scholarly journals Dam Siting: A Review

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2080
Author(s):  
Yang Wang ◽  
Yongzhong Tian ◽  
Yan Cao
Keyword(s):  
Machine Learning ◽  
Power Generation ◽  
Site Selection ◽  
Flood Control ◽  
Research Literature ◽  
Future Research ◽  
Complex Data ◽  
Geological Conditions ◽  
Selection Factors ◽  
The Impact

Dams can effectively regulate the spatial and temporal distribution of water resources, where the rationality of dam siting determines whether the role of dams can be effectively performed. This paper reviews the research literature on dam siting in the past 20 years, discusses the methods used for dam siting, focuses on the factors influencing dam siting, and assesses the impact of different dam functions on siting factors. The results show the following: (1) Existing siting methods can be categorized into three types—namely, GIS/RS-based siting, MCDM- and MCDM-GIS-based siting, and machine learning-based siting. GIS/RS emphasizes the ability to capture and analyze data, MCDM has the advantage of weighing the importance of the relationship between multiple factors, and machine learning methods have a strong ability to learn and process complex data. (2) Site selection factors vary greatly, depending on the function of the dam. For dams with irrigation and water supply as the main purpose, the site selection is more focused on the evaluation of water quality. For dams with power generation as the main purpose, the hydrological factors characterizing the power generation potential are the most important. For dams with flood control as the main purpose, the topography and geological conditions are more important. (3) The integration of different siting methods and the siting of new functional dams in the existing research is not sufficient. Future research should focus on the integration of different methods and disciplines, in order to explore the siting of new types of dams.

scholarly journals Lesbian, Gay, Bisexual, and Trans Aging in a U.K. Context: Critical Observations of Recent Research Literature

2019 ◽  
Vol 89 (1) ◽  
pp. 93-107 ◽  
Author(s):  
Kathryn Almack ◽  
Andrew King
Keyword(s):  
Older Adults ◽  
Empirical Research ◽  
Evidence Base ◽  
Research Literature ◽  
Future Research ◽  
Policy And Practice ◽  
International Evidence ◽  
Care Systems ◽  
The Impact ◽  
People’S Perceptions

In this article, we provide critical observations of empirical research from leading U.K. researchers relating to the lives of lesbian, gay, bisexual, and trans older adults. We suggest learning that may be applied in differing global contexts as well as contributing to the development of an international evidence base. We illustrate the importance of paying attention to distinct health and care systems and legislation, which present global differences as well as similarities in terms of lesbian, gay, bisexual, and trans people’s perceptions and access to resources. With this contextual background, we then discuss the cutting-edge U.K. research in this field from 2010 onward. We identify key strengths including the contribution our evidence has made to policy and practice and the development of theoretical insights such as the impact of intersectionality. The article concludes with a discussion of future research in this field which has relevance at national and international levels.

scholarly journals Identity as Resistance: Identity Formation at the Intersection of Race, Gender Identity, and Sexual Orientation

2019 ◽  
Vol 44 (1) ◽  
pp. 67-83 ◽  
Author(s):  
Alison Cerezo ◽  
Mariah Cummings ◽  
Meredith Holmes ◽  
Chelsey Williams
Keyword(s):  
African American ◽  
Sexual Orientation ◽  
Gender Identity ◽  
Identity Formation ◽  
Psychological Research ◽  
Research Literature ◽  
Constructivist Grounded Theory ◽  
Future Research ◽  
And Gender ◽  
The Impact

Although the concept of intersectionality has gained widespread attention in psychological research, there remains a significant gap related to the impact of intersectionality on identity formation for persons negotiating multiple minority statuses. This gap is especially pronounced among sexual and gender expansive women of Latinx and African American descent—two groups that face disparate personal and public health risks but are largely ignored in the research literature. In response to this gap, we carried out a qualitative study using constructivist grounded theory with 20 Latinx and African American sexual minority, gender expansive women to understand participants’ experiences of forming an intersectional social identity. Following an exploration of identity formation related to the specific domains of race, gender identity, and sexual orientation, we prompted participants to consider how each of the specified identity domains impacted the formation and experience of an overall intersectional identity (e.g., how racial position impacted gender identity and/or sexual identity formation). Findings revealed four major themes that were critical in identity formation: (a) family and cultural expectations, (b) freedom to explore identity, (c) the constant negotiation of insider/outsider status, and (d) identity integration as an act of resistance. Implications for future research and psychological services are discussed.

