Search Results (334,142)

This research paper examines the spatial and time variation of demographic dependency in Europe in a 30-year horizon of the evolution of the demographic dividend regarding the economic dependency ratio (ADR1). We used the Curve Fit Forecast tool to estimate the trends of ADR1 in each of the EU Member States using data on Eurostat projections and a sophisticated geostatistical analysis tool developed in ArcGIS Pro 3.2.2. The findings indicate that the dependency in all countries has increased significantly in a statistically significant manner as the Gompertz function has appeared as the best curve in a third of the cases. It is an S-shaped asymptotic behaviour of this function that effectively describes the nonlinear patterns of acceleration and saturation of demographic ageing. As indicated in the analysis, the European regions are increasingly moving apart, with the southern and eastern nations such as Romania demonstrating the most alarming decline in ADR1. These trends highlight the need to reform labour market policies and social protection mechanisms to an ageing population. The paper combines the curve-fitting, descriptive statistics (median, skewness, interquartile range (IQR)) with time clustering (value, correlation, and Fourier) to provide an effective, replicable approach to early warning and policy prioritisation. Overall, the results highlight the importance of integrating predictive spatial modelling and demographic economics to support anticipatory and evidence-based policy decisions. The proposed approach proves to be a robust and transferable framework, applicable to a wide range of socio-economic phenomena characterised by inertia and structural change. Future research should extend the analysis to subnational levels, incorporate additional explanatory variables, and develop scenario-based simulations, including multivariate Gompertz-type models, to further enhance both predictive accuracy and policy relevance in the context of emerging structural labour scarcity. Full article

Modern cyber–physical infrastructures rely heavily on alarm and notification systems to direct human attention when abnormal conditions occur. These mechanisms support timely and safe responses by informing operators and occupants about potential hazards. At the same time, research in human factors has shown that repeated or excessive alerts can weaken vigilance, slow reactions, and reduce confidence in warning systems. This behavioral pattern is commonly described as alarm fatigue. This paper examines how that vulnerability can be exploited intentionally. We refer to this adversarial strategy as alarm poisoning: the deliberate injection of false or misleading alerts in order to increase alarm pressure, erode trust in the monitoring infrastructure, and degrade organizational responsiveness over time. To study this process, we develop a stochastic Cybersecurity Dynamics model representing the interaction among attackers, defenders, alarm infrastructure, and a population of employees. Employee behavior is modeled through evolving trust and fatigue levels, while the overall system is formulated as a continuous–time Markov chain and simulated using the Gillespie Stochastic Simulation Algorithm. A Monte–Carlo campaign is used to analyze the resulting socio–technical dynamics under alternative attacker strategies. The study evaluates time-dependent trust, fatigue, and alarm-pressure trajectories, the distribution of times to behavioral collapse, and defender timing through Trust–Resilience–Agility–Mitigation (TRAM) metrics. The revised analysis also includes replication-sufficiency diagnostics, one-at-a-time sensitivity analysis, and threshold-robustness checks for the collapse criterion. The results show that false alarms with high perceived severity drive alarm pressure upward and degrade trust faster than nuisance-dominated campaigns, even when the total fake-alarm intensity is held constant across strategies. Collapse timing remains highly variable across stochastic realizations, and a non-negligible fraction of runs do not reach the collapse threshold within the simulation horizon. Sensitivity analysis indicates that the main qualitative ranking of attacker strategies is robust across most tested perturbations, with fatigue recovery and defender escalation emerging as particularly influential mechanisms. Overall, the findings support the view that alarm poisoning is a credible socio–technical attack vector and highlight the importance of rapid mitigation, robust alarm management, and human-centered defensive design in cyber–physical security systems. Full article

