Search

Several research papers have attempted to describe the dynamics of COVID-19 based on systems of differential equations. These systems have taken into account quarantined or isolated cases, vaccinations, control measures, and demographic parameters, presenting propositions regarding theoretical results that often investigate the asymptotic behavior of the system. In this paper, we discuss issues that concern the theoretical results proposed in the paper "An Extended SEIR Model with Vaccination for Forecasting the COVID-19 Pandemic in Saudi Arabia Using an Ensemble Kalman Filter". We propose detailed explanations regarding the resolution of these issues. Additionally, this paper focuses on extending the local stability analysis of the disease-free equilibrium, as presented in the aforementioned paper, while emphasizing the derivation of theorems that validate the global stability of both epidemic equilibria. Emphasis is placed on the basic reproduction number R 0 , which determines the asymptotic behavior of the system. This index represents the expected number of secondary infections that are generated from an already infected case in a population where almost all individuals are susceptible. The derived propositions can inform health authorities about the long-term behavior of the phenomenon, potentially leading to more precise and efficient public measures. Finally, it is worth noting that the examined paper still presents an interesting epidemiological scheme, and the utilization of the Kalman filtering approach remains one of the state-of-the-art methods for modeling epidemic phenomena. [ABSTRACT FROM AUTHOR]

Despite advancements in smart grid (SG) technology, effective load forecasting utilizing big data or large-scale datasets remains a complex task for energy management, planning, and control. The Saudi SGs, in alignment with the Saudi Vision 2030, have been envisioned as future electrical grids with a bidirectional flow of power and data. To that end, data analysis and predictive models can enhance Saudi SG planning and control via artificial intelligence (AI). Recently, many AI methods including deep learning (DL) algorithms for SG applications have been published in the literature and have shown superior time series predictions compared with conventional prediction models. Current load-prediction research for the Saudi grid focuses on identifying anticipated loads and consumptions, on utilizing limited historical data and the behavior of the load's consumption, and on conducting shallow forecasting models. However, little scientific proof on complex DL models or real-life application has been conducted by researchers; few articles have studied sophisticated large-scale prediction models for Saudi grids. This paper proposes hybrid DL methods to enhance the outcomes in Saudi SG load forecasting, to improve problem-relevant features, and to accurately predict complicated power consumption, with the goal of developing reliable forecasting models and of obtaining knowledge of the relationships between the various features and attributes in the Saudi SGs. The model in this paper utilizes a real dataset from the Jeddah and Medinah grids in Saudi Arabia for a full year, 2021, with a one-hour time resolution. A benchmark strategy using different conventional DL methods including artificial neural network, recurrent neural network (RNN), conventional neural networks (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and different real datasets is used to verify the proposed models. The prediction results demonstrate the effectiveness of the proposed hybrid DL models, with CNN–GRU and CNN–RNN with NRMSE obtaining 1.4673% and 1.222% improvements, respectively, in load forecasting accuracy. [ABSTRACT FROM AUTHOR]

Visual pollution (VP) is the deterioration or disruption of natural and man-made landscapes that ruins the aesthetic appeal of an area. It also refers to physical elements that limit the movability of people on public roads, such as excavation barriers, potholes, and dilapidated sidewalks. In this paper, an end-to-end visual pollution prediction (VPP) framework based on a deep active learning (DAL) approach is proposed to simultaneously detect and classify visual pollutants from whole public road images. The proposed framework is architected around the following steps: real VP dataset collection, pre-processing, a DAL approach for automatic data annotation, data splitting as well as augmentation, and simultaneous VP detection and classification. This framework is designed to predict VP localization and classify it into three categories: excavation barriers, potholes, and dilapidated sidewalks. A real dataset with 34,460 VP images was collected from various regions across the Kingdom of Saudi Arabia (KSA) via the Ministry of Municipal and Rural Affairs and Housing (MOMRAH), and this was used to develop and fine-tune the proposed artificial intelligence (AI) framework via the use of five AI predictors: MobileNetSSDv2, EfficientDet, Faster RCNN, Detectron2, and YOLO. The proposed VPP-based YOLO framework outperforms competitor AI predictors with superior prediction performance at 89% precision, 88% recall, 89% F1-score, and 93% mAP. The DAL approach plays a crucial role in automatically annotating the VP images and supporting the VPP framework to improve prediction performance by 18% precision, 27% recall, and 25% mAP. The proposed VPP framework is able to simultaneously detect and classify distinct visual pollutants from annotated images via the DAL strategy. This technique is applicable for real-time monitoring applications. [ABSTRACT FROM AUTHOR]

