Scalable Network Research Articles

Exploration of hydrogen technology diffusion and network characteristics across multiple channels

This study investigates the diffusion and network structure of hydrogen technology from 1980 to 2022. Hydrogen fuel cell technology has the shortest diffusion cycle and high global acceptance. Production technology shows significant global demand, particularly within regional patent families. In contrast, hydrogen storage and fuel cell technologies have more concentrated diffusion, indicating higher adaptation thresholds. Japanese firms are crucial in spreading storage and production technologies, while U.S. enhances diffusion through licensing and transfer. Hydrogen refrigeration technology exhibits a more dispersed bidirectional network. Hydrogen production and fuel cell technologies demonstrate notable activity and network scale in the global market. This research provides valuable insights into the global development of hydrogen technology.

DC near‐area voltage stability constrained renewable energy integration for regional power grids

AbstractWith the increasing deployment of renewable energy resources, the scale of DC networks and renewable capacity continues to grow. System security is challenged by the decrease in inertia from traditional synchronous generators. In order to accommodate high‐penetration renewable energy, voltage stability should be considered in the renewable energy integration planning. Herein, first, a voltage stability‐constrained minimum startup index and algorithm for conventional thermal power plants are proposed. Then, based on time series production simulation, a renewable energy integration capacity analysis algorithm is designed considering voltage stability and peak shaving constraints. Finally, based on the boundary conditions of the “Fifteen‐Five Plan”, the renewable energy capacity considering voltage stability and peak shaving constraints for Northwest and East China power grids are analysed to verify the effectiveness and engineering practicality of the proposed methodology. The results demonstrate that the proposed method can maintain the renewable energy integration goal outlined in the “Fifteen‐Five Plan” while maintaining the voltage stability in these regions.

Manufacture and testing of biomass-derivable thermosets for wind blade recycling.

Wind energy is helping to decarbonize the electrical grid, but wind blades are not recyclable, and current end-of-life management strategies are not sustainable. To address the material recyclability challenges in sustainable energy infrastructure, we introduce scalable biomass-derivable polyester covalent adaptable networks and corresponding fiber-reinforced composites for recyclable wind blade fabrication. Through experimental and computational studies, including vacuum-assisted resin-transfer molding of a 9-meter wind blade prototype, we demonstrate drop-in technological readiness of this material with existing manufacture techniques, superior properties relative to incumbent materials, and practical end-of-life chemical recyclability. Most notable is the counterintuitive creep suppression, outperforming industry state-of-the-art thermosets despite the dynamic cross-link topology. Overall, this report details the many facets of wind blade manufacture, encompassing chemistry, engineering, safety, mechanical analyses, weathering, and chemical recyclability, enabling a realistic path toward biomass-derivable, recyclable wind blades.

Impact of dispersion-shifted fiber on optical communications link through orthogonal channels

Dispersion-Shifted Fiber (DSF) is essential for reducing chromatic dispersion in high-speed optical communication systems. This study investigates the influence of Four Wave Mixing (FWM) on the quality of signals in orthogonal channels. We examine the advantages of DSF technology and analyze the impact of modulation formats such as On-Off Keying with Return-to-Zero (OOK-RZ) and Duo Binary Modulation class-1 (DBM-1) on transmission performance at different distances. This research assesses the efficacy of orthogonal channels in mitigating four-wave mixing (FWM) effects and improving the overall performance of eight-channel systems at distances of 100 km and 200 km through computer simulations. The results of our study show notable enhancements, namely in optimizing the Q-factor (a metric for signal quality) and reducing bit error rates when employing orthogonal channels compared to previous work. By integrating orthogonal channels with OOK-RZ modulation, we achieved higher performance and reduced nonlinear impairments in a simulated eight-channel system with 50 GHz spacing and 80 Gb/s data rates. This effect was particularly pronounced at high input power levels. At an input power of 20 dBm and a distance of 200 km, this particular combination yielded a maximum Q-factor of 27.25 and a minimum FWM power of −54 dBm. In comparison, under the same conditions, the use of OOK-RZ alone resulted in an FWM power of −24 dBm and a Q-factor of only 1.63. This research provides vital insights into enhancing the efficiency and dependability of optical communication systems, hence facilitating breakthroughs in high-speed data transfer and network scalability.

