Search Results (1,546)

This paper analyzes factors affecting school absenteeism due to an injury or illness among the US school student population between 6 and 15 years of age. The number of missed school days displays overdispersion and is modeled using the Finite Mixture Roy (FMR) model for count variables. The married/single parent family status (treatment) is potentially endogenous to the dependent variable (missed days). The Roy structure controls observed heterogeneity due to the mother’s marital status. Finite mixtures are intended to control unobserved heterogeneity due to healthy and unhealthy children in the sample. This approach facilitates identification of latent subpopulations in which treatment and marginal effects are relatively homogeneous. The model also incorporates two application-driven extensions. First, probabilities of the latent components are modeled as functions of regressors. Secondly, the mother’s income affects treatment nonparametrically. The FMR model is estimated with two latent components in each state, corresponding to healthy and unhealthy students. The results indicate that maternal marital status decreases annual missed school days by approximately 13 percent for a randomly drawn child; however, this increases absenteeism by about 14 percent among families that self-select into two-parent households, which is evidence of adverse selection. Full article

Sandstone uranium deposits exhibit stratabound mineralization and strong vertical heterogeneity in geological space, which complicates the identification of uranium anomaly layers and their integration into deposit-scale 3D models using borehole datasets. In this paper, we propose a UAPC Fourier layer identification (UFLI) method for uranium anomaly layer identification. The method is based on multi-log feature construction, random forest-based estimation of a depth continuous uranium anomaly probability curve (UAPC), and improved Fourier vertical variation analysis. We used 19 boreholes arranged on four exploration lines (ZKA-ZKD) of the Daying uranium deposit in the northern Ordos Basin (north central China), for the validation. The proposed UFLI method identified 51 uranium anomaly layers at a 5 m sampling interval, forming discrete vertical clusters within the drilled successions. The results indicate that anomalies are overwhelmingly concentrated in the Middle Jurassic Zhiluo Formation, particularly within the lower Zhiluo member, with an anomaly-bearing depth range of approximately 550–745 m. Comparison with known mineralization records shows that both industrial and ordinary mineralization intervals are captured within the anomaly layers. Then, based on inter-borehole continuity of anomaly layers, we reconstructed five uranium orebodies (orebodies 1–5) and describe their distribution characteristics. The proposed method provides a technical means for subsurface visualization and exploration targeting in sandstone uranium systems. Full article

Sarcopenia is a complex condition marked by reductions in muscle strength, mass, and overall physical performance, which has significant consequences for functional autonomy and metabolic health in elderly women. This study aimed to examine the correlations between lower limb strength, functional capabilities, and metabolic indicators in community-dwelling older women categorized according to the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) criteria. A total of thirty-eight women aged ≥ 60 years underwent assessments, including anthropometric, hemodynamic, and metabolic evaluations, along with functional tests such as handgrip strength, chair-rise test, gait speed, Timed Up-and-Go, and maximal isometric hip extension strength (MIHE). The criteria for probable sarcopenia were established using the handgrip thresholds set by the EWGSOP2. Women identified as having probable sarcopenia displayed markedly lower MIHE, diminished gait speed, inferior performance in chair-rise and Timed Up-and-Go tests, decreased muscle mass, and a lower resting metabolic rate than their non-sarcopenic counterparts. MIHE exhibited robust correlations with muscle mass, resting metabolic rate, and functional performance metrics. These results suggest that assessments of lower limb and trunk strength yield pertinent insights beyond handgrip strength alone. Function-oriented evaluations may improve sarcopenia screening and facilitate the identification of older women at risk of functional and metabolic deficiencies in community-based environments. Full article

The automatic modulation recognition (AMR) of low probability intercept (LPI) signals has received a considerable amount of interest from many researchers who have done much work on electronic reconnaissance. This recognition technology aims to design a classifier that enables the identification of signals with different modulation types. Based on deep learning models such as a convolutional neural network (CNN), the time-frequency images (TFIs) of the signal can be input to further extract features for classification. To improve recognition accuracy, especially under low signal-to-noise ratios (SNRs), we propose an AMR method for radio frequency proximity sensor signals based on a TFI enhancement network. The TFIs are denoised based on a per-pixel kernel prediction network (KPN), which can improve the quality of TFIs and achieves comparable denoising performance to traditional TFI reconstruction methods (e.g., sparse representation-based methods and low-rank approximation methods), while requiring significantly less computational overhead. The denoised TFIs, with enhanced signal quality and reduced noise, are then fed into the RetinalNet-based classifier as high-quality input features. This enhancement is crucial for the subsequent recognition stage, as it significantly improves the modulation recognition accuracy, particularly under challenging low SNR conditions. Simulation results show that the proposed method can accurately identify the modulation types of different radio frequency proximity sensors that are aliased in the time-frequency domain under low SNRs, and the average recognition accuracy rate of the signal remains above 97% when the signal-to-noise ratio is above −10 dB. Full article

