Search Results (690)

Modern healthcare and engineering both rely on robust reliability models, where handling censored data effectively translates into longer-lasting devices, improved therapies, and safer environments for society. To address this, we develop a novel inferential framework for the ZLindley (ZL) distribution under the improved adaptive progressive Type-II censoring strategy. The proposed approach unifies the flexibility of the ZL model—capable of representing monotonically increasing hazards—with the efficiency of an adaptive censoring strategy that guarantees experiment termination within pre-specified limits. Both classical and Bayesian methodologies are investigated: Maximum likelihood and log-transformed likelihood estimators are derived alongside their asymptotic confidence intervals, while Bayesian estimation is conducted via gamma priors and Markov chain Monte Carlo methods, yielding Bayes point estimates, credible intervals, and highest posterior density regions. Extensive Monte Carlo simulations are employed to evaluate estimator performance in terms of bias, efficiency, coverage probability, and interval length across diverse censoring designs. Results demonstrate the superiority of Bayesian inference, particularly under informative priors, and highlight the robustness of HPD intervals over traditional asymptotic approaches. To emphasize practical utility, the methodology is applied to real-world reliability datasets from clinical trials on leukemia patients and hydrological measurements from River Styx floods, demonstrating the model’s ability to capture heterogeneity, over-dispersion, and increasing risk profiles. The empirical investigations reveal that the ZLindley distribution consistently provides a better fit than well-known competitors—including Lindley, Weibull, and Gamma models—when applied to real-world case studies from clinical leukemia trials and hydrological systems, highlighting its unmatched flexibility, robustness, and predictive utility for practical reliability modeling. 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

River water quality is a direct determinant of both drinking water security and regional economic vitality. However, the hydrochemical trajectories and solute provenance of agricultural streams remain only fragmentarily understood. Here, we examine the Jinqian River—a representative agricultural tributary of the Danjiangkou Reservoir source area for the South-to-North Water Diversion Project—by coupling hydrochemistry with multivariate statistics techniques. The results revealed that the pH values of the river water ranged from 7.55 to 8.30, indicating a weakly alkaline condition. During all three hydrological periods, the concentrations of total nitrogen (TN) exceeded the limits set by the Class Ⅲ surface water quality standards in China, suggesting that the agricultural river has been significantly impacted by human activities. Solute dynamics followed three rainfall-modulated patterns: (i) dilution-driven decreases in the flood season (e.g., Na⁺), (ii) concentration via flushing or evaporative concentration (e.g., SO₄²⁻), and (iii) reservoir-induced damping of seasonal contrasts (e.g., TN), the latter attributable to nitrogen retention behind upstream dams. Geochemical fingerprints reveal that Cl⁻ and Na⁺ originate predominantly from halite dissolution; Ca²⁺, Mg²⁺ and HCO₃⁻ from coupled carbonate–silicate weathering; and SO₄²⁻ from evaporite dissolution. Principal component analysis distills four dominant quality controlling factors: agricultural fertilizers, halite weathering, evaporite dissolution, and domestic effluent. These findings provide a quantitative basis for managing nutrient and salt fluxes in agricultural rivers and for safeguarding water sustainability within water-diversion source regions. Full article

Background: Rainfed rice–potato cropping systems represent an emerging agricultural pattern in Yunnan Province. This study investigates the dynamics of soil nutrient release and microbial community structure under rainfed rice–potato cropping systems. Methods: Four experimental treatments were established using two rice cultivation methods (flooded and rainfed cultivation) as the preceding crop, followed by two distinct potato cultivars: rainfed rice–potato Dianshu 23 (DR), rainfed rice–potato Dianshu 1418 (DY), flooded rice–potato Dianshu 23 (WR), and flooded rice–potato Dianshu 1418 (WY). Soil samples were collected before rice planting and at harvest, as well as before potato planting and at 40-, 80-, and 120-days post-planting. Soil nutrient release dynamics and microbial community composition were analyzed across all treatments. Results: Flooded rice cultivation as the preceding crop exhibited higher soil nutrient depletion compared to rainfed systems, accompanied by more pronounced increases in soil urease and invertase activities. Following potato establishment, rainfed rice–potato systems demonstrated an accelerated release of available nitrogen and potassium during the initial growth period relative to flooded rice–potato systems. At potato harvest, soil urease and invertase activities increased in rainfed rice–potato systems compared to pre-planting levels, while decreasing in flooded rice–potato systems. Proteobacteria constituted the dominant bacterial phylum across all treatments. Rainfed rice cultivation significantly enhanced Cyanobacteria relative abundance, whereas flooded rice cultivation promoted increased Thermodesulfobacteria abundance. Ascomycota dominated fungal communities, with flooded rice showing significantly greater reductions in Ascomycota relative abundance compared to rainfed systems. Rainfed rice–potato systems exhibited superior soil microbial community richness, diversity, and species abundance relative to flooded rice–potato systems. Bacterial genera associated with nitrogen metabolism showed higher relative abundance in rainfed rice–potato systems, as did pathogenic fungal genera. Conclusions: Soil nutrient characteristics and microbial community profiles in rainfed rice–potato cropping systems differ markedly from traditional flooded rice–potato rotation practices. These findings provide valuable insights for optimizing water and nutrient management strategies in rainfed rice–potato cropping systems. Full article

