The rate of cognitive decline in Alzheimer's disease (AD) varies considerably from person to person. Numerous epidemiological studies point to the protective effects of cognitive, social, and physical enrichment as potential mediators of cognitive decline in AD; however, there is much debate as to the mechanism underlying these protective effects. The retrosplenial cortex (RSC) is one of the earliest brain regions with impaired functions during AD pathogenesis, and its activity is affected by cognitive, social, and physical stimulation, making it a particularly interesting region to investigate the influences of an enriched lifestyle on AD pathogenesis. In the current study, we use the 5xFAD mouse mode of AD to examine the impact of enriched housing conditions on cognitive function in AD and the viability of a particularly vulnerable cell population within the RSC-parvalbumin interneurons (PV-INs). Enriched housing conditions improved cognitive performance in female 5xFAD mice. These changes in cognitive performance coincided with restored functional connectivity of the RSC and preserved PV-IN density within this region. Along with preserved PV-IN density, there was an increase in the density of Wisteria floribunda agglutinin-positive perineuronal nets (WFA+ PNNs) across the RSC of 5xFAD mice housed in enriched conditions. Direct manipulation of WFA+ PNNs revealed that these extracellular matrix structures protect PV-INs from amyloid toxicity and may be the mechanisms underlying the protective effects of enrichment. Together, these results provide support for the WFA+ PNN-mediated maintenance of PV-INs in the RSC as a potential mechanism mediating the protective effects of enrichment against cognitive decline in AD.

Leak detection in pipelines transporting gas-liquid multiphase flow remains a challenging task due to complex and inherently unsteady flow behavior, particularly under intermittent regimes such as plug flow. Conventional leak detection techniques, which are well established for single-phase flow, often suffer from reduced sensitivity and false alarms when applied to multiphase systems. Motivated by these limitations, the present study investigates leakage characteristics in a horizontal pipeline conveying gas-liquid plug flow under underwater conditions. A three-dimensional transient numerical model based on the Volume of Fluid (VOF) approach is developed to simulate various leakage scenarios, including different discharge sizes and gas-liquid superficial velocities. The numerical results are validated against experimental data obtained from a dedicated multiphase flow loop. Time-series pressure signals are analyzed using statistical metrics, probability density functions, and continuous wavelet transform techniques to assess their effectiveness in identifying leakage occurrence. Furthermore, a non-dimensional analysis is employed to develop a nomograph for estimating gas release velocity from underwater leaks. The results demonstrate that leakage significantly alters pressure fluctuation characteristics and gas void fraction distribution, with detectability strongly influenced by flow conditions and discharge size. The proposed nomograph predicts gas release velocity with reasonable agreement relative to experimental measurements, highlighting its potential applicability for subsea leak assessment and process safety analysis in multiphase pipeline systems.

Collagen fibrils provide mechanical integrity to the extracellular matrix in a variety of biological tissues. In biomedical research, quantification of fibril diameters is essential to describe remodeling of the matrix that can occur during development, disease, and healing. However, current statistical methods to analyze differences in the distribution of fibril diameters have significant limitations. This study evaluated a rigorous alternative method, Statistical nonParametric Mapping (SnPM), to compare fibril diameter distributions. Randomly generated simulated datasets and experimental datasets of fibril diameter distributions were analyzed using both conventional tests and SnPM. Results from each method were compared. Conventional statistical tests to compare fibril diameter distributions demonstrated limitations. Comparison of average diameters detected overall shifts in distributions but not more nuanced changes, while analysis of binned relative frequencies showed dependency on the choice of bin width and location. In contrast, comparison of kernel density estimated probability density functions using SnPM both detected differences between groups and located those differences to specific ranges of fibril diameters. Comparative analysis of groups of fibril diameter distributions using SnPM overcomes the limitations of current techniques and provides a reliable and rigorous technique for these data. Other potential applications for SnPM in biomedical research abound, extending to other distributional or multidimensional data.

The eigenvalue probability density function of the Gaussian unitary ensemble permits a q q -extension related to the discrete q q -Hermite weight and corresponding q q -orthogonal polynomials. A combinatorial counting method is used to specify a positive sum formula for the spectral moments of this model. The leading two terms of the scaled 1 / N 2 1/N^2 genus-type expansion of the moments are evaluated explicitly in terms of the incomplete beta function. Knowledge of these functional forms allows for the smoothed leading eigenvalue density and its first correction to be determined analytically.

