Euclidean Distance Research Articles

Identifikasi Kematangan Buah Pisang Berdasarkan Variasi Jarak Menggunakan Metode K-Nearest Neighbor

This research aims to identify the level of ripeness of kepok bananas based on the color of their skin using the K-Nearest Neighbor (K-NN) method. Bananas are an important commodity in Indonesia, and various ripeness levels need to be identified. The current process of identifying banana ripeness is still done manually, which requires a lot of labor and tends to be subjective. The K-NN method is used to classify bananas based on their skin color. This research involves the collection of banana images with three ripeness levels (raw, ripe, and overripe) and the extraction of RGB color features from these images. Three distance methods, namely Euclidean, Minkowski, and Manhattan, are also employed to compare accuracy results. The evaluation results of this research show that the accuracy value for the Euclidean distance method is 84%, the Minkowski distance method is 82%, and the Manhattan distance method is 80%. Thus, the findings indicate that the K-NN method and the Euclidean distance method provide good results in identifying the ripeness level of bananas. By implementing the K-NN algorithm, this research attempts to address the weaknesses of the time-consuming and subjective manual identification process, with the hope of providing a more accurate and efficient solution for the banana industry. The results of this research can be used to automate the identification process of banana ripeness levels and improve efficiency in banana sorting. It is expected that this research can provide practical benefits to the community and serve as a basis for further research in this field.

Resveratrol enhances the antiliver cancer effect of cisplatin by targeting the cell membrane protein PLA2.

In this study, we aimed to explore the mechanism by which resveratrol promotes cisplatin-induced death of HepG2 cells and to provide a potential strategy for resveratrol in the treatment of cancer. HepG2 cells were exposed to a range of drug concentrations for 24 h: resveratrol (2.5 μg/mL [10.95 μM], 5 μg/mL [21.91 μM], 10 μg/mL [43.81 μM], 20 μg/mL [87.62 μM], 40 μg/mL [175.25 μM], and 80 μg/mL [350.50 μM]), cisplatin (0.625 μg/mL [2.08 μM], 1.25 μg/mL [4.17 μM], 2.5 μg/mL [8.33 μM], 4.5 μg/mL [15.00 μM], and 10 μg/mL [33.33 μM]), 24 μg/mL (105.15 μM) resveratrol + 9 μg/mL (30.00 μM) cisplatin, and 12 μg/mL (52.57 μM) resveratrol + 4.5 μg/mL (15.00 μM) cisplatin. The interaction of two drugs was evaluated by coefficient of drug interaction (CDI), which was based on the Pharmacological Additivity model. The MTT 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay was used to detect the effect of different concentrations of drugs on cell viability, whiletranscriptome sequencing was used to identify pathways associated with higher gene enrichment. Synchrotron radiation FTIR microspectroscopy experiments and data analysis were conducted to obtain detailed spectral information. The second-derivative spectra were calculated using the Savitzky-Golay algorithm. Single-cell infrared spectral absorption matrices were constructed to analyze the spectral characteristics of individual cells. The Euclidean distance between cells was calculated to assess their spectral similarity. The cell-to-cell Euclidean distance was computed to evaluate the spatial relationships between cells. The target protein of resveratrol was verified by performing a Western blot analysis. After 24 h of treatment with resveratrol, HepG2 cell growth was inhibited in a dose-dependent manner. Resveratrol promotes cisplatin-induced HepG2 cell death through membrane-related pathways. It also significantly changes the membrane components of HepG2 cells. Additionally, resveratrol changes the morphology of the HepG2 cell membrane by decreasing the expression of PLA2G2. Resveratrol changes the morphology of the HepG2 cell membrane by decreasing the expression of PLA2G2 and promotes cisplatin-induced HepG2 cell death. The combination of cisplatin and resveratrol can play a synergistic therapeutic effect on HepG2 cells.

Navigating Memorability Landscapes: Hyperbolic Geometry Reveals Hierarchical Structures in Object Concept Memory.

