Search Results (37)

Accurately determining citrus origin is essential for establishing and maintaining regional brands with distinctive qualities while safeguarding the rights and interests of both farmers and consumers. In this study, 2693 navel orange samples were collected from 13 major producing regions in China to establish a comprehensive near-infrared spectroscopy (NIRS) dataset. To address the challenge of citrus origin authentication, this study proposes a novel six-layer one-dimensional convolutional neural network (1D-CNN). The classification accuracy of this model reaches 96.16%. Compared with the support vector machine (SVM), partial least squares discriminant analysis (PLS-DA), and three-layer 1D-CNNs with kernel sizes of 3 and 16, the accuracy of the proposed six-layer model is improved by 9.65%, 3.21%, 3.84%, and 1.98%, respectively. Furthermore, the dataset is augmented using a Wasserstein Generative Adversarial Network (WGAN) and Noise Addition. The results indicate that data augmentation can effectively improve the accuracy of various algorithm models. Among them, the 1D-CNN proposed in this study achieves the best performance on the Noise Addition-augmented dataset, with its accuracy, precision, recall, and F1-score reaching 98.39%, 0.9843, 0.9839, and 0.9840, respectively. Compared with the other four comparative models, the accuracy of this model is increased by 1.48%, 1.36%, 1.48%, and 2.85%, respectively. Finally, a visual analysis of the 1D-CNN’s feature-extraction process was conducted. The results demonstrate that the 1D-CNN can effectively extract discriminative NIR spectral features to accurately distinguish citrus from different origins and that data augmentation markedly improves model performance by increasing data diversity. This work provides a robust tool for citrus origin tracing and offers a new perspective for the origin authentication of other agricultural products. Full article

Taiwan accounts for 90% of the total oolong tea production and enjoys a good global reputation for its quality. In recent years, oolong tea from neighboring countries has been imported into Taiwan and sold as Taiwanese oolong at high prices. This study aimed to rapidly classify oolong tea from four geographical origins (Taiwan, Vietnam, China, and Indonesia) using an electronic nose (E-nose) combined with machine learning. Color measurements were also conducted to support the classification. The electronic nose (E-nose) was utilized to analyze the aroma profiles of tea samples. To classify the samples, five machine learning models—linear discriminant analysis (LDA), support vector machine (SVM), K-nearest neighbor (KNN), artificial neural network (ANN), and random forest (RF)—were developed using 70% of the dataset for training and tested on the remaining 30%. Gray relational analysis (GRA) was applied to measure the relationship between sensor responses and reference tea origins. Multivariate analysis of variance (MANOVA) indicated a statistically significant effect of tea origin on color parameters, as confirmed by both Pillai’s trace and Wilks’ Lambda (Λ) tests (p = 0.000 < 0.05). Among the tested models, LDA and ANN achieved the highest overall classification accuracy (98.33%), with ANN outperforming in the discrimination of Taiwanese oolong tea, achieving 98.89% accuracy. GRA presented higher gray relational grade (GRG) values for Taiwanese tea samples compared to other origins and identified sensors S4, S6, and S14 as the dominant contributors. In conclusion, the E-nose combined with machine learning provides a rapid, non-destructive, and effective approach for geographical origin classification of oolong tea. Full article

A chili pepper variety and origin detection system that integrates a field-programmable gate array (FPGA) with an electronic nose (e-nose) is proposed in this paper to address the issues of variety confusion and origin ambiguity in the chili pepper market. The system uses the AIRSENSE PEN3 e-nose from Germany to collect gas data from thirteen different varieties of chili peppers and two specific varieties of chili peppers originating from seven different regions. Model training is conducted via the proposed lightweight convolutional neural network ChiliPCNN. By combining the strengths of a convolutional neural network (CNN) and a multilayer perceptron (MLP), the ChiliPCNN model achieves an efficient and accurate classification process, requiring only 268 parameters for chili pepper variety identification and 244 parameters for origin tracing, with 364 floating-point operations (FLOPs) and 340 FLOPs, respectively. The experimental results demonstrate that, compared with other advanced deep learning methods, the ChiliPCNN has superior classification performance and good stability. Specifically, ChiliPCNN achieves accuracy rates of 94.62% in chili pepper variety identification and 93.41% in origin tracing tasks involving Jiaoyang No. 6, with accuracy rates reaching as high as 99.07% for Xianjiao No. 301. These results fully validate the effectiveness of the model. To further increase the detection speed of the ChiliPCNN, its acceleration circuit is designed on the Xilinx Zynq7020 FPGA from the United States and optimized via fixed-point arithmetic and loop unrolling strategies. The optimized circuit reduces the latency to 5600 ns and consumes only 1.755 W of power, significantly improving the resource utilization rate and processing speed of the model. This system not only achieves rapid and accurate chili pepper variety and origin detection but also provides an efficient and reliable intelligent agricultural management solution, which is highly important for promoting the development of agricultural automation and intelligence. Full article

