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.

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.

Modeling and Comprehensive Review of Signaling Storms in 3GPP-Based Mobile Broadband Networks:Causes,Solutions,and Countermeasures

Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.

Performance comparison of three artificial neural network methods for classification of electroencephalograph signals of five mental tasks

In this paper, performance of three classifiers for classification of five mental tasks were investigated. Wavelet Packet Transform (WPT) was used for feature extraction of the relevant frequency bands from raw Electroencephalograph (EEG) signal. The three classifiers namely used were Multilayer Back propagation Neural Network, Support Vector Machine and Radial Basis Function Neural Network. In MLP-BP NN five training methods used were a) Gradient Descent Back Propagation b) Levenberg-Marquardt c) Resilient Back Propagation d) Conjugate Learning Gradient Back Propagation and e) Gradient Descent Back Propagation with movementum.

New “Intellectual Networks” (Smart Grid) for Detecting Electrical Equipment Faults, Defects and Weaknesses

The most important elements of “intellectual networks” (Smart Grid) are the systems of monitoring the parameters of electrical equipment. Information-measuring systems (IMS), which described in this paper, were proposed to use together with rapid digital protection against short-circuit regimes in transformer windings. This paper presents an application’s experience of LVI-testing, some results of the use of Frequency Response Analysis (FRA) to check the condition of transformer windings and infra-red control results of electrical equipment. The LVI method and short-circuit inductive reactance measurements are sensitive for detecting such faults as radial, axial winding deformations, a twisting of low-voltage or regulating winding, a losing of winding’s pressing and others.

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].

A Cross-Layer Optimization Framework for Energy Efficiency in Wireless Sensor Networks

We consider the extension of network lifetime of battery driven wireless sensor networks by splitting the sensing area into uniform clusters and implementing heterogeneous modulation schemes at different members of the clusters. A cross-layer optimization has been proposed to reduce total energy expenditure of the network;at network layer, routing is done through uniform clusters;at MAC layer, each sensor node of the cluster is assigned fixed or variable time slots and at physical layer different member of the clusters is assigned different modulation techniques. MATLAB simulation proved substantial network lifetime gains.

Preliminary Network Centric Therapy for Machine Learning Classification of Deep Brain Stimulation Status for the Treatment of Parkinson’s Disease with a Conformal Wearable and Wireless Inertial Sensor

The concept of Network Centric Therapy represents an amalgamation of wearable and wireless inertial sensor systems and machine learning with access to a Cloud computing environment. The advent of Network Centric Therapy is highly relevant to the treatment of Parkinson’s disease through deep brain stimulation. Originally wearable and wireless systems for quantifying Parkinson’s disease involved the use a smartphone to quantify hand tremor. Although originally novel, the smartphone has notable issues as a wearable application for quantifying movement disorder tremor. The smartphone has evolved in a pathway that has made the smartphone progressively more cumbersome to mount about the dorsum of the hand. Furthermore, the smartphone utilizes an inertial sensor package that is not certified for medical analysis, and the trial data access a provisional Cloud computing environment through an email account. These concerns are resolved with the recent development of a conformal wearable and wireless inertial sensor system. This conformal wearable and wireless system mounts to the hand with the profile of a bandage by adhesive and accesses a secure Cloud computing environment through a segmented wireless connectivity strategy involving a smartphone and tablet. Additionally, the conformal wearable and wireless system is certified by the FDA of the United States of America for ascertaining medical grade inertial sensor data. These characteristics make the conformal wearable and wireless system uniquely suited for the quantification of Parkinson’s disease treatment through deep brain stimulation. Preliminary evaluation of the conformal wearable and wireless system is demonstrated through the differentiation of deep brain stimulation set to “On” and “Off” status. Based on the robustness of the acceleration signal, this signal was selected to quantify hand tremor for the prescribed deep brain stimulation settings. Machine learning classification using the Waikato Environment for Knowledge Analysis (WEKA) was applied using the multilayer perceptron neural network. The multilayer perceptron neural network achieved considerable classification accuracy for distinguishing between the deep brain stimulation system set to “On” and “Off” status through the quantified acceleration signal data obtained by this recently developed conformal wearable and wireless system. The research achievement establishes a progressive pathway to the future objective of achieving deep brain stimulation capabilities that promote closed-loop acquisition of configuration parameters that are uniquely optimized to the individual through extrinsic means of a highly conformal wearable and wireless inertial sensor system and machine learning with access to Cloud computing resources.

