching rule. In this paper, the mean square stability of the systems is studied when the deterministic switching is subject to two different dwell time conditions, ie, having a lower bound and having both lower and high bounds. The main contributions of this paper are two relevant stability theorems for the systems under study. A numerical example is provided to demonstrate the theoretical results.
EFFECT OF RECLAMATION AND RESTORATION ON SOIL MICROBIAL COMMUNITY STRUCTURES IN COLD REGION WETLAND

To understand the impact of reclamation and restoration on soil microbes and the corresponding soil biogeochemical processes in cold region wetland, an experiment was established that included two cultivated treatments (upland fields of soybean cultivation and paddy fields of rice cultivation), two restored treatments (with meadow and forest wetland of agricultural abandonment). Changes in soil properties and microbial communities across the different treatments were analyzed. The results showed the following: (1) The contents of SOC, TN, and TP and the Shannon diversity, Ace and Chao1 index of a wetland park nature reserve in the cold region were higher than those of the upland and paddy fields. (2) The diversity and abundance of bacterial communities in reclaimed soils was slightly different from that of restored wetland soils. Actinobacteria, Acidobacteria, Proteobacteria, Chloroflexi and Firmicutes were the predominant bacteria. (3) Some soil physicochemical properties are significantly correlated with the main bacteria in the soil, the environmental factor of AP, TP, AK, TK, TN and SOC driven by reclamation and restoration modes effect the structure microbial community.
Levitation forces between a finite rectangular superconductor and a spherical magnet

A model to describe the interaction between a spherical permanent magnet and a square superconductor in the mixed state was elaborated. The proposal is based on image dipole ideas as is the case of the Frozen dipole model that has been used in the literature. More precisely, a frozen dipole was put in the center of the superconducting sample; this dipole “was born” when the first critical field was reached in that position. This led to the attractive force magnet-superconductor in the penetrated-field state. In the description of the Meissner state, the demagnetizing field was not included in the calculation. Both vertical and horizontal forces are discussed.
Densities, infrared band strengths, and optical constants of solid methanol

Contact. The increasing capabilities of space missions like the James Webb Space Telescope or ground-based observatories like the European Extremely Large Telescope demand high quality laboratory data of species in astrophysical conditions for the interpretation of their findings. Aims. We provide new physical and spectroscopic data of solid methanol that will help to identify this species in astronomical environments. Methods. Ices were grown by vapour deposition in high vacuum chambers. Densities were measured via a cryogenic quartz crystal microbalance and laser interferometry. Absorbance infrared spectra of methanol ices of different thickness were recorded to obtain optical constants using an iterative minimization procedure. Infrared band strengths were determined from infrared spectra and ice densities. Results. Solid methanol densities measured at eight temperatures vary between 0.64 g cm−3 at 20 K and 0.84 g cm−3 at 130 K. The visible refractive index at 633 nm grows from 1.26 to 1.35 in that temperature range. New infrared optical constants and band strengths are given from 650 to 5000 cm−1 (15.4–2.0 μm) at the same eight temperatures. The study was made on ices directly grown at the indicated temperatures, and amorphous and crystalline phases have been recognized. Our optical constants differ from those previously reported in the literature for an ice grown at 10 K and subsequently warmed. The disagreement is due to different ice morphologies. The new infrared band strengths agree with previous literature data when the correct densities are considered.
Research on Women's Employment Discrimination

At present, there are still major problems in employment of female college students China, the most important of which are employment discrimination. The current law has not made practical regulations on employment discrimination of invisible female universities. At the same time, it is too broad for explicit employment discrimination regulations, and the actual effect is not obvious. We should also study and discuss the employment discrimination phenomenon of female college students, and prevent and solve the employment discrimination of female college students from the level of improving the level of employment guidance for female college students, the development and improvement of the protection of female employment rights, and strengthening the intensity of inspection supervision. Different data and data will be collected in the article to reflect the three different aspects of discrimination in the workplace, namely physical discrimination, treatment discrimination and marriage discrimination. Solving this problem is also separate from three perspectives, national policies, ideological education, and self -awareness. The state needs to formulate more laws to protect women. Society needs to change the traditional ideas in the past, and women must strengthen their consciousness, have a better attitude, and become more confident.
Data-Adaptive Modeling using Convex Regression

