Statistics

The objective of this paper is to present the properties and potentials of Gumbel Power function (GuPF) distribution to rainfall and wind speed datasets using the T-X methodology. The density and hazard rate function of the GuPF distribution are unimodal and increasing respectively. Statistical properties of the new distribution such as quantile, moments, and probability weighted moments (PWMs), order statistics and entropy are derived. The Maximum likelihood estimation method is used to estimate the parameters of the proposed model. The superiority of GuPF distribution over other distributions with the same baseline is illustrated using two environmental datasets.

Flexibility in shape and scale of Burr XII distribution can make close approximation of numerous well-known probability density functions. Due to these capabilities, the usages of Burr XII distribution are applied in risk analysis, lifetime data analysis and process capability estimation. In this paper the Cross-Entropy (CE) method is further developed in terms of Maximum Likelihood Estimation (MLE) to estimate the parameters of Burr XII distribution for the complete data or in the presence of multiple censoring. A simulation study is conducted to evaluate the performance of the MLE by means of CE method for different parameter settings and sample sizes. The results are compared to other existing methods in both uncensored and censored situations.

Kemajuan teknologi dunia telah membawa banyak perubahan dalam dunia pendidikan. Tujuan kajian ini adalah untuk meninjau inovasi pengajaran bersepadu dan strategi pengajaran dan pembelajaran di era Revolusi Industri 4.0. Secara khususnya objektif kajian adalah mengenalpasti keperluan penggunaan inovasi dalam pengajaran, pembelajaran dan pemudahcaraan. Metodologi kajian ini adalah berbentuk kepustakaan. Penggunaan teknologi digital dapat mengubah suasana pengajaran dan pembelajaran yang lebih moden dan menarik berbanding kaedah tradisional. Hasil kajian mendapati bahawa strategi pengajaran dan pembelajaran bersepadu yang digunakan di era Revolusi Industri 4.0 adalah berteraskan empat elemen utama merupakan teras pendidikan bersepadu dalam strategi aplikasi iaitu creativity , reflectivity , reciprocity dan responsibility .  Hasil kajian juga mendapati penggunaan teknologi digital seperti pembelajaran teradun ( blended learning ), i-Folio dan Massive Open Online Courses (MOOC) membantu ...

Many phenomena are complex and therefore difficult to measure and to evaluate. Research, in the last years, has been focusing on the development and use of a system of Composite Indicators in order to obtain a global description of a complex phenomenon and to convey a suitable synthesis of information. The existing literature offers several alternative methods for obtaining a Composite Indicators. The work focuses on building them through to Structural Equation Modeling, specifically with the use of Partial Least Squares-Path Modeling. In recent years many advances have been developed, in the context of these models to solve some problems related to the role that the Composite Indicators play within that system; in particular, the research focuses on a particular aspect linked to the high level of abstraction, when a Composite Indicator is manifold, lacks its own manifest variables and is described by various underlying blocks. In this regard we have proposed two alternative methods for analyzing and studying higher-order construct Composite Indicator, on the calculation of the estimates for the determination of endogenous block, so as to be the best estimated and represented by the blocks below.

Partial least squares path modeling presents some inconsistencies in terms of coherence with the predictive directions specified in the inner model (i.e. the path directions), because the directions of the links in the inner model are not taken into account in the iterative algorithm. In fact, the procedure amplifies interdependence among blocks and fails to distinguish between dependent and explanatory blocks. The method proposed in this paper takes into account and respects the specified path directions, with the aim of improving the predictive ability of the model and to maintain the hypothesized theoretical inner model. To highlight its properties, the proposed method is compared to the classical PLS path modeling in terms of explained variability, predictive relevance and interpretation using artificial data through a real data application. A further development of the method allows to treat multi-dimensional blocks in composite-based path modeling.

