Search results for: unbiased estimator

Authors: Mekonen G., Sileshi M., Melkamu M.

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Recharge estimation is important for the assessment and management of groundwater resources effectively. This study applied the soil moisture balance and Baseflow separation methods to estimate groundwater recharge in the Fetam Catchment. It is one of the major catchments understudied from the different catchments in the upper Blue Nile River basin. Surface water has been subjected to high seasonal variation; due to this, groundwater is a primary option for drinking water supply to the community. This research has been conducted to estimate groundwater recharge by using fifteen years of River flow data for the Baseflow separation and ten years of daily meteorological data for the daily soil moisture balance recharge estimating method. The recharge rate by the two methods is 170.5 and 244.9mm/year daily soil moisture and baseflow separation method, respectively, and the average recharge is 207.7mm/year. The average value of annual recharge in the catchment is almost equal to the average recharge in the country, which is 200mm/year. So, each method has its own limitations, and taking the average value is preferable rather than taking a single value. Baseflow provides overestimated result compared to the average of the two, and soil moisture balance is the list estimator. The recharge estimation in the area also should be done by other recharge estimation methods.

Keywords: groundwater, recharge, baseflow separation, soil moisture balance, Fetam catchment

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Authors: Abhaydeep Pandey, Shweta Shukla, Saptamita Goswami, Bhaswati Bandyopadhyay, Vishnampettai Ramachandran, Sudhanshu Vrati, Arup Banerjee

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Background: Long non-coding RNAs (lncRNAs) are the important regulators of gene expression and play important role in viral replication and disease progression. The role of lncRNA genes in the pathogenesis of Dengue virus-mediated pathogenesis is currently unknown. Methods: To gain additional insights, we utilized an unbiased RNA sequencing followed by in silico analysis approach to identify the differentially expressed lncRNA and genes that are associated with dengue disease progression. Further, we focused our study on lncRNAs NEAT1 (Nuclear Paraspeckle Assembly Transcript 1) as it was found to be differentially expressed in PBMC of dengue infected patients. Results: The expression of lncRNAs NEAT1, as compared to dengue infection (DI), was significantly down-regulated as the patients developed the complication. Moreover, pairwise analysis on follow up patients confirmed that suppression of NEAT1 expression was associated with rapid fall in platelet count in dengue infected patients. Severe dengue patients (DS) (n=18; platelet count < 20K) when recovered from infection showing high NEAT1 expression as it observed in healthy donors. By co-expression network analysis and subsequent validation, we revealed that coding gene; IFI27 expression was significantly up-regulated in severe dengue cases and negatively correlated with NEAT1 expression. To discriminate DI from dengue severe, receiver operating characteristic (ROC) curve was calculated. It revealed sensitivity and specificity of 100% (95%CI: 85.69 – 97.22) and area under the curve (AUC) = 0.97 for NEAT1. Conclusions: Altogether, our first observations demonstrate that monitoring NEAT1and IFI27 expression in dengue patients could be useful in understanding dengue virus-induced disease progression and may be involved in pathophysiological processes.

Keywords: dengue, lncRNA, NEAT1, transcriptome

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Authors: Wikanda Phaphan

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Mixture generalized gamma distribution is a combination of two distributions: generalized gamma distribution and length biased generalized gamma distribution. These two distributions were presented by Suksaengrakcharoen and Bodhisuwan in 2014. The findings showed that probability density function (pdf) had fairly complexities, so it made problems in estimating parameters. The problem occurred in parameter estimation was that we were unable to calculate estimators in the form of critical expression. Thus, we will use numerical estimation to find the estimators. In this study, we presented a new method of the parameter estimation by using the expectation – maximization algorithm (EM), the conjugate gradient method, and the quasi-Newton method. The data was generated by acceptance-rejection method which is used for estimating α, β, λ and p. λ is the scale parameter, p is the weight parameter, α and β are the shape parameters. We will use Monte Carlo technique to find the estimator's performance. Determining the size of sample equals 10, 30, 100; the simulations were repeated 20 times in each case. We evaluated the effectiveness of the estimators which was introduced by considering values of the mean squared errors and the bias. The findings revealed that the EM-algorithm had proximity to the actual values determined. Also, the maximum likelihood estimators via the conjugate gradient and the quasi-Newton method are less precision than the maximum likelihood estimators via the EM-algorithm.

Keywords: conjugate gradient method, quasi-Newton method, EM-algorithm, generalized gamma distribution, length biased generalized gamma distribution, maximum likelihood method

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Authors: Hesham Abdin, Mohamed Zakaria, Talaat Abd-Elmonaem, Alaa El-Din Sayed Hafez

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Kalman filter algorithm is an estimator known as the workhorse of estimation. It has an important application in missile guidance, especially in lack of accurate data of the target due to noise or uncertainty. In this paper, a Kalman filter is used as a tracking filter in a simulated target-interceptor scenario with noise. It estimates the position, velocity, and acceleration of the target in the presence of noise. These estimations are needed for both proportional navigation and differential geometry guidance laws. A Kalman filter has a good performance at low noise, but a large noise causes considerable errors leads to performance degradation. Therefore, a new technique is required to overcome this defect using tuning factors to tune a Kalman filter to adapt increasing of noise. The values of the tuning factors are between 0.8 and 1.2, they have a specific value for the first half of range and a different value for the second half. they are multiplied by the estimated values. These factors have its optimum values and are altered with the change of the target heading. A genetic algorithm updates these selections to increase the maximum effective range which was previously reduced by noise. The results show that the selected factors have other benefits such as decreasing the minimum effective range that was increased earlier due to noise. In addition to, the selected factors decrease the miss distance for all ranges of this direction of the target, and expand the effective range which leads to increase probability of kill.

