Search Results (415)

The results of a multiscale study of fault and fracture geometry, distribution, density, and intensity are reported for Mesozoic platform carbonates cropping out along the axial zones of the southern Apennines fold-and-thrust belt, Italy. By integrating field structural observations with digital outcrop analysis, the study focuses on Cretaceous limestone rocks exposed along natural creeks and artificial trails of the Castelsaraceno area, Raparo Mt., southern Italy. There, the limestone beds are bounded by mm- to cm-thick marly–clayey interbeds, forming a well-layered succession made up of a few m-thick bed packages bounded by several cm-thick clayish interlayers. The carbonate multilayer was first affected by thrust tectonics, with the formation of low-angle intra-carbonate thrust faults and fault bend-folding. Then, the multilayer was crosscut by extensional–transtensional high-angle faults, which displaced the previously formed contractional structural elements, and allowed carbonate exhumation from shallow crustal depths. At outcrop scales, thrust-related deformation was solved by low-angle joints and veins, rare high-angle stylolites, and low-angle sheared fractures displaying reverse kinematics. Quantitative analyses of fracture density (P20) and intensity (P21) conducted on selected portions of the thrust fault zones indicate that the low-angle joints and veins attain their highest values in the vicinity of the main slip surfaces, whereas they are almost absent in the surrounding carbonate host rocks. Plio-Quaternary transtensional deformation was solved by NW–SE- and NE–SW striking faults. The latter fault set, nicely exposed along the flanks of the Raganello Creek, was characterized by right-lateral components of slip. Incipient faults, with ca. 1 cm throw, are made up of vertically discontinuous slip surfaces, which crosscut single bed packages and abut against clayish interlayers. The slip surfaces form conjugate geometries, and are associated to high-angle fractures and veins striking NE–SW, dissecting the bed packages. The fault core is virtually absent, whereas the damage zones are very discontinuous along dip. The P20 values computed for the high-angle fractures and veins increase toward the slip surfaces, whereas the P21 values remain nearly constant. These data are interpreted as being due to fault nucleation processes associated with fracture nucleation within the limestone rocks. NE–SW striking small faults displaying throws between 10 and 60 cm are comprised of through-going main slip surfaces crosscutting multiple bed packages, and poorly developed, discontinuous fault cores flanked by m-thick damage zones. The damage zones include sub-parallel high-angle shear fractures, fractures and veins showing a positive correlation between P20 and P21, whose values increase in the vicinity of the main slip surfaces. Such a positive correlation is interpreted as due to fault growth by linkage and coalescence of pre-existing high-angle fractures, and formation of fault-related joints and veins at the extensional quadrants of single shear fractures. Similarly, large-scale NE–SW striking mature faults with throws on the order of tens of meters, made up of a m-thick fault core and 10 s of m-thick damage zones including sub-parallel fractures and veins, also show a positive P20 and P21 correlation. The main outputs of this work are synthesized into a conceptual model illustrating the transition from thrust-related deformation to extensional–transtensional faulting, documenting the evolution of fracture networks from incipient-to-small-to-mature faults. Full article

Rural sustainability planning requires spatially explicit methods that integrate agricultural resource bases, ecological condition, low-carbon feasibility, community implementation support, and cultural landscape values. Although the Circular and Ecological Sphere (CES) concept offers an integrative framework for rural transition, empirical CES studies remain largely qualitative or policy-oriented. This study develops a FarmMap-integrated Python-GIS workflow for proxy-based CES-oriented spatial prioritization in Yangpyeong-gun, a peri-rural county on the eastern fringe of the Seoul metropolitan region in Korea. Public spatial and administrative datasets were integrated into thirteen indicators grouped under five CES-relevant axes. The model does not measure realized circular material flows, governance quality, resident participation, or carbon emission reduction directly; instead, it identifies where CES-relevant spatial potentials co-occur. An axis-balanced entropy model assigned equal total weight to each axis while applying entropy weighting within axes. Robustness was tested through equal-weight, axis-emphasis, raw entropy diagnostic, Monte Carlo perturbation, and spatial-scale sensitivity analyses using 100 m diagnostic, 500 m, and eup/myeon supports. The final 250 m priority surface identified the top fifth of analyzed Yangpyeong-gun area as very-high relative priority and remained stable across weighting and spatial-support diagnostics. Rural-experience villages and village enterprises had significantly higher CES scores than random background locations. The results demonstrate a reproducible first-stage spatial screening workflow for CES-oriented rural planning while clarifying the limits of proxy-based circularity, governance, and low-carbon indicators. Full article

