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This research assesses the economic impact of biomass plant installations on Brazilian municipalities, focusing on (1) labor income, (2) sectoral labor income and (3) income inequality.

Municipal data from the Annual Social Information Report, the National Electric Energy Agency and the National Institute of Meteorology spanning 2002 to 2020 are utilized. The Synthetic Difference-in-Differences methodology is employed for empirical analysis, and robustness checks are conducted using the Doubly Robust Difference in Differences and the Double/Debiased Machine Learning methods.

The findings reveal that biomass plant installations lead to an average annual increase of approximately R$688.00 in formal workers' wages and reduce formal income inequality, with notable benefits observed for workers in the industry and agriculture sectors. The robustness tests support and validate the primary results, highlighting the positive implications of renewable energy integration on economic development in the studied municipalities.

This article represents a groundbreaking contribution to the existing literature as it pioneers the identification of the impact of biomass plant installation on formal employment income and local economic development in Brazil. To the best of our knowledge, this study is the first to uncover such effects. Moreover, the authors comprehensively examine sectoral implications and formal income inequality.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

This paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO₂ prediction models. The purpose of this paper is to pre-process a relatively large data of NO₂ from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO₂ prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison.

This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO₂ pollution concentration. The authors used big data analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months. Weather data from the UK meteorology department and traffic data from the department for transport were collected and merged for the corresponding time and location where the pollution sensors exist.

The results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine learning predictive Model for NO₂ pollution.

This paper's hybrid model provides a basis for giving an informed decision on the NO₂ pollutant avoidance system.

This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO₂ pollution concentration.

This study aims to compare the local climate characteristics of Angkor Wat, Borobudur and Prambanan parks and determine effective strategies for mitigating thermal conditions that could suit Borobudur and Angkor Wat.

The study employed local climate zone (LCZ) indicators and ten-year historical climate data to identify similarities and differences in local climate characteristics. Satellite imagery processing was used to create maps of LCZ indicators. Meanwhile, microclimate models were used to analyze sky view factors and wind permeability.

The study found that the three tropical large-scale archaeological parks have low albedo, a medium vegetation index and high impervious surface index. However, various morphological characteristics, aerodynamic properties and differences in temple stone area and altitude enlarge the air temperature range.

Based on the similarities and differences in local climate, the study formulated mitigation strategies to preserve the sustainability of ancient temples and reduce visitors' heat stress.

The local climate characterization of tropical archaeological parks adds to the number of LCZs. Knowledge of the local climate characteristics of tropical archaeological parks can be the basis for improving thermal conditions.

In social media, crisis information susceptible of generating different emotions could be spread at exponential pace via multilevel super-spreaders. This study aims to interpret the multi-level emotion propagation in natural disaster events by analyzing information diffusion capacity and emotional guiding ability of super-spreaders in different levels of hierarchy.

We collected 47,042 original microblogs and 120,697 forwarding data on Weibo about the “7.20 Henan Rainstorm” event for empirical analysis. Emotion analysis and emotion network analysis were used to screen emotional information and identify super-spreaders. The number of followers is considered as the basis for classifying super-spreaders into five levels.

Official media and ordinary users can become the super-spreaders with different advantages, creating a new emotion propagation environment. The number of followers becomes a valid basis for classifying the hierarchy levels of super-spreaders. The higher the level of users, the easier they are to become super-spreaders. And there is a strong correlation between the hierarchy level of super-spreaders and their role in emotion propagation.

This study has important significance for understanding the mode of social emotion propagation and making decisions in maintaining social harmony.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2024-0192.

The energy generation process through photovoltaic (PV) panels is contingent upon uncontrollable variables such as wind patterns, cloud cover, temperatures, solar irradiance intensity and duration of exposure. Fluctuations in these variables can lead to interruptions in power generation and losses in output. This study aims to establish a measurement setup that enables monitoring, tracking and prediction of the generated energy in a PV energy system to ensure overall system security and stability. Toward this goal, data pertaining to the PV energy system is measured and recorded in real-time independently of location. Subsequently, the recorded data is used for power prediction.

Data obtained from the experimental setup include voltage and current values of the PV panel, battery and load; temperature readings of the solar panel surface, environment and the battery; and measurements of humidity, pressure and radiation values in the panel’s environment. These data were monitored and recorded in real-time through a computer interface and mobile interface enabling remote access. For prediction purposes, machine learning methods, including the gradient boosting regressor (GBR), support vector machine (SVM) and k-nearest neighbors (k-NN) algorithms, have been selected. The resulting outputs have been interpreted through graphical representations. For the numerical interpretation of the obtained predictive data, performance measurement criteria such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R-squared (R²) have been used.

It has been determined that the most successful prediction model is k-NN, whereas the prediction model with the lowest performance is SVM. According to the accuracy performance comparison conducted on the test data, k-NN exhibits the highest accuracy rate of 82%, whereas the accuracy rate for the GBR algorithm is 80%, and the accuracy rate for the SVM algorithm is 72%.

The experimental setup used in this study, including the measurement and monitoring apparatus, has been specifically designed for this research. The system is capable of remote monitoring both through a computer interface and a custom-developed mobile application. Measurements were conducted on the Karabük University campus, thereby revealing the energy potential of the Karabük province. This system serves as an exemplary study and can be deployed to any desired location for remote monitoring. Numerous methods and techniques exist for power prediction. In this study, contemporary machine learning techniques, which are pertinent to power prediction, have been used, and their performances are presented comparatively.