Proposed Caching Scheme for Optimizing Trade-off between Freshness and Energy Consumption in Name Data Networking Based IoT

Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offers opportunities but may also bear risks. A hitherto neglected aspect is the possible increase in power consumption as smart devices in IoT applications are expected to be reachable by other devices at all times. This implies that the device is consuming electrical energy even when it is not in use for its primary function. Many researchers’ communities have started addressing storage ability like cache memory of smart devices using the concept called—Named Data Networking (NDN) to achieve better energy efficient communication model. In NDN, memory or buffer overflow is the common challenge especially when internal memory of node exceeds its limit and data with highest degree of freshness may not be accommodated and entire scenarios behaves like a traditional network. In such case, Data Caching is not performed by intermediate nodes to guarantee highest degree of freshness. On the periodical updates sent from data producers, it is exceedingly demanded that data consumers must get up to date information at cost of lease energy. Consequently, there is challenge in maintaining tradeoff between freshness energy consumption during Publisher-Subscriber interaction. In our work, we proposed the architecture to overcome cache strategy issue by Smart Caching Algorithm for improvement in memory management and data freshness. The smart caching strategy updates the data at precise interval by keeping garbage data into consideration. It is also observed from experiment that data redundancy can be easily obtained by ignoring/dropping data packets for the information which is not of interest by other participating nodes in network, ultimately leading to optimizing tradeoff between freshness and energy required.

Resting-state network complexity and magnitude changes in neonates with severe hypoxic ischemic encephalopathy

Resting-state functional magnetic resonance imaging has revealed disrupted brain network connectivity in adults and teenagers with cerebral palsy. However, the specific brain networks implicated in neonatal cases remain poorly understood. In this study, we recruited 14 termborn infants with mild hypoxic ischemic encephalopathy and 14 term-born infants with severe hypoxic ischemic encephalopathy from Changzhou Children's Hospital, China. Resting-state functional magnetic resonance imaging data showed efficient small-world organization in whole-brain networks in both the mild and severe hypoxic ischemic encephalopathy groups. However, compared with the mild hypoxic ischemic encephalopathy group, the severe hypoxic ischemic encephalopathy group exhibited decreased local efficiency and a low clustering coefficient. The distribution of hub regions in the functional networks had fewer nodes in the severe hypoxic ischemic encephalopathy group compared with the mild hypoxic ischemic encephalopathy group. Moreover, nodal efficiency was reduced in the left rolandic operculum, left supramarginal gyrus, bilateral superior temporal gyrus, and right middle temporal gyrus. These results suggest that the topological structure of the resting state functional network in children with severe hypoxic ischemic encephalopathy is clearly distinct from that in children with mild hypoxic ischemic encephalopathy, and may be associated with impaired language, motion, and cognition. These data indicate that it may be possible to make early predictions regarding brain development in children with severe hypoxic ischemic encephalopathy, enabling early interventions targeting brain function. This study was approved by the Regional Ethics Review Boards of the Changzhou Children's Hospital(approval No. 2013-001) on January 31, 2013. Informed consent was obtained from the family members of the children. The trial was registered with the Chinese Clinical Trial Registry(registration number: ChiCTR1800016409) and the protocol version is 1.0.