In this study, bioceramic composites based on zirconia (ZrO₂) were synthesized and characterized in terms of mechanical properties. Two types of different-sized grains of zirconia powders were used to prepare the composites. A commercial zirconia micropowder (Tosoh) was used as a base for the composites modified with bioactive glass (BG), copper-doped bioactive glass (BGCu), and hexagonal boron nitride (hBN) with a sintering temperature of 1450 °C. The composites with the addition of hydroxyapatite, for which their sintering temperature was 1150 °C, were independently fabricated using a zirconia nanopowder prepared via co-precipitation and hydrothermal methods to achieve high densification and avoid hydroxyapatite decomposition. Mechanical performance of these composites was assessed with regard to biaxial flexural strength, Vickers hardness (HV), and fracture toughness (K_Ic). The reference 3Y-TZP material exhibited Vickers hardness (11.8 GPa) and fracture toughness (6.1 MPa∙m^1/2 values typical for dense tetragonal zirconia ceramics. The addition of all bioactive phases resulted in significant alterations in mechanical properties. Specifically, incorporating 20 wt.% HAp led to a threefold decrease in hardness and a 40% reduction in fracture toughness, while increasing the HAp content to 40 wt.% further reduced these properties. Nonetheless, the fracture toughness of these composites remained higher than that of pure hydroxyapatite materials. The incorporation of BG and BGCu reduced the hardness values by 45% and 30%, respectively, compared to 3Y-TZP. The most significant deterioration of the properties was observed for the 3Y-TZP-hBN composite. The 3Y-TZP–BGCu composite exhibited fracture toughness (5.9 MPa∙m^1/2) representing 95% of the toughness of pure zirconium dioxide, thereby showing the lowest weakness of all the other composites with bioactive additives. A slightly lower fracture toughness value (5.3 MPa∙m^1/2) was also observed in the composite with bioglass but lacking the copper additive. This factor, combined with a relatively small decrease in hardness in both cases, highlights high durability for implantology applications, thus marking the indicated materials the most promising among the composites studied. Full article

Artificial intelligence techniques have made notable progress in supporting learning processes, with increasing adoption across educational contexts. However, despite the increasing work on AI-assisted techniques, explicit and implicit learning mechanisms in AI educational assistants have not been systematically categorised. The study of how these techniques aid in and are implemented for learning remains underexplored. Therefore, a more systematic categorisation of how these techniques support learning through user interaction is needed. This paper presents a systematic review of 38 studies published between 2000 and 2024, spanning domains including programming education, cognitive skills, language learning, and the AI field. This review was conducted and reported in accordance with the PRISMA 2020 guidelines. In this review, we propose a taxonomy of explicit and implicit learning features. We analyse implementation aspects (e.g., knowledge representation, algorithms, and interaction modalities) and synthesise how prior work evaluates learning support capabilities. The findings show that (i) 79% of reviewed studies support explicit and 21% supported implicit learning through interaction; (ii) written interaction dominates (45%), followed by visualisation (34%), while voice-based interaction remains underrepresented (9%); (iii) some implementations lack details (e.g., knowledge bases and validation methods); and (iv) evaluation practices remain uneven, with most studies relying on experiment evaluation, highlighting the need for robust evaluation practices. Full article

With the deepening construction of ecological civilization, the realization of ecological product value, referring to the value derived from ecosystems’ material goods, regulation, support, and cultural services, has become a strategic key point for national sustainable development. Data factors, distinguished from digital technologies as the actual resources used in production, exchange, and consumption, are becoming increasingly important as a new catalyst for empowering the realization of ecological product value. Drawing on panel data spanning 2011 to 2023 across China’s 31 provinces, this research employs the entropy weight method to construct evaluation indices for both the development of data factors and the realization of ecological product value, deriving weights from the data’s intrinsic variability. The effect of data factors on the realization of ecological product value is examined using a two-way fixed effects framework. Our outcomes are presented below. First, data factors can significantly promote the realization of ecological product value, and this conclusion is supported by a series of robustness checks and endogeneity treatments. Second, the mechanism analysis reveals that data factors empower the realization of ecological product value through new quality productive forces, energy consumption intensity, and innovation and entrepreneurship. Third, results from the threshold model suggest that the promoting effect of data factors on the realization of ecological product value is subject to a threshold constraint, characterized by diminishing marginal returns beyond this point. Fourth, regarding regional disparities, the results indicate that data factors primarily drive ecological product value realization in the central region, as it is at a critical stage of digital transformation, with a secondary effect in the east, while their influence in the western region remains insignificant. These findings provide important guidance for integrating data factors and ecological resources to achieve sustainable development. Full article

