Search Results (6,135)

The present study is concerned with heteroclinic trajectories of a Caputo fractional-order Lorenz-type model ($0<\alpha \le 1$), a problem that has remained unresolved. Building upon previously known findings, we initially deduce two asymmetric heteroclinic connections that link the globally attracting equilibrium $E_0$, the repelling equilibrium $E_{+}$ (or $E_{-}$), and the globally attracting equilibrium $E_{-}$ (or $E_{+}$), where $E_0=(0,0,0)$ and $E_{\pm }=({\displaystyle \frac{bd\pm \sqrt{b^2{d}^2+4bc}}{2}},{\displaystyle \frac{bd\pm \sqrt{b^2{d}^2+4bc}}{2}},c+d\left({\displaystyle \frac{bd\pm \sqrt{b^2{d}^2+4bc}}{2}}\right))$. Additionally, a novel heteroclinic trajectory is identified for two separate regimes: (1) joining the repelling $E_0$ with the globally attracting $E_{+}$ ($E_{-}$, respectively), alongside a locally attracting $E_{-}$ ($E_{+}$, respectively); (2) joining the repelling $E_{-}$ ($E_{+}$, respectively) with the globally attracting $E_{+}$ ($E_{-}$, respectively), alongside a locally attracting $E_0$. All analytical claims are supported by numerical experiments. Full article

Metabolic diseases, including obesity and type 2 diabetes mellitus, represent a major global health burden and are closely linked to the composition and function of the gut microbiota. Advances in molecular methods have enabled detailed characterization of microbial communities and their interactions with diet, medications, and host physiology, positioning the microbiome as an active metabolic organ. However, the field faces persistent challenges in distinguishing causal relationships from associations, largely owing to substantial biological, exposure-related, and methodological heterogeneity. This review systematizes the principal lines of evidence connecting the gut microbiome to metabolic disorders and critically examines the sources of variability that limit reproducibility and cross-cohort transferability of these findings. We discuss the taxonomic, functional, and metabolite-based levels of microbiome analysis, evaluate the strengths and limitations of cross-sectional, case–control, cohort, and interventional study designs, and consider approaches for establishing causality, including fecal microbiota transplantation, Mendelian randomization, mediation analysis, causal diagrams, and triangulation of evidence. We conclude that only a comprehensive, standardized, and causally informed approach will allow reliable discrimination between true microbiota-driven effects and methodological artifacts, thereby advancing the integration of microbiome science into the management of metabolic diseases and diabetes. Full article

Poyang Lake is a typical floodplain lake hydrologically linked to the middle-lower Yangtze River. Its peripheral saucer-shaped sub-lakes undergo seasonal hydrological alternation between isolation and connectivity driven by annual water level fluctuations. To date, the coupling between seasonal hydrological rhythms and zooplankton metacommunity assembly in these saucer floodplain lakes remains poorly understood. In this study, four representative saucer sub-lakes within the Poyang Lake National Nature Reserve (Banghu, Zhonghuchi, Shahu and Dahuchi) were selected as research objects. Seasonal quantitative zooplankton surveys and synchronous hydro-physicochemical monitoring were conducted across four hydrological phases from 2012 to 2013: low-water isolation (spring), high-water connectivity (summer), water recession transition (autumn), and extreme low-water closure (winter). We systematically explored how water level fluctuations regulate zooplankton metacommunity assembly processes. In total, 95 zooplankton species were identified, among which rotifers (68 species) constituted the absolute dominant taxon, and 15 species were identified as year-round absolute dominants exhibiting regular seasonal succession in response to hydrological shifts. Our results demonstrate that hydrological connectivity plays a strong role in regulating the relative strength of species dispersal and environmental filtering. During spring and winter, complete hydrological isolation blocked inter-lake species exchange, making local environmental filtering the main driver of metacommunity assembly. Isolated sub-lakes harbored fewer total species but abundant endemic taxa, accompanied by pronounced spatial community heterogeneity. When water levels exceeded the 17.3 m threshold in summer, full water exchange between sub-lakes and the main lake coincided with widespread species dispersal, which appeared to represent the primary assembly process. The four sub-lakes shared 23 co-occurring species during this period, with highly homogenized community structures and minimal spatial differentiation (Global test: R = 0.47, p = 0.001). In autumn, receding water levels weakened inter-lake connectivity, such that environmental filtering and dispersal jointly structured zooplankton metacommunities; significant spatiotemporal differences were detected in zooplankton density, biomass and α-diversity (p < 0.05). Critical hydro-physicochemical factors of environmental filtering included water temperature, chlorophyll a, pH, electrical conductivity and turbidity, yet the dominant limiting factor varied spatially across sub-lakes due to divergent basin topography and water residence time. Species co-occurrence network analysis revealed that zooplankton communities achieved the highest stability during the fully connected summer phase. In early autumn water drawdown, species association networks became fragmented, and community stability reached its annual minimum. This study elucidates the mechanistic link between floodplain lake hydrological rhythms and zooplankton metacommunity assembly, providing theoretical support and baseline data for wetland biodiversity conservation and ecosystem management of Poyang Lake floodplains. Full article

