Search Results (1,265)

The development of cross-border hydrogen energy value chains involves complex interactions between technological, regulatory, and logistical subsystems. Static assessment models often fail to capture the dynamic response of these coupled systems to external perturbations. This study addresses this gap by proposing the Dual Carbon Cooperation Index (DCCI), a data-driven framework designed to quantify the synergy efficiency of the China–Korea hydrogen ecosystem. We construct a dynamic state estimation model integrating three coupled dimensions—Technology Synergy, Regulatory Alignment, and Supply Chain Resilience—utilizing an adaptive weighting algorithm (Triple Dynamic Response). Based on multi-source heterogeneous data (2020–2024), the model employs Natural Language Processing (NLP) for vectorizing unstructured regulatory texts and incorporates an exogenous signal detection mechanism (GPR). Empirical results reveal that the ecosystem’s composite synergy score recovered from 0.38 to 0.50, driven by robust supply chain resilience but constrained by high impedance in technological transfer protocols. Crucially, the novel dynamic weighting algorithm significantly reduces state estimation error during high-volatility periods compared to static linear models, as validated by bootstrapping analysis (1000 resamples). The study provides a quantitative engineering tool for monitoring ecosystem coupling stability and proposes a technical roadmap for reducing system constraints through secure IP data architectures and synchronized standard protocols. Full article

Understanding the molecular basis of heat tolerance in microalgae is crucial for developing resilient strains for industrial biotechnology. This study identified two desert Chlorella strains, XDA024 (thermotolerant) and XDA121 (heat-sensitive), through short-term thermal screening. The thermotolerant strain XDA024 survived exposure to 50 °C for 3 h, whereas XDA121 succumbed within 1 h at 40 °C. Physiological analyses revealed that the superior heat resistance of XDA024 was associated with enhanced activities of key antioxidant enzymes, including superoxide dismutase, catalase, and peroxidase, which effectively mitigated oxidative damage, alongside an elevated proline content contributing to osmoregulation. Transcriptomic profiling under acute heat stress (45 °C, 3 h) revealed that the unique thermotolerance of XDA024 was underpinned by the upregulation of genes related to photosystem stability and lipid synthesis, processes supported by activated calcium signaling and antioxidant pathways. In contrast, XDA121 exhibited significant downregulation of photosynthesis-related genes and promoted lipid degradation, resulting in membrane instability. Exogenous application of phosphatidylethanolamine (PE) and monoethanolamine (MEA) markedly increased the survival rate of XDA121 by more than threefold, primarily by alleviating membrane damage through enhanced membrane integrity and modulated antioxidant enzyme activities. These findings indicate that thermotolerance in desert Chlorella (Chlorophyta) is governed by the integrated coordination of antioxidant defense mechanisms, lipid metabolism, and photosystem protection. This research provides crucial insights and practical strategies for engineering heat-resistant microalgal strains for sustainable biofuel and bioproduct production. Full article

Drought is a pervasive and intensifying climate hazard with profound implications for food security, water availability, and socioeconomic stability, particularly in sub-Saharan Africa. In Kenya, where over 80% of the landmass comprises arid and semi-arid lands (ASALs), recurrent droughts have become a critical threat to agricultural productivity and climate resilience. This study presents a comprehensive spatiotemporal analysis of seasonal drought dynamics in Kenya for June–July–August–September (JJAS) from 2000 to 2024, leveraging remote sensing-based drought indices and geospatial analysis for climate risk planning. Using the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Soil Moisture Anomaly (SMA), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) anomaly, a Combined Drought Indicator (CDI) was developed to assess drought severity, persistence, and impact across Kenya’s four climatological seasons. Data were processed using Google Earth Engine and visualized through GIS platforms to produce high-resolution drought maps disaggregated by county and land-use class. The results revealed a marked intensification of drought conditions, with Alert and Warning classifications expanding significantly in ASALs, particularly in Garissa, Kitui, Marsabit, and Tana River. The drought persistence analysis revealed chronic exposure in drought conditions in northeastern and southeastern counties, while cropland exposure increased by over 100% while rangeland vulnerability rose nearly 56-fold. Population exposure to drought also rose sharply, underscoring the socioeconomic risks associated with climate-induced water stress. The study provides an operational framework for integrating remote sensing into early warning systems and policy planning, aligning with global climate adaptation goals and national resilience strategies. The findings advocate for proactive, data-driven drought management and localized adaptation interventions in Kenya’s most vulnerable regions. Full article

