Search results for: predictive policing

Authors: Li Zhang, Yuehong Su

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Lightpipe as a mature solar light tube technique has been employed worldwide. Accurately assessing the performance of lightpipe and evaluate daylighting available has been a challenging topic. Previous research had used regression model and computational simulation methods to estimate the performance of lightpipe. However, due to the nonlinear nature of solar light transferring in lightpipe, the methods mentioned above express inaccurate and time-costing issues. In the present study, a neural network model as an alternative method is investigated to predict the transmittance of lightpipe. Four types of commercial lightpipe with bended angle 0°, 30°, 45° and 60° are discussed under clear, intermediate and overcast sky conditions respectively. The neural network is generated in MATLAB by using the outcomes of an optical software Photopia simulations as targets for networks training and testing. The coefficient of determination (R²) for each model is higher than 0.98, and the mean square error (MSE) is less than 0.0019, which indicate the neural network strong predictive ability and the use of the neural network method could be an efficient technique for determining the performance of lightpipe.

Keywords: neural network, bended lightpipe, transmittance, Photopia

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Authors: Benjamin Leiby, Darryl Ahner

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This study demonstrates an alternative stochastic imputation approach for large datasets when preferred commercial packages struggle to iterate due to numerical problems. A large country conflict dataset motivates the search to impute missing values well over a common threshold of 20% missingness. The methodology capitalizes on correlation while using model residuals to provide the uncertainty in estimating unknown values. Examination of the methodology provides insight toward choosing linear or nonlinear modeling terms. Static tolerances common in most packages are replaced with tailorable tolerances that exploit residuals to fit each data element. The methodology evaluation includes observing computation time, model fit, and the comparison of known values to replaced values created through imputation. Overall, the country conflict dataset illustrates promise with modeling first-order interactions while presenting a need for further refinement that mimics predictive mean matching.

Keywords: correlation, country conflict, imputation, stochastic regression

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Authors: Hyun-Woo Cho

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Detection of incipient abnormal events in production processes is important to improve safety and reliability of manufacturing operations and reduce losses caused by failures. The construction of calibration models for predicting faulty conditions is quite essential in making decisions on when to perform preventive maintenance. This paper presents a multivariate calibration monitoring approach based on the statistical analysis of process measurement data. The calibration model is used to predict faulty conditions from historical reference data. This approach utilizes variable selection techniques, and the predictive performance of several prediction methods are evaluated using real data. The results shows that the calibration model based on supervised probabilistic model yielded best performance in this work. By adopting a proper variable selection scheme in calibration models, the prediction performance can be improved by excluding non-informative variables from their model building steps.

Keywords: calibration model, monitoring, quality improvement, feature selection

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Authors: Surapol Raadnui

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This particular work describes an experimental investigation on gear wear in which wear and pitting were intentionally allowed to occur, namely, moisture corrosion pitting, acid-induced corrosion pitting, hard contaminant-related pitting and mechanical induced wear. A back to back spur gear test rig and a grease lubricated worm gear rig were used. The tests samples of wear debris were collected and assessed through the utilization of an optical microscope in order to correlate and compare the debris morphology to pitting and wear degradation of the worn gears. In addition, weight loss from all test gear pairs were assessed with utilization of statistical design of experiment. It can be deduced that wear debris characteristics from both cases exhibited a direct relationship with different pitting and wear modes. Thus, it should be possible to detect and diagnose gear pitting and wear utilization of worn surfaces, generated wear debris and quantitative measurement such as weight loss.

Keywords: predictive maintenance, worm gear, spur gear, wear debris analysis, problem diagnostic

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Authors: R. Mohan, V. Jadhav, A. Ahmed, J. Rivas, A. Kelkar

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Cementitious materials are an excellent example of a composite material with complex hierarchical features and random features that range from nanometer (nm) to millimeter (mm) scale. Multi-scale modeling of complex material systems requires starting from fundamental building blocks to capture the scale relevant features through associated computational models. In this paper, molecular dynamics (MD) modeling is employed to predict the effect of plasticizer additive on the mechanical properties of key hydrated cement constituent calcium-silicate-hydrate (CSH) at the molecular, nanometer scale level. Due to complexity, still unknown molecular configuration of CSH, a representative configuration widely accepted in the field of mineral Jennite is employed. The effectiveness of the Molecular Dynamics modeling to understand the predictive influence of material chemistry changes based on molecular/nanoscale models is demonstrated.

