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Realizing CO2 emission reduction in lime and soda ash manufacturing through anion exchange
Get PDFPublisher Copyright: © 2025 The Royal Society of Chemistry.Lime (CaO) and soda ash (Na2CO3) are two foundational chemicals for modern civilization, and the CO2 emissions from their production processes are challenging to reduce. Furthermore, decarbonization of the lime industry could also reduce the CO2 emissions associated with cement production, for which lime is the key precursor. In this paper, we show that an anion exchange process to co-produce CaO and Na2CO3 from CaCO3 and NaOH can reduce the carbon footprint of both chemicals through industrial symbiosis. Heating energy and NaOH production are the major contributing factors towards the cost and CO2 emissions of this process, which can supply the global annual soda ash demand (∼65 Mt) and co-produce ∼50 Mt of lime in an economically sustainable manner (16% gross margin) while immediately reducing global CO2 emission by 37 Mt compared to current production methods. Using electrified industrial heat sources and heat pumps to reuse heating energy would further reduce the cost and CO2 emissions of the anion exchange process.Peer reviewe
Biodegradable Poly(d,l-lactide-co-ϵ-caprolactone) Electrospun Scaffolds Outperform Antifibrotic-Loaded Meshes in an in Vivo Tissue Regeneration Model
No full textPublisher Copyright: © 2025 American Chemical Society.Wound healing is a complex and dynamic process of replacing missing cellular structures and tissue layers. Clinical practice includes the application of a sterile bandage to promote healing and to restrain infection, like the commercial nonbiodegradable meshes. However, while inert, nontoxic, and nonimmunogenic, they can cause calcification, fibrosis, and inflammation, potentially hindering the healing process in the long term. To address this challenge and enhance wound healing, we developed a totally biodegradable electrospun poly(d,l-lactide-co-ϵ-caprolactone) (PDLLCL) drug delivery system that incorporates two already FDA-approved antifibrotics, pirfenidone (PIRF) and triamcinolone acetonide (TA). The PDLLCL meshes, fabricated via electrospinning, exhibited homogeneity and complete degradation after 120 days, consistent with the wound healing process. In vitro, functional analysis on RAW 264.7 macrophages revealed no cytotoxicity and an immunomodulatory effect of PIRF and TA compared with the positive control (lipopolysaccharides, LPS) and the PDLLCL meshes alone. Lastly, subcutaneous in vivo assessment on a rabbit model, following the ISO 10993-6 standard, showed that our tailored made PDLLCL meshes were able to lower both irritation and fibrosis indexes from 2 weeks to 4 weeks of implantation, highlighting the beneficial effect of biodegradable polymers. However, we saw no significant positive effect on the incorporation of antifibrotics in vivo for irritation and fibrosis indexes. This underscores the potential of PDLLCL meshes as a possible alternative for wound healing, reducing the use of intermittent antifibrotic agents during the healing process.Peer reviewe
A numerical study on fatigue strength degradation due to pitting corrosion of S355 structural steel in a marine environment
Get PDFPublisher Copyright: © 2025 Elsevier LtdThis study investigates the influence of pitting corrosion on the fatigue strength of S355 steel, commonly used in offshore wind support structures. A short crack microstructural model is employed to estimate the degraded fatigue strength, effectively capturing the physics of the problem with minimal input parameters. Material characterization tests provide the key mechanical properties of S355 steel. A parametric analysis is used to examine the effects of pit shape, size, and aspect ratio. A generic relationship is identified between degraded fatigue strength and pit size for various pit aspect ratios. A sensitivity analysis reveals that the pit aspect ratio significantly influences fatigue strength, with sharper pits leading to greater reductions. The threshold stress intensity factor has a moderate effect, while in-air fatigue strength and grain size have minimal impacts. A case study is performed to evaluate the fatigue strength degradation of S355 steel exposed to the North Sea environment. Findings indicate that the most severe degradation occurs in the initial years of exposure, with the degradation rate declining over time. The results align well with experimental data, offering a robust framework for assessing structural integrity in a marine environment.Peer reviewe
A Fallback Localization Algorithm for Automated Vehicles Based on Object Detection and Tracking
Get PDFPublisher Copyright: © 2020 IEEE.Integrating Automated Vehicles (AVs) into everyday traffic is an ongoing challenge. Ensuring the safety of all involved agents, even in the presence of system failures, is crucial, especially in urban environments. This paper introduces a fallback-oriented localization algorithm for AVs designed to operate during main localization source failures. The method leverages stationary vehicles as dynamic landmarks, identified through the perception module, despite their initially unknown positions. By tracking relative positions before failure and applying trilateration, the algorithm estimates the ego vehicle's position. The proposed algorithm is evaluated through simulations, a real-world dataset, and practical tests on two vehicle models. The results include an average trajectory error of 0.62 m and 1.58 deg compared to the ground truth over different fallback maneuvers. This translates into an average relative translational error of 1.65% and a relative rotational error of 0.05 deg/m, improving the performance of an IMU-based dead reckoning and, hence, providing localization for performing safe stop maneuvers.Peer reviewe
