Miracle-dz/UniFAR-FTUs huggingface dataset:
“We’re in This Together”: Bridging and Bonding Social Capital in Elementary School PTOs

Scholarship on the links between families and schools encompasses contradictory notions about social capital and its relation to inequality. One view holds that schools can narrow inequality by generating dense relationships among families, while others suggest that advantaged parents can use these networks to hoard opportunities. This multiple case study analyzes qualitative data from diverse North Carolina elementary schools to learn how parents build and deploy social capital. We distinguish between bonding social capital, built in dense, homogeneous networks, and bridging social capital, gained through relationships across a social distance. Our analyses suggest that bonding alone is associated with opportunity hoarding; however, when schools are committed to building both bridging and bonding social capital, they can produce more equitable and inclusive schools.
Vehicle Position Correction: A Vehicular Blockchain Networks-Based GPS Error Sharing Framework

The positioning accuracy of the existing vehicular Global Positioning System (GPS) is far from sufficient to support autonomous driving and ITS applications. To remedy that, leading methods such as ranging and cooperation have improved the positioning accuracy to varying degrees, but they are still full of challenges in practical applications. Especially for cooperative positioning, in addition to the performance of methods, cooperators may provide false data due to attacks or selfishness, which can seriously affect the positioning accuracy. By fully exploiting the characteristics of blockchain and edge computing, this paper proposes a vehicular blockchain-based secure and efficient GPS positioning error evolution sharing framework, which improves vehicle positioning accuracy from ensuring security and credibility of cooperators and data. First, by analyzing the GPS error, a bridge can be established between the sensor-rich vehicles and the common vehicles to achieve cooperation by sharing the positioning error evolution at a specific time and location. Particularly, the positioning error evolution is obtained by a deep neural network (DNN)-based prediction algorithm running on the edge server. We further propose to use blockchain technology for storage and sharing the evolution of positioning errors, mainly to guarantee the security of cooperative vehicles and mobile edge computing nodes (MECNs). In addition, the corresponding smart contracts are designed to automate and efficiently perform storage and sharing tasks as well as solve inconsistencies in time scales. Extensive simulations based on actual data indicate the accuracy and security of our proposal in terms of positioning error correction and data sharing.
An Open-Source Web Platform to Share Multisource, Multisensor Geospatial Data and Measurements of Ground Deformation in Mountain Areas

Nowadays, the increasing demand to collect, manage and share archives of data supporting geo-hydrological processes investigations requires the development of spatial data infrastructure able to store geospatial data and ground deformation measurements, also considering multisource and heterogeneous data. We exploited the GeoNetwork open-source software to simultaneously organize in-situ measurements and radar sensor observations, collected in the framework of the HAMMER project study areas, all located in high mountain regions distributed in the Alpines, Apennines, Pyrenees and Andes mountain chains, mainly focusing on active landslides. Taking advantage of this free and internationally recognized platform based on standard protocols, we present a valuable instrument to manage data and metadata, both in-situ surface measurements, typically acquired at local scale for short periods (e.g., during emergency), and satellite observations, usually exploited for regional scale analysis of surface displacement. Using a dedicated web-interface, all the results derived by instrumental acquisitions and by processing of remote sensing images can be queried, analyzed and downloaded from both expert users and stakeholders. This leads to a useful instrument able to share various information within the scientific community, including the opportunity of reprocessing the raw data for other purposes and in other contexts.
Estimation of Value-at-Risk for Conduct Risk Losses Using Pseudo-Marginal Markov Chain Monte Carlo

We propose a model for conduct risk losses, in which conduct risk losses are characterized by having a small number of extremely large losses (perhaps only one) with more numerous smaller losses. It is assumed that the largest loss is actually a provision from which payments to customers are made periodically as required. We use the pseudo-marginal (PM) Markov chain Monte Carlo method to decompose the largest loss into smaller partitions in order to estimate 99.9% value-at-risk. The partitioning is done in a way that makes no assumption about the size of the partitions. The advantages and problems of using this method are discussed. The PM procedures were run on several representative data sets. The results indicate that, in cases where using approaches such as calculating a Monte Carlo-derived loss distribution yields a result that is not consistent with the risk profile expressed by the data, using the PM method yields results that have the required consistency.
Relación cintura/estatura, una herramienta útil para detectar riesgos cardiovascular y metabólico en niños

