International Research Journal of Multidisciplinary Technovation

Growth, Third-Order Nonlinear Optical, Thermal and Quantum Chemical Investigation of L-Ornithine Monohydrochloride Single Crystals

2026-05-09T04:34:01+00:00

Single crystals of L-ornithine monohydrochloride (LOHCL) were successfully synthesized at ambient temperature using the slow evaporation technique and systematically investigated through complementary experimental and computational techniques to assess their relevance for nonlinear-optical and optoelectronic applications. Single-crystal and powder X-ray diffraction confirmed the formation of the known monoclinic LOHCL phase with good crystallinity. UV–visible spectroscopy revealed high transparency across the visible range with a lower absorption edge near 223 nm, indicating favourable optical transmission. Thermal analyses by TGA, DTA and DSC indicated that the material maintains thermal stability up to approximately 255 °C before undergoing a major transformation. FT-IR and FT-Raman spectra supported the ionic amino-acid hydrochloride structure and were consistent with the diffraction results. Photoluminescence measurements exhibited a blue emission band centred at 474 nm. Z-scan analysis established a positive nonlinear refractive index of 5.056 × 10⁻⁸ cm² W⁻¹ and a nonlinear absorption coefficient of 0.06 × 10⁻⁴ cm W⁻¹, confirming appreciable third-order nonlinear optical characteristics. DFT calculations using the B3LYP/6-311++G(d,p) level further clarified the electronic configuration, charge distribution, thermodynamic behaviour and intermolecular interactions. The calculated HOMO–LUMO energy gap was found to be 3.00 eV. The combined findings support LOHCL for advanced photonic, laser-based and frequency-conversion technology platforms.

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

2026-02-23T07:53:50+00:00

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.

Performance Enhancement of Concrete Incorporating Demolition Waste Aggregates Using PC-200 Superplasticizer

2026-02-05T10:27:11+00:00

The increase in construction work has made a lot of construction and demolition waste that causes environmental and disposal problems. One way to deal with the growing amount of demolition concrete waste in a sustainable way is to use it again as aggregate in new concrete, but this is usually limited due to lower workability and damage to mechanical properties. Solving these problems is important to encourage environmentally friendly and structurally sound manufacturing of concrete. The main goal of the study is to use demolition concrete waste as coarse aggregate in concrete after studying the possibility and to check how well the PC-200 superplasticizer will work to improve the mechanical and fresh properties of concrete made using it. To achieve this goal, concrete samples were made with different amounts of replacement of natural coarse aggregate with demolition waste concrete, the replacement levels being 0, 25, 50, 75, and 100%. Two categories of mixes were included in this study: ordinary concrete with a 0.42 water–cement ratio and modified concrete containing PC-200 superplasticizer with low water–cement ratio of 0.28. The tests included the slump test, compressive strength, splitting tensile and flexural strength, and modulus of elasticity after 28 days of curing. The test results showed reduced workability and mechanical strength of ordinary concrete mixes with the introduction of demolition waste aggregates. High levels of modification with PC-200 superplasticizer showed significant improvement in the fresh and hardened properties. Compressive strength increase from 43.1 MPa for the reference modified mix to 52.5 MPa for the 100% waste aggregate. Similar improvements were also observed in the tensile and flexural strength as well as modulus of elasticity. The results indicate that demolition waste aggregates can be successfully used to produce high performance sustainable concrete for structural applications, given the right level of modification.

Adaptive Deep Linguistic Representation for High-Precision Cyberbullying Detection Using APO-Bi-LSTM with Attention Mechanism

