Search Results (92,702)

Background/Objectives: Molecular docking and molecular dynamics are accurate but computationally expensive, so the compounds entering them must be chosen well. The present study proposes CADT, a confidence-gated affinity–ADME-T docking-triage cascade that decides which compounds are worth docking. Methods: The gate combines an ensemble estimate of drug–target affinity with its epistemic uncertainty and an applicability-domain check. Predicted absorption, distribution, metabolism, excretion, and toxicity (ADME-T) developability is added as a soft flag. All components were trained on openly licensed Therapeutics Data Commons data. Ranking was assessed on the DAVIS and KIBA kinase panels and on BindingDB $K_d$, under three split protocols over five seeds. The routing decision was then examined against molecular docking, in which 407 compound–target pairs were docked into six withheld kinases. Results: A Morgan-fingerprint gradient-boosting model reached a concordance index of $0.866\pm 0.006$, with $0.813$ for unseen targets and $0.720$ for unseen drugs. Across eight ADME-T endpoints, the area under the ROC curve ranged from $0.65$ to $0.91$. On the cold-target split the cascade reduced the compounds sent to docking by $86\%$ while retaining $61\%$ of the true strong binders. Docking measured that reduction at $85\%$, and at an equal budget, the gate enriched true binders more than the docking score itself. Conclusions: A transparent pre-screen can prioritise compounds ahead of structure-based calculation at a fraction of its cost. However, the uncertainty and applicability-domain terms act as an abstention mechanism rather than an accuracy gain, and that abstention is not free. Full article

Digital rights and responsibilities education is a core component of digital citizenship in primary schools, yet existing approaches often prioritize rule transmission over behavioral internalization. This study examined the effects of integrating Digital Game-Based Learning (DGBL) into the final lesson of a digital rights and responsibilities curriculum. A quasi-experimental study was conducted with 88 fifth-grade students during a four-lesson curriculum delivered over four weeks. Both groups received the same teacher-led instruction during the first three lessons. In the final lesson, Cyber Judge was incorporated into instruction for the experimental group (EG) as a game-supported learning activity, while the control group (CG) received conventional instruction on the same content. Data were analyzed using ANCOVA and t-tests, with Holm–Bonferroni adjustments applied for multiple comparisons. The EG showed higher adjusted post-test digital rights and responsibilities literacy, with significant differences in Rights Awareness and Responsibilities Behavior, and higher overall cognitive absorption, particularly in Temporal Separation. No significant between-group differences were found in learning anxiety. These findings highlight the potential of DGBL to connect ethical knowledge with situated decision-making while underscoring the importance of considering learners’ affective responses in the design of game-supported instruction. Full article

To assess the long-term fatigue safety of fully enclosed high-speed railway noise barriers and to optimize maintenance strategies, a time-dependent probabilistic reliability framework focusing on fatigue damage and bolt preload relaxation is developed. The stochastic nature of train velocity, load scaling factor, and daily traffic volume is explicitly considered. Using the 350 km/h two-train passing scenario as the reference case, the stress time history at the column base is extracted. The damage per train pass is computed via rain-flow counting and Miner’s rule, and the time-dependent reliability indices are obtained through parallel Monte Carlo simulations (10⁴ samples, daily time steps over 50 years). The results show that the fatigue failure probability at 50 years is 0.11%, with a reliability index β ≈ 3.06 and a mean cumulative damage of 0.325; failures are concentrated in the 40–50-year period, and train velocity is identified as the most influential factor. Furthermore, 98.3% of the bolts require retightening within 50 years, with a median intervention time of 18.2 years—far earlier than the occurrence of fatigue failure. The joint system analysis reveals that the overall system failure is dominated by bolt relaxation; when the coupling effect is included, the fatigue failure probability increases to 0.18%. A phased maintenance strategy is accordingly proposed: monitor preload during years 0–15, perform comprehensive re-torquing during years 15–30, and intensify fatigue inspections during years 30–50. The proposed methodology provides a quantitative basis for life-cycle safety assessment and operational decision-making for fully enclosed high-speed railway noise barriers. Full article

