Network Applications Research Articles

Tree Sequences as a General-Purpose Tool for Population Genetic Inference.

As population genetic data increase in size, new methods have been developed to store genetic information in efficient ways, such as tree sequences. These data structures are computationally and storage efficient but are not interchangeable with existing data structures used for many population genetic inference methodologies such as the use of convolutional neural networks applied to population genetic alignments. To better utilize these new data structures, we propose and implement a graph convolutional network to directly learn from tree sequence topology and node data, allowing for the use of neural network applications without an intermediate step of converting tree sequences to population genetic alignment format. We then compare our approach to standard convolutional neural network approaches on a set of previously defined benchmarking tasks including recombination rate estimation, positive selection detection, introgression detection, and demographic model parameter inference. We show that tree sequences can be directly learned from using a graph convolutional network approach and can be used to perform well on these common population genetic inference tasks with accuracies roughly matching or even exceeding that of a convolutional neural network-based method. As tree sequences become more widely used in population genetic research, we foresee developments and optimizations of this work to provide a foundation for population genetic inference moving forward.

Modulation of Phase-Locking Characteristics of NbOx Memristor by Ag Doping.

Memristors based on niobium oxide have attracted wide interest due to their applications in artificial neural network. Phase-locking (PL) characteristics of NbOx newly explored roots in complex nonlinear dynamics and exhibits great potential in periodical signal handling because of its bioinspired nature. In this study, we fabricate a Pd/Nb/Ag-NbOx/Nb/Pd memristive structure and study the effects of Ag doping on the PL characteristics. We clearly see that the NbOx memristor responds to signal in three distinct patterns. For the low-frequency input, the memristor fires spike series and locks the phase near during the positive period of sinusoidal input, and it encodes the input features by the spike number per period. For the middle-frequency input, the memristor fires one spike per period at a definite phase. The output has the same frequency as the input. The locked phase is proportional to the input frequency. For the high-frequency input, the memristor transforms high frequency to low frequency signal and locks at a definite phase higher than 2π. We combined the microstructural analysis and chaos dynamics to know that Ag doping will lower the activation energy, enhance the responding rate, and enhance thermal conductivity, which extends the locked frequency to 1.7 times the value of the undoped NbOx memristor. We also obtained the locked frequency of even 40 MHz after modulating the elementary circuit configuration according to the material modification and chaos model. Our study proposes to handle a signal with high efficiency resembling auditory sense and inspires a novel artificial intelligent computation prototype.

Radiation analysis of optimized wearable antenna sensor at 2.4GHz on human body for Wireless Body Area Network Applications

Radiation analysis of optimized wearable antenna sensor at 2.4GHz on human body for Wireless Body Area Network Applications

Nonvolatile reconfigurable polarization rotator at datacom wavelengths based on a Sb2Se3/Si waveguide.

Silicon photonics has become a key platform for photonic integrated circuits (PICs) due to its high refractive index and compatibility with complementary metal-oxide-semiconductor manufacturing. However, the inherent birefringence in silicon waveguides requires efficient polarization management. Here, we report a reconfigurable polarization rotator (PR) using a Sb2Se3/Si waveguide operating at datacom wavelengths (1310 nm), providing nonvolatile switching with zero static power consumption. The polarization conversion relies on the interference of hybrid electric-magnetic (EH) modes, which can be reconfigured by changing the Sb2Se3 state between amorphous and crystalline. Our experimental device exhibits a polarization conversion efficiency (PCE) and a polarization extinction ratio (PER) as high as -0.08 dB and 17.65 dB, respectively, in a compact footprint of just 21 µm length. Therefore, the proposed reconfigurable PR offers a compact and energy-efficient solution for polarization management in silicon photonics, with potential applications in data communication networks and emerging applications benefiting from polarization information encodings, such as optical neural networks and quantum computing.

Multi-Strategy Bald Eagle Search Algorithm Embedded Orthogonal Learning for Wireless Sensor Network (WSN) Coverage Optimization.

