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Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

The paper seeks to investigate the impacts of government's incentives and internal aspects (i.e. firms' ethics and firms' attitudes) on the implementation of sustainability-oriented technology (SOT) among small and medium-sized enterprises (SMEs) in Tonga. Those aspects are imperative to examine as numerous enterprises in developing nations possess insufficient assets that suspend applying innovations, specifically SOT incorporated with enterprise management. Thus, it is unavoidable for an intermediary to intervene in technology implementation, and developing the more effective implementation process is reckoned. Meanwhile, governments possess the assets and authority to motivate the SOT implementation extensively. Therefore, this paper assesses governmental factors as influencing drivers for realizing cost-effective and well-organized implementation.

The paper employs the partial least squares structural equation modeling (PLS-SEM) technique to assess the information collected from 266 Tongan SMEs.

The outcomes indicate that government's policy and subsidies positively and significantly shape firms' ethics and attitudes regarding SOT implementation in Tonga.

The research analyzes the SOT implementation in a single country of Tonga; thus, the findings cannot be generalized to other emerging countries. Besides, this study selects SMEs as the sample; hence, it cannot be used to explain the behaviors of large companies.

The research is the first attempt to assess such impacts in the SMEs of a South Pacific nation.

Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.

The literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.

Analysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.

The outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.

Hough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.

Prefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).

Firstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.

The proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.

In this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

The present study aims to identify the dimensions of destination psychological ownership (DPO) from tourists’ perspectives and to develop a reliable and valid measurement scale.

The mixed method has been applied in this study for the development of a scale to measure psychological ownership (PO) of tourists. The first stage includes identifying the PO dimensions of tourists toward the tourist destinations through in-depth interviews with experts (university professors, managers and experts in the tourism industry). Theme analysis was applied to analyze the data in this stage. A quantitative survey was conducted among tourists during the second stage. Accordingly, a questionnaire was designed and its reliability and validity were investigated. Confirmatory factor analysis and structural equation modeling were used to examine the structural model and its validity.

The findings revealed that the DPO from tourists’ perspectives includes six dimensions for the sense of attachment, responsibility, the sense of trust, the sense of honor, the sense of gratification and self-identity toward tourist destinations. The validity of the conceptual model was confirmed according to the results of the quantitative section of the study.

The present study is one of the limited numbers of studies coping with PO, especially in the field of tourism, from the view of the consumer. The present study identifies the dimensions of the DPO of tourists and develops an instrument to measure them. Therefore, the developed questionnaire can be used as a valid and reliable instrument to measure the DPO in future research.

The present study investigates human behavioral and emotional experiences based on human-built environment interaction with a specific interest in urban graffiti displaying fear and pleasure-inducing facial expressions. Regarding human behavioral and emotional experience, two questions are asked for the outcome of human responses and two hypotheses are formulated. H1 is based on the behavioral experience and posits that the urban graffiti displaying fear and pleasure-inducing facial expressions elicit specified behavioral fear and pleasure responses. H2 is based on emotional experience and states that the urban graffiti displaying fear and pleasure-inducing facial expressions elicit specified emotional fear and pleasure responses.

The research design is developed as a multi-method approach, applying a lab-based experimental strategy (N:39). The research equipment includes a mobile electroencephalogram (EEG) and a Virtual Reality (VR) headset. The behavioral and emotional human responses concerning the representational features of urban graffiti are assessed objectively by measuring physiological variables, EEG signals and subjectively by behavioral variables, systematic behavioral observation and self-report variables, Self-assessment Manikin (SAM) questionnaire. Additionally, correlational analyses between behavioral and emotional results are performed.

The findings of behavioral and emotional evaluations and correlational results show that specialized fear and pleasure response patterns occur due to the affective characteristics of the urban graffiti's representational features, supporting our hypotheses. As a result, the characteristics of behavioral fear and pleasure response and emotional fear and pleasure response are identified.

The present paper contributes to the literature on human-built environment interactions by using physiological, behavioral and self-report measurements as indicators of human behavioral and emotional experiences. Additionally, the literature on urban graffiti is expanded by studying the representational features of urban graffiti as a parameter of investigating human experience in the built environment.

