A surrogate model for interference prevention in the limaçon-to-limaçon machines
- Authors: Sultan, Ibrahim
- Date: 2007
- Type: Text , Journal article
- Relation: Engineering Computations (Swansea, Wales) Vol. 24, no. 5 (2007), p. 437-449
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- Description: Purpose - This paper aims to replace the complicated iterative procedure used to prevent interference in limacon-to-limacon machines by a simplified mathematical equation which can be solved by a straightforward substitution of the required clearance value. Design/methodology/approach - The input data to the iterative procedure and the obtained results have been employed in regression models to construct the sought after equation. Searching for a proper form of this equation involved numerical experiments to study the effects of the various model parameters on the system response. Findings - The numerical experiments conducted proved to be an effective model construction technique, and the regression model proposed has been found extremely accurate in the specified parameter space. Research limitations/implications - The proposed equation is applicable within the parameter range chosen for the study. This range is the one often used for industrial applications. Should the parameters selected for a specific design fall outside the specified range, the proposed model structure may have to be varied to maintain a desirable level of accuracy. Practical implications - The interference study is a part of the iterative procedure employed to design the dimensions of the limaçon-to-limaçon machine. This iterative procedure searches for the proper design amongst hundreds of various possible solutions. The results of this paper will ensure a much faster convergence for the design procedure, since the interference study will be eliminated from the iterative section of the analysis. Originality/value - The paper offers a valid and accurate model that can be efficiently used for the intended purpose. © Emerald Group Publishing Limited.
- Description: C1
- Description: 2003004799
Estimation of a regression function by maxima of minima of linear functions
- Authors: Bagirov, Adil , Clausen, Conny , Kohler, Michael
- Date: 2009
- Type: Text , Journal article
- Relation: IEEE Transactions on Information Theory Vol. 55, no. 2 (2009), p. 833-845
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- Description: In this paper, estimation of a regression function from independent and identically distributed random variables is considered. Estimates are defined by minimization of the empirical L2 risk over a class of functions, which are defined as maxima of minima of linear functions. Results concerning the rate of convergence of the estimates are derived. In particular, it is shown that for smooth regression functions satisfying the assumption of single index models, the estimate is able to achieve (up to some logarithmic factor) the corresponding optimal one-dimensional rate of convergence. Hence, under these assumptions, the estimate is able to circumvent the so-called curse of dimensionality. The small sample behavior of the estimates is illustrated by applying them to simulated data. © 2009 IEEE.
Incremental DC optimization algorithm for large-scale clusterwise linear regression
- Authors: Bagirov, Adil , Taheri, Sona , Cimen, Emre
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Computational and Applied Mathematics Vol. 389, no. (2021), p. 1-17
- Relation: https://purl.org/au-research/grants/arc/DP190100580
- Full Text: false
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- Description: The objective function in the nonsmooth optimization model of the clusterwise linear regression (CLR) problem with the squared regression error is represented as a difference of two convex functions. Then using the difference of convex algorithm (DCA) approach the CLR problem is replaced by the sequence of smooth unconstrained optimization subproblems. A new algorithm based on the DCA and the incremental approach is designed to solve the CLR problem. We apply the Quasi-Newton method to solve the subproblems. The proposed algorithm is evaluated using several synthetic and real-world data sets for regression and compared with other algorithms for CLR. Results demonstrate that the DCA based algorithm is efficient for solving CLR problems with the large number of data points and in particular, outperforms other algorithms when the number of input variables is small. © 2020 Elsevier B.V.