Methanotrophs offer promising avenues for sustainable agriculture and climate mitigation. This study evaluates the efficacy of indigenously isolated methanotrophs, particularly Methylomonas Kb3, as bioinoculants in rice cultivation. Kb3-treated plants exhibited early flowering, increased height, and a grain yield up to 17% higher than that of untreated controls. A mixed inoculation of Methylomonas and Methylomagnum resulted in a 15% increase in yield, indicating limited synergistic benefit. The root-dipping method during transplantation proved to be a practical and scalable inoculation technique for farmers. Genomic analysis revealed that Methylomonas Kb3 harbours genes associated with nitrogen fixation and resistance to heavy metals and antibiotics, potentially underpinning its agronomic performance. Beyond yield enhancement, the application of methanotrophs may contribute to reduced methane emissions in flooded paddy systems, offering dual benefits for both productivity and environmental sustainability. These findings warrant multilocation trials to validate efficacy across diverse agro-climatic zones and support the development of climate-smart biofertilizer strategies. Full article

During water flooding processes, the high viscosity of heavy oil and significant reservoir heterogeneity often lead to severe water channeling and low sweep efficiency. Addressing the limitations of traditional hydrophobically associating polymer-based profile-control agents—such as significant adsorption loss, mechanical degradation during reservoir migration, resulting in a limited effective radius and short functional duration—this study developed a polymeric graded profile-control agent suitable for highly heterogeneous conditions. The physicochemical properties of the system were comprehensively evaluated through systematic testing of its apparent viscosity, salt tolerance, and anti-aging performance. The microscopic oil displacement mechanisms in porous media were elucidated by combining CT scanning and microfluidic visual displacement experiments. Experimental results indicate that the agent exhibits significant hydrophobic association behavior, with a critical association concentration of 1370 mg·L⁻¹, and demonstrates a “low viscosity at low temperature, high viscosity at high temperature” rheological characteristic. At a concentration of 3000 mg·L⁻¹, the apparent viscosity of the solution is 348 mPa·s at 30 °C, rising significantly to 1221 mPa·s at 70 °C. It possesses a salinity tolerance of up to 50,000 mg·L⁻¹, and a viscosity retention rate of 95.4% after 90 days of high-temperature aging, indicating good injectivity, reservoir compatibility, and thermal stability. Furthermore, within a concentration range of 500–3000 mg·L⁻¹, the agent can effectively emulsify Gudao heavy oil, forming O/W emulsion droplets with sizes ranging from 40 to 80 μm, enabling effective plugging of pore throats of corresponding sizes. CT scanning and microfluidic displacement experiments further reveal that the agent possesses a graded control function: in the near-wellbore high-concentration zone, it primarily relies on its aqueous phase viscosity-increasing capability to control the mobility ratio; upon entering the deep reservoir low-concentration zone, it utilizes “emulsion plugging” to achieve fluid diversion, thereby expanding the sweep volume and extending the effective treatment period. This research outcome provides a new technical pathway for the efficient development of highly heterogeneous heavy oil reservoirs. Full article

