Accurate Identification Research Articles

Bayesian structural damage identification using fraction function regularisation model based on natural frequency and mode shape curvature

ABSTRACT The existing models based on Bayesian inference and L 1 regularisation provide a feasible scheme to the uncertainty problem in structural damage identification. However, L 1 regularisation cannot accurately describe the sparsity of damage due to excessive punishment of large damage parameters, and the mode shape data used by such models are insensitive to the damage. This compromises the accuracy of damage identification. Compared with L 1 regularisation, the fraction function regularisation avoids excessive punishment for larger elements in the damage parameters, resulting in a more accurate sparse solution. The mode shape curvature is more sensitive to damage because it can be viewed as the differential form of the mode shape. Inspired by this, a fraction function regularisation model based on natural frequency and mode shape curvature data is proposed in this paper to further improve the accuracy of Bayesian damage identification. In addition, an improved cuckoo search algorithm is proposed to solve the model by introducing a ranking-based mutation strategy. The results obtained from both the numerical investigation and the experimental study consistently indicate that the proposed method can provide accurate and reliable identification results.

IFGAN: Pre- to Post-Contrast Medical Image Synthesis Based on Interactive Frequency GAN

Medical images provide a visual representation of the internal structure of the human body. Injecting a contrast agent can increase the contrast of diseased tissues and assist in the accurate identification and assessment of conditions. Considering the adverse reactions and side effects caused by contrast agents, previous methods synthesized post-contrast images with pre-contrast images to bypass the administration process. However, existing methods pay inadequate attention to reasonable mapping of the lesion area and ignore gaps between post-contrast and real images in the frequency domain. Thus, in this paper, we propose an interactive frequency generative adversarial network (IFGAN) to solve the above problems and synthesize post-contrast images from pre-contrast images. We first designed an enhanced interaction module that is embedded in the generator to focus on the contrast enhancement region. Within it, target and reconstruction branch features interact to control the local contrast enhancement region feature and maintain the anatomical structure. We propose focal frequency loss to ensure the consistency of post-contrast and real images in the frequency domain. The experimental results demonstrated that IFGAN outperforms other sophisticated approaches in terms of preserving the accurate contrast enhancement of lesion regions and anatomical structures. Specifically, our method produces substantial improvements of 7.9% in structural similarity (SSIM), 36.3% in the peak signal-to-noise ratio (PSNR), and 8.5% in multiscale structural similarity (MSIM) compared with recent state-of-the-art methods.

Two new species and three new records of Diachea (Physarales) from China based on morphological and molecular evidence

Diachea is an important genus of myxomycetes, recognized for its ecological role and wide distribution. This study aimed to expand knowledge of species diversity within this genus in China. We collected Diachea specimens from various locations in Shaanxi and Sichuan provinces and characterized them through morphological analysis and phylogenetic analysis using four genetic markers: small subunit ribosomal RNA (nSSU), translation elongation factor 1-alpha (EF-1α), mitochondrial small subunit (mtSSU), and alpha-tubulin gene (α-Tub). Based on these analyses, we describe two new species, namely, Diachea plectophylla and D. sichuanensis, discovered in the Shaanxi and Sichuan provinces, respectively. Diachea plectophylla is distinguished by its dense, rigid capillitium, spore warts, and distinct separation of capillitium ends from the peridium. Diachea sichuanensis, closely related to D. leucopodia, is identified by its blunt-headed columella, clustered spore warts, and robust stalks. In addition to these new species, we recorded five previously documented species, including D. bulbillosa in Gansu province, D. leucopodia in Yunnan and Sichuan provinces, and D. subsessilis in Sichuan province. Detailed descriptions, micrographs, taxonomic comparisons, and an identification key are provided to aid in accurate identification. The discovery of these new species not only enhances the known diversity of slime molds in the region but also provides valuable information for future studies on their geographical distribution and ecological relationships.

Bacterial profile-based body fluid identification using a machine learning approach.