The transmission rate of COVID-19 varies over time. There are many reasons underlying this mechanism, such as seasonal changes, lockdowns, social distancing, and wearing face masks. Hence, it is very difficult to directly measure the transmission rate. The main task of the present paper was to identify the variable transmission rate ( β 1 ) for a SIR-like model. For this, we first propose a new compartmental forced SEYNHRV-S differential model. We then drive the nonlinear differential equation and present the finite difference technique to obtain the time-dependent transmission rate directly from COVID-19 data. Following this, we show that the transmission rate can be represented as a linear combination of radial kernels, where several forms of radial kernels are explored. The proposed model is flexible and general, so it can be adapted to monitor various epidemic scenarios in various countries. Hence, the model may be of interest for policymakers as a tool to evaluate different possible future scenarios. Numerical simulations are presented to validate the prediction of our SEYNHRV and forced SEYNHRV-S models, where the data from confirmed COVID-19 cases reported by the Ministry of Health in Saudi Arabia were used. These confirmed cases show the second wave of the infected population in Saudi Arabia. By using the COVID-19 data, we show that our model (forced SEYNHRV-S) is able to predict the second wave of infection in the population in Saudi Arabia. It is well known that COVID-19 epidemic data cannot be accurately represented by any compartmental approach with constant parameters, and this is also true for our SEYNHRV model. [ABSTRACT FROM AUTHOR]

In this work, we developed, validated, and analysed the behaviour of a compartmental model of COVID-19 transmission in Saudi Arabia. The population was structured into four classes: susceptible (S), exposed (E), infectious (I), and removed (R) individuals. This SEIR model assumes a bilinear incidence rate and a nonlinear recovery rate that depends on the quality of health services. The model also considers a treatment function and incorporates the effect of fear due to the disease. We derived the expression of the basic reproduction number and the equilibrium points of the model and demonstrated that when the reproduction number is less than one, the disease-free equilibrium is stable, and the model predicts a backward bifurcation. We further found that when the reproduction number is larger than one, the model predicts stable periodic behaviour. Finally, we used numerical simulations with parameter values fitted to Saudi Arabia to analyse the effects of the model parameters on the model-predicted dynamic behaviours. [ABSTRACT FROM AUTHOR]

Various published COVID-19 models have been used in epidemiological studies and healthcare planning to model and predict the spread of the disease and appropriately realign health measures and priorities given the resource limitations in the field of healthcare. However, a significant issue arises when these models need help identifying the distribution of the constituent variants of COVID-19 infections. The emergence of such a challenge means that, given limited healthcare resources, health planning would be ineffective and cost lives. This work presents a universal neural network (NN) computational instrument for predicting the mainstream symptomatic infection rate of COVID-19 and models of the distribution of its associated variants. The NN is based on a mixture density network (MDN) with a Gaussian mixture model (GMM) object as a backbone. Twelve use cases were used to demonstrate the validity and reliability of the proposed MDN. The use cases included COVID-19 data for Canada and Saudi Arabia, two date ranges (300 and 500 days), two input data modes, and three activation functions, each with different implementations of the batch size and epoch value. This array of scenarios provided an opportunity to investigate the impacts of epistemic uncertainty (EU) and aleatoric uncertainty (AU) on the prediction model's fitting. The model accuracy readings were in the high nineties based on a tolerance margin of 0.0125. The primary outcome of this work indicates that this easy-to-use universal MDN helps provide reliable predictions of COVID-19 variant distributions and the corresponding synthesized profile of the mainstream infection rate. [ABSTRACT FROM AUTHOR]

