Search Results (8,467)

Improving regional economic resilience is a point addressed in the sustainable development goals (SDGs; i.e., SDG 8 and SDG 11). The Yangtze River Delta (YRD) has demonstrated excellent economic resilience during the COVID-19 pandemic, largely due to the persistent green transition of the YRD in the past two decades. This paper uses a single-case method combined with the perspective of evolutionary economic geography to systematically investigate the process of green transition in the YRD (2000–2023) at both vertical and horizontal levels and proposes an integrated multi-dimensional system framework to reveal the collaborative logic of the overall green transition action and the internal mechanism of enhancing economic resilience in the YRD. The findings indicate that the combination of external factors such as contradiction change, magnifying crises, economic stabilization, and policy steering has driven the historical inevitability of green transition in China. Under such conditions, the YRD not only completed development in terms of primitive accumulation of space (coordinated development, i.e., chassis), industry (orderly upgrade, i.e., engine), and governance (equal supply, i.e., lubricant) earlier but also ensured the stability of this triangle, injecting sustained strong momentum into the rapid recovery of the economy under the impact. The solidification of green concepts further enhances the sustainability and strength of the YRD’s economic resilience. These findings provide beneficial experience on how to resume production after the pandemic or lay out cities in developing countries that are still in rapid urbanization in advance. Full article

Within the domain of Earth observation, tracking terrestrial transitions via high-resolution optical data plays a fundamental role. Nevertheless, current methods face critical challenges, including the difficulty in collaborative modeling of local details and global features and the singularity of bi-temporal difference representation, along with insufficient cross-scale feature communication, thereby constraining both the precision and resilience of models when applied to complicated environments. To solve these problems, we propose LADENet (Locally Adaptive Mamba and Multi-scale Feature Enhancement Network), an innovative framework that synergizes CNN, Transformer, and Mamba paradigms. By leveraging customized local contextual refinement alongside sophisticated hierarchical fusion, this integration delivers highly precise and resilient detection performance. LADENet adopts a weight-sharing multi-level Transformer encoder combined with a sequence reduction mechanism to generate multi-scale global features, achieving precise alignment of bi-temporal features and global context modeling while reducing computational complexity. To realize accurate localization and local enhancement of changed regions, we design a dual spatiotemporal adaptive local feature marking module based on State-Space Scanning (SSS). This module screens high-saliency changed regions through an adaptive scanning strategy, realizes pixel-aligned spatiotemporal feature fusion via cross-temporal state-space scanning, and introduces a sliding window boundary calibration mechanism to alleviate boundary information loss caused by window segmentation. To strengthen the feature representation of changed regions, a dual-branch difference enhancement module is constructed, which collaboratively captures global change trends and fine-grained local features through an attention-enhanced difference branch and a multi-scale convolution concatenation branch, effectively suppressing background interference. To address the semantic gap between cross-scale features, a global cross-scale spatial feature fusion decoder is proposed, which balances local detail preservation and global context perception through the synergy of spatial attention and two-dimensional selective scanning, completing refined multi-scale feature fusion and spatial resolution recovery. To rigorously validate the proposed LADENet, comprehensive experiments were conducted across four widely adopted bi-temporal benchmarks: LEVIR-CD, WHU-CD, CLCD-CD, and GVLM-CD. The presented architecture establishes substantial superiority over existing cutting-edge methodologies across primary evaluation criteria. Specifically, it yields an F1-measure of 91.06% alongside an IoU of 85.28% in the LEVIR-CD tests, while registering 90.51% (F1) and 82.45% (IoU) for WHU-CD. Similarly, robust outcomes are delivered on CLCD-CD (82.15% F1, 72.83% IoU) as well as GVLM-CD (89.12% F1, 77.78% IoU). These results demonstrate that LADENet possesses excellent detection accuracy, boundary delineation capability and generalization performance in diverse and intricate bi-temporal observation environments. Full article