A common misunderstanding found in the literature is that only PLS-PM allows the estimation of SEM including formative blocks. However, if certain model specification rules are followed, the model is identified, and it is possible to estimate a Covariance-Based SEM with formative blocks. Due to the complexity of both SEM estimation techniques, we study, in the framework of the same simulation design, their relative performance, analysing the bias and the variability of the estimates. We find that both PLSPM and ML-SEM perform particularly well in terms of bias and efficiency of the parameter estimates when the variance of the disturbance in the formative block is small. As we increase the variance of the disturbance, the bias of the inner PLS estimates grows significantly, while the variability holds steady to a very low value. On the contrary, the inner ML estimates present a minor degree of bias, but the variability of grows drastically. Nevertheless, the two approaches behave almost equally in the formative outer block.

To what extent does individual student change (growth) over the academic year statistically explain why students differ in end-of-year performance after accounting for performance on interim assessments? The four growth estimates examined in this report (simple difference, average difference, ordinary least squares, and empirical Bayes) all contributed significantly to predicting performance on the end-of-year criterion-referenced reading test when performance on the initial (fall) interim assessment was used as a covariate. The simple difference growth estimate was the best predictor when controlling for mid-year (winter) status, and all but the simple difference estimate contributed significantly when controlling for final (spring) status. Quantile regression suggested that the relations between growth and the outcome were conditional on the outcome, implying that traditional linear regression analyses could mask the predictive relations.

Bayesian estimation of the linear regression parameter system is considered by deploying Median Rank Set Sampling (MRSS). The full conditional distributions and the associated posterior distribution are obtained. Therefore, based on Markov Chain Monte Carlo simulation, the Bayesian point estimates and credible intervals for the regression parameters are determined. To measure the efficiency of the obtained Bayesian estimates concerning the frequentist estimates we compute the asymptotic relative efficiency of the obtained Bayesian estimates using Markov Chain Monte Carlo simulation. This study shows that the Bayesian estimation of the simple linear regression parameters under frequentist MRSS is highly beneficial and much superior to the RSS scheme.

College student voter turnout reflects the degree to which students are being taught to support and protect democracy, long a core mission of the U.S. higher education system. Yet, college student voter turnout is generally very low. In 2020-2021, campuses around the country participated in a coordinated program to increase college student voter registration during the COVID-19 pandemic. Using survey and focus group data collected in spring 2021, we find evidence of understudied barriers to student registration that continue to restrict the effectiveness of those efforts. Specifically, we find that students need help determining whether or how to choose a political party affiliation and whether to register with their campus address. Efforts to help students surmount these understudied challenges are a potentially powerful means of increasing youth registration and turnout. We also find that students operated as knowledge brokers, sharing with family members the information they were receiving on campus about how to register and vote using new virtual options offered due to the pandemic.

In this paper we investigate a technique for fusing approximate knowledge obtained from distributed, heterogeneous information sources. This issue is substantial, e.g., in modeling multiagent systems, where a group of loosely coupled heterogeneous agents cooperate in achieving a common goal. Information exchange, leading ultimately to knowledge fusion, is a natural and vital ingredient of this process. We use a generalization of rough sets and relations , which depends on allowing arbitrary similarity relations. The starting point of this research is , where a framework for knowledge fusion in multiagent systems is introduced. Agents' individual perceptual capabilities are represented by similarity relations, further aggregated to express joint capabilities of teams. This aggregation, expressing a shift from individual to social level of agents' activity, has been formalized by means of dynamic logic. The approach of Doherty et al. (2007) [6] uses the full propositional dynamic logic, which does not guarantee tractability of reasoning. Our idea is to adapt the techniques of Nguyen to provide an engine for tractable approximate database querying restricted to a Horn fragment of serial dynamic logic. We also show that the obtained formalism is quite powerful in applications.

The estimation of parameters of the log normal distribution based on complete and censored samples are considered in the literature. In this article, the problem of estimating the parameters of log normal mixture model is considered. The Expectation Maximization algorithm is used to obtain maximum likelihood estimators for the parameters, as the likelihood equation does not yield closed form expression. The standard errors of the estimates are obtained. The methodology developed here is then illustrated through simulation studies. The confidence interval based on large-sample theory is obtained.

