Search Results (485)

Human identification using gait as a biometric feature has gained significant attention in recent years, showing notable advancements in medical fields and security. A review of recent developments in remote radar-based gait identification is presented in this article, focusing on the methods used, the classifiers employed, trends and gaps in the literature. Particularly, recent trends highlight the increasing use of Artificial Intelligence (AI) to enhance the extraction and classification of features, while key gaps remain in the area of multi-subject detection. In this paper, we provide a comprehensive review of the techniques used to implement such systems over the past 7 years, including a summary of the scientific publications reviewed. Several key factors are compared to determine the most suitable radar for remote gait-based identification, including accuracy, operating frequency, bandwidth, dataset, range, detection, feature extraction, size and number of features extracted, multiple subject detection, radar modules used, AI used and their properties, and the testing environment. Based on the study, it was determined that Frequency-Modulated Continuous-Wave (FMCW) radars were more accurate than Continuous-Wave (CW) radars and Ultra-Wideband (UWB) radars in this field. Despite the fact that FMCW is the most closely related radar to real-world scenarios, it still has some limitations in terms of multi-subject identification and open-set scenarios. In addition, the study indicates that simpler AI techniques, such as Convolutional Neural Network (CNN), are more effective at improving results. Full article

The proliferation of Internet of Things (IoT) devices has introduced significant challenges in cybersecurity, particularly in the realm of intrusion detection. While effective, traditional centralized machine learning approaches often compromise data privacy and scalability due to the need for data aggregation. In this study, we propose a federated learning framework for near-real-time intrusion detection in IoT environments. Federated learning enables decentralized model training across multiple devices without exchanging raw data, thereby preserving privacy and reducing communication overhead. Our approach builds upon a previously proposed hybrid model, which combines a machine learning model deployed on IoT devices with a second-level cloud-based analysis. This previous work required all data to be passed to the cloud in aggregate form, limiting security. We extend this model to incorporate federated learning, allowing for distributed training while maintaining high accuracy and privacy. We evaluate the performance of our federated-learning-based model against a traditional centralized model, focusing on accuracy retention, training efficiency, and privacy preservation. Our experiments utilize actual attack data partitioned across multiple nodes. The results demonstrate that this hybrid federated learning not only offers significant advantages in terms of data privacy and scalability but also retains the previous competitive accuracy. This paper also explores the integration of federated learning with cloud-based infrastructure, leveraging platforms such as Databricks and Google Cloud Storage. We discuss the challenges and benefits of implementing federated learning in a distributed environment, including the use of Apache Spark and MLlib for scalable model training. The results show that all the algorithms used maintain an excellent identification accuracy (98% for logistic R=regression, 97% for SVM, and 100% for Random Forest). We also report a very short training time (less than 11 s on a single machine). The previous very low application time is also confirmed (0.16 s for over 1,697,851 packets). Our findings highlight the potential of federated learning as a viable solution for enhancing cybersecurity in IoT ecosystems, paving the way for further research in privacy-preserving machine learning techniques. Full article

Isopentyltransferase (IPT) is a key rate-limiting enzyme in cytokinin synthesis, playing a crucial role in plant growth, development, and response to adverse conditions. Although the IPT gene family has been studied in various plants, comprehensive identification and functional characterization of IPT genes in maize (Zea mays) remain underexplored. In this study, ten IPT gene family members (ZmIPT1–ZmIPT10) were identified in the maize genome, and their gene structure, physicochemical properties, evolutionary relationships, expression patterns, and stress response characteristics were systematically analyzed. The ZmIPT genes were found to be unevenly distributed across six chromosomes, with most proteins predicted to be basic and localized primarily in chloroplasts. Phylogenetic analysis grouped the ZmIPT family into four subfamilies, showing close evolutionary relationships with rice IPT genes. Conserved motif and gene structure analyses indicated that the family members were structurally conserved, with five collinear gene pairs being identified. Ka/Ks analysis revealed that these gene pairs underwent strong purifying selection during evolution.Cis-element analysis of promoter regions suggested that ZmIPT genes are widely involved in hormone signaling and abiotic stress responses. Tissue-specific expression profiling showed that ZmIPT5, ZmIPT7, and ZmIPT8 were highly expressed in roots, with ZmIPT5 exhibiting consistently high expression under multiple abiotic stresses. qRT-PCR validation confirmed that ZmIPT5 expression peaked at 24 h after stress treatment, indicating its key role in long-term stress adaptation. Protein interaction analysis further revealed potential interactions between ZmIPT5 and cytokinin oxidases (CKX1, CKX5), as well as FPP/GGPP synthase family proteins, suggesting a regulatory role in cytokinin homeostasis and stress adaptation. Overall, this study provides comprehensive insights into the structure and function of the ZmIPT gene family and identifies ZmIPT5 as a promising candidate for improving stress tolerance in maize through molecular breeding. Full article