Public electric vehicle (EV) charging increasingly relies on internet-connected platforms for metering, billing, roaming, and settlement. However, final billing records and charge detail records often provide limited evidence that a session result can be independently linked to the ordered metering data from which it was derived. This paper presents a reproducible blockchain-anchored Proof-of-Charge platform for generating tamper-evident and auditable EV charging receipts. The platform converts charging-session data into canonical receipts, computes cryptographic commitments over receipt content and ordered meter values, aggregates receipt hashes using a temporally ordered and domain-separated Merkle profile, and anchors compact batch commitments in a smart contract while keeping detailed records off-chain. A working prototype implements cross-language canonicalization checks, membership-proof generation, structured storage, batch anchoring, verification services, synthetic workload generation, dataset export, and local blockchain deployment. Across 50 measured runs covering 10 to 1000 receipts, all count reconciliations, batch-root checks, and on-chain comparisons passed. Mean receipt-pipeline latency ranged from 7.152 to 7.853 ms per receipt, with throughput from 127.54 to 141.23 receipts/s. A focused 1000-leaf proof sample produced a 2064-byte proof with ten sibling hashes. The results show that the platform can generate, anchor, and verify auditable EV charging receipts with reproducible performance while keeping detailed charging data off-chain. The proposed architecture provides a practical digital trust layer for internet-enabled EV charging and V2G-ready settlement workflows. Full article

Fast Reroute (FRR) after link failures is essential for carrier-grade Software-Defined Networking (SDN), yet hybrid deployments remain dominated by slow legacy routing convergence. This paper presents two port-state-driven FRR mechanisms for homogeneous and hybrid SDN networks. First, Port-State-Aware Fast Reroute (PSA-FRR) uses OpenFlow port-status events to trigger proactive, rule-based protection in the data plane. Second, Port-State-Aware Neural Fast Reroute (PSAR-FRR) formulates hybrid FRR as a controller-local multi-class classification problem and predicts the backup egress port from a port-centric state representation, enabling microsecond-scale decision latency. We evaluate the methods on the Abilene wide-area network (WAN) topology using Mininet with Open vSwitch (OVS) and a Ryu controller (homogeneous case) and Graphical Network Simulator-3 (GNS3) with Cisco IOS routers (hybrid baseline). In homogeneous SDN emulation, PSA-FRR restores connectivity within 30–100 ms under the evaluated configurations. In the hybrid baseline, conventional routing protocols converge in 13.8–256.1 s (Enhanced Interior Gateway Routing Protocol (EIGRP), Intermediate System to Intermediate System (IS-IS), Open Shortest Path First (OSPF), Border Gateway Protocol (BGP), and Routing Information Protocol (RIP)), confirming that control-plane recovery cannot meet a 50 ms target. Using the collected dataset, PSAR-FRR reduces controller decision time from 6.753 $\mathsf{\mu }$s (PSA-FRR rule evaluation) to 0.214 $\mathsf{\mu }$s (deep neural network (DNN) inference), a 31.5× speedup. These results show that port-state awareness combined with learned, controller-local policies can substantially reduce the decision-to-action latency of FRR, providing a practical path toward low-latency failure recovery in SDN migration scenarios. Full article