Lithium-ion batteries have an essential characteristic in consumer electronics applications and electric mobility. However, predicting their lifetime performance is a difficult task due to the impact of operating and environmental conditions. Additionally, state-of-health (SOH) and remaining-useful-life (RUL) predictions have developed into crucial components of the energy management system for lifetime prediction to guarantee the best possible performance. Due to the non-linear behaviour of the health prediction of electric vehicle batteries, the assessment of SOH and RUL has therefore become a core research challenge for both business and academics. This paper introduces a comprehensive analysis of the application of machine learning in the domain of electric vehicle battery management, emphasizing state prediction and ageing prognostics. The objective is to provide comprehensive information about the evaluation, categorization and multiple machine-learning algorithms for predicti...

This research clarified a complete design for a renewable microgrid for Al-aroub technical college in Palestine. It consists of various renewable energy systems, including the photovoltaic system, biogas as a primary energy source, a fuel cell generator and a hydrogen storage unit, which can provide electricity to developing economies. As the photoelectric generator and the methane generator provide sufficient electrical energy during the day that covers the requirements of the different loads on the farm, while the excess energy is transferred to the electrolyzer for hydrogen production and storage, and when the load needs more energy, the electric fuel cells are turned on where the hydrogen is obtained from the energy storage unit. An energy management strategy was also proposed in this study as the newly developed network control and management system, and Matlab was chosen to undertake this task. Moreover, the RETscreen Expert software that enables to determine the optimum size that meets the potential demand along with the most feasible economic values and guarantees the highest system reliability. Therefore, three scenarios were proposed and tested, the first being the basic model which was a solar system with traditional batteries, the second a solar system, biogas with batteries, and finally, solar energy, biogas, and fuel cell with hydrogen energy storage unit. Technical analysis of the combined generation was also performed using Power world simulator to obtain constant frequency and voltage (stability conditions). The simulation results clearly show that the hybrid renewable energy system (HRES) consist of PV, bioenergy and small-scale fuel cell generator is a more economical configuration than single renewable energy systems with battery which has a total net cost of $ 473570, levelized cost of energy (LCOE) of 0.157 $/kWh and the lowest CO2 emission model that was 2.1 tons per year.

The present data article describes high-school drop-out related web activities in Canada, from 2004 to 2012, obtained mining Google Trends (GT), using high-school drop-out as key-word. The searches volumes were processed, correlated and cross-correlated with statistical data obtained at national and province level and broken down for gender. Further, an autoregressive movingaverage (ARMA) model was used to model the GT-generated data. From a qualitative point of view, GT-generated relative search volumes (RSVs) reflect the decrease in drop-out rate. The peak in the Internet-related activities occurs in 2004 (56.35%, normalized value), and gradually declines to 40.59% (normalized

We analyze a decentralized trading process in a basic labor market where heterogeneous firms and workers meet directly and randomly, and negotiate salaries with each other over time. Firms and workers may not have a complete picture of the entire market and can thus behave myopically in the process. Our main result establishes that, starting from an arbitrary initial market state, there exists a finite sequence of successive myopic (firm-worker) pair improvements, or bilateral trades, leading to a stable matching between firms and workers with a scheme of competitive salary offers. An important implication of this result is that a general random process where every possible bilateral trade is chosen with a positive probability converges with probability one to a competitive equilibrium of the market.

We study a test statistic based on the integrated squared difference between a kernel estimator of the copula density and a kernel smoothed estimator of the parametric copula density. We show for fixed smoothing parameters that the test is consistent and that the asymptotic properties are driven by a U -statistic of order 4 with degeneracy of order 1. For practical implementation we suggest to compute the critical values through a semiparametric bootstrap. Monte Carlo results show that the bootstrap procedure performs well in small samples. In particular size and power are less sensitive to smoothing parameter choice than they are under the asymptotic approximation obtained for a vanishing bandwidth.

We study the asymptotic distribution of Tikhonov Regularized estimation of quantile structural effects implied by a nonseparable model. The nonparametric instrumental variable estimator is based on a minimum distance principle. We show that the minimum distance problem without regularization is locally ill-posed, and consider penalization by the norms of the parameter and its derivatives. We derive pointwise asymptotic normality and develop a consistent estimator of the asymptotic variance. We study the small sample properties via simulation results, and provide an empirical illustration to estimation of nonlinear pricing curves for telecommunications services in the U.S.

