Miracle-dz/UniFAR-FTUs huggingface dataset:
TRANSFORMATIONS OF AN URBAN SOIL FROM THE IMPLANTATION OF AN AGROFOREST

This study aimed to identify and understand the possible transformations occurred in an urban soil due to the implementation of an agroforestry in the Butantã campus of the University of São Paulo. To accomplish this objective, field surveys were carried out to study the soil of a grassy area and an agroforestry zone. Both areas share similar geomorphological and pedological conditions. The soil profiles were described morphologically, and samples were collected to perform physical and chemical analyses in the laboratory. The morphological attributes described in the field are: (i) color, (ii) texture, (iii) structure, (iv) wet consistency, (v) transition between horizons, (vi) depth, (vii) roots, and (viii) soil fauna. The following physical analyses were carried out in the laboratory: (i) grain-size, (ii) density, and (iii) aggregate stability. Finally, the main chemical parameters related to soil fertility were surveyed: pH H 2 O, pH CaCl 2 0.01 mol L -1 , O.M. , P, K + , Ca 2+ , Mg 2+ , Al 3+ , H+Al, SB, CTC, V%, and m%. The results showed that the color of the surface horizon of the agroforestry area is browner than the analogous horizon of the grass area. In addition, an increase in the grade of the structure was identified in the soil of the agroforestry. The soil of this area also presents higher density of roots, which reach deeper horizons with respect to the grass area; ants and termites are absent (in contrast, they are very common in the grass area), but the presence of earthworms and beetles is common. The levels of soil density and acidity are smaller in the soil of the agroforestry. On the other hand, the O.M. , P, K + , Ca 2+ , Mg 2+ content and the SB, CTC, V% levels increased; only Al 3+ , H+Al, and m% decreased. Thus, we conclude that the identified soil changes in the agroforestry area suggest a recovery of soil and its functions faster than expected (1 year and 4 months), even in an urban soil whose natural conditions were deeply modified by anthropic actions
Machine Learning-based Lake Ice Monitoring Using UAVs

The duration and extent of lake ice are vital indicators of climate change. It has been recognized as part of the Essential Climate Variables (ECV) by the Global Climate Observing System (GCOS). One way to monitor the dynamics of lake ice is through semantic segmentation of lake images. In this project, we aim to detect the frozen parts of lake Sils (Graubuenden, Switzerland) using RGB images captured with UAVs. By adopting DeepLabV3+, we achieve an Intersection-over-Union (IoU) value of 88% for the segmentation task, proven to be accurate enough to monitor lake ice changing for three days. Besides, we attempt to adopt up-to-date domain adaptation approaches to help transfer learning between diﬀerent lake ice datasets (Photi-Lakeice webcam dataset and the UAV dataset). Our experiments show the potential application of domain adaptation methods to reduce required manual annotation work and improve the target dataset’s prediction results. Based on the experiment results of various approaches, possible limitations of current unsupervised domain adaptation methods are explored and discussed.
INFLUENCE OF THE PERCEPTION OF SERVICE QUALITY ON CUSTOMER LOYALTY IN TACNA'S BANKING AGENCIES

The general objective of the research is to determine the influence of the perception of service quality on customer loyalty in Tacna's banking agencies. The type of research was explanatory level with a non-experimental design, the data were collected by the cross-section; the sample was composed of 170 customers, the technique used was the survey and the instrument was the questionnaire, and the approach was quantitative. It was found that 54.7% of the clients perceive that the quality of service in Tacna's bank branches is adequate, where the most important dimension was the "Physical aspect"; 49.4% considered their loyalty to their preferred entity to be fair, where the most important dimension was "Relative attitude". It is concluded that there is an influence of perceived service quality on customer loyalty (Nagelkerke coefficient = 75.7%); similarly, physical appearance (Nagelkerke coefficient = 31.7%), service delivery (Nagelkerke coefficient = 57.9%) and new technologies (Nagelkerke coefficient = 79.5%) have a significant influence on customer loyalty (Nagelkerke coefficient = 79.5%).
Realization, maintenance and dissemination of the kilogram in the revised SI

A substantial change to the International System of Units (SI) is currently under discussion and might become effective in 2019. The General Conference on Weights and Measures (CGPM) is scheduled to have its 26 th meeting in 2018 and will likely vote on a proposal put forward by the International Committee for Weights and Measures (CIPM) to revise the SI. According to this proposal, the structure of the SI will change fundamentally. The present SI is built upon seven base units: the metre, the second, the kilogram, the ampere, the kelvin, the mole, and the candela. Each of the seven base units has its own definition, which sometimes draws on other base units. The unit of mass, the kilogram, is defined via an artefact, the International Prototype of the Kilogram (IPK) that is kept at the International Bureau of Weights and Measures (BIPM). The foundation of revised SI speed light vacuum c, luminous efficacy K cd , additional defining constants the Planck constant h, the elementary charge e, Boltzmann k, the A , the the unit the
Study of strange matter in STAR with express analysis