Keywords: proportional navigation, differential geometry, Kalman filter, genetic algorithm

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Authors: Bastien Batardière, Joon Kwon

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For finite-sum optimization, variance-reduced gradient methods (VR) compute at each iteration the gradient of a single function (or of a mini-batch), and yet achieve faster convergence than SGD thanks to a carefully crafted lower-variance stochastic gradient estimator that reuses past gradients. Another important line of research of the past decade in continuous optimization is the adaptive algorithms such as AdaGrad, that dynamically adjust the (possibly coordinate-wise) learning rate to past gradients and thereby adapt to the geometry of the objective function. Variants such as RMSprop and Adam demonstrate outstanding practical performance that have contributed to the success of deep learning. In this work, we present AdaLVR, which combines the AdaGrad algorithm with loopless variance-reduced gradient estimators such as SAGA or L-SVRG that benefits from a straightforward construction and a streamlined analysis. We assess that AdaLVR inherits both good convergence properties from VR methods and the adaptive nature of AdaGrad: in the case of L-smooth convex functions we establish a gradient complexity of O(n + (L + √ nL)/ε) without prior knowledge of L. Numerical experiments demonstrate the superiority of AdaLVR over state-of-the-art methods. Moreover, we empirically show that the RMSprop and Adam algorithm combined with variance-reduced gradients estimators achieve even faster convergence.

Keywords: convex optimization, variance reduction, adaptive algorithms, loopless

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Authors: Takashi Shimizu, Tomoaki Hashimoto

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Controlling the flow of fluids is a challenging problem that arises in many fields. Burgers’ equation is a fundamental equation for several flow phenomena such as traffic, shock waves, and turbulence. The optimal feedback control method, so-called model predictive control, has been proposed for Burgers’ equation. However, the model predictive control method is inapplicable to systems whose all state variables are not exactly known. In practical point of view, it is unusual that all the state variables of systems are exactly known, because the state variables of systems are measured through output sensors and limited parts of them can be only available. In fact, it is usual that flow velocities of fluid systems cannot be measured for all spatial domains. Hence, any practical feedback controller for fluid systems must incorporate some type of state estimator. To apply the model predictive control to the fluid systems described by Burgers’ equation, it is needed to establish a state estimation method for Burgers’ equation with limited measurable state variables. To this purpose, we apply unscented Kalman filter for estimating the state variables of fluid systems described by Burgers’ equation. The objective of this study is to establish a state estimation method based on unscented Kalman filter for Burgers’ equation. The effectiveness of the proposed method is verified by numerical simulations.

Keywords: observer systems, unscented Kalman filter, nonlinear systems, Burgers' equation

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Xingxing Peng

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With the development of society, there are more and more online communication scenarios such as teleconference and online education. In the process of conference communication, the quality of voice communication is a very important part, and noise may cause the communication effect of participants to be greatly reduced. Therefore, voice noise reduction has an important impact on scenarios such as voice calls. This research focuses on the key technologies of the sound transmission process. The purpose is to maintain the audio quality to the maximum so that the listener can hear clearer and smoother sound. Firstly, to solve the problem that the traditional speech enhancement algorithm is not ideal when dealing with non-stationary noise, an adaptive speech noise reduction algorithm is studied in this paper. Traditional noise estimation methods are mainly used to deal with stationary noise. In this chapter, we study the spectral characteristics of different noise types, especially the characteristics of non-stationary Burst noise, and design a noise estimator module to deal with non-stationary noise. Noise features are extracted from non-speech segments, and the noise estimation module is adjusted in real time according to different noise characteristics. This adaptive algorithm can enhance speech according to different noise characteristics, improve the performance of traditional algorithms to deal with non-stationary noise, so as to achieve better enhancement effect. The experimental results show that the algorithm proposed in this chapter is effective and can better adapt to different types of noise, so as to obtain better speech enhancement effect.

Keywords: speech noise reduction, speech enhancement, self-adaptation, Wiener filter algorithm

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Authors: Gabriela Macera DiFilippo

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The world watched in disbelief as George Floyd was killed by a policeman. The images from the scene were made more memorable by Mr. Floyd’s pleas and cries for his mother. In the aftermath of this tragedy, the Black Lives Matter movement gained momentum. Weeks and months after the protests, global interest in learning about tackling systemic racism erupted. One must wonder how school children of color viewed and processed this trauma. This study will examine the kinds of trauma experienced by children of color and the opportunity for school mental health providers to support these children. This study used literature searches that were previously conducted for proven and practical assessment methods that can help deal with racial trauma for children. As part of the assessment, trauma symptoms experienced by children of color were summarized and characterized in a non-imperial manner. The research was also will be done in practical ways to make adequate and effective mental health services available in schools and lessen the stigma. This research study found that there is a need to provide an analysis of the ongoing racial trauma of children of color after the death of George Floyd. Impactful and appropriate assessment methods, such as surveys, were presented to all school professionals. Lastly, this paper attempted to provide mental health professionals with the tools to screen and provide guidance based on unequivocal, unbiased methods for helping these children. There is a need for both schools and community leaders to ensure that every child has access to mental health care and is being assessed for their overall well-being. There is a need to educate the communities about racial trauma and its impact on individuals, especially children. School mental health professionals are encouraged to offer and educate schools and communities about racial trauma awareness, its importance, and ways to cope with it in different settings. The delivery of these informed services should focus on behavioral health and must be sensitive to children of color and different ways of self-care.

