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This study aims at proposing a hybrid model for early cost prediction of a construction project. Early cost prediction for a construction project is the basic approach to procure a project within a predefined budget. However, most of the projects routinely face the impact of cost overruns. Furthermore, conventional and manual cost computing techniques are hectic, time-consuming and error-prone. To deal with such challenges, soft computing techniques such as artificial neural networks (ANNs), fuzzy logic and genetic algorithms are applied in construction management. Each technique has its own constraints not only in terms of efficiency but also in terms of feasibility, practicability, reliability and environmental impacts. However, appropriate combination of the techniques improves the model owing to their inherent nature.

This paper proposes a hybrid model by combining machine learning (ML) techniques with ANN to accurately predict the cost of pile foundations. The parameters contributing toward the cost of pile foundations were collected from five different projects in India. Out of 180 collected data entries, 176 entries were finally used after data cleaning. About 70% of the final data were used for building the model and the remaining 30% were used for validation.

The proposed model is capable of predicting the pile foundation costs with an accuracy of 97.42%.

Although various cost estimation techniques are available, appropriate use and combination of various ML techniques aid in improving the prediction accuracy. The proposed model will be a value addition to cost estimation of pile foundations.

In the recent years, the industries show interest in natural and synthetic fibre-reinforced hybrid composites due to weight reduction and environmental reasons. The purpose of this experimental study is to investigate the properties of the hybrid composites fabricated by using carbon, untreated and alkaline-treated hemp fibres.

The composites were tested for strengths under tensile, flexural, impact and shear loadings, and the water absorption characteristics were also observed. The finite element analysis (FEA) was carried out to analyse the elastic behaviour of the composites and predict the strength by using ANSYS 15.0.

From the experimental results, it is observed that the hybrid composites can withstand the maximum tensile strength of 61.4 MPa, flexural strength of 122.4 MPa, impact strength of 4.2 J/mm² and shear strength of 25.5 MPa. From the FEA results, it is found that the maximum stress during tensile, flexural and impact loading is 47.5, 2.1 and 1.03 MPa, respectively.

The results of the untreated and alkaline-treated hemp-carbon fibre composites were compared and found that the alkaline-treated composites perform better in terms of mechanical properties. Then, the ANSYS-predicted values were compared with the experimental results, and it was found that there is a high correlation occurs between the untreated and alkali-treated hemp-carbon fibre composites. The internal structure of the broken surfaces of the composite samples was analysed using a scanning electron microscopy (SEM) analysis.

This review article offers an insight into employer brand and its importance for organizations, as well as an overview of international employer brand based on research on this topic available to date.

An examination and critical evaluation of 37 research articles, two scientific monographs and a chapter was conducted. The selection of articles was based on conducted content analysis.

Having an employer brand has become of utmost importance for many organizations since it was first described in academic literature in mid-1990s. Despite its key role in organizational success, there is a certain lack of recognition of employer brand in academic literature. While employer brand research is somewhat scarce, international employer brand research is almost non-existent. Organizations that operate on different international markets often recruit their employees internationally as well. However, employer brand developed and managed locally differs from the one developed and managed globally.

This review is based on a small number of articles available in the databases. Additionally, only the research papers written in English were included in the review.

This review paper offers a much-needed overview of literature on employer branding within international context. International employer brands and international employer branding have so far been neglected within employer branding literature, despite the obvious need for differentiation. Therefore, this article seeks to provide a systematic overview and identify relevant characteristics of the international employer brand.

Breast cancer (BC) is one of the leading cancer in the world, BC risk has been there for women of the middle age also, it is the malignant tumor. However, identifying BC in the early stage will save most of the women’s life. As there is an advancement in the technology research used Machine Learning (ML) algorithm Random Forest for ranking the feature, Support Vector Machine (SVM), and Naïve Bayes (NB) supervised classifiers for selection of best optimized features and prediction of BC accuracy. The estimation of prediction accuracy has been done by using the dataset Wisconsin Breast Cancer Data from University of California Irvine (UCI) ML repository. To perform all these operation, Anaconda one of the open source distribution of Python has been used. The proposed work resulted in extemporize improvement in the NB and SVM classifier accuracy. The performance evaluation of the proposed model is estimated by using classification accuracy, confusion matrix, mean, standard deviation, variance, and root mean-squared error.

The experimental results shows that 70-30 data split will result in best accuracy. SVM acts as a feature optimizer of 12 best features with the result of 97.66% accuracy and improvement of 1.17% after feature reduction. NB results with feature optimizer 17 of best features with the result of 96.49% accuracy and improvement of 1.17% after feature reduction.

The study shows that proposal model works very effectively as compare to the existing models with respect to accuracy measures.

The purpose of this paper is to examine the extent to which demographic factors and corporate ethical value impact on ethical decisions of Malaysian accounting practitioners.

A questionnaire survey was carried out to elicit opinions from accounting practitioners on corporate ethical values and ethical judgements. Regression analysis was performed on 201 completed and useable questionnaires.