The STAR experiment provides a perfect machinery for studying strange matter for more than two decades. Recently, we developed the express procedure, which allows online monitoring of the collected physics data. The high quality of express calibration and reconstruction provides a unique pos-sibility to run the express production and observe almost in real time strange particles including mesons, hyperons, resonances and even hypernuclei. The STAR Beam Energy Scan II program, including ﬁxed target Au + Au collisions taken in 2018–2021, is particularly suited to study hypernuclei. Light hypernuclei are expected to be abundantly produced in low energy heavy-ion collisions. Measurements of hypernuclei production and their properties will provide information on the hyperon-nucleon interactions, which are essential in-gredients for understanding nuclear matter equation of state at high net-baryon densities, such as inside neutron stars. With the heavy fragment trigger introduced for the 2021 data taking, we were able to run the express production at the STAR High Level Trigger farm. The collected data were su ﬃ cient to observe the decay process of 5 Λ He → 4 Hep π − with more than 11 σ signiﬁcance, measure binding energy as a function of hypernuclei mass, and study hypernuclei decay properties with the Dalitz plot technique.
$\nu$-flows: Conditional neutrino regression

We present \nuν-Flows, a novel method for restricting the likelihood space of neutrino kinematics in high-energy collider experiments using conditional normalising flows and deep invertible neural networks. This method allows the recovery of the full neutrino momentum which is usually left as a free parameter and permits one to sample neutrino values under a learned conditional likelihood given event observations. We demonstrate the success of \nuν-Flows in a case study by applying it to simulated semileptonic t\bar{t}tt‾ events and show that it can lead to more accurate momentum reconstruction, particularly of the longitudinal coordinate. We also show that this has direct benefits in a downstream task of jet association, leading to an improvement of up to a factor of 1.41 compared to conventional methods.
Time series prediction of personalized insulin dosage for type 2 diabetics

Careful blood glucose monitoring and consistent insulin administration are necessary for managing diabetes. People with demanding schedules or little access to medical personnel may find this difficult. Fortunately, without having to visit a doctor every day, daily insulin dosage may now be customized to a person’s unique needs using technology and customised algorithms based on their food intake, exercise routines, and blood glucose levels. This information can be entered into a diabetes management app or device, where an algorithm will determine the proper insulin dosage and offer real-time feedback to assist maintain ideal blood glucose levels. A patient's dietary preferences, degree of physical activity, and blood sugar are taken into account for determining the proper bolus and basal insulin dosages in this study. According to the tracked body data, a patient’s appropriate insulin dosage is predicted using artificial neural network (ANN)-based models. Based on patient activity, food intake, exercise, and past insulin administration, insulin projections are created. To forecast an individual’s basal and bolus insulin requirements, long short-term memory (LSTM) and random forest regression models are employed. Accuracy of both models are tested and random forest regression shows better accuracy which is used in the prediction system.
Analyzing selected letters by Antonio Gramsci in terms of appraisal theory: A review study

This paper uses the appraisal theory to analyze selected letters by Antonio Gramsci letters because no studies have used the appraisal theory to examine Antonio Gramsci. Thus, the qualitative method has been considered to analyze and compare these categories. Such comparison and analysis benefit from previous research works conducted in this field. The paper identifies the three interconnected levels of analysis to provide a complete picture of the text's interpretation. These levels are: description, explanation and interpretation. A good interpretation cannot be achieved by dividing these levels of analysis. Interpretation and explanation are part of description, which is concerned with the formal aspects of the text. Because power, beliefs, and identities are intertwined with the text's linguistic elements, the researchers is able to decipher ideologically shaped and produced texts to show relations and patterns of power and dominance that are created and shaped by discursive and societal structure. In texts, grammatical emphasis is used to convey authority and persuade the reader, the degree of power and ideology in text production may be seen in the usage of strong words and clauses. The study findings showed that many discursive strategies, including reported verbs, complete and connected concepts, and unambiguous intentions, in text articulation, creation, and understanding were used. Gramsci was able to effectively prepare the route to his argument by connecting the concepts and comparing the present to the past. To make his language strong and compelling, he used more model verbs, transitive verbs, passivisation, repetitious structure, and nominalization”.
Distributed Predictive Policies for Local Residential Energy Communities ⋆

