Search Results (78)

Community detection is a key task in the analysis of complex networks, particularly in social network analysis, where uncovering cohesive and well-separated groups is essential for understanding structural organization and interaction patterns. Many existing centroid-based community detection methods rely primarily on node degree for centroid selection, which often leads to centroid crowding and insufficient spatial separation among communities. To address these limitations, this paper proposes Degree–Distance Centroid–Community Detection with Influential Nodes (DDC-CDIN), a distance-driven and influence-aware community detection framework. In the proposed approach, nodes are first ranked according to an Enhanced Degree Centrality measure that incorporates degree information, neighbourhood structure, and local clustering characteristics to identify structurally influential nodes. Centroids are then selected iteratively from the top-ranked influential nodes by maximizing shortest-path distances, ensuring that the chosen centroids are both representative and well dispersed within the network. Once the centroids are determined, the remaining nodes are assigned to communities based on the minimum geodesic distance, yielding compact, clearly separated clusters. Extensive experiments across multiple real-world networks show that DDC-CDIN achieves competitive performance compared to traditional centroid-based and modularity-driven methods in terms of modularity, community cohesion, and boundary clarity. The results indicate that jointly incorporating influence-aware node ranking with distance-based centroid dispersion effectively mitigates centroid crowding and enhances overall community detection quality. These findings demonstrate the effectiveness and robustness of DDC-CDIN for detecting well-structured and topologically coherent communities in complex networks. Full article

Archaeological sites characterized by the coexistence of extensive above-ground terrain and hypogeum structures present major challenges for accurate and comprehensive geospatial documentation. Conventional survey approaches—such as static terrestrial laser scanning (TLS), total-station measurements, and aerial photogrammetry—often suffer from operational constraints, particularly in the presence of narrow underground spaces, low or absent illumination, harsh environmental conditions, and restrictions on UAV deployment. Additional complexity arises when both surface and subterranean elements must be consistently georeferenced to a common global reference system, especially where establishing a traditional topographic–geodetic control network is impractical. Within the framework of the EIMAWA Egyptian–Italian Mission conducted by the University of Milano since 2018, the Geomatics group of the University of Bologna designed and implemented a multi-scale multi-technique 3D documentation workflow, with a prominent role assumed by Simultaneous Localization and Mapping (SLAM) mobile laser scanning. The approach was supported by GNSS measurements providing centimetric accuracy. SLAM was employed to document both the surface necropolis and multiple hypogeal tombs, enabling rapid acquisition of dense three-dimensional data in environments where traditional techniques are limited. All datasets were integrated within a unified reference system, resulting in a coherent, multi-layered spatial dataset representing both landscape and underground spaces. The results demonstrate that SLAM can produce dense point clouds that document at few-centimetric level accuracy and continuously both above- and below-ground contexts. Quantitative analyses of the co-registration and mutual alignment of multiple SLAM datasets confirm a high degree of internal consistency, further enhanced through post-processing refinement. Overall, the experience indicates that this solution represents a practical and reliable technique for complex archaeological surveying. Full article

Aided by quantum sources, quantum metrology helps enhance measurement precision. Here, we construct a theoretical model for quantum imaging based on squeezed states and present the corresponding numerical results. Through discretization and quantum Fisher information theory, we investigate the two-point resolution and spatial multi-parameter estimation of optical fields with unknown spatial distributions. We calculate and compare imaging results based on squeezed vacuum states, coherent states, and squeezed coherent states; our results show that squeezed coherent states yield greater quantum Fisher information, which can effectively improve imaging quality. In addition, we analyze the influence of imaging basis functions, degree of squeezing, quantum correlations, and other factors on imaging performance. The proposed quantum imaging model and computational method can be extended to more complex scenarios, such as multi-mode squeezed-state imaging schemes and incoherent imaging systems. In the future, this approach is expected to find applications in practical imaging systems, including Raman microscopy and stimulated Brillouin scattering imaging. Full article

This paper introduces a novel hybrid framework called Interval-Valued Picture Fuzzy Soft Rough Sets (IVPFSRS) designed to address complex uncertainty in multi-criteria group decision-making (MCGDM) problems. The model achieves a synergistic integration of three powerful mathematical theories: interval-valued picture fuzzy sets (IVPFS) for representing nuanced, interval-valued degrees of membership, neutrality, and non-membership; soft sets for parameterized problem formulation; and rough sets for handling data granularity and approximation under incompleteness. We formally define the IVPFSRS framework, investigate its fundamental properties and algebraic operations, and develop a comprehensive MCGDM algorithm with explicit weight incorporation to address the critical role of criterion importance. The effectiveness and robustness of the proposed approach are demonstrated through a detailed illustrative example of administrative position selection and a systematic comparative analysis with existing models. Results show that the IVPFSRS framework provides a more powerful, flexible, and logically coherent tool for robust decision making in highly uncertain and information-deficient environments. The proposed framework complements recent advancements in cloud-rough integration for large group decision making while offering unique advantages in parameterized three-way uncertainty representation and structured multi-criteria evaluation. Full article

