Search Results (430)

Background: Intermittent fasting (IF) can improve inflammatory status, but its effects may be dependent on the mode of fasting. Objectives: We performed a systematic review with pairwise and network meta-analyses to investigate the effects of different modes of IF on inflammatory markers in adults. Methods: Three database searches were conducted, including PubMed, Scopus, and Web of Science, from inception to June 2024. The searches used two keyword groups: “intermittent fasting” and “inflammatory markers”. Randomized and non-randomized trials investigating any IF mode on inflammatory markers, including interleukin (IL)-6, tumor necrosis factor (TNF)α, C-reactive protein (CRP), leptin, and adiponectin, were included. Standardized mean differences (SMDs) were calculated using random effects models for both analyses. Results: A total of 21 studies (839 participants) were included. Compared with controls, IF reduced TNF-α [SMD: −0.31, p = 0.009], CRP [SMD: −0.19, p = 0.04], and leptin [SMD: −0.57, p = 0.005] but did not significantly affect IL-6 or adiponectin. Among the IF modes, time-restricted feeding (TRF) showed the largest reduction in TNF-α [−0.39, p = 0.001]. TRF had the highest probability ranking for changes in IL-6, TNF-α, leptin, and adiponectin; however, the effects on IL-6 and adiponectin were not statistically significant. The 5:2 diet ranked highest for CRP. Conclusions: IF may be an effective dietary therapy for improving some inflammatory markers, with effects potentially influenced by the mode of IF. TRF had the highest rankings across multiple markers, though the findings were not uniformly significant. Additional longer-term trials are needed to fully elucidate the anti-inflammatory potential of IF. Full article

This study investigates the impact of outsiders’ demand for more information (or transparency) on corporate social responsibility (CSR) initiatives. Drawing on a dataset of U.S. companies from 2010 to 2023, CSR performance is measured using ASSET4 ratings, while CSR disclosure levels are captured through the number of words and sentences in reports. Utilizing within-industry and -firm OLS regressions, our analyses reveal a positive relationship between the demand for more information and future CSR investments, showing that firms with higher demand for information not only enhance their CSR performance but also expand the length of their CSR reports. These results suggest that increased pressures for information encourage organizations to engage more deeply with social responsibility, resulting in more robust CSR activities and more comprehensive reporting practices. This study contributes to the existing literature by highlighting the strong predictive role of outsiders’ demand for more information in promoting CSR investment and disclosure, and by offering important insights for policymakers and practitioners on fostering corporate responsibility through enhanced transparency. Full article

The genus Macgarvieomyces (Magnaporthales, Sordariomycetes, Ascomycota) currently includes three species, which are associated with leaf spots on plants belonging to the Cyperaceae and Juncaceae families and are known only in Europe and New Zealand. During a comprehensive survey conducted between 2020 and 2022 targeting host plants from these families across various regions of Iran, six novel species of Macgarvieomyces—M. caspica, M. cyperi, M. junci-acuti, M. juncigenus, M. salkadehensis, and M. schoeni—were uncovered. These species were identified based on detailed morphological characterizations and multi-locus phylogenetic analyses using ITS-rDNA, RPB1, ACT, and CAL gene regions. This study provides thorough descriptions and illustrations of the new taxa, including information on their morphology, ecological preferences, and geographic distribution. The phylogenetic relationships among the species are also discussed. This work significantly enhances the known diversity of Macgarvieomyces associated with Cyperaceae and Juncaceae, expands their geographic distribution, and underscores the value of integrating morphological and molecular data in fungal taxonomy; accordingly, the findings of this study lay the groundwork for future ecological and evolutionary studies of this genus. Full article