Keywords: cement composite, mechanical properties, molecular dynamics, plasticizer additives

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Authors: Elizaveta S. Prokofyeva, Svetlana V. Maltseva, Roman D. Zaitsev

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Rapidly evolving modern data analysis technologies in healthcare play a large role in understanding the operation of the system and its characteristics. Nowadays, one of the key tasks in urban healthcare is to optimize the resource allocation. Thus, the application of data analysis in medical institutions to solve optimization problems determines the significance of this study. The purpose of this research was to establish the dependence between the indicators of the effectiveness of the medical institution and its resources. Hospital discharges by diagnosis; hospital days of in-patients and in-patient average length of stay were selected as the performance indicators and the demand of the medical facility. The hospital beds by type of care, medical technology (magnetic resonance tomography, gamma cameras, angiographic complexes and lithotripters) and physicians characterized the resource provision of medical institutions for the developed models. The data source for the research was an open database of the statistical service Eurostat. The choice of the source is due to the fact that the databases contain complete and open information necessary for research tasks in the field of public health. In addition, the statistical database has a user-friendly interface that allows you to quickly build analytical reports. The study provides information on 28 European for the period from 2007 to 2016. For all countries included in the study, with the most accurate and complete data for the period under review, predictive models were developed based on historical panel data. An attempt to improve the quality and the interpretation of the models was made by cluster analysis of the investigated set of countries. The main idea was to assess the similarity of the joint behavior of the variables throughout the time period under consideration to identify groups of similar countries and to construct the separate regression models for them. Therefore, the original time series were used as the objects of clustering. The hierarchical agglomerate algorithm k-medoids was used. The sampled objects were used as the centers of the clusters obtained, since determining the centroid when working with time series involves additional difficulties. The number of clusters used the silhouette coefficient. After the cluster analysis it was possible to significantly improve the predictive power of the models: for example, in the one of the clusters, MAPE error was only 0,82%, which makes it possible to conclude that this forecast is highly reliable in the short term. The obtained predicted values of the developed models have a relatively low level of error and can be used to make decisions on the resource provision of the hospital by medical personnel. The research displays the strong dependencies between the demand for the medical services and the modern medical equipment variable, which highlights the importance of the technological component for the successful development of the medical facility. Currently, data analysis has a huge potential, which allows to significantly improving health services. Medical institutions that are the first to introduce these technologies will certainly have a competitive advantage.

Keywords: data analysis, demand modeling, healthcare, medical facilities

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Authors: Katleho Daniel Makatjane, Diteboho Lawrence Xaba, Ntebogang Dinah Moroke

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The current study investigates the behaviour of time-varying parameters that are based on the score function of the predictive model density at time t. The mechanism to update the parameters over time is the scaled score of the likelihood function. The results revealed that there is high persistence of time-varying, as the location parameter is higher and the skewness parameter implied the departure of scale parameter from the normality with the unconditional parameter as 1.5. The results also revealed that there is a perseverance of the leptokurtic behaviour in stock returns which implies the returns are heavily tailed. Prior to model estimation, the White Neural Network test exposed that the stock price can be modelled by a GAS model. Finally, we proposed further researches specifically to model the existence of time-varying parameters with a more detailed model that encounters the heavy tail distribution of the series and computes the risk measure associated with the returns.

Keywords: generalized autoregressive score model, South Africa, stock returns, time-varying

