Search Results (894)

The increasing use of the internet and social media has transformed how parents manage their children’s health, yet the role of mother–pediatrician Facebook groups remains insufficiently understood. This study investigated how mothers participating in these groups manage their children’s health, with particular attention to Facebook group use, trust and satisfaction toward family and online pediatricians, and the associations of these factors with mothers’ health management behaviors. A total of 354 Italian mothers completed an ad hoc online questionnaire assessing socio-demographic characteristics, perceived social support, use of online health information, trust, satisfaction, and Facebook group use. Data were analyzed using descriptive statistics, paired-samples t-tests, and chi-square tests. Overall trust and satisfaction were significantly higher for family pediatricians than for online pediatricians. Higher trust in online pediatricians was associated with more frequent and direct use of information obtained through Facebook groups, whereas higher trust in family pediatricians was associated with greater verification of online information and continued adherence to pediatric recommendations. Most mothers reported verifying online information before using it. Overall, mother–pediatrician Facebook groups appear to complement rather than replace the family pediatrician, serving primarily as spaces for discussion, information, and social support. Full article

Growing air traffic and dynamic missions require conflict detection and resolution (CD&R) during the execution phase, when ad hoc airspace must be inserted into an already conflict-free baseline plan without global re-optimization. This paper proposes a grid-based online optimization framework built on Geographical Coordinate Subdivision grid with One dimension integer coding on 2ⁿ-tree (GeoSOT) discretization that transforms four-dimensional spatiotemporal conflict judgment into efficient grid-code matching and interval comparison. Incremental conflict detection restricts pairwise checks to candidate ad hoc-related pairs, reducing detection scale by over 99% relative to full screening. A lexicographic two-stage resolution policy prioritizes ad hoc adjustments—incorporating horizontal, altitude, temporal, and grid-shrinkage operations—and activates limited baseline coordination only when necessary. The The Incremental Ad-hoc Operation—Tiered Priority Time-Sliced Search (IAO-TPTS) algorithm implements this policy under a hard time budget through Phase A (ad-hoc-restricted Dimension-wise Conflict-Driven Assignment, DCDA-Lite) for fast ad hoc-only feasibilization and Phase B (Hybrid Adaptive Large Neighborhood Search, Hybrid-ALNS) for tiered refinement, with dual validation to prevent secondary conflicts in neighboring airspace. Experiments including visualization, ablation, algorithm comparison, and scalability analysis on Small, Medium, and Large scenarios show 100% feasibility within 180 s, median solve times as low as 0.069 s, competitive objective values versus mixed-integer linear programming (MILP) and Adaptive Large Neighborhood Search (ALNS), and sub-linear scalability from 20 to 300 baseline airspaces. The novelty is this integrated execution-phase framework (incremental detection, lexicographic baseline-protective scheduling, and time-budgeted IAO-TPTS with dual validation), rather than a new grid-coding scheme or a standalone MILP. Full article

This work presents a dual-core embedded architecture for inner–outer loop control of a brushed DC servomechanism based on a dsPIC33CH128MP202 digital signal controller. The secondary core executes the PWM-synchronized current loop at 20 kHz and computes online performance and timing statistics, while the main core handles the incremental encoder, host communication, and inter-core data exchange. Three control schemes are evaluated on the same plant over five runs: direct PWM, a floating-point proportional-integral (PI) current controller, and the same discrete PI law in Q15 fixed-point arithmetic. The maximum observed loop execution times were 9.17 µs, 31.74 µs, and 10.50 µs, respectively, within the 50 µs sampling period. Independent oscilloscope measurements produced 9.19, 30.45, and 10.50 µs. Q15 reduced the maximum execution time by 66.9% and increased the timing margin from 18.26 to 39.50 µs. The two PI implementations showed no observable difference in position response and nearly identical steady-state current-loop RMSE values. The measurements show that Q15 implementation recovers substantial current-loop timing margin without measurable degradation in closed-loop performance, while also confirming the expected reduction in computational cost of fixed-point arithmetic. Current-reference tests from ±0.25 to ±1.00 A and emulated additional inertias were added for both PI implementations to examine current-loop accuracy and load sensitivity. Full article