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. Although the influence of natural factors on groundwater is well-recognized, the impact of human activities, despite being a major contributor to its change, has been less explored due to the challenges in measuring such effects. To address this gap, our study employed an integrated approach using remote sensing and the Google Earth Engine (GEE) cloud-free platform to analyze the effects of various anthropogenic factors such as built-up areas, cropland, and surface water on groundwater storage in the Lake Urmia Basin (LUB), Iran. Key anthropogenic variables and groundwater data were pre-processed and analyzed in GEE for the period from 2000 to 2022. The processes linking these variables to groundwater storage were considered. Built-up area expansion often increases groundwater extraction and reduces recharge due to impervious surfaces. Cropland growth raises irrigation demand, especially in semi-arid areas like the LUB, leading to higher groundwater use. In contrast, surface water bodies can supplement water supply or enhance recharge. The results were then exported to XLSTAT software2019, and statistical analysis was conducted using the Mann–Kendall (MK) non-parametric trend test on the variables to investigate their potential relationships with groundwater storage. In this study, groundwater storage refers to variations in groundwater storage anomalies, estimated using outputs from the Global Land Data Assimilation System (GLDAS) model. Specifically, these anomalies are derived as the residual component of the terrestrial water budget, after accounting for soil moisture, snow water equivalent, and canopy water storage. The results revealed a strong negative correlation between built-up areas and groundwater storage, with a correlation coefficient of −1.00. Similarly, a notable negative correlation was found between the cropland area and groundwater storage (correlation coefficient: −0.85). Conversely, surface water availability showed a strong positive correlation with groundwater storage, with a correlation coefficient of 0.87, highlighting the direct impact of surface water reduction on groundwater storage. Furthermore, our findings demonstrated a reduction of 168.21 mm (millimeters) in groundwater storage from 2003 to 2022. GLDAS represents storage components, including groundwater storage, in units of water depth (mm) over each grid cell, employing a unit-area, mass balance approach. Although storage is conceptually a volumetric quantity, expressing it as depth allows for spatial comparison and enables conversion to volume by multiplying by the corresponding surface area. Full article

This study conducts a comprehensive and systematic literature review, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, to investigate the impacts of climate change on closed lake systems in Central Asia, with a specific focus on Lakes Balkhash, Issyk-Kul, and Urmia. Based on a detailed analysis of 74 peer-reviewed studies published between 2000 and 2025, this review identifies key thematic patterns and bibliometric trends in the literature. Findings reveal that most studies emphasize hydrological stress, glacier retreat, and an increasing drought frequency, while institutional adaptation and transboundary governance mechanisms remain underdeveloped and inconsistently implemented. National-level adaptation strategies vary considerably, with Kazakhstan and Uzbekistan showing a relatively higher engagement, though rarely supported by enforceable cross-border agreements. This review also highlights the limited participation of local research institutions and insufficient empirical validation of policy measures. The bibliometric analysis indicates that most high-impact publications originate outside the region, particularly from China and Germany. This study provides a structured synthesis of existing knowledge and identifies critical avenues for future research and policy development. It calls for more inclusive, transdisciplinary, and regionally embedded approaches to water governance in the context of accelerating climate risks. Full article

This study investigated the effects of three mulch types (straw, vermicompost and “plastic”) plus an untreated control, and three irrigation regimes (RFD: rainfed conditions; SIF: one supplemental irrigation at the flowering stage; SIVF: two supplemental irrigations at the vegetative and flowering stages) on the growth, seed yield, oil composition, and biochemical status of linseed (Linum usitatissimum L.). Linseed plants were best affected by SIVF and straw mulch in terms of seed yield (300 and 222.4 g m⁻², respectively), biomass yield (887.9 and 703 g m⁻², respectively), and concentration of oleic and linoleic acids. Under rainfed conditions, “plastic” mulch application increased stearic acid concentrations, while SIF increased palmitic acid concentrations. Rainfed conditions promoted the accumulation of proline (10.1 μmol g⁻¹ fresh weight), total phenols (6.68 mg g⁻¹ fresh weight), and DPPH radical scavenging capacity (56.5%). Under RFD, plants grown in straw-mulched soil showed the highest total phenol content and DPPH radical scavenging capacity, while control (unmulched) plants displayed the highest proline concentration at this irrigation regime. Enzyme activities, including catalase and superoxide dismutase, were enhanced under straw and “plastic” mulch compared to control plants under rainfed conditions. Our findings suggest that straw mulch represents an effective, sustainable strategy to successfully manage linseed crops, mitigating the adverse effects of water deficit stress on plant performance. Full article

