Renewable, or green, hydrogen will play a critical role in the decarbonisation of hard-to-abate sectors and will therefore be important in limiting global warming. However, renewable hydrogen is not ...cost-competitive with fossil fuels, due to the moderate energy efficiency and high capital costs of traditional water electrolysers. Here a unique concept of water electrolysis is introduced, wherein water is supplied to hydrogen- and oxygen-evolving electrodes via capillary-induced transport along a porous inter-electrode separator, leading to inherently bubble-free operation at the electrodes. An alkaline capillary-fed electrolysis cell of this type demonstrates water electrolysis performance exceeding commercial electrolysis cells, with a cell voltage at 0.5 A cm
and 85 °C of only 1.51 V, equating to 98% energy efficiency, with an energy consumption of 40.4 kWh/kg hydrogen (vs. ~47.5 kWh/kg in commercial electrolysis cells). High energy efficiency, combined with the promise of a simplified balance-of-plant, brings cost-competitive renewable hydrogen closer to reality.
Graph neural networks (GNNs) have been increasingly employed in the field of Parkinson’s disease (PD) research. The use of GNNs provides a promising approach to address the complex relationship ...between various clinical and non-clinical factors that contribute to the progression of PD. This review paper aims to provide a comprehensive overview of the state-of-the-art research that is using GNNs for PD. It presents PD and the motivation behind using GNNs in this field. Background knowledge on the topic is also presented. Our research methodology is based on PRISMA, presenting a comprehensive overview of the current solutions using GNNs for PD, including the various types of GNNs employed and the results obtained. In addition, we discuss open issues and challenges that highlight the limitations of current GNN-based approaches and identify potential paths for future research. Finally, a new approach proposed in this paper presents the integration of new tasks for the engineering of GNNs for PD monitoring and alert solutions.
Surfaces decorated with uniformly dispersed catalytically active nanoparticles play a key role in many fields, including renewable energy and catalysis. Typically, these structures are prepared by ...deposition techniques, but alternatively they could be made by growing the nanoparticles in situ directly from the (porous) backbone support. Here we demonstrate that growing nano-size phases from perovskites can be controlled through judicious choice of composition, particularly by tuning deviations from the ideal ABO3 stoichiometry. This non-stoichiometry facilitates a change in equilibrium position to make particle exsolution much more dynamic, enabling the preparation of compositionally diverse nanoparticles (that is, metallic, oxides or mixtures) and seems to afford unprecedented control over particle size, distribution and surface anchorage. The phenomenon is also shown to be influenced strongly by surface reorganization characteristics. The concept exemplified here may serve in the design and development of more sophisticated oxide materials with advanced functionality across a range of possible domains of application.
The worldwide effort for the development of more efficient and environmentally friendly ships has led to the development of new concepts. Extensive electrification is a very promising technology for ...this purpose. Together with optimal power management can lead to a substantial improvement in ship efficiency ensuring, at the same time, compliance with the environmental constraints and enhancing ship sustainability. In this paper, a method for optimal demand-side management and power generation scheduling is proposed. Demand-side management is based on the adjustment of the power consumed by ship electric propulsion motors, and no energy storage facility is exploited. Dynamic programming algorithm subjected to ship operation and environmental and travel constraints is used to solve the problem for all-electric ships (AESs). Simulation results prove that the proposed method ensures cost minimization of ship power system operation, greenhouse gas (GHG) emissions limitation, and compliance with all technical and operational constraints.
Although abundant research work has been published in the area of path recommendation and its applications on travel and routing topics, scarce work has been reported on context-aware route ...recommendation systems aimed to stimulate optimal cultural heritage experiences. This paper tries to address this issue, by proposing a personalized and content adaptive cultural heritage path recommendation system, where location is modeled using mean-shift clustering trained with actual user movement patters. Additionally, topic modeling is incorporated to formalize the implicit cultural heritage content, while first order Markov models address the movement as a temporal transition aspect of the problem. The overall architecture is applied on data collected from actual visits to the archaeological sites of Gournia and Çatalhöyük and extensive analysis on visitor movement patterns follows, especially in comparison to the curated paths in the aforementioned sites. Finally, the offline evaluation results of the proposed recommendation scheme are encouraging, validating its efficiency and setting a positive paradigm for cultural heritage route recommendations.
