Search Results (2,029)

Background: Rural fires represent a significant environmental and socioeconomic challenge in Mediterranean regions, particularly in Portugal, which experiences some of the highest fire incidences in Europe. Understanding the temporal evolution of fire occurrences and their relationship with climatic parameters is crucial for developing effective fire management strategies and adapting to climate change impacts. This study aims to analyze the evolution of rural fire occurrences in Guimarães, northern Portugal, during the period 1980–2020, and to investigate their relationship with climatic parameters, specifically temperature and precipitation patterns. Methods: We analyzed a comprehensive dataset of rural fire occurrences and burnt areas in the Guimarães municipality from 1980 to 2020, along with corresponding climatic data including mean annual temperature and total annual precipitation. Statistical analyses included descriptive statistics, Mann–Kendall trend analysis, Pearson and Spearman correlation analyses, and multiple linear regression modeling. The relationships between fire variables and climatic parameters were examined using both parametric and non-parametric approaches. Results: The analysis revealed significant temporal trends and climate–fire relationships. Mean annual temperature showed a statistically significant increasing trend (Mann–Kendall Z = 3.055, p = 0.002) with a Sen’s slope of 0.032 °C/year, representing approximately 1.3 °C warming over the 40-year period. Rural fire occurrences demonstrated a positive correlation with mean temperature (Pearson r = 0.459, p = 0.003; Spearman ρ = 0.453, p = 0.003), while total burnt area also showed significant positive correlation with temperature (Pearson r = 0.426, p = 0.005; Spearman ρ = 0.466, p = 0.002). Precipitation showed no significant correlation with fire variables. Multiple regression models explained 23.1% of the variance in fire occurrences and 18.3% of the variance in burnt area, with temperature being the primary climatic predictor. Conclusions: The study provides evidence of a significant warming trend in Guimarães over the past four decades, which is positively associated with increased rural fire activity. The consistent relationship between temperature and fire variables suggests that continued climate warming may lead to increased fire risk in the region. These findings have important implications for fire management strategies and climate adaptation planning in northern Portugal. Full article

Objectives: The present study mainly aimed to identify whether the envelope and triangular flaps affected wound healing and patient quality of life differently. Secondarily, the study aimed to investigate whether some anatomical and operational variables may also affect healing. Study design: A prospective randomized study was conducted with 56 fully impacted lower third molars, randomly divided into two groups, one treated with the envelope flap and the other with the bayonet flap. Qualitative variables were transformed into quantitative ones and then analyzed using independent samples t-tests or analysis of variance. An analysis of bivariate correlations with Pearson’s coefficient was also used. The chi-square test was used to verify the association between each flap and the categorical variables considered. Results: No statistically significant associations were found between flap types and dehiscence, although the mean dehiscence diameter was consistently greater in the envelope flap group. The maximum diameter of the dehiscence at 14 days was found to be significantly and negatively related to the 14-day wound healing indices. Analyses relating to the quality of life did not show significant associations. Conclusions: Despite some significant healing differences between the two considered flaps exist, they do not have relevant effects on the patient’s post-operative quality of life. Full article

A control chart is a crucial statistical process control (SPC) instrument for identifying method variances that may undermine product efficacy. The combined control chart has been utilized to enhance recognition capability. When testing a methodology, nonparametric statistics make a strong and compelling case when the distribution of a quality feature is uncertain. The primary focus of monitoring this work is to offer a novel control chart to support the surveillance of mean activities. This chart will incorporate a Tukey method, an extended exponentially weighted moving average control chart, and a moving average control chart called the Nonparametric EEWMA-MA chart. The Monte Carlo simulation facilitates assessments for evaluating system performance using average run lengths (ARL) based on zero-state. The comparison analysis demonstrates that the sensitivity of the suggested chart surpasses that of the conventional control chart (including the moving average (MA) chart, the extended exponentially weighted moving average (EEWMA) chart, and the mixed extended exponentially weighted moving average-moving average (EEWMA-MA) chart) in rapidly detecting changes that fluctuate with varying parameter settings by examining the minimal ARL. A simplified monitoring scenario using data on vinyl chloride can be employed to demonstrate the feasibility of the proposed technique. Full article

