Search Results (1,115)

Introduction: Cholera remains a major public health threat in endemic settings, and oral cholera vaccine (Shanchol™) campaigns are increasingly used amid constrained global supply. However, practical decisions on revaccination require clearer, setting-specific estimates of how rapidly vaccine-induced vibriocidal antibodies peak and wane. Methods: We conducted a prospective cohort kinetics analysis in Lukanga Swamps (Central Province, Zambia), enrolling adults (18–65 years) stratified by prior Shanchol™ exposure (0, 1, or 2 previous doses). All participants received two Shanchol™ doses 14 days apart, with serum collected at baseline and days 14, 28, 60, and 90 (end of follow-up). Ogawa and Inaba vibriocidal titres were measured using a complement-based assay and analysed on the log10 scale. Serotype-specific mixed-effects models with natural cubic splines for time (knots: 14, 28, 60 days) assessed trajectories by prior-dose strata, adjusting for age, sex, and HIV status. Peak timing and post-peak half-life were derived from model-based predictions with participant-level bootstrap CIs (1000 replications). Results: The analysis included 225 participants: 68 (30.2%) with zero prior doses, 89 (39.6%) with one, and 68 (30.2%) with two; median age was 33 years (IQR 25–49), 56.4% were female, and 19.2% were HIV-positive. Modelled titres for both serotypes rose steeply after vaccination, peaking around day 36–37 across prior-dose strata. Ogawa titres reached half of peak by about day 73–78, corresponding to post-peak half-lives of 37–41 days; Inaba declined more slowly with half-lives of 42–46 days. Confidence intervals overlapped across prior-dose strata, indicating minimal differences by vaccination history. Conclusions: In this cholera-endemic adult population, Shanchol™ induced vibriocidal responses that peaked at ~5 weeks and waned over the following 5–7 weeks, with broadly similar kinetics regardless of prior vaccination and slightly slower decay for Inaba than Ogawa. These parameters can inform booster timing in hotspot settings. Full article

Connected and autonomous vehicle (CAV) platoons face the dual challenge of maintaining longitudinal formation stability while ensuring lateral safety in dynamic traffic environments, yet existing control approaches often address these objectives in isolation. This paper proposes a hierarchical cooperative control framework that integrates a differential game-based longitudinal controller with a risk potential field-driven model predictive controller (MPC) for lateral motion. At the coordination control layer, a differential game formulation models inter-vehicle interactions, with analytical solutions derived for both open-loop Nash equilibrium under predecessor-following (PF) topology and an estimated Nash equilibrium under two-predecessor-following (TPF) topology. The motion control layer employs a risk potential field model that quantifies collision threats from surrounding obstacles and road boundaries, guiding the MPC to perform real-time trajectory optimization. A comprehensive co-simulation platform integrating MATLAB/Simulink, Prescan, and CarSim validates the proposed framework across three representative scenarios: ramp merging with aggressive cut-in maneuvers, emergency braking by a preceding obstacle vehicle, and multi-lane cooperative obstacle avoidance involving multiple dynamic obstacles. Across all scenarios, the CAV platoon achieves safe obstacle avoidance through autonomous decision-making, with spacing errors converging to zero and smooth velocity adjustments that ensure both formation stability and ride comfort. The results demonstrate that the proposed framework effectively adapts to diverse and complex traffic conditions. Full article

Background/Objectives: SLC13A5 encodes a sodium–citrate cotransporter implicated in early-onset epileptic encephalopathy and metabolic brain dysfunction, yet its developmental regulation and molecular context in the human brain remain incompletely defined. Methods: Leveraging human developmental transcriptomes from the Evo-Devo resource, we delineated tissue trajectories and network context for SLC13A5 across the fetal–postnatal life. Results: In the cerebrum, SLC13A5 expression rises from late fetal stages to peak in the first postnatal year and then declines into adulthood, while cerebellar levels increase across the lifespan; liver shows a fetal decrease followed by sustained postnatal upregulation. A transcriptome-wide scan identified extensive positive and negative associations with SLC13A5, and a signed weighted gene co-expression network analysis (WGCNA) built on biweight midcorrelation placed SLC13A5 in a large module. The module eigengene tracked brain maturation (Spearman rho = 0.802, p = 8.62 × 10⁻⁶) and closely matched SLC13A5 abundance (rho = 0.884, p = 2.73 × 10⁻⁶), with a significant partial association after adjusting for developmental rank (rho = 0.672, p = 6.17 × 10⁻⁴). Functional enrichment converged on oxidative phosphorylation and mitochondria. A force-directed subnetwork of the top intramodular members (|bicor| > 0.6) positioned SLC13A5 adjacent to a densely connected nucleus including CYP46A1, ITM2B, NRGN, GABRD, FBXO2, CHCHD10, CYSTM1, and MFSD4A. Conclusions: Together, these results define a developmentally tuned, mitochondria-centered program that co-varies with SLC13A5 in the human brain across the lifespan. It may provide insights to interrogate age-dependent phenotypes and therapeutic avenues for disorders involving citrate metabolism. Full article

