Search Results (8,745)

Educational platforms increasingly rely on vector databases to store and retrieve embedding representations of large-scale learning corpora (e.g., lecture notes, assignments, feedback, and student Q&A) for retrieval-augmented generation and analytics. However, directly indexing educational text embeddings raises privacy risks (student identities, sensitive performance signals, and protected attributes) and creates a management challenge: embeddings drift as curricula evolve, access policies change, and new content arrives continuously. This paper studies privacy-preserving intelligent management of educational-corpus vector libraries and proposes a novel, end-to-end algorithmic framework that jointly optimizes (i) privacy leakage control, (ii) retrieval quality, and (iii) operational efficiency under streaming updates. We introduce a hierarchical policy-aware vector lifecycle model, a privacy budget scheduler for adaptive re-embedding and re-indexing, and a secure-aware clustering-and-routing mechanism that supports fast query-time filtering with minimal accuracy loss. The resulting system, PP-EDUVec, enables compliant similarity search across multi-tenant educational data while automatically maintaining index health (freshness, redundancy, and utility) over time. On the EDU-Mix benchmark, PP-EDUVec achieves Recall@10 $=0.835$ while reducing representation leakage (LeakRep) from $0.215$ to $0.136$ ($-36.7\%$) and access-pattern leakage (LeakAP) from $0.398$ to $0.255$ ($-35.9\%$), and lowering mean latency from $42.1$ ms to $33.4$ ms ($-20.7\%$) and weekly maintenance time from $55.0$ to $35.8$ min/week ($-34.9\%$) compared with PostFilter. Full article

As nanofabrication advances toward atom-by-atom control of surface morphology, plasmonic electrodes and nanogap devices are being pushed into a regime where atomic-scale protrusions and sub-nanometer separations become accessible. In this extreme limit, classical electrodynamics becomes unreliable because it cannot capture quantum effects. To this end, we compute the optical response of metallic sub-nanometer nanogaps containing atomic-scale protrusions by employing quantum hydrodynamic theory (QHT), and benchmark the predictions against the classical local-response approximation (LRA). We revealed that atomic-scale variations in protrusion can leave the far-field scattering spectrum nearly unchanged while profoundly reshaping tnear-field nanofocusing. Upon a continuous decrease in the nanogap, QHT successfully predicts non-monotonic spectral evolution with a redshift-to-blueshift deflection point accompanied via a suppression of field enhancement, whereas LRA yields a continuous redshift and a monotonic increase in field enhancement. We further demonstrated that such an inflection point is tunable, as determined by the atomic morphology of the electrodes, which provide a theoretical foundation for the experimental observation of varied inflection points. These results provide a practical route to optically diagnose and engineer tunneling-enabled charge exchange and quantum-regulated nanofocusing in extreme plasmonic nanogaps, and offer design guidance for molecular-scale optoelectronic and nanophotonic devices. Full article

In modern communication networks, particularly satellite-based systems, data security faces significant challenges from vulnerabilities such as signal interception, jamming, and latency during long distance transmissions. Traditional cryptographic methods are increasingly vulnerable to quantum computing threats, underscoring the need for advanced solutions to protect data integrity, confidentiality, and availability. This research investigates the fusion of quantum cryptography and Machine Learning (ML) to improve security in satellite communication. The Quantum Key Distribution (QKD), which is grounded in quantum mechanics, enables unbreakable encryption by detecting eavesdropping via quantum state disturbances. The CatBoost ML algorithm is applied to a dataset of 10,000 records featuring categorical attributes for prioritizing security elements such as anomaly detection, encryption types, and access controls. The model yields an accuracy of 89.23% and Area under Curve the Receiver Operating Characteristic (AUC-ROC) score of 94.56%, effectively predicting threat levels. Feature importance reveals anomaly detection (28.5%) and quantum encryption (22.3%) as primary contributors. While hurdles such as high implementation costs and transmission range limitations persist, this quantum ML synergy provides a proactive, adaptive framework for resilient, future-ready communication networks. Full article