In the field of earthquake-resistant design, there is an increasing emphasis on evaluating buildings as integrated systems rather than as assemblies of independent components. Hybrid wall systems based on structural steel and reinforced concrete offer a promising alternative to existing approaches by combining the stiffness and toughness of concrete with the ductility and flexibility of steel, which enhances resilience and seismic performance. The objective of this scientific study is to obtain preliminary analytical estimates of the earthquake response of a prototype hybrid steel RC coupled wall building that is being developed as part of a joint research program between the National Center for Research on Earthquake Engineering (NCREE) and New Zealand’s Centre for Earthquake Resilience (QuakeCoRE). Nonlinear response history analyses were carried out on the prototype building, using scaled ground motions and nonlinear hinge properties assigned to the primary lateral force resisting elements to replicate the expected inelastic behavior of the hybrid system. The results were used to evaluate story drift demands, deformation patterns, coupling beam behavior, and buckling restrained brace behavior, providing a system-level perspective on the expected earthquake performance of the proposed hybrid wall system. To deepen the current experimental understanding of the seismic behavior of the proposed hybrid structural system, a large-scale shaking table test is planned at NCREE as the next stage of this collaborative research. Full article

Microalgae display remarkable resilience to harsh environments, partly through the biosynthesis of diverse secondary metabolites. Cyanobacteria and red algae are well known to produce mycosporine-like amino acids (MAAs)—low-molecular-weight, water-soluble UV-absorbing compounds with anti-inflammatory, anticancer, and antimicrobial activities. By contrast, green microalgae typically lack detectable MAAs under standard conditions, and their responses under abiotic stress remain poorly characterized. Here, we investigated the freshwater green microalga Jaagichlorella luteoviridis grown under three stressors (salinity, heat, and UV) and assessed MAA induction. High-performance liquid chromatography (HPLC) revealed that stressed cultures accumulated multiple MAAs, whereas untreated controls showed no such accumulation. All stress treatments (UV, salinity, and heat) produced a substantial increase in peak intensity at 323–350 nm, whereas the control samples showed significantly lower absorption in this region. We also optimized an MAA extraction protocol suitable for “green” downstream applications in the pharmaceutical, nutraceutical, and cosmeceutical sectors and formulated an emulsion showing preliminary positive results and exhibiting an increased SPF index from 3.60 (control) to 3.78 when 0.2% MAA extract was added. Transcriptomic profiling against a reference genome revealed stress-specific differential gene expression and overexpression of specific genes of the MAA pathway, like ArioC and AroM/Aro1 SAM methyltransferases, thus identifying candidate targets for engineering enhanced MAA production. Given market demand for environmentally friendly and safe bioactives, microalgae represent a promising source of these valuable molecules. Full article

Agricultural systems face mounting pressures from climate change, as rising temperatures, elevated CO₂, and shifting precipitation patterns intensify plant disease outbreaks worldwide. Conventional strategies, such as breeding for resistance, pesticides, and even transgenic approaches, are proving too slow or unsustainable to meet these challenges. Synthetic biology offers a transformative paradigm for reprogramming plant immunity through genetic circuits, RNA-based defences, epigenome engineering, engineered microbiomes, and artificial intelligence (AI). We introduce the concept of synthetic immunity, a unifying framework that extends natural defence layers, PAMP-triggered immunity (PTI), and effector-triggered immunity (ETI). While pests and pathogens continue to undermine global crop productivity, synthetic immunity strategies such as CRISPR-based transcriptional activation, synthetic receptors, and RNA circuit-driven defences offer promising new avenues for enhancing plant resilience. We formalize synthetic immunity as an emerging, integrative concept that unites molecular engineering, regulatory rewiring, epigenetic programming, and microbiome modulation, with AI and computational modelling accelerating their design and climate-smart deployment. This review maps the landscape of synthetic immunity, highlights technological synergies, and outlines a translational roadmap from laboratory design to field application. Responsibly advanced, synthetic immunity represents not only a scientific frontier but also a sustainable foundation for climate-resilient agriculture. Full article

