Search Results (40)

The advancement of harvester technology increasingly relies on automated forest analysis within machine operational ranges. However, real-world testing remains costly and time-consuming. To address this, we introduced the Tree Classification Framework (TCF), a simulation platform for the cost-effective testing of harvester technologies. TCF accelerates technology development by simulating forest environments and machine operations, leveraging machine-learning and computer vision models. TCF has four components: Synthetic Forest Creation, which generates diverse virtual forests; Point Cloud Generation, which simulates LiDAR scanning; Stem Identification and Classification, which detects and characterises tree stems; and Experimental Evaluation, which assesses algorithm performance under varying conditions. We tested TCF across ten forest scenarios with different tree densities and morphologies, using two-point cloud generation methods: fixed points per stem and LiDAR scanning at three resolutions. Performance was evaluated against ground-truth data using quantitative metrics and heatmaps. TCF bridges the gap between simulation and real-world forestry, enhancing the harvester technology by improving efficiency, accuracy, and sustainability in automated tree assessment. This paper presents a framework built from affordable, standard components for stem identification and classification. TCF enables the systematic testing of classification algorithms against known ground truth under controlled, repeatable conditions. Through diverse evaluations, the framework demonstrates its utility by providing the necessary components, representations, and procedures for reliable stem classification. Full article

The case of randomly scattered maize seeds presents the problem of complex background and high density, making detection difficult. To address this challenge, this paper proposes an improved YOLOv8n model (OBW-YOLO) for detecting damaged maize seeds. The introduction of the C2f-ODConv module enhances the model’s ability to extract damaged features, especially in complex scenarios, allowing for better capture of local information. The improved BIMFPN module optimizes the fusion of shallow and deep features, reduces detail loss, and improves detection accuracy. To accelerate model convergence and improve detection precision, the traditional bounding box loss function has been replaced by WIoU, significantly enhancing both accuracy and convergence speed. Experimental results show that the OBW-YOLO model achieves a detection accuracy of 93.6%, with mAP@0.5 and mAP@0.5:0.95 reaching 97.6% and 84.8%, respectively, which represents an improvement of 2.5%, 1%, and 1.2% compared to previous models. Additionally, the number of parameters and model sizes have been reduced by 33% and 22.5%, respectively. Compared to other YOLO models, OBW-YOLO demonstrates significant advantages in both accuracy and mAP. Ablation experiments further validate the effectiveness of the improvements, and heatmap analysis shows that the improved model is more precise in capturing the damaged features of maize seeds. These improvements enable the OBW-YOLO model to achieve significant performance gains in maize seed damage detection, providing an effective solution for the automated quality inspection of maize seeds in the agricultural field. Full article

This study investigated the microencapsulation process of natural taro essence and characterized its physicochemical properties. The effects of core-to-wall ratio, T-20/β-CD mass ratio, and ultrasonic time on encapsulation efficiency were systematically investigated. Optimal conditions, identified through orthogonal experiments, included a core-to-wall ratio of 1:10, a T-20/β-CD mass ratio of 1.6:1, and an ultrasonic time of 40 min, resulting in an encapsulation efficiency of 56.10%. The characterization of the microcapsules revealed satisfactory physical properties, including low moisture content, suitable solubility, appropriate bulk density, and good flowability. Particle size distribution analysis showed consistency, and zeta potential measurements indicated stability against agglomeration. Thermal analysis demonstrated enhanced thermal stability, and FT-IR spectroscopy confirmed successful encapsulation through significant interactions between taro essence and β-CD. SEM imaging revealed a heterogeneous morphology, while XRD patterns validated the formation of stable inclusion complexes. An analysis of volatile components indicated the effective encapsulation of key alkanes, with PCA and heatmap clustering analyses confirming the stability of these components during storage. In conclusion, the optimized microencapsulation process significantly enhances the encapsulation efficiency, stability, and thermal properties of natural taro essence microcapsules. Full article

