Alsalam University College

Dengue virus (DENV) does not have any effective antiviral therapy. The Cinnamomum verum has cinnamic acid and oleic acid that could inhibit important viral proteins. To compare their inhibitory capacity with the key DENV proteins through molecular docking, molecular dynamics and in silico ADMET. Phytochemical profiling of the ethanolic extract of the bark was done by GCMS. AutoDock Vina (version 1.2.0) was used to dock cinnamic acid and oleic acid to key proteins of DENV (NS5, NS3, and envelope) in the presence of ribavirin as the reference. The best complexes were then subjected to 50 ns of molecular dynamics simulation and stability measured by RMSD, RMSF, Rg, SASA, hydrogen bonding and RDF. Validated in silico tools were used to predict the ADMET properties. Analysis of GC-MS revealed cinnamic acid (85.92%) and oleic acid (5.33%). The outcome of docking was that the cinnamic acid had the greatest affinity with NS5 (-5.970 kcal/mol) and the capsid protein (-5.755 kcal/mol), and oleic acid showed the highest affinity with the capsid (-6.150 kcal/mol) and then with NS5 (-5.209 kcal/mol). Both ligands had a relatively weak interaction with NS3. Simulation of the molecular dynamics showed the stability of the top complexes, especially the cinnamic acid-NS5 complex, that retained low RMSD (1.6-1.9 A), stable Rg and SASA profiles, and continued hydrogen bonding during the 50 ns period. The use of cinnamic acid in ADMET projections was more preferable, as it was more soluble, orally bioavailable (0.91), and drug-like (QED 0.65), but oleic acid revealed higher lipophilicity and lower drug-like properties (QED 0.29). Cinnamic acid showed specificity towards the NS5 proteins with the help of stable dynamics and good predicted pharmacokinetics, which are features that make it a promising multi-target anti-DENV scaffold. Oleic acid exhibited poor affinity and poor pharmacokinetic properties. The findings are predictive and must be validated using biochemical, cellular, and toxicological means to prove the antiviral efficacy and safety.

The article presents a novel audio zero-watermarking technique that is free of creating any watermark data in the original audio. The strategy depends on the robust features of the audio in order to generate an audio fingerprint. Primarily, the audio is divided into non-overlapping frames, and time-based, frequency-based, and structural features are obtained out of each frame. The features are then weighted on the basis of its stability to construct a very robust composite feature representation. The adaptive thresholding scheme is used to apply a binary sequence to the composite representation and the watermark data is then appended to the binary sequence to produce the final audio fingerprint. Lastly, the features extracted along with the corresponding binary sequence that acts as a label of every frame are then fed to a Random Forest (RF) classifier. The feature vectors derived when extracting the attacked audio are fed to the trained Random Forest classifier in order to reconstruct the original binary sequence during extraction. This sequence is subsequently combined with the existing fingerprint and it becomes possible to reconstruct the watermark. The results of simulations indicate that the technique preserves the original quality of the audio, but provides good resistance to numerous types of attacks, such as additive noise, filtering, MP3 compression, re-sampling and re-quantization changes, as well as time and pitch rescaling.

The aim of this endeavor is to apply artificial intelligence to provide original solutions for very complicated and challenging physical model. The results of chemical reaction involving Marangoni convection, gyrotactic microbes and thermo-bioconvection in water-based THNF (trihybrid nanofluid) flow across a sheet with heat source, thermal radiation, thermophoretic particle deposition is assessed in this work using a feed-forward neural network in conjunction with back-propagation Bayesian regularization optimization (FFNN-BPBRO). The suggested Bayesian-Regularized Neural Network model for assessing mass and heat transportation in ternary nanofluids including motile microorganisms can be used to improve thermal performance in biomedical systems, microfluidic devices, energy storage, and cooling technologies. It also offers important insights for wastewater treatment, bio-convective transport, and nanofluid-based industrial processes, where precise prediction and thermodynamic optimization are critical for increasing efficiency, stability, and sustainability. As the Peclet number increases, the microorganism profile decreases.

