Search Results (364)

Deep coalbed methane (CBM) resources possess significant potential. However, their development is challenged by geological characteristics such as high in situ stress and low permeability. Furthermore, existing production strategies often prove inadequate. In order to achieve long-term stable production of deep coalbed methane reservoirs and increase their final recoverable reserves, it is urgent to construct a scientific and reasonable drainage system. This study focuses on the deep CBM reservoir in the Daning-Jixian Block of the Ordos Basin. First, a thermal–hydraulic–mechanical (THM) multi-physics coupling mathematical model was constructed and validated against historical well production data. Then, the model was used to forecast production. Finally, key control measures for enhancing well productivity were identified through production strategy adjustment. The results indicate that controlling the bottom-hole flowing pressure drop rate at 1.5 times the current pressure drop rate accelerates the early-stage pressure drop, enabling gas wells to reach the peak gas production earlier. The optimized pressure drop rates for each stage are as follows: 0.15 MPa/d during the dewatering stage, 0.057 MPa/d during the gas production rise stage, 0.035 MPa/d during the stable production stage, and 0.01 MPa/d during the production decline stage. This strategy increases peak daily gas production by 15.90% and cumulative production by 3.68%. It also avoids excessive pressure drop, which can cause premature production decline during the stable phase. Consequently, the approach maximizes production over the entire life cycle of the well. Mechanistically, the 1.5× flowing pressure drop offers multiple advantages. Firstly, it significantly shortens the dewatering and production ramp-up periods. This acceleration promotes efficient gas desorption, increasing the desorbed gas volume by 1.9%, and enhances diffusion, yielding a 39.2% higher peak diffusion rate, all while preserving reservoir properties. Additionally, this strategy synergistically optimizes the water saturation and temperature fields, which mitigates the water-blocking effect. Furthermore, by enhancing coal matrix shrinkage, it rebounds permeability to 88.9%, thus avoiding stress-induced damage from aggressive extraction. Full article

Kinetoplastids are protozoa that possess a unique organelle called a kinetoplast. These include the parasites Trypanosoma cruzi, T. brucei and related African trypanosomes, and Leishmania spp. These parasites cause a variety of neglected tropical diseases in humans and livestock, with devastating consequences. In the absence of any vaccine, pharmaceutical interventions are the mainstay of control, but these have historically been underfunded, fragmented, and inadequately aligned with the complex zoonotic and ecological realities of the parasites’ transmission dynamics. In this review, the landscape of current and emerging drugs for treating leishmaniasis, Chagas disease, and African trypanosomiasis is critically evaluated across both veterinary and human contexts. It examines the challenges of legacy compounds, the pharmacological shortcomings in multi-host, multi-tropic and multi-stage disease systems, and the gaps in veterinary therapeutics, specifically for African animal trypanosomiasis and canine leishmaniasis but also the animal reservoir of T. cruzi. Emphasis is placed on pharmacokinetic divergence between species, the accompanying risks with the use of off-label human drugs in animals, and the ecological effects of environmental drug exposure. We propose a far-reaching One Health framework for pharmaceutical research and development, promoting dual-indication co-development, ecological pharmacology, regulatory harmonisation, and integrated delivery systems. In this context, we argue that the drug development pipeline must be rationalised as a transdisciplinary and ecologically embedded process, able to interrupt parasite transmission to human, animal, and vector interfaces. Our findings reveal that we can bridge age-old therapeutic gaps, advance towards sustainable control, and eventually eliminate the neglected diseases caused by kinetoplastid protozoan parasites by aligning pharmaceutical innovation with One Health principles. This article aims to promote future research and development of innovative drugs that are sustainable under the One Health framework. Full article