Markerless motion capture is increasingly used in pediatric neurorehabilitation, yet its ability to detect patient-specific gait abnormalities in small and heterogeneous cohorts remains unclear. This study evaluated a smartphone-based markerless workflow (OpenCap integrated with OpenSim) as a clinical assessment tool to support individualized planning in the context of robot-assisted gait rehabilitation (RAGT) by characterizing individualized gait deviations in four pediatric patients with neurological gait disorders, referenced against normative data from 30 healthy individuals. Sagittal hip, knee, and ankle kinematics were extracted, normalized, and converted into gait-cycle–dependent Z-scores. Group-level comparisons using one-sample Statistical Parametric Mapping (SPM) revealed no significant deviations between patient-group means and normative trajectories (p ≥ 0.05). In contrast, individualized deviation profiling—including Z-score heatmaps, phase-wise Z-score analysis, and per-patient kinematic overlays—identified distinct, clinically meaningful abnormalities in every patient, such as excessive swing-phase hip and knee flexion, mid-stance knee extension deficits, reduced terminal-stance hip extension, and markedly diminished late-stance ankle plantarflexion and push-off. Several deviations exceeded |2–5| SD from the normative dataset, indicating substantial impairments that were obscured by group averaging. These individualized patterns were consistent with each patient’s clinical presentation and could be interpreted in relation to modifiable gait features that are commonly considered during planning and phase-specific adjustment of robot-assisted gait rehabilitation, rather than serving as direct evidence of therapeutic efficacy. Overall, the findings demonstrate that smartphone-based markerless motion capture enables sensitive, individualized gait assessment even when group-level statistics remain nonsignificant, supporting its use as an exploratory, decision-support framework rather than as an outcome measure of RAGT. Full article

Location-based services generate a large amount of location and trajectory data, which contain rich spatiotemporal and semantic information. Publishing these data without proper protection can seriously threaten users’ trajectory privacy. Existing trajectory privacy protection schemes generally fail to consider the dependency between a stay point and its preceding location and also overlook the relationship between the semantic information of location and privacy. Moreover, they often suffer from issues such as over-protection. Therefore, this paper proposes a trajectory privacy protection scheme based on the replacement of stay points. First, a stay point extraction algorithm is proposed, which extracts users’ stay points by setting distance and time thresholds based on the principle of the sliding window. Then, this paper proposes a location perturbation algorithm based on the vector indistinguishability mechanism and introduces different protection strategies for ordinary stay points and long-duration stay points, respectively. Finally, the perturbed trajectory is adjusted by generating a certain number of location points near the replacement points to maintain the temporal continuity and integrity of the trajectory. The experimental results indicate that it is necessary to provide more meticulous protection for long-duration stay points. Compared with similar schemes, the proposed scheme in this paper achieves higher data utility while ensuring privacy. Full article

This study addresses the challenges of real-time performance, safety, and trajectory smoothness in robot navigation by proposing an innovative variable-horizon model predictive control (MPC) scheme that utilizes evolutionary algorithms. To effectively adapt to the complex and dynamic conditions during navigation, a constrained multi-objective evolutionary algorithm is used to tune the control parameters precisely. The optimized parameters are then used to dynamically adjust the MPC’s prediction horizon online. To further enhance the system’s real-time performance, warm start and multiple shooting techniques are introduced, significantly improving the computational efficiency of the MPC. Finally, simulation and real-world experiments are conducted to validate the effectiveness of the proposed method. Experimental results demonstrate that the proposed control scheme exhibits excellent navigation performance in differential-drive robot models, offering a novel solution for intelligent mobile robot navigation. Full article