The purpose of this paper is to investigate the process by which the level of immersion in virtual reality-based behavioral simulation (VR-BS) impacts on the non-cognitive and cognitive outcomes. The cognitive outcome is measured using the increase in the level of Sharia financial literacy, while the noncognitive outcome is measured using the behavioral intention to use VR-BS.

The method consists of two parts: First, the development of VR-BS, in the context of sharia financial literacy, using the waterfall model. Second, testing the effectiveness of VR-BS using the theory of interactive media effects framework. The participants were 142 students from three secondary schools (two Islamic religious schools and one public school) in Yogyakarta and Central Java, Indonesia. Partial least squares structural equation modeling was used for testing the hypotheses.

VR-BS creates a perceived coolness and vividness, which in turn has an impact on increasing the participants’ engagement. Also, the use of VR has an impact on natural mapping, which increases a user’s engagement through its perceived ease of use. As predicted, the user’s engagement affects VR’s behavior, mediated by the user’s attitude toward VR media. VR’s interactivity, however, does not impact on the cognitive aspect.

The participants were not randomly selected, as the data were collected during the COVID-19 pandemic. As a result, the majority of the participants had never tried VR before this study. The participants, however, were digital natives.

It is implied from the findings that Islamic financial business actors and the relevant government agencies (e.g. the Indonesian Financial Services Authority [OJK], the Ministry of Education, Culture, Research and Technology and the Ministry of Religious Affairs) should collaborate to best prepare the future generation of ummah by using VR-BS in their joint promotion and education programs. The results of the current study reveal that the use of VR-BS may attract people to engage in Islamic financial activities. By engaging in such activities, or at least engaging in real-life simulations/classes/workshops, people may gradually acquire more knowledge about Islamic finance.

As predicted, the user’s engagement has an impact on behavior toward VR-BS, which is mediated by attitude toward VR-BS.

When encountering novel technology, customers often use the term “cool” to express their thoughts; therefore, coolness has become crucial for launching service robots. However, research on the impact mechanism of “coolness” is lacking. This study explored the relationship between delight and behavioral intention regarding the coolness of service robots in the food and beverage industry while discussing the mediating roles of utilitarian and hedonic values.

Questionnaires were distributed online with links to the survey posted on restaurant discussion boards on Facebook and online community platforms such as Dcard. In total, 540 responses were deemed valid. The hypotheses were tested using the partial least squares structural equation modeling method.

The results indicate that coolness positively impacted both utilitarian and hedonic values and that both perceived values positively impacted delight. Moreover, coolness does not directly impact delight but must be mediated by perceived value to be effective.

Increasing customer perceptions of the coolness of service robots is recommended. Moreover, regarding customer revisits, utilitarian value services can delight customers more effectively than hedonic value services.

The stimulus-organism-response model was used to identify the relationships among coolness, perceived value, delight and behavioral intention. Moreover, the authors investigated the impact of coolness on utilitarian and hedonic values. These findings are significant for the development of smart restaurants and provide a critical reference for exploring service robots.

This study aims to evaluate the usage of partial least squares structural equation modeling (PLS-SEM) in journals related to logistics and supply chain management (LSCM).

Based on a structured literature review approach, the authors reviewed 401 articles in the field of LSCM applying PLS-SEM published in 15 major journals between 2014 and 2022. The analysis focused on reasons for using PLS-SEM, measurement model and structural model evaluation criteria, advanced analysis techniques and reporting practices.

LSCM researchers sometimes did not clarify the reasons for using PLS-SEM, such as sample size, complex models and non-normal distributions. Additionally, most articles exhibit limited use of measurement models and structural model evaluation techniques, leading to inappropriate use of assessment criteria. Furthermore, progress in the practical implementation of advanced analysis techniques is slow, and there is a need for improved transparency in reporting analysis algorithms.

This study contributes to the field of LSCM by providing clear criteria and steps for using PLS-SEM, enriching the understanding and advancement of research methodologies in this field.