2026-01-22T05:44:43+00:00

The phenomenon of cyberbullying on different social media has become severe following the further development of user-generated content, which calls for mechanisms that can detect expressions that are detrimental with high precision. Most of the current methods are fairly effective in dealing with offensive language, but are frequently unable to capture contextual subtleties, emotional polarities, and the constant shift in internet vocabulary. The goal of this research is to obtain a high-precision cyberbullying detector by systematically synthesizing an Adaptive Deep Linguistic Representation Model with a multimodal early-fusion strategy. This is achieved through an optimized framework utilizing a Bidirectional Long Short-Term Memory network and Attention Mechanism, tuned dynamically by Artificial Protozoa Optimization (APO-Bi-LSTM-AM). Appropriate publicly available social media text corpora with bullying, abusive, and neutral data is collected and narrowed down to clean data using cleaning tasks, such as noise elimination, lexical normalization, and contextual filtering of tokens. Internal semantic-refinement process is added to remove pretentiousness that emojis, irregular spellings, and redundant characters cause. To extract features, rich semantic vectors that can be used in downstream tasks of deep-learner are obtained using Word2Vec and contextual encoder-based representations. The APO-Bi-LSTM-AM combines both the bidirectional sequence learning and a focused attention layer, whereas the APO component regards some important network parameters that will promote convergence and minimize misclassification in borderline cases. The combination of these allows the architecture to obtain subtle cues, relational dependencies, and intensity variations in aggressive language, and is implemented in Python. Experimental assessments on the 600-instance test set indicate high levels of performance, identifying exactly 120 true positives and 468 true negatives. The model achieved a precision of 96.0%, accuracy of 98.0%, recall of 94.5%, and a corrected F1-score of 95.2%, outperforming traditional models. Its findings suggest that the Adaptive Deep Linguistic Representation Model suggested provides a powerful model of high-precision CBD in the social media setting.

A Robust Shot Detection Method for Regional Language Videos using Edge and Histogram Statistics

2025-11-29T03:45:16+00:00

The huge amount of digital content is being uploaded by users every day, and processing these videos specifically for indexing and retrieval purposes is extremely important. This research is contributing to society by providing a baseline approach for Gujarati news channel video retrieval for regional users. The Gujarati language is spoken by more than 22 million people worldwide. To retrieve these videos correctly, we need to process them frame by frame; hence, shot segmentation is an essential component, followed by keyframe extraction, and, lastly, extracting text features for the retrieval of news videos. A three approaches were developed to detect shot boundaries and identify frames containing meaningful Gujarati text in broadcast news video. The three methods were: (1) An Shot Boundary Detection (SBD) methods based on global threshold (SBD-GT) with 5 distinct frame level features (2) SBD methods employing statistical adaptive thresholding (SBD-AT) with 5 distinct frame level features (3) Development of the proposed Edge Histogram Adaptive Shot Detection (EHASD) system with the fusion of edge and histogram based features. EHASD applied Statistical Edge-Histogram Thresholding (SEHT) together to evaluate edge deviation and histogram difference to improve the detection rate of both abrupt and gradual transitions. The proposed research was evaluated using a TV9 Gujarati news video dataset with 45920 total frames from different categories like weather, Cricket, and politics. The proposed approach, EHASD, achieved a precision of 93.25% and a recall of 92.01%, surpassing the values obtained by the global and adaptive thresholds in SBD approaches. Additionally, the z test & paired t test was also performed on the mean and standard deviation to validate the results of the proposed approach. In addition, the research confirmed that the proposed method can be used further to perform key frame selection followed with text extraction for the video retrieval purpose.

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

2026-02-23T08:09:47+00:00

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.

Deep Learning-Driven Hybrid Beamforming, Channel Estimation, and Resource Allocation for Enhanced mmWave MIMO-OFDM Systems

2025-12-02T07:47:00+00:00

New wireless networks use millimeter-wave large MIMO-OFDM systems, however beam squint effects, pilot overhead, and low power and spectrum allocation under dynamic channel conditions limit their effectiveness. These challenges are addressed by an integrated deep learning-driven system that improves hybrid beamforming, adaptive channel prediction, and dynamic resource allocation in wideband mmWave contexts Framework includes multi-scale graph attention-based hybrid beamforming, transformer-based adaptive channel estimation with frequency-aware memory, meta-learning-enhanced deep reinforcement learning for resource allocation, sparse coding-based beamforming enhancement, and multi-agent reinforcement learning for coordinated beam alignment and powers. Real-world urban mmWave studies indicate considerable improvements in all performance metrics. The framework boosts spectral efficiency by 30-40% across antenna and user configurations, peaking at 28.5 bits/s/Hz in large-scale MIMO. Improved channel estimate accuracy by 35%, lowering normalized mean square error to -22.1 dB and pilot overhead by 35%. Wideband beam misalignment is considerably minimized by 96.5% beamforming accuracy. Dynamic resource allocation efficiency surpasses 97%, saving 25-35% power without compromising throughput. The framework is appropriate for 5G and 6G ultra-reliable and high-capacity wireless networks because closely integrated deep learning architectures can enable scalable, low-latency, and energy-efficient mmWave MIMO-OFDM communication sets.

