Performance study of an integrated solar water supply system for isolated agricultural areas in Thailand : a case-study of the Royal Initiative Project

This article presents a field-performance investigation on an Integrated Solar Water Supply System (SWSS) at two isolated agricultural areas in Thailand. The two case-study villages (Pongluek and Bangkloy) have experienced severe draughts in recent decades, and, therefore, water supply has become a major issue. A stand-alone 15.36 kW solar power and a 15 kW solar submersible pump were installed along with the input power generated by solar panels supported by four solar trackers. The aim is to lift water at the static head of 64 and 48 m via a piping length of 400 m for each village to be stored in 1000 and 1800 m3 reservoirs at an average of 300 and 400 m3 per day, respectively, for Pongluek and Bangkloy villages. The case study results show that the real costs of electricity generated by SWSS using solar photovoltaic (PV) systems intergraded with the solar tracking system yield better performance and are more advantageous compared with the non-tracking system. This study illustrates how system integration has been employed. System design and commercially available simulation predictions are elaborated. Construction, installation, and field tests for SWSS are discussed and highlighted. Performances of the SWSS in different weather conditions, such as sunny, cloudy, and rainy days, were analysed to make valuable suggestions for higher efficiency of the integrated solar water supply systems

Effect of Cross-Departmental Collaboration on Performance: Evidence from the Federal Highway Administration

Cross-departmental collaboration, one of the most salient administrative reforms, has been promoted to resolve cross-jurisdictional administration issues over the previous three decades. Nearly all previous empirical studies have examined the direct impact of cross-departmental collaboration on organizational performance without accounting for the indirect effects of managerial practices. Using data from the Federal Highway Administration, this study develops an integrated structural equation modeling and Bayesian network model used to examine both direct and indirect impacts of cross-departmental collaboration on organizational performance. The structural model indicates that cross-departmental collaboration has a direct effect on organizational performance and indirect effects through its influence on resource acquisition and knowledge creation. The scenario-based simulation suggests the optimal integration of managerial actions to improve agency performance, which is achieved by encouraging cross-departmental collaboration and supporting the knowledge creation process. Finally, implications are provided to present practical managerial actions from the Federal Highway Administration as an exemplar for other highway agencies

Exploring leadership styles for innovation: an exploratory factor analysis

Leadership plays a vital role in building the process, structures, and climate for an organisation to become innovative and to motivate team expectations toward innovations. This study explores the leadership styles that engineers regard as significant for innovation in the public sector. Exploratory factor analysis (EFA) was conducted to identify the principal leadership styles influencing innovation in the Australian Public Service (APS), using survey data extracted from the 2014 APS employee census comprising 3 125 engineering professionals in Commonwealth of Australia departments. EFA returned a two-factor structure explaining 77.6% of the variance of the leadership for innovation construct. In this study, the results from the EFA provided a clear estimation of the factor structure of the measures for leadership for innovation. From the results, the two factors extracted were transformational leadership and consideration leadership. In transformational leadership, a leader values organisational objectives, inspires subordinates to perform, and motivates followers beyond expected levels of work standards. Consideration leadership refers to the degree to which a leader shows concern and expressions of support for subordinates, takes care of their welfare, treats members as equals, and displays warmth and approachability. These findings highlight the role of leadership as the most critical predictor when considering the degree to which subordinates strive for creativity and innovation. Both transformational and consideration leadership styles are recommended to be incorporated into management training and development programs. This study also recommends that Commonwealth departments recruit supervisors who have both of these leadership styles before implementing innovative projects

Drivers and barriers to innovation in the Australian public service: a qualitative thematic analysis

The purpose of this paper was to identify common themes from archival records related to innovation in the Australian Public Service (APS). A thematic analysis was conducted to review and evaluate archival records which consisted of transcripts from senior manager presentations at Innovation Month seminars from 2014 to 2018 and other related official documents. This empirical study addressed innovation from the leaders’ perspective, reflecting upon their experience. Analysing themes within archival records helped to gain insights from various perspectives of leaders on how they regard an innovation agenda for the APS. Three themes emerged from archival records: (1) innovation characteristics; (2) drivers of innovation; and (3) barriers to innovation. Synthesis of these drivers and barriers can provide important insights for senior APS managers on how they can enhance their organisations’ ability to innovate in order to respond to digital disruption challenges and opportunities. Variety of perspectives with leader’s perceptions informs about authors’ selection of the research question among consistent patterns and legitimates the salient themes as input for QSR NVivo 11

Drivers and barriers to innovation in the Australian public service: A qualitative thematic analysis

The purpose of this paper was to identify common themes from archival records related to innovation in the Australian Public Service (APS). A thematic analysis was conducted to review and evaluate archival records which consisted of transcripts from senior manager presentations at Innovation Month seminars from 2014 to 2018 and other related official documents. This empirical study addressed innovation from the leaders’ perspective, reflecting upon their experience. Analysing themes within archival records helped to gain insights from various perspectives of leaders on how they regard an innovation agenda for the APS. Three themes emerged from archival records: (1) innovation characteristics; (2) drivers of innovation; and (3) barriers to innovation. Synthesis of these drivers and barriers can provide important insights for senior APS managers on how they can enhance their organisations’ ability to innovate in order to respond to digital disruption challenges and opportunities. Variety of perspectives with leader’s perceptions informs about authors’ selection of the research question among consistent patterns and legitimates the salient themes as input for QSR NVivo 11

Achieving career satisfaction through fostering innovation: lessons from the engineering profession in the Australian public sector