Nonsmooth optimization algorithms for clusterwise linear regression
- Authors: Mirzayeva, Hijran
- Date: 2013
- Type: Text , Thesis , PhD
- Full Text: false
- Description: Data mining is about solving problems by analyzing data that present in databases. Supervised and unsupervised data classification (clustering) are among the most important techniques in data mining. Regression analysis is the process of fitting a function (often linear) to the data to discover how one or more variables vary as a function of another. The aim of clusterwise regression is to combine both of these techniques, to discover trends within data, when more than one trend is likely to exist. Clusterwise regression has applications for instance in market segmentation, where it allows one to gather information on customer behaviors for several unknown groups of customers. There exist different methods for solving clusterwise linear regression problems. In spite of that, the development of efficient algorithms for solving clusterwise linear regression problems is still an important research topic. In this thesis our aim is to develop new algorithms for solving clusterwise linear regression problems in large data sets based on incremental and nonsmooth optimization approaches. Three new methods for solving clusterwise linear regression problems are developed and numerically tested on publicly available data sets for regression analysis. The first method is a new algorithm for solving the clusterwise linear regression problems based on their nonsmooth nonconvex formulation. This is an incremental algorithm. The second method is a nonsmooth optimization algorithm for solving clusterwise linear regression problems. Nonsmooth optimization techniques are proposed to use instead of the Sp¨ath algorithm to solve optimization problems at each iteration of the incremental algorithm. The discrete gradient method is used to solve nonsmooth optimization problems at each iteration of the incremental algorithm. This approach allows one to reduce the CPU time and the number of regression problems solved in comparison with the first incremental algorithm. The third algorithm is an algorithm based on an incremental approach and on the smoothing techniques for solving clusterwise linear regression problems. The use of smoothing techniques allows one to apply powerful methods of smooth nonlinear programming to solve clusterwise linear regression problems. Numerical results are presented for all three algorithms using small to large data sets. The new algorithms are also compared with multi-start Sp¨ath algorithm for clusterwise linear regression.
- Description: Doctor of Philosophy
Modeling seasonal tropical cyclone activity in the Fiji region as a binary classification problem
- Authors: Chand, Savin , Walsh, Kevin
- Date: 2012
- Type: Text , Journal article
- Relation: Journal of Climate Vol. 25, no. 14 (2012), p. 5057-5071
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- Description: This study presents a binary classification model for the prediction of tropical cyclone (TC) activity in the Fiji, Samoa, and Tonga regions (the FST region) using the accumulated cyclone energy (ACE) as a proxy of TC activity. A probit regression model, which is a suitable probabilitymodel for describing binary response data, is developed to determine at least a fewmonths in advance (by July in this case) the probability that an upcoming TC season may have for high or low TC activity. Years of "high TC activity" are defined as those years when ACE values exceeded the sample climatology (i.e., the 1985-2008 mean value). Model parameters are determined using the Bayesian method. Various combinations of the El Nin{ogonek} o-Southern Oscillation (ENSO) indices and large-scale environmental conditions that are known to affect TCs in the FST region are examined as potential predictors. It was found that a set of predictors comprising low-level relative vorticity, upper-level divergence, and midtropspheric relative humidity provided the best skill in terms of minimum hindcast error. Results based on hindcast verification clearly suggest that the model predicts TC activity in the FST region with substantial skill up to the May-July preseason for all years considered in the analysis, in particular for ENSO-neutral years when TC activity is known to show large variations. © 2012 American Meteorological Society.
Prediction of monthly rainfall in Victoria, Australia : Clusterwise linear regression approach
- Authors: Bagirov, Adil , Mahmood, Arshad , Barton, Andrew
- Date: 2017
- Type: Text , Journal article
- Relation: Atmospheric Research Vol. 188, no. (2017), p. 20-29
- Relation: http://purl.org/au-research/grants/arc/DP140103213
- Full Text: false
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- Description: This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889–2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations. © 2017 Elsevier B.V.
Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique
- Authors: Khandelwal, Manoj , Armaghani, Danial
- Date: 2016
- Type: Text , Journal article
- Relation: Geotechnical and Geological Engineering Vol. 34, no. 2 (2016), p. 605-620
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- Description: The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN.
- Description: The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN. © 2015 Springer International Publishing Switzerland
A comparative assessment of models to predict monthly rainfall in Australia
- Authors: Bagirov, Adil , Mahmood, Arshad
- Date: 2018
- Type: Text , Journal article
- Relation: Water Resources Management Vol. 32, no. 5 (2018), p. 1777-1794
- Relation: http://purl.org/au-research/grants/arc/DP140103213
- Full Text: false
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- Description: Accurate rainfall prediction is a challenging task. It is especially challenging in Australia where the climate is highly variable. Australia’s climatic zones range from high rainfall tropical regions in the north to the driest desert region in the interior. The performance of prediction models may vary depending on climatic conditions. It is, therefore, important to assess and compare the performance of these models in different climatic zones. This paper examines the performance of data driven models such as the support vector machines for regression, the multiple linear regression, the k-nearest neighbors and the artificial neural networks for monthly rainfall prediction in Australia depending on climatic conditions. Rainfall data with five meteorological variables over the period of 1970–2014 from 24 geographically diverse weather stations are used for this purpose. The prediction performance of each model was evaluated by comparing observed and predicted rainfall using various measures for prediction accuracy. © 2018, Springer Science+Business Media B.V., part of Springer Nature.
