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Graph-based clustering commonly provides promising clustering effectiveness as it can preserve samples’ local geometric information. Inspired by it, multi-view graph clustering was developed to integrate complementary information among graphs of diverse views and it has received intensive attention recently. Nevertheless, on the one hand, most existing methods require extra k-means after obtaining embedding representations to generate a discrete cluster indicator, which reduces effectiveness due to the two-stage mismatch. On the other hand, numerous complex noises in real-world multi-view data challenge the robustness of existing clustering methods. In this paper, we established a discrete correntropy-based multi-view anchor-graph clustering (DCMAC) model that not only emphasizes the aforementioned issues but also makes use of anchor graphs to improve the efficiency of the graph construction stage. To optimize this non-convex model, we propose a fast half-quadratic-based coordinate descent strategy to acquire the discrete cluster indicator directly without extra k-means. Furthermore, we extend the DCMAC model to a single-view form and provide optimization strategies for it. Extensive experiments illustrate that the proposed method is effective and robust compared to those advanced baselines. © 2023 Elsevier B.V.

This paper outlines a novel simple protocol for tuning the structure and properties of jute using sodium periodate (NaIO4) oxidation and coating with alginate. When compared to the raw jute, fabrics oxidized with a 0.2 or 0.4 % NaIO4 solution for 30–120 min exhibited an increased aldehyde group content (0.185 vs. 0.239–0.398 mmol/g), a significantly increased negative zeta potential (from − 8.57 down to − 20.12 mV), a slight disruption of fiber crystallinity, 15.1–37.5 % and 27.9–49.8 % lower fabric maximum force and stiffness, respectively. Owing to the removal of hydrophobic surface barrier, decreased crystallinity index and the presence of micropores on the fabrics' surfaces, oxidized fabrics have a 22.3–29.6 % improved ability for moisture sorption compared to raw fabric. Oxidized fabrics characterized by very long wetting times and excellent antioxidant activities (> 98 %), can find applications as hydrophobic packaging materials. To further extend the utilization of jute in biocarpet engineering such as water-binding geo-prebiotic supports, oxidized fabrics were coated with alginate resulting in 7.9–24.9 % higher moisture sorption and 352–660 times lower wetting times than their oxidized counterparts. This modification protocol has never been applied to lignocellulosic fibers and sheds new light on obtaining jute fabrics with tuned structure and properties intended for various applications.

This paper outlines a novel simple protocol for tuning the structure and properties of jute using sodium periodate (NaIO4) oxidation and coating with alginate. When compared to the raw jute, fabrics oxidized with a 0.2 or 0.4 % NaIO4 solution for 30–120 min exhibited an increased aldehyde group content (0.185 vs. 0.239–0.398 mmol/g), a significantly increased negative zeta potential (from − 8.57 down to − 20.12 mV), a slight disruption of fiber crystallinity, 15.1–37.5 % and 27.9–49.8 % lower fabric maximum force and stiffness, respectively. Owing to the removal of hydrophobic surface barrier, decreased crystallinity index and the presence of micropores on the fabrics' surfaces, oxidized fabrics have a 22.3–29.6 % improved ability for moisture sorption compared to raw fabric. Oxidized fabrics characterized by very long wetting times and excellent antioxidant activities (> 98 %), can find applications as hydrophobic packaging materials. To further extend the utilization of jute in biocarpet engineering such as water-binding geo-prebiotic supports, oxidized fabrics were coated with alginate resulting in 7.9–24.9 % higher moisture sorption and 352–660 times lower wetting times than their oxidized counterparts. This modification protocol has never been applied to lignocellulosic fibers and sheds new light on obtaining jute fabrics with tuned structure and properties intended for various applications.

Hyperspectral imaging (HSI) is a non-destructive and contactless technology that provides valuable information about the structure and composition of an object. It has the ability to capture detailed information about the chemical and physical properties of agricultural crops. Due to its wide spectral range, compared with multispectral-or RGB-based imaging methods, HSI can be a more effective tool for monitoring crop health and productivity. With the advent of this imaging tool in agrotechnology, researchers can more accurately address issues related to the detection of diseased and defective crops in the agriculture industry. This allows to implement the most suitable and accurate farming solutions, such as irrigation and fertilization, before crops enter a damaged and difficult-to-recover phase of growth in the field. While HSI provides valuable insights into the object under investigation, the limited number of HSI datasets for crop evaluation presently poses a bottleneck. Dealing with the curse of dimensionality presents another challenge due to the abundance of spectral and spatial information in each hyperspectral cube. State-of-the-art methods based on 1D and 2D convolutional neural networks (CNNs) struggle to efficiently extract spectral and spatial information. On the other hand, 3D-CNN-based models have shown significant promise in achieving better classification and detection results by leveraging spectral and spatial features simultaneously. Despite the apparent benefits of 3D-CNN-based models, their usage for classification purposes in this area of research has remained limited. This paper seeks to address this gap by reviewing 3D-CNN-based architectures and the typical deep learning pipeline, including preprocessing and visualization of results, for the classification of hyperspectral images of diseased and defective crops. Furthermore, we discuss open research areas and challenges when utilizing 3D-CNNs with HSI data.

Additive manufacturing, also known as 3D printing, has become a powerful and versatile means of manufacturing for almost any applications in our daily lives. A frequently encountered problem in printing, particular in consumer or lower-end applications, is how to print a workpiece whose dimensions exceed the working space of the printer at hand. Rather than opting for the costly solution of getting a new and larger printer (and hence more expensive and also other induced hassles), a more plausible solution would be an algorithmic one, which is the objective of this paper. We present a partition-based multi-setup printing process planning algorithm that, given the CAD model of an oversized workpiece, will be able to determine a continuous printing path with multiple workpiece setups to correctly print out the workpiece on the same printer. To answer the challenge of workpiece localization during the change of workpiece setups in the middle of printing, we propose a visual-guided workpiece localization system. Both computer simulation and physical printing experiments are carried out, and the results give a positive confirmation on the feasibility and effectiveness of the proposed multi-setup printing methodology and our enabling printing process planning algorithm.