Waterbody extraction is of great significance for water resource investigation and monitoring. In addition to RGB bands, most common satellite images have a near-infrared (NIR) band. By combining these RGB-NIR bands, certain water, vegetation, and shadow indices can be calculated. The near-infrared band and these indices are very similar to the X modality in RGB+X data (common examples include RGB-D and RGB-Thermal). However, at present, no studies have thoroughly examined multimodal feature fusion from the RGB+X perspective in order to extract waterbodies with high precision. As a result, existing algorithms do not fully utilize satellite image information and have limited generalization ability. To overcome this limitation, we propose a dual-complexity backbone for waterbody extraction from the perspective of RGB+X data semantic segmentation. Its complex Transformer branch is used to extract RGB modality features, while its simple CNN branch is used to extract X modality features. This network structure can effectively capture multimodal, global, and local features in remote sensing images. It can also fully leverage the fact that the scale of RGB image datasets in computer vision is significantly larger than that of remote sensing waterbody extraction datasets. If a large pretrained model is used in the RGB branch, it is unnecessary to freeze the weights. Instead, both branches can be trained jointly, allowing the RGB branch to better adapt to the remote sensing waterbody extraction task without raising concerns that fine-tuning might undermine the pretrained model’s strong representation capability. We also propose two X modality configurations with strong generalization performance. To fully fuse multimodal features, we design a hybrid fusion module combining a CNN and a cross-attention mechanism. To integrate the multi-scale features, we employ a multi-scale Transformer structure in the RGB branch and design a multi-scale decoder. Our algorithm achieves state-of-the-art performance on the GID-5 dataset and competitive performance on the S1S2-Water dataset. Furthermore, it significantly outperforms existing methods in cross-dataset zero-shot transfer between the two datasets, with IoU/F1-score gains of 26.08%/27.33% on GID-5 and 38.74%/31.37% on S1S2-Water over previous SOTA methods. Our processing paradigm of modeling RGB-NIR remote sensing images as RGB+X data shows potential for generalization to other multi-modal remote sensing tasks. The dual-complexity backbone we design also has potential to be extended to other tasks that transfer large pretrained RGB models to remote sensing imagery with RGB-NIR four bands or even more spectral bands. We have open-sourced the code and trained models used in this research. Full article

Soil compaction is a widespread yet insufficiently monitored form of agricultural land degradation, affecting approximately 25% of global soils and nearly 33% of European subsoils, with consequential reductions in soil physical functionality, crop performance, and long-term sustainability; however, approaches for national-scale compaction risk mapping remain limited. A geospatial decision support framework was developed to quantify and map susceptibility to compaction risk across Italy by integrating Earth observation products with multi-criteria decision analysis within a GIS-based Analytic Hierarchy Process. The model combined four indicators: (i) Soil Moisture Index derived from Sentinel 1 C band SAR time series (2018 to 2024), (ii) the Sentinel 2 Normalized Difference Tillage Index, (iii) clay fraction from SoilGrids 2.0, and (iv) an Intensity of Agricultural Practice Index derived from national census statistics. The approach was applied to 74,156 km² of bare soil surfaces across all 20 regions to generate 100 m seasonal and multi-year mean risk maps. Extreme risk (high plus very high) exhibited a bimodal seasonal behavior, occupying 53.6% in winter and 55.5% in autumn, while declining to 24.8% in spring and 26.5% in summer; Southern Italy showed the largest seasonal amplitude (40.7%), and Friuli Venezia Giulia persisted as a hotspot exceeding 50% in all seasons. Comparison with the independent bulk density observations yielded 31.24% accuracy, largely constrained by the temporal mismatch between dynamic processes and static reference data, which represents a constraint of this research. The framework provides an initial screening tool for mapping susceptibility to soil compaction aligned with the EU Soil Strategy 2023 to 2030, supporting targeted interventions by prioritizing spring (March to May) as a low-risk remediation window; however, local conditions must be checked because cultivated crop types are highly diverse, and cropping cycles vary significantly from one species to another. Full article

