Search Results (341)

The design of high-precision total temperature probes for aero-engines is constrained by the massive computational cost of high-fidelity simulations. This paper overcomes this barrier by introducing a surrogate model-based optimization framework for a dual-shield probe. A computationally efficient data-driven framework is established, merging conjugate-heat-transfer Computational Fluid Dynamics (CFDs), a Support Vector Regression (SVR) model, and a Genetic Algorithm (GA), which collectively replace the traditional costly design loop. The surrogate model’s exceptional predictive fidelity is confirmed, and this approach obtains improvement in measurement accuracy, successfully reducing the temperature deviation and meeting the stringent requirement. Finally, the demonstrated framework is geometry-agnostic, establishing a generalizable and cost-effective strategy for the rapid design of high-performance thermometric components in gas turbine systems. Full article

Short-form video platforms such as TikTok, Instagram Reels, and YouTube Shorts have become influential social commerce and interactive marketing tools, shaping consumer attitudes and behaviors beyond the digital environment. This study examines how short-form video content affects consumers’ intention to visit destination retail stores by integrating the Theory of Planned Behavior (TPB) with the constructs of perceived usefulness, curiosity, and envy. Data from 423 Gen Z and Millennial consumers were collected through an online survey and analyzed using structural equation modeling. The findings indicate that perceived usefulness, curiosity, and envy significantly influence attitudes toward short-form video content, which subsequently drive intentions to visit destination retailers. Social influence also emerged as a stronger predictor of behavioral intention than practical barriers such as cost or accessibility, underscoring the importance of peer validation in motivating digital-to-physical consumer behavior. This study advances electronic commerce research by extending TPB to short-form video marketing and identifying key emotional and cognitive triggers that facilitate consumer engagement. Practically, the results highlight strategies for retailers to develop video campaigns that spark curiosity, evoke aspirational emotions, and leverage social endorsement. More broadly, the study demonstrates how short-form video platforms operate as interactive ecosystems that merge emotional engagement, social validation, and technological affordances to shape hybrid consumer journeys from digital exposure to in-store action. Full article

This study develops a satellite-based, machine-learning-based prediction algorithm to predict optimal irrigation scheduling for cotton cultivation within Rahim Yar Khan, Pakistan. The framework leverages multispectral satellite imagery (Landsat 8 and Sentinel-2), GIS-derived climatic, land surface data and real-time weather information obtained from a freely accessible weather API, eliminating the need for ground-based IoT sensors. The proposed algorithm integrates FAO-56 evapotranspiration principles and water stress indices to accurately forecast irrigation requirements across the four critical growth stages of cotton. Supervised learning algorithms, including Gradient Boosting, Random Forest, and Logistic Regression, were evaluated, with Random Forest indicating better predictive accuracy with a coefficient of determination (R²) exceeding 0.92 and a root mean square error (RMSE) of approximately 415 kg/ha, owed its capacity to handle complex, non-linear relations, and feature interactions. The model was trained on data collected during 2023 and 2024, and its predictions for 2025 were validated against observed irrigation requirements. The proposed model enabled an average 12–18% reduction in total water application between 2023 and 2025, optimizing water use deprived of compromising crop yield. By merging satellite imagery, GIS data, and weather API information, this approach provides a cost-effective, scalable solution that enables precise, stage-specific irrigation scheduling. Cloud masking was executed by applying the built-in QA bands with the Fmask algorithm to eliminate cloud and cloud-shadow pixels in satellite imagery statistics. Time series were generated by compositing monthly median values to ensure consistency across images. The novelty of our study primarily focuses on its end-to-end integration framework, its application within semi-arid agronomic conditions, and its empirical validation and accuracy calculation over direct association of multi-source statistics with FAO-guided irrigation scheduling to support sustainable cotton cultivation. The quantification of irrigation capacity, determining how much water to apply, is identified as a focus for future research. Full article