Molecular electronics studies have advanced from early, simple single-molecule experiments at cryogenic temperatures to complex and multifunctional molecules under ambient conditions. However, room-temperature environments increase the risk of contamination, making it essential to identify and quantify clean and contaminated rupture traces (i.e., conductance versus relative electrode displacement) within large datasets. Given the high throughput of measurements, manual analysis becomes unfeasible. Clustering algorithms offer an effective solution by enabling the automatic classification and quantification of contamination levels. Despite the rapid development of machine learning, its application in molecular electronics remains limited. In this work, we present a methodology based on the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to extract representative traces from both clean and contaminated regimes, providing a scalable and objective tool to evaluate environmental contamination in molecular junction experiments. Full article

The Precambrian crystalline basement of Madagascar, shaped by its diverse geological history of magmatic activity, sedimentation, and metamorphism, is divided into six distinct geological units. Within this intricate geological framework, five primary types of uranium deposits are present. Despite the presence of these deposits, their resource potential remains largely unquantified. To address this, a comprehensive study was conducted on Madagascar’s uranium geochemical blocks. This study processed the original data of uranium elements across the region, following the “Theoretical Model Pedigree of Geochemical Block Mineralization” proposed by Xie Xuejin. The analysis is based on the geochemical mapping data of Madagascar at a scale of 1:100,000, which was jointly completed by the China–Madagascar team and involved the delineation of geochemical blocks and the division of their internal structures using the 15 km × 15 km window data. The study used an isoline with a uranium content greater than 3.2 × 10⁻⁶ as a boundary and considered five key factors for the classification of prospective areas. These factors included uranium bulk density, anomaly intensity, block structure, prospective area, and the tracing of uranium enrichment trajectories through the pedigree chart of 5-level geochemical blocks. By integrating these factors with potential resource assessment, uranium mining economics, and conditions for uranium mining and utilization, the study successfully classified and evaluated uranium resources in Madagascar. As a result, 10 uranium prospective areas were identified, ranging from Level I to IV, with 3 being Level I areas deemed highly promising for exploration and investment. For the first time, the study predicted a resource potential of 72,600 t of uranium resources, marking a significant step towards understanding Madagascar’s uranium endowment. Full article

This article conducts a comprehensive bibliometric analysis of 182 papers to trace the progression of research on cryptocurrency taxation. The study highlights prevailing patterns, influential contributors, and collaborative networks by utilising data from Scopus and the Web of Science Core Collection from 2002 to 2023. The findings underscore an interdisciplinary character, encompassing studies in legal frameworks, fiscal policy, economics, and technology. By employing analytical tools such as VOSviewer 1.6.20, Bibliometrix 4.0 and Microsoft Excel, the study identifies key themes and concepts focused on four main themes: international tax frameworks and regulatory variations, classification and reporting of crypto-related income, tax implications for emerging crypto segments, and issues surrounding compliance and enforcement. Tax treatment differs based on jurisdiction. Direct taxation may be levied as capital gains, income, or profit tax. Although cryptocurrency exchanges are not subject to value-added tax, intermediary services offered by platforms might incur this indirect tax. The insights generated are valuable for policymakers, scholars, and professionals aiming to comprehend the relationship between cryptocurrency and tax regulation. A limitation of the study is its exclusion of sources beyond the established timeframe. Given the fast-paced changes in cryptocurrency tax regulation, ongoing updates are crucial to capturing the full scope of this evolving field. Full article