Copper is typically acknowledged as a critical mineral and one of the vital components of various of today’s fast-growing green technologies. Porphyry copper systems, which are an important source of copper and molybdenum, typically consist of large volumes of hydrothermally altered rocks, mainly around porphyry copper intrusions. Mapping hydrothermal alteration zones associated with porphyry copper systems is one of the most important indicators for copper exploration, especially using advanced satellite remote sensing technology. This paper presents a sophisticated remote sensing-based method that uses ASTER satellite imagery (SWIR bands 4 to 9) to identify hydrothermal alteration zones by combining the discrete wavelet transform (DWT) and the median absolute deviation (MAD) algorithms. All six SWIR bands (bands 4–9) were analyzed independently, and band 9, which showed the most consistent spatial patterns and highest validation accuracy, was selected for final visualization and interpretation. The MAD algorithm is effective in identifying spectral anomalies, and the DWT enables the extraction of features at different scales. The Urmia–Dokhtar magmatic arc in central Iran, which hosts the Zafarghand porphyry copper deposit, was selected as a case study. It is a hydrothermal porphyry copper system with complex alteration patterns that make it a challenging target for copper exploration. After applying atmospheric corrections and normalizing the data, a hybrid algorithm was implemented to classify the alteration zones. The developed classification framework achieved an accuracy of 94.96% for phyllic alteration and 89.65% for propylitic alteration. The combination of MAD and DWT reduced the number of false positives while maintaining high sensitivity. This study demonstrates the high potential of the proposed method as an accurate and generalizable tool for copper exploration, especially in complex and inaccessible geological areas. The proposed framework is also transferable to other porphyry systems worldwide. Full article

In recent years, both anthropogenic and climate changes have caused the depletion of surface water resources, shifts in rainfall and accelerations in temperature, which indicates the importance of their examination to flood forecasting analyses. This paper studies the importance of synchronised water management strategies, considering upstream and downstream dynamics using field data from 1986 to 2018. Seasonal and decadal variations show the need for adaptive management strategies to address potential climate change impacts on discharge, precipitation and temperature patterns in the Zarrinehrud River, Iran. The regression analysis was considered via R² values, and the statistical analysis was regarded by p-values. The regression analysis of monthly river peak discharge indicates strong correlations between the discharge of specific months (September–October upstream, December–January downstream). By the 2000s and 2020s, both stations showed a shift in peak precipitation to the spring months (April–May for upstream and May–June for downstream). This confirms a synchronisation of rainfall trends, which are influenced by climate changes or regional hydrological patterns. This temporal offset between stations confirms the spatial and seasonal variation in rainfall distribution across the basin. Higher temperatures during the dominant months, particularly late summer to early autumn, accelerate snowmelt from upstream catchments. This aligns with the river discharge peaks observed in the hydrograph. The statistical analysis of river peak discharge indicated that the Weibull (p-value = 0.0901) and the Lognormal (p-value = 0.1736) distributions are the best fitted distributions for the upstream and downstream stations, respectively. Full article

Alternaria is a large genus of fungi comprising approximately 400 species, currently classified into 29 sections. These fungi exhibit a cosmopolitan distribution, thriving in both natural and human-impacted environments with saprophytic, endophytic, and parasitic lifestyles. As part of our ongoing studies on fungi associated with wetland plants in the families Cyperaceae and Juncaceae across various regions of Iran, we isolated 21 fungal strains displaying morphological traits of Alternaria. Multigene phylogenetic analysis and morphological examination of eight selected strains confirmed their placement within Alternaria with strong support. These isolates formed a basal clade distinct from the 29 previously recognized sections and six monotypic lineages, leading to the establishment of a new section, Alternaria section Iraniana, to accommodate them. Furthermore, two monophyletic lineages within this section were identified, representing two new species, A. avrinica and A. iraniana, which are described and illustrated in this study. The new section is distinguished by long, semi-macronematous to macronematous conidiophores with multiple geniculate and sympodial proliferations, as well as solitary, non-beaked conidia that have only transverse eu-septa to pseudo-septa. The newly described species are differentiated based on conidiophore and conidial characteristics and nucleotide sequence comparisons of genomic regions. These results contribute to a better understanding of the distribution and host range of Alternaria species, while highlighting the importance of ongoing research into fungal taxonomy and biodiversity in Iran, a region rich in potential for the discovery of new fungal species. Full article