The degradation of vegetation cover and the vulnerability of urban market gardening systems to climate risks are a major challenge for food security in peri-urban areas. This study analyzes the spatio-temporal dynamics of vegetation using the NDVI and assesses its correspondence with producers’ perceptions of hydroclimatic impacts. NDVIs were extracted from the MODIS MOD13Q1v6.1 product via Google Earth Engine, with a spatial resolution of 250 m × 250 m and a temporal resolution of 16 days, then processed in Python v3.14.0 using the xarray library. Additionally, 369 producers in Grand Nokoué were surveyed about the risks of flooding, drought, and heat waves, as well as the adaptation strategies they implement. The results reveal a decline in areas with a moderate to high NDVI (between 0.41 and 0.81) and an expansion of areas with a low or very low NDVI (below 0.41), reflecting increased fragmentation and degradation of vegetation cover. Producers’ perceptions confirm this vulnerability and reveal different strategies depending on the type of crop and risk, including irrigation, temporary abandonment of plots, agroforestry, and the adoption of resilient crops. These observations highlight the need to implement targeted policies and appropriate agroecological practices in order to strengthen the resilience of urban market gardening systems to extreme climate risks. Full article

This study develops a comprehensive two-stage hybrid framework to forecast food prices in Türkiye, addressing inflation prediction challenges in volatile emerging markets where sample sizes are limited. In the first stage, systematic relationship analyses—comprising correlation, ARDL, cointegration, and Granger causality tests—identified ten key macroeconomic predictors from Central Bank datasets. In the second stage, we evaluated diverse predictive models, including XGBoost, Gradient Boosting, Ridge, LSTM, and SVR, using rice prices as a pilot case. A critical methodological contribution is the empirical comparison of feature engineering strategies; results demonstrate that traditional “smoothing” techniques dilute volatility signals, whereas the “Log-Return Transformation Strategy” strategy significantly improves accuracy. XGBoost emerged as the champion model, achieving a remarkable R² of 0.932 (MAE: 1.68 TL) on the test set. To strictly validate this performance against small-sample limitations, a Recursive Walk-Forward Validation was conducted, confirming the model’s robustness with a strong R² of 0.870 over a 31-month rolling simulation. Furthermore, Robust Rolling SHAP analysis identified Insurance and Transportation costs as primary drivers, evidencing a strong cost-push mechanism and inflation inertia. These findings integrate econometric rigor with machine learning transparency, offering resilient early warning tools for sustainable inflation management. Full article

Hunan Province from 2000 to 2023 is the study area. Based on NOAA precipitation data and county-level rice yield statistics in Hunan Province, the Mann–Kendall test, extreme precipitation indices, and wavelet analysis examine the spatial and temporal evolution characteristics of extreme precipitation and its multi-scale impact on rice yield. The results show that the extreme precipitation in Hunan Province showed a stable pattern of fluctuation, and the main extreme precipitation indexes had no significant change trend. The spatial distribution showed a pattern of “high value in central-northern Hunan and stable in southern Hunan”, and the precipitation was concentrated in June–August. The rice yield showed the characteristics of “stable increase in the core area, intensified fluctuation in the transition area, and continuous shrinkage in the marginal area”, and the Dongting Lake Plain was a high-yield and stable area. Multi-scale analysis shows significant coupling between extreme precipitation and yield: in the 4–8-year cycle, the peak value of precipitation lags behind the response of 1–2 years, and changes synchronously in a short period. The response of rice to extreme precipitation showed a threshold-type nonlinear characteristic. Moderate wetting was beneficial to stable yield, while the yield decreased significantly when the intensity or continuous precipitation exceeded the threshold. Hunan’s rice system has strong climate resilience but requires a multi-scale climate-adaptive agricultural system via engineering, technology, and policy for long-term stability and sustainable grain production. Full article

