Image super‐resolution via dynamic network

Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction block.The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution.To enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes.To prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained features.Also,a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem.Finally,a construction block is responsible for reconstructing high‐quality images.Designed heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital devices.Experimental results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and complexity.The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.

Model of Universe as Described by Dynamic Universe Model

In this paper we will see the model of Universe according to Dynamic Universe Model of Cosmology by visualizing various processes that are happening in the Universe as per experimental evidences. For simplifying the matter here, we will see in part 1: about the Galaxy life cycle, where the birth and death of Galaxies discussed. Probably Universe gives guidance for the movement of Galaxies. We call this Part 1: Thinking and Reproducing Universe or Mindless Universe? (Galaxy life cycle). We see every day Sun, Stars, Galaxies etc., dissipating enormous energy in the form of radiation by the way of fusion of Hydrogen to helium. So after sometime all the Hydrogen is spent and Universe will die, is it not? … Dynamic Universe Model says that the energy in the form of electromagnetic radiation passing grazingly near any gravitating mass changes in frequency and finally will convert into neutrinos (mass). Hence Dynamic Universe Model proposes another process where energy will be converted back into matter and the cycle energy to mass to energy continues, sustaining the Universe to maintain this present status for ever in this form something like a Steady state model without any expansion. This we will see in Part 2: Energy - Mass - Energy Cycle. After converting energy into mass “how various elements are formed and where they are formed?” will be next logical question. Dynamic Universe Model says that these various particles change into higher massive particles or may get bombarded into stars or planets and various elements are formed. Here we bifurcate the formation of elements into 6 processes. They are for Elementary particles and elements generated in frequency changing process, By Cosmic rays, By Small stars, By Large Stars, By Super Novae and Manmade elements By Neutron Stars. This we will discuss in Part 3: Nucleosynthesis.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

AN ALGORITHM FOR CANONICAL PIECEWISE-LINEAR DYNAMIC NETWORKS CONTAINING DEVICES WITH CHARACTERISTIC FAMILY

This paper improves the modeling method for the device with characteristic family presented by L. O. Chua (1977) and results in the one-dimensional fluctuating canonical piecewise-linear model. It is an efficient model. The algorithm for canonical piecewise-linear dynamic networks with one dimensional fluctuating model is discussed in detail.

Nonlinear Differential Equation of Macroeconomic Dynamics for Long-Term Forecasting of Economic Development

In this article we derive a general differential equation that describes long-term economic growth in terms of cyclical and trend components. Equation is based on the model of non-linear accelerator of induced investment. A scheme is proposed for obtaining approximate solutions of nonlinear differential equation by splitting solution into the rapidly oscillating business cycles and slowly varying trend using Krylov-Bogoliubov-Mitropolsky averaging. Simplest modes of the economic system are described. Characteristics of the bifurcation point are found and bifurcation phenomenon is interpreted as loss of stability making the economic system available to structural change and accepting innovations. System being in a nonequilibrium state has a dynamics with self-sustained undamped oscillations. The model is verified with economic development of the US during the fifth Kondratieff cycle (1982-2010). Model adequately describes real process of economic growth in both quantitative and qualitative aspects. It is one of major results that the model gives a rough estimation of critical points of system stability loss and falling into a crisis recession. The model is used to forecast the macroeconomic dynamics of the US during the sixth Kondratieff cycle (2018-2050). For this forecast we use fixed production capital functional dependence on a long-term Kondratieff cycle and medium-term Juglar and Kuznets cycles. More accurate estimations of the time of crisis and recession are based on the model of accelerating log-periodic oscillations. The explosive growth of the prices of highly liquid commodities such as gold and oil is taken as real predictors of the global financial crisis. The second wave of crisis is expected to come in June 2011.

Neural Network-Powered License Plate Recognition System Design

The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.