The main goal of this work is to design Battery Storage Management solutions for Smart Grids with multiple interconnected microgrids, or local power generation units, using a distributed model predictive control approach. In order to know how much energy should be stored, sold or bought, a model predictive control policy computes the minimum of a cost function that minimizes the net revenue. The solution also encourages some level of storage. The alternating direction method of multipliers computes the proposed distributed model predictive control solution, considering that each system is directly interconnected with its neighbors. The distributed predictive control algorithm is described and tested in simulations.
Self-Compassion Among University Students

Self-compassion among university students, and the current research community was identified by Wasit University students for morning studies for the academic year (2022-2023), and their number is (14394), with (8748) students from scientific disciplines and (5646) students from humanities disciplines. The research sample included (400) male and female students from the University of Wasit, and the study tool included the selfcompassion scale prepared by Neff (2003) and translated by the researcher, consisting of (26) paragraphs, and the psychometric characteristics of honesty and stability were extracted, and the research sample (400) was relied upon and analyzed The data were statistically analyzed using the statistical bag for social sciences (spss). The results showed the following: Wasit University students enjoy a high level of self-compassion, and in the light of the results, the researcher developed a set of conclusions, recommendations and suggestions.
Higher Fashion Education in Perspective: The Effects of Work- Based Learning On Industry Requirements

Change occurs rapidly in the world of work these days. Keeping pace with this rapid change is a continuing challenge for Ghana’s Higher Education (HE) institutions. The study, thus, explored the effects of work-based learning (WBL) on industry requirements through higher fashion education in Ghana. The research adopted the quantitative data collection and analysis method. The population for the study comprised postgraduate and undergraduate students of the HE institution in Ghana, offering the fashion design and textiles option as well as fashion houses (industry). The data was analysed using the hierarchical multiple regression statistical technique. Regarding policy, the findings may be used to inform decisions related to the curriculum of fashion design and textile programmes in Ghana. In terms of research, it provides evidence of the positive impact of WBL experiences on students’ preparedness for the fashion industry.
Accelerated Deep-Learning inference on FPGAs in the Space Domain

Artificial intelligence has found its way into space, and similar to the situation on ground demands powerful hardware to unfold its full potential. With the heterogeneous compute platform that is offered by the space-grade variant of the Versal, AMD Xilinx presents a system that is particularly targeted at accelerating AI inference in space. This paper investigates the design flow and the achievable performance of this novel device. We present benchmark results in terms of concrete figures and measurements, i.e., throughput, latency, and power consumption, achieved by a predesigned hardware accelerator realized on the system, and compare them to a previous generation platform.
Random countable alphabet conformal iterated function systems satisfying the transversality condition

Dealing with with countable (finite and infinite alike) alphabet random conformal iterated function systems with overlaps, we formulate appropriate transversality conditions and then prove the relevant, in such a context, the Moran–Bowen formula which determines the Hausdorff dimension of random limit sets in dynamical terms. We also provide large classes of examples of such random systems satisfying the transversality condition.
Building blocks of microphysiological system to model physiology and pathophysiology of human heart

Microphysiological systems (MPS) are drawing increasing interest from academia and from biomedical industry due to their improved capability to capture human physiology. MPS offer an advanced in vitro platform that can be used to study human organ and tissue level functions in health and in diseased states more accurately than traditional single cell cultures or even animal models. Key features in MPS include microenvironmental control and monitoring as well as high biological complexity of the target tissue. To reach these qualities, cross-disciplinary collaboration from multiple fields of science is required to build MPS. Here, we review different areas of expertise and describe essential building blocks of heart MPS including relevant cardiac cell types, supporting matrix, mechanical stimulation, functional measurements, and computational modelling. The review presents current methods in cardiac MPS and provides insights for future MPS development with improved recapitulation of human physiology.
Independent Curriculum Learning Management to Improve Students’ Literacy and Numerical Competence in Schools

This study aims to describe: 1) students' perceptions of the efforts that must be made to improve literacy and numeracy competencies and 2) significant differences regarding the efforts that must be made to improve literacy and numeracy competencies when viewed from gender and numeracy differences-school status. The descriptive approach was followed in this study. The research sample consisted of 160 students from public and private schools in North Sumatra, which implemented an independent curriculum—a questionnaire of 67 valid and reliable items. The study results prove that students' literacy and numeracy competencies are in good categories, especially in literacy, literacy and numeracy, scientific, digital, financial, cultural, and citizenship. If viewed from the status of the school, there is no difference in the perception of the efforts that must be made to improve literacy and numeracy competencies. However, there are differences in terms of gender differences. The analysis of the research results recommends that educator training is needed to improve student's literacy and numeracy through activities: 1) doing simple research with friends in the numeration group, 2) reasoning systematically in solving problems, 3) drawing conclusions based on facts, 4) getting used to using ICT properly and correctly to improve knowledge, attitudes, and skills, 5) distinguish between needs and desires and 6) can take attitudes in a social environment as part of a culture.
Single atom visibility in STEM optical depth sectioning

