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Two Potamogeton species (Potamogetonaceae) new to the Caucasus
Get PDFThis study presents the first records of Potamogeton compressus L. and P. friesii Rupr. in the Caucasus, where both are confined to Lake Tabatskuri, Georgia. Potamogeton compressus was identified from 1965 herbarium specimens found in TGM, while P. friesii was documented in 2023. These findings extend their known southern ranges, highlighting the importance of high-altitude lakes in preserving boreal aquatic flora in the South Caucasus
Assessment of Battery Degradation Using Rainflow Cycle-Counting Algorithm: A Recent Advancement
No full textBattery based energy storage systems are increasingly popular in power systems as renewable energy continues to grow while ensuring the reliability of power supply. However, battery degradation is a significant issue that can impact power system operations and optimal scheduling strategies. Therefore, estimating the remaining life cycle or assessing the health of batteries due to the degradation process has become a new challenge and research focus in various engineering fields. This topic is relevant in the context of electric vehicles (EVs), where battery degradation caused by continuous and non-continuous operations (i.e., charging and discharging cycles). Degradation can limit the performance of batteries and occur throughout their lifespan whether they are in use or not. The degradation process is complex and influenced by usage and external conditions that are normally measured by state of health (SOH). Therefore, predicting the SOH of batteries is crucial in ensuring the safety, stability, and long-term viability of energy storage and EVs systems. This prediction requires a battery mechanism model that can be established from a complex electrochemical process. Alternatively, a rainflow cycle-counting algorithm (RCCA) has become popular among researchers for battery degradation estimation because of its simplicity. This paper presents a comprehensive review of the battery degradation estimation using RCCA to count the equivalent cycles of charging and discharging profiles
Gut-Lung Axis in Cats: Case Archives (2020-2025) for Further Evidence of Proof in Cats with Several Different Respiratory Patterns Treated with Natural Remedies
No full textThe gut-lung axis is a crucial bidirectional interaction between intestinal and respiratory microbiota, significantly influencing immune homeostasis. Dysbiosis in these microbial communities has been implicated in various respiratory diseases, including feline asthma. While the gut-lung axis has been extensively studied in humans, its role in feline respiratory pathology remains underexplored. This study aimed to investigate the gut-lung axis in cats by retrospectively analysing cases of feline respiratory disease with the classification of different patterns, assessing the efficacy of probiotic and nutraceutical interventions, and evaluating their impact on clinical outcomes. Case records of 117 cats diagnosed with respiratory distress from 2020 to 2025 were reviewed retrospectively. Respiratory patterns were classified into five groups: inspiratory, restrictive, obstructive, paradoxical, and panting. Diagnostic evaluation included fractional exhaled nitric oxide (FeNO) measurement, thoracic ultrasonography, radiography, and bioresonance analysis. Treatment regimens were individualised based on respiratory pattern classification, incorporating targeted probiotics and nutraceuticals. Statistical analyses, including logistic regression and non-parametric tests, were conducted to assess treatment efficacy. Treatment success varied across respiratory patterns, with the highest response observed in the paradoxical (80%) and obstructive (76.47%) groups, whereas restrictive respiratory patterns exhibited the lowest response rate (62.79%). The presence of multiple B-lines on lung ultrasound, indicative of pulmonary pathology, was significantly associated with restrictive and obstructive breathing patterns (P=0.001). Post-treatment FeNO reduction correlated with clinical improvement, supporting the role of gut microbiota modulation in respiratory disease management. This study provides novel evidence supporting the gut-lung axis in feline respiratory diseases. Tailored probiotic and nutraceutical interventions demonstrated potential therapeutic benefits, particularly in obstructive and paradoxical respiratory distress cases. Future studies should explore microbiome profiling and mechanistic pathways to further elucidate the interplay between gut and lung health in veterinary medicine
Intelligent Deep Learning Based Fault Classification Using Dual-Stream Transformer-CNN with Self-Supervised Feature Refinement for Industrial Applications