Ancient underground voids present non-trivial hazards to urban redevelopment, particularly where groundwater conditions change during construction. We propose a staged, groundwater-aware workflow that integrates in-void mapping with area-scale geophysics and explicitly links water state to imaging performance. Following exposure of an undocumented masonry tunnel in a foundation pit in Wuhan (China), we acquired underwater CCTV and sonar during water-filled conditions, and, after drainage, collected ground-penetrating radar (GPR, 75–150 MHz) and ultra-high-density electrical resistivity tomography (UHD-ERT, 1 m electrode spacing) data. Calibration lines over the breach anchored the depth/geometry and reduced interpretational non-uniqueness. Analytical estimates using Archie-type and CRIM relations, together with observed signatures, indicate that drainage increased resistivity and reduced electromagnetic attenuation, improving UHD-ERT contrast and GPR penetration. The merged evidence resolves a straight-walled arch (~1.8 m wide × ~1.9 m high) at ~4–5 m depth with a sealed end 4 m south of the breach. Sonar confirms a northward segment measuring 45 ± 2 m to a sealed wall; a GPR void-type anomaly at ~57 m along trend represents a candidate continuation that remains unverified with current access. Within the resolution and sensitivity of the 2D survey, no additional voids were detected elsewhere on site. This case demonstrates that coupling in-void CCTV/sonar with post-drainage GPR and UHD-ERT, organized by hydrologic stage, yields engineering-grade constraints for risk control. The workflow and boundary conditions provide a transferable template for water-influenced, urban environments. Full article

Floods increasingly threaten semiarid regions, yet their long-term soil ecological impacts remain underdocumented. This study quantifies the hydrologic change and flood-induced soil transformation on the Zhabay River floodplain (Akmola, Kazakhstan) using integrated field, laboratory, and remote sensing data. Gauge records (2012–2024) were analyzed; inundation was mapped from a 0.30 m DEM (Digital Elevation Model) merging SRTM (Shuttle Radar Topography Mission), Landsat 8/Sentinel 2, and UAV (Unmanned Aerial Vehicle) photogrammetry (NDWI (Normalized Difference Water Index) > 0.28) and validated with 54 in situ depths (MAE (Mean Absolute Error) 0.17 m). Soil samples collected before and after floods were analyzed for texture, bulk density, pH, Eh, macronutrients, and heavy metals. Annual maxima increased by 0.08 m yr⁻¹, while extreme floods became more frequent. Thresholds of ≥0.5 m depth and >7 days duration marked compaction onset, whereas >1 m and ≥12 days produced maximum organic carbon loss and Zn/Ni enrichment. The combination of high-resolution DEMs, ROC (Receiver Operating Characteristic) analysis, and soil microbial monitoring provides new operational indicators of soil degradation for Central Asian steppe floodplains. Findings contribute to SDG 13 (Climate Action) and SDG 15 (Life on Land) by linking flood resilience assessment with sustainable land-use planning. Full article

Vehicle platoons offer significant benefits in connected vehicle environments, including reduced travel time, increased throughput, mitigated congestion, and lower energy consumption. To adapt to dynamic traffic conditions, platoon formations must be adjusted flexibly; this process is facilitated by traffic management centers via real-time control and cloud-based data transmission. Given communication constraints, we propose a minimum information set that is sufficient to maintain and adjust platoon formations and that supports data storage, computation, and representation of state changes within platoons. This set comprises two components: the Property Set and the Instruction Set. The Property Set collects vehicle-level and platoon-level attributes, whereas the Instruction Set, which includes communication and control subsets, enables formation maintenance and adjustment. We design a series of algorithms structured along timeline-based and task-based frameworks to specify transition rules and task execution modes across states, thereby describing the complete life cycle of a platoon from independent driving through formation and reorganization to dissolution. Finally, we develop an integrated scenario algorithm and apply it to two representative cases: highway platooning and intersection merging and separation. The results indicate that the proposed Minimum Platoon Set has substantial potential for platoon management, providing a solid theoretical foundation and practical guidance for optimizing platoon control. Full article

