Search Results (2,413)

Electrospinning is a widely used technique for fabricating nanomaterials with tailored morphology and functional properties. This study investigates how two fundamental process parameters—applied voltage and needle tip-to-collector distance—affect the spatial geometry and deposited mass of electrospun nanofiber mats in a top-down vertical electrospinning setup using a 10% (w/v) PVA solution prepared in deionized water. To support this hypothesis, both experimental measurements and 3D geometric modeling were performed to evaluate the area, perimeter, and deposited mass under different parameter combinations. Digital image analysis and cross-sectional reconstruction were applied to model nanofiber deposition. Regression and ANOVA analyses reveal that the tip-to-collector distance has a statistically significant impact on both area and perimeter of the electrospun nanofiber mat, while the applied voltage in the tested range (15–20 kV) has no significant effect. Interestingly, the total deposited mass shows no clear dependence on either parameter, likely due to startup irregularities or solution droplets. Full article

Objective: Alzheimer’s disease (AD) and other forms of dementia are a heterogeneous group of neurodegenerative diseases characterized by progressive cognitive decline. Differential diagnosis between AD and other dementias is crucial for choosing the optimal treatment strategy. Currently, cerebrospinal fluid (CSF) analysis remains the most accurate diagnostic method, but its invasiveness limits its use. In this regard, the search for reliable biomarkers in the blood is an urgent task. Methods: The study included 31 dementia patients (23 women and 8 men) diagnosed via interdisciplinary consultations and neuropsychological testing (MMSE ≤ 24). CSF and blood plasma samples were collected and analyzed using Luminex technology. Biomarker concentrations were measured, and statistical analyses (ANOVA, Kruskal–Wallis, and Pearson correlation) were performed to compare groups and assess correlations. Results: Levels of Aβ40 and Aβ42 in CSF were significantly lower in patients with AD compared with non-AD dementia (p = 0.02 and p < 0.001, respectively). The Aβ42/40 ratio in CSF was higher in patients with non-AD dementia (p = 0.048). The concentration of Aβ42 in blood plasma was increased in patients with AD (p = 0.001). Positive correlations were found between Aβ42 in CSF and TDP-43 in plasma in non-AD dementia (r = 0.97, p < 0.001), as well as between neurogranin and TDP-43 in plasma in AD (r = 0.845, p < 0.001). Conclusions: The study demonstrates the potential of blood biomarkers, in particular Aβ42, for the differential diagnosis of AD and other forms of dementia. The discovered correlations between CSF and plasma biomarkers deepen the understanding of neurodegenerative processes and contribute to the development of noninvasive diagnostic methods. Full article

This study presents the development and mechanical characterization of a full-scale helmet manufactured from a polyester matrix composite reinforced with woven jute fabric using vacuum infusion. Laminates with two and four reinforcement layers were produced and assembled using four joining configurations: seamless, stitched, bonded, and hybrid (bonded + stitched). Tensile tests were performed according to ASTM D3039, while frontal and lateral compression tests followed ABNT NBR 7471, aiming to evaluate the influence of laminate thickness and joining strategy on mechanical performance. In tension, the seamless configuration reached maximum loads of 0.80 kN (two layers) and 1.60 kN (four layers), while the hybrid configuration achieved 0.79 kN and 1.43 kN, respectively. Stitched and bonded joints showed lower strength. Under compression, increasing the laminate thickness from two to four layers reduced frontal elongation from 15.09 mm to 9.97 mm and lateral elongation from 13.73 mm to 7.24 mm, corresponding to stiffness gains of 50.3% and 87.3%, respectively. Statistical analysis (ANOVA/Tukey, α = 0.05) confirmed significant effects of thickness and joint configuration. Although vacuum infusion is a well-established process, the novelty of this work lies in its application to a full-scale natural-fiber helmet, combined with a systematic evaluation of joining strategies and a direct correlation between standardized tensile behavior and structural compression performance. The four-layer hybrid laminate exhibited the best balance between strength, stiffness, and deformation capacity. Full article

