Search Results (3,893)

Given the global importance and limited availability of phosphate, optimizing the use of this critical resource and minimizing its wastage are of paramount importance. In this context, this paper proposes an innovative architecture for the implementation of an intelligent video surveillance system specifically designed for open-pit phosphate mines. The proposed architecture is designed to meet the overall functional requirements of a surveillance system in the challenging environment of open-pit mining, while aligning with the guidelines of the Mine 4.0 revolution. It incorporates advanced technologies that address the critical challenges of latency, data security, and transparency commonly encountered in traditional monitoring systems. By adopting a multi-layered approach that leverages edge, fog, and cloud computing, coupled with blockchain technology and expert collaboration, our architecture offers a comprehensive framework for efficient data processing at every stage, from initial data acquisition to real-time anomaly detection and decision-making. Full article

Short-cycle agricultural product sales forecasting significantly reduces food waste by accurately predicting demand, ensuring producers match supply with consumer needs. However, the forecasting is often subject to uncertain factors, resulting in highly volatile and discontinuous data. To address this, a hierarchical prediction model that combines RF-XGBoost is proposed in this work. It adopts the Random Forest (RF) in the first layer to extract residuals and achieve initial prediction results based on correlation features from Grey Relation Analysis (GRA). Then, a new feature set based on residual clustering features is generated after the hierarchical clustering is applied to classify the characteristics of the residuals. Subsequently, Extreme Gradient Boosting (XGBoost) acts as the second layer that utilizes those residual clustering features to yield the prediction results. The final prediction is by incorporating the results from the first layer and second layer correspondingly. As for the performance evaluation, using agricultural product sales data from a supermarket in China from 1 July 2020 to 30 June 2023, the results demonstrate superiority over standalone RF and XGBoost, with a Mean Absolute Percentage Error (MAPE) reduction of 10% and 12%, respectively, and a coefficient of determination (R²) increase of 22% and 24%, respectively. Additionally, its generalization is validated across 42 types of agricultural products from six vegetable categories, showing its extensive practical ability. Such performances reveal that the proposed model beneficially enhances the precision of short-term agricultural product sales forecasting, with the advantages of optimizing the supply chain from producers to consumers and minimizing food waste accordingly. Full article

Energy communities are a transformative force in the electricity markets and ancillary services, reshaping the energy landscape through collective action. This paper explores the successful strategies adopted by these communities, highlighting real-world cases where they have participated directly in the market, or through aggregators, or sold their energy to retailers, which is of paramount importance because it serves as a foundation for those countries that wish to implement these entities as part of their decarbonization plan. It also serves as a model for the development of future citizen initiatives that aim to turn citizens into active users of the electricity system. The paper examines collaborative dynamics within the energy sector, highlighting how these communities optimize resource sharing and contribute to a more resilient and sustainable energy system. The study emphasizes the potential of energy communities in driving innovation and fostering a participatory approach to energy management. The results show that some pilot projects are being developed and several electricity cooperatives, one of the most common forms of energy communities, are participating in energy trading with their members and other entities. More efforts are also needed for energy communities to participate more directly in the market and/or through aggregators. Full article

This paper presents a novel approach to optimizing the coordination between renewable energy generation enterprises and power grid companies using evolutionary game theory. The research focuses on resolving conflicts and distributing benefits between these key stakeholders in the context of large-scale renewable energy integration. A theoretical model based on replicator dynamics is developed to simulate and analyze the evolutionary stable strategies of power generation enterprises and grid companies with particular emphasis on peak shaving services and electricity bidding. These simulations are based on theoretical models and do not incorporate real-world data directly, but they aim to replicate scenarios that reflect realistic behaviors within the electricity market. The model is validated through dynamic simulation under various scenarios, demonstrating that the final strategic choices of both thermal power and renewable energy enterprises tend to evolve towards either high-price or low-price bidding strategies, significantly influenced by initial system parameters. Additionally, this study explores how the introduction of peak shaving compensation affects the coordination process and stability of renewable energy integration, providing insights into improving grid efficiency and enhancing renewable energy adoption. Although the results are simulation-based, they are designed to offer practical recommendations for grid management and policy development, particularly for the integration of renewable energies such as wind power in competitive electricity markets. The findings suggest that effective government regulation, alongside well-designed compensation mechanisms, can help establish a balanced interest distribution between stakeholders. By offering a clear framework for analyzing the dynamics of renewable energy integration, this work provides valuable policy recommendations to promote cooperation and stability in electricity markets. This study contributes to the understanding of the complex interactions in the electricity market and offers practical solutions for enhancing the integration of renewable energy into the grid. Full article

