Search Results (3,630)

The aim of this study is to evaluate the influence of traditional and industrial smoking technologies on the physicochemical properties, color, texture, and mass loss of selected cured pork products. Four products (dry-cured pork neck, dry-cured pork loin, pancetta, and fermented sausages in natural and collagen casings) were produced using two smoking regimes (traditional and industrial). The samples were analyzed at two processing stages, after smoking and at the end of the production process. Physicochemical composition, pH, water activity (a_w), color parameters (CIE L*a*b*), texture profile parameters, and mass loss were determined using standard analytical methods. Statistical differences between treatments were evaluated using the analysis of variance (ANOVA) followed by Fisher’s least significant difference (LSD) test (p < 0.05). Traditional smoking resulted in greater dehydration, with moisture content reduced by approximately 8–15% and water activity lower by about 0.04–0.09 compared with industrial smoking. Traditionally smoked products also showed higher mass loss (up to 10–12%) and lower L* values, indicating darker color. Texture profile analysis indicated higher hardness values in several traditionally smoked products, particularly in sausages and pancetta. In contrast, industrial smoking resulted in higher moisture retention and more uniform physicochemical characteristics. The differences between smoking regimes were less pronounced in dry-cured pork neck. These results demonstrate that smoking technology significantly influences dehydration dynamics and several technological quality parameters of cured meat products, providing useful information for optimizing smoking regimes in traditional and industrial meat processing. Full article

As a representative large shallow freshwater lake in China, Lake Taihu has suffered from persistent cyanobacterial blooms for a long time. Although intensive restoration actions have been carried out and caused visible improvements, the long-term evolution path and inner driving mechanisms of its ecosystem are still not fully made clear. Based on long-term monitoring data during 2011 to 2024, this study aims to characterize temporal dynamics of the aquatic environment, find out key drivers that shape community succession, and offer a scientific foundation for effective lake management. A series of data about hydrometeorological factors, physicochemical water quality indexes, and biological community data was analyzed by using the Mann–Kendall trend test, Pettitt change-point test, Redundancy Analysis, and correlation heatmaps. The results show that the Taihu ecosystem has experienced a notable regime shift in the past 14 years. First, nitrogenous nutrients reacted quickly to external emission reductions, showing a notable monotonic decline; in comparison, Total Phosphorus and Cyanobacterial Density followed a non-linear “U-shaped” path, with a notable shift happening in 2020, which marks the change from a “deterioration phase” to a “recovery phase.” Second, correlation analysis has confirmed that the lake is mainly phosphorus-limited, and a clear “decoupling” between nitrogen levels and algal outbreaks has taken place. Third, the “10-year Fishing Ban” (initiated in 2020), together with sustained phosphorus control, reduced the competitive exclusion of phytoplankton by cyanobacteria, promoting the recent rebound in biodiversity. This study points out that Lake Taihu has passed a tipping point of ecological restoration, shifting from a turbid “algae-dominated state” to a stable state with higher biodiversity. Future management strategies should put first the mitigation of internal phosphorus loading and adaptive management against extreme climatic events. Full article

Floodplain disconnection caused by channel incision and/or levee construction has led to widespread loss of riparian habitats and ecosystem functions globally. Restoring full stream–floodplain connectivity is increasingly promoted, yet evidence of ecological outcomes remains limited. This study evaluates the initial performance of two Stage 0 restoration projects on Whychus Creek, Oregon, which reconnected incised channels to their historical floodplains in 2012 and 2016. We combined pre- and post-restoration vegetation surveys along fixed transects with hydrogeomorphic-based riparian and wetland function assessments and applied quantitative analyses, including Kruskal–Wallis tests, Jaccard correlations, Sorensen similarity indices, and factor analysis, to compare changes in plant assemblages and ecosystem functions across restored, transitional, and unrestored reaches. Our research results indicate that two years post-restoration, the active riparian area expanded 2.5-fold, species richness and structural diversity increased significantly, and riparian and wetland functions such as water storage, sediment retention, and habitat support for fish and amphibians improved markedly. Numbers of anadromous salmonids also increased markedly. This is important as salmon recovery is a regional stream restoration goal. Comparisons with a reach restored six years earlier suggest a positive trajectory toward mature, resilient ecosystems. These findings demonstrate that Stage 0 restoration can rapidly reestablish complex habitat mosaics and enhance ecosystem services critical for biodiversity, water quality, and flood resilience. Practically, this evidence supports process-based restoration strategies that prioritize full floodplain reconnection as a cost-effective approach to reversing long-term ecological degradation. Continued monitoring is essential to guide adaptive management and strengthen the evidence base for the wide-scale implementation of valley-floor wide stream restoration. Full article

