Global research trends in diagnostic biomarkers for colorectal cancer: a bibliometric and visualization analysis

In recent years, the surge in network traffic has led to a substantial increase in energy consumption, making the construction of green and energy-efficient networks a critical challenge in the field of communications. Software-Defined Networking (SDN), with its centralized control characteristic, provides a new paradigm for the collaborative scheduling of actuators. However, traditional distributed network architectures lack global regulation capabilities, resulting in low resource utilization. Moreover, existing SDN traffic management methods mostly adopt fixed-weight reward functions, which are difficult to adapt to the dynamic fluctuation of network traffic and device heterogeneity, failing to meet the real-time and stability requirements of actuators in control scenarios. To address these issues, this study proposes a Dynamic Weight Generation Deep Q-Network (DWG-DQN) framework. By integrating a Long Short-Term Memory (LSTM) network with the SDN actuator scheduling mechanism, the system dynamically generates adaptive weight vectors, enabling real-time collaborative optimization of energy consumption, load balancing, and bandwidth utilization. Experimental results demonstrate that in fat-tree topology experiments, the proposed method achieves a 12.23% increase in average reward, a 33.93% reduction in energy consumption, a 31.12% improvement in load balancing, and a 24.03% enhancement in bandwidth utilization. Compared with fixed-weight method, it consistently outperforms in key performance indicators. The dynamic weight generation mechanism effectively solves the multi-objective optimization problem of actuators in dynamic network environments, offering a viable solution for the intelligent scheduling of actuators in SDN-based green traffic management.

Multi-source distribution network fault elimination strategy based on real-time risk perception

Wind-solar power generation is inherently uncertain. These uncertainties bring considerable difficulties to the assessment of hosting capacity. To tackle these difficulties, it is essential to create typical scenarios that can precisely capture the statistical traits and interrelationships of wind-solar power. In this research, we systematically integrate various scenario generation techniques, resulting in the creation of a holistic framework grounded in kernel density estimation (KDE) and Copula functions. Our proposed approach represents the stochastic nature of wind-solar power output by constructing their respective probability density functions (PDFs). It comprehensively depicts the potential spatiotemporal complementarity between wind-solar power by utilizing Copula functions and establishing a joint probability distribution model. Through Monte Carlo simulation, we generated a large number of wind-solar output scenarios. Subsequently, we employed the K-means clustering algorithm to reduce the number of scenarios. The findings reveal that the integrated framework, which combines KDE and Copula theory, achieves higher fitting accuracy for the marginal distributions and correlation structures of wind-solar power generation. As a result, the generated scenarios are more representative and reliable, offering strong support for photovoltaic (PV) hosting capacity analysis (HCA) and the formulation of typical plans. We validate the proposed method using historical wind-solar data from several representative regions in China, such as Inner Mongolia, northern Hebei, the Beijing–Tianjin–Hebei region, and Hubei Province. This validation demonstrates the method’s applicability under various geographical and climatic conditions.

Allogenic bone grafting and bone banking have become vital components of modern orthopaedic reconstruction, addressing bone loss following trauma, infection, tumor resection, and revision arthroplasty. Despite expanding clinical use, a comprehensive overview of global research productivity and collaboration in this domain has not been previously undertaken. A bibliometric analysis was conducted using the Scopus database (2000-2025) with defined search terms related to bone banking and allogenic bone grafting in orthopaedics. Data were analyzed using Scopus analytics and VOSviewer for publication trends, source impact, geographic distribution, authorship, funding patterns, and keyword co-occurrence networks. A total of 3497 documents were identified, showing steady publication growth. The majority were original research articles (79.2%). The United States led in publication output (37.7%), followed by China and Italy. Clinical Orthopaedics and Related Research and Spine were the most productive journals. Keyword mapping revealed core themes in revision arthroplasty, spinal fusion, limb reconstruction, and bone defect management. Global research on allogenic bone and bone banking demonstrates robust growth, interdisciplinary collaboration, and emerging regional contributions. Future efforts should emphasize standardization, outcome-based studies, and integration of biomaterials and regenerative technologies to enhance the safety and sustainability of bone banking worldwide.