Impact of Channel Dynamics, Combined Nonlinearities and ASE Noise on Transmission Performance of all Optical Star WDM Networks

For all optical Wavelength Division Multiplexing (WDM) network based on G.653 fibers, we investigate the quality factor deterioration due to combined nonlinear effects and Amplified spontaneous emission (ASE) noise for system parameters based on ITU-T Recommendation G.692. The investigation: (a) emphasizes on stimulated Raman scattering (SRS) and four wave mixing (FWM) effects which are the dominant nonlinearities known to limit WDM system performance and (b) accounts for beating between nonlinearities and beating between ASE noise and nonlinearities. Using the proposed model, performance of the worst affected channels due to SRS and FWM is compared and the results indicate that the worst affected channel due to SRS performs better and hence must be preferred for reliable and efficient transmission over the worst affected channel due to FWM. Further, the results suggest that to achieve a desired error rate (quality factor);there exists an optimal value of channel spacing for a given number of channels. The proposed theoretical model is also validated through extensive simulations over Rsoft OptSimTM simulator and the two sets of results are found to match, indicating that the proposed model accurately calculates the quality factor of the all optical WDM network.

Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources.This paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN method.This technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access decision-making.The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy rate.Additionally,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern organizations.Furthermore,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.

Application of Convolutional Neural Networks in Classification of GBM for Enhanced Prognosis

The lethal brain tumor “Glioblastoma” has the propensity to grow over time. To improve patient outcomes, it is essential to classify GBM accurately and promptly in order to provide a focused and individualized treatment plan. Despite this, deep learning methods, particularly Convolutional Neural Networks (CNNs), have demonstrated a high level of accuracy in a myriad of medical image analysis applications as a result of recent technical breakthroughs. The overall aim of the research is to investigate how CNNs can be used to classify GBMs using data from medical imaging, to improve prognosis precision and effectiveness. This research study will demonstrate a suggested methodology that makes use of the CNN architecture and is trained using a database of MRI pictures with this tumor. The constructed model will be assessed based on its overall performance. Extensive experiments and comparisons with conventional machine learning techniques and existing classification methods will also be made. It will be crucial to emphasize the possibility of early and accurate prediction in a clinical workflow because it can have a big impact on treatment planning and patient outcomes. The paramount objective is to not only address the classification challenge but also to outline a clear pathway towards enhancing prognosis precision and treatment effectiveness.

Effect of sevoflurane preconditioning on astrocytic dynamics and neural network formation after cerebral ischemia and reperfusion in rats

Astrocytes, the major component of blood-brain barriers, have presented paradoxical profiles after cerebral ischemia and reperfusion in vivo and in vitro. Our previous study showed that sevoflurane preconditioning improved the integrity of blood-brain barriers after ischemia and reperfusion injury in rats. This led us to investigate the effects of sevoflurane preconditioning on the astrocytic dynamics in ischemia and reperfusion rats, in order to explore astrocytic cell-based mechanisms of sevoflurane preconditioning. In the present study, 2,3,5-triphenyltetrazolium chloride staining and Garcia behavioral scores were utilized to evaluate cerebral infarction and neurological outcome from day 1 to day 3 after transient middle cerebral artery occlusion surgery. Using immunofluorescent staining, we found that sevoflurane preconditioning substantially promoted the astrocytic activation and migration from the penumbra to the infarct with microglial activation from day 3 after middle cerebral artery occlusion. The formation of astrocytic scaffolds facilitated neuroblasts migrating from the subventricular zone to the lesion sites on day 14 after injury. Neural networks increased in the infarct of sevoflurane preconditioned rats, consistent with decreased infarct volume and improved neurological scores after ischemia and reperfusion injury. These findings demonstrate that sevoflurane preconditioning confers neuroprotection, not only by accelerating astrocytic spatial and temporal dynamics, but also providing astrocytic scaffolds for neuroblasts migration to ischemic regions, which facilitates neural reconstruction after brain ischemia.