The Hippo, as a central pathway regulating cell proliferation, apoptosis, stem cell homeostasis and organ development, is closely associated with the onset and progression of tumors, metabolic reprogramming, drug resistance and immune evasion when it is abnormally inactivated. The Hippo not only directly promotes tumor cell proliferation, maintains cancer stem cell properties, and mediates metabolic reprogramming and treatment resistance, but also reshapes the tumor microenvironment(TME) by regulating the formation, heterogeneity and function of cancer-associated fibroblasts (CAFs). Furthermore, it mediates tumor immunosuppression and immune evasion by modulating programmed death-ligand 1(PD-L1) expression, T-cell function, macrophage polarization and cytokine secretion. At the same time, inflammatory cytokines, growth factors, metabolites and physical signals within the TME can negatively regulate the activity of the Hippo, creating a pro-tumor positive feedback loop. This article provides a systematic review of the composition and regulation of the Hippo , its mechanisms of action in the biological behavior of tumor cells and interactions within the tumor microenvironment, as well as progress in the development of drugs targeting this pathway. It offers a theoretical basis for a deeper understanding of the role of the Hippo in tumors and for the development of novel anti-tumor therapeutic strategies. Full article

The Street Clinic (Consultório na Rua—CnR) is a strategic component of Primary Health Care in Brazil, aimed at populations experiencing homelessness, a group characterized by high levels of social and health vulnerability. This study critically analyzes the experiences of healthcare professionals working within a CnR team, identifying the meanings attributed to their work, the challenges encountered, and the strategies developed within the territory. This is an exploratory study with a qualitative approach, grounded in health narratives and the philosophical hermeneutics of Hans-Georg Gadamer. Four professionals participated, representing the totality of eligible members of a team in a medium-sized municipality in Southern Brazil, with between one and eleven years of experience in the service. Hermeneutic analysis revealed that the CnR functions as an entry point to Primary Health Care and Psychosocial Care, with the bond between team and users serving as the primary mechanism for overcoming barriers to access. Professionals report ethical suffering arising from the tension between their commitment to comprehensive care and the structural limitations of the service, including shortages of supplies, institutional instability, and precarious employment arrangements. It is concluded that strengthening the CnR requires not only investment in infrastructure and expansion of the teams, but also policies that recognize and support the complexity of street-based work, including care for the caregivers themselves. Full article

Skin aging is a complex, multifactorial process driven by intrinsic biological mechanisms and environmental exposures, resulting in progressive functional and structural decline. Chronological age does not adequately capture this variability, highlighting the need for molecular biomarkers that reflect biological aging. In this context, transcriptomic aging clocks have emerged as a promising approach, as gene-expression profiles provide a dynamic representation of cellular and tissue states. This narrative review is based on a targeted literature search in PubMed and IEEE Xplore and focuses on transcriptomic aging clocks in human skin, with emphasis on gene-expression signatures, key biological pathways, and computational modeling strategies. These models consistently capture coordinated alterations in processes such as cellular senescence, DNA damage response, inflammation, and extracellular matrix remodeling. Representative transcriptomic frameworks, including models such as SkinAGE, illustrate the ability of gene-expression-based approaches to quantify biologically meaningful and dynamic aging states in the skin. Advances in machine-learning approaches, including deep learning and pathway-guided models, are critically evaluated, alongside the role of explainable artificial intelligence in enhancing model transparency and biological interpretability. Future developments are expected to integrate multi-omics data and digital twin frameworks, enabling the transition from static biomarkers toward dynamic, predictive, and personalized models of skin aging Full article

The integration of post-quantum digital signature (PQDS) algorithms into coherent wavelength-division multiplexing (WDM) optical networks introduces a non-negligible cryptographic overhead that fundamentally alters physical-layer performance characteristics. Unlike conventional studies that treat security and transmission independently, this work provides a cross-layer evaluation of PQDS-induced payload expansion and its direct impact on coherent optical system behavior under realistic, DSP-aligned conditions. A structured and reproducible evaluation framework is proposed to systematically analyze this interaction across multiple transmission scenarios, ranging from a single-channel QPSK baseline to a 16-channel WDM system employing both QPSK and 16QAM modulation formats. Key system parameters—including launch power, local oscillator power, bit rate, and fiber length—are jointly optimized, while performance is rigorously assessed in terms of bit error rate (BER), Q-factor, and maximum transmission reach. The results demonstrate a clear performance degradation trend driven by both spectral efficiency scaling and cryptographic payload expansion. The single-channel QPSK system achieves a maximum reach of 203 km, which decreases to 194 km in the 16-channel WDM QPSK configuration due to inter-channel interference and nonlinear effects. In contrast, the 16-channel WDM 16QAM system exhibits a significantly reduced reach of 103 km, reflecting its heightened sensitivity to noise, chromatic dispersion, and fiber nonlinearities. Furthermore, increased payload size associated with PQDS schemes is shown to exacerbate transmission impairments by extending frame duration and intensifying inter-channel interactions. These findings identify PQDS-induced overhead as a critical system-level constraint that directly governs transmission efficiency, scalability, and performance limits. The study highlights the necessity of cross-layer co-design strategies, where cryptographic mechanisms and physical-layer parameters are jointly optimized to enable efficient, reliable, and quantum-safe coherent optical communication systems. Full article