Exploring Scalability of BFT Blockchain Protocols through Network Simulations

Novel Byzantine fault-tolerant (BFT) state machine replication protocols improve scalability for their practical use in distributed ledger technology, where hundreds of replicas must reach consensus. Evaluating the performance of BFT protocol implementations requires careful evaluation. We propose a new methodology using scalable network simulations to predict BFT protocol performance. Our simulation architecture allows for the integration of existing BFT implementations without modification or re-implementation, offering a cost-effective alternative to large-scale cloud experiments. We validate our method by comparing simulation results with real-world cloud deployments, showing that simulations can accurately predict performance at larger scales when network limitations dominate. In our study, we applied this methodology to assess the performance of several “blockchain-generation” BFT protocols, including HotStuff, Kauri, Narwhal & Tusk, and Bullshark, under realistic network conditions (with constrained 25 Mbit/s bandwidth) and induced faults. Kauri emerges as the top performer, achieving 6742 operations per second (op/s) with 128 replicas, outperforming BullShark (2318 op/s) and Tusk (1952 op/s). HotStuff, using secp256k1 and BLS signatures, reaches 494 op/s and 707 op/s, respectively, demonstrating the efficiency of BLS-signature aggregation for saving bandwidth. This study demonstrates that state-of-the-art asynchronous BFT protocols can achieve competitive throughput in large-scale, real-world scenarios.

Modulation of electron transfer behavior on Fe2P-Co2P/NPC oxygen electrocatalyst by lattice cation substitution engineering and charge transport network design for rechargeable Zn-air batteries

Crystal structure modulation engineering on the lattice scale and charge transport network design on the micro/nano scale work together to endow oxygen electrocatalysts with high reactivity to fully release the capacity of Zn-air batteries (ZABs) as the next-generation green energy storage devices. Metal phosphide-based oxygen reduction reaction (ORR)/oxygen evolution reaction (OER) electrocatalysts with tunable electronic structures, variable valence states, and moderate overpotentials are recognized as the most promising and ideal candidates for air cathodes. The cation substitution strategy overcomes the drawback that single metal phosphides with fewer active sites and high adsorption barriers are not sufficient to achieve satisfactory electrocatalytic performance. In this paper, the Fe2P-Co2P/NPC oxygen electrocatalyst employing lattice cation site substitution engineering and three-dimensional (3D) conductive network encapsulation strategies are constructed to successfully ensure fast electron transfer behavior and safe charge/discharge energy storage. Noteworthy, the presence of Schottky barriers at the interface of the two materials hinders the electron reflux, which promotes a unidirectional flow of electrons, and increases the electron condensation in the OER and ORR processes. Relying on these features, the Fe2P-Co2P/NPC-assembled liquid ZABs achieved power densities, specific capacities, and energy densities of 175.18 mW cm-2, 805.75 mAh gZn-1, and 958.84 Wh kg-1, respectively, and importantly good rate performances and stability were exhibited even under high-current-density operation. The corresponding flexible solid-state ZABs also possesses superior electrochemical activity and mechanical flexibility. This work paves the way for the construction of bifunctional oxygen electrocatalysts with high-speed electron transfer channels, thus advancing the development of wearable electronic devices.

Variational tensor neural networks for deep learning

Deep neural networks (NNs) encounter scalability limitations when confronted with a vast array of neurons, thereby constraining their achievable network depth. To address this challenge, we propose an integration of tensor networks (TN) into NN frameworks, combined with a variational DMRG-inspired training technique. This in turn, results in a scalable tensor neural network (TNN) architecture capable of efficient training over a large parameter space. Our variational algorithm utilizes a local gradient-descent technique, enabling manual or automatic computation of tensor gradients, facilitating design of hybrid TNN models with combined dense and tensor layers. Our training algorithm further provides insight on the entanglement structure of the tensorized trainable weights and correlation among the model parameters. We validate the accuracy and efficiency of our method by designing TNN models and providing benchmark results for linear and non-linear regressions, data classification and image recognition on MNIST handwritten digits.