Identifying the origins of biological traces is critical for the reconstruction of crime scenes in forensic investigations. Traditional methods for body fluid identification rely on chemical, enzymatic, immunological, and spectroscopic techniques, which can be sample-consuming and depend on simple color-change reactions. However, these methods have limitations when residual samples are insufficient after DNA extraction. This study aimed to develop a method for body fluid identification by leveraging bacterial DNA profiling to overcome the limitations of the conventional approaches. Bacterial profiles were determined by sequencing the hypervariable region of the 16S rRNA gene,using DNA metabarcoding of evidence collected from criminal cases. Amplicon sequence variants (ASVs) were analyzed to identify significant microbial patterns in different body fluid samples. The bacterial profile-based method demonstrated high discriminatory power with a machine learning model trained using the naïve Bayes algorithm, achieving an accuracy of over 98% in classifying samples into one of four body fluid types: blood, saliva, vaginal secretion, and mixture traces of vaginal secretions and semen. Bacterial profiling enhances the accuracy and robustness of body fluid identification in forensic analysis, providing a valuable alternative to traditional methods by utilizing DNA and microbial community data despite the uncontrollable conditions. This approach offers significant improvements in the classification accuracy and practical applicability in forensic investigations.

Consensus recommendations for an integrated diagnostic approach to peripheral nerve sheath tumors arising in the setting of Neurofibromatosis type 1 (NF1).

Consensus recommendation published in 2017 histologically defining atypical neurofibromatous neoplasm of uncertain biologic potential (ANNUBP) and malignant peripheral nerve sheath tumor (MPNST) were codified in the 2021 WHO Classification of Tumors of the Central Nervous System and the 2022 WHO Classification of Tumors of Soft Tissue and Bone. However, given the shift in diagnostic pathology toward the use of integrated histopathologic and genomic approaches, the incorporation of additional molecular strata in the classification of Neurofibromatosis Type 1 (NF1)-associated peripheral nerve sheath tumors should be formalized to aid in accurate diagnosis and early identification of malignant transformation to enable appropriate intervention for affected patients. To this end, we assembled a multi-institutional expert pathology working group as part of a "Symposium on Atypical Neurofibroma: State of the Science". Herein, we provide a suggested framework for adequate interventional radiology and surgical sampling, and recommend molecular profiling for clinically or radiologically worrisome non-cutaneous lesions in patients with NF1 to identify diagnostically-relevant molecular features, including CDKN2A/B inactivation for ANNUBP, as well as SUZ12, EED, or TP53 inactivating mutations, or significant aneuploidy for MPNST. We also propose renaming "low-grade MPNST" to "ANNUBP with increased proliferation" to avoid the use of the "malignant" term in this group of tumors with persistent unknown biologic potential. This refined integrated diagnostic approach for NF1-associated peripheral nerve sheath tumors should continue to evolve in concert with our understanding of these neoplasms.

Integrated Chemical and Ecotoxicological Assessment of Metal Contamination in the Andong Watershed: Identifying Key Toxicants and Ecological Risks

This study employed an integrated field monitoring approach, combining chemical analysis and ecotoxicity testing of multiple environmental matrices—water, sediment, and sediment elutriates—to comprehensively assess the environmental health of the Andong watershed, located near a Zn smelter and mining area. The primary objectives were to evaluate the extent of metal contamination, identify key toxicants contributing to ecological degradation, and trace the sources of these pollutants. Our findings revealed severe metal contamination and significant ecotoxicological effects both in proximity to and downstream from industrial sites. Specifically, Cd, Zn, and Pb were strongly linked to the smelter, while Hg, Ni, Cu, and As were predominantly associated with mining activities in the tributaries. To further assess toxicity of field-collected sediment and their elutriates, a logistic regression analysis was employed to estimate benchmark values for distinguishing between toxic and non-toxic samples, using the sum of toxic units for sediment elutriates and the mean probable effect level (PEL) quotient for sediment toxicity. These models demonstrated greater predictive accuracy than conventional benchmarks for determining toxicity thresholds. Our results highlight that integrating chemical and ecotoxicological monitoring with site-specific concentration–response relationships enhances the precision of ecological risk assessments, facilitating more accurate identification of key toxicants driving mixture toxicity in complex, pollution-impacted aquatic ecosystems.

