Similar Estimates Research Articles

ON ESTIMATIONS IN AN EQUATION WITH A PARAMETER AND A DISCONTINUOUS OPERATOR

In a real reflexive Banach space, an equation with a parameter and a discontinuous nonlinear operator is considered. Both parameter estimations and operator norms are found for the equation. These estimations validate and define concretely the similar estimations obtained earlier in problems with a parameter for elliptic and ordinary differential equations with discontinuous right-hand sides.

Hindlimb biomechanics of Lagosuchus talampayensis (Archosauria, Dinosauriformes), with comments on skeletal morphology.

Lagosuchus talampayensis is a small-bodied (~0.5 m long) Late Triassic dinosauriform archosaur from Argentina. Lagosuchus long has been a pivotal taxon for reconstructing the evolution of form and function on the dinosaur lineage. This importance is because it has a mix of ancestral archosaurian traits, such as a small pelvis with a mostly closed acetabulum lacking prominences that would restrict hip mobility much, with derived "dinosaurian" traits such as bipedalism, proximally shifted thigh muscle insertions, elongate hindlimbs, "advanced mesotarsal" ankle joints and digitigrade feet. Here, to quantify key functional traits related to the locomotor biomechanics of Lagosuchus, we build a three-dimensional musculoskeletal model, focussing on morphofunctional analysis of the pelvic limb. We survey skeletal material that we have digitised, pointing out hitherto undescribed features and elements, many of which are from taxa other than Lagosuchus. Next, we select ideal elements amongst these to construct a composite model, and articulate adjacent body segments into joints, then estimate body shape including centre of mass, and add muscle paths to create a musculoskeletal model. Finally, we use two methods to quantify the hindlimb muscle parameters ("architecture") in the model. We find that they produce similar estimates of force-generating capacities, and compare these data to the few available data from other archosaurs in an evolutionary context, to reconstruct fundamental patterns of changes in muscle architecture and pelvic limb morphology. Our model forms a valuable basis for future quantitative analyses of locomotor function and its evolution in early archosaurs, and an example of how to navigate decision-making for modelling problematic specimens.

Agreement between fat-free mass from bioelectrical impedance analysis and dual-energy X-ray absorptiometry and their use in estimating resting metabolic rate in resistance-trained men.

This study aimed to determine the agreement between fat-free mass (FFM) estimates from bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) and their use in estimating resting metabolic rate (RMR) in men undergoing resistance training. Thirty healthy resistance-trained men (22.7 ± 4.4 years, 70.0 ± 8.7 kg, 174.6 ± 6.7 cm, and 22.9 ± 2.3 kg/m2) were evaluated. The equation developed by Tinsley et al. (RMR = 25.9 × fat-free mass [FFM] + 284) was adopted to calculate the RMR. DXA was used as the reference method for FFM. Furthermore, FFM was also estimated by BIA using a spectral device. No significant difference (p > 0.05) was observed between DXA (1884.2 ± 145.5 kcal) and BIA (1849.4 ± 167.7 kcal) to estimate RMR. A positive and significant correlation (r = 0.89, p < 0.05) was observed between DXA and BIA estimates of RMR. The mean difference between methods indicated that BIA presented a bias of -34.8 kcal. These findings suggest that using FFM derived from DXA or BIA results in similar RMR estimates in resistance-trained men.

Truthful and privacy-preserving generalized linear models

This paper explores estimating Generalized Linear Models (GLMs) when agents are strategic and privacy-conscious. We aim to design mechanisms that encourage truthful reporting, protect privacy, and ensure outputs are close to the true parameters. Initially, we address models with sub-Gaussian covariates and heavy-tailed responses with finite fourth moments, proposing a novel private, closed-form estimator. Our mechanism features: (1) o(1)-joint differential privacy with high probability; (2) o(1n)-approximate Bayes Nash equilibrium for (1−o(1))-fraction of agents; (3) o(1) error in parameter estimation; (4) individual rationality for (1−o(1)) of agents; (5) o(1) payment budget. We then extend our approach to linear regression with heavy-tailed data, using an ℓ4-norm shrinkage operator to propose a similar estimator and payment scheme.