Background: Antimicrobial resistance (AMR), particularly due to extended-spectrum beta-lactamases (ESBL), is a growing threat to public health in sub-Saharan Africa. This study investigates the prevalence, epidemiological characteristics, resistance patterns and resistance dynamic over time of ESBL-producing non-typhoidal Salmonella (NTS) bacteremia in Kisantu, Democratic Republic of Congo (DRC), from 2019 to 2022. Methods: A retrospective observational study used routine bloodstream infection data from the AMR network at Saint Luc Hospital in Kisantu. Blood cultures from suspected bacteremia cases were processed using standard microbiological techniques. Bacterial identification relied on biochemical reactions. Antibiotic susceptibility testing and ESBL-producing NTS detection were performed by disk diffusion following Clinical and Laboratory Standards Institute guidelines. Associations between ESBL production and patient characteristics (age, sex) were assessed using Pearson’s Chi-square test, and annual temporal trends in ESBL-producing NTS from 2019 to 2022 were analyzed by logistic regression using 2019 as the reference year. Results: Of the 19,430 blood cultures, 1681 NTS isolates were identified, and 1568 of these were screened for ESBL. ESBL prevalence was significantly associated with age (p = 0.007), peaking in children under 2 years, but not with sex (p = 0.570). Compared with 2019, the likelihood of isolating ESBL-producing NTS increased markedly through 2022, with adjusted probabilities rising from 58% to 87%, reflecting a strong upward temporal trend. High levels of extensively drug-resistant (94.1%) were observed. No carbapenem resistance was detected. Conclusions: ESBL-producing NTS bacteremia is rising in Kisantu, DRC, mainly affecting children under 2 years. Rising resistance to key antibiotics limits treatment options and highlights the need for strengthened AMR surveillance, optimized antibiotic use, and vaccination strategies. Full article

In the field of structural health monitoring systems, sensors serve as the fundamental components for assessing infrastructure integrity. The rationality of their spatial configuration significantly influences the precision of structural performance assessment, the efficacy of damage detection algorithms, and the operational reliability of the system throughout its designated lifecycle. A robust optimization methodology for the placement of multi-type sensors is proposed in this study, explicitly formulated to mitigate the negative impact of sensor malfunctions during long-term operation. First, a rigorous evaluation framework for sensor placement schemes is established based on Bayesian inference and the minimization of information entropy, thereby quantifying the uncertainty inherent in parameter identification. Then, a probabilistic model of sensor failure is developed utilizing the Weibull distribution to capture time-dependent reliability characteristics, combined with a modified information entropy calculation method that mathematically assimilates these failure probabilities into the optimization objective. Finally, a heuristic search strategy is employed to achieve the robust optimal placement of multi-type sensors, efficiently navigating the complex combinatorial search space. In contrast to deterministic information entropy (DIE) methodologies, which assume ideal sensor functionality, the robust information entropy (RIE) approach comprehensively accounts for the stochastic nature of sensor failures and their impact on the information content of the monitoring network, thereby significantly augmenting the robustness and redundancy of the sensor configuration. Validations utilizing a numerical frame structure and a finite element bridge model demonstrate that the RIE method effectively integrates the sensor failure probability model to yield robust optimal placement schemes, minimizing the risk of information loss and ensuring reliable structural health monitoring throughout the engineering lifecycle. Full article

