Carbon dots have stimulated the curiosity of biomedical researchers due to their unique properties, such as less toxicity and high biocompatibility. The synthesis of carbon dots for biomedical application is a core area in research. In the current research, an eco-friendly hydrothermal technique was employed to synthesize high fluorescent, plant-derived carbon dots from Prosopis juliflora leaves extract (PJ-CDs). The synthesized PJ-CDs were investigated by physicochemical evaluation instruments such as fluorescence spectroscopy, SEM, HR-TEM, EDX, XRD, FTIR, and UV-Vis. The UV-Vis absorption peaks obtained at 270 nm due to carbonyl functional groups shifts of n→π*. In addition, a quantum yield of 7.88 % is achieved. The synthesized PJ-CDs showing the presence of carious functional groups O–H, C–H, C=O, O–H, C–N and the obtained particles in spherical shape with an average size of 8 nm. The fluorescence PJ-CDs showed stability against various environmental factors such as a broad range of ionic strength and pH gradient. The antimicrobial activity of PJ-CDs was tested against a Staphylococcus aureus, and a Escherichia coli. The results suggest that the PJ-CDs could substantially inhibit the growth of Staphylococcus aureus. The findings also indicate that PJ-CDs are effective materials for bio-imaging in Caenorhabditis elegans and they can be also used for pharmaceutical applications.

This study investigates the use of a closed-loop speed control approach based on fuzzy logic for brushless DC (BLDC) motors in Electric Vehicle (EV) applications. The primary objective is to overcome the drawbacks of traditional control techniques by improving dynamic performance, response time, and stability under changing load conditions and parameter uncertainty. Nonlinearities, load fluctuations, and transient overshoots are common problems for traditional PID controllers, which results in suboptimal performance of EV propulsion systems. A state-space modelling technique for the BLDC motor is used in this study to address these issues, incorporating a Fuzzy Logic Controller (FLC) for accurate speed control. The superiority of FLC over PID controllers is demonstrated by a comparison study that was verified by simulation and hardware implementation. The results show that FLC produces smooth speed transitions, no overshoot, and zero steady-state error with a settling time of only 0.05s, as in contrast to 0.1s for the PID controller. Under load fluctuations, the FLC’s torque response stays constant at about 1.05 Nm, however the PID controller shows noticeable oscillations and a larger torque ripple. Additionally, FLC guarantees smooth speed regulation throughout a broad range (1500–3000 rpm), greatly increasing motor lifespan and energy efficiency. When compared to the PID controller, the experimental validation shows that FLC performs robustly in real-time EV settings, exhibiting smoother speed transitions, faster disturbance rejection, and improved adaptability. According to these results, FLC is a better option for BLDC motor speed control in EV applications, guaranteeing effective propulsion, less mechanical stress, and more driving stability. As a potential control strategy for upcoming EV technologies, the proposed strategy not only improves energy utilisation but also strengthens system reliability.

Accurate identification and segmentation of brain tumors in Magnetic Resonance Imaging (MRI) images are critical for timely diagnosis and treatment. MRI is frequently used to diagnose these disorders; however medical professionals find it challenging to manually evaluate MRI pictures because of time restrictions and unpredictability. Computerized methods such as R-CNN, attention models and earlier YOLO variants face limitations due to high computational demands and suboptimal segmentation performance. To overcome these limitations, this study proposes a successive framework that evaluates YOLOv9, YOLOv10, and YOLOv11 for tumor detection and segmentation using the Figshare Brain Tumor dataset (2100 images) and BraTS2020 dataset (3170 MRI slices). Preprocessing involves log transformation for intensity normalization, histogram equalization for contrast enhancement, and edge-based ROI extraction. The models were trained on 80% of the combined dataset and evaluated on the remaining 20%. YOLOv11 demonstrated superior performance, achieving 96.22% classification accuracy on BraTS2020 and 96.41% on Figshare, with an F1-score of 0.990, recall of 0.984, mAP@0.5 of 0.993, and mAP@ [0.5:0.95] of 0.801 during testing. With a fast inference time of 5.3 ms and a balanced precision–recall profile, YOLOv11 proves to be a robust, real-time solution for brain tumor detection in clinical applications.

The incorporation of various reinforcements in aluminium composites markedly improves their thermal, mechanical, and wear characteristics when compared to composites with a single reinforcement. Nevertheless, heightened hardness presents challenges in machining, rendering traditional methods less efficient for obtaining high-quality cuts. This study explores the application of Abrasive Water Jet Machining (AWJM) on aluminium alloy LM26 hybrid composites that are reinforced with graphite and fly ash, which were manufactured through stir casting. AWJM is assessed as a viable machining technique to tackle the challenges presented by these advanced composites. The analysis focuses on key process parameters, including transverse speed, stand-off distance, and abrasive flow rate, to evaluate their impact on essential output responses such as surface roughness, material removal rate (MRR), and overcut error. A systematic experimental design utilizing an L27 orthogonal array is implemented to thoroughly examine the influences of various parameters. Response Surface Methodology (RSM) serves as a vital tool for statistical analysis and optimization, facilitating an in-depth exploration of parameter interactions and their influence on machining performance. The results offer important perspectives on enhancing AWJM parameters for hybrid aluminium composites, leading to better machining precision and efficiency. The ideal machining parameters were determined to be an abrasive flow rate of 440 g/min, a traverse speed of 300 mm/min, and a stand-off distance of 1.50 mm, leading to a peak MRR of 1.201 g/sec and a minimum surface roughness (Ra) of 2.018 µm. The investigation highlights AWJM’s proficiency in processing high-hardness materials while preserving favorable surface qualities. The findings enhance manufacturing methods for reinforced aluminium composites, facilitating their wider use in aerospace, automotive, and other high-performance sectors.

The trend of miniaturizing engineering components represents a significant advancement in technology, enabling the production of small, precise parts that are essential in contemporary life. Titanium alloy has become a well-known material in contemporary applications due to its low density and high strength, along with its excellent heat resistance. As a result, it is widely utilized in aerospace and gas turbine industries. This article presents a novel study aimed at investigating the machining effects of titanium alloy through the optimization of micro-turning process parameters, focusing on tool wear and surface roughness. The study indicates that the primary factors influencing reactions are cutting speed and feed rate. The analysis of numerical and graphical methods aims to determine the optimal process parameters. The gathered results indicate that optimal tool wear and surface roughness are achieved simultaneously at 0.149 mm and 0.691 μm, respectively, with cutting parameters set at 3035 rpm for cutting speed, 7.5 μm/rev for feed, and a depth of cut of 15 μm, resulting in 89% desirability. This approach enhances output and surface quality while minimizing machining costs. A confirmation experiment is conducted and analyzed for the purpose of comparative study, focusing on residual error. The analysis through Scanning Electron Microscopy indicates that the medium order cutting conditions yield superior performance while addressing conflicting objectives, resulting in a smoother surface topography.