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A two-stage joint model approach to handle incomplete time dependent markers in survival data through inverse probability weight and multiple imputation
Fuzzy C-Means clustering and LSTM-based magnitude prediction of earthquakes in the Aegean region of Türkiye
Abstract Türkiye is highly susceptible to earthquakes due to its active tectonic structure and the presence of major fault lines. The accurate estimation of earthquake magnitudes is essential for effective risk mitigation and structural resilience. This study proposes an integrated methodology combining clustering, statistical modeling, and deep learning techniques for the analysis and forecasting of earthquake magnitudes. Initially, earthquakes are classified into three distinct regions using the Fuzzy C-Means (FCM) clustering algorithm. For each region, statistical distributions are applied to characterize magnitude behavior. Subsequently, the Long Short-Term Memory (LSTM) model is used to predict future earthquake magnitudes. The joint application of these three methods provides a comprehensive framework for regional seismic analysis. The findings suggest that the Gumbel distribution offers the best fit for modeling return periods of earthquakes with magnitudes greater than Mwg 5.18, where Mwg denotes the global moment magnitude. Estimated return periods range from 2.56 to 3.63 years in the first region, 2.53 to 3.55 years in the second region, and 2.71–4.22 years in the third region, based on probability levels between 25 and 95%. The LSTM model forecasts that the third region is likely to experience relatively stronger seismic activity, with maximum magnitudes ranging from 2.4 to 6.5 between October 2021 and March 2029. For the same period, expected magnitudes in the first and second regions range from 2.0 to 5.7. These forecasts are supported by model performance metrics that confirm the projected magnitudes are within an acceptable and reliable range of accuracy for medium-term seismic forecasting.
Efficient and robust temporal processing with neural oscillations modulated spiking neural networks
The synergistic effects of linalool and chitosan-carbon nanotubes enhance the healing of infectious wounds
Population assessment and habitat suitability modelling of endangered medicinal plant, Aconitum heterophyllum Wall. ex Royle in the western Himalaya
Direct generation of nitrogen-centered radicals via non-covalent interaction between Cu complexes and BiVO4 photoanodes
Model based planning and layout optimization for multilevel express processing centers through A case study of air express sorting center
Abstract The construction of multi-level express processing centers constitutes a core strategy for addressing escalating logistical demands, with the layout planning of these hubs significantly impacting the efficiency, cost-effectiveness, and customer satisfaction associated with express delivery services. However, two significant limitations were identified in traditional planning processes: insufficient modeling capabilities and an excessive reliance on experiential knowledge in planning and design. This study proposes a planning model for express parcel handling centers that accounts for multi-level demands. The core of this approach lies in integrating key elements, such as functional area division and correlation values, into the layout planning analysis framework. An improved Genetic Algorithm (GA) is incorporated into the system layout design method, and it realizes automated layout planning of express parcel sorting and handling centers. A case study conducted on an air logistics center demonstrates the feasibility, effectiveness, and applicability of the proposed method. The results indicate that this approach can increase space utilization by 15–28%.This research not only provides innovative perspectives and solutions for layout decision-making in multi-level express processing centers but also offers a framework that can be adapted to complex layout optimization challenges across various industries beyond logistics. By optimizing the layout of express processing centers, it enables faster delivery times, improves resource utilization, and lowers energy consumption, delivering tangible benefits to both businesses and consumers.