We address the challenging problem of predicting the biological quality of a water body based on a set of environmental and management-related drivers. While modelling approaches for predicting water quality from hydrological and pollution-load variables are well established, analogous tools for predicting biological status remain less consolidated. Both components are nevertheless required within the Water Framework Directive to assess ecological status (ES) and support restoration planning. This paper concentrates on the latter challenge by presenting an experience developed over four heavily impacted rivers in Regione Lombardia (Northern Italy). Rutinary environmental, hydromorphological and biological data were systematized to develop a predictive framework linking management-related pressures to the macroinvertebrate component of ecological status. A rule-based classifier, denominated MacroSimply, was developed and compared with alternative statistical and machine learning approaches: logistic regression, a classification tree (CHAID), and a multilayer perceptron neural network. The tested models exhibited different strengths and weaknesses: Logistic regression provided a good balance between predictive performance and interpretability; the classification tree generated transparent threshold-based decision rules; and the neural network captured potentially complex non-linear relationships, albeit with reduced interpretability. MacroSimply achieved predictive performance comparable to the alternative approaches, and performing even higher reliability, while maintaining full transparency of the structure and a direct connection between predictors and management actions. The proposed framework was subsequently implemented in a spreadsheet-based tool to be easily used in restoration planning exercises. Although the obtained model should not be considered the definitive modelling solution even for our particular case, the results suggest that the adopted rule-based approach represents a working, valuable option preferrable to more complex data-driven methods when interpretability, reproducibility and practical applicability are key requirements for environmental management. Full article

Wine tourism is increasingly recognized as a key driver of sustainable rural development and the integrated enhancement of cultural heritage. However, in many production areas, including Southern Italy, the sector remains fragmented, with limited integration between wine production, cultural heritage, and territorial identity, restricting its potential for experiential and sustainable tourism. This study addresses this gap by proposing a relational, heritage-based approach that connects wine landscapes with archaeological and cultural resources. The aim of the research is to examine how wine tourism can contribute to sustainable territorial development in Basilicata (Southern Italy) through an integrated interpretative framework. This study also develops a georeferenced wine tourism map and evaluates the readiness of wineries to adopt integrated wine tourism models. Methodologically, the research is structured into three phases: (i) literature review and data collection on wine-related cultural heritage; (ii) development of a georeferenced map identifying 21 points of interest within the main regional DOC areas; and (iii) stakeholder engagement through participation in sector events and a strategic questionnaire administered to 30 wineries (response rate: 50%). Data were analysed using spatial interpretation and descriptive and thematic analysis. Results show that wine tourism in Basilicata is consolidating but remains characterized by territorial imbalance and weak integration between wine production and cultural heritage. Wineries demonstrate strong territorial identity and growing interest in experiential tourism, while communication and interpretation tools remain underdeveloped. The wine tourism map effectively integrates spatial and cultural data and supports cooperation among stakeholders. In conclusion, integrated mapping-based wine tourism can foster sustainable rural development in inner areas and provide a replicable framework linking heritage, identity, and tourism. Full article