This study assesses the innovation performance and convergence dynamics across 237 European regions (NUTS 2 level) from 2016 to 2023, explicitly accounting for the structural and behavioural changes triggered by the COVID-19 pandemic. The article provides a novel regional-level assessment of how an unprecedented external shock reshaped innovation trajectories before and after the pandemic. To this end, the analysis combines Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), sigma-convergence measures, and a Difference-in-Differences (DiD) framework within an integrated multi-method empirical approach to evaluate shifts in regional innovation patterns over time. The results reveal a highly uneven distribution of innovation activities, with increasing polarization in the post-pandemic period. Northern and Western European regions strengthened their competitive advantage through robust digital infrastructure, strong human capital, and substantial R&D investments. In contrast, many Southern and Eastern European regions faced heightened structural barriers, leading to a widening innovation gap. Nevertheless, several regions exhibited notable resilience and achieved significant innovation catch-up, providing new empirical evidence on heterogeneous regional adaptive dynamics supported by targeted regional policies and improved local capabilities. The sigma-convergence analysis indicates a general increase in overall disparities, as reflected by rising dispersion in the Regional Innovation Index (RII) during 2020–2023. However, according to the DiD estimation, regions most severely affected by COVID-19 experienced a statistically significant relative increase (approximately 2.17%) in innovation performance, highlighting the pandemic’s role as a catalyst for accelerated digital transformation and innovation adjustment at the regional level. Full article

Background and Objectives: Heart failure frequently coexists with CKD, compounding prognosis via cardio-renal interplay. Sodium glucose cotransporter 2 (SGLT2) inhibitors have demonstrated cardiovascular and renal benefits in randomized trials, but data remain limited in real-world lower-resource settings. Materials and Methods: We conducted a retrospective single-center cohort study at a tertiary university hospital in western Romania, including adults with chronic HF and LVEF ≤ 45%, monitored between 2021–2024. Patients were stratified based on receipt of SGLT2 inhibitors. The primary endpoint was a composite of cardiovascular death, HF hospitalization, or ≥40% sustained decline in eGFR/initiation of KRT. Annual eGFR slope was analyzed to assess renal trajectory. Results: Among 240 patients, treatment with SGLT2 inhibitors was associated with a lower risk of the composite cardio-vasculo-renal endpoint compared with no treatment (adjusted HR 0.70, 95% CI 0.50–0.98). The reduction was primarily driven by fewer heart failure hospitalizations. Decline in kidney function was slower among SGLT2 inhibitor-treated patients in longitudinal mixed-effects analyses. Conclusions: In this retrospective cohort, SGLT2 inhibitor use was associated with fewer cardio-renal events and a slower decline in kidney function. Given the observational design and residual confounding risk, these findings should be considered hypothesis-generating but provide implementation-relevant signals supporting further prospective evaluation. Full article

This paper introduces a novel architecture for a navigation system that is designed to estimate the position and velocity of a moving vehicle specifically for remote piloting scenarios where GPS availability is intermittent and can be lost for extended periods of time. The purpose of the navigation system is to keep velocity estimation as reliable as possible to allow the vehicle guidance and control systems to maintain close-to-nominal performance. The cornerstone of this system is a landmark-extraction algorithm, which identifies pertinent features within the environment. These features serve as landmarks, enabling continuous and precise adjustments to the vehicle’s estimated velocity. State estimations are performed by a Sequential Kalman filter, which processes camera data regarding the vehicle’s relative position to the identified landmarks. Tracking the landmarks supports a state-of-the-art LiDAR odometry segment and keeps the velocity error low. During an extensive testing phase, the system’s performance was evaluated across various real word trajectories. These tests were designed to assess the system’s capability in maintaining stable velocity estimation under different conditions. The results from these evaluations indicate that the system effectively estimates velocity, demonstrating the feasibility of its application in scenarios where GPS signals are compromised or entirely absent. Full article

