Maritime transportation safety is a strategic priority in Indonesia's national logistics system as an archipelagic country. However, high shipping intensity and limited inspection resources pose serious challenges to the effectiveness of ship inspections. Ship Safety Inspectors (PPKK) play a crucial role in ensuring the seaworthiness of ships through inspections of nautical, technical, and communication (radio) aspects; however, their functional contributions have rarely been systematically examined. This study employs a quantitative approach using survey methods and Structural Equation Modeling–Partial Least Squares (SEM-PLS) analysis techniques. A sample of 64 CSOs from three strategic ports (Tanjung Priok, Soekarno-Hatta, Makassar, and Sorong-Pelra) was analyzed to examine the relationship between inspection functions, workload, and maritime safety. The results indicate that technical, nautical, and communication functions significantly influence workload, while technical functions and workload have a direct impact on maritime safety. Work volume acts as a mediating variable in the model. The implications of these findings emphasize the importance of enhancing the competence of PPKK and developing risk-based inspection policies. This study provides empirical contributions to the reform of the maritime safety supervision system and opens opportunities for cross-national validation to strengthen the generalizability of the model.
Assessing the performance of decision-making units (DMUs) under intuitionistic fuzzy conditions has emerged as an essential area of investigation in today’s performance evaluation studies. The framework demonstrated in Intuitionistic Fuzzy Data Envelopment Analysis (IFDEA) is a way to assess the relative performance of DMUs when the observed data are notably expressed as ambiguity or uncertainty in the inputs and outputs represented by intuitionistic fuzzy numbers (IFN). When the situations define the conditions to use models with traditional input-output distinctions, traditional models are not less applicable when the parameters are vague, thus prompting the need for a set of more flexible tools. In this work, a ranking procedure is utilized that uses the centroid of triangular intuitionistic fuzzy numbers (TIFNs) to address the IFDEA model that defined input and output variable through TIFNs, it allows to calculate the efficiency status of each unit and to differentiate the DMUs between efficient and inefficient groups. An intuitionistic super-efficiency (IFSE) model is provided to obtain a complete ranking of DMUs that identified as efficient. To help decision makers, a reference-set-oriented benchmarking strategy is created to identify relevant peer units of the DMUs identified as inefficient to assist in improving their performance. To demonstrate the strength and practical applicability of the proposed framework, two examples of application are presented, as well as discussed, the technical differences of comparing the outcomes of analysis with the ranking proposals existing in the literature.
Inpari Nutri Zinc rice, a biofortified variety enriched with zinc (Zn), has been developed to enhance the nutritional quality of staple crops and address widespread micronutrient deficiencies. Zinc plays a critical role in human health, particularly for children, pregnant women, and lactating mothers, and its deficiency has been linked to stunting. To evaluate the long-term viability of Inpari Nutri Zinc rice cultivation, a multidimensional sustainability assessment was conducted in Bantul Regency, Indonesia, with data collected from 125 farmers in Selopamiro, Wukirsari, and Kebonagung Villages. The Rapid Appraisal (RAP) approach, based on a multi-dimensional scaling (MDS)-based method, was applied to assess sustainability across economic, social, and ecological dimensions. The overall sustainability index was estimated at 62.86%, categorizing the farming system as moderately sustainable. Dimension-specific results indicated that the economic dimension scored 48.79% (unsustainable), the social dimension 66.15% (moderately sustainable), and the ecological dimension 73.65% (moderately sustainable), with a 24.86% disparity between the highest and lowest scores. Model robustness was confirmed by a Standardized Residual Sum of Squares (STRESS) value of 0.16 and a Coefficient of Determination (R²) value of 0.94, demonstrating high reliability and explanatory strength. The economic dimension emerged as the weakest component, underscoring the need for targeted interventions such as guaranteed government procurement of harvests and the integration of biofortified rice into community health programs in areas vulnerable to stunting. Leveraging factors were identified as market access availability for the economic dimension, farmer–extension worker relations for the social dimension, and water quality management for the ecological dimension. These attributes represent critical entry points for enhancing the sustainability of Inpari Nutri Zinc rice farming. The findings provide evidence-based insights for policymakers, extension services, and development agencies to strengthen economic resilience while maintaining social and ecological sustainability in biofortified rice farming systems.