Flywheel energy storage systems coupled with planetary transmissions require accurate carrier motion control to support efficient energy exchange and operation with carrier torque close to zero. This paper develops a digital cascaded acceleration and velocity control framework for a permanent magnet DC motor used as the carrier actuator in a planetary-transmission flywheel energy storage system. The controller combines an outer velocity loop, an inner acceleration loop, and a feedforward angular acceleration reference derived from the desired motion profile. Two digital implementation approaches are examined. The first preserves the continuous motor plant and includes A/D conversion, aliasing prevention and D/A reconstruction effects. The second uses a fully discrete acceleration plant for adaptive controller design. Based on this discrete model, two adaptive inner acceleration controllers are developed: adaptive deadbeat control and adaptive pole placement control. Both controllers replace only the inner acceleration PI controller, while the outer velocity loop and the feedforward structure remain unchanged. A normalized gradient estimator with projection updates the discrete plant coefficients online and keeps the estimates within calculated admissible bounds. Scilab/Xcos simulations evaluate the controllers under pulse acceleration disturbance, sinusoidal acceleration disturbance, and segmented reference tracking. The results show that both adaptive controllers reduce angular acceleration and angular velocity tracking errors compared with the digital PI baseline. The adaptive deadbeat controller gives the fastest response, whereas the adaptive pole placement controller provides a tunable compromise between response speed and smoothness. Full article

Artificial intelligence (AI) is reorganising corporate work, but whether green skills retain their relative weight during intelligent upgrading remains unclear. This study examines how disclosure-based AI technology exposure is associated with the share of green-skill positions in corporate recruitment. It combines annual-report text, online job postings, and financial and governance data for 31,303 firm-year observations of Chinese A-share listed firms from 2016 to 2024. Panel regressions with industry and year fixed effects show that greater AI exposure is associated with a significantly lower green-skill recruitment share. The result remains stable when the explanatory and dependent variables are remeasured, the 2020 observations are excluded, governance controls are added, a fractional response model is used, and firm years are weighted by recruitment volume. The negative association is stronger under higher media attention, greater industry concentration, stronger tax incentives, and more government subsidies. It is also evident across environmental-governance, carbon-management, and new-energy positions. Further analysis shows that the absolute number of green-skill postings rises while total recruitment expands faster, so the lower share represents relative recruitment reallocation rather than a demonstrated contraction in green hiring. Green-skill recruitment is positively associated with green innovation output. By distinguishing AI exposure from verified adoption and recruitment composition from employment levels, the study identifies green human capital as a link between digital transformation and sustainability and provides evidence for coordinated technology, training, and workforce policies. Full article

This study examines how often adults with a travel-limiting condition or disability use online shopping and home delivery. It uses the person file of the 2022 National Household Travel Survey (NHTS), a household-based survey, restricted to community-dwelling adults aged 18 and over. Delivery counts are heavily overdispersed, so survey-weighted negative binomial models with household-clustered standard errors are used, with delivery frequency as the outcome. Covariates are entered sequentially. Adjusting for demographics alone, adults with a travel-limiting condition do not receive significantly more deliveries; adding socioeconomic status leaves the estimate null; only after employment, driver status, and household vehicle and driver counts are added does a positive association emerge. Because those variables may be consequences rather than causes of a travel-limiting condition, this association is contingent on the model specification and does not constitute evidence of a robust unconditional difference. Within the subsample of adults who receive at least one delivery, however, the pattern is stronger and specification-stable: adults with a travel-limiting condition receive more deliveries overall and disproportionately more food, grocery, and service or personal deliveries than general goods. All estimates are cross-sectional associations and cannot establish direction or cause. The NHTS records how often deliveries occur but not why, so whether delivery compensates for inaccessible travel options remains untested and requires data on accessibility and trip substitution. Full article

Consumer acceptance remains a primary barrier to incorporating organ meats into value-added beef products, despite their nutritional profile. This study evaluated the effectiveness of educational infographics emphasizing either nutritional benefits or economic value on consumer perceptions of organ-enhanced ground beef. A total of 1939 U.S. consumers served as the experimental unit and completed an online survey administered through Qualtrics. Participants were randomly assigned to one of two educational infographic treatments (nutrition or economics and resource utilization). Consumer perception indices were calculated before and after infographic exposure. Changes in perception (Δ) were analyzed with infographic treatment, food familiarity level and their interaction. Educational infographics produced small numerical improvements in consumer perception, although responses did not differ between infographic types. No differences were observed among nutrition- or economic-focused infographics for any perception index (p = 0.1570). Likewise, no interaction between infographic type and Food Familiarity Index were detected (p = 0.4849). However, food familiarity significantly influenced acceptance (p < 0.0001), label transparency (p < 0.0001), and willingness-to-pay (p < 0.0001). Consumers with greater food familiarity reported more favorable perceptions than those with lower familiarity. However, FFI level did not impact economic perception. Current results demonstrate that concise educational messaging can influence consumer perceptions of organ-enhanced ground beef. Furthermore, improving consumer food familiarity may enhance acceptance and marketability of nutrient-dense beef products containing edible organ meats. Full article