Digitalizing greenhouse trials: An automated approach for efficient and objective assessment of plant damage using deep learning
Get PDFPublisher Copyright: © 2025 The AuthorsThe use of image based and, recently, deep learning-based systems have provided good results in several applications. Greenhouse trials are key part in the process of developing and testing new herbicides and analyze the response of the species to different products and doses in a controlled way. The assessment of the damage in the plant is daily done in all trials by visual evaluation by experts. This entails time consuming process and lack of repeatability. Greenhouse trials require new digital tools to reduce time consuming process and to endow the experts with more objective and repetitive methods for establishing the damage in the plants. To this end, a novel method is proposed composed by an initial segmentation of the plant species followed by a multibranch convolutional neural network to estimate the damage level. In this way, we overcome the need for costly and unaffordable pixelwise manual segmentation for damage symptoms and we make use of global damage estimation values provided by the experts. The algorithm has been deployed under real greenhouse trials conditions in a pilot study located in BASF in Germany and tested over four species (GLXMA, TRZAW, ECHCG, AMARE). The results show mean average error (MAE) values ranging from 5.20 for AMARE and 8.07 for ECHCG for the estimation of PDCU value, with correlation values (R2) higher than 0.85 in all situations, and up to 0.92 in AMARE. These results surpass the inter-rater variability of human experts demonstrating that the proposed automated method is appropriate for automatically assessing greenhouse damage trials.Peer reviewe
PRoT-FL: A privacy-preserving and robust Training Manager for Federated Learning
Get PDFPublisher Copyright: © 2024 The AuthorsFederated Learning emerged as a promising solution to enable collaborative training between organizations while avoiding centralization. However, it remains vulnerable to privacy breaches and attacks that compromise model robustness, such as data and model poisoning. This work presents PRoT-FL, a privacy-preserving and robust Training Manager capable of coordinating different training sessions at the same time. PRoT-FL conducts each training session through a Federated Learning scheme that is resistant to privacy attacks while ensuring robustness. To do so, the model exchange is conducted by a “Private Training Protocol” through secure channels and the protocol is combined with a public blockchain network to provide auditability, integrity and transparency. The original contribution of this work includes: (i) the proposal of a “Private Training Protocol” that breaks the link between a model and its generator, (ii) the integration of this protocol into a complete system, PRoT-FL, which acts as an orchestrator and manages multiple trainings and (iii) a privacy, robustness and performance evaluation. The theoretical analysis shows that PRoT-FL is suitable for a wide range of scenarios, being capable of dealing with multiple privacy attacks while maintaining a flexible selection of methods against attacks that compromise robustness. The experimental results are conducted using three benchmark datasets and compared with traditional Federated Learning using different robust aggregation rules. The results show that those rules still apply to PRoT-FL and that the accuracy of the final model is not degraded while maintaining data privacy.Peer reviewe
A Tuning Method for the Supplementary Voltage Controller of Dual-Side Grid Forming Converters in Distributed Storage Systems
Get PDFPublisher Copyright: © 2020 IEEE.Utility-scale battery energy storage systems (BESSs) are currently being used to provide auxiliary services, such as frequency regulation, peak shaving, or grid balancing, among others. Hybrid ac/dc distribution grids where the BESS systems are connected in the dc side and the dc/ac interface is implemented through a grid forming (GF) converter are currently researched. These solutions combine the benefits given by the dc distribution and the possibility to provide emulated inertia and damping to the system through the use of GF control techniques. This article presents a novel tuning method, based on small signal analysis, for the configuration parameters of a dual-side GF controller. It aims to minimize the dynamic performance difference between the dual-side and ideal GF controllers, thus ensuring that the dual-side GF provides the expected support to the grid in terms of inertia, damping and primary response, while simultaneously controlling the dc voltage. This is achieved through the optimum tuning of the supplementary dc voltage regulator embedded in the dual-side GF controller. Real-time estimation of the optimum controller gains by making use of an artificial neural network is proposed. Simulation and experimental results are presented to validate the method.Peer reviewe
Associations Between Transdiagnostic Psychological Processes and Global Symptom Severity Among Outpatients With Various Mental Disorders: A Cross-Sectional Study