A significant part of agricultural farms in the Kingdom of Saudi Arabia (KSA) are in off-grid sites where there is a lack of sufficient water supply despite its availability from groundwater resources in several regions of the country. Since abundant agricultural production is mainly dependent on water, farmers are forced to pump water using diesel generators. This investigation deals with the increase in the effectiveness of a solar photovoltaic water pumping system (SPVWPS). It investigated, from a distinct perspective, the nonlinear behavior of photovoltaic modules that affects the induction motor-pump because of the repeated transitions between the current and the voltage. A new chaotic Horse Herd Optimization (CHHO)-based Maximum Power Point Tracking technique (MPPT) is proposed. This algorithm integrates the capabilities of chaotic search methods to solve the model with a boost converter to maximize power harvest while managing the nonlinear and unpredictable dynamical loads. The analytical modeling for the proposed SPVWPS components and the implemented control strategies of the optimal duty cycle of the DC–DC chopper duty cycle and the Direct Torque Control (DTC) of the Induction Motor (IM) has been conducted. Otherwise, the discussions and evaluations of the proposed model performance in guaranteeing the maximum water flow rate and the operation at MPP of the SPVWPS under partial shading conditions (PSC) and changing weather conditions have been carried out. A comparative study with competitive algorithms was conducted, and the proposed control system's accuracy and its significant appropriateness to improve the tracking ability for SPVWPS application have been proven in steady and dynamic operating climates and PSC conditions. [ABSTRACT FROM AUTHOR]

Hybrid energy systems (HESs) are gaining prominence as a practical solution for powering remote and rural areas, overcoming limitations of conventional energy generation methods, and offering a blend of technical and economic benefits. This study focuses on optimizing the sizes of an autonomous microgrid/HES in the Kingdom of Saudi Arabia, incorporating solar photovoltaic energy, wind turbine generators, batteries, and a diesel generator. The innovative reinforcement learning neural network algorithm (RLNNA) is applied to minimize the annualized system cost (ASC) and enhance system reliability, utilizing hourly wind speed, solar irradiance, and load behavior data throughout the year. This study validates RLNNA against five other metaheuristic/soft-computing approaches, demonstrating RLNNA's superior performance in achieving the lowest ASC at USD 1,219,744. This outperforms SDO and PSO, which yield an ASC of USD 1,222,098.2, and MRFO, resulting in an ASC of USD 1,222,098.4, while maintaining a loss of power supply probability (LPSP) of 0%. RLNNA exhibits faster convergence to the global solution than other algorithms, including PSO, MRFO, and SDO, while MRFO, PSO, and SDO show the ability to converge to the optimal global solution. This study concludes by emphasizing RLNNA's effectiveness in optimizing HES sizing, contributing valuable insights for off-grid energy systems in remote regions. [ABSTRACT FROM AUTHOR]

This paper proposes, validates and analyzes the dynamics of the susceptible exposed infectious recovered (SEIR) model for the propagation of COVID-19 in Saudi Arabia, which recorded the largest number of cases in the Arab world. The model incorporates a saturated incidence rate, a constant vaccination rate and a nonlinear treatment function. The rate of treatment is assumed to be proportional to the number of infected persons when this number is low and reaches a fixed value for large number of infected individuals. The expression of the basic reproduction number is derived, and the model basic stability properties are studied. We show that when the basic reproduction number is less than one the model can predict both a Hopf and backward bifurcations. Simulations are also provided to fit the model to COVID-19 data in Saudi Arabia and to study the effects of the parameters of the treatment function and vaccination rate on disease control. [ABSTRACT FROM AUTHOR]

