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Landslide databases are a potential tool for the analysis of landslide susceptibility, hazard, and risk. Additionally, the spatio-temporal distribution of landslides and their correlation with their triggering factors are inputs that facilitate the evaluation of landslide prediction models and the determination of thresholds necessary for early warning systems (EWS). This study presents an analysis of four widely known global databases—the International Disaster database (EM-DAT), the Disaster Inventory System (DesInventar), the Global Landslide Catalog (GLC), and the Global Fatal Landslide database (GFLD)—which contain relevant landslide information for different regions of the world. These databases were analysed and compared by means of the spatio-temporal distributions of their records. Subsequently, these databases were merged and depurated to obtain a more robust database, namely the Unified Global Landslide Database (UGLD), with 161 countries, 37,946 landslides, and 185,753 fatalities registered between 1903 and 2020. The merging process among the databases resulted in a small number of repeated landslides, indicating that the databases collect very different landslide information and complement each other. Finally, an update of the spatial and temporal analysis of landslides in the world was performed with the new database, in which patterns, trends, and the main triggers were presented and analysed. The results obtained from the analysis of the UGLD database show the American and Asian continents as the continents with the highest number of landslides and associated fatalities, showing a bimodal and unimodal annual temporal pattern, respectively. Regarding the most frequent triggers of landslides, rainfall, anthropogenic intervention, and earthquakes stand out.

Direct access to internal temperature readings in lithium-ion batteries provides the opportunity to infer physical information to study the effects of increased heating, degradation, and thermal runaway. In this context, a method to insert temperature sensors into commercial 18650 cells to determine the short- and long-term effects through characterization testing is developed. Results show that sensor insertion only causes a decrease in capacity of 0.5–2.3%, and an increase in DC resistance of approximately 15 mΩ. The temperatures of the modified cells are approximately 0.5 °C higher than the control cells, the difference between the internal and external temperature readings of the modified cells is approximately 0.4 °C, and the modified cells exhibit the same temperature behavior and trend during cycling as the control cells. The cells are able to operate and collect data for 100–150 cycles before their capacities fade and resistances increase beyond what is observed in the control cells. The results of the testing show that cells modified with internal temperature sensors provide useful internal temperature data for cells that have experienced little or no cyclic aging.

This paper presents a comprehensive framework for real-time energy management in microgrids integrating distributed renewable energy sources and demand response (DR) programs. To address the inherent uncertainties in key operational variables—such as load demand, wind speed, solar irradiance, and electricity market prices—this study employs a probabilistic modeling approach. A two-stage stochastic optimization method, combining mixed-integer linear programming and optimal power flow (OPF), is developed to minimize operational costs while ensuring efficient system operation. Real-time dynamic pricing mechanisms are incorporated to incentivize consumer load shifting and promote energy-efficient consumption patterns. Three microgrid scenarios are analyzed using one year of real historical data: (i) a grid-connected microgrid without DR, (ii) a grid-connected microgrid with 10% and 20% DR-based load shifting, and (iii) an islanded microgrid operating under incentive-based DR contracts. Results demonstrate that incorporating DR strategies significantly reduces both operating costs and reliance on grid imports, especially during peak demand periods. The islanded scenario, while autonomous, incurs higher costs and highlights the challenges of self-sufficiency under uncertainty. Overall, the proposed model illustrates how the integration of real-time pricing with stochastic optimization enhances the flexibility, resilience, and cost-effectiveness of smart microgrid operations, offering actionable insights for the development of future grid-interactive energy systems.

Flash sintering has attracted significant attention as its remarkably rapid densification process at low sintering furnace temperature leads to the retention of fine grains and enhanced dielectric properties. However, high-temperature mechanical behaviors of flash-sintered ceramics remain poorly understood. Here, we present high-temperature (up to 600 °C) in situ compression studies on flash-sintered yttria-stabilized zirconia (YSZ). Below 400 °C, the YSZ exhibits high ultimate compressive strength exceeding 3.5 GPa and high inelastic strain (~8%) due primarily to phase transformation toughening. At higher temperatures, crack nucleation and propagation are significantly retarded, and prominent plasticity arises mainly from dislocation activity. The high dislocation density induced in flash-sintered ceramics may have general implications for improving the plasticity of sintered ceramic materials. Flash sintering allows for rapid ceramic processing, but the mechanical behavior of such ceramics remains poorly understood. Here, the authors compress micropillars of yttria stabilized zirconia to show flash sintering promotes outstanding plasticity.