[Abstract]: Time series are key across industrial and research areas for their ability to model behaviour across time, making them ideal for a wide range of use cases such as event monitoring, trend prediction or anomaly detection. This is even more so due to the increasing monitoring capabilities in many areas, with the subsequent massive data generation. But it is also interesting to consider the potential of time series for Machine Learning processing, often fused with Big Data, to search for useful information and solve real-world problems. However, time series can be studied individually, representing a single entity or variable to be analysed, or in a grouped fashion, to study and represent a more complex entity or scenario. In this latter case we are dealing with multivariate time series, which usually imply different approaches when dealt with. In this paper, we present a pipeline architecture to process and cluster multiple groups of multivariate time series. To implement this, we apply a multi-process solution composed by a feature-based extraction stage, followed by a dimension reduction, and finally, several clustering algorithms. The pipeline is also highly configurable in terms of the stage techniques to be used, allowing to perform a search with several combinations for the most promising results. The pipeline has been experimentally applied to batches of HPC jobs from different users of a supercomputer, with the multivariate time series coming from the monitoring of several node resource metrics. The results show how it is possible to apply this multi-process information fusion to create different meaningful clusters from the batches, using only the time series, without any labelling information, thus being an unsupervised scenario. Optionally, the pipeline also supports an outlier detection stage to find and separate jobs that are radically different when compared to others on a dataset. These outliers can be removed for a better clustering, and lat

This paper describes a framework for regional scale coseismic landslide evaluation that combines physics-based wave propagation simulation at a regional scale, with flexible sliding analysis at a site scale. The physics-based simulation incorporates fault rupture process, complex topography and dynamic site response. The sliding displacements of flexible masses are calculated by integrating seismic parameter k obtained from the physics-based simulation over seismic resistance. The framework is applied to the case study of coseismic landslides during the 2016 Mw 7.0 Kumamoto earthquake in Japan, which has well documented fault mechanism, geologic information and landslide inventory. The performance of the model prediction is evaluated by receiver operating characteristic (ROC) analysis. This study highlights the near-fault effect and soil nonlinear effect on landslide distribution, and it is demonstrated that the landslide prediction can be notably improved with the consideration of topographic amplification of ground motions. Empirical correlations between topographic amplification of key intensity measures and parameterized topographic features are developed. Overall, the simulation captures 58% of observed landslides in the inventory (using a displacement threshold of 15 cm), showing the proposed framework is a promising tool for regional coseismic landslide analysis.

Designing flow fields that can lead to uniform distributions of reactants at a minimum pump work is critical to enhancing the performance of redox flow batteries. This paper reports on an improved design of conventional serpentine flow fields, in which the channel depth is linearly reduced from the inlet to the outlet, speeding up the flow speed along the flow path and enhancing the under-rid convection downstream toward the outlet. Three-dimensional numerical simulations reveal that the optimized gradient at 25% (the channel-depth ratio between the outlet and inlet) can lead to the highest pump-based voltage efficiency. Experimental validations demonstrate that the application of the optimized flow field to a vanadium redox flow battery leads to significant improvements in both energy efficiency and electrolyte utilization, which is 5.0% and 27.7%, respectively, higher than that with the conventional serpentine flow field at a relatively high current density and low flow rate (400 mA cm−2, 12 mL min−1 cm−2). The effectiveness of the flow field design in boosting the uniform reactant distribution provides a feasible approach for scaling up high-performance redox flow batteries. © 2023 Elsevier B.V.

In this paper we prove that a finite group of order r has at most 7.3722⋅r[Formula present]+1.5315 subgroups.

Contemporary manufacturing must prioritise the sustainability of its manufacturing processes and systems. Zero Defect Manufacturing (ZDM) focusses on minimising waste of any kind using data-driven technology, hence enhancing the quality of all manufacturing aspects (product, process, service, etc.). Making things right on the first try is the central tenet of ZDM. In recent years, the application of automation for in-line quality inspection systems has begun to attract the interest of both practitioners and academics because of its capability to detect defects in real-time, and thus adapt the system to disturbances. In this work, we provide a systematic review of the literature on current trends in the application of automation for in-line quality inspection with the ultimate objective of achieving ZDM. Additionally, bibliometric and performance analyses have been performed to gain a complete picture of the field. In this work, we have collected bibliometric data from the most widely referred search engines for academic engineering papers, i.e. Scopus, Web of Science, and IEEE Explorer, involving a total of 145 academic publications from 2011 to 2021. Uniquely for this study, we used three research attributes for the analysis of the selected articles, that is, the level of automation, the condition for quality inspection, and the contribution to ZDM dimensions. The literature suggests that there is a lack of research on the use of in-line detection data for the prediction of defects or repair. Based on the results and our interpretation of the literature, an adapted framework of ZDM (Psarommatis et al., 2020a) and multi-layer quality inspection (Azamfirei et al., 2021a) is presented.

The identification of input parameters for funite element modelling of the cutting process is still a complex task as the experimental testing equipment cannot reach its combined levels of strains, strain rates and temperatures. Inverse identification using Artificial Intelligence method provides a relevant alternative. In this paper, material constitutive and friction models parameters identified with an Efficient Global Optimization algorithm and an ALE orthogonal cutting model are introduced in a CEL model. Assessment of the differences in the results due to the formulation and dependence of parameters identification to the finite element model are then performed.

Industrial manual assembly, especially within the area of large-scale assembly, often lacks process monitoring since the feedback about performed assembly tasks is only generated by the worker. Optical sensor systems thereby offer the potential to monitor assembly states automatically and derive the required information without intervening the work in progress. With the growing trend of customization, not only the assembly process itself, but also the monitoring system must be adapt- able at short notice. However, most machine vision systems, as they are today, are commonly task-specific solutions and are therefore hard to be transferred to another inspection task or other work objects. To lower the barriers on applying machine vision into varying environments, this paper introduces an automated CAD-based sensor planning and implementation pipeline. An analysis and derivation of common constraints in assembly design is laid out, followed by a method of generating and optimizing inspection features and sensor poses. A strategy to implement the image processing pipeline based on the derived features is presented and applied on an assembly use case., Bundesministerium für Wirtschaft und Klimaschutz (BMWK)

Structural design optimization for steel reinforcement (rebar) is a critical part of reinforced concrete structures. In practice, this process is conducted manually or semi-automatically for each element, which is time-consuming and relies on engineers' knowhow and experience for buildability improvement, material savings and clash resolution. This paper presents an automated pipeline for clash-free rebar design optimization integrating graph neural networks (GNN) and exploratory genetic algorithms (EGA). Graph representation is adopted to characterize rebar layouts in both structural elements and different kinds of reinforced concrete joints. GNN leverages graph representation of rebars to consider the parametric relationship between different rebar groups in a single structural element or among multiple structural elements, enhancing the efficiency of clash-free rebar design optimization. EGA supports design checking and further optimization according to building codes to achieve the optimal design. Compared with conventional optimization methods, the proposed pipeline can automatically identify optimal clash-free rebar design in compliance with code-stipulated requirements while reducing 75%–90% of the computation time, which shows enormous potential for practical use in the industry. © 2022 Elsevier B.V.