The worldwide data for COVID-19 for active, infected individuals in multiple waves show that traditional epidemic models with constant parameters are not able to capture this kind of disease behavior. We solved this major open mathematical problem in this report. We first consider the disease transmission rate for the stochastic SIRVI epidemic model, which satisfies the mean-reverting Ornstein–Uhlenbeck (OU) process, and we propose a new stochastic SIRVI model. We then showed the existence and uniqueness of the global solution and obtained sufficient conditions for the persistent mean and exponential extinction of infectious disease, which have not been given before. In the second part, we derive a nonlinear system of differential equations for the time-dependent transmission rate from the deterministic SIRVI model and present an algorithm to compute the time-dependent transmission rate directly from the given active, infected individuals' data. We then show that the time-dependent transmission obtained from and perturbed by the Ornstein–Uhlenbeck process could be represented after using a smoothing technique using a finite linear combination of a Gaussian radial basis function, which was obtained from our algorithm. This novel computer-assisted proof provides a theoretical basis for other epidemic models and epidemic waves. Finally, some numerical solutions of the stochastic SIRVI model are presented using COVID-19 data from Saudi Arabia and Austria. [ABSTRACT FROM AUTHOR]

Because of the fast expansion of electric vehicles (EVs) in Saudi Arabia, a massive amount of energy will be needed to serve these vehicles. In addition, the transportation sector radiates a considerable amount of toxic gases in the form of SO2 and CO2. The national grid must supply a huge amount of electricity on a regular basis to meet the increasing power demands of EVs. This study thoroughly investigates the technical and economic benefits of an off-grid and grid-connected hybrid energy system with various configurations of a solar, wind turbine and battery energy storage system for the electric vehicle charging load in the Qassim region, Saudi Arabia. The goal is to decrease the cost of energy while reducing the chance of power outages in the system. This is achieved by using a new optimization algorithm called the modified salp swarm optimization algorithm (MSSOA), which is based on an evolutionary algorithm approach. MSSOA is an improved version of SSOA, which addresses its shortcomings. It has two search strategies to enhance its efficiency: first, it uses Levy flight distribution (LFD) to help individuals reach new positions faster, and second, it instructs individuals to spiral around the optimal solution, improving the exploitation phase. The MSSOA's effectiveness is confirmed by comparing its results with those of the conventional salp swarm optimization algorithm and particle swarm optimization (PSO). According to simulation findings, MSSOA has excellent accuracy and robustness. In this region, the SPV/WT/BESS-based EV charging station is the optimal option for EV charging stations. The SPV/WT/BESS design has the lowest LCOE of all feasible configurations in the region under study. The optimum values for the LCOE and TNPC using MSSOA are USD 0.3697/kWh and USD 99,928.34, which are much lower than the optimized values for the LCOE (USD 0.4156) and TNPC (USD 1,12,671.75) using SSOA. Furthermore, a comprehensive techno–economic analysis of optimized hybrid systems is assessed by incorporating the grid-connected option. The grid connected system results in optimized values of the LCOE (USD 0.0732/kWh) and TNPC (USD 1,541,076). The impact of different grid purchase prices on the levelized cost of energy is also studied. Our results will assist the researchers to determine the best technique for the optimization of an optimal energy system. [ABSTRACT FROM AUTHOR]

This work presents a new polymorphic, reusable, and comprehensive mathematical model for COVID-19 epidemic transition cycle dynamics. This model has the following characteristics: (1) The core SEIR model includes asymptomatic and symptomatic infections; (2) the symptomatic infection is a multi-variant; (3) the recovery stage provides a partial feed to the symptomatic infection; and (4) the symptomatic and asymptomatic stages have additional feed streams from the protected stage. The proposed formalisation template is a canonical way to achieve different models for the underlying health control environment. This template approach endows the model with polymorphic and reusable capability across different scenarios. To verify the model's reliability and validity, this work utilised two sets of initial conditions: date range and COVID-19 data for Canada and Saudi Arabia. [ABSTRACT FROM AUTHOR]