Rubisco activase (RCA) is an ATP-dependent enzyme that plays a crucial role in plant stress responses by regulating the catalytic activity of Rubisco. However, the alternative splicing and functional characteristics of the RCA gene exhibit notable species-specific diversity. The variable splice forms and functions of the RCA gene in mangrove plants remain poorly understood. Herein, we cloned the RCA cDNA in the leaves of mangrove plant Kandelia candel (L.) in response to combined flooding and salinity stress, and performed systematic expression analysis and functional validation. Our results demonstrated that the RCA gene undergoes alternative splicing to produce two isoforms, designated as KcRCA_l (GenBank accession: MG492021) and KcRCA_s (GenBank accession: MG492022), respectively. The KcRCA_l encodes a 440-amino acid protein (42.49 kDa) belonging to the β-isoforms, while KcRCA_s encodes a 474-amino acid protein (46.10 kDa) classified as the α-isoforms. Moreover, protein structure analysis revealed that both isoforms contain phosphorylation and lysine acetylation modification sites. Phylogenetic analysis indicated that KcRCA shares the closest evolutionary relationship with RCA from Cicer arietinum (chickpea) and Durio zibethinus (durian). Furthermore, RT-qPCR analysis revealed that the expression levels of KcRCA_l and KcRCA_s were significantly upregulated in K. Candel leaves under the combined stress condition. The following functional validation studies in transgenic Arabidopsis demonstrated that overexpression of the KcRCA cDNA enhances the plant’s tolerance to resist flooding and salinity stress while improving antioxidant capacity and increasing RCA and Rubisco activity, thereby maintaining photosynthetic efficiency under combined flooding and salinity stress. Our study not only provides new experimental evidence for understanding the molecular mechanisms of plant flooding and salinity stress, but also offers theoretical foundations for breeding flooding- and salinity-tolerant crops. Full article

In response to the problems encountered during the pressure-driven oil recovery process in low-permeability oil reservoirs, such as slow pressure transmission, poor liquid supply, vulnerability of the reservoir to damage, and difficulties in injection and production, in order to achieve the goal of high-quality water injection development, based on the theories of rock mechanics and seepage mechanics, combined with large-scale physical model experiments, acoustic emission crack monitoring, and microscopic scanning technology, an oil reservoir and fracture model was established to conduct a feasibility analysis of pressure-driven assisted pressure reduction and enhanced injection, and it was successfully applied in the exploration and development practice of the Shengli Oilfield. The research shows the following: (1) During the pressure-driven process, the distribution of the fracture network system is relatively limited. In the early stages of the process, there will be minor fractures, but they do not communicate or activate effectively. The improvement of physical properties and pore-throat structure is negligible. As the injection flow rate increases, the effective fracture network system begins to be established, and the range of fluid coverage begins to expand. With the progress of the pressure-driven process, the hydraulic fractures gradually extend, the number of activated original fractures gradually increases, the communication area between hydraulic fractures and original fractures gradually increases, and the reservoir modification effect gradually improves. (2) Based on the compression cracking experiment of large object molds, it is concluded that generating effective micro-cracks and activating them to form efficient diversion channels is the key to pressure flooding injection. Combining the mechanical characteristics of the rock in the target layer to precisely control the injection speed and injection pressure can maximize the fracture network, thereby improving the reservoir to achieve the purpose of pressure reduction and injection increase. (3) Different pressure flooding injection parameters were set for the low-permeability oil reservoirs in the study area to simulate the fracture network expansion. Finally, it was concluded that the optimal injection speed for fracture expansion was 1.2 m³/min and the optimal total injection volume was 20,000 m³. Through research, the mechanism of pressure-driven injection and the extent of reservoir modification caused by this pressure-driven process have been enhanced in terms of understanding. Full article

Water conveyance channels, as critical components of water diversion projects, feature numerous structures, complex configurations, and intensive operational management requirements, making them vulnerable to multiple risks, such as extreme flooding, channel blockage, structural failures, and management deficiencies. To ensure an accurate assessment of the operational safety risk, this study proposes a comprehensive risk assessment framework that integrates risk probability and risk loss. The former is quantified using the Consequence Reverse Diffusion Method (CRDM), which systematically identifies and categorizes key factors of primary dike failure modes into four domains: hydrological characteristics, channel morphology, engineering structures, and operational management. The latter is assessed by integrating socioeconomic impacts, including population exposure, infrastructure investment, and industrial and agricultural production. A structured assessment framework is established through systematic indicator selection, justified weight assignment, and standardized scoring criteria. Application of the framework to Yangtze-to-Huaihe Water Diversion Project (Henan Reach) reveals that the risk probability across four segments falls within the (1, 3) range, indicating a generally low to moderate risk profile, while channel morphology shows greater spatial variability than hydrological, structural, and management indicators, driven by local differences in crossing structure density, sinuosity, and regime coefficients. Meanwhile, the segments along the Qingshui River face higher risk losses owing to their upstream location and large-scale water supply capacity, resulting in a relatively higher comprehensive risk level. Full article

