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Nowadays, online community platforms provide firms with an important source of information for conducting dynamic marketing research. High-technology companies, in particular, rely heavily on lead users for the development of very novel products or easily adjustable services. In this paper, we present a three-phase methodology that integrates a machine-learning-based algorithm with a sophisticated clustering technique. The purpose of this methodology is to systematically identify lead users and their needs from a complex online community network. We also aim to identify important features, perceptions, and preferences for different groups of lead users. To validate the effectiveness of our approach, we conduct a real-world case study. • Lead users in online communities are valuable for new product development. • A novel three-phase framework is proposed to identify lead users and their needs. • Lead users' need trends are extracted by a sophisticated clustering technique. • Comparative and industrial studies validate the superiority of the method. [ABSTRACT FROM AUTHOR]

Hierarchical text classification (HTC) aims to classify a text into multiple categories organized in a hierarchical structure. The state-of-the-art HTC methods usually employ graph networks, where label graphs are constructed and label representation is learned to interact with text representations for classification. In general, label graphs are built on the intrinsic label hierarchy, label semantic similarity, or label co-occurrence. Such graphs have been proven to be effective, but they only exploit knowledge from training data or simple label descriptions, without considering the vast external knowledge in the open sources. Actually, external knowledge from open sources could bring in complementary information to enhance the label graph's representation power. Motivated by the above considerations, we explore the use of external knowledge for improving HTC in this paper. We categorize knowledge into micro-knowledge and macro-knowledge, which are defined as the fundamental concepts related to a single class label and the correlations among class labels, respectively. For tailor-made incorporation of the two types of knowledge into representation learning and classification, we propose Adaptive Micro- and Macro-Knowledge Incorporation for Hierarchical Text Classification (AMKI-HTC) model in this paper. The micro-knowledge incorporation helps capture class-relevant keywords in the text and hence produce discriminative representations, while the macro-knowledge incorporation improves the accuracy of label graphs. Finally, a confidence maximization fusion strategy is developed for adaptive aggregation of multi-view features. Extensive experiments on three benchmark HTC datasets demonstrate that AMKI-HTC consistently outperforms state-of-the-art models. • Conceptualize extensive external knowledge as micro-knowledge and macro-knowledge. • Aggregate micro- and macro-knowledge into original text features adaptively. • Construct text-knowledge graphs to emphasize discriminant information. • Simplify hierarchy-GCN as SH-GCN with improved performance. • Adaptively fuse features from multiple label graphs by confidence maximization. [ABSTRACT FROM AUTHOR]

As one of fundamental problems in robotics, coverage path planning (CPP) requires the robot path to cover the entire workspace which has been employed in several essential applications such as cleaning robots, land mine detector, lawnmowers and automated harvesters. Unlike most of existing studies considering the CPP problem under a unrealistic assumption of infinity energy, this paper takes the battery limitation of robots into account. This poses a significant challenge for enabling an efficient coverage path while satisfying the limited energy constraint, even in a priori known environment. Handling this challenge, we propose a BWave Framework that guides the robot to move following an improved Boustrophedon-like motion and a special area prioritization and especially, to return a charging station effectively before an exhausted energy. To that end, a weighted map is applied for recognizing the special areas, namely trap regions, and governing the robot to enter these fields in priority. Moreover, a return matrix, which forms the shortest-path tree from the charging station, is pre-computed to not only validate the energy requirement, but also speed up the calculation process of return and advance paths during the robot's operation. We then evaluate BWave Framework extensively in various scenarios in both generated and real-life indoor maps datasets. The results show that compared to typical baseline methods, BWave Framework achieves the CPP solution at a significantly accelerated running time, namely 51.5 to 72.8 times lower for generated maps, and 44.8 to 255 times for real maps, while reducing the total path length by 2.4%–17.6% and by 2.9%–18.5%, respectively. Moreover, the proposed method also outperforms the baselines in terms of overlap rate, number of returns and accounts for a lower number of deadlocks. • This paper tackles coverage path planning with energy constraints. • It introduces BWave, a novel framework for complete coverage. • BWave has two phases: environment knowledge extracting and complete coverage. • BWave is evaluated in various scenarios and compared with state-of-the-art methods. • BWave performs well in environments with many trap areas. [ABSTRACT FROM AUTHOR]

Identifying differences between groups is one of the most important knowledge discovery problems. The procedure, also known as contrast sets mining, is applied in a wide range of areas like medicine, industry, or economics. In the paper we present RuleKit-CS, an algorithm for contrast set mining based on separate and conquer – a well established heuristic for decision rule induction. Multiple passes accompanied with an attribute penalization scheme provide contrast sets describing same examples with different attributes, distinguishing presented approach from the standard separate and conquer. The algorithm was also generalized for regression and survival data allowing identification of contrast sets whose label attribute/survival prognosis is consistent with the label/prognosis for the predefined contrast groups. This feature, not provided by the existing approaches, further extends the usability of RuleKit-CS. Experiments on over 130 data sets from various areas and detailed analysis of selected cases confirmed RuleKit-CS to be a useful tool for discovering differences between defined groups. In particular, the presented method identified contrast sets with higher support and precision than popular competitors like STUCCO, CSM-SD, or pysubgroup. The algorithm was implemented as a part of the RuleKit suite. • Combining multiple separate and conquer models renders reliable contrast sets. • Attribute penalization ensures contrast set diversity. • Regression and survival contrast sets reveal new, interesting knowledge. • Numerical attributes and missing values are supported by the algorithm. • Usability of the presented approach was confirmed on over 130 data sets. [ABSTRACT FROM AUTHOR]

Over the last twenty years, patient opinions of the received healthcare services have emerged as a crucial quality indicator and a valuable instrument for evaluating the service quality of health organizations. However, many surveyors still utilized uncertain information for conducting this evaluation process. Whereas, in the past few years, the issues of inconsistency, indeterminacy, and uncertainty in data have been growing and have become a matter of concern in dealing with decision-making issues. This paper proposes an improved RAFSI method based on a single-valued trapezoidal neutrosophic number (SVTraNN) as a multi-attribute framework for dealing with a healthcare service quality evaluation. The proposed framework consists of two features: a feature for determining numerical criteria weights involving the procedures of the Ordinal Priority Approach (OPA) and a feature for selecting the best polyclinic through contributing our new SVTraNN Harmonic and arithmetic mean to yield criterion functions in the RAFSI method. This paper then compares the results obtained by the proposed method with other SVTraNN decision-making methods. A comparative analysis and rank reversal testing of the improved method demonstrated that the multi-attribute framework proffers the option of capability and stability in overcoming inconsistency, indeterminacy, and uncertainty information efficiently during the healthcare service quality evaluation. [ABSTRACT FROM AUTHOR]

Influence maximization is an important research area in social network analysis where researchers are concerned with detecting influential nodes. The detection of influential nodes is of great interest in several disciplines including computer science, opinion propagation, political movements, or economics, where systems are often modeled as graphs. The Influence Maximization problem is proved NP-hard. This computational complexity is justified by two main factors. The first factor is about the important size of social networks. Modern social networks like TikTok and Facebook have reached an unprecedented number of users. Dynamic social networks, whose topology or/and informational content is able to evolve, represent the second factor. Maximizing influence in such networks remains a significant task. In this light, several methods have been proposed. Being motivated by this fact, we provide in this paper a detailed survey of influence maximization approaches. Our main concern is to provide a taxonomy of existing models in both static and dynamic networks. In addition, we provide a comparison of the state-of-the-art approaches according to a clear categorization. New trends for detecting influential nodes are also discussed. We provide then some challenges as well as future directions. • Taxonomy of existing IM models in both static and dynamic networks is provided. • A comparison of existing approaches according to a clear categorization is presented. • New trends for detecting influential nodes are also discussed. [ABSTRACT FROM AUTHOR]