Overlapping Community Detection in Dynamic Networks

Due to the increasingly large size and changing nature of social networks, algorithms for dynamic networks have become an important part of modern day community detection. In this paper, we use a well-known static community detection algorithm and modify it to discover communities in dynamic networks. We have developed a dynamic community detection algorithm based on Speaker-Listener Label Propagation Algorithm (SLPA) called SLPA Dynamic (SLPAD). This algorithm, tested on two real dynamic networks, cuts down on the time that it would take SLPA to run, as well as produces similar, and in some cases better, communities. We compared SLPAD to SLPA, LabelRankT, and another algorithm we developed, Dynamic Structural Clustering Algorithm for Networks Overlapping (DSCAN-O), to further test its validity and ability to detect overlapping communities when compared to other community detection algorithms. SLPAD proves to be faster than all of these algorithms, as well as produces communities with just as high modularity for each network.

Reduced-Order GPIO Based Dynamic Event-Triggered Tracking Control of a Networked One-DOF Link Manipulator Without Velocity Measurement

In networked robot manipulators that deeply integrate control, communication and computation, the controller design needs to take into consideration the limited or costly system resources and the presence of disturbances/uncertainties. To cope with these requirements, this paper proposes a novel dynamic event-triggered robust tracking control method for a onedegree of freedom(DOF) link manipulator with external disturbance and system uncertainties via a reduced-order generalized proportional-integral observer(GPIO). By only using the sampled-data position signal, a new sampled-data robust output feedback tracking controller is proposed based on a reduced-order GPIO to attenuate the undesirable influence of the external disturbance and the system uncertainties. To save the communication resources, we propose a discrete-time dynamic event-triggering mechanism(DETM), where the estimates and the control signal are transmitted and computed only when the proposed discrete-time DETM is violated. It is shown that with the proposed control method, both tracking control properties and communication properties can be significantly improved. Finally, simulation results are shown to demonstrate the feasibility and efficacy of the proposed control approach.

Semantic model and optimization of creative processes at mathematical knowledge formation

The aim of this work is mathematical education through the knowledge system and mathematical modeling. A net model of formation of mathematical knowledge as a deductive theory is suggested here. Within this model the formation of deductive theory is represented as the development of a certain informational space, the elements of which are structured in the form of the orientated semantic net. This net is properly metrized and characterized by a certain system of coverings. It allows injecting net optimization parameters, regulating qualitative aspects of knowledge system under consideration. To regulate the creative processes of the formation and realization of mathematical know- edge, stochastic model of formation deductive theory is suggested here in the form of branching Markovian process, which is realized in the corresponding informational space as a semantic net. According to this stochastic model we can get correct foundation of criterion of optimization creative processes that leads to “great main points” strategy (GMP-strategy) in the process of realization of the effective control in the research work in the sphere of mathematics and its applications.

In situ atomic-scale observation of size-dependent (de) potassiation and reversible phase transformation in tetragonal FeSe anodes

Potassium-ion batteries(PIBs)are considered promising alternatives to lithium-ion batteries owing to cost-effective potassium resources and a suitable redox potential of-2.93 V(vs.-3.04 V for Li+/Li).However,the exploration of appro-priate electrode materials with the correct size for reversibly accommodating large K+ions presents a significant challenge.In addition,the reaction mecha-nisms and origins of enhanced performance remain elusive.Here,tetragonal FeSe nanoflakes of different sizes are designed to serve as an anode for PIBs,and their live and atomic-scale potassiation/depotassiation mechanisms are revealed for the first time through in situ high-resolution transmission electron micros-copy.We found that FeSe undergoes two distinct structural evolutions,sequen-tially characterized by intercalation and conversion reactions,and the initial intercalation behavior is size-dependent.Apparent expansion induced by the intercalation of K+ions is observed in small-sized FeSe nanoflakes,whereas unexpected cracks are formed along the direction of ionic diffusion in large-sized nanoflakes.The significant stress generation and crack extension originating from the combined effect of mechanical and electrochemical interactions are elucidated by geometric phase analysis and finite-element analysis.Despite the different intercalation behaviors,the formed products of Fe and K_(2)Se after full potassiation can be converted back into the original FeSe phase upon depotassiation.In particular,small-sized nanoflakes exhibit better cycling perfor-mance with well-maintained structural integrity.This article presents the first successful demonstration of atomic-scale visualization that can reveal size-dependent potassiation dynamics.Moreover,it provides valuable guidelines for optimizing the dimensions of electrode materials for advanced PIBs.