This paper describes the background to the collaboration between IFLA, UNESCO Institute for Statistics and ISO to develop new library statistics. It outlines the inadequacies of currently available data and the principles underlying the data elements selected for inclusion in the new questionnaire.

ABSTRAK Rahmawati mohungo, Nim 931 410 209. "Pengaruh Perubahaan Inflasi, Dana Pihak Ketiga, dan Tingkat Suku Bunga Kredit modal Kerja terhadap Kredit Modal Usaha, Studi Kasus Pada Bank Umum di Gorontalo, Periode 2010-2013. Skripsi, Program Studi S1 Manajemen Jurusan Manajemen, Fakultas Ekonomi dan Bisnis, Universitas Negeri Gorontalo. Dibawah bimbingan Bapak Raflin Hinelo., S.Pd. M.Si selaku pembimbing I dan Ibu Dewi Indrayani Hamim., SE., MM selaku pembimbing II. Penelitian ini bertujuan untuk mengetahui pengaruh perubahaan Inflasi, Dana Pihak Ketiga, dan Tingkat Suku Bunga Kredit modal Kerja terhadap Kredit Modal Usaha, Studi Kasus Pada Bank Umum di Gorontalo, Periode 2010-2013. Model analisis yang dipakai dalam penelitian ini adalah model analisis berganda dengan metode uji t dan uji F. Berdasarkan Hasil penelitian menunjukkan bahwa secara simultan Inflasi, Dana Pihak Ketiga, dan Tingkat Suku Bunga Kredit modal Kerja memiliki pengaruh signifikan sebesar 0,907 atau 90,7%. Da...

Each year, the American Cancer Society estimates the number of new cancer cases and deaths expected in the United States in the current year and compiles the most recent data regarding cancer incidence, mortality, and survival based on incidence data from the National Cancer Institute, the Centers for Disease Control and Prevention, and the North American Association of Central Cancer Registries and mortality data from the National Center for Health Statistics. Incidence and death rates are age-standardized to the 2000 US standard million population. A total of 1,529,560 new cancer cases and 569,490 deaths from cancer are projected to occur in the United States in 2010. Overall cancer incidence rates decreased in the most recent time period in both men (1.3% per year from 2000 to 2006) and women (0.5% per year from 1998 to 2006), largely due to decreases in the 3 major cancer sites in men (lung, prostate, and colon and rectum [colorectum]) and 2 major cancer sites in women (breast and colorectum). This decrease occurred in all racial/ethnic groups in both men and women with the exception of American Indian/Alaska Native women, in whom rates were stable. Among men, death rates for all races combined decreased by 21.0% between 1990 and 2006, with decreases in lung, prostate, and colorectal cancer rates accounting for nearly 80% of the total decrease. Among women, overall cancer death rates between 1991 and 2006 decreased by 12.3%, with decreases in breast and colorectal cancer rates accounting for 60% of the total decrease. The reduction in the overall cancer death rates translates to the avoidance of approximately 767,000 deaths from cancer over the 16-year period. This report also examines cancer incidence, mortality, and survival by site, sex, race/ethnicity, geographic area, and calendar year. Although progress has been made in reducing incidence and mortality rates and improving survival, cancer still accounts for more deaths than heart disease in persons younger than 85 years. Further progress can be accelerated by applying existing cancer control knowledge across all segments of the population and by supporting new discoveries in cancer prevention, early detection, and treatment.

The uniaxial compressive strength (UCS) of rocks is one of the key parameters for evaluating the safety and stability of civil and mining structures. In this study, 386 rock samples containing four properties named the load strength (PLS), the porosity (Pn), the P-wave velocity (Vp), and the Schmidt hardness rebound number (SHR) are utilized to predict the UCS using several typical empirical equations (EA) and artificial intelligence (AI) methods, i.e., 16 single regression (SR) equations, 2 multiple regression (MR) equations, and the random forest (RF) models optimized by grey wolf optimization (GWO), moth flame optimization (MFO), lion swarm optimization (LSO), and sparrow search algorithm (SSA). The root mean square error (RMSE), determination coefficient (R2), Willmott’s index (WI), and variance accounted for (VAF) are used to evaluate the predictive performance of all developed models. The evaluation results show that the overall performance of AI models is superior to empirica...