Keywords: trauma, children, black psychology, students

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Authors: Isaac Mugume

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Since the beginning of the 21st century, climate change has been an issue due to the reported rise in global temperature and changes in the frequency as well as severity of extreme weather and climatic events. The changing climate has been attributed to rising concentrations of greenhouse gases, including environmental changes such as ecosystems and land-uses. Climatic projections have been carried out under the auspices of the intergovernmental panel on climate change where a couple of models have been run to inform us about the likelihood of future climates. Since one of the major forcings informing the changing climate is emission of greenhouse gases, different scenarios have been proposed and future climates for different periods presented. The global climate models project different areas to experience different impacts. While regional modeling is being carried out for high impact studies, bias correction is less documented. Yet, the regional climate models suffer bias which introduces uncertainty. This is addressed in this study by bias correcting the regional models. This study uses the Weather Research and Forecasting model under different representative concentration pathways and correcting the products of these models using observed climatic data. This study notes that bias correction (e.g., the running-mean bias correction; the best easy systematic estimator method; the simple linear regression method, nearest neighborhood, weighted mean) improves the climatic projection skill and therefore reduce the uncertainty inherent in the climatic projections.

Keywords: bias correction, climatic projections, numerical models, representative concentration pathways

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Authors: Xiaoqin Wang, Li Yin

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Background and purpose: In many practices, one estimates causal effects arising from a complex stochastic process, where a sequence of treatments are assigned to influence a certain outcome of interest, and there exist time-dependent covariates between treatments. When covariates are plentiful and/or continuous, statistical modeling is needed to reduce the huge dimensionality of the problem and allow for the estimation of causal effects. Recently, Wang and Yin (Annals of statistics, 2020) derived a new general formula, which expresses these causal effects in terms of the point effects of treatments in single-point causal inference. As a result, it is possible to conduct the modeling via point effects. The purpose of the work is to study the modeling of these causal effects via point effects. Challenges and solutions: The time-dependent covariates often have influences from earlier treatments as well as on subsequent treatments. Consequently, the standard parameters – i.e., the mean of the outcome given all treatments and covariates-- are essentially all different (null paradox). Furthermore, the dimension of the parameters is huge (curse of dimensionality). Therefore, it can be difficult to conduct the modeling in terms of standard parameters. Instead of standard parameters, we have use point effects of treatments to develop likelihood-based parametric approach to the modeling of these causal effects and are able to model the causal effects of a sequence of treatments by modeling a small number of point effects of individual treatment Achievements: We are able to conduct the modeling of the causal effects from a sequence of treatments in the familiar framework of single-point causal inference. The simulation shows that our method achieves not only an unbiased estimate for the causal effect but also the nominal level of type I error and a low level of type II error for the hypothesis testing. We have applied this method to a longitudinal study of COVID-19 mortality among Scandinavian countries and found that the Swedish approach performed far worse than the other countries' approach for COVID-19 mortality and the poor performance was largely due to its early measure during the initial period of the pandemic.

Keywords: causal effect, point effect, statistical modelling, sequential causal inference

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Authors: Nureni O. Adeboye, Dawud A. Agunbiade

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This research attempts to investigate the effects of heteroscedasticity and periodicity in a Panel Data Regression Model (PDRM) by extending previous works on balanced panel data estimation within the context of fitting PDRM for Banks audit fee. The estimation of such model was achieved through the derivation of Joint Lagrange Multiplier (LM) test for homoscedasticity and zero-serial correlation, a conditional LM test for zero serial correlation given heteroscedasticity of varying degrees as well as conditional LM test for homoscedasticity given first order positive serial correlation via a two-way error component model. Monte Carlo simulations were carried out for 81 different variations, of which its design assumed a uniform distribution under a linear heteroscedasticity function. Each of the variation was iterated 1000 times and the assessment of the three estimators considered are based on Variance, Absolute bias (ABIAS), Mean square error (MSE) and the Root Mean Square (RMSE) of parameters estimates. Eighteen different models at different specified conditions were fitted, and the best-fitted model is that of within estimator when heteroscedasticity is severe at either zero or positive serial correlation value. LM test results showed that the tests have good size and power as all the three tests are significant at 5% for the specified linear form of heteroscedasticity function which established the facts that Banks operations are severely heteroscedastic in nature with little or no periodicity effects.

Keywords: audit fee lagrange multiplier test, heteroscedasticity, lagrange multiplier test, Monte-Carlo scheme, periodicity

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Authors: Nancy Retzlaff, Sarah J. Berkemer, Trudie Strauss

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In the last couple of decades, the advent of digitalization of any kind of data was probably one of the major advances in all fields of study. This paves the way for also analysing these data even though they might come from disciplines where there was no initial computational necessity to do so. Especially in linguistics, one can find a rather manual tradition. Still when considering studies that involve the history of language families it is hard to overlook the striking similarities to bioinformatics (phylogenetic) approaches. Alignments of words are such a fairly well studied example of an application of bioinformatics methods to historical linguistics. In this paper we will not only consider alignments of strings, i.e., words in this case, but also alignments of syntax trees of selected Indo-European languages. Based on initial, crude alignments, a sophisticated scoring model is trained on both letters and syntactic features. The aim is to gain a better understanding on which features in two languages are related, i.e., most likely to have the same root. Initially, all words in two languages are pre-aligned with a basic scoring model that primarily selects consonants and adjusts them before fitting in the vowels. Mixture models are subsequently used to filter ‘good’ alignments depending on the alignment length and the number of inserted gaps. Using these selected word alignments it is possible to perform tree alignments of the given syntax trees and consequently find sentences that correspond rather well to each other across languages. The syntax alignments are then filtered for meaningful scores—’good’ scores contain evolutionary information and are therefore used to train the sophisticated scoring model. Further iterations of alignments and training steps are performed until the scoring model saturates, i.e., barely changes anymore. A better evaluation of the trained scoring model and its function in containing evolutionary meaningful information will be given. An assessment of sentence alignment compared to possible phrase structure will also be provided. The method described here may have its flaws because of limited prior information. This, however, may offer a good starting point to study languages where only little prior knowledge is available and a detailed, unbiased study is needed.