The vocal repertoire of the domestic pig (Sus scrofa domesticus) was examined in this study under conventional housing conditions. Therefore, direct behavior-associated vocalizations of fattening pigs were recorded and assigned to behavioral categories. Subsequently, a mathematical analysis of the recorded vocalizations was conducted using the frequency-based parameters of 25%, 50% and 75% quantiles of the frequency spectrum and the time-based parameters of variance of the time signal, mean level of the individual amplitude modulation and cumulative amplitude modulation. Most vocalizations were positively/neutrally assessed vocalizations constituting 59.7%, of which grunting was by far the most frequent vocalization. Negatively assessed vocalizations accounted for 37.8% of all vocalizations. Data analysis based on the six parameters resulted in a distinguishability of vocalizations related to negatively valenced behavior from those related to positively/neutrally valenced behavior. The study illustrates the relationship between auditory sensory perception and the underlying mathematical signals. It shows how pig vocalizations assessed by observations, for example, as positive or negative, are distinguishable using mathematical parameters but also which ambiguities arise when objective mathematical features widely overlap. In this way, the study encourages the use of more complex algorithms in the future to solve this challenging, multidimensional problem, forming the basis for future automatic detection of negative pig vocalizations. Full article

Accurate precipitation data are essential for hydrological modeling, climate studies, and water resource management. Indeed, there is an increasing focus on understanding shifts in precipitation events to monitor the risks of floods and droughts, as well as to ensure sustainable water resource management. This study compares four reanalysis and satellite precipitation products (ERA5-Land, CHIRPS, PERSIANN, and TerraClimate) with ground data from 2003 to 2022. Among the datasets evaluated, ERA5-Land has the best performance (overall) in reproducing ground data, with a minimal mean bias error (MBE) of 1.91 mm, the highest correlation coefficient (R² = 0.93), and the most favorable Nash–Sutcliffe efficiency (NSE = 0.93). In contrast, CHIRPS, PERSIANN, and TerraClimate significantly underestimate precipitation as compared to ground data. The categorical metrics also highlight ERA5-Land’s superior performance in identifying wet months. Spatial analysis shows that ERA5-Land and other datasets generally exhibit agreement regarding precipitation patterns. However, PERSIANN displays notable variances, particularly in northern regions, where it overestimates precipitation. To investigate possible changes in precipitation patterns, a longer period (1983–2022) is selected for trend analysis based on gridded precipitation products. Sen’s slope analysis does not reveal any significant annual precipitation trend. In autumn, the PERSIANN dataset indicates a significant increasing trend of +1.81 mm/year, which is also confirmed by ERA5-Land (+2.68 mm/year) and CHIRPS (+1.34 mm/year), although without statistical significance. The findings emphasize the need for more sophisticated satellite algorithms and integration with ground observations to improve precipitation accuracy. Full article

Urbanization affects bird communities by reducing habitat and fragmenting ecosystems. Urban parks can help counteract these effects. However, anthropogenic noise can further alter bird composition. We examined the distribution and abundance of bird species in nine urban parks in Mexico City. We used a ten minute fixed-radius (25 m) point-counting technique to count birds along their annual cycle, with ten minutes allocated for bird counts. The quality of green areas was analyzed in terms of vegetation (Normalized Difference Vegetation Index), park size, and mean noise level dB(A) (based on MIN and MAX values), and species were grouped into trophic guilds. A total of 108 bird species were recorded, 5 of which are under special protection; we grouped all species into 14 trophic guilds, showing different responses to environmental gradients. Redundancy analysis (RDA) explained 89.98% of the variance, with noise and park size being the most influential variables. Granivores and omnivores were more tolerant to noise, while insectivores and frugivores preferred quieter areas with more vegetation. A positive association was observed between the presence of winter resident species and the park size. On the other hand, mean noise level dB(A) was negatively related to permanent resident species, winter resident species, and those with protected status. Conservation efforts should focus on maintaining ample green spaces and reducing noise pollution, as recorded high mean noise levels (>53 dB(A)) exceed the recommended thresholds for avifauna conservation. Full article