Map matching is a fundamental technique for aligning noisy GPS trajectory data with digital road networks and constitutes a key component of Intelligent Transportation Systems (ITS) and Location-Based Services (LBS). Nevertheless, existing approaches still suffer from notable limitations in complex environments, particularly urban and urban-like scenarios characterized by heterogeneous GPS noise and sparse observations, including inadequate adaptability to dynamically varying noise, unavoidable trade-offs between real-time efficiency and matching accuracy, and limited generalization capability across heterogeneous driving behaviors. To overcome these challenges, this paper presents a Meta-learning-driven Progressive map-Matching (MPM) method with a symmetry-aware design, which integrates a two-layer pattern-mining-based noise-robust meta-learning mechanism with a dynamic weight adjustment strategy. By explicitly modeling topological symmetry in road networks, symmetric trajectory patterns, and symmetric noise variation characteristics, the proposed method effectively enhances prior knowledge utilization, accelerates online adaptation, and achieves a more favorable balance between accuracy and computational efficiency. Extensive experiments on two real-world datasets demonstrate that MPM consistently outperforms state-of-the-art methods, achieving up to 10–15% improvement in matching accuracy while reducing online matching latency by over 30% in complex urban environments. Furthermore, the symmetry-aware design significantly improves robustness against asymmetric interference, thereby providing a reliable and scalable solution for high-precision map matching in complex and dynamic traffic environments. Full article

Tomato production is a strategic horticultural sector in Europe, yet it is increasingly exposed to climate variability, input-price volatility, and structural heterogeneity among national production models. This study provides a macro-level, cross-country assessment to benchmark structural performance and derive country typologies of tomato systems in 15 European countries over 2015–2024 using harmonized public statistics on cultivated area, production, and derived yields. A Tool for Agroecology Performance Evaluation (TAPE)—informed interpretive lens is used to frame yield level and interannual yield variability as transition-relevant performance signals, while acknowledging that farm- and territory-level TAPE scoring cannot be replicated with aggregated national data. The analysis combines descriptive benchmarking, trend-adjusted yield stability metrics, area–production relationship diagnostics, and multivariate classification (principal component analysis and Ward hierarchical clustering) to identify coherent national performance profiles. Results show pronounced cross-country contrasts and three recurring macro-patterns: (i) high-yield, low-dispersion systems with stable trajectories; (ii) transitional systems with lower yields and broader distributions; and (iii) high-dispersion systems indicating structural or climatic instability. The resulting typology supports differentiated policy discussion on adaptation, modernization priorities, and transition enabling conditions, and highlights the need to link macro-statistics with comparable agroecological indicators at farm and regional scale for stronger inference on transition pathways. Full article

Background: Early childhood growth trajectories can influence the risk of chronic diseases in adulthood. Improvements in environmental sanitation may affect child development in low-resource settings. Objective: to examine the associations among socioeconomic factors with nutrition indicators, and trajectories of anthropometric indicators across three epidemiological cohorts that reflect different phases of environmental sanitation implementation. Methods: A longitudinal study was conducted in Salvador, Brazil, from 1997 to 2013. A total of 1429 children were recruited across three epidemiological cohorts, corresponding to the phases of a sanitation program: pre-intervention (n = 299), intervention (n = 1007), and post-intervention (n = 123). Height-for-age (HAZ) and BMI-for-age (BAZ) z-scores were assessed at four time points. Multilevel linear models were used to adjust for socioeconomic factors. Results: A total of 992 children (68.7%) completed follow-up. Post-intervention children showed improved HAZ trajectories, with sex-specific patterns that varied across cohorts. Birth weight is positively associated with HAZ across all cohorts (0.34–0.49 kg increase per z-score). Household overcrowding (>2 persons/room) is consistently associated with lower HAZ (−0.34 to −0.63 z-score reduction). Children who were never exclusively breastfed in the post-intervention phase had a higher BAZ (0.76 z-score increase). Caesarean delivery is associated with higher BAZ in the pre-intervention (0.23) and intervention (0.27) cohorts. Conclusions: Children born in later time periods showed better growth trajectories, which may reflect the combined effects of sanitation improvements, economic development, and other societal changes in Brazil during this period. Further research using experimental or quasi-experimental designs is needed to isolate the specific contribution of sanitation to child growth. Full article