To address the challenge of extracting adaptive fault features for unmanned aerial vehicle (UAV) rotor motor bearings and to meet the high accuracy requirements of bearing fault diagnosis, this paper proposes a neural network-based bearing fault diagnosis method using WPT-CEEMD-CNN-LSTM. Initially, the method applies multiple noise reduction processes to the original vibration signals and enhances their time–frequency resolution through Wavelet Packet Transform (WPT) and Complete Ensemble Empirical Mode Decomposition (CEEMD). This effectively removes noise and generates a high-quality dataset. Subsequently, a Convolutional Neural Network (CNN) is employed to automatically extract deep features, while a Long Short-Term Memory (LSTM) network is used for the time-series modeling, thereby constructing an accurate rotor motor bearing fault diagnosis model. The experimental results demonstrate that the fault diagnosis accuracy of this method reaches 96.67%, which is significantly higher than that of the traditional CNN (85%), LSTM (51.33%), and the CEEMD-CNN-LSTM model with single-signal noise reduction (77.33%). This method also exhibits stronger fault identification and generalization capabilities. This study confirms the effectiveness of combining WPT-CEEMD with CNN-LSTM deep learning techniques for UAV bearing fault diagnosis, providing a high-precision and stable diagnostic solution for UAV health monitoring. Full article

Background/Objectives: Cerebral palsy (CP), often caused by early brain injury such as perinatal stroke or hemorrhage, is the most common lifelong motor disability. Early identification of at-risk infants and timely access to rehabilitation interventions are essential for improving long-term outcomes. The General Movements Assessment (GMA), performed in the first months of life, has high sensitivity and specificity to predict CP; however, the neurological correlates of general movements remain unclear. This analysis aimed to investigate the relationship between white matter integrity and general movements in infants with perinatal brain injury using advanced neuroimaging techniques. Methods: Diffusion-weighted MRI data were analyzed in 17 infants, 12 with perinatal brain injury and 5 typically developing infants. Tractography was used to identify the corticospinal tract, a key motor pathway often affected by perinatal brain injury, and tract-based spatial statistics (TBSS) were used to examine broader white matter networks. Diffusion parameters from the diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models were compared between infants with and without typical general movements. Results: Corticospinal tract integrity did not differ between groups when averaged across hemispheres. However, infants with asymmetric general movements exhibited greater corticospinal tract asymmetries. A subset of infants with atypical general movement trajectories at <6 weeks and 3–5 months of age showed reduced corticospinal tract integrity compared to those with typical general movements. TBSS revealed significant differences in white matter integrity between infants with typical and atypical general movements in several white matter pathways, including the corpus callosum, the right posterior corona radiata, bilateral posterior thalamic radiations, the left fornix/stria terminalis, and bilateral tapetum. Conclusions: These findings support and expand upon previous research suggesting that white matter integrity across multiple brain regions plays a role in the formation of general movements. Corticospinal integrity alone was not strongly associated with general movements; interhemispheric and cortical-subcortical connectivity appear critical. These findings underscore the need for further research in larger, diverse populations to refine early biomarkers of neurodevelopmental impairment and guide targeted interventions. Full article

The identification of the most pertinent site parameters to classify soils in terms of their amplification of seismic ground motions is still of prime interest to earthquake engineering and codes. This study investigates many options for improving soil classifications in order to reduce the deviation between “exact” predictions using wave propagation and the method used in seismic codes based on amplification (site) factors. To this end, an exhaustive parametric study is carried out to obtain nonlinear responses of sets of 324 clay and sand columns and to constitute the database for neuronal network methods used to predict the regression equations of the amplification factors in terms of seismic and site parameters. A wide variety of parameters and their combinations are considered in the study, namely, soil depth, shear wave velocity, the stiffness of the underlaying bedrock, and the intensity and frequency content of the seismic excitation. A database of AFs for 324 nonlinear soil profiles of sand and clay under multiple records with different intensities and frequency contents is obtained by wave propagation, where soil nonlinearity is accounted for through the equivalent linear model and an iterative procedure. Then, a Generalized Regression Neural Network (GRNN) is used on the obtained database to determine the most significant parameters affecting the AFs. A second neural network, the Radial Basis Function (RBF) network, is used to develop simple and practical prediction equations. Both the whole period range and specific short-, mid-, and long-period ranges associated with the AFs, F_a, F_v, and F_l, respectively, are considered. The results indicate that the amplification factor of an arbitrary soil profile can be satisfactorily approximated with a limited number of sites and the seismic record parameters (two to six). The best parameter pair is (PGA; resonance frequency, f₀), which leads to a standard deviation reduction of at least 65%. For improved performance, we propose the practical triplet $\left({PGA;V}_{s30};f_0\right)$ with V_s30 being the average shear wave velocity within the upper 30 m of soil below the foundation. Most other relevant results include the fact that the AFs for long periods (F_l) can be significantly higher than those for short or mid periods for soft soils. Finally, it is recommended to further refine this study by including additional soil parameters such as spatial configuration and by adopting more refined soil models. Full article

