Networks in the Implementation of Illegal Gold Mining Countermeasure Policy in Kuantan Singingi Regency

This study aims to determine the implementation of the Illegal Gold Mining (IGM) Countermeasure policy in the Kuantan Singingi Regency. This study uses a multi-sectoral multi-organizational network model consisting of a Contextual Assessment approach to understanding the environmental context and identifying stakeholders in policy implementation and joint visioning. This study critically examines the IGM in the regency in qualitative perspective from various related articles. A systematic literature review was used to analyze national and international journal articles from nine managed databases based on these concerns. From the literature review, 22 relevant research-based articles in the last 15 years from 2007 to 2020 were selected from Google Scholar, Taylor, Francis Outline, Springer Link, Emerald Insight, Science Direct, Sage Journal Online, and Oxford Cambridge. Three stages were carried out: preparation, screening and validation, and content review. The findings include 1) research showing that stakeholders involved from both government and community groups do not play an active and synergistic role in controlling IGM. 2) the implementation model of the IGM countermeasure, the government, the private sector, and the community must support each other in controlling and supervising IGM

Examining intergenerational trauma and mental health supports within the Latinx community: A rapid review

Intergenerational trauma is a phenomenon experienced by many individuals including those within the Latinx community. This literature review examines the mental health challenges faced by Latinx youth, including depression, anxiety, and the impact of bicultural stress and discrimination. Additionally, the literature review discusses existing intergenerational trauma interventions such as the Intergenerational Trauma Treatment Model and a multi-tiered approach in schools. The current rapid review explored what methods have been evaluated to address the mental health needs of Latinx youth. After a search of databases, three studies were selected. The methodology and findings from each publication is briefly summarized, highlighting the influence of a humanistic school-based mental health counseling intervention and the effectiveness of an evidence-based practice called Show me FIRST. Limitations and implications for school psychologists’ practices are discussed

CALPHAD formalism for Portland clinker: thermodynamic models and databases

International audienceThe so-called CALPHAD method is widely used in metallurgy to predict phase diagrams of multi-component systems. The application of the method to oxide systems is much more recent, because of the difficulty of modelling the ionic liquid phase. Since the 1980s, several models have been proposed by various communities. Thermodynamic databases for oxides are available and still under development. The purpose of this article is to discuss the distinct approaches of the method for the calculation of multi-component systems for Portland cement elaboration. The article gives a state of the art of the most recent experimental data and the various calculations for the CaO-Al2O3-SiO2 phase diagram. A literature review of the three binary sub-systems leads to main conclusions: (i) discrepancies are found in the literature for the selected experimental data, (ii) the phase diagram data in the reference books are not complete and up to date and (iii) the two-sublattices model and the modified quasichemical model can be equally used for the modelling of the aluminates liquid. The predictive feature of the CALPHAD method is illustrated using the CaO-Al2O3-SiO2 system with the two-sublattices model: extrapolated (predicted) and fully-assessed phase diagrams are compared in the clinkering zone of interest. The recent application of the predictive method for the calculations of high-order systems (taking into account Fe2O3, SO3, CaF2, P2O5) shows that the databases developed with the two-sublattices model and the modified quasichemical model are no longer equivalent