Influence source locating is important for misinformation detecting and blocking. However, most of existing multiple sources locating methods use only the local structure of the nodes or the shortest path between them. In addition, some methods do not consider the influencing times of the observed nodes and the mutual effect between the influence cascades originated from various sources. These factors hinder these methods from obtaining high-quality multi-source detection results. To overcome such shortcomings, it is necessary to analyze the nodes' latent structure characteristics in spreading and receiving the influences. This paper presents a representation learning-based approach to detect the influence sources. The algorithm detects the sources using the topological features of the influenced observed nodes. Firstly, a set of candidate sources is constructed by eliminating some nodes which obviously cannot influence the observed ones. The latent spaces of influence senders and receivers are defined to reveal the nodes' features in influence propagation. The nodes are mapped into the mentioned two latent spaces according to their influencing probabilities and influenced times. The latent spaces establish an influence propagation model, where each node's representations can be used to obtain the probability that it becomes a source. To optimize the propagation model, negative sampling method is used to reduce the computation time. Our experimental results on data sets of 5 real networks and 3 synthetic networks demonstrate that precision of the result by our algorithm is on average 10 % higher than those of the other similar algorithms. [ABSTRACT FROM AUTHOR]

Selecting weighting factors for H-infinity controller requires tones of knowledge and experience without guaranteeing obtaining the best results. This paper proposes novel idea for H-infinity controller synthesis using metaheuristics (MHs). The optimisation problem for the H-infinity controller synthesis is posed to find the weighting factors to optimise the control performance subject to stability and control handling constraints. A number of established MHs are used to solve the problem while their performance are investigated. The results indicate that the Artificial Hummingbird Algorithm and Grey Wolf Optimizer is the most efficient algorithm for the optimisation problem of H-infinity flight control design. Based on this study, the performance of several MHs on solving the H-infinity flight control design are examined while the optimum weighting factors for H-infinity flight controller are obtained. This paper is said to be a based line of studying in easy and efficient way for H-infinity controller synthesis using MHs. [ABSTRACT FROM AUTHOR]

In order to accurately represent the meaning and pronunciation of Arabic words and sentences, the presence of diacritics plays a crucial role. Over the years, researchers have dedicated significant efforts to enhancing automated diacritization systems. This paper introduces a novel approach for Arabic diacritization utilizing Bidirectional Encoder representations from Transformers (BERT) models. To evaluate the effectiveness of the proposed approach, two publicly available datasets, namely the Arabic Diacritization (AD) dataset and the Tashkeela Processed (TP) dataset, were employed. The performance of the models was assessed using various error metrics, including Diacritic Error Rate (DER) and Word Error Rate (WER). The findings demonstrate the superior performance of BERT in the diacritization process, surpassing all models employed in other diacritization systems. On the AD dataset, the proposed system achieved state-of-the-art (SOTA) syntactic DER and WER of 1.14% and 3.34%, respectively. For morphological diacritization, the best results yielded a DER of 0.92% and a WER of 1.91%. These outcomes reflect a remarkable relative error reduction of over 30% compared to previous research. Additionally, on the TP dataset, the BERT models exhibited a substantial decrease in DER, reducing the benchmark from 4.0% to 1.11%. Furthermore, this study introduces a real-time diacritization system called SUKOUN, which offers diacritized text through a user-friendly website. A comparison with existing automatic diacritization tools, using six example texts, reveals the superior prediction accuracy and preservation of input format provided by SUKOUN. • This study proposes Sukoun (an automated Arabic diacritization system). • Sukoun was validated using two publicly available AD and TP datasets. • Sukoun achieves state-of-the-art results both syntactically and morphologically. • Sukoun outperformed existing Arabic diacritization tools. • Sukoun reduced error rates by over 30%, highlighting the potential of the proposed model. [ABSTRACT FROM AUTHOR]

Anchor graphs are powerful tools for large-scale applications, but their quality depends on the selection of representative anchors, which is a difficult endeavor. Another challenge is to cluster data with incomplete multi-view features, which have missing values in some views. This paper proposes a novel method for incomplete multi-view clustering based on local and global bagging of anchor graphs. Our method does not require a specific anchor selection scheme but leverages bagging techniques to reduce the variance and discover the intrinsic anchor graph. Specifically, we first construct bagged partial anchor graphs for each incomplete view by randomly sampled anchors. Then, we integrate the bagged partial anchor graphs from multiple views into a unified and complete anchor graph. Finally, we apply global bagging to the unified anchor graph to further boost performance. Our method can generate robust anchor graphs for incomplete multi-view clustering scenarios, as evidenced by extensive experiments. • Bagging is exploited to learn robust intact anchor graph and ease difficult anchor selection. • Unstable anchor graphs induced by random anchors can be significantly enhanced with bagging. • Our method outperforms the state-of-the-art methods on six challenging datasets. [ABSTRACT FROM AUTHOR]

Robust design in analog integrated circuits (ICs) is intricate due to process variations, culminating in notable performance uncertainties. Contemporary surrogate-based techniques depend on lengthy pre-training and are prone to prediction inaccuracies amidst process, voltage, and temperature (PVT) fluctuations. This paper introduces a framework that amalgamates a problem reformulation strategy with an evolutionary algorithm, expediting robust analog IC design in the face of PVT variations. Initially, by segmenting decision variables into subgroups and correlating them with weight variables, the decision space is adeptly reconstructed. An indicator function is subsequently utilized to minimize the objective space's dimensionality, thereby reshaping the original problem into a low-dimensional single-objective optimization problem. Given the reduced dimensionality of both objective and decision spaces, the Bayesian optimization algorithm is invoked to efficiently diminish performance disparity under variations. Consequently, a collection of quasi-optimal, robust solutions is acquired. In the final phase, a multi-objective evolutionary algorithm ensures the even distribution of these quasi-optimal solutions along the approximate Pareto optimal front. To validate the effectiveness of our framework, we conducted tests on three integrated voltage references and compared the results with four cutting-edge methods. The empirical data highlight the superior performance of our framework, which yields a 3 to 40-fold increase in the quality factor and a 30% to 60% reduction in necessary function evaluations. Additionally, the framework led to a 40% to 70% decrease in the overall execution time. • Novel framework for robust analog IC optimization under PVT variations. • Reformulating the original problem into a low-dimensional single-objective one. • Obtaining PVT-robust quasi-optimal solutions via problem reformulation. • Enhancing circuit performance with multi-objective optimization. • Reducing function evaluations and simulation costs via worst-case-driven strategy. [ABSTRACT FROM AUTHOR]

The multi-objective multi-task evolutionary algorithm is emerging in the field of evolutionary computating. A great deal of multi-objective multi-tasking algorithms have been proposed recently and proved to be superior in solving many problems. However, in the real world, there are numerous high-dimensional objective problems that need to be solved. With the increasing number of objectives, the slow convergence speed, high computational complexity and reduced population diversity will occur in the existing multi-objective multi-tasking algorithms. There is a growing need to study high-dimensional objective algorithms in multi-tasking environment. To fulfill this research gap, a novel many-objective multi-tasking evolutionary algorithm (MaMTO-ADE) is put forward in this paper. The reference points-based non-dominated sorting method is introduced, which guarantees the diversity of the population in high-dimensional space. And a new offspring generation strategy is proposed to accelerate the population convergence and enables the population to generate high-quality offspring. The performance of MaMTO-ADE is verified on the classical benchmark problems, and the experimental results emphasize the excellent competitiveness of MaMTO-ADE compared to other related algorithms. • A novel many-objective multitask framework is proposed to solve the high-dimension multi-objective problems. • This algorithm uses a new mixture probability model-based knowledge transfer method. • A novel offspring generation strategy is proposed. • The new individual selection method is used for inter-task crossover. [ABSTRACT FROM AUTHOR]

Although the distributed permutation flowshop scheduling problem (DPFSP) has recently received extensive research attention, most studies assume that either all jobs have due date constraints or none of them do. Nevertheless, in practice, it is very common to schedule jobs with due dates alongside jobs without a due date. This paper addresses a DPFSP with part of jobs subject to a common deadline (DPFSP-PJCD). The objective is to minimize the total completion time. We establish a mathematical model and propose a Self-adaptive Population-based Iterated Greedy (SPIG) algorithm that is specifically tailored to the characteristics of the problem. We design a hybrid constructive heuristic to generate a population of potentially high-quality solutions. We introduce an insertion-based acceleration method that combines three distinct accelerations to improve operational efficiency. We propose some effective operators to carry out the selection, destruction, and construction of solutions, as well as a local search mechanism, to balance the exploitation and exploration of the algorithm. Additionally, we employ a self-adaptive method to determine a key algorithmic parameter depending on the search phase and search space. We also utilize a self-adjustment insertion procedure to handle infeasible solutions. Through comprehensive experimental evaluations, we demonstrate that the proposed SPIG outperforms five state-of-the-art metaheuristics from the closely related literature, providing effective solutions for the DPFSP-PJCD considered. [ABSTRACT FROM AUTHOR]