With the rapid growth of vessel traffic and the widespread adoption of the Automatic Identification System (AIS) in recent years, analyzing maritime traffic flow characteristics has become an essential component of modern maritime supervision. Clustering analysis is one of the primary data-mining approaches used to extract traffic patterns from AIS data. Addressing the challenge of assigning appropriate weights to the multidimensional features in AIS trajectories, namely latitude and longitude, speed over ground (SOG), and course over ground (COG). This study introduces an adaptive parameter optimization mechanism based on evolutionary algorithms. Specifically, Particle Swarm Optimization (PSO), a representative swarm intelligence algorithm, is employed to automatically search for the optimal feature-distance weights and the core parameters of Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling dynamic adjustment of clustering thresholds and global optimization of model performance. By designing a comprehensive clustering evaluation index as the objective function, the proposed method achieves optimal parameter allocation in a multidimensional similarity space, thereby uncovering maritime traffic clusters that may be overlooked when relying on single-dimensional features. The method is validated using AIS trajectory data from the Xiamen Port area, where 15 traffic clusters were successfully identified. Comparative experiments with two other clustering algorithms demonstrate the superior performance of the proposed approach in trajectory pattern analysis, providing valuable reference for maritime regulatory and traffic management applications. Full article

Water scarcity and ecological degradation driven by the expansion of irrigated agriculture in arid regions urgently necessitate a rigorous assessment of the combined impacts of climate change and crop-structure adjustments on irrigation water requirements (IWR). Taking the Qarqan River Basin as a case study, this study establishes an integrated framework that incorporates remote sensing (Landsat/MODIS), the AquaCrop-OS crop model, and a CNN-LSTM deep learning architecture to simulate historical IWR (2000–2024) and project future trajectories under CMIP6 climate scenarios. The results indicate that: (1) from 2000 to 2024, fruit tree area expanded from 120.3 to 320.3 km², cotton stabilized at approximately 165.3 km² after peaking at 187.9 km² in 2014, wheat recovered to 113.1 km², and maize varied between 23.7 and 85.0 km², indicating that fruit trees have become the dominant crop type. (2) Over the same period, total basin-wide IWR increased by 91% (3.7 × 10⁸ to 7.1 × 10⁸ m³), with fruit trees accounting for 44–68% of this growth. Logarithmic mean Divisia index (LMDI) decomposition further shows that meteorological factors and human activities jointly drove the increase in IWR, with cultivated-area expansion and cropping-structure change contributing most, while improvements in agricultural water-use efficiency partially offset the rise. (3) Projections for 2025–2100 suggest stronger structural dominance of fruit trees and cotton; the growing share of water-intensive cash crops may further elevate irrigation pressure. Under SSP5-8.5, a 30% reduction in fruit tree area in the late century could save 4.3% of irrigation water (0.33 × 10⁸ m³). Overall, this study provides dynamic projections and decision support for adaptive regulation of agricultural water resources in arid regions. Full article

Background and Objectives: SGLT2 inhibitors are foundational in heart failure therapy, yet their impact on left ventricular (LV) remodeling immediately following acute coronary syndrome (ACS) remains less defined. This study evaluated the association between early SGLT2 inhibitor initiation and structural recovery in a real-world post-ACS cohort. Materials and Methods: We conducted a retrospective observational study including 238 revascularized ACS patients, stratified into an SGLT2 inhibitor group (n = 71) and a control group (n = 167). Changes in LV ejection fraction (LVEF) and indexed LV mass (LVMi) were assessed by echocardiography at baseline and follow-up (mean 286 days). Multivariable regression models were adjusted for baseline imbalances and tested for interactions with diabetes status. Results: A significant “confounding by indication” was observed; the SGLT2 group presented a high-risk phenotype with higher diabetes prevalence (56.3% vs. 25.7%, p < 0.001), lower baseline LVEF (38.3% vs. 43.3%), and greater hypertrophy. After adjustment, statistical independence was attenuated by baseline severity, yet the SGLT2 group achieved follow-up structural outcomes comparable to lower-risk controls. Interaction analysis indicated these trends were consistent regardless of diabetes status (p > 0.05). Conclusions: In this high-risk ACS population, early SGLT2 inhibitor therapy was associated with stabilization of cardiac structure. Despite a profound baseline disadvantage, the recovery trajectory effectively aligned with that of a lower-risk population, highlighting a clinically relevant pattern of structural stabilization consistent across metabolic subgroups. Full article