Keywords: alignments, bioinformatics, comparative linguistics, historical linguistics, statistical methods

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

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In this paper, we propose a novel inference algorithm for the multi-class Gaussian process classification model that can be used in the field of human behavior recognition. This algorithm can drive simultaneously both a posterior distribution of a latent function and estimators of hyper-parameters in a Gaussian process classification model with multi-class. Our algorithm is based on the Laplace approximation (LA) technique and variational EM framework. This is performed in two steps: called expectation and maximization steps. First, in the expectation step, using the Bayesian formula and LA technique, we derive approximately the posterior distribution of the latent function indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. Second, in the maximization step, using a derived posterior distribution of latent function, we compute the maximum likelihood estimator for hyper-parameters of a covariance matrix necessary to define prior distribution for latent function. These two steps iteratively repeat until a convergence condition satisfies. Moreover, we apply the proposed algorithm with human action classification problem using a public database, namely, the KTH human action data set. Experimental results reveal that the proposed algorithm shows good performance on this data set.

Keywords: bayesian rule, gaussian process classification model with multiclass, gaussian process prior, human action classification, laplace approximation, variational EM algorithm

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Authors: Hany Mustafa, Mahmoud Roushdi, Khaled Kheireldin

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Rainfall variability is an important feature of semi-arid climates. Climate change is very likely to increase the frequency, magnitude, and variability of extreme weather events such as droughts, floods, and storms. The Blue Nile Basin is facing extreme climate change-related events such as floods and droughts and its possible impacts on ecosystem, livelihood, agriculture, livestock, and biodiversity are expected. Rainfall variability is a threat to food production in the Blue Nile Basin countries. This study investigates the long-term variations and trends of seasonal and annual precipitation over the Blue Nile Basin for 102-year period (1901-2002). Six statistical trend analysis of precipitation was performed with nonparametric Mann-Kendall test and Sen's slope estimator. On the other hands, four statistical absolute homogeneity tests: Standard Normal Homogeneity Test, Buishand Range test, Pettitt test and the Von Neumann ratio test were applied to test the homogeneity of the rainfall data, using XLSTAT software, which results of p-valueless than alpha=0.05, were significant. The percentages of significant trends obtained for each parameter in the different seasons are presented. The study recommends adaptation strategies to be streamlined to relevant policies, enhancing local farmers’ adaptive capacity for facing future climate change effects.

Keywords: Blue Nile basin, climate change, Mann-Kendall test, trend analysis

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Authors: Kassaye Balkew Workagegn, Gunnar Klemetsdal, Hans Magnus Gjøen

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In this study, the first objective was to investigate whether non-lethal or non-invasive methods, utilizing body measurements, could be used to efficiently predict fillet weight and fillet yield for a complete diallel cross of three Nile tilapia (Oreochromis niloticus) strains collected from three Ethiopian Rift Valley lakes, Lakes Ziway, Koka and Chamo. The second objective was to estimate heritability of body weight, actual and predicted fillet traits, as well as genetic correlations between these traits. A third goal was to estimate additive, reciprocal, and heterosis effects for body weight and the various fillet traits. As in females, early sexual maturation was widespread, only 958 male fish from 81 full-sib families were used, both for the prediction of fillet traits and in genetic analysis. The prediction equations from body measurements were established by forward regression analysis, choosing models with the least predicted residual error sums of squares (PRESS). The results revealed that body measurements on live Nile tilapia is well suited to predict fillet weight but not fillet yield (R²= 0.945 and 0.209, respectively), but both models were seemingly unbiased. The genetic analyses were carried out with bivariate, multibreed models. Body weight, fillet weight, and predicted fillet weight were all estimated with a heritability ranged from 0.23 to 0.28, and with genetic correlations close to one. Contrary, fillet yield was only to a minor degree heritable (0.05), while predicted fillet yield obtained a heritability of 0.19, being a resultant of two body weight variables known to have high heritability. The latter trait was estimated with genetic correlations to body weight and fillet weight traits larger than 0.82. No significant differences among strains were found for their additive genetic, reciprocal, or heterosis effects, while total heterosis effects were estimated as positive and significant (P < 0.05). As a conclusion, prediction of prediction of fillet weight based on body measurements is possible, but not for fillet yield.

