Search Results (6,192)

Background/Objectives: This technical report introduces an innovative hybrid digital workflow that integrates diagnostic plaster-cast scanning with intraoral scanning to produce an accurate 3D-printed model for fabricating distal-extension removable dental prostheses (RDPs). Methods: The technique aims to overcome the challenges of reproducing the mobile mucosa in free-end saddles, a critical factor for denture base accuracy and stability. The workflow began with conventional clinical procedures, including clinical examination, impression-making, and cast surveying. After performing the required mouth preparations according to the prosthetic design, the diagnostic cast was digitized and selectively modified to allow intraoral rescanning. The prepared teeth were then scanned intraorally and merged with the digitalized cast, producing a refined virtual model for CAD-based metal framework design. The framework was digitally designed, 3D-printed to verify adaptation, and cast in cobalt–chromium. Standard RDP fabrication steps were followed, including intraoral framework try-in, fabrication of acrylic bases, occlusal registration, tooth arrangement, and functional and esthetic try-in. The final prosthesis was installed and adjusted without the need for an additional impression. Results: This hybrid workflow enabled a highly accurate reproduction of the distal extension region, outperforming models derived solely from direct intraoral scanning. By digitally capturing the physiological morphology of the mobile mucosa, the method eliminates the need for the traditional altered-cast technique, reducing clinical time, technical sensitivity, and material costs. Conclusions: The proposed approach enhances denture base accuracy, improves adaptation, and promotes more uniform occlusal load distribution in free-end RDPs. This streamlined and reproducible digital protocol offers a clinically relevant advancement, with potential to improve prosthesis stability and long-term outcomes. Full article

Mining is an economic activity with significant socio-environmental implications, particularly in regions where communities depend directly on natural resources. This study aimed to analyze the perceptions of residents of the Pungala parish regarding the impacts of mining in the Alao River basin. A questionnaire was administered, considering sociodemographic, social, and environmental variables. The surveyed population was predominantly older adults and had a balanced gender distribution. The majority identified as indigenous or mestizo, with primarily secondary school educational levels and with a labor structure characterized by independent work. At the social level, mining is perceived as a source of economic benefits through job creation and increased income. However, negative impacts are also recognized, including conflicts over water use, displacement of families, and increased costs of goods and services. From an environmental perspective, the majority perceived negative changes, particularly water pollution, deforestation, erosion, and biodiversity loss. Regarding the ecosystem services, provisioning services were perceived as having the greatest importance and frequency of use, especially water for human consumption, irrigation, and productive activities. These results demonstrate the coexistence of benefits and risks and highlight the need for sustainable management strategies that integrate ecosystem conservation and community well-being. Full article

We introduce fuzzy skew maps as a levelwise ($\alpha$-cut) extension of robustly chaotic skew transformations of S-unimodal maps to epistemically uncertain environments. Our central hypothesis is that the robust-chaos mechanism of the underlying skew family transfers to fuzzy parameter uncertainty in a set-based (not probabilistic) sense is as follows: for every $\alpha \in [0,1]$, the induced crisp family $\{F(·,q):q\in {\left[\tilde{q}\right]}_{\alpha }\}$ preserves the absence of periodic windows and maintains strictly positive Lyapunov exponents. This yields a precise notion of fuzzy robustness that is distinct from interval enclosures (pure bounds) and stochastic robustness (average-case guarantees). We also formalize fuzzy topological entropy via the extension principle and discuss its basic structural properties under mild continuity assumptions. For chaos-based image encryption, fuzzification provides an uncertainty-aware key representation and stabilizes cryptographic indicators across $\alpha$-cuts as follows: in our experiments, NPCR remains within $99.58$–$99.64\%$, UACI within $33.41$–$33.52\%$, and the cipher entropy is near 8 bits, while pixel correlation stays close to zero. These results support fuzzy skew maps as a robust primitive for secure information systems operating under parametric uncertainty. Full article