Correlation between social network and mental health in family-oriented older migrants: A study in two cities in China

BackgroundFamily-oriented mobility has become increasingly prevalent in recent years, leading to the emergence of a new demographic: older migrants. These individuals, who are often accompanied by their family members, relocate to another place of residence while retaining their household registration in their original location. The existing literature has largely neglected the social perspective on the mental health of this population group. MethodsThis study recruited participants from five communities in two cities and employed the Geriatric Social Network Scale, the Self-Rating Anxiety Scale, and the Geriatric Depression Scale to assess their mental health and social networks. ResultsAmong the 317 qualified participants, 31.23 % reported experiencing anxiety and 15.77 % reported experiencing depression. The mean social network score was 3.83 ± 0.54 points. A negative correlation was observed between social network scores and both anxiety (r = −0.272, P < 0.001) and depression (r = −0.338, P < 0.001). Two distinct types of social networks were identified through latent profile analysis, with a low level of social support and a low-level social network being significantly correlated with depression (P < 0.001). ConclusionsMental health issues are prevalent among family-oriented older migrants. A medium-level social network was found to be negatively correlated with anxiety and depression. The presence of a low-level social network was found to be associated with depression. It is imperative to prioritize the mental health status of family-oriented older migrants. Collaboration between community nurses and social workers should be encouraged to aid in exploring more effective ways of promoting the mental health status and social networks of this population.

Effects of Social Isolation and Loneliness on Heart Failure Self-care: A Cross-sectional Analysis.

Patients with heart failure (HF) who engage in effective HF self-care have better quality of life, and lower risks of all-cause and HF-related hospital readmission and mortality. It is unclear whether social isolation and loneliness, which are prevalent among patients with HF and known to affect other self-care behaviors, can predict HF self-care. The aim was to explore the relationship between social isolation, loneliness, and HF self-care. This was a cross-sectional secondary analysis (n = 49) of the GEtting iNTo Light Exercise for HF randomized controlled trial, a 6-month home-based live group gentle exercise intervention for patients with HF. Measures included the following: 6-item Lubben Social Network Scale for social isolation, Patient-Reported Outcomes Measurement Information System Social Isolation survey for loneliness, Self-Care of Heart Failure Index, and Patient-Reported Outcomes Measurement Information System Depression survey. Multiple linear regression modeling was used to examine the relationships of 4 HF self-care processes to social isolation and loneliness, adjusting for depression and grouping (control group or intervention group). Scores indicating less social isolation predicted higher self-care maintenance (B = 0.937, P = .015), monitoring (B = 0.799, P = .041), and management (B = 1.812, P < .001). Loneliness did not predict HF self-care. To our knowledge, this is the first study to predict HF self-care using distinct measures for social isolation and loneliness. Patients who were less socially isolated engaged in better HF self-care; loneliness had no relationship with HF self-care. Prospective studies are needed to investigate causal relationships between social isolation and HF-self-care engagement to determine the effect on outcomes such as hospital readmission and mortality.

Use of information and communication technologies by elderly people: analysis of social support in interpersonal relationships

The present research aimed to identify the use of Information and Communication Technologies (ICT) by elderly people, as well as analyze the level of social support for this population. Study with a quantitative-qualitative approach, cross-sectional and analytical in nature. Sociodemographic and health data collection instruments were used, Technology Use Acceptance Scale, questionnaire on the use of ICT, Lubben Brief Social Network Scale. We observed that 100% of participants have contact with some type of information and communication technology and 100% of participants also agree with the importance of technologies in interpersonal relationships. The population interviewed demonstrated a good affinity in general with technologies, where communicating via the internet, for example, with friends, family and even enabling new meetings. The study identified relevant results for the elderly's social interaction with technology and also regarding the social and support network. The importance of using communication technologies in matters of supporting personal and social life is highlighted, which has led to some elderly people frequently using social networks such as Whatsapp and Facebook.

Fully Scalable Fuzzy Neural Network for Data Processing.