The radio source J0028+0035 is a recently discovered double–double radio galaxy at redshift $z=0.398$. Its relic outer lobes are separated by about $3^{\prime }$ in the sky, corresponding to ∼1 Mpc projected linear size. Inside this large-scale structure, the inner pair of collinear lobes span about 100 kpc. In the arcsec-resolution radio images of J0028+0035, there is a central radio feature that offers the intriguing possibility of being resolved into a pc-scale, third pair of innermost lobes. This would make this radio galaxy a rare triple–double source where traces of three distinct episodes of radio activity could be observed. To reveal the compact radio structure of the central component, we conducted observation with the European Very Long Baseline Interferometer Network and the enhanced Multi Element Remotely Linked Interferometer Network. Our $1.66$ GHz image with high (∼5 milliarcsec) resolution shows a compact central radio core with no indication of a third, innermost double feature. The observation performed in multi-phase-centre mode also revealed that the physically unrelated but in projection closely separated background source 5BZU J0028+0035 has a single weak, somewhat resolved radio feature, at odds with its blazar classification. Full article

To evaluate the nutritional value and development potential of Pterocladiella capillacea in the marine environment of Naozhou Island, Zhanjiang, this study conducted species classification and identification, followed by an analysis of key nutritional components. The combination of morphological and molecular results confirmed the identification of the collected samples as P. capillacea. Further analysis showed that P. capillacea in Zhanjiang had a moisture content of 74.9% and a protein content of 24%. In comparison, the fat (0.4%) and carbohydrate (15.4%) contents were relatively low, with moderate ash (14.3%) and crude fiber (9.1%) content. It contains a diverse range of fatty acids, with saturated fatty acids accounting for 51.82% and unsaturated fatty acids accounting for 48.18% of the total. The amino acid composition was also diverse, with essential amino acids comprising 31.58% and flavor-enhancing amino acids constituting 54.85%. The minerals contained four major elements and four trace elements, while heavy metal levels were within safety limits, ensuring their edibility. In conclusion, P. capillacea is a high-protein, low-fat economic seaweed with a favorable amino acid and fatty acid composition, rich in minerals, and with significant nutritional and developmental potential. This study provides important data to support future research and utilization of this seaweed. Full article

In orthodontics, the manual tracing of cephalometric radiographs is a common practice, where the Sella Turcica (ST) serves as a reference point. The radiologist often manually traces the outline of the sella using manual tools (e.g., calipers on radiographs). Perhaps the inherent complexity and variability in the shapes of sella and the lack of advanced assessment tools make the classification of sella challenging, as it requires extensive training, skills, time, and manpower to detect subtle changes that often may not be apparent. Moreover, existing semi-supervised learning (SSL) methods face key limitations such as shift invariance, inadequate feature representation, overfitting on small datasets, and a lack of generalization to unseen variations in ST morphology. Medical imaging data are often unlabeled, limiting the training of automated classification systems for ST morphology. To address these limitations, a novel semi-supervised deep subspace embedding (SSLDSE) framework is proposed. This approach integrates real-time stochastic augmentation to significantly expand the training dataset and introduce natural variability in the ST morphology, overcoming the constraints of small and non-representative datasets. Non-linear features are extracted and mapped to a non-linear subspace using Kullback–Leibler divergence, which ensures that the model remains consistent despite image transformations, thus resolving issues related to shift invariance. Additionally, fine-tuning the Inception-ResNet-v2 network on these enriched features reduces retraining costs when new unlabeled data becomes available. t-distributed stochastic neighbor embedding (t-SNE) is employed for effective feature representation through manifold learning, capturing complex patterns that previous methods might miss. Finally, a zero-shot classifier is utilized to accurately categorize the ST, addressing the challenge of classifying new or unseen variations. Further, the proposed SSLDSE framework is evaluated through comparative analysis with the existing methods (Active SSL, GAN SSL, Contrastive SSL, Modified Inception-ResNet-v2) for ST classification using various evaluation metrics. The SSLDSE and the existing methods are trained on our dataset (sourced from PGI Chandigarh, India), and a blind test is conducted on the benchmark dataset (IEEE ISBI 2015). The proposed method improves classification accuracy by 15% compared to state-of-the-art models and reduces retraining costs. Full article

