Search Results (1,336)

Existing research in extended reality for education emphasizes learning outcomes rather than the process for developing their materials. Design thinking, a method in Research through Design, which often generates artefacts and systems, can help address this limitation. As such, this paper presents a process for developing 360° videos based on the six steps of the design thinking process with a new step for planning. The authors also propose a novel approach emphasizing co-creation and Indigenous Research Values throughout the process, showing respect, and minimizing misinterpretations, appropriations, and weak translations that often result from recording stories. Presented through an example titled ‘Tipi Teachings’, a digital story rooted in Indigenous Knowledge of Engineering, the authors demonstrate how design thinking and co-creation can be applied to digital storytelling, proposing a procedure which aims to provide guidance to future researchers utilizing digital storytelling, minimizing trial and error, and providing an opportunity for researchers to share and document lessons learned. While the proposed process was created within a Canadian Indigenous research context, and centers Indigenous storybasket values, these values require researchers to listen to and build relationships with the community, incorporating their core values, regardless of whether they directly align with the storybasket values, adjusting the process to their specific context. The decolonial design process aligned with design thinking also considers decolonization globally, rather than locally. Full article

The Baltic Dry Index (BDI) measures the cost of transporting dry bulk commodities such as coal, iron ore, and grain. As a key indicator of global trade, supply chain dynamics, and overall economic activity, accurate short-term forecasting of the BDI is crucial. This paper compares six univariate methods to obtain a more precise short-term BDI prediction model, providing valuable insights for decision-makers. The six forecasting techniques include Grey Forecast, ARIMA, Support Vector Regression, LSTM, GRU and EMD-SVR-GWO. Model performance is evaluated using three common metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Our findings reveal that the novel EMD-SVR-GWO model outperforms the other univariate methods, demonstrating superior accuracy in forecasting monthly BDI trends. This study contributes to improved BDI prediction, aiding managers in strategic planning and decision-making. Full article

To address the limited environmental perception capability and the difficulty of achieving consistent and efficient representation of the workspace feasible domain caused by high dust concentration, uneven illumination, and enclosed spaces in underground coal mines, this paper proposes a digital spatial construction and representation method for underground environments by integrating RGB-D depth vision with ORB-SLAM3. First, a ChArUco calibration board with embedded ArUco markers is adopted to perform high-precision calibration of the RGB-D camera, improving the reliability of geometric parameters under weak-texture and non-uniform lighting conditions. On this basis, a “dense–sparse cooperative” OAK-DenseMapper Pro module is further developed; the module improves point-cloud generation using a mathematical projection model, and combines enhanced stereo matching with multi-stage depth filtering to achieve high-quality dense point-cloud reconstruction from RGB-D observations. The dense point cloud is then converted into a probabilistic octree occupancy map, where voxel-wise incremental updates are performed for observed space while unknown regions are retained, enabling a memory-efficient and scalable 3D feasible-space representation. Experiments are conducted in multiple representative coal-mine tunnel scenarios; compared with the original ORB-SLAM3, the number of points in dense mapping increases by approximately 38% on average; in trajectory evaluation on the TUM dataset, the root mean square error, mean error, and median error of the absolute pose error are reduced by 7.7%, 7.1%, and 10%, respectively; after converting the dense point cloud to an octree, the map memory footprint is only about 0.5% of the original point cloud, with a single conversion time of approximately 0.75 s. The experimental results demonstrate that, while ensuring accuracy, the proposed method achieves real-time, efficient, and consistent representation of the 3D feasible domain in complex underground environments, providing a reliable digital spatial foundation for path planning, safe obstacle avoidance, and autonomous operation. Full article