Tourism destinations exposed to chronic natural hazards require robust analytical frameworks to understand and prioritize the factors that sustain post-disaster resilience. This study examines Baños de Agua Santa (Ecuador), a volcano-exposed destination whose long recovery trajectory illustrates the complexity of socio-ecological adaptation. Using a multidimensional FAHP model grounded in expert judgments, eight dimensions and fifty-six criteria were evaluated through fuzzy triangular numbers and the extended analysis method of Chang to capture uncertainty and ambiguity in decision-making. Results show a consistent and hierarchical structure of resilience, with experiential, economic-entrepreneurial, and socio-community dimensions emerging as the most influential drivers of post-disaster adaptability. Fifteen criteria—primarily perceptual, community-based, and endogenous—achieved “very high impact” status, including risk perception, basic education, individual resilience capacities, institutional coordination, and entrepreneurial environment. Conversely, limited healthcare infrastructure, low economic diversification, and national-level vulnerabilities were identified as critical weaknesses. The study concludes that post-disaster recovery in Baños is shaped by a bottom-up dynamic that emphasizes agency, learning and socio-ecological memory. It also proposes an evidence-based Action Matrix for adaptive governance to guide prioritized, time-phased interventions. The FAHP model proves effective for transparent, context-sensitive prioritization in highly uncertain tourism environments. Full article

This article presents an online additive fault-detection system for the speed sensor of a 200 W shunt-type direct current (DC) motor, integrated into a power module controlled by an Insulated Gate Bipolar Transistor (IGBT). The system is designed to trigger an alarm signal when an additive fault occurs by comparing the Kalman Filter (KF) residual against a predefined detection threshold. Three specific fault types in the speed sensor were analyzed: offset, disconnection, and sinusoidal noise. Experimental results demonstrate effective fault detection across a speed range of 80 to 690 rpm under no-load conditions. However, when a constant torque of 0.5 Nm is applied, both the detection threshold and the subset of reliably identifiable faults must be adjusted. The main contribution of this study is the development of a customized real-time fault detection framework and the characterization of residual variations caused by unmodeled load disturbances in actual hardware. This approach improves the monitoring and fault-diagnosis capabilities of sensor systems in DC motors by quantifying the stochastic behavior of residuals under different operating constraints. Full article

The growing pollution caused by plastics with slow degradation kinetics is demanding the search for biodegradable alternatives. Starch-based films are a promising option, but their practical application may be limited by their potential susceptibility to rapid ultraviolet (UV) exposure degradation. This study evaluates the effect of prolonged UV-C irradiation (254 nm, 168 h) on plasticizer-free films derived from the starch of an Ecuadorian potato Solanum tuberosum (Chola variety). Films formulated at 3% and 5% (w/v) starch were characterized before and after UV exposure. The analysis includes the evaluation of optical, mechanical, and physicochemical properties, along with Fourier Transform Infrared spectroscopy (FTIR) and atomic force microscopy (AFM) for nanoscale surface inspection. UV irradiation increased the opacity of the films but reduced slightly their tensile strength, elongation at break, moisture content, and total soluble matter. In contrast, the elastic modulus remained relatively high. FTIR analysis revealed no significant formation of new functional groups. AFM measurements indicated that irradiation caused only minor nanoscale alterations in the same film regions. These alterations were more pronounced in films with higher starch concentrations. The results demonstrate that UV-C exposure induces minor structural adjustments in plasticizer-free starch films derived from the Chola variety, without compromising their fundamental integrity. Consequently, this work advances the understanding of the environmental stability of these films and supports their potential application as sustainable materials, even in conditions involving UV exposure. Full article

This review examines the implementation dimensions of integrated lemon biorefinery systems, including cascade valorisation design, circular-economy integration, life-cycle assessment, techno-economic feasibility, and regulatory frameworks. Bibliometric analysis of Web of Science data (2015–2025) reveals exponential growth in citrus-biorefinery research, with lemon representing a burgeoning subset. Techno-economic assessments indicate that cascade biorefineries recovering essential oils, pectin, polyphenols, nanocellulose, and bioenergy can achieve cumulative revenues of USD 400–650 per tonne of dry peel. Whilst small-scale units (<500 tonnes per year) struggle to achieve viability, industrial simulations demonstrate Internal Rates of Return exceeding 18% at processing scales above 100,000 tonnes annually (2025 basis). Life-cycle assessments confirm environmental benefits, with greenhouse gas reductions of 60–85% relative to conventional disposal. Critical success factors include adopting green extraction technologies to preserve bioactive integrity and mitigating D-limonene inhibition in downstream anaerobic digestion. These findings establish essential oil extraction and pectin recovery as commercially mature technologies, whilst integrated multi-product lemon biorefineries remain economically promising based on techno-economic modelling and pilot-scale demonstrations, provided regulatory hurdles are effectively navigated. Full article