Numerous studies support that the development of digital teaching competence is essential in 21st century schools. This paper examines Technological Pedagogical Content Knowledge (TPACK) to gain a deeper understanding of ICT integration in teaching. By assessing TPACK, we uncover opportunities to enhance teacher competencies and, consequently, improve student learning. This research evaluated the initial TPACK of primary school teachers from a public school in the Colombian Caribbean. Eight teachers participated in a professional development program based on the Lesson Study (LS) methodology. Adopting an interpretive qualitative approach and a case study for the operational analysis of LS, the findings indicate that teachers, in self-reports and performance-based assessments, highlight high competence in the PK, CK, and PCK domains. This demonstrates their ability to select and adapt effective teaching strategies. They excel in guiding learning and understanding academic content, showcasing a remarkable capacity to adapt to the diverse socioeconomic realities of their students. However, these findings also highlight areas for improvement in developing the technological components of TPACK, specifically TK, TPK, TCK, TPCK, and XK. Full article

This paper focuses on studying the concept of walkability in Riyadh, Saudi Arabia, after the implementation of the Humanizing Neighborhoods Initiative. Al-Falah neighborhood was selected as a case study because it was one of the first neighborhoods to implement walkability strategies. By adopting a qualitative critical approach, this research collected data through a literature review and on-site observation to assess walkability in the neighborhood. Analysis of the observations highlighted the challenges and opportunities of walkability in Riyadh and the wider Gulf cities. Recommendations for promoting walkable communities in Riyadh include enhancing pedestrian infrastructure, incorporating mixed-use developments, integrating green infrastructure, implementing smart urban planning principles, and integrating public transportation systems. This paper emphasizes the importance of community engagement, preserving local identity, and involving various stakeholders to transform Riyadh into a pedestrian-friendly city that prioritizes public health and sustainability. Full article

This study explores the obstacles to electric vehicle (EV) adoption in Indonesia, focusing on technological, environmental, economic, and regulatory factors. Despite government initiatives, such as the Presidential Regulation 55 of 2019, intended to encourage the adoption of EVs and mitigate air pollution, the EV market share in Indonesia remains low, at 1.47%. The main challenges include inadequate charging infrastructure, limited public revenue, and financial constraints. This research highlights the need for improved government policies, incentives for producers, and increased public awareness to encourage EV adoption. Factors influencing consumer decisions include operational costs, environmental concerns, and the availability of charging stations. Key findings suggest that electric motorcycle users have a lower understanding of technology than electric car users, with particular attention to initial costs, maintenance costs, and the accessibility of charging infrastructure. This study recommends that manufacturers and policymakers consider the different preferences of electric car and motorcycle users in their EV adoption promotion strategies. The study seeks to elucidate the determinants affecting EV adoption in Indonesia and propose potential solutions to accelerate the transition to electric mobility. Full article

The snow ablation optimizer (SAO) is a meta-heuristic technique used to seek the best solution for sophisticated problems. In response to the defects in the SAO algorithm, which has poor search efficiency and is prone to getting trapped in local optima, this article suggests a multi-strategy improved (MISAO) snow ablation optimizer. It is employed in the unmanned aerial vehicle (UAV) path planning issue. To begin with, the tent chaos and elite reverse learning initialization strategies are merged to extend the diversity of the population; secondly, a greedy selection method is deployed to retain superior alternative solutions for the upcoming iteration; then, the Harris hawk (HHO) strategy is introduced to enhance the exploitation capability, which prevents trapping in partial ideals; finally, the red-tailed hawk (RTH) is adopted to perform the global exploration, which, enhances global optimization capability. To comprehensively evaluate MISAO’s optimization capability, a battery of digital optimization investigations is executed using 23 test functions, and the results of the comparative analysis show that the suggested algorithm has high solving accuracy and convergence velocity. Finally, the effectiveness and feasibility of the optimization path of the MISAO algorithm are demonstrated in the UAV path planning project. Full article