This paper shows how the ideas of Archimedes about integrating “mechanical methods” and formal reasoning can be connected with the modern-day use of three computer programs—Wolfram Alpha, Maple, and Excel—in exploring topics related to the elementary theory of numbers. Explorations deal with the subsequences of integer sequences through the step-by-step elimination of every other term obtained in the previous step. This process, resembling the sieve of Eratosthenes and some modern-day sieving algorithms, is applied to tetrahedral numbers stemming from an ancient tradition to associate integers with geometric shapes and social contexts. It is demonstrated that symbolic computations in Wolfram Alpha enable generalization in the construction of sieves that is confirmed by Maple and a spreadsheet. This conceptual paper addresses one of the aims of the Special Issue by demonstrating the duality of mathematics and technology in the sense that whereas the latter facilitates new approaches to knowledge acquisition, the former can be used to improve the efficiency of computations by reflecting on the results made possible by these approaches. The activities conducted advocate for the value of a pedagogical framework that integrates ancient ideas, digital tools, and elementary number theory in the education of mathematics teachers. Reflective comments by teacher candidates are included as appropriate and linked to established educational frameworks to illustrate conformity. Full article

To address the difficulty in accurately characterizing the fault current response of renewable energy grid-connected devices during high-/low-voltage ride-through, this paper proposes a fault current response modeling and parameter identification method based on adaptive nonlinear compensation. First, with the fault voltage and pre-fault operating point as input variables, a basic quadratic equivalent model is established to describe the main variation characteristics of active and reactive currents during high-/low-voltage ride-through. Second, nonlinear compensation terms are introduced into the basic model to correct the response deviation caused by the simplification of fast electromagnetic control links in the electromechanical transient equivalent process, thereby improving the representation capability of the model for complex fault current characteristics. Furthermore, considering that the structural parameters of the nonlinear compensation terms are difficult to directly identify using the traditional least squares method, a differential evolution–ridge regression (DE–Ridge) hierarchical identification method is proposed. In this method, the differential evolution algorithm is used in the outer layer to adaptively optimize the nonlinear structural parameters, while ridge regression is used in the inner layer to solve the corresponding linear coefficients. Case study results show that, compared with the traditional quadratic equivalent model and the fixed nonlinear compensation model, the proposed method further reduces the fault current identification error on the validation set and improves the identification accuracy and generalization capability of fault current responses during high-/low-voltage ride-through. Full article

Urban forests are expected to cool cities, sustain biodiversity, and distribute access to nature; however, evidence is often produced at scales that do not align with implementation. This structured evidence map characterizes English-language articles and reviews indexed in OpenAlex from 2000 to 15 July 2026 and interprets the patterns through three implementation cases. Five title-and-abstract searches returned 1201 records; 1103 remained after DOI/OpenAlex-ID deduplication, and a precision-oriented deterministic screen retained a 140-record core set. Metadata and abstracts were coded for outcome, scale, method, and management/governance language. Management/governance-related language occurred in 110 records, thermal outcomes in 74, biodiversity in 59, and explicit equity terms in 26. Thirty-three records were coded at two or more specified spatial scales and seven at all three; 14 jointly contained thermal and equity codes. These are record-level co-occurrences, not evidence of causal integration. Melbourne, Singapore, and Barcelona illustrate, without serving as controlled comparisons, three ways to translate citywide ambition into precinct stewardship, connected networks, and heat-health priorities. Within the defined corpus, canopy cover alone is an incomplete performance measure. The review proposes a non-aggregated decision chain and indicator portfolio linking network configuration, neighborhood vulnerability, site-level forest quality, and funded stewardship. Because screening and coding used one index and title/abstract metadata, the numerical patterns describe explicit framing rather than evidence quality, independent studies, or pooled effects. Full article