Stock price manipulation has a serious negative impact on the normal market trading environment and order, and among them, trade-based manipulation is particularly common and hidden, which makes it difficult to be detected by regulatory agencies. A variety of algorithms have been applied onto solving this problem, among which RNN-based models perform well due to their adeptness at processing time-series data. Several works have been conducted to integrate RNNs into ensemble models by treating RNNs as traditional classifiers, but the training process and the sub-model connection of traditional ensemble models could drop potentially informative features learned by RNNs. In this paper, we propose a model utilizing the stacking generalization technique that combines predictions of multiple submodules to give the final result while keeping the structure of neural networks. To validate the performance of our model, we collected public data from China Securities Regulatory Commission and created a dataset based on these data. The results show that our model performs better than the baseline in multiple metrics. [ABSTRACT FROM AUTHOR]

Multi-view learning (MVL) is a promising field that seeks to make the most of information shared across different views, ultimately improving generalization performance. Numerous multi-view support vector machines (SVMs) have been proposed and shown great success. However, hinge loss is widely used by existing methods, which makes these SVMs sensitive to noise and unstable when re-sampling. Furthermore, many researchers have demonstrated the effectiveness of universum data for supervised learning. This paper proposes a novel MVL method called multi-view universum support vector machine with insensitive pinball loss (Pin-MvUSVM). Specifically, we first integrate the pinball loss into the multi-view learning framework, which is dependent on the quantization distance and is robust to noise. Then, we utilize universum data to explore prior knowledge for MVL, which improves the generalization performance. Moreover, we develop multi-view universum twin support vector machine with insensitive pinball loss (Pin-MvUTSVM) as a low-complexity alternative to improve computational efficiency. It solves a pair of smaller-scale quadratic programming problems (QPPs) and utilizes the dual form of the model to solve two convex optimization problems for obtaining the prediction classifier. We conduct classification experiments on four multi-view datasets, and our results show that the proposed convex models outperform several state-of-the-art models. • We propose two novel multi-view learning methods. • They enhance generalization with the assistance of universum data. • The insensitive pinball loss is introduced into multi-view learning. • Experimental results validate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Large indoor cluttered environment representation is still a challenging task when non-uniform triangle cell-based or quadrangle cell-based decomposition is used to build the map. This paper aims at proposing a new method to represent a large indoor environment for efficient and global robot path planning using a trade-off among three criteria: path length, distance to obstacles, and path search complexity. In this regard, three steps are involved: (i) the design of a tiled map that represents several regular sub-regions of the given large environment; (ii) the selection of the best representation between non-uniform triangle cell-based submap and regular quadrangle cell-based submap for each tile using the predefined efficient path planning criteria. Hence, the entire large environment is represented by a hybrid cell-based map; (iii) the path search using the well-known A* algorithm. Moreover, to find the best representation, firstly, a generative method based on cellular automata is used to build a large synthetic database of maps of the same size as the tiles. Each map of the database is associated with the representation which provides the most efficient path planning. Given a tile, the corresponding image is used to find the closed tile image from the database, and the associated representation is selected as the best representation. Extensive simulations, as well as experiments, suggest that for a given large, cluttered environment, the hybrid representation with a mix of triangle-based cells and quadrangles-based cells can provide a more efficient global path compared to the traditional regular quadrangle or triangle representations. [ABSTRACT FROM AUTHOR]

Imbalanced class distributions in machine learning, where the minority class is often under-represented, pose a substantial challenge. Synthetic Minority Over-sampling Technique (SMOTE) has been widely employed to address this issue by generating synthetic minority samples through interpolation. Despite its popularity, SMOTE exhibits certain drawbacks caused by the implementation of random interpolation samples. In this paper, we introduce a new data level technique for oversampling, called Fuzzy C-Means Center-SMOTE (FCM-CSMOTE), which generates synthetic samples in each cluster using its center considered as the memory of the main data components. We demonstrate that the proposed selective strategy has a very low probability to generate noise. The experimental results demonstrate that the proposed method performs better than the state-of-the-art approaches on 21 real unbalanced data sets (regular and large size data set) in terms of several metrics, including Geometric Mean (GM), F-Measure (FM), Area Under the Curve (AUC), and Accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach that merges a lightweight parallel depth-wise separable convolutional neural network (LPDCNN) with a ridge regression extreme learning machine (Ridge-ELM) for precise classification of three lung cancer types alongside normal lung tissue (adenocarcinoma, large cell carcinoma, normal, and squamous cell carcinoma) using CT images. The proposed methodology combines contrast-limited adaptive histogram equalization (CLAHE) and Gaussian blur to enhance image quality, reduce noise, and improve visual clarity. The LPDCNN extracts discriminant features while minimizing computational complexity (0.53 million parameters and 9 layers). The Ridge-ELM model was developed to enhance classification performance, replacing the traditional pseudoinverse in the ELM approach. Through comprehensive evaluation against state-of-the-art models, the framework achieves remarkable average recall and accuracy values of 98.25 ± 1.031 % and 98.40 ± 0.822 %, respectively, through rigorous five-fold cross-validation for four-class classifications. In binary classifications, outstanding results are obtained with recall and accuracy values of 99.70 ± 0.671 % and 99.70 ± 0.447 %%, respectively. Notably, the framework exhibits exceptional efficiency, with a testing time of only 0.003 s. Additionally, integrating the SHAP (Shapley Additive Explanations) in the proposed framework enhances Explain-ability, providing insights into decision-making and boosting confidence in real-world lung cancer diagnoses. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment classification aims to infer the sentiment expression towards a specific aspect in a sentence. The key to this task is to utilize the relationship between sentiment words and aspect words. The mainstream methods use Recurrent Neural Networks (RNN), Attention mechanisms, or Graph Neural Networks (GNN) to explore the syntactic information. Though these methods are undoubtedly effective, they still encounter several challenges: (1) Since most of the studies used only syntactic dependency graphs, they lacked a more optimal representation of inter-word relationships. (2) Some studies have explored multiple relationship graphs, but they fail to effectively integrate syntactic dependencies with semantic or other information, thereby impeding the exchange of multiple information elements. Moreover, the inclusion of more information graphs increases the computational burden on the model. In this paper, we construct a word-level relational hypergraph containing various syntactic and semantic relationships between aspect words and other context words. We propose an aspect-specific hypergraph attention network (ASHGAT) to thoroughly investigate the hypergraph's information. Furthermore, we design an aspect-oriented syntactic distance-based weight distribution mechanism to optimize hypergraph attention. We conducted extensive experiments on four benchmark datasets from SemEval 14, 15, and 16. The results show that ASHGAT demonstrates the other SOTA baselines. • We enriched word relationships through our approach. • We design simple hypergraph matrix representation for relationships. • We improved hypergraph attention network for ABSC. • We introduced syntax distance for weight supervision. [ABSTRACT FROM AUTHOR]

The significant proliferation of AI-empowered systems and machine learning (ML) across various examined domains underscores the vital necessity for comprehensive and customised explainability frameworks to lead to usable and trustworthy systems. Especially in the medical domain, where validation of methodologies and outcomes is as important as the adoption rate of such systems, the requirements of the depth and the level of abstraction of the explainability are particularly important and necessitate a systemic approach to ensure a proper definition. Explainability and interpretability are important usability and trustworthiness properties of AI-empowered systems and, as such, constitute important factors for technology acceptance. In this paper, we propose a novel framework for explainability requirements in AI-empowered systems using the Technology Acceptance Model (TAM). This framework employs targeted ML (hierarchical clustering, k-means or other) to acquire a user model for personalised, multi-layered explainability. Our novel framework integrates a rule-based system, which guides the degree of trustworthiness to be achieved based on user perception and AI literacy level. We test this methodology in the case of AI-empowered medical systems to (1) assess and quantify the doctors' abilities and familiarisation with technology and AI, (2) generate layers of personalised explainability based on user ability and user needs in terms of trustworthiness and (3) provide the necessary environment for transparency and validation. To assess and quantify the doctors' abilities we have considered Rapid Estimate of Adult Literacy in Medicine (REALM) a tool commonly used in the medical domain to bridge the communication gap between patients and doctors. • Framework for personalised explainability in Artificial intelligence, based on Levels. • Levels refer to AI literacy level and level of abstraction. • Behavioural models were used that measure perception towards an innovation. • Framework is evaluated and implemented in an AI-empowered medical application. [ABSTRACT FROM AUTHOR]