Bayesian model updating based on Gaussian Process (GP) models has received attention in recent years, which incorporates kernel-based GPs to provide enhanced fidelity response predictions. Although most kernel functions provide high fitting accuracy in the training data set, their out-of-sample predictions can be highly inaccurate. This paper investigates this problem by reformulating the problem on a consistent probabilistic foundation, reviewing common choices of kernel covariance functions, and proposing a new Bayesian model selection for kernel function selection, aiming to create a balance between fitting accuracy, generalizability, and model parsimony. Computational aspects are addressed via Laplace approximation and sampling techniques, providing detailed algorithms and strategies. Numerical and experimental examples are included to demonstrate the accuracy and robustness of the proposed framework. As a result, an exponential-trigonometric covariance function is characterized and justified based on the Bayesian model selection approach and observations of the sample autocorrelation function of the response discrepancies.

This paper distills the serial work of Li et al., 2020a, Li et al., 2021, Li et al., 2022a, Li et al., 2022b, Li et al., 2022c, Li et al., 2023a, Li et al., 2023b into a practice guide for the Dynamic Mode Decomposition (DMD) with a particular emphasis on the quick learning and adaptation of the technique from a wind engineering perspective. The guide begins with discussions of the DMD's connections with the Koopman, Fourier & Laplace, the Proper Orthogonal Decomposition, and machine learning theories. Then, based on 1.5 × 106 core hours of parametric investigations on paradigmatic cases, recommendations that broadly apply to bluff-body wind engineering problems—especially regarding the input sequence—are formulated. In the temporal aspect, results highlight the DMD's preference for steady, stationary, equilibrium, or quasi-equilibrium systems, and suggest a sampling resolution 15–20 times the frequency of the dynamics of interest. Emphasis is also placed on the necessity of the Stabilization state for sampling convergence while avoiding the input of overdetermined systems. In the spatial aspect, high-dimensional, mean-subtracted data with minimal interpolation and noise proved to yield better Koopman models. Different input variables, though derived from the same system, may also result in non-unique Koopman modes depending on their nonlinear richness. Finally, two open-source MATLAB algorithms have been discussed and made open-source to facilitate the easy dissemination and usage for wind engineers. Via pedagogical examples, Algorithm 1 outlines a standard procedure to check for sampling convergence, and Algorithm 2 performs the similarity-expression DMD and visualizes the outputs.

The popularity of precast concrete in construction is rising due to its capacity for improving building efficiency and quality in comparison to cast-in-situ concrete. While researchers have focused on enhancing constructability factors like logistics and sequencing, the need for developing standardization in this area remains evident. This paper describes a framework for studying the relationship between construction cost and standardization by optimizing precast component dimensions and rebar designs. The framework involves using Building Information Modeling techniques to extract semantic information from architectural plans, followed by a gradient-based Optimality Criteria method to optimize the sizing variables of precast components. Finally, a hybrid approach called NSGA-II-GD, which combines the Non-dominated Sorting Genetic Algorithm II and Great Deluge Algorithm, is used to optimize the rebar layout design of each precast component. The Results demonstrate that an optimal point exists between construction cost and standardization, particularly for components subjected to similar stresses. Additionally, the NSGA-II-GD algorithm improves the searching efficiency in terms of convergence, computational time, and searching space. © 2023 Elsevier B.V.

This paper considers a large-scale multiple testing problem for spatiotemporal data with multiple change points. A data-driven procedure that aims to fully utilize the clustering information is proposed. Specifically, we first develop a new change-point detection algorithm that integrates the kernel-based aggregation of spatial observations with a global loss function at the temporal level to group data into several sets, and then derive an FDR control scheme for set-wise multiple testing. Under some mild conditions on the spatiotemporal dependence structure, FDR is shown to be strongly controlled. Theoretical analysis and numerical studies demonstrate the advantages of the algorithm over competing methods. © 2023

Automated medical image classification is the key component in intelligent diagnosis systems. However, most medical image datasets contain plenty of samples of common diseases and just a handful of rare ones, leading to major class imbalances. Currently, it is an open problem in intelligent diagnosis to effectively learn from imbalanced training data. In this paper, we propose a simple yet effective framework, named Class Attention to REgions of the lesion (CARE), to handle data imbalance issues by embedding attention into the training process of Convolutional Neural Networks (CNNs). The proposed attention module helps CNNs attend to lesion regions of rare diseases, therefore helping CNNs to learn their characteristics more effectively. In addition, this attention module works only during the training phase and does not change the architecture of the original network, so it can be directly combined with any existing CNN architecture. The CARE framework needs bounding boxes to represent the lesion regions of rare diseases. To alleviate the need for manual annotation, we further developed variants of CARE by leveraging the traditional saliency methods or a pretrained segmentation model for bounding box generation. Results show that the CARE variants with automated bounding box generation are comparable to the original CARE framework with manual bounding box annotations. A series of experiments on an imbalanced skin image dataset and a pneumonia dataset indicates that our method can effectively help the network focus on the lesion regions of rare diseases and remarkably improves the classification performance of rare diseases. © 2023 Elsevier B.V.

[Abstract]: The taxonomic and functional composition of microbial communities from environmental, agricultural, and therapeutic settings is increasingly being studied using metagenomic methodologies in large-scale genomic applications. This has led to exponential growth in the field and has impacted on healthcare, pharmacology and biotechnology. However, with the current methodologies, it is sometimes difficult to obtain conclusive identification of an organism. In addition, the growth of the metagenomic field has led to the creation of large amounts of data held by different hosts, which characterize data differently and make analysis difficult. Therefore, correct data aggregation and classification improve and facilitate the discovery of repositories of interest. This paper tackles these issues by proposing a methodology for organism identification, data aggregation and content characterization, visualization and selection. We propose a three-step pipeline for organism identification that uses compression-based metrics, an aggregation mechanism for content characterization, and a web database catalogue for data exposition and visualization.