Emergency Medical Services (EMS) personnel require exceptional spatial awareness and rapid decision-making within the confined environment of an ambulance. While Virtual Reality (VR) offers a safe alternative to traditional training, the lack of high-fidelity, regionally accurate, and VR-optimized 3D ambulance models limits its application. This study presents a scenario-driven methodology for developing a VR-ready 3D ambulance environment prototype tailored for Emergency Medical Technician (EMT) training. Utilizing reality-capture techniques, terrestrial laser scanning was performed to accurately document the interior of a standard Hungarian ambulance simulator. The resulting point cloud underwent systematic processing, manual retopology, PBR shading, and the implementation of a custom dual-rigging animation system to optimize complex mechanical movements—such as stretcher operations—for standalone VR platforms. The workflow successfully reduced the vertex count to 25,373 while maintaining millimeter-level spatial fidelity. Technical evaluation confirmed that geometrical, functional, and material objectives were fulfilled, whereas pedagogical implementation remains incomplete. Structural accuracy and animation readiness were verified through preliminary inspection within Blender’s VR viewport inspector. However, interactive game-engine integration remains future work, and educational effectiveness has not yet been tested with EMT learners. Overall, this workflow delivers a 3D asset foundation that establishes the necessary technical basis for subsequent software implementation and clinical evaluation. Full article

Satellite downlinks reliably connect remote unmanned aerial vehicle (UAV) swarms, but broad coverage exposes information-bearing signals to unauthorized receivers. This paper proposes a multi-beam cooperative time-varying directional modulation (TVDM) framework for secure satellite downlinks. Two asymmetrically partitioned subarrays form cooperative beams whose pointing states and beam-dependent symbol mappings are randomly updated at the symbol rate, thereby introducing controlled randomness for physical-layer security. In each interval, the source symbol is mapped to two transmit symbols whose superposition remains in the correct phase-shift-keying decision region at legitimate UAVs, while the equivalent constellation varies at unauthorized locations. A relaxed transparent-transmission constraint based on constructive decision-region margins enables conventional detection without instantaneous TVDM-state estimation. Mutual information (MI) defines the pointwise multi-UAV secrecy capacity (SC), main-lobe insecure area, and sidelobe leakage. A mixed discrete–continuous problem jointly optimizes the trajectory radius, relative pointing phase, mapping parameters, and subarray partition to reduce insecure coverage and sidelobe leakage. A block alternating algorithm combines Monte Carlo MI evaluation, projected Armijo updates, and finite partition search to coordinate continuous and discrete variables. Simulations using representative low-Earth-orbit satellite parameters show that the proposed design reduces the central insecure-interval length by 82.7% relative to conventional beamforming. Full article

This paper introduces a Joint 4-channel wavelength-division multiplexed (WDM) Dual-Polarization Autoencoder (J-4WDM-DPAE) framework to address the scaling limitations of existing end-to-end learning architectures for long-haul coherent optical transmission. The proposed approach employs a unified one-dimensional residual convolutional neural network (CNN) decoder that processes all eight complex symbol streams (4 WDM channels × 2 polarizations) at the symbol rate, enabling simultaneous exploitation of inter-channel and inter-polarization correlations with low inference latency. The transceiver is trained through a fully differentiable dual-polarization Manakov split-step Fourier method (SSFM) model including span-wise amplified spontaneous emission (ASE) noise and an effective combined transmitter–local-oscillator phase-noise process, enabling co-optimization of a shared geometric constellation shaping (GCS) encoder under realistic nonlinear and linewidth constraints. Robustness is further enhanced by randomized launch powers and signal-to-noise ratio (SNR) conditions during training. Additional robustness is assessed by cross-SPS evaluation (SSFM-resolution mismatch) and SSFM convergence checks, indicating that the reported achievable-rate trends are not an artifact of the baseline SSFM discretization. Evaluations over standard single-mode fiber (SSMF) for 16-, 32-, and 64-quadrature amplitude modulation (QAM) show that at 1000 km, the learned constellations achieve generalized mutual information close to the dual-polarization limits, with pre-FEC bit-error rates remaining below an adopted threshold of $2\times {10}^{-2}$ (used as a representative soft-decision FEC operating target) up to 4000 km when the model is re-trained for each distance. Complexity analysis further indicates that the unified WDM-aware decoder provides a quantitative performance–computational trade-off compared to existing counterparts under matched link conditions. Full article