Geothermal systems face adoption challenges due to their high initial investment cost. Accurate cost analyses and a more precise understanding of updated prices could assist geothermal industry projects in obtaining investment financing and better money management with the right equipment. As the cost of geothermal installations can vary widely depending on case and location, it seems essential to clarify the factors and parameters that determine the cost of the system. These include the type of loop system, the ground conditions, the type of heat pump, the system size, and the geographical location. The scope of this study is to compare the operation of various types of ground heat exchangers (GHEs) present in a Ground Source Heat Pump (GSHP) system installed in the coastal area of the Mediterranean climate zone of Cyprus. The highlight of this work is that it presents real installation cost data as well as recorded total energy contributed by the GHEs to the GSHP system of a HP cooling and heating capacities of 101 kW and 117 kW, respectively. The input contribution from the GHEs to the HP is 85,650 kWh (308,340 MJ) in summer and 25,880 kWh (93,168 MJ) in winter. It is shown that, among the three groups of GHEs investigated, the open-well GHE complex has the lowest cost per kWh ratio (0.32 EUR/kWh), followed by the vertical GHE complex (1.05 EUR/kWh), and lastly by the helical coil GHE (2.77 EUR/kWh). This clearly suggests that when underground water is available, the open-well GHE is much more favorable than other GHE types. Full article

Liquid-core nanocapsules (NCs) coated with amphiphilic hyaluronic acid (AmHA) have been proposed for the preparation of drug and food formulations. Herein, we focused on the use of ultrasound techniques to (i) optimize the polysaccharide chain length with respect to the properties of NCs stabilized with AmHAs and (ii) form oil-core nanocapsules with a coating composed of AmHAs. The results indicate that sonication is a convenient and effective method that allows for a controlled reduction in HA molecular weight. The initial (H-HA) and degraded (L-HA) polysaccharides were then reacted with dodecylamine to obtain hydrophobic HA derivatives (HA-C12s). Then, NCs were prepared based on HA-C12s using ultrasound-assisted emulsification of glyceryl triacetate oil. The nanocapsules coated with L-HA-C12 showed greater stability compared to the longer-chain polysaccharide. Molecular dynamics (MD) simulations revealed that HA-C12 readily adsorbs at the water–oil interphase, adopting a more compact conformation compared to that in the aqueous phase. The dodecyl groups are immersed in the oil droplet, while the main polysaccharide chain remaining in the aqueous phase forms hydrogen bonds or water bridges with the polar part of the triglycerides, thus increasing the stability of the NC. Our research underscores the usefulness of ultrasound technology in preparing suitable formulations of bioactive substances. Full article

Optical and synthetic aperture radar (SAR) images exhibit non-negligible intensity differences due to their unique imaging mechanisms, which makes it difficult for classical SIFT-based algorithms to obtain sufficiently correct correspondences when processing the registration of these two types of images. To tackle this problem, an accurate optical and SAR image registration algorithm based on the SIFT algorithm (OS-PSO) is proposed. First, a modified ratio of exponentially weighted averages (MROEWA) operator is introduced to resolve the sudden dark patches in SAR images, thus generating more consistent gradients between optical and SAR images. Next, we innovatively construct the Harris scale space to replace the traditional difference in the Gaussian (DoG) scale space, identify repeatable key-points by searching for local maxima, and perform localization refinement on the identified key-points to improve their accuracy. Immediately after that, the gradient location orientation histogram (GLOH) method is adopted to construct the feature descriptors. Finally, we propose an enhanced matching method. The transformed relation is obtained in the initial matching stage using the nearest neighbor distance ratio (NNDR) and fast sample consensus (FSC) methods. And the re-matching takes into account the location, scale, and main direction of key-points to increase the number of correctly corresponding points. The proposed OS-PSO algorithm has been implemented on the Gaofen and Sentinel series with excellent results. The superior performance of the designed registration system can also be applied in complex scenarios, including urban, suburban, river, farmland, and lake areas, with more efficiency and accuracy than the state-of-the-art methods based on the WHU-OPT-SAR dataset and the BISTU-OPT-SAR dataset. Full article