Collagen and gelatin are promising cell scaffold materials, but their structural instability under physiological conditions necessitates crosslinking treatment. This study evaluated UV crosslinking (254 nm, 0–180 min) as a non-toxic alternative to chemical crosslinking for collagen sheets (CS) and gelatin sheets (GS). Physicochemical properties were characterized by gel fraction analysis, atomic force microscopy (AFM) in PBS, and Fourier transform infrared spectroscopy (FT-IR), while NIH-3T3 fibroblast proliferation was evaluated by CCK-8 assay. UV crosslinking dramatically improved GS water resistance (gel fraction increased from 22% to 80% at 60 min) while maintaining smooth nanoscale surfaces (R_q: 1–4 nm), whereas CS exhibited inherent high stability (90% gel fraction without UV treatment, reaching 95–98% after irradiation). Both materials achieved maximum elastic modulus at 60 min (CS: 2.0 MPa; GS: 1.5 MPa). UV irradiation significantly enhanced cell proliferation on both substrates compared to untreated controls (p < 0.05). CS showed consistently high proliferation across all UV-treated conditions (day 3 absorbance: ~2.5–2.7), while GS exhibited progressive increases reaching a maximum at 180 min (absorbance: ~2.9). The continued GS enhancement despite slightly decreased elastic modulus suggests that chemical factors, possibly related to RGD motif accessibility, contribute beyond mechanical optimization. UV crosslinking effectively establishes structural stability essential for cell scaffold function, with both materials representing effective, biocompatible scaffolds for tissue engineering applications. Full article

Positivity of a linear control system is interpreted as a symmetry with respect to transformations preserving the nonnegative cone of the state space. We study systems on arbitrary time scales and compare positivity criteria for different time scales. This allows us to establish conditions under which Euler discretization of a continuous-time positive system gives rise to a positive system on a discrete time scale. Then we study positive reachability and accessibility of positive systems on various time scales and show invariance of these properties with respect to Euler discretization. Full article

In 2022, the World Health Organization (WHO) released a list of four fungi identified as the most medically important global pathogens, resulting in Cryptococcus neoformans, Candidozyma auris (formerly Candida auris), Aspergillus fumigatus and Candida albicans being classified as the critical priority fungi. The purpose of this list is to encourage the prioritization of fungal research and public policies to strengthen its control and combat fungal diseases. Among the criteria used in the analysis by the WHO to determine these critical threat pathogens were numbers of deaths; annual incidence; current global distribution; trends in the last 10 years; hospitalization; complications and sequelae; preventability; access to diagnostic tests; evidence-based treatments; and antifungal resistance. Difficulties in treatment, including due to antifungal resistance, are a major factor in the morbidity and mortality of these fungi. The fungal cell wall plays a fundamental role in maintaining cellular architecture and contributing to fungal survival. Thus, new approaches targeting the cell wall have been and are being developed. This review article aims to bring together studies from the last ten years focusing on the development of new treatment alternatives targeting the cell walls of the four critical priority fungi and discussing their potential for combating these deadly fungi of worldwide clinical importance. Full article

This study examined the role of work–family conflict as a linking mechanism between difficulties in emotion regulation (ER) and perceived parental self-efficacy (PPSE). Participants were 228 working parents (74.6% women; mean age = 45.24, SD = 7.16), who completed self-report measures of ER difficulties, work–family conflict, and PPSE. Lack of emotional awareness and non-acceptance of emotions were positively related to PPSE, but these associations were not significant when work–family conflict dimensions were considered. Difficulties in goal-directed behavior and limited access to ER strategies were associated with lower PSSE, with strain-based conflict emerging as a relevant pathway for difficulties in goal-directed behavior (95% CI [0.04, 2.67]). Difficulties controlling impulsive behaviors and lack of emotional clarity were linked to lower perceived PPSE through behavior-based conflict (95% CI [−2.04, −0.15]; [−2.01, −0.09], respectively). Overall, the findings suggest that specific ER difficulties may reduce parents’ confidence by increasing strain and behavioral interference between work and family roles. Promoting ER skills may help parents manage these demands more effectively and maintain a stronger sense of parental efficacy. Full article