A difference of convex optimization algorithm for piecewise linear regression
- Authors: Bagirov, Adil , Taheri, Sona , Asadi, Soodabeh
- Date: 2019
- Type: Text , Journal article
- Relation: Journal of Industrial and Management Optimization Vol. 15, no. 2 (2019), p. 909-932
- Relation: http://purl.org/au-research/grants/arc/DP140103213
- Full Text: false
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- Description: The problem of finding a continuous piecewise linear function approximating a regression function is considered. This problem is formulated as a nonconvex nonsmooth optimization problem where the objective function is represented as a difference of convex (DC) functions. Subdifferentials of DC components are computed and an algorithm is designed based on these subdifferentials to find piecewise linear functions. The algorithm is tested using some synthetic and real world data sets and compared with other regression algorithms.
Forecasting tropical cyclone formation in the Fiji region: A probit regression approach using bayesian fitting
- Authors: Chand, Savin , Walsh, Kevin
- Date: 2011
- Type: Text , Journal article
- Relation: Weather and Forecasting Vol. 26, no. 2 (2011), p. 150-165
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- Description: An objective methodology for forecasting the probability of tropical cyclone (TC) formation in the Fiji, Samoa, and Tonga regions (collectively the FST region) using antecedent large-scale environmental conditions is investigated. Three separate probabilistic forecast schemes are developed using a probit regression approach where model parameters are determined via Bayesian fitting. These schemes provide forecasts of TC formation from an existing system (i) within the next 24 h (W24h), (ii) within the next 48 h (W48h), and (iii) within the next 72 h (W72h). To assess the performance of the three forecast schemes in practice, verification methods such as the posterior expected error, Brier skill scores, and relative operating characteristic skill scores are applied. Results suggest that the W24h scheme, which is formulated using large-scale environmental parameters, on average, performs better than that formulated using climatology and persistence (CLIPER) variables. In contrast, the W48h (W72h) scheme formulated using large-scale environmental parameters performs similar to (poorer than) that formulated using CLIPER variables. Therefore, large-scale environmental parameters (CLIPER variables) are preferred as predictors when forecasting TC formation in the FST region within 24 h (at least 48 h) using models formulated in the present investigation. © 2011 American Meteorological Society.
Prediction of index properties of different rocks using non-destructive testing
- Authors: Khandelwal, Manoj
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 52nd U.S. Rock Mechanics/Geomechanics Symposium; Seattle, Washington; 17th-20th June 2018 p. 1-6
- Full Text: false
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- Description: Index properties of rocks are vital in the planning and design of geo-mining structures. It is a time consuming and laborious job to determine index properties in laboratory and also involves expensive equipment and proficiency, but the determination of P-wave velocity in laboratory is an easy, dependable, and less difficult task. So, in this paper, an attempt has been made to correlate Impact strength index, Schmidt hammer rebound number, Slake durability index and Protodyakonov strength index of different rocks with the P-wave velocity. A simple linear regression analysis was performed and a strong correlation was established between the P-wave velocity and different index properties of various rock types with very higher coefficient of determination. Student’s t-test were performed to confirm the validity of the proposed linear relations.