Cemented filling technology is an effective approach to solving tailings accumulation and goaf, with rheological properties serving as key indicators of slurry fluidity. Since slurry rheology is influenced by multiple factors, accurate prediction of its parameters is essential for optimizing filling design. In this study, we developed a model to predict static and dynamic yield stress using the extreme gradient boosting (XGBoost) algorithm, trained on 140 experimental samples (105 for training and 35 for validation, split 75:25). For comparison, adaptive boosting tree (ADBT), gradient boosting decision tree (GBDT), and random forest (RF) algorithms were also applied. Model performance was evaluated using four metrics: coefficient of determination (R²), mean absolute error (MAE), root mean square error (RMSE), and explained variance score (EVS). The Shapley additive explanation (SHAP) method was employed to interpret model outputs. The results show that XGBoost achieved superior predictive accuracy for slurry yield stress compared with other models. Analysis of importance revealed that underflow concentration had the strongest influence on predictions, followed by the binder-to-tailings ratio, while the fine-to-coarse tailings ratio contributed least. These findings highlight the potential of machine learning as a powerful tool for modeling the rheological parameters of filling slurry, offering valuable guidance for engineering applications. Full article

Introduction: The aim of this study was to investigate the spatial relationship between the incisive canal (IC) and apical region of the maxillary central incisors in the Korean population, using cone-beam computed tomography (CBCT) imaging. The findings are intended to inform and improve the planning and execution of immediate implant placement in the maxillary esthetic zone. Materials and methods: CBCT data were collected from 94 patients (48 men, 46 women) aged 20–79 years at Gangnam Dental Clinic, Seoul, South Korea. The sample was divided according to age into three groups: 20–39 years, 40–59 years, and 60–79 years. Exclusion criteria included missing maxillary anterior teeth, severe crowding, periodontitis, pathology, and image artifacts. Measurements of the distance from the root apex to the incisive canal (RIC-11-P, RIC-21-P) and from the root apex to the buccal bone (RBB-11-B, RBB-21-B) were taken from CBCT images. Statistical analyses were conducted using Welch’s t-test, ANOVA, and Pearson correlation, with significance set at p < 0.05. Results: The mean distances from the root apex to the incisive canal were 3.77 mm (RIC-11-P) and 3.62 mm (RIC-21-P), while the mean distances to the buccal bone were 0.86 mm and 0.94 mm, respectively. Males exhibited significantly greater distances compared to females, both in the NPC-to-root apex and buccal bone measurements. Age-related variations were observed, with younger individuals showing shorter distances from the IC to the root apex. However, ANOVA tests and Pearson correlation analysis indicated no statistically significant correlation in these distances across different age groups. The study highlights significant gender differences in maxillary central incisor anatomy, with males having larger distances from the root apex to both the IC and buccal bone, which has implications for implant placement. While age-related changes were observed, they did not significantly affect the mean distances in a statistically meaningful way. Conclusions: These findings underscore the need for personalized treatment planning in immediate implant placement, particularly in relation to gender and age. Comparisons with other population studies suggest that these anatomical differences may be consistent across various ethnic groups, though individual variance factors should still be considered. Full article

