International Research Journal of Multidisciplinary Technovation

Palladium (II)-Quinoxaline Complex as A Potent Antioxidant, Antimicrobial, and Antidiabetic Agent: Design, Synthesis, in Vitro and in Silico Evaluation

Periyannan M — 2026-05-06

A Schiff base ligand (E)-11-(2-phenylhydrazono)-11H-indeno[1,2-b] quinoxaline was synthesized by the condensation of o-phenylenediamine with ninhydrin followed by its Pd (II) complex under refluxing condition. The newly formed compounds have been systematically characterized by various spectroscopic and analytical techniques which include UV–Vis, FT-IR, ¹H-NMR, Mass spectroscopy; additionally supported by elemental analysis for their chemical composition. A square-planar geometry has been proposed for the Pd(II) complex. The Pd (II) complex demonstrated notable antioxidant potential in ABTS and FRAP assays. Pd(II) complex demonstrated antimicrobial activity against Staphylococcus aureus and Candida albicans. Dose-dependent α-amylase inhibition (IC₅₀ = 326.47 µg/mL) exhibiting a mixed mode was observed. In silico studies using molecular docking indicate a binding energy of -7.65 kcal/mol and stable interactions at the α-amylase active site. Molecular dynamics simulations (100 ns) revealed structural stability of the ligand–enzyme complex. MM/GBSA free energy calculations estimated a binding free energy of −61.4 kcal/mol, dominated by van der Waals and lipophilic interactions. These findings underscore the potential of the Pd(II) complex as a promising antidiabetic and antimicrobial agent and warrant further investigation into its mechanism of action and in vivo efficacy.

G-Transgan: Semantic Translation of Gujarati Texts using GAN-based Augmentation and Optimized Transformer Models in Low-Resource Settings

Mehulkumar Dalwadi — 2026-04-20

Gujarati is an Indo-Aryan language with more than 55 million speakers, making it an important language to consider in machine translation. It has limited parallel corpora, complex morphology, and no context preservation. The typical neural machine translation methods tend to fail in low-resource settings, resulting in syntactic errors and semantic drifts. To overcome these shortcomings, this paper presents Gujarati-Translation with Generative Adversarial Network (G-TransGAN), a new hybrid model that combines conditional Generative Adversarial Networks (cGANs), morphology-sensitive Sentence Piece tokenization, multilingual transformer embeddings (XLM-RoBERTa and Indic BERT), and optimization techniques such as Sharpness-Aware Minimization (SAM) and Low-Rank Adaptation (LoRA). The main goal is to maximize fluency, semantic retention, and domain flexibility in low-resource Gujarati-English translation. The workflow includes five steps: data augmentation, pre-processing and tokenization, contextual embedding, semantic translation, and optimization. The experimental findings indicate that G-TransGAN had better performance on various measures, including BLEU (38.4), METEOR (0.76), and TER (0.46). Such results support the model as able to produce high-quality, human-like translations and yet remain computationally feasible in low-resource real-world settings.

Optimal Fractional-Order PID Control for BLDC Motor Drives: A Robust and IOT-Enabled Approach

Megha Sharma — 2026-04-15

Brushless DC motors have a very wide range of applications in industrial automation, however traditional PID controllers cannot provide stability with nonlinear and noisy operating conditions. To overcome this, a novel optimized Nelder–Mead algorithm is employed for an intelligent control structure using a Fractional Order PID controller, targeting the minimization of the Integral Time-weighted Absolute Error (ITAE). The proposed NM-FOPID framework is benchmarked against industrial standards, including Ziegler–Nichols (ZN) and Cohen–Coon (CC) methods, as well as metaheuristic benchmarks like PSO and GA. Experimental validation under different loading conditions (0.5–2.0 kg·m²) showed substantial performance improvement over traditional PID control. The FOPID controller reduced overshoot from 48.3% (traditional PID) to 8.24%, reduced settling time from 31.8 s to 5.9 s (≈ 81% improvement), and raised damping ratio to 1.73, leading to more robustness against disturbances. A White-noise test and frequency-domain analysis also verified high gain stability with a 25° phase margin. The proposed FOPID-based control realizes 70–80% improvement in transient performance and noise robustness, providing an optimal, Industry 4.0-compatible solution to smart BLDC motor control. Finally, the framework is implemented on a Raspberry Pi platform with Firebase integration, providing a scalable Industry 4.0 solution. The IoT layer achieves a measured jitter of ±1.2 ms and an 85 ms cloud latency, successfully decoupling high-speed local regulation from remote monitoring. These findings confirm that the NM-optimized FOPID provides a resilient, energy-efficient, and practical alternative for high-performance electric drive systems.

A Computational Ensemble Framework for Multiclass Blastocyst Segmentation: Modelling Morphological Complexity in Human Embryos

Barkavi R — 2026-05-06

Artificial intelligence is evolving in the field of embryology, offering exciting possibilities for improved results in Assisted Reproductive Technologies such as In Vitro Fertilization. The morphological quality of blastocysts of day 5 human embryo is a vital factor for determining the success of In Vitro Fertilization Hence making accurate and automated analysis of embryonic structures essential. To achieve an automated assessment of human embryo quality on the basis of morphological image features, it is crucial to precisely segment the regions of the embryo. In this research, a comprehensive analysis of U-Net and its variants for the semantic segmentation of human embryo day 5 blastocyst images is performed. Based on this insights gained from the comparative analysis, a novel Ensemble segmentation model is proposed to exploit the complementary strengths of multiple models. The proposed ensemble approach demonstrates robust performance, achieving an overall segmentation accuracy of 98%, with an F1-score of 0.95081 and a Jaccard index of 0.90625, indicating high spatial agreement between predicted and ground-truth segmentations. The framework effectively addresses key challenges inherent to blastocyst imaging, including low-contrast boundaries, heterogeneous cellular organization, and limited annotated data. By enabling precise and reproducible segmentation of critical embryonic regions, the proposed method provides a reliable foundation for automated embryo quality assessment and grading systems. This work also contributes to the integration of biologically inspired computational models into clinical embryology and supports the broader adoption of AI-driven decision support tools in reproductive medicine.

