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This paper proposes a class of estimators for population correlation coefficient when information about the population mean and population variance of one of the variables is not available but information about these parameters of another variable (auxiliary) is available, in two phase sampling and analyzes its properties. Optimum estimator in the class is identified with its variance formula. The estimators of the class involve unknown constants whose optimum values depend on unknown population parameters. In earlier research it has been shown that when these population parameters are replaced by their consistent estimates the resulting class of estimators has the same asymptotic variance as that of optimum estimator. An empirical study is carried out to demonstrate the performance of the constructed estimators.

A purely theoretical approach has been found to be of limited value in the solution of practical Pattern Recognition problems. Difficulties arise when relating infinite mathematics to reality, e.g. “algorithmic convergence” must be replaced by a vaguer notion of “satisfactory performance”. Experimentation has been used to study this and related problems: a) Learning in noise; b) Similarity of classifiers; c) Instability of classifiers; d) Relating infinite‐sample analysis to finite data sets (reference to pdf estimation). Finally, the system requirements for effective experimentation are discussed.

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

The purpose of this paper is to present how prior knowledge about the impact of real estate features on value might be utilised in the econometric models of real estate appraisal. In these models, price is a dependent variable and real estate features are explanatory variables. Moreover, these kinds of models might support individual and mass appraisals.

A mixed estimation procedure was discussed in the research. It enables using sample and prior information in an estimation process. Prior information was provided by real estate experts in the form of parameter intervals. Also, sample information about the prices and features of undeveloped land for low-residential purposes was used. Then, mixed estimation results were compared with ordinary least squares (OLS) outcomes. Finally, the estimated econometric models were assessed with regard to both formal criteria and valuation accuracy.

The OLS results were unacceptable, mostly because of the low quality of the database, which is often the case on local, undeveloped real estate markets. The mixed results are much more consistent with formal expectations and the real estate valuations are also better for a mixed model. In a mixed model, the impact of each real estate feature could be estimated, even if there is no variability in the sample information. Valuations are also more precise in terms of their consistency with market prices. The mean error (ME) and mean absolute percentage error (MAPE) are lower for a mixed model.

The crucial problem in econometric property valuation is that it involves the unreliability of databases, especially on undeveloped, local markets. The applied mixed estimation procedure might support sample information with prior knowledge, in the form of stochastic restrictions imposed on parameters. Thus, that kind of knowledge might be obtained from real estate experts, practitioners, etc.

Classification techniques have been applied to many applications in various fields of sciences. There are several ways of evaluating classification algorithms. The analysis of such metrics and its significance must be interpreted correctly for evaluating different learning algorithms. Most of these measures are scalar metrics and some of them are graphical methods. This paper introduces a detailed overview of the classification assessment measures with the aim of providing the basics of these measures and to show how it works to serve as a comprehensive source for researchers who are interested in this field. This overview starts by highlighting the definition of the confusion matrix in binary and multi-class classification problems. Many classification measures are also explained in details, and the influence of balanced and imbalanced data on each metric is presented. An illustrative example is introduced to show (1) how to calculate these measures in binary and multi-class classification problems, and (2) the robustness of some measures against balanced and imbalanced data. Moreover, some graphical measures such as Receiver operating characteristics (ROC), Precision-Recall, and Detection error trade-off (DET) curves are presented with details. Additionally, in a step-by-step approach, different numerical examples are demonstrated to explain the preprocessing steps of plotting ROC, PR, and DET curves.

Keynes' criticisms of Tinbergen's pioneering econometric work are traced back to Keynes' concept of “inductive probability logic”. Induction had already been rejected by Popper as the basis of scientific method. He argued that theories could be corroborated but not proved by the failure of attempts to falsify them by observation and experiment. Economic theory is proto‐theory, which is not fully falsifiable, but which yields falsifiable results if appropriate econometric methods, or a method‐theory is applied to it. A useful method‐theory needs to go beyond description and forecasting to policy optimisation.