Data accountability (i.e., data integrity and traceability) concerns existing construction cost management platforms due to the risk of data manipulation in a centralized paradigm. Blockchain is a promising technology that provides immutable and traceable data storage with a decentralized architecture. However, the transparency of blockchain conflicts with the confidentiality nature of cost information. Therefore, this paper presents a novel framework based on blockchain and encryption to preserve both data accountability and confidentiality in construction cost management. The proposed new framework involves the development of a cost data model containing the required confidential cost information to facilitate partially transparent recordings on the blockchain. An access control model based on symmetric and asymmetric encryption as well as proxy re-encryption mechanisms is developed to prevent unauthorized access to sensitive cost data on the blockchain and transfer data access in dynamic construction projects (i.e., construction projects involving new members continuously). Subsequently, encryption-integrated smart contracts are developed for automatic and secure cost activities on the blockchain. The framework is validated with a desirable latency (at the millisecond level), throughput (at the hundred level), and storage cost (at the MB level) in three illustrative cost management scenarios. The results indicate that project members can have accountable and confidential cost data for dispute resolution and cost analysis, respectively, as well as efficient cost data access transfer.

The efficient separation and transmission of photogenerated charges is crucial for photocatalysis. In this paper, a novel continuous surface Z-Scheme and Schottky heterojunction composite Au/La2Ti2O7/Ag3PO4 with dual heterointerface channels was constructed for the steering separation and transport of charges. Excitedly, the composites exhibited outstanding photocatalytic activity under natural sunlight toward the organic photodegradation in contrast to the latest published researches because of the synergism of Z-Scheme and Schottky heterojunction. And the wide applicability of this photocatalytic system was well verified with various cations/anions, solution pH, reaction temperature, water quality, and peroxydisulfate (PDS) addition. Simultaneously, the rapid charge transport pathway and its induced active substances were deduced from the analysis of structural characteristics of the composite. Furthermore, the degradation mechanism was proposed based on the analysis of organic molecules’ reaction sites calculated by density functional theory (DFT) and the degradation intermediates detected by HPLC/MS. Additionally, the toxicity of degradation products was significantly diminished, which can secure the aquatic environment safety of receiving water body. This work not only affords a strategy for efficient separation of photogenerated carriers, but also provides an efficient photocatalytic system for environmental remediation of refractory organic polluted wastewater under natural sunlight irradiation. © 2023 Elsevier B.V.

In this article we used data from the Understanding Society study as part of our control group. While we did explain that the data was provided by Understanding Society in the paper, we did not include the statement they required for use of these data which is the following: “Understanding Society: The UK Household Longitudinal Study is led by the Institute for Social and Economic Research at the University of Essex and funded by the Economic and Social Research Council. The survey was conducted by NatCen and the genome-wide scan data were analysed and deposited by the Wellcome Trust Sanger Institute. Information on how to access the data can be found on the Understanding Society website https://www.understandingsociety.ac.uk/.” We apologise for any inconvenience caused.

Electroencephalograms (EEGs) are brain dynamics measured outside of the brain, which have been widely utilized in non-invasive brain-computer interface applications. Recently, various neural network approaches have been proposed to improve the accuracy of EEG signal recognition. However, these approaches severely rely on manually designed network structures for different tasks which normally are not sharing the same empirical design cross-task-wise. In this paper, we propose a cross-task neural architecture search (CTNAS-EEG) framework for EEG signal recognition, which can automatically design the network structure across tasks and improve the recognition accuracy of EEG signals. Specifically, a compatible search space for cross-task searching and an efficient constrained searching method is proposed to overcome challenges brought by EEG signals. By unifying structure search on different EEG tasks, this work is the first to explore and analyze the searched structure difference in cross-task-wise. Moreover, by introducing architecture search, this work is the first to analyze model performance by customizing model structure for each human subject. Detailed experimental results suggest that the proposed CTNAS-EEG could reach state-of-the-art performance on different EEG tasks, such as Motor Imagery (MI) and Emotion recognition. Extensive experiments and detailed analysis are provided as a good reference for follow-up researchers.

Intelligent Transportation Systems are essential due to the increased number of traffic congestion problems and challenges nowadays. Traffic Signal Control (TSC) plays a critical role in optimizing the traffic flow and mitigating the congestion within the urban areas. Various research works have been conducted to enhance the behavior of TSCs at intersections and subsequently reduce the traffic congestion. Researchers recently leveraged Deep Learning (DL) and Reinforcement Learning (RL) techniques to optimize TSCs. In RL framework, the agent interacts with surrounding world through states, rewards and actions. The formulation of these key elements is crucial as they impact the way the RL agent behaves and optimizes its policy. However, most of existing frameworks rely on hand-crafted state and reward designs, restricting the RL agent from acting optimally. In this paper, we propose a novel approach to better formulate state and reward definitions in order to boost the performance of the traffic signal controller agent. The intuitive idea is to define both state and reward in a consistent and straightforward manner. We advocate that such a design approach helps achieving training stability and hence provides a rapid convergence to derive best policies. We consider the double deep Q-Network (DDQN) along with prioritized experience replay (PER) for the agent architecture. To evaluate the performance of our approach, we conduct series of simulations using the Simulation of Urban MObility (SUMO) environment. The statistical analysis of our results show that the performance of our proposal outperforms the state-of-the-art state and reward design approaches

The paper examines the determinants of FDI profit repatriation in the Czech Republic and OECD countries as a group from 2013 to 2019. By applying the Lintner model about repatriation decisions at the national level, the paper finds the following findings. Firstly, the repatriation rate in the Czech Republic is higher than in most OECD countries. Secondly, financial development and investment opportunities negatively affect the payout ratio of OECD countries as a group, while the appreciation of host countries' currency and higher effective rates positively affect the payout ratio of OECD countries as a group. However, in the case of the Czech Republic, two factors that cause a higher payout ratio are financial development and anti-corruption enforcement. It implies that the Czech Republic should improve its financial market development and reduce the level of corruption to keep the profit from FDI in the country. © 2023 Elsevier B.V.