The Gadouras Reservoir, Rhodes Island’s primary water source, experiences recurrent whiting events—milky turbidity from calcium carbonate precipitation—that challenge treatment operations, with impacts compounded by a major 2023 wildfire in this fire-prone Mediterranean setting. To elucidate these dynamics, a pragmatic, multi-source monitoring framework integrates archived Sentinel-2 and Landsat imagery with treatment-plant records (2017–mid-2025). Unitless spectral indices (e.g., AreaBGR) for whiting detection and chlorophyll-a proxies are combined with laboratory measurements of turbidity, pH, total organic carbon, manganese, and hydrological metrics, analyzed via spatiotemporal Hovmöller diagrams, Pearson correlations, and interrupted time-series models. Two seasonal whiting regimes are identified: a biogenic summer mode (southern origin; elevated chlorophyll-a; water temperature > 15 °C; pH > 8.5) and a non-biogenic winter mode (northern inflows). Following the wildfire, the system exhibits characteristics that could be related to possible hypolimnetic anoxia, prolonged whiting, a ~50% rise in organic carbon, and a manganese excursion to ~0.4 mg L⁻¹ at the deeper intake. Crucially, the post-fire period shows a decoupling of AreaBGR from turbidity (r ≈ 0.233 versus ≈ 0.859 pre-fire)—a key diagnostic finding that confirms a fundamental shift in the composition and optical properties of suspended particulates. The manganese spike is best explained by the confluence of a wildfire-induced biogeochemical predisposition (anoxia and Mn mobilization) and a consequential operational decision (relocation to a deeper, Mn-rich intake). This framework establishes diagnostic baselines and thresholds for managing fire-impacted reservoirs, supports the use of remote sensing in data-scarce systems, and informs adaptive operations under increasing climate pressures. Full article

This research presents a comprehensive spatiotemporal assessment of the effects of climate change and anthropogenic pressures on the water surface area and quality of the Sidi Salem Dam, the largest reservoir in Northern Tunisia. Located within a sub-humid to Mediterranean humid bioclimatic zone, the dam plays a vital role in regional water supply, irrigation, and flood control. Utilizing a 40-year dataset (1985–2025), this study integrates multi-temporal satellite imagery and geospatial analysis using Geographic Information System (GIS) and remote sensing (RS) techniques. The temporal variability of the dam’s surface water extent was monitored through indices such as the Normalized Difference Water Index (NDWI). The analysis was further supported by climate data, including records of precipitation, temperature, and evapotranspiration, to assess correlations with observed hydrological changes. The findings revealed a significant reduction in the dam’s surface area, from approximately 37.8 km² in 1985 to 19.8 km² in 2025, indicating a net loss of 18 km² (47.6%). The Mann–Kendall trend test confirmed a significant long-term increase in annual precipitation, while annual temperature showed no significant trend. Nevertheless, recent observations indicate a decline in precipitation during the most recent period. Furthermore, Pearson correlation analysis revealed a significant negative relationship between precipitation and temperature, suggesting that wet years are generally associated with cooler conditions, whereas dry years coincide with warmer conditions. This hydroclimatic interplay underscores the complex dynamics driving reservoir fluctuations. Simultaneously, land use changes in the catchment area, particularly the expansion of agriculture, urban development, and deforestation have led to increased surface runoff and soil erosion, intensifying sediment deposition in the reservoir. This has progressively reduced the dam’s storage capacity, further diminishing its water storage efficiency. This study also investigates the degradation of water quality associated with declining water levels and climatic stress. Indicators such as turbidity and salinity were evaluated, showing clear signs of deterioration resulting from both natural and human-induced processes. Increased salinity and pollutant concentrations are primarily linked to reduced dilution capacity, intensified evaporation, and agrochemical runoff containing fertilizers and other contaminants. Full article