Unions and organizations interests are often seen to be in competition. However, union-voice hypothesis suggests that unions can provide a distinctive mechanism to lower organizational costs by reducing exit behavior, absence from work and conflict levels at work. This study aims to look at union participation as a form of voice which is affected by a number of antecedents and in turn has an effect upon the workers performance (i.e. worker behavior effectiveness [WBE]) in an organization.

The study draws on data from 340 permanent labors working in 19 manufacturing units across different regions of India to explore both the antecedents and outcomes of union participation. Hypotheses are tested using mediation analysis.

Results indicate statistically significant relationships between union participation, its antecedents and WBE, with union participation partially influencing the relationship between the constructs.

Uniqueness of the study lies in its findings which report positive relationship among union participation, its antecedents and behavior effectiveness. Contrary to the traditional belief that unions are detrimental to the health of any organization, the study suggests that workers decision to join and participate in unions should be viewed positively because only if a person is willing to stay with the organization, he/she seeks to resolve the issues/problems through collective mechanism of union participation and which in turn leads to enhanced performance, reduced absenteeism at the workplace.

This paper aims to investigate the overhead costs of construction contractors at the Gaza Strip, Palestine, specifically the level of contractors' awareness of the concept of overhead cost, their perception of main components of overhead cost, percentage of overhead to total project cost, method used to manage and control overhead cost, and reasons for increasing overhead cost.

In the research 40 contractors classified under the Palestinian Contractors Union (PCU) and United Nation Relief Works Agency (UNRWA) list were surveyed.

The findings indicate that the majority of contractors are aware of overhead costs in construction projects. The staff wages are the highest overhead costs component. The currency exchange rates, inflation, increase in financial costs among others lead to increase in overhead costs. The findings illustrate that the overhead costs are on average 11.1 percent of the total project cost. Controlling and managing overhead costs are considered the main tools to improve the companies' financial situation.

The trend in most companies is to adopt techniques to manage and control their cost components. Using activity based costing (ABCs) is one of the main techniques which the companies should take into consideration. The contractors' knowledge and awareness of the research finding will increase their chances of winning bids within an acceptable profit margin. Applied training courses are recommended for contractors to improve their awareness regarding the importance of overhead costs.

This study will extend contractors' awareness and knowledge through guidance on how to gear their financial resources carefully, and how to bid correctly in order to remain competitive in the market place.

The notion of social performance of the company (SPC) is a fundamental concept of the research on ethics of business and work on company-society relationships. The study raises several debates concerning SPC’s determinants. The purpose of this paper is to provide a framework of SPC along with its social and technological determinants. After identification of the determinants, the authors have searched through a managerial perspective to recognize the effects of these determinants on SPC.

Content analysis of 18 semi-structured interviews with the HR managers, and statistical analysis of data collected from Managers/HR Managers (n=250) working in private and public sector banks of Tunisia was undertaken. Structural equation modeling (SEM), has been used to test the hypotheses and statistically validate the proposed relationships. Data for the study were collected online.

Results indicate a strong interrelationship between SPC and its determinants. Such an interrelation aims to enrich the framework of analysis of the SPC by considering the action of social responsibility of the company, organizational commitment and managers’ characteristics on one hand, and human resources information system, the practices of knowledge management, and facilitating conditions for the use of the information and communication technologies on the other.

The study reconciles various perspectives in the SPC literature and presents a comprehensive model of SPC by identifying its determinants – social and technological, which could stimulate the SPC in Tunisian context.

This paper is concerned with the problem of estimating the uncertainty associated with a variable in a finite population. The study of this problem leads to the following conclusion: The classical measure of uncertainty, Shannon's entropy, is not suitable for sampling from finite populations; nevertheless, by using the entropy of order ? = 2, proposed by Havrda and Charvat, one can define an unbiased estimator of the uncertainty associated with the variable in both, the sampling with replacement and the sampling without replacement. This conclusion will be illustrated by an example.