Keywords: additive, fillet traits, genetic correlation, heritability, heterosis, prediction, reciprocal

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Authors: Maria Monica D. Espinosa

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This study aimed to identify and examine the characteristics of an effective leader in a Hospital institution from the perspectives of military nurse-managers. The researcher extracted the different facets of leadership from the stories of six nurse- managers from a military hospital. The stories which are in pre-reflective stage convey an unbiased perspective from which clinical leadership may be defined. Using Phenomenology as a method of Research, the lived experiences of the military nurse-managers served as empirical data which were reflected upon until the formulation of insights. The information from the co-researchers became gallows from which the characteristics of effective leadership in the clinical area were drawn. These insights were synthesized through layers of reflection that resulted to the knowledge about clinical leadership. The reflections are the following, (a) Clinical leaders develop their skills through experiences and hardwork; (b) Clinical leaders are devoted; (c) Clinical leaders are focused; (d) Clinical leaders are good in interpersonal relationship; (e) Clinical leaders are mentors; (f) Clinical leaders seek affirmation and recognition; and (g) Clinical leaders are responsible and dependable. The common themes that emerged from the nurse manager’s stories showed that clinical leadership maybe attained if leaders possessed the following traits, (a) The gift to establish a steadfast and firm management; (b) The proficiency to guide and encourage others towards the achievement of their goals and objectives; (c) The ability to instigate participative and collaborative work among his/her subordinates and (d) The aptitude and skill to address the institutional concerns in their unit. In the future, Clinical leaders should continually adapt an evaluation program on how they can relate socially with their subordinates, the result of which can be used as a basis in developing strategies on relationship enhancement. Moreover, they should empower the nurses by allowing them to voice out their opinions and concerns regarding assignments, role expectations, and workload issues to improve and strengthen the relationships among nurses. Lastly, they can incorporate a collaborative strategy to promote professional socialization attitudes of nurse managers who work with staff nurses to improve the quality of their proficiencies and enhance a positive clinical environment.

Keywords: clinical leadership, experiences, implications, military nurse - managers, phenomenology

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Authors: Odejayi Adedayo Olugbenga, Aina Adebisi

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Weather variables (rainfall and temperature) affect the diversity and abundance of both fauna and flora species. This study compared the weather variables with spider abundance in two ecological zones of Ogun State, Nigeria namely Ago-iwoye (Rainforest) in the Ijebu axis and Aiyetoro (Derived Savannah) in the Yewa axis. Seven study sites chosen by Simple Random Sampling in each ecosystem were used for the study. In each sampling area, a 60 m x 120 m land area was marked and sampled, spider collection techniques were; hand picking, use of sweep netting, and Pitfall trap. Adult spiders were identified to the species level. Species richness was estimated by a non-parametric species estimator while the diversity of spider species was assessed by Simpson Diversity Index and Species Richness by One-way Analysis of Variance. Results revealed that spiders were more abundant in rainforest zones than in derived savannah ecosystems. However, the pattern of spider abundance in rainforest zone and residential areas were similar. During high temperatures, the activities of spiders tended to increase according to this study. In contrast, results showed that there was a negative correlation between rainfall and spider species abundance in addition to a negative and weak correlation between rainfall and species richness. It was concluded that heavy downpour has lethal effects on both immature and sometimes matured spiders, which could lead to the extinction of some unknown species of spiders. Tree planting should be encouraged, as this shelters the spider.

Keywords: spider, abundance, species richness, species diversity

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Authors: Peiming Li

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This paper presents an innovative blockchain-based approach to enhance the reliability and efficiency of federated learning systems. By integrating a decentralized oracle consensus mechanism into the federated learning framework, we address key challenges of data and model integrity. Our approach utilizes a network of redundant oracles, functioning as independent validators within an epoch-based training system in the federated learning model. In federated learning, data is decentralized, residing on various participants' devices. This scenario often leads to concerns about data integrity and model quality. Our solution employs blockchain technology to establish a transparent and tamper-proof environment, ensuring secure data sharing and aggregation. The decentralized oracles, a concept borrowed from blockchain systems, act as unbiased validators. They assess the contributions of each participant using a Hidden Markov Model (HMM), which is crucial for evaluating the consistency of participant inputs and safeguarding against model poisoning and malicious activities. Our methodology's distinct feature is its epoch-based training. An epoch here refers to a specific training phase where data is updated and assessed for quality and relevance. The redundant oracles work in concert to validate data updates during these epochs, enhancing the system's resilience to security threats and data corruption. The effectiveness of this system was tested using the Mnist dataset, a standard in machine learning for benchmarking. Results demonstrate that our blockchain-oriented federated learning approach significantly boosts system resilience, addressing the common challenges of federated environments. This paper aims to make these advanced concepts accessible, even to those with a limited background in blockchain or federated learning. We provide a foundational understanding of how blockchain technology can revolutionize data integrity in decentralized systems and explain the role of oracles in maintaining model accuracy and reliability.

Keywords: federated learning system, block chain, decentralized oracles, hidden markov model

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Authors: Radha Krishan, Deepak Khare, Bhaskar R. Nikam, Ayush Chandrakar

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Every irrigation project is planned considering long-term historical climatic conditions; however, the prompt climatic shift and change has come out with such circumstances which were inconceivable in the past. Considering this fact, scrutiny of rainfall and temperature trend has been carried out over the command area of Eastern Ganga Canal project for pre-climate shift period and post-climate shift periods in the present study. Non-parametric Mann-Kendall and Sen’s methods have been applied to study the trends in annual rainfall, seasonal rainfall, annual rainy day, monsoonal rainy days, average annual temperature and seasonal temperature. The results showed decreasing trend of 48.11 to 42.17 mm/decade in annual rainfall and 79.78 tSo 49.67 mm/decade in monsoon rainfall in pre-climate to post-climate shift periods, respectively. The decreasing trend of 1 to 4 days/decade has been observed in annual rainy days from pre-climate to post-climate shift period. Trends in temperature revealed that there were significant decreasing trends in annual (-0.03 ºC/yr), Kharif (-0.02 ºC/yr), Rabi (-0.04 ºC/yr) and summer (-0.02 ºC/yr) season temperature during pre-climate shift period, whereas the significant increasing trend (0.02 ºC/yr) has been observed in all the four parameters during post climate shift period. These results will help project managers in understanding the climate shift and lead them to develop alternative water management strategies.