The urban heat island (UHI) effect represents a critical urban climate phenomenon arising from the combined pressures of rapid urbanization and climate warming. Although its association with carbon emissions has received increasing scholarly attention, the underlying behavior-mediated pathways and cross-regional spillover patterns remain insufficiently understood. Using multi-source geospatial data for the Yangtze River Delta urban agglomeration from 2014 to 2023, this study develops a multi-scale analytical framework integrating 1 km urban agglomeration exploratory analysis and 5 km spatial econometric modeling. Anthropogenic Energy Activity Intensity (AEAI) is constructed as a proxy for energy-related human activities, and a spatial Durbin model, combined with a spatial mediation approach, is employed to examine the spatial associations and statistically mediated pathways within the “heat-energy-carbon” nexus. The results indicate that: (1) carbon emissions exhibit significant positive spatial spillover effects, consistent with thermal diffusion processes and socioeconomic network interactions; (2) AEAI represents a substantial partial statistical mediation pathway in the association between UHI and carbon emissions, accounting for 44.63% of the total association. This suggests that the UHI–carbon emission linkage is partly embedded in spatial patterns of energy-intensive human activities rather than reflecting a purely direct thermal effect. These findings suggest that regional climate governance may need to move beyond single-city interventions and purely physical cooling strategies toward integrated approaches that combine cross-regional coordination with behavioral regulation. Promoting passive cooling-oriented urban planning and demand-side energy transitions may help reduce carbon lock-in risks and support the development of climate-resilient urban agglomerations. Full article

With global warming and rapid urbanization, short-duration summer rainstorms are becoming more intense and localized, posing growing challenges to urban flood resilience. However, their spatiotemporal characteristics, vertical structures, and environmental drivers remain poorly understood. Here, we combine multi-source remote sensing datasets and China’s new-generation satellite-borne dual-frequency precipitation radar observations to investigate summer rainstorms in Nanjing, China, during 2017–2024. Results reveal pronounced spatiotemporal heterogeneity, with higher rainfall intensities concentrated over urban and adjacent areas. During the study period, rainstorm intensity and duration increased by 7.44% and 38.63%, respectively, while the affected area decreased by 8.18%, indicating a transition toward more localized yet more intense rainfall events. Environmental analyses suggest that large-scale thermodynamic conditions and regional topographic forcing provide a favorable background for convection development, while local urban thermal effects may further modulate rainfall enhancement. Three-dimensional radar detection of an illustrative rainstorm event indicates an inverted-cone vertical structure, suggesting a mixed convective-stratiform precipitation structure involving both warm-rain and ice-phase processes. An Explainable Bayesian-Optimized XGBoost (EBOX) model further identifies near-surface air temperature and specific humidity as the primary environmental factors associated with rainstorm occurrence and development. Overall, this study highlights the value of integrating satellite remote sensing with explainable artificial intelligence to improve understanding of urban extreme rainfall and provide new insights into how climate change, topography, and urbanization jointly shape precipitation extremes in rapidly urbanizing monsoon regions. Full article

Energy Geographies has a unique position at the intersection of geospatial and social science, and it now faces a defining methodological development with the rapid rise in Geospatial Artificial Intelligence (GeoAI). This paper examines where GeoAI has and has not been applied within energy research through two bibliometric analyses using the Dimensions database. The first establishes an updated picture of energy geographies scholarship from 2020 to 2026, mapping the field’s current priorities and geographic distribution as a baseline for evaluating GeoAI’s role. The second conducts a bibliometric analysis of GeoAI-specific energy publications from 2020 to 2026, which reveals significant GeoAI Application Gaps: a heavy concentration in energy extraction and production research and in renewable energy siting and grid optimization, while energy transition, justice, and the energy problems of underrepresented regions remain substantially underserved. GeoAI energy research is also more geographically concentrated than the broader field, dominated by a small number of countries, raising questions about the applicability of these tools to the energy challenges facing the rest of the world. We argue that this gap reflects a pattern of problem selection as much as technological limitation, and that energy geographers are well positioned to redirect the development of this new field. We outline three directions for future research: developing Explainable GeoAI to ensure transparency and accountability, expanding geographic coverage to address data biases that favor a small set of well-resourced countries, and confronting the computational energy paradox of carbon-intensive AI applied to sustainability-oriented research. Full article

