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To assess the currency of descriptions found in SilverPlatter's Medline CDROM edition precisely, all the descriptions from 1993 were studied. The objectives were not only to quantify the mean currency of the articles, but also to establish whether the priority level, the periodicity and the subset had any influence on currency. As a secondary result, a database summarises characteristics and currency values for each serial The results show that the currency of a description can vary tremendously. Priority level, periodicity and prior database were found to have a low but highly significant influence on currency (p < 0.0001). The mean currency was 6.92 months, when Priority 1 journal articles had a mean currency of 4.53 months and appeared on CDROM two to three months earlier than other priority‐assigned journal articles. 40.28% of Priority 1 journals appeared on CDROM within three months of publication. The 10 most important serials (regarding ISI's impact factor for general medicine) are shown as examples of the database for secondary results.

The purpose of this paper is to assess the extent to which personal values affect entrepreneurial intentions and the extent to which this relationship depends on gender among the millennial generation.

This relationship was examined using the list of values (LOV). Based on a sample of 600 respondents born between 1977 and 1994, a self-administered online questionnaire was conducted.

The partial least squares structural equation modeling (PLS-SEM) approach demonstrated that Generation Y members who give higher priority to self-direction, social affiliation and hedonic orientation values have greater entrepreneurial intentions. Across gender, the PLS-multigroup analysis (MGA) approach reveals that self-direction values enhance entrepreneurial intention for Generation Y females but not for males. Social affiliation values improve entrepreneurial intention for Generation Y males but not for females. Hedonic orientation values rise entrepreneurial intentions for both Generation Y males and females similarly. The findings give also a ranking of the nine LOV.

Across-cultural comparisons are lacking in this research. This study only focuses on the value–intention relationship. Future research could study the value–attitude–behavior.

The results provide implications to all agents concerned by promoting new enterprises and feminine entrepreneurship regarding the implementation of personal values in fostering the venture creation process and stimulation of people to become business owners.

Little is known about the role of personal values in venture creation. The findings provide support for the role personal values play in building entrepreneurial intentions. The focus here was on Generation Y. The generation that faces problems of unemployment, job loss and poverty specifically in the time of crises of the COVID-19 pandemic. The value-based entrepreneurship approach is a proliferating field of research as the world seeks to rebuild economies.

For decades, continuous research work is going on to maximize the power harvested from the sun; however, there is only a limited analysis on exploiting the microwatt output power from indoor lightings. Microelectronic system has power demand in the µW range, and therefore, indoor photovoltaics would be appropriate for micro-energy harvesting appliances. “Energy harvesting is defined as the transfer process by which energy source is acquired from the ambient energy, stored in energy storage element and powered to the target systems”. The theory of energy harvesting is: gathering energy from surroundings and offering technological solutions such as solar energy harvesting, wind energy collection and vibration energy harvesting. “The solar cell or photovoltaic cell (PV), is a device that converts light into electric current using the photoelectric effect”. Factors such as light source, temperature, circuit connection, light intensity, angle and height can manipulate the functions of PV cells. Among these, the most noticeable factor is the light intensity that has a major impact on the operations of solar panels.

This paper aims to design an enhanced prediction model on illuminance or irradiance by an optimized artificial neural network (ANN). The input attributes or the features considered here are temperatures, maxim, TSL, VI, short circuit current, open-circuit voltage, maximum power point (MPP) voltage, MPP current and MPP power, respectively. To enhance the performance of the prediction model, the weights of ANN are optimally tuned by a new self-improved brain storm optimization (SI-BSO) model.

The superiority of the implemented work is compared and proved over the conventional models in terms of error analysis and prediction analysis. Accordingly, the presented approach was analysed and its superiority was proved over other conventional schemes such as ANN, ANN-Levenberg–Marquardt (LM), adaptive-network-based fuzzy inference system (ANFIS) and brainstorm optimization (BSO). In addition, analysis was held with respect to error measures such as mean absolute relative error (MARE), mean square root error (MSRE), mean absolute error and mean absolute percentage error. Moreover, prediction analysis was also performed that revealed the betterment of the presented model. More particularly, the proposed ANN + SI-BSO model has attained minimal error for all measures when compared to the existing schemes. More particularly, on considering the MARE, the adopted model for data set 1 was 23.61%, 48.12%, 79.39% and 90.86% better than ANN, ANN-LM, ANFIS and BSO models, respectively. Similarly, on considering data set 2, the MSRE of the implemented model was 99.87%, 70.69%, 99.57% and 94.74% better than ANN, ANN-LM, ANFIS and BSO models, respectively. Thus, the enhancement of the presented ANN + SI-BSO scheme has been validated effectively.