Why are some object concepts (e.g., birds, cars, vegetables, etc.) more memorable than others? Prior studies have suggested that features (e.g., color, animacy, etc.) and typicality (e.g., robin vs. penguin) of object images influences the likelihood of being remembered. However, a complete understanding of object memorability remains elusive. In this study, we examine whether the geometric relationship between object concepts explains differences in their memorability. Specifically, we hypothesize that image concepts will be geometrically arranged in hierarchical structures and that memorability will be explained by a concept's depth in these hierarchical trees. To test this hypothesis, we construct a Hyperbolic representation space of object concepts (N=1,854) from the THINGS database (Hebart et al., 2019), which consists of naturalistic images of concrete objects, and a space of 49 feature dimensions derived from data-driven models. Using ALBATROSS (Stier, A. J., Giusti, C., & Berman, M. G., In prep), a stochastic topological data analysis technique that detects underlying structures of data, we demonstrate that Hyperbolic geometry efficiently captures the hierarchical organization of object concepts above and beyond a traditional Euclidean geometry and that hierarchical organization is related to memorability. We find that concepts closer to the center of the representational space are more prototypical and also more memorable. Importantly, Hyperbolic distances are more predictive of memorability and prototypicality than Euclidean distances, suggesting that concept memorability and typicality are organized hierarchically. Taken together, our work presents a novel hierarchical representational structure of object concepts that explains memorability and typicality.

A modality separation approach for facial sketch synthesis

AbstractThe technology for face‐to‐sketch synthesis transforms optical face images into a sketch‐style format. However, traditional style losses are insufficient to discern the modal differences between optical and sketch domain images, leading to unclear images. At the same time, generated images lack clarity due to traditional approaches' disregard for high‐frequency texture. To address these issues, a modality separation approach for facial sketch synthesis is proposed. First, a modality separation structure is proposed, using a quicksort algorithm to merge features of optical and sketch images as target modality (positive samples), ensuring the generated images' feature distribution matches real sketches. By controlling the Euclidean distance between generated images (anchors) and both target and filtered modality (positive and negative samples), irrelevant information is effectively filtered out. Next, an edge‐promoting module feeds processed blurry sketch images into the discriminator to enhance robustness. Lastly, a detail optimization module uses Laplacian filtering to extract high‐frequency texture from optical face images for local enhancement. Experimental validation on CUHK, AR, and XM2VTS datasets shows that this method outperforms mainstream sketch face synthesis methods in terms of Fréchet inception distance and learned perceptual image patch similarity, producing more realistic and natural images with richer texture details.

Bringing circuit theory into spatial occupancy models to assess landscape connectivity

Abstract Occupancy models were originally developed to better understand species distribution while accounting for imperfect detection. Because species distribution is not only shaped by habitat quality but also by the ability of individuals to reach suitable habitats, spatial dynamic occupancy models have been proposed to extend the original framework by defining that site colonisation was a function of the Euclidean distance to occupied sites. However, not all sites in the landscape are equally accessible due to the presence of barriers, that of corridors, etc. To account for connectivity between sites, the Euclidean distance has recently been replaced by a least‐cost path distance, which explicitly accounts for landscape resistance, but assumes that individuals will follow the optimal route. To relax this assumption, we first developed a new spatial occupancy model that incorporates commute‐time distance derived from circuit theory to model accessibility across sites. This distance has the advantage of modelling movement as a random walk and accounting for the fact that colonisation could be achieved from multiple paths. Our approach allows for the explicit estimation of landscape connectivity from detection/non‐detection data and a direct measure of connectivity uncertainty. We implemented the model in the Bayesian framework using the nimble R package, which allows useful R connectivity functions to be called from within the model. Second, we carried out a simulation study to assess the performance of our model by considering four scenarios depicting an increasing level of landscape resistance. Third, to illustrate our new approach, we studied the recolonisation of two carnivores in France. We quantified the degree to which rivers facilitate Eurasian otter (Lutra lutra) colonisation and highways impede Eurasian lynx (Lynx lynx) colonisation. Overall, spatial occupancy models provide a flexible framework to accommodate any distance metric designed to align with species dispersal ecology.