The primary objective of the research presented in this article is to introduce an artificial neural network that demands less computational power than a conventional deep neural network. The development of this ANN was achieved through the application of Ordered Fuzzy Numbers (OFNs). In the context of Industry 4.0, there are numerous applications where this solution could be utilized for data processing. It allows the deployment of Artificial Intelligence at the network edge on small devices, eliminating the need to transfer large amounts of data to a cloud server for analysis. Such networks will be easier to implement in small-scale solutions, like those for the Internet of Things, in the future. This paper presents test results where a real system was monitored, and anomalies were detected and predicted.

Downscaling soil moisture in regions with high soil heterogeneity: the solution based on ensemble learning with sequential and parallel learner

Soil moisture plays an important role in the water and heat exchanges between the land surface and atmosphere, and it has great importance for agricultural production, ecological planning, and water resources management. Although microwave remote sensing has been widely used in large-scale soil moisture monitoring, the accuracy of the downscaled retrieval results cannot be guaranteed for regions with high vegetation coverage and high soil heterogeneity. To address these challenges, this study built soil moisture indice set based on MODIS and elevation data by calculating the Pearson correlation coefficient (R) and Maximum Information Coefficient (MIC), then constructed decision tree models (Gradient Boosting Decision Tree and Random Forest) about the indice set and low-resolution Soil Moisture Active Passive (SMAP) by using two ensemble learning methods (Bagging and Boosting). The models were applied to the high-resolution soil moisture indices in Jilin Province for the years 2017 to 2020 to generate 1 km-resolution products. In the validation process, Triple Collocation Analysis (TCA), comparison of soil moisture maps with coarse and fine resolution, and in-situ measurements in Lishu County, Tongyu County, and Jilin City were used to evaluate the differences between downscaling soil moisture results and ground observations at network, seasonal and point scales. The results were as follows: (1) The correlation coefficient (R2) calculated by the TCA method was 0.733 (GBDT_36km) > 0.649 (RF_36km), and the error variance was 0.0004 (GBDT_36km) < 0.00058 (RF_36km). (2) R at network scale was 0.798 (GBDT_SM) > 0.662 (RF_SM), RMSE was 0.040 (GBDT_SM) < 0.044 (RF_SM), the point scale R was 0.864 (GBDT_SM) > 0.833 (RF_SM), RMSE was 0.029 (GBDT_SM) < 0.039 (RF_SM). The R in four stages of the growth period was GBDT_SM > RF_SM, RMSE was GBDT_SM < RF_SM. In conclusion, the GBDT and RF models can reliably downscale soil moisture in Jilin Province, and the Boosting ensemble learning method represented by GBDT had a better estimation performance.

Einsamkeit bei Arbeitslosen mit psychischen Erkrankungen

ZusammenfassungHintergrundEinsamkeit ist ein weitverbreitetes Phänomen und steht in Zusammenhang mit gesundheitlichen Beeinträchtigungen. Dabei stellen Arbeitslose mit psychischen Erkrankungen (ALPE) eine Hochrisikogruppe in Hinblick auf Gesundheit und berufliche Wiedereingliederung dar. Ziel dieser Studie ist ein besseres Verständnis der Zusammenhänge zwischen Soziodemografie, psychischer Gesundheit und Einsamkeit bei ALPE.MethodenFür die vorliegende Studie wurden die Fragebögen von 526 arbeitslosen Personen im ALG-2-Bezug und mit mindestens einer psychiatrischen Diagnose aus dem Erhebungszeitraum 09/2020–09/2023 ausgewertet. Es wurden 2 Regressionsanalysen mit robusten Schätzern und der Outcome-Variable Einsamkeit (University of California, Los Angeles, Loneliness Scale, UCLA) durchgeführt. Bei der ersten Regression wurde der alleinige Prädiktor soziales Netzwerk (Lubben Social Network Scale, LSNS-6) verwendet, bei der zweiten wurden Schulden (Ja/Nein), Depression (Patient Health Questionnaire, PHQ-9), Angst (Mini-Symptom-Checkliste, MSCL) und Somatisierung (MSCL) ergänzt sowie Alter, Geschlecht, Bildung und Zusammenleben mit Partner als Kontrollvariablen.ErgebnisseEs zeigen sich ein signifikant negativer Zusammenhang zwischen sozialem Netzwerk und Einsamkeit sowie ein signifikant positiver Zusammenhang zwischen hoher Bildung, Depression, Angst und Einsamkeit.DiskussionBei den ALPE fanden sich im Vergleich zu anderen Studien hohe Belastungen durch Depression, Angst, Somatisierung und Einsamkeit. Die identifizierten Zusammenhänge zwischen sozialem Netzwerk, psychischer Gesundheit und Einsamkeit verdeutlichen die Wichtigkeit von psychologischen Screeningverfahren und/oder Diagnostik in dieser Hochrisikogruppe und bieten Ansatzpunkte für die Prävention.