Keyword Searches and Schema Transformation for Multi-Model Databases

The Variety of data is promoting the evolution and development of databases. One of the influence results is the emergence of multi-model databases. So far, the database community has proposed quite a few multi-model databases to support different data models, but these databases adopt diverse methods to implement their data storage and query, which results in a heavy burden for novices to use multi-model databases. Considering this, we present our first research topic - how to employ the keyword searches method as an alternative way to explore and query multi-model databases. Besides, compared with the mature and robust relational databases dominating the current market, multi-model databases - which can not yet match them in transaction management, query optimization, security, etc. - still need time to perfect their foundations of the mathematic theory and boost performance. Considering this, we present our second research topic - how to use relational databases as an alternative way to store and query well-structured data and NoSQL data uniformly. For the first research problem, we utilize the probabilistic formalism of quantum physics to bring the problem into vector spaces and exploit non-classical probabilities to find top-k the most relevant results. As for the second research topic, it requires designing a good relational schema to store these various data in relational databases. But the challenge is that we need to address the difference of structure between flat relational tables and complex multi-model data. To address this problem, we review all relevant works, analyze existing methods, and give a literature review. As a result, we find these works focusing on handling one single data model by relational databases. There is no relevant research to handle multi-model data. Against this challenge, we prepare to employ the reinforcement learning method. This is because this method could automatically obtain an excellent relational schema from the given multi-model data and queries by interacting with the outer environment. To make this idea work in the field of databases, we define the input, goal, reward, policy, and observation according to our purpose, respectively. Besides, we present a Double Q-tables algorithm to assist in decreasing the complexity of the learning process.Datan monimuotoisuus edistää tietokantojen kehittymistä. Eräs vaikuttavimmista kehityskuluista on monimallisten tietokantojen synty,. Tietokantayhteisö on kehittänyt useita monimallisia tietokantoja tukemaan erilaisia tietomalleja. Näissä monimallisissa tietokannoissa on toteutettu monipuolisesti erilaisia tapoja tallentaa dataa ja suorittaa tietokantakyselyjä, mikä toisaalta aiheuttaa aloittelijoille vaikeuksia monimallisten tietokantojen käyttämisessä. Aloittelijoiden omaksuttava jokaisen monimallisen tietokannan kyselykieli erikseen. Näiden lisäksi käyttäjien täytyy hallita monimutkaisia ja dynaamisesti kehittyviä tietokantakaavioita, jotta he voivat muodostaa kyselyitä monimallisissa tietokannoissa. Ottaen huomioon nämä haasteet esitämme ensimmäisen tutkimuskysymyksen: kuinka käyttää avainsanahakua vaihtoehtoisena tapana suorittaa kyselyitä monimallisissa tietokannoissa? Ensimmäisen tutkimuskysymyksen osalta hyödynnämme kvanttifysiikkaan liittyvää todennäköisyyslaskennan formalismia, joka muotoilee ongelman vektoriavaruuksien avulla ja hyödyntää ei-klassisia todennäköisyyksiä. Tällöin löydetään k olennaisinta tulosta, jotka koostuvat useasta komponentista ja tietomallista. Lähestymme toista tutkimusongelmaa havaitsemalla, että monimallisen tiedon tallentaminen relaatiotietokantaan vaatii hyvän relaatiotietokantakaavion kehittämistä. Meidän täytyy ottaa huomioon yksiulotteisten relaatioiden ja monimallisen tiedon rakenteelliset erot. Aloitamme katsauksella nykyiseen aiheeseen liittyvään tutkimukseen, analysoimme olemassa olevia menetelmiä sekä kokoamme kirjallisuuskatsauksen aiheesta. Selvityksen perusteella voimme havaita, että nämä tutkimukset keskittyvät yhden tietomallin käsittelemiseen relaatiotietokannoissa eikä monimallista tietoa ole toistaiseksi käsitelty tutkimuksissa lainkaan. Vastataksemme tähän haasteeseen kehitämme vahvistusoppimiseen perustuvan menetelmän, jolla pystymme tuottamaan erinomaisen relaatiokaavion monimalliselle tiedolle sekä kyselyille vuorovaikutuksessa ympäristön kanssa. Jotta kykenemme soveltamaan tätä ideaa tietokantatutkimuksessa, määrittelemme tarkoituksiimme sopivan syötteen, tavoitteen, palkkiosysteemin, menettelytavan ja havainnot. Lisäksi esittelemme ns. Double Q-tables -algoritmin, joka auttaa koneoppimisprosessin vaativuuden vähentämisessä

A Survey on Mapping Semi-Structured Data and Graph Data to Relational Data

The data produced by various services should be stored and managed in an appropriate format for gaining valuable knowledge conveniently. This leads to the emergence of various data models, including relational, semi-structured, and graph models, and so on. Considering the fact that the mature relational databases established on relational data models are still predominant in today's market, it has fueled interest in storing and processing semi-structured data and graph data in relational databases so that mature and powerful relational databases' capabilities can all be applied to these various data. In this survey, we review existing methods on mapping semi-structured data and graph data into relational tables, analyze their major features, and give a detailed classification of those methods. We also summarize the merits and demerits of each method, introduce open research challenges, and present future research directions. With this comprehensive investigation of existing methods and open problems, we hope this survey can motivate new mapping approaches through drawing lessons from eachmodel's mapping strategies, aswell as a newresearch topic - mapping multi-model data into relational tables.Peer reviewe

Conceptualising women's perinatal well-being: a systematic review of theoretical discussions

Background: Perinatal well-being has increasingly become the focus of research, clinical practice and policy. However, attention has mostly been on a reductionist understanding of well-being based on a mind-body duality. Conceptual clarity around what constitutes well-being beyond this is lacking. Aim: To systematically review theoretical discussions of perinatal well-being in the academic literature.Design and methods: A search of online databases identified papers which discussed perinatal well-being theoretically, taking a multi-dimensional approach to well-being. Thematic synthesis was used to identify and synthesize relevant elements within the included papers.Findings: Eight papers were identified for inclusion in this review. All contributed a number of elements towards a theoretical discussion of perinatal well-being. Three themes were developed: (1) the importance of a number of general domains of women’s lives and domains specific to the perinatal period, (2) well-being as a subjective and individual experience with physical/embodied, affective, and psychological/cognitive aspects, and (3) the dynamic nature of well-being. Conclusions and implications for practice: Perinatal well-being is a complex, multi-dimensional construct. Current theoretical discussions in the academic literature do not provide a comprehensive model or conceptualisation covering all aspects of well-being during the perinatal period. Further theoretical work is required, particularly with regards to theorising well-being during labour and birth, the perinatal period as a continuum, and the role played by women’s expectations. The themes identified in this review contribute to a tentative model of perinatal well-being, taking note particularly of the dynamic nature of well-being. This model should be refined and validated through empirical work and can then be used to underpin further research and the development of a multi-dimensional measure of perinatal well-being

