Deccan College Post Graduate and Research Institute

The built environment of childcare spaces plays an important role in the psychosocial wellbeing of a child. An appropriate physical environment encourages curiosity, independence, and a sense of belonging by serving as an active contributor to a child’s holistic growth. Natural light, safe and adaptable layouts, and access to outdoor spaces promote physical activity, cognitive engagement, and emotional well-being. Conversely, overcrowded and noisy spaces undermine a child’s sense of autonomy and security. Therefore, designing spaces that are child-friendly and comprehensible, and measuring the attributes of such a built environment, is essential. In India, childcare institutions (CCIs) are a dominant form of alternative care for orphaned and vulnerable children. There are limited tools that comprehensively measure and evaluate the built environment attributes of CCIs. Thus, a new Children’s Built Environment Attributes Scale (CBEAS) was constructed. Factor analyses of this scale yielded five factors: Crowding, Housing Quality, Space Personalization, Outdoor Spaces, and Spatial Autonomy. The scale showed internal consistency reliability (alpha). Data were collected from 147 Orphaned and Vulnerable Children (OVC) residing in three different CCIs in Maharashtra, India, using a paper-and-pencil method. Various factors showed association differently and significantly predicted children’s well-being. These findings suggest the need to assess not only the overall built environment but also its individual components. A higher score for each component reflects a better built environment in relation to that specific component, except for crowding, where a higher score reflects lower crowding. The authors propose that the scale could be further modified and restandardized for use in various applications, such as childcare centers, residential schools, and similar settings.

Road surface damages such as potholes and cracks significantly affect transportation safety, vehicle maintenance costs, and travel efficiency. Traditional road inspection methods rely on manual surveys, which are time-consuming, inaccurate, and costly. This paper presents a deep learning-based software framework for automated pothole detection and road quality evaluation using computer vision techniques. The proposed system employs YOLO (You Only Look Once) for real-time object detection and OpenCV for image preprocessing and feature enhancement. A dataset of road images and videos is used to train and validate the model, enabling accurate identification and classification of potholes under varying lighting and weather conditions. The detected potholes are further analysed to determine their severity using shape, area, and texture parameters. The system integrates real-time visualization and reporting tools for effective road maintenance planning. The experimental results demonstrate the efficiency of the proposed AI-based approach in providing fast, reliable, and scalable road condition monitoring without requiring any specialized hardware setup

Wearable devices are transforming the healthcare sector by facilitating continuous health monitoring, promoting proactive patient management, and enhancing overall health outcomes. From basic fitness bands to sophisticated health sensors, these devices gather real-time data on vital signs, physical activity, and other physiological metrics, providing crucial insights for both patients and medical professionals. Despite the growing adoption of wearables, there remains a need to understand user perceptions, actual usage patterns, and challenges. Such insights can help bridge gaps in healthcare delivery and improve patient outcomes, for various patient groups. This study investigates patients’ perceptions, usage behaviour, and the perceived limitations of wearable health technologies through a structured survey. Findings indicate that while awareness and adoption levels are relatively high, a significant proportion of users remain sceptical about the effectiveness of wearables for long-term health monitoring. The most valued benefits cited by respondents are physical activity tracking, sleep monitoring, and stress tracking. However, key challenges include concerns over data privacy and accuracy, lack of seamless integration with formal healthcare systems, limited digital literacy, accessibility issues, high costs, and inadequate battery performance. The findings highlight a critical gap between adoption and trust in wearable health technologies, underscoring the need for improved reliability, data privacy, user education, and healthcare integration to enhance patient confidence and outcomes. Overcoming these challenges is key to unlocking the full benefits of wearables in delivering accessible and effective healthcare.

It is crucial for organizations to secure their data in the internet era. The use of Intrusion Detection Systems (IDS) implies this security. Several researchers used various tools and methods to implement various IDS models. However, a few performance concerns that must be resolved are crucial from a security standpoint. The problems pertain to the IDS time efficiency referred as timeliness, accuracy as well as the fault tolerance. The proposed model of intrusion detection has two phases of detection. Every phase uses a different set of machine learning algorithms. Phase I employs Support Vector Machine (SVM) and k nearest neighbor (kNN), whereas Phase II uses Decision Tree and Naïve Bayes. This two phase detection takes care of reducing false positives and false negatives. To compensate the execution time of these four techniques, the big data environment—Hadoop Distributed File System (HDFS)—is utilized as the underlying storage and processing structure. With such arrangement of two phases, the model gives accuracy of 97.29% overall for known and unknown attacks. For known attacks it gives 99.49% and for unknown attacks it gives 96.28% accuracy in detecting intrusion. Also, the time efficiency is measured for training and testing of the model, for training with 10,000 records, it took 0.7 seconds which is very efficient as considered to existing systems. The detailed performance achievements are discussed in results section. Also, because of HDFS, it becomes distributed and fault tolerant intrusion detection system.

