The task of deploying an energy -conscious wireless sensor networks (WSNs) is challenging. One of the most effective methods for conserving WSNs energy is clustering. The deployed sensors are divided into groups by th...
详细信息
The task of deploying an energy -conscious wireless sensor networks (WSNs) is challenging. One of the most effective methods for conserving WSNs energy is clustering. The deployed sensors are divided into groups by the clustering algorithm, and each group's cluster head (CH) is chosen to gather and combine data from other sensors in the group. Mobile Wireless Sensor Networks, which enable moving the sink node, aid in reducing energy consumption. Thus, this paper introduces an energy efficient clustering algorithm and optimized path for a mobile sink using a swarm intelligence algorithms. The Chaotic Grey Wolf Optimization (CGWO) approach is used to form clusters and identify CHs. While utilizing the Slime Mould algorithm (SMA) for determining the shortest path between a mobile sink and CHs. The effectiveness of the suggested routing strategy is evaluated against that of other current, cutting -edge protocols. The findings demonstrate that in terms of overall energy consumption and network lifetime, the suggested algorithm performs better than others. While for stability period the proposed algorithm outperforms three of compared algorithms and was close to the fourth.
The high number of connected nodes in Internet of Vehicles (IoVs) drives to high data exchange between nodes, which increases the network overhead. Moreover, the recurrent change in vehicle mobility in Internet of Veh...
详细信息
The high number of connected nodes in Internet of Vehicles (IoVs) drives to high data exchange between nodes, which increases the network overhead. Moreover, the recurrent change in vehicle mobility in Internet of Vehicles (IoVs) drives to frequent changes in network topology which in turn causes frequent link disconnections. Therefore, the most addressed issues in IoVs are to manage the high quantity of packets sent by the huge number of vehicles connected with IoT devices, to reduce communication delays and guarantee the longest communication stability. clustering techniques have been utilized to reduce network overhead in IoVs networks. Classical clustering algorithms have been proposed to enhance network performances. However, IoVs environment is characterized by the high dynamicity of nodes, therefore, the optimization methods already proposed cannot perfectly deal with the characteristics of IoVs. Reinforcement learning (RL) is a machine learning algorithm, where the agent learns from its environment and tries to enhance its policies to obtain the best reward. In this paper, we propose to use deep reinforcement learning (DRL) to select the best cluster heads based on node's degree, node's buffer size, and signal strength. In the proposed work, the vehicle can perfectly select the cluster heads by choosing the best state-action values taking in consideration the high dynamicity of the network.
This paper addresses the heterogeneity of the digital divide and internet use among citizens in the 28 European Union (EU) countries (at the time of the survey). Drawing from the Eurobarometer Surveys, three indicator...
详细信息
This paper addresses the heterogeneity of the digital divide and internet use among citizens in the 28 European Union (EU) countries (at the time of the survey). Drawing from the Eurobarometer Surveys, three indicators of the digital divide are used to define the groups: frequency of internet access, means of internet access, and online activities. The categorical clustering algorithm identifies six groups of internet users: Non-Users, Basic Users, Information Exchangers, Instrumental Users, Socializers, and Advanced Users, each with distinct socio-demographic profiles. The study reveals significant socio-economic and demographic profiling variables characterizing these patterns, including age, education, gender, occupation, type of community and geographic location. A major digital divide is detected in many countries;Notably, Romania, Greece, and Bulgaria have the largest proportion of Non-Users, emphasizing the need for targeted policy interventions. These results provide crucial insights for the European Commission's digitization strategy, suggesting that more nuanced and targeted measures are needed to ensure equitable digital access across the EU.
We propose a novel FFT2 parallel multiscale computational method to predict the nonlinear behavior and failure of composite materials. Unlike traditional multiscale methods, the proposed approach reformulates the mech...
详细信息
We propose a novel FFT2 parallel multiscale computational method to predict the nonlinear behavior and failure of composite materials. Unlike traditional multiscale methods, the proposed approach reformulates the mechanical boundary value problem into Lippmann-Schwinger integral equations at both the micro- and macro-scale, thereby leveraging the numerical ciency of the fast Fourier transform (FFT) method at both scales. The application of generic non-periodic) boundary conditions at the macro-scale is carried out by using the virtual boundary technique and buffer zones. In addition, the introduction of a clustering algorithm further proves the computational efficiency of the numerical method during the information transfer between scales. To ensure accurate damage prediction and mitigate spurious strain localization both scales, suitable regularization techniques are employed. The proposed multiscale method applied to investigate the transverse tension of unidirectional composite dog-bone specimens. After experimental verification, the method is applied to simulate 2D and 3D brittle fracture, elasto-plastic damage, and examples with non-uniform material orientation. The results demonstrate the robustness and adaptability of the clustering approach, which achieves up to 65.90 speedup and 81.62-fold reduction in memory usage compared to non-clustered multiscale methods, while maintaining a comparable level of accuracy.
