Various authors from around the world have extended the fuzzy concept to study the uncertainty condition and define its degree of certainty in various real-life experiments. At the same time, many authors have discussed the shortcomings of the definition of fuzzy sets that currently exist. However, no author has properly highlighted the problem of not following the two main classical set theories logically. To address this issue, an imprecise set definition is introduced as an extended definition of fuzzy sets, where the new concept applies two parameters, namely the functions of membership and reference, instead of one, and is helpful in defining the uncertainty problem in a more convenient manner than the existing one. In our previous work, we have studied imprecise subgroup using this new concept addressed by Baruah. In this paper, using the concept of complement of imprecise subgroup, we have introduced anti imprecise subgroup and some properties of anti imprecise subgroup with examples. Imprecise subgroup is an extended version of fuzzy group theory developed using the definition of imprecise set defined by Baruah. In addition, we expected an application developed from an anti imprecise subgroup that can be used to resolve various networking problems.

Mathematical modeling plays a crucial role in epidemiology by helping us understand how an epidemic unfolds under different conditions. Respiratory infectious diseases have emerged in our history, the virus has significantly impacted all aspects of life. In the absence of a definitive treatment, vaccination and Non-Pharmaceutical Interventions (NPIs) such as social distancing, handwashing, wearing face masks, quarantine, isolation, and contact tracing have been essential in controlling its spread. This study develops a deterministic mathematical model to explore the dynamics of respiratory infectious diseases under key mitigation measures, including vaccination, face mask usage, quarantine, and isolation. The system of Ordinary Differential Equations (ODEs) is solved using Wolfram Mathematica, while the Next Generation Matrix (NGM) method is employed to determine the basic reproduction number. Stability analysis is conducted using the Jacobian matrix, and numerical simulations are carried out in Python using Jupyter Notebook. The analysis indicates that the model has a disease-free equilibrium (DFE), which is locally asymptotically stable when the basic reproduction number is less than one. This suggests that respiratory infectious diseases can be effectively controlled if vaccination and NPIs are implemented together. Sensitivity analysis highlights that the most critical factors for eradicating respiratory infectious diseases are the vaccine coverage rate (the proportion of susceptible individuals vaccinated) and vaccine efficacy.

This research focuses on developing an automated framework for evaluating distress on flexible and rigid pavement surfaces through deep learning and algorithms, enhancing infrastructure monitoring by efficiently identifying, assessing, and measuring road distresses. The methodology begins with identifying road stretches from ground-level images, followed by capturing photos of distresses and applying algorithms to measure their dimensions accurately. A YOLOv5 model is developed to evaluate the length and width of identified distresses, with an exploration of the relationship between camera position and measurement accuracy. Physical measurements using tape are employed for validation, ensuring that the automated results align with real-world dimensions. Results indicate that the average errors of 26.1% for length and 26.9% for width for flexible pavement and the average percentage error in length is about 29% and average percentage error in width is about 1% for rigid pavement. This highlights the importance of precise measurements for effective road rehabilitation. The integration of computer vision in road maintenance, validated through physical measurements, promises significant improvements in the accuracy, efficiency, and resilience of road networks.

Emotions significantly influence human behaviour, decision-making, and communication, making their accurate recognition essential for various applications. This study introduces a novel approach for emotion extraction from electrocardiogram (ECG) and galvanic skin response (GSR) signals using Bidirectional Long Short-Term Memory (BiLSTM) networks. Unlike conventional emotion recognition methods that rely on facial expressions or self-reports, our model utilizes physiological signals to capture emotional states with high precision. ECG provides insights into cardiac activity, while GSR reflects changes in skin conductance, both serving as reliable indicators of emotional responses. By leveraging advanced signal processing techniques and deep learning algorithms, the model effectively identifies intricate patterns within these biosignals, enabling accurate emotion classification. Experimental validation demonstrates the model’s effectiveness in distinguishing between different emotional states, surpassing traditional methods. This research contributes to affective computing and human-computer interaction (HCI) by enhancing the capability of intelligent systems to recognize and respond to human emotions, paving the way for applications in mental health monitoring, driver assistance systems, and adaptive user interfaces.

People around the world use fresh water daily for drinking, sanitation, and washing. At the same time, they discharge wastewater into canals, which can be harmful to both human health and the ecosystem of surface water sources. A significant amount of water is consumed for washing purposes. However, it is possible to disinfect and purify this large volume of wastewater for reuse. The process of treating used wastewater is known as refinement. This study aims to develop a two-stage stochastic recourse model that refines wastewater before it is released into the environment. The goal is to ensure that the refined wastewater does not harm the ecosystem. The treated water can then be repurposed for various secondary uses. The proposed model will account for uncertainties related to the availability of water from the supplying authority. To evaluate the effectiveness of this model, we will compare the costs of the water supply system both with and without refinement. The advantages of the proposed model will be assessed through calculations of the expected value of perfect information (EVPI), the value of the stochastic solution (VSS), the recourse solution (RS), the wait-and-see solution (WS), and the expected solution based on first-stage decisions (EEV). Additionally, a risk-averse (RA) optimization model will be used to analyze the sensitivity of system costs.