Analysis of Mesogenic Properties Exhibited by Linear Supramolecular Hydrogen Bonded Thermotropic Liquid Crystals

2026-03-11T11:45:34+00:00

A supramolecular hydrogen-bonded thermotropic liquid-crystalline complex was synthesized by combining myristic acid (MC) and 4-n-pentyloxybenzoic acid (5BAO) in an equimolar ratio, and its mesogenic, optical, and thermal characteristics were systematically investigated. The formation of intermolecular hydrogen bonding between the carboxylic acid groups of the precursor molecules was confirmed by Fourier transform infrared spectroscopy, which revealed characteristic shifts in the O-H and C=O stretching vibrations after complexation. The mesomorphic behaviour of the prepared complex was examined by polarizing optical microscopy, which identified the presence of nematic and smectic C mesophases through their characteristic optical textures during the cooling cycle. Differential scanning calorimetry further established the corresponding phase-transition temperatures and enthalpy changes in both heating and cooling runs. The complex exhibited phase transitions associated with crystal, smectic C, nematic, and isotropic states, demonstrating rich phase polymorphism and good thermal response. Thermal analysis also indicated that the nematic phase possessed a wider mesophase range and greater thermal stability than the smectic C phase. In addition, specific heat and transition-order analyses supported the thermal behaviour of the system. The observed spectral response suggests that the complex may serve as a promising candidate for tunable optical filtering and thermally responsive liquid-crystalline applications.

Multi-Lag Smoothing Techniques for Stock Market Time-Series Forecasting and Performance Evaluation

2026-02-28T04:40:49+00:00

Financial time series are nonlinear, non-stationary and highly contaminated with high-frequency noise thus they are difficult to forecast accurately. Although advanced machine learning models have shown a high predictive accuracy, very little has been done on systematic signal conditioning before making a prediction. This paper suggests a systematic multi-lag smoothing model of predicting time-series of stock markets with six moving averages, namely, SMA, EMA, WMA, DEMA, HMA, and AMA. The filter properties of these methods are measured in time and frequency domain with different lag windows (5-100 days). A baseline model that is done using regression is used to isolate the effect of smoothing on predictive accuracy. MSE, MAPE, R2, and Directional Accuracy are used to determine performance. Comparison of performance on benchmark indices (NIFTY 50 and S&P 500) through cross-dataset validation shows that HMA demonstrates consistently lower error in short- and medium-term forecasting, while AMA shows improved performance in long-term trend modeling. Statistical significance is formally validated for HMA versus SMA at the 25-day lag, while other observations are based on comparative empirical results rather than formal hypothesis testing. Paired t-tests are statistically confirmed to be significant (p < 0.05), but moderate effect sizes (Cohen d) (0.42–0.55) are significant. The proposed framework unites the classical FIR filter theory and computational financial modeling, which provides a statistically valid and computationally efficient framework applicable in a real-time financial analytics system.

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

2025-10-27T06:21:24+00:00

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

2025-10-22T06:21:07+00:00

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.

LabVIEW-Based Remote Laboratory with Pipelined Face Recognition for Multi-User Attendance Monitoring

2026-03-18T09:46:59+00:00

This paper outlines an open source LabVIEW based Remote Laboratory System for Automated Attendance Monitoring through Facial Recognition to assist with both User Authentication and Multi-User Scalability within Online Practical Courses. The proposed RLS has been designed utilizing a Pipeline-Parallel Architecture where multiple Webcam Streams can be processed in parallel at different stages, namely Capture, Detection, Recognition and Logging. Evaluation of the proposed RLS involved testing with six Users simultaneously over a controlled environment. Results indicate Real-Time Operation with an average Per-User Latency Time of ~0.2 seconds. Average Throughput is estimated to be ~8 frames/sec/user (~45-50 frames/sec aggregate). Accuracy of Recognitions is reported as being ~96-99% under typical Lighting Conditions. A PCA/Eigenfaces Path was utilized for Fast Continuous Verification, and Periodic FaceNet-Based Checks were employed to Improve Robustness with moderate Overhead. Resource Profiling results estimate ~85% CPU Utilization from a Commodity Multi-Core Host (8-Cores / 16-Threading), 300 MB Memory Demand, and 3 Mbps Network Demand. These estimates suggest Straightforward Scaling via Additional Cores, GPU/FPGA Acceleration or Frame-Rate Throttling. The Proposed Design also Integrates Embedded/IoT Endpoints, which aligns with Computational Thinking and Education 4.0 Priorities that enable Authentic Participation, Learning Management System Integration, and Data-Driven Pedagogy. Wide-Area Conditions were Characterized Using a Laboratory WAN Emulator (tc/netem) that Simulated Controlled Levels of Latency, Jitter and Loss Representative of Campus, 4G and 5G Profiles. Due to the Complexity of Deploying an Internet-Scale Multi-Site Version of the Proposed RLS, such a Deployment is identified as Future Work.

