Multi-Assignment Scheduler: A New Behavioral Cloning Method for the Job-Shop Scheduling Problem

Publisher 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

Using offline data to speed up Reinforcement Learning in procedurally generated environments

Publisher Copyright: © 2024 The AuthorsOne of the key challenges of Reinforcement Learning (RL) is the ability of an agent to generalize its learned policy to unseen settings. Moreover, training an RL agent requires large numbers of interactions with the environment. Motivated by the success of Imitation Learning (IL), we conduct a study to investigate whether an agent can leverage offline data in the form of trajectories to improve the sample-efficiency in procedurally generated environments. We consider two settings of using IL from offline data for RL: (1) pre-training a policy before online RL training and (2) concurrently training a policy with online RL and IL from offline data. We analyze the impact of the quality (optimality of trajectories), quantity and diversity of available offline trajectories on the effectiveness of both approaches. Across four well-known sparse reward tasks in the MiniGrid environment, we find that using IL for both pre-training and concurrently during online RL training, consistently improves sample-efficiency, and in some tasks achieves higher returns compared to using either IL or RL alone. Furthermore, we show that training a policy from as few as two trajectories can make the difference between learning an optimal policy at the end of online training and not learning at all. Evaluation in two tasks of the Procgen environment further highlights that the diversity of the training data is more important than its quality. Our findings motivate the widespread adoption of IL for pre-training and concurrent IL in procedurally generated environments whenever offline trajectories are available or can be generated.Peer reviewe

A Singular Theory of Sensorimotor Coordination: On Targeted Motions in Space

Publisher Copyright: Copyright © 2024 the authors.Gravity has long been purported to serve a unique role in sensorimotor coordination, but the specific mechanisms underlying gravity-based visuomotor realignment remain elusive. In this study, astronauts (nine males, two females) performed targeted hand movements with eyes open or closed, both on the ground and in weightlessness. Measurements revealed systematic drift in hand-path orientation seen only when eyes were closed and only in very specific conditions with respect to gravity. In weightlessness, drift in path orientation was observed in two postures (seated, supine) for two different movement axes (longitudinal, sagittal); on Earth, such drift was only observed during longitudinal (horizontal) movements performed in the supine posture. In addition to providing clear evidence that gravitational cues play a fundamental role in sensorimotor coordination, these unique observations lead us to propose an “inverted pendulum” hypothesis to explain the saliency of the gravity vector for eye–hand coordination—and why eye–hand coordination is altered during body tilt or in weightlessness.Peer reviewe

Corrosion Behavior of Additively Manufactured Steels: A Comprehensive Review

Publisher Copyright: © 2025 Wiley-VCH GmbH.Additive manufacturing (AM) is transforming the production of steel components, offering unique advantages such as design freedom and the ability to create complex geometries. This review examines the corrosion behavior of various steel types, including austenitic stainless steels (SS), martensitic SS, duplex SS, low-alloy steels, and maraging steels, produced through AM technologies. In addition, the topic of material hybridization through AM is addressed, which allows for the optimization of the properties of the base materials. While AM often generates finer grain structures, particularly in SS, which enhances corrosion resistance, it can also lead to undesirable phases, precipitates, or defects like porosity that degrade performance. Controlling AM process parameters is crucial to achieving the desired microstructure and optimizing corrosion resistance. The review highlights current knowledge, identifies challenges, and underscores the importance of standardized testing methodologies to enable better cross-study comparisons and guide future advancements in corrosion-resistant AM steels.Peer reviewe

Blockchain-Based Evidence Trustworthiness System in Certification

Publisher Copyright: © 2024 by the authors.Digital evidence is a critical component in today’s organizations, as it is the foundation on which any certification is based. This paper presents a risk assessment of evidence in the certification domain to identify the main security risks. To mitigate these risks, it also proposes an adaptation of an existing Blockchain-based audit trail system to create an evidence trustworthiness system enhancing security and usability. This system covers specific additional requirements from auditors: evidence confidentiality and integrity verification automation. The system has been validated with cloud service providers to increase the security of evidence for a cybersecurity certification process. However, it can be also extended to other certification domains.Peer reviewe

Plasma nitrided ferritic stainless steel surfaces as hydrogen permeation barriers

Publisher Copyright: © 2025 The AuthorsHydrogen has the potential to replace fossil fuels in certain sectors where decarbonization presents significant challenges. However, components manufactured in metallic alloys that come into contact with hydrogen are susceptible to hydrogen induced embrittlement (HE) to varying degrees. Plasma based surface treatments might provide a barrier to hydrogen diffusion, a prerequisite for HE. This study aims at investigating the performance as hydrogen diffusion barrier of active screen plasma nitrided treatments on a ferritic stainless steel (X6Cr17). The research has focused on the nitriding parameters (mainly processing temperature), as well as the thickness and microstructure of the steel. A variety of techniques, including X-ray diffraction spectroscopy, microscopy, indentation and hydrogen permeation tests were employed throughout the study on different nitrided surfaces. The findings of the study indicate that plasma nitrided surfaces act as effective hydrogen permeation barriers. Results show a reduction of the hydrogen permeation flow by up to two orders of magnitude compared to the same untreated steel alloy (2.0 × 10−9 vs. 4.2 × 10−7 Pa.m3/s). This is accompanied by a delay in the hydrogen permeation uptake of >25 times compared to the same untreated steel alloy. However, the findings also indicate that the surface treatment effectiveness is influenced by both the presence of surface defects and the depths and microstructure of the nitrided surfaces.Peer reviewe