Spain has one of Europe’s most extraordinary biodiverse environments and a significant risk of fires in its forests. At the same time, rural areas are affected by several challenges, such as desertification, population decrease, and loss of income. Fortunately, some green sparks arise on the horizon. Among them, we use critical enabling technologies for fire prevention and extinction, renewable energy, and resilience solutions by adopting a system of systems approach given by the systems engineering frameworks. We analyse recent Research and Development (R&D) projects focused on fire prevention to detect (1) the key enabling technologies used and (2) engineering practices. A motivational case is presented, which evaluates the potential interest of the mineral water industry sector in applied R&D with key enabling technologies, including the replicability potential and the replicability potential for using the R&D results. After that, the authors initialize an innovative R-ISSUES model to promote early interoperability between energy and environment systems engineering towards the shared mission of designing digital and rural jobs to preserve our biosphere. The model is used to answer specific research questions and detect gaps or potential improvements for the model itself by using the recent scientific literature. Full article

At present, higher education institutions (HEIs) are increasingly expected to incorporate sustainability into all aspects by integrating it not only into education and research but also into operational processes, including procurement. In some cases, national legislation hinders public universities from adhering to sustainability requirements. This paper aims to introduce a leadership approach model that enables public universities to overcome the constraints imposed by the legal framework. To explore the relatively new area of Sustainable Public Procurement (SPP), where still little is known, the Technical University of Sofia (TU-Sofia) was chosen as a testbed for the case study. Firstly, the challenges posed by external factors—such as non-discrimination principles stated in Bulgaria’s Public Procurement Act (PPA) and internal incentives at TU-Sofia for sustainable procurement initiatives were identified. Secondly, based on the findings, the recommendation is to adopt a leadership approach model by developing and implementing a Sustainable Public Procurement Policy (SPPP) tailored to harness the university’s sustainable development drivers while complying with legislative requirements. As a result of the leadership approach model implementation, the university will not only enhance economic benefits but also mitigate risks and drive transformative change in procurement management processes, contributing to broader societal and environmental goals. Full article

Feed costs represent a significant component of milk production expenses, making up a share of 50% or more of the total cost. The optimization of feed costs is important to achieve financial stability, competitiveness, and sustainability in dairy farming. Optimizing dairy rations not only improves economic efficiency but also plays a significant role in reducing the environmental impact of dairy farming. On the other hand, dairy rations need to meet the specific nutritional requirements of cattle to ensure their health, well-being, and maximum milk production. The formulation of an optimal dairy ration is not an easy task for farmers due to the many challenges they face in this process. This study presents an intuitive web-based tool called OPTIMILK that is used for the optimization of dairy rations using the Simplex method of linear programming. This tool enables farmers to efficiently find a least-cost ration considering the nutritional needs of dairy cows, feed availability and costs, and the nutritional composition of feed. The tool was created for a dairy farmers’ association in Bosnia and Herzegovina and made available to its members for free. After almost four years of operation, the tool’s usage was evaluated by analyzing the collected data. The results show that after an initial period of low activity, there was evidence of an increase in the level of use, which implies the gradual adoption of the tool in dairy farming practice. Full article

Livestock farming has been a practice of great importance for the evolution of civilization, not only influencing social, economic, and cultural aspects at a global level, but also food, the economy, and sustainability, especially in developing countries, where it generates significant pressure on natural resources and biodiversity. In this context, conflict arises between wildlife, mainly top predators, and livestock farmers. Despite the efforts of different communities to implement measures against predation, the conflict continues to increase. In Latin America, the livestock sector is growing at a much higher rate than in the rest of the world, particularly in Chile, where around a third of agricultural production units use livestock as their main source of livelihood. To understand the factors influencing the behavior of goat farmers when adopting measures, we applied a hurdle model with social, spatial, economic, and productive information to assess the decision to adopt measures and the intensity of the adoption of such practices. To perform this, we used data from a survey, administered in 2014 to 476 farmers located in the three provinces of the Coquimbo Region. Our dependent variable was defined by six measures: a protection dog, night confinement of the herd, supervised grazing, anti-carnivore corral, the death or capture of the predator, and repelling the predator. The adoption decision, as well as the intensity of adoption, were influenced by the location, household size, the type of livestock, the income generated by the livestock, health management, and access to technical advice. The decision to adopt measures was influenced by the production system and whether it was self-sustaining, while the intensity of adoption was influenced by herd size and the number of losses due to predation. The results showed the importance of developing and adjusting livestock support initiatives in the study area, including those that could be created, based on differentiated measures according to the profiles of farmers in the territory. Full article