Research on Soybean Seedling Stage Recognition Based on Swin Transformer

Accurate identification of the second and third compound leaf periods of soybean seedlings is a prerequisite to ensure that soybeans are chemically weeded after seedling at the optimal application period. Accurate identification of the soybean seedling period is susceptible to natural light and complex field background factors. A transfer learning-based Swin-T (Swin Transformer) network is proposed to recognize different stages of the soybean seedling stage. A drone was used to collect images of soybeans at the true leaf stage, the first compound leaf stage, the second compound leaf stage, and the third compound leaf stage, and data enhancement methods such as image rotation and brightness enhancement were used to expand the dataset, simulate the drone’s collection of images at different shooting angles and weather conditions, and enhance the adaptability of the model. The field environment and shooting equipment directly affect the quality of the captured images, and in order to test the anti-interference ability of different models, the Gaussian blur method was used to blur the images of the test set to different degrees. The Swin-T model was optimized by introducing transfer learning and combining hyperparameter combination experiments and optimizer selection experiments. The performance of the optimized Swin-T model was compared with the MobileNetV2, ResNet50, AlexNet, GoogleNet, and VGG16Net models. The results show that the optimized Swin-T model has an average accuracy of 98.38% in the test set, which is an improvement of 11.25%, 12.62%, 10.75%, 1.00%, and 0.63% compared with the MobileNetV2, ResNet50, AlexNet, GoogleNet, and VGG16Net models, respectively. The optimized Swin-T model is best in terms of recall and F1 score. In the performance degradation test of the motion blur level model, the maximum degradation accuracy, overall degradation index, and average degradation index of the optimized Swin-T model were 87.77%, 6.54%, and 2.18%, respectively. The maximum degradation accuracy was 7.02%, 7.48%, 10.15%, 3.56%, and 2.5% higher than the MobileNetV2, ResNet50, AlexNet, GoogleNet, and VGG16Net models, respectively. In the performance degradation test of the Gaussian fuzzy level models, the maximum degradation accuracy, overall degradation index, and average degradation index of the optimized Swin-T model were 94.3%, 3.85%, and 1.285%, respectively. Compared with the MobileNetV2, ResNet50, AlexNet, GoogleNet, and VGG16Net models, the maximum degradation accuracy was 12.13%, 15.98%, 16.7%, 2.2%, and 1.5% higher, respectively. Taking into account various degradation indicators, the Swin-T model can still maintain high recognition accuracy and demonstrate good anti-interference ability even when inputting blurry images caused by interference in shooting. It can meet the recognition of different growth stages of soybean seedlings in complex environments, providing a basis for post-seedling chemical weed control during the second and third compound leaf stages of soybeans.

Rapid bacterial identification through volatile organic compound analysis and deep learning.

The increasing antimicrobial resistance caused by the improper use of antibiotics poses a significant challenge to humanity. Rapid and accurate identification of microbial species in clinical settings is crucial for precise medication and reducing the development of antimicrobial resistance. This study aimed to explore a method for automatic identification of bacteria using Volatile Organic Compounds (VOCs) analysis and deep learning algorithms. AlexNet, where augmentation is applied, produces the best results. The average accuracy rate for single bacterial culture classification reached 99.24% using cross-validation, and the accuracy rates for identifying the three bacteria in randomly mixed cultures were SA:98.6%, EC:98.58% and PA:98.99%, respectively. This work provides a new approach to quickly identify bacterial microorganisms. Using this method can automatically identify bacteria in GC-IMS detection results, helping clinical doctors quickly detect bacterial species, accurately prescribe medication, thereby controlling epidemics, and minimizing the negative impact of bacterial resistance on society.