Maintenance planning and scheduling are an essential part of manufacturing companies to prevent machine breakdowns and increase machine uptime, along with production efficiency. One of the biggest challenges is to effectively address uncertainty (e.g., unexpected machine failures, variable time to repair). Multiple approaches have been used to solve the maintenance scheduling problem, including dispatching rules (DR), metaheuristics and simheuristics, or most recently reinforcement learning (RL). However, to the best of our knowledge, no study has ever studied to what extent these techniques are effective when faced with different levels of uncertainty. To overcome this gap in research, this paper presents an approach by analyzing the impact of categorized levels of uncertainty, specifically high and low, on the failure distribution and time to repair. Upon the formalization of the maintenance scheduling problem, the experiments conducted are performed in simulated scenarios with different degrees of uncertainty, and also considering a real-life manufacturing use case. The results indicate that rescheduling based on a genetic algorithm (GA) simheuristic outperforms RL and DR in terms of total machine uptime, but not in terms of the mean time to repair when configured with high re-optimization frequencies (i.e., hourly re-optimization), but rapidly underperforms when the re-optimization frequency decreases. Furthermore, our study demonstrates that GA-simheuristic is highly computationally demanding compared to RL and rule-based policies. • Problem of dynamic maintenance scheduling under uncertainty is assessed. • Reinforcement Learning, Genetic Algorithms and Dispatching rules were compared. • Exploration of uncertainty factors such as time to repair and failure distribution. • Artificial and real-based scenarios were evaluated. [ABSTRACT FROM AUTHOR]

• To develop a hybrid deep learning model for student behavior analysis. • To develop an Opposition-based Food Foraging-Pelican Optimizer (O-FFPO) algorithm. • To carry out optimal feature selection by utilizing developed O-FFPO algorithm. • To develop an O-FFPO to enhance the classification performance of developed model. • To adopt a Radian Basis-Recurrent Neural Networks (RB-RNN) deep learning model. With the development of the internet, e-learning is rapidly growing in worldwide, which pave more attention to the students. Further, the learning materials like video lectures, shared assignments as well as lecture slides are performed during the online courses. Moreover, these activities among the students exhibit different behaviors. Hence, the analysis of student behavior becomes complicated in the existing models. This research focuses on developing a deep learning model for analyzing student behaviour in an accurate manner. This paper aims to develop a new student behavior prediction model for E-learning by evaluating brain signals and chatbot interaction. This suggested model collects data from both chatbots and the Brain waves of students. The collected textual data from the chatbot are forwarded to the feature extraction phase by word2vector and bag-of-n grams. Then, the optimal features are selected using an Opposition-based Food Foraging-Pelican Optimizer (O-FFPO). Similarly, the second set of Brain waves of students are forwarded to the phase feature extraction stage by Short-time Fourier transforms (STFT), and then, deep features are extracted using Convolutional Neural Network (CNN). These gathered data can also be forwarded to the optimal feature selection process using the same O-FFPO. Finally, these fused features are fed to Radian Basis-Recurrent Neural Networks (RB-RNN) to get student behaviors, where the constraints are optimized by the same O-FFPO. Various performance metrics are compared and validated with the existing approaches. Moreover, the developed model shows 95% and 93% concerning accuracy and F1-score. Finally, experimental evaluation was carried out with the estimation of both brain waves and chatbot queries of students. [ABSTRACT FROM AUTHOR]

As a novel and economical transportation way, ride-sharing has attracted more and more passengers and drivers to participate. How to match passengers with drivers efficiently has become a key issue. Specifically, drivers are usually heterogeneous with different costs, and they may behave strategically (e.g. reveal their private cost information untruthfully) in order to make more profits. Drivers' strategic behavior may lead to inefficient matching, which results in the loss of social welfare of ride-sharing platforms and drivers. In this paper, an incentive-compatible and efficient mechanism is proposed to solve this issue, which can match passengers with drivers and determine the payments to drivers in order to maximize social welfare while ensuring drivers reveal their cost information truthfully. Specifically, an order matching algorithm with branch and bound based route planning is designed to accelerate the matching process. Meanwhile, the payments to drivers are computed based on the second-pricing algorithm. In so doing, a second-pricing based ride-sharing mechanism (SPRM) is proposed, which satisfies incentive compatibility, individual rationality, budget balance and computational efficiency. Based on the real Manhattan taxi order data and vehicle fuel consumption data, extensive experiments are conducted to evaluate the proposed mechanism. The experimental results show that SPRM can guarantee drivers' profits and improve the ratio of drivers' participation and the ratio of served orders, and eventually achieve greater social welfare than four typical benchmark approaches, GPri, ND, mT-share and mdp. The research of this paper can provide useful insights for designing efficient ride-sharing system to maximize the social welfare of all participants in the real life. • An efficient mechanism to match orders with drivers is proposed. • A branch and bound based route planning method is used to accelerate the matching. • A second pricing based mechanism is proposed to compute the payments to drivers. • The proposed mechanism can outperform benchmark approaches in the social welfare. [ABSTRACT FROM AUTHOR]

Visual question answering (VQA) is a critical multimodal task in which an agent must answer questions according to the visual cue. Unfortunately, language bias is a common problem in VQA, which refers to the situation where the model generates answers solely based on the surface-level correlations between the question-answer pairs in the training set, without fully understanding the visual content. To reduce the language bias, Several recent approaches increase the image-dependency by introducing auxiliary tasks. However, these auxiliary tasks balance the data by adding extra manual image annotations or simply constructing counterfactual samples, without fully exploring the intrinsic information of the samples themselves. In this paper, we tackle the language bias problem by proposing an adaptive self-supervised counterfactual learning (ASCL) method to enhance the model's understanding of images. We propose a new adaptive feature selection module to mine the intrinsic information of the samples. This module can adaptively divides the image into question-relevant visual positive objects and question-irrelevant visual negative objects based on the given question. The question-relevant visual positive objects are used directly to generate the predicted answer, in order to reduce the influence of visual distracting information on the model's understanding of the image and ensure the actual cause of the answer. The question-irrelevant visual negative objects are treated as counterfactual samples to guide model training and prevent the model from being driven by language bias. To avoid incorrect classification of images on the classification edge during training, we propose an adaptive contrastive loss learning method that automatically adjusts the measurement distance to increase the distance between images on the classification edge. Our method has been extensively evaluated on the VQA-CP dataset, demonstrating its effectiveness and yielding improved results. Specifically, by leveraging the LMH model as a foundation, we achieve state-of-the-art performance on both the VQA CPv1 and VQA CPv2 datasets. Notably, our method significantly enhances the accuracy of the baseline, with improvements of 10.36% on the VQA CPv2 dataset and 9.38% on the VQA CPv1 dataset. The source code is publicly available at: https://github.com/shuxy0120/ASCL. • A feature selection method mines the intrinsic information of samples. • A novel learnable adaptive method for feature selection. • A counterfactual learning method uses causal inference to mitigate language bias. • An adaptive contrastive loss learning method to automatically adjust the distance. [ABSTRACT FROM AUTHOR]