Hydroxyapatite–calcium sulfate (HACaS) bone cements have been clinically established. Combining HACaS with an antiresorptive (zoledronic acid, ZA) and osteoanabolic agent (bone morphogenic protein 2; BMP-2) may enhance the performance of HACaS bone cements in challenging indications, but it must be ensured that this does not impair their setting and mechanical properties. This study established a Vicat/Gillmore-inspired indentation protocol to quantify force-based endpoints and the setting of HACaS with biological adjuvants. HACaS was mixed with or without ZA and/or BMP-2 at 0 min and after a 2 min pre-setting phase with reduced NaCl content (lower liquid-to-powder ratio). For each time point (3–90 min), three cylindrical pellets (Ø 4 mm, height 6 mm) underwent single indentation. Setting was defined as the maximum force at needle penetration, and endpoint hardness was defined as peak force at failure. For 24 h endpoints, specimens were incubated in blood at 37 °C. One-way ANOVA with Tukey’s H post hoc test was performed per time point (n = 3; 24 h endpoints n = 5). All 2 min protocols showed accelerated setting, consistent with the initial lower liquid-to-powder ratio. ZA significantly delayed setting and remained lowest at 90 min and after 24 h in blood. Mixing sequence and vehicle composition critically influenced early mechanical properties and should be considered in the further preclinical evaluation of HACaS with osteoanabolic or antiresorptive agents. Full article

This paper presents the electrochemical behavior of stainless steel 304 (SS304), a material often utilized in the wine industry, in the presence of varying concentrations of ascorbic acid (AcAS), introduced in a neutral solution (Na₂SO₄ 0.25 M + 12% (v/v) EtOH). The experimental part of this paper included potentiodynamic polarization and chronoamperometry techniques to evaluate the influence of ascorbic acid on the corrosion processes in the test solutions. Electrochemical impedance spectroscopy (EIS) has been used to investigate the charge transfer at the interface and the formation of a protective film in the absence and presence of AcAS. The Tafel method was employed to determine the kinetic parameters of the corrosion process studied. Additionally, several models of adsorption isotherms were applied to describe the interactions between AcAS and the stainless steel surface, with the Freundlich and Dubinin–Radushkevich isotherms demonstrating the most robust correlation, based on the R² correlation coefficients. Quantum chemical calculations (DFT) were also performed to clarify the molecular mechanism via which AcAS functions as an eco-friendly corrosion inhibitor in winemaking-related environments. Full article

Background: Migraine is a complex neurovascular disorder with a substantial genetic component, yet many contributing loci remain poorly characterised. Methods: This study investigated the association between 21 biologically prioritised single nucleotide variants (SNVs) and migraine susceptibility in a case-control cohort of 548 individuals of European ancestry, of whom 304 (164 cases, 140 controls) remained after quality control and principal component analysis (PCA). Genotyping was performed using a targeted Sequenom MassARRAY platform, and substantial missingness (mean 30.3% per SNV) was addressed using multiple imputation by chained equations (MICE). Association testing was conducted using three complementary logistic regression frameworks: unadjusted single-variant analysis, covariate-adjusted marginal models, and a multivariable joint model incorporating all SNVs with L2 regularisation. Results: Across analyses, two variants in ASTN2 (rs1052053 and rs6478241) showed the most robust associations with migraine, surviving Bonferroni correction in the joint model (p = 0.001 and p = 0.002, respectively) and false discovery rate (FDR) correction in marginal models (q = 0.003 for both). A third variant, rs7304841 (12p12), demonstrated a risk-increasing effect that reached FDR significance in marginal analysis (q = 0.035) and remained nominally significant in the joint model. In contrast, rs62624978 in CTC1 showed a strong signal in unadjusted analysis (OR = 0.217, p = 0.0014) and remained nominally significant after adjustment (p = 0.011), although it did not survive multiple-testing correction in imputed models. The joint model demonstrated good discriminatory performance (AUC = 0.822), though this is not intended as a predictive tool. Biologically, implicated loci suggest contributions from both neuronal circuit organisation (ASTN2) and telomere and vascular maintenance pathways (CTC1), supporting a broader neurovascular model of migraine susceptibility. Conclusions: These findings are consistent with shared genetic architecture between migraine and microvascular dysfunction, potentially involving endothelial integrity, neurovascular coupling, and cortical excitability mechanisms. Full article

