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Multivariate Time series forecasting finds numerous applications across various fields, including society, industry, market, etc. Recently, gated recurrent unit neural networks (GRU) have shown high efficiency in processing sequential time series data in recent years. While traditional GRUs can learn and understand time series data, with the explosion and increasing complexity of data, there has not been much research on GRU networks that considers the fuzziness and periodicity of the data’s nature. Thus, the novel developed complex fuzzy-gated recurrent neural network (CFGRU) is proposed in this study to improve the ability of GRU networks to resolve multivariate time series forecasting issues. Complex fuzzy theory, which represents the uncertainty and periodicity of the data space from the input data, is integrated with GRU regression neural networks and the proposed CFGRU network. Furthermore, this paper also suggests a stacked residual complex fuzzy-gated recurrent neural network architecture for multivariate time series data forecasting. An experiment was carried out on multivariate time series data sets comprising 05 multivariate time series datasets (weather, sunspots, PM2.5, air quality, and power consumption) to validate the success and efficiency of the suggested model. Comparison results on three indices—MAE, RMSE, and SMAPE—indicate that the proposed model performs forecasting better than both complex fuzzy forecasting models and conventional GRU models.

In recent decades, mangroves have been seriously devastated by shrimp farming development in the Vietnamese Mekong Delta. As a result, integrated mangrove-shrimp farming has emerged as a potential solution to culture shrimps and protect mangroves. The present study aims to understand whether mangrove-to-pond cover ratios influence shrimp yields in an integrated mangrove-shrimp farming system. Five integrated mangrove-shrimp ponds in the Tam Giang Commune, Nam Can District, Ca Mau Province (Southern Vietnam) were chosen for this study. The study estimated that the mangrove-to-pond cover ratios ranged from 42.00 % to 72.50 %. The total shrimp yield per year (kg.ha-1 yr-1 ) was generally high, ranging from 76.62 to 249.09 (including 37.93 to 108.64 for the black tiger shrimp (Penaeus monodon Fabricius, 1798), and 38.69 to 140.45 for other shrimps, namely Penaeus indicus Milne Edwards, 1837, Penaeus merguiensis de Man, 1888, Metapenaeus ensis (De Haan, 1844), and Metapenaeus lysianassa (de Man, 1888)). Moreover, a strong positive correlation between the mangrove-to-pond cover ratios and the shrimp yields were observed (r > 0.71, P < 0.05). In conclusion the mangrove-to-pond cover ratios have a direct impact on the total shrimp yield. The mangrove-to-pond cover ratios should be 50 % to enhance shrimp yields in this system.

One of the most efficient tools for modeling uncertainty in decision-making problems is the neutrosophic set (NS) and its extensions, such as complex NS (CNS), interval NS (INS), and interval complex NS (ICNS). Linguistic variables have been long recognized as a useful tool in decision-making problems for solving the problem of crisp neutrosophic membership degree. In this paper, we aim to introduce new concepts: single-valued linguistic complex neutrosophic set (SVLCNS-2) and interval linguistic complex neutrosophic set (ILCNS-2) that are more applicable and adjustable to real-world implementation than those of their previous counterparts. Some set-theoretic operations and the operational rules of SVLCNS-2 and ILCNS-2 are designed. Then, gather classifications of the candidate versus criteria, gather the significance weights, gather the weighted rankings of candidates versus criteria and a score function to arrange the candidates are determined. New TOPSIS decision-making procedures in SVLCNS-2 and ICNS-2 are presented and applied to lecturer selection in the case study of the University of Economics and Business, Vietnam National University. The applications demonstrate the usefulness and efficiency of the proposal.

Dynamic multi-criteria decision-making (DMCDM) models have many meaningful applications in real life in which solving indeterminacy of information in DMCDMs strengthens the potential application of DMCDM. This study introduces an extension of dynamic internal-valued neutrosophic sets namely generalized dynamic internal-valued neutrosophic sets. Based on this extension, we develop some operators and a TOPSIS method to deal with the change of both criteria, alternatives, and decision-makers by time. In addition, this study also applies the proposal model to a real application that facilitates ranking students according to attitude-skill-knowledge evaluation model. This application not only illustrates the correctness of the proposed model but also introduces its high potential appliance in the education domain.

Decision-making activities are prevalent in human life. Many methods have been developed to address real-world decision problems. In some practical situations, decision-makers prefer to provide their evaluations over a set of criteria and weights. However, in many real-world situations, problems include a lack of weight information for the times, criteria, and decision-makers (DMs). To remedy such discrepancies, an optimization model has been proposed to determine the weights of attributes, times, and DMs. A new concept related to the correlation measure and some distance measures for the dynamic interval-valued neutrosophic set (DIVNS) are defined in this paper. An extend Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method in the interval-valued neutrosophic set with unknown weight information in dynamic neutrosophic environments is developed. Finally, a practical example is discussed to illustrate the feasibility and effectiveness of the proposed method.

Urogenital and anorectal infections caused by Neisseria meningitidis have been reported in several countries and found to be more prevalent among men who have sex with men (MSM) than among [...]

