The management of End-of-Life Tires (ELT) presents a growing concern about environmental pollution that has led to a tightening of legislation worldwide. One approach gaining attention is Extended Producer Responsibil...
The management of End-of-Life Tires (ELT) presents a growing concern about environmental pollution that has led to a tightening of legislation worldwide. One approach gaining attention is Extended Producer Responsibility (EPR), which makes producers responsible for the life-cycle management of their products. This work introduces a multi-objective optimization model that supports decision-making through the integration of a techno-political framework to enhance traditional sustainability assessment, studying the impact of introducing collection goals, valuation quotas and emerging valorization technologies. A Chilean case study is presented considering the implementation of pyrolysis plants for the recycling of ELT under the regulatory framework of the EPR Law. The results show that introducing political and technological parameters and variables into economic, environmental, and social objectives facilitates convergence towards a solution, despite the initial opposition of the objectives. Applying the ϵ -constraint method allows to find profitable alternatives for this project. However, those solutions that further reduce the gap and achieve a balance between the three objectives tend to produce negative economic outcomes, suggesting that fully sustainable scenarios may not be economically feasible. Sensitivity analyses highlight the trade-offs involved in balancing economic feasibility in sustainable scenarios with technological and regulatory measures. Considering all the above, it is necessary to strengthen regulatory policies that support reverse logistics, ELT recovery technologies, and the development of decentralized ELT managementsystems. Lastly, modifications in the collection goals, robust optimization for dealing with uncertainty, and multiple recycling technologies assessment are suggested as future guidelines.
Coastal areas, essential for human settlement and marine biodiversity, face persistent flood hazards. Integrating vegetation with traditional coastal defense structures, such as seawalls, offers a promising solution f...
Coastal areas, essential for human settlement and marine biodiversity, face persistent flood hazards. Integrating vegetation with traditional coastal defense structures, such as seawalls, offers a promising solution for robust and cost-effective flood mitigation. However, optimizing hybrid vegetation-seawall solutions to enhance coastal protection while addressing varying risk tolerances is a challenging task. This study develops a novel framework combining a non-hydrostatic wave model, a data-driven surrogate model, and a multi-objective optimization algorithm to optimize hybrid designs. Results demonstrate that vegetation integration significantly reduces wave impacts, enhancing seawall performance. Optimized designs reveal that higher vegetation area provides greater wave energy dissipation, while vegetation density plays a more nuanced role depending on available space and risk tolerance levels. For critical infrastructure with low-risk tolerance, designs emphasize seawall height and moderate vegetation density, whereas high-risk tolerance prioritizes larger vegetated areas with lower density. The developed framework equips decision-makers to design hybrid systems that balance coastal protection and cost-effectiveness based on their specific objectives and constraints.
The increasing share of renewables and storage units in the energy systems has sparked the interest in several optimization problems. One of them is the long-term power and storage expansion planning problem, specific...
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The increasing share of renewables and storage units in the energy systems has sparked the interest in several optimization problems. One of them is the long-term power and storage expansion planning problem, specifically tailored for microgrid applications. In order to formulate and solve problems of this class, inherent uncertainties about the future that may be disruptive to the planning process, such as the economic and technical characteristics of the microgrid with multiple energy entities, need to be considered carefully. Furthermore, the highly electrified future requires that critical facilities have an uninterrupted power supply, which can only be realized via mathematical planning models that explicitly account for reliability consideration. In this study we propose a novel problem formulation and a reinforcement learning-based algorithm which can make the solutions of real-scale problems reachable. This model not only incorporates several stochastic components for which we extend previous studies, but is also dynamic in response to changes in the energy sector. Reliability is quantified through Chance Constraint Probability (CCP) and Loss of Load Probability (LOLP). These metrics are integrated into the objective function via Lagrange multipliers, which ensures that the optimization subjects to reliability requirements. By testing the proposed artificial intelligence techniques in real-scale microgrids integrating renewable energy sources and energy storage using location-specific data, we are able to derive dynamic optimized policies for the problem. We show the benefits of utilizing approaches that account for delayed rewards from the managerial scope, quantifying the potential savings that could be incurred and relating our results to the United Nations Sustainable Development Goals. This highly detailed and comprehensive approach manages to capture effectively the inherent uncertainties of multi-energy microgrid expansion planning problem and provide
Building-integrated photovoltaics (BIPV) are increasingly used to build low-carbon buildings and promote energy transition. However, the absence of precise three-dimensional (3D) building models may hinder accurate es...