Optimal Partitioning of Distribution Networks for Micro-Grid Operation

A great concern for the modern distribution grid is how well it can withstand and respond to adverse conditions. One way that utilities are addressing this issue is by adding redundancy to their systems. Likewise, distributed generation (DG) is becoming an increasingly popular asset at the distribution level and the idea of microgrids operating as standalone systems apart from the bulk electric grid is quickly becoming a reality. This allows for greater flexibility as systems can now take on exponentially more configurations than the radial, one-way distribution systems of the past. These added capabilities, however, make the system reconfiguration with a much more complex problem causing utilities to question if they are operating their distribution systems optimally. In addition, tools like Supervisory Control and Data Acquisition (SCADA) and Distribution Automation (DA) allow for systems to be reconfigured faster than humans can make decisions on how to reconfigure them. As a result, this paper seeks to develop an automated partitioning scheme for distribution systems that can respond to varying system conditions while ensuring a variety of operational constraints on the final configuration. It uses linear programming and graph theory. Power flow is calculated externally to the LP and a feedback loop is used to recalculate the solution if a violation is found. Application to test systems shows that it can reconfigure systems containing any number of loops resulting in a radial configuration. It can connect multiple sources to a single microgrid if more capacity is needed to supply the microgrid’s load.

谣言实现的社会机制及对信息的治理

Rumors are a type of false information, a consequence of an asymmetrical informational structure. This paper focuses on the social mechanisms of rumor fulfilling. Rumors with important contents related to people’s personal interests win acceptance through changing people’s expected payoffs, misleading people to the belief that acceptance of the rumor would beneficially outperform rejection of it. Nevertheless, it is risky to believe rumors; therefore, people make their decision whether to believe a rumor or not by referring to other people’s choices. An analysis was performed first within a game model that incorporated the variables of an individual’s expectancy and other people’s impact to predict whether the individual would accept or reject a rumor. Another analysis followed to further examine the functions of some dynamic mechanisms in rumor fulfilling when group pressure and network effects were introduced. Finally, an exploratory discussion on how to prevent rumors and erase their effects via information management strategies was presented.

Fractal Networks of Real Worlds of Fluorescing DNA in Complete Set of Chromosomes inside Blood Cells for Medical Diagnostics

We analyze fluorescence due to oxidizing activity of DNA in neutrophils of peripheral blood in the large populations ~104 - 105 of cells. Fluorescence is registered by flow cytometry method. Spatial resolution is about a few nanometers for varied complex three-dimensional (3D) DNA nanostructures of all non-coding and coding parts of DNA. It’s shown that oxidative activity of all 3D DNA in the full set of chromosomes inside cells is defined by new standards for complex networks of “exponentially small worlds”, with more dense packing than in the well known networks of “small worlds”. Analysis of various blood samples in vivo and during medical treatment shown that only two classes of Good and Bad Networks of DNA for a good and a bad health existed. This division is defined by any network to one from two classes of “n” or “s” shaped curves for typical deviations and from straight line in perfect networks of “exponentially small worlds”, as for two types of hysteresis curves at phase transitions or at switching of bistability. These deviations coincide with two types of positive and negative trends of changing fractal dimension by changing the scales of multi-scale networks of fluorescing DNA. These trends give the overall assessments of human immunity, including hidden and unidentified diseases, and as a sum of all kinds of health and illness of given person, from the point of view the inner life of neutrophils, living in different parts of human body in given time. Characteristics of deviations associated with type, level and complexity of illness in the dependence on

DDoS Attack Detection Scheme Based on Entropy and PSO-BP Neural Network in SDN

SDN (Software Defined Network) has many security problems, and DDoS attack is undoubtedly the most serious harm to SDN architecture network. How to accurately and effectively detect DDoS attacks has always been a difficult point and focus of SDN security research. Based on the characteristics of SDN, a DDoS attack detection method combining generalized entropy and PSOBP neural network is proposed. The traffic is pre-detected by the generalized entropy method deployed on the switch, and the detection result is divided into normal and abnormal. Locate the switch that issued the abnormal alarm. The controller uses the PSO-BP neural network to detect whether a DDoS attack occurs by further extracting the flow features of the abnormal switch. Experiments show that compared with other methods, the detection accurate rate is guaranteed while the CPU load of the controller is reduced, and the detection capability is better.

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.

Stochastic Modeling and Power Control of Time-Varying Wireless Communication Networks

Wireless networks are characterized by nodes mobility, which makes the propagation environment time-varying and subject to fading. As a consequence, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) that varies from one observation instant to the next. This paper is concerned with dynamical modeling of time-varying wireless fading channels, their estimation and parameter identification, and optimal power control from received signal measurement data. The wireless channel is characterized using a stochastic state-space form and derived by approximating the time-varying DPSD of the channel. The expected maximization and Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Moreover, we investigate a centralized optimal power control algorithm based on predictable strategies and employing the estimated channel parameters and states. The proposed models together with the estimation and power control algorithms are tested using experimental measurement data and the results are presented.