This paper proposes a novel approach that integrates the capability of empirical validation of structural equation modelling (SEM) and the prediction ability of Bayesian networks (BN). The Hybrid SEM–BN approach was used as a decision support framework to examine the interplay between salient organisational constructs and their ability to influence engineers’ career satisfaction in the Australian Public Service (APS). The results emphasise that the ambidextrous culture for innovation was the most important factor that needed to be implemented in their organisation. Managerial implications are recommended for senior managers on how they can implement innovation culture to increase workplace innovation, which could, in turn, help reduce the turnover rate of engineers employed in the APS

Unraveling key drivers for engineer creativity and meaningfulness of work: Bayesian network approach

This study builds on an existing structural model developed to examine the influence of leadership and organizational culture on innovation and satisfaction of engineers in Australian public sectors (APS). The objective of this study is to increase the understanding of innovation process with a focus on causal relationships among critical factors. To achieve this objective, the study develops an assessment approach to help predict creativity and work meaningfulness of engineers in the APS. Three quantitative analysis methods were sequentially conducted in this study including correlation analysis, path analysis, and Bayesian networks. A correlation analysis was conducted to pinpoint the strong association between key factors studied. Subsequently, path analysis was employed to identify critical pathways which were accordingly used as a structure to develop Bayesian networks. The findings of the study revealed practical strategies for promoting (1) transformational leadership and (2) innovative culture in public sector organizations since these two factors were found to be key drivers for individual creativity and work meaningfulness of their engineers. This integrated approach may be used as a decision support tool for managing the innovation process for engineers in the public sectors

Optimized neural network-based state-of-the-art soft computing models for the bearing capacity of strip footings subjected to inclined loading

Determining the bearing capacity of a strip footing under inclined loading is crucial in designing foundations. Due to the complex correlations, the subject area remains predominantly unexplored, or it has been simulated using only limited datasets. This paper presents the development of a prediction model based on machine learning (ML), leveraging advanced hybrid artificial neural network (ANN) models for estimating the bearing capacity of strip footings under inclined loading. The ANN models are hybridized with four different optimization algorithms, ant colony optimization (ACO), artificial lion optimization (ALO), the imperialist competitive algorithm (ICA), and shuffled complex evolution (SCE), which enhance the accuracy and efficiency of the predictive capabilities of ANN. The models are trained on a dataset of 920 records, and their performance is evaluated using a range of significant performance metrics. The ANN-ICA model achieved the highest rank in the score analysis (R2 =0.912, RMSE=0.165 in testing), followed by ANN-ALO and ANN-ACO. To reinforce the trustworthiness of the predictions, external validation is employed, and visual analysis is conducted using the Taylor diagram. The findings suggest that the proposed models are robust, and the incorporation of optimization techniques has improved the performance of traditional ANNs. The research findings have significant implications for the field of geotechnical engineering, providing engineers and researchers with valuable insights into the applicability of hybrid artificial neural network (ANN) models and alternative machine learning (ML)-based prediction models in assessing the bearing capacity of strip footings

Liquefaction susceptibility using machine learning based on SPT data

Assessing the potential for liquefaction using traditional experimental or empirical analysis procedures is both time-consuming and arduous. Employing a machine learning model that can accurately predict liquefaction potential for a specific site can reduce the time, effort, and associated costs. This study proposes several empirical machine learning models, including deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), and bi-directional long short-term memory (BILSTM), to assess the liquefaction potential of soil deposits based on SPT-based post liquefaction datasets. To train the proposed models, a dataset comprising 834 liquefied and non-liquefied cases was collected to perform the liquefaction analysis. A Pearson correlation matrix was also conducted to examine the correlation between soil and seismic parameters and the probability of liquefaction. Furthermore, a sensitivity analysis was adopted to assess the impact of soil and seismic parameters on the probability of liquefaction. The proposed model's prediction capability was assessed using several performance indices, including rank analysis, accuracy matrix, and AIC criteria. The comparative analysis of the proposed models' predictive ability to determine liquefaction probability revealed that the RNN model outperformed the others, displaying the highest accuracy and lowest error index values. Subsequently, the RNN model achieved the first rank with a total score value of 70, followed by the CNN (55), DNN (52), BILSTM (47), and LSTM (16) models. The parametric analysis, rank analysis, accuracy matrix, and AIC criteria collectively demonstrate the proposed models' ability to predict liquefaction probability. Furthermore, the robustness of these models was assessed through external validation and comparative analysis

Stability evaluation of elliptical tunnels in natural clays by integrating FELA and ANN

The stability of tunnels in clayey soil is a major concern for underground space technology. Clay has anisotropy in shear strength induced by depositional and sedimentation processes. For the numerical analysis of geotechnical stability problems, the anisotropic undrained shear (AUS) model can account for this anisotropy of clayey soils. In this study, the stability of the elliptical tunnel (stability factor: σs-σt/suc) with varying elliptical shape (width-depth ratio: B/D) placed at different embedment depths (cover-depth ratio: C/D) in clay with different anisotropy (anisotropic strength ratio: re) and varying dimensionless overburden factor (overburden factor: γD/suc) is evaluated using finite element limit analysis and the AUS model. The failure planes are also evaluated for the above variations. Based on the numerical outcome, the artificial neural network (ANN) is utilized to establish the equation for predicting the stability of the elliptical shape tunnel with different shapes (i.e., width-depth ratio), and varying overburden, cover-depth ratio, varying anisotropic strength ratio of clay. The present study results are presented as design charts, tables, and equations so that they can be used in practice