Coverage control is a fundamental and critical issue in plentiful wireless sensor network (WSN) applications. Aiming at the high-dimensional optimization problem of sensor node deployment and the complexity of the monitoring area, an orthogonal learning multi-strategy bald eagle search (OLMBES) algorithm is proposed to optimize the location deployment of sensor nodes. This paper incorporates three kinds of strategies into the bald eagle search (BES) algorithm, including Lévy flight, quasi-reflection-based learning, and quadratic interpolation, which enhances the global exploration ability of the algorithm and accelerates the convergence speed. Furthermore, orthogonal learning is integrated into BES to improve the algorithm's robustness and premature convergence problem. By this way, population search information is fully utilized to generate a more superior position guidance vector, which helps the algorithm jump out of the local optimal solution. Simulation results on CEC2014 benchmark functions reveal that the optimization performance of the proposed approach is better than that of the existing method. On the WSN coverage optimization problem, the proposed method has greater network coverage ratio, node uniformity, and stronger optimization stability when compared to other state-of-the-art algorithms.

ICTS as Tools for Local Development: Santa Elena Province; A View from the SDGs

Introduction: Currently, there are social problems that affect humanity, one of them is the gap between knowledge of ICTs and their application in social networks for access to information in a timely manner in order to contribute to personal and professional development and contribution to the development of the community. The State provides opportunities based on programs, projects and strategies to direct aid and educate the population in the digital age. Objective: The research objective is to determine the way in which ICTs are used as tools for local - community development through the qualitative-quantitative approach to strengthen the development of the Amantes de Sumpa neighborhood in the Province of Santa Elena. Method: The methodological process applied in this topic is descriptive and deductive, as well as the types of documentaries, field; as well as the population that will be stratified in the neighborhood, which was investigated through the survey and the interview with the neighborhood leader. The reliability of the Likert-type instrument was also carried out by calculating Cronbach's Alpha, which was 0.833. Results and conclusions: The people participating in the research stated that it is important to develop care agreements, promote technological services, since access will allow improving the productive activities of entrepreneurs in the Amantes de Sumpa neighborhood, and promote an electronic commerce system that can be installed from public or community institutions in such a way that this serves for the permanent dissemination of the economic activities carried out by the neighborhood sector.

1 × 2 broadband polarization-independent beam splitter based on air annular photonic crystal designed by a particle swarm optimization algorithm

We propose a 1×2 broadband polarization-independent beam splitter based on air annular photonic crystals. The inner and outer radii of the air annular photonic crystals at the V-shaped waveguide side and corners can influence the transmittance and broadband performance. To improve the design efficiency and transmittance of the broadband splitter, particle swarm optimization was used to reverse-design the splitter. The performance of the designed splitter was studied, and the results show that the designed splitter has a wide bandwidth range of 1530–1565 nm. It exhibits a minimum transmittance of 84% and an average transmittance of 87.8% for TE polarization and a minimum transmittance of 81% and an average transmittance of 83% for TM polarization. The response times for both polarizations are below 0.7 ps. This device has promising applications in all-optical communication networks and photonic high-density integration.

Correction: A novel design of non-bending narrow wall slotted waveguide array antenna for X-band wireless network applications

Correction: A novel design of non-bending narrow wall slotted waveguide array antenna for X-band wireless network applications

Effect of shear rate, temperature, and salts on the viscosity and viscoelasticity of semi‐dilute and entangled xanthan gum