Keywords: climate shift, rainfall trend, temperature trend, Mann-Kendall test, sen slope estimator, eastern Ganga canal command

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Authors: Kornelius Ronald Demu, Dewi Retno Sari Saputro, Purnami Widyaningsih

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Human Development Index (HDI) is a standard measurement for a country's human development. Several factors may have influenced it, such as life expectancy, gross domestic product (GDP) based on the province's annual expenditure, the number of poor people, and the percentage of an illiterate people. The scatter plot between HDI and the influenced factors show that the plot does not follow a specific pattern or form. Therefore, the HDI's data in Indonesia can be applied with a nonparametric regression model. The estimation of the regression curve in the nonparametric regression model is flexible because it follows the shape of the data pattern. One of the nonparametric regression's method is a truncated spline. Truncated spline regression is one of the nonparametric approach, which is a modification of the segmented polynomial functions. The estimator of a truncated spline regression model was affected by the selection of the optimal knots point. Knot points is a focus point of spline truncated functions. The optimal knots point was determined by the minimum value of generalized cross validation (GCV). In this article were applied the data of Human Development Index with a truncated spline nonparametric regression model. The results of this research were obtained the best-truncated spline regression model to the HDI's data in Indonesia with the combination of optimal knots point 5-5-5-4. Life expectancy and the percentage of an illiterate people were the significant factors depend to the HDI in Indonesia. The coefficient of determination is 94.54%. This means the regression model is good enough to applied on the data of HDI in Indonesia.

Keywords: generalized cross validation (GCV), Human Development Index (HDI), knots point, nonparametric regression, truncated spline

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Authors: Afsha Shaikh, Mohammed Azizur Rahman, Ibrahim Hassan, Mayur Pal

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Student engagement in learning is instantly affected by the sources of learning methods available for them, such as videos showing the applications of the concept or showing a practical demonstration. Specific to the engineering discipline, there exist enormous challenging concepts that can be simplified when they are connected to real-world scenarios. For this study, the concept of heat exchangers was used as it is a part of multidisciplinary engineering fields. To make the learning experience enjoyable and impactful, 3-D printed heat exchanger models were created for students to use while working on in-class activities and assignments. Students were encouraged to use the 3-D printed heat exchanger models to enhance their understanding of theoretical concepts associated with its applications. To assess the effectiveness of the method, feedback was received by students pursuing undergraduate engineering via an anonymous electronic survey. To make the feedback more realistic, unbiased, and genuine, students spent nearly two to three weeks using the models in their in-class assignments. The impact of these tools on their learning was assessed through their performance in their ungraded assignments as well as their interactive discussions with peers. ‘Having to apply the theory learned in class whilst discussing with peers on a class assignment creates a relaxed and stress-free learning environment in classrooms’; this feedback was received by more than half the students who took the survey and found 3-D models of heat exchanger very easy to use. Amongst many ways to enhance learning and make students more engaged through interactive models, this study sheds light on the importance of physical tools that help create a lasting mental representation in the minds of students. Moreover, in this technologically enhanced era, the concept of augmented reality was considered in this research. E-drawings application was recommended to enhance the vision of engineering students so they can see multiple views of the detailed 3-D models and cut through its different sides and angles to visualize it properly. E-drawings could be the next tool to implement in classrooms to enhance students’ understanding of engineering concepts.

Keywords: student engagement, life-long-learning, visual demonstration, 3-D printed models, engineering education

Procedia PDF Downloads 84

Authors: O. S. Ebrahim, K. O. Shawky, M. O. S. Ebrahim, P. K. Jain

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Induction Motor (IM) driving pump is the main consumer of electricity in a typical fluid transportation system (FTS). It was illustrated that changing the connection of the stator windings from delta to star at no load could achieve noticeable active and reactive energy savings. This paper proposes a supervisory hysteresis liquid-level control with three-state energy saving mode (ESM) for IM in FTS including storage tank. The IM pump drive comprises modified star/delta switch and hydromantic coupler. Three-state ESM is defined, along with the normal running, and named analog to computer ESMs as follows: Sleeping mode in which the motor runs at no load with delta stator connection, hibernate mode in which the motor runs at no load with a star connection, and motor shutdown is the third energy saver mode. A logic flow-chart is synthesized to select the motor state at no-load for best energetic cost reduction, considering the motor thermal capacity used. An artificial neural network (ANN) state estimator, based on the recurrent architecture, is constructed and learned in order to provide fault-tolerant capability for the supervisory controller. Sequential test of Wald is used for sensor fault detection. Theoretical analysis, preliminary experimental testing and, computer simulations are performed to show the effectiveness of the proposed control in terms of reliability, power quality and energy/coenergy cost reduction with the suggestion of power factor correction.