This study creates and refines a risk–effectiveness–integrated dynamic simulation framework that brings together risk and effectiveness factors affecting qualified workforce allocation in multi-project contexts, specifically in the construction of industrial production facilities. Based on a case study of three overlapping projects in West Java, Indonesia, this study examines the requirements for an expert workforce across the Engineering, Procurement, and Construction (EPC) phases. Conventional mitigation measures generally assume that a qualified expert workforce is immediately available. However, hiring the right personnel with specific qualifications for a project takes time. To fill this gap, this paper presents a system dynamics-based model that explicitly integrates quantified project risks and execution effectiveness to determine expert workforce requirements at the multi-project level. This aspect is often addressed implicitly in the existing workforce planning approaches. This mixed-methods strategy includes a literature review, variable validation, simulation modeling, and case analysis. The results show that workforce planning based on integrated risk and effectiveness factors significantly improves project delivery by anticipating expert workforce shortages and reducing the need for reactive solutions. Model validation using real project data demonstrates that the simulated expert workforce demand reproduces both the average behavior and variability observed in real-world practice, satisfying quantitative behavioral validation criteria across projects and the EPC phases. The model contributes to sustainability by enhancing long-term workforce resilience, reducing resource waste, and supporting more efficient industrial project delivery. Full article

Background: Aging brains are shaped by a persistent dialogue between declining neurogenesis and rising neuroinflammation. Neural stem cells progressively lose regenerative capacity, while microglia and astrocytes shift toward maladaptive states that erode synaptic plasticity and cognition. This convergence defines inflammaging, a slow yet relentless process that undermines resilience. However, the field remains hampered by critical gaps: incomplete mapping of microglial heterogeneity, poorly understood epigenetic scars from inflammasome signaling, lack of longitudinal data, unclear niche-specific immune mechanisms, and uncertain cross-species relevance. This review addresses these pressing barriers, aiming to transform fragmented insights into actionable strategies. Summary: I chart how neurogenesis and neuroinflammation operate in continuous dialogue, identify five major knowledge gaps, and evaluate strategies to reprogram this interaction. Approaches include longitudinal imaging, niche-focused immunomodulation, glial subtype reprogramming, brain-penetrant inflammasome inhibitors, and CRISPR-based epigenetic editing. Each strategy is mapped against translational potential, short-term feasibility, and long-term vision, with emphasis on how mechanistic precision can guide clinical innovation. Conclusions: Here I highlight that neurogenic potential is not entirely lost with age but may be preserved or restored by tuning immune and epigenetic environments. This review proposes a roadmap for reshaping the aging brain’s fate, offering mechanistically grounded strategies to delay cognitive decline. Beyond neurology, the work underscores a broader principle: by integrating cellular plasticity with immune modulation, science edges closer to re-engineering resilience across the lifespan. Full article

Salinity shock severely impairs the partial denitrification/anammox (PD/A) process, leading to prolonged functional deterioration and slow reactivation of anaerobic ammonium-oxidizing bacteria (anammox). To develop an effective strategy for mitigating salinity-induced inhibition, this study systematically examined the role of exogenous hydroxylamine (NH₂OH) in accelerating PD/A recovery using short-term batch assays and long-term reactor operation. Hydroxylamine exhibited a clear concentration-dependent effect on system reactivation. In batch tests, low-dose hydroxylamine (10 mg/L) markedly enhanced anammox activity, increasing the ammonium oxidation rate to 5.5 mg N/(g VSS·h), representing a 42.5% increase, indicating its potential to stimulate key nitrogen-transforming pathways following salinity stress. During continuous operation, hydroxylamine at 5 mg/L proved optimal for restoring reactor performance, achieving stable nitrogen removal with 87% NH₄⁺-N removal efficiency. The nitrite transformation ratio (NTR) reached approximately 80% within 13 cycles, 46 cycles ahead of the control, while simultaneously promoting the enrichment of key functional microbial taxa, including Thauera and Candidatus Brocadia. Hydroxylamine addition also triggered the production of tyrosine- and tryptophan-like proteins within extracellular polymeric substances, which enhanced protective and metabolic functionality during recovery. In contrast, a higher hydroxylamine dosage (10 mg/L) resulted in persistent NO₂⁻-N accumulation, substantial suppression of Candidatus Brocadia (declining from 0.67% to 0.09%), and impaired system stability, highlighting a dose-sensitive threshold between stimulation and inhibition. Overall, this study demonstrates that controlled low-level hydroxylamine supplementation can effectively reactivate salinity-inhibited PD/A systems by enhancing nitrogen conversion, reshaping functional microbial communities, and reinforcing stress-response mechanisms. These findings provide mechanistic insight and practical guidance for improving the resilience and engineering application of PD/A processes treating saline wastewater. Full article

