Search Results (179)

In the field of deep learning, the traditional Vision Transformer (ViT) model has some limitations when dealing with local details and long-range dependencies; especially in the absence of sufficient training data, it is prone to overfitting. Structures such as retinal blood vessels and lesion boundaries have distinct fractal properties in medical images. The Fractional Convolution Vision Transformer (FCViT) model is proposed in this paper, which effectively compensates for the deficiency of ViT in local feature capture by fusing convolutional information. The ability to classify medical images is enhanced by analyzing frequency domain features using fractional-order Fourier transform and capturing global information through a self-attention mechanism. The three-branch architecture enables the model to fully understand the data from multiple perspectives, capturing both local details and global context, which in turn improves classification performance and generalization. The experimental results showed that the FCViT model achieved 93.52% accuracy, 93.32% precision, 92.79% recall, and a 93.04% F1-score on the standardized fundus glaucoma dataset. The accuracy on the Harvard Dataverse-V1 dataset reached 94.21%, with a precision of 93.73%, recall of 93.67%, and F1-score of 93.68%. The FCViT model achieves significant performance gains on a variety of neural network architectures and tasks with different source datasets, demonstrating its effectiveness and utility in the field of deep learning. Full article

Autism spectrum disorder (ASD) deals with several symptoms, including language and speech impairment and developmental delays. The main brain regions affected could be the prefrontal cortex (PFC) or the temporal lobe. The detrimental features could include oxidative stress, mitochondrial dysfunction, and neuroinflammation. Most often, these phenomena are interrelated and can lead to one another, creating a vicious cycle. They also influence the regulation of certain genes involved in the pathogenesis of ASD or related behavior. In the brain regions prone to these detrimental features, a cascade of free radicals, inflammatory cytokines, and mitochondrial energy disruptions is initiated. These actions during the prenatal or developmental stage of the child potentially lead to ASD symptomatic features, such as social isolation, communication difficulty, speech and language impairment, cognitive dysfunction, and intellectual disability. The more recent theories, including genetics, epigenetics, and the gut–brain axis, have been demonstrated to play a greater role in ASD pathology, often being associated with the more common ones as mentioned above. We also introduced some of the neurological disorders possessing shared genetic and behavioral traits with ASD. Many genes playing a role in ASD-like features and their potential targeted drugs were explained briefly. However, there are limited therapeutic options, and molecular pathways related to this disorder are less explored. Currently, researchers and therapists are racing to uncover a concrete remedy. This review also provides a brief outline of potential antioxidant, mitochondrial, and anti-inflammatory therapies. We finally included some novel strategies to diagnose and manage autistic pathology and symptoms. Full article

With the large-scale integration of renewable energy through power electronic inverters,
modern power systems are gradually transitioning to low-inertia systems. Grid-forming
inverters are prone to power overshoot and frequency deviation when facing external
disturbances, threatening system stability. Existing methods face two main challenges in
dealing with complex disturbances: neural-network-based approaches have high computational
burdens and long response times, while traditional linear algorithms lack sufficient
precision in adjustment, leading to inadequate system response accuracy and stability. This
paper proposes an innovative coordinated adaptive control strategy for virtual inertia and
damping. The strategy utilizes a Radial Basis Function neural network for the adaptive
regulation of virtual inertia, while the damping coefficient is adjusted using a linear algorithm.
This approach provides refined inertia regulation while maintaining computational
efficiency, optimizing the rate of change in frequency and frequency deviation. Simulation
results demonstrate that the proposed control strategy significantly outperforms traditional
methods in improving system performance. In the active power reference variation
scenario, frequency overshoot is reduced by 65.4%, active power overshoot decreases by
66.7%, and the system recovery time is shortened. In the load variation scenario, frequency
overshoot is reduced by approximately 3.6%, and the maximum frequency deviation is
reduced by approximately 26.9%. In the composite disturbance scenario, the frequency
peak is reduced by approximately 0.1 Hz, the maximum frequency deviation decreases by
35%, and the power response improves by 23.3%. These results indicate that the proposed
method offers significant advantages in enhancing system dynamic response, frequency
stability, and power overshoot suppression, demonstrating its substantial potential for
practical applications. Full article