Early-exit deep neural networks (DNNs) can reduce edge-inference latency, but abrupt variations in wireless and computing resources can destabilize split-inference policies. This paper proposes a meta-learning-driven adaptive control framework for joint backbone splitting and early-exit routing in MobileViT. The framework formulates multi-exit splitting as a constrained Markov decision process (CMDP) and introduces splitting-aware multi-dimensional adaptive proximal policy optimization (SMAPPO). SMAPPO combines nonlinear quality-of-service (QoS) penalties with topology-aware action masking, while cross-environment meta-initialization supports edge-local adaptation after resource disturbances. Under the stated simulation assumptions, SMAPPO reached the highest performance-index plateau among six methods in a representative 500-episode stationary trace and achieved the lowest normalized total cost across three latency–energy preference settings. Across ten seeds and nine stationary or disturbed scenarios, online SMAPPO achieved a 77.20% measured accuracy and 22.40 mJ of system energy. With an adaptation horizon of $K=14$, SMAPPO yielded a post-disturbance mean latency of 37.68 ms, a QoS-violation rate of 2.24%, and an on-time completion rate of 98.69%. These results indicate that combining meta-initialization, nonlinear constraint shaping, and topology-aware action masking improves stationary optimization and disturbance recovery within the controlled simulator. Full article

Background/Objective: Accurate measurement of serum 25-hydroxyvitamin D (25OHD) is essential for the diagnosis and management of vitamin D deficiency. Although automated immunoassays are widely used in routine clinical practice, analytical variability between platforms may lead to inconsistent patient classification around clinically relevant decision thresholds. This study comprehensively compared three automated immunoassays (DiaSorin Liaison XL, Beckman Coulter DxI 9000, and Roche cobas e602) with a Vitamin D Standardization Certification Program (VDSCP)-traceable liquid chromatography–tandem mass spectrometry (LC-MS/MS) reference method. Methods: A total of 400 anonymized adult outpatient serum samples were prospectively collected and equally distributed across four predefined vitamin D concentration categories (<25, 25–49, 50–74, and ≥75 nmol/L). Samples were stored at −70 °C before simultaneous analysis on the three automated immunoassays and the reference LC-MS/MS platform. Method comparison included Deming regression, Bland–Altman analysis, and evaluation of diagnostic performance at clinically relevant thresholds (<25, <50, and <75 nmol/L). The effects of sample storage, vitamin D metabolites (C3-epi-25OHD₃ and 24,25-dihydroxyvitamin D₃), and external quality assessment (DEQAS) performance were also investigated. Results: Significant analytical differences were observed between all immunoassays and LC-MS/MS. Roche cobas e602 consistently overestimated 25OHD concentrations, resulting in reduced sensitivity but excellent specificity and positive predictive value across all clinical thresholds. Beckman Coulter demonstrated the highest sensitivity for detecting vitamin D deficiency but generated more false-positive classifications, particularly at the <75 nmol/L threshold. DiaSorin Liaison XL achieved the best overall balance between sensitivity, specificity, accuracy, and agreement with LC-MS/MS, with Cohen’s κ values ranging from 0.818 to 0.895 across decision thresholds. Higher concentrations of C3-epi-25OHD₃ and 24,25-dihydroxyvitamin D₃ were associated with greater positive bias in Roche measurements, while Beckman Coulter showed no significant metabolite-related interference, although the present correlation analyses could not establish independent analytical interference. DEQAS performance did not consistently reflect agreement with LC-MS/MS in patient samples. Conclusions: Despite ongoing international standardization efforts, clinically important differences remain among automated 25OHD immunoassays. Assay-specific analytical characteristics significantly influence patient classification around established decision thresholds. DiaSorin Liaison XL demonstrated the closest overall agreement with the reference LC-MS/MS method, whereas Beckman Coulter prioritized sensitivity and Roche specificity. These findings highlight the importance of method-aware interpretation of vitamin D results and support continued assay harmonization to improve comparability in routine clinical practice. Full article