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Surface displacements associated with the average subsidence due to hydrocarbon exploitation in southwest of Iran which has a long history in oil production, can lead to significant damages to surface and subsurface s...
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Building rooftop data are of importance in several urban applications and in natural disaster management. In contrast to traditional surveying and mapping, by using high spatial resolution aerial images, deep learning...
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Nanoplastics (NPs) are widespread in wastewater systems and more toxic than microplastics (MPs), necessitating effective removal techniques. Although membranes can effectively remove MPs, their efficiency and mechanis...
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Nanoplastics (NPs) are widespread in wastewater systems and more toxic than microplastics (MPs), necessitating effective removal techniques. Although membranes can effectively remove MPs, their efficiency and mechanisms for NPs removal require further study. A novel hydroxyapatite functionalized PVDF (HAPF) membrane was developed for NPs removal. The optimized HAPF membrane was prepared via a one-step method with 800 mg/L HAP at pH 7.3 for 8 hours. It achieved a water flux of 4376.44 LMH, 3.4 times higher than that of the pristine PVDF membrane, while maintaining a polystyrene (PS) NPs rejection above 99.5 %. The HAPF membrane maintained 95 % of its pure water flux when treating wastewater with only PS NPs, but exhibited severe flux decline under varying conditions, such as surface-functionalized PS NPs, pH changes, or coexisting contaminants. Among these, the presence of MgSO alongside PS NPs caused the most severe water flux reduction, with the HAPF membrane undergoing complete blocking, followed by the formation of a cake layer. XDLVO results revealed that there is an attractive interaction between the HAPF membrane and PS NPs, primarily driven by acid-base interaction. The HAPF membrane exhibited significantly lower R and R values than the PVDF membrane and achieved a flux recovery rate of 92.94 % over multiple fouling-cleaning cycles. Its water flux was 18.9 times that of the PVDF membrane during long-term filtration. The developed HAPF membrane holds significant potential for advanced water treatment and fouling resistance .
Medical procedures, in particular those invasive, generate significant ethical, legal, and social concerns, including the issues of humanity and person-hood, stigma, autonomy, privacy and security, research ethics, sa...
Medical procedures, in particular those invasive, generate significant ethical, legal, and social concerns, including the issues of humanity and person-hood, stigma, autonomy, privacy and security, research ethics, safety, moral and legal responsibility, fair access, and justice. It was pointed out that future research should focus on developing practical solutions to the ethical challenges of medical experimental procedures with human participants, facilitated by the collection of empirical data on the perspectives shared by the general public, researchers and of course study participants.
Discriminative training techniques define state-of-the-art performance for automatic speech recognition systems. However, they are inherently prone to overfitting, leading to poor generalization performance when using...
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Problem definition: Emergency department (ED) boarding refers to the practice of holding patients in the ED after they have been admitted to hospital wards, usually resulting from insufficient inpatient resources. Boa...
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Problem definition: Emergency department (ED) boarding refers to the practice of holding patients in the ED after they have been admitted to hospital wards, usually resulting from insufficient inpatient resources. Boarded patients may compete with new patients for medical resources in the ED, compromising the quality of emergency care. A common expedient for mitigating boarding is patient overflowing, i.e., sending patients to beds in other specialties or accommodation classes, which may compromise the quality of inpatient care and bring on operational challenges. We study inpatient bed assignment to shorten boarding times without excessive patient overflowing. Academic/practical relevance: As a cross-departmental issue, boarding is caused by periodic mismatches between the demand for inpatient care and the supply of inpatient resources. Besides reducing overall boarding times, mitigating the time-of-day effect is also essential for patients’ safety. With numerous bed assignment constraints, it is challenging to strike a balance between boarding and overflowing. Methodology: We use a queue with multiple customer classes and multiple server pools to model hospital wards. Exploiting patient flow data from a hospital, we propose a computationally tractable approach to formulating the bed assignment problem, where the joint probability of all waiting patients meeting their respective delay targets is maximized. Results: By dynamically adjusting the overflow rate, the proposed approach is capable not only of reducing patients’ waiting times, but also of mitigating the time-of-day effect on boarding times. In numerical experiments, our approach greatly outperforms both early discharge policies and threshold-based overflowing policies, which are commonly used in practice. Managerial implications: We provide a practicable approach to solving the bed assignment problem. This data-driven approach captures critical features of patient flow management, while the resulting optim
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