Network Capacity Research Articles

Organic-inorganic hybrids cathode with Hydrogen Bonding Network for highly efficient zinc-ion batteries

Organic-inorganic hybridization has been explored to improve the electrochemical performance of electrodes for aqueous zinc-ion batteries. In this work, 4,4′-diamino-2,2′-bipyridine (DB) and 2,6-diaminoanthraquinone (DAAQ) intercalated vanadium pentoxide (HVO-DB and HVO-DAAQ) are prepared by pre-intercalation techniques. The incorporation of DAAQ and DB play a pivotal role in not merely enhancing the interlayer spacing of VOx slabs substantially, which consequently elevates the efficiency of ion diffusion processes, but also in reinforcing the overall structural stability through promoting the establishment of a robust hydrogen bond network. Moreover, the abundant CO redox-active sites present in DAAQ actively coordinate with Zn2+ during the insertion process, leading to a notable enhancement in specific capacity. HVO-DAAQ demonstrates an exceptional specific capacity of 395 mA h g−1 when charged at 0.1 A g−1, coupled with remarkable cycling stability, retaining 87.8 % of its capacity even after enduring 2000 cycles at a high rate of 5 A g−1, and superior rate performance. Ex-situ characterization reveals the exceptional reversibility of Zn2+ insertion, alongside the remarkable capacity of hydrogen bond networks to undergo disruption and subsequent reconstruction, showcasing a dynamic and resilient structural adaptability. Furthermore, this work provides insights into the synergistic energy storage mechanism in the organic-inorganic hybrids cathode for aqueous zinc-ion batteries.

Pneumonia detection on x-ray image using improved depthwise separable convolutional neural networks

A single neural network model cannot capture intricate and diverse features due to its ability to learn only a finite set of patterns from the data. Additionally, training and utilising a single model can be computationally demanding. Experts propose incorporating multiple neural network models to address these constraints to extract complementary attributes. Previous research has highlighted challenges network models face, including difficulties in effectively capturing highly detailed spatial features, redundancy in network structure parameters, and restricted generalisation capabilities. This study introduces an innovative neural network architecture that combines the Xception module with the inverse residue structure to tackle these issues. Considering this, the paper presents a model for detecting pneumonia in X-ray images employing an improved depthwise separable convolutional network. This network architecture integrates the inverse residual structure from the MobileNetV2 model, using the rectified linear unit (ReLU) non-linear activation function throughout the entire network. The experimental results show an impressive recognition rate with a test accuracy of 97.24% on the chest x-ray dataset. This method can extract more profound and abstract image features while mitigating overfitting issues and enhancing the network's generalisation capacity.

Development of Semi-Mountainous and Mountainous Areas: Design of Trail Paths, Optimal Spatial Distribution of Trail Facilities, and Trail Ranking via MCDM-VIKOR Method

This study proposes a sustainable ecotourism framework for the development of semi-mountain and mountain regions of Paiko in Greece, focusing on the strategic design and ranking of trail paths using the multi-criteria decision-making (MCDM) VIKOR method. Aiming to balance environmental conservation with economic benefits, we designed 19 trails paths and allocated signage for resting and recreation facilities. The trail paths were assessed based on criteria such as length, difficulty, scenic appeal, and accessibility. This approach identified key trails that combine scenic beauty with infrastructure suitable for a broad range of visitors, thereby enhancing sustainable tourism appeal. Stakeholder engagement was integral to shaping the trail network, ensuring that the selected paths reflect local values and priorities. This study highlights how the VIKOR method can optimize resource allocation by ranking trails according to their environmental and visitor-centered attributes, supporting regional economic growth through ecotourism. This framework offers a replicable model for other mountainous regions seeking to harness ecotourism’s potential while preserving natural ecosystems. The findings demonstrate the capacity of well-planned trail networks to attract nature-based tourism, stimulate local economies, and respond to the rising post-pandemic interest in outdoor recreation, while promoting long-term conservation efforts. This approach offers a replicable model for the sustainable development of mountainous and semi-mountainous areas in Greece and beyond.