With the rapid advancement of quantum computing, traditional cryptographic techniques are at risk of devolution, necessitating quantum-resilient alternatives for future communication networks. This systematic literature review evaluates the role of Quantum Key Distribution (QKD) in enhancing the security of sixth-generation (6G) wireless communications. Employing the PRISMA methodology, 48 peer-reviewed studies published between 2016 and May 2025 were identified and analyzed. The review addresses three key research questions: the identification of QKD protocols applicable to 6G, challenges in their integration, and proposed solutions for seamless deployment. Findings reveal that protocols such as BB84, E91, CV-QKD, and MDI-QKD, transmitted via optical fiber and satellite channels, offer promising security guarantees. This review concludes that while QKD can significantly strengthen 6G communications against quantum threats, further interdisciplinary efforts in hardware development, standardization, and pilot implementations are essential. The study offers valuable insights for researchers, engineers, and policymakers working toward secure, quantum-resistant future networks. The study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to ensure transparency, rigor, and reproducibility. A comprehensive search was conducted across major scientific databases, including IEEE Xplore, SpringerLink, ScienceDirect, and arXiv, using well-defined keywords and Boolean search strategies related to QKD, 6G networks, and quantum communication security. After removing duplicates and applying predefined inclusion and exclusion criteria, a total of 48 peer-reviewed studies published between 2016 and May 2025 were selected for detailed analysis. The selected literature was systematically classified to address three primary research questions: (i) identification of QKD protocols and technologies applicable to 6G networks, (ii) challenges hindering the integration of QKD into 6G architectures, and (iii) solutions and frameworks proposed to facilitate practical deployment. The findings reveal that prominent QKD protocols, including BB84, E91, Continuous-Variable QKD (CV-QKD), and Measurement-Device-Independent QKD (MDI-QKD), demonstrate strong potential for securing 6G communications when deployed over optical fiber and satellite-based channels. However, practical integration faces significant challenges such as scalability limitations, synchronization issues, quantum channel coexistence with classical networks, hardware complexity, and high deployment costs. The review further highlights emerging solutions that leverage Software-Defined Networking (SDN), Network Function Virtualization (NFV), blockchain-based key management, and hybrid classical-quantum security architectures to overcome these obstacles. Ongoing standardization efforts by organizations such as NIST, ETSI, and ITU-T are also identified as critical enablers for real-world adoption .

Abstract In pursuit of a global quantum key distribution (QKD) network, a service based on untrusted nodes on geostationary satellites could offer wide coverage, continuous operation, and enhanced security compared to the trusted node alternative. Although this scenario has been studied for entanglement-based protocols, such an approach would require large-area telescopes both on the ground and in space. In this work, we analyze the performance of two QKD protocols well adapted to this scenario, namely twin-field (TF) and mode-pairing (MP) QKD, which exhibit high resilience to high-loss channels. Leveraging an in-depth simulation of communication channels corrected with adaptive optics, we assess the expected secret key rates for both protocols in a configuration involving two 50 cm telescopes on board the satellite and ground-based telescopes ranging from 20 cm to 1 m in aperture. Our results show that, in the best case and considering realistic detectors, it is possible to achieve secret key rates on the order of a few hundred bit/s for both TF and MP-QKD. We show, notably, that secret key generation is potentially feasible even with 20 cm ground telescopes, highlighting the high scalability potential of such a configuration.

The maintenance of natural gas wayleaves is important for ensuring the safety, efficiency, and reliability of gas distribution systems, particularly as global energy demands continue to rise. This study explores the factors influencing the maintenance of natural gas wayleaves, focusing on operations at GASCO. Using a mixed-methods approach, the study collected data from 50 GASCO staff in Dar es Salaam, employing surveys, interviews, focus group discussions, and document reviews. Quantitative analysis was conducted using descriptive statistics and the Relative Importance Index (RII), while qualitative data were analysed using Content Analysis. Key findings revealed that budget constraints, environmental factors such as rainfall intensity, limited access to wayleaves, and human activities significantly impact maintenance operations. Technological adoption, manpower shortages, insufficient training, equipment failures, and regulatory compliance also influence maintenance practices, but to a lesser extent. The results show the need for improved funding, enhanced infrastructure access, weather-resilient strategies, community engagement, and targeted training initiatives to address these challenges effectively. This study provides actionable insights for stakeholders, emphasising the importance of prioritising key factors to enhance maintenance efficiency and safety. By adopting advanced maintenance frameworks and best practices, GASCO and similar organisations can strengthen the resilience of their natural gas distribution networks, aligning with sustainability and energy security goals