The pimary goal of this research was to delineate optimal zones for the establishment of wells by integrating geophysical and hydrogeological techniques, namely electrical resistivity tomography and piezometric analysis. Carried out on the southern flank of Mount Bamboutos within the Menoua Division in Cameroon, the current study addressed the local issue of inadequate water supply, which persists in view of the scarcity of water resources and limited success achieved by previous initiatives. A total of 21 wells and 31 Vertical Electrical Sounding (VES) locations were investigated and seven distinct geophysical anomalies were identified, with resistivity values ranging from 28.61 to 216 703 $\Omega \cdot$m, and thicknesses varying from 0.228 to 46.64 meters. The anomalies were associated with weathered geological formations, including decomposed rocks, fractured basaltic trachytes, and alteritic layers. Considerable spatial variations were found in hydraulic parameters: (i) Hydraulic conductivity ranged between 0.004 and 16.915 m/day; (ii) Transmissivity values extended from 0.017 to 227.841 m$^2$/day; and (iii) Porosity estimates fluctuated between 0.736% and 38.226%. Aquifers hosted in alteritic materials were found at depths about 1.63 to 26 m whereas those associated with fractured basaltic trachytes exceeded 26 m in depth. Piezometric measurements revealed a predominant groundwater flow direction from the northeast toward the southwest. Depressed hydraulic head zones, particularly in the southwestern and central areas, were considered favorable for groundwater exploitation. Aquifer thicknesses ranged from 14.7 to 46.6 m primarily concentrated in the southwestern, southeastern, central, and northern parts of the study area. Based on the integration of geophysical and piezometric data, a hydrogeological map was generated to highlight several promising zones for borehole development. The map serves as a practical decision-support tool to select favorable drilling sites, reduce borehole failure rates and directly support the planning of local water supply. The outcome of this multidisciplinary investigation provided valuable contributions to guide the sustainable management and development of groundwater resources in the region.
Effective traffic management at signalized intersections is crucial for enhancing fuel efficiency, safety, and mobility; however, this is challenging for human drivers due to a lack of predictability. This paper proposes a predictive vehicle control system that extends a traditional human-based driving model to optimize traffic flow, reduce intersection transit time, and fuel consumption. The proposed system utilizes an optimal trajectory prediction model to determine the stopping velocity pattern at traffic signals and employs safety gap synchronization, thereby exhibiting human-like car-following behavior. Specifically, the optimal velocity profiles are generated based on a trajectory optimization model over a long-time horizon. A polynomial function is fitted with these optimal trajectories to find the ideal stopping pattern. Instead of repeating the optimization at each step, as in the Model Predictive Control (MPC) approach, our method determines the control acceleration with necessary adjustments while ensuring driving safety. Moreover, the synchronization compensation factor improves the transition from idling to driving conditions. Performance evaluation through microscopic traffic simulations demonstrates improvements in intersection throughput and fuel efficiency, showcasing the effectiveness of the proposed predictive vehicle control system. Unlike the computationally demanding MPC approach, our proposed system offers a practical balance between real-time applicability and traffic flow efficiency.
This study investigates how cloud accounting technologies contribute to the sustainable transformation of business management in Romania. It aims to assess the relationship between digitalization and sustainability by examining how cloud-based systems enhance financial transparency, operational efficiency, and the integration of Environmental, Social, and Governance (ESG) principles in accounting practices. The research applies a multi-utility global method (MUGM) framework to evaluate sustainability-oriented accounting practices across multiple economic sectors, including IT, automotive, energy, and food industries. Data were collected through structured surveys and expert validation to determine sector-specific performance scores. The analysis integrates both subjective assessments and objective indicators, such as reductions in paper usage, cost efficiency, and reporting timeliness—to evaluate sustainability outcomes and potential biases. The results reveal significant sectoral variation in the adoption and sustainability impact of cloud accounting. The IT and automotive sectors lead in digital integration and ESG-oriented financial reporting, while the energy and food industries demonstrate moderate progress constrained by regulatory and investment limitations. Cloud technologies are shown to facilitate improved ESG data management, enhance corporate accountability, and support the EU twin transition, the simultaneous pursuit of digitalization and sustainability. This research extends sustainability accounting literature by positioning cloud computing as a driver of responsible corporate governance and ESG transparency. It bridges the gap between digital transformation and sustainability by demonstrating how intelligent technologies can operationalize sustainability objectives in financial management and reporting.