Strategies for Strengthening Network Capacity in the Implementation of Food Security Policies at the Local Level in Indonesia

This study aims to analyze network management strategies in the interactive governance perspective in the implementation of food security policies in North Luwu Regency, Indonesia. The complexity of food security issues requires effective collaboration and interaction among various actors in the policy network. Using a qualitative approach with an explanatory case study design, this research examines seven indicators of network management strategies: network (de-)activating, constitutional reform, (de)coupling games, changing incentives, changing internal structure and position of actors, changing relations, and management by chaos. Data were collected through in-depth interviews, participatory observation, and document analysis, involving relevant stakeholders from government agencies, farmer organizations, and private sector partners. The findings reveal that the application of network management strategies facilitates interaction among network actors and contributes to the success of interactive governance in implementing food security policies. Network (de-)activating involves the engagement of new coalitions and resource mobilization to strengthen cooperation and interaction. Constitutional reform through changes in rules and resources helps develop coordination mechanisms and strengthen policy integration. (De) coupling games demonstrate the separation of strategies carried out by various actors to support a holistic approach in achieving food security. Changing incentives through incentive interventions can increase motivation and active participation of actors in realizing food security. Changing relations indicate changes in cooperative relationships and actor roles to enhance effective collaboration. Management by chaos through the application of principles of flexibility, adaptability, and the ability to manage uncertainty can strengthen interactive governance in facing complex and dynamic challenges. This study recommends strengthening cross-sectoral collaboration, developing a structured incentive system, enhancing the capacity of farmer organizations, and optimizing the use of information technology to improve the success of interactive governance in food security policy implementation.

Neural Methods for Amortized Inference

Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new revolution as it embraces the representational capacity of neural networks, optimization libraries, and graphics processing units for learning complex mappings between data and inferential targets. The resulting tools are amortized, in the sense that, after an initial setup cost, they allow rapid inference through fast feed-forward operations. In this article we review recent progress in the context of point estimation, approximate Bayesian inference, summary-statistic construction, and likelihood approximation. We also cover software and include a simple illustration to showcase the wide array of tools available for amortized inference and the benefits they offer over Markov chain Monte Carlo methods. The article concludes with an overview of relevant topics and an outlook on future research directions.

Optimal Placement of TCSC and DPFC on Nigeria Power Transmission Network for Capacity Enhancement

The increasing demand for electrical energy and the high costs and challenges associated with building new transmission lines highlight the need to optimize existing power transmission networks. This study focuses on optimizing or enhancing Nigeria's 28-bus power transmission system using Flexible Alternating Current Transmission System (FACTS) devices, specifically, Dynamic Power Flow Controller (DPFC) and Thyristor-Controlled Series Compensator (TCSC). This research employed the Bacterial Foraging Optimization Algorithm (BFOA) in MATLAB to determine the optimal placement and sizing of DPFC and TCSC to minimize power losses, voltage deviations, and installation costs. Simulation results were compared, before and after incorporating the FACTS devices. In the base case, 32% of the buses had voltage profiles outside the acceptable range (0.95 to 1.05 p.u.), with real power loss of 602.91 kW and reactive power loss of 4559.69 kVAR. After optimally placing two DPFC and two TCSC devices in the system, all bus voltages were regulated to fall between 0.95 and 1.06 p.u., while real and reactive power losses were reduced by approximately 84.6% and 84.7%, respectively. This demonstrates a significant improvement in the efficiency and capacity of the power transmission network.

Assessing the Generalization Capacity of Convolutional Neural Networks and Vision Transformers for Deforestation Detection in Tropical Biomes

Assessing the Generalization Capacity of Convolutional Neural Networks and Vision Transformers for Deforestation Detection in Tropical Biomes

Ultra-high-capacity band and space division multiplexing backbone EONs: multi-core versus multi-fiber