Advancements in Blind Source Separation for EEG Artifact Removal: A comparative analysis of Variational Mode Decomposition and Discrete Wavelet Transform approaches

Electroencephalography (EEG) is a vital tool for elucidating cerebral processes; however, it is inherently vulnerable to physiological interference, including cardiac rhythm, ocular movement, and muscular activity. To guarantee the reliability of essential neuronal data, it is imperative to implement efficacious denoising methodologies. This study compares the efficacy of two advanced blind source separation (BSS) techniques applied to EEG signals: variational mode decomposition-based BSS (VMD-BSS) and discrete wavelet transform-based BSS (DWT-BSS). The efficacy of these methods is rigorously assessed using performance metrics such as the Euclidean Distance (ED) and the Spearman Correlation Coefficient (SCC), which evaluate the precision of signal reconstruction and the correlation between the original and denoised signals, respectively. The findings indicate that both methods yield robust results, with minimal Euclidean distances of 704.04 for VMD-BSS and 703.64 for DWT-BSS, and a strong correlation coefficient of 0.82. The results demonstrate the effectiveness of the proposed techniques in removing artifacts while preserving essential neural information in EEG recordings. Furthermore, the proposed techniques are benchmarked against previous studies, considering factors such as signal properties, computational complexity, frequency localization, and flexibility. These findings highlight the importance of customized parameter selection tailored to the specific characteristics of EEG datasets and research objectives.

Suppressed possibilistic fuzzy c-means clustering based on shadow sets for noisy data with imbalanced sizes

Possibilistic fuzzy c-means clustering (PFCM) algorithm generates fewer overlapping clustering centers than the possibilistic c-means clustering (PCM) algorithm and possesses better noise immunity than fuzzy c-means clustering (FCM) algorithm. However, with the increasing noise intensity and number of clusters, PFCM still faces the problem of getting partially overlapping clustering centers or mislocated centers in noise regions. Moreover, the sample-size imbalance increasingly intensifies the difficulty of positioning centers of small clusters. To solve the above problems, a suppressed possibilistic fuzzy c-means clustering algorithm based on shadow sets (S-SPFCM) is proposed by introducing the shadow set theory and the "suppressed competitive learning" strategy. Firstly, KL divergence is introduced in the objective function of PFCM to increase the anti-noise robustness of fuzzy memberships against long-range noise and outliers. Secondly, to reduce the number of overlapping centers caused by possibilistic memberships, the shadow set theory is introduced to divide each class adaptively into three regions (core, shadow, and exclusion regions) by an uncertainty balance method. The suppressed competitive learning method is extended by modifying the memberships of points within the three regions, thus artificially guiding the iterative track of clustering centers. Meanwhile, to further reduce the influence of imbalanced sizes, a scheme to reset mislocated centers in the core and shadow regions is also designed. In addition, to improve the segmentation performance of S-SPFCM for noisy images, a suppressed possibilistic fuzzy c-means clustering algorithm based on shadow sets and local information (SL-SPFCM) is also proposed. The SL-SPFCM first improves the Euclidean distance by a distance filtering scheme. Then SL-SPFCM introduces the local median membership of each pixel into the KL divergence of the objective function. Finally, experiments on synthetic datasets and color images which are characteristic of imbalanced sizes and noise injection demonstrate the proposed S-SPFCM and SL-SPFCM algorithms achieve smaller center deviations and higher clustering accuracy compared with several state-of-the-art clustering algorithms.