AbstractXanthan gum, a naturally‐occurring, high‐molecular‐weight, anionic polyelectrolyte, exhibits significant drag‐reducing properties at low concentrations in water, suggesting promising applications in geothermal networks. The flow behavior of xanthan solutions are explored, specifically at concentrations in the semi‐dilute (1000 ppm by mass) and entangled (4000 ppm) regimes as well as in both water and various salt solutions across a temperature range of 5–85°C. The viscosity and viscoelastic properties of xanthan solutions examine relationships between polyelectrolyte concentration, temperature, and salt concentration. Viscosity decreases by two orders of magnitude in 4000 ppm xanthan solutions in deionized water and with 50 mM NaCl (in the high salt limit) as the temperature increases from 5 to 85°C. Next, the addition of salts affects viscosity differently in the two concentration regimes. Specifically, at 25°C, the zero‐shear rate viscosity upon salt addition decreases by 63% for 1000 ppm solutions (from 0.54 to 0.2 Pa s) but increases by 280% for 4000 ppm solutions (from 28 to 107 Pa s). At 1000 ppm, salt ions cause chain collapse, reducing viscosity; At 4000 ppm, entanglements led to structural changes, resulting in higher viscosity. Furthermore, three salts commonly found in geothermal waters, namely NaCl, Na2CO3, and NaHCO3, exhibited similar changes in viscosity and shear thinning behavior in the entangled regime across temperatures from 5 to 85°C.

Demonstration of high-frequency self-pulsing oscillations in an active silicon micro-ring cavity

We experimentally investigated the self-pulsing (SP) oscillations induced by the thermo-optic, free carrier, and Kerr nonlinear effects in integrated active silicon microring resonators. We demonstrate high frequency self-pulsing oscillations (up to 30 MHz) by applying a few millivolts of reverse bias voltage to the PIN junction of the active cavity. We illustrate that the shape of those oscillations (i.e., frequency and duty cycle) can be controlled by adjusting the CW input power and applying a reverse bias voltage to the PIN junction for carrier removal. This controlling is important for synchronizing the cavity which is crucial for neural network applications. Furthermore, we utilize a mathematical model for visualizing the stability regions by numerically studying coupled mode theory in silicon microcavity under different conditions.

PMU-based voltage estimation and distributed generation effects in active distribution networks

The integration of distributed generation (DG) and phasor measuring units (PMUs) has significantly impacted electrical distribution networks. This study focuses on real-time control of renewable energy intermittency and strategic PMU deployment. By optimizing the placement of PMUs and DGs, the study aims to achieve several goals: maximize power loss reduction, increase DG penetration, and maintain acceptable voltage profiles. The recommended PMU-based approach incorporates the optimal number of DGs into the distribution network for load flow analysis. Testing this technique on 12-bus, 33-bus, and 69-bus networks yielded substantial gains. For example, power loss was 81.044 % lower in the 33-bus system and 79.256 % lower in the 69-bus test system compared to the base-case scenario. We also compared these results with other methods found in the literature. The developed algorithm is recommended for application in a real electrical power distribution network for more efficient integration of PMUs and new distributed generation units. Integrating PMUs with DG benefits active distribution network management, enabling proactive grid management and stability.

BPFNN Based Football Player Value Prediction by Introducing Historic Information

With extensively successful applications of artificial neural networks in many scenarios of classification and prediction, research related to the world most popular sport - football - has undoubtedly attracted researchers to carry out their works with aid of such powerful tools. Despite that most of these works are oriented to predict match results and game strategies, only very a few are designed for player value prediction, which is of great importance with the rapid growth of the football players’ transfer market. In this paper, we propose a BP feedforward neural network framework which integrates both the empirically determined variables and some historical information. Comparative ablation experiments on one dataset FINAL verified the necessity of some factors especially the highest_values which inspired the inclusion of available historical information into the framework. For further verifying the efficiency of integrating historical information, we embedded the value columns of FIFA17 to FIFA21 into the data of FIFA22. Comparative experiments showed an 11% improvement on the precision with the help of historical values, verifying the efficiency of this strategy. Experiments were also conducted for determining the choice of the number of layers, the ratio of the training set and test set, the learning rate, and the process of the missed information.

