Search Results (13)

Quality of life is a multidimensional concept that includes characteristics of the residence and the wider living environment. The aim of the paper is to examine whether age groups differ in satisfaction with selected spatial characteristics of the residence and the immediate living environment, and whether comparable differences occur in the perceived healthy living environment. The research considers age and perceived quality of life on three distinct levels: specific spatial characteristics of the residence (for example, the presence of a balcony, terrace, daylighting and window view) with selected environmental characteristics of the immediate surroundings (open view, view of green areas, accessibility of green areas) and a broader perception of a healthy living environment (general perception of health and well-being). The research is based on questionnaire results that included 473 participants. The data were analyzed using analysis of variance, Tukey–Kramer comparisons and correlation analysis. The results show statistically significant differences between age groups, especially in regard to satisfaction with the presence of a balcony, terrace or atrium, to daylighting and the quality of the window view in the residences, with the satisfaction being lowest in the younger and highest in the older age group. Interestingly, there were no statistically significant differences in the general perception of a healthy living environment. The correlation analysis further showed that satisfaction and age was associated with home ownership, dwelling type, residential location and housing-cost burden, whereas proximity to green areas was not linearly associated with age. The findings showed that specific age groups perceived the quality of the living environment more pronouncedly when linked with specific spatial characteristics of the dwelling rather than the broader perception of the living environment. Full article

Blade Tip Timing (BTT) is an essential non-contact technique for monitoring vibrations in rotating machinery, but its practical accuracy is often degraded by noise, undersampling, and spectral leakage. This paper proposes a multi-stage robust Bayesian high-resolution identification framework that systematically addresses these challenges. A recursive digital algorithm based on Kalman filtering estimates the rotational speed without requiring once-per-revolution probes, effectively suppressing sensor noise. An attention-enhanced dynamic convolutional autoencoder then generates channel-specific window functions to minimize spectral leakage. The core identification algorithm extracts phases via all-phase FFT and employs sub-bin interpolation to overcome the resolution limitation of conventional FFT. A Tukey-biweight-based robust aggregation strategy is used to suppress the influence of abnormal or unequal-quality sensor channels during multi-channel phase fusion. A Bayesian prior distribution over the vibration order guides the estimation toward physically plausible values under noisy conditions. Finally, a coarse-to-fine multi-stage search strategy drastically reduces computational burden while preserving accuracy. Experiments on a rotor-blade test bench at constant and variable speeds show that the method reduces the noise floor by about 60 dB, achieves a maximum frequency identification error of 7.84%, and accelerates the search by approximately 48.6% compared to exhaustive search. The proposed method provides a reliable and efficient solution for blade health monitoring. Full article

To address the limitations of traditional non-local mean (NLM) denoising algorithms in terms of neighborhood similarity metrics, weight calculation, and computational efficiency, this paper proposed a structural-tensor-driven and dynamic window-based fast non-local mean denoising algorithm with dual kernel weighting. First, a Gaussian–Tukey dual-kernel weighting function was designed to optimize similarity metrics. Then, spatial neighborhood features were adopted. By measuring both grayscale similarity and spatial correlation, the weight distribution rationality was further enhanced. Second, structural tensor eigenvalues were used to quantify regional structural properties. A dynamic window allocation function was designed to adaptively match search window sizes to different image regions. Finally, an integral image acceleration mechanism was proposed, significantly improving algorithm execution efficiency. Experimental results demonstrated that the proposed algorithm achieved both excellent denoising performance and edge/texture preservation capabilities. In high-noise environments, its Peak Signal-to-Noise Ratio (PSNR) outperformed the Gauss kernel non-local mean algorithm by an average of 1.96 dB, while Structural Similarity (SSIM) improved by an average of 5.7%. Moreover, the algorithm’s execution efficiency increased by approximately 7–11 times, indicating strong potential for real-time application in digital image processing. Full article