(1) Background: This retrospective cohort study aimed to investigate the cephalometric evaluation of facial height ratio (FHR) and growth patterns. (2) Methods: We assessed facial height ratios, the y-axis to SN angle, and growth patterns in 94 participants from Timis County using digital cephalograms. Angle’s classification guided the categorization of participants. We digitally traced and analyzed cephalograms using the WebCeph imaging software. We conducted the statistical analysis using Python version 3.11.9. We performed the following statistical tests: Welch’s t-test or ANOVA (analysis of variance), Mann–Whitney U test or the Kruskal–Wallis test, χ² test or Fisher’s, and logistic regression. (3) Results: Significant correlations were observed between FHR and craniofacial development, especially in hypodivergent growth patterns. Among the molar classes, the most predominant growth pattern in Class I was normodivergent (61.5%), followed by hypodivergent (33.3%). In Class II, hypodivergent growth was the most common (52%), with a smaller proportion of normodivergent cases (30.8%). Class III was characterized by a mix of growth patterns, with hypodivergent being predominant (14.7%). Across all groups, the y-axis to SN angle remained within normal limits, and a strong negative correlation with Jarabak’s ratio was found (r = −0.72, p < 0.001). This shows the importance of using holistic assessment methods in orthodontic practice. (4) Patients from Timis County mostly have a hypodivergent growth pattern across all types of malocclusions. Understanding these patterns is essential for comprehensive orthodontic treatment planning. We need to conduct further research to investigate the implications of these findings on treatment outcomes and patient care. Full article

Baijiu is popular with a long history and balanced flavor. Flavor type is the most widely used classification mode for Baijiu. However, the evolutionary relationships of Baijiu flavor types and the differential markers between flavor types are still unclear, significantly impacting the development of the Baijiu industry. In this study, a total of 319 trace components were identified using gas chromatography–olfactometry–mass spectrometry and gas chromatography–mass spectrometry. Among them, 91 trace components with high odor active values or taste active values were recognized as flavor components. Then random forests were conducted to screen differential markers between the derived and basic flavor types, while a principal component analysis assessed their effectiveness in distinguishing the flavor types of Baijiu. Finally, 19 differential markers (including 3-methylbutyric acid, pentanoic acid, 2-butanol, 2,3-butanediol, ethyl pro-panoate, isobutyl acetate, ethyl butanoate, ethyl hexanoate, ethyl heptanoate, ethyl lactate, ethyl 2-hydroxy butanoate, isopentyl hexanoate, ethyl nonanoate, isopropyl myristate, ethyl tetradecanoate, ethyl benzoate, 2,4-di-t-butylphenol, 2-methylbutanal and 3-octanone) were screened and proven to effectively reveal the evolution of Baijiu flavor types; these were further verified as key differential markers using addition tests and a correlation analysis. Full article

This study addresses the complex hydrogeological conditions and frequent inrush water incidents in the Donghuantuo coal mine by proposing a novel spectral tracing technique aimed at rapidly and accurately identifying the sources of inrush water. Through the analysis of electrical data from the Donghuantuo mine, the electrical characteristics of the mine floor were examined. Systematic sampling of water from the primary aquifers within the mining area was conducted, followed by detailed spectral measurements, resulting in the establishment of a spectral database for inrush water sources in the Donghuantuo mine. The chaotic sparrow search optimization algorithm (CSSOA) was employed to optimize the key parameters of the random forest (RF) model, leading to the development of the CSSOA-RF spectral tracing identification model. This model demonstrated outstanding classification performance in the test set, achieving an accuracy of 100%. This research offers a novel, more accurate, and reliable method for identifying the sources of inrush water, facilitating the rapid identification of sources in coal-bearing regions of North China and reducing disaster losses. Although the geological structure of the study area is relatively simple, the research achieved significant results in identifying both single and mixed water sources. However, further validation and optimization are needed for its applicability in more complex geological conditions. The findings of this study provide crucial technical support for safe mining operations and hold significant reference value for water hazard prevention in similar regions. Full article