The purpose of this study is to analyze the soundness level of PT BCA Syariah. This study is descriptive research with a quantitative approach.The paper used data on the annual financial statements of PT BCA Syariah for the period 2017 until 2021. The analysis using CAMELS, RGEC and Altman Z-Score methods. The results show that overall PT BCA Syariah in the 2017 until 2021 period was at the Health level in the CAMELS, RGEC and Altman Z-Score assessment methods. The highest health and health condition of PT BCA Syariah during the Covid-19 pandemic is a management effort in carrying out appropriate, focused and planned strategies and risk mitigation. The three analytical methods show the same results so that they can be used as a reference for assessing the health of other Islamic banks.

Selection indexes represent the real efforts of a breeding program to obtain genetic gains for various significant traits simultaneously. As such, this study selected superior F 5 lines by combined selection for fruit quality using an index based on predicted genotypic values via residual or restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) methodology weighted by agronomic weights. To do so, 97 F 5 papaya lines obtained by the single seed descent method, resulting from the biparental cross between the genotypes JS-12 and Sekati, were evaluated for the main traits related to fruit quality. Results of the analysis of deviance and the genetic parameters indicated that there was genetic variability, indicating possible success in the selection process. Based on the selection index, 29 lines were selected as superior for fruit quality with 'Formosa' pattern. The strategy of the selection index combining BLUPs of multiple traits of interest associated with agronomic weights allows maximizing genetic progress while discarding less-promising genotypes.

We present mass spectrum and magnetic moments of the nnQQ states, where n = u, d, s and Q = c, b. We solve four-body Schrödinger equation with a quark potential model by using diffusion Monte Carlo (DMC) method. The quark potential is based on the Coulomb, confinement and spin-spin interaction terms. We find mass and magnetic moment of the T + cc state as M T + cc = 3892 MeV and µ = 0.28µN ,respectively. We also find the mass and magnetic moment of T - bb as M T - bb = 10338 MeV and µ = -0.32µN , respectively. We find some bound state candidates of doubly heavy tetraquark systems with I(J P ) = 0(1) + nn bb , I(J P ) = 0(0) + nnc b, I(J P ) = 0(1) + nnc b, and I(J P ) = 1/2(1) + ns bb . We compare our results with other approaches in the literature.

Safety equivalence studies may be required to demonstrate that a new procedure or process is at least as safe as a previous one. They usually involve low or very low outcome rates that are often not precisely determined, making patient-based sample sizing uncertain. Using a reverse sampling approach, a method is derived from standard equations to estimate the number of events that need to be observed to demonstrate equivalence using the confidence interval approach. For instance, for a one-sided (nonsuperiority) hypothesis, 5% alpha risk, and 80% power, almost 100 events need to be observed in each study arm to demonstrate equivalence within 30%, or 250 events for 20% equivalence. The number of patients to be included can be derived directly from expected event rates.

In this paper, the delete-mj jackknife estimator is proposed. This estimator is based on samples obtained from the original sample by successively removing mutually exclusive groups of unequal size. In a Monte Carlo simulation study, a hierarchical linear model was used to evaluate the role of nonnormal residuals and sample size on bias and efficiency of this estimator. It is shown that bias is reduced in exchange for a minor reduction in efficiency. The accompanying jackknife variance estimator even improves on both bias and efficiency, and, moreover, this estimator is mean-squared-error consistent, whereas the maximum likelihood equivalents are not.