Design and Characterization of a Cyclic Stress Imposed Accelerated Aging Test Method – Assessment on Tyre Tread Compound

2026-01-17T17:22:14+00:00

Surface mobility has been one of the greatest enablers of human development, and tyres have played a pivotal role in this journey. The tyre is the only link between the vehicle and the road to enable safety. As a major requirement, the traction is achieved by the tyre with the help of several components of its structure. The tread patterns provide physical interlocking with road asperities and the tread compound interfaces with road textures, to effect necessary grip in dynamic conditions. As the properties of tread compound play the most vital role in affecting dynamic grip, the aging of the rubber and consequent loss of properties have become key topics to study in tyre design for safety and durability assessment. Tread ageing is affected by multiple simultaneous conditions experienced by the tyre, related to thermo-oxidative exposure and mechanical stress variations. While aging is a critical influencer in tread performance preservation, the studies related to aging have made many assumptions due to difficulties in replicating actual service conditions in the laboratory. Traditionally, aging studies were conducted by inducing a thermo-oxidative environment at an elevated temperature above ambient temperature and mapping property degradation as a function of time. This generic approach was fraught with many limitations due to its inability to replicate the degradation characteristics as they happen in real service conditions. A novel attempt is made to develop a facility that simulates real-life service conditions to a greater extent by imposing a cyclic mechanical stress during the accelerated aging process. This study investigates the effect of aging on Natural Rubber (NR) based tyre tread compound under conditions dominant in dynamic service conditions. Unlike conventional accelerated aging without mechanical stimuli (may be called static aging), the current study involves cyclical mechanical stress imposed on the tread compound under elevated temperatures (dynamic aging) to mimic the effect on tyre tread in service conditions. This paper addresses the synergistic interactions mechanical behaviour of tread rubber compound and its microstructure alterations when exposed to static and dynamic aging processes. Various methods in mechanical and viscoelastic studies to verify representative parameters of rubber compound behaviour, such as hysteresis ratio, Mullins coefficient, stress relaxation, and macromolecular network alteration, are introduced. A macromolecular network-alteration mechanism, linking changes in crosslink density and chain scission to the mode and state of aging, is established. The findings significantly reinforce the understanding of compound behaviour alterations with more realistic operating conditions of aging to support the design of materials to enhance product safety.

Benchmarking YOLO Variants for Vision-Based Flap State Detection in Fixed-Wing UAVs

2026-02-25T11:04:07+00:00

Fixed-wing UAVs depend on their flap state for safe operation, to do that conventional sensors cannot be used such as the potentiometers because they are rigid, weigh so much, and lack adaptation according to the place they are used. This creates a critical gap in the need for a good fault-tolerant method used for monitoring the UAV’s control surface. This study aims to benchmark this fault tolerant method, as the fault tolerant method used here will be using Oak D Lite stereo vision camera and the implementation of YOLO model in it, the YOLO model to be used is benchmarked for superior performance, thus establishing a good and lightweight alternative to traditional sensors. A thorough dataset of about 1,920 raw images were taken by mounting the Oak D Lite camera at the tail section of the fixed-wing UAV, it has six flap state images taken under various lighting conditions, depth, and background conditions. After augmentation, 4,623 images are been used to train and then evaluate the YOLOv5(s/m/l), YOLOv8(s/m/l), and YOLO11(s/m/l) models, other models are either too old or too much minimal for this research. These models are evaluated under standard parameters run on an RTX 4090 GPU. Found out that all the models achieved mAP@0.5 exceeding 98%, with a good precision and recall consistency that are above 98.1% and 95.5% respectively. YOLOv5s achieved the best F1-score with the lowest confidence threshold with only 7 misdetections, while other models like YOLOv5s and YOLOv8s had a good inference speed of about 0.6ms. These findings provide a validated baseline benchmark for UAV control surface monitoring and offer practical deployment guidelines for real-time fault-tolerant UAV systems.

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

2026-03-05T05:21:11+00:00

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.

