Search Results (9,352)

The article presents a long-term assessment of the surface water quality of six small rivers in the East Kazakhstan region (Breksa, Tikhaya, Ulba, Glubochanka, Krasnoyarka, and Oba) based on hydrochemical monitoring data from the Kazhydromet State Enterprise for the period 2017–2024. A unified water quality classification system was applied, along with statistical methods, including multiple linear regression. The Glubochanka and Krasnoyarka rivers were identified as the most polluted (reaching classes 4–5), with multiple exceedances of Zn (up to 2.96 mg/dm³), Cd (up to 0.8 mg/dm³), and Cu (up to 0.051 mg/dm³). The most stable and highest water quality was recorded in the Oba River, where from 2021 to 2024, water consistently corresponded to Class 2. Regression models of water quality class as a function of time and annual precipitation were constructed to assess the influence of climatic factors. Statistical analysis revealed no consistent linear correlation between average annual precipitation and water quality (correlation coefficients ranging from −0.49 to +0.37), indicating a complex interplay between climatic and anthropogenic factors. Significant relationships were found for the Breksa (R² = 0.903), Glubochanka (R² = 0.602), and Tikhaya (R² = 0.555) rivers, suggesting an influence of temporal and climatic factors on water quality. In contrast, the Oba (R² = 0.130), Ulba (R² = 0.100), and Krasnoyarka (R² = 0.018) rivers exhibited low coefficients, indicating the predominance of other, likely local, sources of pollution. It was found that summer periods are characterized by the highest pollution due to low water flow, while episodes of acid runoff occur in spring. A decrease in pH below 7.0 was first recorded in 2023–2024 in the Ulba and Tikhaya rivers. Forecasts to 2030 suggest relative stability in water quality under current climatic conditions; however, by 2050, the risk of water quality deterioration is expected to rise due to increased precipitation and extreme weather events. This study presents, for the first time, a systematic long-term analysis of small rivers in the East Kazakhstan region, offering deeper insight into the dynamics of surface water quality and providing a scientific foundation for developing adaptive strategies for the protection and sustainable use of water resources under climate change and anthropogenic pressure. The results emphasize the importance of prioritizing rivers with high variability in water quality for regular monitoring and the development of adaptive conservation measures. The research holds strong applied significance for shaping a sustainable water use strategy in the region. Full article

The worldwide air pollution situation reveals significant environmental challenges. In addition to being a major contributor to the deterioration of air quality, particulate matter (PM) is also an important factor affecting the performance of solar energy systems given its ability to decrease light transmission to solar panels. As part of our research, the present investigation involves monitoring concentrations of PM using a high-performance optical instrument, the in situ calibration protocol of which is described in detail. For the city of Rabat, observations revealed significant variations in concentrations between day and night, with peaks observed around 8 p.m. correlating with high relative humidity and low wind speeds, and the highest levels recorded in February with a monthly average value reaching 75 µm/m³. In addition, an experimental protocol was set up for an analysis of the elemental composition of particles in the same city using SEM/EDS, providing a better understanding of their morphology. To assess the impact of meteorological variables on PM concentrations in two distinct climatic environments, a database from the city of Marrakech for the year 2024 was utilized. Overall, the distribution of PM values during this period did not fluctuate significantly, with a monthly average value not exceeding 45 µm/m³. The random forest method identified the most influential variables on these concentrations, highlighting the strong influence of the type of environment. The findings provide crucial information for the modeling of solar installations’ soiling and for improving understanding of local air quality. Full article