Data-Adaptive Modeling using Convex Regression Ashley Petersen Co-Chairs of the Supervisory Committee: Dr. Daniela Witten Departments of Biostatistics and Statistics Dr. Noah Simon Department of Biostatistics The collection and storage of large amounts of data has greatly increased in recent years, which has led to a greater interest in developing methods to explore and model this data. In settings in which little is known about the relationship among variables in advance, exploratory data analysis is particularly important. Furthermore, when the number of covariates is large, traditional exploratory data analysis becomes challenging. In this setting, fitting flexible and interpretable models can be a useful tool for visualizing the conditional relationships between the covariates and outcome. In Chapter 2, we present a method that adaptively selects covariates to include in the model and for those included, models their conditional associations with the outcome as piecewise constant functions with adaptivelychosen knots. In Chapter 3, we present a related method that is useful in settings in which interactions between pairs of covariates are of particular interest. In Chapter 4, we turn our attention to a specific type of data: calcium imaging data, which measures large populations of neurons at cellular resolution in behaving animals. As a first step of analyzing calcium imaging data, two goals must be accomplished: neuron identification and calcium quantification. Through combining image segmentation, clustering, and convex regression, we are able to extract the locations of neurons in the field of view, as well as estimate the neurons’ intracellular calcium concentrations over time. This extracted data is useful in downstream analyses, which aim to provide unprecedented insight into neural activity.
DIFFERENCES OF COPEPODA COMPOSITION ANALYSIS IN MARICULTURE PONDS THROUGH MORPHOLOGICAL TAXONOMY AND DIFFERENT METAGENETIC APPROACHES

Copepods play a fundamental role in aquatic ecosystems and are an important food source for fish at an early stage. However, little attention has been given to changes in copepod communities in aquaculture water. This is mainly limited by the copepod analysis method. In this study, we designed a pair of copepod primers, Cop18SF/Cop18SR, for the analysis of copepod communities to promote the investigation of copepods in aquaculture water. Our results showed that the common-used primer pair Uni18S/Uni18SR non-specifically amplified many DNA sequences from bacteria, whereas the primer pair LSUCop-D2F/LSUCop-D2R abnormally enriched OTU denovo438, which was an unidentified Eukaryota zooplankton. Although Eurytemora pacifica was classified into 26 OTUs with 97% sequence identity using our designed primer pair Cop18SF/Cop18SR, other results indicated that the primer pair Cop18SF/Cop18SR was superior to the other two primer pairs. Moreover, different primer pairs yielded different plankton community structures, which caused their correlations with environmental factors to be obviously different. These results imply that the primer pair Cop18SF/Cop18SR can be used to analyze the dynamic changes in the copepod community structure and the relationship between the community structure and abiotic and biological factors in aquaculture water.
Molecular and genetic regulations of fleshy fruit shape and lessons from Arabidopsis and rice

Abstract Fleshy fruit shape is an important external quality trait influencing the usage of fruits and consumer preference. Thus, modification of fruit shape has become one of the major objectives for crop improvement. However, the underlying mechanisms of fruit shape regulation are poorly understood. In this review we summarize recent progress in the genetic basis of fleshy fruit shape regulation using tomato, cucumber, and peach as examples. Comparative analyses suggest that the OFP-TRM (OVATE Family Protein - TONNEAU1 Recruiting Motif) and IQD (IQ67 domain) pathways are probably conserved in regulating fruit shape by primarily modulating cell division patterns across fleshy fruit species. Interestingly, cucumber homologs of FRUITFULL (FUL1), CRABS CLAW (CRC) and 1-aminocyclopropane-1-carboxylate synthase 2 (ACS2) were found to regulate fruit elongation. We also outline the recent progress in fruit shape regulation mediated by OFP-TRM and IQD pathways in Arabidopsis and rice, and propose that the OFP-TRM pathway and IQD pathway coordinate regulate fruit shape through integration of phytohormones, including brassinosteroids, gibberellic acids, and auxin, and microtubule organization. In addition, functional redundancy and divergence of the members of each of the OFP, TRM, and IQD families are also shown. This review provides a general overview of current knowledge in fruit shape regulation and discusses the possible mechanisms that need to be addressed in future studies.
An Arithmetic Operation on Hexadecagonal Fuzzy Number

In this paper, a new form of fuzzy number named as Hexadecagonal Fuzzy Number is introduced as it is not possible to restrict the membership function to any specific form. The cut of Hexadecagonal fuzzy number is defined and basic arithmetic operations are performed using interval arithmetic of cut and illustrated with numerical examples.
Impulse noise removal from colour images using fuzzy genetic algorithm

Noise removal from colour images is an important pre-processing step in any task involving analysis of these images. Many methods have been proposed for noise removal, but they are either inefficient or quite complicated. This paper presents a simple method for removing impulse noise from colour images, where a set of standard filters are successively applied on the noisy image. The type and the order of application of these filters are determined using fuzzy genetic algorithm. The results of simulations performed on a set of standard test images for a wide range of noise corruption levels shows that the proposed method outperforms the standard procedures both visually and in terms of objective quality measures such as Peak Signal-to-Noise Ratio (PSNR), Image Quality Index (IQI) and Mean Absolute Error (MAE).
A Revisit to the Notation of Martensitic Crystallography