Comparative efficacy and safety of cognitive enhancers for treating vascular cognitive impairment: systematic review and Bayesian network meta-analysis

Objective: To assess and compare the clinical efficacy and safety of cognitive enhancers(donepezil, galantamine, rivastigmine, and memantine) on cognition, behavior, function, and global status in patients with vascular cognitive impairment.Data sources: The initial literature search was performed with PubMed, EMBASE, the Cochrane Methodology Register, the Cochrane Central Register of Controlled Trials, and Cumulative Index to Nursing & Allied Health(CINAHL) from inception to January 2018 for studies regarding donepezil, galantamine, rivastigmine, and memantine for treatment of vascular cognitive impairment.Data selection: Randomized controlled trials on donepezil, galantamine, rivastigmine, and memantine as monotherapy in the treatment of vascular cognitive impairment were included. A Bayesian network meta-analysis was conducted. Outcome measures: Efficacy was assessed by changes in scores of the Alzheimer's Disease Assessment Scale, cognitive subscale, Mini-Mental State Examination, Neuropsychiatric Inventory scores and Clinician's Interview-Based Impression of Change Scale Plus Caregiver's Input, Activities of Daily Living, the Clinical Dementia Rating scale. Safety was evaluated by mortality, total adverse events(TAEs), serious adverse events(SAEs), nausea, vomiting. diarrhea, or cerebrovascular accidents(CVAs). Results: After screening 1717 citations, 12 randomized controlled trials were included. Donepezil and rivastigmine(mean difference(e) = –0.77, 95% confidence interval(CI): 0.25–1.32; MD = 1.05, 95% CI: 0.18–1.79) were significantly more effective than placebo in reducing Mini-Mental State Examination scores. Donepezil, galantamine, and memantine(MD = –1.30, 95% CI: –2.27 to –0.42; MD = –1.67, 95% CI: –3.36 to –0.06; MD = –2.27, 95% CI: –3.91 to –0.53) showed superior benefits on the Alzheimer's Disease Assessment Scale–cognitive scores compared with placebo. Memantine(MD = 2.71, 95% CI: 1.05–7.29) improved global status(Clinician's Interview-Based Impression of Change Scale Plus Caregiver's Input) more than the placebo. Safety results revealed that donepezil 10 mg(odds ratio(OR) = 3.04, 95% CI: 1.86–5.41) contributed to higer risk of adverse events than placebo. Galantamine(OR = 5.64, 95% CI: 1.31–26.71) increased the risk of nausea. Rivastigmine(OR = 16.80, 95% CI: 1.78–319.26) increased the risk of vomiting. No agents displayed a significant risk of serious adverse events, mortality, cerebrovascular accidents, or diarrhea.Conclusion: We found significant efficacy of donepezil, galantamine, and memantine on cognition. Memantine can provide significant efficacy in global status. They are all safe and well tolerated.

Flatness predictive model based on T-S cloud reasoning network implemented by DSP

The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.

Performance Analysis of Hybrid MAC Protocol for Cognitive Radio Networks

The rapid growth in demand for broadband wireless services coupled with the recent developmental work on wireless communications technology and the static allocation of the spectrum have led to the artificial scarcity of the radio spectrum. The traditional command and control model (Static allocation) of spectrum allocation policy allows for severe spectrum underutilization. Spectrum allocated to TV operators can potentially be shared by wireless data services, either when the primary service is switched off or by exploiting spatial reuse opportunities. This paper describes a hybrid access scheme based on CSMA/CA and TDMA MAC protocols for use in the TV bands. The approach allows secondary users (SU) to operate in the presence of the primary users (PU) and the OPNET simulation and modelling software has been used to model the performance of the scheme. An analysis of the results shows that, the proposed schemes protect the primary user from harmful Interference from the secondary user. In terms of delay, it was found that packet arrival rates, data rates and the number of secondary users have significant effects on delay.

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.