In this paper certain interpretability criteria are taken into account in order to extract a set of linear inequality constraints for enhancing the fuzzy model interpretability. Among others, the ...criteria of model distinguishability, completeness, compactness, and fuzzy set sharing between rules are considered. To support distinguishability, the distances between fuzzy set centers are lower bounded and the widths are manipulated as to control the overlap between fuzzy sets. Sufficient conditions are given to satisfy the completeness criterion, whereas the compactness requirement is addressed by comparing models with different number of rules. Finally, fuzzy set sharing between rules is achieved through a model optimization procedure that involves fuzzy set merging. It turns out that the feasible region is a compact and convex set. The tradeoff between interpretability and accuracy is established by minimizing the model's square error over the feasible region through constrained particle swarm optimization. The method is tested using a number of high-dimensional datasets and conducting two kinds of experiments. The first focuses on interpretability. The second studies the accuracy by comparing the method to other algorithms that perform unconstrained optimization, using nonparametric statistics.
In this paper, a novel method to modify color images for the protanopia and deuteranopia color vision deficiencies is proposed. The method admits certain criteria, such as preserving image ...naturalness and color contrast enhancement. Four modules are employed in the process. First, fuzzy clustering-based color segmentation extracts key colors (which are the cluster centers) of the input image. Second, the key colors are mapped onto the CIE 1931 chromaticity diagram. Then, using the concept of confusion line (i.e., loci of colors confused by the color-blind), a sophisticated mechanism translates (i.e., removes) key colors lying on the same confusion line to different confusion lines so that they can be discriminated by the color-blind. In the third module, the key colors are further adapted by optimizing a regularized objective function that combines the aforementioned criteria. Fourth, the recolored image is obtained by color transfer that involves the adapted key colors and the associated fuzzy clusters. Three related methods are compared with the proposed one, using two performance indices, and evaluated by several experiments over 195 natural images and six digitized art paintings. The main outcomes of the comparative analysis are as follows. (a) Quantitative evaluation based on nonparametric statistical analysis is conducted by comparing the proposed method to each one of the other three methods for protanopia and deuteranopia, and for each index. In most of the comparisons, the Bonferroni adjusted
-values are <0.015, favoring the superiority of the proposed method. (b) Qualitative evaluation verifies the aesthetic appearance of the recolored images.
In this paper we investigate the implementation of particle swarm optimization in the design of radial basis function neural networks under the framework of input–output fuzzy clustering. The problem ...being studied concerns the optimal estimation of the basis function centers, provided that the learning process is guided by the information of the output space. The proposed method encompasses a cost function, which is defined by a reformulated version of the fuzzy c-means applied in the product (i.e. input–output) space. The minimization of this function is accomplished by using the particle swarm optimization, where each particle encodes a set of cluster centers associated to a single fuzzy partition. The algorithm is simple and easy to implement, yet very effective. The performance of the resulting network is tested and verified through a number of experimental cases in terms of a 10-fold cross validation analysis.
In decentralized systems, the quest for heightened security and integrity within blockchain networks becomes an issue. This survey investigates anomaly detection techniques in blockchain ecosystems ...through the lens of unsupervised learning, delving into the intricacies and going through the complex tapestry of abnormal behaviors by examining avant-garde algorithms to discern deviations from normal patterns. By seamlessly blending technological acumen with a discerning gaze, this survey offers a perspective on the symbiotic relationship between unsupervised learning and anomaly detection by reviewing this problem with a categorization of algorithms that are applied to a variety of problems in this field. We propose that the use of unsupervised algorithms in blockchain anomaly detection should be viewed not only as an implementation procedure but also as an integration procedure, where the merits of these algorithms can effectively be combined in ways determined by the problem at hand. In that sense, the main contribution of this paper is a thorough study of the interplay between various unsupervised learning algorithms and how this can be used in facing malicious activities and behaviors within public and private blockchain networks. The result is the definition of three categories, the characteristics of which are recognized in terms of the way the respective integration takes place. When implementing unsupervised learning, the structure of the data plays a pivotal role. Therefore, this paper also provides an in-depth presentation of the data structures commonly used in unsupervised learning-based blockchain anomaly detection. The above analysis is encircled by a presentation of the typical anomalies that have occurred so far along with a description of the general machine learning frameworks developed to deal with them. Finally, the paper spotlights challenges and directions that can serve as a comprehensive compendium for future research efforts.
Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), ...such as ChatGPT, and persuasive technologies have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity, and personalization, recommender systems can effectively influence user decision making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present pilot experiments with a case study involving a hotel recommender system. Our inhouse commercial hotel marketing platform, eXclusivi, was extended with a new software module working with ChatGPT prompts and persuasive ads created for its recommendations. In particular, we developed an intelligent advertisement (ad) copy generation tool for the hotel marketing platform. The proposed approach allows for the hotel team to target all guests in their language, leveraging the integration with the hotel’s reservation system. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between ChatGPT and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue.
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