The multi-objective multi-item transportation problem is a challenging issue in the context of supply chain management which deals with optimizing several conflicting objectives, considering the allocation of different products departing from many source nodes to multiple demand destinations. In this paper we propose a systematic mathematical approach based on linear programming to solve this challenging optimization problem. Based on these assumptions the study designs a multi-objective linear programming (MOLP) model with cost, delivery time and environment as the main objectives. The model is developed under clear-cut restrictions that consider supply avai lability, demand requirements, vehicle capacity and multi-product allocation rules. A practical example is considered with real operational data of a regional distribution network for optimal transportation planning and WinQSB software is used to find the best routes. Results show that the proposed model can effectively compromise conflicting multi-objectives, reducing total cost by 18.5%, delivery time by 12.3% and CO2 emissions by 15.2%. The research uses the weighted sum-constraint method for Pareto optimization based decisiontrade-offs, and results into a full tradeoffs analysis and possible transportation planning solutions to decision-making people.

Point-to-multipoint network for continuous-variable quantum key distribution with passive-state preparation

Abstract Hydropower sector is undergoing a transformation of replacing old analog control systems and leveraging the effect of digitalization to increase operational flexibility, production and safety using digital elements like sensors, wireless platforms, real time monitoring, predictive maintenance and decision support systems etc.. Implementation of digitalization to hydropower plants has a potential of 42 TWh increase in annual production worldwide, hence creates a potential for USD 5 billion annual operational cost savings and reduction in greenhouse gas emissions. One of the critical and common applications of digitalization in hydropower sector is unmanned/remote operated hydropower plants, of which there are several examples of in the Water-Energy nexus projects. In Water Distribution Networks (WDN), Pressure Reduction Valves (PRV) are the most common tools to manage excessive pressure due to the topography, resulting in energy waste. To harvest this waste energy, micro hydropower plants can be used for pressure reduction and the harvested energy can be used in remote areas lacking grid connectivity or directly supplied to the local grid. Digitalization has potential advantages on both micro, mini and small sized hydropower plants installed in water networks such as optimal control of water pressure using digital twins of the WDNs, autonomous operation of the plants and proactive or predictive maintenance to ensure trouble-free operation. In this paper, we present the insights from a technical point of view from a practical Water-Energy nexus project and from a short term scientific mission study conducted with a COST action PEN@Hydropower member institute. This research aims to reveal main challenges to be encountered during implementation of digital control and monitoring solutions in a small hydropower plant, including hands on observations during erection, commissioning and operation phases. Review on data collection and storage issues from critical equipment, cleaning out the collected data for analysis and machine learning applications, cyber security issues brought by digital transformation along with the convenience in installation and operation it brings is presented as a guide for future research.

With the continuous improvement of power system intelligence, multimodal data generated during distribution network maintenance have grown exponentially. However, existing power multimodal datasets commonly suffer from issues such as low sample quality, frequent factual errors, and inconsistent instruction expressions caused by regional differences.Traditional sample correction methods mainly rely on manual screening or single-feature matching, which suffer from low efficiency and limited adaptability. This paper proposes a multimodal sample correction framework based on large-model instruction enhancement and knowledge guidance, focusing on two critical modalities: temporal data and text documentation. Multimodal sample correction refers to the task of identifying and rectifying errors, inconsistencies, or quality issues in datasets containing multiple data types (temporal sequences and text), with the objective of producing corrected samples that maintain factual accuracy, temporal consistency, and domain-specific compliance. Our proposed framework employs a three-stage processing approach: first, temporal Bidirectional Encoder Representations from Transformers (BERT) models and text BERT models are used to extract and fuse device temporal features and text features, respectively; second, a knowledge-injected assessment mechanism integrated with power knowledge graphs and DeepSeek’s long-chain-of-thought (CoT) capabilities is designed to achieve precise assessment of sample credibility; third, beam search algorithms are employed to generate high-quality corrected text, significantly improving the quality and reliability of multimodal samples in power professional scenarios. Experimental results demonstrate that our method significantly outperforms baseline models across all evaluation metrics (BLEU: 0.361, ROUGE: 0.521, METEOR: 0.443, F1-Score: 0.796), achieving improvements ranging from 21.1% to 73.0% over state-of-the-art methods: specifically, a 21.1% improvement over GECToR in BLEU, 26.5% over GECToR in ROUGE, 30.3% over Deep Edit in METEOR, and 11.8% over Deep Edit in F1-Score, with a reduction of approximately 35% in hallucination rates compared to existing approaches. These improvements provide important technical support for intelligent operation and maintenance of power systems, with implications for improving data quality management, enhancing model reliability in safety-critical applications, and enabling scalable knowledge-guided correction frameworks transferable to other industrial domains requiring high data integrity.

