The proliferation of fake news, characterized by the dissemination of inaccurate information to deceive audiences, has become a pressing concern in recent times. Traditional approaches to phony news detection, often focused on analyzing Twitter content, are susceptible to noise and variations in input sequences, leading to suboptimal performance. To address these challenges, this study proposes a novel method called MULTI-Head Attention-Hierarchical Bidirectional Long Short-Term Memory (MHA-HBiLSTM) Networks. Our approach involves two phases: training and testing, wherein we employ tweet pre-processing techniques such as stemming, punctuation removal, stop-word elimination, URL handling, and Twitter control removal. Features are represented using the Glove word embedding technique for experimental evaluation and comparison. The MHA-HBiLSTM model integrates MULTI-head attention and hierarchical concepts, allowing meaningful information extraction from Twitter data. Notably, our model utilizes dual-level attention mechanisms and a hierarchical structure, reflecting the inherent hierarchy in documents and prioritizing key material during document representation. The effectiveness of the proposed MHA-HBiLSTM algorithm is evaluated using the Whale & MULTI-VERSE (W-MVO) Optimizer approach, with tests conducted on Kaggle and FakeNewsNet datasets. Comparative analysis with traditional machine learning approaches and deep learning models demonstrates the superior performance of the MHA-HBiLSTM approach in fake news detection.

Power system these days appears to work at high-stress load, which could trigger voltage security problems. This is due to the fact that the system will operate under low voltage conditions, which could be possibly below the allowable voltage limit. The voltage collapse phenomenon can become one of the remarkable issues in the power systems which can lead to severe consequences of voltage instability. This paper proposes a method for managing the voltage stability risk using two methods which are evolutionary programming (EP) and MULTIVERSE OPTIMIZATION (MVO). Consequently, EP and MVO were used to manage the risk in the power system due to load variations. The risk assessment is made in order to determine the risk of collapse for the system utilizing a pre-developed voltage stability index termed as Fast Voltage Stability Index (FVSI). It is used as the indicator of voltage stability conditions. Results obtained from the study revealed that the MVO technique is much more effective compared to EP.

One of the effective parameters in performance-based design methods is the determination of lateral displacement demand. Due to the existence of uncertainties in the parameters of analytical models such as mechanical properties of structures and model simplifications, accurate calculation of structural responses is associated with complexities. The use of training-based prediction methods can be a good alternative to accurate analysis in assessing the seismic behavior of a building structure. In this paper, an efficient training approach for modeling and predicting the response of building structures with nonlinear behavior is studied. To perform the training process, an adaptive scheme of fuzzy inference system with the TSK model combined with MULTI-VERSE OPTIMIZATION algorithm is used to model the seismic behavior of structures. The proposed training model is implemented by optimizing the parameters of the TSK model using the OPTIMIZATION algorithm based on comparing the previous time steps responses. To implement the adaptive design and increase the accuracy of the prediction, three training cases based on the responses of 2, 5, and 10 previous time steps were used. To train this system, the data collected from the results of nonlinear time history analysis under 100 seismic events with different characteristics have been used. Also, 10 events were used to test the inference system. The performance of the proposed design was evaluated on a shear frame structural model with nonlinear hysteresis behavior. The results show that the inference system of the TSK model by combining the OPTIMIZATION method is an efficient computational method for predicting the response of nonlinear structures. The average MSE for the test group ground motions, using three training modes with 2, 5, and 10 previous time steps, 2.817e-03, 1.228e-03, and 2.953e-04, respectively. By increasing the number of time steps from 2 to 10, the prediction error decreases by 89.52%.

In this paper, recommended spiral passive micromixer was designed and simulated. spiral design has the potential to create and strengthen the centrifugal force and the secondary flow. A series of simulations were carried out to evaluate the effects of channel width, channel depth, the gap between loops, and flowrate on the micromixer performance. These features impact the contact area of the two fluids and ultimately lead to an increment in the quality of the mixture. In this study, for the flow rate of 25 μl/min and molecular diffusion coefficient of 1×10-10 m2/s, mixing efficiency of more than 90% is achieved after 30 (approximately one-third of the total channel length). Finally, the optimized design fabricated using proposed 3D printing method.