- Description: 52nd U.S. Rock Mechanics/Geomechanics Symposium
Nonsmooth DC programming approach to clusterwise linear regression : Optimality conditions and algorithms
- Authors: Bagirov, Adil , Ugon, Julien
- Date: 2018
- Type: Text , Journal article
- Relation: Optimization Methods and Software Vol. 33, no. 1 (2018), p. 194-219
- Relation: http://purl.org/au-research/grants/arc/DP140103213
- Full Text: false
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- Description: The clusterwise linear regression problem is formulated as a nonsmooth nonconvex optimization problem using the squared regression error function. The objective function in this problem is represented as a difference of convex functions. Optimality conditions are derived, and an algorithm is designed based on such a representation. An incremental approach is proposed to generate starting solutions. The algorithm is tested on small to large data sets. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
Development of a precise model for prediction of blast-induced flyrock using regression tree technique
- Authors: Hasanipanah, Mahdi , Faradonbeh, Roohollah , Armaghani, Danial , Amnieh, Hassan , Khandelwal, Manoj
- Date: 2017
- Type: Text , Journal article
- Relation: Environmental Earth Sciences Vol. 76, no. 1 (2017), p. 1-10
- Full Text: false
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- Description: Drilling and blasting is the predominant rock fragmentation method in open-cast mines and civil construction works. Flyrock is one of the most hazardous effects caused by blasting operation. Therefore, the ability to make accurate predictions of the blast-induced flyrock is essential to reduce the environmental problems. This paper aimed to develop a precise and applicable model based on regression tree (RT) to predict blast-produced flyrock distance in Ulu Tiram quarry, Malaysia. In this regard, 65 blasting operations were investigated and the most influential factors on the flyrock, i.e. blast-hole length, spacing, burden, stemming, maximum charge used per delay and powder factor, were measured. Also, the flyrock distance values for the considered blasting events were carefully measured. In order to check the precision of the proposed RT model, multiple linear regression (MLR) model was also developed and both of the predictive models were compared. For this work, some statistical functions, i.e. median absolute error, coefficient of determination (R2) and root mean squared error, were used and computed. The results revealed that the RT can be introduced as a powerful technique to predict flyrock distance and the proposed RT model can estimate flyrock distance better than MLR model. Also, sensitivity analysis was performed and it was found that the powder factor is the most influential parameter on the flyrock in the studied case. © 2016, Springer-Verlag Berlin Heidelberg.
Effects of grazing exclusion on plant species richness and phytomass accumulation vary across a regional productivity gradient
- Authors: Schultz, Nick , Morgan, John , Lunt, Ian
- Date: 2011
- Type: Text , Journal article
- Relation: Journal of Vegetation Science Vol. 22, no. 1 (2011), p. 130-142
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- Description: Question: Does long-term grazing exclusion affect plant species diversity? And does this effect vary with long-term phytomass accumulation across a regional productivity gradient? Location: Lowland grassy ecosystems across the state of Victoria, southeast Australia. Methods: Floristic surveys and phytomass sampling were conducted across a broad-scale productivity gradient in grazing exclusion plots and adjacent grazed areas. Differences in species richness, evenness and life-form evenness between grazed and ungrazed areas were analysed. The environmental drivers of long-term phytomass accumulation were assessed using multiple linear regression analysis. Results: Species richness declined in the absence of grazing only at the high productivity sites (i.e. when phytomass accumulation was >500 gm-2). Species evenness and life-form evenness also showed a negative relationship with increasing phytomass accumulation. Phytomass accumulation was positively associated with both soil nitrogen and rainfall, and negatively associated with tree cover. Conclusions: Competitive dominance is a key factor regulating plant diversity in productive grassy ecosystems, but canopy disturbance is not likely to be necessary to maintain diversity in less productive systems. The results support the predictions of models of the effects of grazing on plant diversity, such as the dynamic equilibrium model, whereby the effects of herbivory are context-dependent and vary according to gradients of rainfall, soil fertility and tree cover. © 2010 International Association for Vegetation Science.
A bayesian regression approach to seasonal prediction of tropical cyclones affecting the Fiji region
- Authors: Chand, Savin , Walsh, Kevin , Chan, Johnny
- Date: 2010
- Type: Text , Journal article
- Relation: Journal of Climate Vol. 23, no. 13 (2010), p. 3425-3445
- Full Text: false
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- Description: This study presents seasonal prediction schemes for tropical cyclones (TCs) affecting the Fiji, Samoa, and Tonga (FST) region. Two separate Bayesian regression models are developed: (i) for cyclones forming within the FST region (FORM) and (ii) for cyclones entering the FST region (ENT). Predictors examined include various El Niño-Southern Oscillation (ENSO) indices and large-scale environmental parameters. Only those predictors that showed significant correlations with FORM and ENT are retained. Significant preseason correlations are found as early as May-July (approximately three months in advance). Therefore, May-July predictors are used to make initial predictions, and updated predictions are issued later using October-December early-cyclone-season predictors. A number of predictor combinations are evaluated through a cross-validation technique. Results suggest that a model based on relative vorticity and the Niño-4 index is optimal to predict the annual number of TCs associated with FORM, as it has the smallest RMSE associated with its hindcasts (RMSE = 1.63). Similarly, the all-parameter-combined model, which includes the Niño-4 index and some large-scale environmental fields over the East China Sea, appears appropriate to predict the annual number of TCs associated with ENT (RMSE = 0.98). While the all-parameter-combined ENT model appears to have good skill over all years, the May-July prediction of the annual number of TCs associated with FORM has two limitations. First, it underestimates (overestimates) the formation for years where the onset of El Niño (La Niña) events is after the May-July preseason or where a previous La Niña (El Niño) event continued through May-July during its decay phase. Second, its performance in neutral conditions is quite variable. Overall, no significant skill can be achieved for neutral conditions even after an October-December update. This is contrary to the performance during El Niño or La Niña events, where model performance is improved substantially after an October-December early-cyclone-season update. © 2010 American Meteorological Society.