Keywords: ANN, ESM, IM, star/delta switch, supervisory control, FT, reliability, power quality

Procedia PDF Downloads 158

Authors: Helena Baffoe

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Context: In the United States, there have been significant advancements in programs aimed at improving the lives of individuals with mental health disorders and substance abuse problems. However, public attitudes and beliefs regarding these issues have not improved correspondingly. This study aims to explore the perceptions and beliefs surrounding mental health disorders and substance abuse in the context of data analytics in the field of social work. Research Aim: The aim of this research is to provide empirical evidence on the beliefs and perceptions regarding mental health disorders and substance abuse. Specifically, the study seeks to answer the question of whether being diagnosed with a mental disorder implies a diagnosis of substance abuse. Additionally, the research aims to analyze the specific roles that social workers can play in addressing individuals with mental disorders. Methodology: This research adopts a data-driven methodology, acquiring comprehensive data from the Substance Abuse and Mental Health Services Administration (SAMHSA). A noteworthy causal connection between mental disorders and substance abuse exists, a relationship that current literature tends to overlook critically. To address this gap, we applied logistic regression with an Instrumental Variable approach, effectively mitigating potential endogeneity issues in the analysis in order to ensure robust and unbiased results. This methodology allows for a rigorous examination of the relationship between mental disorders and substance abuse. Empirical Findings: The analysis of the data reveals that depressive, anxiety, and trauma/stressor mental disorders are the most common in the United States. However, the study does not find statistically significant evidence to support the notion that being diagnosed with these mental disorders necessarily implies a diagnosis of substance abuse. This suggests that there is a misconception among the public regarding the relationship between mental health disorders and substance abuse. Theoretical Importance: The research contributes to the existing body of literature by providing empirical evidence to challenge prevailing beliefs and perceptions regarding mental health disorders and substance abuse. By using a novel methodological approach and analyzing new US data, the study sheds light on the cultural and social factors that influence these attitudes.

Keywords: mental health disorder, substance abuse, empirical evidence, logistic regression with IV

Procedia PDF Downloads 29

Authors: Eva L. Suarez, Daniel E. Meeroff, Yan Yong

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Current flooding risk modeling focuses on resilience, defined as the probability of recovery from a severe flooding event. However, the long-term damage to property and well-being by nuisance flooding and its long-term effects on communities are not typically included in risk assessments. An approach was developed to address the probability of recovering from a severe flooding event combined with the probability of community performance during a nuisance event. A consolidated model, namely the conflation flooding recovery (&FR) model, evaluates risk-coping mitigation strategies for communities based on the recovery time from catastrophic events, such as hurricanes or extreme surges, and from everyday nuisance flooding events. The &FR model assesses the variation contribution of each independent input and generates a weighted output that favors the distribution with minimum variation. This approach is especially useful if the input distributions have dissimilar variances. The &FR is defined as a single distribution resulting from the product of the individual probability density functions. The resulting conflated distribution resides between the parent distributions, and it infers the recovery time required by a community to return to basic functions, such as power, utilities, transportation, and civil order, after a flooding event. The &FR model is more accurate than averaging individual observations before calculating the mean and variance or averaging the probabilities evaluated at the input values, which assigns the same weighted variation to each input distribution. The main disadvantage of these traditional methods is that the resulting measure of central tendency is exactly equal to the average of the input distribution’s means without the additional information provided by each individual distribution variance. When dealing with exponential distributions, such as resilience from severe flooding events and from nuisance flooding events, conflation results are equivalent to the weighted least squares method or best linear unbiased estimation. The combination of severe flooding risk with nuisance flooding improves flood risk management for highly populated coastal communities, such as in South Florida, USA, and provides a method to estimate community flood recovery time more accurately from two different sources, severe flooding events and nuisance flooding events.

Keywords: community resilience, conflation, flood risk, nuisance flooding

Procedia PDF Downloads 61

Authors: Joonas Pääkkönen

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In sports, individuals and teams are typically interested in final rankings. Final results, such as times or distances, dictate these rankings, also known as places. Places can be further associated with ordered random variables, commonly referred to as order statistics. In this work, we introduce a simple, yet accurate order statistical ordinal regression function that predicts relay race places with changeover-times. We call this function the Fenton-Wilkinson Order Statistics model. This model is built on the following educated assumption: individual leg-times follow log-normal distributions. Moreover, our key idea is to utilize Fenton-Wilkinson approximations of changeover-times alongside an estimator for the total number of teams as in the notorious German tank problem. This original place regression function is sigmoidal and thus correctly predicts the existence of a small number of elite teams that significantly outperform the rest of the teams. Our model also describes how place increases linearly with changeover-time at the inflection point of the log-normal distribution function. With real-world data from Jukola 2019, a massive orienteering relay race, the model is shown to be highly accurate even when the size of the training set is only 5% of the whole data set. Numerical results also show that our model exhibits smaller place prediction root-mean-square-errors than linear regression, mord regression and Gaussian process regression.

Keywords: Fenton-Wilkinson approximation, German tank problem, log-normal distribution, order statistics, ordinal regression, orienteering, sports analytics, sports modeling

Procedia PDF Downloads 100

Authors: Alfiah Hasanah, Silvia Mendolia, Oleg Yerokhin

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This paper explores the impacts of adult child migration on the health of elderly parents left behind. The maternal and children health are a priority of health-related policy in most low and middle-income country, and so there is lack of evidence on the health of older population particularly in Indonesia. With increasing life expectancy and limited access to social security and social services for the elderly in this country, the consequences of increasing number of out-migration of adult children to parent health are important to investigate. This study use Indonesia Family Life Survey (IFLS), the only large-scale continuing longitudinal socioeconomic and health survey that based on a sample of households representing about 83 percent of the Indonesian population in its first wave. Using four waves of IFLS including the recent wave of 2014, several indicators of the self-rated health status, interviewer-rated health status and days of illness are used to estimate the impact of labour out-migration of adult children on parent health status. Incorporate both individual fixed effects to control for unobservable factors in migrant and non-migrant households and the ordered response of self-rated health, this study apply the ordered logit of “Blow-up and Cluster” (BUC ) estimator. The result shows that labour out-migration of adult children significantly improves the self-rated health status of the elderly parent left behind. Findings of this study are consistent with the view that migration increases family resources and contribute to better health care and nutrition of the family left behind.