This study examines the operational resilience of Limassol Port, Cyprus’s primary maritime hub, amid disruptions caused mainly by the COVID-19 pandemic. Utilizing high-resolution port-level data from 2018 to 2025, we evaluate performance across five key performance indicators: port calls, anchorage utilization, berth utilization, waiting times, and arrival punctuality. The analysis adopts a longitudinal approach, spanning pre-pandemic, peak-pandemic, post-pandemic, and recent phases, while differentiating impacts across vessel categories. Unlike many regional ports, Limassol’s cruise sector exhibited unique counter-cyclical growth, with calls doubling during the pandemic as the port transitioned into a strategic safe haven and repositioning base. This surge normalized over the 2024–2025 period as temporary operational disruptions resolved. Conversely, container and Ro-Ro (roll-on/roll-off) segments demonstrated robust stability, achieving rapid post-pandemic normalization, while bulk and tanker operations exhibited higher volatility linked to shifting commodity demands. These findings, validated through one-way analysis of variance (ANOVA) and Cohen’s d effect sizes, underscore the adaptive capacity of mid-sized Mediterranean hubs. The study concludes that operational flexibility, coupled with enhanced digital coordination and strategic capacity planning, is essential for maintaining the resilience of short sea shipping networks during global crises. Full article

This paper investigates the impact of Environmental, Social, and Governance (ESG) performance on the financial outcomes of 68 European and 60 U.S. banks over the period 2010–2022 using a Panel Smooth Transition Regression (PSTR) framework. Unlike traditional linear models, the PSTR approach captures the nonlinear, regime-dependent effects of ESG engagement on bank profitability, measured by ROA and ROE, and financial stability, measured by the Z-score. Our empirical findings reveal a critical ESG threshold in both regions, above which banks experience substantial improvements in profitability and resilience. Comparative analysis indicates that while ESG enhances stability slightly more in European banks, U.S. banks tend to achieve marginally higher profitability gains. Control variables, including bank size, capital adequacy, leverage, and macroeconomic conditions, also play a significant role in shaping performance. These results underscore the importance for banks to attain a minimum ESG maturity to fully realize the benefits of sustainable practices. The study provides valuable insights for bank managers, investors, and policymakers seeking to promote a sustainable and resilient banking sector across Europe and the United States. Full article

Compound flash drought–heatwave extremes (FDHW) expose vegetation to rapid water and heat stress, but regional assessments often conflate event detection with vegetation response and rarely resolve delayed canopy trajectories. We quantified FDHW across China’s Northeast Black Soil Region during the 1995–2024 growing seasons using ERA5-Land soil-moisture and temperature thresholds, applied a spatiotemporal graph neural network to regularize threshold-derived event masks, and reserved AVHRR NDVI for independent post-event impact assessment. Flash drought and FDHW frequencies exhibited strong interannual variability rather than a significant monotonic trend. FDHW occurrence increased from 3.8 to 4.8 d per growing season between 1995–2005 and 2016–2024, but the Theil–Sen trend was near zero (0.05 d per decade). Land–atmosphere composites indicate progressive soil-moisture depletion before FDHW occurrence and a transition from latent to sensible heat release roughly three days before maximum temperature anomalies. NDVI composites revealed a delayed greenness response: anomalies were negative through the first two post-event weeks, reached their minimum approximately one week after the reference FDHW grid-day, and then partially recovered during days 16–30. Mean NDVI suppression was modest (short-term −0.009; long-term −0.006), but persistent negative anomalies remained in 12.1% of southern cropland-dominant trajectories and 10.7% of northern forest–crop ecotone trajectories. These results show that FDHW impacts in the NBSR are expressed less as a steady rise in event frequency than as delayed and spatially heterogeneous vegetation stress, highlighting the need for post-event monitoring windows and cross-sensor validation to support agricultural risk assessment and adaptation planning. Full article