Ketamine produces clinical states ranging from subanesthetic analgesia and dissociation to anesthetic-dose behavioral unresponsiveness. Its electroencephalographic (EEG) effects differ from the slow-delta and frontal-alpha patterns commonly observed with GABAergic-dominant anesthetics and vary with exposure, administration kinetics, and co-administered agents. After anesthetic bolus dosing, ketamine may produce alternating slow-delta and gamma activity; at lower exposures, spectral and connectivity findings are more heterogeneous. When ketamine is added to propofol or volatile anesthesia, bispectral index and spectral-entropy values may remain elevated or increase, limiting their interpretation as stand-alone measures of hypnotic state. Prior syntheses have largely addressed molecular, cellular, and cortical-circuit mechanisms of dissociation; this narrative review instead synthesizes human EEG, connectivity, imaging, and selected mechanistic evidence using a dose–state framework. We distinguish behavioral responsiveness, environmental connectedness, and conscious experience; assess which network measures are technically derivable from clinical scalp recordings; and consider implications for operating-room and critical-care monitoring. Available evidence supports a cautious interpretation of processed indices and greater attention to the raw EEG, spectrogram, background hypnotic, administration pattern, and clinical context. Validated ketamine-specific EEG biomarkers and prospective monitoring algorithms are not yet available. Potential links between acute EEG effects and antidepressant mechanisms are discussed as shared upstream pathways rather than a single electrophysiological state. Full article

Background: To date, there are no known published studies in Portugal that link the perceived neighborhood walkability of a specific geographical area to the functional capacity and social participation of older adults living in the community. Thus, the main objective of this study, conducted in Portugal, is to examine whether the perceived neighborhood walkability is associated with functional capacity, walking confidence, social participation, and exercise self-efficacy among community-dwelling older adults, thereby contributing to the national evidence base on the influence of contextual factors on human functioning, with particular relevance to active and healthy aging. Methods: An exploratory, cross-sectional study was conducted using data from older adults (aged 50 and over) living in the community, in Portugal. A member of the research team assessed physical function using objective measures of upper and lower body function, balance and mobility. To assess the perceived neighborhood walkability, a validated questionnaire for Portuguese adults living in the community with good internal consistency (α = 0.848) was used. The walkability dimensions assessed were safety, facilities/surroundings, comfort/esthetics and accessibility/connectivity. The Pearson correlation coefficient was used to analyze the relationships between continuous variables. Differences between groups were analyzed using independent sample Student’s t-tests or analysis of variance (ANOVA) as appropriate. Results: Perceived neighborhood walkability was associated with several sociodemographic, health, and psychosocial variables. Higher educational levels and better self-perceived health were associated with more positive perceptions of walkability. Participants without urinary incontinence and osteoarthritis reported significantly higher walkability scores, particularly in the safety, facilities/surroundings, and comfort/esthetics dimensions. Older age was associated with higher scores in the facilities/surroundings dimension, whereas slower gait speed and higher anxiety levels were associated with lower perceptions in this dimension. Few significant associations were found between perceived neighborhood walkability and objective physical function measures, and these associations were generally weak. Conclusions: Overall, the findings suggest that neighborhood walkability is a multidimensional construct influenced not only by environmental characteristics but also by health status, psychological factors, and individual perceptions. These results reinforce the importance of considering contextual and environmental factors when promoting mobility, participation, and healthy aging among community-dwelling older adults. Full article

Coal mining landscapes require restoration strategies that account not only for where ecological risk is concentrated but also for how risk constrains landscape connectivity. However, landscape ecological risk assessment is still commonly used as a zoning tool, with weak links to resistance surface parameterization and node-level restoration. Using the Shenmu coal mining area in northern China as a case study, we developed a risk-informed ecological network framework based on multi-source spatial data from 1995 to 2020. The framework combined landscape ecological risk assessment, GeoDetector-based driver analysis, ecological source screening, resistance surface construction, minimum cumulative resistance modeling, a gravity model, and circuit theory-based node diagnosis. Landscape dominance showed the highest explanatory power within the tested factor set (q = 0.06083), followed by land use type, water body proximity, and landscape fragmentation, while most factor interactions showed bivariate or nonlinear enhancement. Risk zoning delineated ecological conservation (467.62 km²), enhancement (1434.86 km²), and restoration areas (2566.49 km²). The framework identified 10 ecological sources; 18 potential corridors with a total length of 213.18 km; and 89 key nodes, including 52 pinch points, 4 barrier points, and 33 fracture points. The main contribution of this framework lies not in combining established ecological network tools, but in transferring ecological risk information into resistance surface parameterization and linking different types of critical nodes to differentiated restoration priorities. These outputs should be interpreted as model-based structural and potential functional connectivity priorities, rather than as direct evidence of realized species movement. Full article