This study aimed to understand the effects of foliar application of selenium sources at different soybean development stages, focusing on leaf morphology, development, and grain yield. The experiment was carried out in December 2024, at the “Capão da Onça” School Farm of the State University of Ponta Grossa (UEPG), in the South-Central region of the State of Paraná, Brazil. The experimental design was a completely randomized block (RBC) design in a 2 x 3 factorial arrangement, where the first factor was application at the Vegetative7 and Reproductive1 development stages, interacting with the selenium sources: selenate, selenite, and absence of the nutrient, totaling 6 treatments with four replicates, totalling 24 plots The selenium influences soybean plant development. The use of 10 µM selenate improves soybean crop development, grain yield, palisade parenchyma thickness, xylem diameter, phloem diameter, stomatal density and stomatal functionality. Selenium application at the Vegetative7 development stage can result in increased plant height, palisade parenchyma thickness, and phloem diameter.

Noncontact ultrasonic materials identification based on improved frequency responses.

Nyström-aware approximations for matrix-based Rényi's entropy.

We investigate the fundamental limits of using total-scattering measurements to simultaneously determine the atomic number density (ρ) and pair distribution function (g(r)) of disordered materials. Building on rigorous Fourier-transform relationships between the structure factorS(Q) andg(r), we first show analytically that even infinitely precise, noise-freeS(Q) data-spanning an unboundedQ-range-cannot uniquely specify bothρandg(r). This non-uniqueness arises from phase information loss, finite-dimensional projections inherent in one-dimensional pair distributions, and the mathematical insensitivity ofS(Q) to coordinated rescaling of density and radial distances. In addition, we highlight practical problems arising from mathematical methods aimed at extractingρvia Fourier transform of data. Direct calculation from integratingg(r)-1(Yarnell method) converges badly for high density because of long-range structure ing(r), and at low density because of a bias coming from the central atom ing(r). Indirect calculation from the slope off⋅[g(r)-1](Eggert method) depends sensitively on having good quality high-Qdata. To address these ambiguities, we introduce a density-sweep protocol using the empirical potential structure refinement (EPSR) within theab initioaugmented structure solving engine framework. By systematically varying trial densities around target values (±5%-50%) and evaluating both the internal EPSRR-factor and an externalR-factor based on finalF(Q), one can identify a clear minimum bracketing the trueρwithout reliance on external equations of state or arbitrary fitting ranges. We showcase the effectiveness of the method by application to supercritical krypton at multiple pressures, liquid D2O at 298 K and amorphous silica and reliably recover known densities within±5%.

Across India, medium-sized towns frequently experience unplanned urban growth and the ensuing environmental problems. In addition to notable reductions in water bodies and vegetation cover, unchecked, rapid urbanisation has led to a marked expansion of built-up areas. Such transformations of once-green urban landscapes have intensified environmental risks and exacerbated climate-related issues. Geospatial technology is a powerful tool for quantifying land-cover transformations and the resulting temperature increase. The present study investigates the spatio-temporal dynamics of urban temperature rise and its association with accelerated urban growth and resultant land-use/land-cover (LULC) transformations in the Kozhikode city region, from 1993 to 2023. To quantify these LULC changes, a combination of remote sensing and Geographic Information System (GIS)-based analytical techniques (Various spectral indices (NDMI, NDII, GNDVI, NDbaI) and Land surface temperature) was employed. The study reveals a pronounced intensification of built-up land and a corresponding depletion of vegetation cover. The mean GNDVI value decreased from 0.53 in 1993 to 0.51 in 2023. Similarly, the moisture index showed a declining trend. It transformed from 0.27 in 1993 to 0.23 in 2023. Conversely the bareness index and impervious index showed a marked acceleration over the research period. It transformed into -0.43 in 1993 to -0.17 in 2023 and -0.43 in 1993 to -0.17 in 2023 respectively. The steadily increasing land-surface temperature readings during the research period provide further evidence of accelerating urban warming in the study area. The mean Land Surface Temperature value accelerated into 35.59°C in 2023 from 29.24°C in 1993. The spatial analysis of Land Surface Temperature (LST) reveals that elevated temperature zones are predominantly concentrated in intensively built-up areas, particularly within the 5 km buffer surrounding the city core. The findings clearly indicate that regions characterised by higher built-up density function as urban heat island (UHI) zones within the city.

