Search Results (6,063)

Shallow gas is an unconventional natural gas resource with great potential and has received growing attention recently. Accurate estimation of gas saturation is crucial for reserves assessments and for development program formulations. However, such reservoirs are characterized by weak diagenesis, a high clay content, and low resistivity. These properties pose significant challenges for saturation evaluations. To address the challenge of insufficient accuracy in evaluating the saturation of gas-bearing reservoirs, we propose an acoustic-based saturation evaluation method. In this study, a shallow unconsolidated rock physics model is first constructed to investigate the effect of variations in the gas saturation on elastic wave velocities. The model especially considers the patchy distribution of fluids within pores. In addition, we propose an iterative algorithm based on the updated relationship between porosity and gas saturation by introducing a correction term for the saturation to the density porosity, and successfully apply it to the logging data collected from the shallow gas reservoirs in the Pearl River Mouth Basin of the South China Sea. It is evident from the results that the saturation derived from the array acoustic logs is comparable to that obtained from the resistivity logs, with a mean absolute error of less than 6%. Additionally, it is also consistent with the drill stem test (DST) data, which further verifies the validity and reliability of this method. This study provides a novel non-electrical method for estimating the saturation of shallow gas reservoirs, which is essential to promote the evaluation of unconsolidated sandstone gas reservoirs. Full article

Facing the increasingly scarce supply of rare-earth resources, a cobalt-doped metal–organic framework-derived carbon–metallic sulfide composite (Co-FeS₂@C) was successfully synthesized via the hydrothermal method and the following carbonization/sulfidation treatments and used for the efficient electrosorption of rare earths from aqueous solution. Comparative characterizations revealed that Co doping effectively expanded the interlayer spacing of FeS₂, introduced crystalline defects, and optimized the electronic structure, thereby synergistically enhancing active site exposure and electron transfer kinetics. In addition, the electrochemical analysis demonstrated a significant increase in the surface-controlled capacitive contribution from 57.1% to 83.3%, indicating the markedly improved electric double-layer effects and mass transport efficiency. Under the optimal conditions, the Co-FeS₂@C electrode achieved a high Yb³⁺ adsorption capacity of 129.2 mg g⁻¹ along with an exceptional cycling stability (92.63% retention after 20 cycles), substantially outperforming the undoped counterpart FeS₂ (88.4 mg g⁻¹ and 74.61%). Furthermore, the mechanistic investigations confirmed that the electrosorption process follows a monolayer physico-chemical synergistic mechanism, primarily driven by the pseudo-capacitive effect arising from the redox reaction of FeS₂ and the enhanced charge-transfer driving force resulting from the higher electronegativity of cobalt. This work provides an innovative electronic structure modulation strategy for developing the high-performance capacitive deionization electrodes for rare earth recovery via the electrosorption process. Full article

As tourism increasingly drives the revitalization of traditional villages, rural spaces are undergoing a transformation from functional living areas to spaces for cultural display and leisure. This shift has amplified the spatial usage discrepancies between multiple stakeholders, such as tourists and villagers, highlighting conflicts in spatial resource allocation and behavior path organization. Using Wulin Village, a typical example of a Minnan overseas Chinese village, as a case study, this paper introduces social network analysis to construct a “spatial–behavioral” dual network model. The model integrates both architectural and public spaces, alongside behavior path data from villagers and tourists, to analyze the spatial structure at three scales: village-level network completeness, district-level structural balance, and point-level node vulnerability. The study integrates two dimensions—architectural space and public space—along with behavioral path data from both villagers and tourists. It reveals the characteristics of spatial structure under the intervention of multiple behavioral agents from three scales: village-level network completeness, district-level structural balance, and point-level node vulnerability. The core research focus of the spatial network includes the network structure of architectural and public spaces, while the behavioral network concerns the activity paths and behavior patterns of tourists and villagers. The study finds that, at the village scale, Wulin Village’s spatial network demonstrates good connectivity and structural integrity, but the behavior paths of both tourists and villagers are highly concentrated in core areas, leading to underutilization of peripheral spaces. This creates an asymmetry characterized by “structural integrity—concentrated behavioral usage.” At the district scale, the spatial node distribution appears balanced, but tourist behavior paths are concentrated around cultural nodes, such as the ancestral hall, visitor center, and theater, while other areas remain inactive. At the point scale, both tourist and villager activities are highly dependent on a few high-degree, high-cluster nodes, improving local efficiency but exacerbating systemic vulnerability. Comparison with domestic and international studies on cultural settlements shows that tourism often leads to over-concentration of spatial paths and node overload, revealing significant discrepancies between spatial integration and behavioral usage. In response, this study proposes multi-scale spatial optimization strategies: enhancing accessibility and path redundancy in non-core areas at the village scale; guiding behavior distribution towards multifunctional nodes at the district scale; and strengthening the capacity and resilience of core nodes at the point scale. The results not only extend the application of behavioral network methods in spatial structure research but also provide theoretical insights and practical strategies for spatial governance and cultural continuity in tourism-driven cultural villages. Full article