A Dynamic Distributed Spectrum Allocation Mechanism Based on Game Model in Fog Radio Access Networks

With the explosive growth of highspeed wireless data demand and the number of mobile devices, fog radio access networks(F-RAN) with multi-layer network structure becomes a hot topic in recent research. Meanwhile, due to the rapid growth of mobile communication traffic, high cost and the scarcity of wireless resources, it is especially important to develop an efficient radio resource management mechanism. In this paper, we focus on the shortcomings of resource waste, and we consider the actual situation of base station dynamic coverage and user requirements. We propose a spectrum pricing and allocation scheme based on Stackelberg game model under F-RAN framework, realizing the allocation of resource on demand. This scheme studies the double game between the users and the operators, as well as between the traditional operators and the virtual operators, maximizing the profits of the operators. At the same time, spectrum reuse technology is adopted to improve the utilization of network resource. By analyzing the simulation results, it is verified that our proposed scheme can not only avoid resource waste, but also effectively improve the operator's revenue efficiency and overall network resource utilization.

Network pharmacology and molecular dynamics study of the effect of the Astragalus-Coptis drug pair on diabetic kidney disease

BACKGROUND Diabetic kidney disease(DKD)is the primary cause of end-stage renal disease.The Astragalus-Coptis drug pair is frequently employed in the management of DKD.However,the precise molecular mechanism underlying its therapeutic effect remains elusive.AIM To investigate the synergistic effects of multiple active ingredients in the Astragalus-Coptis drug pair on DKD through multiple targets and pathways.METHODS The ingredients of the Astragalus-Coptis drug pair were collected and screened using the TCMSP database and the SwissADME platform.The targets were predicted using the SwissTargetPrediction database,while the DKD differential gene expression analysis was obtained from the Gene Expression Omnibus database.DKD targets were acquired from the GeneCards,Online Mendelian Inheritance in Man database,and DisGeNET databases,with common targets identified through the Venny platform.The protein-protein interaction network and the“disease-active ingredient-target”network of the common targets were constructed utilizing the STRING database and Cytoscape software,followed by the analysis of the interaction relationships and further screening of key targets and core active ingredients.Gene Ontology(GO)function and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichments were performed using the DAVID database.The tissue and organ distributions of key targets were evaluated.PyMOL and AutoDock software validate the molecular docking between the core ingredients and key targets.Finally,molecular dynamics(MD)simulations were conducted to simulate the optimal complex formed by interactions between core ingredients and key target proteins.RESULTS A total of 27 active ingredients and 512 potential targets of the Astragalus-Coptis drug pair were identified.There were 273 common targets between DKD and the Astragalus-Coptis drug pair.Through protein-protein interaction network topology analysis,we identified 9 core active ingredients and 10 key targets.GO and KEGG pathway enrichment analyses revealed that Astragalus-Coptis drug pair treatment for DKD involves various biological processes,including protein phosphorylation,negative regulation of apoptosis,inflammatory response,and endoplasmic reticulum unfolded protein response.These pathways are mainly associated with the advanced glycation end products(AGE)-receptor for AGE products signaling pathway in diabetic complications,as well as the Lipid and atherosclerosis.Molecular docking and MD simulations demonstrated high affinity and stability between the core active ingredients and key targets.Notably,the quercetin-AKT serine/threonine kinase 1(AKT1)and quercetin-tumor necrosis factor(TNF)protein complexes exhibited exceptional stability.CONCLUSION This study demonstrated that DKD treatment with the Astragalus-Coptis drug pair involves multiple ingredients,targets,and signaling pathways.We propose a novel approach for investigating the molecular mechanism underlying the therapeutic effects of the Astragalus-Coptis drug pair on DKD.Furthermore,we suggest that quercetin is the most potent active ingredient and specifically targets AKT1 and TNF,providing a theoretical foundation for further exploration of pharmacologically active ingredients and elucidating their molecular mechanisms in DKD treatment.