No full textFault classification plays a crucial role in industrial engineering, particularly in manufacturing and power generation, where accurate fault detection is essential to prevent system failures, reduce maintenance costs, and enhance operational safety. With the advancement of Industry 4.0 and 5.0, intelligent fault classification techniques leveraging real-time data processing have become increasingly important. This study proposes a deep learning-based fault classification model integrating Dual-Stream Transformer-CNN with Self-Supervised Feature Refinement (DSTC-SSFR) to improve classification accuracy and robustness. The core architecture consists of two parallel processing streams designed to effectively extract both spatial and temporal features from multivariate industrial sensor signals. The spatial stream uses multi-scale 1D Convolutional Neural Networks (1D CNNs) with varying kernel sizes to capture localized fault-related features at different frequency scales. The Bobcat is used for hyperparameter tuning, further enhancing model performance. The proposed approach achieves an overall accuracy of 95.07%, with a precision of 95.10%, recall of 95.07%, and F1-score of 95.07%. Additionally, the model attains a logarithmic loss of 0.1070, a Matthews correlation coefficient (MCC) of 0.9343, and an area under the ROC curve (AUC) of 99.54%. These results demonstrate the model's effectiveness in fault classification, offering a robust and efficient solution for industrial applications in smart manufacturing environments
Hybrid Swarm Intelligence for FPGA-Based Noise Mitigation in Gradient-Sensitive MRI Systems
No full textThis research presents an advanced Magnetic Resonance Imaging (MRI) signal processing framework to address time-varying noise and environmental disturbances. A novel adaptive filtering mechanism is proposed to enhance noise mitigation while dynamically adjusting coefficients based on gradient field variations and magnetic coil responses. The core contribution lies in the development of a Modified Opposition-Based Artificial Ant Colony Optimization (MOACO) algorithm, marking the first application of an optimization algorithm tailored for MRI systems. The proposed method integrates swarm intelligence by combining opposition-based ant bee colony optimization, leveraging the cooperative behaviors of ants and bees to optimize the adaptive filter's weight estimation. The optimization process dynamically adjusts the step size, ensuring faster convergence and improved noise suppression. A Parallel Architecture Opposition-Based ABC algorithm is introduced for efficient hardware implementation. The effectiveness of the proposed solution is evaluated against conventional filtering methods, including Two-Dimensional LMS (TDLMS), Recursive Least Squares (RLS), and Adaptive Filtering Least Mean Error (AFLME). The algorithms are implemented on a Field-Programmable Gate Array (FPGA) platform, specifically the EP-FPGA-256C6 Cyclone, utilizing 65 nm and 90 nm CMOS technology nodes to ensure optimal power efficiency, reduced Look-Up Table (LUT) utilization, and minimal delay. Experimental results demonstrate that the MOACO-enhanced adaptive filtering framework significantly improves power efficiency, circuit performance, and noise reduction, leading to enhanced MRI image reconstruction and minimized motion artifacts. This study establishes a novel direction for integrating AI-driven optimization techniques with hardware-based MRI signal processing, paving the way for more efficient medical imaging systems
Mapping Community Facility Diversity in Chengdu: A POI and Clustering-Based Approach
No full textUrban community service facilities play a critical role in shaping residents' quality of life and influencing urban livability and development patterns. This study examines the diversity and spatial differentiation of community service facilities in five districts of Chengdu, China, using Points of Interest (POI) data. The Shannon-Wiener diversity index is employed to quantify facility distribution, while spatially constrained multivariate clustering identifies distinct spatial patterns and facility concentration areas. Findings reveal that community facility diversity is highest in the central urban areas, forming a "fan-shaped" pattern of service clusters extending toward the periphery. The clustering results indicate unequal service distribution, with some peripheral areas lacking key community facilities. These insights offer valuable implications for urban planners and policymakers, emphasizing the need for balanced facility allocation, improved accessibility, and community-driven infrastructure planning. Future research should explore dynamic changes in facility distribution over time and the integration of multi-source urban data to refine spatial planning strategies