Groundwater-dependent irrigated agriculture in semi-arid basins is increasingly threatened by climate variability and aquifer depletion. Karapınar district, located within Türkiye’s Konya Closed Basin, represents a water-stressed melon-producing area where deep-well drip irrigation and rigid calendar-based scheduling remain common. This study developed a prototype AquaCrop-based decision-support framework for adapting mature and pickled melon irrigation to climatic conditions classified by the crop-season weighted three-month Standardized Precipitation Index (SPI-3). SPI-3 was selected to represent short-term seasonal precipitation anomalies relevant to agricultural water availability, and three historical years were used as representative scenarios: Dry (2020), Normal (2018), and Wet (2017). Fourteen irrigation treatments, including farmer practice and phenologically targeted Late-Season Event (L.S.E.) deficit strategies, were simulated using FAO AquaCrop v7.1. Irrigation timing was more important than seasonal irrigation volume alone. Under Normal conditions, an L.S.E. strategy that redistributed irrigation toward four late fruit-development events (T4.2) maintained yield stability comparable to farmer practice (CV < 2%), increased mature melon yield by 12.7% relative to farmer practice, and reduced applied irrigation by 19–21%. Under Dry conditions, a lower-volume L.S.E. strategy with the same late-season targeting (T4.3) maintained baseline-level yield while reducing applied irrigation by 28–31%. The resulting SPI-based decision matrix is forecast-compatible but not operationally validated; field validation, long-term climate testing, and forecast-skill evaluation remain necessary before deployment. Full article

Measuring the functional size of business intelligence (BI) and digital marketing analytics dashboard development can provide quantitative input for estimating the required time and cost of software development efforts. However, digital analytics practitioners currently lack practical guidance for planning dashboard development using measurement-based inputs that support reliable estimation models. Therefore, this study proposes the first Common Software Measurement International Consortium (COSMIC) ISO/IEC 19761 framework to measure the functional size of software user requirements of Power BI, Data Studio, and Tableau. An empirical study was conducted to measure the functional sizes of two datasets based on COSMIC. These functional sizes were considered inputs to several machine learning (ML) models to predict the effort required for BI dashboard development. The outcomes of these ML models showed that projects with consistent, low-variability effort should be prioritized when collecting or selecting data for training effort-estimation models and recommended the use of Stacking Regressor, Linear Regression, and Voting Regressor models for similar datasets because these models recorded the highest standardized accuracy for predicting BI development efforts with high generalization performance. These findings demonstrate the importance of applying COSMIC as a software engineering standard to BI and digital marketing analytics projects by offering measurements that allow for decisions informed by data rather than intuition or guesswork. Full article

Perceived safety determines whether urban greenways are used in everyday life, yet it is rarely measurable at the boundary scale where design decisions are made. Existing street-view models split into black-box networks whose predictions cannot be traced to design elements and pixel-ratio regressions whose interpretability rests on weak, unstructured representations, while greenspace studies lean on GIS proximity variables that confound design with context. We present the CPTED-Guided Perception Network (CGPN), which fuses a visual branch with a masked, learnable projection of segmentation ratios onto five CPTED dimensions. Because the mask confines learning to a theory-defined support, the prior regularizes the representation while every coordinate of the model remains tied to a named CPTED dimension, whose directional effect on the prediction we verify by perturbation. On 110,633 street-view images, CGPN is statistically equivalent to the strongest black-box baseline in pairwise ranking accuracy (0.649 vs. 0.652; equivalence test within a 1.5-point margin, $p=0.006$, attains the best $R^2$ (0.192), and improves on its unconstrained variant in goodness of fit across three seeds ($\mathrm{\Delta }{R}^2=+0.031$, $p=0.042$). Applied to 218 greenway-adjacent residential boundaries in Boston and New York, it uncovers a threshold-like negative association for barrier-dominated access control and an inverted-U distance profile whose weakest segment lies within 100 m of the greenway edge ($p=0.007$). Full article