Reliable and up-to-date measurements of public space remain scarce in small and medium-sized towns (SMSTs), where conventional geospatial datasets are often outdated, inconsistent, or inaccessible. This study presents a low-cost and fully reproducible computational pipeline that integrates nadir RGB imagery captured by a DJI Mini 3 UAV with a lightweight instance-segmentation model (Ultralytics YOLOv12-seg) and GIS-based post-processing to derive class-specific surface indicators at the neighborhood scale. The workflow consists of four components: autonomous UAV acquisition over three representative zones of Sesquilé, Colombia; planar mosaic generation and georeferencing using ad hoc ground control points; fine-tuning of a YOLOv12-seg model trained on locally annotated images; and transformation of predicted masks into OSM and GeoPackage geometries for metric analysis. The trained model achieved stable convergence with mask mAP50 ≈ 0.85 and mAP50–95 ≈ 0.70, supported by balanced precision–recall behavior across classes. Spatial outputs exhibit coherent morphological contrasts between the analyzed zones. Buildings occupy 48.17% of the mapped area, vegetation 25.88%, and transport- and plaza-related public space (roadways, sidewalks, and hardscape areas) 25.95%. These proportions capture a clear gradient from a dense urban core to less consolidated peripheral sectors. Results demonstrate that very-high-resolution UAV imagery, combined with open-source deep-learning tools and structured GIS post-processing, can reliably produce operational public-space indicators for SMSTs at low cost. The methodology provides an accessible and scalable framework for evidence-based urban assessment in municipalities with limited technical and financial resources. Full article

Electronic Medical Records (EMRs) are mandatory in Indonesia following the Ministry of Health regulation, which raises significant challenges in data security and patient-centric access control. Current implementations rely on centralized healthcare systems or third-party vendors, creating risks of unauthorized access, data leakage, and uncertain data integrity. To address these issues, this study proposes DecMed, a decentralized EMR management framework built on IOTA Distributed Ledger Technology (DLT). DecMed integrates Capability-Based Access Control (CapBAC), Proxy Re-Encryption (PRE), and the InterPlanetary File System (IPFS) to enforce patient ownership of medical data. Patients actively grant or revoke access, define access duration, and selectively share data with healthcare personnel. The system is implemented using smart contracts in the Move programming language on the IOTA ledger, while encrypted clinical data is stored on IPFS. Evaluation through unit testing of various unauthorized access scenarios demonstrates that DecMed effectively enforces fine-grained access rules, preserves data confidentiality and integrity, and ensures compliance with national healthcare requirements. Full article

Background: This longitudinal study assessed the Uric Acid/HDL-Cholesterol (UA/HDL-C) ratio as a prognostic biomarker for fatty liver disease (FLD) during a five-year follow-up of 1022 participants from the Genetics of Atherosclerotic Disease (GEA) Study. FLD is a multifactorial disease associated with cardiometabolic comorbidities, and genetic variants affecting uric acid transport (ABCG2 rs2231142) and hepatic lipid metabolism (PNPLA3 rs738409). Early diagnosis is essential to prevent disease progression; however, standard diagnostics are expensive and not widely accessible, highlighting the need for noninvasive tools. Objectives: The study aimed to validate the UA/HDL-C as a long-term predictor for FLD and its effectiveness in risk stratification, including adjustment for cardiometabolic factors and genetics. Methods: Non-contrast computed tomography was used to diagnose FLD and rs738409 and rs2231142 were genotyped by real-time PCR. ROC curves, Kaplan–Meier survival analysis, and logistic regression were used. Results: The findings show that FLD patients exhibited significantly higher UA/HDL-C than controls at both baseline and follow-up (p < 0.0001). Higher UA/HDL-C quartiles were associated with greater FLD prevalence, exceeding 50% in the highest quartile. The index cut-off points were 0.18 in men and 0.09 in women. ROC analysis showed significant discrimination for FLD (AUC: 0.637 overall, 0.650 in men, 0.626 in women). Conclusions: Logistic regression confirmed a strong independent association between UA/HDL-C and FLD over five years, even after adjustment for genetic, biochemical, and anthropometric factors, OR = 3.53, 95% CI: 2.39–4.68, p < 0.0001. Results suggest this ratio could be an alternative to find and follow FLD early on, especially in places with few resources. Full article