In practical research, nonlinear equation systems (NESs) are common mathematical models widely applied across various fields. Solving these nonlinear equation systems is crucial for addressing many engineering challen...
详细信息
ISBN:
(纸本)9789819771837;9789819771844
In practical research, nonlinear equation systems (NESs) are common mathematical models widely applied across various fields. Solving these nonlinear equation systems is crucial for addressing many engineering challenges. However, due to the inherent complexity and diverse solutions of nonlinear equation systems, traditional optimization algorithms and intelligent optimization algorithms have certain limitations. Neural network algorithms, which have gained significant popularity in recent years, excel in fitting nonlinear relationships. This research aims to explore different neural network models to develop efficient and accurate computational models for solving various types of nonlinear equation systems, thus overcoming some of the limitations of traditional and intelligent optimization algorithms. By leveraging the adaptability and generality of neural networks, we seek to enhance their performance in solving complex nonlinear equation systems. Furthermore, by integrating iterative algorithms and clustering algorithms, we aim to improve solution accuracy and effectively address the multiple roots problem associated with nonlinear equation systems.
Efficiently identifying cancer driver genes is critical to drug design, cancer diagnosis and treatment. Current unsupervised cancer driver gene prediction approaches mainly exploit mutual exclusivity of mutated driver...
详细信息
ISBN:
(数字)9789819750870
ISBN:
(纸本)9789819750863;9789819750870
Efficiently identifying cancer driver genes is critical to drug design, cancer diagnosis and treatment. Current unsupervised cancer driver gene prediction approaches mainly exploit mutual exclusivity of mutated driver genes and integrate multi-omics data with gene function networks. Some of them identify driver genes based on the gene features learned by network embedding algorithms. However, these methods are limited to using the mutual exclusivity from original data without considering the mutual exclusivity implanted in the learned features. Additionally, they simply assume that all driver genes have high mutation frequencies. Thus, we propose a novel unsupervised framework FCMEDriver, which utilizes the mutual exclusivity from the learned features and mutation frequency to predict driver genes. In FCMEDriver, a feature clustering algorithm is designed to obtain modules. Based on the modules, our extensive experiments show that the Euclidean distances between learned features are highly related with the mutual exclusivity defined on the original data, and they can reveal more information compared to mutual exclusivity. Thus, we apply the Euclidean distances of learned gene features for each module to calculate a module importance score for each gene. Since the fact that most of driver genes have intermediate mutation frequencies, we design a mutation frequency scoring function for each gene to optimize the existing mutation frequency scoring strategy in which genes with intermediate mutation frequencies are more inclined to obtain similar high scores as those genes with high mutation frequencies. The weighted sum of the module importance score and the mutation frequency score is used to prioritize the genes. The experiment results show that FCMEDriver outperforms other four state-of-the-art methods for cancer driver identification.
This article presents a dataset containing messages from the Digital Teaching Assistant (DTA) system, which records the results from the automatic verification of students' solutions to unique programming exercise...
详细信息
This article presents a dataset containing messages from the Digital Teaching Assistant (DTA) system, which records the results from the automatic verification of students' solutions to unique programming exercises of 11 various types. These results are automatically generated by the system, which automates a massive Python programming course at MIREA-Russian Technological University (RTU MIREA). The DTA system is trained to distinguish between approaches to solve programming exercises, as well as to identify correct and incorrect solutions, using intelligent algorithms responsible for analyzing the source code in the DTA system using vector representations of programs based on Markov chains, calculating pairwise Jensen-Shannon distances for programs and using a hierarchical clustering algorithm to detect high-level approaches used by students in solving unique programming exercises. In the process of learning, each student must correctly solve 11 unique exercises in order to receive admission to the intermediate certification in the form of a test. In addition, a motivated student may try to find additional approaches to solve exercises they have already solved. At the same time, not all students are able or willing to solve the 11 unique exercises proposed to them;some will resort to outside help in solving all or part of the exercises. Since all information about the interactions of the students with the DTA system is recorded, it is possible to identify different types of students. First of all, the students can be classified into 2 classes: those who failed to solve 11 exercises and those who received admission to the intermediate certification in the form of a test, having solved the 11 unique exercises correctly. However, it is possible to identify classes of typical, motivated and suspicious students among the latter group based on the proposed dataset. The proposed dataset can be used to develop regression models that will predict outbursts of student activ
Digital geological survey methods have become supplementary approaches for traditional geological survey in the last two decades. In this paper, the Unmanned Aerial Vehicle (UAV)-based photogrammetry technology is use...