This work has established an improved illuminance/irradiance prediction model using the optimization concept. Here, the attributes, namely, temperature, maxim, TSL, VI, Isc, Voc, Vmpp, Impp and Pmpp were given as input to ANN, in which the weights were chosen optimally. For the optimal selection of weights, a novel ANN + SI-BSO model was established, which was an improved version of the BSO model.

Data mining plays a major role in forecasting the open price details of the stock market. However, it fails to address the dimensionality and expectancy of a naive investor. Hence, this paper aims to study a future prediction model named time series model is implemented.

In this model, the stock market data are fed to the proposed deep neural networks (DBN), and the number of hidden neurons is optimized by the modified JAYA Algorithm (JA), based on the fitness function. Hence, the algorithm is termed as fitness-oriented JA (FJA), and the proposed model is termed as FJA-DBN. The primary objective of this open price forecasting model is the minimization of the error function between the modeled and actual output.

The performance analysis demonstrates that the deviation of FJA–DBN in predicting the open price details of the Tata Motors, Reliance Power and Infosys data shows better performance in terms of mean error percentage, symmetric mean absolute percentage error, mean absolute scaled error, mean absolute error, root mean square error, L1-norm, L2-Norm and Infinity-Norm (least infinity error).

The proposed model can be used to forecast the open price details.

The investors are constantly reviewing past pricing history and using it to influence their future investment decisions. There are some basic assumptions used in this analysis, first being that everything significant about a company is already priced into the stock, other being that the price moves in trends

This paper presents a technique for time series modeling using JA. This is the first work that uses FJA-based optimization for stock market open price prediction.

The purpose of this study is to assess the incorporation of fiber reinforced polymer (FRP) bars in concrete as a reinforcement enhances the corrosion resistance in a concrete structure. However, FRP bars are not practically used due to a lack of standard codes. Various codes, including ACI-440-17 and CSA S806-12, have been established to provide guidelines for the incorporation of FRP bars in concrete as reinforcement. The application of these codes may result in over-reinforcement. Therefore, this research presents the use of a machine learning approach to predict the accurate flexural strength of the FRP beams with the use of 408 experimental results.

In this research, the input parameters are the width of the beam, effective depth of the beam, concrete compressive strength, FRP bar elastic modulus and FRP bar tensile strength. Three machine learning algorithms, namely, gene expression programming, multi-expression programming and artificial neural networks, are developed. The accuracy of the developed models was judged by R², root means squared and mean absolute error. Finally, the study conducts prismatic analysis by considering different parameters. including depth and percentage of bottom reinforcement.

The artificial neural networks model result is the most accurate prediction (99%), with the lowest root mean squared error (2.66) and lowest mean absolute error (1.38). In addition, the result of SHapley Additive exPlanation analysis depicts that the effective depth and percentage of bottom reinforcement are the most influential parameters of FRP bars reinforced concrete beam. Therefore, the findings recommend that special attention should be given to the effective depth and percentage of bottom reinforcement.

Previous studies revealed that the flexural strength of concrete beams reinforced with FRP bars is significantly influenced by factors such as beam width, effective depth, concrete compressive strength, FRP bars’ elastic modulus and FRP bar tensile strength. Therefore, a substantial database comprising 408 experimental results considered for these parameters was compiled, and a simple and reliable model was proposed. The model developed in this research was compared with traditional codes, and it can be noted that the model developed in this study is much more accurate than the traditional codes.