Soil liquefaction remains a critical geotechnical hazard during earthquakes, posing significant risks to infrastructure and urban resilience. Traditional empirical methods, while practical, often fall short in capturing complex parameter interactions and providing interpretable outputs. This study presents an interpretable machine learning (IML) framework for soil liquefaction assessment using Rough Set Theory (RST) to generate a transparent, rule-based predictive model. Leveraging a standardized SPT-based case history database, the model induces IF–THEN rules that relate seismic and geotechnical parameters to liquefaction occurrence. The resulting 25-rule set demonstrated an accuracy of 86.2% and strong alignment (93.8%) with the widely used stress-based semi-empirical model. Beyond predictive performance, the model introduces scenario maps and parameter interaction diagrams that elucidate key thresholds and interdependencies, enhancing its utility for engineers, planners, and policymakers. Notably, the model reveals that soils with high fines content can still be susceptible to liquefaction under strong shaking, and that epicentral distance plays a more direct role than previously emphasized. By balancing interpretability and predictive strength, this rule-based approach advances site-adaptive, explainable, and technically grounded liquefaction assessment—bridging the gap between traditional methods and intelligent decision support in geotechnical engineering. Full article

This study addresses the key scientific problem of urban safety in complex, hazard-inducing geological environments by focusing on representative towns in the Three Gorges Reservoir area. Through the integrated use of field investigations, numerical simulations, and multivariate statistical analysis, we developed a comprehensive model for assessing geological safety risk in reservoir-area towns. A four-tier deep safety evaluation system was constructed for two types of hazard-inducing geological environments, and a classification scheme for shallow susceptibility was proposed. On this basis, a five-tier integrated urban geological safety risk evaluation model was established that combines deep safety level, engineering sensitivity, shallow susceptibility, and prevention difficulty. The model exhibited strong performance (pseudo R² ≥ 0.914, p < 0.001) and indicates that risk is predominantly moderate (Grade III), with 85.7% of the 21 representative areas in Wushan, Fengjie, and other “2 + 4” towns falling into this category. Overall, the results provide an operational tool to support disaster risk reduction, risk-informed land-use governance, and resilient infrastructure planning, thereby contributing to sustainable urban development in reservoir-side mountainous regions. Full article

This study examines the impact of extreme weather events on electrical transmission lines, with a particular focus on high-impact, low-probability (HILP) meteorological conditions. Investigating how these conditions affect transmission lines and the potential effects of power outages is crucial for the reliability and continuity of electrical grids. The study conducts a comprehensive review of the literature on the effects of extreme weather events on electrical grids. Specifically, it categorizes and analyzes faults occurring on transmission lines caused by high-impact, low-probability meteorological conditions such as storms, hurricanes, and ice storms. Identifying and classifying these faults is a fundamental step in enhancing the reliability of power systems. Another focus of the study is examining various strategies to prevent power outages, including probabilistic modeling and resilience enhancement technologies. Solutions such as the development of advanced warning systems, design modifications to enhance the physical resilience of transmission lines, and emergency response plans have the potential to increase the reliability of electrical grids. In conclusion, the findings of this study contribute significantly to understanding the impact of HILP meteorological conditions on electrical transmission lines and identifying measures to enhance the reliability of electrical grids. The results of this study can provide valuable guidance to planners, engineers, and decision-makers in the energy sector. Full article