Node Identification Research Articles

Identification of important nodes in multi-layer hypergraphs based on fuzzy gravity model and node centrality distribution characteristics

Hyperedge is a common structure that represents high-order interactions between nodes in complex networks, and multi-layer networks provide more diverse node interactions than single-layer networks. Therefore, multi-layer hypergraphs can more clearly represent the relationships between nodes. However, there are few studies on identifying important nodes in this framework. This paper proposes a method called HCT to fill this gap. It consists of three parts, namely: Hypergraph Fuzzy Gravity Model (HFGM), Layer Weight Calculation Method based on Node Centrality Distribution Characteristics (CDLW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). HCT progressively analyzes the importance of a node from three levels: local (the node itself), semi-local (the hyperedges and community to which the node belongs), and global (the layer to which the node belongs). By combining these three results, the global centrality of a node in the entire network can be calculated. Simulation experiments demonstrate that the important nodes identified by HCT exhibit stronger contagion capabilities in nine networks compared to nine combinatorial methods and removing these nodes will seriously damage the connectivity and robustness of the network. The centrality of each node calculated by HCT is also consistent with its actual importance.

Optimal configuration of distributed energy storage based on the identification of voltage-vulnerable nodes

Optimal configuration of distributed energy storage based on the identification of voltage-vulnerable nodes

A Multimodal Protocol Combining 99mTc-Tilmanocept with Indocyanine Green Fluorescence Lympho-Angiography for Sentinel Lymph Node Biopsy in Early-Stage Oral Cancer: A Case Series.

Sentinel lymph node biopsy (SLNB) is currently considered as a viable alternative to elective neck dissection (END) for the management of cN0 oral cavity squamous cell carcinoma (OCSCC). However, some difficulties were detected in sentinel lymph node (SLN) identification in floor of mouth (FOM) and ventral tongue tumors because of the so-called "shine-through radioactivity" of the injection site, which may mask nodal hotspots in proximity. We assessed the feasibility and the potential strengths of combining 99mTc-Tilmanocept with indocyanine green (ICG) fluorescence lympho-angiography in a dedicated multimodal protocol for SLNB in T1/T2N0 oral cancer to evaluate the synergistic role of each of these two tracers in providing the appropriate sensitivity and ease of learning, even in such a critical anatomical subsite. A detailed, stepwise description of our multimodal protocol is provided, together with the presentation of its application in two cases of early-stage ventral tongue tumors. Radioactive guidance with 99mTc-Tilmanocept was used preoperatively to perform planar lymphoscintigraphy and single-photon emission computed tomography/computed tomography and to define the nodal hotspot(s) and the surgical "roadmap". In addition, it was used intraoperatively to pinpoint the SLN location within each nodal hotspot with high specificity but limited spatial resolution. Optical guidance with ICG injection at the tumor bed and near-infrared fluorescence imaging was then added, providing intuitive intraoperative guidance within each nodal hotspot with high spatial resolution. Our small experience with this protocol is illustrated and future perspectives are highlighted.

Glucose metabolic rate from four-dimensional [18F]FDG PET/CT to differentiate sarcoid lesions from malignant lesions.