Sensitive imaging of actinide materials in shielded radioactive waste

This paper reports on the development of a method for enhanced non-destructive assay (NDA) of radioactive waste using the novel technique neutron-gamma emission tomography (NGET). The technique relies on the detection of correlated fast neutrons and gamma rays emitted in spontaneous or induced fission. It is based on fast organic scintillators and enables sensitive detection and three-dimensional (3D) localization of the fission events. The technique is passive and does not require moving components. In this work, we apply the NGET technique to the category of radioactive waste which is often referred to as historic or legacy waste. This can include mixed wastes encased in shielded containers many decades ago, before the advent of detailed waste description criteria. These low or intermediate level wastes are often associated with lacking, limited or conflicting documentation. This poses a challenge when assigning the waste to the proper disposal route as well as in deciding whether the waste needs to undergo sorting and conditioning to fulfil waste acceptance criteria both with regards to safe interim storage and to its ultimate disposal. Actinides, such as isotopes of uranium and plutonium, with their typically long half-lives and decay chains are of special interest in this regard since they may challenge the long-term safety assessment in repositories predicated on mainly shorter half-life radionuclides if undetected. Accurate identification and localisation of actinides is also important from a safeguards perspective, especially since they are generally difficult to detect and localise by established passive means due to their relatively weak radiation emissions, in particular in shielded containments and in the presence of strong radiation fields from other radioactive materials. In this paper we present findings of measurements on shielded containments of long-lived radioactive waste performed at the Studsvik site in Sweden, as well as measurements on a laboratory assembly simulating a grouted waste drum. Similarities and differences between the novel NGET technique and a commercially available gamma imaging system are also briefly discussed.

ViralPrimer: a Web Server to Monitor Viral Nucleic Acid Amplification Tests' Primer Efficiency during Pandemics, with Emphasis on SARS-CoV-2 and Mpox.

Accurate pathogen identification is crucial during outbreaks, especially with the emergence of new variants requiring frequent primer updates. However, resources for maintaining up-to-date verification of primer sequences are often limited, which poses challenges for reliable diagnosis and hinders potential monitoring efforts based on genome sequencing. To address this, we introduce ViralPrimer, a web server facilitating primer design, SARS-CoV-2 and Mpox variant monitoring, and adaptation to future threats. ViralPrimer aims to enhance diagnostic accuracy with its comprehensive primer database, mutation analysis assistance, and user primer upload feature. Its adaptable design allows monitoring of other rapidly mutating pathogens, contributing to broader public health protection efforts. ViralPrimer is freely accessible and open to all users with no login requirement at https://viralprimer.elte.hu/. The application is hosted on a DJANGO v3.2.13 web server, with a PostgreSQL database, and the frontend was implemented using jQuery v3.6.0, vanilla JavaScript vES6, and Bootstrap v5.1. https://viralprimer.elte.hu/help#downloads.

Application of 3D printing in bacterial detection

Diseases caused by bacterial infections, especially pathogen variations and drug-resistant bacteria, are a serious threat to human health globally. Accurate and rapid identification of bacterial pathogens is essential for early diagnosis of infections and timely treatment of disease. At present, the routine diagnostic methods for identifying bacterial pathogens in clinic are bacterial culture, immunological detection, genetic analysis, etc. These methods occupy foundational position for bacterial detection that favors for clinical diagnosis of pathogen infection in daily life. Notably, 3D printing technology, together with 3D bioprinting technology, provides more options for bacterial detection due to its personalized design and manufacturing. Compared with traditional methods, 3D printing provides a more convenient, rapid and accurate bacterial detection platform, thus meeting the urgent needs of clinical and scientific research. Here, we have summarized the research progress and given a comprehensive review of 3D printing technology in bacterial detection, including bacterial detection sensors, microfluidic chips, bioprinting microarray technology, AI and spectroscopy technology, providing a scientific reference and filling in gaps in 3D printing-assistance bacterial detection. At the same time, technical advantages, challenges and future development trends will also be discussed in related healthcare fields.