Surface albedo is a key variable influencing ground-reflected solar irradiance, which is a vital factor in boosting the energy gains of bifacial solar installations. Therefore, surface albedo is crucial towards estimating photovoltaic power generation of both bifacial and tilted solar installations. Varying across daylight hours, seasons, and locations, surface albedo is assumed to be constant across time by various models. The lack of granular temporal observations is a major challenge to the modeling of intra-day albedo variability. Though satellite observations of surface reflectance, useful for estimating surface albedo, provide wide spatial coverage, they too lack temporal granularity. Therefore, this paper considers a novel approach to temporal downscaling with imaging time series of satellite-sensed surface reflectance and limited high-temporal ground observations from surface radiation (SURFRAD) monitoring stations. Aimed at increasing information density for learning temporal patterns from an image series and using visual redundancy within such imagery for temporal downscaling, we introduce temporally shifted heatmaps as an advantageous approach over Gramian Angular Field (GAF)-based image time series. Further, we propose Multispectral-WaveMix, a derivative of the mixer-based computer vision architecture, as a high-performance model to harness image time series for surface albedo forecasting applications. Multispectral-WaveMix models intra-day variations in surface albedo on a 1 min scale. The framework combines satellite-sensed multispectral surface reflectance imagery at a 30 m scale from Landsat and Sentinel-2A and 2B satellites and granular ground observations from SURFRAD surface radiation monitoring sites as image time series for image-to-image translation between remote-sensed imagery and ground observations. The proposed model, with temporally shifted heatmaps and Multispectral-WaveMix, was benchmarked against predictions from models image-to-image MLP-Mix, MLP-Mix, and Standard MLP. Model predictions were also contrasted against ground observations from the monitoring sites and predictions from the National Solar Radiation Database (NSRDB). The Multispectral-WaveMix outperformed other models with a Cauchy loss of 0.00524, a signal-to-noise ratio (SNR) of 72.569, and a structural similarity index (SSIM) of 0.999, demonstrating the high potential of such modeling approaches for generating granular time series. Additional experiments were also conducted to explore the potential of the trained model as a domain-specific pre-trained alternative for the temporal modeling of unseen locations. As bifacial solar installations gain dominance to fulfill the increasing demand for renewables, our proposed framework provides a hybrid modeling approach to build models with ground observations and satellite imagery for intra-day surface albedo monitoring and hence for intra-day energy gain modeling and bifacial deployment planning. Full article

Wave breaking is a fundamental process in ocean energy dissipation and plays a crucial role in the exchange between ocean and nearshore sediments. Foam, the primary visible feature of wave breaking areas, serves as a direct indicator of wave breaking processes. Monitoring the distribution of foam via remote sensing can reveal the spatiotemporal patterns of nearshore wave breaking. Existing studies on wave breaking processes primarily focus on individual wave events or short timescales, limiting their effectiveness for nearshore regions where hydrodynamic processes are often represented at tidal cycles. In this study, video imagery from a typical low-tide terrace (LTT) beach was segmented into four categories, including the wave breaking foam, using the DeepLabv3+ architecture, a convolutional neural networks (CNNs)-based model suitable for semantic segmentation in complex visual scenes. After training and testing on a manually labelled dataset, which was divided into training, validation, and testing sets based on different time periods, the overall classification accuracy of the model was 96.4%, with an accuracy of 96.2% for detecting wave breaking foam. Subsequently, a heatmap of the wave breaking foam distribution over a tidal cycle on the LTT beach was generated. During the tidal cycle, the foam distribution density exhibited both alongshore variability, and a pronounced bimodal structure in the cross-shore direction. Analysis of morphodynamical data collected in the field indicated that the bimodal structure is primarily driven by tidal variations. The wave breaking process is a key factor in shaping the profile morphology of LTT beaches. High-frequency video monitoring further showed the wave breaking patterns vary significantly with tidal levels, leading to diverse geomorphological features at various cross-shore locations. Full article

Smoking is a well known risk factor for coronary artery disease (CAD). However, the effects of smoking on gene expression in the blood of CAD subjects in Hungary have not been extensively studied. This study aimed to identify differentially expressed genes (DEGs) associated with smoking in CAD subjects. Eleven matched samples based on age and gender were selected for analysis in this study. All subjects were non-obese, non-alcoholic, non-diabetic, and non-hypertensive and had moderate to severe stenosis of one or more coronary arteries, confirmed by coronary angiography. Whole blood samples were collected using PAXgene tubes. Next-generation sequencing was employed using the NextSeq 500 system to generate high-throughput sequencing data for transcriptome profiling. The differentially expressed genes were analyzed using the R programming language. Results: The study revealed that smokers exhibited non-significant higher levels of total cholesterol, low-density lipoprotein-cholesterol, and triglycerides compared to non-smokers (p > 0.05), although high-density lipoprotein-cholesterol was also elevated. Despite this, the overall lipid profile of smokers remained less favorable. Non-smokers had a higher BMI (p = 0.02). Differential gene expression analysis identified 58 DEGs, with 38 upregulated in smokers. The key upregulated genes included LILRB5 (log₂FC = 2.88, p = 1.05 × 10⁻⁵) and RELN (log₂FC = 3.31, p = 0.024), while RNF5_2 (log₂FC = −5.29, p = 0.028) and IGHV7-4-1_1 (log₂FC = −2.86, p = 0.020) were notably downregulated. Heatmap analysis showed a distinct clustering of gene expression profiles between smokers and non-smokers. However, GO analysis did not identify significant biological pathways associated with the DEGs. Conclusions: This research illuminates smoking’s biological effects, aiding personalized medicine for predicting and treating smoking-related diseases. Full article