Background/Objectives: To identify US military unknowns, the Armed Forces Medical Examiner System’s Armed Forces DNA Identification Laboratory has historically relied upon mitochondrial DNA and Y-chromosomal short tandem repeat testing. Where no appropriate family reference sample (FRS) is available or skeletal samples are degraded, autosomal single nucleotide polymorphism (SNP) testing with next-generation sequencing could assist. Methods: A method utilizing hybridization capture enrichment of a 95,000 (95K) SNP panel, amenable to FRS and extremely challenging samples, was validated. The Parabon Fx Forensic Analysis Platform was used for analysis and extended kinship inference. Skeletal samples (n = 65) and associated FRS (n = 64) were selected for a performance evaluation and case-type sample study. Results: Considering FRS with ≥7 ng DNA input into library preparation, 94% yielded ≥66,320 SNPs at ≥5X coverage. SNP recovery for skeletal samples at ≥1X coverage ranged from 5 to 94,197 SNPs, averaging 40,770 SNPs. When skeletal samples resulted in ≥13,000 SNPs, the most likely relationship category was consistent with the expected relationship. A log10 likelihood ratio of ≥4 and a posterior probability of ≥99.99% were established as thresholds for strong statistical support, and 87% of inferences met these thresholds while 13% were considered inconclusive. Pairwise kinship inference between unrelated individuals yielded an unrelated result in 85% of comparisons, 66% with strong statistical support. There were 170 instances of false positive 4th degree relationship inferences with strong statistical support. All false positives involved skeletal samples from individuals of admixed ancestry. Conclusions: With this approach, autosomal SNP testing can result in reliable kinship inferences between related individuals out to 3rd, and in some cases 4th, degree relationships, increasing the scope of eligible FRS to aid in identifications. Full article

Background: Visceral leishmaniasis (VL) is a life-threatening protozoan disease prevalent in tropical and subtropical regions and a frequent coinfection among people living with HIV. Early identification of patients at high risk of death may reduce case-fatality. This study evaluated the post-test prognostic value of C-reactive protein (CRP) and interleukin-6 (IL-6) as biomarkers of mortality in VL. Methods: A retrospective hospital-based cohort of 101 VL patients was analyzed. CRP and IL-6 concentrations at admission were correlated with clinical findings, the Kala-Cal^® prognostic score, and in-hospital mortality. Results: Eight patients died, most presenting with hemorrhagic manifestations. At admission, 87.1% of patients had both biomarkers above the predefined cut-offs. CRP and IL-6 levels were markedly elevated in patients with hemorrhage or fatal outcomes. The AUC was 0.85 for CRP and 0.87 for IL-6, with no significant difference between markers. Optimal prognostic cut-offs were 150 mg/L for CRP and 90 pg/mL for IL-6. Conclusions: In this sample, CRP and IL-6 showed good prognostic performance in VL. In patients with low initial clinical risk, positive biomarker results substantially increased the probability of death. When combined with Kala-Cal^®, these markers may improve risk stratification and guide referral decisions. Full article

Tracking highly maneuvering, non-cooperative UAVs poses significant challenges due to rapid and unpredictable changes in target dynamics. Under such conditions, traditional single-model filters often fail to maintain reliable state estimates, resulting in degraded tracking performance. Multiple-Model Kalman Filter (MMKF) approaches, including the Generalized Pseudo Bayesian (GPB1) and Interacting Multiple-Model (IMM) algorithms, improve robustness by simultaneously considering multiple candidate motion models and weighting them according to the observed target behavior. Adaptive strategies, such as ${\chi }^2$-test-based or t-test-based methods, further enhance performance by dynamically responding to changes in maneuvering patterns. This paper presents a multi-criteriacomparative assessment of four MMKF formulations—GPB1, IMM, ${\chi }^2$-test-based, and t-test-based filters—under a consistent modeling and simulation framework. Particular emphasis is placed on systematically analyzing the role of the transition probability matrix (TPM), investigating how fixed, adaptive, and TPM-free strategies affect estimation accuracy, robustness to noise, and mode-identification performance. Beyond conventional Root Mean Square Error (RMSE) metrics, the filters’ comparison is carried out through confusion matrices and dwell time analysis to highlight performance nuances and trade-offs. This allows to establish which filter formulation is preferable in different operational conditions. Full article