Disaster headlines often underscore devastation and loss while overlooking success stories where proactive disaster risk reduction (DRRM) measures have averted catastrophe, saved lives, and reduced economic damage. This study addresses the gap in documentation and analysis of DRRM success stories in Africa, particularly within the Southern African Development Community (SADC), arguing that the absence of such narratives hampers a shift from reactive to proactive disaster risk governance. The research aims to extract critical lessons from success stories for enhancing future preparedness and response frameworks. A qualitative research design was employed, integrating document analysis, expert interviews, field observations, and practitioner workshops. Data was triangulated from diverse sources, including national disaster management agency reports (e.g., South Africa’s NDMC, Botswana’s NDMO, Mozambique’s INGC), peer-reviewed literature, UNDRR reports, SADC policy documents, and first-hand experiences from the authors’ consultancy work in the African Union’s biennial DRRM reporting processes. Case studies examined include Mozambique’s response to Cyclone Idai in 2019, South Africa’s drought and flood risk governance (e.g., the 2023 floods in Eastern and Western Cape), and Malawi’s flood resilience programs. Findings reveal that successful DRRM outcomes are driven by a combination of anticipatory governance, community-based preparedness, integration of Indigenous Knowledge Systems (IKSs), and investment in infrastructure and ecosystem-based adaptation. These cases demonstrate that locally embedded, yet scientifically informed, interventions enhance resilience and reduce disaster impacts. The study underscores the relevance of theoretical frameworks such as resilience theory, narrative theory, and social learning in interpreting how success stories contribute to institutional memory and adaptive capacity. Policy recommendations emphasize the need for institutionalizing success-story documentation in national DRRM frameworks, scaling up community engagement in risk governance, and fostering regional knowledge-sharing platforms within the SADC. Furthermore, the paper advocates for making DRRM success stories more visible and actionable to transition toward more anticipatory, inclusive, and effective disaster risk management systems. Full article

River ice is a common feature in most Canadian rivers and streams during the cold season. River channel hydraulics under ice conditions may cause higher water levels at a relatively lower discharge compared to the open-water flood events. Elevated water levels resulting from river ice processes throughout fall freeze-over, mid-winter, and spring break-up are important hydrologic events with diverse morphological, ecological, and socio-economic impacts. This study analyzes the timing of maximum water levels (occurring during freeze-over, spring break-up, and open-water periods) and the typology of maximum ice-related events (at freeze-over, mid-winter, and spring break-up) using data from the Canadian River Ice Database. The study also compares annual maximum water levels during the river ice and open-water periods at selected hydrometric stations from 1966 to 2015, divided into two 25-year windows: 1966–1990 and 1991–2015. A return period classification method was applied to define ice-influenced, open-water, and mixed-regime conditions. The results indicate that the majority of ice-influenced maximum water levels occurred during spring break-up (~79% in 1966–1990 and ~69% in 1991–2015), followed by fall freeze-up (~13% and ~23%) and mid-winter break-up (~8% and ~7%) for the two periods, respectively. Among 15 stations analyzed for 1966–1990 and 42 stations for 1991–2015, the proportion of annual maximum water levels dominated by open-water conditions increased from 47% to 55%, while ice-dominated events decreased from 13% to 12%, and mixed-regime events dropped from 40% to 33%. However, a focused comparison of eight common stations revealed minimal change in the distribution of water level-generating events between the two periods. The findings offer valuable insights into the spatial distribution of maximum water level-generating mechanisms across Canada. Full article