This study provides a comprehensive characterization of the low-to-intermediate sulfidation (LS-to-IS) epithermal Molai Zn-Pb±(Ag,Ge) ore (Vigla-Mesovouni orebody) in Laconia, Greece, and provides insights on how such data may be employed in beneficiation flow-sheet design. Detailed mineralogical, chemical, textural, and physicochemical characterization defines a systematic transition from early refractory Ge-rich to late-stage refractory Ag-rich mineralization, including sulfides and fahlores. Germanium, although present in all sphalerite varieties (Sp-I, Sp-II, and Sp-III), is predominantly enriched in early sphalerite (Sp-I, up to 1891.60 ppm). Interestingly, Ge is also enriched in early Py-I pyrite, with content reaching up to 383 ppm. Silver is mainly concentrated in late-stage tetrahedrite Ttr-II (up to 3.60%), galena (Ga-II), and, to a lesser extent, late sphalerite (Sp-III). Liberation studies reveal effective liberation of Py-I and Sp-I, major Ge carriers, in the coarser fractions (+0.150 mm) and near complete liberation of all ore phases below 0.036 mm. Combined beneficiation via Wilfley pre-concentration and differential flotation produced up to ~35% Pb and ~65% Zn at >85% recovery for the smallest fractions (−0.036 mm). Ore characterization revealed that secondary circuits may be developed to further enhance the economic value of Molai ore (Ge from Py-I, and Ag±[Sb,As] from Ttr-II and Ag-bearing sulfosalts), which are dismissed as wastes in Pb and Zn flotation circuits. The results of our study establish a robust foundation for the design of tailored, multi-stage metallurgical flow-sheets aimed at maximizing the economic value of the Molai epithermal resource. Full article

Accurate satellite-based precipitation estimates are crucial for climate studies and water resource management, particularly in regions with sparse meteorological station coverage. This study evaluates the improvements of the Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run version 07 (V07) relative to the previous version (V06). The evaluation employed gridded data from the Brazilian Daily Weather Gridded Data (BR-DWGD) product and ground observations from 58 rain gauges distributed across the Parnaíba River Basin in Northeast Brazil. The analysis comprised three main stages: (i) an intercomparison between BR-DWGD gridded data and rain gauge records using correlation, bias, and Root Mean Square Error (RMSE) metrics; (ii) a comparative assessment of the IMERG Final V06 and V07 products, evaluated with statistical metrics (correlation, bias, and RMSE) and complemented by performance indicators including the Kling-Gupta Efficiency (KGE), Probability of Detection (POD), and False Alarm Ratio (FAR); and (iii) the application of cluster analysis to identify homogeneous regions and characterize seasonal rainfall variations across the basin. The results show that the IMERG Final V07 product provides notable improvements, with lower bias, reduced RMSE, and greater accuracy in representing the spatial distribution of precipitation, particularly in the central and southern regions of the basin, which feature complex topography. IMERG V07 also demonstrated higher consistency, with reduced random errors and improved seasonal performance, reflected in higher POD and lower FAR values during the rainy season. The cluster analysis identified four homogeneous regions, within which V07 more effectively captured seasonal rainfall patterns influenced by systems such as the Intertropical Convergence Zone (ITCZ) and Amazonian moisture advection. These findings highlight the potential of the IMERG Final V07 product to enhance precipitation estimation across diverse climatic and topographic settings, supporting applications in hydrological modeling and extreme-event monitoring. Full article