The discovery of the fifth gravitational wave, GW170817, and its electromagnetic counterpart, resulting from the merger of neutron stars by the LIGO and Virgo teams, marked a major milestone in astronomy. It was the first time that gravitational waves and light from the same cosmic event were observed simultaneously. The LIGO detectors in the United States recorded the signal for 100 s, longer than in previous detections. The merging of neutron stars emits both gravitational and electromagnetic waves across all frequencies—from radio to gamma rays. However, pinpointing the exact source remains difficult, requiring rapid sky scanning to locate it. To address this challenge, the Gravitational-Wave Optical Transient Observer (GOTO) project was established. It is specifically designed to detect optical light from transient events associated with gravitational waves, enabling faster follow-up observations and a deeper study of these short-lived astronomical phenomena, which appear and disappear quickly in the universe. In astrophysics, it has become more important to find astronomical transient events like supernovae, gamma-ray bursts, and stellar flares because they are linked to extreme cosmic processes. However, finding these short-lived events in huge sky survey datasets, like those from the GOTO project, is very hard for traditional analysis methods. This study suggests a deep learning methodology employing Convolutional Neural Networks (CNNs) to enhance transient classification. CNNs are based on how biological vision systems work and how they are structured. They mimic how animal brains hierarchically process visual information, making it possible to automatically find complex spatial patterns in astronomical images. Transfer learning and fine-tuning on pretrained ImageNet models are utilized to emulate adaptive learning observed in biological organisms, enabling swift adaptation to new tasks with minimal data. Data augmentation methods like rotation, flipping, and noise injection mimic changes in the environment to improve model generalization. Dropout and different batch sizes are used to stop overfitting, which is similar to how biological systems use redundancy and noise tolerance. Ensemble learning strategies, such as Soft Voting and Weighted Voting, draw inspiration from collective intelligence in biological systems, integrating multiple CNN models to enhance decision-making robustness. Our findings indicate that this bio-inspired framework substantially improves the precision and dependability of transient detection, providing a scalable solution for real-time applications in extensive sky surveys such as GOTO. Full article

This study explores the integration of point cloud data obtained from unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) to address limitations in photogrammetry and to create comprehensive models of aquatic environments. The UAV platform (AUTEL EVO II) employs structure-from-motion (SfM) photogrammetry using optical imagery, while the USV (equipped with a NORBIT iWBMS multibeam sonar system) collects underwater bathymetric data. UAVs commonly face constraints in battery life and image-processing capacity, making it necessary to merge smaller UAV point clouds into larger, more complete models. The USV-derived bathymetric data are integrated with UAV-derived surface data to construct unified terrain models that include both above-water and underwater features. This study evaluates three coordinate transformation (CT) methods—4-parameter, 6-parameter, and 7-parameter—across three study areas in Taiwan to assess their effectiveness in registering USV–UAV and UAV–UAV point clouds. For USV–UAV integration, all CT methods improved alignment accuracy compared with results without CT, achieving decimeter-level precision. For UAV–UAV integrations, the 7-parameter method provided the best accuracy, especially in areas with low terrain roughness such as rooftops and pavements, while improvements were less pronounced in areas with high roughness such as tree canopies. These findings demonstrate that the 7-parameter CT method offers an effective and straightforward approach for accurate point cloud integration from different platforms and sensors. Full article

The dedifferentiation of smooth muscle cells (SMCs) is the main cause of atherosclerosis and vascular calcification. This study integrated the gene expression data of multiple microarrays to identify relevant marker molecules. A total of 72 Gene Expression Omnibus (GEO) samples (GSM) were collected from 10 gene expression data series (GSE) and divided into five groups: non-SMC, SMC, atherosclerotic SMC (SMC-ath), calcified SMC (SMC-calc), and treated SMC (SMC-t). The SMC-t group included synthetic SMCs that had undergone treatment to inhibit proliferation, migration, or inflammation. The gene expression data were merged, normalized, and batch effects were removed before differential gene expression (DGE) analysis was performed via linear models for microarray data (limma) and statistical analysis of metagenomic profiles (STAMPs). The genes with expressions that significantly differed were subsequently subjected to protein-protein interaction (PPI) and functional prediction analyses. In addition, the random forest method was used for classification. Twelve proteins that may be marker molecules for SMC differentiation and dedifferentiation were identified, namely, Proprotein convertase subtilisin/kexin type 1 (PCSK1), Transforming growth factor beta-induced (TGFBI), Complement C1s (C1S), Phosphomannomutase 1 (PMM1), Claudin 7 (CLDN7), Calcium binding and coiled-coil domain 2 (CALCOCO2), SAC3 domain-containing protein 1 (SAC3D1), Natriuretic peptide B (NPPB), Monoamine oxidase A (MAOA), Regulator of the Cell Cycle (RGCC), Alpha-crystallin B Chain (CRYAB), and Alcohol dehydrogenase 1B (ADH1B). Finally, their possible roles in SMCs are discussed. This study highlights the feasibility of bioinformatics analysis for studying SMC dedifferentiation. Full article