Periapical radiographs remain the first-line imaging modality in endodontics due to accessibility and low radiation dose, whereas cone-beam computed tomography (CBCT) is reserved for inconclusive cases or suspected anatomical complexity. We propose a physics- and geometry-aware preprocessing pipeline coupled with sliding-window Viterbi tracking to enhance canal visibility and recover plausible root canal trajectories directly from routine periapical images. The pipeline standardizes row-wise brightness, compensates for the cone-like tooth density profile (Tukey window), and suppresses noise prior to dynamic-programming inference, requiring only minimal operator input (two-point orientation and region of interest). In a retrospective evaluation against micro-computed tomography (micro-CT)/CBCT reference anatomy, the approach accurately localized canals on periapicals under study conditions, suggesting potential as a rapid, chairside aid when 3D imaging is unavailable or deferred. Full article

This paper presents a novel method for damage identification in aluminum beams using the contourlet transform. Four aluminum beams were used in the study: one was undamaged, while the other three had different damage scenarios. The damage included middle and side slots with depth-to-thickness ratios of 7% and 28%. Damage is identified using the proposed index of contourlet transform of the modal rotations and modal curvatures of the beams for the free-free condition. The beam’s first three modal rotations are directly measured with digital shearography, and the corresponding modal curvatures are obtained through their numerical differentiation. The results indicated that to detect the exact locations and identify damage severities using the proposed damage indices, instead of modal rotations, the modal curvatures should be introduced as the input. Moreover, they revealed that the proposed damage indices need modal data of the undamaged state as a baseline to identify smaller damage. In addition, comparing the proposed contourlet-based damage indices with previously suggested wavelet-based damage detection methods revealed that, although the wavelet-based damage index is more sensitive to damage severity, it also exhibits higher noise levels in undamaged locations. The Tukey windowing process was introduced to address the boundary effect problem. Full article

Air pollution forecasting is essential for understanding environmental patterns and mitigating health risks, especially in urban areas. This study investigates the forecasting of criterion pollutants—$CO,O_3,S{O}_2,N{O}_2,P{M}_{2.5},$ and $P{M}_{10}$—across multiple temporal frames (hourly, daily, weekly, monthly) in Salamanca, Mexico, utilizing temporal, meteorological, and pollutant data from local monitoring stations. The primary objective is to identify robust models capable of short- and mid-term predictions, despite challenges related to data inconsistencies and missing values. Leveraging the low-code PyCaret framework, a benchmark analysis was conducted to identify the best-performing models for each pollutant. Statistical evaluations, including ANOVA and Tukey HSD tests, were employed to compare model performance across different time frames. The results reveal significant variations in prediction accuracy depending on both the pollutant and temporal windows, with stronger predictive performance observed in the weekly and monthly frames. The research indicates that the incorporation of temporal and environmental variables enhances forecast accuracy and highlights the value of low-code AutoML tools, such as PyCaret, in streamlining model selection and improving overall forecasting efficiency. Full article

With the development of digital holography, the accuracy requirements for the reconstruction phase are becoming increasingly high. The transfer function of the double fast transform (D-FFT) algorithm is distorted when the diffraction distance is larger than the criterion distance ${\mathrm{d}}_{\mathrm{t}}$, which reduces the accuracy of solving the phase. In this paper, the Fresnel diffraction integration algorithm is improved by using the low-pass Tukey window to obtain more accurate reconstructed phases. The improved algorithm is called the D-FFT (Tukey) algorithm. The D-FFT (Tukey) algorithm adjusts the degree of edge smoothing of the Tukey window, using the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) to remove the ringing effect and obtain a more accurate reconstructed phase. In a simulation of USAF1951, the longitudinal resolution of the reconstructed phase obtained by D-FFT (Tukey) reached 1.5 $\mathsf{\mu }\mathrm{m}$, which was lower than the 3 $\mathsf{\mu }\mathrm{m}$ obtained by the T-FFT algorithm. The results of Fresnel holography experiments on lung cancer cell slices also demonstrated that the phase quality obtained by the D-FFT (Tukey) algorithm was superior to that of the T-FFT algorithm. D-FFT (Tukey) algorithm has potential applications in phase correction, structured illumination digital holographic microscopy, and microscopic digital holography. Full article