As a critical alpine transition zone linking the Qinghai–Tibet Plateau and the Loess Plateau, Gannan Prefecture acts as an important water conservation area in the upper Yellow River basin of China. Based on GLDAS-2.1 surface soil moisture (SSM) datasets spanning 2000–2022 and interpretable machine learning tools (SHAP and ALE), this paper analyzes the spatiotemporal evolution, future trend sustainability, and nonlinear statistical associations between environmental predictors and SSM. The main results were as follows: (1) SSM exhibited a significant upward trend with an annual growth rate of 0.18 kg·m⁻²·a⁻¹ (p < 0.001), and an abrupt turning point occurred in 2017. The spatial pattern of high SSM in the southeast and low SSM in the northwest remained relatively stable, with the centroid migration distance being less than 1.81 km; most regions presented statistically significant moistening trends (p < 0.05). (2) Natural environmental predictors jointly carried 95.79% of the total statistical explanatory weight for modeled SSM variability. Precipitation possessed the highest explanatory proportion (37.93%), followed by temperature (27.30%), potential evapotranspiration (ET_p, 12.26%), elevation (10.44%), and fractional vegetation cover (FVC, 7.77%). One-dimensional ALE curves identified sample-limited statistical breakpoints: SSM gradually plateaued when precipitation reached 650–700 mm, while modeled SSM decreased substantially once ET_p exceeded 800 mm·a⁻¹. Two-dimensional ALE further characterized combined statistical correlations among precipitation, temperature, and ET_p. Model outputs also indicated that FVC above 0.45 corresponded to enhanced soil water retention within the observed sample range, which only reflects statistical patterns captured in this dataset rather than universal regulatory standards. This study offers quantitative statistical understanding of SSM variations across alpine transition zones. Full article

Background/Objectives: Cervical spinal cord injury (SCI) carries a significant burden in Saudi Arabia, frequently resulting in permanent ventilator dependence and high morbidity. While laparoscopic diaphragmatic pacing (DP) has emerged as an alternative to long-term mechanical ventilation (MV) globally, regional evidence regarding its application within the Middle East remains limited. This study evaluates a single-center cohort of ventilator-dependent cervical SCI patients undergoing laparoscopic DP. Methods: We conducted a retrospective analysis of all ventilator-dependent patients with cervical SCI admitted to a tertiary hospital in Riyadh between 2012 and 2024 who underwent laparoscopic DP after failing traditional weaning attempts. Inclusion criteria required at least 3 months of MV dependence, intraoperative diaphragmatic stimulability and a minimum one-year follow-up post-implantation. Across the entire cohort, the long-term follow-up duration reached a median of 60.0 months (interquartile range [IQR]: 36.0–84.0 months; range: 12.0–120.0 months). Results: Out of 30 initial candidates with cervical SCI, 28 patients (22 males, 6 females; median age 24.0 years (interquartile range [IQR]: 15.0–33.0 years)) were included. Patients had been on MV for a median of 13.0 months (IQR: 10.5–16.0 months) prior to the procedure. Utilizing a combined weaning success rate (complete or partial weaning), 26 patients (92.86%; 95% CI: 77.42–98.01%) were successfully transitioned to the pacing protocol, while 2 patients (7.14%) experienced DP failure. Complete (24 h) daily MV independence was achieved by 18 patients (64.29%), and partial weaning (≥4 h/day of MV-free time) was achieved by 8 patients (28.57%). Age at the time of injury ranged from 5 to 62 years. No major intraoperative or postoperative complications occurred. Minor exit-site skin irritation was observed in 3 patients (10.71%), all of which resolved completely with conservative local care alone without requiring antibiotic therapy. Conclusions: In this selected single-center Saudi cohort of ventilator-dependent cervical SCI patients, laparoscopic DP was feasible and was associated with high rates of partial or complete ventilator-free breathing. Larger prospective multicenter studies with standardized selection criteria, safety reporting, respiratory outcomes, quality-of-life measures, and longer follow-up are needed. Full article

Adjustable steam ejectors exhibit significant adaptability to various operating conditions. However, the coupling regulation mechanism between ejector performance and the internal flow field remains insufficiently understood, thereby limiting further optimization. The novelty of this study lies in elucidating the ejector’s performance regulation mechanism by examining the influence of spindle position on non-equilibrium condensation in wet steam. This approach clarifies the flow–thermal–phase-change coupling mechanism and interprets the resulting condensation suppression and shock wave dynamics. In this study, the effects of operating conditions and spindle position on ejector performance were quantitatively characterized. The flow-field evolution was further analyzed through key flow-field variables (pressure, Mach number, temperature, and condensate mass fraction). Moreover, the relationship between ejector performance and flow characteristics was investigated. The flow–thermal–phase-change coupling analysis reveals that the spindle effectively regulates steam ejector performance, internal thermodynamic behavior, and phase-transition processes by adjusting the equivalent throat diameter. Under a representative operating condition, compared with the baseline position (d_t = 5.66 mm), moving the spindle in the positive x-axis direction (to d_t = 5 mm) decreased the equivalent throat diameter and the motive-fluid mass flow rate by 11.7% and 22.6%, respectively. Consequently, the distance between adjacent shock waves gradually decreased along the flow direction (by approximately 14.1%), and the global maximum Mach number decreased sharply from 2.0 to 1.6 (a 20% reduction). The jet core was significantly shortened, while both the intensity and number of shock waves in the diffuser were reduced. Additionally, the local backflow near the wall of the mixing chamber’s contraction section was suppressed, resulting in a weaker temperature rise in the backflow region. The fluid temperature approached the outlet temperature more gradually, while the average flow-field temperature increased. Meanwhile, the condensate mass fraction in the mixing chamber was significantly reduced (from 0.1 to 0), and the entrainment ratio was enhanced. This configuration is suitable for applications requiring low discharge pressure, high motive pressure, or high suction pressure. Conversely, moving the spindle in the negative x-axis direction enlarged the equivalent throat diameter, which generated higher Mach numbers and stronger shock waves. This enlarged throat configuration enhances the ejector’s resistance to elevated discharge pressure and increases the critical discharge pressure, making it more suitable for high discharge pressure, low motive pressure, or low suction pressure conditions. Full article