This study focuses on human awareness, a critical component in human–robot interaction, particularly within agricultural environments where interactions are enriched by complex contextual information. The main objective is identifying human activities occurring during collaborative harvesting tasks involving humans and robots. To achieve this, we propose a novel and lightweight deep learning model, named 1D-ResNeXt, designed explicitly for recognizing activities in agriculture-related human–robot collaboration. The model is built as an end-to-end architecture incorporating feature fusion and a multi-kernel convolutional block strategy. It utilizes residual connections and a split–transform–merge mechanism to mitigate performance degradation and reduce model complexity by limiting the number of trainable parameters. Sensor data were collected from twenty individuals with five wearable devices placed on different body parts. Each sensor was embedded with tri-axial accelerometers, gyroscopes, and magnetometers. Under real field conditions, the participants performed several sub-tasks commonly associated with agricultural labor, such as lifting and carrying loads. Before classification, the raw sensor signals were pre-processed to eliminate noise. The cleaned time-series data were then input into the proposed deep learning network for sequential pattern recognition. Experimental results showed that the chest-mounted sensor achieved the highest F1-score of 99.86%, outperforming other sensor placements and combinations. An analysis of temporal window sizes (0.5, 1.0, 1.5, and 2.0 s) demonstrated that the 0.5 s window provided the best recognition performance, indicating that key activity features in agriculture can be captured over short intervals. Moreover, a comprehensive evaluation of sensor modalities revealed that multimodal fusion of accelerometer, gyroscope, and magnetometer data yielded the best accuracy at 99.92%. The combination of accelerometer and gyroscope data offered an optimal compromise, achieving 99.49% accuracy while maintaining lower system complexity. These findings highlight the importance of strategic sensor placement and data fusion in enhancing activity recognition performance while reducing the need for extensive data and computational resources. This work contributes to developing intelligent, efficient, and adaptive collaborative systems, offering promising applications in agriculture and beyond, with improved safety, cost-efficiency, and real-time operational capability. Full article

Understanding global precipitation trends is critical for managing water resources, anticipating extreme events, and assessing the impacts of climate change. This study analyzes spatial and temporal patterns of precipitation from 1998 to 2024 using NASA’s Global Precipitation Measurement Mission (GPM) Integrated Multi-satellite Retrievals for (IMERG) Version 7, which merges satellite observations with rain-gauge data at 0.1° resolution. A total of 324 monthly datasets were aggregated into annual and seasonal composites to evaluate annual and seasonal trends in global precipitation. The non-parametric Mann–Kendall test was applied at the pixel scale to detect statistically significant monotonic trends, and Sen’s slope estimator method was used to quantify the magnitude of change in mean annual and seasonal global precipitation. Results reveal robust and geographically consistent patterns: significant wetting trends are evident in high-latitude regions, with the Arctic and Southern Oceans showing the strongest increases across multiple seasons, including +0.04 mm/day in December–January–February for the Arctic Ocean and +0.04 mm/day in June–July–August for the Southern Ocean. Northern China also demonstrates persistent increases, aligned with recent intensification of extreme late-season precipitation. In contrast, significant drying trends are detected in the tropical East Pacific (up to −0.02 mm/day), northern South America, and some areas in central-southern Africa, highlighting regions at risk of sustained hydroclimatic stress. The North Atlantic south of Greenland emerges as a summer drying hotspot, consistent with Greenland Ice Sheet melt enhancing stratification and reducing precipitation. Collectively, the findings underscore a dual pattern of wetting at high latitudes and drying in tropical belts, emphasizing the role of polar amplification, ocean–atmosphere interactions, and climate variability in shaping Earth’s precipitation dynamics. Full article

This research presents a novel conceptual framework for inclusive education by integrating Internet of Things (IoT)-driven real-time environmental and behavioral monitoring with adaptive teaching strategies. Unlike traditional methods, our model leverages sensor-based data collection to analyze classroom conditions, teacher mobility, and student interactions, enabling dynamic adjustments that aim to enhance engagement and inclusivity. While the framework is theoretical and has not yet undergone experimental validation, we discuss how optimizing spatial configurations, voice dynamics, and movement patterns could support student participation, particularly for learners with diverse needs. Pilot implementations and empirical testing are planned for future research. By merging data-driven insights with educators’ expertise, our approach offers a scalable vision for creating responsive, inclusive learning environments that proactively address barriers to education. Full article