The Internet of Vehicles (IoV) relies on frequent vehicle-to-roadside-unit (RSU) access over open wireless channels, making efficient authentication and key establishment essential. In many existing authentication and key agreement (AKA) schemes, a target RSU receives and processes a request before an invalid sender is rejected, which can waste roadside computation under dense invalid-request traffic. This paper presents EDAKA-IoV, an elliptic-curve-cryptography-based AKA scheme that separates admission filtering from end-to-end session-key establishment. A trusted authority (TA) performs Lightweight Polynomial-based Pre-Verification (LPPV) to discard invalid authentication requests before they reach the target RSU, while the vehicle and RSU establish the final session key. A current–pending dual-state mechanism prevents permanent de-synchronization during dynamic pseudo-identity renewal without adding another communication round. Formal analysis under an eCK-style model, ProVerif verification, and heuristic analysis evaluate session-key secrecy, injective mutual authentication, privacy, and resistance to the considered attacks. Optional offline precomputation moves two fixed-base scalar multiplications outside the online phase and reduces the non-polynomial online computation component by approximately 48.8%. With fixed-length compressed point encoding, the authentication exchange requires 2336 bits. The workload analysis shows that the RSU-side processing reduction is proportional to the invalid-request ratio, while the TA still performs record lookup, hashing, and degree-dependent polynomial evaluation for every received request. EDAKA-IoV therefore provides a balanced authentication solution for resource-sensitive IoV deployments. Full article

Graphite ore grade sorting is a key step toward intelligent mineral processing; however, it faces three representational contradictions: ambiguous classification posteriors at grade boundaries, asymmetric multi-scale feature interaction, and the mismatch between class-agnostic self-distillation assignment and sample-level difficulty. Targeting these, this paper adopts D-FINE as the baseline and introduces three decoupled improvements at its decoder, encoder, and criterion layers. (1) Boundary-Grade-aware Distribution Refinement (BG-FDR) online identifies boundary samples via the Top-2 classification score gap and modulates regression-distribution refinement, yielding +2.69 percentage points in mAP@0.5 with zero additional trainable parameters. (2) Bidirectional Feature Pyramid with Global–Local Spatial Attention (BiFPN-GLSA) builds a learnable weighted bidirectional multi-scale fusion path. (3) Difficulty-Aware Decoupled Distillation with Wise-Inner-Shape-IoU (DADD+Wise-IoU) imposes class- and sample-level difficulty-aware constraints. In the integrated full model, this increases Precision from 66.21% to 71.43% (+5.22 pp), F1 from 73.57% to 77.57%, and mean IoU from 97.81% to 98.35%, while false positives drop by 19.6%; the only parameter overhead (+3.84M) comes from BiFPN-GLSA, with BG-FDR and DADD adding effectively no network weights. Ablation on a self-built 3800-image dataset reveals a non-monotonic AP–Precision relationship: the mAP-optimal configuration (BG-FDR+BiFPN-GLSA, 94.17%) and the Precision-optimal one (DADD+Wise-IoU, 77.54%) do not coincide, providing a quantitative basis for objective-driven module selection in industrial sorting. Full article