Mortality modelling is a practical method for the government and various fields to obtain a picture of mortality up to a specific age for a particular year. However, some information on the phenomenon may remain in the residual vector and be unrevealed from the models. We handle this issue by employing a multivariate control chart to discover substantial cohort changes in mortality behavior that the models still need to address. The Hotelling T2 control chart is applied to the externally studentized deviance model, which is already optimized using a machine-learning decision tree. This study shows a mortality model with the lowest MSE, MAPE, and deviance, by accomplishing simulations in various countries. In addition, the model that is more sensitive in detecting signals on the control chart is singled out so that we can perform a decomposition to determine the attributes of death in the specific outlying age group in a particular year. The case study in the decomposition uses data from the country Saudi Arabia. The overall results demonstrate that our method of processing and producing mortality models with machine learning can be a solution for developing countries or countries with limited mortality data to produce accurate predictions through monitoring control charts. [ABSTRACT FROM AUTHOR]

This research examines the impact of financial knowledge on risky investment intention via the lens of the theory of planned behavior (TPB). The research developed a comprehensive model to test the mediation effect of the three TPB antecedents on the link between financial knowledge and risky investment intention. The research investigates the moderating effect of risk-taking on the link between three TPB constructs and risky investment intention. For these purposes, we used a pre-tested survey, was directed to senior university students in public universities in Saudi Arabia. The findings of SmartPLS showed a significant positive influence of financial knowledge on attitudes towards risky investment, subjective norms (SNs), and perceived behavioral control (PBC). Both SNs and PBC have a significant positive influence on risky investment intention. Nonetheless, the personal attitude of students failed to have a significant direct or mediating influence on risky investment intention. Additionally, risk-taking did not have a moderating effect on the link between personal attitude and risky investment intention. Students belong to a risk-adverse culture, which could justify the insignificant impact of their personal attitudes on risky investment intention. On the other side, SNs and PBC have a mediating effect on the link between financial knowledge and risky investment intention. Risk-taking has a moderating effect on the link between SNs, PBC, and risky investment intention. The research extends the use of TPB by validating its assumptions about driving the investment intention of university graduates. [ABSTRACT FROM AUTHOR]

The current study examines the direct effect of investment awareness and university education support on students' risky financial investment intentions. Additionally, it examines the mediating effect of social influence and the moderating effect of self-control on these relationships. For this purpose, we directed an online questionnaire to senior students at three public universities in Saudi Arabia. The results of SmartPLS showed positive significant effects of investment awareness and university education support on social influence towards investment. The results also showed direct positive significant effects of investment awareness and university education support on students' risky financial investment intentions. The results confirmed a partial mediating effect of social influence on the link between investment awareness and university education support on students' risky financial investment intentions. Moreover, self-control was found to have a moderating effect on the link between investment awareness, university education support and social influence. Self-control failed to confirm the other moderating effects; i.e., the link between university education support and investment awareness, nor the link between investment awareness, university education support and risky financial investment intention. Implications of these findings for academics and policymakers to stimulate investment intention among higher education graduates in the Kingdom of Saudi Arabia (KSA) are discussed. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a leading cause of blindness in middle-aged diabetic patients. Regular screening for DR using fundus imaging aids in detecting complications and delays the progression of the disease. Because manual screening takes time and is subjective, deep learning has been used to help graders. Pre-trained or brute force CNN models are used in existing DR grading CNN-based approaches that are not suited to fundus image complexity. To solve this problem, we present a method for automatically customizing CNN models based on fundus image lesions. It uses k-medoid clustering, principal component analysis (PCA), and inter-class and intra-class variations to determine the CNN model's depth and width. The designed models are lightweight, adapted to the internal structures of fundus images, and encode the discriminative patterns of DR lesions. The technique is validated on a local dataset from King Saud University Medical City, Saudi Arabia, and two challenging Kaggle datasets: EyePACS and APTOS2019. The auto-designed models outperform well-known pre-trained CNN models such as ResNet152, DenseNet121, and ResNeSt50, as well as Google's AutoML and Auto-Keras models based on neural architecture search (NAS). The proposed method outperforms current CNN-based DR screening methods. The proposed method can be used in various clinical settings to screen for DR and refer patients to ophthalmologists for further evaluation and treatment. [ABSTRACT FROM AUTHOR]