Climate change, particularly the increasing frequency of droughts, poses a critical challenge for agriculture. Rising temperatures and water scarcity threaten both agricultural productivity and ecosystem stability, making the identification of effective drought mitigation strategies essential. This study introduces an innovative approach to agricultural drought monitoring in Poland, utilizing remote sensing (RS) satellite data, collected from 2001 to 2020, and the Drought Identification Satellite System (DISS) index at a 1 km × 1 km spatial resolution, in combination with Copernicus High-Resolution Layers (HRL). To assess areas’ capacities to mitigate drought risks, a multi-criteria decision (MCD) analysis of regional environmental conditions was conducted. Focusing on the Mazowieckie Voivodeship, an algorithm was developed to evaluate regional susceptibility to drought. Spatial datasets were used to analyze environmental indicators, producing a map of communal temperature mitigation capacities. Statistical analysis identified drought vulnerability, highlighting areas in need of urgent intervention, such as increased mid-field tree planting. The study revealed that the frequency of droughts in this region during the growing season from 2001 to 2020 exceeded 40%. As a result, 40 LAU 2 administrative units have been affected by multiple negative environmental factors that contribute to drought formation and its long-term persistence. The proposed methodology, integrating diverse satellite data sources and spatial analyses, offers an effective tool for drought monitoring, mitigation planning, and ecosystem protection in a changing climate. This approach provides valuable insights for policymakers and land managers in addressing agricultural drought challenges and enhancing regional resilience to the impacts of climate change. Full article

Cotton aphids are the primary pests that adversely affect cotton growth, and they also transmit a variety of viral diseases, seriously threatening cotton yield and quality. Although the traditional remote sensing method with a single data source improves the monitoring efficiency to a certain extent, it has limitations with regard to reflecting the complex distribution characteristics of aphid pests and accurate identification. Accordingly, there is a pressing need for efficient and high-precision UAV remote sensing technology for effective identification and localization. To address the above problems, this study began by presenting a fusion of two kinds of images, namely panchromatic and multispectral images, using Gram–Schmidt image fusion technique to extract multiple vegetation indices and analyze their correlation with aphid damage indices. After fusing the panchromatic and multispectral images, the correlation between vegetation indices and aphid infestation degree was significantly improved, which could more accurately reflect the spatial distribution characteristics of aphid infestation. Subsequently, these machine learning techniques were applied for modeling and evaluation of the performance of multispectral and fused image data. The results of the validation revealed that the GBDT (Gradient-Boosting Decision Tree) model for GLI, RVI, DVI, and SAVI vegetation indices based on the fused data performed the best, with an estimation accuracy of R² of 0.88 and an RMSE of 0.0918, which was obviously better than that of the other five models, and that the monitoring method of combining fusion of panchromatic and multispectral imagery with the accuracy and efficiency of the GBDT model were noticeably higher than those of single multispectral imaging. The fused panchromatic and multispectral images combined with the GBDT model significantly outperformed the single multispectral image in terms of precision and efficiency. In conclusion, this study demonstrated the effectiveness of image fusion combined with GBDT modeling in cotton aphid pest monitoring. Full article