The COVID-19 outbreak has made it evident that the nature and behavior of SARS-CoV-2 requires constant research and surveillance, owing to the high mutation rates that lead to variants. This work focuses on the statistical analysis of energy measures as biomarkers of SARS-CoV-2. The main purpose of this study is to determine which energy measure can differentiate between SARS-CoV-2 variants, human cell receptors (GRP78 and ACE2), and their combinations. The dataset includes energy measures for different biological structures categorized by variants, receptors, and combinations, representing the sequence of variants and receptors. A multiple analysis of variance (ANOVA) test for equality of means and a Bartlett test for equality of variances are applied to energy measures. Results from multiple ANOVA show (a) the presence of significant differences in energy across variants, receptors, and combinations, (b) that average energy is significant only for receptors and combinations, but not for variants, and (c) the absence of significant differences observed for standard deviation across variants or combinations, but that there are significant differences across receptors. The results from the Bartlett tests show that (a) there is a presence of significant differences in the variances in energy across the variants and combinations, but no significant differences across receptors, (b) there is an absence of significant differences in variances across any group (variants, receptors, combinations), and (c) there is an absence of significant differences in variances for standard deviation of energy across variants, receptors, or combinations. In summary, it is concluded that energy and mean energy are the key biomarkers used to differentiate receptors and combinations. In addition, energy is the primary biomarker where variances differ across variants and combinations. These findings can help to implement tailored interventions, address the SARS-CoV-2 issue, and contribute considerably to the global fight against the pandemic. Full article

The article examines the implementation of 3D printing in civil protection and crisis management with a focus on the educational process, while 3D printing technology enables the creation of various teaching aids that streamline teaching and enrich theoretical knowledge. The empirical part of the study is based on a quantitative questionnaire survey among students of the Faculty of Safety Engineering of the University of Žilina in Žilina, with hypotheses set in advance and forming the basis for the construction of the questionnaire. The questionnaire collected data on the subjective evaluation of 3D printing through continuous, nominal, and ordinal responses and was completed by 277 students. Statistical methods of simple and group classification, as well as t-test, ANOVA, Kruskal–Wallis and Pearson’s correlation analysis were used to evaluate the data. Statistical significance was used to determine whether observed differences and relationships were unlikely to have arisen by chance. In addition, effect size measures were used in correlation and regression analyses to assess the strength and practical relevance of statistically significant relationships. The results of the study show that 3D printing significantly contributes to improving education and preparedness in civil protection, as it allows for more material-efficient and flexible production of educational aids compared to traditional custom production. Thus, it supports the development of more resilient communities and contributes to long-term sustainability. The findings confirmed that 3D printing is a suitable tool for improving public preparedness for emergencies. Full article

To evaluate the influence of different surface conditioning protocols—sandblasting (SB), tribochemical silica coating (TBC), CO₂ laser irradiation, and plasma-enhanced chemical vapor deposition (PECVD-Si coating for 49 min) on surface roughness (Ra), surface morphology, and composite-to-zirconia shear bond strength (SBS). Eighty 3Y-TZP plates were randomly allocated into four groups (n = 20) based on surface conditioning protocol: Group 1 (SB), Group 2 (CO₂ laser), Group 3 (TBC), and Group 4 (PECVD-Si coating for 49 min). From each group, five specimens underwent Ra assessment using a contact profilometer, and five specimens were examined for surface morphology via scanning electron microscopy (SEM). The remaining ten specimens received resin composite buildup, followed by artificial aging. Subsequently, SBS testing was performed using a universal testing machine, and failure modes were evaluated under a stereomicroscope. Statistical analysis was conducted using one-way ANOVA with post hoc Tukey test and chi-square for fracture assessment(α = 0.05). Group 1 (SB) demonstrated the lowest Ra (0.844 ± 0.063 µm) and SBS (12.21 ± 4.6 MPa), whereas Group 4 (PECVD-Si coating for 49 min) exhibited the highest Ra (1.388 ± 0.098 µm) and SBS (30.48 ± 2.5 MPa). Intergroup comparison revealed no statistically significant differences between Groups 2 and 3 for both Ra and SBS values (p > 0.05). However, Groups 1 and 4 differed significantly in both parameters (p < 0.05). PECVD-based silica coating for 49 min demonstrated superior surface conditioning efficacy for 3Y-TZP, yielding significantly higher Ra and SBS values compared to sandblasting, tribochemical silica coating, and CO₂ laser irradiation. Full article