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Authors: Estela Díaz

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This article aims to make three contributions. First, build on the literature on ethical decision-making literature by exploring factors that influence the intention of adopting veganism. Second, study the superiority of extended models of the Theory of Planned Behavior (TPB) for understanding the process involved in forming the intention of adopting veganism. Third, analyze the moderating effect of gender on TPB given that attitudes and behavior towards animals are gender-sensitive. No study, to our knowledge, has examined these questions. Veganism is not a diet but a political and moral stand that exclude, for moral reasons, the use of animals. Although there is a growing interest in studying veganism, it continues being overlooked in empirical research, especially within the domain of social psychology. TPB has been widely used to study a broad range of human behaviors, including moral issues. Nonetheless, TPB has rarely been applied to examine ethical decisions about animals and, even less, to veganism. Hence, the validity of TPB in predicting the intention of adopting veganism remains unanswered. A total of 476 non-vegan Spanish university students (55.6% female; the mean age was 23.26 years, SD= 6.1) responded to online and pencil-and-paper self-reported questionnaire based on previous studies. TPB extended models incorporated two background factors: ‘general attitudes towards humanlike-attributes ascribed to animals’ (AHA) (capacity for reason/emotions/suffer, moral consideration, and affect-towards-animals); and ‘general attitudes towards 11 uses of animals’ (AUA). SPSS 22 and SmartPLS 3.0 were used for statistical analyses. This study constructed a second-order reflective-formative model and took the multi-group analysis (MGA) approach to study gender effects. Six models of TPB (the standard and five competing) were tested. No a priori hypotheses were formulated. The results gave partial support to TPB. Attitudes (ATTV) (β = .207, p < .001), subjective norms (SNV) (β = .323, p < .001), and perceived control behavior (PCB) (β = .149, p < .001) had a significant direct effect on intentions (INTV). This model accounted for 27,9% of the variance in intention (R2Adj = .275) and had a small predictive relevance (Q2 = .261). However, findings from this study reveal that contrary to what TPB generally proposes, the effect of the background factors on intentions was not fully mediated by the proximal constructs of intentions. For instance, in the final model (Model#6), both factors had significant multiple indirect effect on INTV (β = .074, 95% C = .030, .126 [AHA:INTV]; β = .101, 95% C = .055, .155 [AUA:INTV]) and significant direct effect on INTV (β = .175, p < .001 [AHA:INTV]; β = .100, p = .003 [AUA:INTV]). Furthermore, the addition of direct paths from background factors to intentions improved the explained variance in intention (R2 = .324; R2Adj = .317) and the predictive relevance (Q2 = .300) over the base-model. This supports existing literature on the superiority of enhanced TPB models to predict ethical issues; which suggests that moral behavior may add additional complexity to decision-making. Regarding gender effect, MGA showed that gender only moderated the influence of AHA on ATTV (e.g., βWomen−βMen = .296, p < .001 [Model #6]). However, other observed gender differences (e.g. the explained variance of the model for intentions were always higher for men that for women, for instance, R2Women = .298; R2Men = .394 [Model #6]) deserve further considerations, especially for developing more effective communication strategies.

Keywords: veganism, Theory of Planned Behavior, background factors, gender moderation

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Authors: Bronwen Wade

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Over the last half-century, child welfare systems have increasingly relied on quantitative risk assessment tools, such as actuarial or predictive risk tools. These tools are developed by performing statistical analysis of how attributes captured in administrative data are related to future child maltreatment. Some scholars argue that attributes in administrative data can serve as proxies for race and that quantitative risk assessment tools reify racial bias in decision-making. Others argue that these tools provide more “objective” and “scientific” guides for decision-making instead of subjective social worker judgment. This study performs a systematic review of the literature on the impact of quantitative risk assessment tools on racial disproportionality; it examines methodological biases in work on this topic, summarizes key findings, and provides suggestions for further work. A search of CINAHL, PsychInfo, Proquest Social Science Premium Collection, and the ProQuest Dissertations and Theses Collection was performed. Academic and grey literature were included. The review includes studies that use quasi-experimental methods and development, validation, or re-validation studies of quantitative risk assessment tools. PROBAST (Prediction model Risk of Bias Assessment Tool) and CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) were used to assess the risk of bias and guide data extraction for risk development, validation, or re-validation studies. ROBINS-I (Risk of Bias in Non-Randomized Studies of Interventions) was used to assess for bias and guide data extraction for the quasi-experimental studies identified. Due to heterogeneity among papers, a meta-analysis was not feasible, and a narrative synthesis was conducted. 11 papers met the eligibility criteria, and each has an overall high risk of bias based on the PROBAST and ROBINS-I assessments. This is deeply concerning, as major policy decisions have been made based on a limited number of studies with a high risk of bias. The findings on racial disproportionality have been mixed and depend on the tool and approach used. Authors use various definitions for racial equity, fairness, or disproportionality. These concepts of statistical fairness are connected to theories about the reason for racial disproportionality in child welfare or social definitions of fairness that are usually not stated explicitly. Most findings from these studies are unreliable, given the high degree of bias. However, some of the less biased measures within studies suggest that quantitative risk assessment tools may worsen racial disproportionality, depending on how disproportionality is mathematically defined. Authors vary widely in their approach to defining and addressing racial disproportionality within studies, making it difficult to generalize findings or approaches across studies. This review demonstrates the power of authors to shape policy or discourse around racial justice based on their choice of statistical methods; it also demonstrates the need for improved rigor and transparency in studies of quantitative risk assessment tools. Finally, this review raises concerns about the impact that these tools have on child welfare systems and racial disproportionality.

