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This paper aims to address the pressing issue of melanoma classification by leveraging advanced neural network models, specifically basic Convolutional Neural Networks (CNN), ResNet-18, and EfficientNet-B0. Our objectives encompass presenting and evaluating these models based on established practices in medical image diagnosis. Additionally, we aim to demonstrate their effectiveness in contributing to the critical task of saving lives through early and accurate melanoma diagnosis.Our methodology involves a multi-stage process, which includes image normalization and augmentation, followed by segmentation, feature extraction, and classification. Notably, the neural network models underwent rigorous evaluation, with EfficientNet-B0 exhibiting exceptional performance as the winning model. EfficientNet-B0 achieved a remarkable accuracy of 97%, surpassing ResNet-18 (87%) and basic CNN (80%) in classifying malignant and benign cases. In addition to accuracy, a comprehensive set of evaluation metrics was employed for EfficientNet-B0: sensitivity of 99%, specificity of 93%, F1-score of 97%, precision of 95%, and an error rate of 3%. It also demonstrated a Mathew’s correlation coefficient of 94% and a geometric mean of 1.01. Across these metrics, EfficientNet-B0 consistently outperformed ResNet-18 and basic CNN. The findings from this research suggest that neural network models, particularly EfficientNet-B0, hold significant promise for precise and efficient melanoma skin cancer detection.

Proteases are important industrial biocatalysts that constitute the largest group of enzymes acting as proteinases, peptidases, and amidases with a broad range of industrial applications. In this review, particular attention has been given to comprehensively scrutinize the proteases. After the succinct introduction, classification of proteases as exopeptidases (amino and carboxy proteases) and endopeptidases (serine, aspartic, cysteine, and metalloproteases), sources of alkaline, acidic and neutral protease like animal, plant and microbial sources along with the multi-industrial applications have been dissertated. Now a day’s, mostly proteases, which are present in the market, are produced from microbial sources because of the fast production rate and the limited requirement of cultivation. In addition to this, a critique on the applications of proteases in food, detergent, leather, pharmaceutical, cosmetics, silk degumming, silver recovery, chemical industry, and wastewater treatment industries is also concisely addressed. Finally, protein engineering and immobilization strategies to improve the catalytic properties of protease are thoroughly vetted. The quest for novel sources of protease enzyme has been encouraged to fulfill their ever-increasing demands for industrial exploitation. Graphic Abstract

Cancer is a leading cause of morbidity and mortality worldwide. The development of cancer involves aberrations in multiple pathways, representing promising targets for anti-cancer drug discovery. Natural products are regarded as a rich source for developing anti-cancer therapies due to their unique structures and favorable pharmacology and toxicology profiles. Deoxyelephantopin and isodeoxyelephantopin, sesquiterpene lactone compounds, are major components of Elephantopus scaber and Elephantopus carolinianus, which have long been used as traditional medicines to treat multiple ailments, including liver diseases, diabetes, bronchitis, fever, diarrhea, dysentery, cancer, renal disorders, and inflammation-associated diseases. Recently, deoxyelephantopin and isodeoxyelephantopin have been extensively explored for their anti-cancer activities. This review summarizes and discusses the anti-cancer activities of deoxyelephantopin and isodeoxyelephantopin, with an emphasis on their modes of action and molecular targets. Both compounds disrupt several processes involved in cancer progression by targeting multiple signaling pathways deregulated in cancers, including cell cycle and proliferation, cell survival, autophagy, and invasion pathways. Future directions of research on these two compounds towards anti-cancer drug development are discussed.

In data-based modeling, correlations between explanatory variables often lead to the formation of distinct gene blocks. This study focuses on identifying influential gene blocks and key variables within these blocks, with a particular application in mind: genotype-phenotype mapping in Saccharomyces. To overcome the challenges of a limited sample size, we use partial least squares (PLS). These gene blocks, which consist of combinations of genes, play a critical role in explaining phenotypic variations. Using partial least squares with multiple blocks, we propose a novel approach, weighted block importance on projection in partial least squares (BwIP-mbPLS), to identify influential gene blocks. Variable importance on projection is used to select significant genes within these blocks. Our study models copper chloride at 0.375mM and melibiose at 2% efficiency and rate in Saccharomyces cerevisiae yeast. Analysis based on silhouette index and total distance within clusters using k-means shows the classification of 5629 genes into 18 gene blocks. Remarkably, BwIP-mbPLS identifies 4 gene blocks on average and significantly improves the prediction of efficiency-based phenotypes. In contrast, traditional block importance in partial least squares projection identifies 6 gene blocks on average and shows comparable or better performance than BIP-mbPLS for rate-based phenotypes. Remarkably, most gene blocks contain fewer than 10 influential genes. Both proposed variants consistently outperform conventional approaches such as partial least squares and multi-block partial least squares in predicting phenotypes. These results highlight the potential of our methods for advancing data-based modeling and genotype-phenotype mapping.