XAIP: An eXplainable AI‐Based Pipeline for Identifying Key Factors of Surface Defects in Strip Steel

The surface defect has a direct impact on the performance and quality of the final product, so it is crucial to identify the key factors causing the defect. To achieve accurate key factor identification, an eXplainable AI‐based Pipeline (XAIP) is proposed herein. The proposed XAIP combines data mode clustering and local model construction to meet practical application requirements. Meanwhile, after analyzing the characteristics of practical data, a strategy for defect rate calculation is designed to provide more fine‐grained defect‐related information to supervise model training. The final identification results are presented by a two‐stage explainable method, which is designed to reveal the information of data modes and key variables and can give engineers more specific defect‐related information. The experiment results based on two practical manufacturing datasets show the effectiveness of the proposed method.

YOLOX-S-TKECB: A Holstein Cow Identification Detection Algorithm

Accurate identification of individual cow identity is a prerequisite for the construction of digital farms and serves as the basis for optimized feeding, disease prevention and control, breed improvement, and product quality traceability. Currently, cow identification faces challenges such as poor recognition accuracy, large data volumes, weak model generalization ability, and low recognition speed. Therefore, this paper proposes a cow identification method based on YOLOX-S-TKECB. (1) Based on the characteristics of Holstein cows and their breeding practices, we constructed a real-time acquisition and preprocessing platform for two-dimensional Holstein cow images and built a cow identification model based on YOLOX-S-TKECB. (2) Transfer learning was introduced to improve the convergence speed and generalization ability of the cow identification model. (3) The CBAM attention mechanism module was added to enhance the model’s ability to extract features from cow torso patterns. (4) The alignment between the apriori frame and the target size was improved by optimizing the clustering algorithm and the multi-scale feature fusion method, thereby enhancing the performance of object detection at different scales. The experimental results demonstrate that, compared to the traditional YOLOX-S model, the improved model exhibits a 15.31% increase in mean average precision (mAP) and a 32-frame boost in frames per second (FPS). This validates the feasibility and effectiveness of the proposed YOLOX-S-TKECB-based cow identification algorithm, providing valuable technical support for the application of dairy cow identification in farms.

MXene/WO3 Sensor Array with Improved SNN Algorithm for Accurate Identification of Toxic Gases.

Gas sensing is pivotal in critical areas such as industrial production and food safety. This study explores the gas classification capabilities of MXene-based gas sensors. Pure V2CTx MXene and an MXene/WO3 nanocomposite were synthesized, and MXene-based gas sensors were integrated into a 2 × 2 rudimentary electronic nose array. The tests on gas sensitivity revealed that the inclusion of WO3 nanoparticles (NPs) boosted the sensor's response to 10 ppm of NO2 from 2.82 to 3.45 at room temperature. Moreover, the sensor showcased a rapid response/recovery duration of 74.5/149.0 s, excellent environmental stability, and long-term reliable sensing performance. Furthermore, we have improved the method of accurately identifying four toxic gases detected by an MXene-based sensor array using a spiking neural network (SNN) based on the memristive system. Also, the performance of this identification method revealed that the method achieved 95.83% accuracy in the identification of the four gases. Notably, the improved SNN demonstrated approximately 5% higher accuracy than the other gas recognition algorithm. These results highlight the potential of SNN as a powerful tool to accurately and reliably identify toxic gases based on the gas sensor array.