Dams: Effects of Hydrological Infrastructure on Development

2019 ◽  
Vol 11 (1) ◽  
pp. 125-148
Author(s):  
Andrew Dillon ◽  
Ram Fishman
Keyword(s):  
Economic Development ◽  
Flood Control ◽  
The Body ◽  
Future Research ◽  
Emerging Issues ◽  
Potential Benefits ◽  
The Impact ◽  
Benefits And Costs ◽  
Distributional Impacts

Hydrological investments, particularly irrigation dams, have multiple potential benefits for economic development. Dams also have financial, environmental, and distributional impacts that can affect their benefits and costs. This article reviews the evidence on the impact of dams on economic development, focusing on the levels and variability of agricultural productivity, and its effect on poverty, health, electricity generation, and flood control. We also review the evidence on irrigation efficiency and collective action of dam maintenance. Throughout the discussion, we highlight the empirical challenges that restrict the body of causally interpretable impact estimates and areas in which the evidence is particularly thin. We conclude with a discussion of emerging issues pertaining to the long-term sustainability of dams’ impacts and suggest directions for future research.

Artificial Intelligence, Machine Learning, Automation, Robotics, Future of Work and Future of Humanity

2019 ◽  
Vol 30 (1) ◽  
pp. 61-79 ◽  
Author(s):  
Weiyu Wang ◽  
Keng Siau
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Industrial Revolution ◽  
Future Research ◽  
Way Of Life ◽  
Negative Effects ◽  
Future Research Directions ◽  
Negative Impacts ◽  
The Impact ◽  
The Way

The exponential advancement in artificial intelligence (AI), machine learning, robotics, and automation are rapidly transforming industries and societies across the world. The way we work, the way we live, and the way we interact with others are expected to be transformed at a speed and scale beyond anything we have observed in human history. This new industrial revolution is expected, on one hand, to enhance and improve our lives and societies. On the other hand, it has the potential to cause major upheavals in our way of life and our societal norms. The window of opportunity to understand the impact of these technologies and to preempt their negative effects is closing rapidly. Humanity needs to be proactive, rather than reactive, in managing this new industrial revolution. This article looks at the promises, challenges, and future research directions of these transformative technologies. Not only are the technological aspects investigated, but behavioral, societal, policy, and governance issues are reviewed as well. This research contributes to the ongoing discussions and debates about AI, automation, machine learning, and robotics. It is hoped that this article will heighten awareness of the importance of understanding these disruptive technologies as a basis for formulating policies and regulations that can maximize the benefits of these advancements for humanity and, at the same time, curtail potential dangers and negative impacts.

The Health Effects of Worksite HIV/AIDS Interventions: A Review of the Research Literature

1996 ◽  
Vol 11 (2) ◽  
pp. 150-157 ◽  
Author(s):  
Mark G. Wilson ◽  
Cynthia Jorgensen ◽  
Galen Cole
Keyword(s):  
Health Effects ◽  
Organizational Factors ◽  
Research Literature ◽  
Future Research ◽  
Control Group ◽  
Search Methods ◽  
Positive Effects ◽  
Introductory Article ◽  
The Impact ◽  
Hiv Aids

Purpose. To examine the individual and organizational health effects of HIV/AIDS interventions conducted at the worksite. Search Methods. This review is part of a series of reviews that used search methods described in an introductory article. To supplement these methods, HIV/AIDS-specific periodicals were searched to include journals that might not be incorporated in the computerized databases. Twelve of the 20 articles identified through the Centers for Disease Control and Prevention and our own subsequent searches met the criteria and were included in this review. Findings. Ten of the 12 studies reviewed reported positive effects of employee education programs on knowledge or attitudes. Nine of the studies involved health care workers or employees with potential occupational exposure to HIV, and nine lacked a comparison or control group. None of the studies however, examined the effects of policies, manager training, or family education on the organization or person. Conclusions. Methodologic weaknesses in many of the studies reviewed, coupled with the small number of studies, led us to conclude that the research literature on worksite HIV/AIDS interventions is weak. Impact is, however, plausible. Future research should be directed toward developing valid measures of key variables, controlling for confounding factors, and ultimately examining the impact of organizational factors.

scholarly journals Detection of Leek Rust Disease under Field Conditions Using Hyperspectral Proximal Sensing and Machine Learning

2021 ◽  
Vol 13 (7) ◽  
pp. 1341
Author(s):  
Simon Appeltans ◽  
Jan G. Pieters ◽  
Abdul M. Mouazen
Keyword(s):  
Machine Learning ◽  
Field Conditions ◽  
Environmental Cost ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Complex Data ◽  
Rust Disease ◽  
Proximal Sensing ◽  
Full Field ◽  
Extensive Evaluation