Robust Characterization of Photonic Integrated Circuits

AbstractPhotonic integrated circuits (PICs) offer ultra‐broad optical bandwidths that enable unprecedented data throughputs for signal processing applications. Dynamic reconfigurability enables compensation of fabrication flaws and fluctuating external environments, tuning for adaptive equalization and training of optical neural networks. The initial step in PIC reconfiguration entails measuring its dynamic performance, often described by its frequency response. While measuring the amplitude response is straightforward, e.g. using a tunable laser and optical power meter, measuring the phase response presents challenges due to various factors, including phase variations in test connections and instrumentation limitations. To address these challenges, a universal and robust characterization technique is proposed, which uses an on‐chip reference path coupled to the signal processing core (SPC), with a delay gap larger than the total delay across the signal processing paths. A Fourier transform of the chip's power response reveals the SPC's impulse response. The method is more robust against low reference‐path power and imprecise delays. Experiments using a finite‐impulse‐response (FIR) structure demonstrate rapid SPC training, overcoming thermal crosstalk and device imperfections. This approach offers a promising solution for PIC characterization, facilitating expedited physical parameter training for advanced applications in communications and optical neural networks.

Vibration-resonance chimeras in coupled excitable systems with heterogeneous aperiodic excitations

In this work, we present a novel chimera-like state known as the vibration-resonance chimera (VRC) state, which is induced by heterogeneous aperiodic (HA) excitations and emerges within the optimal range of amplitude for the HA excitations. We investigate the dynamic behaviors of the FitzHugh-Nagumo model in relation to HA excitations and coupling strength in parameter spaces, identifying specific regions where VRC states exist and analyzing the transition mechanisms between different dynamic behaviors. Using basin stability measures, we find that the mean period of HA excitations affects the multi-stable states of neural systems. Specifically, the model excited by HA excitations with a larger mean period is more likely to exhibit multi-stable states. Additionally, we explore the effects of two levels of heterogeneity in HA excitations, namely amplitude heterogeneity and period heterogeneity, on VRC states. Amplitude heterogeneity can suppress the occurrence of VRC states, whereas period heterogeneity can promote their generation. This work provides valuable insights into complex system dynamics, enhancing our understanding of chimera states in neuronal models and their potential applications in neural networks.

Design of Concurrent Dual Band Low Noise Amplifier for WLAN Applications

This paper presents a dual band LNA CMOS designed for a Wireless Local Area Network (WLAN) application using CMOS 0.13-µm Silterra technology. The proposed design incorporates forward body bias technique with cascode and notch filter configuration to achieve both low power consumption and high gain. The simulation results demonstrate a total power consumption of 2.39 mW at a low supply voltage of 0.5 V, with a gain of 16 dB at 2.4 GHz and 12 dB at 5.2 GHz. Additionally, input return loss of -12.3 dB and -14.2 dB with noise figures of 2.93 dBm and 4.23 dBm were obtained at 2.4 GHz and 5.2 GHz, respectively. Overall, this research contributes to the development of dual band LNA designs for WLAN applications.

Proposed Methods for Preventing Overfitting in Machine Learning and Deep Learning

The article discusses various approaches and methods to combat overfitting, which is a key problem in the field of artificial intelligence. Overfitting occurs when the model over-adapts to the training dataset, losing the ability to generalize to new data. The main causes and signs of overfitting are also discussed, including excessive complexity of models and limited data. The focus is on methods such as regularization, dropout, and the use of ensemble methods that can significantly reduce the risk of overfitting. These approaches are evaluated using examples from various fields of neural network applications, providing the reader with a comprehensive understanding of the problem and its solution methods.

Analysis and Implementation of Metamaterial-Inspired Microstrip Antenna for Wireless Applications