Wearable laser Doppler devices are susceptible to complex noise interferences, such as Gaussian white noise, baseline drift, and motion artifacts, with motion artifacts significantly impacting clinical diagnostic accuracy. Addressing the limitations of existing denoising methods—including traditional adaptive filtering that relies on prior noise information, modal decomposition techniques that depend on empirical parameter optimization and are prone to modal aliasing, wavelet threshold functions that struggle to balance signal preservation with smoothness, and the high computational complexity of deep learning approaches—this paper proposes an ISSA-VMD-AWPTD denoising algorithm. This innovative approach integrates an improved sparrow search algorithm (ISSA), variational mode decomposition (VMD), and adaptive wavelet packet threshold denoising (AWPTD). The ISSA is enhanced through cubic chaotic mapping, butterfly optimization, and sine–cosine search strategies, targeting the minimization of the envelope entropy of modal components for adaptive optimization of VMD’s decomposition levels and penalty factors. A correlation coefficient-based selection mechanism is employed to separate target and mixed modes effectively, allowing for the efficient removal of noise components. Additionally, an exponential adaptive threshold function is introduced, combining wavelet packet node energy proportion analysis to achieve efficient signal reconstruction. By leveraging the rapid convergence property of ISSA (completing parameter optimization within five iterations), the computational load of traditional VMD is reduced while maintaining the denoising accuracy. Experimental results demonstrate that for a 200 Hz test signal, the proposed algorithm achieves a signal-to-noise ratio (SNR) of 24.47 dB, an improvement of 18.8% over the VMD method (20.63 dB), and a root-mean-square-error (RMSE) of 0.0023, a reduction of 69.3% compared to the VMD method (0.0075). The processing results for measured human blood flow signals achieve an SNR of 24.11 dB, a RMSE of 0.0023, and a correlation coefficient (R) of 0.92, all outperforming other algorithms, such as VMD and WPTD. This study effectively addresses issues related to parameter sensitivity and incomplete noise separation in traditional methods, providing a high-precision and low-complexity real-time signal processing solution for wearable devices. However, the parameter optimization still needs improvement when dealing with large datasets. Full article

Galenic preparations are patient-centered medicines prepared by pharmacists or veterinarians, which allow personalizing dosages, overcoming allergy problems, reducing costs, and dealing with rare diseases. However, the current manual production process of galenic preparations poses several challenges to human workers. This paper proposes the use of collaborative robots to help pharmacists carry out the most tiresome, precise, error-prone, and time-consuming tasks. In particular, an end-user development (EUD) environment called PRAISE (pharmaceutical robotic and AI system for end users) has been designed to support pharmacists in programming the tasks to be performed by a collaborative robot. The EUD environment integrates artificial intelligence (AI) features based on large language models but ensures that end users always have complete control over the generated output, that is, a robot program. The paper focuses on the application of a human-centered methodology adopted to design PRAISE by involving representative end users (experts in the pharmaceutical sector) from system ideation to its evaluation. Design implications related to AI-enabled EUD for collaborative robots are the main findings of the paper. Full article

In view of the challenges brought by a complex environment, diverse data sources and urban development needs, our study comprehensively reviews the application of algorithms in urban residential vacancy rate observation. First, we explore the definition and measurement of urban residential vacancy rate, pointing out the difficulties in accurately defining vacant houses and obtaining reliable data. Then, we introduce various algorithms such as traditional statistical learning, machine learning, deep learning and ensemble learning, and analyze their applications in vacancy rate observation. The traditional statistical learning algorithm builds a prediction model based on historical data mining and analysis, which has certain advantages in dealing with linear problems and regular data. However, facing the high nonlinear relationships and complexity of the data in the urban residential vacancy rate observation, its prediction accuracy is difficult to meet the actual needs. With their powerful nonlinear modeling ability, machine learning algorithms have significant advantages in capturing the nonlinear relationships of data. However, they require high data quality and are prone to overfitting phenomenon. Deep learning algorithms can automatically learn feature representation, perform well in processing large amounts of high-dimensional and complex data, and can effectively deal with the challenges brought by various data sources, but the training process is complex and the computational cost is high. The ensemble learning algorithm combines multiple prediction models to improve the prediction accuracy and stability. By comparing these algorithms, we can clarify the advantages and adaptability of different algorithms in different scenarios. Facing the complex environment, the data in the observation of urban residential vacancy rate are affected by many factors. The unbalanced urban development leads to significant differences in residential vacancy rates in different areas. Spatiotemporal heterogeneity means that vacancy rates vary in different geographical locations and over time. The complexity of data affected by various factors means that the vacancy rate is jointly affected by macroeconomic factors, policy regulatory factors, market supply and demand factors and individual resident factors. These factors are intertwined, increasing the complexity of data and the difficulty of analysis. In view of the diversity of data sources, we discuss multi-source data fusion technology, which aims to integrate different data sources to improve the accuracy of vacancy rate observation. The diversity of data sources, including geographic information system (GIS) (Geographic Information System) data, remote sensing images, statistics data, social media data and urban grid management data, requires integration in format, scale, precision and spatiotemporal resolution through data preprocessing, standardization and normalization. The multi-source data fusion algorithm should not only have the ability of intelligent feature extraction and related analysis, but also deal with the uncertainty and redundancy of data to adapt to the dynamic needs of urban development. We also elaborate on the optimization methods of algorithms for different data sources. Through this study, we find that algorithms play a vital role in improving the accuracy of vacancy rate observation and enhancing the understanding of urban housing conditions. Algorithms can handle complex spatial data, integrate diverse data sources, and explore the social and economic factors behind vacancy rates. In the future, we will continue to deepen the application of algorithms in data processing, model building and decision support, and strive to provide smarter and more accurate solutions for urban housing management and sustainable development. Full article