This study investigates the spatial relationship between population distribution and tree cover loss in Nepal from 2000 to 2020, using satellite-based forest cover and population data along with statistical and geospatial analysis. Two statistical methods—linear regression (LR) and Geographically Weighted Regression (GWR)—were used to assess the influence of population on forest cover change. The correlation between total population and forest loss at the national level suggested little to no direct impact of population growth on forest loss. However, sub-national analysis revealed localized forest degradation, highlighting the importance of spatial and regional assessments to uncover land cover changes masked by national trends. While LR showed a weak national-level correlation, GWR revealed substantial spatial variation, with the coefficient of determination values increasing from 0.21 in 2000 to 0.59 in 2020. In some regions, local R² exceeded 0.75 during 2015 and 2020, highlighting emerging hotspot clusters where population pressure is strongly linked to deforestation, especially along major infrastructure corridors. Using very high-resolution spatial data enabled pixel-level analysis, capturing fine-scale deforestation patterns, and confirming hotspot accuracy. Overall, the findings emphasize the value of spatially explicit models like GWR for understanding human–environment interactions guiding targeted land use planning to balance development with environmental sustainability in Nepal. Full article

As a commercially valuable aquaculture species, rainbow trout (Oncorhynchus mykiss) urgently require solutions to growth inhibition associated with reproductive development. To elucidate the function of the cell cycle regulator Cyclin E genes (CCNE1 and CCNE2) in this process, we cloned the genes and analyzed their relative expression across various tissues and gonadal developmental stages. Using RNA interference (RNAi) and overexpression in RTG2 cells, we examined the effects of CCNE on cell viability, proliferation, and meiotic gene expression. Results showed that the open reading frame lengths of CCNE1 and CCNE2 were 1230 bp and 1188 bp, encoding 408 and 395 amino acids, respectively. Both proteins contain two conserved cyclin boxes, exhibit high structural similarity, and are phylogenetically most closely related to Oncorhynchus tshawytscha and Oncorhynchus kisutch. Expression and localization analyses revealed that CCNE1 was highly expressed in the ovary, while CCNE2 was highly expressed in the testis. Both proteins were expressed during fertilized egg development and key gonadal stages (at 13, 21, and 35 months post-fertilization). CCNE expression positively correlated with RTG2 cell viability and proliferation, with immunofluorescence confirming that CCNE is localized in the nucleus. Knockdown or overexpression of CCNE induced the differential expression of reproductive-related genes and key meiotic regulators. These findings suggest that CCNE1 and CCNE2 balance meiosis and gamete development through specific regulatory mechanisms, and their dysregulation may be a key factor underlying meiosis inhibition and reproductive development abnormalities. Full article

The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm³/cm³, 0.019 cm³/cm³, and 0.034 cm³/cm³, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm³/cm³ (ASCAT) and 0.0662 cm³/cm³ (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product. Full article

The urban heat island (UHI) effect significantly impacts urban environments, particularly along roads, a phenomenon known as urban linear heat (UHI_ULI). Numerous factors contribute to roads influencing the UHI_ULI; however, effective mitigation strategies remain a challenge. This study examines the relationship between canopy cover percentage, normalized difference vegetation index, land use types, and three road typologies (local, regional, and state) with land surface temperature. This study is based on data from the city of Adelaide, Australia, using spatial analysis, and statistical modelling. The results reveal strong negative correlations between land surface temperature and both canopy cover percentage and normalized difference vegetation index. Additionally, land surface temperature tends to increase with road width. Among land use types, land surface temperature varies from highest to lowest in the order of parkland, industrial, residential, educational, medical, and commercial areas. Notably, the combined influence of the road typology and land use produces varying effects on land surface temperature. Canopy cover percentage and normalized difference vegetation index consistently serve as dominant cooling factors. The results highlight a complex interplay between built and natural environments, emphasizing the need for multi-factor analyses and a framework based on the local climate and the type of roads (local, regional, and state) to effectively evaluate UHI_ULI mitigation approaches. Full article