Understanding the impact of additives on cobalt leaching efficiency using a citric acid-based deep eutectic solvent

Publisher Copyright: © 2025 The Royal Society of Chemistry.Recovery of critical metals such as cobalt from secondary sources is an effective way to reduce the supply risk of metals that are necessary in clean energy technologies, but such recovery processes need to be more benign. Hence, this study presents new insights into leaching cobalt using deep eutectic solvents under mild conditions. The role of ethylene glycol (EG) and water as additives in cobalt leaching was investigated using a mixture containing citric acid (CA):choline chloride (ChCl) in 1 : 1 molar ratio. While the water concentration and Co leaching efficiency were directly related, that was not the case for the EG content. A larger amount of EG in the mixture (CA : ChCl : EG from 1 : 1 : 0.3 to 1 : 1 : 4 molar ratio) decreased the cobalt leaching efficiency, which was attributed to the presence of EG in different coordination forms, as suggested by FTIR spectroscopy. The optimal solvent mixture CA : ChCl : EG (1 : 1 : 1.1) led to leaching efficiencies of 43% cobalt and 65% lithium from lithium cobalt oxide (LiCoO2) at 60 °C for 48 h. Although lithium(I) was the key to increasing the leaching efficiency, we also observed that the presence of lithium(I) in the leachate could negatively impact the electrochemical reduction process. This may be due to the different speciation of cobalt(ii) in the presence and absence of lithium(I), as indicated by NMR spectroscopy.Peer reviewe

Diverse policy generation for the flexible job-shop scheduling problem via deep reinforcement learning with a novel graph representation

Publisher Copyright: © 2024 The AuthorsIn scheduling problems common in the industry and various real-world scenarios, responding in real-time to disruptive events is important. Recent methods propose the use of deep reinforcement learning (DRL) to learn policies capable of generating solutions under this constraint. However, current DRL approaches struggle with large instances, which are common in real-world scenarios. The objective of this paper is to introduce a new DRL method for solving the flexible job-shop scheduling problem, with a focus on these type of instances. The approach is based on the use of heterogeneous graph neural networks to a more informative graph representation of the problem. This novel modeling of the problem enhances the policy's ability to capture state information and improve its decision-making capacity. Additionally, we introduce two novel approaches to enhance the performance of the DRL approach: the first involves generating a diverse set of scheduling policies, while the second combines DRL with dispatching rules (DRs) constraining the action space, with a variable degree of freedom depending on the chosen policy. Experimental results on two public benchmarks show that our approach outperforms DRs and achieves superior results compared to three state-of-the-art DRL methods, particularly for large instances.Peer reviewe

Scaling up lignin-based polyols for PU coatings

Publisher Copyright: © 2025 RSC.Lignin, a promising sustainable feedstock, has been utilized to produce polyols through a novel anionic ring opening polymerization of oxiranes. This approach overcomes the limitations of lignin's heterogeneity, enabling the synthesis of aliphatic polyols with tailored properties at room temperature and atmospheric pressure. By optimization of reaction conditions, polyols with specific characteristics suited for polyurethane dispersion coatings have been achieved. Notably, the process has been successfully scaled up by a factor of 330, from 15 mL to 5 L reactors, while the desired properties have been maintained. The resulting polyols have been used to partially substitute traditional polyols in polyurethane dispersions, demonstrating their potential in wood coating applications. This breakthrough has paved the way for the large-scale production of lignin-based polyols, offering a more sustainable alternative for the coatings industry.Peer reviewe

The future need for critical raw materials associated with long-term energy and climate strategies: The illustrative case study of power generation in Spain

Publisher Copyright: © 2024 The AuthorsThe deployment of renewable energy technologies, though necessary to decarbonise our society, poses a risk stemming from the massive increase in the use of critical raw materials. This work presents a prospective evaluation of a national electricity generation mix up to 2050 and discusses the increase in several critical and strategic materials used in this transition. Results indicate that the deployment of solar photovoltaics and wind energy will raise material criticality concerns in the coming decades. When comparing a decarbonisation scenario aligned with the 2030 Spanish policy with a business-as-usual scenario, results show that a higher penetration of renewables would involve increases of up to 53 % in silicon, 27 % in aluminium, 11 % in copper, and less than 1 % in other materials by 2050. Overall, the decarbonisation scenario would involve up to 12 % more materials. Furthermore, criticality indicators show increases of 0.06 % and 5 % by 2050 depending on the selected indicator. Differences in figures highlight discrepancies in the way criticality is evaluated, suggesting that further research is needed. Nevertheless, national long-term energy policies such as the Spanish one are urged to implement criticality issues in their formulation. Consequently, the authors recommend including critical material usage within energy and climate planning models.Peer reviewe