The aim of this study was to evaluate the adhesion and biofilm formation of Candida albicans (C. albicans) on conventionally fabricated, milled, and 3D-printed denture base resin materials in order to determine the susceptibility of denture contamination during clinical use. Specimens were incubated with C. albicans (ATCC 10231) for 1 and 24 h. Adhesion and biofilm formation of C. albicans were assessed using the field emission scanning electron microscopy (FESEM). The XTT (2,3-(2-methoxy-4-nitro-5-sulphophenyl)-5-[(phenylamino) carbonyl]-2H-tetrazolium hydroxide) assay was used for the quantification of fungal adhesion and biofilm formation. The data were analyzed using GraphPad Prism 8.02 for windows. One-way ANOVA with Tukey’s post hoc testing were performed with a statistical significance level set at α = 0.05. The quantitative XTT biofilm assay revealed significant differences in the biofilm formation of C. albicans between the three groups in the 24 h incubation period. The highest proportion of biofilm formation was observed in the 3D-printed group, followed by the conventional group, while the lowest candida biofilm formation was observed in the milled group. The difference in biofilm formation among the three tested dentures was statistically significant (p < 0.001). The manufacturing technique has an influence on the surface topography and microbiological properties of the fabricated denture base resin material. Additive 3D-printing technology results in increased candida adhesion and the roughest surface topography of maxillary resin denture base as compared to conventional flask compression and CAD/CAM milling techniques. In a clinical setting, patients wearing additively manufactured maxillary complete dentures are thus more susceptible to the development of candida-associated denture stomatitis and accordingly, strict oral hygiene measures and maintenance programs should be emphasized to patients. Full article

In this paper, the effect of fuzzy time series on estimates of the spectral, bispectral and normalized bispectral density functions are studied. This study is conducted for one of the integer autoregressive of order one (INAR(1)) models. The model of interest here is the dependent counting geometric INAR(1) which is symbolized by (DCGINAR(1)). A realization is generated for this model of size n = 500 for estimation. Based on fuzzy time series, the forecasted observations of this model are obtained. The estimators of spectral, bispectral and normalized bispectral density functions are smoothed by different one- and two-dimensional lag windows. Finally, after the smoothing, all estimators are studied in the case of generated and forecasted observations of the DCGINAR(1) model. We investigate the contribution of the fuzzy time series to the smoothing of these estimates through the results. Full article

This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering external stimulus exposure that causes users to start to feel fatigued. Measuring the accuracy and exposure time can be carried out through the information transfer rate—ITR, which is defined as a relationship between the precision, the number of stimuli, and the required time to obtain a result. NLCCA performance was evaluated by comparing it with two other approaches—the well-known canonical correlation analysis (CCA) and the least absolute reduction and selection operator (LASSO), both commonly used to solve the SSVEP paradigm. First, the best average ITR value was found from a dataset comprising ten healthy users with an average age of 28, where an exposure time of one second was obtained. In addition, the time sliding window responses were observed immediately after and around 200 ms after the flickering exposure to obtain the phase effects through the coefficient of variation (CV), where NLCCA obtained the lowest value. Finally, in order to obtain statistical significance to demonstrate that all approaches differ, the accuracy and ITR from the time sliding window responses was compared using a statistical analysis of variance per approach to identify differences between them using Tukey’s test. Full article