Multi-source coordinated scheduling has become the predominant operational paradigm in power systems. However, substantial differences among hydropower, thermal power, wind power, and photovoltaic sources in terms of response speed, regulation capability, and operational constraints—particularly the complex generation characteristics and spatiotemporal hydraulic coupling of large-scale cascade hydropower stations—significantly increase the complexity of coordinated scheduling. Therefore, this study proposes an optimization method for determining the day-ahead generation intervals of cascade hydropower, applicable to multi-source coordinated scheduling scenarios. The method fully accounts for the operational characteristics of hydropower and the requirements of coordinated scheduling. By incorporating stochastic operational processes, such as reservoir levels and power outputs, feasible boundaries are constructed to represent the inherent uncertainties in hydropower operations. A stochastic optimization model is then formulated to determine the generation intervals. To enhance computational tractability and solution accuracy, a linearization technique for stochastic constraints based on duality theory is introduced, enabling efficient and reliable identification of hydropower generation capability intervals under varying system conditions. In practical applications, other energy sources can develop their generation schedules based on the feasible generation intervals provided by hydropower, thereby effectively reducing the complexity of multi-source coordination and fully leveraging the regulation potential of hydropower. Multi-scenario simulations conducted on six downstream cascade reservoirs in a river basin in Southwest China demonstrate that the proposed method significantly enhances system adaptability and scheduling efficiency. The method exhibits strong engineering applicability and provides robust support for multi-source coordinated operation. Full article

Shale oil extraction efficiency hinges on the interfacial interactions between oil molecules and reservoir clay minerals, such as kaolinite, whose slit-like pores serve as primary storage spaces for alkanes. This study introduces a novel multi-dimensional quantification method using four angular parameters—elevation angle (θ), azimuth angle (φ), rotation angle (ω), and dihedral angle (τ)—to systematically investigate the adsorption configuration of n-octane in kaolinite slit pores ranging from 0.45 to 14.05 nm. Through molecular simulations and advanced trajectory analysis, we elucidate the impact of pore sizes on alkane adsorption density, layering, and molecular configurations. Results reveal that pore size regulates molecular behavior via steric hindrance and potential field superposition, while the four angular parameters can effectively capture subtle changes in. this molecular behavior: (1) the elevation angle (θ) around 0° indicates complete alignment parallel to surface, but is modulated at increasing distance from the surface into the pore-region highlighting a disordered state; (2) the azimuth angle (φ) is concentrated at 60° and 120° on the siloxane tetrahedral surface due to lattice regulation, but shows a disordered distribution on the hydroxyl octahedral surface; (3) the rotation angle (ω) is mainly concentrated at 0° and 90° indicating molecular plane being either parallel or perpendicular to the surface; (4) the dihedral angle (τ) remains at ~0°, indicating that the molecular chains are straight. In pores smaller than 4.26 nm, strong confinement yields ordered molecular arrangements (θ = 0°, φ at 60° or 120°, ω = 0°) with high adsorption density; for larger pores than 4.26 nm, disordered configurations and increased layering (up to eight layers) with stable density and adsorption capacity per unit area are observed. The proposed parameter system overcomes limitations of traditional qualitative approaches, offering a standardized, scalable tool for quantifying alkane-clay interactions. This framework enhances understanding of shale oil occurrence mechanisms and supports optimized extraction strategies, with broad applicability to other chain molecules and 2D materials in interface science. Full article

HIV/AIDS remains a major global health challenge, with immune dysfunction, chronic inflammation, and comorbidities sustained by latent viral reservoirs that evade antiretroviral therapy. Euclea natalensis, a medicinal plant widely used in Southern African ethnomedicine, remains underexplored for its potential against HIV. An integrative systems pharmacology and molecular modeling framework was employed, including ADME profiling, target mapping, PPI network analysis, GO and KEGG pathway enrichment, BA-TAR-PATH analysis, molecular docking, MD simulations, and MM/GBSA calculations, to investigate the mechanistic roles of E. natalensis phytochemicals in HIV pathogenesis. Sixteen phytochemicals passed ADME screening and mapped to 313 intersecting host targets, yielding top ten hub genes with GO annotations in immune-metabolic, apoptotic, and nuclear signaling pathways. KEGG analysis revealed the enrichment of HIV-relevant pathways, including Th17 cell differentiation (hsa04659), PD-L1/PD-1 checkpoint (hsa05235), IL-17 signaling (hsa04657), HIF-1 signaling pathway (hsa04066), and PI3K-Akt (hsa04151). Lead phytochemicals, diospyrin and galpinone, strongly targeted key hub proteins (NFκβ1, STAT3, MTOR, HSP90AA1, and HSP90AB1), demonstrating favorable binding affinities, conformational stability, and binding free energetics compared to reference inhibitors. E. natalensis phytochemicals may modulate Th17 differentiation, HIV latency circuits, and comorbidity-linked signaling by targeting multiple host pathways, supporting their potential as multi-target therapeutic candidates for adjunct HIV/AIDS treatment and immunotherapy. Full article