Microplastic pollution in tropical urban rivers has become an increasing environmental concern due to rapid urbanization, inadequate waste management, and hydrological transport processes. This study investigated the occurrence, characteristics, and spatiotemporal distribution of microplastics in the Can Tho River, Vietnam, along an urban–peri-urban–rural gradient during dry and wet seasons. Surface-water samples were collected at 15 sites and analyzed for microplastic abundance, density, shape, color, and size composition using stereomicroscopic identification and statistical analyses. Microplastics were detected at all sampling sites in both seasons, indicating widespread contamination throughout the river system. Although seasonal differences in overall abundance and density were not statistically significant at the basin scale, clear spatial variability was observed, particularly in urban and peri-urban regions. Fibers and fragments were the dominant shapes, while blue, purple, and green particles were the most common color categories. Particles larger than 1000 µm accounted for the largest proportion of detected microplastics, and continuous size-distribution analysis revealed broadly similar overall distributions, although a greater proportion of smaller particles was observed during the dry season. The results suggest that hydrological conditions, urbanization, and land-use characteristics may contribute to the observed spatial and seasonal patterns of microplastic distribution in the Can Tho River. Peri-urban zones exhibited the greatest seasonal variability, highlighting their role as transitional areas that may influence microplastic redistribution in tropical river systems. This study provides baseline information for understanding microplastic pollution in the Mekong Delta and supports future river management strategies. Full article

Sustainable agricultural development requires regional agricultural systems to balance output growth, resource efficiency, ecological protection, and long-term resilience. In mountainous and ecologically sensitive regions, identifying the development constraints and statistically derived marginal contribution thresholds of agriculture is essential for promoting green transformation and sustainable land use. Taking Yunnan Province, China, as a representative plateau mountainous agricultural region, this study uses provincial annual data from 1990 to 2023 to quantitatively identify the key drivers and threshold characteristics of agricultural development under multidimensional constraints. A multidimensional indicator system was constructed covering fiscal and investment support, agricultural production inputs, rural infrastructure, and labor and population conditions. Ridge regression was employed to address multicollinearity among explanatory variables, Bootstrap approximate inference was used to improve the robustness of coefficient estimation, and the SHAP interpretation framework was introduced to rank key driving factors and identify marginal contribution thresholds. By integrating ridge regression, Bootstrap approximate inference, SHAP-based contribution ranking, and threshold identification, the proposed framework advances prior agricultural sustainability studies by linking coefficient-based factor analysis with interpretable marginal contribution thresholds under conditions of high multicollinearity and multidimensional resource constraints. The results show that agricultural development in Yunnan is characterized by multidimensional resource and infrastructure constraints. Rural electricity consumption, total reservoir storage capacity, fixed asset investment in agriculture, forestry, animal husbandry and fisheries, local public fiscal budget expenditure, and agricultural population generally act as positive supporting factors. Rural electricity consumption is the most stable and core driver across the aggregate and three sectoral models. In contrast, pesticide and fertilizer inputs show significant negative associations in most models, suggesting that future agricultural development in Yunnan is unlikely to be sustainably supported by continued expansion of high-intensity chemical inputs. Sectoral heterogeneity is also evident: agriculture and animal husbandry are more dependent on energy, water resources, and mechanization, whereas forestry shows a more distinct operational structure. The SHAP dependence analysis identifies several statistically derived marginal contribution thresholds, including rural electricity consumption of approximately 6.055 billion kWh, total reservoir storage capacity of approximately 10.395 billion m³, total agricultural machinery power of approximately 19.8324 million kW, pesticide use of approximately 37,500 tons, and fertilizer application of approximately 1.5238 million tons. These values should be interpreted as empirical transition points in the modeled marginal contributions rather than definitive biophysical ecological limits. They indicate that the sustainability-related constraint structure of agricultural development in Yunnan is not a single output ceiling but a composite interval shaped by infrastructure support capacity, factor allocation conditions, and the declining marginal contribution of high-intensity chemical inputs. The findings provide directional quantitative evidence for sustainable agricultural governance, agricultural green transformation, and differentiated policy discussion in mountainous agricultural regions and offer reference implications for advancing SDG 2 and SDG 15 through the coordination of food-related production, resource use efficiency, and ecosystem conservation. The identified thresholds should be interpreted as model-derived marginal contribution transition points rather than operational policy cutoffs or directly enforceable ecological standards. Full article