Encapsulation of fluoxetine by cucurbit[7]uril: A computational and experimental study

Fluoxetine (FLX), a selective serotonin reuptake inhibitor, is widely prescribed for the treatment of depression and obsessive-compulsive disorders. However, the administration of FLX can lead to side effects, which may be mitigated by encapsulating the drug within a macrocyclic host. In this study, we explored the complexation of FLX with cucurbit[7]uril (CB7) using 19F and 1H NMR spectroscopy, fluorescence displacement titrations, and molecular dynamics (MD) simulations. Our fluorescence results indicate that CB7 forms a 1:1 complex with FLX, with a binding constant of 1.7 × 10³ M−1. Additionally, NMR data suggest that the (trifluoromethyl) phenyl moiety of FLX is encapsulated within the CB7 cavity. Molecular dynamics simulations revealed two binding modes for FLX with CB7: one involving the inclusion of the (trifluoromethyl) phenyl moiety and the other involving the phenyl moiety within the CB7 cavity. In both modes, the protonated amine group of FLX interacted with the CB7 portal. Binding free energy calculations using the MM-PBSA method suggested a preference for the inclusion of the phenyl moiety, which contrasts the NMR findings. The interactions between the host and guest were primarily driven by electrostatic forces, with van der Waals interactions playing a lesser, but still favorable, role. In contrast, DFT calculations produced similar binding free energy estimates for the two modes.

Optimising sub-metre resolution 3D geomorphic change detection over large areas using multitemporal airborne laser scanning with Sentinel-1 InSAR and Sentinel-2 optical observations

Airborne laser scanning (ALS) is widely used in studies of Earth surface change and has potential to inform targeted landscape remediation over large areas. Leveraging this capability requires geomorphic change detection methods that exploit the full 3D information contained in ALS point clouds but remains challenging over large areas (i.e., > 10 km2). We developed a methodology for geomorphic change detection over large areas using multitemporal ALS in a multiscale model-to-model cloud comparison (M3C2) framework adapted for volumetric estimation. Time series Sentinel-2 optical observations were used to isolate persistently-bare areas as candidate sites to co-register the ALS point clouds. Geomorphic stability of those sites was determined from coherence change detection using time series Sentinel-1 InSAR, thereby ensuring only geomorphically-stable sites were used for co-registration. Results showed the Sentinel-based co-registration produced a closer vertical alignment (0.00 ± 0.09 m) between ALS point clouds over stable parts of the landscape, while co-registration using an iterative closest-point algorithm contained bias (0.07 ± 0.10 m). The methodology was used to estimate annual sediment yield for a semi-arid catchment in northeastern Australia and results were compared with long-term field-based stream sediment monitoring. The ALS-based geomorphic change detection estimated 2.58 ± 0.54 t·ha−1·a−1 sediment yield and stream sediment monitoring estimated 1.40 t·ha−1·a−1. These similar estimates indicate multitemporal ALS can produce realistic whole-of-catchment sediment yield estimates in ungauged catchments (i.e., with no stream sediment monitoring) and improves the spatial detail of those estimates. Accurately detecting geomorphic change from multitemporal ALS also required a strategy to manage vegetation-related error due to misclassification of ALS point clouds. Combined identification of fine-scale erosion processes and reliable estimation of catchment-scale erosion rates indicates the proposed methodology provides a valuable tool for planning landscape remediation over large areas.

Genetic Assessment of a Captive Population of Eurasian Stone-Curlew (Burhinus oedicnemus), Source for the Reinforcement of Wild Populations

Although ex situ conservation programs are increasingly valuable support tools for in situ conservation measures, success depends on these captive individuals to be genetically representative of the recipient population. The Eurasian stone-curlew (Burhinus oedicnemus) inhabit steppes that represent some of the most degraded and exploited habitats worldwide. A captive breeding program was implemented in Morocco as a pre-emptive effort for the conservation of the North African subspecies Burhinus oedicnemus saharae. However, the genetic origins of the founders of the captive flock were unknown. We applied a multi-locus approach to characterize the genetic ancestry of the current captive breeding flock by comparing it to wild populations from both Western and Eastern Morocco. Mitochondrial DNA and microsatellite markers were employed to assess levels of genetic diversity and relatedness within each sample, as well as potential genetic differentiation between wild and captive samples through PCA and admixture analyses. We recovered similar genetic diversity estimates, low levels of relatedness, and little differentiation between captive and wild samples. These results confirmed the Moroccan origin of the founders. We provide recommendations for the optimization of the Eurasian Stone Curlew conservation breeding program but also for future conservation breeding programs to ensure the effective conservation of genetic diversity and wild populations.