Decision-making problem is a fundamental activity in human life every day. Regarding group decision-making, individuals have insufficient comprehension of the decision objects owing to the limited experience and knowledge and drawback the evaluation process. Therefore, the evaluation information is missing and the group cannot get the precise information for decision objects resulting in the generation of incomplete information systems. It is meaningful for individuals finding an approach to Leverage and tap into existing information. On the basis, the work presented in this paper focuses on group decision-making problems (GDM) with incomplete information. Under the framework of group decision-making, a GDM approach with an incomplete information system is presented combining the formal concept analysis and rough set theory. Firstly, we propose a concept called formal decision context and discuss the properties of the formal decision context, which could divide the incomplete information system into many cognitive units, and the definitions of cognitive units will be given. Then, the property and the relations of various cognitive units will be discussed. Additionally, the upper and lower approximation space under the formal decision context is founded considering the risk attitudes integrating fuzzy rough set and formal concept context. Considering the order relation of various cognitive units and decision objects, we introduce the dominance relation and the importance degree of each cognitive unit under the framework of formal decision context. Furthermore, a theoretical framework of the multi-criteria decision-making problem is presented and two models for the multi-criteria group decision-making problem are discussed combined with fuzzy rough set and formal decision context from two perspectives, including qualitative analysis and quantitative analysis. Finally, an illustrated example is applied to verify the validity and effectiveness of the proposed models. The contributions of this paper include two aspects. On the one hand, this paper provides a new perspective and paradigm considering MCGDM problems with the incomplete information system from the perspective of group consensus. On the other hand, this paper gives two approaches for group decision-making problems from sorting and set theory by the perspective of multi-granular rough set and group consensus. • A model of formal decision context with incomplete information to describe the cognitive situation. • An approach to MCDM problem combined rough set with formal decision context with incomplete information. • Two approaches to MCGDM problem from qualitative analysis and quantitative analysis. [ABSTRACT FROM AUTHOR]

Telco players are constantly evaluating their loss in customer revenue or customer churn, especially on the prevention, control, and risk assessment of churn. In this paper, a domain-order based shallow fusion deep learning model, the Fully Connected Layer Convolutional Neural Network - Long Short-Term Memory (FCLCNN-LSTM) is designed and applied to a decision support system to assess the risk of customer churn. First, a majority least absolute shrinkage and selection operator (Maj-LASSO) algorithm is proposed to identify churn predictors that have an important impact on classification. The proposed Maj-LASSO algorithm is different from previous studies that solved the study of unbalanced data and feature selection separately, but solved both as one problem to give the feature selection method under unbalanced data. Then, a fully connected layer based on multiple ReLU neurons is used to balance the importance of the features. Doing so addresses the problem where some key features are overlooked due to their unequal contribution to the risk assessment of telco customer churn. Third, a 2D convolutional neural network based on domain information and an LSTM model based on ordinal information are combined to learn the complex mapping between the features and labels, to improve the accuracy and generalizability of the predictive model. The proposed FCLCNN-LSTM model is different from the previous single spatial model or temporal model, but improves the network classification feature extraction capability from both temporal and spatial dimensions, thus innovatively solving the problem of model development for large-scale and high-dimensional data, and applying it in the field of telecommunication customer churn prediction. Finally, using three public customer churn datasets from the Kaggle and UCI platform, the FCLCNN-LSTM model is compared against other classification models, namely, Logistic Regression, Support Vector Machine, Random Forest, eXtreme Gradient Boosting (XGBoost), Convolutional Neural Network, and Long Short-Term Memory. The experimental results inform that the accuracy of the FCLCNN-LSTM is 3.43% higher than that of the other comparison models, while the AUC is 4.84% higher. Thus, the FCLCNN-LSTM model provides a better decision-making reference for telco players to identify potential churners. [ABSTRACT FROM AUTHOR]

Forest wildfires are one of the most catastrophic natural disasters, which poses a severe threat to both the ecosystem and human life. Therefore, it is imperative to implement technology to prevent and control forest wildfires. The combination of unmanned aerial vehicles (UAVs) and object detection algorithms provides a quick and accurate method to monitor large-scale forest areas. Nevertheless, most available datasets on forest wildfires comprise single-mode ground-fixed-angle pictures that inadequately represent the intricate terrain, high humidity, low visibility meteorological conditions, and multiscale light flux densities of forest wildfires. To address these limitations, we developed the Multiple scenarios, Multiple weather conditions, Multiple lighting levels and Multiple wildfire objects Synthetic Forest Wildfire Dataset (M 4 SFWD), which provides remote sensing data on forest fires across diverse terrain types, weather conditions, light flux densities as well as different numbers of wildfire objects. Researchers can employ this dataset to improve the efficacy of fire and smoke detection algorithms, promoting continuous forest monitoring. This paper presents a Multi-Faceted Synthetic Forest Wildfire Dataset based on Unreal Engine 5. We first constructed eight forest scenes with different terrains, weather conditions, and texture effects. We also simulated the light flux density at different times of the day by utilizing real-time ray tracing technology, which created realistic lighting and shadows. Secondly, we introduced a range of wildfire targets with varying scales and numbers into each scenario to enable multiple-angle shooting simulations from a UAV's viewpoint. During evening hours and in foggy conditions, many objects resemble wildfires. To enhance the dataset's precision and reliability for fire and smoke detection, 3,974 images were undergone pixel-level manual annotation using tools like labelImg. This annotation yielded 17,763 bounding boxes, which were subsequently statistically analyzed to ascertain their positions and proportions. Finally, we assessed the applicability of M 4 SFWD in single-stage, two-stage, and lightweight object detection algorithms by inputting the dataset into various algorithms with different parameter sizes. Based on the experimental results' visualization, M 4 SFWD exhibited superior performance in scenarios with standard light flux density and large-scale wildfire objects. However, due to its complex contextual information and multiscale object features, false detections and missed detections occurred in other complex multi-faceted scenarios. Thus, optimizing the existing object detection algorithms will be necessary for future research. The dataset is available at: https://github.com/Philharmy-Wang/M4SFWD. [ABSTRACT FROM AUTHOR]

Sine Cosine Algorithm(SCA) is a population-based optimization algorithm, to find the optimal solution. However, SCA has problems such as premature convergence, insufficient solution precision for high-dimensional functions, and slow convergence speed. To solve the problems above, this paper proposes a multi-strategy-based Adaptive Sine Cosine Algorithm (ASCA). Firstly, a more uniform initial population is generated by the Halton sequence so that the initial population covers the entire search space to maintain the diversity of the initial population. Secondly, the adaptive grading strategy is adopted to sort according to the fitness value, and the population dynamics are divided into 4 grades: excellent, good, medium and poor. For the purpose of improving the convergence accuracy of the algorithm and enhancing the ability to jump out of the local optimum, hybrid mutation and elite guidance methods are applied to different levels of populations for perturbing mutations. Finally, in order to improve the convergence speed of the algorithm, a dynamic opposition-based learning global search strategy is proposed. The ASCA is tested on a set of 20 functions in low- dimensional and high-dimensional, and the improved algorithm is compared with Particle Swarm Optimization (PSO), Backtracking Search Algorithm(BSA), Genetic Algorithm(GA)and other improved Sine Cosine Algorithms. The results show the improved convergence accuracy and speed of the ASCA. Moreover, the ASCA proposed in this paper is applied to engineering optimization design. The solution results show that the ASCA is better than other algorithms in superiority-seeking ability, and can effectively solve the optimization problems in engineering. [ABSTRACT FROM AUTHOR]

A botnet is a group of hijacked devices that conduct various cyberattacks, which is one of the most dangerous threats on the internet. Individuals or organizations can effectively detect botnets by analyzing abnormal behaviors in network traffic. Existing works focus on extracting the deterministic behavioral features, which highly rely on statistical features and existing botnet interaction structures, resulting in unsatisfactory detection accuracy, especially for unknown botnet traffic. The botnet detection method based on the original traffic bytes has more advantages in this regard, especially the use of mining payload information in the traffic to enhance the identification of abnormal botnet behavior is the focus of this study. In this paper, we propose a dual-mode botnet detection scheme, which takes the original traffic bytes as the object, one is to encode the implicit semantic relationship between the traffic bytes through a multi-layer Transformer encoder, and the other is the network traffic Image representation, the spatial relationship of traffic bytes is captured by a deep neural network, and then botnet detection is achieved by maximizing the mutual information between the two. We conduct comprehensive experiments with both known botnets and unknown botnets to evaluate our scheme. Experimental results show that for known botnets, our approach achieves 99.84% and 91.92% detection accuracy with CTU-13 and ISCX-2014 datasets, respectively, which is 3.04% and 2.54% more accurate compared with the state-of-art (DL). For unknown datasets, our scheme is 10.19% more accurate than the existing traffic representation. • We extract the implicit semantic relationship of raw traffic for botnet detection. • We propose a dual-modal botnet detection method (Bot-DM) based on raw traffic. • We conduct extensive experiments to validate the superiority of the proposed methods. • We compare the detection performance of unknown botnets in different traffic modes. [ABSTRACT FROM AUTHOR]

