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Design and Fabrication of Walking E-Bike for Pedestrians
No full textIn the normal electric bike system, every time we need to replace the battery, in the same way treadmill waking system is required continues electric power supply. These two methods having their own advantages and disadvantages. These two methods advantages are combined and developed a new innovative Walking E-Bike project. The waking E-Bike can work has E-bike and also treadmill, when the vehicle is moving plain or downhill side the waking E-Bike works as treadmill and increase the physical activity also it works as in regenerative mode it will charge the battery. If the vehicle is moving uphill side, it works as normal E-Bike and the stored energy is utilized to run the vehicle. In this project PWM Control, Variable Frequency Drives (VFD) are used effective synchronization and speed control of motor. Smart Battery Management system is used for enhance battery life and smooth ride of Vehicle
Multi-Disease Diagnostic Framework Using VGG19-Based Deep Visual Feature Extraction from Medical Images
No full textEffective clinical decision-making depends on the early and accurate diagnosis of thoracic illnesses. In order to analyze chest X-ray and CT pictures, this work proposes a deep learning-based multi- disease diagnosis system that uses transfer learning with the VGG19 convolutional neural network. The suggested model is trained on publically accessible datasets that have undergone extensive preprocessing, such as data augmentation, scaling, and standardization to improve robustness. According to experimental findings, training accuracy steadily increases to about 70%, while validation accuracy stays steady at about 50%, suggesting that dataset complexity and inter-class similarity limit generalization. While the validation loss exhibits minor variations, indicating the existence of moderate overfitting, the training loss generally displays a decreasing trend. In spite of this, the framework successfully acquires discriminative characteristics pertinent to the categorization of lung diseases. The suggested method supports dependable and data-driven healthcare decision-making by showcasing the potential of transfer learning for medical imaging applications and offering a scalable basis for AI-assisted diagnostic systems
Wood Protection and Service Life Extension
No full textWood is a renewable, ecologically friendly material used on a large scale in building construction and furniture manufacturing, but its service life is short because of corners that can degrade it under ultraviolet (UV) radiation, humidity changes, and biological attack. This paper conducts experiments to compare the effectiveness of three protection tactics, namely, thermal modification of wood and acetylation and coating wood with nanotechnology, in enhancing wood durability. Accelerated weathering was observed in samples of beech (Fagus sylvatica) and pine (Pinus sylvestris), fungus resistance experiment and moisture absorption experiment. Thermal modification greatly improved the dimensional stability, and water uptake was minimised by a factor of 35 relative to untreated controls. Acetylated samples showed strong potential against fungal decay, with weight loss reduced by 60%. The use of nanoparticle-reinforced coatings containing TiO2and ZnO were shown to have good UV protection, maintain surface colour, and mechanical integrity in 1000 hours of exposure. The outcomes demonstrate that integrative modification methods can be successfully applied to enhance the service lifetime of wood and to provide sustainable, environmentally friendly technologies that replace traditional chemical preservatives and support the continued use of wood in modern engineering and eco-friendly building sectors
Benign-Malignant Breast Histology Classification using MobileNet Variants: An Analysis
No full textAutomatic analysis of the medical data is one of the common practices followed to detect diseases with better accuracy. Deep Learning (DL) tool-based medical image examination is one of the approved clinical practices, and the outcome of this process supports the treatment planning and execution. This work proposes a DL tool based on the ConvNeXt (CN) scheme to classify the chosen Breast Histology Images (BHI) into benign and malignant classes. The various phases of the proposed DL-tool include: image collection from the database and resizing it to 224x224x3 pixels, feature extraction using the chosen CN-model, feature reduction using 50% dropout, and serial features fusion to get fused- features-vector (FFV), and binary classification with 5-fold cross-validation. The merit of the developed scheme is confirmed using the classification executed with the chosen CN-feature and the FFV. The outcome of this study confirms that the FFV-based classification provides a detection result upto 99% when the SoftMax-based classification is executed. This confirms that the proposed DL-tool provides a better result on the chosen image database
Evaluation of the Floater N219 Structure with CEVL Material in Response to Random Wave Excitation
No full textAmphibious aircraft are seaplanes fitted with dual floats attached to the fuselage, allowing for landing and take-offs on aquatic surfaces. This research presents a method for evaluating the structural integrity of amphibious aircraft subjected to stochastic wave load stimulation through a probabilistic framework. The wave loads on the aircraft are assessed using the panel approach in a time domain simulation with ANSYS AQWA. Aircraft operations are simulated under three wave height: 0.5 m, 1.0 m, and 1.5 m, with three variations in relative wave direction: 90̊, 180̊, and 0̊, within a wave frequency range of up to 2 rad/s. The simulation of the floater model attempts to predict the vertical bending moment experienced by the structure; this value is subsequently utilized as input in static load modelling through the finite element method to determine the maximum stress value. A probabilistic approach was employed to account for the stochastic characteristics of wave loads, with all potential loads represented as a probability density function (PDF). Moreover, the structural reliability evaluation, which ascertains the likelihood of structural failure, was estimated by combining the load PDF with the strength PDF, derived from CEVL material testing. The evaluation results indicate that the probability of structural failure is -0.34, -0.23 and -0.029 for wave heights of 0.5 m, 1.0 m, and 1,5 m, respectively. The reliability of the floater structure might be enhanced by diminishing the stress induced by wave loads by reinforcement of floater’s longitudinal structure and/or the fortification of the CEVL material