The arithmetic average of the first n primes, ${\overline{p}}_n={\displaystyle \frac{1}{n}}{\sum }_{i=1}^n{p}_i$, exhibits very many interesting and subtle properties. Since the transformation from $p_n\to {\overline{p}}_n$ is extremely easy to invert, $p_n=n{\overline{p}}_n-(n-1){\overline{p}}_{n-1}$, it is clear that these two sequences $p_n⟷{\overline{p}}_n$ must ultimately carry exactly the same information. But the averaged sequence ${\overline{p}}_n$, while very closely correlated with the primes, (${\overline{p}}_n\sim {\displaystyle \frac{1}{2}}{p}_n$), is much “smoother” and much better behaved. Using extensions of various standard results, I shall demonstrate that the prime-averaged sequence ${\overline{p}}_n$ satisfies prime-averaged analogues of the Cramer, Andrica, Legendre, Oppermann, Brocard, Fourges, Firoozbakht, Nicholson, and Farhadian conjectures. (So these prime-averaged analogues are not conjectures; they are theorems). The crucial key to enabling this pleasant behaviour is the “smoothing” process inherent in averaging. While the asymptotic behaviour of the two sequences is very closely correlated, the local fluctuations are quite different. Full article

Sediment accumulation in irrigation channels poses a significant challenge to water resource management, impacting hydraulic efficiency and agricultural sustainability. This study introduces an innovative multidisciplinary framework that integrates advanced image analysis (FIJI/ImageJ 1.54p), statistical validation (RStudio), and vector field modeling with a novel Sinusoidal Morphodynamic Bedload Transport Equation (SMBTE) to predict sediment deposition patterns with high precision. Conducted along the Malacatos River in La Tebaida Linear Park, Loja, Ecuador, the research captured a natural sediment transport event under controlled flow conditions, transitioning from pressurized pipe flow to free-surface flow. Observed sediment deposition reduced the hydraulic cross-section by approximately 5 cm, notably altering flow dynamics and water distribution. The final SMBTE model (Model 8) demonstrated exceptional predictive accuracy, achieving RMSE: 0.0108, R²: 0.8689, NSE: 0.8689, MAE: 0.0093, and a correlation coefficient exceeding 0.93. Complementary analyses, including heatmaps, histograms, and vector fields, revealed spatial heterogeneity, local gradients, and oscillatory trends in sediment distribution. These tools identified high-concentration sediment zones and quantified variability, providing actionable insights for optimizing canal design, maintenance schedules, and sediment control strategies. By leveraging open-source software and real-world validation, this methodology offers a scalable, replicable framework applicable to diverse water conveyance systems. The study advances understanding of sediment dynamics under subcritical (Fr ≈ 0.07) and turbulent flow conditions (Re ≈ 41,000), contributing to improved irrigation efficiency, system resilience, and sustainable water management. This research establishes a robust foundation for future advancements in sediment transport modeling and hydrological engineering, addressing critical challenges in agricultural water systems. Full article

Chemically strengthened glass is widely used for its remarkable fracture strength, mechanical performance, and scratch resistance. Assessing its hardness is crucial to evaluating improvements from chemical tempering. However, conventional methods like Vickers hardness tests are destructive, altering the sample surface. This study presents a novel, rapid, and nondestructive testing (NDT) approach by correlating the nonlinear refractive index (n₂) with surface hardness. Using ultrafast laser pulses, we measured the n₂ cross-section via the nonlinear ellipse rotation (NER) signal in Gorilla^®-type glass subjected to ion exchange (Na⁺ by K⁺). A microscope objective lens provided a penetration resolution of ≈5.5 μm, enabling a localized NER signal analysis. We demonstrate a correlation between the NER signal and hardness, offering a promising pathway for advanced, noninvasive characterization. This approach provides a reliable alternative to traditional destructive techniques, with potential applications in industrial quality control and material science research. Full article