Keywords: aging, migration, panel data, self-rated health

Procedia PDF Downloads 320

Authors: Naci Büyükkaracığan

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Hydroclimatic observation values ​​are used in the planning of the project of water resources. Climate variables are the first of the values ​​used in planning projects. At the same time, the climate system is a complex and interactive system involving the atmosphere, land surfaces, snow and bubbles, the oceans and other water structures. The amount and distribution of precipitation, which is an important climate parameter, is a limiting environmental factor for dispersed living things. Trend analysis is applied to the detection of the presence of a pattern or trend in the data set. Many trends work in different parts of the world are usually made for the determination of climate change. The detection and attribution of past trends and variability in climatic variables is essential for explaining potential future alteration resulting from anthropogenic activities. Parametric and non-parametric tests are used for determining the trends in climatic variables. In this study, trend tests were applied to annual total precipitation data obtained in period of 1972 and 2012, in the Konya Basin. Non-parametric trend tests, (Sen’s T, Spearman’s Rho, Mann-Kendal, Sen’s T trend, Wald-Wolfowitz) and parametric test (mean square) were applied to annual total precipitations of 15 stations for trend analysis. The linear slopes (change per unit time) of trends are calculated by using a non-parametric estimator developed by Sen. The beginning of trends is determined by using the Mann-Kendall rank correlation test. In addition, homogeneities in precipitation trends are tested by using a method developed by Van Belle and Hughes. As a result of tests, negative linear slopes were found in annual total precipitations in Konya.

Keywords: trend analysis, precipitation, hydroclimatology, Konya

Procedia PDF Downloads 188

Authors: Venkatesh Kumar, Kasun Hewage, Rehan Sadiq

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Residential buildings consume significant amounts of energy and produce a large amount of emissions and waste. However, there is a substantial potential for energy savings in this sector which needs to be evaluated over the life cycle of residential buildings. Life Cycle Assessment (LCA) methodology has been employed to study the primary energy uses and associated environmental impacts of different phases (i.e., product, construction, use, end of life, and beyond building life) for residential buildings. Four different alternatives of residential buildings in Vancouver (BC, Canada) with a 50-year lifespan have been evaluated, including High Rise Apartment (HRA), Low Rise Apartment (LRA), Single family Attached House (SAH), and Single family Detached House (SDH). Life cycle performance of the buildings is evaluated for embodied energy, embodied environmental impacts, operational energy, operational environmental impacts, total life-cycle energy, and total life cycle environmental impacts. Estimation of operational energy and LCA are performed using DesignBuilder software and Athena Impact estimator software respectively. The study results revealed that over the life span of the buildings, the relationship between the energy use and the environmental impacts are identical. LRA is found to be the best alternative in terms of embodied energy use and embodied environmental impacts; while, HRA showed the best life-cycle performance in terms of minimum energy use and environmental impacts. Sensitivity analysis has also been carried out to study the influence of building service lifespan over 50, 75, and 100 years on the relative significance of embodied energy and total life cycle energy. The life-cycle energy requirements for SDH is found to be a significant component among the four types of residential buildings. The overall disclose that the primary operations of these buildings accounts for 90% of the total life cycle energy which far outweighs minor differences in embodied effects between the buildings.

Keywords: building simulation, environmental impacts, life cycle assessment, life cycle energy analysis, residential buildings

Procedia PDF Downloads 431

Authors: Mark Morgan, Christine Li, Shuangyu Xu, Jenny McCarty

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The National Wild and Scenic Rivers System was created by the U.S. Congress in 1968 (Public Law 90-542; 16 U.S.C. 1271 et seq.) to preserve outstanding natural, cultural, and recreational values of some U.S. rivers in a free-flowing condition for the enjoyment of present and future generations. This Act is notable for safeguarding the special character of these rivers while supporting management action that encourages public participation for co-creating river protection goals and strategies. This is not an easy task. To meet the challenges of modern ecosystem management, federal resource agencies must address many legal, environmental, economic, political, and social issues. The U.S. Forest Service manages a 44-mile section of the Eleven Point National Scenic River (EPR) in southern Missouri, mainly for outdoor recreation purposes. About half of the acreage is in private lands, while the remainder flows through the Mark Twain National Forest. Private land along the river is managed by scenic easements to ensure protection of scenic values and natural resources, without public access. A portion of the EPR lies adjacent to a 16,500-acre tract known as the Irish Wilderness. The spring-fed river has steep bluffs, deep pools, clear water, and a slow current, making it an ideal setting for outdoor enthusiasts. A 10-month visitor study was conducted at five access points along the EPR during 2019 so the US Forest Service could update their river management plan. A mail-back survey was administered to 560 on-site visitors, yielding a response rate of 53%. Although different types of visitors use the EPR, boating and fishing were the predominant forms of outdoor recreation. Some river use was from locals, but other visitors came from farther away. Formulating unbiased policies for outdoor recreation is difficult because managers must assign relative values to recreational activities and travel distance. Because policymaking is a subjective process, management decisions can affect user groups in different ways (i.e., boaters vs. fishers; proximate vs. distal visitors), as seen through a GIS analysis.

Keywords: activity selection, forest recreation, policy, travel distance

Procedia PDF Downloads 105