As an emerging technology-intensive industry, the low-altitude economy (hereafter LAE) has attracted growing attention for its potential contribution to sustainable development. However, little is known about whether its expansion can simultaneously promote environmental improvement and economic growth. Using panel data from 30 Chinese provinces from 2012 to 2023, this study examines the relationship between the LAE and the synergistic development of carbon reduction, pollution mitigation, green transition, and economic growth (hereafter CPGE). Green technological innovation (hereafter GT) is incorporated as a mediating variable, while artificial intelligence (hereafter AI) is introduced as both a moderating and a threshold variable to explore the underlying mechanisms and nonlinear effects. The results show that the LAE is significantly and positively associated with CPGE. GT exhibits a significant negative mediating effect, suggesting that the benefits of green innovation may not yet have been fully translated into coordinated green development outcomes during the sample period. AI not only strengthens the positive association between the LAE and CPGE but also exhibits a significant threshold effect. The contribution of the LAE becomes substantially stronger once AI development surpasses a critical level, highlighting the important role of digital intelligence in amplifying the environmental benefits of emerging industries. In addition, the impact of the LAE displays pronounced regional heterogeneity, with stronger effects observed in non-resource-based and non-central regions. This study contributes to the literature by revealing that the environmental effects of the LAE depend not only on innovation channels but also on the level of digital intelligence development. AI serves as a critical enabling condition for translating the growth potential of the LAE into coordinated green development. By revealing the mediating role of GT and the moderating and threshold effects of AI, this study provides new evidence on how emerging industries contribute to sustainable development. The findings underscore the importance of aligning LAE development with AI-driven digital transformation to advance sustainable regional development. Full article

The Mangyahedong gold deposit—recently discovered in the Qimantage segment of the East Kunlun orogenic belt—is a high-priority exploration target. Key unknowns include its mineralization age, the sources of sulfur and gold, and the tectonic–magmatic–hydrothermal controls on formation. These gaps have hindered genetic classification and stage-specific research. We addressed them through integrated petrography, TIMA mineral mapping, in situ LA-ICP-MS analysis of pyrite from three mineralization stages, and U-Pb dating of hydrothermal apatite spatially and temporally linked to the main sulfide-precipitation event. The stages are: (I) early sericite–quartz alteration; (II) main ore stage—carbonate–chlorite–sulfide + native gold; and (III) late calcite–pyrite veins. Pyrite zoning shows that early pyrite cores are enriched in As and Au. In contrast, the main-stage pyrite has As-poor cores, with As, Au, and Co progressively enriched toward the rims. This zoning pattern indicates evolving fluid redox conditions and metal complexation during ore deposition. A ²⁰⁷Pb/²⁰⁶Pb age of 406 ± 13 Ma from apatite in gold-bearing quartz–sulfide veins constrains gold deposition to the Late Silurian–Early Devonian transition. Age, texture, and geochemistry collectively support a regional metamorphic–deformational origin, consistent with the orogenic gold model. Isotopic and elemental data point to the Qimantage Group volcanic rocks as the dominant source of ore-forming elements—indicating strong potential for discovery along strike and at depth. Full article