The integration of railway stations into urban environments necessitates a detailed examination of their vitality and influencing factors. This study assesses urban vitality around four major railway stations in Beijing utilizing a variety of analytical models including Ordinary Least Squares, Geographically Weighted Regression, Multi-Scale Geographically Weighted Regression, and machine learning approaches such as XGBoost 2.0.3, Random Forest 1.4.1.post1, and LightGBM 4.3.0. These analyses are grounded in Baidu heatmaps and examine relationships with spatial form, functional distribution, and spatial configuration. The results indicate significant associations between urban vitality and variables such as commercial density, average number of floors, integration, residential density, and housing prices, particularly in predicting weekday vitality. The MGWR model demonstrates enhanced fit and robustness, explaining 84.8% of the variability in vitality, while the Random Forest model displays the highest stability among the machine learning options, accounting for 76.9% of vitality variation. The integration of SHAP values with MGWR coefficients identifies commercial density as the most critical predictor, with the average number of floors and residential density also being key. These findings offer important insights for spatial planning in areas surrounding railway stations. Full article

In the field of wireless communication, transmitter localization technology is crucial for achieving accurate source tracking. However, the extant methodologies for localization face numerous challenges in wireless sensor networks (WSNs), particularly due to the constraints posed by the sparse distribution of sensors across large areas. We present DSLoc, a deep learning-based approach for transmitter localization in sparse WSNs. Our method is based on an improved high-resolution network model in neural networks. To address localization in sparse wireless sensor networks, we design efficient feature enhancement modules, and propose to locate transmitter locations in the heatmap using an image centroid-based method. Experiments conducted on WSNs with a 0.01% deployment density demonstrate that, compared to existing deep learning models, our method significantly reduces the transmitter miss rate and improves the localization accuracy by more than double. The results indicate that the proposed method offers more accurate and robust performance in sparse WSN environments. Full article

Investigating urban vitality and comprehending the influence mechanisms of the built environment is essential for achieving sustainable urban growth and improving the quality of life for residents. Current research has rarely addressed the nonlinear relationships and synergistic effects between urban vitality and the built environment at the neighborhood scale. This oversight may overlook the influence of key neighborhoods and overestimate or underestimate the influence of different factors on urban vitality. Using Guangzhou’s central urban area as a case study, this research develops a comprehensive urban vitality assessment system that includes economic, social, cultural, and ecological dimensions, utilizing multi-source data such as POI, Dazhong Dianping, Baidu heatmap, and NDVI. Additionally, the XGBoost-SHAP model is applied to uncover the nonlinear impacts of different built environment factors on neighborhood vitality. The findings reveal that: (1) urban vitality diminishes progressively from the center to the periphery; (2) proximity to Zhujiang New Town is the most critical factor for neighborhood vitality (with a contribution of 0.039), while functional diversity and public facility accessibility are also significant (with contributions ranging from 0.033 to 0.009); (3) built environment factors exert nonlinear influences on neighborhood vitality, notably with a threshold effect for subway station accessibility (feature value of 0.1); (4) there are notable synergistic effects among different built environment dimensions. For example, neighborhoods close to Zhujiang New Town (feature value below 0.12) with high POI density (feature value above 0.04) experience significant positive synergistic effects. These findings can inform targeted policy recommendations for precise urban planning. Full article

