Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing Total Phosphorus in Current and Geologic Utah Lake Sediments: Implications for Water Quality Management Issues

Utah Lake is highly eutrophic with large phosphorous inflows and a large internal phosphorous reservoir in the sediment. There are debates over whether this phosphorous is from geologic or more recent anthropologic sources. This study characterizes total phosphorous in geologic and current lake sediments to attempt to address that question. The average total phosphorous concentrations in the lake sediment were 666 ppm, with most samples in the 600 to 800 ppm range with a few larger values. Concentrations in historic geologic sediments were not statistically different from lake sediments. A spatial analysis showed that phosphorous distributions appeared continuous from the lake to the shore and that high and low values could be attributed to areas of seeps and springs (low) or feed lots and waste water discharge (high). These results indicate that geologic sediments without anthropogenic impacts are not statistically different than current lake sediments. The high values indicate that internal natural phosphorous loadings could be significant and the impaired state may be relatively insensitive to external anthropogenic loadings. If this is the case, then mitigation efforts to address anthropogenic sources may have minimal impacts. This case study presents an impaired water body where non-anthropogenic nutrient sources are significant and shows that reservoir management decisions should consider these non-anthropogenic phosphorous sources relative to anthropogenic sources. This study can serve as a template for evaluating the importance of geologic phosphorous sources for management decisions.
Optimal physical preprocessing for example-based super-resolution A

Fourier ptychographic microscopy is a technique that achieves a high space-bandwidth product, i.e. high resolution and high field-of-view. In Fourier ptychographic microscopy, variable illumination patterns are used to collect multiple low-resolution images. These low-resolution images are then computationally combined to create an image with resolution exceeding that of any single image from the microscope. Due to the necessity of acquiring multiple low-resolution images, Fourier ptychographic microscopy has poor temporal resolution. Our aim is to improve temporal resolution in Fourier ptychographic microscopy, achieving single-shot imaging without sacrificing space-bandwidth product. We use example-based super-resolution to achieve this goal by trading off generality of the imaging approach. In example-based super-resolution, the function relating low-resolution images to their high-resolution counterparts is learned from a given dataset. We take the additional step of modifying the imaging hardware in order to collect more informative low-resolution images to enable better high-resolution image reconstruction. We show that this “physical preprocessing” allows for improved image reconstruction with deep learning in Fourier ptychographic microscopy. In this work, we use deep learning to jointly optimize a single illumination pattern and the parameters of a post-processing reconstruction algorithm for a given sample type. We show that our joint optimization yields improved image reconstruction as compared with sole optimization of the post-processing reconstruction algorithm, establishing the importance of physical preprocessing in example-based super-resolution. © 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
A Case for Serverless Machine Learning

The scale and complexity of ML workflows makes it hard to provision and manage resources—a burden for ML practitioners that hinders both their productivity and effectiveness. Encouragingly, however, serverless computing has recently emerged as a compelling solution to address the general problem of data center resource management. This work analyzes the resource management problem in the specific context of ML workloads and explores a research direction that leverages serverless infrastructures to automate the management of resources for ML workflows. We make a case for a serverless machine learning framework, specializing both for serverless infrastructures and Machine Learning workflows, and argue that either of those in isolation is insufficient.
Semantic Web Technologies for Big Data Modeling from Analytics Perspective: A Systematic Literature Review

The present study addresses research on the application of semantic technologies (Semantic web technologies) to assist analysts in selecting, building, and explaining big data models. It is motivated by the established lack of a comprehensive and up-to-date systematic scientific review aimed at the use of semantic technologies for big data modeling for the purposes of their analysis. Research questions are defined, which refer to tracking the research interest in this topic; identification of the big data models to which the focus is directed and the semantic technologies applied to them and the solved analytics tasks; formulation of trends, guidelines for future work. The scientific papers included in the review are 44, collected from well-known digital libraries for scientific literature covering the period between 2011 and the beginning of 2021. As a result of the conducted research, useful conclusions are summarized for the most frequently studied big data models, semantic technologies and the research tasks solved through them.
Nobel Lecture: The double heterostructure concept and its applications in physics, electronics, and technology