Both multi-band and space division multiplexing (SDM) independently represent cost-effective approaches for next-generation optical backbone networks, particularly as data exchange between core data centers reaches the petabit-per-second scale. This paper focuses on different strategies for implementing band and SDM elastic optical network (BSDM EON) technology and analyzes the total network capacity of three sizes of backbone metro-core networks: ultra-long-, long-, and medium-distance networks related to the United States, Japan, and Spain, respectively. Two BSDM strategies are considered, namely, multi-core fibers (MCFs) and BSDM based on standard single-mode fiber (SSMF) bundles of multi-fiber pairs (BuMFPs). For MCF-based BSDM, we evaluated the performance of four manufactured trench-assisted weakly coupled (TAWC) MCFs with 4, 7, 13, and 19 cores. Simulation results reveal that, in the regime of ultra-low (UL) loss and inter-core crosstalk (ICXT), MCF-based throughput can be up to 14% higher than SSMF BuMFP-based BSDM when the core pitch exceeds 43 µm and the loss coefficient is lower than that of standard single-mode fibers. However, increasing the number of cores with (non-)standard cladding diameters, UL loss, and ICXT coefficient is not beneficial. As core counts increase up to 13 for non-standard cladding diameters (<230µm), the core pitch and loss coefficient also increase, leading to degraded performance of MCF-based BSDM compared to SSMF BuMFP-based BSDM. The results indicate that, in scenarios with 19 MFPs, SSFM BuMFP-based BSDM outperforms 19-core MCF-based scenarios, increasing the throughput by 55% to 73%, from medium-backbone networks to ultra-long ones.

Autonomous and ubiquitous in-node learning algorithms of active directed graphs and its storage behavior

The brain’s memory system is extraordinarily complex, evidenced by the multitude of neurons involved and the intricate electrochemical activities within them, as well as the complex interactions among neurons. Memory research spans various levels, from cellular and molecular to cognitive behavioral studies, each with its own focus, making it challenging to fully describe the memory mechanism. Many details of how biological neuronal networks encode, store, and retrieve information remain unknown. In this study, we model biological neuronal networks as active directed graphs, where each node is self-adaptive and relies on local information for decision-making. To explore how these networks implement memory mechanisms, we propose a parallel distributed information access algorithm based on the node scale of the active directed graph. Here, subgraphs are seen as the physical realization of the information stored in the active directed graph. Unlike traditional algorithms with global perspectives, our algorithm emphasizes global node collaboration in resource utilization through local perspectives. While it may not achieve the global optimum like a global-view algorithm, it offers superior robustness, concurrency, decentralization, and biological feasibility. We also tested network capacity, fault tolerance, and robustness, finding that the algorithm performs better in sparser network structures.

Solving blockage problem of the 5G mmWaves Wireless Communications using Jellyfish Search Optimizer

The 5G technologies are aimed to support a variety of vertically connected applications with heterogeneous devices and machines with significant improvements in terms of higher quality of service, increased network capacity and improved system throughput. However, 5G systems still face a number of challenges mentioned by researchers and organizations, and among these challenges, we highlight that millimeter wave links are very susceptible to blockage. If the line-of-sight communication path is blocked, reflexive path communication can help maintain communication. The shortest route was to take an alternate route when the first route was blocked. Among the proposed solutions to the blocking problem is an algorithm to distribute the power between different mmWave communication paths in order to overcome the masses of links under randomly distributed obstacles. The problem has been resolved with an exact method. In this article, we have optimized all the results of this exact method by applying a powerful metaheuristic technique called jellyfish search optimizer to maximize the overall capacity of the path between two nodes. The results showed that our approach procures an improvements exceed 100% over the results of the exact method.

Research on assessment method of maximum distributed generation hosting capacity in distribution system with high shares of renewables and power electronics.

With the rapid development of distributed generation (DG) within the framework of modern power systems, accurately assessing the maximum DG hosting capacity in distribution networks is crucial for ensuring the safe and stable operation of the power grid. This paper first introduces an assessment model of maximum DG hosting capacity in distribution network based on optimal power flow (OPF). Then, a two-step method that combines the linearization method and the recursive method is proposed, which consists of two parts: firstly, using linearization method to quickly calculate the preliminary assessment value of maximum DG hosting capacity, and then using a recursive method to accurately correct the preliminary assessment value. Additionally, the proposed improved comprehensive sensitivity index and safety constraint verification method can enhance the computational efficiency and accuracy of the recursive algorithm. Finally, the proposed methods were simulated and validated on the IEEE 33-bus system.

Antibiotic treatment recommendations for acute respiratory tract infections in Scandinavian general practices—time for harmonization?