Passwords remain a universal mechanism for user authentication, yet they are troubled by weak choices and vulnerabilities. The arrival of quantum computing threatens to worsen these issues by enabling faster password cracking. Conventional wisdom suggests using longer or more complex passwords to counter quantum attacks, but this approach can be impractical.In this paper, we propose a novel quantum-resistant password generation framework that leverages a Quantum Neural Network (QNN) to create highly secure passwords. Our approach combines quantum machine learning with entropy-based rigorous analysis to ensure that generated passwords are resilient even against adversaries equipped with quantum computing capabilities. We design and train a QNN using large-scale password datasets (including RockYou, Have I Been Pwned, and others) to learn the distribution of common passwords and then generate passwords that avoid these patterns. To rigorously quantify security, we replace simplistic entropy estimations with a von Neumann entropy analysis, measuring the quantum entropy of the password generation process. The experimental results demonstrate that our QNN-generated passwords achieve significantly higher entropy and resistance to guessing attacks compared to conventional methods. This research highlights how quantum computing can be harnessed not only as a threat but as a tool to enhance security.

This research project aims to understand how the concept of new museology helps to communicate with differently abled people. Though the policy of exclusion for various kind visitors is also prevalent in museum due to vision centric nature of museum and architecturally designed for people without any difficulties. One such example is the exclusion of visually impaired people from the museum. The role of the museum has significantly changed in the recent years and has been attributed to play its most important role as an educator. This being an important aspect; there have been developments also to justify it being an organization in the public sector and for the public sector. Museums need to be accessible to all and this has been worked upon in different museums across the globe to make it all inclusive and not exclusive in nature. The cultural exclusion is not only from the perspective of inaccessibility for audience but also from the perspective of the lack of meaningful representation of disability within museum’s collection. Since the development of concept of museology, the differently abled people have been historically and contemporarily excluded from cultural spaces. The National Museum has made a significant endeavour to make museum more inclusive comprising visually impaired people by making Anubhav Gallery. In this research it is observed that the Anubhav gallery is not just being a repository of materials on display but it serves an institution, which enables dialogue between the audience and the objects. Providing an interactive model for accessible gallery for all visitors or designing of a specific gallery dedicated to particular visitors are not enough but museums should also promote the facilities through various programmes among the marginalized as well as common visitors also. Hence, there is no need for individuals with different abilities to separate for museum experience. This can allow families or groups of friends to enjoy the experience together and to build their relationships with each other.Keywords- Visually Impaired, Disabled, Vision loss and Accessibility

We successfully deposited In 2 Se 3 films on the interdigital electrode (IDE) substrates using the radio frequency (RF)-magnetron sputtering method with optimized parameters. The formation of high-quality γ -In 2 Se 3 using X-ray diffraction (XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FE-SEM), and energy dispersive spectroscopy (EDS) is explored. Subsequently, we fabricated γ -In 2 Se 3 -based photodetectors on indium tin oxide (ITO)-coated IDE using optimized parameters. The detailed investigation focused on the influence of IDE spacing, bias voltage, and light intensity on the photodetector properties. The photodetector fabricated with an IDE spacing of 335 µm exhibited outstanding properties, including the highest photoresponsivity of 14.8 µA/W and detectivity of 31.3 × 10 7 Jones. It also demonstrated a fast rise time of 99 ms and a decay time of 61 ms. In the bias voltage variation study, the γ -In 2 Se 3 -based photodetectors exhibited a linear relationship between the change in current and the bias potential, indicating the formation of ohmic contact between γ -In 2 Se 3 and ITO electrodes. Examining light intensity photoresponse, we varied the power density of light from 5 to 30 mW/cm 2 . We observed a direct proportionality between the generated photocurrent and the incident light intensity. However, at higher light intensities, there was a decrease in photodetectivity from 3.97 × 10 8 to 1.16 × 10 8 Jones and a reduction in photoresponsivity from 33.36 to 9.73 µA/W for the γ -In 2 Se 3 -based photodetectors. In conclusion, the photodetector properties of γ -In 2 Se 3 -based devices are critically influenced by IDE spacing, bias voltage, and light intensity. Index Terms-γ -In 2 Se 3 , interdigital electrode (IDE), photodetector, radio frequency (RF) magnetron sputtering.