This study compares the two (2D)- and three-dimensional (3D) accuracy of tooth reduction depths in porcelain laminate veneer prepared using conventional and 3D-printed guide techniques. Forty 3D-printed maxillary casts were divided into four groups: freehand (FH) (n = 10), silicone guide (SG) (n = 10), cross-shaped 3D-printed guide (3D_C) (n = 10), and stackable 3D-printed guides (3D_S) (n = 10). Butt-joint veneer preparation was performed on the left central incisor. Two-dimensional analysis was performed to assess trueness using mean absolute differences (MADs) from the planned depth at eight designated points, while precision was compared within groups. Three-dimensional analysis evaluated trueness by superimposing post-preparation scans with reference casts and precision via intra-group superimposition, with deviation errors measured using the Root Mean Square (RMS) method. One-way ANOVA and Bonferroni post hoc tests were used (α = 0.05). In 2D analysis, 3D_S exhibited a significantly lower MAD than FH at most of the measured points (p < 0.05), more accurate incisal reduction at mesial and distal points compared to 3D_C (p < 0.001), and more accurate mesial (p = 0.011) and distal (p = 0.001) cervical margin preparation than SG. In the 3D trueness assessment, 3D_S exhibited significantly lower deviation errors than FH (p < 0.001) and SG (p = 0.012) while also achieving the highest overall 3D precision with the lowest RMS (0.067 ± 0.013), followed by 3D_C (0.086 ± 0.019). Veneer preparation guided by a stackable 3D-printed guide resulted in more accurate tooth reduction depths compared to the other three techniques. Full article

A hybrid machine learning framework has been developed in this study to estimate Terrestrial Water Storage Anomalies (TWSA) in Morocco’s Casablanca–Settat region, which faces serious groundwater stress due to rapid urbanization, intensive agriculture, and climate variability. In this study, TWSA is used as an integrated proxy for groundwater-related storage changes, while acknowledging that it also includes contributions from soil moisture and surface water. The approach combines satellite-based observations from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) with key environmental indicators such as rainfall, evapotranspiration, and land use data to track changes in groundwater availability with improved spatial detail. After preprocessing the data through feature selection, normalization, and outlier handling, the model applies six base learners, i.e., Huber regressor, automatic relevance determination regression, kernel ridge, long short-term memory, k-nearest neighbors, and gradient boosting. Their predictions are aggregated using a random forest meta-learner to improve accuracy and stability. The ensemble achieved strong results, with a root mean square error of 0.13, a mean absolute error of 0.108, and a determination coefficient of 0.97—far better than single-model baselines—based on a temporally independent train-test split. Spatial analysis highlighted clear patterns of groundwater depletion linked to land cover and usage. These results can guide targeted aquifer recharge efforts, drought response planning, and smarter irrigation management. The model also aligns with national goals under Morocco’s water sustainability initiatives and can be adapted for use in other regions with similar environmental challenges. Full article

The generalization capability of the decision-making modules in unmanned ground vehicles (UGVs) is critical for their safe deployment in unseen environments. Prevailing evaluation methods, which rely on aggregated performance over static benchmark sets, lack the granularity to diagnose the root causes of model failure, as they often conflate the distinct influences of scenario similarity and intrinsic difficulty. To overcome this limitation, we introduce a fine-grained, dynamic evaluation framework that deconstructs generalization along the dual axes of multi-level difficulty and similarity. First, scenario similarity is quantified through a four-layer hierarchical decomposition, with results aggregated into a composite similarity score. Test scenarios are independently classified into ten discrete difficulty levels via a consensus mechanism integrating large language models and task-specific proxy models. By constructing a three-dimensional (3D) performance landscape across similarity, difficulty, and task performance, we enable detailed behavioral diagnosis. The framework assesses robustness by analyzing performance within the high-similarity band (90–100%), while the full 3D landscape characterizes generalization under distribution shift. Seven interpretable metrics are derived to quantify distinct facets of both generalization and robustness. This initial validation focuses on the path-planning layer under full state observability, establishing a foundational proof-of-concept for the framework. It not only ranks algorithms but also reveals non-trivial behavioral patterns, such as the decoupling between in-distribution robustness and out-of-distribution generalization. It provides a reliable and interpretable foundation for evaluating the readiness of UGVs for safe deployment in unseen environments. Full article