The global transition of offshore wind energy into deep-water environments necessitates precise modeling of the complex, nonlinear dynamic responses of floating offshore wind turbines (FOWTs) to stochastic loads. Traditional industry-standard simulation tools often rely on potential flow theory, which neglects critical viscous effects and requires manual, empirical tuning of damping coefficients, reducing model reliability, while CFD modeling demands large computational resources. This paper introduces an application of advanced neural network techniques to model the coupled dynamic response of FOWTs under varied ocean conditions, reducing the simulation time required for training high-fidelity models. The architecture was trained using experimental data from the OC5 semi-submersible platform under the LC4.1 load case and further validated across a matrix of heterogeneous conditions, encompassing steady, turbulent, and irregular wind and wave environments. Results demonstrate exceptional predictive accuracy across coupled degrees of freedom (Heave, Pitch, and Surge), with the model achieving a coefficient of determination (${\mathrm{R}}^2>0.9$) and maintaining superior phase coherence without discernible time lag. Power spectral density analysis confirms the model’s robust ability to capture resonant frequencies and hydrodynamic restoration across varied sea states. This data-driven framework provides a robust, near-instantaneous alternative for simulating FOWTs global dynamics. By successfully capturing complex nonlinear interactions and inertial effects, the methodology enables rapid decision-making in preliminary design, real-time digital twinning, and accelerated long-term fatigue analysis for safety-critical offshore applications. Full article

Community detection plays a central role in understanding the modular structure of complex networks. This work introduces Spider Community Detection, a hybrid local–global algorithm that constructs communities through a depth-bounded geodesic expansion process. Each spider originates from a structurally strong seed node selected using a composite score based on degree, triangle participation, and local clustering. From each seed, the algorithm grows a localized spider-shaped subgraph through bounded breadth-first exploration, where candidate nodes are evaluated using true modularity gain together with a triangle-closure signal. After the initial spider construction, the method applies modularity-guided attachment of the remaining vertices, Louvain-style local refinement, and greedy merge matching under conductance constraints to reconcile local structure with global partition coherence. Experimental evaluations on real and benchmark datasets, including Karate Club, High School, Political Blogs, Cora, and DBLP, show that Spider produces partitions that are competitive with the established methods in terms of ground-truth recovery and structural quality, while yielding communities with sharp boundaries under conductance-sensitive evaluation. Full article

We investigate the structure of correlation singularities for the Laguerre–Gauss beam of order $n=0$ and $m=2$ in the transverse plane during the propagation of the beam in the beam-wander model. We explicitly derive analytical expressions for the cross-spectral density of the corresponding beam order and the analytic expressions representing the singular behavior. We also verify that the singular points disappear at certain z values and reappear at other z values as shown in the previous numerical study. We investigate the dependence of the absolute value of the complex degree of coherence $\mu$ on the parameter $\delta$ of the beam-wander model during the propagation of the Laguerre–Gauss beam in the corresponding order. The complex degree of coherence depends not only on $\delta$ but also on the relative positions of two transverse observation points ${\mathit{\rho }}_1$ and ${\mathit{\rho }}_2$, as well as on the propagation variable z for the fixed values of the beam waist and the wavelength of the Laguerre–Gauss beam. Experiments on $\mu$ can demonstrate the range of the applicability of the beam-wander model in the turbulent atmosphere. Finally, we examine the orbital angular momentum flux density of the beam and confirm that the general behaviors of the previous studies also hold for $m=2$. Full article

Universities must prepare future professionals with critical thinking skills to effectively address complex social and environmental challenges. In engineering degrees, while technical competences are strongly developed, the acquisition of ethical and social skills remains challenging within the framework of traditional subjects. This paper explores how the integration of the Sustainable Development Goals (SDGs), following a competence-based educational model, can contribute to the development of ethical, social, and sustainability-related competences in an engineering degree. A set of activities, exercises, and tasks grounded in real professional contexts was designed to encourage students to explore sustainable solutions to social and environmental problems, supported by experiential learning and visible thinking routines. These activities were coherently aligned through interdisciplinary coordination among professors teaching in the degree. The results indicate that the proposed approach was positively received by both professors and students, who valued its contribution to personal and professional development. Students demonstrated enhanced critical thinking and greater awareness of the social and environmental implications of engineering decisions. This work aims to support and inspire educators seeking to integrate SDGs into their teaching by offering a feasible, transferable, and easy-to-implement framework for embedding ethical, social and sustainability-related competences in engineering teaching. Full article