Background/Objectives: While the benefits of almond consumption in reducing levels of TC and LDL-C are well established, the effects on additional lipids that have emerged as important predictors of cardiovascular disease, such as ApoB and the ratio of ApoB:ApoA, are not well characterized. In this systematic review and meta-analysis, the effects of almond consumption on blood lipids were comprehensively assessed. Methods: On 12 May 2025, ProQuest Dialog™ was used to search ten literature databases (AdisInsight: Trials; Allied & Complementary Medicine™; BIOSIS Previews^®; CAB ABSTRACTS; Embase^®; Embase Preprints; Foodline^®: SCIENCE; FSTA^®; MEDLINE^®; National Technical Information Service). Randomized controlled trials at least 4 weeks in duration were included if the investigational product was almonds; the control was void of nuts/tree nuts; the subjects were adults without CVD; and blood lipid levels were assessed. Health Canada’s Quality Appraisal Tool for Intervention Studies was used to assess each study’s risk of bias. The mean difference in the effect for each parameter was pooled across studies in a random effects model, using the inverse of the variance as the weighting factor. Results: 36 publications (48 almond–control datasets) representing 2485 participants were included. Almond consumption significantly reduced LDL-C (−0.132 mmol/L; 95% CI: −0.190, −0.075 mmol/L; p < 0.001), TC (−0.160 mmol/L; 95% CI: −0.218, −0.101 mmol/L; p < 0.001), non-HDL-C (−0.204 mmol/L; 95% CI: −0.281, −0.127 mmol/L; p < 0.001), TC:HDL-C (−0.154; 95% CI: −0.246, −0.063; p = 0.001), LDL-C:HDL-C (−0.112; 95% CI: −0.199, −0.026; p = 0.011), ApoB (−4.552 mg/dL; 95% CI: −6.460, −2.645 mg/dL; p < 0.001), and ApoB:ApoA (−0.027; 95% CI: −0.046, −0.008; p = 0.006), with a borderline significant reduction in TG (−0.037 mmol/L; 95% CI: −0.079, 0.005; p = 0.085) and no effects on HDL-C, ApoA, or Lp[a]. The effects persisted when the analyses were limited to higher quality studies, except for the reduction in TG. Conclusions: Almond consumption improves levels of LDL-C, TC, non-HDL-C, TC:HDL-C, LDL-C:HDL-C, ApoB, and ApoB:ApoA, though dedicated clinical trials are needed to better understand effects on TG levels. Full article

Background/Objectives: Developmental dyslexia is characterised by neuropsychological processing deficits and marked hemispheric functional asymmetries. To uncover latent neurophysiological features linked to reading impairment, we applied dimensionality reduction and clustering techniques to high-density electroencephalographic (EEG) recordings. We further examined the functional relevance of these features to reading performance under standardised test conditions. Methods: EEG data were collected from 200 children (100 with dyslexia and 100 age- and IQ-matched typically developing controls). Principal Component Analysis (PCA) was applied to high-dimensional EEG spectral power datasets to extract latent neurophysiological components. Twelve principal components, collectively accounting for 84.2% of the variance, were retained. K-means clustering was performed on the PCA-derived components to classify participants. Group differences in spectral power were evaluated, and correlations between principal component scores and reading fluency, measured by the TILLS Reading Fluency Subtest, were computed. Results: K-means clustering trained on PCA-derived features achieved a classification accuracy of 89.5% (silhouette coefficient = 0.67). Dyslexic participants exhibited significantly higher right parietal–occipital alpha (P8) power compared to controls (mean = 3.77 ± 0.61 vs. 2.74 ± 0.56; p < 0.001). Within the dyslexic group, PC1 scores were strongly negatively correlated with reading fluency (r = −0.61, p < 0.001), underscoring the functional relevance of EEG-derived components to behavioural reading performance. Conclusions: PCA-derived EEG patterns can distinguish between dyslexic and typically developing children with high accuracy, revealing spectral power differences consistent with atypical hemispheric specialisation. These results suggest that EEG-derived neurophysiological features hold promise for early dyslexia screening. However, before EEG can be firmly established as a reliable molecular biomarker, further multimodal research integrating EEG with immunological, neurochemical, and genetic measures is warranted. Full article

Lifetime reproductive output (LRO), also called lifetime reproductive success (LRS) is often described by its mean (total fertility rate or net reproductive rate), but it is in fact highly variable among individuals and often positively skewed. Several approaches exist to calculating the variance and skewness of LRO. These studies have noted that a major factor contributing to skewness is the fraction of the population that dies before reaching a reproductive age or stage. The existence of that fraction means that LRO has a zero-inflated distribution. This paper shows how to calculate that fraction and to fit a zero-inflated Poisson or zero-inflated negative binomial distribution to the LRO. We present a series of applications to populations before and after demographic transitions, to populations with particularly high probabilities of death before reproduction, and a couple of large mammal populations for good measure. The zero-inflated distribution also provides extinction probabilities from a Galton-Watson branching process. We compare the zero-inflated analysis with a recently developed analysis using convolution methods that provides exact distributions of LRO. The agreement is strikingly good. Full article

