Search Results (650)

To address the concerns of contrast deterioration, detail loss, and color distortion in images produced under haze conditions in scenarios such as intelligent driving and remote sensing detection, an algorithm for image defogging that combines Retinex theory and the dark channel prior is proposed in this paper. The method involves building a two-stage optimization framework: in the first stage, global contrast enhancement is achieved by Retinex preprocessing, which effectively improves the detail information regarding the dark area and the accuracy of the transmittance map and atmospheric light intensity estimation; in the second stage, an a priori compensation model for the dark channel is constructed, and a depth-map-guided transmittance correction mechanism is introduced to obtain a refined transmittance map. At the same time, the atmospheric light intensity is accurately calculated by the Otsu algorithm and edge constraints, which effectively suppresses the halo artifacts and color deviation of the sky region in the dark channel a priori defogging algorithm. The experiments based on self-collected data and public datasets show that the algorithm in this paper presents better detail preservation ability (the visible edge ratio is minimally improved by 0.1305) and color reproduction (the saturated pixel ratio is reduced to about 0) in the subjective evaluation, and the average gradient ratio of the objective indexes reaches a maximum value of 3.8009, which is improved by 36–56% compared with the classical DCP and Tarel algorithms. The method provides a robust image defogging solution for computer vision systems under complex meteorological conditions. Full article

Cancer is a serious group of diseases that is a huge problem on a global scale and is the second most common cause of death. Commonly used therapies do not always lead to the complete elimination of diseased cells or tissues and are also burdened with side effects that reduce the quality of life of patients. Due to these difficulties, new therapeutic approaches are still being sought. In recent years, there has been a return to interest in natural methods of treating various diseases, among which phytochemicals are particularly interesting. This article reviews plant extracts with anticancer properties with different mechanisms of action (proapoptotic, antiproliferative, antiangiogenic, immunomodulatory). In addition, plant extracts that reduce the side effects of chemotherapy and the limitations and prospects for the use of plant extracts in anticancer therapy are described. Our goal was to create an up-to-date information base that would encourage scientists to intensify research into supplementing targeted anticancer therapies with additional protective and preventive measures, in which natural mixtures of phytochemicals (plant extracts) are effective allies. At the same time, we encourage discussion on the limitations of their use in light of the orthodox principles of classical medicine and pharmacy (issues of safety, quality, drug purity, and dose precision), which are a priori correct but have not yet led to the elimination of cancer from the group of incurable diseases. Full article

The systematics and phylogeny of the most speciose genus (Leopardus) of the felidae have historically been contentious and problematic. These issues have been compounded with the recent advancement of genetic techniques that make it possible to detect events such as incomplete lineage sorting (ILS), punctual historical ancestral introgression (PHAI), and repetitive introgression or recent hybridization (RI-RH). Each of these events have noteworthily affected the Leopardus genus. One Leopardus taxon (Leopardus tigrinus, herein called tigrina) has been especially complex from a phylogenetic point of view. In the last decade, one new species has been reported (L. guttulus) and two other new species likely exist within the tigrinas (L. emiliae and L. pardinoides). However, the most surprising find was the discovery of a new and not previously reported tigrina, the Nariño cat, from the southern Andean region of Colombia (2023). Later that same year, a new paper criticized the discovery. In response to that criticism, herein, we provide new molecular genetics results of the Nariño cat as well as new insights into the molecular phylogeny of the tigrinas inside the Leopardus genus: (1) In this new work, we analyzed the mtND5 gene of Nariño cat samples collected over four years (2001, 2007, 2017, 2023) as well as analyzed mitogenomes of Nariño cat samples collected in three different years (2001, 2017, 2023). The temporal Nariño cat samples (2001, 2007, 2017, 2023) refer to samples taken from a single specimen across different years. Based on these analyses, data from 2001 and 2007 represent the most reliable information. In contrast, samples from 2017 and 2023 may be contaminated with DNA from the Pampas cat and tigrina, respectively. (2) On the other hand, based on sequencing the mtND5 gene of 164 specimens of Leopardus, northern Andean and Central American tigrinas (37 specimens) are divided into at least six different groups (without counting the Nariño cat). Based on our analysis of sequenced mitogenomes of 102 specimens (including 34 northern Andean and Central American tigrinas) of the Leopardus genus, there are at least eight different groups of tigrinas (without counting the Nariño cat). Henceforth, there are strong datasets which support the existence of multiple lineages within the presumed “a priori” northern Andean tigrina and thus much of the genetic diversity of this wild cat has gone unnoticed. There are a series of potential taxa that have gone unnoticed due to a lack of sampling of this polyphyletic Andean feline. Full article