In plant ecology, biochemical analyses of bryophytes and vascular plants are often conducted on dried herbarium specimen as species typically grow in distant and inaccessible locations. Here, we present an automated in silico compound classification framework to annotate metabolites using an untargeted data independent acquisition (DIA)–LC/MS–QToF-sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH) ecometabolomics analytical method. We perform a comparative investigation of the chemical diversity at the global level and the composition of metabolite families in ten different species of bryophytes using fresh samples collected on-site and dried specimen stored in a herbarium for half a year. Shannon and Pielou’s diversity indices, hierarchical clustering analysis (HCA), sparse partial least squares discriminant analysis (sPLS-DA), distance-based redundancy analysis (dbRDA), ANOVA with post-hoc Tukey honestly significant difference (HSD) test, and the Fisher’s exact test were used to determine differences in the richness and composition of metabolite families, with regard to herbarium conditions, ecological characteristics, and species. We functionally annotated metabolite families to biochemical processes related to the structural integrity of membranes and cell walls (proto-lignin, glycerophospholipids, carbohydrates), chemical defense (polyphenols, steroids), reactive oxygen species (ROS) protection (alkaloids, amino acids, flavonoids), nutrition (nitrogen- and phosphate-containing glycerophospholipids), and photosynthesis. Changes in the composition of metabolite families also explained variance related to ecological functioning like physiological adaptations of bryophytes to dry environments (proteins, peptides, flavonoids, terpenes), light availability (flavonoids, terpenes, carbohydrates), temperature (flavonoids), and biotic interactions (steroids, terpenes). The results from this study allow to construct chemical traits that can be attributed to biogeochemistry, habitat conditions, environmental changes and biotic interactions. Our classification framework accelerates the complex annotation process in metabolomics and can be used to simplify biochemical patterns. We show that compound classification is a powerful tool that allows to explore relationships in both molecular biology by “zooming in” and in ecology by “zooming out”. The insights revealed by our framework allow to construct new research hypotheses and to enable detailed follow-up studies. Full article

The biomechanical properties of soft tissues vary with pathological phenomenon. Ultrasound elasticity imaging is a noninvasive method used to analyze the local biomechanical properties of soft tissues in clinical diagnosis. However, the echo signal-to-noise ratio (eSNR) is diminished because of the attenuation of ultrasonic energy by soft tissues. Therefore, to improve the quality of elastography, the eSNR and depth of ultrasound penetration must be increased using chirp-coded excitation. Moreover, the low axial resolution of ultrasound images generated by a chirp-coded pulse must be increased using an appropriate compression filter. The main aim of this study is to develop an ultrasound elasticity imaging system with chirp-coded excitation using a Tukey window for assessing the biomechanical properties of soft tissues. In this study, we propose an ultrasound elasticity imaging system equipped with a 7.5-MHz single-element transducer and polymethylpentene compression plate to measure strains in soft tissues. Soft tissue strains were analyzed using cross correlation (CC) and absolution difference (AD) algorithms. The optimal parameters of CC and AD algorithms used for the ultrasound elasticity imaging system with chirp-coded excitation were determined by measuring the elastographic signal-to-noise ratio (SNRe) of a homogeneous phantom. Moreover, chirp-coded excitation and short pulse excitation were used to measure the elasticity properties of the phantom. The elastographic qualities of the tissue-mimicking phantom were assessed in terms of Young’s modulus and elastographic contrast-to-noise ratio (CNRe). The results show that the developed ultrasound elasticity imaging system with chirp-coded excitation modulated by a Tukey window can acquire accurate, high-quality elastography images. Full article

Prior information about the average dielectric properties of breast tissue can be implemented in microwave breast imaging techniques to improve the results. Rapidly providing this information relies on acquiring a limited number of measurements and processing these measurement with efficient algorithms. Previously, systems were developed to measure the transmission of microwave signals through breast tissue, and simplifications were applied to estimate the average properties. These methods provided reasonable estimates, but they were sensitive to multipath. In this paper, a new technique to analyze the average properties of breast tissues while addressing multipath is presented. Three steps are used to process transmission measurements. First, the effects of multipath were removed. In cases where multipath is present, multiple peaks were observed in the time domain. A Tukey window was used to time-gate a single peak and, therefore, select a single path through the breast. Second, the antenna response was deconvolved from the transmission coefficient to isolate the response from the tissue in the breast interior. The antenna response was determined through simulations. Finally, the complex permittivity was estimated using an iterative approach. This technique was validated using simulated and physical homogeneous breast models and tested with results taken from a recent patient study. Full article