Rhythmic gene expression underlies core physiological processes across organisms, from circadian timekeeping to stress responses. Recent experiments suggest that the regulation of such rhythmic dynamics involves protein compartmentalisation mediated by liquid-liquid phase separation (LLPS), yet the mechanisms by which LLPS feeds back onto oscillatory behaviour remain unclear. Here we develop a minimal two-phase gene-expression model in which proteins are synthesised in the dilute phase, reversibly partition into a protein-dense droplet phase, and repress their own production via condensate-mediated regulation. In the deterministic limit, LLPS does not generate limit cycles; instead, nonlinear partitioning and timescale separation between phase separation and protein turnover convert purely relaxational dynamics into damped oscillatory transients, altering the approach to equilibrium without producing sustained oscillations. In the stochastic regime, intrinsic noise interacting with this near-focus dynamics is amplified into noise-sustained, near-periodic fluctuations with a characteristic timescale, as revealed by the power spectral density and autocorrelation functions. These results show how LLPS reshapes oscillatory signatures by encoding and filtering temporal signals in phase-specific ways, providing a quantitative framework for interpreting LLPS-rhythm coupling and for engineering biomolecular systems with tunable dynamic behaviour.

Abstract With the advancement of semiconductor manufacturing processes, etching technology has to satisfy more stringent requirements, such as reduced ion bombardment damage, enhanced plasma uniformity, tunable electron energy probability distribution function (EEPF), and so on. The implementation of a metal grid in inductively coupled plasmas (ICPs) offers a promising approach to address these challenges. This study employs a 2D fluid/electron Monte Carlo collision hybrid model to investigate the effects of grid height and number of metal blocks on the electron density, electron temperature, plasma potential, EEPF and ion energy distribution function (IEDF). The results demonstrate that by comparing with conventional ICP configurations, introduction of a metal grid could obviously increase the electron density above the grid, while the magnitude below it is reduced remarkably. Additionally, both the electron temperature and ion energy beneath the grid are notably lower than in grid-free setups. The number of metal blocks has a pronounced impact on the IEDF, with the ion energy peak shifting from 9.5 eV to 7 eV. In contrast, the axial position of the metal grid has minimal effect on the IEDF. Precise tuning of grid height and block number enables refined control over both the plasma properties and particle dynamics.

In this paper, the surge characteristics of a centrifugal compressor are investigated under various operating conditions through experimental analysis. The stable operational range of the centrifugal compressor at low flow rates is predominantly constrained by the presence of unstable phenomena such as stall and surge. The aerodynamic features spanning from the stall stage (preceding surge occurrence) to the surge stage (following surge occurrence) are comprehensively documented within the experimental framework. The power spectrum density (PSD) of mass flow rate and pressure is utilized to elucidate the fluid dynamics within the centrifugal compressor at deep surge condition (5° valve opening), moderate surge condition (12°), and mild surge condition (15°), respectively. Subsequently, a thorough comparison is conducted between the time-domain and frequency-domain distributions of pressure fluctuations during the transitional zone between the stall and surge stages. The evolution trend of mass flow rate and pressure are compared at deep, moderate, and mild surge conditions, and the transition zone between the stall stage and the surge stage, which could be detected in advance and contribute to prevent surge, is pointed out. The utilization of probability density function (PDF) distribution is implemented to deeply explore a detailed examination of pressure fluctuation disparities between the stall and surge stages, elucidating the impact of surge-induced fluctuations on flow stability within the centrifugal compressor. The characteristics of shaft vibration are deeply explored. The above comparison can provide a profound insight into understanding the physical mechanism of centrifugal compressor at surge conditions.