Simplified Coarse-Grained Dynamic Model for Real Gases

A simplified model is proposed for an easy understanding of the coarse-grained technique and for achieving a first approximation to the behavior of gases. A mole of a gas substance, within a cubic container, is represented by six particles symmetrically moving. The impacts of particles on container walls, the inter-particle collisions, as well as the volume of particles and the inter-particle attractive forces, obeying a Lennard-Jones curve, are taken into account. Thanks to the symmetry, the problem is reduced to the nonlinear dynamic analysis of a SDOF oscillator, which is numerically solved by a step-by-step time integration algorithm. Five applications of proposed model, on Carbon Dioxide, are presented: 1) Ideal gas in STP conditions. 2) Real gas in STP conditions. 3) Condensation for small molar volume. 4) Critical point. 5) Iso-kinetic energy curves and iso-therms in the critical point region. Results of the proposed model are compared with test data and results of the Van der Waals model for real gases.

A High-level Architecture for Intrusion Detection on Heterogeneous Wireless Sensor Networks: Hierarchical, Scalable and Dynamic Reconfigurable

Networks protection against different types of attacks is one of most important posed issue into the network and information security domains. This problem on Wireless Sensor Networks (WSNs), in attention to their special properties, has more importance. Now, there are some of proposed solutions to protect Wireless Sensor Networks (WSNs) against different types of intrusions;but no one of them has a comprehensive view to this problem and they are usually designed in single-purpose;but, the proposed design in this paper has been a comprehensive view to this issue by presenting a complete Intrusion Detection Architecture (IDA). The main contribution of this architecture is its hierarchical structure;i.e. it is designed and applicable, in one, two or three levels, consistent to the application domain and its required security level. Focus of this paper is on the clustering WSNs, designing and deploying Sensor-based Intrusion Detection System (SIDS) on sensor nodes, Cluster-based Intrusion Detection System (CIDS) on cluster-heads and Wireless Sensor Network wide level Intrusion Detection System (WSNIDS) on the central server. Suppositions of the WSN and Intrusion Detection Architecture (IDA) are: static and heterogeneous network, hierarchical, distributed and clustering structure along with clusters' overlapping. Finally, this paper has been designed a questionnaire to verify the proposed idea;then it analyzed and evaluated the acquired results from the questionnaires.

Using Neural Networks to Predict Secondary Structure for Protein Folding

Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate prediction results. The goal of this paper is to develop and implement an intelligent based system to predict secondary structure of a protein from its primary amino acid sequence by using five models of Neural Network (NN). These models are Feed Forward Neural Network (FNN), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN), Convolutional Neural Network (CNN), and CNN Fine Tuning for PSSP. To evaluate our approaches two datasets have been used. The first one contains 114 protein samples, and the second one contains 1845 protein samples.

Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:predictors of patient prognosis

Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly understood.Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neuro biological markers after mild traumatic brain injury.This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury.G raph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function.However,most previous mild traumatic brain injury studies using graph theory have focused on specific populations,with limited exploration of simultaneous abnormalities in structural and functional connectivity.Given that mild traumatic brain injury is the most common type of traumatic brain injury encounte red in clinical practice,further investigation of the patient characteristics and evolution of structural and functional connectivity is critical.In the present study,we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury.In this longitudinal study,we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 wee ks of injury,as well as 36 healthy controls.Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis.In the acute phase,patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network.More than 3 months of followup data revealed signs of recovery in structural and functional connectivity,as well as cognitive function,in 22 out of the 46 patients.Furthermore,better cognitive function was associated with more efficient networks.Finally,our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury.These findings highlight the importance of integrating structural and functional connectivity in unde rstanding the occurrence and evolution of mild traumatic brain injury.Additionally,exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.