On 18F-Fludeoxyglucose (FDG) PET/CT, active sarcoid lesions are often difficult to differentiate from malignant lesions. We investigated the potential of the glucose metabolic rate (MRglc, mg/min/100 mL), a new quantification of glucose metabolic kinetics derived from direct reconstruction based on linear Patlak analysis, to distinguish between sarcoidosis and malignant lesions. A total of 100 patients with cardiac sarcoidosis (CS) and 67 patients with cancer who underwent four-dimensional FDG PET/CT were enrolled. The lesions with a standardized uptake value (SUV) ≥ 2.7 on the standard scan were included as active lesions in the analysis. SUV and MRglc were derived using data acquired between 30 min and 50 min on four-dimensional FDG PET/CT. The mean value in the volume of interest (size 1.5 cm3) was measured. The diagnostic performance of sarcoidosis using MRglc and SUV was evaluated using receiver-operating-characteristic (ROC) analysis. A total of 90 sarcoidosis lesions from 44 CS patients (18 males, 63.4 ± 12.2 years) and 87 malignant lesions from 57 cancer-bearing patients (32 males, 65 ± 14 years) were analyzed. SUV and MRglc for sarcoid lesions were significantly lower than those for malignant lesions (SUV, 4.98 ± 2.00 vs 6.21 ± 2.14; MRglc, 2.52 ± 1.39 vs 3.68 ± 1.61; p < 0.01). ROC analysis indicated that the ability to discriminate sarcoid patients from those with malignancy yielded areas under the curves of 0.703 and 0.754, with sensitivities of 64% and 77% and specificities of 75% and 72% for SUV 5.025 and MRglc 2.855, respectively. MRglc was significantly lower in sarcoid lesions than malignant lesions, and improved sarcoid lesions identification over SUV alone. MRglc improves sarcoid lymph node identification over SUV alone and is expected to shorten the examination time by eliminating delayed scans. Active sarcoid lesions are sometimes associated with FDG accumulation and should be differentiated from malignant lesions. SUV and metabolic rate of glucose (MRglc) strongly positively correlated, and MRglc could differentiate sarcoid and malignant lesions. MRglc allows for accurate evaluation and staging of malignant lesions.

Optimizing cluster head selection for energy efficiency in wireless sensor networks: A hybrid algorithm combining grey wolf and enhanced sunflower optimization

AbstractIn this letter, we introduce a novel cluster head selection algorithm namely mixed grey wolf and improved sunflower optimization algorithm (MGWISFO). This algorithm leverages both energy requirements and inter‐node distances to select cluster heads (CH). Within this algorithm, the Grey Wolf Optimizer facilitates exploration, offering a broader search, while the improved Sunflower Optimization focuses on exploitation, delivering a narrower search. This balance between exploration and exploitation leads to the identification of the optimal CH node, thereby enhancing network performance. To validate its effectiveness, the proposed algorithm is benchmarked against existing strategies such as particle swarm optimization (PSO), genetic algorithm (GA), grey wolf optimization (GWO), and sunflower optimization (SFO) across various performance parameters including throughput, the number of live and dead nodes, and residual energy. Simulation results unequivocally establish the unparalleled performance of our proposed algorithm, surpassing the capabilities of existing algorithms.

Sustainable Transportation: Exploring the Node Importance Evolution of Rail Transit Networks during Peak Hours

The scientific and rational assessment of the evolution of node importance in rail transit line networks is important for the sustainability of transportation systems. Based on the complex network theory, this study develops a weighted network model using the Space L method. It first considers the network topology, the mutual influence of neighboring nodes of the transportation system, and the land use intensity in the station influence domain to construct a comprehensive index evaluation system of node importance. It then uses the covariance-weighted principal component analysis algorithm to more comprehensively evaluate the node importance evolution mechanism and analyzes the similarity and difference of the sorting set by adopting three different methods. The interaction mechanism between the distribution of important nodes and the evolution of land use intensity is explored in detail based on the fractal dimension theory. The Xi’an rail transit network is considered an example of qualitative and quantitative analysis. The obtained results show that the importance of nodes varies at different times of the day and the complexity of the morning peak is more prominent. Over time, articulated fragments with significance values greater than 0.5 are formed around the station, which are aligned with the direction of urban development, creating a sustainable mechanism of interaction. As the network’s crucial nodes in the center of gravity increase and the southern network expands, along with the increased intensity of the city’s land utilization, the degree of alignment in evolution becomes increasingly substantial. Different strategies for attaching the network, organized based on the size of Si can lead to the rapid damage of the network (reducing it to 0.2). The identification of crucial nodes highlighted in this paper serves as an effective representation of the functional characteristics of the nodes in transportation networks. The results obtained can provide a reference for the operation and management of metro systems and further promote the sustainable development of transportation networks.