Background: Ultrasonography is commonly employed during thoracic regional anesthesia; however, its accuracy can be affected by factors such as obesity and poor penetration through the rib window. Needle-sized ultrasound transducers, known as intra-needle ultrasound (INUS) transducers, have been developed to detect the pleura and fascia using a one-dimensional radio frequency mode ultrasound signal. In this study, we aimed to use time-frequency analysis to characterize the pleural signal and develop an automated tool to identify the pleura during medical procedures. Methods: We developed an INUS system and investigated the pleural signal it measured by establishing a phantom study, and an in vivo animal study. Signals from the pleura, endothoracic fascia, and intercostal muscles were analyzed. Additionally, we conducted time- and frequency-domain analyses of the pleural and alveolar signals. Results: We identified the unique characteristics of the pleura, including a flickering phenomenon, speckle-like patterns, and highly variable multi-band spectra in the ultrasound signal during the breathing cycle. These characteristics are likely due to the multiple reflections from the sliding visceral pleura and alveoli. This automated identification of the pleura can enhance the safety for thoracic regional anesthesia, particularly in difficult cases. Conclusions: The unique flickering pleural signal based on INUS can be processed by time-frequency domain analysis and further tracked by an auto-identification algorithm. This technique has potential applications in thoracic regional anesthesia and other interventions. However, further studies are required to validate this hypothesis. Key Points Summary: Question: How can the ultrasound pleural signal be distinguished from other tissues during breathing? Findings: The frequency domain analysis of the pleural ultrasound signal showed fast variant and multi-band characteristics. We suggest this is due to ultrasound distortion caused by the interface of multiple moving alveoli. The multiple ultrasonic reflections from the sliding pleura and alveoli returned in variable and multi-banded frequency. Meaning: The distinguished pleural signal can be used for the auto-identification of the pleura for further clinical respiration monitoring and safety during regional anesthesia. Glossary of Terms: intra-needle ultrasound (INUS); radio frequency (RF); short-time Fourier transform (STFT); intercostal nerve block (ICNB); paravertebral block (PVB); pulse repetition frequency (PRF). Full article

Background: Children with disabilities face an increased risk of neglect and maltreatment due to their dependence on caregivers, social isolation, and challenges in seeking help. While extensive research has examined child abuse, neglect remains an underexplored yet pervasive issue affecting this vulnerable population. Objective: This scoping review synthesizes literature from the past decade to assess the prevalence, characteristics, and risk factors of neglect among children with disabilities, aiming to identify gaps in research and inform policy and intervention efforts. Methods: Following the PRISMA-ScR guidelines, a systematic search was conducted across multiple electronic databases, including PsycNET, Social Services Abstracts, ERIC, PubMed, and EBSCO. Studies were included if they focused on neglect among children with disabilities and were published in English within the last ten years. Thematic analysis was employed to extract and categorize findings. Results: Sixteen studies met the inclusion criteria, revealing a significantly higher prevalence of neglect among children with disabilities compared to their typically developing peers. The type and severity of disability influenced the likelihood and nature of neglect, with children with intellectual disabilities (ID), autism spectrum disorder (ASD), and sensory impairments facing particularly high risks. Key risk factors included parental stress, economic hardship, limited access to resources, and systemic failures in early identification and intervention. Despite the severity of neglect, evidence-based preventive strategies remain scarce, and existing child protection frameworks often fail to account for the unique needs of children with disabilities. Conclusions: The findings underscore the urgent need for targeted interventions, specialized training for professionals, and policy reforms to address the neglect of children with disabilities. Future research should focus on developing and evaluating culturally sensitive and disability-specific support systems to mitigate the long-term consequences of neglect. Full article

Accurate and rapid diagnosis of fault causes is crucial for ensuring the stability and safety of power distribution systems, which are frequently subjected to a variety of fault-inducing events. This study proposes a novel multimodal data fusion approach that effectively integrates external environmental information with internal electrical signals associated with faults. Initially, the TabTransformer and embedding techniques are employed to construct a unified representation of categorical fault information across multiple dimensions. Subsequently, an LSTM-based fusion module is introduced to aggregate continuous signals from multiple dimensions. Furthermore, a cross-attention module is designed to integrate both continuous and categorical fault information, thereby enhancing the model’s capability to capture complex relationships among data from diverse sources. Additionally, to address challenges such as a limited data scale, class imbalance, and potential mislabeling, this study introduces a loss function that combines soft label loss with focal loss. Experimental results demonstrate that the proposed multimodal data fusion algorithm significantly outperforms existing methods in terms of fault identification accuracy, thereby highlighting its potential for rapid and precise fault classification in real-world power grids. Full article