Keywords: actuarial risk, child welfare, predictive risk, racial disproportionality

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Authors: Miraji H. Mohamed

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The purpose of this study is to critique ‘radicalisation’ and more particularly ‘youth radicalisation’ by exploring its usage in online newspapers. ‘Radicalisation’ and ‘extremism’ have become the most common terms in terrorism studies since the 9/11 attacks. Regardless of the geographic location, when the word terrorism is used the terms ‘radicalisation’ and ‘extremism’ always follow to attempt to explore the journey of the perpetrators towards violence. These terms have come to represent a discourse of dominantly pejorative traits often used to describe spaces, groups, and processes identified as problematic. Even though ambiguously defined they feature widely in government documents, political statements, news articles, academic research, social media platforms, religious gatherings, and public discussions. Notably, ‘radicalisation’ and ‘extremism’ have been closely conflated with the term youth to form ‘youth radicalisation’ to refer to a discourse of ‘youth at risk’. The three terms largely continue to be used unquestioningly and interchangeably hence the reason why they are placed in single quotation marks to deliberately question their conventional usage. Albeit this comes timely in the Kenyan context where there has been a proliferation of academic and expert research on ‘youth radicalisation’ (used as a neutral label) without considering the political, cultural and socio-historical contexts that inform this label. This study seeks to draw these nuances by employing a genealogical approach that historicises and deconstructs ‘youth radicalisation’; and by applying a Discourse-Historical Approach (DHA) of Critical Discourse Analysis to analyse Kenyan online newspaper - The Daily Nation between 2015 and 2018. By applying the concept of representation to analyse written texts, the study reveals that the use of ‘youth radicalisation’ as a discursive strategy disproportionately affects young people especially those from cultural/ethnic/religious minority groups. Also, the ambiguous use of ‘radicalisation’ and ‘youth radicalisation’ by the media reinforces the discourse of ‘youth at risk’ which has become the major framework underpinning Countering Violent Extremism (CVE) interventions. Similarly, the findings indicate that the uncritical use of ‘youth radicalisation’ has been used to serve political interests; and has become an instrument of policing young people, thus contributing to their cultural shaping. From this, it is evident that the media could thwart rather than assist CVE efforts. By exposing the political nature of the three terms through evidence-based research, this study offers recommendations on how critical reflective reporting by the media could help to make CVE more nuanced.

Keywords: discourse, extremism, radicalisation, terrorism, youth

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Authors: Kefaifi Aissa, Sahraoui Tahar, Kheloufi Abdelkrim, Anas Sabiha, Hannane Farouk

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The aim of this study is to show how the Design of Experiments Method (DOE) can be put into use as a practical approach for silica sand etching behavior modeling during its primary step of leaching. In the present work, we have studied etching effect on particle size during a primary step of leaching process on Algerian silica sand with florid acid (HF) at 20% and 30 % during 4 and 8 hours. Therefore, a new purity of the sand is noted depending on the time of leaching. This study was expanded by a numerical approach using a method of experiment design, which shows the influence of each parameter and the interaction between them in the process and approved the obtained experimental results. This model is a predictive approach using hide software. Based on the measured parameters experimentally in the interior of the model, the use of DOE method can make it possible to predict the outside parameters of the model in question and can give us the optimize response without making the experimental measurement.

Keywords: acid leaching, design of experiments method(DOE), purity silica, silica etching

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Authors: Nikki Berrange, Gizelle Willows

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Business rescue has become increasingly popular since its inclusion in the Companies Act of South Africa in May 2011. The Alternate Exchange (AltX) of the Johannesburg Stock Exchange has experienced a marked increase in the number of companies entering business rescue. This study sampled twenty companies listed on the AltX to determine whether Altman’s Z-score model for emerging markets (ZEM) or Taffler’s Z-score model is a more accurate model in predicting financial distress for small to medium size companies in South Africa. The study was performed over three different time horizons; one, two and three years prior to the event of financial distress, in order to determine how many companies each model predicted would be unlikely to succeed as well as the predictive ability and accuracy of the respective models. The study found that Taffler’s Z-score model had a greater ability at predicting financial distress from all three-time horizons.

Keywords: Altman’s ZEM-score, Altman’s Z-score, AltX, business rescue, Taffler’s Z-score

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Authors: Merve Aygün Esastürk, Deren Ataç Yılmaz, Görkem Oğur, Emre Özgen Kuzu, Sadık Ödemiş

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Hydrocracker plants are one of the most complicated and most profitable units in the refinery process. It takes long chain paraffinic hydrocarbons as feed and turns them into smaller and more valuable products, mainly kerosene and diesel under high pressure with the excess amount of hydrogen. Controlling the product qualities well directly contributes to the unit profit. Control of a plant is mainly based on PID and MPC controllers. Controlling the reaction section is important in terms of reaction severity. However, controlling the fractionation section is more crucial since the end products are separated in fractionation section. In this paper, the importance of well-configured base layer control mechanism, composed of PID controllers, is highlighted. For this purpose, two different base layer control scheme is applied in a hydrocracker fractionator column performances of schemes, which is a direct contribution to better product quality, are compared.