Identifying tobacco retailers in the absence of a licensing system : lessons from Australia
- Authors: Baker, John , Masood, Mohd , Rahman, Muhammad Aziz , Thornton, Lukar , Begg, Stephen
- Date: 2021
- Type: Text , Journal article
- Relation: Tobacco Control Vol. 31, no. 4 (2021), p. 543-548
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- Description: ObjectivesTo estimate the proportion of retailers that sell tobacco in the absence of appropriate local government oversight, and to describe the characteristics by which they differ from those that can expect to receive such oversight.MethodsA database of listed tobacco retailers was obtained from a regional Victorian local government. Potential unlisted tobacco retailers were added using online searches, and attempts to visit all retailers were undertaken. GPS coordinates and sales type information of retailers that sold tobacco were recorded and attached to neighbourhood-level data on socioeconomic disadvantage and smoking prevalence using ArcMap. Logistic regression analyses, χ2 tests and t-tests were undertaken to explore differences in numbers of listed and unlisted retailers by business and neighbourhood-level characteristics.ResultsOf 125 confirmed tobacco retailers, 43.2% were trading potentially without government oversight. Significant differences were found between listed and unlisted retailers by primary business type (p<0.001), and sales type (p<0.001) but not by the other characteristics.ConclusionsThe database of tobacco retailers was inaccurate in two ways: (1) a number of listed retailers no longer operated or sold tobacco, and (2) 43.2% of businesses confirmed as selling tobacco were missing. As no form of licensing system exists in Victoria, it is difficult to identify the number of retailers operating, or to determine how many receive formal regulatory oversight. A positive licensing system is recommended to regulate the sale of tobacco and to generate a comprehensive database of retailers, similar to that which exists for food registration, gaming and liquor-licensed premises.
The role of internet gaming in the association between anxiety and depression : a preliminary cross-sectional study
- Authors: Stavropoulos, Vasileios , Vassallo, Jeremy , Burleigh, Tyrone , Gomez, Rapson , Colder Carras, Michelle
- Date: 2022
- Type: Text , Journal article
- Relation: Asia-Pacific Psychiatry Vol. 14, no. 2 (2022), p.
- Full Text: false
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- Description: Background: Disordered Internet gaming is thought to be perpetuated by one's need to escape their real-life distress or mental health symptoms, which may in turn generate depressive feelings. Nevertheless, moderate engagement with Internet games has also been suggested to provide relief, thus improving one's mood. This study aspires to clarify the contribution of Internet gaming and gender in the association between anxiety and depression. Methods: A large sample of Internet gamers (N = 964) were recruited online. Disordered Internet gaming was assessed with the Internet Gaming Disorder Scale, 9 Items Short Form (IGD9S-SF). Anxiety and depression symptoms were assessed using the Depression, Anxiety and Stress Scale, 21 items (DASS-21). Results: Regression, moderation and moderated moderation analyses accounting for the effects of gender on the relationship between disordered gaming, anxiety, and depression found a significant effect for anxiety symptoms on depression symptoms and a significant interaction between anxiety and Internet gaming disorder on depression symptoms. Findings support the theory that although anxious gamers bear a higher depression risk, this is buffered with lower and exacerbated with higher disordered gaming symptoms. Conclusion: Findings suggest a dual role of Internet gaming in the association between anxiety and depression, depending on the intensity of one's disordered gaming symptoms. Depression prevention and intervention protocols should be optimized by considering the effects of Internet gaming among anxious gamers by focusing on the intensity of a gamer's involvement and any gaming disorder symptoms. Further research should include clinical samples to better understand this interaction. © 2021 John Wiley & Sons Australia, Ltd.