Rust disease is an important problem for leek cultivation worldwide. It reduces market value and in extreme cases destroys the entire harvest. Farmers have to resort to periodical full-field fungicide applications to prevent the spread of disease, once every 1 to 5 weeks, depending on the cultivar and weather conditions. This implies an economic cost for the farmer and an environmental cost for society. Hyperspectral sensors have been extensively used to address this issue in research, but their application in the field has been limited to a relatively low number of crops, excluding leek, due to the high investment costs and complex data gathering and analysis associated with these sensors. To fill this gap, a methodology was developed for detecting leek rust disease using hyperspectral proximal sensing data combined with supervised machine learning. First, a hyperspectral library was constructed containing 43,416 spectra with a waveband range of 400–1000 nm, measured under field conditions. Then, an extensive evaluation of 11 common classifiers was performed using the scikit-learn machine learning library in Python, combined with a variety of wavelength selection techniques and preprocessing strategies. The best performing model was a (linear) logistic regression model that was able to correctly classify rust disease with an accuracy of 98.14 %, using reflectance values at 556 and 661 nm, combined with the value of the first derivative at 511 nm. This model was used to classify unlabelled hyperspectral images, confirming that the model was able to accurately classify leek rust disease symptoms. It can be concluded that the results in this work are an important step towards the mapping of leek rust disease, and that future research is needed to overcome certain challenges before variable rate fungicide applications can be adopted against leek rust disease.

scholarly journals Impact Tech Startups: A Conceptual Framework, Machine-Learning-Based Methodology and Future Research Directions

2021 ◽  
Vol 13 (18) ◽  
pp. 10048
Author(s):  
Benjamin Gidron ◽  
Yael Israel-Cohen ◽  
Kfir Bar ◽  
Dalia Silberstein ◽  
Michael Lustig ◽  
...  
Keyword(s):  
Machine Learning ◽  
Conceptual Framework ◽  
Impact Category ◽  
Private Investment ◽  
Social Enterprises ◽  
Future Research ◽  
Research Directions ◽  
For Profit ◽  
Future Research Directions ◽  
The Impact

The Impact Tech Startup (ITS) is a new, rapidly developing type of organizational category. Based on an entrepreneurial approach and technological foundations, ITSs adopt innovative strategies to tackle a variety of social and environmental challenges within a for-profit framework and are usually backed by private investment. This new organizational category is thus far not discussed in the academic literature. The paper first provides a conceptual framework for studying this organizational category, as a combination of aspects of social enterprises and startup businesses. It then proposes a machine learning (ML)-based algorithm to identify ITSs within startup databases. The UN’s Sustainable Development Goals (SDGs) are used as a referential framework for characterizing ITSs, with indicators relating to those 17 goals that qualify a startup for inclusion in the impact category. The paper concludes by discussing future research directions in studying ITSs as a distinct organizational category through the usage of the ML methodology.

scholarly journals A Meta-Analysis of the Effect of Phonological Awareness and/or Phonics Instruction on Word and Pseudo Word Reading of English as an L2

SAGE Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 215824402110591
Author(s):  
Dennis Murphy Odo
Keyword(s):  
Word Reading ◽  
Meta Analysis ◽  
Research Literature ◽  
Future Research ◽  
Instructional Interventions ◽  
Phonics Instruction ◽  
L2 Reading ◽  
Beginning Learners ◽  
Phonics Intervention ◽  
The Impact

Despite considerable efforts made to understand the impact that instructional interventions have upon L2 reading development, we still lack a clear picture of the influence that PA and phonics instruction has upon reading in English as an L2. A search of the research literature published from 1990 to 2019 yielded 45 articles with 46 studies containing 3,841 participants in total. Effect sizes were recorded for the effect of various PA and/or phonics instructional interventions on word and pseudo word reading. Results demonstrated that L2 PA and phonics instruction has a moderate effect on L2 word reading ( g = 0.53) and a large effect on pseudo word reading ( g = 1.51). Moderator analyses revealed effects of a number of moderators including testing method, type of PA/phonics intervention, and context where the intervention occurred. Based upon these conclusions, policymakers and educators can provide beginning learners of English as an L2 with PA and phonics instruction that will enable them to read, understand and enjoy English better. Future research should also strive to adhere to more stringent standards of excellence in educational research.

scholarly journals Evolution of corporate reputation during an evolving controversy