The foundation of nuclear power plants is special as large-scale earth filling is often required. The properties of the backfill soil differ significantly from naturally deposited soils with regard to deformation and bearing capacity. For pile foundations, a thick backfill layer near the top may change the bearing mode around the pile. In this paper, six cast-in-place rock-socketed piles were tested, with three vertical loading tests and three horizontal loading tests. The lengths of four piles are 35–40 m, while the other two piles reach 55 m. The results show that shorter piles with more parts in the backfill layer can endure a hoop-tightening effect that caused by dilatancy at the upper part of the pile, resulting in very little frictional resistance being provided by the lower soil and smaller vertical displacement of the whole pile. The typical mechanism of transition from static to dynamic friction between soil and piles that leads to shaft resistance is more apparent for longer piles, but inhomogeneous soil like the backfill layer will make the transition complex. When subjected to lateral loading, piles with better integrity show more pronounced elastic features, smaller maximum horizontal displacement, and less residual horizontal displacement. The selection of the proportional coefficient for determining piles’ horizontal bearing capacity should correspond to the specific load and displacement in backfill soil. The results and in-depth analysis of the piles’ bearing capacity in backfill soil will provide intuitive experience for the analysis of pile foundations, thus offering references for the design and construction in similar engineering. Full article

This paper presents HydroAir, a novel air-propelled Unmanned Surface Vehicle (USV) specifically designed for operation in shallow waters and obstacle-rich aquatic environments such as lakes, reservoirs, and large dams. Unlike conventional aquatic robots, HydroAir employs an aerial propulsion system that enables it to overcome partially submerged obstacles, vegetation, and extremely shallow regions where traditional propeller-based platforms fail. The vehicle features a system with a very reliable internal architecture, providing high maneuverability and robustness in both manual and autonomous navigation modes. The primary objective of HydroAir is to serve as a mobile sensing platform for three-dimensional reconstruction of aquatic environments, particularly the underwater terrain. The onboard sensing suite enables bathymetric data acquisition, while a dedicated monitoring and control software integrates these data with aerial reconstructions obtained from Unmanned Aerial Vehicles (UAVs), allowing for the fusion of above-water and underwater spatial information into a unified 3D model. Experimental validations were conducted in large-scale, real-world environments, including tests in a hydroelectric dam operated by Santo Antônio Energia on the Madeira River in Brazil, demonstrating the platform’s operational feasibility, stability, and reconstruction capabilities. The results indicate that HydroAir is a promising solution for environmental monitoring, inspection, and mapping in challenging aquatic environments where conventional autonomous surface vehicles are limited. Full article

Medium-resolution cryo-electron microscopy (cryo-EM) density maps preserve substantial information about protein secondary-structure organization; however, accurately recovering the topology and connectivity of α-helices and β-strands remains challenging due to noise, structural heterogeneity, and the intrinsic resolution limitations that obscure residue-level detail. Topology determination is a key intermediate step toward building atomic protein models from medium-resolution cryo-EM density maps. It requires identifying the correct correspondence and orientation between secondary-structure elements (SSEs), i.e., α-helices and β-strands, predicted from the amino-acid sequence and those detected in the three dimensional (3D) density map. Despite significant advances in cryo-EM reconstruction and molecular modelling, this correspondence problem remains a challenging task, particularly in the presence of noisy density maps and in large, topologically complex α/β proteins. To address this issue, we propose a fully automated, classification-based framework that infers protein secondary-structure topology directly from medium-resolution cryo-EM density maps. Specifically, we cast topology determination as a supervised classification problem in three-dimensional space, leveraging geometric learning on model-derived Cα coordinate representations to establish SSE correspondences, and a Dynamic Time Warping (DTW)-based procedure to resolve density-stick directionality. Validation on a benchmark of 38 proteins spanning both simulated and experimental cryo-EM maps and covering diverse fold classes (α, β, and α/β) demonstrates strong and consistent performance. Among the evaluated predictors, the Voronoi (1-NN) classifier achieves the highest average correspondence quality, with a mean F1-score of 96.82% across the full benchmark. The framework also scales to large, topologically dense targets containing up to 65 secondary-structure elements while preserving very fast correspondence inference (<3 ms), offering a substantial improvement over prior baselines in both accuracy and computational cost. Overall, the classification-driven strategy provides reliable SSE-to-density matching and, when coupled with DTW-based direction selection, yields stronger topology constraints that directly support model building and refinement from medium-resolution cryo-EM reconstructions, while remaining easy to integrate into existing structural interpretation pipelines. Full article