Image segmentation is one of the vital steps in medical image analysis. The segmentation precision and delicacy of targets affect the diagnosis accuracy to a great extent. Deep learning-based technology is currently a hot research direction in the field of medical image analysis. Compared with manual labeling, deep convolutional neural networks (DCNN) can extract valuable deep features that are difficult for humans to identify. However, some existing DCNN methods tend to only focus on extracting deep features and ignore the semantics loss in the process of continuous pooling and convolution. In addition, medical image datasets are characterized by small-scale, mass noise, and imperceptible differences between foreground and background. How to construct sufficient contextual dependency and feature compensation from multiple perspectives to improve the segmentation performance is still faced with great challenges. In this paper, we propose a deep attention network with multi-perspective feature compensation, called Multi-Perspective Feature Compensation Network (MPFC-Net), to supplement semantics loss during continuous pooling and convolution as much as possible and establish strong contextual dependency. Merits of MPFC-Net are as follows: (1) Serializing Multi-scale Aggregation Blocks (SMAB) are constructed to extract features with different scale receptive fields, capture semantics loss in convolutions, and enhance the nonlinear fitting capability of the network. (2) S pace and C hannel Compound M ultidimensional I nteraction Attention (SCMI Attention) is designed to consider the weights of feature maps of space and channel and build long-term information interactions between different dimensions. (3) Multi-Perspective Feature Compensation (MPFC) is proposed to establish dense context interaction and compensate for semantics loss in continuous pooling and convolution. (4) Deep Feature Reuse (DFR) module is adopted to strengthen the propagation and advanced features reuse. We conduct extensive experiments on the 2018 Data Science Bowl (2018DSB), the ISIC-2018 Challenge (ISIC2018), the JSUAH-Cerebellum, and the DRIVE dataset, and the results show that MPFC-Net achieves competitive performance with simultaneous increases in various metrics. [ABSTRACT FROM AUTHOR]

An increasing number of image perception models are being utilized in the field of autonomous driving. During nighttime driving, the visual perception capabilities of a single-modality RGB sensor become compromised. To address this issue, image fusion methods that utilize multi-modality data to provide a more comprehensive visual representation for nighttime scenes. Nevertheless, the existing image fusion methods are limited by poor generalization abilities and lighting conditions, resulting in inadequate handling of the contrast and spectral corruption. In this paper, we propose a cross-sensor low-light enhancement algorithm that provides more accurate visual perceptions. Our approach employs multiple sensors as preceptors for nighttime driving scenes. Compared with typical fusion algorithms that use a simple one-stage workflow for cross-sensor data, our proposed method adopts an advanced content-enhancement strategy by recursively and interactively scaling up informative pixels. More specifically, our approach employs information measurement to describe the information and then utilizes a cross-sensor content enhancement module to dynamically enhance the mutual information between the infrared spectrum and the RGB streams. Our experiments show that CROSE effectively preserves texture details, resulting in clearer and more solid fusion results for nighttime driving scenes. [ABSTRACT FROM AUTHOR]

The University Course Scheduling Problem (UCSP) is a complex combinatorial optimization problem. Most universities still use hand-operated scheduling, which usually takes a long time and results in numerous irrationalities. The imperative of conceiving a algorithm to navigate this predicament is undeniable. This paper introduces an advanced mixed integer linear programming paradigm, incorporating constraints encompassing both the temporal coherence of courses and the equitable dispersion of these courses throughout the academic week. To tackle this issue, we design a novel two-stage meta-heuristic algorithm; the first stage harnesses the genetic algorithm to cluster courses into sets by exploiting graph coloring techniques, while the subsequent stage leverages an enhanced tabu search heuristic to assign these clusters into distinct temporal intervals. The effectiveness and efficiency of the proposed heuristic has been validated by extensive computational studies based on authentic data gleaned from Huazhong University of Science and Technology's School of Management and the results are compared with CPLEX and common heuristic. Moreover, our research examines the algorithm's convergence properties and the effects of fast neighborhood evaluation strategy. Sensitivity analysis is performed to probe the algorithm's responsiveness to the new constraints. [ABSTRACT FROM AUTHOR]

Far-sea ship repair and maintenance capability evaluation is a typical complex system capability evaluation problem, and the corresponding evaluation indices include additional quantitative and qualitative indices. With the development of modern naval ships, higher requirements have been put forward for far-sea ship repair and maintenance. To accurately evaluate the capabilities of a naval ship's repair and maintenance system far at sea, a hybrid evaluation method based on binary semantic information and the evidential reasoning approach is proposed. First, we construct an evaluation indicator system to assess the capability of naval ship repair and maintenance systems. Second, based on binary semantic information and the analytic hierarchy process, a group expert weighting method is proposed to determine the weight distribution of the indicators. Finally, to address the multiattribute problem of the indicator system, a comprehensive evaluation method is proposed based on the evidential reasoning approach. The results of the calculation examples show that the proposed group expert weighting method and the hybrid evaluation method are feasible for capability evaluation of a naval ship's repair and maintenance system far at sea. By evaluating the calculation results, we can determine advantages and disadvantages in far-sea ship repair and maintenance, and through the sensitivity analysis of the evaluation indices, we can explore the contributions of different indices to the far-sea ship repair and maintenance capability, which can provide a theoretical reference for evaluating far-sea ship repair and maintenance. The results indicate that the method proposed in this paper can also be used as a general method in other similar studies. [ABSTRACT FROM AUTHOR]

In this paper, a novel neural-network iterative learning control (IL, ILC) protocol for generalized regression neural networks (GRNN) based on global space–time is proposed for a class of unknown nonlinear discrete-time systems. The proposed controller is an ideal controller with multidimensional gain based on the global space–time in the iteration domain. The control gain is automatically learned by updating the matrix of the GRNN with correction factors based on the accumulation of global system input and output (I/O) data along the iteration axis. By a rigorous theoretical analysis, properties that can drive the tracking error to be uniformly bounded in the iteration axis are established for the proposed control protocol with the goal of achieving optimal control. The effectiveness and adaptability of the proposed control protocol have been validated through numerical simulations and two comparative examples on general high-frequency signal tracking. • A novel ILC protocol for nonlinear non-affine systems is proposed via GRNN. • The proposed ILC directly constructs the input and control objectives through GRNN. • The controller is designed by the accumulation of global system I/O data along the iteration axis. • The control gain is automatically learned by updating GRNN matrix with correction factors. [ABSTRACT FROM AUTHOR]

In recommender system, the proportion of interacted items with users is extremely sparse compared to the total number of items. This data sparsity problem particularly affects collaborative filtering methods. Even in modern collaborative filtering recommendation models based on deep learning, effectively acquiring more neighbor information and exploring how to utilize this information efficiently remains a promising research area. This paper not only explores heuristic algorithms for neighbor selection but also extends the application of neighbor selection to the GAN framework and negative sampling. To capture user neighbors that contain more potential feature information, this paper proposes a neighbor strategy that integrates explicit and implicit user information. It aggregates reciprocal neighbors of users based on the fusion of their explicit and implicit information, aiming to capture user preference information through neighborhood information. Furthermore, by designing an MRNGAN model based on a twin-tower generator, this paper successfully embeds user neighborhood information as feature embedding into the generator of GAN. In addition, this paper also proposes a heuristic negative sampling mechanism in the recall phase. It uses explicit and implicit information to fuse the nearest neighbors of items with the farthest neighbors of users. Negative samples that contain user neighborhood information are embedded into the discriminator of MRNGAN. The deep relationships between nearest and farthest neighbors are explored through the adversarial training of GAN, and their impact on recommendation performance is validated. The experimental results on three real-world datasets show that the MRNGAN model significantly improves the accuracy of Top-N recommendation. [ABSTRACT FROM AUTHOR]