Triple-negative breast cancer (TNBC) is the most clinically aggressive subtype of breast cancer, characterized by the absence of targetable hormone receptors and HER2 amplification, significantly constraining treatment choices. Estrogen Receptor Beta (ERβ) has emerged as a biologically relevant yet underutilized target in TNBC, with its re-expression linked to tumor suppression and improved prognosis, prompting the development of selective ERβ modulators as a precision therapeutic approach. We introduce ERβ-Score, an interpretable machine learning scoring system developed using a curated dataset of 1699 ERβ bioactive chemicals obtained from ChEMBL, characterized by 39 physicochemical and three-dimensional molecular descriptors. After implementing scaffold-disjoint train/test partitioning to avert structural data leakage, a Gradient Boosting Classifier, fine-tuned through Bayesian hyperparameter optimization, attained in five-fold cross-validation a Precision–Recall AUC (Area Under the Curve) of 0.891, a ROC-AUC (Receiver Operating Characteristic) of 0.888, a Matthews Correlation Coefficient of 0.664, an F1-score of 0.838, and a balanced accuracy of 0.831; on the scaffold-disjoint hold-out test set it attained a Precision–Recall AUC of 0.905, a ROC-AUC of 0.864, and a Matthews Correlation Coefficient of 0.578, indicating strong and balanced discrimination between active and inactive ERβ modulators. We note explicitly that this scaffold-disjoint hold-out constitutes internal validation, since it derives from the same curated ChEMBL workflow used for model development, and it is therefore reported throughout as scaffold-disjoint internal validation rather than as independent external validation. The applicability domain boundaries were established using a k-nearest-neighbor Tanimoto-similarity method with ECFP4 (Extended-Connectivity Fingerprint with a Diameter of 4) fingerprints, offering a quantitative confidence metric that identifies structurally new molecules beyond the model’s reliable prediction range. External validation against independent Tox21 ERβ bioassay data confirmed genuine, statistically significant predictive signal (ROC-AUC = 0.71) while revealing reduced sensitivity for structurally novel active compounds. The model was subsequently used for extensive virtual screening of natural product and drug-like compound libraries, with prioritized candidates undergoing structure-based molecular docking against the ERβ co-crystal structure (PDB: 7XWQ) using Smina, facilitating a comprehensive evaluation of hits based on both ligand and structural properties. To enhance accessibility, the complete pipeline was implemented as an open-access interactive web application utilizing Streamlit, enabling researchers to input any SMILES string and obtain, in real time, an activity prediction with a probability score, applicability domain classification, Lipinski drug-likeness assessment, interactive three-dimensional visualization of protein–ligand interactions, and on-demand docking within the ERβ active site. Full article

Focusing on China’s information technology policy system, this study investigates the spatio-temporal evolution of policy–reference patterns and their implications for policy diffusion. Using 33,702 policy documents and 3150 citation links from the PKULaw database, we construct a policy citation network and combine social network analysis with ordinary least squares (OLS) and geographically and temporally weighted regression (GTWR). The results show that observed policy–reference relationships remain strongly characterized by top-down administrative coordination, while the policy–reference network has expanded to involve a broader range of regions and increasingly diverse interregional connections. These changes are accompanied by increasing citation intensity and textual differentiation, while pronounced regional disparities persist. The regression results reveal that citation-based diffusion indicators are associated with regional development capacity. Economic and industrial foundations are positively associated with policy–reference outcomes, whereas the associations of urbanization and R&D investment vary across different diffusion dimensions. GTWR further reveals that these associations vary across space and time. By integrating policy citation networks with spatio-temporal analysis, this study advances understanding of policy-system evolution under centralized governance and informs differentiated digital policy coordination. Full article