In regions lacking sufficient data, remote sensing (RS) offers a reliable alternative for precipitation estimation, enabling more effective drought management. This study comprehensively evaluates four commonly used RS datasets—Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS), Tropical Applications of Meteorology using Satellite data (TAMSAT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) against ground-based data—with respect to their performance in detecting precipitation and drought patterns in the Great Ruaha River Basin (GRRB), Tanzania (1983–2020). Statistical metrics including the Pearson correlation coefficient (r), mean error (ME), root mean square error (RMSE), and bias were employed to assess the performance at daily, monthly, seasonal (wet/dry), and annual timescales. Most of the RS products exhibited lower correlations (r < 0.5) at daily timestep and low RMSE, bias, and ME. Monthly performance improved substantially (r > 0.8 at most stations) particularly during the wet season (r = 0.52–0.82) while annual and dry-season performance declined (r < 0.5 and r < 0.3, respectively). Performance under RMSE, bias, and ME declined at higher timescales, particularly during the wet season and annually. CHIRPS, MSWEP, and PERSIANN generally overestimated precipitation while TAMSAT consistently underestimated it. Spatially, CHIRPS and MSWEP reproduced coherent basin-scale patterns of drought persistence, with longer dry-spells concentrated in the northern, central, and western parts of the basin and shorter dry-spells in the eastern and southern regions. Trend analysis further revealed that most products captured consistent large-scale changes in dry-spell characteristics, although localized drought events were more variably detected. CHIRPS and MSWEP showed superior performance especially in capturing monthly precipitation patterns and major drought events in the basin. Most products struggled to detect extreme dry conditions with the exception of CHIRPS and MSWEP at certain stations and periods. Based on these findings, CHIRPS and MSWEP are recommended for drought monitoring and water resource planning in the GRRB. Their appropriate use can help water managers make informed decisions, promote sustainable resource use, and strengthen resilience to extreme weather events. Full article

Salinity is an emerging constraint for Mediterranean coastal agriculture, where shallow groundwater, seawater intrusion, and summer evapo-concentration generate relevant intra-seasonal variability in soil electrical conductivity. Camelina [Camelina sativa (L.) Crantz] has been proposed as a diversification oilseed for constrained environments, but its field performance under realistic, dynamic salinity in Mediterranean soils remains unexplored. This two season on farm study compared three commercial camelina lines at an inland non-saline site and a coastal saline–sodic site in northeastern Italy, combining agronomic measurements with phenology aligned monitoring of soil saturated paste electrical conductivity (ECe). At the saline site, ECe increased from 1.8 dS m⁻¹ at the vegetative stage to 6.2 dS m⁻¹ at seed filling, while camelina completed its cycle earlier than at the inland site. Despite similar aboveground and root biomass yield at flowering across lines, performance diverged during the reproductive phase. Two lines maintained similar seed yields (1.30 Mg ha⁻¹) at the coastal site compared with the inland site, whereas one line declined from 1.45 Mg ha⁻¹ to 0.40 Mg ha⁻¹. Differences among lines in seed yield under salinity were accompanied by contrasting responses in seed oil composition. Oil yield at the saline site was more strongly associated with the increase in ECe from flowering to seed filling than with absolute ECe at seed filling. These results provide the first field-based evidence of line-specific salinity responses in camelina and highlight its potential to diversify moderately salt-affected Mediterranean coastal cropping systems, while emphasizing the need to account for temporal salinity dynamics in genotype selection and crop planning. Full article

Due to their large scale, long duration, complex geological conditions, and multiple stakeholders, water conservancy engineering projects are subject to diverse, interrelated, and uncertain risk factors that affect the construction timeline. Traditional critical chain buffer calculation methods, such as the cut-and-paste method and the root variance method, typically assume the independence of risks, which limits their effectiveness in addressing schedule delays caused by correlated risk events. To overcome this limitation, this paper proposes a novel critical chain buffer calculation approach that explicitly incorporates risk correlation analysis. A fuzzy DEMATEL-ISM-BN model is employed to systematically identify the interrelationships and influence pathways among schedule risk factors. Bayesian network inference is then used to quantify the overall occurrence probability while accounting for risk correlations. By integrating critical chain management theory, risk impact coefficients are introduced to improve the traditional root variance method, resulting in a buffer calculation model that captures interdependencies among schedule risks. The effectiveness of the proposed model is validated through a case study of the X Pumped Storage Power Station. The results indicate that, compared with conventional methods, the proposed approach significantly enhances the robustness of project schedule planning under correlated risk conditions while appropriately increasing buffer sizes. Consequently, the adaptability and reliability of schedule control are improved. This study provides novel theoretical tools and practical insights for schedule risk management in complex engineering projects. Full article