The Cretaceous dinosaur record from Normandy, in NW France, is reviewed. It includes several enigmatic specimens that were briefly mentioned in short notes published during the 19th and 20th centuries that have since then been destroyed in World War II or lost. Since they were neither described in detail nor illustrated, their identification must remain uncertain, but some may have been ankylosaur remains, while another specimen may have belonged to a bird or a non-avian theropod. Specimens that were properly described and are kept in museums in Normandy come from Albian and Cenomanian horizons in the coastal cliffs of Seine-Maritime. The Albian record, from Cape La Hève (Le Havre) includes an incomplete titanosaurian sauropod skeleton, described as Normanniasaurus genceyi, and an isolated caudal vertebra from the same provenance, probably belonging to that taxon. The Cenomanian record is limited to a group of bones and a tooth of the furileusaurian abelisaurid theropod Caletodraco cottardi from the glauconitic Chalk at Saint-Jouin-Bruneval. All these specimens come from marine sediments and are in all likelihood derived from floating carcasses that drifted over a fairly long distance from an emergent land area corresponding to the Armorican massif in the west. Although scanty, the record from Normandy sheds some light on the poorly known dinosaurs that inhabited north-western Europe during the middle part of the Cretaceous, some of which apparently had Gondwanan affinities. Full article

With the advancement of UAV (Unmanned Aerial Vehicle) technology, flying ad-hoc networks (FANETs), composed of multiple coordinating UAVs, demonstrate tremendous application potential in disaster relief, environmental monitoring and intelligent logistics. However, inherent resource constraints and unpredictable operating environments make UAV failures a frequent and critical challenge. Particularly in mission-critical applications, simultaneous or consecutive failures of multiple UAVs can severely disrupt network topology, leading to catastrophic consequences such as network fragmentation and service interruptions. Furthermore, traditional topology reconstruction algorithms suffer from high computational overhead and significant communication delays. Primarily designed for single-node failure recovery, they are ill-equipped to address the challenge of concurrent multi-node failures. To address these challenges, this paper proposes a topology reconstruction algorithm tailored for multi-node failure scenarios in FANETs. The core objective of this algorithm is to minimize communication overhead and secondary damage to the network during the reconstruction process while ensuring basic reconstruction results, thereby improving the system’s energy efficiency and robustness. The proposed framework integrates three key phases: First, overlapping communication coverage areas among neighbors of failed nodes are leveraged to define first and second regions, enabling rapid identification of connection restoration candidate positions and avoiding computationally intensive global calculations. Second, a comprehensive importance evaluation mechanism is constructed based on the topological and functional attributes of node, categorizing nodes into different importance types. For failed nodes of varying importance, differentiated search ranges and retry strategies are employed to ensure the most suitable nodes are selected for reconstruction tasks. Third, the inflexibility of repulsion ranges in traditional artificial potential field (APF) method is addressed by introducing dynamic repulsion influence zones and a composite repulsion model. The improved APF algorithm enhances safety in high-speed scenarios and reduces the probability of UAVs becoming trapped in local minima. Finally, extensive simulations validate that the proposed algorithm accurately identifies critical network nodes and promptly implements effective reconstruction measures to minimize network damage. Full article

Nosy Be in the northwestern Madagascar hosts one of the largest known seasonal feeding aggregations of whale sharks. However, the environmental drivers influencing whale shark surface feeding behaviour in this area remain poorly understood. This study investigates the relationship between environmental variability and surface feeding strategies of immature whale sharks at Nosy Be. Boat-based surveys were conducted in November 2018, 2019, 2022, and 2023, resulting in the photo-identification of 88 individuals and the recording of 85 surface feeding events. The influence of environmental factors on feeding behaviour was assessed using multicollinearity among the environmental covariates and three-level step approach: permanova, multinomial logistic regression, marginal effects, and Cochran’s Q, to evaluate whether environmental conditions discriminate feeding-behaviour categories and to quantify how individual covariates relate to behavioural composition under a multi-step framework. Results showed that there is not a strong enough predictive signal for behaviour based on environmental variables; however, thanks to the marginal effects, it is possible to better assess the probability of a certain type of eating behaviour in the presence of an increase in one of the environmental variables, for example, chlorophyll-a appears to be the most interesting, because its increase is associated with a greater probability of some behaviours instead the others. These preliminary observations provide the first insights to evaluate environmental influences on immature whale shark surface feeding behaviour in Nosy Be, highlighting that it is therefore necessary to deepen and increase data collection to have long and significant series of data, integrated also with data on the preys subject to feeding behaviour and to evaluate which other unobserved aspects, perhaps linked precisely to the consistency and quality of the prey, could allow us to predict feeding behaviour. Improving the understanding of these relationships is essential for predicting whale shark habitat use and for supporting conservation and management strategies in a region increasingly affected by climate variability and anthropogenic pressures. Full article