La Organizacion Mundial de Salud reporta que las enfermedades cardiovasculares son las principales causas de muerte en todo el mundo; cada ano mueren mas personas por ellas que por cualquier otra causa y mas de tres cuartas partes de esas defunciones ocurren en los paises de ingresos bajos y medios; sin embargo, la mayoria de dichas enfermedades puede prevenirse actuando sobre factores de riesgo como: consumo de tabaco, dietas no saludables, obesidad, inactividad fisica o el consumo aumentado de alcohol; esto idealmente con estrategias que abarquen a toda la poblacion. En personas con enfermedades cardiovasculares o con alto riesgo cardiovascular debido a presencia de uno o mas factores como hipertension arterial, diabetes, hiperlipidemia o enfermedad cardiovascular confirmada, son fundamentales la deteccion precoz y el tratamiento temprano, por medio de servicios de orientacion o administracion de farmacos, segun corresponda.
The Time Series Design Application of Clear Lob Basic Skill Mastery: An Analysis of the Functions of the Types of Goal Setting

This research was aimed at examining the affectivity of the types of goal setting as one of psychological skill training (PST) strategies in mastering clear lob basic skill (clear lob-BS) of badminton game. The research was conducted by administering quasi-experimental method and time series as the design of the study. The participants of the study were 60 beginner badminton student-athletes aged 10-12 years. The data were collected through the observation of clear lob-BS mastery instrument. The result of the analysis found that goal setting is effective to be used to accelerate the clear lob-BS mastery.
Exploring gender subversion and recuperation in anal fisting among gay men

Anal fisting among gay men, that is, the sexual(ised) and erotic (single or partnered) practice of inserting the hand(s) and/or forearm(s) into the anus and rectum, has historically been framed in medical and medico-forensic case studies as a violent and dangerous sexual practice associated with the contraction of disease, heightened risk of sexual injury, and the possibility of death. In contrast to this, pro-Queer scholars of gender and sexuality have conceptually reframed the act of anal fisting among gay men as a subversive sexual practice which in its discursive and material performances radically transgresses the heteronormative tropes which typically underwrite human sex/uality, especially in ways that render the preferred representations of (gay) sex as ‘vanilla’. By drawing from unstructured individual interviews with a sample of eight (self-identifying) South African gay men who regularly incorporate anal fisting into their sexual relations, this study explores the gendered contradictions which rhetorically circumscribe how these men discursively construct and experience the corpo-erotic practice of anal fisting. In doing so, the findings highlight that while anal fisting among gay men may in fact engender shades of a sexually subversive practice by the way it radically (re)makes the material and erotic possibilities and connections of gay men’s bodies in exceptionally non-normative and perhaps Queer ways; it is, at the same time, also invested with, and reiterative of, discursive repertoires which actively recuperate heteronormative as well as heteromasculinist tropes and gendered power relations that, in some instances, appear femiphobic.
Role of climate smart agriculture in promoting sustainable agriculture: a systematic literature review

In the face of global environmental change, the notion of climate smart agriculture (CSA) has emerged as an approach which can contribute to increased agricultural production and income of poor households. This paper explores the suitability of adopting CSA practices for promoting sustainable agriculture in order to attain global food and nutritional security. It also explores the links among the components of CSA (productivity, adaptation and mitigation) and their contribution to achieving the goal of sustainable agricultural development. Despite the potential of CSA to attain sustainable agriculture, poor connections exist among the components of CSA at the field level. More importantly, the concept is sometimes poorly understood by various levels of stakeholders. In order to attain a better future, it is crucial to disseminate CSA approaches to field level by including them in academic syllabi, seminars, symposiums, and research.
Characterization and physicochemical properties of chlorophyll extract from Spirulina sp.

Spirulina sp. is a group of microalgae which has a number of pigments with diverse bioactivities. Chlorophyll is one of the dominant pigments in Spirulina sp. that play an important role in capturing light as a crucial element for photosynthesis. This study aims at investigating the characteristic of chlorophyll extract from Spirulina sp. and its physicochemical properties. The effects of light exposure (white, yellow, and UV-C) and metal ions (Sn2+, K+, and Fe3+) on chlorophyll stability were investigated. The green spots with RF 0.45 and 0.37 values were observed in the TLC experiment, indicating the chlorophyll a and b, respectively. The UV-Vis analysis was employed to identify the presence of chlorophyll and FTIR was utilized to detect the typical absorbance of characteristics functional groups for chlorophyll. The results show the chlorophyll was less resistance to all kinds of light (white, yellow, and UV-C). Metal ions of Sn2+, K+, and Fe3+ exhibited no obvious effect on the chlorophyll stability in the dark condition, but some metal ions gave a noteworthy effect in the presence of light.
The relationship of sociodemographic and clinical profiles to the duration of untreated psychosis among adolescents with first-episode psychosis in an Oman tertiary hospital