Medical image recognition based on multilayer neural network

As a common accidental injury, accurate identification of burns and scalded injuries is of great significance to the development of treatment programs and prognosis assessment. Traditional identification methods are subjective and inaccurate, this paper proposes a burns and scalds grade identification technique based on multilayer neural network. The three-degree classification standard of burns and scalds is described, and the structure and principle of multilayer neural network are introduced, including the network structure composed of input layer, hidden layer and output layer, forward propagation, back propagation and training process. The implementation steps of burns and scald grade recognition technology based on multi-layer neural network are discussed in detail, such as data acquisition, image pre-processing, feature extraction, classifier design, model training and evaluation. Through experiments, the model is tested using a dataset containing 362 burns and scalds images, and the model achieves more than 90% accuracy on the test set with high accuracy and reliability. The multi-layer neural network model is used to classify the burn and scald data, which improves the diagnostic accuracy, shortens the delay time of the disease, reduces the pressure of professional doctors, and helps patients have a preliminary understanding of the degree of burn.

Looking at the Modern to Better Understand the Ancient: Is It Possible to Differentiate Mars Pigments from Archaeological Ochres?

This article offers a discussion of the possibility of distinguishing ochres from Mars pigments. The discussion addresses technological, archaeological, and artistic aspects. Natural earth pigments such as ochres, siennas, and umbers have been widely used from the Paleolithic to the present day and still find wide application despite the development of synthetic iron oxide pigment synthesis processes, called Mars pigments. The potential ability of today’s analytical techniques to distinguish between two classes of pigments of the same color with very similar chemical composition—but perhaps sufficient for reliable recognition—is also discussed. The paper begins by addressing the proper use of the terms “ochres” and “Mars pigments” and their accurate identification in artworks. It reviews the literature on the chemical–mineralogical characterization of yellow and red iron pigments and analyzes pigment catalogs to understand how companies distinguish ochres from Mars pigments. An experimental analysis using External Reflection Infrared Spectroscopy (FTIR-ER) compared painting samples made with natural ochres and Mars pigments, confirming the literature findings and suggesting future research directions. Key differences such as hematite in yellow ochres and specific spectral peaks in red ochres support the potential of FTIR-ER spectroscopy as a noninvasive tool for distinguishing pigments, especially for fragile artifacts and archaeological applications.

Periodontal diseases in Africa.

Periodontal diseases, a group of complex conditions marked by an excessive immune response and periodontal tissue destruction, are a global health concern. Since 1990, the incidence of these diseases has doubled, with Western sub-Saharan Africa experiencing the highest burden. Accurate diagnosis and case identification are crucial for understanding the etiology, features of disease, research, treatment and prevention. Modern perspectives on periodontal disease classification are based on commonality among those affected. However, current literature is often plagued by methodological inconsistencies and focused on disease mechanisms in European populations. Health inequalities in low- and middle-income countries (LMICs) are exacerbated by these challenges, with sub-Saharan Africa, and Nigeria specifically, facing unique difficulties such as clinical personnel shortages and limited research infrastructure. This review explored disparities in periodontal disease research, care and outcomes in African populations. We highlighted these disparities and identified the factors contributing to inequities in periodontal health outcomes. We further demonstrated the critical need for inclusive and equitable healthcare and research practices tailored to the unique challenges faced by diverse populations and regions with limited resources. Addressing these disparities is essential for ensuring that advancements in healthcare are accessible to all, thereby improving global oral health and general health.

Root Cause Attribution of Delivery Risks via Causal Discovery with Reinforcement Learning

Managing delivery risks is a critical challenge in modern supply chain management due to the increasing complexity and interdependencies of global supply networks. Existing methods often rely on correlation-based approaches, which fail to uncover the true causes behind delivery delays. This limitation makes it difficult for supply chain managers to identify actionable factors that can mitigate risks effectively. To address these challenges, we propose a novel method that integrates causal discovery with reinforcement learning to identify the root causes of delivery risks. Unlike traditional correlation-based methods, our approach uncovers both the direction and strength of causal relationships between variables, allowing for more accurate identification of the key drivers behind delivery delays. By applying causal strength quantification, we further measure the impact of each factor on delivery performance. Using real-world supply chain data, our results demonstrate that the proposed method reveals hidden causal relationships between factors such as shipping mode, order size, and delivery status. These insights enable supply chain managers to implement more targeted interventions, significantly improving risk mitigation strategies.