This study proposes a reproducible and accessible methodological framework for non-planar path generation to enable scaffold biofabrication on irregular anatomical surfaces replicating the native morphology of human tissue. By integrating a simulation-based trajectory optimization system with a robotic arm, lattice paths are generated using an intersection-based method with parallel planes. This method is processed by intersecting the anatomical object with orthogonal planes, allowing for the creation of paths that conform to complex geometries. The proposed approach relies on widely available and commonly used tools, such as MATLAB, avoiding the need for highly specialized software. Thus, a MATLAB-based kinematic model computes optimal end-effector trajectories, while a coaxial nozzle facilitates the simultaneous extrusion of an alginate-based biomaterial. The proposed method ensures smooth trajectory execution, achieving positional standard deviation within the reproducibility threshold of the robotic arm for an optimal path discretization density. Unlike conventional planar methods, the optimized approach achieves positional accuracy within the robotic arm’s reproducibility threshold while demonstrating superior geometric conformity on complex anatomical patches. The approach successfully fabricates scaffolds with controlled deposition on anatomical patches, demonstrating improved geometric conformity over traditional planar methods. This method provides a pathway for patient-specific scaffold fabrication, supporting advances in tissue engineering and regenerative medicine. Full article

Military–civilian interface zones (MCIZs) adjacent to the Korean Demilitarized Zone (DMZ) represent complex wildfire environments shaped by restricted access, intensive military activities, and adjacent civilian land use. This study develops a spatially explicit wildfire ignition risk assessment framework for the DMZ and Civilian Control Zone (CCZ) in Paju, South Korea, employing Random Forest (RF), Generalized Additive Models (GAM), and Geographically Weighted Regression (GWR) in a complementary analytical design. A dataset of 318 wildfire ignition events (2001–2024), including 78 associated with military activities, was analyzed. The RF model achieved high predictive accuracy (AUC = 0.81), identifying proximity to military training zones, relative humidity, wind speed, and proximity to built infrastructure as dominant ignition drivers. GAM revealed narrow nonlinear thresholds—relative humidity at 13.8%–14.0% and wind speed at 13.5–14.0 m/s—corresponding to peak ignition probabilities. GWR demonstrated pronounced spatial heterogeneity, with military proximity exerting a stronger influence in the eastern and northern sectors, while the meteorological effects varied geographically. Based on these outputs, a unified analytical framework was established in which RF-derived ignition probabilities were interpreted alongside GAM- and GWR-based explanatory layers to provide spatially explicit ignition susceptibility assessments without numerical map fusion. The proposed approach provides a scientifically rigorous and operationally applicable method for quantifying ignition risk in politically sensitive, access-restricted landscapes, offering valuable insights for adaptive wildfire prevention and spatially informed governance of transboundary fire risk. Full article