The evolving macroeconomic landscape, shaped by the global financial crisis and the COVID-19 pandemic, poses significant challenges for economies worldwide. However, Central, Eastern, and Southeastern European (CESEE) countries have demonstrated resilience and rapid recovery during crises, driven by a surge in consumption fueled by domestic credit and robust export growth supported by flexible exchange rates and adaptive monetary policies. Prior to EU accession, substantial foreign direct investment (FDI) during privatization and restructuring facilitated knowledge and technology transfers in CESEE economies. This study examines the interplay of exports, real exchange rates, GDP growth, FDI, inflation, domestic credit, and the human development index (HDI) in the CESEE region from 1995 to 2022, covering the transition period, EU accession, and major crises. Employing a panel ARDL model, we account for asymmetric effects of these variables on exports. The results reveal that GDP, FDI, inflation, domestic credit, and HDI significantly and positively influence exports, with HDI and GDP exerting the strongest effects, underscoring the pivotal roles of human capital and economic growth in enhancing export competitiveness. Conversely, real exchange rate depreciation negatively impacts exports, though non-price factors, such as product quality, mitigate this effect. These findings provide a robust basis for targeted policy measures to strengthen economic resilience and export performance in the CESEE region. Full article

This study evaluates the predictive performance of advanced machine learning models, including DeepBoost, XGBoost, CatBoost, RF, and MLP, in estimating the Ω₂, Ω₄, and Ω₆ parameters based on a comprehensive set of input variables. Among the models, DeepBoost consistently demonstrated the best performance across the training and testing phases. For the Ω₂ prediction, DeepBoost achieved an R² of 0.974 and accuracy of 99.895% in the training phase, with corresponding values of 0.971 and 99.902% in the testing phase. In comparison, XGBoost ranked second with an R² of 0.929 and accuracy of 99.870% during testing. For Ω₄, DeepBoost achieved a training phase R² of 0.955 and accuracy of 99.846%, while the testing phase results included an R² of 0.945 and accuracy of 99.951%. Similar trends were observed for Ω₆, where DeepBoost obtained near-perfect training phase results (R² = 0.997, accuracy = 99.968%) and testing phase performance (R² = 0.994, accuracy = 99.946%). These findings are further supported by violin plots and correlation analyses, underscoring DeepBoost’s superior predictive reliability and generalization capabilities. This work highlights the importance of model selection in predictive tasks and demonstrates the potential of machine learning for capturing complex relationships in data. Full article

This paper introduces a novel MEMS-based thermal flow sensor designed for high sensitivity and linearity across a wide range of gas flow velocities. The sensor incorporates a single microheater and two pairs of thermistors symmetrically arranged around the heater, with strategically placed obstacles to enhance performance. To ensure accuracy under varying flow conditions, the sensor is divided into two functional regions: one optimized for low flow velocities (0–1 m/s) and the other for high flow velocities (1–6 m/s). Simulations conducted using COMSOL Multiphysics reveal that including obstacles improves heat transfer and increases the interaction time between the heated surface and the flow, particularly in the high-flow region. In the low-flow regime, the sensor achieves a sensitivity of 2.5 SK/m with 91% linearity. In contrast, in the high-flow regime, the sensitivity increases to 6.5 SK/m with similarly high linearity. This dual-region design highlights the sensor’s versatility in handling a broad range of flow velocities, making it suitable for applications in medical, industrial, and environmental monitoring. These findings underscore the advantages of the dual-region design and obstacle integration, providing a robust solution for accurate flow measurement under diverse operating conditions. Full article