As a policy-driven land use transition initiative bridging poverty eradication and sustainable development, China’s Poverty Alleviation Relocation (PAR) program exemplifies how state-led resettlement can reconfigure land use patterns while balancing immediate livelihood security with long-term community capacity development. The integration of large-scale PAR communities into new urbanization is a critical postrelocation task that is essential for consolidating poverty eradication achievements and enhancing endogenous development capacity. This study examined how the configuration of policy instruments shapes the endogenous development capacity of PAR communities during their transition to new urbanization. Employing a “tool–goal” analytical framework, we conducted a content analysis of 38 provincial-level policy documents (2021–present) using NVivo 20 software. The findings reveal that while local governments have established a preliminary policy system, structural imbalances persist: (1) uneven deployment of policy tools, (2) underutilization of demand-based policy tools, (3) tool–goal misalignment, and (4) insufficient market/societal participation in government-led measures. The discussion further reveals that the land use transition in the PAR program emphasizes the “living mode” (housing and public services) over the “livelihood mode” (productive resources and nonagricultural employment), creating structural dependency and leaving industrial land underutilized—as evidenced by weak policy support for industrial development (14.83%) and labour outmigration from resettlement areas. Drawing on the sustainable livelihoods framework, we further demonstrate how this exogenous-dominated policy mix disproportionately enhances physical and financial capital while constraining the accumulation of human and social capital—the very foundations of endogenous development capacity. To address these issues, we propose three key recommendations: (1) optimizing the policy mix to strengthen the endogenous development capacity of PAR communities; (2) realigning policy tools with objectives to achieve diversified yet coordinated goals; and (3) addressing implementation gaps to better leverage market mechanisms and social forces in promoting the sustainable urban integration of resettlement areas. Full article

The Middle Triassic Tredian Formation of the Salt Range, Pakistan, consists of sandstones with interbedded shale in the lower part and minor dolomite in the upper part. Conventional grain-size analysis has been widely used as a sedimentological tool to elucidate depositional environments and the mode of transportation of detrital sediments. This study presents the first integrated application of a Decision Tree Classifier (a machine learning model) with field and petrographic evidence to interpret grain-size statistics for the analysis of depositional environments of the Tredian Formation in the Salt Range, Pakistan. Stratigraphic sections of the Tredian Formation were measured and sampled in the Nammal Gorge and Zaluch Nala in the Salt Range for detailed sedimentological and grain-size analyses. The lower part of the Tredian Formation (Landa Member) consists of interbedded sandstone and shale (LF-1) characterized by large-scale slumps, parallel lamination, ripple marks, and cross-bedding. The LF-1 is overlain by the Katkhiara Member, which is dominated by thick sandstone (LF-2) with planar and trough cross-bedding and contains dolomite beds (LF-3) in the upper part. Grain-size statistics show that the sandstones are fine-to-medium-grained, well-to-very-well-sorted, near-symmetrical, and very platykurtic. Machine learning-based bivariate plots suggest that most of the samples are grouped, with some showing scattered trends. The Linear Discriminant Function (LDF) analysis indicates that the Tredian Formation was deposited in fluvial–deltaic to shallow marine environments with sand reworking and redistribution under aeolian/beach settings. The Decision Tree Classifier Model (DTCM) predicted fluvial to shallow marine depositional environments for the Tredian Formation and shows strong agreement with field-based lithofacies interpretation, demonstrating its reliability as a predictive tool. Thus, the present study demonstrates that integrating grain-size-based machine learning and statistical analysis with traditional sedimentology provides valuable insights into depositional settings and enhances the reliability of interpretations of ancient sedimentary environments. Full article