Background: The aim of the present study was to apply machine learning (ML) techniques to define clusters relating patient demographics, compliance, and outcome variables in colorectal enhanced recovery after surgery (ERAS) patients and improve data-driven, predictive decision-making. Methods: To uncover inherent patient subgroups from the data without pre-defined labels, the unsupervised K-means clustering algorithm was utilized. This technique was selected for its effectiveness in partitioning patients into distinct groups by iteratively assigning them to the nearest cluster mean, thereby minimizing within-cluster variance across key variables. The top five recovery goals and the top 10 clinical outcome variables were defined based on clinical considerations (incidence and importance). In a second step, the cluster transition was traced by monitoring the transitions between clusters from demographic through compliance to outcome variables. Results: A total of 1381 patients were available for final analysis, revealing three clusters (low risk, n = 490, 36%; intermediate risk, n = 157, 11%; and high risk, n = 734, 53%) for demographic, two clusters (high compliance, n = 1011, 73%, and low compliance n = 370, 27%) for perioperative, and two clusters (good and poor outcomes) for the top five recovery goals and the top 10 clinical outcomes, respectively. The cluster transition for the top five recovery goals and the top 10 clinical outcomes revealed that most patients (488/490, 99.6%) of the low-risk demographic cluster had high perioperative compliance, and over 90% of them had favorable functional and clinical outcomes. Of the 2/3 of intermediate risk patients who had poor perioperative compliance, over 40% had a poor functional recovery, whereas 83% had good clinical outcomes. Of the high-risk demographic group, 100% (734/734) had low perioperative compliance, and over 40% of them had poor functional recovery. Conclusions: This ML-based analysis of demographic, compliance, and recovery clusters and associated cluster transition allowed us to identify patient clusters as a first step to tailored ERAS protocols aiming to improve compliance and outcomes. Full article

Received Signal Strength Indicator (RSSI) prediction is valuable for network planning and optimization as it helps determine the optimal placements of wireless access points and enables better coverage planning. It is also crucial for efficient handover management between cells or access points, reducing dropped connections and improving service quality. Additionally, RSSI prediction supports indoor positioning systems, power management optimization, and cost-efficient network deployment. Path loss models have historically served as the foundation for RSSI prediction, providing a theoretical framework for estimating signal strength degradation. However, modern machine learning approaches have emerged as a revolutionary solution for network optimization, providing more versatile and data-driven methods to enhance wireless network performance. In this paper, an adaptive machine learning framework integrating environmental sensing parameters such as temperature, relative humidity, barometric pressure, and particulate matter for RSSI prediction is proposed. Performance analysis reveals that RSSI values are influenced by environmental factors through complex, non-linear interactions, thereby challenging the conventional linear assumptions of traditional path loss models. The proposed model demonstrates improved predictive accuracy over the baseline, with relative increases in variance explained of 6.02% and 2.04% compared to the baseline model excluding and including environmental parameters, respectively. Additionally, the root mean squared error is reduced to 1.40 dB. These results demonstrate that cognitive methods incorporating environmental data can substantially enhance RSSI prediction accuracy in wireless communications. Full article