A review of flipped learning in innovative math education

Many systematic reviews have examined flipped learning models in various fields. However, there is little research on the use of the flipped learning model in mathematics education, which could help researchers and practitioners use and develop a model to implement mathematics learning processes. To fill this gap, this study aimed to analyze and synthesize current knowledge and practices in the implementation of flipped learning in mathematics education. Systematic literature review was adopted as the research method following an article selection and screening process using the preferred reporting items for systematic review and meta-analysis (PRISMA) protocol. Articles published from 2012 to 2021 in some reputable databases (Web of Science, Scopus, and ERIC) were reviewed, and 17 of 137 articles were included for detailed analysis and synthesis. The findings of this study showed that research in the implementation of the flipped learning model in mathematics education focused on documenting the affectivity of the implementation of the flipped learning model, developing learning processes, and sharing preliminary findings and student feedback. Future research is highly recommended to examine different aspects of flipped learning implementation, promote longitudinal data based on multi-year research for implementing flipped learning, and review various learning media to strengthen students’ understanding of mathematics

'Staying safe' – A narrative review of falls prevention in people with Parkinson’s -'PDSAFE'

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this record.Background: Parkinson's disease demonstrates a spectrum of motor and non-motor symptoms. Falling is common and disabling. Current medical management shows minimal impact to reduce falls, or fall related risk factors such as deficits in gait, strength and postural instability. Despite evidence supporting rehabilitation in reducing fall risk factors, the most appropriate intervention to reduce overall fall rate remains inconclusive. This paper aims to 1) synthesise current evidence and conceptual models of falls rehabilitation in Parkinson's in a narrative review; and based on this evidence 2) introduce the treatment protocol used in the falls prevention, multi-centre clinical trial 'PDSAFE'. Method: Search of four bibliographic databases using the terms ‘Parkinson*’ and ‘Fall*’ combined with each of the following; ‘Rehab*, Balanc*, Strength*, Strateg*and Exercis*' and a framework for narrative review was followed. 3557 papers were identified, 416 were selected for review. The majority report the impact of rehabilitation on isolated fall risk factors. Twelve directly measure the impact on overall fall rate. Discussion: Results were used to construct a narrative review with conceptual discussion based on the 'International Classification of Functioning’, leading to presentation of the 'PDSAFE' intervention protocol. Conclusion: Evidence suggests training single, fall risk factors may not affect overall fall rate. Combining with behavioural and strategy training in a functional, personalised multi-dimensional model, addressing all components of the ‘International Classification of Functioning’ is likely to provide a greater influence on falls reduction. 'PDSAFE' is a multi-dimensional, physiotherapist delivered, individually tailored, progressive, home-based programme. It is designed with a strong evidence based approach and illustrates a model for the clinical delivery of the conceptual theory discussed.This project was funded by the National Institute for Health Research Health Technologies Assessment programme (project number 10/57/21). VG is supported by the National Institute of Health Research Collaboration for Applied Health Research and Care South West Peninsula.

Methods and quality of disease models incorporating more than two sexually transmitted infections: a protocol for a systematic review of the evidence

INTRODUCTION: Disease models can be useful tools for policy makers to inform their decisions. They can help to estimate the costs and benefits of interventions without conducting clinical trials and help to extrapolate the findings of clinical trials to a population level.Sexually transmitted infections (STIs) do not operate in isolation. Risk-taking behaviours and biological interactions can increase the likelihood of an individual being coinfected with more than one STI.Currently, few STI models consider coinfection or the interaction between STIs. We aim to identify and summarise STI models for two or more STIs and describe their modelling approaches. METHODS AND ANALYSIS: Six databases (Cochrane, Embase, PLOS, ProQuest, Medline and Web of Science) were searched on 27 November 2018 to identify studies that focus on the reporting of the methodology and quality of models for at least two different STIs. The quality of all eligible studies will be accessed using a percentage scale published by Kopec et al. We will summarise all used approaches to model two or more STIs in one model. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework will be used to report all outcomes. ETHICS AND DISSEMINATION: Ethical approval is not required for this systematic review. The results of this review will be published in a peer-reviewed journal and presented at a suitable conference. The findings from this review will be used to inform the development of a new multi-STI model. PROSPERO REGISTRATION NUMBER: CRD42017076837

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table