The use of uncrewed aerial vehicles (UAVs) for remote sensing continues to increase, and, in missions such as search and rescue (SAR) and landmine detection, a vision-based detector with high recall is critical. Flying at a higher altitude covers a larger area in each pass but enlarges the ground sample distance, reducing recall and precision. Recall can be maintained by lowering the detector’s confidence threshold, at the cost of precision. In those instances, any additional false positives can be resolved by a verification flight. This work compares two strategies for completing a survey in the minimum time while maintaining high recall. Strategy 1 surveys the entire area at a constant low altitude, where precision is high enough that flagged locations need no verification. Strategy 2 performs a rapid high-altitude survey, then visits each flagged location on a low-altitude verification flight, ordered efficiently by a Travelling Salesman Problem (TSP) solver. Experimental results indicate that Strategy 1 is more effective when targets are dense, and Strategy 2 when targets are sparse, provided its survey altitude is well above the verification altitude. Our approach also generates a decision table and a break-even altitude ratio that mission planners can use to select the better strategy from the estimated target density and the detector’s measured false-positive rate. Full article

In this study, we use gratifications (U&G) theory to explore how social media shapes Millennials’ gastronomic destination choices. Rather than treating social media influence as uniform, we identify three key gratifications obtained from gastronomy-related content: experiential–inspirational, utilitarian decision-making, and information search and assurance. The study investigates how these gratifications directly and indirectly affect destination choice through pre-visit destination impressions. Data were collected from 418 Chinese Millennials. Validity of measurements was confirmed through exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and reliability tests, with hypotheses tested using Hayes’ PROCESS Model 4. Findings indicate that pre-visit destination impression significantly predicts destination choice and mediates the influence of all three gratifications. Experiential–inspirational gratification has the greatest overall impact; experiential–inspirational and information search and assurance gratifications operate through both directly and indirectly, while utilitarian decision-making gratification operates primarily through pre-visit impressions. By integrating U&G theory with pre-visit impressions, this study offers a novel pathway-specific explanation of social media’s role in gastronomic destination choice, suggesting that different gratifications activate distinct psychological mechanisms rather than a uniform process. It also advances destination-choice research by highlighting pre-visit impression as a rapid evaluative mechanism linking digital gratification and behavioral decision. Full article

Background: Population ageing, multimorbidity, and chronic conditions have increased the demand for person-centred care in Primary Health Care. Current stratification systems rely on diagnoses or resource utilization and fail to capture the multidimensional nature of care complexity, which also depends on functional, social, environmental factors. Objective: To develop a HealthCare Complexity Index using routinely collected electronic health record (EHR) data and the Person-Centred Care Knowledge Model. Methodology: A retrospective, cross-sectional observational study was conducted in Primary Health Care in the Community of Madrid (Spain). Deductive Care Methodology was used to identify functional assessment variables associated with four care dimensions: vulnerability, risk, etiology, and signs/symptoms. A multidisciplinary expert panel selected and validated the variables through iterative consensus rounds. A weighted scoring algorithm, the Care Complexity Index, was designed to quantify care complexity on a 0–100 scale. Relationships between the selected variables and NANDA-I nursing diagnoses were established and validated by an expert in standardized nursing languages. Results: 27 assessment variables were identified and classified into the 4 dimensions of the care model. These variables were integrated into a weighted complexity index with five severity levels ranging from low to critical complexity. Furthermore, 48 NANDA-I nursing diagnoses were linked to the selected variables. Conclusions: The HealthCare Complexity Index provides a standardized, person-centred approach for estimating care complexity using routinely available (EHR) data. It may support early identification of individuals with complex care needs, facilitate individualized care planning, and strengthen clinical decision-making. Future studies should validate its predictive performance and implementation in other clinical practice. Full article

The existing research on digital–intelligent innovation collaboration networks has not effectively connected the evolution of real collaborative relationships with governance methods, and governance models often struggle to accommodate both empirically identified evolutionary mechanisms and sequential decision-making capability. Using Chinese digital–intelligent innovation co-patent data from 2016 to 2025, this study develops a machine learning analytical framework centered on relationship evolution and collaboration resilience governance and employs OSR-DDQN to optimize dynamic intervention strategies under limited budgets. The results show that state transitions in digital–intelligent innovation collaboration exhibit pronounced path specificity, with the innovation capability, relational capital, and technological complementarity displaying differentiated conditional associations across transition paths. Further simulation experiments show that, across different budget conditions, OSR-DDQN improves the mean reward by 11.56% to 12.83% relative to the best-performing standard deep Q-network. Within the specified governance environment, once the budget reaches a minimally sufficient level, further performance gains arise primarily from optimizing the action timing, intervention targets, and stopping decisions rather than from simply increasing governance resources. Full article