Understanding how climate and land use interact to shape freshwater quality remains challenging across heterogeneous river basins. This study analysed monthly citizen-science measurements of nitrate (NO₃), phosphate (PO₄), and turbidity, collected between 2016 and 2024, across seven Italian river basins representing contrasting climatic and land-use contexts. A non-parametric analytical framework combining Kruskal–Wallis tests, aligned rank transform analyses, principal component analysis (PCA), and basin-specific Somers’ D statistics was applied to ordinal concentration data. Significant differences among basins revealed persistent spatial structuring of water-quality regimes. PCA identified two largely independent gradients: a dominant nutrient axis defined by NO₃ and PO₄, and a secondary turbidity axis. Urban and industrial land use aligned with higher nutrient categories, while vegetated landscapes were associated with lower concentrations. Climatic effects were basin specific. Precipitation showed opposing relationships with NO₃, suggesting both mobilisation and dilution processes, whereas temperature was positively associated with PO₄ in several basins and negatively related to NO₃. Turbidity displayed variable links with precipitation and temperature, reflecting hydrological and seasonal controls. Overall, results indicate that land use represents the primary structural driver of nutrient variability, while climatic factors modulate basin-specific responses. The integration of citizen science observations with robust non-parametric approaches provides a scalable framework for detecting environmental drivers and supporting the targeted management of Mediterranean river systems. Full article

Background: Sleep disturbances are highly prevalent, affecting approximately 21% of the European population, with chronic insomnia disorder estimated at 6%. Non-pharmacological alternatives to pharmacotherapy are needed. Sulfurous balneotherapy represents a potential intervention, yet real-world evidence remains limited. Objective: To explore changes in self-reported sleep quality following sulfurous balneotherapy at Terme di Saturnia (Italy). Methods: Retrospective single-arm observational study of 76 participants (mean age 47.3 years, 54% female) undergoing a 7–12-day consecutive balneotherapy cycle with daily sulfurous thermal water immersion sessions (60–90 min/session). The Oviedo Sleep Questionnaire (OSQ) was administered pre- and post-treatment. Participants were stratified by baseline insomnia severity into Group A (OSQ ≥ 22, n = 47) and Group B (OSQ < 22, n = 29). The primary outcome was change in OSQ insomnia score in Group A. Statistical analysis was performed using the Wilcoxon signed-rank test. Results: In Group A, insomnia severity decreased significantly from 26.4 ± 8.3 at baseline to 20.1 ± 7.5 post-treatment (Δ = −6.3, 95% CI: −7.9 to −4.7, p < 0.001, r = 0.54). Sleep satisfaction also improved significantly from 3.2 ± 1.1 to 4.6 ± 1.2 (Δ = +1.4, 95% CI: 1.1–1.7, p < 0.001, r = 0.60). In Group B, no statistically significant changes were observed, consistent with ceiling effects. However, in an open-ended question, 72.4% (21/29; 95% CI: 54.3–85.3) of Group B participants reported enhanced relaxation during the spa stay. Due to the single-arm observational design without control groups, the observed improvements cannot be distinguished from non-specific factors, including the vacation effect, reduced work-related stress, placebo and expectancy responses, regression to the mean, or the effects of warm water immersion itself independent of sulfurous mineral content. Conclusions: This exploratory study documents pre–post improvements in self-reported sleep quality in a cohort undergoing sulfurous balneotherapy during a spa vacation. The absence of control groups and unmeasured confounders precludes causal inferences. Future randomized trials with heated non-mineral water controls are needed to isolate specific therapeutic contributions of sulfurous thermal waters. Full article

Maintaining high quality of water resources is essential to the physical health of mankind and sustainable development of society. Accordingly, it is necessary to detect anomalies in water quality variations, which may be caused by pollution. However, prompt anomaly detection is a challenging task, either demanding a lot of human effort or yielding low accuracy, due to the nonlinear and non-stationary characteristics of water quality data. In this paper, we present an efficient real-time anomaly detection scheme which boosts detection accuracy while mitigating human effort. The scheme takes a prediction–detection–verification approach in which a deep learning prediction model is built from historical data and is used to predict future values. The predicted values are compared with the actual measurements, and the residuals are inspected by a detection model. An alarm is sent to field engineers for verification for each anomaly detected by the detection model, and the verification result is analyzed by the scheme to maintain high prediction and detection accuracy. Experiments on multiple water quality datasets show that the proposed scheme achieves significantly higher recall rates and lower false alarm rates in almost all test scenarios, compared with schemes that do not utilize verification. Full article