ABSTRACT Background: The duration of untreated psychosis (DUP) among adolescents with first-episode psychosis has received scant attention in non-western societies. Aim: This study aims to explore both sociodemographic and clinical profiles and examine their relationship to the DUP among adolescents with first-episode psychosis admitted to a tertiary care hospital in Oman Method: This is a retrospective, cross-sectional, analytical study conducted at a tertiary care hospital in Oman from January 2010 to December 2018. The subjects of the study included 121 drug-naive adolescents between the ages of 10 and 19 years old with first-episode psychosis. Sociodemographic data and risk factors were also solicited. Result: A total of 121 adolescents fulfilled the study criteria. The mean DUP was 4 months (SD 4.5). Place of residence, family history of psychosis, presence of both positive and negative symptoms, use of traditional healing systems, length of stay, and leaving against medical advice were the significant factors associated with the duration of untreated psychosis. Discussion: Some of the factors presently found to contribute to the DUP could lay the groundwork for the reduction in the duration of untreated psychosis and redesigning services for such population.
Geometric Analysis of Nonconvex Optimization Landscapes for Overcomplete Learning

Learning overcomplete representations finds many applications in machine learning and data analytics. In the past decade, despite the empirical success of heuristic methods, theoretical understandings and explanations of these algorithms are still far from satisfactory. In this work, we provide new theoretical insights for several important representation learning problems: learning \emph{(i)} sparsely used overcomplete dictionaries and \emph{(ii)} convolutional dictionaries. We formulate these problems as $\ell^4$-norm optimization problems over the sphere, and study the geometric properties of their nonconvex optimization landscapes. For both problems, we show the nonconvex objective has benign (global) geometric structures, which enable development of efficient optimization methods finding the target solutions. Finally, our theoretical results are justified by numerical simulations.
Surfing Multiple Conformation-Property Landscapes via Machine Learning: Designing Single-Ion Magnetic Anisotropy

Computational statistical disciplines, such as machine learning, are leading to a paradigm shift in the way we conceive the design of new compounds, offering a way to directly design the best compound for specific applications. This approach, known as reverse engineering, requires the construction of models able to efficiently predict continuous structure-property maps. Here we show that machine-learning offers such a possibility by designing a model that predicts both the energy and magnetic properties as function of the molecular structure of single-ion magnet. This model is then used to explore the molecular conformational landscapes in search of structures that maximise magnetic anisotropy. We find that a 5% change in one of the coordination angles leads to a ∼50% increase in the anisotropy. This approach can be applied to any structure-property relation and paves the way for a machine-learning-driven optimization of chemical compounds.
Descriptive Exploratory Study of Individuals' Use of Pulsed Electromagnetic Fields, the Micro-Pulse, for Pain Relief

Pain has caused innumerable suffering to countless individuals and has impacted their lives in profound ways. There are many detrimental effects of pain including decreased ability to work, depression, isolation, increased pharmaceutical use, and addiction. Pain, on a worldwide scale, remains ineffectively treated and alternative solutions for managing pain are needed. Pain is conventionally treated with pharmaceuticals, primarily narcotics. Continuation of medications for these painful conditions often causes dependence and addiction. The pain and narcotics cycle contributes to the opioid epidemic. The cost in human lives is immense. Pulsed Electromagnetic Field (PEMF) is a holistic modality used for various ailments. This exploratory descriptive research study focused on the experience of individuals using the Micro-Pulse, PEMF, for pain relief. The mind/body connection was a foundation of the holistic theoretical framework for this study. The theoretical grounding for this study was Watson’s (2018) theory of human caring, which is based on a foundation of holistic healing incorporating mind, emotional body, physical body, and spirit for treatment of pain. Understanding an individual’s experience of pain relief will potentially raise awareness and promote the exploration of holistic therapeutic approaches for patients in pain.
When Do Plans Matter?

Abstract Problem, research strategy, and findings: Plans often sit on the shelf and fail to promote action. This raises the question of how and when plans matter. This is especially important in sustainability planning where local governments are more likely to take actions with short-term benefits that are easy to adopt. How can planners promote more sustainability actions, especially those with longer term benefits? In this study we examine factors influencing local sustainability actions by tracking 651 U.S. local governments’ adoption of 34 sustainability actions from 2010 to 2015. We differentiate places that recently adopted a sustainability plan from those that have had a plan for a longer period and those that have never adopted a sustainability plan. We use difference-in-difference (DID) modeling to assess what effect plans have on the level of sustainability actions. We find a plan may have its greatest effect in motivating actions when it is newly created. Places that adopted a plan between 2010 and 2015 exhibited a significant increase in sustainability actions during those same years. By contrast, places that already had a plan in 2010 showed higher initial levels of sustainability actions but did not show a higher level of growth in the number of actions adopted compared with places without plans. In general, we find local governments with higher levels of sustainability actions articulate social equity goals, devote staff and budget resources to the effort, engage the public, and promote interdepartmental coordination. Local governments under Republican control enact fewer sustainability actions. Takeaway for practice: Sustainability plans are most effective in spurring local sustainability actions when they are newly created. However, this initiation impact in motivating actions wanes over time. Promoting continued adoption of sustainability actions requires a comprehensive approach with attention to political support, public participation, social equity, interdepartmental coordination, and local capacity.
An evaluation of a short course in mindfulness for Health Visitor Practice Teachers