Design and Analysis of a Compact Multi Input Multi Output Truncated Circular Planar Antenna using Defected Ground Structure for Millimeter Applications

2026-01-22T06:55:47+00:00

A novel planar multi-input multi output (MIMO) antenna operating at 28 GHz is proposed, featuring a four-elements with rotationally symmetric circular resonators with defected ground plane (DGP) to enhance performance of gain and isolation. The single element circular patch antenna design is extended into a MIMO configuration to improve gain and directivity, making it suitable for 5G. The proposed antenna is analyzed through simulated S-parameters, including S₁₁ (like the single-element antenna) and S₂₁, S₃₁ for the MIMO configuration for evaluate impedance matching and coupling effects using the commercially available CST microwave studio suite software. A very fine mesh size used in the simulation for better accuracy for getting the good simulation results. The radiation characteristics at the resonant frequency are examined using both polar and 3D radiation patterns, demonstrating the antenna directional behavior and effective radiation performance. The antenna structure overall size is 45 x 45 x 0.254 mm3. The Rogers 5880 with 2.2 dielectric constant is used as the substrate for the material. The defected ground plane (DGP) is used to improve the isolation between the ports in the MIMO structure. The proposed work examined in terms of the parameters like S11, S21, ECC (Envelope correlation coefficient), DG (Diversity Gain), CCL (Channel capacity Loss), TARC (Total Active Reflection Coefficient) and the radiation pattern. The mutual coupling/isolation is >25 dB and the MIMO parameters i.e., ECC <0.00033 with a DG >9.9 dB is achieved. The antenna is fabricated using photolithography techniques and validated using the vector network analyzer (VNA). Both the simulation and measured results are in good agreement in terms of S-parameters as well as radiation characteristics.

A Hybrid Machine Learning and Swarm Optimization Framework for Real-Time Energy Prediction and Optimization in Additive Manufacturing

2025-12-15T01:33:26+00:00

Additive Manufacturing (AM) enables unprecedented design flexibility and decentralized production; however, its high energy consumption remains a critical sustainability challenge. While recent digital twin and machine learning (ML)-based studies have focused primarily on quality monitoring, defect detection, or thermal modeling, limited research has developed integrated predictive–optimization pipelines specifically targeting real-time energy management across multiple AM platforms. This study proposes a hybrid machine learning framework that combines predictive modeling, clustering-based data compression, and swarm intelligence optimization to estimate and minimize energy consumption using real-time process parameters. Experimental data were collected from Selective Laser Sintering (SLS) and Fused Deposition Modeling (FDM) systems, incorporating layer-wise geometric descriptors, process variables (laser power, scan speed, build temperature, extrusion rate), and environmental conditions. Multiple supervised learning models—including Support Vector Regression (SVR), XGBoost, Deep Neural Networks (DNN), and a DBSCAN–XGBoost hybrid—were trained and evaluated under a controlled benchmarking protocol. The DNN with feature selection achieved the best predictive performance (RMSE = 0.72 kWh, R² = 0.96), outperforming conventional regression and ensemble models across identical datasets. Statistical comparisons confirmed a consistent improvement in prediction accuracy relative to baseline linear and non-deep learning models. The predicted energy profiles were integrated into a Particle Swarm Optimization (PSO) module to identify energy-efficient process parameter configurations under manufacturability constraints. The optimization stage achieved up to an 18% reduction in energy consumption for both SLS and FDM builds while maintaining tensile strength and dimensional accuracy within acceptable tolerances. By explicitly integrating clustering-based compression, deep learning prediction, and swarm-based optimization into a unified pipeline, this framework extends beyond existing standalone digital twin or energy estimation approaches. The results demonstrate the potential of hybrid ML–optimization architectures for scalable, real-time, energy-aware additive manufacturing and sustainable industrial deployment.

Preparation and in Vitro Evaluation of Biophytum Sensitivum-Loaded Cellulose Acetate Films for Diabetic Wound Management

2026-04-11T11:39:55+00:00

Diabetic wound healing presents a significant clinical challenge in the expanding medical sector due to impaired blood circulation, neuropathy, protracted tissue regeneration, and increased susceptibility to persistent infection. Traditional wound dressings often fail to provide the multifunctional environment required for effective diabetic wound management. In this study, a biodegradable polymer-based wound dressing film incorporated with Biophytum sensitivum with a common name Mukkutti extract was developed to address these limitations. The plant extract which is rich in anti-inflammatory, antioxidant, and antimicrobial phytochemicals, was successfully integrated into a cellulose acetate thin-film matrix. The resulting film, loaded with phytochemicals, exhibited improved physicochemical stability, uniform surface morphology, and enhanced mechanical integrity compared to the polymer control. Spectroscopic and structural analyses confirmed effective interactions between polymers and phytochemicals, along with a uniform distribution of bioactive substances throughout the film. Additionally, the developed dressing exhibited considerable antimicrobial activity against both Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli and Pseudomonas aeruginosa) bacteria and supports cell viability towards 3T3 mouse fibroblast cell lines. Overall, the findings suggest that the developed film possesses a combination of physicochemical and biological properties relevant to wound management at an in vitro level. However, this study is limited to preliminary evaluation, and further investigations involving wound-specific functional parameters such as water vapor transmission, degradation behavior, extract release kinetics, and in vivo validation are required to establish its suitability for diabetic wound healing applications.