The steel sector is one of the main contributors to carbon dioxide emissions among the industrial activities. It is mostly the use of carbon-rich blast furnaces and natural gas direct reduction processes that cause this. Hydrogen-based direct iron reduction (H-DRI) is a demonstrated method of lowering steel production carbon emissions by using hydrogen rather than carbon monoxide as the reducing agent; therefore, water vapor is released instead of carbon dioxide. This work offers a detailed analysis of the trends, operating concepts, industrial-scale trials, difficulties, and advantages of H-DRI. It is well supported by both energetic and reaction rate considerations that hydrogen is an efficient agent for the reduction of iron oxides to iron metal, giving metallization rates up to those of the traditional processes and at the same time significantly reducing GHG emissions. Moreover, industrial trials confirm that the method is technically feasible on a large scale, which is not yet realized because green hydrogen is very expensive, infrastructure needs are high, and there are still hurdles to be overcome in process optimization, such as water vapor management, pellet quality, and reactor design. According to the studies of product life cycles, if the hydrogen is extracted from renewable sources of energy, then the reduction in CO can be as high as 90%. The article also discusses different aspects of the economy, environment, and law that are already there and the ones that need to be developed so that research, technological breakthroughs, and industrial harmonization can be directed to the right spots. Practical deployment requires control of hydrogen supply, optimizing reduction processes, integrating renewable energy, and regulatory support. The results offer operational insights to the steel industry, policymakers, and academia on the path to sustainable, energy-efficient, and carbon-neutral steel production while retaining the metallurgical quality and industrial scale of the steelmaking processes. Full article

Solar radio bursts at very low frequencies are key phenomena in the Sun–Earth space environment, providing crucial diagnostics of the acceleration and propagation of solar wind, coronal mass ejection (CME), and non-thermal energetic particles and serving as important indicators for space weather forecasting. To meet the demand for rapid screening of burst events in large-scale observational datasets, we present an end-to-end automatic detection and evaluation framework tailored for Type III bursts, built upon long-term radio dynamic spectra from STEREO-A/SWAVES. We formulate radio burst detection as a one-dimensional interval localization task along the time axis and, in view of the scarcity of annotated samples, cast it as a few-shot object detection task. Building upon the Faster R-CNN architecture with a ResNet50-FPN backbone, we propose the Meta-FSOD framework, which adopts an episodic training paradigm to construct support–query episode pairs. The framework incorporates a metric-guided prototype learning branch to semantically align and calibrate region-of-interest (RoI) features via class prototypes, and integrates a dynamic Beta-Gating mechanism coupled with Soft-NMS to effectively suppress false positives while preserving high-recall performance. Experimental results demonstrate that, despite being trained on a significantly smaller dataset than comparable studies, Meta-FSOD achieves competitive performance, closely matching that of conventional supervised model. The proposed framework exhibits strong cross-temporal generalization capabilities and holds considerable potential for engineering applications in deep space exploration missions. Full article

Semantic indexing ensures better organization and optimized searching of heterogeneous, autonomous, and distributed data sources. This approach leverages meaning and context rather than just keywords to better manage the increasing volume, complexity, and heterogeneity of modern data, enabling precise searching, optimized integration, and improved interoperability between domains. Several approaches to semantic indexing are available: ontology-based indexing, machine learning and automated semantic annotation of data sources. However, the main challenge remains scaling up. This article focuses on a conceptual framework designed for scalable semantic indexing of data sources based on a structured peer-to-peer architecture adapted for managing a very large number of nodes, Hilbert curve renowned for its preservation of semantic affinity while scaling, hypercube structure with its efficient diffusion algorithm, semantic annotation of data sources based on keywords, as well as machine learning techniques, in particular, multidimensional data analysis. An illustrative exploratory example of the Meta Skills semantic class is presented to outline the proposed architecture. This study proposes a conceptual and exploratory framework for large-scale semantic indexing of data sources. The proposed approach has not yet been implemented or validated on a large scale; its objective is to provide an initial structured model to serve as a basis for future empirical research. Full article

With the rapid and ongoing expansion of urban areas, the need for accurate, reliable, and regularly updated topographic maps has become increasingly critical for planning and sustainable development. While traditional aerial photogrammetry—whether analog or digital—has long been the standard for such tasks, it remains costly, time-consuming, and logistically demanding, particularly when large or inaccessible regions are involved. This study proposes an alternative approach based on very-high-resolution satellite imagery, focusing specifically on data acquired from Morocco’s Mohammed VI A and B satellites. The research evaluates the capacity of this satellite imagery to support large-scale topographic mapping, both in terms of geometric accuracy and the ability to identify essential urban features. To validate the results, we conducted a comparative analysis of satellite data with conventional photogrammetric imagery from analog cameras (RMK TOP) and digital sensors (ADS, DMC), using ground control points (GCPs) and differential GPS (DGPS) measurements for calibration and accuracy assessment. The outcomes demonstrate that planimetric accuracy from satellite imagery meets the required standards for mapping at 1:10,000 and 1:5000 scales. However, altimetric accuracy is closer to the upper permissible limits, especially in applications requiring finer detail. While major urban elements such as roads, buildings, and vegetation are well identified, smaller infrastructure components, such as power lines, remain challenging to detect. Despite these limitations, the study highlights the growing potential of satellite imagery as a cost-effective and operationally efficient alternative to traditional methods, particularly in rapidly evolving urban environments where frequent map updates are essential. Integration with GeoAI workflows is identified as a key direction for future research and is not part of the current methodology. Full article