Applying an advance dynamic panel second-generation two-step system generalized method of movement modeling, this study endeavors to bridge the gap in the literature by examining the association between shadow economy and economic stability in multiple countries with a special case of the Kingdom of Saudi Arabia. The study has been motivated by the evidence that the shadow economy is a persistent source of the decline in tax revenue, which is the main source of funds for any economy to run welfare packages for the public. In addition, it also impacts the economy's financial stability and amplifies the losses. Saudi Arabia is selected as a special case given its global significance and participation in the group of the fastest growing economies in the world. Thus, this study finds a negative association between the shadow economy and economic stability in the case of the full sample and a sub sample of Saudi Arabia. In other words, our study supports the point of view of those scholars who claim that the shadow economy is harmful to the economy. Based on the empirical findings, the study recommends that policymakers need to give importance to the shadow economy while formulating economic stability policies in the case of a full sample, especially in Saudi Arabia. Similarly, the robustness of the results is tested using different model specifications and alternative estimation techniques. [ABSTRACT FROM AUTHOR]

The Saudi economy ought to maintain a significant amount of foreign exchange reserves due to the pegged exchange rate regime. As a hydrocarbon economy, we measure the dynamic response of external assets and liabilities of banks to the international oil price in Saudi Arabia. In the presence of extreme observations, we apply sophisticated frameworks, including cross-quantilograms, quantile-on-quantile and TVP-VAR approaches, to analyze weekly time-series data from 1993 to 2021. Our results from the cross-quantilogram and quantile-on-quantile frameworks demonstrate that foreign assets and liabilities responded asymmetrically to the volatilities of international oil prices under the bullish and bearish states of the market over different memories. The TVP-VAR results indicate that, during the COVID-19 pandemic, the Saudi economy encountered negative net foreign assets, which occurred mainly as a significant plague of international oil prices. Our findings are robust under different estimators. [ABSTRACT FROM AUTHOR]

Technological advancements have created a plethora of opportunities for entrepreneurs to develop and extend their business operations. Hence, internet has promoted to the emergence of digital entrepreneurship as a growing form of entrepreneurship among many entrepreneurs, especially digital natives. This research examines to what extent personal traits of digital natives' impact on their digital entrepreneurship intention. The research examined the direct impact of the big five personal traits, i.e., openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism, on digital entrepreneurship intention and the indirect impact through personal attitude. For this purpose, a pre-examined questionnaire was directed to senior students in computer sciences and information technology colleges at public universities in Kingdom of Saudi Arabia (KSA). The results of structural equation modeling using SmartPLS (version 4) confirmed a direct positive and significant impact of the big five personal traits on personal attitude. However, the results revealed that the impact of the big five personal traits (except agreeableness) on digital entrepreneurship intention were positive but insignificant. Additionally, a mediating effect was confirmed for personal attitude in the link between personal traits and digital entrepreneurship intention among senior students in KSA higher education. The results contributed to the research gap in relation to personal traits and its impact on personal attitude and ultimatly on digital entrepreneurship intention, especially among digital natives. Several impactions were merged and discussed for scholars, policy makers and educators in higher education institutions. [ABSTRACT FROM AUTHOR]

In line with today's economy, investment and financial awareness are necessary for success and an individual's well-being, specifically for the younger generations. Therefore, this study aims to examine the relationships between financial literacy, saving behavior, a lack of self-control, family financial socialization, and investment awareness. Further, it investigates the moderating role of both family financial socialization and the lack of self-control in these relationships. Employing a quantitative study technique and partial least squares structural equation modeling (PLS-SEM), we analyzed a sample of 409 students representing young adults at King Faisal University, specifically in the School of Business. Our results indicate that financial literacy, saving behavior, and family financial socialization are significantly and positively related to investment awareness. Interestingly and as expected, a lack of self-control negatively and significantly affects investment awareness. For the moderating impact, it was found that the connection between financial literacy, saving behavior, and investment awareness is positively and strongly moderated by family financial socialization. Likewise, a lack of self-control significantly and negatively moderated the association between financial literacy, saving behavior, and investment awareness. The results of this study provide substantial implications for regulators, educational organizations, individuals, and their families. [ABSTRACT FROM AUTHOR]