Lead-free tin halide perovskites constitute a nontoxic alternative to lead-based solar absorbers, but their development is stifled by low performance and material instability, attributed primarily to Sn 2+ oxidation, high levels of defects, and slow charge transfer. We demonstrate glycine-functionalized Ti 3 C 2 T x MXene (MXG) as a multifunctional additive in FASnI 3 perovskite films. The amino groups on MXG have a two-fold role in that they chemically passivate the under-coordinated Sn sites and iodine vacancies, while at the same time provide moderate reductants to suppress Sn 2+ oxidation. Aside from passivation, the MXene with layered conductive properties also acts as a favorable template for perovskite crystallization, allows the vertical grain orientation for better light absorption into absorber layer, improves interfacial connection between layers and charge carrier transfer/extraction. For the MXG devices, better film quality and reduced trap state density and carrier lifetime with enhanced energy level alignment were observed. The champion MXG/FASnI 3 device shows a power conversion efficiency of 15.82% with improved stability (maintaining over 94% of its initial efficiency after 1000 hours). This investigation highlights the dual electrical and structural benefits of MXene engineering toward achieving earth‐abundant, efficient, stable, and scalable Sn perovskite PVs.

Accurate prediction of the heat conduction coefficient (HCC) of renewable fuels is essential for improving thermal management and advancing clean combustion technologies. Existing empirical correlations lack robustness across wide thermodynamic and molecular domains, creating a significant research gap. This study applies six machine‑learning techniques, Support Vector Machine (SVM), Artificial Neural Networks (ANN), Decision Tree (DT), Bagged Tree (BT), Least Squares Boosting Trees (LSBT), and Genetic Programming (GP), to develop generalized HCC models using 2051 high‑quality experimental data points described by reduced pressure, reduced temperature, and molecular weight. Among these models, SVM demonstrated the strongest predictive capability, achieving an overall Mean Absolute Percentage Error (MAPE) of 0.96 % and a coefficient of determination (R²) of 98.78 % on unseen data. Complementarily, GP provided a transparent analytical correlation with MAPE of 1.97 % and R² of 95.18 %, offering a novel interpretable alternative to black‑box models. Additional evaluations, cross‑plot analysis, violin–box deviation profiling, five-fold cross‑validation, and training‑size generalization tests, confirmed SVM’s robustness, stability, and minimal sensitivity to dataset partitioning. Trend analysis further revealed physically consistent dependencies of HCC on pressure, temperature, and molecular chain length, while the SHapley Additive exPlanations (SHAP) interpretability demonstrated that reduced temperature is the dominant factor, followed by molecular weight and reduced pressure. Overall, this research delivers both high‑accuracy predictive models and a practically deployable mathematical correlation, bridging engineering usability with modern machine learning performance. The findings offer a comprehensive and reliable framework for estimating HCC in renewable fuels across broad operating conditions. • Machine learning models developed for accurate prediction of biofuel HCC. • SVM achieved superior accuracy with MAPE below 1 % across wide conditions. • GP delivered an explicit, interpretable HCC correlation for engineering use. • SHAP analysis revealed temperature as the dominant factor governing HCC. • Robust validation confirms reliability across diverse esters and regimes.

Bispecific antibodies (BsAbs) and multispecific antibodies (MsAbs) represent a transformative class of immunotherapeutics in oncology. These engineered molecules possess the unique ability to simultaneously target two or more distinct antigens, thereby facilitating precise immune cell engagement and disrupting multiple signaling pathways. This comprehensive review focuses on their pivotal role in redirecting both innate (natural killer cells, phagocytes) and adaptive (T cells) immunity, with a particular emphasis on their application against hematologic cancers. Significant clinical successes, exemplified by the FDA approval of agents such as blinatumomab, underscore the profound impact these therapies have had on patient outcomes. Despite their promise, inherent challenges persist, including managing treatment-related toxicities, overcoming tumor resistance mechanisms, and optimizing pharmacokinetic profiles. Ongoing research, however, continues to drive innovative strategies to address these hurdles, highlighting the immense potential of immune engagers to deliver more personalized, scalable, and highly effective cancer treatments in the future.