Serine/threonine protein kinases (CK2, PIM-1, RIO1) are constitutively active, highly conserved, pleiotropic, and multifunctional kinases, which control several signaling pathways and regulate many cellular functions, such as cell activity, survival, proliferation, and apoptosis. Over the past decades, they have gained increasing attention as potential therapeutic targets, ranging from various cancers and neurological, inflammation, and autoimmune disorders to viral diseases, including COVID-19. Despite the accumulation of a vast amount of experimental data, there is still no “recipe” that would facilitate the search for new effective kinase inhibitors. The aim of our study was to develop an effective screening method that would be useful for this purpose. A combination of Density Functional Theory calculations and molecular docking, supplemented with newly developed quantitative methods for the comparison of the binding modes, provided deep insight into the set of desirable properties responsible for their inhibition. The mathematical metrics helped assess the distance between the binding modes, while heatmaps revealed the locations in the ligand that should be modified according to binding site requirements. The Structure-Binding Affinity Index and Structural-Binding Affinity Landscape proposed in this paper helped to measure the extent to which binding affinity is gained or lost in response to a relatively small change in the ligand’s structure. The combination of the physico-chemical profile with the aforementioned factors enabled the identification of both “dead” and “promising” search directions. Tests carried out on experimental data have validated and demonstrated the high efficiency of the proposed innovative approach. Our method for quantifying differences between the ligands and their binding capabilities holds promise for guiding future research on new anti-cancer agents. Full article

Cold chain logistics are crucial for reducing agricultural product loss, yet the environmental impact of energy and packaging consumption, among others, demands attention, making the search for eco-friendly development modes essential. Based on data from 30 provinces in China from 2015 to 2021, this study analyzes the basic correlation between the development of cold chain logistics of fresh agricultural products (CCLFAP) and the ecological environment (EE) by using a random forest regression model in comparison with the XGBoost model. Correlation heatmaps were used to analyze the relationships between the cold chain logistics of fresh agricultural products and various factors of the ecological environment. The generalized additive model was then used to establish the connection between cold chain logistics and the ecological environment, identifying significant factors impacting EE. The results demonstrate that a higher development level of cold chain logistics corresponds to a better development trend of EE. The economic efficiency and technical aspects of cold chain logistics for fresh agricultural products are closely related to ecological pressures and responses. The number of employees in the logistics industry, the trading volume of fresh agricultural products, the number of refrigerated vehicles, and the capacity of the cold room have significant positive correlations with the ecological environment, while the per capita consumption of fresh agricultural products, the number of cold chain logistics patent applications, and the road density had significant negative correlations with the ecological environment. The effects of the number of cold chain logistics enterprises and the freight turnover of agricultural products transported by the cold chain on the ecological environment fluctuated. These findings contribute to reducing climate and environmental emergencies throughout the life cycle, offering sustainable development solutions for the fresh agricultural product cold chain logistics industry. Full article

Cytarabine (Ara-C) is a synthetic isomer of cytidine that differs from cytidine and deoxycytidine only in the sugar. The use of arabinose instead of deoxyribose hinders the formation of phosphodiester linkages between pentoses, preventing the DNA chain from elongation and interrupting the DNA synthesis. The minor structural alteration (the inversion of hydroxyl at the 2′ positions of the sugar) leads to change of the biological activity from anti-depressant and DNA/RNA block builder to powerful anti-cancer. Our study aimed to determine the molecular nature of this phenomenon. Three ¹H-¹⁴N NMR-NQR experimental techniques, followed by solid-state computational modelling (Quantum Theory of Atoms in Molecules, Reduced Density Gradient and 3D Hirshfeld surfaces), Quantitative Structure–Property Relationships, Spackman’s Hirshfeld surfaces and Molecular Docking were used. Multifaceted analysis—combining experiments, computational modeling and molecular docking—provides deep insight into three-dimensional packing at the atomic and molecular levels, but is challenging. A spectrum with nine lines indicating the existence of three chemically inequivalent nitrogen sites in the Ara-C molecule was recorded, and the lines were assigned to them. The influence of the structural alteration on the NQR parameters was modeled in the solid (GGA/RPBE). For the comprehensive description of the nature of these interactions several factors were considered, including relative reactivity and the involvement of heavy atoms in various non-covalent interactions. The binding modes in the solid state and complex with dCK were investigated using the novel approaches: radial plots, heatmaps and root-mean-square deviation of the binding mode. We identified the intramolecular OH···O hydrogen bond as the key factor responsible for forcing the glycone conformation and strengthening NH···O bonds with Gln97, Asp133 and Ara128, and stacking with Phe137. The titular butterfly effect is associated with both the inversion and the presence of this intramolecular hydrogen bond. Our study elucidates the differences in the binding modes of Ara-C and cytidine, which should guide the design of more potent anti-cancer and anti-viral analogues. Full article