The regression analysis shows that corporate ethical value is a significant factor determining ethical judgements. Age is also a significant factor, with older accounting practitioners being stricter in their ethical stance. To a lesser extent, gender is also significant, with females exhibiting higher ethical judgements than males.

The regression model reports an adjusted R-squared of 19.2%, which suggests further work in this area is necessary to identify other determinants for (un)ethical judgements. A qualitative approach such as interviewing corporate players may shed light on other possible factors.

The findings suggest that regulatory efforts have contributed towards a more ethically imbued corporate environment. The Malaysian Code on Corporate Governance (2012), which recommends corporations to have formalized ethical standards and women on corporate boards, appears to have positive influence on creating a more ethical working climate. In addition, the enactment of the Minimum Retirement Age Act (2012) also proves relevant in further promoting ethical judgements.

The study highlights the applicability of the theory of moral development to an Asian developing country, and that gender, age and corporate ethical values are complementary in influencing ethical judgements of accounting practitioners in Malaysia.

Due to the importance of performance management in any organizational structure, the present study aims to analyze the mediating role of an employee's reaction to the employee's supervisor' feedback on the impact of the performance management system on job satisfaction and supervisor–employee relationship.

A quantitative study was conducted, with data collected by questionnaire, where 1815 workers from a customer service company in Portugal participated and with the data analyzed using structural equation model.

Three effects were observed in this study: first, the importance of performance management on the reaction to feedback and on the supervisor–employee relationship; second, reaction to feedback fully mediated the effect of performance management on job satisfaction and third, reaction to feedback partially mediated the effect of the performance management on the supervisor–employee relationship.

Despite the growing interest in research on performance management, this study suggests that there are still some areas in need of additional research attention, namely on the important role that adequate feedback to the employee on his/her performance can have. Implications for research on performance management are developed.

Aircraft pitch control is fundamental for the performance of micro aerial vehicles (MAVs). The purpose of this paper is to establish a simple experimental procedure to calibrate pitch instrumentation and classical control algorithms. This includes developing an efficient pitch angle observer with optimal estimation and evaluating controllers under uncertainty and external disturbances.

A wind tunnel test bench is designed to simulate fixed-wing aircraft dynamics. Key elements of the instrumentation commonly found in MAVs are characterized in a gyroscopic test bench. A data fusion algorithm is calibrated to match the gyroscopic test bench measurements and is then integrated into the autopilot platform. The elevator-angle to pitch-angle dynamic model is obtained experimentally. Two different control algorithms, based on model-free and model-based approaches, are designed. These controllers are analyzed in terms of parametric uncertainties due to wind speed variations and external perturbation because of sudden weight distribution changes. A series of experimental tests is performed in wind-tunnel facilities to highlight the main features of each control approach.

With regard to the instrumentation algorithms, a simple experimental methodology for the design of optimal pitch angle observer is presented and validated experimentally. In the context of the platform design and identification, the similitude among the theoretical and experimental responses shows that the platform is suitable for typical pitch control assessment. The wind tunnel experiments show that a fixed linear controller, designed using classical frequency domain concepts, is able to provide adequate responses in scenarios that approximate the operation of MAVs.

The aircraft orientation observer can be used for both pitch and roll angles. However, for simultaneousyaw angle estimation the proposed design method requires further research. The model analysis considers a wind speed range of 6-18 m/s, with a nominal operation of 12 m/s. The maximum experimentally tested reference for the pitch angle controller was 20°. Further operating conditions may require more complex control approaches (e.g. scheduling, non-linear, etc.). However, this operating range is enough for typical MAV missions.

The study shows the design of an effective pitch angle observer, based on a simple experimental approach, which achieved locally optimum estimates at the test conditions. Additionally, the instrumentation and design of a test bench for typical pitch control assessment in wind tunnel facilities is presented. Finally, the study presents the development of a simple controller that provides adequate responses in scenarios that approximate the operation of MAVs, including perturbations that resemble package delivery and parametric uncertainty due to wind speed variations.

COVID-19 accelerated the already growing prevalence of employees working remotely, and limited research exists on the effectiveness of proactive influence tactics in remote work settings. This study aims to identify which proactive influence tactics may best facilitate employee work engagement in a remote work setting.

Survey data stems from 231 employees who work remotely in the USA. Hierarchical regression was used to analyze the data and assess interaction effects.

Evidence was found for positive relationships between work engagement and multiple proactive influence tactics (collaboration, consultation, inspirational appeals, exchange, apprising, rational persuasion, personal appeals and ingratiation) and a negative relationship between work engagement and pressure. The percentage of time an employee spends working remotely moderated the proactive influence tactic-work engagement relationship. Significant interaction effects were found for exchange and personal appeals.

The results highlight several influence tactics that managers can consider using to increase employee work engagement. The findings also demonstrate the increasing effectiveness of certain influence tactics, such as exchange and personal appeals, as employees spend more time working remotely, shedding light on important considerations for managers seeking to optimize employee engagement in remote work environments.

The study contributes to the limited literature on proactive influence tactics and work engagement and examines these relationships in a remote work setting. In addition, it examines the moderating effect of the percentage of time an employee spends working remotely.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.