Perceptual Pigeon Galvanized Optimization of Multi-objective CNN on the Identification and Classification of Mango Leaves Disease

Aims The aim of this study is to develop a strong Multi-objective Convolutional Neural Network (MOCNN) optimized using Perceptual Pigeon Galvanized Optimization (PPGO) for accurate identification and classification of mango leaf diseases. This approach aims to increase classification accuracy, computational efficiency, and generalization ability. The ultimate goal is to improve disease management in mango crops through advanced image-based diagnostic techniques. Background Demands for the consumption of mango (Mangifera indica) fruit and its leaves were growing exponentially in all parts of the world due to its large health benefits for various organs in the human body. However, these plants were largely exposed to various kinds of microbial diseases during cultivation despite the application of pesticides. Hence, it is becoming a significant threat to the farmers and to the food industry. Objective The objectives of this study are to develop reliable methods for the early identification of mango leaf diseases, enabling prompt intervention and reducing crop damage. Additionally, the study aims to provide effective disease management applications that will help farmers minimize crop losses and maintain their economic stability. Methods PPGO is an advanced optimization algorithm inspired by the natural foraging behavior of pigeons. It integrates perceptual hashing and galvanic responses to adaptively adjust the search process, allowing for efficient exploration and exploitation of the solution space. The multi-objective convolutional neural network is trained to minimize a composite loss function that considers classification accuracy, computational efficiency, and generalization error. Results The Perceptual Pigeon Galvanized Optimization (PPGO) with a Multi-objective Convolutional Neural Network (MOCNN) demonstrates superior performance compared to traditional CNN optimization techniques. The results show an accuracy of 96%, recall of 94%, precision of 92%, and an F1 score of 92%. These metrics surpass those of existing methods such as Efficient Supervised Learning based on Deep Neural Network (ESDNN), Hierarchical Deep Learning Support Vector Machine (HDLSVM), Ordinal Regression Neural Network (ORNN), Deep Convolutional Generative Adversarial Network (DCGAN), Convolutional Neural Network (CNN), Local Contrast Normalization Convolutional Neural Network (LCNN), and Visual Geometry Group Network (VGGNET 19). Conclusion The integration of Perceptual Pigeon Galvanized Optimization with a Multi-objective Convolutional Neural Network offers a powerful approach for identifying and classifying mango leaf diseases. The proposed method effectively balances multiple performance metrics, leading to a robust and efficient model suitable for real-world agricultural applications.

Modal Parameter Identification of Electric Spindles Based on Covariance-Driven Stochastic Subspace

Electric spindles are a critical component of numerically controlled machine tools that directly affect machining precision and efficiency. The accurate identification of the modal parameters of an electric spindle is essential for optimizing design, enhancing dynamic performance, and facilitating fault diagnosis. This study proposes a covariance-driven stochastic subspace identification (SSI-cov) method integrated with a simulated annealing (SA) strategy and fuzzy C-means (FCM) clustering algorithm to achieve the automated identification of modal parameters for electric spindles. Using both finite element simulations and experimental tests conducted at 22 °C, the first five natural frequencies of the electric spindle under free, constrained, and dynamic conditions were extracted. The experimental results demonstrated experiment errors of 0.17% to 0.33%, 1.05% to 3.27%, and 1.29% to 3.31% for the free, constrained, and dynamic states, respectively. Compared to the traditional SSI-cov method, the proposed SA-FCM method improved accuracy by 12.05% to 27.32% in the free state, 17.45% to 47.83% in the constrained state, and 25.45% to 49.12% in the dynamic state. The frequency identification errors were reduced to a range of 2.25 Hz to 20.81 Hz, significantly decreasing errors in higher-order modes and demonstrating the robustness of the algorithm. The proposed method required no manual intervention, and it could be utilized to accurately analyze the modal parameters of electric spindles under free, constrained, and dynamic conditions, providing a precise and reliable solution for the modal analysis of electric spindles in various dynamic states.