Prognostic value of systemic inflammation markers in early stage non-small cell lung cancer
Mechanism for the substrate recognition by a eukaryotic DNA N6-adenine methyltransferase complex
Two Tabata cycles in a single training set maximize fat oxidation after exercise in male college students with overweight/obesity
Abstract Tabata, which is involved 20 seconds of maximum-intensity exercise followed by 10 seconds of complete rest, repeated for 8 cycles totaling 4 minutes, has been identified to enhance energy expenditure and fat oxidation in humans. The study aims to find an optimal Tabata volume for weight loss. 32 male university students with overweight/obesity participated in three tests. Test I consisted of a single Tabata cycle, Test II consisted of two, and Test III consisted of three. Each cycle was separated by a 10-minute interval, and each test was was separated by 7 days. Gas exchange indices were monitored during the last Tabata cycle of the test and the 30-minute recovery period. Subsequently, fat and glucose oxidation amounts, rates, and energy expenditure were calculated. During the 10-minute recovery period, the fat oxidation amounts of Test II (0.80±0.26 g) and Test III (0.87±0.24 g) were higher than Test I (0.50±0.11 g, p<0.001). There was no significant difference between Test II and Test III. During the 20-minute and 30-minute recovery periods, Test II (3.21±0.50 g; 5.04±1.02 g) showed significantly higher fat oxidation amount than Test I (2.47±0.59 g, p<0.001; 4.41±0.98 g, p<0.05) and Test III (2.80±0.43 g, p<0.001; 4.11±0.96 g, p<0.001), there was no significant difference between Test I and Test III (p>0.05). No significant differences in energy expenditure were observed among the three tests during recovery periods (p>0.05). We conclude that two Tabata cycles show the highest fat oxidation amount with the same energy expenditure amount during the recovery period, which is the optimal Tabata volume for weight loss.
Drug-related problems and challenges encountered by pharmacists in nirmatrelvir/ritonavir counselling during the covid-19 pandemic: a multi-center study
Cost-effectiveness analysis of screening for congenital Chagas disease in a non-endemic area
Optimization of operational parameters for pneumatic planting of cotton seeds using a standard metering plate
Enhancing punching shear and post-punching behavior of flat slabs using wire mesh, integrity bars and fibers
Cryo-electron tomography reconstructs polymer in liquid film for fab-compatible lithography
Drinking water resources suitability assessment in Brahmani river Odisha based on pollution index of surface water utilizing advanced water quality methods
A swin transformer-based hybrid reconstruction discriminative network for image anomaly detection
Abstract Industrial anomaly detection algorithms based on Convolutional Neural Networks (CNN) often struggle with identifying small anomaly regions and maintaining robust performance in noisy industrial environments. To address these limitations, this paper proposes the Swin Transformer-Based Hybrid Reconstruction Discriminative Network (SRDAD), which combines the global context modeling capabilities of Swin Transformer with complementary reconstruction and discrimination approaches. Our approach introduces three key contributions: a natural anomaly image generation module that produces diverse simulated anomalies resembling real-world defects; a Swin-Unet based reconstruction subnetwork with enhanced residual and pooling modules for accurate normal image reconstruction, utilizing hierarchical window attention mechanisms, and an anomaly contrast discrimination subnetwork based on convolutional Unet that enables end-to-end detection and localization through contrastive learning. This hybrid approach combines reconstruction and discrimination paradigms to improve anomaly detection performance. Experimental results on the industrial dataset MVTec AD demonstrate that SRDAD achieves competitive performance, with improvements of 0.6% in detection accuracy and 0.7% in localization precision. The method demonstrates improved performance in detecting small anomalies and maintaining performance in noisy environments, highlighting its potential for industrial applications.
A doubly stochastic renewal framework for partitioning spiking variability
Abstract The firing rate is a prevalent concept used to describe neural computations, but estimating dynamically changing firing rates from irregular spikes is challenging. An inhomogeneous Poisson process, the standard model for partitioning firing rate and spiking irregularity, cannot account for diverse spike statistics observed across neurons. We introduce a doubly stochastic renewal point process, a flexible mathematical framework for partitioning spiking variability, which captures the broad spectrum of spiking irregularity from periodic to super-Poisson. We validate our partitioning framework using intracellular voltage recordings and develop a method for estimating spiking irregularity from data. We find that the spiking irregularity of cortical neurons decreases from sensory to association areas and is nearly constant for each neuron under many conditions but can also change across task epochs. Spiking network models show that spiking irregularity depends on connectivity and can change with external input. These results help improve the precision of estimating firing rates on single trials and constrain mechanistic models of neural circuits.