This study presents an explainable machine learning framework to forecast groundwater storage dynamics, quantified as the Lake Water Equivalent (LWE), in the Urmia Lake Basin from 2003 to 2023. Satellite-based observations (GRACE, GLDAS) and climatic variables were integrated to model LWE variability. An ensemble learning approach was employed, combining Ridge Regression and Random Forest enhanced through feature re-weighting based on XGBoost-derived importance scores. Model interpretability was addressed using SHapley Additive exPlanations (SHAP), offering transparent insights into the contributions of climatic drivers. Results demonstrated that the Random Forest model achieved superior performance (RMSE = 3.27; $R^2$ = 0.89), with SHAP analysis highlighting the dominant influence of recent LWE values, temperature, and soil moisture. The proposed framework outperformed baseline models including Persistence, Standard Ridge Regression, and XGBoost in terms of both accuracy and explainability. The objectives of this study are (i) to forecast the LWE in the Urmia Lake Basin using an ensemble-based machine learning framework, (ii) to enhance predictive modeling through XGBoost-guided feature weighting, and (iii) to improve model transparency and interpretation using SHAP-based explainability techniques. By integrating ensemble learning with explainable AI, this work advances the transparent data-driven forecasting essential for sustainable groundwater management under climatic uncertainty. Full article

Background/Objectives: High-intensity interval training (HIIT) and time-restricted feeding (TRF) have shown potential in enhancing glucose metabolism, increasing insulin sensitivity, and promoting muscle health. This study investigates the combined effects of HIIT and TRF on the AKT-IGF-1-mTOR signaling pathway in the muscle tissue of type 2 diabetic (T2D) rats. Methods: 42 male Wistar rats (4–5 weeks of age) were included in the study. The animals were randomly divided into two groups: 1. Standard diet (SD) non-diabetic (n = 7) and 2. High-fat diet (HFD n = 35) for 4 weeks. T2D was induced by intraperitoneal injection (IP) of streptozotocin (STZ) at 35 mg/kg. Animals with blood glucose levels ≥ 250 mg/dL were considered diabetic. Diabetic rats were randomly divided into five groups (n = 7): 1. Diabetes-HIIT (D-HIIT), 2. Diabetes-TRF (D-T), 3. Diabetes-combined TRF and HIIT (D-T+HIIT), 4. Diabetes-Untreated Control (D), and 5. Diabetes with metformin (D-MET). The HIIT protocol and TRF regimen were followed for 10 weeks. Muscle tissue was collected for histological analysis, and the expression of proteins related to the AKT-IGF-1-mTOR pathway was measured. Results: Blood glucose levels, insulin resistance (IR), and markers of muscle degradation were significantly improved in the D-T+HIIT and D-MET groups compared to the non-diabetes group. Furthermore, the activation of the AKT and mTOR signaling proteins, as well as increased IGF-1 expression, was significantly elevated in the D-T+HIIT group compared to the diabetic control group and other treatment groups, and approached levels observed in the non-diabetes group. Additionally, muscle fiber size and overall tissue structure were improved in the treatment groups, particularly in the D-T+HIIT group. Conclusions: The combination of HIIT and TRF appears to offer superior benefits in improving muscle protein synthesis, and glucose regulation in T2D rats, as compared to either HIIT or TRF alone. These findings highlight the potential of this combined approach for addressing muscle-related complications in T2D. Full article

In this work, we explore the impact of integral operators such as the Libera and Alexander operators on specific families of analytic functions introduced in the literature and find some of their remarkable results. Using techniques from differential subordination and convolution theory, we establish results concerning the radius of convexity and convolution properties for these function classes. Additionally, we investigate how these integral operators influence the geometric properties of functions in $\mathcal{BS}\left(\mu \right)$ and $\mathcal{KS}\left(\mu \right)$, leading to new insights into their structural behavior. Full article