The continuing development of aberration correctors for the scanning transmission electron microscope (STEM) offers the possibility of locating single atoms in crystals in 3D via optical depth sectioning. The main factors that determine the feasibility of such an approach are visibility and dose requirements. Here, we show how Poisson's statistics can be quantitatively incorporated into STEM image simulations and demonstrate that the 3D location of single cerium atoms in wurtzite-type aluminum nitride is indeed feasible under large-angle illumination conditions with a relatively low dose. We also show that chromatic aberration does not presently represent a limitation provided a cold field emission source is used. These results suggest efforts into improved aberration corrector designs for larger illumination angles that offer significant potential for 3D structure determination of materials.
Algebraic Systems for Normal and Hierarchical Sociograms

Sociometric techniques have proved very fruitful for questions directed within the parts of a social structure, but the potential of sociometry for studying total social structures has been neglected. Since this neglect seems in large part due to the conceptual difficulties involved in describing total social structures, it is proposed that relational algebras can facilitate progress in this direction. The first two sections develop an algebra, appropriate for the familiar mutual-choice sociogram, which allows both identification of cliques and preservation of the essential relationships between cliques. Then, since most sociological ideas about group structure imply hierarchy, a procedure for inferring hierarchy in sociograms is presented. Finally, an algebra appropriate for studying hierarchical sociograms is considered, and some of the parallels between this algebra and Friedell's semilattice algebra for organizations are developed.
Parameter estimation for time-varying system based on improved genetic algorithm

In this paper, the authors first analyze the drawback of genetic algorithm with traditional mutation operator in preventing GA from converging prematurely, then study its influence on the diversity of genes in the same locus, and point out that the traditional mutation, to some extent, can result in premature convergence of genes in the same locus. This will cause the loss of critical alleles. Inspired by digital technique, we import two kinds of Boolean operation into GA to develop a novel mutation operator and discuss its contribution to prevent the loss of critical alleles. The experimental results of function optimization show that the improved mutation operator can prevent premature convergence effectively, and can provide a wide range of selecting control parameters for GA. Then we present a new parameter estimation algorithm based on the improved GA. Simulation experiments have been carried out and their results show that this estimation algorithm has good tracking ability and is not sensitive to the noise.
Aliphatic hydrocarbon content of interstellar dust

Abstract The mid-IR spectrum of the interstellar medium contains both aromatic and aliphatic hydrocarbon features. These are generally attributed to carbonaceous dust. The aliphatic component is of particular interest because it produces a significant 3.4 μm absorption feature. The optical depth of this feature is related to the number and type of aliphatic carbon C–H bonds in the line of sight. It is possible to estimate the column density of aliphatic carbon from quantitative analysis of the 3.4 μm interstellar feature, providing that the absorption coefficient of interstellar aliphatic hydrocarbon is known. We produced interstellar dust analogues with spectra closely matching astronomical observations. Using a combination of FTIR and 13C NMR spectroscopy, we determined an integrated absorption coefficient of the aliphatic component. The results thus obtained permit direct calibration of astronomical observations, providing rigorous estimates of the amount of aliphatic carbon in the ISM.
Systolic blood pressure estimation using PPG during physical exercise

A model to estimate systolic blood pressure (SBP) using finger-derived photoplethysmography (PPG) signals is proposed for 19 healthy subjects during physical exercise. The estimation was done for every 30 s during the period of exercise, before which an initialization process for each subject at rest was performed. Eighteen features were extracted from PPG signals and a multiple linear regression (MLR) method was employed to combine all derived features to estimate SBP. Our results showed that the proposed model had a performance in estimating SBP with a mean difference of -0.40 mmHg, a standard deviation of difference of 14.07 mmHg, and a median intra-subject correlation coefficients of 0.85. The present work demonstrated promising results of estimating SBP using PPG during physical exercise.
Spectral Estimation of the Multivariate Impulse Response

One of the applications of cross-spectral estimation of stationary time series, developed some five decades ago, is the estimation of the lagged response of an output series causally dependent on an input series, in the absence of feedback from the output to the input. The direct application of cross-spectral analysis for this purpose is no longer appropriate in the presence of feedback or more general inter-dependence of the series. In that case, vector autoregressive modeling has been used, particularly in the econometric context, to estimate the response of one series to a shock or impulse in the innovations of another series. To achieve the same end, cross-spectral analysis requires the application of spectral factorization, and in this article, we demonstrate this methodology, explaining how it may be used to construct impulse response function estimates and their statistical properties. Our presentation includes an information criterion for choosing the smoothing bandwidth to be used for cross-spectral estimation.