Applied Sciences

Emmer (Triticum turgidum ssp. dicoccum) is attracting renewed interest as a nutrient-dense ancient wheat for sustainable cereal foods; however, product-level evidence for region-specific landraces remains limited. This study characterizes pasta produced exclusively from 100% Triticum dicoccum semolina cultivated in Ardahan (Türkiye) by integrating proximate composition, cooking performance, and instrumental texture (TPA). The emmer pasta contained 12.70% protein, 4.93% total dietary fiber, and 1.68% ash, with an energy value of 366.25 kcal/100 g. Cooking tests revealed 10.86% cooking loss, 219.98% water absorption, and 101.62% volume increase, indicating limited cooking tolerance consistent with a weaker starch–protein matrix. In comparison with conventional T. durum pasta, cooked emmer pasta exhibited comparable hardness, gumminess, and chewiness, but higher adhesiveness and springiness alongside lower resilience and cohesiveness. These results highlight Ardahan-grown T. dicoccum as a nutritionally valuable pasta raw material, albeit with technological constraints (particularly cooking loss) that warrant further optimization for industrial use.

As dockless bike-sharing systems rapidly expanded, this study aims to develop a flow-integrated framework for assessing bicycle usage efficiency, which addresses a critical gap in conventional static indicators. Existing studies rely primarily on big data to evaluate location-specific efficiency using Time-to-Booking (ToB). However, ToB ignores network flow effects while bicycles departing from the same location may reach destinations with vastly different ToB values. To overcome this, we propose a flow-integrated ToB (FwToB) index that incorporates the idle time at both the trip origin and destination. Applying this index to central Beijing reveals significant spatial heterogeneity while maintaining the original core-periphery pattern, indicating that most bicycles flow to areas with similar efficiency. Geographically weighted regression further shows that factors like population density, healthcare, shopping facilities, and distance to metro stations influence efficiency with substantial spatial non-stationarity. These findings advance the understanding of bike-sharing efficiency and offer insights for operators and urban planners.

This study presents a real-time framework for generating two-dimensional (2D) orthomosaic maps directly from UAV video. The method targets operational scenarios in which a continuously updated 2D overview is required during flight or immediately after landing, without relying on time-consuming offline photogrammetry workflows such as structure-from-motion (SfM) and multi-view stereo (MVS). The proposed procedure incrementally registers sparsely sampled video frames on standard CPU hardware using classical feature-based image registration. Each selected frame is converted to grayscale and processed under a fixed keypoint budget to maintain predictable runtime. Tentative correspondences are obtained through descriptor matching with ratio-test filtering, and outliers are removed using random sample consensus (RANSAC) to ensure geometric consistency. Inter-frame motion is modeled by a planar homography, enabling the mapping process to jointly account for rotation, scale variation, skew, and translation that commonly occur in UAV video due to yaw maneuvers, mild altitude variation, and platform motion. Sequential homographies are accumulated to warp incoming frames into a global mosaic canvas, which is updated incrementally using lightweight blending suitable for real-time visualization. Experimental results on three UAV video sequences with different durations, flight patterns, and scene targets report representative orthomosaic-style outputs and per-step CPU runtime statistics (mean, 95th percentile, and maximum), illustrating typical operating behavior under the tested settings. The framework produces visually coherent orthomosaic-style maps in real time for approximately planar scenes with sufficient overlap and texture, while clarifying practical failure modes under weak texture, motion blur, and strong parallax. Limitations include potential drift over long sequences and the absence of ground-truth references for absolute registration-error evaluation.