The role of nanofluids is of fundamental significance in the cooling process of small electronic devices including microchips and other associated gadgets in microfluidics. With such astounding applications of nanofluids in mind, it is intended to examine the flow of magnetohydrodynamic nanofluid comprising a novel combination of multi-walled carbon nanotubes and engine oil over a stretched rotating disk. The concentration equation is modified by considering the autocatalytic chemical reaction. The succor of the bvp4c numerical technique amalgamated with the response surface methodology is secured for the solution of a highly nonlinear system of equations. The sensitivity analysis is performed using a response surface methodology. The significant impacts of the prominent arising parameters versus involved fields are investigated through graphical illustrations. It is observed that the skin friction coefficient and local Nusselt number are positively sensitive to nanoparticle volume fraction while it is positively sensitive to the suction parameter. It is negatively sensitive to the Magnetic parameter. The skin friction coefficient is negatively sensitive to all input parameters.

In the modern era, with the emergence of the Internet of Things (IoT), big data applications, cloud computing, and the ever-increasing demand for high-speed internet with the aid of upgraded telecom network resources, users now require virtualization of the network for smart handling of modern-day challenges to obtain better services (in terms of security, reliability, scalability, etc.). These requirements can be fulfilled by using software-defined networking (SDN). This research article emphasizes one of the major aspects of the practical implementation of SDN to enhance the QoS of a virtual network through the load management of network servers. In an SDN-based network, several servers are available to fulfill users’ hypertext transfer protocol (HTTP) requests to ensure dynamic routing under the influence of the SDN controller. However, if the number of requests is directed to a specific server, the controller is bound to follow the user-programmed instructions, and the load on that server is increased, which results in (a) an increase in end-to-end user delay, (b) a decrease in the data transfer rate, and (c) a decrease in the available bandwidth of the targeted server. All of the above-mentioned factors will result in the degradation of network QoS. With the implementation of the proposed algorithm, dynamic active sensing server load management (DASLM), on the SDN controller, the load on the server is shared based on QoS control parameters (throughput, response time, round trip time, etc.). The overall delay is reduced, and the bandwidth utilization along with throughput is also increased.

This study explores a comprehensive assessment of deep learning models for classification of four Brassica species ( Brassica juncia , Brassica napus , Brassica oleracea , and Brassica rapa ) based on codon usage frequency patterns mined from their whole CDS genomes. We compared the performance of a novel Gradient Guided Adaptive Regularized (GGAR) Multilayer Perceptron (MLP) model against five panelized approaches of MLP, Adaptive, Elastic Net, Fixed L1, Fixed L2, base line MLP and one traditional 1D-CNN model, across multiple hyper parameter configurations (learning rates: 0.01, 0.001, 0.0001; batch sizes: 32, 64, 128, 256). The models were evaluated using 10-fold cross-validation, with performance metrics including accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). The results shows that GGAR consistently performed better than its existing models models in low learning rate of 0.0001 and batch sizes of 32, 64 and 128 settings, attaining near perfect classification accuracy, recall, mcc and F1 approximately equals to 1. Statistical validation via Kruskal–Wallis and ANOVA tests confirmed GGAR’s superiority ( p  < 0.001) over comparative models as well as over traditional CNN model in all evaluation scenarios. Notably, Fixed L1 and CNN excelled at higher learning rates of 0.01 and 0.001, while GGAR dominated in fine-tuned, low rate regimes, signifying its effectiveness in handling indirect genomic patterns. The analysis of training durations showed that Fixed L1 was computationally efficient, completing analysis in 5.90–91.52 min. In contrast, GGAR demanded more time from 6.38 to 124.78 min but achieved higher accuracies. While the MLP baseline performed competitively, its results were less consistent, and Elastic Net and Fixed L2 demonstrated clear speed versus precision tradeoffs. The CNN also gives exceptional performance with very low execution speed from 99.49 to 179.25 min. These results highlights the significance of adaptive regularization in genomic classification, with GGAR showing particularly effective for precise species classification. This study introduces a practical guidance for filtering deep learning models in bioinformatics, stressing how regularization approaches and hyper parameter tuning influence deep learning model performance. Graphical abstract