Practice Teachers (PTs) participated in an 8 week course, Mindfulness Based Cognitive for well-being and resilience. The evaluation was undertaken using three (3) evidenced based questionnaires: Perceived stress(PSS) (Cohen et al 1983), Self-Compassion Score SCS (Neff 2003) and Five Facet Mindfulness Questionnaire (FFMQ) (Baer et al 2006) to identify outcomes for the mindfulness training. Findings indicate the impact on personal wellbeing and professional practice with recommendations for retention of students & Health Visitor (HV) workforce. Summary Mindfulness skills to support top performance and good mental health were offered in eight two-hour sessions to 18 Practice Teachers from September- December 2014 and was evaluated. The course drew on the approach of Mindfulness Based Cognitive Therapy, where participants learn to focus their attention and develop the possibility of creating the space for reflection and response rather than reacting to life’s difficulties. Participants had the opportunity to develop self–care and self-compassion in the sessions as well as in between sessions and which research has shown can have a positive effect on others. Compassion is a contemporary theme in the CNO’s strategy to return to the fundamentals of nursing; mindfulness enables the foundation of practising compassion on self and ultimately to practice compassion with others we work and care for. The attendees were encouraged to review and refresh their practice through with the ultimate planned outcome, to reduce student attrition and prevent breakdown of the practice placement. However, this is a the complex area of adult learning and the nature of student attrition from courses as well as from the workforce cannot necessarily be reduced to one factor. The practice of mindfulness can have an influential effect on health, well-being and resilience, as shown by scientific and medical evidence. This experiential course was neither therapy nor religious instruction but an effective means of helping to alleviate stress and promoting well-being, resilience and flourishing. The course was delivered at a time when practitioners, including PTs were experiencing an increase of student numbers and service delivery changes with associated imperative data collection. The perception of practitioners about the declared increase in the HV workforce (DH 2011) to support and implement these changes was not evident and the evaluation indicates the need for self-care and resilience in the workforce which includes students and practitioners. The project outcomes make recommendations based on the impact of this mindfulness training with PTs to inform future developments within the programme and practice education as well as the HV workforce where a number of interventions call for 'mindfulness' with infants, children and families.
Matrix factorization with stochastic gradient descent for recommender systems

MATRIX FACTORIZATION WITH STOCHASTIC GRADIENT DESCENT FOR RECOMMENDER SYSTEMS Ömer Faruk Aktulum M.S. in Computer Engineering Advisor: Cevdet Aykanat February 2019 Matrix factorization is an efficient technique used for disclosing latent features of real-world data. It finds its application in areas such as text mining, image analysis, social network and more recently and popularly in recommendation systems. Alternating Least Squares (ALS), Stochastic Gradient Descent (SGD) and Coordinate Descent (CD) are among the methods used commonly while factorizing large matrices. SGD-based factorization has proven to be the most successful among these methods after Netflix and KDDCup competitions where the winners’ algorithms relied on methods based on SGD. Parallelization of SGD then became a hot topic and studied extensively in the literature in recent years. We focus on parallel SGD algorithms developed for shared memory and distributed memory systems. Shared memory parallelizations include works such as HogWild, FPSGD and MLGF-MF, and distributed memory parallelizations include works such as DSGD, GASGD and NOMAD. We design a survey that contains exhaustive analysis of these studies, and then particularly focus on DSGD by implementing it through message-passing paradigm and testing its performance in terms of convergence and speedup. In contrast to the existing works, many real-wold datasets are used in the experiments that we produce using published raw data. We show that DSGD is a robust algorithm for large-scale datasets and achieves near-linear speedup with fast convergence rates.
A Hybrid Controller for DOS-Resilient String-Stable Vehicle Platoons

This paper deals with the design of resilient Cooperative Adaptive Cruise Control (CACC) for homogeneous vehicle platoons in which communication is vulnerable to Denial-of-Service (DOS) attacks. We consider DOS attacks as consecutive packet dropouts. We present a controller tuning procedure based on linear matrix inequalities (LMI) that maximizes the resiliency to DOS attacks, while guaranteeing performance and string stability. The design procedure returns controller gains and gives a lower bound on the maximum allowable number of successive packet dropouts. A numerical example is employed to illustrate the effectiveness of the proposed approach.