Reservoirs face growing challenges in safety and sustainable management, requiring systematic approaches that integrate monitoring, analysis, and decision support. To address this need, this study develops an integrated information system framework with a four-layer architecture, encompassing “perception,” “data,” “model,” and “application.” The perception layer establishes a multi-platform monitoring network based on fused multi-sensor data. The data layer manages heterogeneous information through correlation mechanisms at the physics, semantics, and application levels. The model layer supports decision-making through a cross-coupled analytical framework for the coordinated management of water safety, resources, environment, and ecology. Finally, the application layer utilizes virtual-physical mapping and dynamic reasoning to implement a closed-loop management system encompassing forecasting, warning, simulation, and planning. This framework was implemented and validated at the Ye Fan Reservoir in Hubei Province, China. By integrating components like “One Map,” flood dispatching, safety monitoring, early warning, video surveillance, and operational supervision, a three-dimensional perception network was constructed. This deployment significantly improved the precision, reliability, and scientific basis of reservoir operation. The integrated monitoring and management system presented in this paper, driven by heterogeneous sensor networks, provides an effective and generalizable solution for modern reservoir management, with the potential for extension to broader water resource and infrastructure systems. Full article

This study proposes an integrated computer vision system for automated petrological analysis of tight sandstone micro-structures. The system combines Zero-Shot Segmentation SAM (Segment Anything Model), Mask R-CNN (Region-Based Convolutional Neural Networks) instance segmentation, and an improved MetaFormer architecture with Cascaded Group Attention (CGA) attention mechanism, together with a parameter analysis module to form a hybrid deep learning system. This enables end-to-end mineral identification and multi-scale structural quantification of granulometric properties, grain contact relationships, and pore networks. The system is validated on proprietary tight sandstone datasets, SMISD (Sandstone Microscopic Image Segmentation Dataset)/SMIRD (Sandstone Microscopic Image Recognition Dataset). It achieves 92.1% mIoU segmentation accuracy and 90.7% mineral recognition accuracy while reducing processing time from more than 30 min to less than 2 min per sample. The system provides standardized reservoir characterization through automated generation of quantitative reports (Excel), analytical images (JPG), and structured data (JSON), demonstrating production-ready efficiency for tight sandstone evaluation. Full article

This article presents the results of numerical modeling for a hypothetical CO₂-EGS system in the volcanic rocks of the Gorzów Block, Poland. Modeling was carried out in the following stages: in phase 0, modeling of the fracturing process was performed, as a result of which the permeability distribution for the newly created fractured zone was obtained. Next, the process of saturating the EGS reservoir with CO₂ was modeled until pure CO₂ could enter the production well (phase 1). Then, a multi-variant simulation of heat production was performed (phase 2). The obtained results allowed for drawing interesting conclusions: (1) the duration of phase 1 may take several years unless a sufficiently high injection rate of CO₂ is supplied, (2) the higher the injection rate of CO₂, the lower the cumulative storage ratio of CO₂, and (3) most of the CO₂ storage in the formation takes place in phase 1, while even 92% of the CO₂ injected in phase 2 can be recovered via the production well. Despite the environmental benefits connected with structural trapping of CO₂, the Gorzów Block has probably too low formation temperature (145 °C) and too low stimulated volume (~0.1 km³) to deliver satisfactory and stable thermal output. Full article