Parasitic infections pose a significant threat to the wild population of Southern catfish (Silurus meridionalis) in Dongting Lake, yet the specific pathogen identity and epidemiological drivers remain unclear. This study combined morphological assessment, 28S rDNA molecular identification, and Generalized Linear Models (GLM) to elucidate the infection dynamics and pathogenicity of trematodes. Molecular analysis confirmed the pathogen as Isoparorchis eurytremus. GLM analysis revealed that apparent spatiotemporal variations in infection were actually sampling bias in fish host size structure; the total length was identified as the decisive predictor of infection risk. The infection probability followed a sigmoid growth pattern with a median infection length (L₅₀) of 70.4 cm, a phenomenon attributed to the host’s ontogenetic diet shift from insectivory to obligate piscivory. Anatomical observations indicated that the infection induced systemic pathology; beyond severe fibrosis and mechanical damage to the swim bladder, varying degrees of parenchymal lesions were evident in the liver, spleen, and kidney. These findings indicate that I. eurytremus infection in S. meridionalis is a size-dependent, accumulative process maintaining a homogenous high pressure across the lake ecosystem, necessitating a shift in perspective from localized lesions to systemic disease management. Full article

Rapid urban air mobility (UAM) developments and new classes of vertical takeoff and landing (eVTOL) aircraft have changed the safety paradigm in urban airspace. eVTOL aircraft operations in dense urban environments are characterized by increased variability of external factors, highly dynamic flight scenarios, and an increased likelihood of rare but potentially critical events. Traditional safety assessment approaches do not capture the specific features of eVTOL designs, power plants, autonomy algorithms, and urban air traffic characteristics; this results in low threat prediction accuracy and limited development of modern incident prevention systems. Herein, the risk profile of eVTOL aircraft is analyzed, accounting for the multifactorial nature of urban environments and the complexity of integrating such vehicles into existing UAM infrastructure. The need for quantitative methods for assessing the probability of critical situation risks is also substantiated. These methods provide a statistically accurate description of extreme events and enable the identification of hidden dependencies in complex technical and organizational systems. Approaches based on probabilistic models, extreme value analysis, and systemic processing of operational data are considered, providing increased risk assessment accuracy and a deeper understanding of mechanisms underlying hazardous events. Results demonstrate the importance of applying the extreme value theory (EVT)–Copula model, which enables the quantitative assessment of the probability of extreme situations and loss of stability of eVTOL vehicles in the context of developing UAM. This model can be employed to obtain realistic predictions of flight processes, reduce uncertainty, and create scientifically valid tools for developing effective measures to minimize the risks of extreme events—a key factor in ensuring the safety of eVTOL flights in urban airspace. Full article

Toppling failure is a fundamental mode of instability in rock slopes and occurs predominantly in reservoir bank anti-dip bedded rock masses. Reservoir impoundment changes seepage conditions and weakens slopes, whereas discontinuity non-persistence introduces uncertainty and complicates the identification of coupled toppling–sliding mechanisms. To address this, a probabilistic framework using the Goodman–Bray limit equilibrium method is developed. Equivalent strength parameters are introduced to unify the strength contrast between unsaturated and saturated segments along a common basal surface. Basal discontinuity connectivity is modeled as a random variable, and a Monte Carlo simulation is used to derive failure mode probabilities and a probability-weighted factor of safety. The framework is applied to the Huangcaoping anti-dip slope in the Dagangshan reservoir area at a normal water level of 1130 m. The most probable scenario has a probability of 0.116, involving sliding at 1120–1420 m and toppling at 1420–1550 m, with a probability-weighted mean factor of safety of 0.978. Predicted failure characteristics and deformation intervals are consistent with engineering observations, confirming the method’s effectiveness. This integration enables the simultaneous characterization of stability levels and the evolution mechanism. The approach provides mechanism-explicit mode likelihoods and a robust stability metric to support hazard assessment, monitoring placement, and reinforcement design. Full article