Honey is a natural product of high nutritional and therapeutic value, whose biological properties are closely linked to its botanical origin and chemical composition. This study aimed to characterize avocado honey in terms of botanical origin, physicochemical parameters, phenolic content, antioxidant activity, chemical profile by LC-MS/MS, and antibacterial potential. Melissopalynological analysis revealed 86.21% avocado pollen, allowing classification as monofloral honey. The sample presented amber color and a high total phenolic content (269.79 ± 1.10 mg GAE 100 g⁻¹), values higher than those commonly reported for Brazilian and international honeys. Antioxidant activity, assessed by the DPPH method, confirmed the strong radical-scavenging capacity, consistent with the phenolic profile identified (EC₅₀ 10.250 ± 0.003 mg mL⁻¹). LC-MS/MS analysis allowed the annotation of nine compounds, including caffeine, scopoletin, abscisic acid, and vomifoliol, compounds associated with antioxidant, anti-inflammatory, and metabolic regulatory activities. Although no antibacterial effect was detected against the tested oral bacterial strains, the results highlight the chemical diversity and functional potential of avocado honey. Overall, the findings reinforce the bioactive potential of avocado honey, particularly due to its strong antioxidant capacity and diversity of metabolites, supporting its value as a natural resource of nutritional and therapeutic interest. Full article

Efficient water management is essential for optimizing agricultural productivity in water-scarce regions such as the Lis Valley, Portugal. In situ measurements of soil moisture content (SMC) and electrical conductivity (EC), together with Sentinel-2-derived vegetation indices, were used to assess the crop water status and evapotranspiration dynamics of pumpkin (Cucurbita moschata ‘Butternut’) during the 2020 growing season. SMC and EC were measured at depths of 10, 20, 30, 50, and 70 cm using a TDR sensor, with strong correlations observed in the upper layers, indicating that EC can complement direct SMC measurements in characterizing near-surface moisture conditions. Sentinel-2 imagery was acquired to compute NDVI, SAVI, EVI, and GCI. In addition, NDVI values obtained from both a GreenSeeker^® sensor and Sentinel-2 imagery were compared, showing a similar temporal pattern during the season. By replacing the standard FAO-56 K_c values with those derived from each vegetation index, ET_a was recalculated to incorporate actual crop condition variability detected via satellite. ET_a estimates from RS-assisted vegetation indices agreed with those obtained using the FAO-56 method; independent ET_a measurements were not available for validation. Although such agreement is partly expected due to calibration, its confirmation for Cucurbita moschata under Mediterranean conditions—where published references are scarce—reinforces the method’s practical applicability for water management in data-limited settings. Water Productivity (WP) was estimated as 8.32 kg m⁻³, and Water Use Efficiency (WUE FAO-56) was calculated as 0.64 kg m⁻³, indicating high water use efficiency under Mediterranean smallholder irrigation conditions. These findings demonstrate that integrating high-resolution RS with continuous soil moisture monitoring can enhance precision irrigation strategies, increase crop yields, and conserve water resources in the Lis Valley. Full article

