Objective: To obtain better human segmentation by enhancing model inference speed, this paper applies the Human Spatial Prior Module (HSPM) and the Dynamic Token Pruning Module (DTPM) to deal with real-time three-dimensional environments.

Materials and Methods: In this paper, we have applied the Human Spatial Prior Module (HSPM) and the Dynamic Token Pruning Module (DTPM), designed to seize human-unique features in malformed images by dynamically extracting complex information. Meanwhile, the DTPM enhances the inference pace by pruning the point tokens at each Vision Transformer layer (VT). Unlike conventional cropping techniques, the pruned tokens are retained through function maps and selectively reactivated in subsequent layers to enhance model performance.

Results and Discussion: To show the effectiveness of our Vision Transformer for Malformed Images (VT-MI), we expand the dataset with an input image size of 512 x 512 and conduct behavior experiments on this prolonged dataset as well as on the COCO, MPII Human Pose, and a few real-time captured image datasets. Our technique achieves a mIoU growth of 86.5 and an FPS improvement of 7.9 on the COCO dataset with 93.21% accuracy, together with mIoU (person) of 86.11 with dynamic token pruning and reactivation on the entire dataset, while additionally decreasing the model size by using approximately 120 GFLOPs.

Conclusion: This work contributes deeper insights into vision transformer networks and provides a clear direction towards both image classification and dense prediction tasks. Moreover, extensive experiments help in showcasing comparative analysis that benchmarks the frameworks contributing to image analysis advancements.

Methods: This paper introduces the Automatic Cloth Drying Tube Machine (ACDTM), a smart drying system designed to optimize fabric drying through enhanced airflow and temperature regulation tailored to urban drying constraints. The ACDTM integrates an AC 220V turbofan, LED strip, XH-W3001 thermostat, and soil moisture sensor to dynamically monitor and manage drying conditions. It adjusts airflow and heating based on fabric type and moisture levels, thereby reducing energy consumption. Performance evaluation involved comparing the drying times of various fabrics with those of conventional air-drying methods.

Results: Experimental findings demonstrate significant reductions in drying times using the ACDTM, achieving a 50.89% improvement in efficiency compared to traditional methods. Specifically, drying times were reduced by 108 minutes for jeans and 204 minutes for cotton towels. The system ensures precise moisture control, minimizes energy use, and preserves fabric integrity.

Discussion: The ACDTM represents a compact, automated, and energy-efficient solution to urban drying challenges. By optimizing drying parameters and reducing reliance on environmental conditions, it offers a hygienic and environmentally friendly alternative to conventional drying techniques. Its suitability for smart home integration enhances user convenience and supports sustainable living practices.

Conclusion: The ACDTM presents a novel approach to addressing urban drying challenges, offering substantial efficiency gains and environmental benefits. Its implementation underscores a shift towards smarter, more sustainable household solutions, promoting hygiene and energy conservation in urban settings.

Methods: A systematic literature review (SLR) was conducted to classify UAV swarm pathplanning algorithms into four categories: sampling-based, graph-based, biologically inspired, and mathematical model-based approaches. The review adhered to established SLR guidelines, employing systematic database searches and predefined inclusion and exclusion criteria. Each algorithm was evaluated in terms of computational efficiency, adaptability, and application suitability.

Results: Biologically inspired algorithms demonstrated high adaptability and flexibility, whereas graph-based approaches were effective in structured environments. Sampling-based methods proved scalable in high-dimensional spaces, and mathematical models emphasized optimization. Persistent challenges include real-time adaptability, energy efficiency, and operations in interactive environments.

Discussion: The comparative analysis highlights the trade-offs inherent in each approach and underscores the unresolved challenges that limit practical implementation. Addressing these gaps is essential to advance the reliability and applicability of UAV swarms in real-world scenarios.

Conclusion: This review systematically evaluates UAV swarm path-planning algorithms, identifying their respective advantages, limitations, and application contexts. The findings provide a reference framework to inform future research directions aimed at enhancing UAV swarm autonomy and operational efficiency.

Methods: To assess various mitigation techniques, the study uses ENVI-met software for scenario analysis, model calibration, and data gathering. The study looks at how vegetation cover, traffic emissions, and urban morphology affect air quality, with an emphasis on using green remedies to lower pollution.