It is impossible to imagine now modern solid-state physics without semiconductor heterostructures. Semiconductor heterostructures and, particularly, double heterostructures, including quantum wells, wires, and dots, are today the subject of research of two-thirds of the semiconductor physics community. The ability to control the type of conductivity of a semiconductor material by doping with various impurities and the idea of injecting nonequilibrium charge carriers could be said to be the seeds from which semiconductor electronics developed. Heterostructures developed from these beginnings, making it possible to solve the considerably more general problem of controlling the fundamental parameters inside the semiconductor crystals and devices: band gaps, effective masses of the charge carriers and the mobilities, refractive indices, electron energy spectrum, etc. Development of the physics and technology of semiconductor heterostructures has resulted in remarkable changes in our everyday life. Heterostructure electronics are widely used in many areas of human civilization. It is hardly possible to imagine our recent life without double heterostructure (DHS) laser-based telecommunication systems, heterostructure-based light-emitting diodes (LED’s), heterostructure bipolar transistors, or lownoise high-electron-mobility transistors for highfrequency applications including, for example, satellite television. Double-heterostructure lasers now enter practically every house with CD players. Heterostructure solar cells have been widely used for space and terrestrial applications. Our interest in semiconductor heterostructures was not occasional. Systematic studies of semiconductors were started in the early 1930s at the Physico-Technical Institute under the direct leadership of its founder, Abraham Ioffe. V. P. Zhuze and B. V. Kurchatov studied the intrinsic and impurity conductivity of semiconductors in 1932, and the same year Ioffe and Ya. I. Frenkel created a theory of rectification in a metalsemiconductor contact based on the tunneling phenom-
Aircraft Detection in Remote Sensing Image based on Multi-scale Convolution Neural Network with Attention Mechanism

Detecting aircraft from remote sensing image (RSI) is an important but challenging task due to the variations of aircraft type, size, pose, angle, complex background and small size of aircraft in RSIs. An aircraft detection method is proposed based on multi-scale convolution neural network with attention (MSCNNA), consisting of encoder, decoder, attention and classification. In MSCNNA, the multiple convolutional and pooling kernels with different sizes are utilized to learn the multi-scale discriminant features, and the global attention mechanism (GAM) is employed to capture the spatial and channel dependencies and adaptively preserve the relationships of the entire image. Compared with the standard deep CNN, multi-scale convolution neural networks (CNN) and GAM are integrated to learn the multi-scale features for aircraft detection, especially small aircrafts. Experiment results on the aircraft image dataset of the public EORSSD dataset show that the proposed method outperforms the state-of-the-art method on the same dataset and the obtained multi-size aircraft edge is clearer.
Local sunspot oscillations and umbral dots

Data analysis of sunspot oscillations based on a 6-hr SDO run of an observation showed that low frequency ( 0.2 < ω < 1 mHz ) oscillations are locally similar to three and five minute oscillations. The oscillations in the sunspot are concentrated in cells of a few arcsec, each of which has its own oscillation spectrum. The analysis of two scenarios for sunspot oscillations leads to a conclusion that local sunspot oscillations occur due to a subphotospheric resonator for slow MHD waves. Empirical models of a sunspot atmosphere and the theory of slow waves in thin magnetic flux tubes are applied to modeling the subphotospheric resonator. The spectrum of local oscillations consists of a great number of lines. This kind of spectrum can occur only if the subphotospheric resonator is a magnetic tube with a rather weak magnetic field. Magnetic tubes of this sort are umbral dots that appear due to the convective tongues in monolithic sunspots. The interrelation of local oscillations with umbral dots and wavefronts of traveling waves in sunspots is discussed.
Effect of Soy Isoflavones Supplementation on Adiponectin Levels in Postmenopausal Women

Decreased adiponectin levels has been demonstrated in postmenopausal (PMP) women. Soy isoflavones, as an herbal product have been shown to increase adiponectin level but the results are inconclusive and inconsistent. The present study reassessed the data on the impact of soy isoflavones supplementation on adiponectin levels in PMP women through a meta-analysis. A systematic search was performed in the databases of PubMed, Web of science, Scopus and the Cochrane library. The literature search identified 830 studies with duplicates. Out of those, 80 were screened for title and abstract and 12 articles were ultimately selected for the analysis. Meta-regression and subgroup analyses, based on the moderator variables such as treatment duration, dose of soy isoflavones and BMI were performed. The quality of the studies was evaluated using the Grading of Recommendation Assessment, Development and Evaluation (GRADE) approach. The results revealed that soy isoflavones supplementation significantly increased the circulating level of adiponectin in PMP women (SMD: 0.36 µg/mL; 95% CI (0.05 to 0.66); P= 0.02). No publication bias was observed using Begg's (P = 0.38) and Egger's (P = 0.07) tests. Sensitivity analysis indicated the results were completely powerful and stable. Moreover, Meta-regression and subgroup analyses indicated a significant increase of adiponectin levels in subgroups of dose > 50 mg and treatment duration less or equal 3 months. Our findings showed significantly increase in adiponectin levels after isoflavones-supplemented soy consumption in postmenopausal women, who received dose > 50 mg of soy isoflavones in treatment duration ≤ 3 months.
Investigating the Impact Factors of the Logistics Service Supply Chain for Sustainable Performance: Focused on Integrators