Long COVID is not the same for everyone: a hierarchical cluster analysis of Long COVID symptoms 9 and 12 months after SARS-CoV-2 test

BackgroundIdentifying symptom clusters in Long COVID is necessary for developing effective therapies for this diverse condition and improving the quality of life of those affected by this heterogeneous condition. In this study, we aimed to identify and compare symptom clusters at 9 and 12 months after a SARS-CoV-2 positive test and describe each cluster regarding factors at infection.MethodsThis is a cross-sectional study with individuals randomly selected from the Portuguese National System of Epidemiological Surveillance (SINAVE) database. Individuals who had a positive SARS-CoV-2 test in August 2022 were contacted to participate in a telephonic interview approximately 9 and 12 months after the test. A hierarchical clustering analysis was performed, using Euclidean distance and Ward’s linkage. Clustering was performed in the 35 symptoms reported 9 and 12 months after the SARS-CoV-2 positive test and characterised considering age, sex, pre-existing health conditions and symptoms at time of SARS-CoV-2 infection.Results552 individuals were included at 9 months and 458 at 12 months. The median age was 52 years (IQR: 40–64 years) and 59% were female. Hypertension and high cholesterol were the most frequently reported pre-existing health conditions. Memory loss, fatigue or weakness and joint pain were the most frequent symptoms reported 9 and 12 months after the positive test. Four clusters were identified at both times: no or minor symptoms; multi-symptoms; joint pain; and neurocognitive-related symptoms. Clusters remained similar in both times, but, within the neurocognitive cluster, memory loss and concentration issues increased in frequency at 12 months. Multi-symptoms cluster had older people, more females and more pre-existing health conditions at 9 months. However, at 12 months, older people and those with more pre-existing health conditions were in joint pain cluster.ConclusionsOur results suggest that Long COVID is not the same for everyone. In our study, clusters remained similar at 9 and 12 months, except for a slight variation in the frequency of symptoms that composed each cluster. Understanding Long COVID clusters might help identify treatments for this condition. However, further validation of the observed clusters and analysis of its risk factors is needed.

Time-Series Feature Selection for Solar Flare Forecasting

Solar flares are significant occurrences in solar physics, impacting space weather and terrestrial technologies. Accurate classification of solar flares is essential for predicting space weather and minimizing potential disruptions to communication, navigation, and power systems. This study addresses the challenge of selecting the most relevant features from multivariate time-series data, specifically focusing on solar flares. We employ methods such as Mutual Information (MI), Minimum Redundancy Maximum Relevance (mRMR), and Euclidean Distance to identify key features for classification. Recognizing the performance variability of different feature selection techniques, we introduce an ensemble approach to compute feature weights. By combining outputs from multiple methods, our ensemble method provides a more comprehensive understanding of the importance of features. Our results show that the ensemble approach significantly improves classification performance, achieving values 0.15 higher in True Skill Statistic (TSS) values compared to individual feature selection methods. Additionally, our method offers valuable insights into the underlying physical processes of solar flares, leading to more effective space weather forecasting and enhanced mitigation strategies for communication, navigation, and power system disruptions.

Fuzzy-Based Decision Support Model for Assessing Green Building Performance

Global warming is currently a major environmental issue that is capable of causing unpredictable climate changes. The phenomenon is due to the accumulation of gases and carbon dioxide in the earth’s atmosphere, partly attributed to building operation and construction. The Green Building Rating System (GBRS) is developed to assess and measure the level of green building practices to address this problem. The assessments have typically been conducted using conventional methods that require parameters to meet specific criteria. However, certain parameter values cannot be calculated using objective methods, such as bias, time series, and distance values. The existence of these challenges leads to the development and integration of the Decision Support Model (DSM) into the GBRS in the research. The DSM uses a mathematical model, Tsukamoto Fuzzy Inference System (FIS), and conventional methods to handle the parameter values. Moreover, data related to the parameters are collected and analyzed quantitatively. As a result, the DSM-GBRS model is successfully implemented with two findings. First, there are 83 parameters, related to policy, retrofit, construction, and utilization aspects based on Peraturan Menteri Pekerjaan Umum dan Perumahan Rakyat Nomor 21 Tahun 2021. Second, the model provides precise decision values by splitting the treatment into four types: conventional, Fuzzy logic, slope, and Euclidean distance to ensure a comprehensive assessment of green building performance.