This paper presents an energy-focused analysis of structural materials used in passenger cars, with a particular emphasis on the impact of construction materials on total energy consumption throughout the vehicle’s life cycle. Three production periods (2000, 2010, and 2020) were analysed for B- and C-segment vehicles using inventory data from Life Cycle Assessment databases, the scientific literature, and certified dismantling stations. The embodied energy of key material groups—steel, aluminium, plastics, and other materials—was calculated based on representative mass shares and material-specific energy intensity indicators. The computational model was supplemented with statistical analyses (Kolmogorov–Smirnov test, Levene’s test, ANOVA, and Tukey’s post hoc tests) to verify whether observed temporal trends were statistically significant. The results indicate that total material-related energy inputs increased from approximately 57 GJ to 64 GJ per vehicle, while the specific energy intensity per kilogram decreased from 47.6 MJ/kg to 42.6 MJ/kg. Aluminium exhibited a pronounced reduction in unit energy intensity due to the rising share of secondary materials, whereas plastics and other materials showed substantial increases. Steel remained the largest contributor in absolute terms because of its dominant mass share. This study highlights the growing importance of the production phase in the environmental balance of modern vehicles, particularly in the context of the rising share of lightweight materials and recycling-based components. The results emphasise the importance of energy-efficient material use and underscore the significance of material selection and recycling strategies in reducing energy demand within the automotive sector. Full article

This study presents a statistically robust quality-engineering evaluation of an industrial cathodic electrodeposition (CED) process applied to large electric-charging station components. In contrast to predominantly laboratory-scale studies, the analysis is based on 1250 thickness measurements, enabling reliable assessment of process uniformity, positional effects, and long-term stability under real production conditions. The mean coating thickness was specified at 21.84 µm with a standard deviation of 3.14 µm, fully within the specified tolerance window of 15–30 µm. One-way ANOVA revealed statistically significant but technologically small inter-station differences (F(49, 1200) = 3.49, p < 0.001), with an effect size of η² ≈ 12.5%, indicating that most variability originates from inherent within-station common causes. Shewhart X^¯–R–S control charts confirmed process stability, with all subgroup means and dispersions well inside the control limits and no evidence of special-cause variation. Distribution tests (χ², Kolmogorov–Smirnov, Shapiro–Wilk, Anderson–Darling) detected deviations from perfect normality, primarily in the tails, attributable to the superposition of slightly heterogeneous station-specific distributions rather than fundamental non-Gaussian behaviour. Capability and performance indices were evaluated using Statistica and PalstatCAQ according to ISO 22514; the results (Cp = 0.878, Cpk = 0.808, Pp = 0.797, Ppk = 0.726) classify the process as conditionally capable, with improvement potential mainly linked to reducing positional effects and centering the mean closer to the target thickness. To complement the statistical findings, an AIAG–VDA FMEA was conducted across the entire value stream. The highest-risk failure modes—surface contamination, incorrect bath chemistry, and improper hanging—corresponded to the same mechanisms identified by SPC and ANOVA as contributors to thickness variability. Proposed corrective actions reduced RPN values by 50–62.5%, demonstrating strong potential for capability improvement. A predictive machine-learning model was implemented to estimate layer thickness and successfully reproduced the global trend while filtering process-related noise, offering a practical tool for future predictive quality control. Full article

This study focuses on the diversity of phytoplankton in the Koh Yaa region of Thailand and their relationship with environmental variables, aiming to assess whether human activities (primarily tourism) pose potential threats to the marine ecosystem and provide scientific support for eco-sustainable tourism management decisions in the region. In April, August, and December 2024, corresponding to peak season, off-season, and shoulder season, a total of 156 discrete samples were collected from four coastal sites to analyze water quality parameters such as temperature, pH, total nitrogen (TN), and total phosphorus (TP), along with plankton diversity and abundance. Statistical analyses including two-way ANOVA with Duncan’s Multiple Range Test (DMRT), Pearson correlation analysis, and principal component analysis (PCA) were applied. The results showed a declining trend in plankton abundance over time, peaking at 1009 × 10⁶ cells/m³ in April and dropping to 281 × 10⁶ cells/m³ by December. A total of 15 types of phytoplankton were identified across four phyla: Bacillariophyta, Cyanobacteria, Dinoflagellata, and Chlorophyta. Notably, Chaetoceros from Bacillariophyta accounted for 47% of phytoplankton, while Oscillatoria from Cyanobacteria made up 29.6%. The diversity index and evenness index improved from 1.34 and 0.46 in April to 1.88 and 0.64 in December, respectively. Environmental factors like pH, temperature, and TP significantly affected phytoplankton abundance (p < 0.01), with TP levels ranging from 0.27 to 0.69 mg/L. These results indicate possible pollution in this region, and changes in phytoplankton abundance were linked to seasonal climate variations—especially during peak tourist seasons—which may exacerbate eutrophication affecting community structures. Full article