The main aims of this study were to develop analytical scales for yachting tourism push-pull motivations and constraints, and analyze how these factors may influence the revisit intention of yachting tourists in China.

The analysis was conducted using the PLS-SEM, including the evaluation of measurement models and the structural models. SPSS18.0 and SmartPLS 3.3.5 software were used for statistical analysis.

We conducted a survey of 451 respondents who participate in yachting activities in Dalian, China and identified six push motivational factors (novelty and stimulation, sightseeing and leisure, sports and learning, social relationships, self-esteem and prestige and self-realization), three pull motivational factors (featured activities and services, destination environment, destination facilities) and two constraints (internal and external). Partial least squares structural equation modelling showed that all hypothesized interactions between identified factors were statistically significant and meaningful.

The push-pull-constraint model offers a new interpretation to the traditional push-pull model in theory, and the results contribute to local yacht industry sectors.

Previous research has demonstrated that Digital Twins (DT) are extensively employed to improve sustainable construction methods. Nonetheless, their uptake in numerous nations is still constrained. This study seeks to identify and examine the digital twin’s implementation barriers in construction building projects to augment operational performance and sustainability.

An iterative two-stage approach was adopted to explore the phenomena under investigation. General DT Implementation Barriers were first identified from extant literature and subsequently explored using primary questionnaire survey data from Hong Kong building industry professionals.

Survey results illustrated that Lack of methodologies and tools, Difficulty in ensuring a high level of performance in real-time communication, Impossibility of directly measuring all data relevant to the DT, need to share the DT among multiple application systems involving multiple stakeholders and Uncertainties in the quality and reliability of data are the main barriers for adopting digital twins' technology. Moreover, Ginni’s mean difference measure of dispersion showed that the stationary digital twin’s barriers adoption is needed to share the DT among multiple application systems involving multiple stakeholders.

The study’s findings offer valuable guidance to the construction industry. They help stakeholders adopt digital twins' technology, which, in turn, improves cost efficiency and sustainability. This adoption reduces project expenses and enhances environmental responsibility, providing companies a competitive edge in the industry.

This research rigorously explores barriers to Digital Twin (DT) implementation in the Hong Kong construction industry, employing a systematic approach that includes a comprehensive literature review, Ranking Analysis (RII) and Ginni’s coefficient of mean difference (GM). With a tailored focus on Hong Kong, the study aims to identify, analyze and provide novel insights into DT implementation challenges. Emphasizing practical relevance, the research bridges the gap between academic understanding and real-world application, offering actionable solutions for industry professionals, policymakers and researchers. This multifaceted contribution enhances the feasibility and success of DT implementation in construction projects within the Architecture, Engineering and Construction (AEC) sector.

The purpose of this paper is to explore the application of smart systems and emerging technologies for disaster risk management (DRM) in Nepal. This delves into specific technologies, including advanced connection and communication technologies, AI, big data analytics, autonomous vehicles and advanced robotics, examining their capabilities and potential contributions to DRM. Further, it discusses the possibility of implementing these technologies in Nepal, considering the existing policies and regulations, as well as the challenges that need to be addressed for successful integration.

For this review journal series of search strategy for identifying relevant journals, the initial examination of results, a manual assessment, geographical refinement, establishment of criteria for the final selection, quality assessment and data management, along with a discussion of limitations. Before delving into the relevant literature within the field of research interest, the authors identified guiding keywords. Further, the authors refined the list by filtering for articles specifically related to Nepal, resulting in a final selection. The final selection of these 95 articles was based on their direct relevance to the research topics and their specific connection in the context of Nepal.

The way technology is used to reduce disaster risk has changed significantly in Nepal over the past few years. Every catastrophe has given us a chance to shift to something innovative. The use of new emerging technologies such as artificial intelligence (AI), big data analytics, autonomous vehicles, advanced robotics and advanced connection and communication technologies are increasing for the purpose of generating risk knowledge, reducing disaster risk and saving the loss of lives and properties. The authors conclude that the successful implementation of smart systems and emerging technologies for disaster risk management in Nepal has the potential to significantly improve the country's resilience and minimize the impact of future disasters. By leveraging data-driven decision-making, enhanced connectivity and automation, Nepal can build a more proactive, adaptive and efficient disaster management ecosystem.

Studies on the application of smart systems in Nepal are limited and scattered across different database. This work collects together such literatures to understand the current status of the application of the smart system and technologies and highlights the challenges and way forward for effective disaster risk management in Nepal. Therefore, this work is an original one and adds value to the existing literatures.

This paper aims to propose a bi-level mixed integer linear location-allocation problem. The upper-level objective function is dedicated to minimizing the total distances covered by customers to meet the p-selected facilities and the fixed cost values for establishing these facilities. While in the lower level, a customer preference function evaluates the priority of customers in selecting facilities.

The solution approach to the proposed model uses the Karush–Kuhn–Tucker (KKT) optimality conditions to the lower-level problem where a set of p-selected facilities are introduced as the selection of the upper-level decision maker. The bi-level model reduces to a single-level model with some added binary variables.

Sensitivity analysis of the proposed bi-level model concerning variations of such different parameters as customers’ preferences and the number of selected facilities have been provided, using some numerical examples. Also, locating a recreational facility in Mazandaran province, Iran, has been provided to evaluate the reliability of the proposed model and efficiency of the solution approach, as well.

To the best of the authors’ knowledge, this paper is original and its findings are not available elsewhere.