Classification of movements of collegiate female soccer players using inertial measurement units

This study aimed to classify the movements of female soccer players during matches using raw data measured by inertial measurement units (IMUs). Twelve collegiate female soccer players were equipped with IMUs (100 Hz), and raw triaxial acceleration data from eight official matches were analyzed. The measurement data were separated every 3 s, and a Fast Fourier Transform (FFT) was performed. After FFT, the Euclidean distances between the data when the players were in the stationary state and other states were calculated to classify the movements of the players using the k-means method. The data of the clustering numbers classified as the stationary state were eliminated after analyzing the movements of players by video filming the matches. After classification, the average Euclidean distances between the stationary state and other movements were calculated. Consequently, the results showed that the upward and downward directions of the raw data affected the classification. Using the methods of this study, it was also shown that the distribution of Euclidean distances differed from player to player. Our findings indicate that the method used in this study can be used to classify and characterize the movements of female collegiate soccer players.

Approximation schemes for Min-Sum [formula omitted]-Clustering

We consider the Min-Sum k-Clustering (k-MSC) problem. Given a set of points in a metric which is represented by an edge-weighted graph G=(V,E) and a parameter k, the goal is to partition the points V into k clusters such that the sum of distances between all pairs of the points within the same cluster is minimized.The k-MSC problem is known to be APX-hard on general metrics. The best known approximation algorithms for the problem obtained by Behsaz et al. (2019) achieve an approximation ratio of O(log|V|) in polynomial time for general metrics and an approximation ratio 2+ϵ in quasi-polynomial time for metrics with bounded doubling dimension. No approximation schemes for k-MSC (when k is part of the input) is known for any non-trivial metrics prior to our work. In fact, most of the previous works rely on the simple fact that there is a 2-approximate reduction from k-MSC to the balanced k-median problem and design approximation algorithms for the latter to obtain an approximation for k-MSC.In this paper, we obtain the first Quasi-Polynomial Time Approximation Schemes (QPTAS) for the problem on metrics induced by graphs of bounded treewidth, graphs of bounded highway dimension, graphs of bounded doubling dimensions (including fixed dimensional Euclidean metrics), and planar and minor-free graphs. We bypass the barrier of 2 for k-MSC by introducing a new clustering problem, which we call min-hub clustering, which is a generalization of balanced k-median and is a trade off between center-based clustering problems (such as balanced k-median) and pair-wise clustering (such as Min-Sum k-clustering). We then show how one can find approximation schemes for Min-hub clustering on certain classes of metrics.

Specific emitter identification unaffected by time through adversarial domain adaptation and continual learning

Timely identifying the emitter of target signals is crucial for communication security in complex electromagnetic environments. Specific emitter identification (SEI) is a technique to identify emitters using the hardware fingerprints of emitters. In this paper, the impact of changes in hardware fingerprints over time on SEI is solved, which significantly deteriorates identification performance. This issue is addressed from two aspects: one is mitigating the impact of these changes, and the other is tracking and adapting to them. For the aspect of mitigating impact, an alternating adversarial domain adaptation (AADA) method is proposed to eliminate the time-varying component in hardware fingerprints. Subsequently, a feature map calculation method using weighted Euclidean distance is designed, preserving the main parameters of feature maps for each emitter. For the aspect of tracking and adapting to changes, a continual learning method was designed based on feature maps of each emitter. This approach incorporates the selective annotation of unlabeled new data with an iterative optimization training process. To validate the effectiveness of the proposed method, we independently collected comprehensive time-variant datasets as well as simpler datasets with varying receivers and environments. The proposed method was tested on these datasets and compared with existing conventional and advanced methods. The experimental results indicate that the proposed SEI method exhibits superior recognition performance. Compared to existing methods, it achieved an average recognition accuracy improvement of over 8% on the time-variant dataset, and demonstrated enhanced robustness against these three types of variations.