Project manager’s competency is crucial in the construction sector for the successful completion of projects, particularly in the case of developing countries like Nepal. Therefore, it is very essential to select competent project managers by finding the competency factors required by them. Hence, this study aims to identify the characteristics of competent project managers by expert opinion method and to evaluate their competency level by a questionnaire survey to develop a prediction model using a supervised machine learning approach via Waikato Environment for Knowledge Analysis (WEKA), a machine learning tool which predicts Project manager’s performance as “Higher than expected,” “Expected” or “Lower than expected” for the medium complexity construction projects of Nepal (from US$200,000 up to US$10M).

The data collection procedure for this research is based on an expert opinion method and survey. Expert opinion method is conducted to find the characteristics of a competent project manager by validating the top 15 competency factors based on literature review. The survey is conducted with the top management to assess their project manager’s competency level. Both qualitative and quantitative approaches are used to collect data for classification and prediction in WEKA, a machine learning tool.

The results illustrate that the project managers in Nepal have a high score in leadership skills, personal characteristics, team development and delegation, communication skills, technical skills, problem-solving/coping with situation skills and stakeholder/relationship management skills. Furthermore, among the seven classifiers (naïve Bayes, sequential minimal optimization [SMO], multilayer perceptron, logistic, KStar, J48 and random forest), the accuracy given by the SMO algorithm is highest of all in both the percentage split and k-folds cross validation method. The model developed using SMO classifier by k-folds cross-validation (k = 10) is acknowledged as a final model.

This research focuses to develop a prediction model to predict and analyze the competency of project managers by applying a supervised machine learning approach. Seven extensively used algorithms (Naïve Bayes, SMO, multilayer perceptron, logistic, KStar, J48, random forest) are used to check the accuracy of models and an algorithm that gives the highest accuracy is adopted. Data collection for this research is carried out by expert opinion method to validate the characteristics (factors) essential for competent project managers in the first round and the description of each factor as high, medium and low is inquired with the same experts in the second round. After an expert opinion, a structured questionnaire is prepared for the survey to assess the competency level of project managers (PMs). The competency level of PMs working under government funded, foreign aided or private projects from the contractor’s side is measured. This research is limited to the medium scale construction projects of Nepal.

This model can be a huge asset in the human resource department of construction companies as it helps to know the performance level of project managers in terms of “Higher than expected,” “Expected” or “Lower than expected” for the medium complexity construction projects of Nepal. Also, the model will assist human intelligence to make the decision while recruiting a new project manager/s for different types of projects at a time. Moreover, the model can be used for self-assessment of project manager/s to know their performance level. The model can be used to develop a user friendly interface system or an application such that it can be conveniently used anywhere any time.

This research shows that most of the project managers working in a medium complexity construction project of Nepal are male, maximum of them hold bachelor’s degree and study for road projects. Furthermore, most of the project managers scored high in leadership skills, personal characteristics, communication skills, technical skills, problem-solving/coping with situation skills, team development and delegation and stakeholder/relationship management skills. The model has given the “Personal characteristics” attribute the highest weightage. Likewise, other attributes having high weightage are communication skills, analytical abilities, project budget, stakeholder/relationship management, team development and delegation and time management skills.

This research was conducted to find the competency factors and to study the competency level of project managers in Nepal to develop a prediction model to predict the PM’s performance using a machine learning approach in medium scale construction projects. There is a lack of research to develop a model that predicts project manager’s competency using the machine learning approach. Therefore, the predictive model developed here helps in the identification of a competent project manager as it will be advantageous for project completion with a high success rate.

This paper aims to examine the extent to which students’ experiences and perceptions of their literature classroom align with their teacher’s instructional goals for literary inquiry and what teachers can learn from gaining access to students’ perspectives on their classroom experiences.

Thematic analyses were used to examine the data sources: mid-year and end-of-year interviews with six students, audio recordings of the teacher’s rationale for her instructional designs and a reflective discussion with the teacher upon reading the student interviews three years later.

Much of what the teacher intended students to get out of her instruction was what they expressed learning and experiencing in the class, yet some understood the purpose of the class to be far from her intentions. All the interviewed students had deeply personal and varied ways of relating what they learned in class to the world and their own lives. The teacher’s reflection on the interviews highlighted the importance of making space for multiple meanings and perspectives on literary works.