Psychological distress is associated with problematic online shopping, but the relative and independent associations of depression, anxiety, and stress require careful evaluation without causal interpretation. This study examined their associations with continuous Online Shopping Addiction Scale (OSAS) scores after accounting for sociodemographic characteristics and online shopping frequency. A cross-sectional convenience sample included 438 adults in Türkiye (54.0% female among respondents reporting gender; mean age = 33.79, SD = 10.24); a total of 317 participated online and 121 completed printed questionnaires face to face. Pearson correlations were estimated in the full sample. Hierarchical regressions used a common complete-case sample (n = 375). Because residual diagnostics indicated heteroscedasticity and tail departures, HC3 robust standard errors and 95% confidence intervals were reported. Anxiety, depression, and stress showed statistically significant but modest correlations with OSAS total scores (r = 0.34, 0.32, and 0.27, respectively; all p < 0.001). In separate adjusted models, anxiety (ΔR² = 0.070; β = 0.283), depression (ΔR² = 0.068; β = 0.276), and stress (ΔR² = 0.042; β = 0.217) each increased explained variance (all p < 0.001). In the combined model, the DASS-21 block added ΔR² = 0.091; anxiety (β = 0.206, p = 0.002) and depression (β = 0.200, p = 0.001) had comparable independent associations, whereas stress did not (β = −0.050, p = 0.473). Online shopping frequency had the largest standardized coefficient among concurrent covariates (β = 0.280), but it is conceptually proximal to the OSAS outcome. The combined model explained 31.6% of the variance (adjusted R² = 0.291). Recruitment-mode comparisons were small and nonsignificant after Holm correction. Depression and anxiety were independently associated with higher OSAS scores, while most outcome variance remained unexplained. The findings do not establish temporal prediction, causation, or clinical diagnosis and should be interpreted in light of self-report measurement, construct overlap, residual confounding, and the digitally engaged convenience sample. Full article

Real-time monitoring of radioactive liquid effluents from nuclear power plants (NPPs) is essential for environmental safety assurance, yet existing liquid scintillation counting (LSC) instruments are bulky (>200 kg), laboratory-bound, and incapable of autonomous online deployment. This paper presents the design and implementation of a fully automated online LSC monitoring process integrating seawater sampling, distillation pre-treatment, liquid scintillator mixing, dual photomultiplier tube (PMT) coincidence detection, field-programmable gate array (FPGA)-based digital signal processing, and 4G remote data transmission in a single portable unit weighing 21.59 kg. The automated process executes a complete sample-to-result cycle in approximately 45 min without human intervention. The signal processing chain comprises a dual-PMT coincidence system, a custom two-stage pre-amplifier, a 14-bit 40 MSPS analogue-to-digital converter (ADC; AD9245, Analog Devices, Norwood, MA, USA), and a five-stage FPGA pipeline implementing anti-coincidence rejection, pulse amplitude discrimination, charge comparison method (CCM) waveform discrimination, and convolutional neural network (CNN)-based alpha/beta classification achieving 97.4% accuracy on a Geant4-simulated test set. System performance was validated against a PerkinElmer 1220 QUANTULUS reference spectrometer across a five-point calibration range (0–400 Bq/L; R² = 0.9987, recovery 99.4–101.6%), confirmed via third-party environmental testing (−10 °C to +50 °C, GB/T 2423.1-2008), and verified in field measurements at Tianwan Nuclear Power Plant. The experimentally determined system background is (1.83 ± 0.12) cpm; the calculated minimum detectable activity (MDA) for tritium is 0.073 Bq/mL at 30 min counting time (η = 3.4%, V = 10 mL, T_s = 1800 s per the Currie formulation), satisfying the GB 14587 (the Chinese national standard: Limits of Radioactivity for Liquid Effluents from Nuclear Power Plant) regulatory reference limit of 0.5 Bq/mL with a 7× safety margin. The proposed system is, to the authors’ knowledge, the first reported instrument combining full process automation (including distillation pre-treatment), single-person portability, and real-time 4G remote data transmission for continuous NPP liquid effluent surveillance in high-salinity seawater environments. Full article

Active distribution grids require analysis methods that combine physical fidelity with low computational cost for repeated screening, optimization workflows, and operational decision support. Local linearized power-flow models can support these tasks, but their accuracy depends strongly on the operating point around which they are derived and on how the relevant operating range is represented. This paper benchmarks operating-point-dependent linearized power-flow models for active distribution grids across five SimBench networks. We compare operating-point selection strategies, library sizes, and targeted library extensions for three separate target quantities: voltage magnitude, line loading, and transformer loading. The evaluation combines equal-budget accuracy, critical- and near-limit operating states, post-action AC validation, and controlled runtime measurements. At an equal budget of 36 linearization points, k-medoids provides the most consistent general-purpose accuracy and achieves the first target-specific rank for all three quantities. Increasing the library size yields diminishing gains, with the largest improvement between 10 and 22 points. A k-medoids/k-center hybrid improves all six primary accuracy metrics relative to a common 28-point basis and provides the best upper-tail voltage accuracy in critical states. In post-action validation, nominal linear actions are AC-feasible in 31 of 35 cases, while adding a safety margin yields successful AC results in all 35 cases without new violations. Online evaluation requires about 3.5 ms per state and achieves a median speed-up of about 26–28 relative to AC power flow. The results show that operating-point libraries can provide a practical accuracy–runtime compromise when representative coverage, critical-state validation, and residual decision margins are considered jointly. Full article

