ICFAI Foundation for Higher Education

This study investigates the mechanical, thermal, and surface behavior of poly lactic acid (PLA)-based composites reinforced with silane-treated luffa vine stem microfiber and silane-treated volcanic pumice filler. Mechanical characterization revealed significant improvements in tensile strength, flexural strength, impact energy, and hardness with the incorporation of treated reinforcements. Among all specimens, PLV2 (67 vol.

Female mortality is frequently caused by ovarian cancer (OC). Because of its late detection, ovarian cancer seems to have a low survival rate, and new techniques are required for its early identification. One of the more prevalent gynecologic cancers is ovarian cancer. The various diagnoses of ovarian cancer depend on the efficient classification of the various forms. Patients with ovarian tumours require accurate diagnosis. When compared to a deep convolutional neural network, previous neural networks are an outmoded technology that offers fewer characteristics, which demonstrates that deep convolutional layers supply essential and healthy features. To get over these limitations, ovarian tumours are identified using the krill herd optimization-based convolutional neural network (KHO-CNN) mechanism, a novel optimized deep neural network approach. The system analyses datasets related to ultrasound-detected ovarian cancer. The obtained real-world ultrasound images of ovarian cancer also contain additional noise, which is removed using a Wavelet Transform. An enhanced KHO model has been used in the segmentation process. Features were extracted by use of a local binary pattern. Ovarian tumours are classified as benign, malignant, or normal by the KHO-CNN. To identify ovarian cancers using deep learning techniques that utilize optimised convolutional neural networks, this model was developed and utilised with a set.

Epoxy resins are a widely used polymeric material for a wide range of applications. Nevertheless, the intrinsic flammable nature of epoxy resins limits their applications in extreme conditions. Chemists look for suitable flame-retardants (FRs) to impart flame retardance to the epoxy resin. Adapting suitable organic synthetic approaches to synthesize FRs is always viable. Schiff base chemistry has emerged as a promising approach for developing FRs in epoxy resins, mainly due to its efficient char-forming ability. The imine group (  C N  ) not only serves as a functional site for attaching FR agents like DOPO, but also contributes to flame retardance in the condensed phase by facilitating the formation of cross-linked, nitrogen-containing cyclic networks between Schiff base units. The present review provides an overview of possible approaches to make FRs for epoxy resins via Schiff base chemistries. The flame-retardant mechanism of Schiff base-containing epoxy thermosets is also proposed. Significant findings from multiple research groups, as well as major insights and viewpoints, are discussed.

Correction for ‘Towards advanced mineral identification for future space mining applications employing LIBS and machine learning’ by Homa Saeidfirozeh et al. , J. Anal. At. Spectrom. , 2026, 41 , 615–632, https://doi.org/10.1039/D5JA00377F.

Wearable sensors are increasingly deployed in healthcare, fitness, and human activity recognition (HAR). Adversarial attacks on deep learning models used in HRA increase security concerns in consumer goods. Adversarial time-series perturbations decrease the accuracy of prediction models. This paper investigates the impact of Time-Warping Adversarial Attacks (TWAA), which manipulate the temporal axis of sensor signals while preserving amplitude ranges. Thereby, generating adversarially misleading samples. We propose a trust-centric framework comprising attack execution, detection, and mitigation modules, and evaluate it on three benchmark datasets (UCI HAR, WISDM and MHEALTH) using baseline deep models (CNN, LSTM, and CNN-LSTM). Experimental results show that TWAA significantly degrades classification performance. It reduces the adversarial accuracy by up to 30% and achieving attack success rates exceeding 40% at moderate perturbation parameters. Our detection mechanism achieves AUROC >0.85 across different types of TWAA, while the mitigation strategy restores model accuracy to above 80% with inference latency under 0.01 ms/sample. These findings demonstrate both the severity of temporal adversarial threats in HAR and the effectiveness of our proposed trust-centric defense. Unlike existing mechanisms that rely on adversarial training or offline robustness enhancement, the proposed framework enables runtime detection of physically plausible time-warping adversarial attacks and applies conditional input-level temporal repair using dynamic time warping. Thus, providing a holistic pipeline for the analysis, detection and mitigation of TWAA on wearable consumer goods.