In steel enterprises, the long-term interval prediction of downstream gas demand plays an increasingly significant role in the scheduling decision-making of gas supply networks. Accurate long-term interval prediction remains a challenge owing to the intermittent and fluctuating characteristics of ironmaking and steelmaking. In this paper, a novel granulation-based interval prediction model (STGLUBE) is developed, which considers various data granule features and spatial–temporal correlated features. Granular evaluation indices and a corresponding training method are designed for STGLUBE training. A hybrid long-term interval prediction method integrated with a multiscale decomposition and error correction mechanism is further proposed to satisfy the actual requirements. In this method, an empirical wavelet transform is used to extract simplified modes from the original gas demand. The proposed STGLUBE is implemented in the primary mode, whereas the point predictions are performed in other modes. In addition, an error correction mechanism is introduced to enhance prediction accuracy. The effectiveness of the proposed method is verified by conducting two industrial case studies, and the results illustrate that the proposed method outperforms the existing widely used methods. [ABSTRACT FROM AUTHOR]

In this paper, series of ionic polymers were synthesized by crosslinking alkyl quaternary ammonium salts with 1,4-bis(chloromethyl)benzene. Among them, hyper-crosslinked polymer fabricated with dodecyl dimethyl benzyl ammonium chloride (HCP-DD) as monomer delivered superior adsorption performance for endocrine disrupting chemicals (EDCs). The adsorption mechanism mainly includes π-π stacking, hydrophobic and electrostatic interaction. With HCP-DD as solid phase extraction sorbent, a high performance liquid chromatography-diode array detection method was developed for the detection of four phenolic EDCs in water and fish samples. The detection limits of the method were 0.005–0.02 ng mL−1 for water samples and 3–30 ng g−1 for fish samples. The recoveries of EDCs in water samples and fish samples were 80–119% and 81.3–117% (relative standard deviations <4.4%), respectively. The study not only provides a route for preparation ionic porous polymers, but also highlights the applications of ionic polymers as efficient adsorbent to enrich organic pollutants. In this work, a hyper-crosslinked polymer (HCP-DD) was prepared and used as a sorbent to adsorb and isolate EDCs from water and fish samples prior to high performance liquid chromatography-fluorescence detection. A sensitive and reliable method was established for the determination of four EDCs (BPF, BPA, BPB and PTBP) in water and fish samples. [Display omitted] • An ionic porous polymer (HCP-DD) was synthesized. • HCP-DD exhibited large adsorption capacity and fast adsorption kinetics for EDCs. • Adsorption mechanism involved electrostatic force, hydrophobic and π-π interaction. • A HPLC method was developed for determination of phenolic EDCs in water and fish. [ABSTRACT FROM AUTHOR]

People with celiac disease or gluten sensitivity may experience an immune reaction to the protein called gluten, which is present in wheat, barley, and rye. A strict gluten-free diet is the sole cure for these ailments. There are chances of food fraud about the claim of being gluten-free food items. As a result, there is a rising need for trustworthy and precise ways to identify gluten. There are many methods to detect gluten in food samples viz., enzyme-linked immunosorbent assay 1 1 ELISA: Enzyme-linked immunosorbent assay Surface plasmon resonance (SPR), Electrochemical sensors, Fluorescence-based sensors, etc. The use of sensors is one of the most promising methods for gluten detection. For detecting gluten, a variety of sensors, including optical, electrochemical, and biosensors, have been developed with different limits of detection and sensitivity. The present review reports the recent advancements (2019–2023) in the development of sensors for gluten detection in food. We may conclude that sensitivity and limit of detection are not related to the type of sensor used (aptamer or antibody-based), however, there are advancements, with the year, on the simplicity of the material used like paper-based sensors and paradigm shift to reagent free sensors by the spectral analysis. Also, recent work shows the potential of IoT-based studies for gluten detection. [Display omitted] • Concerns about gluten-free food fraud drive the need for reliable detection methods. • Various methods, including ELISA, SPR, sensors, and fluorescence-based sensors, detect gluten in food. • Sensor technology is a promising approach for gluten detection. Recent advancements (2019–2023) in sensor development are discussed. • Advancements include simplifying sensor materials (e.g., paper-based sensors) and shifting to reagent-free sensors with spectral analysis. • The potential of IoT-based studies for gluten detection [ABSTRACT FROM AUTHOR]

Diquat (DQ) is a typical bipyridine herbicide widely used to control weeds in fields and orchards. The severe toxicity of diquat poses a serious threat to the environment and human health. Metal-organic frameworks (MOFs) have received widespread attention due to their unique physical and chemical properties and applications in the detection of toxic and harmful substances. In this work, a two-dimensional (2D) Tb(III) functionalized MOF Tb(III)@ 1 (1 = [Cd(HTATB)(bimb)] n ·H 2 O (Cd-MOF), H 3 TATB = 4,4′,4″-triazine-2,4,6-tribenzoicacid, bimb = 1,4-bis((1H-imidazol-1-yl)methyl)benzene) has been prepared and characterized. Tb(III)@ 1 has excellent optical properties and high water and chemical stability. After the Tb(III) is fixed by the uncoordinated -COO- in the 1 framework, Tb(III)@ 1 emits the typical green fluorescence of the lanthanide ion Tb(III) through the "antenna effect". It is worth noting that Tb(III)@ 1 can be used as a dual emission fluorescence chemical sensor for the ratio fluorescence detection of pesticide DQ, exhibiting a relatively low detection limit of 0.06 nM and a wide detection range of 0–50 nM. After the addition of DQ, a rapid color change of Tb(III)@ 1 fluorescence from green to blue was observed due to the combined effects of IFE, FRET and dynamic quenching. Therefore, a simple test paper box has been designed for direct on-site determination of pesticide DQ. In addition, the developed sensor has been successfully applied to the detection of DQ in real samples (fruits a Yin-Xia Sun and Bo-Tao Ji contributed equally to this work and should be considered co-first authors.nd vegetables) with satisfactory results. The results indicate that the probe developed in this study has broad application prospects in both real sample detection and actual on-site testing. A two-dimensional (2D) Tb(III) functionalized metal organic framework Tb(III)@ 1 (1 = [Cd(HTATB)(bimb)] n ·H 2 O, H 3 TATB = 4,4′,4″-triazine-2,4,6-tribenzoicacid, bimb = 1,4-bis((1H-imidazol-1-yl)methyl)benzene) was reported. After the Tb(III) was fixed by the uncoordinated -COO- in the 1 framework, Tb(III)@ 1 emitted the typical green fluorescence of the lanthanide ion Tb(III) through the "antenna effect". It was worth noting that Tb(III)@ 1 could be used as a dual emission fluorescence chemical sensor for the ratio fluorescence detection of pesticide DQ. After the addition of DQ, there was a rapid color change of Tb(III)@ 1 fluorescence from green to blue. Therefore, a simple test paper box has been designed for direct on-site determination of pesticide DQ. In addition, the developed sensor has been successfully applied to the detection of DQ in real samples (fruits and vegetables) with satisfactory results. [Display omitted] • A two-dimensional (2D) Tb(III) functionalized metal organic framework Tb(III)@ 1 was reported. • Tb(III)@ 1 can be used as a dual emission fluorescence sensor for the ratio fluorescence detection of pesticide DQ. • Tb(III)@ 1 exhibited a relatively low detection line of 0.06 nM and a wide detection range of 0–50 nM. • A simple test paper box has been designed for direct on-site determination of pesticide DQ. • The developed sensor has been applied to the detection of DQ in real samples (fruits and vegetables) with satisfactory results. [ABSTRACT FROM AUTHOR]

Maple syrup, a popular natural sweetener has a high content of sucrose, whose consumption is linked to different health issues such as obesity and diabetes. Hence, within this paper, the conversion of sucrose to prebiotics (fructo-oligosaccharides, FOS) was proposed as a promising approach to obtaining a healthier, value-added product. Enzymatic conversion was optimized with respect to key experimental factors, and thereafter derived immobilized preparation of fructosyltransferase (FTase) from Pectinex® Ultra SP-L (FTase-epoxy Purolite, 255 IU/g support) was successfully utilized to produce novel functional product in ten consecutive reaction cycles. The product, obtained under optimal conditions (60 °C, 7.65 IU/mL, 12 h), resulted in 56.0% FOS, 16.7% sucrose, and 27.3% monosaccharides of total carbohydrates, leading to a 1.6-fold reduction in caloric content. The obtained products' prebiotic potential toward the probiotic strain Lactobacillus plantarum 299v was demonstrated. The changes in physico-chemical and sensorial characteristics were esteemed as negligible. [Display omitted] • Maple syrup modification to value-added product with prebiotic potential. • In situ enzymatic transformation of sucrose to fructo-oligosaccharides (FOS). • Decrease in sucrose concentration (82%) led to a 1.6-fold reduction in caloric value. • Product retained original physico-chemical and sensorial properties. • Product remains stable for at least one year without FOS degradation. [ABSTRACT FROM AUTHOR]