In the field of autonomous driving, object detection under point clouds is indispensable for environmental perception. In order to achieve the goal of reducing blind spots in perception, many autonomous driving schemes have added low-cost blind-filling LiDAR on the side of the vehicle. Unlike point cloud target detection based on high-performance LiDAR, the blind-filling LiDARs have low vertical angular resolution and are mounted on the side of the vehicle, resulting in easily mixed point clouds of pedestrian targets in close proximity to each other. These characteristics are harmful for target detection. Currently, many research works focus on target detection under high-density LiDAR. These methods cannot effectively deal with the high sparsity of the point clouds, and the recall and detection accuracy of crowded pedestrian targets tend to be low. To overcome these problems, we propose a real-time detection model for crowded pedestrian targets, namely RTCP. To improve computational efficiency, we utilize an attention-based point sampling method to reduce the redundancy of the point clouds, then we obtain new feature tensors by the quantization of the point cloud space and neighborhood fusion in polar coordinates. In order to make it easier for the model to focus on the center position of the target, we propose an object alignment attention module (OAA) for position alignment, and we utilize an additional branch of the targets’ location occupied heatmap to guide the training of the OAA module. These methods improve the model’s robustness against the occlusion of crowded pedestrian targets. Finally, we evaluate the detector on KITTI, JRDB, and our own blind-filling LiDAR dataset, and our algorithm achieved the best trade-off of detection accuracy against runtime efficiency. Full article

Ensuring food security has become of paramount importance due to the rising global population. In particular, the agriculture sector in South Korea faces several challenges such as an aging farming population and a decline in the labor force. These issues have led to the recognition of smart farms as a potential solution. In South Korea, the smart farm is divided into three generations. The first generation primarily concentrates on monitoring and controlling precise cultivation environments by leveraging information and communication technologies (ICT). This is aimed at enhancing convenience for farmers. Moving on to the second generation, it takes advantage of big data and artificial intelligence (AI) to achieve improved productivity. This is achieved through precise cultivation management and automated control of various farming processes. The most advanced level is the 3rd generation, which represents an intelligent robotic farm. In this stage, the entire farming process is autonomously managed without the need for human intervention. This is made possible through energy management systems and the use of robots for various farm operations. However, in the current Korean context, the adoption of smart farms is primarily limited to the first generation, resulting in the limited utilization of advanced technologies such as AI, big data, and cloud computing. Therefore, this research aims to develop the second generation of smart farms within the first generation smart farm environment. To accomplish this, data was collected from nine sensors spanning the period between 20 June to 30 September. Following that, we conducted kernel density estimation analysis, data analysis, and correlation heatmap analysis based on the collected data. Subsequently, we utilized LSTM, BI-LSTM, and GRU as base models to construct a stacking ensemble model. To assess the performance of the proposed model based on the analyzed results, we utilized LSTM, BI-LSTM, and GRU as the existing models. As a result, the stacking ensemble model outperformed LSTM, BI-LSTM, and GRU in all performance metrics for predicting one of the sensor data variables, air temperature. However, this study collected nine sensor data over a relatively short period of three months. Therefore, there is a limitation in terms of considering the long-term data collection and analysis that accounts for the unique seasonal characteristics of Korea. Additionally, the challenge of including various environmental factors influencing crops beyond the nine sensors and conducting experiments in diverse cultivation environments with different crops for model generalization remains. In the future, we plan to address these limitations by extending the data collection period, acquiring diverse additional sensor data, and conducting further research that considers various environmental variables. Full article

The aim of this cross-sectional study was to explore the eye tracking (ET) performance of postgraduate pediatric dental students in correctly detecting abnormalities in different sets of panoramic radiographs. This observational study recruited postgraduate pediatric dental students to evaluate seven panoramic radiographs. RED-m^® SMI software (Sensomotoric Instruments, Teltow, Germany) was used to track the participants’ eye movements as they looked at the radiographs. The data collected for areas of interest (AOIs) included revisit counts, fixation counts, fixation times, entry times, and dwell times. Univariate and bivariate analyses were conducted to summarize the participants’ characteristics and ET measures. The overall percentage of correctly located AOIs was 71.7%. The residents had significantly more revisits and fixation counts in AOIs located in one sextant than in multiple sextants (p < 0.001). Similar patterns were observed for fixation and dwell times (p < 0.001), but not for entry time. Heatmaps showed that the highest density of fixations was on the AOIs and the residents fixated more on dentition than on bony structures. In single-sextant radiographs, residents had significantly more revisits and fixation counts for AOIs compared to those of multiple sextants. Residents had slower entry times and dwelled less on AOIs located in multiple sextant(s). The reported findings can direct dental educators to develop a standardized scan scheme of panoramic radiographs to minimize misdiagnosis. Full article