Metabolic and bariatric surgery (MBS) has evolved into a highly effective neurohormonal intervention for severe obesity; however, it introduces unique long-term vulnerabilities, particularly regarding alcohol (AUD) and substance use disorders (SUD). This review synthesizes the epidemiological, pharmacokinetic, and neurobiological drivers of postoperative substance misuse. Procedures like Roux-en-Y gastric bypass (RYGB) radically alter ethanol metabolism, eliminating first-pass metabolism and accelerating gastric emptying, while simultaneously recalibrating reward pathways, creating a “reward gap” that facilitates addiction transfer. These physiological shifts exacerbate critical micronutrient deficiencies (thiamine, B12, iron), increase the risk of post-bariatric hypoglycemia, and correlate with higher rates of liver cirrhosis and suicide. Furthermore, substance use is a primary driver of suboptimal weight loss trajectories and weight regain. Mitigation requires a lifelong, multidisciplinary framework involving preoperative risk stratification, validated screening (e.g., AUDIT-C), and targeted nutritional supplementation to safeguard the long-term metabolic and psychological benefits of MBS. Full article

This article develops a rigorous and reproducible systematic review of the integration of artificial intelligence (AI) in financial education during the period 2020–2025, structured in accordance with -5.3-PRISMA and explicitly oriented toward detecting narrative and perception. The search was conducted in three complementary databases (Scopus, ScienceDirect, and Taylor & Francis), using search strings equivalent to those of the platform and a selection workflow that begins with 388 records and culminates in 50 included studies, prompting a narrative synthesis given the methodological heterogeneity. From a methodological contribution perspective, the study combines bibliometric mapping with text mining and an NLP process that triangulates sentiment using lexicon-based approaches (VADER, TextBlob) and a multilingual transformer model (XLM-RoBERTa), producing continuous indicators (sentiment index) and reproducible research artifacts. The results position AI as an integrative nexus linking financial literacy, decision-making, sustainability, and language technologies (including ChatGPT-5.3.), highlighting its potential for personalization, virtual tutoring, and immediate gains in comprehension and motivation; however, evidence of sustained behavioral change remains nascent. Critical gaps remain, such as a shortage of longitudinal/controlled studies, a lack of standardized metrics, limited transparency and validation of models, and constraints in terms of geographic and cultural diversity, while privacy, fairness, and algorithmic bias emerge as structural conditions for responsible adoption. Full article

As network slicing gains traction in cloud computing environments, efficient management and orchestration systems are required to realize the benefits of this technology. These systems must enable dynamic provisioning and resource optimization of virtualized services spanning multiple network slices. Nevertheless, the common resource overprovisioning practice implemented by service providers leads to the inefficient use of resources, limiting the ability of Mobile Network Operators (MNOs) to rent new network slices to more vertical customers. Hence, efficient resource allocation mechanisms are essential to achieve optimal network performance and cost-effectiveness. This paper proposes a predictive model for network slice resource optimization based on resource sharing between Virtualized Network Functions (VNFs). The model employs deep learning models based on Long Short-Term Memory (LSTM) and Transformers for CPU resource usage prediction and a reactive algorithm for resource sharing between VNFs. The model is powered by a telemetry system proposed as an extension of the 3GPP network slice management architectural framework. The extended architectural framework enhances the automation and optimization of the network slice lifecycle management. The model is validated through a practical use case, demonstrating the effectiveness of the resource sharing algorithm in preventing VNF overload and predicting resource usage accurately. The findings demonstrate that the sharing mechanism enhances resource optimization and ensures compliance with service level agreements, mitigating service degradation. This work contributes to the efficient management and utilization of network resources in 5G networks and provides a basis for further research in network slice resource optimization. Full article

The rapid growth of wind energy has increased the need for advanced condition monitoring (CM), predictive maintenance, and remaining useful life (RUL) estimation strategies for wind turbines. In this context, digital twins (DTs) have emerged as a key tool for improving reliability, availability, and operational efficiency by integrating physical models, operational data, and artificial intelligence (AI). This paper presents a systematic literature review (SLR) aimed at analyzing the state of the art, classifying the main applications, and identifying research gaps. A rigorous search protocol was applied across scientific databases, considering inclusion and exclusion criteria and analysis categories aligned with four research questions. The results show a high concentration of studies on critical wind turbine components, a predominance of hybrid physics-based and data-driven approaches, and an increasing use of deep learning (DL) models. However, several research gaps remain, including the predominance of component-level digital twin implementations rather than system-level architectures, the lack of standardized datasets and benchmarking frameworks, and challenges related to SCADA data heterogeneity and real-time scalability. It is concluded that DTs are evolving toward more autonomous and prescriptive systems; however, they still require further maturation for widespread industrial adoption. Full article