Modern agriculture faces increasing pressure to maintain productivity while reducing soil degradation, chemical inputs, and ecological footprint, making biologically based soil-improvement strategies highly relevant. This study examined whether microbial inoculation, combined with conservation tillage practices (loosening and no-tillage), can enhance soil physical quality during pea (Pisum sativum) cultivation in an agroecological market garden in Hungary. A 2 × 2 factorial field experiment was established, testing tillage (loosening vs. no-tillage) and microbial inoculation (with vs. without) in a randomized design with three replications per treatment (12 plots total). A single microbial application was performed prior to planting using a consortium of Rhizobium spp., Ensifer spp., Pseudomonas spp., and Bacillus spp. The research focused on (I) soil penetration resistance, (II) soil moisture dynamics, and (III) infiltration capacity, with most parameters measured before and after planting. Microbial inoculation significantly reduced penetration resistance under both tillage systems and influenced soil moisture behavior, indicating improved soil structure and water retention. Infiltration rate did not change significantly within the study period. Overall, the results demonstrate that microbial amendments can rapidly improve key soil physical properties, offering a practical, nature-based strategy for resilient, low-input farming systems. Full article

Background/Objectives: Quantifying cerebrovascular reactivity (CVR) is essential for stroke risk assessment in patients with Moyamoya Angiopathy (MMA). Breath-hold functional MRI (bh-fMRI) is an easily implementable method to assess CVR. Determining the optimal time period of the BOLD signal for analyzing the best bh-fMRI data quality remains an open question. Methods: A retrospective analysis of 46 bh-fMRI data sets of MMA patients was conducted. The percentage BOLD signal changes were evaluated at different time periods (time point of the maximum cerebellar signal peak (TP_{cereb. max}) ± 0 s, TP_{cereb. max} ± 1 s, TP_{cereb. max} ± 2 s, TP_{cereb. max} ± 3 s, TP_{cereb. max} ± 4 s, TP_{cereb. max} ± 5 s). The agreement between the bh-fMRI maps and [¹⁵O]water PET maps was independently and consensually rated on a 4-point Likert scale (1 = poor, 2 = moderate, 3 = good, 4 = excellent) and compared with the Friedman test. The inter-rater agreement was calculated separately for each time period using quadratic weighted Cohen’s kappa κ_w. Results: The selected time period had a significant impact on the agreement between bh-fMRI and [¹⁵O]water PET (χ²(5) = 79.448, p < 0.001, W = 0.345). Short time periods of TP_cereb.max ± 0 s or TP_cereb.max ±1 s demonstrated the highest level of concordance between bh-fMRI and [¹⁵O]water PET (median = 3.5 for TP_cereb.max ± 0 s; median = 3 for TP_cereb.max ± 1 s, modus = 4 in both cases). The agreement between bh-fMRI and [¹⁵O]water PET was significantly higher when evaluating time periods of TP_cereb.max ± 0 s than when evaluating all time periods ≥ TP_{cereb. max} ± 2 s. The inter-rater agreement was almost perfect for all time periods except one (TP_{cereb. max} ± 1 s). Conclusions: Short time periods should be selected when evaluating CVR with bh-fMRI, as this study suggests a high level of validity in comparison to [¹⁵O]water PET. Full article

Satellite remote sensing increasingly supports water quality monitoring, yet the temporal transferability of machine learning (ML) models remains insufficiently tested, particularly in coastal shallow lakes subject to hydrological variability. This study evaluates the predictive robustness of satellite-based ML models for electrical conductivity (EC), turbidity (TUR), water temperature (WT), and dissolved oxygen (DO) in Vrana Lake, Croatia. A total of 409 in situ measurements collected during 2023–2024 and 2025 were paired with Sentinel-2 and Landsat 8–9 imagery. Pearson, Spearman, and Kendall correlation analyses were applied for parameter-specific band selection using original, inverse, quadratic, and logarithmic feature transformations. Seventeen regression algorithms were evaluated under six training–testing split strategies, including strict temporal projection. WT exhibited high robustness (R² ≈ 0.90 under temporal projection) due to its strong dependence on thermal bands, while DO achieved moderate temporal stability (R² = 0.51) using log-transformed predictors. EC and TUR demonstrated substantial performance degradation under temporal separation (R² = 0.14 and −4.62, respectively), reflecting sensitivity to distribution shifts. For parameters showing sufficient stability, interpretable band-based retrieval equations were derived using the most strongly correlated spectral predictors. These findings highlight the importance of temporally structured validation and demonstrate that model complexity does not guarantee operational robustness in shallow, dynamically evolving lake systems. Full article