The impingement of a molten droplet on a solid surface, forming a “splat,” is a fundamental phenomenon observed across numerous industrial surface engineering techniques. For example, thermal spray deposition is widely used to create metal, ceramic, polymer, and composite coatings that are vital for aerospace, biomedical, electronics, and energy applications. Significant progress has been made in understanding droplet impact behavior, largely driven by advancements in high-resolution and high-speed imaging techniques, as well as computational resources. Although droplet impact dynamics, splat morphology, and interfacial bonding mechanisms have been extensively reviewed, a comprehensive overview of the mechanical behaviors of single splats, which are crucial for coating performance, has not been reported. This review bridges that gap by offering an in-depth analysis of bonding strength and residual stress in single splats. The various experimental techniques used to characterize these properties are thoroughly discussed, and a detailed review of the analytical models and numerical simulations developed to predict and understand residual stress evolution is provided. Notably, the complex interplay between bonding strength and residual stress is then discussed, examining how these two critical mechanical attributes are interrelated and mutually influence each other. Subsequently, effective strategies for improving interfacial bonding are explored, and key factors that influence residual stress are identified. Furthermore, the fundamental roles of splat flattening and formation dynamics in determining the final mechanical properties are critically examined, highlighting the challenges in integrating fluid dynamics with mechanical analysis. Thermal spraying serves as the primary context, but other relevant applications are briefly considered. Cold spray splats are excluded because of their distinct bonding and stress generation mechanisms. Finally, promising future research directions are outlined to advance the understanding and control of the mechanical properties in single splats, ultimately supporting the development of more robust and reliable coating technologies. Full article

Geopolymer materials possess several outstanding advantages, including the wide availability of raw materials, an energy-saving and environmentally friendly production process, and excellent engineering technical performance. They are regarded as a new type of green building material that can achieve high-value-added resource utilization of industrial solid waste. They are one of the current research hotspots in the field of materials. Fly ash and slag, the most common industrial wastes in China, have been discharged in large quantities, significantly impacting the country’s ecological environment. Based on this, this paper primarily investigates the mechanical properties and strength formation mechanism of geopolymer paste to develop geopolymer materials with enhanced mechanical properties. This research uses metakaolin as the silicate raw material and uses sodium silicate mixed with NaOH as the alkali activator to prepare geopolymer paste. By adding fly ash and slag, the mechanical properties of the geopolymer paste are improved. The effects of the alkali activator modulus, Na₂O equivalent, and content of fly ash and slag on the setting time and strength of geopolymer paste are studied. XRD, FTIR, and SEM are employed to characterize the phase, molecular structure, and microscopic morphology of geopolymer paste, as well as to analyze the microstructure and reaction mechanism of these materials. The results show that the setting time of the geopolymer increases with the increase in modulus and shortens with the increase in Na₂O equivalent. Fly ash and slag, respectively, act as retarders and early strength promoters. The ratio of n(SiO₂)/n(A1₂O₃) (that is, the modulus of the alkali activator) of the geopolymer is an important factor affecting its strength. The metakaolin and fly ash–slag–metakaolin exhibit the best mechanical properties when their molar ratios are 2.97 and 3.26, respectively. Through microscopic characterization using XRD, FTIR, and SEM, it is observed that fly ash–slag–metakaolin exhibits the most complete polymerization reaction, generates the most amorphous silicate aluminosilicate gel, and displays the best inter-gel bonding effect, resulting in the best mechanical properties. Full article

Biodegradable packaging based on starch–polycaprolactone (PCL) composites is a promising route to reduce reliance on petroleum-derived plastics. Here, wheat starches with A- and B-type crystallinity—sourced from Kazakhstani varieties—were dual-modified by electron-beam irradiation followed by acetylation and incorporated into PCL (30–50 wt%) via melt extrusion and compression molding. The resulting films were characterized for morphology, mechanical performance, water-vapor permeability (WVP), thermal behavior, antibacterial activity, and biodegradation under soil and composting conditions. Acetylated A-type starch dispersed more uniformly within the PCL matrix, yielding smoother surfaces, higher tensile strength, and moderate WVP. In contrast, B-type starch produced a more porous microstructure with increased WVP and accelerated mass loss during composting (up to ~45% within 10 days at higher starch loadings). Incorporation of starch slightly decreased thermal stability relative to neat PCL, while agar-diffusion assays against Escherichia coli and Staphylococcus aureus showed loading-dependent inhibition zones, with A-type composites generally outperforming B-type at equivalent contents. Taken together, A-type starch–PCL films are better suited for applications requiring mechanical integrity and controlled moisture transfer, whereas B-type systems favor breathable packaging and rapid compostability. These results clarify how starch crystalline type governs structure–property–degradation relationships in PCL composites and support the targeted design of sustainable packaging materials using regionally available starch resources. Full article