This study explores the integration of Georgi Lozanov’s Suggestopedia with Alain Berthoz’s theory of simplexity as a pedagogical paradigm for inclusive and creative educational design. The research, conducted within the specialization courses for educational support at the University of Salerno, involved 230 trainee teachers engaged in a participatory action-research process aimed at translating suggestopedic principles, positive suggestion, music, and relational harmony into didactic planning. Through a combination of theoretical training, laboratory design activities, and reflective evaluation, participants produced 21 interdisciplinary educational projects assessed according to the properties and rules of simplexity. The results show a high degree of methodological coherence, aesthetic quality, and curricular inclusiveness, with music emerging as a key factor in fostering attention, cooperation, and emotional engagement. Data analysis indicates that the fusion of suggestopedic and simplex approaches promotes adaptive, modular, and meaning-oriented design processes that enhance teachers’ creativity and metacognitive awareness. Overall, the findings highlight the educational value of a pedagogy of resonance, in which body, mind, and environment interact harmoniously. The study concludes that the suggestopedic—simplex model represents a regenerative framework for contemporary didactics, capable of transforming complexity into harmony and restoring to education its aesthetic, relational, and human dimension. Full article

Heatwaves pose increasing risks to human health and socio-economic systems, yet their spatiotemporal organization and underlying synergistic mechanisms remain insufficiently understood, particularly with respect to daytime and nighttime processes. Using a dual identification framework combining absolute and relative temperature thresholds, this study systematically investigates the spatiotemporal evolution of daytime and nighttime heatwaves across China during 1961–2022. A complex network approach is further introduced to characterize the interannual co-variability and interdecadal structural evolution of heatwave activity from a system-level perspective. Results reveal a pronounced interdecadal transition in the early 1990s, accompanied by a fundamental reorganization of heatwave co-occurrence networks. Heatwave frequency exhibits a clear post-transition desynchronization, characterized by a sharp decline in network connectivity and fragmented local clustering, indicating a shift from large-scale, circulation-dominated coherence toward increasingly localized and heterogeneous heatwave occurrences. In contrast, heatwave duration shows an opposite evolution, with significantly enhanced spatial synchronization after the transition. Degree centrality and clustering coefficients increase markedly, and high-connectivity cores expand from coastal regions into inland areas, including North, Central, and Northwest China. This coexistence of desynchronized heatwave occurrence and strongly synchronized persistence suggests an emerging high-risk regime in which heatwaves occur more randomly but, once initiated, tend to persist coherently across large regions. Furthermore, a dual-layer network analysis reveals previously undocumented cross-temporal coupling between daytime and nighttime heatwaves, with pronounced regional differences. The middle and lower reaches of the Yangtze River are more strongly influenced by local processes, whereas northern China is increasingly governed by large-scale circulation control and enhanced regional clustering after the transition. These findings demonstrate that complex network analysis provides a powerful framework for uncovering hidden structural changes in extreme heat events and offer new insights into the evolving risks of compound and persistent heatwaves under climate change. Full article

Urban sustainability has become a central theme in contemporary city planning and policy-making, reflecting the growing need to address complex environmental, social, and economic challenges. However, the range of metrics used to measure sustainability often results in fragmentation and inconsistency, limiting their practical application. The present study aims to analyze and systematize the urban sustainability indicators most commonly found in the literature and employed at the international level. The research seeks to develop a comprehensive framework of economic, environmental, and social indicators, providing a more coherent and standardized tool to support informed and effective urban regeneration strategies. In particular, in this work a critical examination of the indicators is carried out, highlighting the inherent limitations, potential distortions, and the standardizability level. To ensure more reliable and transparent measurement tools, the outcome of the analysis is the definition of a structured abacus of key urban sustainability indicators, classified across three main domains (economic, environmental, and social), able to orient the choices processes to promote sustainable cities development. Overall, a total of 85 indicators have been identified (27 economic, 36 environmental, 22 social), of which 47 show a high degree of standardization, 37 a moderate level, and only 1 a low level. The majority of the selected indicators are fully operational at the city scale, strengthening their applicability in supporting local governance and urban transformation processes. Full article