Amidst the rapid advancement of artificial intelligence and information technology, the emergence of big data and machine learning provides a new research paradigm for mineral exploration. Focusing on the Tethyan metallogenic domain, this paper conducted a series of research works based on machine learning methods to explore the critical geochemical element signals that affect the metallogenic potential of porphyry deposits and reveal the metallogenic regularity. Binary classifiers based on random forest, XGBoost, and deep neural network are established to distinguish zircon fertility, and these machine learning methods achieve higher accuracy, exceeding 90%, compared with the traditional geochemical methods. Based on the random forest and SHapley Additive exPlanations (SHAP) algorithms, key chemical element characteristics conducive to magmatic mineralization are revealed. In addition, a deposit classification model was constructed, and the t-SNE method was used to visualize the differences in zircon trace element characteristics between porphyry deposits of different mineralization types. The study highlights the promise of machine learning algorithms in metallogenic potential assessment and mineral exploration by comparing them with traditional chemical methods, providing insights into future mineral classification models utilizing sub-mineral geochemical data. Full article

As a common vegetable variety, asparagus is rich in B vitamins, vitamin A, and trace elements such as folate, selenium, iron, manganese, and zinc. With the increasing market demand, China has become the world’s largest cultivated area for asparagus production and product exportation. However, traditional asparagus grading mostly relies on manual visual judgment and needs a lot of manpower input to carry out the classification operation, which cannot meet the needs of large-scale production. To address the high labor cost and labor-intensive production process resulting from the large amount of manpower input and low accuracy of existing asparagus grading devices, this study proposed an improved asparagus grading system and method based on TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) objective evaluation and SVM (support vector machine) prediction. The key structure of classification device was analyzed first, the key components were designed, and the structural parameters were determined by theoretical calculation. Through analysis of the factors affecting asparagus quality, three key attributes were determined: length, diameter, and bruises, which were used as reference attributes to conduct experimental analysis. Then, the graded control groups were set up, combining the TOPSIS principle with weighting, and a score for each asparagus sample was determined. These scores were compared with those of a graded control group to derive the grade of each asparagus, and these subsets of the dataset were used as the training set and the test set, excluding the error caused by the subjectivity of the manual judgment. Based on a comparison of the accuracies of different machine learning models, the support vector machine (SVM) was determined to be the most accurate, and four SVM methods were used to evaluate the test set: linear SVM, quadratic SVM, cubic SVM, and medium Gaussian SVM. The test results showed that the grading device was feasible for asparagus. The bruises had a large influence on asparagus quality. The training accuracy of the medium Gaussian SVM method was high (96%), whereas its test accuracy was low (86.67%). The training accuracies and test accuracy of the quadratic and cubic SVM methods were 93.34%. The quadratic SVM and cubic SVM were demonstrated to have better generalization ability than the medium Gaussian SVM method for predicting unknown grades of asparagus and meeting the operational requirements of the asparagus grading. Full article

Power-based Side-Channel Analysis (SCA) began with visual-based examinations and has progressed to utilize data-driven statistical analysis. Two distinct classifications of these methods have emerged over the years; those focused on leakage exploitation and those dedicated to leakage detection. This work primarily focuses on a leakage detection-based schema that utilizes Welch’s t-test, known as Test Vector Leakage Assessment (TVLA). Both classes of methods process collected data using statistical frameworks that result in the successful exfiltration of information via SCA. Often, statistical testing used during analysis requires the assumption that collected power consumption data originates from a normal distribution. To date, this assumption has remained largely uncontested. This work seeks to demonstrate that while past studies have assumed the normality of collected power traces, this assumption should be properly evaluated. In order to evaluate this assumption, an implementation of Tiny-AES-c with nine unique substitution-box (s-box) configurations is conducted using TVLA to guide experimental design. By leveraging the complexity of the AES algorithm, a sufficiently diverse and complex dataset was developed. Under this dataset, statistical tests for normality such as the Shapiro-Wilk test and the Kolmogorov-Smirnov test provide significant evidence to reject the null hypothesis that the power consumption data is normally distributed. To address this observation, existing non-parametric equivalents such as the Wilcoxon Signed-Rank Test and the Kruskal-Wallis Test are discussed in relation to currently used parametric tests such as Welch’s t-test. Full article