Preoperative and Intraoperative Identification of Sentinel Lymph Nodes in Melanoma Surgery.

According to the American Joint Commission on Cancer (AJCC) 8th edition guidelines, SLN biopsy is recommended for primary melanomas with a Breslow thickness of at least 1 mm. Additionally, the National Comprehensive Cancer Network (NCCN) recommends that a SLN biopsy may be considered for melanoma patients with T1b lesions, which are 0.8-1 mm thick or less than 0.8 mm thick with ulceration. It can also be considered for T1a lesions that are less than 0.8 mm thick but have other adverse features, such as a high mitotic rate, lymphovascular invasion, or a positive deep margin. To reduce the false negative rate of melanoma SLN biopsy, we have introduced the intraoperative use of Sentinella, a gamma camera, to enhance the identification rate of SLNs beyond that of the traditional gamma hand-held probe. At the Center for Melanoma Research and Treatment at the California Pacific Medical Center, a multidisciplinary approach has been established to treat melanoma patients when the diagnosis of primary melanoma is made with a referral to our melanoma center. This comprehensive approach at the melanoma tumor board, including the efforts of pathologists, radiologists, dermatologists, surgical, medical and radiation oncologists, results in a consensus to deliver personalized and high-quality care for our melanoma patients. This multidisciplinary program for the management of melanoma can be duplicated for other types of cancer. This article consists of current knowledge to document the published methods of identification of sentinel lymph nodes. In addition, we have included new data as developed in our melanoma center as newly published materials in this article to demonstrate the utility of these methods in melanoma sentinel lymph node surgery. Informed consent has been waived by our IRB regarding the acquisition of clinical data as presented in this study.

A Tumor-Activatable Liposomal Nanoprobe for Selective Visualization of Metastatic Lymph Nodes.

The precise identification of sentinel lymph nodes (SLNs) during surgery and assessment of their benign status is crucial for accurate tumor staging and optimal treatment strategizing. Currently, a deficiency exists in non-invasive in vivo diagnostic techniques that can accurately pinpoint SLNs during surgery while simultaneously evaluating their benign status. Here, a tumor-activatable liposomal nanoprobe (nTAL) is developed, remotely loaded with clinically approved photosensitizer, methyl aminolevulinate (MAL), to noninvasively visualize the tumor metastasis lymph nodes (LNs) with precision. Benefited from the highly efficient LNsdraining of nanosized liposome and tumor cell-specific transformation of the non-fluorescent MAL to fluorescent protoporphyrin IX (PPIX), nTAL succeeded in targeting the SLNs and differentiated the metastatic from the benign ones with a positive correlation between PPIX generation and tumor cell infiltration in LNs. Moreover, the nTAL technology is capable of probing the early metastatic stage with a primary tumor size of 50 mm3. This study provides a new strategy for intraoperative visualization of real-time sentinel node dissection.

Land suitability analysis for blue-green infrastructure implementation in an urban stormwater management system in Surabaya, Indonesia

ABSTRACT In low-lying areas, pluvial, fluvial, and coastal flooding can combine to form complex flood events. In this context, integrated blue-green infrastructure (BGI) has emerged as a viable approach to environmentally friendly stormwater management. This study concentrated on two primary objectives: i) the development of a geospatial technology-based land suitability map for BGI implementation and ii) the identification of optimal BGI nodes and corridors. Surabaya City in the Delta Brantas region of Indonesia was selected as the location for a pilot study. The land suitability analysis considered five criteria (slope, potential drainage density, land use/land cover, soil types, and proximity to roads), and the analytic hierarchy process (AHP) was employed to assign weights to these criteria before calculating a BGI index using the raster calculator tool in GIS. A BGI index map was then employed to identify potential BGI nodes on public land and priority corridors. The eastern and western regions of Surabaya, which are characterized by agriculture, fishponds, salt ponds, and mangrove forests, were identified as prime candidates for BGI implementation. Areas of public land under the jurisdiction of the city government, such as government offices, police stations, and educational zones, also offered the opportunity for BGI initiatives.