Prediction of the mobile game players’ payments-related retention from the Big Data perspective

The paper presents the methodology for evaluating mobile game players retention, which is the basis for generating economic income for its creators. The system for collecting and processing the in-game data (events related to the players’ actions) exploiting the Big Data cloud platform is described. The player profile with the crucial features allowing for the retention analysis is introduced. Datasets generated for the My Spa Resort mobile game by CherryPick company are described. The retention prediction approach based on the similarity estimation between the analyzed and already inactive players is presented. Results of the prediction using the k Nearest Neighbors (kNN) classifier are discussed.

Examining Virtual Delivery of Strength at Home for Intimate Partner Violence Perpetration

This study examined the effectiveness of the virtual delivery of the Strength at Home (SAH) intervention program for intimate partner violence in a sample of 605 military veterans across 69 Veterans Affairs (VA) Medical Centers through a national implementation of the program. Outcome measures included physical IPV, psychological IPV, coercive control behaviors, post-traumatic stress disorder (PTSD) symptoms, and alcohol misuse. Significant pre-intervention to post-intervention reductions were found for all the outcomes, with similar effect size estimates relative to a prior investigation of in-person-delivered SAH through the same national VA implementation. Study findings suggest that the virtual delivery of SAH may be as effective as in-person delivery which has important implications for program access and impact.

Association between chronic long-term exposure to airborne dioxins and breast cancer

Breast cancer is the most common type of cancer among women. Environmental pollutants, specifically those with endocrine disrupting properties like dioxins, may impact breast cancer development. Current epidemiological studies on the association between exposure to dioxins and the risk of breast cancer show inconsistent results. To address these uncertainties, our objective was to investigate the impact of airborne dioxin exposure on breast cancer risk within the E3N cohort, encompassing 5222 cases identified during the 1990–2011 follow-up and 5222 matched controls. Airborne dioxin exposure was assessed using a Geographic Information System-based metric considering residential proximity to dioxin emitting sources, their technical characteristics, exposure duration and wind direction. Additional analyses were performed using dioxin concentrations estimated by a chemistry transport model, CHIMERE. The results suggest a slightly increased risk between cumulative dioxin exposure at the residential address and overall breast cancer risk (adjusted odds ratio (OR) = 1.03, 95% confidence interval (CI): 0.99–1.07, for a one standard deviation (SD) increment among controls (14.47 log-μg-TEQ/m2). The associations remained consistent for sources within 3, 5, and 10 km, and when restricting exposure to dioxin emissions from household waste incinerators. Similar OR estimates (OR = 1.02, 95% CI: 0.97–1.07, for a one SD increment) were obtained using the CHIMERE model. The findings of this study suggest the possibility of an increased risk of breast cancer associated with long-term residential exposure to dioxins and emphasize the importance of efforts to mitigate air pollution exposure.

Accuracy of healthcare systems data for identifying cardiovascular outcomes after stroke due to intracerebral haemorrhage in the United Kingdom

BackgroundHealthcare systems data (HCSD) could improve the efficiency of clinical trials, but their accuracy and validity are uncertain. Our objective was to assess the accuracy of HCSD as the sole method of outcome detection in the REstart or STop Antithrombotics Randomised Trial (RESTART; ISRCTN71907627) compared with adjudicated questionnaire follow-up and compare estimates of treatment effect.MethodsRESTART was a prospective, open, assessor-blind, parallel-group randomised controlled trial (RCT) of antiplatelet therapy after intracerebral haemorrhage (ICH) in the UK.We included 496 (92%) of 537 RESTART participants, who were resident in England or Scotland at randomisation. Computerised randomisation incorporating minimisation allocated participants (1:1) to start or avoid antiplatelet therapy.RESTART used annual questionnaires to detect its primary outcome (recurrent ICH) and secondary outcome (a composite of haemorrhagic or ischemic major adverse cardiovascular events [MACE]) over a median of 2.0 years; an independent adjudication committee verified outcomes using medical records and brain imaging. We obtained ICD10-coded HCSD on hospital admissions and deaths in England and Scotland to identify primary and secondary outcomes. We compared HCSD with a reference standard of adjudicated outcomes. We estimated the effects of antiplatelet therapy using HCSD alone in a Cox proportional hazards model adjusted for minimisation variables.ResultsIn the original RESTART trial, 31 people experienced a primary outcome event. HCSD had sensitivity of 84% (95% CI 66 to 95%) and positive predictive value of 68% (51 to 82%) for recurrent ICH. HCSD estimated an effect of antiplatelet therapy (adjusted hazard ratio [aHR] 0.51, 95% CI 0.27 to 0.98; p = 0.044) that was almost identical to adjudicated outcomes (aHR 0.51, 95% CI 0.25 to 1.03; p = 0.060). HCSD had sensitivity of 84% (76 to 91%) and positive predictive value of 78% (69 to 85%) for MACE, on which HCSD estimated an effect of antiplatelet therapy (aHR 0.81, 95% CI 0.56 to 1.16; p = 0.247) that was similar to adjudicated outcomes (aHR 0.65, 95% CI 0.44 to 0.95; p = 0.025).ConclusionsIn a RCT of antiplatelet therapy for people with ICH, HCSD was reasonably accurate and provided similar estimates of treatment effect compared with adjudicated outcomes.Trial registrationISRCTN71907627. Registered on 25 April 2013.

