Network Applications Research Articles

A multi-queue-based ECN marking strategy for multi-class QoS guarantee in programmable networks

Currently, network applications are experiencing explosive growth, and various types of network applications are showing a trend of varied demands for quality of network service. However, the existing Explicit Congestion Notification (ECN) marking methods have not taken into account the diversified Quality of Service (QoS) requirements of network applications. This article introduces a multi-queue ECN marking strategy targeting multiple QoS guarantees. The strategy utilizes virtual queues and dynamic weighted round-robin scheduling to achieve traffic partitioning in a programmable data plane. It constructs a multi-queue, multi-class QoS queuing model based on the QoS requirements of different traffic and network conditions. The model is solved by real-time to obtain the ECN marking thresholds and round-robin weights for different queues, in order to achieve dynamic QoS requirements of different network applications. We implemented this strategy in Mininet and BMv2, and compared it with DCQCN, P4QCN, and TCN. The experimental results indicate that this policy demonstrates good performance in terms of queue length, RTT, and throughput, while also ensuring fairness between traffics. Results of the experiment indicate that the proposed approach is superior to DCQCN and P4QCN in the field of performance fluctuation and rapid feedback, and it exhibits notable advantages over TCN, and also ensures the fairness of traffic.

Predicting Work-in-Process in Semiconductor Packaging Using Neural Networks: Technical Evaluation and Future Applications

This review paper focuses on the application of neural networks in semiconductor packaging, particularly examining how the Back Propagation Neural Network (BPNN) model predicts the work-in-process (WIP) arrival rates at various stages of semiconductor packaging processes. Our study demonstrates that BPNN models effectively forecast WIP quantities at each processing step, aiding production planners in optimizing machine allocation and thus reducing product manufacturing cycles. This paper further explores the potential applications of neural networks in enhancing production efficiency, forecasting capabilities, and process optimization within the semiconductor industry. We discuss the integration of real-time data from manufacturing systems with neural network models to enable more accurate and dynamic production planning. Looking ahead, this paper outlines prospective advancements in neural network applications for semiconductor packaging, emphasizing their role in addressing the challenges of rapidly changing market demands and technological innovations. This review not only underscores the practical implementations of neural networks but also highlights future directions for leveraging these technologies to maintain competitiveness in the fast-evolving semiconductor industry.

Advanced Beamforming and Multi‐Access Edge Computing: Empowering Ultra‐Reliable and Low‐Latency Applications in 6G Networks

ABSTRACT6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. Multi‐Access Edge Computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource‐intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC‐enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.

Artificial Neural Network Application to Permeability Prediction from Nuclear Magnetic Resonance Log

Artificial Neural Network Application to Permeability Prediction from Nuclear Magnetic Resonance Log

A survey on feature extraction and learning techniques for link prediction in homogeneous and heterogeneous complex networks

Complex networks are commonly observed in several real-world areas, such as social, biological, and technical systems, where they exhibit complicated patterns of connectedness and organised clusters. These networks have intricate topological characteristics that frequently elude conventional characterization. Link prediction in complex networks, like data flow in telecommunications networks, protein interactions in biological systems, and social media interactions on platforms like Facebook, etc., is an essential element of network analytics and presents fresh research challenges. Consequently, there is a growing emphasis in research on creating new link prediction methods for different network applications. This survey investigates several strategies related to link prediction, ranging from feature extraction based to feature learning based techniques, with a specific focus on their utilisation in dynamic and developing network topologies. Furthermore, this paper emphasises on a wide variety of feature learning techniques that go beyond basic feature extraction and matrix factorization. It includes advanced learning-based algorithms and neural network techniques specifically designed for link prediction. The study also presents evaluation results of different link prediction techniques on homogeneous and heterogeneous network datasets, and provides a thorough examination of existing methods and potential areas for further investigation.

Integrating an AI-powered symptom assessment application (SAA) in a hospital network in Portugal

Abstract Introduction Patients often struggle to understand medical information and healthcare worker shortage strains health systems. Integrating SAAs that guide patients and provide diagnostic decision support (DDS) to doctors can help. This study evaluates the impact of the SAA ‘Ada’, a class IIa medical device, after its integration into the patient portal of a Portuguese hospital network, CUF, on clinical workflow and patient health. Methods This on-going post-market quality improvement study, started in Nov 2023 and has enrolled 664 participants. Participating doctors complete a baseline survey. Consenting patients conduct symptom assessments and answer survey questions within the app. The patient’s symptom report is shared with their physician, who then completes a post-consultation survey. Results After using Ada, 86% of patients could enter all their symptoms, 79% felt more prepared for their consultation, and 39% felt less health anxiety (N = 664). In the baseline survey, most doctors utilize online resources (96%) in the absence of a symptom checker, followed by discussions with colleagues (65%) for challenging cases (N = 26). In the post-consultation survey, 95% of doctors found Ada’s condition suggestions reasonable, with only 10% disagreeing with its triage classification. Doctors reported time saving and increased consultation efficiency (64%), and 59% felt more confidence in their diagnosis. A majority said that Ada provided them with additional information about patients (77%). Ada also improved consultation efficiency (74%), preparation (78%), patient engagement (82%), and health knowledge (77%; N = 22). Conclusions By providing personalized health information, Ada prepares patients for their consultation, resulting in improved patient engagement and facilitation of clinical conversation. Incorporating a SAA into routine patient care enhances quality of consultation, reduces consultation time, increases efficiency, and provides DDS to doctors. Key messages • Digital health enhances patient engagement, clinical efficiency, and diagnostic decision support for doctors in healthcare settings. • Ada’s personalized health information empowers patients, improves consultations, and reduces health-related anxiety.

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.

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.

Heterometallic [2]Catenane-Crosslinked Supramolecular Networks with Improved Antibacterial Activity.

The construction of supramolecular networks with novel crosslinks is of great significance in expanding their chemical structures and exploring their advanced functions. Herein, we prepare a type of [2]catenane-cored supramolecular networks based on the crosslinking of polyethylene glycol (PEG) using a heterometallic [2]catenane unit. By adjusting the molecular weight of PEG, the solubility of the networks can be tuned and gels are formed using low molecular weight PEG. The introduction of heterometallic [2]catenane offers the networks good antibacterial properties owing to the synergistic antimicrobial activity of Pt(II) and Cu(I) ions in the [2]catenane. This study provides a simple and efficient strategy for constructing supramolecular networks with topological crosslinks as antibacterial materials, which will promote the structural design and biological applications of supramolecular networks.

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.