In developing countries, adolescent girls and young women (AGYW) continue to experience high rates of unintended pregnancy and sexually transmitted infections (STIs), including Human Immunodeficiency Virus (HIV). Several healthcare services are available at the primary level of healthcare to address the sexual and reproductive needs of adolescents in South Africa. Healthcare providers often face challenges such as limited resources, inadequate funds, and inadequate training, which hinder their ability to provide integrated care. Furthermore, cultural stigma and a lack of privacy prevent adolescents from seeking care. In response to increasing international calls for developing and implementing integrated person-centered care, which addresses both quality and access to care, this paper aims to develop and validate an integrated HIV/STI, and pregnancy prevention program for adolescent girls and young women in the Vhembe District of Limpopo. Multiphase mixed methods were employed in this study. This study consisted of three interconnected phases. As part of phase 1 of this study, a comprehensive literature review was conducted. In phase 2, an empirical study conducted using a concurrent triangulation strategy to collect and analyze both qualitative and quantitative data as a form of confirmation, dis-confirmation, cross-validation or corroboration of the findings. Consequently, a conceptual framework was developed using qualitative and quantitative analysis by merging, comparing, and interpreting the results. The findings of phase 2 interface were analyzed using the Political, Environmental, Social, and Technological (PEST) and Strength, Weakness, Opportunity, and Threat (SWOT) analyses. Additionally, the outcomes of the Logical Framework Analyses (LFA) informed the development of an integrated programme aimed at preventing HIV, STIs, and teenage pregnancy. Several stakeholders and experts (n = 35) were consulted as part of the Reduce the Risk (RTR) Coalition to validate the proposed integrated programme with an average of 94.3% on acceptability, feasibility, and appropriateness. In the Vhembe District of Limpopo province, there has been no published study that has developed an integrated HIV, STIs, and pregnancy prevention programme to improve the sexual health outcomes of adolescent girls and young women. Full article

The airborne 2D-S optical array probe has worked for more than ten years and has collected a large number of cloud particle images. However, existing detection methods cannot detect cloud particles with high precision due to the size differences of cloud particles and the occurrence of particle fragmentation during imaging. So, this paper proposes a novel cloud particle detection method. The key innovation is an adaptive anchor SSD module, which overcomes existing limitations by generating anchor points that adaptively align with cloud particle size distributions. Firstly, morphological transformations generate multi-scale image information through repeated dilation and erosion operations, while removing irrelevant artifacts and fragmented particles for data cleaning. After that, the method generates geometric and mass centers across multiple scales and dynamically merges these centers to form adaptive anchor points. Finally, a detection module integrates a modified SSD with a ResNet-50 backbone for accurate bounding box predictions. Experimental results show that the proposed method achieves an mAP of 0.934 and a recall of 0.905 on the test set, demonstrating its effectiveness and reliability for cloud particle detection using the 2D-S probe. Full article

Education has been steadily incorporating technology to support and enhance teaching and learning practices. One illustrative example is the use of augmented reality (AR), which seamlessly merges virtual elements with the physical world. Children are acquainted with emerging technology as they are the new generation who have been exposed to smart phones and tablets. They belong to a new generation profoundly influenced by these devices. In this research, an AR-based edutainment mobile application with digital visual elements and sound, namely ARKiD, is developed as an alternative to traditional educational mechanisms. It aims to enhance the learning experience for preschool children. This research investigates teachers’ and preschoolers’ perceptions and behavioral patterns in using ARKiD. A mixed method approach was used to collect data from 12 teachers and 65 preschoolers aged 4–5. During data collection, both qualitative and quantitative methods are used. Qualitative methods include observation based on psychomotor aspects, for example, controlling, turning, inspecting, and interview while quantitative refers to the use of questionnaires. The questionnaire was designed based on the technology acceptance model (TAM) which consisted of four antecedents, namely perceived usefulness (PU), perceived ease of use (PEOU), attitude (A) and behavioral intention (BI). This research revealed that the teachers and preschoolers enjoyed using ARKiD despite some concerns regarding AR technology. Overall, preschoolers can operate the ARKiD independently and it shows the learning effectiveness. This research has presented a new type of educational technology to bridge the gap in the field. Full article