Quantifying the unseen benefits: an innovative economic valuation of the social and cultural values of trees, woods, and forests at risk

ABSTRACT Trees, woods, and forests in England are associated with a wide range of social and cultural values (S&CV). However, these treescapes (i.e. trees in any location) face increasing risks from pests, diseases, climate change, and biosecurity threats. This study addresses the urgent need for comprehensive estimates of S&CV related to England’s treescapes. Current UK policies and resource allocations are based on limited “value at risk” estimates. The study proposes a novel anchor value method for holistic S&CV valuation, resulting in an annual benefits flow ranging from £7 to £10 billion. This estimate surpasses the current natural capital accounts for England’s woodlands and underscores the importance of including S&CV in total ecosystem benefits assessments. The study also applies traditional methods, namely Willingness to Pay (WTP) and Travel Cost Method (TCM), to economically value S&CV of trees at risk from pests and diseases. These methods, focusing on local use values, yield similar estimates of just under £1 billion per year. This preliminary valuation invites further debate and scrutiny.

MRI-free processing of tau PET images for early detection

Abstract Tau positron emission tomography (PET) imaging in Alzheimer’s Disease (AD) is becoming increasingly common to assess in vivo tau burden. MR images are often acquired to assist with processing of PET data, including for region-of-interest definitions in native space and for normalization to template space. However, in the real-world setting, corresponding MRIs may not be available and PET processing may require MRI-free pipelines. This is particularly important and challenging as the field moves towards early detection among clinically unimpaired (CU) individuals where changes in tau PET signal are expected to be subtle. We used two independent [18F]Flortaucipir tau PET datasets to evaluate whether MRI-free PET processing can detect subtle tau PET uptake differences in Amyloid+ (A+) CU individuals (preclinical AD) versus A-. Standardized Uptake Value Ratios (SUVRs) from MRI-free compared to MRI-based methods were evaluated using linear regression and linear mixed-effects regression models. Effect size differences between A+/- CU groups in MRI-free processed cross-sectional and longitudinal tau PET SUVRs were compared to differences quantified through MRI-based processing. Regional MRI-free SUVRs were highly correlated with MRI-based SUVRs within CU individuals (average ICC = 0.90 for ADNI CU and 0.81 for A4 CU). MRI-free and MRI-based pipelines resulted in similar estimates of cross-sectional and longitudinal differences between A- and A+ CU, even in early focal regions within the medial temporal lobe.

Individual tree mortality: Risks of climate change in the eastern Brazilian Amazon region