The overall scale of China’s logistics industry is growing rapidly, and the environment and conditions for its development are constantly improving, which lays a solid foundation for further accelerating the development of the logistics industry. However, logistics services are characterized by subordination, immediacy, demand volatility, and substitutability. Low-level integrated management of logistics services seriously hinders the development of the logistics service supply chain (LSSC) for sustainable performance. Many studies have been limited to the performance evaluation of LSSCs, and the factors affecting LSSC performance have generally been ignored. This study focuses on integrated LSSCs by using an integrator’s opportunistic behavior as the entry point of research and investigates the factors that affect the sustainability of LSSC performance. On the basis of relevant theories, a model for a hypothesis is constructed and eight hypotheses are subsequently proposed. Moreover, 271 survey responses from functional logistics service providers (research object) are utilized to develop a structural equation model for empirical research. Findings show that integrators with opportunistic behavior inhibit the information-sharing behavior of supply chain members. Consequently, the integration capability and agility of LSSCs are reduced, which affects the overall performance of LSSCs. The findings of this study can provide management insights into the behaviors of supply chain members and governments.
Second order BVPs with state dependent impulses via lower and upper functions

The paper deals with the following second order Dirichlet boundary value problem with p ∈ ℕ state-dependent impulses: z″(t) = f (t,z(t)) for a.e. t ∈ [0, T], z(0) = z(T) = 0, z′(τi+) − z′(τi−) = Ii(τi, z(τi)), τi = γi(z(τi)), i = 1,. , p. Solvability of this problem is proved under the assumption that there exists a well-ordered couple of lower and upper functions to the corresponding Dirichlet problem without impulses.
A generalized conserved phase-field system based on type III heat conduction

In this paper, we are interested in the study of the asymptotic behavior, in terms of finite-dimensional attractors, of a generalization of the conserved phase-field system proposed by G. Caginalp. This model is based on a heat conduction law recently proposed in the context of thermoelasticity and known as type III law. In particular, we prove the existence of exponential attractors and, thus, of finite-dimensional global attractors.
Teaching-learning methodologies from the perspective of nursing students

Objective: to evaluate the teaching-learning methodologies adopted by teachers of a Nursing course from the perspective of students. Methods: research with mixed methods of the convergent type. This is an excerpt of a comprehensive project of evaluation a program, using the Context, Input, Process, and Product evaluation model. Quantitative data (cross-sectional study with secondary data) and qualitative data were collected concomitantly and then triangulated. Results: regarding the teaching methodologies adopted by teachers, the participating students reported greater use of expository and dialogic classes, 161 (67.6%), among teachers of the basic subjects; and non-dialogic expository classes, 226 (92.6%), by teachers of specific subjects of the nursing area. Traditional teaching and traditional assessment methodologies predominated in all courses. Conclusion: the participating students considered predominantly traditional methodologies and wanted to experience active methods, highlighting the need for interdisciplinarity and greater teaching-service-community integration.
2 7 O ct 2 01 7 An integral quadratic constraint framework for real-time steady-state optimization of linear time-invariant systems *

Achieving optimal steady-state performance in real-time is an increasingly necessary requirement of many critical infrastructure systems. In pursuit of this goal, this paper builds a systematic design framework of feedback controllers for Linear Time-Invariant (LTI) systems that continuously track the optimal solution of some predefined optimization problem. The proposed solution can be logically divided into three components. The first component estimates the system state from the output measurements. The second component uses the estimated state and computes a drift direction based on an optimization algorithm. The third component computes an input to the LTI system that aims to drive the system toward the optimal steady-state. We analyze the equilibrium characteristics of the closed-loop system and provide conditions for optimality and stability. Our analysis shows that the proposed solution guarantees optimal steady-state performance, even in the presence of constant disturbances. Furthermore, by leveraging recent results on the analysis of optimization algorithms using integral quadratic constraints (IQCs), the proposed framework is able to translate input-output properties of our optimization component into sufficient conditions, based on linear matrix inequalities (LMIs), for global exponential asymptotic stability of the closed loop system. We illustrate the versatility of our framework using several examples.
Assessing the quality of the fossil record: insights from vertebrates