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

2025-04-03T14:25:39+00:00

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

2026-01-05T05:16:39+00:00

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.

Copy-Move Image Forgery Detection via Weighted Multi-Similarity Matching and Adaptive Thresholding

2025-12-04T08:40:56+00:00

One popular digital image forgery technique for identifying regions of image forgery is Copy-Move Forgery Detection (CMFD). Copy-move forging is the procedure of attaching a specific section of an image to a new element of an identical image to replicate the forged image elements as an original. The fake appears realistic because the image preserves all the fundamental characteristics of the original image, even after its creation in the target region. Because editing tools and image capture have been more widely available, the quantity of phony photographs on the internet has exploded. Further, social media and other networks have emerged as the primary means of distributing modified photos, rumors, fake news, and other such content. Therefore, creating efficient methods for identifying these forgeries has become crucial. The Copy Move Forgery (CMF), which uses the patches inside the image to change it, is among the most prevalent kinds of forgeries. Deep structured learning-based methods generally perform better but have a focus on generalization. Besides feature matching, this paper also proposes a new deep-learning algorithm to detect forgeries. First, the images from the standard dataset are collected. Preprocessing methods include Retinex and Contrast Limited Adaptive Histogram Equalization (CLAHE) are applied. Further, each of the collected images is subjected to post-processing to improve image effectiveness. The pre-processed images are then fed into an Efficient Convolutional Transformer with Spatial Attention Network (ECT-SANet) for feature extraction. Then we perform the feature matching operation using the Weighted Multi-Similarity Check (WMS) method. The Adaptive Threshold is optimized by Randomized Improved Orca Predation Algorithm (RE-OPA). The matched features are then filtered for false-positives using a Random Sample Consensus (RANSAC) algorithm. The performance of the proposed approach is evaluated in terms of the CMFD.

Machine Learning-Driven Intrusion Detection for Next-Generation Information Security Systems

2026-01-02T04:41:57+00:00

The proliferation of cloud, IoT, edge, and 5G infrastructures has dramatically expanded the attack surface of modern networks, while many existing intrusion detection systems (IDS) remain centralized, poorly interpretable, and brittle to concept drift and adversarial manipulation. Traditional machine learning-based IDS architectures demand centralization of raw data, offer limited decision transparency, degrade as traffic distributions evolve, and scale poorly to privacy-sensitive and resource-constrained deployments. In this paper, the Adaptive Explainable Federated Intrusion Detection System (AEF-IDS) is proposed, incorporating privacy-preserving federated learning, Kolmogorov-Smirnov (KS) test-based drift detection, differential privacy, adversarial robustness training, and multi-level explainability within a unified edge-oriented framework. Evaluated on three widely adopted benchmarks, namely NSL-KDD, UNSW-NB15, and CIC-IDS2018, AEF-IDS achieves detection accuracies of 96.74%, 93.92%, and 95.87%, false positive rates of 1.68%, 2.61%, and 2.19%, and AUC-ROC scores of 0.9781, 0.9573, and 0.9683, respectively. The system satisfies strict real-time performance requirements, achieving per-sample inference latencies of 47.3, 44.8, and 46.1 ms across the three benchmarks, all within the 50 ms operational threshold. AEF-IDS further demonstrates high resilience against white-box adversarial attacks, including FGSM, PGD, C&W, and DeepFool, maintaining a mean under-attack detection accuracy exceeding 88% across all evaluated datasets. Through federated optimization and KS-triggered adaptive retraining, the system effectively mitigates distributional shift while preserving local data sovereignty, and SHAP/LIME-based explanations provide both global and local attribution transparency for security analysts. These results collectively demonstrate that AEF-IDS constitutes a robust, privacy-preserving, and interpretable solution for next-generation IDS deployment at the network edge. Future work will address cross-domain generalization, online hyperparameter adaptation, and large-scale real-world field validation.