With the expansion of the power system scale and the increasing complexity of distribution network structures, the safety of power facilities has become increasingly prominent under natural disasters, such as earthquakes. As the core support of distribution networks, the seismic performance of reinforced concrete pole–conductor systems directly affects the safe operation of power systems. Compared with single-pole structures, the coupling effect between poles and conductors significantly complicates the mechanical characteristics of the system. This paper focuses on a typical 10 kV distribution line-reinforced concrete pole–conductor system. A refined “three-pole two-conductor” finite element model considering the geometric nonlinearity of conductors is established via ANSYS (Analysis System) software. Through modal analysis and nonlinear dynamic time–history analysis, the natural vibration frequencies, displacement responses of poles, and root stress distribution patterns under different conductor spans (60 m, 80 m, and 100 m) and span-to-height ratios (5–6.7) were systematically investigated. The results indicate that the mass–sag effect of conductors reduces the natural vibration frequency of the pole–conductor system by 10–18%, and its dynamic influence exhibits nonlinear differences as the span increases. When the span-to-height ratio is within the range of 5–6.7, the vibration of conductors significantly amplifies the stress at the pole roots, suggesting that a dynamic amplification factor of up to 1.17 was observed in this study, which can serve as a reference for the seismic design of similar distribution lines.

Multi-scenario robust stochastic programming based distributed energy resources allocation in distribution networks: Balancing economic efficiency and resilience

Hybrid game-based dispatch for 5G BS-BSC synergy in distribution networks

Preventive dispatch method for coordinated topology optimization of transmission and distribution networks

ABSTRACT With the high penetration of renewable energy sources, distribution networks face significant challenges with voltage fluctuations and energy imbalances. Grid‐Forming Distribution Areas (GFDA), with their active voltage regulation capabilities, offer a novel technical solution for the distribution network operations. However, existing research predominantly employs multi‐objective dispatch models with fixed weights, which struggle to adapt to dynamic changes in grid operational states. To address this issue, this paper proposes an adaptive multi‐objective coordination dispatch method for distribution areas based on deep reinforcement learning. First, a power support framework is established for the collaborative operation of Grid‐Forming and Grid‐Following Distribution Areas (GFLDA). Second, a state‐aware reward weight dynamic allocation mechanism is designed to achieve adaptive adjustment of weights between voltage quality and economic objectives. Finally, a stable and efficient reinforcement learning training framework is constructed based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Simulation results on the IEEE‐33 bus system demonstrate that, compared to fixed‐weight schemes, the proposed method achieves significant improvements in both voltage stability and economy, particularly in extreme scenarios with sudden load increases, effectively suppressing voltage limit violations while reducing network losses under normal operating conditions.

Microplastics hack the water supply system: What it means for water safety and human health?

Three-dimensional inversion method based on multi-source fused physical information networks for leachate distribution in landfills

ABSTRACT Bryophytes play key roles in ecosystem functioning but have received limited attention in functional ecology, especially in tropical regions. This review synthesized data from 75 studies that measured functional traits in Brazilian bryophytes over the past 26 years. We analyzed traits across eight categories, traits distribution across Brazilian biomes, and among bryophyte groups (mosses, liverworts, and hornworts), and related these patterns to research focus, geographic distribution, author productivity, and collaboration networks. Reproductive traits predominated, and research was concentrated in the Atlantic Forest, while the Amazon, Caatinga, Cerrado, Pantanal, and Pampa remain poorly explored. Despite growing interest in functional ecology, these spatial and thematic biases reflect the dominance of a few highly productive specialists within a predominantly national collaboration network, shaping methodological approaches and reinforcing knowledge gaps. Our review also identified ongoing challenges in trait classification and functional interpretation. Nevertheless, Brazilian researchers have made important advances in bryophyte functional trait ecology, despite structural constraints that limit broader progress. We advocate expanding trait coverage, improving methodological standardization, and strengthening interdisciplinary and international collaborations to reduce existing biases. Establishing a regional trait database and developing a common conceptual framework for bryophyte functional traits would support hypothesis‐driven research, enable cross‐regional comparisons, and inform conservation strategies at multiple scales. Overall, our review maps the current state of bryophyte functional trait research in Brazil and outlines pathways to enhance its ecological and biogeographic relevance nationally and globally.