The mortality of trees in humid tropical forests plays a fundamental role in understanding forest development, particularly after disturbances such as those caused by logging and extreme weather events. The aim of this study was to evaluate estimates of individual tree mortality following Reduced Impact Logging (RIL) in the Eastern Brazilian Amazon at biennial intervals from 2005 to 2012. RIL is based on operations planning, personnel training, and investments in forest management, and harvesting through RIL must: (a) minimize environmental damage, (b) diminish operation cost by increasing work efficiency, and (c) reduce operational waste. A mortality model was constructed based on the estimation of three distance-independent competition-indices (DII) and five models for predicting the probability of individual tree mortality. The Kolmogorov-Smirnov statistical test was used to determine the most representative model, from which a Neural Network Autoregressive (NNAR) model was constructed to forecast mortality after RIL. Mortality data was correlated with the El Niño–Southern Oscillation (ENSO) and climate (Rainfall, Maximum, Minimum, and Average air temperature). The tested models showed similar and accurate estimates with R2 exceeding 0.90, although underestimation and overestimation trends were observed. The NNAR satisfactorily represented species mortality over the simulated years. The period from 2012 to 2014 was characterized by a Neutral and Weak El Niño event, and exhibited the highest mortality value for a 25 cm DBH (diameter at breast height), the smallest DBH class measured in this study. In the correlation matrix analysis, maximum air temperature showed the highest positive correlation with trees mortality. Despite the challenges in estimating individual tree mortality in tropical forests after selective logging, accurate estimates were achieved using traditional regression techniques and NNAR. These results can support technical and silvicultural decisions regarding forest management in the Eastern Amazon region of Brazil.

Learning Interpretable Brain Functional Connectivity via Self-Supervised Triplet Network With Depth-Wise Attention.

Brain functional connectivity has been widely explored to reveal the functional interaction dynamics between the brain regions. However, conventional connectivity measures rely on deterministic models demanding application-specific empirical analysis, while deep learning approaches focus on finding discriminative features for state classification, having limited capability to capture the interpretable connectivity characteristics. To address the challenges, this study proposes a self-supervised triplet network with depth-wise attention (TripletNet-DA) to generate the functional connectivity: 1) TripletNet-DA firstly utilizes channel-wise transformations for temporal data augmentation, where the correlated & uncorrelated sample pairs are constructed for self-supervised training, 2) Channel encoder is designed with a convolution network to extract the deep features, while similarity estimator is employed to generate the similarity pairs and the functional connectivity representations, 3) TripletNet-DA applies Triplet loss with anchor-negative similarity penalty for model training, where the similarities of uncorrelated sample pairs are minimized to enhance model's learning capability. Experimental results on pathological EEG datasets (Autism Spectrum Disorder, Major Depressive Disorder) indicate that 1) TripletNet-DA demonstrates superiority in both ASD discrimination and MDD classification than the state-of-the-art counterparts, where the connectivity features in beta & gamma bands have respectively achieved the accuracy of 97.05%, 98.32% for ASD discrimination, 89.88%, 91.80% for MDD classification in the eyes-closed condition and 90.90%, 92.26% in the eyes-open condition, 2) TripletNet-DA enables to uncover significant differences of functional connectivity between ASD EEG and TD ones, and the prominent connectivity links are in accordance with the empirical findings, thus providing potential biomarkers for clinical ASD analysis.

Distance similarity entropy: a sensitive nonlinear feature extraction method for rolling bearing fault diagnosis

Entropy-based methods are widely used in machinery fault diagnosis for characterizing system disorder and complexity. However, conventional entropy techniques often fail to capture local signal variations when analyzing relationships between vectors, especially in complex settings. This leads to incomplete representations of subtle features and dynamic behaviors, resulting in inaccurate estimations of system complexity and affecting diagnostic accuracy and reliability. To address these limitations, a novel distance similarity entropy (DSEn) is proposed in this paper: (1) It leverages element-wise distances to precisely capture local shifts and subtle distortions between subsequences. (2) It employs a Gaussian kernel function for vector similarity, enhancing signal pattern analysis by preserving subtle differences and mitigating the impact of outliers. (3) It uses probability density estimation of distance similarities between adjacent vectors to track changes in internal signal patterns, enabling more accurate and sensitive estimations of signal complexity. Synthetic signal experiments demonstrate that DSEn excels in detecting dynamic time series changes and characterizing signal complexity. Tests on two bearing datasets reveal that DSEn's extracted features show significant differences, highlighted by Hedges’ g effect size. Compared to other commonly used entropies (SampEn, PermEn, FuzzEn, DistEn, etc.), DSEn shows superior fault identification accuracy, computational efficiency, and noise resistance.