Automated inspection of leather surface defects is critical in evaluating product quality, yet manual inspection is still time-consuming and error-prone. Conventional automated methods, on the other hand, exhibit high computational complexities, are rigid in dealing with varied defects, and often require extensive manual parameter tuning. To counter these challenges, we propose a lightweight model integrated with symmetry for efficient defect classification and segmentation. The model consists of a streamlined semantic segmentation network that uses depthwise separable convolution and symmetric padding to preserve edge features while eliminating deconvolution layers, thus considerably reducing computational overhead. Moreover, a discrimination network automates defect detection without requiring manual thresholds, and a segmentation suggestion stage refines defect masks for practical cutting recommendations. Experimental results demonstrate a 96.75% detection accuracy and 89.41% mean pixel accuracy, achieving performance comparable to state-of-the-art models (e.g., KMDNet, U-Net) while reducing training time by 40% and model size by 60%. The symmetry-driven architecture enhances computational efficiency (0.05 s/img) and robustness across multiple defect types. Furthermore, the modular design enables independent updates for new defect types without requiring full retraining, addressing a major limitation of prior methods. These findings highlight the potential of symmetry-based architectures in industrial quality control, offering a scalable and efficient solution for automated defect detection. Full article

Under complex conditions, the collaborative control capability of UAV swarms is considered to be the key to ensuring the stability and safety of swarm flights. However, in complex environments such as forest firefighting, traditional swarm control methods struggle to meet the differentiated needs of UAVs with differences in behavior characteristics and mutually coupled constraints, which gives rise to the problem that adjustments and feedback to the control policy during training are prone to erroneous judgments, leading to decision-making dissonance. This study proposed a swarm control method for complementary collaboration of UAVs under complex conditions. The method first generates training data through the interaction between UAV swarms and the environment; then it captures the potential patterns of UAV behaviors, extracts their differentiated behavior characteristics, and explores diversified behavior combination scenarios with complementary advantages; accordingly, dynamic behavior allocations are made according to the differences in perception accuracy and action capability to achieve collaborative cooperation; and finally, it optimizes the neural network parameters through behavior learning to improve the decision-making policy. According to the experimental results, the UAV swarm control method proposed in this study demonstrates high formation stability and integrity when dealing with the collaborative missions of multiple types of UAVs. Full article

Celiac disease is a chronic inflammatory condition of the small bowel caused, in genetically predisposed subjects, by the ingestion of gluten and characterised by a broad clinical polymorphism, ranging from patients with an asymptomatic or paucisymptomatic disease. The clinical presentation ranges from the presence of minor, apparently unrelated symptoms or first-degree kinship with known patients to severe intestinal malabsorption and all its clinical consequences and complications. Even if a large body of research improved our understanding of the molecular basis of celiac disease pathophysiology, enhancing the identification of new targets for future new treatments, an accurate gluten-free diet remains the mainstay of the therapy for this condition, restoring a normal absorptive mucosa. It is very rare, nowadays, to deal with patients with severe malabsorption syndrome secondary to celiac disease. Consequently, physicians are currently less prone to search for nutritional deficiencies in celiac disease. To pinpoint the possibility of both a disease-related and a diet-induced vitamin deficiency, we reviewed the literature on vitamin deficiency in this condition and reported the impact both in untreated and treated patients with celiac disease. A gluten-free diet must be tailored for each patient to meet nutritional targets: the pre-existence or diet-induced intake inadequacies should be carefully considered for an effective management of celiac disease. Full article