The response of five Passiflora species as rootstocks for yellow passion fruit was evaluated against the Meloidogyne incognita complex and Fusarium oxysporum f. sp. passiflorae. Individual, sequential, and simultaneous inoculations were applied, quantifying disease severity, nematode reproduction (RF), biomass, and plant vigour. In addition, integrated analysis was performed using the Combined Tolerance Index (CTI) to confirm the simultaneous interaction of the inoculation condition. The graft compatibility index (GCI) of the materials under study was also determined. The results showed critical functional differences; P. maliformis showed tolerance in terms of compensatory vigour but presented high susceptibility to the nematode and low graft affinity (GCI = 1.39). In contrast, P. platyloba emerged as the superior genotype, combining effective resistance to Meloidogyne (zero incidence at critical stages), excellent anatomical compatibility (deviation from the ideal of 0.04), and physiological stability superior to the control. Although P. nitida showed resilience in biomass under severe stress conditions, it is concluded that P. platyloba is the most promising alternative for use as rootstock. This is because its morphological affinity and health resistance ensure crop sustainability in field conditions and promote more sustainable agricultural practices. Full article

The genus Xylaria comprises numerous species, particularly prevalent in tropical ecosystems such as those of Ecuador. Despite its ecological importance, the taxonomy of the genus remains challenging, and much of its diversity in the Neotropics remains under-documented. This study provides a preliminary characterization of the Xylaria diversity at the Estación Científica San Francisco, an Andean biodiversity hotspot in Southern Ecuador. Through an integrated approach including detailed macro- and micro-morphological descriptions and nuclear ribosomal DNA (nrDNA ITS and LSU) phylogenetic analyses, 20 Xylaria specimens were examined. As a result, ten species were recognized: Xylaria adscendens, X. cf. anisopleura, X. apiculata, X. curta, X. enterogena, X. fissilis, X. globosa, X. aff. telfairii, X. tuberoides, and X. aenea, the latter representing a new record for Ecuador. The phylogenetic analysis presented here serves as a preliminary systematic positioning of these specimens within the genus rather than a comprehensive global reconstruction. While these ribosomal markers provided preliminary insights into species relationships, partial incongruence with morphospecies highlights the evolutionary complexity of certain lineages and underscores the need for future multilocus studies. Furthermore, four additional phylotypes found in their anamorphic state are documented, suggesting that local diversity exceeds current records. By providing detailed morphological documentation supported by preliminary barcode data from a poorly sampled region, this study contributes vital information to the global understanding of Xylaria and underscores the importance of Southern Ecuador as a reservoir of fungal diversity. Full article

Zinc titanate (ZnTiO₃) is a chemically stable and non-toxic oxide perovskite whose photovoltaic potential remains largely unexplored due to its wide indirect bandgap. This study evaluates whether oxygen-vacancy (F-center) engineering can tailor its electronic structure and improve its suitability as a photovoltaic absorber. Density Functional Theory (DFT) calculations using VASP (PAW − GGA/PBE + U) were performed to evaluate structural stability, electronic properties, and electron affinity, while optical absorption was modeled through a combined Tauc–Gaussian approach. Device performance was assessed via SCAPS-1D simulations in an FTO/ZnO/ZnTiO₃/Spiro-OMeTAD architecture. Oxygen vacancies induce bandgap narrowing from ~2.96 eV to ~1.47 eV and generate Ti-3d-dominated donor-like and deep intragap states. The calculated electron affinity is ~3.77 eV. Simulated single-layer devices reach Voc ≈ 1.11 V, Jsc ≈ 8.27 mA·cm⁻², FF ≈ 83%, and a maximum efficiency of ~7.65%, primarily limited by moderate absorption strength and defect-assisted recombination. Multilayer configurations indicate that geometric optimization can significantly enhance projected efficiency, approaching 19.25% under idealized conditions. Although vacancy engineering extends visible-light absorption, the intrinsic indirect band-gap character constrains the ultimate photovoltaic performance of ZnTiO₃. Full article

We propose a robust method for outlier detection in functional data analysis. This approach uses the robust Minimum Covariance Determinant estimator to compute the Mahalanobis distance applied to functional principal component scores. The main contribution of this research is the detection of outlier curves using the robust covariance matrix of functional principal components, in contrast to existing methods that use principal components on the discrete dataset. The proposed method is practical because it considers the entire functional form of the data, through their functional principal components, providing a comprehensive analysis that can detect anomalies across the entire functional range. A simulation study compares this approach with existing methods to evaluate their performance, followed by applications to El Niño Sea Surface Temperature data and SCImago Journal Rank data. The results show that the proposed method provides greater accuracy, demonstrating its effectiveness in detecting outlier curves. Full article