This paper describes the design and synthesis of a series of terpyridine-based complexes of the first-row transition metals Cr, Mn, Fe, and Co for non-aqueous redox flow batteries (NARFBs). Electrochemical studies reveal that these complexes can undergo multi-electron transfer redox reactions. In particular, the Mn and Fe-based complexes exhibit both low negative redox potentials and high positive redox potential, permitting them to serve as a bipolar electrolyte for symmetric RFBs with a cell voltage of more than 2 V. The solubility of these complexes can be effectively improved by incorporating a polyether substituent on the terpyridine ligand and counter anion optimization. The iron complex [Fe(tpy-O(CH2CH2O)3CH3)2][TFSI]2 shows a high solubility of 0.76 M in MeCN. The fabricated iron-based symmetric NARFB demonstrates a superior battery performance with a high cell voltage of 2.3 V, columbic efficiency of 97%, energy efficiency as high as 88%, and stable charge-discharge capacity retention of 60% after 160 cycles, corresponding to 99.75 % capacity retention per cycle. The post-cycling cyclic voltammetry (CV), UV-Vis, and 1H-NMR characterizations indicate only minor chemical decomposition of the cycled complex, confirming its good charging-discharging stability.

In this paper, by using the time-varying parameter vector autoregression model (TVP-VAR) with the asymmetric connectedness indicator and network diagrams, we investigate the dynamic and asymmetric return connectedness in the global “Carbon-Energy-Stock” system, including carbon markets and stock markets of the three largest economies, namely the United States, European Union and China, and fossil energies of crude oil and natural gas under exogenous shocks. Our study shows that (i) the risk spillover level of the global system has significantly increased after the outbreak of two exogenous events, the COVID-19 pandemic and the Russo-Ukrainian war, and the global shock from the COVID-19 pandemic has more widespread and greater impact on the system than the geopolitical shock from the Russo-Ukrainian war, (ii) the global “Carbon-Energy-Stock” system is more sensitive to negative information on price returns than positive information, and the asymmetry of the connectedness is much larger when the system is active and in the presence of exogenous shocks, (iii) risks in the global “Carbon-Energy-Stock” system usually transformed from stock markets, especially the stock markets of the United States and European Union, to the carbon markets. These findings provide valuable guidance and have economic implications for both investors and policymakers worldwide. © 2023 Elsevier B.V.

Iiboshi Y., Iwata A., Hayashi T., et al. Energy trapping of circumferential shear horizontal wave in a hollow cylinder. Ultrasonics 133, 107044 (2023); https://doi.org/10.1016/j.ultras.2023.107044., This paper describes energy trapping of circumferential shear horizontal wave (C-SH wave) at a circumferential inner groove of a hollow cylinder. We first derive exact solutions of resonant frequencies of the C-SH wave from the classical theory of guided waves propagating in a hollow cylinder and approximated solutions from the relationship between wavelength of the C-SH wave and length of circumferential path of the hollow cylinder. Next, we examined the energy trapping conditions using the dispersion curves of a guided wave propagating in the longitudinal direction of a hollow cylinder, and showed that C-SH waves trap energy when the hollow cylinder has a circumferential groove on the inner surface, rather than on the outer surface. The energy trapping at an inner groove was confirmed for the C-SH wave with the circumferential order of n = 6 using eigen frequency analysis of finite element method and experiments with electromagnetic transducers. Moreover, when the energy trap mode was used to determine the change in resonance frequency for glycerin solution of different concentrations, it was confirmed that the resonance frequency decreased monotonically as the concentration increased, indicating the possibility of using the energy trap mode as a QCM-like sensor.

Iiboshi Y., Iwata A., Hayashi T., et al. Energy trapping of circumferential shear horizontal wave in a hollow cylinder. Ultrasonics 133, 107044 (2023); https://doi.org/10.1016/j.ultras.2023.107044., This paper describes energy trapping of circumferential shear horizontal wave (C-SH wave) at a circumferential inner groove of a hollow cylinder. We first derive exact solutions of resonant frequencies of the C-SH wave from the classical theory of guided waves propagating in a hollow cylinder and approximated solutions from the relationship between wavelength of the C-SH wave and length of circumferential path of the hollow cylinder. Next, we examined the energy trapping conditions using the dispersion curves of a guided wave propagating in the longitudinal direction of a hollow cylinder, and showed that C-SH waves trap energy when the hollow cylinder has a circumferential groove on the inner surface, rather than on the outer surface. The energy trapping at an inner groove was confirmed for the C-SH wave with the circumferential order of n = 6 using eigen frequency analysis of finite element method and experiments with electromagnetic transducers. Moreover, when the energy trap mode was used to determine the change in resonance frequency for glycerin solution of different concentrations, it was confirmed that the resonance frequency decreased monotonically as the concentration increased, indicating the possibility of using the energy trap mode as a QCM-like sensor.

To provide direction and advice for future research on Industry 4.0 maintenance, we conducted a comprehensive analysis of 344 eligible journal papers published between 2013 and 2022. Our systematic literature review identifies key trends in advanced maintenance techniques and the consolidation of traditional maintenance concepts, which are driven by the increasing adoption of Industry 4.0 technologies and the need to optimize manufacturing systems' performance and reliability. In light of our findings, we highlight the importance of addressing sustainability factors, human aspects, and the implementation of environmental KPIs in future research. Building upon these insights, we introduce the Maintenance 5.0 framework, which emphasizes the integration of human-centered and AI-driven strategies for achieving efficient and sustainable maintenance in Zero-Defect Manufacturing (ZDM) systems. We propose a novel framework that links traditional and advanced maintenance policies for small and medium-sized enterprises (SMEs) to facilitate the adoption of Industry 4.0 technologies in the maintenance field. This work underscores the need for future research to bridge the gap between these policies, enabling a seamless transition for SMEs towards Industry 4.0 maintenance practices, while fostering sustainable and socially responsible operations.