Time-stamped data is fast and constantly growing and it contains significant information thanks to the quick development of management platforms and systems based on the Internet and cutting-edge information communication technologies. Mining the time series data including time series prediction has many practical applications. Many new techniques were developed for use with various types of time series data in the prediction problem. Among those, this work suggests a unique strategy to enhance predicting quality on time-series datasets that the time-cycle matters by fusing deep learning methods with fuzzy theory. In order to increase forecasting accuracy on such type of time-series data, this study proposes integrating deep learning approaches with fuzzy logic. Particularly, it combines the long short-term memory network with the complex fuzzy set theory to create an innovative complex fuzzy long short-term memory model (CFLSTM). The proposed model adds a meaningful representation of the time cycle element thanks to a complex fuzzy set to advance the deep learning long short-term memory (LSTM) technique to have greater power for processing time series data. Experiments on standard common data sets and real-world data sets published in the UCI Machine Learning Repository demonstrated the proposed model's utility compared to other well-known forecasting models. The results of the comparisons supported the applicability of our proposed strategy for forecasting time series data. [ABSTRACT FROM AUTHOR]

In human existence, making decisions is a common event. Various techniques have been devised to tackle decision-making troubles in practical situations. Over the past ten years, a great deal of study has concentrated on quantifying the degree of ambiguity and unpredictability in knowledge using the concept of neutrosophic sets or extensions thereof. An efficient framework for handling information in decision-making problems involving uncertain, indeterminate, and time-related aspects is the Temporal Complex Neurosophic Set (TCNS). Measures of entropy and similarity can be helpful for evaluating data to solve multicriteria decisionmaking (MCDM) challenges in practical situations. However, TCNS information measurements were of no concern or relevance to the current technique. In the context of the TCNS, this work suggests multiple novel similarity and entropy measurements. The proposed metrics have been validated and shown to comply with the explicit definition of the entropy measure and similarity for the TCNS. The novel similarity and entropy measures on the TCNS environment are proposed in this research. The four similarity measures on the TCNS contain Dice, Jaccard, Cosine, and Cotangent. Also, a numerical example concerning selecting a Vietnam tourist destination is provided to validate the usefulness of the suggested measures. The practical application shows that proposed TCNS similarity and entropy metrics can produce accurate and significant outcomes for real-world decision-making problems. [ABSTRACT FROM AUTHOR]

The Temporal Complex Neutrosophic Set (TCNS) is a powerful extension of the Neutrosophic theory, and TCNS has added advantage to represents uncertain, indeterminate, inconsistent and time related factors. In the literature TCNS has been used for multi-criteria decision-making problems, especially in the time-aware systems. In order to tackle effectively decision-making problems in the TCNS environment effectively, it is crucial to make suite aggregation operators that enable decision makers to process preferences in a nuanced manner. In this paper, we introduce novel TCNS aggregation operators based on Aczel and Alsina t-norm and t-conorm using Aczel-Alsina operations, and we propose a multi-criteria decision-making model that utilizes those operators for TCNS-based multi criteria decision making problems (MCDMs). Theorytically, the soundness and completeness of new aggregation operators are proved, and to demonstrate the practicality and effectiveness of our model a real world case study for tourist destination selection in Vietnam is completed along with analysis. The result proved the application potential of the proposal to solve the time-related MCDMs in practical, such as creating a MCDM model for tourist destination selection. [ABSTRACT FROM AUTHOR]

Manganese oxides (MnxOy) have been widely applied in various chemical industrial processes owing to their long lifetime, low cost and high abundance. They have been used as co-reactants for the elimination of volatile organic compounds (VOCs); however, their oxidation mechanism is not clearly established. In this theoretical study, interaction capacities between benzene (C6H6) and MnxOy clusters, which were modeled with MnO2 and Mn2O3 molecules, were investigated by quantum chemical computations using density functional theory (DFT) with the PBE-D3 functional. The interaction capacity between C6H6 and MnxOy was evaluated, and the probing of the initial stage of the C6H6 oxidation at a molecular level offers an in-depth oxidation reaction path. Interaction energies computed in several spin states, along with the analysis of the electron distribution using the quantum theory of atoms in molecules, natural bond orbital and Wiberg bond index techniques as well as local softness values and MO energies of fragments, point out that the interaction between C6H6 and Mn2O3 is stronger than that with MnO2, amounting to −43 and −35 kcal mol−1, respectively, and the metal atom is identified as the primary active site. During the oxide cluster-assisted oxidation, benzene firstly undergoes an oxidation reaction by active oxygen to generate intermediates such as hydroquinone and benzoquinone. The pathway involving p-benzoquinone as the product (noted as PR1) is the most energetically favored one through a transition structure lying at 19 kcal mol−1, below the energy reference of the reactants, leading to an energy barrier significantly lower than that of 36 kcal mol−1 found for the gas phase oxidation reaction with molecular oxygen without the assistance of the oxide clusters. Potential energy profiles illustrating the reaction paths and molecular mechanisms were described in detail. [ABSTRACT FROM AUTHOR]

Neurexins are key adhesion proteins that coordinate extracellular and intracellular synaptic components. Nonetheless, the low abundance of these multidomain proteins has complicated any localization and structure-function studies. Here we combine an ALFA tag (AT)/nanobody (NbALFA) tool with classic genetics, cell biology and electrophysiology to examine the distribution and function of the Drosophila Nrx-1 in vivo. We generate full-length and ΔPDZ ALFA-tagged Nrx-1 variants and find that the PDZ binding motif is key to Nrx-1 surface expression. A PDZ binding motif provided in trans, via genetically encoded cytosolic NbALFA-PDZ chimera, fully restores the synaptic localization and function of NrxΔPDZ-AT. Using cytosolic NbALFA-mScarlet intrabody, we achieve compartment-specific detection of endogenous Nrx-1, track live Nrx-1 transport along the motor neuron axons, and demonstrate that Nrx-1 co-migrates with Rab2-positive vesicles. Our findings illustrate the versatility of the ALFA system and pave the way towards dissecting functional domains of complex proteins in vivo. It is challenging to localize synaptic proteins and dissect their functional domains in vivo. Here, the authors use the ALFA tag system to examine the trafficking and distribution of Drosophila Neurexin-1 and the role of the PDZ binding motif, and to accomplish its functional reconstitution in vivo. [ABSTRACT FROM AUTHOR]