MIT World Peace University

Interfacial load-transfer inefficiency between carbon fibers and epoxy matrices limits the mechanical reliability of carbon fiber–reinforced polymer composites. This study treats Triton X-100 surfactant concentration as a controlled interfacial design variable and evaluates its effect on the mechanical behavior of short carbon fiber–epoxy composites. Fibers were surface-modified at 1, 2, and 3 wt.% Triton X-100 and incorporated into compression-molded epoxy composites at ∼40% fiber volume fraction. Tensile and compressive properties were measured per ASTM D3039 and D695; wettability, surfactant uptake, void fraction, and fracture morphology were characterized by contact angle goniometry, gravimetric analysis, Archimedes densitometry (ASTM D792), and scanning electron microscopy. The 1 wt.% composite exhibited tensile strength of 116.00 MPa (∼40.3% above untreated: 82.67 MPa) and a comparable compressive strength gain, while elastic modulus remained constant at ∼2.0 GPa across all conditions. Gravimetric analysis confirmed monolayer adsorption at 1 wt.% and excess deposition at 2 wt.%, consistent with the observed mechanical optimum. SEM revealed reduced fiber pull-out (64 ± 9 μm to 22 ± 4 μm) and suppressed debonding in the optimally treated composite. Controlled surfactant treatment provides a scalable, non-destructive route to enhance interfacial efficiency and mechanical performance in short carbon fiber–epoxy systems.

ABSTRACT Grape disease is one of the most common diseases that impacts grapevines, affecting both the yields of the plants and the quality of the fruits that are harvested. Currently, fungicide treatments are often used throughout the season to combat the disease. In terms of public health and the environment, fewer treatments are necessary. This issue could be solved by identifying vineyards that are more likely to suffer severe attacks in the spring and treating them only with fungicidal treatments. Computers, categorization, bioinformatics, marketing, healthcare, gaming and industry are just a few of the many areas that have made use of machine learning in the past several years. These technologies are used to identify high‐quality grapes for export after they have been photographed and pre‐processed. Accurate illness detection and disease preventive management methods are critical for increasing quality and productivity. This proposed study aims to effectively predict the quality of grape yield and perceive illnesses such as powdery mildew and downy mildew. Initially, sensors located on farms are used to gather climate data. Then, the input data is pre‐processed using min‐max normalization and a one‐hot encoding method to remove the unwanted data. Four distinct machine learning classifiers are then employed on the pre‐processed input: K Nearest Neighbours (KNN), Logistic Regression (LR), Multinomial Naive Bayes (MNB) and Bernoulli Naive Bayes (BNB). A variety of performance measures are used to assess the performance of the proposed model. In terms of disease prediction, the KNN classifier outperforms with 82% accuracy on plots 2 and 3. Additionally, plots 2 and 3 have a yield prediction accuracy of 98%. The results obtained are more efficient than other existing models.

Abstract Intrusion detection is a major concern in network systems where numerous smart devices are interconnected to handle sensitive information. Such interactions expose networks to threats like weak authentication, eavesdropping, malicious payloads, and a high false alarm rate. To address these challenges, a Channelized Spatial Attention enabled Adam optimized Convolutional Network (ChSp-ACN) is developed to identify malicious activities. The proposed ChSp-ACN effectively manages data imbalance and refines input features using KNN imputation and normalization. Its spatial attention mechanism enhances detection accuracy by focusing on relevant attack features, while the Adam optimizer fine-tunes model parameters to minimize false positives. Experimental evaluation demonstrates that ChSp-ACN achieves superior results compared to existing methods, attaining an accuracy of 97.22%, specificity of 97.24%, sensitivity of 97.20%, and a False positive rate of 0.03, which attains maximal performance effectiveness under the BoT-IoT dataset.

Translating spoken words into emotionally and contextually aligned video content remains an open challenge in generative AI. Subtle vocal patterns—such as pauses and pitch modulations—often obscure emotional cues, resulting in visuals that feel emotionally disconnected or flat. While several models excel at text-to-image generation, they struggle with interpreting speech-based inputs, often misreading paralinguistic cues and contextual intent. To address these limitations, this research introduces EchoVid, an audio-to-video synthesis model designed to prioritize contextual fidelity and emotional alignment. A scalable web interface built with React.js and TypeScript connects to Node.js backend with the MongoDB Atlas for near-real-time generation (≈ 1.2× input-duration latency at 512² frames on RTX A4000) interaction. Using PyAudio input, EchoVid guides Hugging Face’s Stable Diffusion v2.1 via emotion-aware prompts, with CNN-enhanced diffusion transformers supporting the video generation process. Preliminary results show that EchoVid can generate visuals that reflect both emotional tone (e.g., joyful imagery for upbeat speech) and context. The proposed EchoVid model is compared with MoCoGAN and Stable Video Diffusion variants based on metrics like, FVD, FID-VID and CLIPScore. Further this research introduces two novel evaluation metrics namely Temporal Semantic Stability (TSS) and Perceptual Flicker Index (PFI) that scores the semantic consistency and frame-to-frame change in the generated video. The results show that EchoVid outperforms the other models and can generate relatively better videos.