This study aims to determine whether the dimensions in service quality that is tangible, reliability, responsiveness, assurance, and empathy affects customer satisfaction at Tyas Kebaya boutiques and analyze the most dominant factor in influencing customer satisfaction at Tyas Kebaya boutiques The population in this study are those customers who order kebaya at Tyas Kebaya Boutique. Samples taken as many as 100 respondents using the technique of Non-Probability Sampling with purposive sampling approach, the technique of sampling data source with a certain considerations which data sources are considered most knowledgeable about what to expect, making it easier for researchers to explore objects or social situations under investigation. Based on statistical data analysis, indicators in this study are valid and are reliable variables. The sequence individually on each of the most influential variable is variable assurance with a regression coefficient of 0.410, and empathy with a regr...

Six simple, dynamic soft sensor methodologies with two update conditions were compared on two experimentally-obtained datasets and one simulated dataset. The soft sensors investigated were: moving window partial least squares regression (and a recursive variant), moving window random forest regression, feedforward neural networks, mean moving window, and a novel random forest partial least squares regression ensemble (RF-PLS). We found that, on two of the datasets studied, very small window sizes (4 samples) led to the lowest prediction errors. The RF-PLS method offered the lowest one-step-ahead prediction errors compared to those of the other methods, and demonstrated greater stability at larger time lags than moving window PLS alone. We found that this method most adequately modeled the datasets that did not feature purely monotonic increases in property values. In general, we observed that linear models deteriorated most rapidly at more delayed model update conditions while nonli...

This paper discusses a modification of the kappa measure of surprise and uses it to build semi-qualitative probabilistic networks. The new measure is designed to enable the definition of partial-order relations on its conditional values and is hence used to define qualitative influences over the edges of the network, similarly to Qualitative Probabilistic Networks. The resulting networks combine the advantages of kappa calculus of robustness and ease of assessment and the efficiency of Qualitative Probabilistic Networks. The measure also enables a built-in tradeoff resolution mechanism for the proposed network.

Purpose: This paper aims to develop a corporate failure prediction model for construction companies in Poland that allow assessing their financial situation and credit risk. Design/Methodology/Approach: For this purpose, the following research methods have been used, descriptive and comparative analysis, subject literature review, and logit analysis. The Polish construction companies' financial data in this research come from the Emerging Markets Information Service (EMIS). To achieve the main goal of the research, the logit model was built. The significance test, error matrix, and ROC curve were used to assess the quality of the estimated binary logit model. Findings: Based on the research, we identify seven financial indicators that significantly impact the probability of poor financial condition. The following variables are current assets turnover, debt to assets ratio, operating profit to assets, gross profit to assets, operating profit plus amortization to short-term liabilities, current assets to assets ratio, and equity to assets ratio. The research results show that corporate failure prediction models are interesting and important tools to assess the financial situation. Based on the developed model, it has been found that the growth of debts increases the credit risk of construction companies. Moreover, the increase in the share of current assets in the total assets harms the financial condition. Also, the risk of insolvency decreases with growing profitability measured by the rate of return on assets. Practical Implications: The built logit model can be beneficial for investment loan providers, insurance companies, and entities selecting contractors in construction projects due to the possibility of the credit risk assessment. Originality/Value: The use of logit models to identify statistically significant corporate failure prediction factors for construction companies in Poland.

Stem diameter variability is an essential inventory result that provides useful information in forest management decisions. Little has been done to explore the modelling potentials of standard deviation (SDD) and coefficient of variation (CVD) of diameter at breast height (dbh). This study, therefore, was aimed at developing and testing models for predicting SDD and CVD in stands of Pinus caribaea Morelet (pine) in south west Nigeria. Sixty temporary sample plots of size 20 m×20 m, ranging between 15 and 37 years were sampled, covering the entire range of pine in south west Nigeria. The dbh (cm), total and merchantable heights (m), number of stems and age of trees were measured within each plot. Basal area (m 2 ), site index (m), relative spacing and percentile positions of dbh at 24 th , 63 rd , 76 th and 93 rd (i.e. P24, P63, P76 and P93) were computed from measured variables for each plot. Linear mixed model (LMM) was used to test the effects of locations (fixed) and plots (random). Six candidate models (3 for SDD and 3 for CVD), using three categories of explanatory variables (i.e. (i) only stand size measures, (ii) distribution measures, and (iii) combination of i and ii). The best model was chosen based on smaller relative standard error (RSE), prediction residual sum of squares (PRESS), corrected Akaike Information Criterion (AICc) and larger coefficient of determination (R 2 ). The results of the LMM indicated that location and plot effects were not significant. The CVD and SDD models having only measures of percentiles (i.e. P24 and P93) as predictors produced better predictions than others. However, CVD model produced the overall best predictions, because of the lower RSE and stability in measuring variability across different stand developments. The results demonstrate the potentials of CVD in modelling stem diameter variability in relationship with percentiles variables.

