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Slope stability analysis is essential for ensuring the safe design of road embankments. While various conventional methods, such as the finite element approach, are used to determine the safety factor of road embankments, there is ongoing interest in exploring the potential of machine learning techniques for this purpose.

Within the study context, the outcomes of the ensemble machine learning models will be compared and benchmarked against the conventional techniques used to predict this parameter.

Generally, the study results have shown that the proposed machine learning models provide rapid and accurate estimates of the safety factor of road embankments and are, therefore, promising alternatives to traditional methods.

Although machine learning algorithms hold promise for rapidly and accurately estimating the safety factor of road embankments, few studies have systematically compared their performance with traditional methods. To address this gap, this study introduces a novel approach using advanced ensemble machine learning techniques for efficient and precise estimation of the road embankment safety factor. Besides, the study comprehensively assesses the performance of these ensemble techniques, in contrast with established methods such as the finite element approach and empirical models, demonstrating their potential as robust and reliable alternatives in the realm of slope stability assessment.

Earthquakes pose a significant challenge to human safety and the durability of infrastructure, highlighting the urgent need for innovative disaster management strategies. This study addresses the gap in current earthquake disaster management approaches, which are often related to issues of transparency, centralization and sluggish response times. By exploring the integration of blockchain technology into seismic hazard management, the purpose of the research is to overcome these limitations by offering a novel framework for integrating blockchain technology into earthquake risk mitigation and disaster management strategies of smart cities.

This study develops an innovative approach to address these issues by introducing a blockchain-based seismic monitoring and automated decision support system for earthquake disaster management in smart cities. This research aims to capitalize on the benefits of blockchain technology, specifically its real-time data accessibility, decentralization and automation capabilities, to enhance earthquake disaster management. The methodology employed integrates seismic monitoring data into a blockchain framework, ensuring accurate, reliable and comprehensive information. Additionally, smart contracts are utilized to handle decision-making and enable rapid responses during earthquake disasters, offering an effective alternative to traditional approaches.

The study results highlight the system’s potential to foster reliability, decentralization and efficiency in earthquake disaster management, promoting enhanced collaboration among stakeholders and facilitating swift actions to minimize human and capital loss. This research lays the foundation for further exploration of blockchain technology’s practical applications in other disaster management contexts and its potential to transform traditional practices.

Current methodologies, while contributing to the reduction of earthquake-related impacts, are often hindered by limitations such as lack of transparency, centralization and slow response times. In contrast, the adoption of blockchain technology can address these challenges and offer benefits over various aspects, including decentralized control, improved security, real-time data accessibility and enhanced inter-organizational collaboration.

The study analysed the influence of organizational justice on the duty orientation of employees in the mining sector of Ghana. Also, it examined the mediating role of supervisor-provided resources in the relationship between organizational justice and duty orientation.

The study obtained data through a self-administered questionnaire from 291 employees of a mining firm. The data were analysed and interpreted in light of the hypotheses using the partial least squares structural equation modelling technique.

The findings revealed that organizational justice had a significant positive relationship with duty orientation and supervisor-provided resources. The results again established that supervisor-provided resources had a significant positive relationship with duty orientation. The study finally documented that supervisor-provided resources partially mediate the relationship between organizational justice and duty orientation.

We recommended that the management of the mining companies devote resources to developing organizational justice policies based on fairness in resource allocation, clear roles, employee feedback and effective information dissemination. Furthermore, supervisors should place priority on acquiring and dispensing resources as employees demonstrate their willingness to improve duty orientation.

The study contributes to knowledge in a novel research area. It adds to empirical evidence by highlighting the possible variables that may influence employees to engage in duty orientation.

本研究擬分析於迦納的採礦部門裏,組織公平感對僱員職責導向的影響;研究亦擬探討主管提供的資源,如何在組織公平感與職責導向間的關係上起著中介角色。

研究人員透過一間採礦公司291名僱員自我測試的問卷,取得研究所需的數據,繼而以偏最小平方結構方程式模式分析法,進行數據分析,並按照研究的假設,對數據進行闡釋的工作。

研究結果顯示,組織公平感與職責導向和主管提供的資源之間存在顯著的正向關係;研究結果亦確定了主管提供的資源與職責導向之間存在顯著的正向關係。最後,研究結果證明了主管提供的資源,會一定程度調節組織公平感與職責導向之間的關係。

我們建議採礦企業的管理層應根據資源的公平分配、明確的角色、僱員的回饋和有效的信息傳播,把資源專用於發展組織公平感的政策上;而且,當僱員展示他們願意改善職責導向時,主管應把獲取資源,並加以發放列為優先事項。

本研究在一個新穎的研究領域裏,幫助我們增進知識;研究透過強調影響僱員參與職責導向的可能變數,增加有關的經驗證據。