Jalpaiguri Government Engineering College

Entrained saline liquid contaminates the vapor, making the condensation output unsuitable for drinking. Thus, estimating the liquid entrained fraction in spray flash evaporation systems is crucial for improving desalination performance, ensuring effective vapor-liquid separation, and promoting cleaner production. In this study, liquid carrying experiments during spray flash evaporation are carried out in 1000 mm long and 220 mm internal diameter test sections. The experiments covered four distinct pressure conditions (11.3 kPa to 41.3 kPa) and a wide range of water feed temperature conditions (95-70 degrees C). Experiments use two nozzle length arrangements (300 mm and 410 mm) to inject normal tap water downward into the flash chamber. These experiments examined operating conditions, including droplet residence time, vacuum tank pressure, initial spray mass, and superheat degree. The system's efficiency is evaluated using the final evaporated mass, yield ratio, and liquid carrying factor. The study indicated that the final evaporated mass is increased by 89.2% by increasing the initial feed temperature by 25 degrees C, keeping the other conditions the same. While comparing the results, it was observed that the yield ratio obtained from the longer nozzle height of 410 mm is close to the theoretical results. It is reported that at a superheat of 46.75 degrees C, the final carrying mass is about 296 g, whereas for superheats of 36.75 degrees C and 21.75 degrees C, the final carrying mass is 213 g and 193 g, respectively. The results suggested that the liquid entrained mass and fraction increase with the degree of superheat and residence time but decrease with an increase in nozzle height. The findings highlight the need for demisters to prevent excessive liquid carryover, improve flash efficiency, and enhance overall system performance, particularly in desalination and cooling applications.

Understanding how nighttime sleep affects next-day thermal comfort is critical in naturally ventilated (NV) buildings, especially in warm-humid climates where mechanical cooling is absent. This study presents a novel investigation linking previous night’s sleep quality to next-day’s thermal sensation, comfort, and adaptive behavior in NV hostels in eastern India. Field data were collected over two seasons from four NV hostels using ASHRAE Class II measurements and subjective responses, including the Pittsburgh Sleep Quality Index (PSQI). Statistical analyses including correlation, multiple regression and probit modeling were used to quantify the relationships between environmental variables, sleep-quality and comfort. Results revealed significant seasonal variation in operative temperature (22.8 °C winter vs. 31.2 °C summer), clothing insulation (0.68 vs. 0.38 clo), PMV (–0.48 vs. +1.83), and TSV (–0.74 vs. +0.69). Poor sleep (PSQI > 5) was associated with up to 1.5 units extreme TSV, and lower satisfaction and acceptance. PMV overpredicted discomfort compared to TSV, reinforcing the limitations of heat-balance models in NV settings. Thermal neutrality shifted seasonally (TnG: 24.2 29.8 °C). This study provides the first empirical evidence that prior sleep quality significantly influences thermal comfort perception, highlighting the need to integrate sleep dynamics into adaptive comfort frameworks.

Cyberbullying has become a pervasive issue on social media, significantly impacting the well-being of individuals, and decision explainability has become a concerning issue in cyberbullying detection. This study aims to enhance the decision explainability of cyberbullying detection by extracting metadata through textual data analysis, based on contextual factors like gender, race, religion, and sentiment. Utilizing the HateXplain dataset, which provides annotated social media posts, we prepare, restructure, and preprocess the data through key steps such as dataset preparation, cleaning, tokenization, and label encoding. Our comprehensive machine learning-based approach focuses on detecting cyberbullying across sensitive categories like race, religion, gender, sexual orientation, and miscellaneous criteria. We employ a Bi-LSTM model to capture the contextual and sequential dependencies inherent in online harassment. The model’s performance is evaluated using various metrics, including top-K accuracies, precision, recall, and F1-score. The results highlight the model’s effectiveness in addressing the nuanced and context-dependent nature of cyberbullying, offering a well-prepared dataset and a robust solution for fostering safer online environments.

Human behavior analysis significantly depends on facial expression recognition, where deep learning has enabled the development of visionary models that can surpass human-level performance. Explainable Artificial Intelligence (XAI) techniques are employed to validate the trustworthiness of a trained convolutional neural network by providing interpretable heatmaps, generated using recent techniques, including GradCAM, GradCAM++, LayerCAM, and ScoreCAM. These saliency heatmaps highlight the critical facial regions used by the classifiers, thus aligning the system’s behavior with human cognitive processes. Metrics such as average drop, confidence increase, and win percentage are utilized to assess the system’s reliability by analyzing these heatmaps, but they can not quantify the measure of trustworthiness. This study introduces Thresholding-based Evaluation Metrics in terms of Precision, Recall, and F-measure that not only assess the system’s reliability but also quantify the measure of trustworthiness of an XAI technique for a given classifier. Experiments are conducted on three benchmark datasets: CK+, RAFD, and RAF-DB, using classifiers including VGG19, ResNet18, GoogleNet, DenseNet121, and EfficientNet, and evaluated for different XAI techniques. The results demonstrate that the proposed metrics are as efficient as the traditional metrics and advance the assessment by quantifying the reliability measure, increasing their acceptability for human-centered applications. The source code will be made publicly available at https://github.com/Sayankumar007/FER-XAI-ThreshEvalMetrics .

In present study, an energy efficient higher-order leaky integrate-and-fire (HLIF) neurone model is proposed to enhance the temporal learning capability and classification performance compared to first-order LIF leaky integrate-and-fire, generalized LIF (GLIF) and relaxation LIF neuron models. The theoretical formulations of the proposed neurone are articulated through an analysis of membrane potential dynamics and the rate of membrane decay (RoD), offering insights into temporal retention, membrane persistence, and spike propagation characteristics of HLIF neurone model. Furthermore, spike encoding framework is employed via spike, image, and raster formation, along with temporal membrane state visualisation on benchmark datasets (i.e. MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100), to assess the temporal dynamics of the encoded spike streams. Subsequently, an HLIF-based SNN architecture is also developed and assessed for image classification on benchmark datasets. Experimental results indicate that the proposed HLIF neurone attains a remarkable 96