详细信息
Digital geological survey methods have become supplementary approaches for traditional geological survey in the last two decades. In this paper, the Unmanned Aerial Vehicle (UAV)-based photogrammetry technology is used to obtain the 3D point cloud model of rock outcrops. The clustering algorithm is used to automatically identify the rock discontinuity parameters. However, the obtained 3D point cloud model with high resolution often has a huge point data which usually poses a great challenge to the computational efficiency of the automatic identification. In fact, too-dense point cloud data may not be necessary for cases when the rock mass is relatively intact. The optimal point cloud resolution, which balances the accuracy and efficiency, depends on the degrees of fragmentation of the rock mass under investigation. For a model with the same resolution, large-size discontinuities may have quite a few redundant point cloud data that are of little use to improve the identification accuracy whereas small-size discontinuities may not be properly identified due to insufficient number of data points. In this paper, the influence of the degree of fragmentation of rock mass on the identified results was investigated. The uniform grid method was adopted to sparse the 3D point cloud model. The optimal point cloud resolution for different discontinuities was suggested. The applicability and feasibility of the proposed approach were verified via three illustrative examples of typical rock slopes.
BackgroundThe loss of mechanical homeostasis between tumor cells and microenvironment is an important factor in tumor metastasis. In the process, mechanical forces affect cell proliferation, differentiation, migration...
详细信息
BackgroundThe loss of mechanical homeostasis between tumor cells and microenvironment is an important factor in tumor metastasis. In the process, mechanical forces affect cell proliferation, differentiation, migration and tissue *** high spatial resolution of Atomic force microscopy (AFM) technology, our study provides the direct measurement of the nanomechanical properties of prostate cancer clinical tissue *** and MethodsAFM was used to determine the biomechanical properties of prostate tissue with different grade scores. K-means clustering method and fuzzy C-means were used to distinguish the cellular component in prostate tissue from non-cellular component based on their viscoelasticity. Futhermore, AFM measurements in vitro cells, including metastatic prostate cells (PC-3) and normal human prostate cells (PZ-HPV-7) were carried *** Young's modulus was decreased in prostate cancer progression, and the elasticity of cellular component in prostate cancer tissue was smaller than that of normal prostate tissue. PC-3 cells were softer than PZ-HPV-7 cells. Further mechanism investigation showed that the difference in modulus between cancerous and normal prostate tissue may be associated with a greater actin cytoskeleton distribution inside the cancer *** results suggests that the nanomechanical properties can classify the prostate tumor, which could be used as an index for the identification and classification of cancer at cellular level.
This study based on the 25-year wave hindcast database of the western Pacific and used three unsupervised learning clustering algorithms to classify the wave energy resources in the China East Adjacent Seas. Five wave...
详细信息
This study based on the 25-year wave hindcast database of the western Pacific and used three unsupervised learning clustering algorithms to classify the wave energy resources in the China East Adjacent Seas. Five wave energy characteristic parameters are comprehensively considered in the calculation process of the clustering algorithm. According to the analysis of the classification results, it can be seen that the Class IV is the most suitable for wave energy development in the China East Adjacent Seas, followed by the Class III and Class V. The Class VI is too far away from the coast to be used as the intended area for wave energy development. The Class I is mostly located in inland seas and harbors, which are not suitable for wave energy development. By analyzing the annual average captured power, it can be seen that the optimal capture interval of the existing wave energy converters with mature technology is too large compared with the wave conditions in the China East Adjacent Seas. We should vigorously develop wave energy converters that are more suitable for the wave conditions of Class III, Class IV and Class V, and improve the capture efficiency of the wave energy converters.
暂无评论