The rapid growth of construction and demolition waste (CDW) presents significant challenges to sustainable urban development, particularly in densely populated regions, such as the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Despite substantial disparities in CDW management (CDWM) performance across cities, the key influencing factors and effective strategies remain underexplored, limiting the development of localized and evidence-based CDWM solutions. Therefore, this study formulated three hypotheses concerning the relationships among CDWM performance, city attributes, and governance capacity to identify the key determinants of CDWM outcomes. These hypotheses were tested using clustering and correlation analysis based on data from 11 GBA cities. The study identified three distinct city clusters based on CDW recycling, reuse, and landfill rates. Institutional support and recycling capacity were key determinants shaping CDWM performance. CDW governance capacity acted as a mediator between city attributes and performance outcomes. In addition, the study examined effective strategies and institutional measures adopted by successful GBA cities. By highlighting the importance of institutional and capacity-related factors, this research offers novel empirical insights into CDW governance in rapidly urbanizing contexts. Full article

Background/Objectives: The aim of this study was to investigate the relationship between the PRIMARY score derived from [⁶⁸Ga]Ga-PSMA PET/CT and key clinical and pathological parameters of prostate cancer aggressiveness, including the PSA level, ISUP Grade Group, and D’Amico risk classification, in patients with biopsy-proven prostate cancer. A secondary aim was to evaluate the interobserver agreement of the PRIMARY score in routine clinical practice. Methods: This retrospective analysis included 51 patients with histopathologically confirmed prostate adenocarcinoma who underwent [⁶⁸Ga]Ga-PSMA PET/CT imaging for staging. PRIMARY scores were determined based on the intraprostatic uptake pattern, intensity, and zonal localization. These scores were compared with PSA levels, ISUP GG, D’Amico risk classification, and histopathological features such as the cribriform pattern, intraductal carcinoma, perineural invasion, extraprostatic extension, and lymphovascular invasion. The PRIMARY scores were independently assigned by a total of three nuclear medicine physicians, and interobserver agreement was calculated using Fleiss’ kappa analysis. Results: Significant associations were found between the PRIMARY scores and the PSA level, ISUP Grade Group, and D’Amico risk classification. The most prevalent score was PRIMARY 5 (54.9%), which was significantly associated with ISUP GG 5 and the high-risk category in D’Amico classification. Among patients with PRIMARY Score 2, a substantial proportion (64.7%) had ISUP GG ≥ 3, and 58.8% were in the high-risk group, highlighting the limitations of binary PRIMARY classification. No statistically significant correlations were found between the PRIMARY scores and specific histopathologic features. Interobserver agreement was excellent (κ = 0.833). Conclusions: The PRIMARY score demonstrates high reproducibility and clinical relevance in stratifying prostate cancer aggressiveness. However, the findings challenge the reliability of binary classifications, particularly for patients with Score 2, who may still harbor high-grade disease. Integrating imaging-based scores with clinical and histopathological data is essential, particularly for accurate staging and decision-making regarding active surveillance. Full article

Purpose: To investigate if radiomics signatures generated from longitudinal CT scans could predict IRE treatment effectiveness and outcomes in patients with locally advanced pancreatic cancer (LAPC). Methods: A cohort of 50 (60% male, mean [SD] age 60.7 [8.7] years) LAPC patients treated with IRE were retrospectively selected. Preoperative and 12-week follow-up CT scans were reviewed by two radiologists for tumor segmentation. A total of 2078 features were extracted: shape (n = 16), texture (n = 68), filter (n = 1892), intensity (n = 18), and local texture (n = 84). Principal component analysis (PCA) was applied to develop composite radiomics features. Composite signatures and clinically relevant radiomics features were correlated with time to recurrence (TTR), time to local recurrence (TTLR), time to distant recurrence (TTDR), recurrence-free survival (RFS) and overall survival (OS). Risk stratification performance was evaluated using hazard ratios (HRs), and significance was evaluated using the log-rank test. Results: Statistically significant separation between high and low patient TTR risk groups was observed in the following: gray-level co-occurrence matrix (HR = 2.65, p < 0.01, median survival difference = 6.6 mo); composite radiomics features derived from the following feature groups: all radiomics features (HR = 2.27, p = 0.01, median survival difference = 6.4 mo), intensity features (HR = 3.13, p < 0.01, median survival difference = 14.0 mo), and filter features (HR = 2.27, p = 0.01, median survival difference = 6.4 mo). Conclusions: Pre-treatment radiomics signatures were significantly associated with LAPC patient outcomes. The observed correlations used pre-treatment CT scans, implying that the features predict the individual risk of disease recurrence. Full article