This paper speaks to the importance of surfacing students’ individual and varied ways of making sense of literary texts as part of instruction that values students’ thinking as well as the epistemic commitments of literary reading.

The paper introduced the use of a hybrid system of neural networks support vector machines (NNSVMs) consisting of artificial neural networks (ANNs) and support vector machines (SVMs) to price single-family properties.

The mechanism of the hybrid system is such that its output is given by the SVMs which utilise the results of the ANNs as their input. The results are compared to other property pricing modelling techniques including the standalone ANNs, SVMs, geographically weighted regression (GWR), spatial error model (SEM), spatial lag model (SLM) and the ordinary least squares (OLS). The techniques were applied to a dataset of 3,225 properties sold during the period, January 2012 to May 2014 in Cape Town, South Africa.

The results demonstrate that the hybrid system performed better than ANNs, SVMs and the OLS. However, in comparison to the spatial models (GWR, SEM and SLM) the hybrid system performed abysmally under with SEM favoured as the best pricing technique.

The findings extend the debate in the body of knowledge that the results of the OLS can significantly be improved through the use of spatial models that correct bias estimates and vary prices across the different property locations. Additionally, utilising the result of the hybrid system is thus affected by the black-box nature of the ANNs and SVMs limiting its use to purposes of checks on estimates predicted by the regression-based models.

To investigate specific barriers that might be hindering Maltese women from achieving a managerial position.

This study is based on research by Cromie. Barriers are classified into two main categories; internal and external barriers. Job‐involvement and work‐based self‐esteem are considered to be internal barriers, whereas attitudes towards women in management are considered to be external barriers. The total population was 200, consisting of male and female middle managers, female and male employees and B. Commerce students.

Results indicate no differences between job involvement and work‐based self‐esteem of male and female managers. On the other hand, both male employees and students seem to hold more stereotypical attitudes towards women in management than their female counterparts.

One of the basic limitations of this study was the sample size since small samples make it difficult to generalize. Further research may focus on two main areas. First, it would be useful to have qualitative research on the work experiences of female managers to further investigate the various factors that have helped and hindered women thorough their career advancement. Secondly, research on corporate climate can be helpful in identifying organizational practices that might be blocking female career prospects. Finally, a study considering how attitudes can be reshaped through the educational system and through the use of the media can also help to reduce gender stereotypes.

This study indicates that women often have to face several attitudinal barriers, which in turn may explain the lack of female participation in managerial occupations. A change in organizational policies can help women to overcome these barriers.

This paper confirms that, as in several countries, Maltese women are facing several barriers, which are hindering their career prospects. It also highlights the important role of organizations in reducing workplace barriers.

The purpose of this paper is to explore the use of narratives in biodiversity reports as a mechanism to raise the awareness of biodiversity’s importance. By classifying biodiversity narratives into 14 categories of biodiversity values this paper investigates whether the explanations for biodiversity conservation used by UK local councils are line with shallow, intermediate or deep philosophies.

This study used content analysis to examine the disclosures on biodiversity’s importance in the biodiversity action plans published by UK local councils. The narratives were first identified and then allocated into 14 categories of biodiversity value. Then, they were ascribed to either shallow (resource conservation, human welfare ecology and preservationism), intermediate (environmental stewardship and moral extensionism) or deep philosophies.

UK local councils explained biodiversity’s importance mainly in terms of its instrumental value, in line with shallow philosophies such as human welfare ecology and resource conservation. UK local councils sought to raise awareness of biodiversity’ importance by highlighting values that are important for the stakeholders that are able to contribute towards biodiversity conservation such as landowners, residents, visitors, business and industries. The authors also found that local councils’ biodiversity strategies were strongly influenced by 2010, the International Year of Biodiversity.

This paper is one of the few accounting studies that engages with the literature on environmental ethics to investigate biodiversity. In line with stakeholder theory, it indicates that explanations on biodiversity’s importance based on anthropocentric philosophies are considered more effective in informing those stakeholders whose behaviour needs to be changed to improve biodiversity conservation.