Misuse of amide herbicides in the fisheries environment can pose unpredictable harm to aquatic products and ultimately human health. Thus, the development of a real-time, rapid on-site detection method is crucial. This study proposes for the first time, a paper-based visual detection method for amide herbicides in fish serum, by coating the molecularly imprinted polymer layer onto quantum dots, prepared fluorescent sensing materials (QDs@MIPs) for the detection of amide herbicides in aquatic products. These materials specifically cause fluorescence quenching in the presence of amide herbicides resulting in a color change. For practical application, this research designed a rapid test strip based on QDs@MIPs, meanwhile, incorporate a smartphone or a fluorescence spectrophotometer for qualitative and quantitative measurements, the limit of detection ranges of 0.061–0.500 μM. The method can be used for on-site evaluation of aquatic products, providing new technology for monitoring the safety of aquatic products. • The sensing system possessed high sensitivity and rapid detection technology. • Test paper was prepared and could be successfully used for qualitative detection. • Smartphone was suitable for colorimetric sensing as detector. • New on-site inspection and monitoring methods for food safety have been established. [ABSTRACT FROM AUTHOR]

Heterocyclic aromatic amines (HAAs), arising as chemical derivatives during the high-temperature culinary treatment of proteinaceous comestibles, exhibit notable carcinogenic potential. In this paper, a composite aerogel (AGD-UiO-66) with high-capacity and fast adsorption of HAAs was made with anchoring defective UiO-66 (D-UiO-66) mediated by lauric acid on the backbone of cellulose nanofibers (CNF). AGD-UiO-66 with hierarchical porosity reduced the mass transfer efficiency for the adsorption of HAAs and achieved high adsorption amount (0.84–1.05 μmol/g) and fast adsorption (15 min). The isothermal adsorption model demonstrated that AGD-UiO-66 belonged to a multilayer adsorption mechanism for HAAs. Furthermore, AGD-UiO-66 was successfully used to adsorb 12 HAAs in different food (roasted beef, roasted pork, roasted salmon and marinade) with high recoveries of 94.65%–104.43%. The intrinsic potential of AGD-UiO-66 demonstrated that it could be widely applicable to the adsorption of HAAs in foods. [Display omitted] • The "defective design" solves the mass transfer hindrance of micropores in UiO-66. • Combination of MOFs with CNFs to form aerogels with perfectly hierarchical porosity. • AGD-UiO-66 can be used for the adsorptive removal of hazardous substances from food. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid three-term conjugate gradient (CG) method is proposed to solve constrained nonlinear monotone operator equations. The search direction is computed such that it is close to the direction obtained by the memoryless Broyden–Fletcher–Goldferb–Shanno (BFGS) method. Without any condition, the search direction is sufficiently descent and bounded. Moreover, based on some conditions, the search direction satisfy the conjugacy condition without using any line search. The global convergence of the method is established under mild assumptions. Comparison with existing methods is done to test the efficiency of the proposed method through some numerical experiments. Lastly, the applicability of the proposed method is shown. [ABSTRACT FROM AUTHOR]

This paper develops a two-stage stochastic model to investigate the evolution of random fields on the unit sphere S 2 in R 3. The model is defined by a time-fractional stochastic diffusion equation on S 2 governed by a diffusion operator with a time-fractional derivative defined in the Riemann–Liouville sense. In the first stage, the model is characterized by a homogeneous problem with an isotropic Gaussian random field on S 2 as an initial condition. In the second stage, the model becomes an inhomogeneous problem driven by a time-delayed Brownian motion on S 2. The solution to the model is given in the form of an expansion in terms of complex spherical harmonics. An approximation to the solution is given by truncating the expansion of the solution at degree L ≥ 1. The rate of convergence of the truncation errors as a function of L and the mean square errors as a function of time are also derived. It is shown that the convergence rates depend not only on the decay of the angular power spectrum of the driving noise and the initial condition, but also on the order of the fractional derivative. We study sample properties of the stochastic solution and show that the solution is an isotropic Hölder continuous random field. Numerical examples and simulations inspired by the cosmic microwave background (CMB) are given to illustrate the theoretical findings. [ABSTRACT FROM AUTHOR]

The outbreak of uncertain events, e.g., financial crisis, regional conflict and abrupt contagion, has a significant impact on residents' income. Hence, the wealth management and portfolio selection become more and more important. In addition, the behavioral finance believes that decision-making process of the investors not only depend on utility maximization, but also on who to compare with. It differs from the traditional finance based on rational cognitive acquiring and decision-making of agents, in this paper, we consider both investment uncertainty and investment utility with reference level for portfolio selection. First, we introduce the uncertainty theory and loss aversion utility with reference level based on behavioral finance. Then we study an uncertain multi-period portfolio selection problem. Moreover, we formulate an uncertain behavioral portfolio selection model with three objective functions for which we propose an equivalent program consisting of a main model and two auxiliary models for tri-objective optimization model. Thereafter, we use a modified evolutionary root system growth algorithm to solve the transformed models. Finally, a numerical simulation allows to show the practicability of proposed models and validity of modified algorithm. [ABSTRACT FROM AUTHOR]

This paper aims to present in a systematic form the stability and convergence analysis of a numerical method defined in nonuniform grids for nonlinear elliptic and parabolic convection–diffusion–reaction equations with Neumann boundary conditions. The method proposed can be seen simultaneously as a finite difference scheme and as a fully discrete piecewise linear finite element method. We establish second convergence order with respect to a discrete H 1 -norm which shows that the method is simultaneously supraconvergent and superconvergent. Numerical results to illustrate the theoretical results are included. [ABSTRACT FROM AUTHOR]

In this paper, the compound Poisson phase-type process is defined and analyzed. This paper proves that for a non-negative compound Poisson phase-type process, the compound value for all the arrivals by a given time can be approximated by a phase-type distribution. As an application of this process, three different shock models are studied: the cumulative shock model, a degradation-threshold-shock model, and a shock model for the multi-component system. A novel approach is proposed to compute the system's reliability under the aforementioned shock models for a general counting process. Numerical illustrations are presented. [ABSTRACT FROM AUTHOR]

Diagonal curve is one of the most important shape measurements of tensor-product Bézier surfaces. An approach to construct Bézier surfaces with energy-minimizing from two prescribed diagonal curves is presented in this paper. Firstly, a general second order functional energy is formulated with several parameters. This functional includes many common functionals as special cases, such as the Dirichlet energy, the biharmonic functional and the quasi-harmonic functional etc. Secondly, the necessary and sufficient conditions for tensor-product Bézier surface with minimal energy from prescribed diagonal curves are derived. Besides prescribing the diagonal curves, other related problems are considered, those where boundary curves are also prescribed. Finally, the effectiveness of the proposed method is illustrated by several surface modeling examples. [ABSTRACT FROM AUTHOR]

In this work, a Generalized Finite Difference (GFD) scheme is presented for effectively computing the numerical solution of a parabolic–elliptic system modeling a bacterial strain with density-suppressed motility. The GFD method is a meshless method known for its simplicity for solving non-linear boundary value problems over irregular geometries. The paper first introduces the basic elements of the GFD method, and then an explicit–implicit scheme is derived. The convergence of the method is proven under a bound for the time step, and an algorithm is provided for its computational implementation. Finally, some examples are considered comparing the results obtained with a regular mesh and an irregular cloud of points. [ABSTRACT FROM AUTHOR]

In this paper, we apply the coordinate increment discrete gradient (CIDG) method to solve the Lorentz force system which can be written as a non-canonical Hamiltonian system. Then we can obtain a new energy-preserving CIDG-I method for the system. The CIDG-I method can combine with its adjoint method CIDG-II which is also a energy-preserving method to form a new method, namely CIDG-C method. The CIDG-C method is symmetrical and can conserve the Hamiltonian energy directly and exactly. With comparison to the well-used Boris method, numerical experiments indicate that the CIDG-C method holds advantage over the Boris method in terms of energy-conserving. [ABSTRACT FROM AUTHOR]