For modern power plants to be dependable, safe, sustainable, and provide the highest operational efficiency (i.e., enhance dynamic load distribution with a faster response time at reduced reactive losses), there must be an intelligent dynamic load management system based on modern computational techniques to prevent overloading of power devices (i.e., alternators, transformers, etc.) in grid systems. In this paper, a co-simulation framework (Panda-SDN Load Balancer) is designed to achieve maximum operational efficiency from the power grid with the prime objective of real-time intelligent load balancing of operational power devices (i.e., power transformers, etc.). This framework is based on the integration of two tools: (a) PandaPower (an open-source Python tool) used for real-time power data (voltage; current; real power, PReal; apparent power, PApparent; reactive power, PReactive; power factor, PF; etc.) load flow analysis; (b) Mininet used for the designing of a Software-Defined Network (SDN) with a POX controller for managing the load patterns on power transformers after load flow analysis obtained through PandaPower via the synchronization tool Message Queuing Telemetry Transport (MQTT) and Intelligent Electrical Devices (IEDs). In this research article, the simulation is performed in three scenarios: (a) normal flow, (b) loaded flow without the proposed framework, and (c) loaded flow with the proposed framework. As per simulation results, the proposed framework offered intelligent substation automation with (a) balanced utilization of a transformer, (b) enhanced system power factor in extreme load conditions, and (c) significant gain in system operational efficiency as compared to legacy load management methods.

Chemotherapy appeared to be a significant advancement in cancer research, with fewer side effects. Methotrexate (MTX) is a widely used anticancer drug with strong activity but serious side effects. Several MTX derivatives have been reported, with modifications at various sites to reduce side effects and increase efficacy. The current study uses FTIR spectroscopy to predict the survival fraction of human malignant glioma U87 (MG-U87) cell lines against MTX derivatives. Together with Parent MTX several aldehydes viz. Benzaldehyde, Chlorobenzaldehyde, 2-Chlorobenzaldehyde, 3-Nitrobenzaldehyde, 5-Chloro-2-hydroxybenz-aldehyde, 2-Hydroxy-5-Nitrobenzaldehyde, 2-Thiocarboxyaldehyde, Trans-2-pentenal, and Glutaraldehyde are treated with MTX to obtain MTX derivatives. The prediction of survival fraction of malignant glioma cells is carried out by Lasso, Elastic net and Soft PLS at different concentration levels of synthesized derivatives, including 400 μM, 200 μM, 100 μM, 50 μM, 25 μM and 12.5 μM. The cross-validated prediction error is minimised to optimise spectral wavelength selection and model parameters. It appears that the RMSE computed from test data is significantly varying with the change of models (p = 0.012), with the change of concentrations levels (p  ≤ 0.001 ) and with the change of combination of models and concentration level (p ≤ 0.001 ). StPLS outperforms in predicting survival fraction of glioma cells at the concentration level 50 μM, 100 μM and 400 μM respectively with relative RMSE = 0.1,0.14 and 0.55. Lasso outperforms at the concentration level 12.5 μM, and 200 μM respectively with relative RMSE = 0.4 and 0.14. Elastic net outperforms at the concentration level 25 μM with relative RMSE = 0.8. Consistently appeared influential wavelength identifies the influential functional compounds which best predicts the survival fraction. Hence FTIR appears potential candidate for estimating survival fraction of MTX derivatives.

In competitive industry, economical and environmentally friendly production techniques are essential. In this sense, cleaner and more sustainable machining techniques are the industry’s focus. In addition to green methods, effective parametric control is necessary for hard-to-cut materials, particularly titanium Ti-6Al-4V, which is extensively used in a diversity of industries, including aerospace, medical, and military applications. Therefore, the current study aims to improve the machining performance of Ti-6Al-4V alloy using sustainable lubrication conditions. The effect of Al2O3 nanoparticles based on the minimum quantity lubrication (N-MQL) condition on surface quality and productivity are compared with minimum quantity lubrication (MQL). The performance measures, including surface roughness (Ra), material removal rate (MRR), and temperature, are evaluated at three machining variables, i.e., cutting speed (Vc), feed rate (f), and depth of cut (ap). These performance measures are further assessed by tool wear and surface morphology analysis. ap, f, and Vc are the most influencing parameters for Ra, MRR, and temperature, regardless of lubrication mode. The optimized values of RA of 0.728443 µm, MRR of 2443.77 m3/min, and temperature of 337 °C are achieved at N-MQL. For the N-MQL state, the optimized values of Ra of 0.55 µm, MRR of 2579.5 m3/min, and temperature of 323.554 °C are attained through a multi-response optimization desirability approach. Surface morphology analysis reveals a smooth machined surface with no obvious surface flaws, such as feed marks and adhesion, under N-MQL conditions, which significantly enhances the surface finish of the parts. The machining performance under the N-MQL condition has been enhanced considerably in terms of an improvements in surface finish of 32.96% and MRR of 11.56%, along with a decrease in temperature (17.22%) and higher tool life (326 s) than MQL. Furthermore, Al2O3 is advised over MQL because it uses less energy and has reduced tool wear and improved surface quality, and it is a cost-effective and sustainable fluid.