Introduction During recent years, the world—including Scandinavia—has experienced significant challenges with shortages of antibiotics. In Scandinavia, phenoxymethylpenicillin is recommended as first-line antibiotic treatment for most acute respiratory tract infections (ARTIs). However, the Scandinavian countries each constitute rather small markets for phenoxymethylpenicillin. The aim of this discussion paper is to enlighten the differences in Scandinavian ARTI antibiotic treatment recommendations. This information is fundamental for exploring the potential of harmonizing treatment recommendations in Denmark, Norway and Sweden—to help ensure sufficient future supply of phenoxymethylpenicillin. Methods Information from national ARTI antibiotic treatment recommendations from respectively Denmark, Norway and Sweden has been collated. Results Several discrepancies exist in recommendations. Adult dosage varies from a minimum of 660 mg x 4 (Denmark) to a maximum of 2000 mg × 3 (Sweden). Within Norway and Sweden, variations in recommended dosage also exist between the different types of ARTIs. A main challenge is that the tablet strengths recommended, and available on the market in the three countries, differs. Also, antibiotic treatment durations vary significantly between countries and infections treated—from five to 10 days of treatment. Conclusion In the capacity of a well-established network for antibiotic stewardship, we have enlightened the differences in Scandinavian ARTI antibiotic treatment recommendations. This paper is the first step moving forward to scrutinizing the potential for harmonizing recommendations for Denmark, Norway and Sweden—to help ensure continued supply of phenoxymethylpenicillin for use within the Scandinavian countries.

DeepPipe: A Multi-Stage Knowledge-Enhanced Physics-Informed Neural Network for Hydraulic Transient Simulation of Multi-Product Pipeline

In the chemical pipelining industry, owing to the high-pressure transportation process, an accurate hydraulic transient simulation tool plays a central role in preventing the slack line flow and overpressure from causing pipeline operation treacherous. Nevertheless, the current model-driven method often faces challenges in balancing computational efficiency with accuracy, and the existing data-driven models struggle to produce explainable results from the physics perspectives since insufficient theoretical principles are incorporated into the model training. Additionally, the existing physics-informed learning architecture fails to achieve a gradient-balanced training, resulting from the significant magnitude difference in outputs and multiple loss terms. Consequently, a Multi-Stage Knowledge-Enhanced Physics-Informed Neural Network (MS-KE-PINN) is proposed for the hydraulic transient simulation of multi-product pipelines. To enforce the neural network producing simulation results with high consistency to physical laws, the governing equations, boundary, and initial condition are incorporated into the training process for an efficient mesh-free simulation. Then, considering that the significant magnitude difference between outputs can easily lead to deficient performance in the gradient descent, the magnitude conversion on the outputs and the equivalent conversion of the governing equations are implemented to enhance the training effect of the neural network. Subsequently, to tackle the imbalanced gradient of multiple loss terms with fixed weights, a multi-stage hierarchical training strategy is designed to improve the approximation capacity of the neural network. Numerical simulation cases demonstrate a better approximation function of the proposed model than the state-of-art models, while the mean absolute percentage errors yielded by MS-KE-PINN are reduced by 77.4 %, 88.7 %, and 87.8 % in three simulation operation conditions for pressure prediction. Furthermore, experimental investigations from a real-world multi-product pipeline suggest that the proposed model can still draw accurate simulation results even under complex and dynamic hydraulic transient scenarios in practice, with root mean squared errors reduced by 94.8 % and 80 % than that of the physics-informed neural network. To this end, the proposed model can conduct accurate and effective hydraulic transient analysis, thus ensuring the safe operation of the pipeline.