Multi-horizon forecasting of nonlinear dynamic time series with exogenous inputs is challenging when the target variable exhibits strong temporal persistence and the exogenous variables provide horizon-dependent corrective information. Direct forecasting models must learn both the carry-forward behavior of the target and the nonlinear deviations caused by changes in the process inputs. This study proposes a residual exogenous–autoregressive gated forecasting framework for nonlinear dynamic prediction. The proposed model decomposes the forecasting operator into a persistence component and a learnable residual correction term. Historical target dynamics and exogenous input dynamics are encoded using two dedicated CNN-LSTM branches, and their latent representations are combined through a sample-dependent sigmoid gating mechanism. The final prediction is obtained by adding the learned correction to the most recent target observation. The framework is evaluated on a benchmark sulfur recovery unit dataset for multi-horizon hydrogen sulfide H₂S concentration forecasting using a leakage-aware nested blocked hyperparameter selection and evaluation protocol. Three forecasting horizons are considered: one-step, five-step, and ten-step ahead prediction. The proposed method achieved the lowest RMSE at the one-step and five-step horizons and remained highly competitive at the ten-step horizon, where its RMSE was nearly identical to the best PatchTST baseline. Across the three horizons, the proposed model obtained RMSE values of $0.0096\pm 0.0020$, $0.0436\pm 0.0097$, and $0.0521\pm 0.0138$, corresponding to RMSE reductions over the persistence baseline of 39.7%, 10.0%, and 13.7%, respectively. The model also maintained a compact parameter count and sub-millisecond inference latency, supporting its feasibility for online soft-sensing applications. Regression, time-series, error distribution, Taylor diagram, and SHAP analyses show that the residual gated formulation is particularly effective for short- and medium-horizon forecasting, while longer-horizon prediction remains more difficult because of increasing temporal uncertainty. The SHAP results indicate that historical H₂S dominates short-horizon prediction, whereas airflow-related variables become more influential at the longer horizon. The results demonstrate that the proposed framework provides an interpretable and computationally compact learning approach for residual forecasting in persistent nonlinear dynamic systems. Full article

This study examines the complex dynamics of advertising avoidance in the social media context, with particular emphasis on the roles of psychological reactance perceived surveillance, privacy concerns, and algorithmic fatigue. It delineates the intricate interplay between personalization technologies and users’ surveillance, privacy awareness, and exhaustion from continuous media consumption, which may contribute to the rising tendency of users to avoid digital advertisements. Drawing on the psychological reactance theory (PRT), the study proposes that perceived algorithmic personalization increases perceptions of surveillance, privacy concerns, and algorithmic fatigue, which trigger defensive responses—advertising avoidance. Furthermore, the study examines the moderating role of perceived privacy control in the relationships between reactance-related antecedents and ad avoidance. An online survey of Instagram users was conducted over a one-month period starting from the 15th of December to empirically test the hypotheses. The findings reveal that perceived algorithmic personalization has a significant positive effect on algorithmic fatigue, privacy concerns, and surveillance. Moreover, perceived control weakens the impact of algorithmic fatigue on ad avoidance, highlighting its role as a critical boundary condition. This study contributes to the literature by integrating technological and psychological determinants of ad avoidance into a unified framework, offering a more comprehensive understanding of user resistance in algorithm-driven environments. Full article

In recent years, interest in machine learning applications has grown rapidly, particularly in the medical domain, where large amounts of data are available for training these models. This review focuses on the potential of machine learning for early diagnosis and prognosis of chronic kidney disease (CKD) by examining the most recent literature. Articles published from 2016 to 2025 were collected from online databases such as PubMed, Web of Science, and Embase. After abstract and full-text screening, 57 articles were included in the results section. Machine learning was applied to clinical and laboratory data, medical imaging, urine samples, retinal images, and at-home measurements to diagnose CKD and predict CKD progression and related complications. Although many studies reported high discriminatory performance, the evidence base was dominated by retrospective, single-center, and methodologically heterogeneous studies, with frequent high-risk-of-bias findings and limited external validation. Furthermore, most published models are not yet sufficiently validated for clinical deployment. Before these tools can be adopted in routine care, prospective, multicenter studies are required that report calibration and clinical utility, adhere to established reporting standards, and demonstrate added value over the current standard of care. Full article