The potential for carbon emissions avoidance through post-consumer recycling has been highlighted in the "Zero-Waste City" initiatives in China, which call for increasing household participation in the community recycling programs. A shift from a facility-oriented strategy to behavior-oriented norm building at the community level provides the local niches for emerging business models in post-consumer recycling. This paper proposes a framework based on the Theory of Planned Behavior to simulate the impact on the participation rate of households in community-based recycling program in the context of different recycling business models in Beijing. Firstly, a questionnaire survey was conducted in 2021 in Beijing (N = 1153) to test the key factors which affect household recycling behavior. Secondly, an agent-based model for community-based recycling which was developed in 2016 has been updated to incorporate the institutional change in the development of zero-waste city initiatives. Scenario analysis is adopted to compare the effects of introducing two new business models to the baseline situation that is prevalent at present. The settings for the two new business models represent the diversified directions of the upgrading of urban recycling sector: one is to improve the intelligence of the collection facilities to save labors, and the other is to involve the informal recyclers to provide door-to-door collection services in person so as to save time for the residents. Additional scenario shows the effects of norm-based solutions in combination with the two strategies addressed above in the long run. The simulation results show that the potential for carbon emissions reduction through post-consumer recycling in Beijing can range from 1 million tons per year at the basic scenario to more than 4.5 million tons per year in the community level norm-based solution scenario. In conclusion, the proposal for sustaining the community-based new business models through the capture

[Abstract]: Federated learning (FL) is a distributed approach to developing collaborative learning models from decentralized data. This is relevant to many real applications, such as in the field of the Internet of Things, since the models can be used in edge computing devices. FL approaches are motivated by and designed to protect privacy, a highly relevant issue given current data protection regulations. Although FL methods are privacy-preserving by design, recently published papers show that privacy leaks do occur, caused by attacks designed to extract private data from information interchanged during learning. In this work, we present an FL method based on a neural network without hidden layers that incorporates homomorphic encryption (HE) to enhance robustness against the above-mentioned attacks. Unlike traditional FL methods that require multiple rounds of training for convergence, our method obtains the collaborative global model in a single training round, yielding an effective and efficient model that simplifies management of the FL training process. In addition, since our method includes HE, it is also robust against model inversion attacks. In experiments with big data sets and a large number of clients in a federated scenario, we demonstrate that use of HE does not affect the accuracy of the model, whose results are competitive with state-of-the-art machine learning models. We also show that behavior in terms of accuracy is the same for identically and non-identically distributed data scenarios.

The special issue on Nonlinear Economic Dynamics contains a selection of papers presented at the 11th International Conference on Nonlinear Economic Dynamics (NED) that took place on September 4–6, 2019, at the Kyiv School of Economics, Ukraine. Organized under the auspices of the Nonlinear Economic Dynamics Society, the NED conference is a biennial (odd-year) international meeting of scholars interested in the development of methods for studying nonlinear dynamical systems and in their applications in economics and social sciences. The aim of this meeting is to bring together different streams of the growing literature in this field and to stimulate a fruitful exchange between theoretical research and applications in economics.

This paper evaluates the gender differences in intergenerational effects of laid-off parents on children’s adult outcomes. Using the China Health and Nutrition Survey (CHNS) over the period from 1991 to 2015, we construct the children’s lengths of exposure to fathers’/mothers’ layoffs from state-owned enterprises (SOEs) retrenchment starting in the 1990 s before they turn 18 years old. We find that, when experiencing fathers’ layoffs for a longer period, only girls suffer a significant reduction in education outcomes and are hence more likely to have manual occupations; when suffering mothers’ layoffs for a longer period, boys’ education levels increase, but this advantage does not reduce their probability of having manual occupations. Although the gender difference in children’s education persists in their occupations, girls do not underperform in adult earnings compared to boys with similar family backgrounds. Instead, for girls with manual occupations, fathers’ layoffs increase their gardening income.

Multi-level image thresholding is a common approach to image segmentation where an image is divided into several regions based on its histogram. Otsu's method is the most popular method for this purpose, and is based on seeking for threshold values that maximise the between-class variance. This requires an exhaustive search to find the optimal set of threshold values, making image thresholding a time-consuming process. This is especially the case with increasing numbers of thresholds since, due to the curse of dimensionality, the search space enlarges exponentially with the number of thresholds. Population-based metaheuristic algorithms are efficient and effective problem-independent methods to tackle hard optimisation problems. Over the years, a variety of such algorithms, often based on bio-inspired paradigms, have been proposed. In this paper, we formulate multi-level image thresholding as an optimisation problem and perform an extensive evaluation of 23 population-based metaheuristics, including both state-of-the-art and recently introduced algorithms, for this purpose. We benchmark the algorithms on a set of commonly used images and based on various measures, including objective function value, peak signal-to-noise ratio, feature similarity index, and structural similarity index. In addition, we carry out a stability analysis as well as a statistical analysis to judge if there are significant differences between algorithms. Our experimental results indicate that recently introduced algorithms do not necessarily achieve acceptable performance in multi-level image thresholding, while some established algorithms are demonstrated to work better.

Modern additive manufacturing systems still suffer from low fabrication accuracy and speed. This issue is particularly severe for thermal reaction bonding material extrusion systems, where the motion and the deposition processes are coupled. To enhance the deposition accuracy, this work established a method to use real-time in-situ thermal images to capture and compensate for the geometrical accuracy of the printed parts and establish the spatial and temporal relationship between the inaccuracy and the deposition trajectory commands. The paper derives an analytical slender condition to process the printing regions with low curvature while establishing deposition direction allocation methods to account for the trajectory regions with high curvature. Real-time thermal image localization is performed with key printing feature detection and calibration. The localized and calibrated thermal images are aligned with the defined deposition direction to acquire the deposition inaccuracies. With the deposition dynamics considered, the deposition inaccuracy is reduced via dynamic deposition rate compensation. The proposed thermal imaging and compensation techniques have been experimentally verified with a material extrusion experimental platform to illustrate its capabilities to alleviate the deposition inaccuracy during the corner regions. © 2023 Elsevier B.V.