Background and Purpose: Efficient forest stand management requires reliable estimates of growing stock. The reliability of stem volume estimates depends on the range and extent of available sample data. The potentials of canopy layers stratification in pure plantations as a means of improving the accuracy of stem volume equations have not been fully explored. Linear Mixed Model (LMM) approach is a statistical technique capable of yielding a more efficient prediction under clustered data structure. This study investigates the existence and potentials of canopy stratifications for improving the reliability of stem volume prediction equations under pure plantations using linear mixed model approach. Materials and Methods: Pinus caribaea Morelet plantations in Oluwa Forest Reserve, Ondo State, Nigeria were investigated. Individual tree growth variables, including diameters, heights and crown measurements were obtained in 2010 on twenty-five 0.04 ha plots representing five different stands planted between 1979 and 1991. Visual assessment of the trees within each plot was also done to classify them into four canopy strata (i.e. dominant, co-dominant, intermediate and suppressed). Linear mixed model approach was used to analyze the tree growth data using SAS Proc Mixed. Two variants of volume equations; simple linear and exponential were investigated. Results: Results show that simple linear mixed model consistently give better fit criteria (e.g. AIC) of 135.8, 18.9, -214.7 and -174.6 under dominant, co-dominant, intermediate and suppressed canopy layers, respectively. The covariance parameter estimate for dominant canopy (0.2219) is about 370 as large as that of suppressed (0.0006). This implies that canopy layers not only influence stem volume prediction but also reduce within-stand variance as well. Simple linear mixed model produced better fitting criteria in terms of lower values of Akaike's and Bayesian Information Criteria. Canopy stratification in pure stands of Pinus caribaea showed great potentials in improving the predictive ability of volume equations.

Rutin is a citrus flavonoid glycoside and exhibits anti-inflammatory, antihepatotoxic, antiulcer, antiallergic, antiviral and antioxidant activity. It is found in many herbal plants. The aim of this research work was to develop and validate a simple, accurate, precise and economic UV spectroscopic method for the determination of rutin in Cocculus hirsutus. The estimation of rutin by spectroscopic with maximum absorption at λmax 262.10 nm using developed solvent [n-butanol : water : acetic acid (7:1:1)]. Beer-Lambert's law was obeyed in the concentration range of 0-18 μg/ml and was described by the regression equation y = 0.032x + 0.008 with a regression coefficient (r 2 ) = 0.999 (n = 5). For Rutin, the value of molar absorptivity and Sandell's sensitivity were 1.6429 x 10 6 L/mol/cm and 0.0372 x 10 -2 μg/cm 2 , respectively and of LOD and LOQ were found to be 0.6135 and 1.8593 μg/ml, respectively. The percentage recovery of rutin was found to be 98.22 %. The % RSD for intraday and interday precision was 1.33 and 1.52 respectively. It showed excellent % RSD which is less than 2. The developed method was validated in terms accuracy, precision, linearity and robustness. The statistically validated results indicate that the proposed method has good sensitivity, accuracy and precision. The method is simple and economic as compare to chromatographic methods. The developed method was successfully applied for the determination of Rutin in herbal plants and its product.

