There are two approaches for simulating memory as well as learning in artificial intelligence; the functionalistic approach and the cognitive approach. The necessary condition to put the second approach into account is to provide a model of brain activity that contains a quite good congruence with observational facts such as mistakes and forgotten experiences. Given that human memory has a solid core that includes the components of our identity, our family and our hometown, the major and determinative events of our lives, and the countless repeated and accepted facts of our culture, the more we go to the peripheral spots the data becomes flimsier and more easily exposed to oblivion. It was essential to propose a model in which the topoGraphical differences are quite distinguishable. In our proposed model, we have translated this topoGraphical situation into quantities, which are attributed to the nodes. The result is an edge-weighted Graph with mass-based values on the nodes which demonstrates the importance of each atomic proposition, as a truth, for an intelligent being. Furthermore, it Dynamically develops and modifies, and in successive phases, it changes the mass of the nodes and weight of the edges depending on gathered inputs from the environment.

Special conditions of wireless sensor networks, such as energy limitation, make it essential to accelerate the convergence of algorithms in this field, especially in the distributed compressive sensing (DCS) scenarios, which have a complex reconstruction phase. This paper presents a DCS reconstruction algorithm that provides a higher convergence rate. The proposed algorithm is a distributed primal-dual algorithm in a bidirectional incremental cooperation mode where the parameters change with time. The parameters are changed systematically in the convex optimization problems in which the constraint and cooperation functions are strongly convex. The proposed method is supported by simulations, which show the higher performance of the proposed algorithm in terms of convergence rate, even in stricter conditions such as the small number of measurements or the lower degree of sparsity.

Robots have become integral to modern society, taking over both complex and routine human tasks. Recent advancements in depth camera technology have propelled computer vision-based robotics into a prominent field of research. Many robotic tasks—such as picking up, carrying, and utilizing tools or objects—begin with an initial grasping step. Vision-based grasping requires the precise identification of grasp locations on objects, making the segmentation of objects into meaningful components a crucial stage in robotic grasping. In this paper, we present a system designed to detect the graspable parts of objects for a specific task. Recognizing that everyday household items are typically grasped at certain sections for carrying, we created a database of these objects and their corresponding graspable parts. Building on the success of the Dynamic Graph CNN (DGCNN) network in segmenting object components, we enhanced this network to detect the graspable areas of objects. The enhanced network was trained on the compiled database, and the visual results, along with the obtained Intersection over Union (IoU) metrics, demonstrate its success in detecting graspable regions. It achieved a grand mean IoU (gmIoU) of 92.57% across all classes, outperforming established networks such as PointNet++ in part segmentation for this dataset. Furthermore, statistical analysis using analysis of variance (ANOVA) and T-test validates the superiority of our method.

Detecting fake news on social media platforms remains a significant challenge due to the Dynamic nature of these networks, evolving user-news relationships, the difficulty in distinguishing real from fake information, and the use of advanced generative models to create fake content. In this study, we propose a novel approach, the Dynamic Graph Attention Network (DynGAT), for effective fake news detection. The DynGAT model utilizes the Dynamic Graph structure of social networks to capture the evolving interactions between users and news sources. It includes a Graph construction module that updates the Graph based on temporal data and a Graph attention module that assigns importance to nodes and edges within the Graph. The model applies attention mechanisms to prioritize critical interactions and uses deep learning techniques to classify news articles as real or fake. Experimental results on the TweepFake dataset (20,712 samples) show that DynGAT achieves 95% accuracy, outperforming existing methods such as Static GNN (87%), Transformer-based models (91%), and Hybrid models (89%). The model also demonstrates improvements in precision, recall, and F1 score. This work contributes to the ongoing efforts to combat misinformation and promote reliable information on social media platforms.

Due to the lack of measurements in many regions, wave characteristics are estimated using different methods. Wave climate hindcasting/forecasting is mostly conducted by numerical models or empirical methods. Until now, different empirical methods have been developed for wave hindcasting. However, with the development of high speed processors, several sophisticated numerical models have been developed for wave prediction. These models are mostly phase-averaged spectral wave models developed in three generations. In the last two decades, third generation wave models have been used widely in academic and practical projects. In this regard, Port and Maritime Organization has produced his own model, PMO Dynamic. This model has been developed as a part of first three phases of Monitoring and Modeling of Study of Iranian Coasts project. PMO Dynamic package is a software available for engineering purposes. It has several modules that have been developed for different objectives. Wave model is the module which is used for the generation and transformation of wind waves in coastal areas. In this paper, in order to test the PMO Dynamic model capabilities, it has been applied for the prediction of wave parameters in Bushehr Bay and the results have been compared with MIKE21 SW model and measured data.

Background: In intertrochanteric fractures، Dynamic hip screw (DHS) is the most frequently used tool، but several studies did not give good results for this device in reverse-oblique and transverse fractures. Current study aimed to compare fixation results of reverse-oblique intertrochanteric and short-segment subtrochanteric fractures using Dynamic condylar screw (DCS) and DHS in patients referred to Alzahra and Kashani Hospitals in Isfahan, Iran.Methods: In a cross-sectional study، medical files of the patients referred to emergency units of the two hospitals who were diagnosed with intertrochanteric or subtrochanteric fractures and underwent DHS or DCS surgery more than 6 months before were assessed; they were invited for follow-up visit and examination. Written consent forms were taken from all the patients upon their entry; and they were examined in terms of complications including nonunion and failure of device and level of fracture improvement. These cases were evaluated by simple radioGraphy ,too. Results obtained from examination of patients in addition to demoGraphic information were recorded in the data collection form for each patient.Findings: Perfect union was observed in 14 (of 25) patients in DHS group and 21 (of 25) patients in DCS group (56% vs. 84%) six months after the surgery. Bone union was significantly better in DCS group (P=0.031). In addition, six months after the surgery, the devices used in 14 people in DHS group and 24 people in DCS group were fixed and the difference between the two groups was significant (P=0.001). 14 and 24 patients had total recovery in DHS and DCS groups، respectively (20% vs. 44%) with a significant difference (P=0.020).Conclusion: Considering obtained results, it can be concluded that using DCS is recommended over DHS due to severity of pain, bone union and fixation of the device.

The independence Graph Ind(G) of a Graph G is the Graph with vertices as maximum independent sets of G and two vertices are adjacent, if and only if the corresponding maximum independent sets are disjoint. In this work, we find the independence Graph of Cartesian product of d copies of complete Graphs Kq, which is known as the Hamming Graph H(d, q). Greenwell and Lovasz [7] found that the independence number of direct product of d copies of Kq as qd−1. We prove that the independence number of Hamming Graph H(d, q), which is cartesian product of d copies of Kq, is also qd−1. As an application of our findings, we find answers for rook problem in higher dimensional square chess board.

In this paper, we find the star chromatic number of central Graph of complete bipartite Graph and corona Graph of complete Graph with path and cycle.