Pellets produced from raw or torrefied shredded logging residues have been investigated in the study. The research material came from pine and spruce stands in Poland, Slovakia, Czechia and Hungary. Torrefaction temperatures (T_t) of 250, 300, and 400 °C were applied. Before pressure agglomeration, 3% wheat flour was added to the torrefaction material as a binding agent. Pellets with a diameter of 8 mm were produced at constant humidity, compaction pressure (P) of 140 or 180 MPa and agglomeration temperature (T_a) of 100, 120 or 140 °C. The produced pellets were assessed for their physicomechanical parameters (density, radial compressive strength, compression ratio, modulus of elasticity), chemical parameters (extractive compounds, cellulose, lignin) and energy parameters (ash content, elemental composition, calorific value). The results were subjected to basic statistical analysis and multi-way ANOVA. The produced pellets varied in physical, mechanical, chemical and energy properties. A significant effect of torrefaction temperature, agglomeration temperature and compaction pressure on the results was observed. In terms of physicomechanical parameters, the best pellets were produced from the raw material, while in terms of energy parameters, those produced from the torrefied material were superior. Pellets of satisfactory quality produced from torrefied logging residues could be obtained at T_t = 250 °C, T_a = 120 °C and P = 180 MPa. Pellets with specific density of approximately 1.1 g·cm⁻³, radial compressive strength of 3–3.5 MPa, modulus of elasticity of 60–80 MPa and calorific value of 20.3–23.8 MJ·kg⁻¹ were produced in the process. Full article

Objective: This in vitro study evaluated the accuracy and precision of five intraoral scanners (IOSs) by examining the interaction between gingival model color and linear measurement distances. Materials and Methods: Seven color-distinct models were scanned to obtain absolute deviation data from six linear distances between four reference points. Measurements were analyzed using Zeiss Inspect software v2025.3.3.4. Due to non-normal data distribution, all factors (Scanner, Model, Pair) and their interactions were assessed using Aligned Rank Transform (ART) ANOVA. Accuracy was defined as median absolute deviation, and precision as the coefficient of variation (CV%). Results: Statistical analysis identified significant differences in absolute deviation across all main factors and their three-way interactions (p < 0.001). The Medit i700 and Trios 5 demonstrated the lowest overall median deviation (0.09 mm), followed closely by Trios 3 (0.10 mm), with no statistically significant differences among them. The P5 model yielded lower deviations, while extreme colors increased variability. In terms of precision, values varied significantly based on specific interactions; the highest precision was recorded for the Shining scanner on the White model (A–C pair, CV: 7.33%), whereas the lowest precision was observed for the Sirios scanner on the Black model (A–D pair, CV: 158.10%). Conclusions: Within the limitations of this in vitro study, deviation values varied according to gingival color and pair distance. Gingival colors with a higher pink saturation (P5) and shorter distances yielded lower deviations, whereas extreme colors and longer distances were associated with reduced precision. Full article

Background/Objectives: The rotation of maxillary canines represents one of the least predictable movements with clear aligners, particularly in cases requiring rotations greater than 10°, due to the rounded crown morphology and limited aligner grip. The aim of this retrospective study was to compare three different crescent-shaped attachment designs (vertical, horizontal, and oblique) for maxillary canine rotations greater than 10° with clear aligners. Methods: Seventy-eight maxillary canines were retrospectively selected and allocated into three equal groups (n = 26) according to the orientation of the applied attachment: vertical, horizontal, or oblique crescent-shaped attachments. Digital STL models (initial, predicted, and final) were imported into Dolphin 3D software 12.0.63 to assess the accuracy of maxillary canine’s rotation through the comparison between planned and achieved values. Results: Mean rotational accuracy was 55.10% ± 15.60 for the vertical group, 62.40% ± 16.10 for the horizontal group, and 64.60% ± 19.40 for the oblique group. One-way ANOVA showed no statistically significant differences among groups (p = 0.09). Pairwise analysis revealed a statistically significant difference between the oblique and vertical designs (p = 0.05). Conclusions: Attachment orientation may influence the accuracy of maxillary canine rotation with clear aligners, with oblique crescent-shaped attachments showing a trend toward improved rotational control. Full article