Aviation armament system-of-systems modeling and identification method of vulnerable nodes based on interdependent network

Aiming at the problem that it is difficult to build model and identify the vulnerable equipment for aviation armament System-of-Systems (SoS) due to complex equipment interaction relationships and high confrontation, the interdependent network theory is introduced to solve it. Firstly, a two-layer heterogeneous interdependent network model for aviation armament SoS is proposed, which reflects the information interaction, functional dependency and inter-network dependence effectively. Secondly, using the attack cost to describe the confrontation process and taking the comprehensive impact on kill chains as the entry point, the node importance index and the attack cost measurement method are constructed. Thirdly, the identification of vulnerable nodes is transformed into the optimization problem of node combinatorial selection, and the vulnerable node identification method based on tabu search is proposed. Based on vulnerable nodes, a robustness enhancement strategy for aviation armament SoS network is presented. Finally, the above methods are used to an aerial confrontation SoS, and the results verify the rationality and effectiveness of the proposed methods.

Multi-Scale Higher-Order Dependencies (MSHOD): Higher-Order Interactions Mining and Key Nodes Identification for Global Liner Shipping Network

Liner shipping accounts for over 80% of the global transportation volume, making substantial contributions to world trade and economic development. To advance global economic integration further, it is essential to link the flows of global liner shipping routes with the complex system of international trade, thereby supporting liner shipping as an effective framework for analyzing international trade and geopolitical trends. Traditional methods based on first-order global liner shipping networks, operating at a single scale, lack sufficient descriptive power for multi-variable sequential interactions and data representation accuracy among nodes. This paper proposes an effective methodology termed “Multi-Scale Higher-Order Dependencies (MSHOD)” that adeptly reveals the complexity of higher-order interactions among multi-scale nodes within the global liner shipping network. The key step of this method is to construct high-order dependency networks through multi-scale attributes. Based on the critical role of high-order interactions, a method for key node identification has been proposed. Experiments demonstrate that, compared to other methods, MSHOD can more effectively identify multi-scale nodes with regional dependencies. These nodes and their generated higher-order interactions could have transformative impacts on the network’s flow and stability. Therefore, by integrating multi-scale analysis methods to mine high-order interactions and identify key nodes with regional dependencies, this approach provides robust insights for assessing policy implementation effects, preventing unforeseen incidents, and revealing regional dual-circulation economic models, thereby contributing to strategies for global, stable development.

An interactive 3D atlas of sentinel lymph nodes in breast cancer developed using SPECT/CT

BackgroundThe identification and assessment of sentinel lymph nodes (SLNs) in breast cancer is important for optimised patient management. The aim of this study was to develop an interactive 3D breast SLN atlas and to perform statistical analyses of lymphatic drainage patterns and tumour prevalence.MethodsA total of 861 early-stage breast cancer patients who underwent preoperative lymphoscintigraphy and SPECT/CT were included. Lymphatic drainage and tumour prevalence statistics were computed using Bayesian inference, non-parametric bootstrapping, and regression techniques. Image registration of SPECT/CT to a reference patient CT was carried out on 350 patients, and SLN positions transformed relative to the reference CT. The reference CT was segmented to visualise bones and muscles, and SLN distributions compared with the European Society for Therapeutic Radiology and Oncology (ESTRO) clinical target volumes (CTVs). The SLN atlas and statistical analyses were integrated into a graphical user interface (GUI).ResultsDirect lymphatic drainage to the axilla level I (anterior) node field was most common (77.2%), followed by the internal mammary node field (30.4%). Tumour prevalence was highest in the upper outer breast quadrant (22.9%) followed by the retroareolar region (12.8%). The 3D atlas had 765 SLNs from 335 patients, with 33.3–66.7% of axillary SLNs and 25.4% of internal mammary SLNs covered by ESTRO CTVs.ConclusionThe interactive 3D atlas effectively displays breast SLN distribution and statistics for a large patient cohort. The atlas is freely available to download and is a valuable educational resource that could be used in future to guide treatment.