Low-Carbon Water–Rail–Road Multimodal Routing Problem with Hard Time Windows for Time-Sensitive Goods Under Uncertainty: A Chance-Constrained Programming Approach

In this study, a low-carbon freight routing problem for time-sensitive goods is investigated in the context of water–rail–road multimodal transportation. To enhance the on-time transportation of time-sensitive goods, hard time windows are employed to regulate both pickup and delivery services at the start and end of their transportation. The uncertainty of both the demand for time-sensitive goods and the capacity of the transportation network are modeled using L-R triangular fuzzy numbers in the routing process to make the advanced routing more feasible in the actual transportation. Based on the carbon tax policy, a fuzzy linear optimization model is established to address the proposed problem, and an equivalent chance-constrained programming formulation is then obtained to make the solution to the problem attainable. A numerical experiment is carried out to verify the feasibility of incorporating the carbon tax policy, uncertainty, and water–rail–road multimodal transportation to optimize the low-carbon freight routing problem for time-sensitive goods. Furthermore, a multi-objective optimization is used to reveal that lowering the transportation costs, reducing the carbon emissions, and avoiding the risk are in conflict with each in the routing. We also analyze the sensitivity of the optimization results concerning the confidence level of the chance constraints and the uncertainty degree of the uncertain demand and capacity. Based on the numerical experiment, we draw several conclusions to help the shipper, receiver, and multimodal transportation operator to organize efficient water–rail–road multimodal transportation for time-sensitive goods.

Interpretable ADMM-CSNet for interrupted sampling repeater jamming suppression

Interrupted sampling repeater jamming (ISRJ) is a category of coherent jamming that greatly influences radars' detection performance. Since the ISRJ has greater power than true targets, ISRJ signals can be removed in the time domain. Due to frequency band loss, grating lobes will be produced if pulse compression (PC) is performed directly, which may generate false targets. Compressive sensing (CS) is an effective method to restore the original PC signal. However, it is challenging for classic CS approaches to manually select the optimization parameters (e.g., penalty parameters, step sizes, etc.) in different ISRJ backgrounds. In this article, a network method based on the Alternating Direction Method of Multipliers (ADMM), named ADMM-CSNet, is introduced to solve the problem. Based on the strong learning capacity of the deep network, all parameters in the ADMM are learned from radar data utilizing back-propagation rather than manually selecting in traditional CS techniques. Compared with classic CS approaches, a higher ISRJ removal signal restoration accuracy is reached faster. Simulation experiments indicate the proposal performs effectively and accurately for ISRJ removal signal reconstruction.

Development model of a high-performance multiple input multiple output microstrip antenna based on a planar series array with 8×2 elements for 5G communication systems

MIMO (Multiple Input Multiple Output) makes a major contribution to 5G communication systems by increasing network capacity and spectrum efficiency. In 5G, MIMO enables the use of multiple antennas at base stations and user devices, allowing simultaneous sending and receiving of data over multiple paths. This significantly increases data throughput and connection reliability, especially in environments with high user density. In addition, MIMO technology supports the implementation of beamforming, which focuses signals on a specific direction, reduces interference, and improves signal coverage and quality, making it one of the keys to achieving faster and more responsive 5G performance. Therefore, antennas with wide bandwidth, high gain and MIMO performance are crucial for supporting 5G communication systems. This paper proposes a high-performance MIMO microstrip antenna based on a series planar array with 8×2 elements operating at a resonant frequency of 3.5 GHz for 5G communication systems. A spiral stub and a feed inset are proposed to control the reflection coefficient and bandwidth of the antenna while the series planar array is proposed to increase the gain. To support the MIMO communication system, the proposed antenna is separated into two different ports with a certain distance. From the measurement results, the proposed antenna has high performance indicated by a wide bandwidth of 680 MHz (3–3.68 GHz) and a high gain of 17.8 dB at a resonant frequency of 3.5 GHz. In addition, the proposed antenna has high mutual coupling and diversity indicated by ECC and DG of 0.001 and 9.99 dB, respectively. This work provides a solution to design a high-performance microstrip antenna and can be implemented as a receiving antenna for 5G communication systems

Layout design of densest weakly coupled multi-core fibers to minimize the network blocking rate