Background: DNMT3B is frequently overexpressed in molecular subsets of acute myeloid leukemia (AML) and is associated with poor prognosis. Unlike DNMT3A, DNMT3B is rarely mutated, suggesting dysregulation through epigenetic mechanisms. The regulatory basis and downstream consequences of DNMT3B overexpression in AML remain incompletely defined. Methods: We integrated analyses of BeatAML, TCGA, and BLUEPRINT cohorts with multi-omic profiling (RNA-seq, DNA methylation, ATAC-seq, and proteomics) in DNMT3B-high AML models. Nanaomycin A (NanA) was used as a DNMT3B-directed functional probe to interrogate cis-regulatory remodeling, transcriptional circuitry, and apoptotic dependencies. Results: DNMT3B overexpression was linked to enhancer-associated chromatin activation rather than recurrent genetic mutation, particularly in CEBPA- and NPM1-mutant AML. NanA exposure produced focal epigenomic remodeling, including 6900 differentially methylated CpGs, with 268 CpGs located within regions of altered chromatin accessibility. These changes were accompanied by coordinated transcriptomic and proteomic reprogramming enriched for cell-cycle, checkpoint, and stress-response pathways. Functionally, DNMT3B perturbation induced redistribution of cell-cycle phases with increased S-phase fraction and progressive apoptosis. Transcriptional profiling demonstrated induction of BH3-only sensitizers (NOXA, PUMA), repression of BCL2, and compensatory upregulation of MCL1 and BCL-XL, collectively reshaping apoptotic dependency. Combined DNMT3B perturbation and BCL2 inhibition produced cooperative cytotoxicity in DNMT3B-high AML models. Conclusion: DNMT3B functions as a context-dependent epigenetic regulator linking enhancer-associated chromatin organization with proliferative control and apoptotic resistance in AML. DNMT3B-directed epigenetic perturbation remodels cis-regulatory circuitry and is associated with increased venetoclax responsiveness, supporting DNMT3B-governed networks as a candidate co-targeting axis in high-risk AML. Full article

Type 2 diabetes mellitus (T2DM) is a major global public-health problem, and physical inactivity contributes to poor disease control. In settings with limited access to health services, as in the Brazilian Amazon, interventions delivered by Community Health Workers (CHWs) within Primary Health Care (PHC) may offer a pragmatic strategy to increase physical activity (PA). We aimed to evaluate the effect of a CHW-led, theory-based intervention on PA among adults with T2DM in PHC in a cluster-randomized, community-based trial. A total of 274 participants were enrolled (intervention: n = 140, control: n = 134). CHWs in the intervention group completed a blended training (e.g., asynchronous modules, printed educational materials, and hands-on guidance). They conducted six home visits over six months to support behavior change, including increased PA. PA was measured using the International PA Questionnaire (IPAQ-LF), which assessed active commuting, walking, moderate-to-vigorous PA (MVPA), and total PA. Group-by-time effects were examined using mixed-effects zero-inflated Gamma models. No significant intervention effects were observed for the conditional mean of minutes or the probability of participation in active commuting, walking, or total PA. However, for MVPA, the zero-inflated Gamma model revealed a significant intervention effect on the probability of engaging in activity. The intervention group showed a marked reduction in the likelihood of remaining at zero minutes of MVPA (Odds Ratio = 0.08; 95% CI = 0.01–0.79; p = 0.001) compared to the control group, indicating effective behavioral activation among previously inactive participants. These findings suggest that empowering CHWs to deliver structured, theory-driven interventions within PHC can reduce inactivity among high-risk adults with T2DM in underserved communities. Full article

Peer-to-peer (P2P) energy-trading has emerged as a promising mechanism for decentralized electricity markets, but its practical deployment is often limited by the difficulty of accounting for physical network constraints and transmission losses in real time. This paper presents a decentralized P2P energy trading mechanism that incorporates network constraints and transmission losses directly into the market-clearing process. The framework combines Power Transfer Distribution Factors (PTDFs) for pre-trade feasibility validation with an Enhanced Least Squares Method (ELSM) for loss estimation, enabling loss-aware settlement without computationally intensive and redundant AC power flow calculations. The mechanism is implemented on Hyperledger Fabric using Attribute-Based Access Control, Access Control Lists and Private Data Collections to ensure privacy and auditability. Numerical studies on a 3-bus and the IEEE 39-bus system show that the proposed approach closely reproduces AC Optimal Power Flow dispatch and cost outcomes, while significantly improving simplified DC-based loss models. The results demonstrate that physically feasible and economically efficient decentralized trading can be achieved in a permissioned blockchain environment. Full article