MMSI Based Anomaly Classification in AIS: A Rules-First Baseline from International Numbering Standards
No full textResearch on Automatic Identification System (AIS) anomalies has largely focused on vessel trajectories and kinematics, while identifier validity is often assumed. This study fills that gap by using the nine-digit Maritime Mobile Service Identity (MMSI) to build a rules-first baseline for anomaly classification. Validation rules are derived from ITU-R M.585-9 and operational guidance (USCG NA VCEN, AMSA), covering format constraints, category and prefix patterns, and MID ranges. The same rules are applied to two public data sources (Global Fishing Watch and NOAA/Access AIS). The pipeline assigns per-record labels for validity, category, and diagnostic notes, and defines a taxonomy of identity anomalies: invalid format, misclassification or misuse, MID and policy inconsistencies, and spatiotemporal “cloned MMSI” detected via overlap tests when positions are available. Results indicate that identity screening reduces noise, highlights priority cases, and produces cleaner inputs for downstream behavioral models without relying on speed or trajectories. Contributions include a reproducible MMSI rule set, an anomaly taxonomy, and a per-source evaluation protocol to avoid misleading generalizations. The approach is transparent, computationally efficient, and easy to integrate as a first-stage filter in maritime analytics pipelines
A Stochastic Optimization Approach for an Integrated Energy System with Electricity, Heat, Gas, and Hydrogen Considering Carbon Trading
No full textTargeting low-carbon energy transition, this study proposes a stochastic optimization model for an integrated electricity-heat-gas-hydrogen system with high renewable penetration. The configuration couples four storage assets—battery, heat storage tank, gas storage tank and hydrogen storage tank—with three hydrogen devices: electrolyzers, fuel cells and methanation reactors. Operating and carbon-trading costs are both embedded. Numerical results indicate that enabling carbon trading mildly raises computational load yet increases operating cost while curbing renewable curtailment. Integrating hydrogen devices significantly heightens model complexity, further lowers curtailment and reduces overall cost, validating the practical value of the approach
Numerical optimization of PEM fuel cell electrocatalytic layers via an agglomerate level model
No full textThe objective of developing the physicochemical model is to formulate recommendations for the fabrication of the membrane–electrode assembly and its components with parameters that meet the requirements imposed on fuel cells. The agglomerate model of the catalytic layers assumes that catalyst particles (platinum on carbon black) are grouped into small spherical agglomerates, each bounded and filled with a polymer electrolyte. Numerical analysis of the cathode catalytic layer shows that the optimal polymer electrolyte content depends on the catalytic layer porosity and air humidity. For porosities in the range of 30–60%, the optimal polymer electrolyte mass fraction lies between 20–30% and decreases with increasing porosity. Increasing air humidity shifts the optimal polymer electrolyte content from approximately 30–40 wt% to about 60 wt%. These results characterize the influence of key parameters on the composition of cathode catalytic layers in proton exchange membrane (PEM) fuel cells. The model-based optimization of cathode catalytic layer structure enhances platinum utilization and minimizes transport losses, enabling reduced noble-metal loading and higher electrochemical efficiency in support of the United Nations (UN) Sustainable Development Goals (SDG 7: Affordable and Clean Energy; SDG 13: Climate Action)
The Positive Effect and Negative Effect of Artificial Intelligence in English Language Teaching: A Review of Current Trends
No full textRecent advances in technology have significantly transformed the landscape of English teaching and learning. Innovative tools such as chatbots, automated essay grading systems, and specialized pronunciation apps are making lessons more interactive and feedback more immediate and personalized. These digital resources allow schools to reach larger groups of students and to track learners’ progress with greater accuracy and efficiency. Despite these benefits, there are still notable challenges. Teachers must constantly adapt to new tools and methods, which can be overwhelming without proper training and ongoing professional development. Furthermore, it is essential for schools to ensure that the use of technology aligns with their educational goals, rather than simply following the latest trends. Technology also has limitations, especially when it comes to assessing natural spoken language and authentic conversational skills. There are concerns about students and teachers becoming overly reliant on technology, which could potentially marginalize the role of skilled educators. Ultimately, the most effective English learning experiences occur when technology is thoughtfully blended with teachers’ expertise, creating a balanced and meaningful approach that supports diverse student needs
Adolescent Depression and Suicide Consequences: Risk Factors, Protective Mechanisms, and Intervention Strategies
No full textAs a disease, adolescent depression has negative consequences and is a major problem in public health. Adolescence is an urgent phase, an age when timely and evidence-based measures may change future courses of action. The underlying symptoms of depression may persist into adulthood and may take many forms, such as anxiety, suicidal outcome, mood disorders, poor health, poor performance in education, joblessness, and substance and drug abuse. The recent evidence of scholarly research is reviewed in this paper that demonstrate the major aspects of adolescent depression and attempt to commit suicide, and its effects on society, family, and individually on society and gender equality. Other theories of heterogeneity in depression outcomes also exist, and some of these were identified in this paper. The life course framework and the stress diathesis model, and the primary evidence-based interventions of aims, such as school mental health programs, student counselling and computer interventions, are also discussed. The article explores complex consequences of adolescent depression, mental illness, suicidality, and psychosocial functioning. It also examines the potential and constraints of prevention and treatment actions. The results suggest that coping with depression and suicidal symptoms should be addressed through early detection and prevention measures in adolescents