A graph convolutional network model based on regular equivalence for identifying influential nodes in complex networks

The identification of influential nodes in complex networks is an interesting problem. Traditional approaches, such as metric- and machine learning-based approaches, often fail to adequately consider the network structure or node features, limiting their applications in certain scenarios. Deep learning-based methods, particularly those using graph neural networks (GNNs), have yielded remarkable results in this field. However, there is a lack of utilization of the regular equivalence-based similarity between nodes to feature construction, which is critical to deep learning-based methods. Nevertheless, regular equivalence-based similarity is extremely suitable to the quantification of the similarity between influential nodes, as these nodes usually have similar structural properties but do not require common neighbors. Building upon graph convolutional network (GCN, a specific type of GNN) and the regular equivalence-based similarity, this study proposes a novel method for identifying influential nodes in complex networks. Particularly, the regular equivalence-based similarities between nodes are considered to construct node features; a GCN is enhanced to improve the identification of influential nodes. Experiments on several datasets indicate that the proposed method is superior to state-of-the-art methods, demonstrating the advantages of the proposed feature construction based on regular equivalence-based similarity over the identification of influential nodes.

Sentinel lymph node biopsy in early breast carcinoma using fluorescein sodium and methylene blue dye: A validation study.

The availability of radioisotopes for sentinel lymph node biopsy (SLNB) in breast carcinoma is limited in low- and middle-income countries and thus the need for other reliable tracers exists. We aimed to validate the effectiveness of fluorescein sodium (FS) together with methylene blue dye (MBD) for patients with node-negative early breast carcinoma in a prospective cross-sectional study. Patients underwent SLNB using FS and MBD followed by axillary dissection to validate results. Sentinel lymph node (SLN) identification rate and false negative rate were assessed for all three tracers/combinations (MBD, FS, and MBD + FS). We concluded that SLNB using a combination of FS and MBD has an acceptable rate of SLN identification but the addition of FS provided no additional benefit.

Identifying critical nodes in interdependent networks by GA-XGBoost

Once a critical node is destroyed, the interdependent network is prone to experience severe cascading failure. Due to the coupling, traditional methods are challenging to apply to interdependent networks. Here, we propose a novel comprehensive model based on machine learning. The main work of this data-driven approach is to train the model on a small set of nodes (5 % of the graph) and do the critical node identification on the rest. We collect node centrality indicators to describe the node features and provide informative input data from different dimensions. The uniform node sampling is improved to cluster oversampling, which combines K-means and Synthetic Minority Over-sampling Technique (SMOTE) to select and recreate uniformly distributed training samples. We optimize the XGBoost based on the Genetic algorithm (GA) to overcome the instability of manual parameters. Kendall's τ correlation coefficient, Jaccard similarity coefficient, R2, and RMSE are used as the model performance evaluation metrics. Experiment results confirm that the proposed GA-XGBoost model outperforms others, demonstrating higher adaptability and stability in various situations. The heuristic algorithm-optimized machine learning model offers a viable solution for identifying critical nodes in interdependent networks, which is of great significance for controlling virus propagation and preventing failures.