Improving decision-making in the autonomous maneuvering of unmanned aerial vehicles (UAVs) is of great significance to improving flight safety, the mission execution rate, and environmental adaptability. The method of deep reinforcement learning makes the autonomous maneuvering decision of UAVs possible. However, the current algorithm is prone to low training efficiency and poor performance when dealing with complex continuous maneuvering problems. In order to further improve the autonomous maneuvering level of UAVs and explore safe and efficient maneuvering methods in complex environments, a maneuvering decision-making method based on hierarchical reinforcement learning and Proximal Policy Optimization (PPO) is proposed in this paper. By introducing the idea of hierarchical reinforcement learning into the PPO algorithm, the complex problem of UAV maneuvering and obstacle avoidance is separated into high-level macro-maneuver guidance and low-level micro-action execution, greatly simplifying the task of addressing complex maneuvering decisions using a single-layer PPO. In addition, by designing static/dynamic threat zones and varying their quantity, size, and location, the complexity of the environment is enhanced, thereby improving the algorithm’s adaptability and robustness to different conditions. The experimental results indicate that when the number of threat targets is five, the success rate of the H-PPO algorithm for maneuvering to the designated target point is 80%, which is significantly higher than the 58% rate achieved by the original PPO algorithm. Additionally, both the average maneuvering distance and time are lower than those of the PPO, and the network computation time is only 1.64 s, which is shorter than the 2.46 s computation time of the PPO. Additionally, as the complexity of the environment increases, the H-PPO algorithm outperforms other compared networks, demonstrating the effectiveness of the algorithm constructed in this paper for guiding intelligent agents to autonomously maneuver and avoid obstacles in complex and time-varying environments. This provides a feasible technical approach and theoretical support for realizing autonomous maneuvering decisions in UAVs. Full article

This article investigates the performance of the Galileo Open Service Navigation Message Authentication (OSNMA) system in real-life environments prone to RF interference (RFI), jamming, and/or spoofing attacks. Considering the existing data that indicate a relatively high number of RFI- and spoofing-related incidents reported in Eastern Europe, this study details a data-collection campaign along various roads through urban, suburban, and rural settings, mostly in three border counties in East and South-East of Romania, and presents the results based on the data analysis. The key performance indicators are determined from the perspective of an end user relying only on Galileo OSNMA authenticated signals. The Galileo OSNMA signals were captured using one of the few commercially available GNSS receivers that can perform this OSNMA authentication algorithm incorporating the satellite signals. This work includes a presentation of the receiver’s operation and of the authentication results obtained during test runs that experienced an unusually high number of RFI-related incidents, followed by a detailed analysis of instances when such RFI impaired or fully prevented obtaining an authenticated position, velocity, and time (PVT) solution. The results indicate that Galileo OSNMA demonstrates significant robustness against interference in real-life RF-degraded environments, dealing with both accidental and intentional interference. Full article

The Loess Plateau in northwest China features fragmented terrain and is prone to landslides. However, the complex environment of the Loess Plateau, combined with the inherent limitations of convolutional neural networks (CNNs), often results in false positives and missed detection for deep learning models based on CNNs when identifying landslides from high-resolution remote sensing images. To deal with this challenge, our research introduced a CNN–transformer hybrid network. Specifically, we first constructed a database consisting of 1500 loess landslides and non-landslide samples. Subsequently, we proposed a neural network architecture that employs a CNN–transformer hybrid as an encoder, with the ability to extract high-dimensional, local-scale features using CNNs and global-scale features using a multi-scale lightweight transformer module, thereby enabling the automatic identification of landslides. The results demonstrate that this model can effectively detect loess landslides in such complex environments. Compared to approaches based on CNNs or transformers, such as U-Net, HCNet and TransUNet, our proposed model achieved greater accuracy, with an improvement of at least 3.81% in the F1-score. This study contributes to the automatic and intelligent identification of landslide locations and ranges on the Loess Plateau, which has significant practicality in terms of landslide investigation, risk assessment, disaster management, and related fields. Full article

Background: This paper examines the economic implications of market segmentation on consumer purchasing behavior with a particular emphasis on intertemporal pricing strategies in dynamic markets. Methods: In order to analyze optimal discount rates and the timing for price reductions for consumer segments, including loyal and deal-prone customers, a detailed mathematical model was developed. The model incorporates theories of consumer behavior and pricing elasticity to simulate market responses to price changes throughout a product’s lifecycle. Results: This research indicates that market segmentation enhances sales by targeting the distinct preferences of loyal consumers, who are less price-sensitive and who stabilize revenue streams, and deal-prone consumers, who respond to price reductions. Customizing pricing strategies for loyal consumers and deal-prone consumers increases sales volumes and optimizes profitability. Conclusions: This research improves our comprehension of market segmentation and dynamic pricing, providing a practical framework for businesses to create effective pricing strategies that can be promptly implemented. It emphasizes the significance of understanding consumer behavior and price sensitivity in the interest of revenue promotion. This study also emphasizes the social implications of equitable pricing practices, promoting the implementation of transparent and value-based strategies to promote market inclusivity and consumer trust. Full article