Microvascular density and endothelial glycocalyx function may provide insights into early atherosclerosis and cardiovascular disease (CVD) risk. Sublingual sidestream darkfield (SDF) microscopy permits imaging of red blood cells (RBC) to assess the microcirculation. We sought to define reference ranges for SDF microscopy parameters in healthy populations and individuals with coronary artery disease (CAD) and to assess factors correlated with microcirculatory abnormalities. Adults with and without CVD risk factors and epicardial CAD underwent SDF microscopy using a CapiScope Handheld Video Capillaroscopy System and Glycocheck analytic software to measure the perfused boundary region (a measure of RBC glycocalyx penetration), percent RBC filling (%RBC, a measure of microvascular perfusion) and microvascular density. A total of 413 individuals underwent SDF microscopy, including 86 healthy adults, 273 adults with ≥1 CVD risk factors without established obstructive CAD, and 54 with obstructive CAD. Among healthy adults, median perfused boundary region was 1.89 μm [interquartile range 1.71-2.06], median %RBC was 74.4% [71.2-77.6], and median microvascular density was 411 μm/mm2 [331-480]. Compared with healthy adults, participants with obstructive CAD had higher median perfused boundary region (2.02 [1.89-2.15], P=0.004), lower %RBC (71.1% [67.2-74.6], P=0.002), and lower microvascular density (298 [250-384], P<0.0001), though values overlapped substantially; after adjustment for demographics and CVD risk factors, obstructive CAD was not independently associated with sublingual microvascular parameters. Obstructive CAD was not associated with sublingual microvascular parameters after accounting for demographics and CVD risk factors. The overlap of microvascular parameters in patients with and without CAD limits the clinical utility of SDF microscopy to identify traditional CVD.

Objective.We propose a new formulation for ideal observers (IOs) that incorporate stochastic object models (SOMs) for data acquisition optimization.Approach. A data acquisition system is considered as a (possibly nonlinear) discrete-to-discrete mapping from a finite-dimensional object space,x∈Rnd, to a finite-dimensional measurement space,y∈Rm. For binary tasks, the two underlying SOMs,H0andH1, are specified by two probability density functions (PDFs)p0(x),p1(x). This leads to the notion of intrinsic likelihood ratio (LR)ΛI(x)=p1(x)/p0(x)and intrinsic class separability (ICS), the latter quantifies the population separability that is independent of data acquisition. With respect to ICS, the IO employs the 'extrinsic' LRΛ(y)=pr(y|H1)/pr(y|H0)of the data and quantifies the extrinsic class separability (ECS). The difference between ICS and ECS measures the efficiency of data acquisition. We show that the extrinsic LRΛ(y)is the expectation of the intrinsic LRΛI(x), where the expectation is with respect to the posterior PDFpr(x|y,H0)underH0.Main results. We use two examples, one to clarify the new IO and the second to demonstrate its potential for real world applications. Specifically, we apply the new IO to spectral optimization in dual-energy CT projection domain material decomposition (pMD), for which SOMs are used to describe variability of basis material line integrals. The performance rank orders obtained by IO agree with physics predictions.Significance.The main computation in the new IO involves sampling from the posterior PDFpr(x|y,H0), which are similar to (fully) Bayesian reconstruction. Thus our IO computation is amenable to standard techniques already familiar to CT researchers. The example of dual-energy pMD serves as a prototype for other spectral optimization problems, e.g., for photon counting CT or multi-energy CT with multi-layer detectors.

Abstract The average treatment effect (ATE) is commonly used to quantify the main effect of a binary treatment on an outcome. Extensions to continuous treatments are usually based on the dose response curve or shift interventions, but both require strong overlap conditions and the resulting curves may be difficult to summarise. We focus instead on average derivative effects (ADEs) that are scalar estimands related to infinitesimal shift interventions requiring only local overlap assumptions. ADEs, however, are rarely used in practice because their estimation usually requires estimating conditional density functions. By characterising the Riesz representers of weighted ADEs,weproposeanewclassofestimandsthat provides a unified view of weighted ADEs/ATEs when the treatment is continuous/binary. We derive the estimand in our class that minimises the nonparametric efficiency bound, thereby extending optimal weighting results from the binary treatment literature to the continuous setting. We develop efficient estimators for two weighted ADEs that avoid density estimation and are amenable to modern machine learning methods, which we evaluate in simulations and an applied analysis of Warfarin dosage effects.