IoT-based trusted wireless communication framework by machine learning approach

The traditional Radio-Frequency Systems (RFS) authentication methods, designed to ensure secure data transmission on the web, may not always effectively prevent adversaries from gaining access to concealed IDs or asymmetric cryptography through infiltrative, side-channel, training, and computer attacks. In contrast, Unaccounted Information (UAI) has the potential to exploit irregularities in production systems to automatically identify microchips, offering a highly robust and cost-effective security solution. This approach introduces RFS-UAI, a deep neural network-based system that efficiently manages wireless node identification by leveraging synthetic RFS characteristics of remote controls (Tx) learned through supervised methods in Wireless Sensor Networks (WSN). Unlike traditional methods that require the development of specialized transistors for UAI or semantic segmentation, this approach utilizes the existing asymmetrical RFS communication networks. Similar to the way the human brain processes information, Rx handles the entire device identification process at the gateway. According to test results, which include assessing process capability at a specified 65 nm threshold voltage and characteristics such as Local Oscillator (LO) misalignment and I-Q disparity using a probabilistic model with 52 hidden units, the system can distinguish up to 4800 transmitters with a remarkable 99.9 % accuracy under various channel conditions, all without the need for regular preambles. This recommended method can serve as a standalone security measure or be integrated into a biometric identification system.

Travel Characteristics and Vulnerability Analysis of Road Resource Utilization Based on Taxi GPS Data

Residents’ travel and logistics are greatly affected by urban transportation networks, which are one of the most important supports for urban socio-economic activities. Urban transportation systems tend to cripple when faced with challenges such as natural hazards and social unrest. This paper proposes a framework for a vulnerability analysis of urban road networks (URNs) based on real traffic flows with GPS data. An improved K-shell critical node identification method is proposed based on structural and traffic characteristics. Then, a cascade failure model is proposed to analyze the structural and functional vulnerability of the URN by combining the load capacity model and the vulnerability model. This paper takes the Harbin main city URN as an example and first analyzes the passenger travel distribution and the relationship between travel orders, population and POI. Four deliberate attack methods are proposed to analyze the vulnerability of the URN under deliberate attack on commute days and rest days. The experimental results show that URNs exhibit intense vulnerability, with the fastest cascading failure occurring based on improved K-shell node failure. Furthermore, URNs are more vulnerable on rest days compared to commuter days. These findings could be used to inform a vulnerability-based spatiotemporal design of UBNs and provide theoretical support for managing traffic congestion on different days.

Network analysis of gut microbial communities reveal key genera for a multiple sclerosis cohort with Mycobacterium avium subspecies paratuberculosis infection

BackgroundIn gut ecosystems, there is a complex interplay of biotic and abiotic interactions that decide the overall fitness of an individual. Divulging the microbe-microbe and microbe-host interactions may lead to better strategies in disease management, as microbes rarely act in isolation. Network inference for microbial communities is often a challenging task limited by both analytical assumptions as well as experimental approaches. Even after the network topologies are obtained, identification of important nodes within the context of underlying disease aetiology remains a convoluted task. We therefore present a network perspective on complex interactions in gut microbial profiles of individuals who have multiple sclerosis with and without Mycobacterium avium subspecies paratuberculosis (MAP) infection. Our exposé is guided by recent advancements in network-wide statistical measures that identify the keystone nodes. We have utilised several centrality measures, including a recently published metric, Integrated View of Influence (IVI), that is robust against biases.ResultsThe ecological networks were generated on microbial abundance data (n = 69 samples) utilising 16 S rRNA amplification. Using SPIEC-EASI, a sparse inverse covariance estimation approach, we have obtained networks separately for MAP positive (+), MAP negative (-) and healthy controls (as a baseline). Using IVI metric, we identified top 20 keystone nodes and regressed them against covariates of interest using a generalised linear latent variable model. Our analyses suggest Eisenbergiella to be of pivotal importance in MS irrespective of MAP infection. For MAP + cohort, Pyarmidobacter, and Peptoclostridium were predominately the most influential genera, also hinting at an infection model similar to those observed in Inflammatory Bowel Diseases (IBDs). In MAP- cohort, on the other hand, Coprostanoligenes group was the most influential genera that reduces cholesterol and supports the intestinal barrier.ConclusionsThe identification of keystone nodes, their co-occurrences, and associations with the exposome (meta data) advances our understanding of biological interactions through which MAP infection shapes the microbiome in MS individuals, suggesting the link to the inflammatory process of IBDs. The associations presented in this study may lead to development of improved diagnostics and effective vaccines for the management of the disease.