Results: The simulation findings demonstrate a significant decrease in pollutant levels following the installation of green infrastructure, with PM2.5 concentrations falling from 29.00 μg/m3 to 10 μg/m³ and PM10 concentrations falling from 74.00 μg/m³ to 35 μg/m³.

Discussion: According to the results, green initiatives like aeroponics and algae systems considerably lower air pollution levels. The report highlights how green infrastructure can help reduce pollution and enhance urban air quality in crowded places like Mint Street.

Conclusion: Green infrastructure integration is anticipated to provide a sustainable solution to urban air pollution by improving public health, reducing respiratory issues, and improving general comfort by lowering pollution levels.

Methods: A system integrating a fine-tuned LLaMA-3 model with a hybrid interface combining conversational and form-based inputs was developed. Implicit feedback (dwell time, clicks) was incorporated via a weighted scoring algorithm to refine recommendations in real time. A user study involving 50 participants was conducted to compare three interfaces: conversational, formbased, and hybrid. Standardized metrics were used, including usability (SUS), cognitive load (NASA-TLX), efficiency (the completion time and the number of iterations), and satisfaction.

Results: The hybrid interface achieved a significantly higher usability score than conversational or form-based interfaces (86.1 ± 5.9), lowest cognitive load (28.9 ± 4.7), and fastest itinerary completion time (11.8 ± 1.6 minutes). Recommendation accuracy reached 92%, supported by a 28% reduction in planning iterations due to the integration of implicit feedback. Satisfaction ratings were highest for the hybrid interface (4.6 ± 0.5 on a 5-point scale), with 92% of participants indicating they were satisfied or very satisfied.

Discussion: Findings confirm that hybrid interfaces strike a balance between conversational flexibility and structured clarity, substantially improving the user experience compared to traditional approaches. Implicit feedback mechanisms reduce user effort and enhance the relevance of recommendations, demonstrating the advantage of adaptive interaction. Limitations include occasional LLM misinterpretations and minor latency (mean 1.2 seconds per query). Broader benchmarking against traditional recommender baselines suggests clear efficiency gains, though further generalization across domains is warranted. Findings should be interpreted cautiously, as the study was limited to 10 destinations and had a modest participant pool (N = 50). Future work should investigate multimodal inputs (such as voice and image) and conduct longitudinal studies to assess sustained engagement.

Conclusion: LLM-guided hybrid interfaces provide a scalable and effective solution for personalized itinerary planning, significantly optimizing usability, accuracy, and efficiency. By integrating implicit behavioral signals with adaptive human–computer interaction, this work advances both intelligent recommendation systems and HCI research, with potential applicability to related domains beyond travel. This work builds upon prior patented approaches in AIdriven itinerary generation and conversational travel assistants, extending them with LLMguided adaptive interaction.

Methods: This study proposes an enhanced architecture that incorporates the Highly Efficient Channel Attention (HECA) mechanism into YOLOv11 to improve feature selection. HECA performs Global Average Pooling (GAP) and Max Average Pooling (MAP) on input feature maps. Using spatial transformations and pixel-domain enhancements, the research establishes and augments a four-class dataset containing monkeypox, measles, chickenpox, and normal skin images. Gradient-Weighted Class Activation Mapping (Grad-CAM) generates heatmaps for lesion localization visualization, while a PyQt5-based interactive diagnostic interface streamlines clinical workflows.

Results: Experimental results demonstrate the proposed model’s superior performance compared to existing approaches (EfficientNet-v2-l, GoogLeNet, ResNet50, VGG19, MobileNetv2, DenseNet201, and YOLOv11s) on the test set, with enhanced detection speed, accuracy (98.9%), precision (99%), recall (99.3%), and F1-score (99.1%). HECA-YOLOv11 outperforms SE-YOLOv11 and ECA-YOLOv11 by over 5.1% across all evaluation metrics. Heatmap analysis confirms the model’s focus on lesion regions, aligning with medical reasoning.

Discussion: The integrated attention mechanism and interpretability techniques effectively address key limitations in automated skin lesion detection. The high performance and low latency signify a major advancement towards reliable clinical decision support, with important implications for early outbreak containment.

Conclusion: The HECA-YOLOv11 framework provides a highly accurate and efficient tool for early monkeypox screening. Its standardized interface holds significant potential to accelerate public health responses and improve global control of emerging infectious diseases.