In response to the technical challenges of traditional reservoir inversion techniques in determining inter-well wavelets and estimating geological statistical parameters, this study proposes an artificial intelligence high-resolution reservoir inversion technique based on seismic waveform constraints. This technology integrates multi-source heterogeneous data such as lithology characteristics, logging curves, and seismic waveforms through a deep learning neural network framework, and constructs an intelligent reservoir prediction model with geological and physical constraints. Results demonstrate that the proposed technique significantly enhances prediction accuracy for thin sand layers by effectively extracting high-frequency seismic information and establishing robust nonlinear mapping relationships. Inversion errors of reservoir parameters were reduced by more than 25%, while a vertical resolution of 0.5 m was achieved. Predictions agreed with actual drilling data with an accuracy of 86%, representing an 18% improvement over traditional methods. In practical applications, the technique successfully supported new well placement, contributing to a 22% increase in initial oil production in the pilot area. Furthermore, this study establishes a standardized technical procedure: “Time–Depth Modeling-Phase-Controlled Interpolation-Intelligent Inversion”. This workflow provides an innovative solution for high-precision reservoir characterization in regions with limited well control and complex terrestrial depositional systems, offering both theoretical significance and practical value for advancing reservoir prediction technology. Full article

Targeting the classification and evaluation of chemical flooding well groups in medium-thick sandstone reservoirs (single-layer thickness: 5–15 m), this study proposes a multi-level classification model based on decision trees. Through the comprehensive analysis of key static factors influencing chemical flooding efficiency, a four-tier classification index system was established, comprising: interlayer/baffle development frequency (Level 1), thickness-weighted permeability rush coefficient (Level 2), reservoir rhythm characteristics (Level 3), and pore-throat radius-based reservoir connectivity quality (Level 4) as its core components. The model innovatively transforms common reservoir physical parameters (porosity and permeability) into pore-throat radius parameters to enhance guidance for polymer molecular weight design, while employing a thickness-weighted permeability rush coefficient to simultaneously characterize heterogeneity impacts from both permeability and thickness variations. Unlike existing classification methods primarily designed for thin-interbedded reservoirs—which consider only connectivity or apply fuzzy mathematics-based normalization—this model specifically addresses medium-thick reservoirs’ unique challenges of interlayer development and intra-layer heterogeneity. Furthermore, its decision tree architecture clarifies classification logic and significantly reduces data preprocessing complexity. In terms of engineering practicality, the classification results are directly linked to well-group development bottlenecks, as validated in the J16 field application. By implementing customized chemical flooding formulations tailored to the study area, the production performance in the expansion zone achieved comprehensive improvement: daily oil output dropped from 332 tons to 243 tons, then recovered to 316 tons with sustained stabilization. Concurrently, recognizing that interlayer barriers were underdeveloped in certain well groups during production layer realignment, coupled with strong vertical heterogeneity posing polymer channeling risks, targeted profile modification and zonal injection were implemented prior to flooding conversion. This intervention elevated industrial replacement flooding production in the study area from 69 tons to 145 tons daily post-conversion. This framework provides a theoretical foundation for optimizing chemical flooding pilot well-group selection, scheme design, and dynamic adjustments, offering significant implications for enhancing oil recovery in medium-thick sandstone reservoirs through chemical flooding. Full article

With the increasing attention to China’s ecological environment protection and the prominence of lake water environment problems, the impact of soil erosion on lake ecosystems has become an important research topic for regional sustainable development. Based on the CiteSpace bibliometric method, this study systematically analyzed 225 research articles on the impact of soil erosion on the water environment of lakes in China in the core collection of Web of Science from 1998 to 2025, aiming to reveal the research hotspots, evolution trends and regional differences in this field. The results show that China occupies a dominant position in this field (209 papers), and the Chinese Academy of Sciences is the core research institution (93 papers). The research hotspots show obvious policy-driven characteristics, which are divided into slow start periods (1998–2007), accelerated growth periods (2008–2015), explosive growth periods (2016–2020) and stable development periods (2021–2025). A keyword cluster analysis identified nine main research directions, including sedimentation effect (#0 cluster), soil loss (#2 cluster) and nitrogen and phosphorus migration (#11 cluster) in the Three Gorges Reservoir area. The study found that the synergistic effects of climate change and human activities (such as land use change) are becoming a new research paradigm, and the Yangtze River Basin, the Loess Plateau and the Yunnan–Guizhou Plateau constitute the three core research areas (accounting for 72.3% of the total literature). Future research should focus on a multi-scale coupling mechanism, a climate resilience assessment and an ecological engineering effectiveness verification to support the precise implementation of lake protection policies in China. This study provides a scientific basis for the comprehensive management of the soil erosion–lake water environment system, and also contributes a Chinese perspective to the sustainable development goals (SDG6 and SDG15) of similar regions in the world. Full article