Enhancement on CT for preoperative diagnosis of metastatic lymph nodes in thyroid cancer: a comparison across experience levels.

To investigate the diagnostic performance and interobserver agreement of quantitative CT parameters indicating strong lymph node (LN) enhancement in differentiated thyroid cancer (DTC), comparing them with qualitative analysis by radiologists of varying experience. This study included 463 LNs from 399 patients with DTC. Three radiologists independently analyzed strong LN enhancement on CT. Qualitative analysis of strong enhancement was defined as LN cortex showing greater enhancement than adjacent muscles on the arterial phase. Quantitative analysis included the mean attenuation value (MAV) of LN on arterial phase (LNA) and venous phase (LNV), LNA normalized to the common carotid artery (NAVCCA), internal jugular vein (NAVIJV), and sternocleidomastoid muscle (NAVSCM), attenuation difference [AD; (LNA - MAVSCM)], and relative washout ratio [((LNA - LNV)/LNA) × 100]. The interobserver agreement and diagnostic performance of the quantitative and qualitative analyses were evaluated. Interobserver agreement was excellent for all quantitative CT parameters (ICC, 0.83-0.94) and substantial for qualitative assessment (κ = 0.61). All CT parameters except for LNV showed good diagnostic performance for metastatic LNs (AUC, 0.81-0.85). NAVCCA (0.85, 95% CI: 0.8-0.9) and AD (0.85, 95% CI: 0.81-0.89) had the highest AUCs. All quantitative parameters except for NAVIJV had significantly higher AUCs than qualitative assessments by inexperienced radiologists, with no significant difference from assessments by an experienced radiologist. Quantitative assessment of LN enhancement on arterial phase CT showed higher interobserver agreement and AUC values than qualitative analysis by inexperienced radiologists, supporting the need for a standardized quantitative CT parameter-based model for determining strong LN enhancement. When assessing strong LN enhancement in DTC, quantitative CT parameters indicating strong enhancement can improve interobserver agreement, regardless of experience level. Therefore, the development of a standardized diagnostic model based on quantitative CT parameters might be necessary. Accurate preoperative assessment of LN metastasis in thyroid cancer is crucial. Quantitative CT parameters indicating strong LN enhancement demonstrated excellent interobserver agreement and good diagnostic performance. Quantitative assessment of contrast enhancement offers a more objective model for the identification of metastatic LNs.

On the Impact of Renewable Generation on the Sicilian Power System in Near-Future Scenarios: A Case Study

This paper was conceived to investigate some central issues related to the upheaval of current energy scenarios in Sicily. New power connection lines that are about to be built in the Mediterranean area, planned with a view to a constantly increasing renewable generation, encourage the carrying out of analyses on how the Sicilian electric power system will be able to make itself ready to support large power injections, especially due to new renewables plants that will be established in the region soon. This study, carried out in close collaboration with the Italian TSO Terna S.p.A and the University of Palermo, defines what the impacts of new renewable power plants will be on the Sicilian power transmission grid under intact and non-intact grid conditions. This study consists of steady-state simulations carried out using WinCreso® in two energy scenarios estimated for the years 2024 and 2027, based on real connection requests by producers to Terna, and allows one to go beyond the studies conducted so far on a 2030 basis through the precise identification of network nodes or lines in difficulty. Finally, as well as presenting an interesting case study due to Sicily’s strategic position in the Mediterranean Sea, this article proposes a methodological approach that can easily be adopted in other contexts and by other TSOs to analyze similar situations.