Apparent survival and population turnover in a long-lived generalist raptor: a comparison of estimation methods

Adult survival and turnover rates are often estimated in long-lived species as the primary drivers of population dynamics. Various methods have been used, yet comparison of estimation methods is seldom undertaken. To address this gap, we used colour-ringing across different age groups, as well as GPS-tracking and genetic analysis of shed feathers, to estimate these critical parameters in a population of the medium-sized bird of prey, the Lesser Spotted Eagle. According to the colour-ringing data, the apparent survival during the first year of life (0.21) was more than four times lower than that of birds older than one year (0.88); the GPS-tracking (0.84) and genetic analysis (0.81) yielded similar survival estimates. All used techniques indicated a stable trend in apparent survival rates. However, population turnover rates derived from genetic feather analysis (0.17–0.43) exceeded expectations based on apparent survival estimates, as well as those derived from colour-rings (0.05–0.17) and GPS-tracking (0.05–0.08). Notably, all markers indicated high site fidelity among adult eagles, with breeding dispersal events being infrequent. Our findings underscore the utility of diverse methods for estimating apparent survival, though the ecological characteristics of the species may influence the efficacy of each method. However, estimates of population turnover rates were notably influenced by the choice of method and should be interpreted cautiously before recommending management actions.

A comparison of thermal drones and camera trap population estimates for Sitka black‐tailed deer in Alaska

One of the most difficult challenges for wildlife managers is reliably estimating wildlife populations. Camera traps combined with spatial capture–recapture (SCR) models are a popular tool for population estimation. They have limitations, however, including long data processing times. Drones with thermal imagery are an emerging tool for estimating wildlife populations, but how they compare to other methods remain poorly studied. We compared the use of camera traps and SCR models to drone surveys for estimating population densities of Sitka black‐tailed deer Odocoileus hemionus sitkensis on Afognak Island, Alaska. We deployed 26 camera traps from 1 September until 6 October 2022 and individually identified males using antler characteristics, for the SCR model. At the same site we conducted three drone surveys between October and December 2022, identified sex composition and obtained deer counts. The estimated density from the SCR model was 3.7 males ± 0.8 (SE) km‐2, and 14.1 ± 3.1 adults km‐2 of clear‐cut forest. Results from the drone survey produced similar estimates with 2.1 ± 0.9 males km‐2 and 13.4 ± 1.6 adults km‐2. The similarity in estimates suggests that both methods converged on an accurate representation of the population in this habitat, but these methods diverge in levels of sampling effort, duration, and financial cost. Camera traps offer further insights on behavior and home‐range size but require longer data processing times, can be subject to malfunctions, and are difficult to deploy and maintain in remote areas. Drones are subject to legal restrictions, have difficulty in closed canopy habitat and can be initially costly, but they provide results faster and require less data analysis. Camera traps and drones are useful for determining population dynamics but are subject to their limitations. Wildlife managers should make survey decisions based on their specific goals, habitat type, focal species ecology and financial limitations.

Divergent Population Trends of Two Sympatric Auk Species in the Rapidly Warming Gulf of Maine.

Rapidly warming global temperatures are having a widespread influence on wildlife communities across taxa, with southern-edge populations often experiencing the greatest negative impacts. However, sympatric species may exhibit divergent demographic responses due to differences in life history strategies and niche separation. We used integrated population models to estimate abundance, survival, and productivity for Atlantic Puffins and Razorbills nesting at the southern edge of their breeding range in the rapidly warming Gulf of Maine. We then conducted transient life table response experiments to understand the relative importance of demographic parameters in driving population dynamics. We found that the Atlantic Puffin population remained relatively stable over the 22-year study period, whereas the Razorbill population increased substantially. Estimates of mean survival and productivity were similar between the study species but were at the lower range of values reported in the literature across their range. Despite similar estimates of mean productivity, interannual variation in this demographic rate was much higher in Puffins than Razorbills. Overall, adult survival was found to be the primary driver of population dynamics for both species yet shows evidence of long-term decline in Puffins. For Razorbills, we found similar evidence of long-term decline in first-year survival. Overall, our findings suggest that these sympatric species may be responding differently to shared environmental conditions. Given the observed long-term decrease in Puffin adult survival, future monitoring and conservation efforts for this species should be focused outside the breeding season in critical overwintering areas and migratory locations where adult mortality is typically concentrated. Similarly, given the observed long-term decline in Razorbill first-year survival, additional monitoring and tracking of chicks is warranted for this species to understand where immature individuals are going after they fledge from the colony.

Spectral Similarity Estimation and ML-Based Classification of Speech Using PDMS-Embedded FBG Sensor-Based Contact Microphone

Spectral Similarity Estimation and ML-Based Classification of Speech Using PDMS-Embedded FBG Sensor-Based Contact Microphone