Keywords: controller, distillation, configuration selection, hydrocracker, model predictive controller, proportional-integral-derivative controller

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Authors: Milica Karadzic, Lidija Jevric, Sanja Podunavac-Kuzmanovic, Strahinja Kovacevic, Aleksandra Tepic-Horecki, Zdravko Sumic

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In this paper, the influence of six different phytochemical content (phenols, carotenoids, chlorophyll a, chlorophyll b, chlorophyll a + b and vitamin C) on antioxidant potential of Murai and Levistro lettuce varieties was evaluated. Variable selection was made by generalized pair correlation method (GPCM) as a novel ranking method. This method is used for the discrimination between two variables that almost equal correlate to a dependent variable. Fisher’s conditional exact and McNemar’s test were carried out. Established multiple linear (MLR) models were statistically evaluated. As the best phytochemicals for the antioxidant potential prediction, chlorophyll a, chlorophyll a + b and total carotenoids content stand out. This was confirmed through both GPCM and MLR, predictive ability of obtained MLR can be used for antioxidant potential estimation for similar lettuce samples. This article is based upon work from the project of the Provincial Secretariat for Science and Technological Development of Vojvodina (No. 114-451-347/2015-02).

Keywords: antioxidant activity, generalized pair correlation method, lettuce, regression analysis

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Authors: Jose Ramon Calvo-Ferrer

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Video games have been extensively employed in educational contexts to teach contents and skills, upon the premise that they engage students and provide instant feedback, which makes them adequate tools in the field of education and training. Term frequency, along with metacognition and implicit corrective feedback, has often been identified as powerful variables in the learning of vocabulary in a foreign language. This study analyses the learning of L2 mobile operating system terminology by a group of students and uses the data collected by the video game The Conference Interpreter to identify the predictive strength of term frequency (times a term is shown), positive metacognition (times a right answer is provided), and negative metacognition (times a term is shown as wrong) regarding L2 vocabulary learning and perceived learning outcomes. The regression analysis shows that the factor ‘positive metacognition’ is a positive predictor of both dependent variables, whereas the other factors seem to have no statistical effect on any of them.

Keywords: digital game-based learning, feedback, metacognition, frequency, video games

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Authors: Wanhyun Cho, Soonja Kang, Sanggoon Kim, Soonyoung Park

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We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered an efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior distribution for various parameters and latent function by using the variational Bayesian approximations and important sampling method, and next we derived a predictive distribution of latent function needed to classify new samples. The proposed model is applied to classify the synthetic multivariate dataset in order to verify the performance of our model. Experiment result shows that our model is more accurate than the other approximation methods.

Keywords: multinomial dirichlet classification model, Gaussian process priors, variational Bayesian approximation, importance sampling, approximate posterior distribution, marginal likelihood evidence

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Authors: D.Mendes, J. Henriques, P. Carvalho, T. Rocha, S. Paredes, R. Cabiddu, R. Trimer, R. Mendes, A. Borghi-Silva, L. Kaminsky, E. Ashley, R. Arena, J. Myers

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The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption).

Keywords: cardiorespiratory fitness, data-driven models, knowledge extraction, machine learning

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Authors: Rundong Li, Peng Wu, Junfeng Zhang, Zhipeng Ren, Chen Yang, Jiahui Gan, Lu Feng, Haibo Tong, Xuemei Xiao, Yuying Chen

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The research of Beidou-3 satellite navigation is on the rise, but in actual work, it is inevitable that satellite data is insecure, research and development is inefficient, and there is no ability to deal with failures in advance. Digital twin technology has obvious advantages in the simulation of life cycle models of aerospace satellite navigation products. In order to meet the increasing demand, this paper builds a Beidou-3 satellite navigation digital twin platform (BDSDTP). The basic establishment of BDSDTP was completed by establishing a digital twin double, Beidou-3 comprehensive digital twin design, predictive maintenance (PdM) mathematical model, and visual interaction design. Finally, this paper provides a time application case of the platform, which provides a reference for the application of BDSDTP in various fields of navigation and provides obvious help for extending the full cycle life of Beidou-3 satellite navigation.