Application of the network equivalent concept for external system representation for power system transient analysis is well known. However, the challenge to utilize an equivalent network, approximated by a rational function, is to guarantee the passivity of the corresponding model. In this regard, special techniques are required to enforce the passivity of the equivalent model through a post processing approach that minimizes its impact on the original model characteristics. In this paper, the passivity is enforced by expressing the problem in terms of a convex OPTIMIZATION problem that guarantees the global optimal solution. The convex OPTIMIZATION problem is efficiently solved by recently developed numerical interior–point methods. This passivity enforcement is also global which indicates that the passivity enforcement in one region does not lead to passivity violation in other regions.

The VERSE “Spend your wealth in the cause of Allah and do not let your own hands throw you into destruction and…” is one of the VERSEs of the Holy Quran that jurists have cited it in various discussions (Al-Baqarah, 2:195). There are many debates in determining the extent of the prohibition and how to interpret this VERSE, although the use of the prohibition of casting (throwing) from this VERSE is not debatable. This paper which has been done by descriptive analytical method, seeks to answer the question: Is the criterion of the prohibition of casting into Tahluka (Arabic: التَّهْلُکَة, destruction), probability, presumption, fear with individual criterion or generic fear? This research criticizes the presented criteria after stating the meaning of the VERSE and presenting the viewpoints and firstly, the presented criteria accepts the generality of casting into Tahluka (Arabic: التَّهْلُکَة, destruction), considering the criticisms of other views and considering that the case cannot limit (restrict) the general and expanded meaning of the VERSE and secondly, due to the fact that rulings are enacted on titles and subjects in Islamic Sharia (Arabic: شریعة, Romanized: sharīʿa) and it is also accepted the theory of generic fear that is the customary ruling for the destruction of the person due to the improbability of jurists’ obligation to personal fear essential concomitants and due to the recension (the effective cause) of the fatwā (Arabic: فتوی; plural fatāwā فتاوی) of jurists in VERSEs such as the mentioned VERSE. Therefore, casting into Tahluka (Arabic: التَّهْلُکَة, destruction) is forbidden, if it is from the perspective of custom for the human race.

MULTI-label classification aims at assigning more than one label to each instance. Many real-world MULTI-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. MULTI-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm OPTIMIZATION and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm OPTIMIZATION algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of MULTI-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.

This study aims to provide an e cient MULTI-Objective MULTIdisciplinary Robust Design OPTIMIZATION (MOMRDO) framework. To this end, Bi-Level Integrated System Synthesis (BLISS) framework was implemented as a fast MULTI-disciplinary Design OPTIMIZATION (MDO) framework. Progressive Latin Hypercube Sampling (PLHS) was developed as a Design Of Experiment (DOE) for the Uncertainty Analysis (UA). This systematic approach leads to a fast, adaptive, and e cient Robust Design OPTIMIZATION (RDO) framework of complex systems. The accuracy and performance of the proposed algorithm have been evaluated through various tests. Finally, the RDO of a hydrazine monopropellant thruster was selected as a case study. The results showed that this method is a fast and e ective method for the MULTI-objective OPTIMIZATION design of complex systems, and it can also be used in other engineering applications.

One of the major factors of supply chain is distribution network and the problem of locating distribution centers is considered as one of the important decisions in supply chain. Also, one of the most important goals of the supply chain is customer satisfaction. So, Reliability can also be effective in delivering adequate productions to customers. A new MULTI-item MULTI-period MULTI-objective nonlinear mixed integer programming model is developed which aims minimizing total cost, warehouse space of distribution centers, tardiness and earliness times and maximizing distribution center’ s reliability. The model is developed for a four-echelon supply chain. E-constraint and BOM methods are used to solve the model. Finally, some numerical examples are generated in different dimensions and solved to evaluate the performance of proposed model and solution methods and the results are compared together.