Overuse of chemical fertilizers can threaten the agro-ecological balance, including an excessive accumulation of certain elements, such as nitrogen and phosphorus. On the other hand, organic fertilizers and biofertilizers, which are eco-friendly and cost-effective, increase biological nitrogen fixation and enhance the availability of nutrients to plants. The aim of this research was to study the possibility of using a full (22.50 t/ha) and 50% (11.25 t/ha) treatment of sheep manure with azotobacter (100 mL/20 L) instead of inorganic fertilizers for increasing savory (Satureja hortensis L.) growth production and yield value as well as improving chemical and biological properties. The results showed that the treatment with 50% sheep manure recorded the highest total dry herb (3.18 t/ha) yield. The inorganic fertilizer resulted in the highest essential oil content (1.43% v/w) and γ-terpinene (10.38% v/v), cymol (5.90% v/v), and α-bisabolene (5.28% v/v) values. The maximum carvacrol value (42.54% v/v) was recorded in the savory herb after applying no fertilization to the plants, while the highest concentration of thymol (16.09% v/v) was obtained by applying the full sheep manure treatment. The full sheep manure + azotobacter treatment had the highest mean α-terpinene value (7.22% v/v), and the 50% sheep manure + azotobacter treatment had the highest mean α-phellandrene value (6.44% v/v). The highest DPPH activity (60.86%) and FRAP value (69.64 mg TE/g DW) were observed with the azotobacter + full sheep manure treatment, while the highest total phenolic content (96.87 mg GAE/g DW) and total flavonoid content (45.97 mg QE/g DW) in the savory herb were obtained from the combination treatment of 50% sheep manure doses + azotobacter. Principal coordinate analysis (PCA) revealed distinct clustering of treatments, with PC1 and PC2 explaining >60% of the variance, highlighting the dominant role of sheep manure doses in morphological/yield properties. Heatmap analysis grouped the treatments (right) and examined properties (bottom) as two main groups. The full sheep manure + biofertilizer and inorganic fertilizer treatments were found in the first group, depending on the treatments. Moreover, the heatmap analysis revealed that the full and 50% sheep manure (SM) treatments played critical roles in separating the examined properties, and the DPPH and carvacrol properties were grouped together compared to other properties. Thus, the results suggest that treatment with azotobacter could be employed in combination with appropriate rates of sheep manure to obtain the maximum benefits regarding herb yield, biological activity, and essential oil components. Full article

Background: Reduced bond strengths in hypmineralised enamel have been reported with increased restorative failures. This study aimed to investigate the shear bond strengths of resin composite to hypomineralised enamel and dentin bonded with two different adhesive systems and pre-treatments. Methods: Thirty-six freshly extracted first permanent molars with MIH and 17 sound third molars were used for shear bond strength tests in enamel and dentin. Specimens of control groups were bonded to resin composite using Scotchbond^TM Universal Plus and Adper^TM Scotchbond 1XT. MIH-affected enamel specimens of six test groups were pre-treated with various chemical agents, such as 35% phosphoric acid, 5% NaOCl, resin infiltration with ICON^®, or a combination of these agents prior to bonding with composite resin using Scotchbond^TM Universal Plus. Bonded specimens were subsequently sheared at a crosshead speed of 1.0 mm/min, after which their fracture modes were recorded. The mean bond strengths of all groups were compared using a one-way analysis of variance test (ANOVA) and a Bonferroni–Holm analysis was performed for pairwise comparison between the groups. The association between modes of failure was examined with Pearson’s chi-square test. Results: Mean shear bond strength values were highest for sound dentin specimens (Group SD 2) bonded with Scotchbond^TM Universal Plus (23.76 ± 7.68 MPa). Sound enamel specimens (Group SE 2) exhibited significantly higher mean bond strength values than MIH-enamel specimens (Group HE 2) when bonded with Scotchbond^TM Universal Plus (19.68 ± 6.25 vs. 11.53 ± 3.29 MPa, p < 0.001). Oxidative pre-treatment followed by resin infiltration significantly improved bond strengths to hypomineralised enamel (Group HE 6) (17.84 ± 2.98 MPa, p < 0.05). Bond strengths to sound and hypomineralised enamel and dentin did not differ significantly for both adhesives. Conclusions: Within the limitations of an in vitro study, oxidative pre-treatment in combination with resin infiltration seems to be beneficial when planning adhesive restorations with composite in hypomineralised enamel. Both Scotchbond^TM Universal Plus and Adper^TM Scotchbond 1XT can be used for bonding of resin composite to MIH-affected enamel and dentin. Full article