The transition toward sustainability in the electric power sector, driven by increasingly renewable integration, has amplified the need to understand complex market dynamics. This study addresses a critical gap in the existing literature by presenting a systematic and reproducible methodology for profiling generating-unit operators’ macro-behaviour in the Italian Ancillary Services market (MSD). Focusing on the Northern zone (NORD) during the pivotal period of 2022–2024, a stability-driven k-means clustering framework is applied to a dataset of capacity-normalized features from the day-ahead market (MGP), intraday market (MI), and MSD. The number of clusters is determined using the Gap Statistic with a 1-SE criterion and validated with bootstrap stability (Adjusted Rand Index), resulting in a robust and reproducible 13-group taxonomy. The use of up-to-date data (2022–2024) enabled a unique investigation into post-2021 market phenomena, including the effects of geopolitical events and extreme price volatility. The findings reveal clear operator-coherent archetypes ranging from units that mainly trade in the day-ahead market to specialists that monetize flexibility in the MSD. The analysis further highlights the dominance of thermoelectric and dispatchable hydro technologies in providing ancillary services, while illustrating varying degrees of responsiveness to price signals. The proposed taxonomy offers regulators and policymakers a practical tool to identify inefficiencies, monitor concentration risks, and inform future market design and policy decisions. Full article

The coordinated development of a complex system refers to the harmonious and coherent evolution of its subsystems. From the perspective of the coordinated development of ecological–economic–social complex systems, this paper analyzes the coordinated development mechanism (CDM) of the mariculture ecological–economic–social (MEES) complex system, constructs a coordinated development evaluation indicator system for the MEES complex system, and adopts the comprehensive evaluation model and the coupling coordination degree (CCD) model to empirically analyze the coordinated development level of the MEES complex system in China from 2009 to 2020. The results show that the comprehensive development level of China’s MEES complex system has improved significantly during this period, with the comprehensive development index increasing from 0.25 in 2009 to 0.76 in 2020, transitioning from a poor to an excellent status. Simultaneously, the CCD of the system increased progressively, experiencing phases of near dissonance, barely coupling coordination, primary coordination, and intermediate coordination, before finally reaching a stage of good coordination. Based on these findings, we further discuss and propose countermeasures to promote the coordinated development of China’s MEES complex system. Full article

Topic modeling in digital news faces the dual challenge of thematic overlap and evolving semantic boundaries, especially in morphologically rich languages like Turkish. To address these obstacles, we propose a topic modeling framework enhanced with knowledge graphs that explicitly incorporates uncertainty in topic assignment. We focus on the diversity of Fuzzy Latent Semantic Analysis (FLSA) and compare the performance with Latent Dirichlet Allocation (LDA), BERTopic, and embedding-based Top2Vec on a corpus drawn from two Turkish news agencies. We evaluate each model using standard metrics for topic coherence, diversity, and interpretability. We propose Shannon entropy of node-degree distributions to measure the network complexity of knowledge graphs as topic similarity. Our results indicate that FLSA achieves perfect topic diversity, 1.000 and improved interpretability, 0.33 over LDA, 0.09 while also enhancing coherence, 0.33 vs. 0.27. Top2Vec demonstrates the strongest coherence, 0.81 and interpretability, 0.78 with high diversity, 0.97, reflecting its capacity to form semantically cohesive clusters. Entropy analysis further shows that FLSA produces the most information-rich topic networks. These findings suggest that fuzzy modeling and embedding-based approaches offer complementary strengths, uncertainty-aware flexibility, and semantic precision, thereby improving topic discovery in complex, unstructured news environments. Full article

Mammary Paget disease (MPD) is a rare cutaneous malignancy associated with underlying ductal carcinoma in situ (DCIS) or invasive ductal carcinoma (IDC). Clinically, it appears as eczematous changes in the nipple and areola complex (NAC), which may include itching, redness, crusting, and ulceration; these symptoms can sometimes mimic benign dermatologic conditions such as nipple eczema, making early diagnosis challenging. A 56-year-old woman presented with persistent erythema and scaling of the left nipple, which did not respond to conventional dermatologic treatments: a high degree of suspicion prompted further investigation. Reflectance confocal microscopy (RCM) revealed atypical, enlarged epidermal cells with irregular boundaries, while line-field confocal–optical coherence tomography (LC-OCT) demonstrated thickening of the epidermis, hypo-reflective vacuous spaces and abnormally large round cells (Paget cells). These non-invasive imaging findings were consistent with an aggressive case of Paget disease despite the absence of clear mammographic evidence of underlying carcinoma: in fact, several biopsies were needed, and at the end, massive surgery was necessary. Non-invasive imaging techniques, such as dermoscopy, RCM, and LC-OCT, offer a valuable diagnostic tool in detecting Paget disease, especially in early stages and atypical forms. Full article