In this paper, a novel metamaterial-inspired microstrip antenna for wireless applications is proposed. The proposed design consists of a radiating path on top and a uniformly distributed split ring-shaped metamaterial structure on the ground. The presented antenna of 50´38 mm with a thickness of 1.6 mm is printed on FR4 substrate and resonates at 1.80 GHz. The design was fabricated and the measured results were found to be in accordance with the simulations. The goal is accomplished by loading uniformly distributed split ring-shaped metamaterial structures on the ground plane of this antenna. The results of the experiments show that using the metamaterial structure on the ground plane improved gain from 4.34 to 7.3 dB, efficiency from 5.94 to 7.8 dB compared to the conventional patch antenna. This introduction in the ground plane exhibits return loss up to –38 dB and modified the gain and directivity to 7.3 and 7.8 dB respectively. The presented antenna has 45 MHz bandwidth. The presented design is proven by simulated surface current, S parameter, VSWR, radiation pattern. We have also investigated the effect of substrate permittivity, split width, and inter-element spacing in a split ring-shaped metamaterial structure on return loss. This directive antenna is designed for the applications of wireless local area networks and other Internet of things-based applications.

Tree sequences as a general-purpose tool for population genetic inference.

As population genetics data increases in size new methods have been developed to store genetic information in efficient ways, such as tree sequences. These data structures are computationally and storage efficient, but are not interchangeable with existing data structures used for many population genetic inference methodologies such as the use of convolutional neural networks (CNNs) applied to population genetic alignments. To better utilize these new data structures we propose and implement a graph convolutional network (GCN) to directly learn from tree sequence topology and node data, allowing for the use of neural network applications without an intermediate step of converting tree sequences to population genetic alignment format. We then compare our approach to standard CNN approaches on a set of previously defined benchmarking tasks including recombination rate estimation, positive selection detection, introgression detection, and demographic model parameter inference. We show that tree sequences can be directly learned from using a GCN approach and can be used to perform well on these common population genetics inference tasks with accuracies roughly matching or even exceeding that of a CNN-based method. As tree sequences become more widely used in population genetics research we foresee developments and optimizations of this work to provide a foundation for population genetics inference moving forward.

A review of graph neural network applications in mechanics-related domains

Mechanics-related tasks often present unique challenges in achieving accurate geometric and physical representations, particularly for non-uniform structures. Graph neural networks (GNNs) have emerged as a promising tool to tackle these challenges by adeptly learning from graph data with irregular underlying structures. Consequently, recent years have witnessed a surge in complex mechanics-related applications inspired by the advancements of GNNs. Despite this process, there is a notable absence of a systematic review addressing the recent advancement of GNNs in solving mechanics-related tasks. To bridge this gap, this review article aims to provide an in-depth overview of the GNN applications in mechanics-related domains while identifying key challenges and outlining potential future research directions. In this review article, we begin by introducing the fundamental algorithms of GNNs that are widely employed in mechanics-related applications. We provide a concise explanation of their underlying principles to establish a solid understanding that will serve as a basis for exploring the applications of GNNs in mechanics-related domains. The scope of this paper is intended to cover the categorisation of literature into solid mechanics, fluid mechanics, and interdisciplinary mechanics-related domains, providing a comprehensive summary of graph representation methodologies, GNN architectures, and further discussions in their respective subdomains. Additionally, open data and source codes relevant to these applications are summarised for the convenience of future researchers. This article promotes an interdisciplinary integration of GNNs and mechanics and provides a guide for researchers interested in applying GNNs to solve complex mechanics-related tasks.

Comparison of Cloud Computing, SaaS, SOA and Microservices

A network application is an application that runs on a single host and facilitates communication with another application running on a different computer or host. Numerous network applications have been introduced, including cloud computing, SaaS, SOA, and microservices. However, these technologies have often been used interchangeably, leading to confusion among developers and researchers. Therefore, the objective of this study is to review and compare the available network applications, specifically cloud computing, Software as a Service (SaaS), Service Oriented Architecture (SOA), and microservices. This study conducts a comparative analysis by examining several important criteria such as definition, type, language, interoperability, characteristics, and examples. By providing clear and precise definitions for each technology, the study aims to clarify their distinctions and appropriate usage contexts. The findings successfully establish the correct terminology for each technology and offer detailed discussions on the specific platforms and situations where each is most suitable. This comprehensive review serves as a valuable resource for developers and researchers seeking to navigate the complexities of modern network applications.