Cascading hazards occur frequently. Unexpected casualties and losses of properties, or even impacts on the society and the environment may ensue from failure to anticipate the amplified risks induced by cascading hazards. Current risk assessment methods pay relatively less attention to quantifying the increased human risk related to “cascading” events. An improved framework for quantifying the human risk caused by cascading hazards is proposed in this paper. The framework considers the interactions among the cascading hazards and among the vulnerabilities of elements to these hazards. Its kernel is to scientifically anticipate and gear up for any new intensified hazards, which may otherwise lead to serious social aftermath. The framework is illustrated with a multi-hazard example close to the epicenter of the Wenchuan earthquake, which involved a chain of hazards including slope failures, a large debris flow, river damming, and flooding. © 2022 Elsevier B.V.

The current era is characterized by rapid developing in all areas of society, but the most rapid expansion is in the field of science, knowledge, and technology. Galloping accessibility of the novel materials and technologies creates requirements of complex evaluation of their deployment to obtain economic and ecological sustainability. The authors of the paper present a proposal for a methodology enabling an effective decision-making process when choosing material and technological processes suitable for the manufacturing of products. This methodology was developed to enable the efficient evaluation of a wide range of materials, manufacturing technologies, including existing additive and hybrid and with focus on future and emerging technologies, too. The evaluation focuses on sustainability approach to minimize negative social and environmental impact while ensuring the required level of safety and profitability. © 2023 The Authors. Published by Elsevier B.V.

The article deals with the problem related to the service life of the coin dies. The faultless condition of tools during the long-time process of the production of the coins is a basement for their quality. It is influenced by several aspects. The most important factors can be considered the material of the coin die - especially its purity, relief production technology, heat treatment - especially the correct observance to procedures and the choice of equipment, achieved mechanical properties, method of final relief treatment - e.g., coating and the existence of residual stresses in the tool. The aim of the present research was to examine the cause of the failure and damage of the coin dies after production of a small number of coins and, accordingly, to make changes in the production process to prolong their service life. By monitoring the individual steps of the production process, it was possible to identify specific causes of premature decommissioning of coin dies. Innovations have been made at each step of the production process to extend the life of the coin. The paper presents the modifications in the heat treatment of the coin dies as one of the basic improvements in their no-failure long time operation. This, together with other identified shortcomings in the tool manufacturing process, has contributed to an increase in the number of coins minted from around 6,000 to 740,000 coins. © 2023 The Authors. Published by Elsevier B.V.

This paper introduces JAX-FEM, an open-source differentiable finite element method (FEM) library. Constructed on top of Google JAX, a rising machine learning library focusing on high-performance numerical computing, JAX-FEM is implemented with pure Python while scalable to efficiently solve problems with moderate to large sizes. For example, in a 3D tensile loading problem with 7.7 million degrees of freedom, JAX-FEM with GPU achieves around 10× acceleration compared to a commercial FEM code depending on platform. Beyond efficiently solving forward problems, JAX-FEM employs the automatic differentiation technique so that inverse problems are solved in a fully automatic manner without the need to manually derive sensitivities. Examples of 3D topology optimization of nonlinear materials are shown to achieve optimal compliance. Finally, JAX-FEM is an integrated platform for machine learning-aided computational mechanics. We show an example of data-driven multi-scale computations of a composite material where JAX-FEM provides an all-in-one solution from microscopic data generation and model training to macroscopic FE computations. The source code of the library and these examples are shared with the community to facilitate computational mechanics research. Program summary: Program Title: JAX-FEM CPC Library link to program files: https://doi.org/10.17632/hgwshjbcw6.1 Developer's repository link: https://github.com/tianjuxue/jax-am/tree/main/jax_am/fem Licensing provisions: GPLv3 Programming language: Python Nature of problem: Implementation of the finite element method (FEM) with several appealing features that classic FEM software typically does not have: realized with pure Python; running on CPU/GPU; differentiable for solving PDE-constrained optimization problems; seamless integration with machine learning. Solution method: Our framework JAX-FEM is based on Google JAX, a high-performance numerical computing library with automatic differentiation features and supporti

In this paper a group contribution model for predicting the viscosity values of n-alkanes and iso-alkanes in the liquid phase based on Free-Volume Theory was developed. A database with 2129 experimental data with a temperature range up to 573.15 K and a pressure range up to 4000 bar was used to develop the model. The total weighted average percentage error is 4.894%. The model was tested on the next 472 experimental data with a temperature range up to 564.15 K and a pressure range up to 2019 bar and a mean percentage error that was obtained is 5.155%.

Recent research has focused on developing real-time sign language recognition systems (SLRSs) based on gesture recognition to classify hand motions into their equivalent meaning in spoken language, but no comprehensive system with all desirable features has been presented. The existence of different systems has hindered the process of selecting the most preferred system. Therefore, many researchers have compared and evaluated several recognition systems to identify the best one using multicriteria decision-making methods. These studies extended the fuzzy decision by opinion score method (FDOSM) using a single Likert scale under the Pythagorean fuzzy set (PFS) or one of its extensions. However, no comparative study has examined the influence of using multiple Likert scales with a single fuzzy set. Furthermore, the effect of employing multiple Likert scales on benchmarking results is a challenging task. Therefore, this paper examines the three Likert scales (five-, seven- and ten-point) under the same fuzzy environment. This paper extends FDOSM into PFSs based on the power Bonferroni mean (PBM) operator (named PFDOSM-PBM) to benchmark the real-time SLRS. The decision matrix is constructed based on 30 real-time SLRS-based wearable sensory devices and the 11 evaluation criteria. The results reveal that the five-point Likert scale is superior to other scales (i.e., seven- and ten-point) as it is flexible, easy to use and generates more accurate findings on the basis of uncertainty compared to other scales. Systematic ranking and comparative analysis are conducted to validate and evaluate the proposed method.

The European plan for a green transition includes the Fit for 55 package, designed to pave the way for climate neutrality. Despite its significant implications for cleaner technologies, it potentially correlates with high investment requirements, necessitating the pursuit of cost-effective environmental policies. Starting from the reference scenario previously envisaged in the Energy and Climate Plan, socioeconomic and environmental impacts are assessed using mixed methods. It is estimated that €1120 bn in investments are needed to meet decarbonization targets, while the total impact on public finance revenues to 2030 is projected at €529 bn. Additionally, the avoided costs of emissions amount to €36 bn, while those from energy savings are expected to reach €30 bn. This paper adds value by contributing to the literature on European climate policies, offering an in-depth appraisal of implications that integrates technoeconomic and environmental perspectives. Furthermore, it informs policymakers' public spending decisions for decarbonization.