To address the technical challenges of difficult deduction, limited field measurement, and ambiguous instability determination of roof separation critical values in mining roadways within the weakly cemented coal-bearing strata of Xinjiang, this paper proposes a discrete element method that integrates the fracture of anchor bolt and anchor cable support materials with the damage degree of the surrounding rock. Taking a specific mine in the Hosh Tolgay coalfield as the research object, a systematic study was conducted. The research process was as follows. (1) Model parameter calibration was performed. Intact rock parameters were obtained through laboratory basic mechanical tests, and rock mass parameters were corrected based on reduction empirical formulas and the Hoek–Brown criterion. Numerical model verification showed that the errors between the simulated and theoretical values of the elastic modulus, compressive strength, and tensile strength of the rock mass were all less than 10%, indicating that the corrected parameters are reasonable. (2) The critical damage values of the rock mass considering a non-constant confining pressure environment were proposed. Through triaxial compression simulations, the differential evolution patterns of rapid damage increase in sandy mudstone under low confining pressure and stable damage accumulation in coal were revealed, thereby clarifying the damage thresholds for rock mass instability under different confining pressures. (3) A large-scale model was established to analyze the evolution laws of the fracture field, support field, and displacement field of the roadway surrounding rock. A comprehensive determination method for the instability of the roof anchored bearing structure was proposed. By comparing the damage thresholds of the scaled rock mass and the roadway surrounding rock and analyzing the fracture conditions of the roadway support system, a dual-criterion consisting of surrounding rock damage and support material fracture was constructed. Based on this criterion theory, the critical values for deep and shallow separation were obtained. The research results indicate that the evolution patterns of damage in coal and sandy mudstone differ with confining pressure. The sandy mudstone layers in the shallow part of the roof are more sensitive to mining-induced unloading disturbances. Consequently, the surrounding rock damage and support fracture of the mine roof exhibit distinct distribution characteristics: the dominant failure of the roadway is shear failure, with wide-range coalescence of shallow fractures and gradual development of deep fractures, alongside the concentrated failure of shallow anchor bolts and partial failure of deep anchor cables. Based on the instability state of the roof monitoring zones, the critical value for shallow separation was determined to be 90.7 mm, and the critical value for deep separation was 129.03 mm. These results are very close to the field measured values, verifying the engineering applicability of the method. This paper reveals the damage characteristics of the rock mass and surrounding rock in weakly cemented strata, as well as the mechanism of roof separation initiation and evolution. The proposed method for determining critical values provides a scientific and feasible practical reference for the support optimization and monitoring and early warning of roadway roofs in weakly cemented strata, possessing significant engineering value for ensuring safe and efficient mine production. Full article

Water electrolysis has emerged as a strategically appealing route for the sustainable production of green hydrogen (H₂) via the hydrogen evolution reaction (HER), though the sluggish kinetics of the oxygen evolution reaction (OER) remains a bottleneck hindering large-scale practical applications. In this regard, an attractive solution is offered by the integration of the ethanol oxidation reaction (EOR) into hybrid water-splitting systems, favorably reducing anodic overpotentials. Nonetheless, an open challenge is related to the fabrication of eco-friendly and economically viable catalysts free from noble metals, combining efficiency and stability. Herein, we explore nickel-oxide-based nanostructures grown onto porous Ni foam scaffolds by a scalable hydrothermal (HT) approach as EOR electrocatalysts. Material properties arising from modulation of the sole HT growth time are investigated by complementary structural, microscopic, and spectroscopic techniques. Electrochemical tests demonstrate good durability and very attractive EOR performances, mainly influenced by the morphology and the NiOOH surface content of the target systems. Overall, the present work advances an attractive route to transition-metal-based electrocatalysts for efficient alcohol-oxidation-assisted water electrolysis. Full article