Background and objectives: Deep learning (DL)-based models for predicting the survival of patients with local stages of breast cancer only use time-fixed covariates, i.e., patient and cancer data at the time of diagnosis. These predictions are inherently error-prone because they do not consider time-varying events that occur after initial diagnosis. Our objective is to improve the predictive modeling of survival of patients with localized breast cancer to consider both time-fixed and time-varying events; thus, we take into account the progression of a patient’s health status over time. Methods: We extended four DL-based predictive survival models (DeepSurv, DeepHit, Nnet-survival, and Cox-Time) that deal with right-censored time-to-event data to consider not only a patient’s time-fixed covariates (patient and cancer data at diagnosis) but also a patient’s time-varying covariates (e.g., treatments, comorbidities, progressive age, frailty index, adverse events from treatment). We utilized, as our study data, the SEER-Medicare linked dataset from 1991 to 2016 to study a population of women diagnosed with stage I–III breast cancer (BC) enrolled in Medicare at 65 years or older as qualified by age. We delineated time-fixed variables recorded at the time of diagnosis, including age, race, marital status, breast cancer stage, tumor grade, laterality, estrogen receptor (ER), progesterone receptor (PR), and human epidermal receptor 2 (HER2) status, and comorbidity index. We analyzed six distinct prognostic categories, cancer stages I–III BC, and each stage’s ER/PR+ or ER/PR− status. At each visit, we delineated the time-varying covariates of administered treatments, induced adverse events, comorbidity index, and age. We predicted the survival of three hypothetical patients to demonstrate the model’s utility. Main Outcomes and Measures: The primary outcomes of the modeling were the measures of the model’s prediction error, as measured by the concordance index, the most commonly applied evaluation metric in survival analysis, and the integrated Brier score, a metric of the model’s discrimination and calibration. Results: The proposed extended patients’ covariates that include both time-fixed and time-varying covariates significantly improved the deep learning models’ prediction error and the discrimination and calibration of a model’s estimates. The prediction of the four DL models using time-fixed covariates in six different prognostic categories all resulted in approximately a 30% error in all six categories. When applying the proposed extension to include time-varying covariates, the accuracy of all four predictive models improved significantly, with the error decreasing to approximately 10%. The models’ predictive accuracy was independent of the differing published survival predictions from time-fixed covariates in the six prognostic categories. We demonstrate the utility of the model in three hypothetical patients with unique patient, cancer, and treatment variables. The model predicted survival based on the patient’s individual time-fixed and time-varying features, which varied considerably from Social Security age-based, and stage and race-based breast cancer survival predictions. Conclusions: The predictive modeling of the survival of patients with early-stage breast cancer using DL models has a prediction error of around 30% when considering only time-fixed covariates at the time of diagnosis and decreases to values under 10% when time-varying covariates are added as input to the models, regardless of the prognostic category of the patient groups. These models can be used to predict individual patients’ survival probabilities based on their unique repertoire of time-fixed and time-varying features. They will provide guidance for patients and their caregivers to assist in decision making. Full article

The internal combustion engines of long-endurance UAVs are optimized for cruises, so they are prone to overheating during climbs, when power requests increase. To counteract the phenomenon, step-climb maneuvering is typically operated, but the intermittent high-power requests generate repeated heating–cooling cycles, which, over multiple missions, may promote thermal fatigue, performance degradation, and failure. This paper deals with the development of a model-based monitoring of the cylinder head temperature of the two-stroke engine employed in a lightweight fixed-wing long-endurance UAV, which combines a 0D thermal model derived from physical first principles with an extended Kalman filter capable to estimate the head temperature under degraded conditions. The parameters of the dynamic model, referred to as nominal condition, are defined through a particle-swarm optimization, minimizing the mean square temperature error between simulated and experimental flight data (obtaining mean and peak errors lower than 3% and 10%, respectively). The validated model is used in a so-called condition-based extended Kalman filter, which differs from a conventional one for a correction term in section prediction, leveraged as degradation symptom, based on the deviation of the model-state derivative with respect to the actual measurement. The monitoring algorithm, being executable in real-time and capable of identifying incipient degradations of the thermal flow, demonstrates applicability for online diagnostics and predictive maintenance purposes. Full article