This study introduces the Novel Alpha Power Gumbel-X (NAPG-X) family of distributions, developed through the T-X transformation with a logarithmic generalizer. The NAPG-Exponential (NAPGEX) distribution is studied as a sub-model, with key mathematical properties derived, including the probability density function, cumulative distribution function, moments, moment generating function, Rényi entropy, and order statistics. The hazard rate function exhibits versatile shapes including increasing-decreasing, reversed-J, and L-shaped, making it suitable for diverse reliability applications. Ten classical estimation methods are evaluated through extensive Monte Carlo simulations across varying sample sizes and three parameter combinations. Results demonstrate that maximum likelihood and Anderson-Darling consistently provide superior performance with minimal bias, mean relative errors and root mean square errors. Furthermore, the practical applicability of the NAPGEX distribution is validated using three real-life datasets. Comprehensive comparisons using various performance measures reveal that the NAPGEX significantly outperforms competing models. These findings establish the NAPG-X family as a flexible and powerful tool for modeling asymmetric, positively skewed lifetime datasets across several scientific disciplines.

. The Gram-Charlier expansion provides a flexible and effective framework for analyzing complex data distributions. Recognizing the skewness and heavy-tailed characteristics commonly observed in financial and other real-world datasets, this study constructs a novel asymmetric distribution derived from the logistic distribution, designed to capture both heavy tails and central peaks. We first demonstrate that the probability density function of the logistic distribution can serve as a weight function, providing a solid mathematical foundation for constructing new distributions. The Newton-Raphson algorithm is then employed to obtain maximum likelihood estimates of the distribution parameters, with numerical simulations verifying the feasibility and accuracy of these estimates. Empirical analysis of daily log-returns for four representative financial assets, the CSI 300 Index, S & P 500 Index, German DAX Index, and Gold Futures, demonstrates that the logistic expansion distribution significantly outperforms the normal distribution, effectively modeling both asymmetry and extreme observations. These results highlight the distribution’s potential as a robust tool for modeling complex financial data, assessing tail risk, and supporting statistical inference in contexts where conventional distributions fail to capture skewness and kurtosis adequately.

This paper discusses a novel technique to creating distribution families by combining the transformation of alpha power and the cosine function. The proposed technique have been named the cosine alpha power-generated family. The Weibull distribution is employed to produce a distinctive model for the cosine alpha power generated family, the specific model is called the cosine alpha power-Weibull (CAP-W). The distribution statistical characteristics are investigated, involving quantiles, Rényi entropies, and order statistics. The CAP-W has a density function that is right-skewed, symmetrical, and decreases continuously, along with J-shape, upside down bathtub, increasing and decreasing hazard rate function. Various methods of estimation-maximum likelihood, ordinary least-squares, weighted least-squares, and cramér-von mises were utilized to estimate the distribution parameters, and a simulation study is carried out to examine their performance. Furthermore, the efficiency of the provided distribution is demonstrated by four real data sets. Ultimately, the log cosine alpha power Weibull regression model is constructed and examined with a real dataset.

We present a theoretical calculation for the A = 2 , 3 , and 4 nuclear contact coefficients within the generalized contact formalism, using both local and nonlocal chiral potentials. The hyperspherical harmonics method is employed to calculate the nuclear wave functions, from which we derive two-body momentum distributions and density functions to extract the contact coefficients. We have extracted the contact coefficients from two-body momentum distributions or from density functions, for a given nucleus and potential, and we have found that the generalized contact formalism predictions are verified in the triplet spin channel for local and nonlocal potentials. On the other hand, some significant tensions exist for the singlet channels, especially when studied with nonlocal potentials. We have also analyzed the model-independence of the ratios between the contact coefficients, which we have found to be quite satisfied. This study extends previous works based on local interaction models only.

As indoor robots are expected to operate in increasingly complex environments, the need for rich and scalable semantic representations has become critical. While semantic mapping is a standard tool, existing representations often fall at two extremes: dense voxel-based maps that are computationally expensive to query, or schematic graphs that lack geometric detail. This trade-off limits the scalability of semantic maps in real-world tasks. We propose an object-aware semantic mapping framework that models key static objects using probability density functions (PDFs). Objects like beds, fridges or desks are detected via 3D point cloud processing and encoded as 2D probabilistic occupancy distributions. This formulation provides a compact, robust representation that preserves semantic identity and geometric shape, while handling noise and partial views. The map is structured around rooms and their contents, enabling global relocalization and semantically informed path planning. Using Differential Evolution and Kullback-Leibler divergence, our method achieves robust relocalization without prior pose. Its object-centric, probabilistic nature also supports functional scene understanding for context-aware navigation. We validate our approach on a benchmark dataset and in a real apartment, showing improved performance over traditional methods in ambiguous or cluttered scenes, and demonstrating the advantages of a unified representation for multiple robotic behaviors.