The Gromov–Wasserstein Distance Between Spheres

AbstractThe Gromov–Wasserstein distance—a generalization of the usual Wasserstein distance—permits comparing probability measures defined on possibly different metric spaces. Recently, this notion of distance has found several applications in Data Science and in Machine Learning. With the goal of aiding both the interpretability of dissimilarity measures computed through the Gromov–Wasserstein distance and the assessment of the approximation quality of computational techniques designed to estimate the Gromov–Wasserstein distance, we determine the precise value of a certain variant of the Gromov–Wasserstein distance between unit spheres of different dimensions. Indeed, we consider a two-parameter family $$\{d_{{{\text {GW}}}p,q}\}_{p,q=1}^{\infty }$$ { d GW p , q } p , q = 1 ∞ of Gromov–Wasserstein distances between metric measure spaces. By exploiting a suitable interaction between specific values of the parameters p and q and the metric of the underlying spaces, we are able to determine the exact value of the distance $$d_{{{\text {GW}}}4,2}$$ d GW 4 , 2 between all pairs of unit spheres of different dimensions endowed with their Euclidean distance and their uniform measure.

Semantic Segmentation-Driven Integration of Point Clouds from Mobile Scanning Platforms in Urban Environments

Precise and complete 3D representations of architectural structures or industrial sites are essential for various applications, including structural monitoring or cadastre. However, acquiring these datasets can be time-consuming, particularly for large objects. Mobile scanning systems offer a solution for such cases. In the case of complex scenes, multiple scanning systems are required to obtain point clouds that can be merged into a comprehensive representation of the object. Merging individual point clouds obtained from different sensors or at different times can be difficult due to discrepancies caused by moving objects or changes in the scene over time, such as seasonal variations in vegetation. In this study, we present the integration of point clouds obtained from two mobile scanning platforms within a built-up area. We utilized a combination of a quadruped robot and an unmanned aerial vehicle (UAV). The PointNet++ network was employed to conduct a semantic segmentation task, enabling the detection of non-ground objects. The experimental tests used the Toronto 3D dataset and DALES for network training. Based on the performance, the model trained on DALES was chosen for further research. The proposed integration algorithm involved semantic segmentation of both point clouds, dividing them into square subregions, and performing subregion selection by checking the emptiness or when both subregions contained points. Parameters such as local density, centroids, coverage, and Euclidean distance were evaluated. Point cloud merging and augmentation enhanced with semantic segmentation and clustering resulted in the exclusion of points associated with these movable objects from the point clouds. The comparative analysis of the method and simple merging was performed based on file size, number of points, mean roughness, and noise estimation. The proposed method provided adequate results with the improvement of point cloud quality indicators.

Automated confidence estimation in deep learning auto-segmentation for brain organs at risk on MRI for radiotherapy.

We have built a novel AI-driven QA method called AutoConfidence (ACo), to estimate segmentation confidence on a per-voxel basis without gold standard segmentations, enabling robust, efficient review of automated segmentation (AS). We have demonstrated this method in brain OAR AS on MRI, using internal and external (third-party) AS models. Thirty-two retrospectives, MRI planned, glioma cases were randomly selected from a local clinical cohort for ACo training. A generator was trained adversarialy to produce internal autosegmentations (IAS) with a discriminator to estimate voxel-wise IAS uncertainty, given the input MRI. Confidence maps for each proposed segmentation were produced for operator use in AS editing and were compared with "difference to gold-standard" error maps. Nine cases were used for testing ACo performance on IAS and validation with two external deep learning segmentation model predictions [external model with low-quality AS (EM-LQ) and external model with high-quality AS (EM-HQ)]. Matthew's correlation coefficient (MCC), false-positive rate (FPR), false-negative rate (FNR), and visual assessment were used for evaluation. Edge removal and geometric distance corrections were applied to achieve more useful and clinically relevant confidence maps and performance metrics. ACo showed generally excellent performance on both internal and external segmentations, across all OARs (except lenses). MCC was higher on IAS and low-quality external segmentations (EM-LQ) than high-quality ones (EM-HQ). On IAS and EM-LQ, average MCC (excluding lenses) varied from 0.6 to 0.9, while average FPR and FNR were ≤0.13 and ≤0.21, respectively. For EM-HQ, average MCC varied from 0.4 to 0.8, while average FPR and FNR were ≤0.37 and ≤0.22, respectively. ACo was a reliable predictor of uncertainty and errors on AS generated both internally and externally, demonstrating its potential as an independent, reference-free QA tool, which could help operators deliver robust, efficient autosegmentation in the radiotherapy clinic.