Background/Objectives: Effective removal of organic tissue extruded beyond the apex is crucial in regenerative endodontics, particularly in teeth with immature apices; therefore, this study aims to compare the efficacy of standard needle irrigation (SNI), ultrasonic irrigation (UI), photon-induced photoacoustic streaming (PIPS), and shock wave-enhanced emission photoacoustic streaming (SWEEPS) techniques in dissolving periapical tissue in a simulated model. Methods: Sixty single-rooted human premolars and sixty bovine palatal mucosa specimens were used. A custom model was created by placing mucosal tissue in contact with the apical area. Specimens were divided into four groups (n = 15) according to the irrigation method: SNI, UI, PIPS, and SWEEPS. Each canal received 15 mL of 2% NaOCl. Tissue samples were weighed before and after treatment. One-way ANOVA and Tukey’s post hoc test were used for statistical analysis (p < 0.05). Results: UI showed significantly less tissue dissolution than the other methods (p < 0.05). SNI, PIPS, and SWEEPS showed no significant differences (p > 0.05). Conclusions: All methods led to tissue loss, but UI was significantly less effective. SNI, PIPS, and SWEEPS performed similarly. Full article

Background/Objectives: The cognitive processes underlying learning are critical for educational practices. While mindfulness-based approaches to strengthening these cognitive processes have become widespread, studies focusing on game-based development of executive functions, particularly in preschool settings, are limited. The primary objective of this study is to develop a play-based mindfulness intervention program for preschool children and to examine the effects of this program on preschool children’s resilience, emotion regulation skills, and executive functions. Methods: The study employed a pretest–post-test control-group experimental design. The study group consisted of 40 children (20 experimental and 20 control) aged 5–6 years, attending a kindergarten in Malatya province, Türkiye. The Devereux Early Childhood Assessment Scale (DECA-P2), Emotion Regulation Scale (ERS), and Childhood Executive Functions Inventory (CHEXI) were used as data collection tools. Independent-samples t-tests were used for baseline analysis, and a two-way repeated-measures ANOVA was used to evaluate the program’s effects. Results: Findings showed that there was a statistically significant difference between the pre-test and post-test mean scores of the children in the experimental group compared with those in the control group for resilience, emotion regulation, and executive function (p < 0.05). Conclusions: Strong evidence was obtained that play-based mindfulness training has positive effects on the cognitive and emotional development of preschool children. Full article

Flash flood events are some of the most life-threatening natural disasters, so it is important to predict extreme rainfall events effectively. This study introduces an LSTM model that utilizes a customized loss function to effectively predict extreme rainfall events. The proposed model incorporates dynamic environmental variables, such as rainfall, LST, and NDVI, and incorporates additional static variables such as soil type and proximity to infrastructure. Wavelet transformation decomposes the time series into low- and high-frequency components to isolate long-term trends and short-term events. Model performance was compared against Random Forest (RF), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and an LSTM-RF ensemble. The custom loss LSTM achieved the best performance (MAE = 0.022 mm/day, RMSE = 0.110 mm/day, R² = 0.807, SMAPE = 7.62%), with statistical validation via a Kruskal–Wallis ANOVA, confirming that the improvement is significant. Model uncertainty is quantified using a Bayesian MCMC framework, yielding posterior estimates and credible intervals that explicitly characterize predictive uncertainty under extreme rainfall conditions. The sensitivity analysis highlights rainfall and LST as the most influential predictors, while wavelet decomposition provides multi-scale insights into environmental dynamics. The study concludes that customized loss functions can be highly effective in extreme rainfall event prediction and thus useful in managing flash flood events. Full article