This study investigates whether combining singular value decomposition with wavelet analysis can provide new insights into the spatiotemporal complementarity between wind turbine sites, surpassing previous findings. Earlier studies predominantly relied on various forms of correlation analysis to quantify complementarity. While correlation analysis offers a way to compute global metrics summarizing the relationship between entire time series, it inherently overlooks localized and time-specific patterns. The proposed approach overcomes these limitations by enabling the identification of spatially explicit and temporally resolved complementarity patterns across a large number of wind turbine sites in the study area. Because complementarity information is derived from orthogonal components obtained through singular value decomposition of a wind power density matrix, there is no need to adjust for phase shifts between sites. Moreover, the complementary contributions of these components to overall wind power density are expressed in watts per square meter, directly reflecting the magnitude of the analyzed data. This facilitates a site-specific, complementarity-optimized strategy for further wind energy expansion. Full article

Methylation is a biochemical process involving the addition of methyl groups to proteins, lipids, and nucleic acids (both DNA and RNA). DNA methylation predominantly occurs on cytosine and adenine nucleobases, and the resulting products—most frequently 5-methylcytosine and N⁶-methyladenine epigenetic marks—can significantly influence gene activity at the affected genomic sites without modifying the DNA sequence called nucleotide order. Various environmental factors can alter the DNA methylation pattern. Among these, methyl donor micronutrients, such as specific amino acids, choline, and several B vitamins (including folate, pyridoxine, thiamine, riboflavin, niacin, and cobalamin), primarily regulate one-carbon metabolism. This molecular pathway stimulates glutathione synthesis and recycles intracellular methionine. Glutathione plays a pivotal role during oocyte activation by protecting against oxidative stress, whereas methionine is crucial for the production of S-adenosyl-L-methionine, which serves as the universal direct methyl donor for cellular methylation reactions. Because local DNA methylation patterns at genes regulating fertility can be inherited by progeny for multiple generations even in the absence of the original disrupting factors to which the parent was exposed, and DNA methylation levels at specific genomic sites highly correlate with age and can also be passed to offspring, nutrition can influence reproduction and life span in a transgenerational manner. Full article

Image dehazing technology is a crucial component in the fields of intelligent transportation and autonomous driving. However, most existing dehazing algorithms only process images in the spatial domain, failing to fully exploit the rich information in the frequency domain, which leads to residual haze in the images. To address this issue, we propose a novel Frequency-domain Information Guided Symmetric Dual-branch Dehazing Network (FIGD-Net), which utilizes the spatial branch to extract local haze features and the frequency branch to capture the global haze distribution, thereby guiding the feature learning process in the spatial branch. The FIGD-Net mainly consists of three key modules: the Frequency Detail Extraction Module (FDEM), the Dual-Domain Multi-scale Feature Extraction Module (DMFEM), and the Dual-Domain Guidance Module (DGM). First, the FDEM employs the Discrete Cosine Transform (DCT) to convert the spatial domain into the frequency domain. It then selectively extracts high-frequency and low-frequency features based on predefined proportions. The high-frequency features, which contain haze-related information, are correlated with the overall characteristics of the low-frequency features to enhance the representation of haze attributes. Next, the DMFEM utilizes stacked residual blocks and gradient feature flows to capture local detail features. Specifically, frequency-guided weights are applied to adjust the focus of feature channels, thereby improving the module’s ability to capture multi-scale features and distinguish haze features. Finally, the DGM adjusts channel weights guided by frequency information. This smooths out redundant signals and enables cross-branch information exchange, which helps to restore the original image colors. Extensive experiments demonstrate that the proposed FIGD-Net achieves superior dehazing performance on multiple synthetic and real-world datasets. Full article