Eastern Thailand serves as a critical case study for the escalating tension between agricultural preservation and urban expansion, a dynamic recently intensified by the COVID-19 pandemic. This study addresses a pivotal research question: To what extent do emerging socio-economic realities, such as policy shifts, labor fluctuations, and climatic extremes, alter the spatiotemporal continuity of urban expansion? Employing a mixed-methods approach, we integrated multi-stakeholder insights with quantitative spatial modeling to simulate context-specific land use futures through 2030. Qualitative findings indicate that while COVID-19 accelerated agricultural modernization, evidenced by increased mechanization and e-commerce integration, these shifts have limited long-term impact on land use patterns. Instead, regional policy, climate change, and technological innovation emerged as the primary drivers of landscape transformation. Quantitative simulations reveal that urban growth will concentrate in the western provinces bordering Bangkok and the southern coastal corridors of Chon Buri and Rayong. Crucially, across all scenarios, approximately 60% of new urban land is projected to be converted from existing croplands, followed by significant losses in natural forest cover. These results demonstrate that current growth-oriented policies may undermine regional food security and ecosystem services. This study provides a framework for balancing agricultural modernization with ecological preservation, offering essential evidence for developing the integrated, sustainability-focused land use frameworks required to meet 2030 development goals. Full article

The rapid proliferation of Internet of Things (IoT) devices increases the demand for task offloading mechanisms that satisfy strict latency constraints while limiting security exposure in edge computing environments. This paper proposes a security-aware task offloading framework for IoT edge networks, using Software-Defined Networking (SDN) as a centralized control plane. The SDN controller combines real-time monitoring, threat-aware risk estimation, and a lightweight heuristic decision engine to assign tasks to heterogeneous edge nodes according to latency constraints, resource availability, and task security sensitivity. To avoid optimistic scalability assumptions, the evaluation explicitly models contention through load-dependent queueing delay at edge nodes and reduced effective bandwidth on shared links. Simulation results with realistic IoT task parameters and heterogeneous edge capacities show that the proposed framework achieves an average latency of approximately $125\pm 5$ ms, a task completion ratio (TCR) of about $92\pm 2$%, and a security success rate (SSR) near $95\pm 1.5$%, compared to the considered baselines. These results indicate that incorporating risk assessment into SDN-based offloading decisions can improve security-related outcomes while maintaining practical performance under contention. Limitations include the use of an analytical risk model and a single-controller SDN setting; future work will investigate multi-controller deployments, attack-trace-driven evaluation, and energy-aware extensions. Full article

Federated learning, a decentralized machine learning framework, allows multiple participants to jointly train models while keeping their raw data local and unshared. Nevertheless, during the exchange of model updates, the communicated information can still introduce privacy vulnerabilities and potentially result in the exposure of user data. Over the past few years, differential privacy methods have been broadly incorporated into federated learning frameworks to strengthen the protection of sensitive data. Nevertheless, the noise required to satisfy differential privacy guarantees often causes significant degradation in model performance. Prior studies have typically employed a fixed noise-injection strategy following gradient clipping. Although such methods provide privacy protection, they overlook the varying importance of different gradient dimensions, resulting in noise being injected into unimportant or redundant parameters, thereby causing unnecessary performance loss. To address these limitations, we propose an adaptive differentially private federated learning scheme with importance-based gradient updates (AIGU-DPFL). Specifically, we focus on coordinates with high information content and introduce an adaptive noise injection mechanism, which perturbs gradient updates to satisfy differential privacy guarantees while dynamically controlling noise intensity, thereby achieving sparse and noise-effective gradient updates. AIGU-DPFL markedly enhances the training effectiveness of federated learning models. Comprehensive evaluations conducted on real-world datasets indicate that the proposed method achieves superior performance compared to existing differentially private federated learning techniques. Full article