This study investigates the perceptions of internal auditors regarding the effectiveness of Artificial Intelligence (AI) in detecting fraudulent activities and strengthening internal control systems within public universities in Ghana. While AI is being increasingly integrated into audit practices globally, its application and perceived impact in public sector institutions, particularly in developing countries, remain underexplored. Ghanaian public universities, facing resource constraints, bureaucratic inefficiencies, and weaknesses in audit frameworks, present a compelling context for examining AI’s role in improving governance and transparency. A mixed-methods approach was employed, combining survey data from 176 internal auditors with qualitative insights from six audit leaders. The Technology Acceptance Model (TAM) and Agency Theory were applied to analyze the perceived usefulness (PU) of AI and its potential to mitigate information asymmetry. Results reveal that internal auditors generally regard AI as highly effective in enhancing fraud detection, particularly in terms of real-time anomaly identification, increasing accuracy, and reducing false positives. AI’s contribution to strengthening internal control mechanisms was also recognized, though challenges related to limited technical training, suboptimal integration of audit and IT systems, and underutilization of advanced AI tools were identified. The study highlights the need for focused auditor training, improved inter-departmental collaboration, and institutional policies that foster AI adoption. These findings contribute to the growing body of literature on the role of AI in public sector auditing, particularly in Sub-Saharan Africa. By integrating quantitative and qualitative data, the study offers a comprehensive analysis of AI’s perceived effectiveness in addressing governance challenges in Ghana’s higher education sector, filling a significant gap in existing research.
Under the influence of technological advancement, digitalisation, and mobile networks, sharing has gained a new dimension in the contemporary era. In the context of rising consumption and economic pressures, the sharing economy has emerged as a global model to promote efficient utilization of limited resources. Recent controversies have questioned how sharing city practices are integrated into urban space and whether they enable the equitable use of underutilised areas. In this context, the “sharing city” approach has been adopted in many cities worldwide. This study explored how sharing practices shaped urban spaces and examined the role of city governments in this process. The research was designed in two stages. First, a systematic review of the Scopus database selected 499 publications from 2016 to early 2025, of which 61 met the inclusion criteria and were analysed to understand the spatial and social impacts of sharing city practices. Second, twelve global cities that adopted sharing city strategies were compared in terms of policy orientation and roles of governance. The analysis demonstrated that the sharing economy produced both enabling and constraining effects on cities, particularly in housing, mobility, and public space. City governments employing a range of regulatory, incentive-based, and partnership-oriented instruments assume different roles, depending on local urban characteristics. By combining insights from the literature and cross-case analysis, the study developed a governance framework that linked municipal roles to specific sharing domains and highlighted areas where equity and data governance remained weak. The findings provide practical guidance for municipalities seeking to balance innovation with regulation, thus offering implementable tools to integrate sharing practices into sustainable urban planning.
Despite their influence on the stability of underground excavations, mineralized veinlets, particularly those composed of pyrite and chalcopyrite, are often underestimated in traditional geomechanical models. The lack of experimental data on their tensile behavior under direct stress represents a critical gap in rock mass characterization. This study experimentally evaluated the direct tensile strength of pyrite and chalcopyrite veinlets from the El Teniente mine, in order to enhance the accuracy of geotechnical models for complex geological contexts. Following the Organization for Economic Cooperation and Development (OECD) 203 (2019) guidelines, a fully randomized experimental design was employed to conduct direct tensile testing of 19 veinlet samples. The results showed that chalcopyrite veinlets exhibited greater internal cohesion with significantly higher tensile strength, reaching up to 3.17 MPa, compared to pyrite veinlets of lower values. Furthermore, chalcopyrite veinlets demonstrated a more homogeneous and cohesive failure behavior compared to pyrite, which displayed greater surface roughness and interfacial failure. This study highlights the importance of incorporating veinlet mineralogy into geotechnical models to improve underground design and safety.
The effectiveness of risk management within the Jordanian banks’ internal control systems and internal auditing is the focus of the study considering the moderate impact of AI in the form of expert systems and neural networks. This study aims to examine the impact of integrating AI in auditing and corporate governance in order to improve the organization’s ability to withstand adversity and endure over time. The study obtained data from 350 internal auditors from Jordanian conventional and Islamic banks through a structured survey. Using partial least square structural equation modeling (PLS-SEM), the study established a positive correlation between the effectiveness of internal auditing and the control of internal systems with risk management. Moreover, while neural networks have a weaker moderating impact, expert systems have a moderating impact on the relationship between the control internal systems and risk management. The study concludes that AI in the form of expert systems enhances the ability to recognize and eliminate risks through the development of internal control and audit functions. It also proposes that the study enhances the understanding of agency theory and the theory of technological superiority by demonstrating the role of AI in aiding human auditors to improve the governance systems in an organization. Moreover, the results assist bank managers, policymakers, and regulators to inform the integration of AI systems and tools to improve risk management practices.
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