Circular economy‐based operational strategies in the management of solar photovoltaics e‐waste: A multi‐stakeholder perspective

AbstractThe growing field of circular economy (CE) offers a promising framework for the sustainable management of end‐of‐life (EOL) electronic waste (e‐waste). However, translating this concept into a practical solution necessitates a nuanced understanding of implementation hurdles. This study delves into the complexities of EOL solar photovoltaic (PV) e‐waste management within the Indian context. We propose a novel, structurally defined CE model specifically designed to address these challenges. Our research engages a diverse group of stakeholders, encompassing academic experts, e‐waste collectors, and recyclers. By leveraging established literature and expert insights, we identify and analyze 18 key inhibitors to CE implementation. The Grey‐DEMATEL method is employed to elucidate the causal relationships and interdependencies among these inhibitors, while Euclidean distance analysis reveals areas of consensus and divergence in stakeholder perspectives. Our findings highlight prevalent challenges, including a lack of incentives for e‐waste collection and recycling, alongside shortcomings in extended producer responsibility and producer responsibility organization strategies. Ultimately, this investigation informs the development of targeted interventions aimed at bolstering reduce‐reuse‐recycle initiatives for EOL solar PV e‐waste within the Indian solar energy sector.

67 Precision identification and weight assessment of cattle using supervised machine learning on body surface keypoints

Abstract Computer vision systems (CVS) offer identification solutions for animals with distinct coat patterns, but are less effective for solid-colored herds. In addition, they can be used to measure critical phenotypes, such as body weight (BW). Providing both BW and identification for solid-colored animals can help farmers make decisions. This study aimed to 1) develop an automatic CVS capable of identifying using the Euclidean distance between keypoints located at specific anatomical landmarks (e.g., bony prominences), and 2) predict the BW using features extracted from these keypoints. The keypoint model was trained to identify seven keypoints per cow, located at the left and right hips, left and right pin bones, tail head, sacral, and cervical vertebrae. These locations remain constant as body condition score of a cow changes. The Euclidean distance between these keypoints was used to generate biometric features for each cow. The keypoint detection model was trained using 3,928 images over 900 epochs with a batch size of 14 and validated using 391 images. This trained model was used to predict keypoints on a different set of 41 cows over 5 d, resulting in a total of 6,944 images which correspond to the dataset for cow identification. The Random Forest (RF) algorithm for cow identification was trained with the first 4 d (5,376 images), and the last day served as the test set (1,568 images). For the cow identification, a RF algorithm was employed on the Euclidean distance of the keypoints. The hyperparameters were determined through a grid search on the training set, utilizing 5-fold cross-validation. The hyperparameters ‘mtry’ (which ranged from 1 to 10) and ‘ntree’ (which ranged from 250 to 500, in increments of 50) were optimized. For the second objective, due to the completely new environment, the keypoint model was fine-tuned using transfer learning for 240 epochs with a batch size of 14, utilizing 310 images. The model predicted keypoints in 1,593 images from different beef-on-dairy animals. This dataset was used to train the BW prediction model. The Euclidean distances from the predicted images and sex were used as features. A RF model was trained using a leave-one-animal-out cross-validation approach. The hyperparameters were determined through a grid search on the training set, using 10-fold cross-validation. The ‘mtry’ parameter ranged from 2 to 16, in increments of 2. For animal identification, the model achieved an accuracy, precision, recall, and F1-score of 92.7%, 89.0%, 90.2%, and 92.7%, respectively. To predict BW, the RF model achieved an R² of 0.86 and a root mean squared prediction error of 36.9 kg, representing 6.7% of the observed values average. These results suggest that keypoints located on the dorsal body surface can identify and weigh individual animals, even those lacking distinct coat color patterns.