With the increasing complexity of power systems and the widespread application of power electronic equipment, power quality issues have become increasingly prominent, among which power quality disturbances are one of the key factors affecting the stable operation of power systems and the normal functioning of electrical equipment. Current research methods are still limited by feature extraction, insufficient model generalization ability, and strong data dependence. This paper proposes a power quality disturbance classification strategy based on the fast S-transform (FST) and an improved convolutional neural network–long short-term memory (CNN-LSTM) model to achieve accurate classification and identification of various power quality disturbances. Firstly, the FST is employed to process the power quality disturbance signals, enabling efficient analysis and feature extraction while effectively preserving the time–frequency characteristics of the signals and significantly reducing the computational burden. Secondly, to address the limitations of traditional CNN models in power quality disturbance classification, this paper introduces an improved CNN-LSTM hybrid classification model that integrates mechanism fusion. This model improves the classification performance and generalization ability for power quality disturbances by incorporating an enhanced sparrow search algorithm and learning mechanisms. Finally, the proposed strategy is experimentally validated using a large dataset of power quality disturbances. After analysis and comparison, the method proposed in this paper maintains an identification accuracy of over 97% even in strong noise environments when subjected to a single type of disturbance. Under complex conditions involving mixed disturbances of multiple types, the identification accuracy remains above 95%. Compared to existing methods, the proposed method achieves an improvement in identification accuracy by up to 3.2%. Additionally, its identification accuracy in scenarios with small data samples is significantly better than that of traditional methods, such as single CNN models and LSTM models. The experimental results demonstrate that the proposed strategy can accurately classify and identify various power quality disturbances, outperforming traditional methods in terms of classification accuracy and robustness. Full article

Acute myocardial infarction (AMI) and type 2 diabetes mellitus (T2DM) share common risk factors. To evaluate the long-term incidence and predictors of new-onset T2DM (NODM) among post-AMI adults, we conducted a retrospective analysis of AMI survivors hospitalized between 2002 and 2017. Eligible patients were followed for up to 16 years to identify NODM, stratified by demographic and clinical characteristics. Among 5147 individuals (74.2% males, mean age 64.6 ± 14.9 years) without pre-existing T2DM, 23.4% developed NODM (cumulative incidence: 0.541). Key risk factors included an age of 50–60 years, a minority ethnicity (Arabs), smoking, metabolic syndrome (MetS), hemoglobin A1C (HbA1C) ≥ 5.7%, and cardiovascular comorbidities. A total score (TS), integrating these factors, revealed a linear association with the NODM risk: each 1-point increase corresponded to a 1.2-fold rise (95% CI 1.191–1.276, p < 0.001). HbA1C ≥ 6% on the “Pre-DM sub-scale” conferred a 2.8-fold risk (p < 0.001), while other risk factors also independently predicted NODM. In conclusion, post-AMI patients with multiple cardiovascular risk factors, particularly middle-aged individuals, Arab individuals, and those with HbA1C ≥ 6% or MetS, are at a heightened risk of NODM. Early identification and targeted interventions may mitigate this risk. Full article

For the non-homogeneous fractional-order Hammerstein multiple input single output (MISO) system, a method for identifying system coefficients and fractional-order parameters in stages is proposed. The coefficients of the system include the coefficients of nonlinear terms and the coefficients of the transfer function. In order to estimate them, we derived the coupling auxiliary form between the original system coefficients, developed a multi-innovation principle combined with the LM (Levenberg–Marquardt) parameter identification method, and introduced a decoupling strategy for the coupling coefficients. The entire identification process of fractional orders is split into three stages. The division of stages is based on assuming that the system is of different fractional order types, including global homogeneous fractional-order systems, local homogeneous fractional-order systems, and non-homogeneous fractional-order systems. Except for the first stage, the estimated initial value of the fractional order in each stage is derived from the estimated value of the fractional order in the previous stage. The fractional order iteration will re-drive the iteration of the system coefficients to achieve the purpose of alternate estimation. To validate the proposed algorithm, we modeled the fractional-order system of heat flow density through a two-layer wall system, demonstrating the algorithm’s effectiveness and practical applicability. Full article

In this paper, we study the performance of second-order compensated measurement to generate a multi-bit response in physically unclonable functions (PUFs). The proposed technique is based on a novel second-order compensated measurement generating multiple bits instead of a single bit provided by the conventional compensated measurement. A PUF based on this technique has been proposed and implemented in 40 Artix-7 FPGAs, and its uniqueness and reproducibility have been compared to those of another PUF using the compensated measurement technique. In addition, we demonstrate that the best trade-off between identifiability and computation time performance is obtained when using only two bits. At the same time, the good performance of the technique has been demonstrated, improving the identifiability of a ring oscillator PUF (RO-PUF) between 70 and 90% compared to a RO-PUF that uses conventional compensated measurement. In particular, equal error rates (EER) of the order of EER∼${10}^{-16}$ can be achieved by combining the sign bit with another bit extracted using the proposed technique; and up to EER∼${10}^{-19}$ by using one more extra bit. In addition, the high reliability of the responses generated by this technique against possible temperature and voltage variations has been proved. These results show how this new technique improves the performance of the PUF in terms of identifiability, so it can be effectively used for device identification purposes. Full article