This study investigated the effects of black chokeberry (Aronia melanocarpa) (Michx.) Elliott powder addition (0.1–0.4%) on the quality attributes of cheese snacks produced from a blended camel–goat–cow milk base (60:20:20) using microwave vacuum drying. The snacks were evaluated for chemical composition, colour parameters, texture profile and water activity in order to assess how black chokeberry incorporation influences their physicochemical and sensory-related properties. Chemical analysis showed that the high protein content of the dried cheese matrix was maintained across all formulations, while fat, carbohydrate and energy values varied within a relatively narrow range, without a clear dose-dependent trend attributable solely to black chokeberry addition. Black chokeberry powder induced concentration-dependent colour changes, with decreased lightness and increased redness and overall colour difference, indicating visually noticeable shifts that may enhance product differentiation. Texture profile analysis revealed a significant reduction in fracturability at intermediate inclusion levels, suggesting a less brittle structure, whereas other texture parameters showed non-linear but statistically non-significant variations due to limited replication. All snacks exhibited very low water activity, consistent with shelf-stable, low-moisture products. A preliminary sensory test with untrained assessors indicated that black chokeberry-enriched snacks, particularly at around 0.3%, were generally well accepted, although the small panel size limits the strength of these conclusions. Overall, the findings suggest that small additions of black chokeberry powder can be used to develop visually attractive, high-protein cheese snacks with promising textural and sensory characteristics, while more comprehensive studies are needed to characterise their antioxidant properties, detailed nutritional profile and long-term stability. Full article

Precise water quality forecasting is vital for sustainable resource management and public health, especially in semi-arid environments. This study investigates the predictive capabilities of ten Machine Learning (ML) algorithms using a dataset of 308 drinking water samples collected from various districts in Şanlıurfa Province, Türkiye. We evaluated ten predictive models, including Support Vector Regressor (SVR) and Extreme Gradient Boosting (XGBoost), both integrated with dimensionality reduction and hyperparameter optimization. Nineteen physicochemical and microbiological parameters—Temperature, chlorine (Cl⁻), pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), nitrite (NO₂⁻), nitrate (NO₃⁻), ammonium (NH₄⁺), sulfate (SO₄²⁻), Free Chlorine (Cl₂), calcium (Ca²⁺), magnesium (Mg²⁺), sodium (Na⁺), potassium (K⁺), fluoride (F⁻), trihalomethanes (THMs), Escherichia coli, Enterococci, Total Coliform—were used as input features. The dataset was split into training (75%) and testing (25%) subsets, and model performance was assessed through 10-fold cross-validation and hold-out testing procedures. To improve model generalization and mitigate the effects of class imbalance, we implemented the Adaptive Synthetic Sampling (ADASYN) technique. ML algorithms were evaluated using standard regression metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). The LSTM model optimized using Randomized Search outperformed the SVR and XGBoost models, demonstrating the highest accuracy and generalization capability, as evidenced by the superior R² value of 0.999 following ADASYN balancing and the lowest RMSE (1.206). These findings underscore the effectiveness of the LSTM framework in modeling the complex variance of the Weighted Arithmetic Water Quality Index (WAWQI). The findings of this study are expected to support future water quality monitoring strategies, inform policy development, and contribute to sustainable water resource management in arid and semi-arid regions. Full article

Circular pipe-jacking construction in gravel strata faces significant technical challenges, including high frictional resistance, elevated permeability, and susceptibility to collapse. Optimizing the formulation of thixotropic slurry is crucial for improving the construction quality and efficiency of such projects. This study, based on the Ruyang Water Supply Project of the North Main Canal in the Qianping Irrigation Area, Henan Province, China, systematically investigated slurry formulation using bentonite, soda ash, sodium carboxymethyl cellulose (CMC), polyacrylamide (PAM), and shell powder as raw materials. An orthogonal experimental design was employed to optimize the mix proportions, and the friction-reduction performance was validated through drag-friction model tests. The results indicate that the optimal slurry formulation is: bentonite 8%, soda ash 0.3%, CMC 0.2%, PAM 0.15%, shell powder 4%, and water 87.35%. This formulation exhibits excellent fluidity and thixotropy, facilitating the formation of a stable slurry film. Consequently, the friction coefficient between concrete specimens and gravel soil was reduced by 35.6%. The inclusion of shell powder significantly enhanced the slurry’s cohesiveness and improved the anti-seepage capacity of the surrounding stratum due to its filling effect. The optimized thixotropic slurry effectively mitigates frictional resistance during pipe jacking in gravel strata and enhances the formation’s resistance to collapse. The findings of this study provide a viable technical reference for pipe-jacking projects under similar geological conditions. Full article