Keywords: BDS-3, digital twin, visualization, PdM

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Authors: Nadjib Melkemi, Salah Belaidi

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Quantitative Structure-Activity Relationship (QSAR) studies have been performed on nineteen molecules of 1,2-dithiole-3-thione analogues. The compounds used are the potent inducers of enzymes involved in the maintenance of reduced glutathione pools as well as phase-2 enzymes important to electrophile detoxication. A multiple linear regression (MLR) procedure was used to design the relationships between molecular descriptor and detoxication properties of the 1,2-dithiole-3-thione derivatives. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based of the following descriptors: qS2, qC3, qC5, qS6, DM, Pol, log P, MV, SAG, HE and EHOMO for the specific activity of quinone reductase; qS1, qS2, qC3, qC4, qC5, qS6, DM, Pol, logP, MV, SAG, HE and EHOMO for the production of growth hormone. To confirm the predictive power of the models, an external set of molecules was used. High correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR models.

Keywords: QSAR, quinone reductase activity, production of growth hormone, MLR

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Authors: Taoufik Benchelha, Toufik Remmal, Rachid El Hamdouni, Hamou Mansouri, Houssein Ejjaouani, Halima Jounaid, Said Benchelha

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During the earthworks for the construction of Marrakech-Agadir highway in southern Morocco, which crosses mountainous areas of the High Western Atlas, the main problem faced is the stability of the slopes. Indeed, the use of explosives as a means of excavation associated with the geological structure of the terrain encountered can trigger major ruptures and cause damage which depends on the intrinsic characteristics of the rock mass. The study consists of a geological and geotechnical analysis of several unstable zones located along the route, mobilizing millions of cubic meters of rock, with deduction of the parameters influencing slope stability. From this analysis, a predictive model for rock mass stability is carried out, based on a statistic method of logistic regression, in order to predict the geomechanical behavior of the rock slopes constrained by earthworks.

Keywords: explosive, logistic regression, rock mass, slope stability

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Authors: S. M. Gupta, Vanita Aggarwal, Som Nath Sachdeva

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The work reported through this paper is an experimental work conducted on High Performance Concrete (HPC) with super plasticizer with the aim to develop some models suitable for prediction of compressive strength of HPC mixes. In this study, the effect of varying proportions of fly ash (0% to 50% at 10% increment) on compressive strength of high performance concrete has been evaluated. The mix designs studied were M30, M40 and M50 to compare the effect of fly ash addition on the properties of these concrete mixes. In all eighteen concrete mixes have been designed, three as conventional concretes for three grades under discussion and fifteen as HPC with fly ash with varying percentages of fly ash. The concrete mix designing has been done in accordance with Indian standard recommended guidelines i.e. IS: 10262. All the concrete mixes have been studied in terms of compressive strength at 7 days, 28 days, 90 days and 365 days. All the materials used have been kept same throughout the study to get a perfect comparison of values of results. The models for compressive strength prediction have been developed using Linear Regression method (LR), Artificial Neural Network (ANN) and Leave One Out Validation (LOOV) methods.

Keywords: high performance concrete, fly ash, concrete mixes, compressive strength, strength prediction models, linear regression, ANN

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Authors: H. Zidoum, A. AlShareedah, S. Al Sawafi, A. Al-Ansari, B. Al Lawati

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Systemic lupus erythematosus (SLE) is an autoimmune disease with genetic and environmental components. SLE is characterized by a wide variability of clinical manifestations and a course frequently subject to unpredictable flares. Despite recent progress in classification tools, the early diagnosis of SLE is still an unmet need for many patients. This study proposes an interpretable disease classification model that combines the high and efficient predictive performance of CatBoost and the model-agnostic interpretation tools of Shapley Additive exPlanations (SHAP). The CatBoost model was trained on a local cohort of 219 Omani patients with SLE as well as other control diseases. Furthermore, the SHAP library was used to generate individual explanations of the model's decisions as well as rank clinical features by contribution. Overall, we achieved an AUC score of 0.945, F1-score of 0.92 and identified four clinical features (alopecia, renal disorders, cutaneous lupus, and hemolytic anemia) along with the patient's age that was shown to have the greatest contribution on the prediction.