Get PDFPublisher Copyright: © 2025 The Author(s). Clinical Psychology & Psychotherapy published by John Wiley & Sons Ltd.Objective: Knowledge about transdiagnostic factors associated with global symptom severity among patients diagnosed with various mental disorders remains limited. This study examined the cross-sectional associations between transdiagnostic processes including global emotion regulation and specific emotion regulation strategies (i.e., amount of physical activity and sedentary behaviour, repetitive negative thinking and sleep routines) with global symptom severity, while controlling for sociodemographic data (age, gender, employment status, relationship status, and educational level) and fear of the coronavirus. Methods: Data from 401 outpatients, aged 42.08 years on average (SD = 13.26; 71.3% female), diagnosed with depressive disorders, non-organic primary insomnia, agoraphobia, panic disorder and/or post-traumatic stress disorder were examined. This study is a secondary analysis of a randomized controlled trial. Data were collected from 10 different study sites between March 2021 and May 2022 for cross-sectional analysis. The influence of predictors of global symptom severity was determined using three-step hierarchical multiple regression: (1) control variables, (2) global emotion regulation and (3) specific emotion regulation strategies. Global symptom severity was measured using the Global Severity Index, derived from the Brief Symptom Inventory-18. Predictors were measured using validated scales, and physical activity was additionally assessed via accelerometer-based sensors. Results: In the first step, control variables accounted for 4% of variance in global symptom severity. The inclusion of global emotion regulation in the second step explained 26% of the outcome variance, and the incorporation of specific emotion regulation strategies in the third step increased the explained variance to 37%. Significant predictors included global emotion regulation (β = 0.28), repetitive negative thinking (β = 0.26) and sleep routines (β = 0.25). Conclusion: Global emotion regulation along with repetitive negative thinking and sleep routines as specific emotion regulation strategies are identified as transdiagnostic psychological processes that may serve as treatment targets for evidence-based interventions designed to enhance emotion regulation, particularly in transdiagnostic samples of stress-related disorders. Additional prospective longitudinal studies with transdiagnostic samples are necessary to explore possible causal relationships.Peer reviewe
High-throughput CALPHAD-guided design and experimental study on the development of a novel multicomponent as-cast Al-Si-Cu-Zn-Fe-Mn-Mg based alloy through the direct melting of post-consumer scrap
Get PDFPublisher Copyright: © 2025 The AuthorsIn the present study, high-throughput CALPHAD calculations are performed to obtain the phase equilibria and solidification properties of an as-cast aluminum alloy containing Si, Cu, Fe, Zn, Mn and Mg. The appropriate alloy compositions were reversely designed to optimize the solidification interval, promote eutectic solidification of Si and Fe-rich phases, and avoid the formation of primary intermetallic compounds, and the overall reduction of other type of intermetallic compounds. The alloy was developed entirely from post-consumer scrap by two-step simple low-cost gravity casting technology that is easily scalable. To validate the CALPHAD-guided design, the microstructure and mechanical properties of the developed alloy were studied. The developed alloy successfully demonstrated the feasibility of creating alloys from post-consumer scrap that meet industry standards, using only adjusting elements, grain refiners and modifiers. Consequently, this study provides a viable approach to increase the scrap recycling rate and contributes to the industrialization of aluminum castings with a low carbon footprint.Peer reviewe
Multi-Assignment Scheduler: A New Behavioral Cloning Method for the Job-Shop Scheduling Problem
No full textPublisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Recent advances in applying deep learning methods to address complex scheduling problems have highlighted their potential in learning dispatching rules. However, most studies have predominantly focused on deep reinforcement learning (DRL). This paper introduces a novel methodology aimed at learning dispatching policies for the job-shop scheduling problem (JSSP) by employing behavioral cloning and graph neural networks. By leveraging optimal solutions for the training phase, our approach sidesteps the need for exhaustive exploration of the solution space, thereby enhancing performance compared to DRL methods proposed in the literature. Additionally, we introduce a novel modelling of the JSSP with the aim of improving efficiency in terms of solving an instance in real time. This involves two key aspects: firstly, the creation of an action space that allows our policy to assign multiple operations to machines within a single action, substantially reducing the frequency of model usage; and secondly, the definition of a state space that only includes significant operations. We evaluated our methodology using a widely recognized open JSSP benchmark, comparing it against four state-of-the-art DRL methods and an enhanced metaheuristic approach, demonstrating superior performance.Peer reviewe