Postharvest dehydration is a major determinant of cucumber freshness and marketability, yet early reductions in internal water status are difficult to detect using conventional quality assessment methods. This study presents a non-destructive, physiology-informed deep learning approach that links cucumber surface color and texture patterns to internal hydration level for automated freshness classification. A time-resolved dataset comprising 4160 RGB images of cucumber fruits was paired with gravimetrically determined relative water content (RWC), used as an objective indicator of internal hydration status. Based on RWC, fruits were classified into four freshness categories: Very Fresh (≥98%), Moderately Fresh (95–98%), Low Freshness (90–95%), and Spoiled (<90%). A custom convolutional neural network (CNN) was trained using standardized RGB images and evaluated on an independent test set. The model achieved an overall classification accuracy of 91.35% and a Cohen’s Kappa coefficient of 0.875, indicating strong agreement between predicted and actual freshness classes. Classification performance was highest for the extreme freshness states, with F1-scores exceeding 0.94 for Very Fresh and Spoiled fruits, while intermediate classes showed greater overlap, reflecting the gradual nature of postharvest water loss. Model interpretability analyses revealed that the CNN consistently focused on physiologically meaningful surface color and texture features associated with dehydration. Overall, these findings highlight the potential of physiology-informed deep learning to advance non-destructive freshness assessment in cucumbers, offering a realistic pathway toward hydration-based sorting, improved shelf-life management, and intelligent quality monitoring in modern postharvest supply chains. Full article

Early-stage fungal contamination in maize kernels is difficult to identify visually and it can cause severe quality and safety risks during storage and transportation. Short-wave infrared (SWIR) hyperspectral imaging offers a rapid, non-destructive approach by capturing chemical information related to water, proteins, and lipids. This study investigates the early detection and classification of Gibberella zeae contamination in maize kernels using SWIR hyperspectral imaging combined with machine learning. Two maize varieties were artificially inoculated and cultured under controlled conditions, followed by hyperspectral data collection over six contamination stages. Various preprocessing techniques including standard normal variate (SNV), second derivative (SD), multiplicative scatter correction (MSC), and derivatives were evaluated to enhance data quality. Feature wavelength selection was performed using successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE), significantly reducing redundancy and improving classification performance. Multiple models, including linear discriminant analysis (LDA), multilayer perceptron (MLP), support vector machine (SVM), a convolutional neural network (CNN), long short-term memory (LSTM) network, and a hybrid architecture Transformer that integrated a CNN, a LSTM network, and a Transformer (abbreviated as CLT), were constructed for both binary (healthy vs. contaminated) and multiclass classification tasks. Specifically, the multiclass task consisted of six contamination stages corresponding to contamination time from Day 0 to Day 5. The best binary classification task accuracy of 100% was achieved using SNV-preprocessed data with the MLP model. For multiclass classification task, the SD-preprocessed LDA model reached a test accuracy of 92.56%. Combined with appropriate preprocessing, feature selection and modeling, these results demonstrate that hyperspectral imaging is a powerful tool for the non-destructive, early-stage identification of fungal contamination in maize kernels, offering strong support for food safety and quality monitoring. Full article

Similar to other municipal facilities, landfills are a substantial source of emissions of various biological pollutants. Numerous sustainability challenges result from the extremely high variability of emissions of harmful biological agents, which necessitates precise detection of microbiological emissions from these municipal facilities. This study aimed to assess whether a municipal waste landfill impacts indicator microorganisms and bacterial endotoxins occurring in soils within the landfill’s zone of influence. The research was conducted directly at the landfill site and in the surrounding area. Soil samples were collected monthly from eight sites over three years. Microbiological analyses included determination of total Salmonella counts and bacteria of the coliform group, Clostridium spp., Clostridium perfringens, and bacterial endotoxin concentrations. Results revealed a significant effect of the landfill on soil sanitary quality, indicating that adverse impacts depended mainly on the distance from the active waste sector of the landfill. The results also confirmed the usefulness of bacterial endotoxins as indicators of soil contamination with microorganisms within the municipal landfill and surroundings. Parametric statistical analyses effectively characterised contamination levels, and the Newman–Keuls multiple comparison test proved to be a rapid and reliable tool for assessing exceedances of established sanitary standards. Findings indicate that fresh waste is a critical source of microbiological contamination in soils, and they emphasise the value of combined microbial and endotoxin monitoring for sustainable landfill environmental assessment and management. While the current study focuses on soil contamination, future research should evaluate the impact of landfill on indicator microorganisms and bacterial endotoxins in air and water. Full article