Keystone College

Fire incidents in residential and industrial environments present serious risks to human safety and infrastructure, requiring rapid detection and effective mitigation systems. This study proposes the design and experimental evaluation of an automatic fire-fighting robot equipped with gas detection and real-time alert capabilities. The system is built around an ESP32 microcontroller and integrates a flame sensor for fire detection, an MQ-135 sensor for hazardous gas monitoring, and a GSM module for transmitting remote alert notifications. A motor-driven water pump is employed to autonomously suppress detected fires, while a dual-mode control mechanism enables both autonomous and manual operation. Experimental evaluations were conducted under controlled indoor conditions to assess system responsiveness and reliability. The results show that the robot is capable of detecting flame sources at distances of up to 80 cm, with an average response time of approximately 3.2 s from detection to fire suppression activation. The gas detection module successfully identified abnormal gas concentrations and triggered SMS alerts with a success rate exceeding 95%. The proposed robot demonstrates a low-cost, portable, and scalable solution for early fire detection and suppression in small-scale indoor environments such as homes, offices, and warehouses. By combining autonomous navigation, gas monitoring, and real-time communication within a single robotic platform, this work contributes to the advancement of intelligent fire-safety systems and IoT-based emergency response technologies.

The traditional teacher preparation model and traditional assessments are outdated, necessitating flexible and accelerated pathways to address declining enrollment and educator shortages. Apprenticeships and competency-based education offer viable alternatives, emphasizing skill attainment over traditional timelines. Effective alternative certification programs require structured support to ensure teacher retention and preparedness, addressing critical gaps in education. Through a review of traditional and alternative certification pathways, key terminology is defined, current literature is reviewed, and practical, research-based solutions are proposed.

Aim: Cloud computing (CC) is a revolutionary new archetype in which users pool their computing resources to provide greater efficiency for everyone. Data become increasingly vulnerable to diverse security threats from attackers when millions of users circulate the same network for data transmission. Protecting these reports has shifted to the vanguard of priorities. The existing data security approach prioritizes protecting data at rest in cloud storage but gives less thought to protecting data in transit. During transmission, the data are vulnerable to intrusion attempts. Methods: The third-party auditor is provided access to data during the transfer phase, which is also the current pattern. As the attacker can now pose as a trusted third party, it makes the data more susceptible to unauthorized access. However, growing concerns regarding data privacy and security have made outsourcing sensitive information to faraway data centers difficult. As a result, new security concerns in the cloud necessitate an improved version of the tried-and-true advanced encryption standard (AES) algorithm. Key aspects presented in this study include a secure and private framework for owner data. It improves upon the 128 AES technique by adding a second round of encryption using a different key, allowing for a throughput of 1000 blocks per second. However, the standard method uses a single round key and only 800 blocks per second. The suggested approach reduces energy consumption, improves load distribution, and optimizes network trust and resource management. Results: The proposed architecture allows for the use of AES with cipher lengths of 16, 32, 64, and 128 bytes. The effectiveness of the algorithm in terms of attaining target quality metrics is illustrated graphically via simulation results. This strategy reduces power consumption by 13.23%, network utilization by 12.43%, and delay by 16.53%, according to the outcomes. Conclusion: As a result, the recommended architecture enhances safety, cuts down on wasted resources, and speeds up the rollout of computational cloud services.

Tackling Spanish as a foreign language (ELE) in India poses challenges from a linguistic, didactic and cultural point of view, given the lack of a significant number of native speakers of Spanish and the presence of vernacular languages. In this study, we have analysed Spanish language teaching in India using a case study approach in which we have tried to assess the importance of language policies and teaching methods as well as the most common difficulties encountered by learners in areas such as pronunciation, conjugation and syntax. We have also sought to reflect on the importance of teacher training, materials and support networks that can be found in other study institutions, as well as the role that can be played in the whole learning process by people outside the educational sphere. We have also tried to identify the strategies which, according to the educational community, have worked best to keep students motivated and to involve them fully in the teaching-learning process outside the classroom. Finally, we propose some lines of action and encourage the entire Hispanic community in India to continue working together and to establish alliances and collaborative relationships with educational institutions and with the government itself in order to continue expanding the teaching of Spanish in India. Resumen: Abordar el español como lengua extranjera (ELE) en India plantea desafíos desde el punto de vista lingüístico, didáctico y cultural, al no contar con un número significativo de hablantes nativos de español y con la presencia de las lenguas vernáculas. En este estudio, hemos analizado la enseñanza del español en India utilizando un enfoque de estudio de casos en el que hemos tratado de valorar la importancia de las políticas lingüísticas y de los métodos de enseñanza así como las dificultades más comunes con las que se encuentran los estudiantes en áreas como la pronunciación, la conjugación y la sintaxis. También hemos querido reflexionar sobre la importancia de la formación del profesorado, de los materiales y de las redes de apoyo que puedan encontrar en otras instituciones de estudio así como del papel que pueden desempeñar en todo este proceso de aprendizaje de personas ajenas al ámbito educativo. Asimismo, hemos tratado de identificar cuáles son las estrategias que según la comunidad educativa han funcionado mejor para mantener motivados a los estudiantes y hacerles partícipes de una forma plena del proceso de enseñanza-aprendizaje que se desarrolla fuera del aula. Por último, proponemos algunas líneas de acción y animamos a toda la comunidad hispanista de la India a seguir trabajado conjuntamente y establecer alianzas y relaciones de colaboración con instituciones educativas y con el propio gobierno para seguir expandiendo la enseñanza del español en India.