Majorization is a fundamental model of uncertainty that possesses several applications in areas like thermodynamics or entanglement theory, and constitutes one of the pillars of the modern resource-theoretic approach to physics. Here, we improve on its relation to measurement apparatus. In particular, after discussing what the proper notion of second law in this scenario is, we show that, for a sufficiently large state space, any family of entropy-like functions constituting a second law is necessarily countably infinite. Moreover, we provide an analogous result for a variation of majorization known as thermo-majorization which, in fact, does not require any constraint on the state space provided the equilibrium distribution is not uniform. Lastly, we conclude by discussing the applicability of our results to molecular diffusion.

We define common thermodynamic concepts purely within the framework of general Markov chains and derive Jarzynski's equality and Crooks' fluctuation theorem in this setup. In particular, we regard the discrete-time case, which leads to an asymmetry in the definition of work that appears in the usual formulation of Crooks' fluctuation theorem. We show how this asymmetry can be avoided with an additional condition regarding the energy protocol. The general formulation in terms of Markov chains allows transferring the results to other application areas outside of physics. Here, we discuss how this framework can be applied in the context of decision-making. This involves the definition of the relevant quantities, the assumptions that need to be made for the different fluctuation theorems to hold, as well as the consideration of discrete trajectories instead of the continuous trajectories, which are relevant in physics.

Information-theoretic principles for learning and acting have been proposed to solve particular classes of Markov Decision Problems. Mathematically, such approaches are governed by a variational free energy principle and allow solving MDP planning problems with information-processing constraints expressed in terms of a Kullback-Leibler divergence with respect to a reference distribution. Here we consider a generalization of such MDP planners by taking model uncertainty into account. As model uncertainty can also be formalized as an information-processing constraint, we can derive a unified solution from a single generalized variational principle. We provide a generalized value iteration scheme together with a convergence proof. As limit cases, this generalized scheme includes standard value iteration with a known model, Bayesian MDP planning, and robust planning. We demonstrate the benefits of this approach in a grid world simulation.

Cowpea is a dual-purpose protein crop which has a high nutritional value. However, many problems such as cowpea aphid-borne mosaic virus limit its production. This study aimed at assessing the resistance of varieties of cowpea to cowpea aphid-borne mosaic virus (CABMV). Thus, a complete diallel cross between five cowpea varieties was done in a partially balanced incomplete block plan (alpha design) with three replications. The F 1 descendants obtained and their parents were evaluated using five characters. Results obtained reveal that all characters discriminate the parents and F 1 descendants. All five parents tested revealed their resistance or susceptibility status to CABMV. The F 1 descendants from the cross between local Gorom x KVx640, KVx30-309-6G x KVx396-4-5-2D, KVx61-1 x KVx640, KVx640 x local Gorom and KVx640 x KVx61-1 which have shown a low severity value and a high weight of one hundred seeds are the best. They could be used in Burkina Faso' cowpea breeding program to develop resistant varieties to cowpea aphid-borne mosaic virus.

The annual global production of chickens exceeds 25 billion birds, and they are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an “iceberg indicator” of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotations, which is very labour-intensive and time-consuming. Thus, a novel light-VGG11 was developed to automatically identify chicken distress calls using recordings (3,363 distress calls and 1,973 natural barn sounds) collected on intensive chicken farms. The light-VGG11 was modified from VGG11 with a significantly smaller size in parameters (9.3 million vs 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e., precision (94.58%), recall (94.89%), F1-score (94.73%), and accuracy (95.07%), therefore more useful for model deployment in practice. To further improve the light-VGG11’s performance, we investigated ...

Background Many studies rely on self-reported height and weight. While a substantial body of literature exists on misreporting of height and weight, little exists on improving accuracy. The aim of this study was to determine, using an experimental design and a comparative approach, whether the accuracy of self-reported height and weight data can be increased by improving how these questions are asked in surveys, drawing on the relevant evidence from the psychology and survey research literatures. Methods Two surveys from two separate studies were used to test our hypotheses (Science Survey, n = 1,200; Eating Behaviours Survey, n = 200). Participants were randomly assigned to one of six conditions, four of which were designed to improve the accuracy of the self-reported height and weight data (“preamble”), and two of which served as the control conditions ( “no preamble”). Four hypotheses were tested: (H1) survey participants read a preamble prior to being asked their height and weig...