Load-balanced Routing Heuristics for Bandwidth Allocation of AVB Flow in TSN

Time-Sensitive Networking (TSN) is a new technology developed from Ethernet that guarantees deterministic transmission of various types of flows, such as Time-triggered (TT) flows and Audio-video-bridging (AVB) flows, in the same network. Currently, Time-aware Shaping (TAS) and Credit-based Shaping (CBS) are widely used for scheduling hybrid flows, where the credit value in CBS is directly linked to the logical bandwidth value idleSlope , which affects the real-time performance of AVB flows. However, as the network scale increases, existing bandwidth allocation methods result in higher computation times and lower allocation success rates. In this paper, the aforementioned problem is addressed by two-step: first, we design a load-balanced routing heuristic (LBRH) to improve the allocation success rate; second, we accelerate the CBS bandwidth allocation by using unified bandwidth allocation scheme, which integrates LBRH and further improves the allocation success rate. Performance evaluation in multiple test cases shows that LBRH can improve the success rate of bandwidth allocation, and the unified bandwidth allocation scheme integrating LBRH significantly reduces the overall execution time while improving the success rate of bandwidth allocation, which is more suitable for large-scale TSN networks with complex routing.

ScTab: Scaling cross-tissue single-cell annotation models

Identifying cellular identities is a key use case in single-cell transcriptomics. While machine learning has been leveraged to automate cell annotation predictions for some time, there has been little progress in scaling neural networks to large data sets and in constructing models that generalize well across diverse tissues. Here, we propose scTab, an automated cell type prediction model specific to tabular data, and train it using a novel data augmentation scheme across a large corpus of single-cell RNA-seq observations (22.2 million cells). In this context, we show that cross-tissue annotation requires nonlinear models and that the performance of scTab scales both in terms of training dataset size and model size. Additionally, we show that the proposed data augmentation schema improves model generalization. In summary, we introduce a de novo cell type prediction model for single-cell RNA-seq data that can be trained across a large-scale collection of curated datasets and demonstrate the benefits of using deep learning methods in this paradigm.

“Separated, but far from alone”

Our article examines how women built an expansive community during the 1970s and 1980s through the pages of Lesbian Connection, one of the era’s most widely circulated publications of the lesbian feminist movement. To reveal this community, we map thousands of locations that appeared in the periodical, revealing a geography that tilted toward rural areas and mid-sized cities instead of the major metropolitan centers that have received much of the attention within LGBTQ scholarship. Through the magazine’s reader-submitted content, advertisements for lesbian-owned businesses, and a directory of volunteers who served as local contacts for an area, Lesbian Connection bridged the spatial scales of long-distance media networks and in-person interactions to create a vibrant lesbian community that transcended region and locality.

Calcium Carbonate Formation in Groundwater-Supplied Drinking Water Systems: Role of CO2 Degassing Rate and Scaling Indices Applicability

CaCO3 precipitation is a ubiquitous and vital process with far-reaching implications for various natural systems. In drinking water supply networks, it creates malfunctions in the system, especially by pipes clogging. This is a common problem in Tunisia, particularly for systems supplied with groundwater. This work attempts to highlight the effect of dissolved CO2 degassing kinetics and determine the most reliable scaling index to predict scaling. For this, a diagnosis of two drinking water circuits is followed by a laboratory study. Results of the field study show that the network scaling is controlled by the dissolved CO2 content, which is significantly affected by the water/atmospheric air contact. The scale formed is mainly CaCO3–calcite. A laboratory-scale simulation of the natural phenomenon using an experimental setup of the fast-controlled precipitation method (FCP) was performed. The result shows that a low CO2 content is a necessary condition for a supersaturated system regarding calcite but not sufficient for precipitation to take place. The precipitation can occur at very low supersaturations if time is allowed for stable nuclei to form, explaining the scaling of drinking water networks. The fundamental and applied study of the scaling indices shows that the Ryznar stability index (RSI) is the most adaptable index for predicting scale formation.