<div class="section abstract"><div class="htmlview paragraph">The global environmental pollution issue and global warming caused by internal combustion engines (ICE) have prompted automotive manufacturers to pioneer the development of emission-free or pure electric vehicles. The Indian government declared that all ICE cars will be replaced by electric vehicles by 2030. Thus, after 2030, ICE vehicle scrapping will be prevented by retrofitting. Transforming traditional cars into electric vehicles in Indian markets reduces emissions and enhances sustainability. This work aims to transform the Maruti Suzuki Zen petrol car into a fully electric vehicle while keeping its pristine transmission system with an onboard charging system. During the fieldwork, all unnecessary components of the ICE are removed to transform it into an electric vehicle. The E Zen’s maximum speed, gradeability, and driving range on both level and sloping roads were also examined. The performance was assessed using a 72 V, 144 Ah lithium-ion battery pack and an AC induction motor with a peak output of 15 kW. The vehicle’s dynamic performance was evaluated, and the findings indicate that the vehicle’s maximum speed, grading capacity, and driving range are 60 kmph, 12.5%, and 100 km, respectively. The weight of the E Zen gains a mere 6%, which is deemed acceptable according to the ARAI standard for retrofitted vehicles in India. The modified vehicle has a 73.41% lower operational cost than the gasoline-powered car. This technique stops global warming and shields the environment from damaging vehicle emissions.</div><div class="htmlview paragraph">The results were promising, and the proposed system functioned adequately for its intended purpose. This work offers exceptionally viable outcomes by achieving efficiency in both economic and environmental aspects.</div></div>

In Symposium , Aristophanes introduces a myth of trigender: male, female, and their union, asserting that beings were once split in half by Zeus. This separation compels individuals to spend their earthly lives in search of reunion, ultimately striving for the island of the blessed. This paper investigates the implications of Aristophanes' claims regarding the nature of love as both a fatalistic force and a divine mission. It explores whether Plato hints at a predetermined aspect of love or emphasizes its role as an aspirational journey of the soul. Additionally, the essay examines how the existence of separate genders influences the pursuit of love and the potential for reunion. What does it mean for individuals of a single gender in this framework? How does this framework shape our understanding of love's mission? By addressing these questions, the essay aims to elucidate the complexities of love in Symposium, highlighting the tension between Aristophanes' myth and the broader philosophical discourse initiated by prior speakers and echoed by Socrates. Ultimately, this paper will attempt to comment upon the existential and metaphysical dimensions of love as a quest for unity and fulfillment.

In today fast-paced society, psychological health issues such as anxiety, depression, and stress have become increasingly prevalent across diverse populations.This paper explores the prediction of anxiety, depression, and stress levels via machine learning algorithms. Five different machine learning algorithms were employed to predict the severity of anxiety, depression, and stress across the sampled population. These algorithms were chosen for their high accuracy, making them particularly well-suited for predicting psychological problems. However, applying these algorithms made it apparent that class imbalances existed in the confusion matrix, necessitating incorporating the F1 score measure. The F1 score proved crucial in identifying the most accurate model among the five applied algorithms, ultimately revealing that the random forest classifier was the most effective in predicting anxiety, depression, and stress levels. Moreover, the specificity parameter was examined, showing that the algorithms exhibited notable sensitivity to negative results. The authors emphasized the trade-off between accuracy and execution time, particularly in the hybrid approach. While the hybrid model combined the strengths of multiple algorithms to achieve higher prediction accuracy for anxiety, depression, and stress levels, it also resulted in longer execution times. This trade-off is a common challenge in machine learning, where increasing model complexity can lead to improved accuracy but at the cost of slower processing speeds. This study contributes to the growing body of research on machine learning in mental health prediction, offering valuable insights into the intricacies of class imbalances and the importance of performance metrics such as the f1 score and specificity. The findings underscore the potential of machine learning algorithms, particularly the random forest classifier, in enhancing our understanding and prediction of psychological health issues in diverse and dynamic populations.

In recent years, natural failures including floods, landslides, earthquakes, volcanic eruptions, and wildfires have come to be increasingly Ubiquitous, These events, frequently worsened with of human activities like deforestation, immoderate chemical use, heavy use of gasoline production, and international warming, those problems result in sizable loss of existence and belongings damage global, fortunately, technological improvements have furnished valuable tools for know-how and mitigating the risks associated with these screws-ups. The Internet of Things (IoT), with its various competencies, has appeared as a promising solution for catastrophe management. Wi-fi sensor networks (WSNs) and IoT platforms can establish centralized facts systems to reveal environmental situations. Through sensor information evaluation, we will discover capability risks in real time. When unusual readings are detected, the device can cause signals, ship SMS messages, and make calls to exact contacts. These signals offer statistics about the threat factor, its location, and suggested movements. Using uploading actual-time statistics to cloud servers and issuing alerts through various communication channels, this machine ambitions to reinforce communities with critical data, thereby enhancing their disaster response and preparedness

As the development of artificial intelligence (AI) accelerates, it is increasingly crucial for societies to discuss its potential impacts. Some who hail AI as a technology heralding another major technological revolution in human history claim it will be a strong presence in many fields, from education to finance to entertainment. There is merit to this enthusiastic claim. AI's power might not necessarily rest in its capabilities but in its magic to transform many industries in the long run.