The suppression of inter-core crosstalk (IC-XT) that affects each lightpath is crucial for resource allocation in space-division multiplexing elastic optical networks (SDM-EONs) with multi-core fibers (MCFs). Resource allocation approaches that limit the simultaneous use of adjacent cores in the same frequency band to the MCFs composing each lightpath have been widely adopted to suppress IC-XT. However, in principle, such methods are inefficient because they cannot fully utilize all cores. This study examines the core density from the perspective of the core layout in weakly coupled MCFs and the IC-XT suppression requirement. The densest MCF layout maximizes the network capacity while restricting the amount of IC-XT within the tolerance threshold for each lightpath. Specifically, we propose an XT-free condition, maintaining the IC-XT to each lightpath within the acceptable tolerance level. In addition, we evaluated numerous MCFs that satisfy or do not satisfy the XT-free condition with various network topologies and cladding diameters. This evaluation also validates the IC-XT reduction performance of the proposed framework compared with that of the conventional resource-allocation approach. Here, we incorporate our indirect IC-XT calculation method that affects lightpaths from other cores via its nearest cores, which was overlooked in the resource allocation problem. Based on these comprehensive examinations, we propose a method to determine the densest core layout for a given network topology and route and modulation format selection algorithm.

Organizational predators of disability sports participation of secondary school students with special needs in Ibadan, Nigeria

Active participation of students with disability in adapted sport is dependent on several organizational predators. The purpose of this study is to examine how organizational predators independently and jointly contribute to disability sport participation among secondary school students with special needs in Ibadan, Nigeria. The methodology featured Chain-Referral (snowball) to identify stakeholders, coaches, organizers and teachers involved in organizing disability sport for students with disability and probability sampling technique to choose the schools to be visited. The findings revealed that Strategic planning (ß=0.289, t=2.816, p=0.008), logistics (ß=0.612, t=7.103, p=0.000), interest of coaches (ß=0.536, t=7.795, p=0.000) and knowledge of organizing personnel (ß=0.500, t=7.836, p=0.000) were independently tested significant on disability sport participation while network capacity (ß=0.171, t=1.772, p=0.085), funding sources (ß=0.082, t=1.360, p=0.182) and sport infrastructure (ß=0.111, t=1.948, p=0.059) were not independently tested significant on disability sport participation. Also, there is a composite joint contribution of strategic planning, network capacity, infrastructure, funding, logistics, interest of coaches, and knowledge of organizing personnel on disability sports participation among secondary school students with special needs in Ibadan, Nigeria (F(7,39)=100.210, p=0.00). The result also generated a coefficient multiple regression of R=0.977 and R2 =0.955; implying that about 95.5% of variance was accounted for by the independent variable. Strategic planning, logistics, interest of coaches, and knowledge of organizing personnel individually and collectively showed a positive contribution to disability sport participation, hence, there is a need to consider them on the subject of disability sport participation in schools of individuals with special needs in Ibadan, Nigeria.

National biodiversity data infrastructures: ten essential functions for science, policy, and practice

Abstract Today, at the international level, powerful data portals are available to biodiversity researchers and policymakers, offering increasingly robust computing and network capacities and capable data services for internationally agreed-on standards. These accelerate individual and complex workflows to map data-driven research processes or even to make them possible for the first time. At the national level, however, and alongside these international developments, national infrastructures are needed to take on tasks that cannot be easily funded or addressed internationally. To avoid gaps, as well as redundancies in the research landscape, national tasks and responsibilities must be clearly defined to align efforts with core priorities. In the present article, we outline 10 essential functions of national biodiversity data infrastructures. They serve as key providers, facilitators, mediators, and platforms for effective biodiversity data management, integration, and analysis that require national efforts to foster biodiversity science, policy, and practice.

Incorporating policy mixes to multimodal network capacity considering second-best constraints

In the pursuit of capacity-oriented urban sustainable development (SD), this study aims to enhance the multimodal transport network by making it more accessible, efficient, and environmentally friendly. A sustainable framework combining policy mixes and second-best constraints is developed as a multimodal transportation network capacity (TNC) model with the bi-level programming (BLP) formulation. The upper-level model is to maximise the total origin-destination (OD) demand with considering second-best constraints. The lower-level model is a combined mode split and traffic assignment with elastic demand (CMSTA-ED) model which considers policy mixes. Numerical experiments are conducted to demonstrate the effectiveness of the hybrid PSO–GWO algorithm, the synergy of the method for selecting and mixing policy instruments based on the proposed assessment system, and a methodology to establish a policy mix. The findings of this research offer practical guidance for formulating well-informed and no-redundant policy mixes, contributing to the improvement of sustainable multimodal transportation network performance.