In the context of increasing climate variability and flood risk, this study explores how long-standing agricultural practices in the Hunter Valley, New South Wales, Australia, have fostered flood resilience through the integration of local agro-environmental knowledge and geomorphologic conditions. Employing a morpho-typological framework, the research identifies three flood adaptation landscape types (FALTs)—rolling hills, foot slopes, and flood plains—each reflecting distinct interactions between landform, soil, biodiversity, hydrology, and viticultural management. Through geospatial analysis, field surveys, and interviews with local farmers, the study reveals how adaptive strategies—ranging from flood avoidance to attenuation and acceptance—have evolved in response to site-specific hydrological and ecologic dynamics. These strategies demonstrate a form of ‘sponge landscape’ design, where agricultural systems are co-shaped with natural processes to enhance systemic resilience and long-term productivity. The findings underscore the value of preserving biocultural legacies and suggest that spatially explicit, context-based approaches to flood adaptation can inform sustainable landscape planning and climate resilience strategies in other rural regions. The FALT framework offers a replicable methodology for identifying flood adaptation patterns across diverse agricultural systems in Australia, supporting proactive land use planning and nature-based solutions. This research contributes to the discourse on climate adaptation by bridging traditional environmental knowledge with contemporary planning frameworks, offering practical insights for policy, landscape management, and rural development. Full article

The increasing frequency and severity of natural disasters—such as floods, storms, droughts, and earthquakes—have created a growing demand for temporary housing. These facilities must be rapidly deployed to provide safe, functional living environments for displaced individuals. This study proposes a design methodology for temporary housing exteriors using the text-to-image capabilities of generative artificial intelligence (GenAI) to address urgent post-disaster housing needs. The approach aims to improve both the efficiency and practicality of early-stage design processes. The study reviews global trends in temporary housing and the architectural applications of GenAI, identifying five key environmental factors that influence design: type of disaster, location and climate, duration of residence, materials and structure, and housing design. Based on these factors, hypothetical disaster scenarios were developed using ChatGPT, and corresponding exterior designs were generated using Stable Diffusion. The results show that diverse, scenario-specific design alternatives can be effectively produced using GenAI, demonstrating its potential as a valuable tool in architectural planning for disaster response. Expert evaluation of the generated designs confirmed their ability to adhere to text prompts but revealed a significant gap in terms of architectural plausibility and practical feasibility, highlighting the essential role of expert oversight. This study offers a foundation for expanding GenAI applications in emergency housing systems and supports the development of faster, more adaptable design solutions for communities affected by natural disasters. Full article

Cloud cover can delay landslide detection in optical satellite imagery for weeks, complicating disaster response. Synthetic Aperture Radar (SAR) backscatter imagery, which is widely used for monitoring floods and avalanches, remains underutilised for landslide detection due to a limited understanding of landslide signatures in SAR data. We developed a conceptual model of landslide expression in SAR backscatter (σ°) change images through iterative investigation of over 1000 landslides across 30 diverse study areas. Using multi-temporal composites and dense time series Sentinel-1 C-band SAR data, we identified characteristic patterns linked to land cover, terrain, and landslide material. The results showed either increased or decreased backscatter depending on environmental conditions, with reduced visibility in urban or mixed vegetation areas. Detection was also hindered by geometric distortions and snow cover. The diversity of landslide expression illustrates the need to consider local variability and multi-track (ascending and descending) satellite data in designing representative training datasets for automated detection models. The conceptual model was applied to three recent disaster events using the first post-event Sentinel-1 image, successfully identifying previously unknown landslides before optical imagery became available in two cases. This study provides a theoretical foundation for interpreting landslides in SAR imagery and demonstrates its utility for rapid landslide detection. The findings support further exploration of rapid landslides in SAR backscatter data and future development of automated detection models, offering a valuable tool for disaster response. Full article

Huge amounts of water and chemicals have been injected into subsurface oil reservoirs in secondary and tertiary oil recovery processes. Although the effects of injected water and chemicals on microbial communities have been investigated, knowledge about their long-term dynamic changes in oil reservoirs remains limited. To address this gap, we used 16S rRNA sequencing from cDNA and chemical analysis to track the dynamic changes in microbial communities in oil reservoirs under a long-term flooding operation over three years and five months using bio-chemical flooding in the Daqing Oilfield, China. Researchers observed dynamic changes in microbial composition and diversity during the flooding process. Long-term bio-chemical drainage leads to alterations in dominant bacterial community structure, with a decrease in methanogenic archaeal abundance. Bacterial metabolic functions remained stable, but archaeal functions changed notably. Our results indicate that the microbial community and its functions in the oil reservoirs have experienced significant dynamic changes under the long-term flooding intervention of bio-chemical flooding, which opens up a new window for further understanding the impact of injected water and chemicals on microbial community in oil reservoirs and expands our knowledge about the role of microbial community changes in reservoirs under the flooding process. Full article