Informal healthcare sector and marginalized groups: Repeat visits in rural North India
- Authors: Iles, Richard
- Date: 2018
- Type: Text , Journal article
- Relation: PLoS One Vol. 13, no. 7 (2018), p. e0199380-e0199380
- Full Text: false
- Reviewed:
- Description: The interrelationship between the public and private sectors, and formal and informal healthcare sectors effects market-level service quality, pricing behaviour and referral networks. However, health utilisation analysis of national survey data from many low and middle income countries is constrained by the lack of disaggregated health provider data. This study is concerned with the pattern of repeat outpatient consultations for a single episode of fever from public and private qualified providers and private unqualified providers. Cross-sectional survey data from 1173 adult respondents sampled from three districts within India's most populous state-Uttar Pradesh is analysed. Data was collected during the monsoon season-September to October-in 2012. Regression analysis focuses on the pattern of repeats visits for a single episode of mild-sever fever as the dependent variable. Results show that Women and Muslims in rural north India are more likely to not access healthcare, and if they do, consult with low quality unqualified outpatient healthcare providers. For fever durations of four or more days, men are more likely to access unqualified providers compared to women. Results of the current study supports the literature that women's utilisation of outpatient healthcare for communicable illnesses in LMICs is often less than men. A relative lack of access to household resources explains why fever duration parameter estimates for women and men differ.
A risky investment? Examining the outcomes of emotional investment in Instagram
- Authors: Lowe-Calverley, Emily , Grieve, Rachel , Padgett, Christine
- Date: 2019
- Type: Text , Journal article
- Relation: Telematics and informatics Vol. 45, no. (2019), p. 101299
- Full Text: false
- Reviewed:
- Description: •First to operationalise Instagram Investment.•Instagram Investment significantly predicts depression and stress.•Instagram Investment does not significantly predict anxiety.•Number of followers indirectly reduces self-esteem via Instagram Investment. Recent research demonstrates links between aspects of Instagram use and negative psychological outcomes. It is therefore important to be able to predict the users who may be at a greater risk of experiencing negative consequences as a result of their use. Instagram is an immersive platform and peoples’ behaviour on Instagram can be important to their self-concept and self-esteem users are potentially deeply emotionally invested in their Instagram use. This paper presents three studies investigating an Instagram-specific form of emotional investment – Instagram Investment. Study 1 (N = 167) examined Instagram Investment as a predictor of depression, anxiety, and stress, within a series of hierarchical multiple regression models, and demonstrated the potential utility of Instagram Investment for the prediction of depression and stress. In Study 2 (N = 120) we expanded our understanding of Instagram Investment within the context of self-esteem. A mediation model revealed an indirect effect of number of followers on self-esteem via Instagram Investment. Finally, in Study 3 (N = 259) we examined the structural properties of the 6 items used to measure Instagram Investment using a confirmatory factor analysis. Together, these studies demonstrate that Instagram Investment is a new and valuable construct for explaining the way that individuals are impacted by their use of Instagram.
A new solar power prediction method based on feature clustering and hybrid-classification-regression forecasting
- Authors: Nejati, Maryam , Amjady, Nima
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE transactions on sustainable energy Vol. 13, no. 2 (2022), p. 1188-1198
- Full Text: false
- Reviewed:
- Description: Solar generation systems are globally extending in terms of scale and number, which highlights the increasing importance of solar power forecast. In this paper, a day-ahead solar power prediction method is proposed including 1) a novel feature selecting/clustering approach based on relevancy and redundancy criteria and 2) an innovative hybrid-classification-regression forecasting engine. The proposed feature selecting/clustering approach filters out irrelevant features and partitions relevant features to two separate subsets to decrease the redundancy of features. Each of these two subsets is separately trained by one forecasting engine and the final solar power prediction of the proposed method is obtained by a relevancy-based combination of these two forecasts. The proposed forecasting engine classifies the historical data based on the learnability of its constituent regression models and assigns each class of training samples to one regression model. Each regression model predicts the outputs of the test samples that belong to its class. The effectiveness of the proposed solar power prediction method is illustrated by testing on two real-world solar farms.