Keywords: feature selection, classification, systemic lupus erythematosus, model interpretation, SHAP, Catboost

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Authors: Yu Zhang, Pedro Desouza

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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modelling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: sentiment analysis, social media, Twitter, Amazon, data mining, machine learning, text mining

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Authors: Adji Achmad Rinaldo Fernandes

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The research was conducted to apply semiparametric SEM modeling to the timeliness of paying credit. Semiparametric SEM is structural modeling in which two combined approaches of parametric and nonparametric approaches are used. The analysis method in this research is semiparametric SEM with a nonparametric approach using a truncated spline. The data in the study were obtained through questionnaires distributed to Bank X mortgage debtors and are confidential. The study used 3 variables consisting of one exogenous variable, one intervening endogenous variable, and one endogenous variable. The results showed that (1) the effect of capacity and willingness to pay variables on timeliness of payment is significant, (2) modeling the capacity variable on willingness to pay also produces a significant estimate, (3) the effect of the capacity variable on the timeliness of payment variable is not influenced by the willingness to pay variable as an intervening variable, (4) the R^2 value of 0.763 or 76.33% indicates that the model has good predictive relevance.

Keywords: structural equation modeling semiparametric, credit bank, accuracy of payment time, willingness to pay

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Authors: Chupicai Manuel

Abstract:

The global efforts to end child labour have been increasingly challenged by adages of global capitalism, inequalities and poverty affecting the global south. In the face the of rising inequalities whose origin can be explained from historical and political economy analysis between the poor and the rich countries, child labour is also on the rise particularly on the global south. The socio-economic and political context of Zimbabwe has undergone serious transition from colonial times through the post-independence normally referred to as the transition period up to the present day. These transitions have aided companies and entities in the business and agriculture sector to exploit child labour while country provided conditions that enhance child labour due to vulnerability of children and anomic child welfare system that plagued the country. Children from marginalised communities dominated by plantations and farms are affected most. This paper explores the experiences and perceptions of children working in tea estates, plantations and farms, and the adults who formerly worked in these plantations during their childhood to share their experiences and perceptions on child labour in Zimbabwe. Childhood theories that view children as apprentices and a human rights perspectives were employed to interrogate the concept of childhood, child labour and poverty alleviation strategies. Phenomenological research design was adopted to describe the experiences of children working in plantations and interpret the meanings they have on their work and livelihoods. The paper drew form 30 children from two plantations through semi-structured interviews and 15 key informant interviews from civil society organisations, international labour organisation, adults who formerly worked in the plantations and the personnel of the plantations. The findings of the study revealed that children work on the farms as an alternative model for survival against economic challenges while the majority cited that poverty compel them to work and get their fees and food paid for. Civil society organisations were of the view that child rights are violated and the welfare system of the country is malfunctional. The perceptions of the majority of the children interviewed are that the system on the plantations is better and this confirmed the socio-constructivist theory that views children as apprentices. The study recommended child sensitive policies and welfare regime that protects children from exploitation together with policing and legal measures that secure child rights.

Keywords: child labour, child rights, phenomenology, poverty reduction

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Authors: Fakieh Alrabai

Abstract:

The present study examines the predictive power of foreign language anxiety and motivation, as two significant affective variables, in English as a foreign language (EFL) achievement. It also explores the causal relationship between these two factors (i.e. which variable causes the other); and which one of them best predicts other affective factors including learner attitude, self-esteem, and autonomy. The study utilized experimental treatments among 210 Saudi EFL learners divided into four groups. Group 1 was exposed to anxiety-controlling moments, group 2 was exposed to motivational moments, group 3 was exposed to anxiety-controlling and motivational moments together, and group 4 was exposed to no specific anxiety or motivation strategies. The influence of the treatment on the study variables was evaluated using a triangulation of measurements including questionnaires, classroom observations, and achievement tests. Descriptive analysis, ANOVA, ANCOVA, and regression analyses have been deployed to figure out the study findings. While both motivation and anxiety significantly predicted learners EFL achievement, motivation has been found to be the best predictor of learners’ achievement; and therefore, operates as the mediator of EFL achievement.

Keywords: motivation, anxiety, achievement, autonomy

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Authors: Hanan Alsaeid Alshenawy

Abstract:

Background: Cervical dysplasia, which is potentially precancerous, has increased in young women. Detection of cervical is important for reducing morbidity and mortality in cervical cancer. This study analyzes the immunohistochemical expression of p16, HPV L1 capsid protein and Ki67 in cervical intraepithelial lesions and correlates them with lesion grade to develop a set of markers for diagnosis and detect the prognosis of cervical cancer precursors. Methods: 75 specimens were analyzed including 15 cases CIN 1, 28 CIN 2, 20 CIN 3, and 12 cervical squamous carcinoma, besides 10 normal cervical tissues. They were stained for p16, HPV L1 and Ki-67. Sensitivity, specificity, predictive values and accuracy were evaluated for each marker. Results: p16 expression increased during the progression from CIN 1 to carcinoma. HPV L1 positivity was detected in CIN 2 and decreased gradually as the CIN grade increased but disappear in carcinoma. Strong Ki-67 expression was observed with high grades CIN and carcinoma. p16, HPV L1 and Ki67 were sensitive but with variable specificity in detecting CIN lesions. Conclusions: p16, HPV L1 and Ki67 are useful set of markers in establishing the risk of high-grade CIN. They complete each other to reach accurate diagnosis and prognosis.