Subsurface geophysical investigations are useful for minimization and optimizing the association of conventional direct investigation methods, resulting in faster and effective advancement of underground development systems. Geophysical techniques provide expensive and cost-effective ways to enhance data obtained by direct exploratory techniques such as borehole drillings, trial test pits and in situ testing, confirming local anomalies that may not be distinguishable by other methods of investigations.In the present study, the operation of Seismic Refraction Method (SRT) in urban tunnelling is investigated during the planning phase (before construction). The lithology interpreted using the SRT technique is compared to a nearby executed borehole log and found to be comparable. A cost and duration comparison between the direct system (boring) and the seismic refraction method for a 1.0 km tunnel stretch is also carried out. It is revealed that the use of SRT required only 2.5 days, whereas borehole drilling takes a much longer duration, 17 days, to perform and complete the same task. The use of SRT reduces exploration duration by over 7.0 times compared with the boring method. The cost required for conducting SRT is 425,000 INR, whereas the borehole drilling technique is 2,800,000 INR, which is almost 6.5 times less than the boring method. This demonstrates that SRT is significantly more efficient in time and cost than borehole drilling.

Tunnel Lining is One of the Primary Components, and its Cost is Directly Related to its Size; Therefore, Constructing the Lining in a Cost-Effective Manner is Crucial. Given the Adaptability of Modern Engineering Practices, Numerical Models Have Become Indispensable in this Study. this Work Uses Numerical Analysis(ANSYS) to Compare the Design Parameters for Tunnel Segments by Incorporating Macro-Synthetic Fibers. the Aim is to Assess the Impact of these Fibers on the Structural Performance and Safety of Tunnel Linings. the Analysis is Carried out Using ANSYS Software, which Simulates Tunnel Segment Behavior with Varying Fibercontents (0%, 5%, 10%, and 15%). the Study Focuses on Segmentdesign Parameters Including Total Deformation, Elastic Strain (equivalent, Normal, and Shear). the Results Reveal that Tunnel Segments with Higher Fiber Content, Especially at 15%, Demonstrate Improved Performance, with much Less Deformationand Elastic Strain than Segment without Fiber. the Present Study’s Total Deformations Results are Comparable to Winterberg, and Nitschke and Winterberg Methods. the Findings Highlight the Benefit of Fiber Reinforcement in Improving the Structural Stability and Safety of Tunnel Linings. this Study Highlights the Importance of Advanced Analytical Tools like ANSYS in Accurately Predicting Tunnel Segment Behavior under Operational Conditions, Guiding Future Tunnel Design Strategies.

In the extensive growth of social media platforms, fake news is false information spread among people around the world. Fake news weakens public trust and societal stability; it has negative impacts that necessitate for fake news. In order to enhance the public trust and improve the social media users, fake news detection is crucial, which is a complex and multi-dimensional task due to the diverse characteristics of fake news. More researchers have explored several deep learning and machine learning approaches for fake news detection approaches yet the existing binary classification mechanism is not sufficient for handling imbalanced training data to enhance poor performance in the fake news detection process also it affects the interpretability and consumes high computational duration. With the aim of rectifying these complexities, an automated fake news detection mechanism is proposed in this research work to ultimately recognise unreliable information for improving social media users’ effectiveness. Initially, the necessary data are taken from the publicly available datasets, such as the WELFake dataset and Fake News dataset; it is fed into the preprocessing phase. Here, the punctuation, special characters, redundant content and inappropriate data are mitigated, which can generate cleaned data. Then, the preprocessed data is given to the feature extraction process to efficiently extract the relevant informative features for enhancing better detection outcomes. Here, the Bidirectional Encoder Representations from Transformers (BERT) is initially used to retrieve the first set of features, the Recurrent Neural Network (RNN) extracts the temporal features and the Convolution Neural Network (CNN) is used to extract the spatial features. Subsequently, these features are fused in the weighted feature selection process, where the weights are quickly tuned through the Improved Horse Herd Optimisation Algorithm (IHHOA). Further, it is passed to the Residual Convolution Bidirectional Long Short Term Memory (RConv-Bi-LSTM) technique for significantly detecting fake news in a limited duration. The entire detection process is estimated with several performance metrics and contrasted over various conventional methods to generate optimal outcomes. Here, the designed framework’s FM value (dataset 1) is enhanced over 22.9% DNN, 8.7% CNN, 17.6% LSTM and 7% Bi-LSTM methods (BERT features) to show its better performance over the existing approaches.

It is now commonplace for individuals all over the world to incorporate the internet and several other types of information and communication technology into their way of life. It has the potential to enhance the capacity of society to engage in open discussion and debate. No state can rightfully claim to be a democracy if this freedom is unfairly or criminally withheld. In this chapter, we will explore the theoretical inquiry, pertinent legislation, constitutional requirements, and pertinent court declarations in order to understand how ANI's institutionalised processes for copyright infringement litigation have affected Indian YouTubers.