The rising prevalence of elderly obesity in developed countries poses a public health challenge, since body composition changes during aging are associated with higher risks of chronic diseases. We cross-sectionally explored the relationship between diet, physical activity, and sex-specific differences in body composition among 378 elderly previously enrolled in the Florence European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Information on dietary habits and lifestyle was collected through validated questionnaires. Adherence to the Italian Mediterranean Index (IMI), Dietary Approaches to Stop Hypertension (DASH), and Greek Modified Mediterranean Diet (GMMD) a priori dietary patterns was calculated. Anthropometric measures were taken by trained personnel, and body composition parameters were estimated via bioelectrical impedance. In age- and energy-intake-adjusted regression models, adherence to the DASH and IMI patterns was associated with healthier body composition among women, while no significant relationship emerged among men. Fitness activities and total recreational physical activity revealed positive associations with healthier body composition (lower % fat mass, higher % muscle mass, and reduced waist circumference) in both sexes. These findings highlight the synergistic effect of diet and physical activity on body composition in the elderly and underscore the need for sex-specific interventions for promoting healthy aging. Full article

Airborne radar-based ground moving target tracking faces challenges such as low detection rates and high clutter density. While lowering the detection threshold can improve detection performance, it introduces significant false alarms, thereby degrading tracking performance. To address these challenges, this paper proposes a novel multi-information assisted Joint Detection and Tracking (JDT) framework for ground moving targets. This study enhances detection and tracking performance by integrating multi-source information, specifically echo information, road network data, and velocity limits, enabling bidirectional data exchange between the detector and tracker for multiple ground targets. An adaptive threshold detector is developed by incorporating a priori information and tracker feedback. Additionally, we innovatively propose an improved Variable Structure Interacting Multiple Model (VS-IMM) filter that leverages road network constraints and detector outputs for tracking, featuring an enhanced model probability calculation to significantly reduce computational time. Simulation results demonstrate that the proposed method significantly improves data association accuracy and tracking precision. Full article

Half-cell potential (HCP) is widely acknowledged as a nondestructive method for assessing the durability of concrete, although the variability in environmental and material conditions compromises its accuracy. The reliability of traditional prediction models, which are often derived from limited data, is questionable under various conditions. This study employed a Bayesian-enhanced probabilistic model to predict corrosion reinforcement using HCP, addressing both known and unknown uncertainties. Constructed as a piecewise function, the model integrates insights from the literature with the results of an accelerated corrosion experiment conducted by the research team, thereby validating the effectiveness of the probabilistic approach. This study also examines the influence of prior knowledge on the accuracy of predictions. The findings revealed a biphasic relationship between HCP and the corroded mass reduction ratio. HCP decreased exponentially with a corroded mass reduction ratio below 15%, whereas beyond this threshold, the decline became more pronounced, modeled by a combination of exponential and cubic polynomial functions. These results underscore the critical role of employing a piecewise function to accurately define the relationship between HCP and corrosion in reinforced concrete, thereby providing a solid foundation for future durability assessments. Full article