Keywords: p16, HPV L1, Ki67, CIN, cervical carcinoma

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Authors: Yaxin Bi

Abstract:

Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data.

Keywords: LSTM, GAN, earthquake, synthetic data, generative AI, seismic precursors

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Authors: Delphine Ribes Lemay, Valentine Bernasconi, André Andrade, Lara DéFayes, Mathieu Salzmann, FréDéRic Kaplan, Nicolas Henchoz

Abstract:

Digitization and preservation of large heritage induce high maintenance costs to keep up with the technical standards and ensure sustainable access. Creating impactful usage is instrumental to justify the resources for long-term preservation. The Museum für Gestaltung of Zurich holds one of the biggest poster collections of the world from which 52’000 were digitised. In the process of building a digital installation to valorize the collection, one objective was to develop an algorithm capable of predicting the next poster to show according to the ones already displayed. The work presented here describes the steps to build an algorithm able to automatically create sequences of posters reflecting associations performed by curator and professional designers. The exposed challenge finds similarities with the domain of song playlist algorithms. Recently, artificial intelligence techniques and more specifically, deep-learning algorithms have been used to facilitate their generations. Promising results were found thanks to Recurrent Neural Networks (RNN) trained on manually generated playlist and paired with clusters of extracted features from songs. We used the same principles to create the proposed algorithm but applied to a challenging medium, posters. First, a convolutional autoencoder was trained to extract features of the posters. The 52’000 digital posters were used as a training set. Poster features were then clustered. Next, an RNN learned to predict the next cluster according to the previous ones. RNN training set was composed of poster sequences extracted from a collection of books from the Gestaltung Museum of Zurich dedicated to displaying posters. Finally, within the predicted cluster, the poster with the best proximity compared to the previous poster is selected. The mean square distance between features of posters was used to compute the proximity. To validate the predictive model, we compared sequences of 15 posters produced by our model to randomly and manually generated sequences. Manual sequences were created by a professional graphic designer. We asked 21 participants working as professional graphic designers to sort the sequences from the one with the strongest graphic line to the one with the weakest and to motivate their answer with a short description. The sequences produced by the designer were ranked first 60%, second 25% and third 15% of the time. The sequences produced by our predictive model were ranked first 25%, second 45% and third 30% of the time. The sequences produced randomly were ranked first 15%, second 29%, and third 55% of the time. Compared to designer sequences, and as reported by participants, model and random sequences lacked thematic continuity. According to the results, the proposed model is able to generate better poster sequencing compared to random sampling. Eventually, our algorithm is sometimes able to outperform a professional designer. As a next step, the proposed algorithm should include a possibility to create sequences according to a selected theme. To conclude, this work shows the potentiality of artificial intelligence techniques to learn from existing content and provide a tool to curate large sets of data, with a permanent renewal of the presented content.

Keywords: Artificial Intelligence, Digital Humanities, serendipity, design research

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Authors: S. O. Oyamakin, A. U. Chukwu,

Abstract:

We proposed a Hyperbolic Gompertz Growth Model (HGGM), which was developed by introducing a stabilizing parameter called θ using hyperbolic sine function into the classical gompertz growth equation. The resulting integral solution obtained deterministically was reprogrammed into a statistical model and used in modeling the height and diameter of Pines (Pinus caribaea). Its ability in model prediction was compared with the classical gompertz growth model, an approach which mimicked the natural variability of height/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using goodness of fit tests and model selection criteria. The Kolmogorov-Smirnov test and Shapiro-Wilk test was also used to test the compliance of the error term to normality assumptions while using testing the independence of the error term using the runs test. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic gompertz growth models better than the source model (classical gompertz growth model) while the results of R2, Adj. R2, MSE, and AIC confirmed the predictive power of the Hyperbolic Monomolecular growth models over its source model.

Keywords: height, Dbh, forest, Pinus caribaea, hyperbolic, gompertz

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