Restoration of forests in low- and middle-income countries (LMICs) has the potential to contribute to international carbon mitigation targets. However, high upfront costs and variable cashflows are obstacles for many landholders. Carbon payments have been promoted as a mechanism to incentivize restoration and economists have suggested cost-sharing by third parties to reduce financial burdens of restoration. Yet empirical evidence to support this theory, based on robust, dynamic field sampling is lacking. Here we use large, long-term datasets from Panama to evaluate the financial prospects of three forest restoration methods under different cost-sharing and carbon payment designs where income is generated through timber harvests. We show some, but not all options are economically viable. Further work combining growth and survival data from field trials with more sophisticated financial analyses is essential to understanding barriers and realizing the potential of forest restoration in LMICs to help meet global carbon mitigation commitments. Forest restoration in LMICs can contribute to global C mitigation targets. Here, the authors assess the economic feasibility of forest restoration methods in Panama, i.e. natural regeneration, native species plantings, and enrichment planting, showing that not all methods are economically viable.

The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn 2 O 4 chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97 R 2 score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries.

The current study proposes a strategy for sensing fault detection in the secondary control of an isolated Microgrid based on a high-order Sliding Mode Robust Observers design. The proposed strategy’s main objective is to support future diagnostic and fault tolerance systems in handling these extreme situations. The proposal is based on a generation system and a waste management system. Four test scenarios were generated in a typical Microgrid to validate the designed strategy, including two Battery Energy Storage Systems in parallel, linear, and non-linear loads. The scenarios included normal grid operation and three types of sensing faults (abrupt, incipient, and random) directly affecting the secondary control of a hierarchical control strategy. The results showed that the proposed strategy could provide a real-time decision for detection and reduce the occurrence of false alarms in this process. The effectiveness of the fault detection strategy was verified and tested by digital simulation in Matlab/Simulink R2023b.

In this research, an alternative methodology is proposed for the location of Static VAR Compensators (SVC) in power systems, considering the reconfiguration of reactive power flows through the optimal switching of the transmission stage, which resembles the contingency restriction N-1 usually considered in transmission expansion planning. Based on this methodology, the contingency index was determined, which made it possible to determine which is the contingency that generates the greatest voltage degradation in the system. For the quantification of reactive flows, optimal AC power flows were used, which minimize the operating costs of the power system subject to transmission line switching restrictions, line charge-ability, voltages and node angles. To determine the node in which the compensation should be placed, the contingency index criterion was used, verifying the voltage profile in the nodes. The proposed methodology was tested in the IEEE test systems of 9, 14 nodes and large-scale systems of 200, 500 and 2000 bus-bars; to verify that the proposed methodology is adequate, the stability of the EPS was verified. Finally, the model allows satisfactorily to determine the node in which the SVC is implemented and its compensation value.

Some physically based landslide models analyse pore water pressure changes due to rainfall infiltration and its effects on slope stability. The physically-based model TRIGRS has been successfully used in rainfall-induced shallow landslide assessments in different studies around the world; nevertheless, evaluating its performance in tropical mountain terrains, such as the Colombian Andes, is necessary. In this study, the TRIGRS model was applied to the La Arenosa basin (San Carlos, Colombia) and ROC (receiver operating characteristic) analysis was used to assess its effectiveness (performance) at predicting areas susceptible to shallow landslides in this tropical mountainous area. The results were compared with those obtained using the SHIA_Landslide (Simulación Hidrológica Abierta, or SHIA, in Spanish) and SHALSTAB models in the same case study. The three models performed well, especially TRIGRS and SHIA_Landslide. The predictive results using TRIGRS were thoroughly analysed, describing the effect of the slope angle and its relationship with the estimated soil depth on the variation of the pressure head and the factor of safety (FS) during the simulated rainfall event. The high dependence of FS on soil thickness demonstrated that defining this variable must be carefully accomplished. The results suggest that TRIGRS can be a valuable tool in tropical mountain terrains, such as the Colombian Andes basins, and it can be useful despite the lack of data and the high parameter uncertainty that is common in many study areas.

This study investigates the effects of high levels of photovoltaic (PV) generation on the unbalanced distribution network using the quasi-dynamic simulation method on DIgSILENT PowerFactory. We are motivated by the need to diversify the national energy matrix, following the power blackout that occurred in Ecuador in 2024 and the energy limitations characterized by the use of fossil fuels. For this purpose, we deployed the simulation of the PJM 13-Node Test Feeder, which is a low-voltage distribution network and mimics the U.S. system, and represents a realist distribution network with residential and commercial load profiles. We simulated realistic PV generation dynamics for a typical day, capturing stochastic solar irradiance, ambient temperature variation, and the impacts of cloud cover. In those conditions, PV generation reached 31.6% of the system total load. We found that during peak irradiance hours, the voltage levels on certain nodes, predominantly low-load buses, exceed nominal levels. The average power factor is noted to diminish by 0.90 p.u to 0.82 p.u at the feeder bus, and further drops to 0.35 p.u at the most PV-penetrated site. While distributed PV generation can effectively reduce line loading and improve energy efficiency, without reactive power compensation, the highest penetration PV generation scenario could result in deterioration of voltage stability and power quality. The prescribed quasi-dynamic framework is practical and computationally feasible, allowing for the assessment of operational performance of distribution networks with high renewables penetration.