In this paper, we investigate the microlensing effects of wormholes associated to black hole spacetimes. Specifically, we work on three typical wormholes (WH): Schwarzschild WH, Kerr WH, and RN WH, as well as their blackhole correspondences. We evaluate the deflection angle upon the second order under weak field approximation using Gauss–Bonnet theorem. Then, we study their magnification with numerics. We find that a Kerr WH could lead to multi peaks in the magnification with certain parameters in the prograde case, while a Kerr BH predicts one peak. Therefore, the multi-peak feature of can be used to distinguish the Kerr WH from other compact objects. We also find that the magnification of RN BH will be one peak compared to RN WH, in which the magnification of RN WH is negative in some situations. For other cases, the behavior of magnification from wormholes and their corresponding blackholes is similar. Our result may shed new light on exploring compact objects through the microlensing effect.

“Motion generation by imitating” enables a robot to generate its trajectory in a new environment. Research works on dynamic movement primitives (DMP) has reported promising results, with good imitation effect and convergence to the target. However, DMP still has issues such as learning from multiple demonstrations for different initial conditions and achieving obstacle avoidance considering the distribution and motion of obstacles. One of the effective solutions is combining DMP and model predictive control (MPC). The imitation process was transformed into a receding horizon planning procedure, letting the robot to learn more from nearer demonstrations. It is solved as an optimization problem with obstacles modeled as constraints. However, its drawback includes the heavy computation burden, which can be even aggravated in a multi-obstacle scenario where complicated constraints occur. Thus, in this paper, we propose an enhanced MPDMP+ method that combines the advantages of MPC with potential function for both multi-demonstration imitation and multi-obstacle avoidance effect. A proximal augmented Lagrangian method is proposed to solve the optimization problem. This proposed method has a faster convergence rate and small errors. We conducted the simulation and robot experiments for imitation learning for obstacle avoidance scenarios. Our results illustrate the superior performance of the proposed method. © 2023 Elsevier B.V.

This paper investigates data correlation in remote sensing networks and how it can be characterized through diverse models quantifying the Value of Information (VoI), a metric that describes how informative the data transmitted by the sensors are. For each sensor, the VoI evaluations comprise the average node-specific Age of Information (AoI), the average cost spent for sending updates, and the AoI of neighbor nodes, assumed to be correlated sources of information and therefore benefiting the VoI of other sensors nearby. We discuss how this metric can be tracked through a two-dimensional Markov chain, but we also show how this representation can be simplified by including the impact of neighbor nodes within the transition probabilities, so as to obtain a simpler model that gives the same insight in terms of VoI evaluations.

Adaptation to climate change is impacted by a range of interrelated processes operating from local to global levels. There are often significant disconnects between different people's perceptions of responsibilities, capabilities and motivations, and divergent understandings of how the system works across actors, sectors and levels of governance. This results in misalignments of policies and practices, plus ineffective flows of resources and knowledge across the network of climate adaptation actors. As these disconnects are rooted in deep misunderstandings of the grounded realities of different actors, an experiential process of mutual discovery is required to build shared understanding and mutual respect. While it is common in the literature for people to talk about multi-level governance, most existing planning processes involve the production of separate plans at each individual level, based on the often-mistaken assumption that they will aggregate into an effective multi-level approach. This paper presents a new, multi-level integrated planning and implementation (MIPI) process, bringing together diverse actors from community, district, regional and national levels in the same workshop. The MIPI process creates a safe space that allows participants to interact directly in conducting systemic, cross-level analyses, as well as the multi-level integration of policies, plans and programs. The paper describes how the MIPI process was designed and facilitated in Ghana to address climate change, agricultural development and food security. This methodology has potential for much broader applicability to complex, multi-level planning and implementation processes.

[Abstract]: In recent years, mainly due to the application of smartphones in this area, research in human activity recognition (HAR) has shown a continuous and steady growth. Thanks to its wide range of sensors, its size, its ease of use, its low price and its applicability in many other fields, it is a highly attractive option for researchers. However, the vast majority of studies carried out so far focus on laboratory settings, outside of a real-life environment. In this work, unlike in other papers, progress was sought on the latter point. To do so, a dataset already published for this purpose was used. This dataset was collected using the sensors of the smartphones of different individuals in their daily life, with almost total freedom. To exploit these data, numerous experiments were carried out with various machine learning techniques and each of them with different hyperparameters. These experiments proved that, in this case, tree-based models, such as Random Forest, outperform the rest. The final result shows an enormous improvement in the accuracy of the best model found to date for this purpose, from 74.39% to 92.97%.

Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on the amount and quality of the annotated data. However, it is time-consuming and expensive for professional pathologists to provide accurate pixel-level ground truth, while it is much easier to get coarse labels such as point annotations. In this paper, we propose a weakly-supervised learning method for nuclei segmentation that only requires point annotations for training. First, coarse pixel-level labels are derived from the point annotations based on the Voronoi diagram and the k-means clustering method to avoid overfitting. Second, a co-training strategy with an exponential moving average method is designed to refine the incomplete supervision of the coarse labels. Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm. We comprehensively evaluate the proposed method using two public datasets. Both visual and quantitative results demonstrate the superiority of our method to the state-of-the-art methods, and its competitive performance compared to the fully-supervised methods. Codes are available at https://github.com/hust-linyi/SC-Net. © 2023 Elsevier B.V.

With the vigorous development of today’s wireless communication industry, the Internet of Vehicles (IoV), as one of its application scenarios, has received extensive attention from researchers. Ensuring the security of information transmission is one of the many problems to be solved in the IoV system. To fit the actual scene, this paper considers the impacts of both the channel characteristics of Nakagami-m fading and the distribution of the vehicles’ positions in the real road scenario of the IoV. According to the random distribution characteristics of vehicle terminal position, a system model with a transmitter base station, a legitimate vehicle terminal, and an eavesdropper is established. The approximate and asymptotic analytical expressions of the secrecy outage probability over the Nakagami-m fading channels are derived. Finally, the correctness of the proposed analysis models established in this paper is verified by Monte Carlo simulation and numerical analysis. The secrecy outage performance and the influencing factors of the considered model in the Nakagami-m fading environment are discussed and analyzed.