From Champion Cells to Bankable Modules: Stability, Scalable Manufacturing, and Standardized Reliability Testing for Perovskite Photovoltaics

Punithaveni B — 2026-05-04

Metal halide perovskite solar cells have experienced unprecedented growth over the last fifteen years. The certified single-junction efficiency has now reached 26.95%, while perovskite-silicon tandems have already exceeded the practical Shockley-Queisser limit by achieving efficiencies above 34.6%, as confirmed by certified measurements. Nevertheless, bankable perovskite modules are still lacking. The central challenge is no longer simply material degradation, but the absence of a validated approach that links laboratory performance metrics, such as power conversion efficiency in small-area cells or ISOS L-1 T80 lifetime, to certified performance metrics, such as power conversion efficiency in large-area cells, which must be statistically reproducible and IEC-qualified before they can be accepted by financiers and independent engineers in decision-making processes. This work addresses that gap by introducing a qualification-oriented analytical framework. A systematic literature search was conducted on peer-reviewed papers published between 2018 and 2025 using databases such as Web of Science, Scopus, and ScienceDirect, with keywords related to perovskite solar cell efficiencies, ISOS L-1 stability, IEC qualification, encapsulation, scalable deposition techniques, and techno-economic studies. Only papers in which test conditions, active area, and encapsulation state were clearly defined were included in the review. Device-level and module-level data were treated as distinct types of evidence. Separate tables are provided for certified performance, operational stability, qualification-relevant module tests, and techno-economic studies to avoid misleading comparisons between different evidence types. A four-domain translation framework is introduced to connect efficiency, degradation physics, engineering readiness, and financial bankability within an analytical structure that includes explicit cross-domain dependencies. To standardise comparison across research groups and pilot-scale demonstrations, a composite Perovskite Module Readiness Index is proposed. The analysis also defines the critical validation milestones that must be achieved before commercially viable implementation and outlines the baseline technical evidence needed to support bankability assessments.

Endo-CNN: A Novel Deep Learning Model for Gastrointestinal Diseases

Esha Saxena — 2026-04-15

Gastrointestinal (GI) diseases often represent the most frequent and common high-risk diseases. Wireless capsule endoscopy (WCE) has changed the landscape of diagnosing and treating patients. Endoscopists commonly utilize wireless capsule endoscopy to assess the majority of intestinal conditions, particularly with respect to polyps and ulcers. The use of WCE has shown a ten percent increase in Indian hospitals. Medical assessments are typically time-consuming and expensive, especially given the necessity to investigate directly from endoscopic videos. These confines are alleviated with the assistance of artificial intelligence and deep learning, which provide an efficient platform for instantaneous defect detection. The objective served by this examination is to assist endoscopic image classification work for clinical investigators. The paper proposed a deep-learning model named Endo-CNN based on convolutional neural network to classify endoscopic images according to the identified disease. The classes of images include polyps, ulcerative colitis, esophagitis and a healthy colon. Data augmentation occurs to reduce the imbalance of datasets and to evaluate the model performance that exceeds 48,000 images. The model achieves a positive accuracy rate with all the image classes. There are various aspects of an identified disease because of the variety of sizes, shapes and textures as well as colors. The paper also performs a comparative study of the designed model and against other pre-trained models. This paper can act as a baseline for many future solutions in the field of gastroenterology.

Computational Analysis of Phloroglucinol as an Anti-Inflammatory Agent Targeting TNF-α in Rheumatoid Arthritis

Manoj Kumar Karuppan Perumal — 2026-05-06

This study utilised computational analysis to investigate the therapeutic potential of phloroglucinol against rheumatoid arthritis (RA). The Differentially expressed genes were identified from the GEO dataset GSE1919 and RA-associated targets were retrieved from the Comparative Toxicogenomics Database (CTD). By comparing these datasets, we identified 17 overlapping targets using a Venn diagram. Then, the top five genes are identified using Cytoscape and CytoHubba plugin. Gene Ontology and KEGG pathway enrichment analyses revealed involvement of these targets in inflammatory signalling. The ADME analysis through the QikProp module demonstrated favourable pharmacokinetic properties, including a molecular weight of 126.112 Da, QPlogPo/w of -0.020, oral bioavailability of 70.508% and no violation of Lipinski's Rule of Five. The molecular docking analysis indicated moderate binding affinities with key inflammatory proteins such as IL6 (-3.583) IL-10 (-2.735), IL-1β (-3.764), ICAM1 (-2.890), and TNF-α. (-4.568). The phloroglucinol-TNF-α complex was subjected to 500 ns molecular dynamics simulation, which confirmed structural stability as evidenced by RMSD values and preserved secondary structure throughout the simulation. These findings identify phloroglucinol as a promising natural small-molecule with TNF-α as its primary molecular target.