Bacterial cellulose (BC) is a type of biomass composed entirely of cellulose. This characteristic favors the presence of a multitude of active sites, which facilitate the exchange of heavy metals present in polluting effluents. Upon contact with water contaminated with metals such as chromium, arsenic, and lead, among others, this biomass offers a potential solution to the environmental problem of industrial pollutants in water. This is particularly pertinent given the well-documented harmful effects of heavy metals in aquatic ecosystems. In this context, the objective is to determine the impact of temperature on Cr (IV) adsorption using bacterial cellulose biomass as an adsorbent, under different temperature scenarios, similar to the conditions of discharge of contaminated effluents into rivers, lagoons, and wetlands. In this study, the biomass was previously characterized through FTIR and SEM images, and isothermal models were subsequently evaluated along with batch adsorption kinetics. The findings demonstrate that bacterial cellulose biomass has great potential for Cr (VI) removal at various temperatures, with an adsorption capacity of 140 mg/g at high temperatures and a reduction of up to 125 mg/g at low temperatures. The findings of this study constitute a valuable contribution to decision-making when considering the expansion of these treatment processes, facilitating this task by offering a comparative analysis of effluent discharge conditions in relation to various scenarios involving contaminated liquid temperatures. The use of this biomaterial in an environmental sustainability initiative focused on water resource conservation is a very promising prospect. Full article

The AGC kinases constitute a large and ancient gene superfamily with origins that coincided with the appearance of multicellularity. Three AGC kinase families—protein kinase A (PKA), protein kinase G (PKG), and protein kinase C (PKC)—mediate the actions of neuropeptide hormones, biogenic amines, and other ligands on various physiological processes in metazoans. Metazoans express two PKG types. Jawed vertebrates express three PKA catalytic (C) subunits, four regulatory (R) subunits, and twelve PKCs, organized into conventional, novel delta-like, novel epsilon-like, atypical, and protein kinase N (PKN) subfamilies. By contrast, invertebrate PKA and PKC sequences are not well characterized. Consequently, limited database resources can result in misidentification or mischaracterization of proteins and can lead to misinterpretation of experimental data. A broad phylogenetic and sequence analysis of CrusTome transcriptome and GenBank databases was used to characterize 640 PKA-C sequences, 1122 PKA-R sequences, and 1844 PKC sequences distributed among the Annelida, Arthropoda, Chordata, Cnidaria, Nematoda, Mollusca, Echinodermata, Porifera, Platyhelminthes, and Tardigrada. Phylogenetic analysis and multiple sequence alignments revealed conservation of certain PKA-C, PKA-R and PKC isoforms across metazoans, as well as diversification of additional taxon-specific isoforms. Decapods expressed four PKA-C isoforms, designated PKA-C₁, -C_D1, -C_GLY1, and -C_GLY2; five PKA-R isoforms, designated PKA-RI₁, -RI_D1, -RII_GLY, and -RII_D1; and five PKC isoforms, designated PKN_D1-3, conventional cPKC_D1, novel nPKC_D1δ and nPKC_D1ε, and atypical aPKC_D1. PKA-C_GLY1, -C_GLY2, and -RII_GLY had glycine-rich N-terminal sequences that were unique to crustaceans. These data suggest lineage-specific diversification that retained the core catalytic function of each kinase, while regions outside of the kinase domain may provide specialized regulatory mechanisms and/or spatiotemporal subcellular localization in invertebrate tissues. Full article

