The present research aims to establish an RP-HPLC method for the simultaneous estimation of Sulopenem etzadroxil and Probenecid in a synthetic mixture and a combined dosage form by Design of Experiment and green chemistry approach. The Box-Behnken design with quadratic model and response surface methodology was adopted to ensure the effect of independent variables (Temperature, flow rate, and percentage of aqueous mobile phase) on the responses or dependent variables (RT of both drugs, resolution, and USP plate count of both drug peaks) to optimize the method confirmation of design space and robustness. The effective separation of Sulopenem etzadroxil and Probenecid was achieved with Interstil column C18 (250 × 2.1 mm,1.8 μm), 0.1
Quantum cellular automata (QCAs) are a promising alternative to traditional CMOS technology due to their lower power consumption and ability to function at the nanoscale. However, challenges such as fault tolerance and energy efficiency remain, especially for arithmetic circuits like multipliers. The Vedic multiplier, known for its reduced computational complexity, presents a valuable opportunity to address these issues. By implementing fault-tolerant mechanisms within the QCA architecture, we aim to improve the reliability and performance of n x n multipliers in critical applications, such as cryptography, signal processing, and neural network accelerators. The proposed Vedic multiplier is designed using a hybrid of Urdhva Tiryakbhyam Sutra (vertical and crosswise technique) and error-correcting QCA gates to ensure fault tolerance. The design is implemented in a hierarchical manner, utilizing optimized QCA logic gates to form the partial product generation and summation stages. Error detection and correction techniques, such as cellular redundancy and parity-based correction, are embedded within the architecture to ensure resilience against cell misalignment and tunneling errors. Power consumption is minimized by optimizing the layout to reduce wire crossings and cell interactions. The energy efficiency and fault tolerance of the design are evaluated using QCADesigner. Simulation results demonstrate that the proposed Vedic multiplier achieves a 30
Zebrafish (Danio rerio) have proven to be a necessary model organism in the field of stress research. Their biology has been well-characterized, their genetics is amenable, and their transparent embryos allow for direct visualization of physiological and developmental events. High genetic conservation with humans and rapid development combined with lower cost make zebrafish one of the most popular models for stress studies. Stress can be induced in zebrafish through environmental factors, social stressors and pharmacological agents. This diversity makes zebrafish a versatile tool for investigating the behavioral and physiological outcomes of both acute and chronic stress. This makes zebrafish models particularly useful in studying the impact of stress on both behavioral aspects and physiological aspects such as neuroendocrine regulation by the hypothalamic-pituitary-interrenal axis, which has similarities to the mammalian HPA axis. These studies offer the basic insight of how stress works in neurodevelopment, neuroplasticity, and eventually in the pathogenesis of psychiatric disorders, including anxiety, depression, and post-traumatic stress disorder. Zebrafish are being widely applied in drug discovery and preclinical testing for identifying potential therapeutic agents against the stress-related pathways. This review would focus on the importance of the zebrafish model for stress research. It emphasizes this role in helping advance our knowledge of stress physiology and behavior while discussing some challenges, such as variability in responses and ethical issues, underpinning the call for standardized protocols for reproducible and ethical stress research.
Mn-doped ZnGeP2 ternary chalcopyrite semiconductor has garnered significant interest for their unique optical and magnetic properties, making them ideal candidates for spintronics and advanced technological applications. This study investigates the structural, electronic, and magnetic properties of Mn-doped ZnGeP2 using the full potential linearized augmented plane wave (FP-LAPW) method within the density functional theory (DFT) framework, employing both the generalized gradient approximation (GGA) and the modified Becke-Johnson (TB-mBJ) potential. Manganese (Mn) doped ZnMnxGe(1 − x)P2 compound properties are analyzed across a doping concentration range of 0 ≤ x ≤ 0.5, where Mn act as a selective dopant to replace germanium (Ge) and enhance the magnetic characteristics of the compound. This reveals that increasing manganese concentration in ZnGeP2 compound leads to substantial alterations in the electronic band structure and an average bandgap increment of 0.02 eV per Mn concentration doping also observed. The partial density of states (PDOS) analysis demonstrates the significant contributions from Mn d-states, highlighting effective p–d hybridization with host atoms. Furthermore, magnetic moment calculations indicate the enhancement in magnetic interactions as the Mn doping concentration increases, suggesting its potential applications for ferromagnetism.
The rising incidence of cardiovascular diseases necessitates the development of real-time, intelligent, and privacy-preserving health monitoring systems. Traditional wearable solutions often rely on cloud processing, which compromises latency, energy efficiency, and user privacy. To address these challenges, this study proposes an advanced IoT-enabled embedded wearable system empowered by Edge AI for continuous cardiovascular health monitoring. The system integrates a novel hybrid deep learning model combining 1D Depthwise Separable Convolution (1D-DSC) and Temporal Attention-Gated Recurrent Unit (TA-GRU) to capture both spatial and temporal dependencies in ECG, PPG, and heart rate signals. This study is implemented on an ARM Cortex-M-based microcontroller using TensorFlow Lite. The model achieves exceptional performance with 99.8 % accuracy, 99.62 % precision, 99.54 % recall, and an AUROC of 0.997. The model size is compressed from 22 KB to 11 KB using post-training quantization (PTQ), reducing inference time from 15 ms to just 7 ms without significant loss in accuracy (a drop of only 0.25 %). A real-time alert system visualizes abnormal conditions through LED color codes and haptic feedback, ensuring immediate user awareness. Compared to state-of-the-art models like CNN, Bi-LSTM, and 1D CNN-LSTM, the proposed method exhibits superior accuracy and 70 % lower latency. This work demonstrates a compelling solution for deploying deep learning models on resource-constrained wearables, enabling energy-efficient, secure, and continuous cardiovascular monitoring in real-world scenarios.