In this research, we consider the inverse problem for the wave equation under an unknown initial condition. A generalized solution to the direct problem was formulated, its correctness was established, and the stability assessment was obtained. The inverse problem was reduced to an optimization problem, where the objective function was minimized using gradient methods, including the accelerated Nesterov algorithm. The conjugate problem was constructed, and the functional gradient was computed, while the existence of the Frechet derivative was proved. For the first time, the quaternion Fourier transform (QFT) was applied to the numerical solution of a direct problem, making it possible to analyze multidimensional wave processes more efficiently. A computational experiment was carried out, which demonstrated that if there is insufficient additional information, the restoration of the initial condition is incomplete. The introduction of the second boundary condition makes it possible to significantly improve the accuracy and stability of the solution. The results confirm the importance of an integrated approach and the availability of sufficient a priori information when solving inverse problems. Full article

The extension of high-speed rail (HSR) lines around the world is increasing. The largest network today is in China, followed by Spain, Japan, France, and Italy; currently, new lines are being built in Morocco and Saudi Arabia. The goal of the new lines built is to drastically reduce the time distances between the extreme railway terminals by intervening on the two main components of time: space and speed. The two components have been investigated in various fields of engineering for design conditions (ex ante/a priori). In the literature, there is no analysis of what happened in the realization of the projects (ex post/retrospective). The research problem that arises is to analyze the high-speed lines built in order to verify, given a pair of extreme terminals, how much the length is reduced by passing from a conventional line to a high-speed line, and to verify how this length is getting closer and closer to the distance as the crow flies. The reduction of spatial distance produces direct connections between two territories, making the railway system (HSR) more competitive compared to other transport alternatives (e.g., air travel). To address the problem posed, information and data are collected on European HSR lines, which constitute a sufficiently homogeneous set in terms of railway and structural standards. The planimetric characteristics of specially built lines such as HSR are examined. A test method is proposed, consisting of a model that is useful to compare the length along the HSR line, with direct lengths, and existing conventional lines. The results obtained from the elaborations offer a first answer to the problem posed, demonstrating that in the HSR lines realized the spatial distances approach the distance as the crow flies between the cities located at the extremes, and are always shorter than the lengths of conventional lines. The final indications that can be drawn concern the possibility of using the results obtained as a reference for decision-makers and planners involved in the transport planning process at national and international level. Future research directions should study the values of the indicators in other large HSR networks, such as those built in Asia, and more generally study all the elements of the lines specially built to allow better sustainable planning, reducing the negative elements found and increasing the positive ones. Full article

A sufficient number of sensors installed in all structural components is a prerequisite for obtaining the full-field temporal–spatial displacement and is essential for large-scale structure health monitoring. In this paper, a novel lightweight vision-based temporal–spatial deflection measurement method is proposed to measure the full-field temporal–spatial displacement of slender structures. First, the geometric and mechanical properties of slender members are introduced as the priori information to vision-based measurement. Then, a slice template matching model is proposed by deploying a one-dimensional template matching model in every pixel column of each image frame, based on traditional digital image correlation (DIC) method. An indoor experiment was carried out to verify the proposed method, and experiment results show that measurement precision of STMM agrees well with the theory and the laser ranger, with a maximum measurement error of 0.03 pixels and the root-mean-square error (RMSE) of 0.052 mm, for transient beam deflection curve; with the correlation coefficient and coefficient of determination of 0.9994 and 0.9986, for dynamic deflection–time history curves at the middle-span point. Finally, further investigation reveals that brightness inconstancy is the source of STMM measurement error. Full article

Real aperture radar (RAR) can acquire the forward-looking target scene of interest continuously in scanning mode by arbitrary imaging geometry; however, the achievable angular resolution is predominantly governed by the physical dimensions of the antenna’s aperture. In contemporary radar imaging methodologies, the reconstruction of sparsely distributed targets can be effectively formulated as an $L_1$-regularized optimization framework through the exploitation of a priori sparsity constraints, thereby enabling the generation of enhanced-resolution forward-looking radar imagery. Nevertheless, traditional target reconstruction methods based on the sparse regularization framework are implemented after batch data collection, which comes at the cost of significant operational complexity and storage space. To address this challenge, an online sparse reconstruction method based on a beam recursive-sliding (BRS) updating framework is proposed to achieve fast target reconstruction. First, the antenna measurement matrix is repaired to reduce the imaging edge information error. Then, due to the independence of the echo data within two beamwidths, a beam recursive updating method is proposed for each two beamwidths echo data by the structural properties of the repaired antenna measurement matrix. Finally, based on the proposed beam recursive updating method, a sliding updating approach is proposed for the whole imaging region to reduce the computational redundancy and storage requirement. Simulation and experimental data demonstrate the effectiveness of the proposed BRS updating framework. Full article