As one of the most successful crystallographic theories for phase transformations, martensitic crystallography has been widely applied in understanding and predicting the microstructural features associated with structural phase transformations. In a narrow sense, it was initially developed based on the concepts of lattice correspondence and invariant plane strain condition, which is formulated in a continuum form through linear algebra. However, the scope of martensitic crystallography has since been extended; for example, group theory and graph theory have been introduced to capture the crystallographic phenomena originating from lattice discreteness. In order to establish a general and rigorous theoretical framework, we suggest a new notation system for martensitic crystallography. The new notation system combines the original formulation of martensitic crystallography and Dirac notation, which provides a concise and flexible way to understand the crystallographic nature of martensitic transformations with a potential extensionality. A number of key results in martensitic crystallography are reexamined and generalized through the new notation.
Artificial intelligence as a pioneering step in school institutions

In this paper, we deal with the potential applications of Artificial Intelligence in educational institutions. One of the main goals of applying the mentioned technology is to make the education system more efficient if possible. Based on research in this area in the world, our impression is that there is a long-term plan for introducing AI in schools, as a new kind of learning standard. The idea is not for artificial intelligence to completely take over the domain of education, but for some processes to be better organized or improved, so that the result of learning would be of better quality. New technologies, in the form of modern learning platforms that incorporate AI as one of the most important components of the system, significantly improve the communication and research work of students. It is evident that new generations are coming whose experience will be significantly different from ours, and that the new technologies from the domain of AI that we consider in this paper will significantly affect the design processes in the future.
SPECIAL INVITED PAPER COEXISTENCE IN STOCHASTIC SPATIAL MODELS 1

In this paper I will review twenty years of work on the question: When is there coexistence in stochastic spatial models? The answer, announced in Durrett and Levin [Theor. and that we explain in this paper is that this can be determined by examining the mean-field ODE. There are a number of rigorous results in support of this picture, but we will state nine challenging and important open problems, most of which date from the 1990's. Introduction. There is an incredible diversity of species that coexist in the world. At the Botanic Garden in Singapore one can see 1000 species of orchids. These are cultivated, of course, but if one examines the food web in a small lake one finds dozens of species coexisting. An important problem in ecology is to identify mechanisms that permit the coexistence of species. In this paper we will examine that question in the context of stochastic spatial models. In these interacting particle systems, space is represented by the d-dimensional integer lattice Z d. With ecological problems in mind, we will usually take d = 2. Our story begins with a very simple and natural model, but one that is still not well understood.
Modulation of excitatory and inhibitory systems in autism spectrum disorder: the role of cannabinoids

Introduction Autism Spectrum Disorder (ASD) includes a group of developmental disabilities characterized by patterns of delay and deviance in the development of social, communicative, cognitive skills and the presence of repetitive and stereotyped behaviors as well as restricted interests (APA, 2013 DSM 5th ed.). Although the etiopathogenesis of autism has not yet been elucidated, past literature has highlighted an imbalance between glutamatergic and gamma-aminobutyric acid (GABA)-ergic neurotransmission (Harada et al. J Autism Dev Disord 2011;41:447-54.). A cortical deficiency of GABA in young people with ASD has been reported (Rojas et al. Neuroimage 2013;86:28-34.). Endocannabinoids act in numerous synapses of the central nervous system, maintaining an adequate synaptic homeostasis, preventing excess stimulation at the level of excitatory or inhibitory synapses. They therefore appear to be fundamental for the short- and long-term control of synaptic plasticity (Castillo et al. Neuron 2012;76,70-81). The endocannabinoid system appears to play an important role in some clinical presentations of autism, such as socialization. Indeed, Autism Spectrum Disorder seems to be characterized by a hypo-functionality of the endocannabinoid system (Aran et al. Mol Autism 2019;10, 2). Objectives The present work aims to describe the current state of the art regarding the possible role of cannabinoids in the modulation of the excitatory and inhibitory systems in individuals with ASD. Methods We carried out a search on PubMed concerning the randomized clinical trials on the modulating effect of excitatory and inhibitory cannabinoid systems in autism. Three eligible articles were found according to the purpose of the present study. Results The results of the three articles considered highlighted a cannabinoid (CBD)-related increase in glutamate in subcortical regions (basal ganglia) and a decrease in cortical regions (dorsomedial prefrontal cortex), both in subjects with and without ASD. CBD increased GABA transmission in the subcortical regions of neurotypical subjects, while it decreased it in the same areas of the ASD group. Furthermore, CBD modulated low-frequency activity, used as a measure of brain activity and functional connectivity in the brains of adults with ASD. Conclusions Data from the three functional MRI studies demonstrated that CBD influences cortical and subcortical connectivity on an adult sample. This effect was notable only in the ASD group but not in the controls. However, further studies are needed to confirm the results obtained so far. Disclosure of Interest None Declared