AlUla, a unique conservation and tourism hub in Saudi Arabia, is undergoing extensive biodiversity restoration efforts, including the reintroduction of threatened wild species. However, interactions among wildlife, domestic, and synanthropic animals in these reserves raise significant concerns about disease transmission to reintroduced species. This study aimed to assess disease risks at the domestic–synanthropic–wildlife interface and identify infectious diseases posing the greatest threat to reintroduced species. A multi-phased prioritization system was developed to guide monitoring based on transmissibility to protected wildlife, susceptibility of reintroduced species, reservoir hosts, vector-borne potential, likelihood of occurrence, and disease severity. A comprehensive expert review identified 61 diseases important to the reintroduced wildlife. From this, 11 priority pathogens were selected for monitoring. A total of 7760 samples were collected from 1367 domestic and synanthropic animals and were analyzed using Real-Time PCR and/or ELISA. All priority pathogens, or prior exposure to these pathogens, were detected. Disease presence was affected by factors such as species, location, health status, and grazing habits. Taken together, these findings underscore the need for robust preventive measures to mitigate disease transmission risks and ensure the sustainability of AlUla’s conservation initiatives. This study also offers a model approach to support reintroduction programs and guide future conservation efforts. Full article

This study presents an integrated geomechanical modeling framework for predicting multi-scale fracture networks and their activity in the Longmaxi Formation shale reservoir, northern Luzhou region, southeastern Sichuan Basin—an area shaped by complex, multi-phase tectonic deformation that poses significant challenges for resource prospecting. The workflow begins with quantitative characterization of key mechanical parameters, including uniaxial compressive strength, Young’s modulus, Poisson’s ratio, and tensile strength, obtained from core experiments and log-based inversion. These parameters form the foundation for multi-phase finite element simulations that reconstruct paleo- and present-day stress fields associated with the Indosinian (NW–SE compression), Yanshanian (NWW–SEE compression), and Himalayan (near W–E compression) deformation phases. Optimized Mohr–Coulomb and tensile failure criteria, coupled with a multi-phase stress superposition algorithm, enable quantitative prediction of fracture density, aperture, and orientation through successive tectonic cycles. The results reveal that the Longmaxi Formation’s high brittleness and lithological heterogeneity interact with evolving stress regimes to produce fracture systems that are strongly anisotropic and phase-dependent: initial NE–SW-oriented domains established during the Indosinian phase were intensified during Yanshanian reactivation, while Himalayan uplift induced regional stress attenuation with limited new fracture formation. The cumulative stress effects yield fracture networks concentrated along NE–SW fold axes, fault zones, and intersection zones. By integrating geomechanical predictions with seismic attributes and borehole observations, the study constructs a discrete fracture network that captures both large-scale tectonic fractures and small-scale features beyond seismic resolution. Fracture activity is further assessed using friction coefficient analysis, delineating zones of high activity along fold–fault intersections and stress concentration areas. This principle-driven approach demonstrates how mechanical characterization, stress field evolution, and fracture mechanics can be combined into a unified predictive tool, offering a transferable methodology for structurally complex, multi-deformation reservoirs. Beyond its relevance to shale gas development, the framework exemplifies how advanced geomechanical modeling can enhance resource prospecting efficiency and accuracy in diverse geological settings. Full article