Background/Objectives: Virtual reality (VR) is increasingly being explored as a non-pharmacological therapy to enhance the well-being of people with cognitive impairment (PwCI). Studies suggest that VR-based interventions improve mood, reduce apathy, and enhance emotional engagement, making VR a valuable tool for cognitive and emotional support. This scoping review synthesizes evidence on VR-based reminiscence therapy (VRRT) for PwCI. It aims to map existing knowledge, highlight implementation challenges, and offer practical, technical design, and evidence-informed recommendations for clinical integration—building on prior reviews that have touched on these aspects, but placing a stronger and more structured emphasis on real-world applicability and translational insights. This review draws extensively on qualitative findings across the included studies to better capture contextual factors, user experiences, facilitator roles, and barriers to usability. Moreover, unlike previous research, we included only studies involving individuals—either directly or via proxies—with an age-related cognitive impairment, formally diagnosed by a qualified authority. Methods: A systematic search based on the PRISMA-ScR guideline identified 310 studies, of which 11 met the inclusion criteria. These studies assessed the effectiveness and feasibility of immersive VRRT. Research methodologies included longitudinal (n = 2), cross-sectional (n = 2), mixed-methods (n = 4), and randomized controlled trials (n = 3)—with most studies focusing on feasibility—with a cumulative sample size of approximately 287 participants. The quality of the included studies was generally moderate; common limitations included small sample sizes, short intervention periods, and limited control conditions. Results: The findings highlight VRRT’s potential to enhance engagement, emotional well-being, and cognitive function. However, usability challenges and technical limitations persist. While VR offers promising benefits, further research is needed to refine interventions, address personalization barriers, and assess long-term effects. Conclusions: This review underscores the importance of integrating VRRT into care programs and improving accessibility. Future research should enhance methodological rigor to ensure reliable outcomes and maximize VR’s impact on PwCI well-being. The scoping review protocol is registered a priori with the Center for Open Science (OSF) (registration type: OSF Preregistration, data registered: 15 November 2024, associated project: osf.io/r7jha, identifier: DOI 10.17605/OSF.IO/R7JHA). Full article

The decomposition of a signal is a fundamental tool in many fields of research, including signal processing, geophysics, astrophysics, engineering, medicine, and many more. By breaking down complex signals into simpler oscillatory components, we can enhance the understanding and processing of the data, unveiling hidden information contained in them. Traditional methods, such as Fourier analysis and wavelet transforms, which are effective in handling mono-dimensional stationary signals, struggle with non-stationary datasets and they require the selection of predefined basis functions. In contrast, the empirical mode decomposition (EMD) method and its variants, such as Iterative Filtering (IF), have emerged as effective non-linear approaches, adapting to signals without any need for a priori assumptions. To accelerate these methods, the Fast Iterative Filtering (FIF) algorithm was developed, and further extensions, such as Multivariate FIF (MvFIF) and Multidimensional FIF (FIF2), have been proposed to handle higher-dimensional data. In this work, we introduce the Multidimensional and Multivariate Fast Iterative Filtering (MdMvFIF) technique, an innovative method that extends FIF to handle data that varies simultaneously in space and time, like the ones sampled using sensor arrays. This new algorithm is capable of extracting Intrinsic Mode Functions (IMFs) from complex signals that vary in both space and time, overcoming limitations found in prior methods. The potentiality of the proposed method is demonstrated through applications to artificial and real-life signals, highlighting its versatility and effectiveness in decomposing multidimensional and multivariate non-stationary signals. The MdMvFIF method offers a powerful tool for advanced signal analysis across many scientific and engineering disciplines. Full article