2019 ◽  
Vol 23 (1) ◽  
pp. 52-71 ◽  
Author(s):  
Siyoung Chung ◽  
Mark Chong ◽  
Jie Sheng Chua ◽  
Jin Cheon Na
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Corporate Reputation ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Content Type ◽  
Twitter Users ◽  
Corporate Crisis ◽  
The Impact

PurposeThe purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those sentiments.Design/methodology/approachUsing a very large data set of tweets (i.e. over 2.6m) about Company A’s food poisoning case (2015–2016). This case was selected because it is widely known, drew attention from various stakeholders and had many dynamics (e.g. multiple outbreaks, and across different locations). This study employed a supervised machine learning approach. Its sentiment polarity classification and relevance classification consisted of five steps: sampling, labeling, tokenization, augmentation of semantic representation, and the training of supervised classifiers for relevance and sentiment prediction.FindingsThe findings show that: the overall sentiment of tweets specific to the crisis was neutral; promotions and marketing communication may not be effective in converting negative sentiments to positive sentiments; a corporate crisis drew public attention and sparked public discussion on social media; while corporate apologies had a positive effect on sentiments, the effect did not last long, as the apologies did not remove public concerns about food safety; and some Twitter users exerted a significant influence on online sentiments through their popular tweets, which were heavily retweeted among Twitter users.Research limitations/implicationsEven with multiple training sessions and the use of a voting procedure (i.e. when there was a discrepancy in the coding of a tweet), there were some tweets that could not be accurately coded for sentiment. Aspect-based sentiment analysis and deep learning algorithms can be used to address this limitation in future research. This analysis of the impact of Chipotle’s apologies on sentiment did not test for a direct relationship. Future research could use manual coding to include only specific responses to the corporate apology. There was a delay between the time social media users received the news and the time they responded to it. Time delay poses a challenge to the sentiment analysis of Twitter data, as it is difficult to interpret which peak corresponds with which incident/s. This study focused solely on Twitter, which is just one of several social media sites that had content about the crisis.Practical implicationsFirst, companies should use social media as official corporate news channels and frequently update them with any developments about the crisis, and use them proactively. Second, companies in crisis should refrain from marketing efforts. Instead, they should focus on resolving the issue at hand and not attempt to regain a favorable relationship with stakeholders right away. Third, companies can leverage video, images and humor, as well as individuals with large online social networks to increase the reach and diffusion of their messages.Originality/valueThis study is among the first to empirically investigate the dynamics of corporate reputation as it evolves during a crisis as well as the effects of corporate apology on online sentiments. It is also one of the few studies that employs sentiment analysis using a supervised machine learning method in the area of corporate reputation and communication management. In addition, it offers valuable insights to both researchers and practitioners who wish to utilize big data to understand the online perceptions and behaviors of stakeholders during a corporate crisis.

Examining the Effects of Controlling for Shared Variance among the Dark Triad Using Meta–Analytic Structural Equation Modelling

2018 ◽  
Vol 32 (1) ◽  
pp. 46-61 ◽  
Author(s):  
Colin E. Vize ◽  
Katherine L. Collison ◽  
Joshua D. Miller ◽  
Donald R. Lynam ◽  
Mitja Back
Keyword(s):  
Structural Equation Modelling ◽  
Structural Equation ◽  
Research Literature ◽  
Dark Triad ◽  
Future Research ◽  
Personality Psychology ◽  
Residual Content ◽  
Equation Modelling ◽  
Recent Developments ◽  
The Impact

Multivariate procedures (e.g. structural equation modelling) are essential to personality psychology, but interpretive difficulties can arise when examining the relations between residualized variables (i.e. the residual content of a variable after its overlap with other variables has been statistically controlled for) and outcomes of interest. These issues have been the focus of recent debate within the research literature on the Dark Triad, which is a collection of interrelated but theoretically distinct personality constructs made up of narcissism, Machiavellianism and psychopathy. The present paper highlights previous work on the issue of partialling and also makes use of recent developments surrounding meta–analytic structural equation modelling to reliably assess the impact of partialling on the empirical profiles of the Dark Triad components. The results show that numerous interpretive difficulties arise after partialling the overlap among the Dark Triad components, most notably for narcissism and Machiavellianism. The results are discussed in the context of contemporary Dark Triad research in addition to discussing the implications for structural equation modelling methods in personality psychology more generally. Recommendations are made for how future research can mitigate the interpretive difficulties that may arise from partialling. Copyright © 2018 European Association of Personality Psychology

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