This research examines the role of gender in the link between entrepreneurship orientation and entrepreneurial intention. More exactly, the research examines the differences between male and female senior students in relation to the effect of risk-taking, innovativeness, and pro-activeness on their entrepreneurial intention. For this purpose, a quantitative research method was conducted through a self-administered questionnaire to a sample of students at King Faisal University, Kingdom of Saudi Arabia. The results of structural equation modeling (SEM) by AMOS software showed a significant positive direct impact of risk-taking on entrepreneurial intention and a significant positive indirect impact through innovativeness and pro-activeness for the structural model of male and female. In the comparison between the two groups using multi-group analysis, the results showed that impacts of risk-taking and innovativeness on entrepreneurial intention were found to be positive and significant in the two groups and the differences in p-value were significant. This means that there are significant differences between males and females in relation to the impact of risk-taking and innovativeness on entrepreneurial intention. These differences were because males were found to be more risk-taking than females whereas females were found to be more innovative than males. On the other side, the results confirmed no significant differences between the two groups in relation to the effect of pro-activeness on entrepreneurial intention. The findings of the study have noble implications for scholars and policymakers, which we have discussed and elaborated on. [ABSTRACT FROM AUTHOR]

Since the inauguration of the United Nations Sustainable Development Goals (UNSDGs), environmental performance and sustainability have become more important to decision makers, scientists and leaders of organizations than before. In response to this, leaders of different organizations spend all endeavors conserving resources and ensuring environmental sustainability. In this context, transformational leaders have the capacity to ensure the green performance of their organization. The purpose of this study is to test the link between green transformational leadership (GTL), green innovation (GI), corporate social responsibility (CSR) and green performance (GP) in the hotel industry in the Kingdom of Saudi Arabia (KSA). The study empirically tests the mediating effect of GI and CSR on the link between GTL and GP. The study used a quantitative research method via a pre-test instrument, self-distributed and collected from employees in large hotels at different regions of the KSA. The findings from 732 valid responses, analyzed with structural equation modeling (SEM) showed that GTL had a significant effect on GI (β = +0.72, t-value = 14.603, p < 0.001), CSR (β = +0.58, t-value = 8.511, p < 0.001) and GP (β = +0.17, t-value = 2.585, p < 0.001). Moreover, GI and CSR had a direct positive effect on GP (β = +0.10, t-value = 2.866, p < 0.01 and β = +0.61, t-value = 4.358, p < 0.001, respectively). GI had a partial mediation effect (p = 0.048 < 0.05) on the link between GTL and GP. On the other hand, CSR had a perfect mediation effect (p = 0.077 > 0.05) on the link between GTL and GP. This reflects the vital part that CSR plays in this relationship, which can be changed based on the status of CSR. In addition, this reflects the value of CSR in achieving GP, which contributes to the achievement of environmental sustainability at a national level (i.e., the Green Saudi Initiative) at a regional level (i.e., the Green Middle East Initiative) and at an international level (i.e., UNSDGs). [ABSTRACT FROM AUTHOR]

Developing a promising technology that copes with the industrial warehousing environment requires special preparation. It includes infrastructure, equipment, resources, knowledge, efficiencies, and strategies for dealing with failures. This study examines Technology 4.0 driven warehouse practices and performance based on a thorough literature review. The study presents a unique proposition as it considers a two-fold fuzzy Delphi analysis to rank the Technology 4.0 driven practices using best-worst method (BWM) based on experts' responses. Warehouse performance measures are evaluated by the Combined Compromise Solution (CoCoSo) method. The results indicate the contributions of a 'Man-machines or robots for facilitating human'; 'Planning system for management'; 'Storage systems' as as leading practices contributing to 'improved inventory management', 'effective storage and distribution', and 'improved distribution and shipping or delivery process'. Using this study, researchers and managers will better understand how to adopt technology in warehouse management system. [ABSTRACT FROM AUTHOR]