Background: Malnutrition may increase the risk of frailty in individuals with musculoskeletal pain. However, this scenario has not been explored in detail. As such, the present study was conducted to examine the effects of malnutrition on the risk of incident and worsening frailty in community-dwelling older adults with musculoskeletal pain. Methods: Data from 895 community-dwelling older adults participating in the Toledo Study of Healthy Ageing who reported experiencing musculoskeletal pain during the month preceding data collection (mean age: 74.9 ± 5.6 years) were analyzed. Pain characteristics (i.e., intensity, locations, and treatment) were assessed based on self-reported information regarding the last month. Malnutrition was operationalized according to the GLIM criteria. Frailty status was assessed at baseline and at follow-up (~2.99 years), according to the Frailty Phenotype paradigm, operationalized through the Frailty Trait Scale 5. Associations between the variables were tested using logistic regression analyses adjusted for many covariates established a priori. Results: Malnutrition increased the risk of frailty (odds ratio [OR] = 4.41) and worsening of frailty status (OR = 6.25) in the participants who used ≥2 groups of painkillers in comparison to their non-undernourished peers. Conclusions: The findings of the present study indicate that malnutrition increases the risk of both developing and worsening frailty in older adults with musculoskeletal disorders. In particular, an increased risk of incident frailty and worsening frailty status was found in undernourished individuals using ≥2 analgesic drugs. Our results suggest that nutritional assessment should be included in the evaluation of old people living with musculoskeletal pain. Full article

The development of underwater high-precision navigation technology is of great significance for the application of autonomous underwater vehicles (AUVs). Traditional long baseline (LBL) positioning systems require pre-deployment and the calibration of multiple beacons, which consumes valuable time and manpower. In contrast, the range-only single-beacon (ROSB) positioning technology can help autonomous underwater vehicles (AUVs) obtain accurate position information by deploying only one beacon. This method greatly reduces the time and workload of deploying beacons, showing high application potential and cost ratio. Given the operational constraints of AUV open-ocean navigation with single-beacon weak observations and absence of valid a priori positioning data in calibration zones, a multi-sensor underwater virtual beacon localization framework was established, proposing a differential Chan–Gauss–Newton (DCGN) methodology for submerged vehicles. Based on inertial navigation, the method uses the distance measurement information from a single beacon and observations from multiple sensors, such as the Doppler velocity log (DVL) and pressure sensor, to obtain accurate position estimates by discriminating the initial position of multiple hypotheses. A simulation experiment and lake test show that the proposed method not only significantly improves the positioning accuracy and convergence speed, but also shows high reliability. Full article

This paper presents a new matrix representation of ant colony optimization (ACO) for solving parametric problems. This representation allows us to perform calculations using matrix processors and single-instruction multiple-data (SIMD) calculators. To solve the problem of stagnation of the method without a priori information about the system, a new probabilistic formula for choosing the parameter value is proposed, based on the additive convolution of the number of pheromone weights and the number of visits to the vertex. The method can be performed as parallel calculations, which accelerates the process of determining the solution. However, the high speed of determining the solution should be correlated with the high speed of calculating the objective function, which can be difficult when using complex analytical and simulation models. Software has been developed in Python 3.12 and C/C++ 20 to study the proposed changes to the method. With parallel calculations, it is possible to separate the matrix modification of the method into SIMD and multiple-instruction multiple-data (MIMD) components and perform calculations on the appropriate equipment. According to the results of this research, when solving the problem of optimizing benchmark functions of various dimensions, it was possible to accelerate the method by more than 12 times on matrix SIMD central processing unit (CPU) accelerators. When calculating on the graphics processing unit (GPU), the acceleration was about six times due to the difficulties of implementing a pseudo-random number stream. The developed modifications were used to determine the optimal values of the SARIMA parameters when forecasting the volume of transportation by airlines of the Russian Federation. Mathematical dependencies of the acceleration factors on the algorithm parameters and the number of components were also determined, which allows us to estimate the possibilities of accelerating the algorithm by using a reconfigurable heterogeneous computer. Full article