Abstract Assessing the quality of the fossil record is notoriously hard, and many recent attempts have used sampling proxies that can be questioned. For example, counts of geological formations and estimated outcrop areas might not be defensible as reliable sampling proxies: geological formations are units of enormously variable dimensions that depend on rock heterogeneity and fossil content (and so are not independent of the fossil record), and outcrop areas are not always proportional to rock exposure, probably a closer indicator of rock availability. It is shown that in many cases formation counts will always correlate with fossil counts, whatever the degree of sampling. It is not clear, in any case, that these proxies provide a good estimate of what is missing in the gap between the known fossil record and reality; rather they largely explore the gap between known and potential fossil records. Further, using simple, single numerical metrics to correct global-scale raw data, or to model sampling-driven patterns may be premature. There are perhaps four approaches to exploring the incompleteness of the fossil record, (1) regional-scale studies of geological completeness; (2) regional- or clade-scale studies of sampling completeness using comprehensive measures of sampling, such as numbers of localities or specimens or fossil quality; (3) phylogenetic and gap-counting methods; and (4) model-based approaches that compare sampling as one of several explanatory variables with measures of environmental change, singly and in combination. We suggest that palaeontologists, like other scientists, should accept that their data are patchy and incomplete, and use appropriate methods to deal with this issue in each analysis. All that matters is whether the data are adequate for a designated study or not. A single answer to the question of whether the fossil record is driven by macroevolution or megabias is unlikely ever to emerge because of temporal, geographical, and taxonomic variance in the data.
The Role of Leader-Follower Defensive Pessimism (In)Congruence in Fostering Perceptions of Followers’ Isolation

This field study examines the joint effect of leaders’ and followers’ different cognitive characteristics (i.e., defensive pessimism) on followers’ isolation. We examine the interplay between leaders’ defensive pessimism and followers’ defensive pessimism in fostering perceptions of followers’ isolation. Data from 291 working professionals are analyzed following a series of hierarchical linear modeling and polynomial regression analyses. Polynomial regression analysis indicates that when both leaders and followers are in agreement in their defensive pessimism, the level of followers’ perceived isolation is lower than when leaders’ and followers’ defensive pessimism deviate from each other (i.e., high-low and low-high leader-follower defensive pessimism). However, when followers’ defensive pessimism is higher than leaders’ defensive pessimism, followers’ perceived isolation also is higher. By suggesting that followers’ perception of leaders’ defensive pessimism may be more complex than previously recognized, we conclude that studies of leadership need to develop a much deeper understanding of leader-follower congruence in cognitive styles in order to decrease followers’ isolation in the workplace. DOI: 10.15458/85451.58
Dual-Functional Peroxidase-Copper Phosphate Hybrid Nanoflowers for Sensitive Detection of Biological Thiols

An effective strategy to detect biological thiols (biothiols), including glutathione (GSH), cysteine (Cys), and homocysteine (Hcy), holds significant incentive since they play vital roles in many cellular processes and are closely related to many diseases. Here, we demonstrated that hybrid nanoflowers composed of crystalline copper phosphate and horseradish peroxidase (HRP) served as a functional unit exhibiting dual catalytic activities of biothiol oxidase and HRP, yielding a cascade reaction system for a sensitive one-pot fluorescent detection of biothiols. The nanoflowers were synthesized through the anisotropic growth of copper phosphate petals coordinated with the amine/amide moieties of HRP, by simply incubating HRP and copper(II) sulfate for three days at room temperature. Copper phosphates within the nanoflowers oxidized target biothiols to generate H2O2, which activated the entrapped HRP to oxidize the employed Amplex UltraRed substrate to produce intense fluorescence. Using this strategy, biothiols were selectively and sensitively detected by monitoring the respective fluorescence intensity. This nanoflower-based strategy was also successfully employed for reliable quantification of biothiols present in human serum, demonstrating its great potential for clinical diagnostics.
The Impact of Banking Performance in Banking Sector - Evidence for Kosovo

In this research paper is analyzed the impact of banking performance in banking sector in Kosovo during the period of time 2009 - 2014. The main arguments are focused on challenges and contributions that banking performance has in Kosovo banking sector. Then, is identified the consequences of financial global crisis, the participation of banking performance in Kosovo's GDP and about NPL in Kosovo. The results of analysis have shown that interest rate of loans and capital adequacy ratio (CAR) have positive impact on banking sector, but non performing loans (NPL) has negative impact on the banking sector. In the conclusion, the commercial banks in Kosovo should increase further their profit on the investments in securities, as well as annual growth of assets. DOI: 10.5901/mjss.2016. v7n6p355