In this commentary, we discuss the concept of field homology, particularly as applied to the brain in comparative neuromorphology. We emphasize its roots in the topological properties of the Bauplan notion (organization plan) as well as in the still scarcely understood concept of developmental fields, and we criticize their modernly frequent erred substitution by the search of similarity of characters. We defend the logical causal connection of embryonic homology with adult homology, irrespective of the regulatory aspects of ontogeny. Full article

Urbanization in mountainous regions alters hydrologic systems, yet the spatial patterning of residential (RA) and non-residential (NRA) areas in response to hydrologic constraints remains poorly quantified. In this study, we analyzed how such constraints shaped the distinct locational logic of RA and NRA expansion in the mountainous Kunming Core Region (KCR), Southwest China, from 1975 to 2020. Using the Global Human Settlement Layer (GHS-BUILT-S) built-up fraction data and its functionally classified RA and NRA layers at 100 m resolution, we quantified multi-decadal urban land changes via regression and centroid migration analyses. Six hydrologic factors, namely altitude, slope, surface roughness, distance to river (DTR), depth to water table (DTWT) and vegetation root depth (VRD), were derived from global terrain, groundwater, and rooting depth datasets, and harmonized to a common grid. Results show a two-phase urbanization pattern: moderate, compact growth before 1995 followed by rapid, near-exponential expansion, dominated by RA. RA consistently clustered in hydrologically favorable zones (low–moderate roughness, mid-altitudes, lower slopes, proximal rivers, shallow–moderate DTWT, moderate VRD), whereas NRA expanded into more hydrologically variable terrain (higher roughness, intermediate DTR, deeper DTWT, higher altitudes, deeper VRD). Contribution-weighting analysis revealed a temporal shift in dominant drivers: for RA, from river proximity and slope in 1975 to terrain roughness in 2020; for NRA, from vegetation root depth and moderate topography to root depth plus altitude. Geographic centroids of both RA and NRA migrated northeastward, indicating coordinated yet functionally distinct peri-urban and corridor-oriented growth. These findings provide a hierarchical, factor-based framework for integrating hydrologic constraints into risk-informed land-use planning in topographically complex basins. Full article

In the mining industry, where equipment breakdowns cause expensive unplanned downtime, operational continuity is paramount. Internet of Things (IoT) technologies have the potential to make predictions; however, most solutions lack a holistic view and mapping of complex system interdependencies. This study presents a comprehensive predictive maintenance (PdM) framework specifically designed for continuous-operation mining environments, with a primary focus on Semi-Autogenous Grinding (SAG) mills. By combining exploratory data analysis, advanced feature engineering, classical machine learning (Gradient Boosting Classifier), and deep learning (LSTM with multiple time-window configurations), the system achieves real-time anomaly detection, root-cause explanation, and failure forecasting up to 48 h in advance (average lead time: 17 h). A four-layer digital twin architecture integrated with Streamlit enables actionable alerts classified as emergency, planned, or preventive interventions. Applied to a one-year dataset comprising 99,854 hourly records from an industrial SAG mill, the hybrid model prevented an estimated 219.5 h of unplanned downtime, yielding substantial economic benefits. The proposed solution is deliberately designed for high adaptability across multiple equipment types and industrial sectors beyond mining. Full article