Runoff simulation and forecasting is of great significance for flood control, disaster mitigation, and water resource management. Alpine regions are characterized by complex terrain, diverse precipitation patterns, and strong snow-and-ice melt influences, making accurate runoff simulation particularly challenging yet crucial. To enhance predictive capability and model applicability, this study takes the Upper Jinsha River as a case study and comparatively evaluates the performance of a physics-based hydrological model BTOP and the data-driven deep learning models LSTM and BiLSTM in runoff simulation and short-term forecasting. The results indicate that for daily-scale runoff simulation, the LSTM and BiLSTM models demonstrated superior simulation capabilities, achieving Nash–Sutcliffe efficiency coefficients (NSE) of 0.82/0.81 (Zhimenda Station) and 0.87/0.86 (Gangtuo Station) during the test period. These values are significantly better than those of the BTOP model, which achieved a validation NSE of 0.57 at Zhimenda and 0.62 at Gangtuo. However, the hydrology-based structure of the BTOP model endowed it with greater stability in water balance and long-term simulation. In short-term forecasting (1–7 d), LSTM and BiLSTM performed comparably, with the bidirectional architecture of BiLSTM offering no significant advantage. When it came to flood events, the data-driven models excelled at capturing peak timing and hydrograph shape, whereas the physical BTOP model demonstrated superior stability in flood peak magnitude. However, forecasts from the data-driven models also lacked hydrological consistency between upstream and downstream stations. In conclusion, the present study confirms that deep learning models achieve superior accuracy in runoff simulation compared to the physics-based BTOP model and effectively capture key flood characteristics, establishing their value as a powerful tool for hydrological applications in alpine regions. Full article

The characterization of lithology within Quaternary aquifers holds significant geological importance for the protection, management, and utilization of groundwater resources, yet it continues to present considerable challenges. Indicator Kriging (IK) is a non-parametric, probability-based method of spatial interpolation. It considers the correlation and variability between data points, and its popularity stems from its alignment with geological experts’ principles. However, it still encounters issues in complex geological conditions. To address the limited capacity of conventional IK in reproducing geological variables within heterogeneous geological settings, this study develops an ordered IK method incorporating field variogram function parameters. This framework dynamically extends IK applications by integrating stratigraphic extension trends, requiring experts to formalize spatial variation trends into geological knowledge data, subsequently transformed into constraint parameters for interpolation. Estimation paths are determined via Euclidean distances between points-to-be-estimated and valid data, executing ordered IK following near-to-far and bottom-to-top principles. Results directly depict QLS formation spatial distributions or undergo expert modification for quantitative analysis, demonstrating superior integration of geological knowledge compared to empirical variogram fitting and partitioned IK estimation. The method reduces deviation from expert-interpreted spatial distributions while maintaining computational efficiency and multi-factor integration, with three case analyses confirming enhanced accuracy in lithology distribution reproduction and improved geostructural congruence in complex geological reconstruction. This approach revitalizes Kriging applications in complex geological research, synergizing domain cognition with computational efficacy to advance precision in geological characterization and support government decision-making. Full article

Understanding the interactions among ecosystem services (ESs) and their spatiotemporal dynamics is pivotal for sustainable ecosystem management, particularly in arid regions where water scarcity imposes significant constraints. This study focuses on the Ili River Valley, a representative arid region, to investigate the evolution of ESs, their trade-offs and synergies, and the underlying driving mechanisms from a water-resource-constrained perspective. We assessed five key ESs—soil retention (SR), habitat quality (HQ), water purification (WP), carbon sequestration (CS), and water yield (WY)—utilizing multi-source remote sensing and statistical data spanning 2000 to 2020. Employing the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, Spearman correlation analysis, Geographically Weighted Regression (GWR), and the Geodetector method, we conducted a comprehensive analysis at both sub-watershed and 500 m grid scales. Our findings reveal that, except for SR and WP, the remaining three ESs exhibited an overall increasing trend over the two-decade period. Trade-off relationships predominantly characterize the ESs in the Ili River Valley; however, these interactions vary temporally and across spatial scales. Natural factors, including precipitation, temperature, and soil moisture, primarily drive WY, CS, and SR, whereas anthropogenic factors significantly influence HQ and WP. Moreover, the impact of these driving factors exhibits notable differences across spatial scales. The study underscores the necessity for ES management strategies tailored to specific regional characteristics, accounting for scale-dependent variations and the dual influences of natural and human factors. Such strategies are essential for formulating region-specific conservation and restoration policies, providing a scientific foundation for sustainable development in ecologically vulnerable arid regions. Full article