The novel triangular spectral indices for characterizing winter wheat drought

Agricultural drought threatens food security and agricultural sustainable development. There have been numerous spectral indices from remote sensing images developed for monitoring crop drought. However, most present spectral indices are focusing on crop growth and Land Surface Temperature (LST), and the crop canopy water content are in less consideration simultaneously. Additionally, the Normalized Difference Vegetation Index (NDVI) is used for characterizing crop growth in almost all spectral drought indices, with the spectral saturation problem of NDVI for closed crop canopy. When vegetation cover is high, NDVI values tend to saturate, which makes them insensitive to further changes in crop health. Therefore, the NDVI saturation phenomenon may lead to an underestimation of the extent of crop drought, as it is not effective in identifying subtle changes in crops under high-density vegetation conditions. Hence, we propose three novel triangular spectral indices for characterizing winter wheat drought using three features including LAI, Land Surface Water Index (LSWI) and LST. For validating the proposed spectral indices, we compared the agreement between these indices with measured Relative Soil Moisture (RSM) and Volumetric Water Content (VWC) of soil in agricultural meteorological station and present popular indices including Crop Water Stress Index (CWSI), Temperature-Vegetation Drought Index (TVDI), and Vegetation Health Index (VHI). The results revealed that our proposed indices including Euclidean distance Crop Health Index (ECHI), Difference Crop Health Index (DCHI) and Perpendicular Water Stress Index (PWSI) outperformed the popular CWSI, TVDI and VHI, with stronger correlations with measured RSM and VWC in agricultural meteorological station. Secondly, there are spatial consistencies for characterizing winter wheat drought between proposed ECHI, DCHI and PWSI with popular CWSI, TVDI and VHI. In addition, our proposed ECHI, DCHI and PWSI have achieved good performance of drought monitoring both in irrigated and rainfed croplands. All these results suggest that our proposed indices have great potential in crop drought monitoring.

A method for compressing AIS trajectory based on the adaptive core threshold difference Douglas–Peucker algorithm

Traditional trajectory compression algorithms, such as the siliding window (SW) algorithm and the Douglas–Peucker (DP) algorithm, typically use static thresholds based on fixed parameters like ship dimensions or predetermined distances, which limits their adaptive capabilities. In this paper, the adaptive core threshold difference-DP (ACTD-DP) algorithm is proposed based on traditional DP algorithm. Firstly, according to the course value of automatic identification system (AIS) data, the original trajectory data is preprocessed and some redundant points are discarded. Then the number of compressed trajectory points corresponding to different thresholds is quantified. The function relationship between them is established by curve fitting method. The characteristics of the function curve are analyzed, and the core threshold and core threshold difference are solved. Finally, the compression factor is introduced to determine the optimal core threshold difference, which is the key parameter to control the accuracy and efficiency of the algorithm. Five different algorithms are used to compress the all ship trajectories in the experimental water area. The average compression ratio (ACR) of the ACTD-DP algorithm is 87.53%, the average length loss ratio (ALLR) is 23.20%, the AMSED (mean synchronous Euclidean distance of all trajectories) is 68.9747 mx, and the TIME is 25.6869 s. Compared with the other four algorithms, the ACTD-DP algorithm shows that the algorithm can not only achieve high compression ratio, but also maintain the integrity of trajectory shape. At the same time, the compression results of four different trajectories show that ACTD-DP algorithm has good robustness and applicability. Therefore, ACTD-DP algorithm has the best compression effect.