The rhizosphere, a confined area of soil plant roots, is an intersection of microbial activity and root exudates. Known as the rhizosphere effect, it enhances crop yield and sustainability by improving nutrient availability, beneficial compounds, and pathogen control. This study combines a field-based rhizosphere–bulk soil comparison for peanut with a geostatistical approach to quantify the spatial variability of rhizosphere-driven changes in soil quality indicators in the Ghardaïa region (southern Algeria), which is known for its sandy–clay and sandy–loam soils. Samples of rhizosphere and bulk soils were prospected using a systematic plan. Subsequently, the pH, electrical conductivity, calcium carbonate, organic matter, total nitrogen, available phosphorus, total potassium, and soluble sodium were determined for each soil (rhizosphere and bulk soil). To assess the spatial variability of rhizosphere soil parameters, semi-variograms of the fitted models were generated using auto-kriging. The results showed that both types of soils were moderately alkaline, with a reduction of 5.52% in the pH of the rhizosphere compared to the bulk soils. Soils were relatively low in organic matter, with only 3.3% of soils having organic matter levels above 20 g kg⁻¹. However, organic matter contents were consistently higher in the rhizosphere (8.51 ± 4.59 g kg⁻¹) than in the bulk soil (6.78 ± 3.52 g kg⁻¹). In the rhizosphere, an increase of 10% in labile phosphorus was noted. Total nitrogen was increased by 52.57%. T-tests suggested no significant difference in potassium and sodium levels, and they were moderately present in both soils. Significantly positive relationships were noted between available phosphorus and total nitrogen (R = 0.59, p < 0.001). However, negative correlations were revealed between pH and organic matter available phosphorus (R = −0.77, p < 0.001) and pH and total nitrogen (R = −0.56, p < 0.01). These results indicate the effects of rhizosphere interactions on soil property improvements and their implications for sustainable agricultural practices, including crop rotation, intercropping, and green manure applications. Full article

Accurate sales prediction is crucial for inventory and marketing in e-commerce. Cross-border sales involve complex patterns that traditional models cannot capture. To address this, we propose an improved Bidirectional Long Short-Term Memory (BiLSTM) model, enhanced with an attention mechanism and Bayesian hyperparameter optimization. The attention mechanism focuses on key temporal features, improving trend identification. The BiLSTM captures both forward and backward dependencies, offering deeper insights into sales patterns. Bayesian optimization fine-tunes hyperparameters such as learning rate, hidden-layer size, and dropout rate to achieve optimal performance. These innovations together improve forecasting accuracy, making the model more adaptable and efficient for cross-border e-commerce sales. Experimental results show that the model achieves an Root Mean Square Error (RMSE) of 13.2, Mean Absolute Error (MAE) of 10.2, Mean Absolute Percentage Error (MAPE) of 8.7 percent, and a Coefficient of Determination (R²) of 0.92. It outperforms baseline models, including BiLSTM (RMSE 16.5, MAPE 10.9 percent), BiLSTM with Attention (RMSE 15.2, MAPE 10.1 percent), Temporal Convolutional Network (RMSE 15.0, MAPE 9.8 percent), and Transformer for Time Series (RMSE 14.8, MAPE 9.5 percent). These results highlight the model’s superior performance in forecasting cross-border e-commerce sales, making it a valuable tool for inventory management and demand planning. Full article

This study investigates the determinants of green energy penetration in the Next Eleven (N-11) economies over the period 2000–2022, with a particular focus on the roles of foreign direct investment (FDI), green transition, governance quality, industrial growth, and urbanization. The primary objective of the study is to assess how investment flows, structural transformation, and institutional capacity jointly shape the adoption of renewable energy in fast-growing emerging economies. To achieve this goal, the study employs a second-generation panel econometric and machine-learning framework that accounts for cross-sectional dependence, slope heterogeneity, and long-run equilibrium relationships. Specifically, cross-sectional dependence and slope homogeneity tests are conducted, followed by CADF and CIPS unit root tests and the Westerlund cointegration approach. Long-run effects are then estimated using Partialing-Out LASSO and Cross-Fit machine-learning estimators, complemented by SHAP analysis to interpret nonlinear and heterogeneous effects. The results indicate that green transition, governance quality, and urbanization significantly promote green energy penetration. In contrast, FDI and industrial growth exert adverse effects, reflecting carbon-intensive investment and production structures. The findings highlight the importance of coordinated investment strategies, institutional strengthening, and urban planning in accelerating renewable energy transitions in emerging economies. These results provide policy-relevant insights for achieving sustainable energy development while supporting long-term economic growth in the N-11 countries. Full article