Automatic response framework for large complex natural gas pipeline operation optimization based on data-mechanism hybrid-driven

In recent years, the scale of gas transmission networks has been continuously expanding, and the operational conditions are becoming increasingly complex. This poses higher requirements for the centralized control of the pipeline network operation. Relying solely on the experience of scheduling personnel may not comprehensively address the operational issues within the network. There is an urgent need for efficient automatic response methods (ARM) to assist in formulating operation schemes and ensuring the safe and stable operation of the pipeline network. Therefore, this paper proposes an automatic response framework of large complex natural gas pipeline operation optimization based on data-mechanism coupling to provide operation schemes that ensure safety, stability, and economic benefits. First, two ARM is proposed, namely the Data-Based ARM using operation scheme database, and the Opti-Model ARM based on optimization modeling. Subsequently, the rapid response features of Data-Based ARM and the optimal response characteristics of Opti-Model ARM are combined to establish the Integrated ARM. Finally, these three ARMs are compared and analyzed through a regional natural gas pipeline network in China. The result indicates that the Data-Based ARM can quickly produce a variety of matched solutions but cannot ensure economic optimality. Response solutions obtained through Opti-Model ARM reduce the compressor energy costs by 4.6–10.8 % compared to on-site operational schemes, but they take longer in response time. In contrast, the economic attributes of solutions derived from the Integrated ARM are on par with Opti-Model ARM, but with a 58.7 % improvement in response speed. The proposed Integrated ARM can swiftly and accurately offer economically viable operation schemes tailored to the varying needs of pipeline operators, which can help to address the energy demand challenges of the future, fostering sustainable development.

Enabling Seamless Connectivity: Networking Innovations in Wireless Sensor Networks for Industrial Application.

The wide-ranging applications of the Internet of Things (IoT) show that it has the potential to revolutionise industry, improve daily life, and overcome global challenges. This study aims to evaluate the performance scalability of mature industrial wireless sensor networks (IWSNs). A new classification approach for IoT in the industrial sector is proposed based on multiple factors and we introduce the integration of 6LoWPAN (IPv6 over low-power wireless personal area networks), message queuing telemetry transport for sensor networks (MQTT-SN), and ContikiMAC protocols for sensor nodes in an industrial IoT system to improve energy-efficient connectivity. The Contiki COOJA WSN simulator was applied to model and simulate the performance of the protocols in two static and moving scenarios and evaluate the proposed novelty detection system (NDS) for network intrusions in order to identify certain events in real time for realistic dataset analysis. The simulation results show that our method is an essential measure in determining the number of transmissions required to achieve a certain reliability target in an IWSNs. Despite the growing demand for low-power operation, deterministic communication, and end-to-end reliability, our methodology of an innovative sensor design using selective surface activation induced by laser (SSAIL) technology was developed and deployed in the FTMC premises to demonstrate its long-term functionality and reliability. The proposed framework was experimentally validated and tested through simulations to demonstrate the applicability and suitability of the proposed approach. The energy efficiency in the optimised WSN was increased by 50%, battery life was extended by 350%, duplicated packets were reduced by 80%, data collisions were reduced by 80%, and it was shown that the proposed methodology and tools could be used effectively in the development of telemetry node networks in new industrial projects in order to detect events and breaches in IoT networks accurately. The energy consumption of the developed sensor nodes was measured. Overall, this study performed a comprehensive assessment of the challenges of industrial processes, such as the reliability and stability of telemetry channels, the energy efficiency of autonomous nodes, and the minimisation of duplicate information transmission in IWSNs.

Analysis of support vector machine and random forest models for predicting the scalability of a broadband network

Analysis of support vector machine and random forest models for predicting the scalability of a broadband network