000			02631nam a22002417a 4500
003			ft8861
005			20251209134432.0
008			251209b ||||| |||| 00| 0 eng d
041			_aengtag
050			_aT58 B85 2025
082			_a.
100	1		_aBulan, Mike P.; Garcia, Steven Q.
245			_aThermoshield: An IoT Arduino-based cooling and heat sensor monitoring system with weather forecasting and advisory using linear regression for enhanced mobile phone protection
264		1	_a. _b. _cc2025
300			_bCapstone Project: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2025
336			_2text _atext _btext
337			_2 unmediated _a unmediated _b unmediated
338			_2volume _avolume _bvolume
505			_aABSTRACT: Mobile phones are essential for communication, navigation, and task management. For some individuals, such as the delivery riders, these devices are crucial for their work, enabling navigation and communication. However, frequent outdoor use exposes phones to environmental factors like extreme heat and rain, affecting functionality and usability. Prolonged use can also cause overheating, reducing device lifespan and performance. Adverse weather conditions further pose safety risks for riders and users. To address these challenges, this study focused on three objectives. First, a protective outbox cover was designed and prototyped to shield devices in weather conditions such as extreme heat or rainfall. Second, a cooling system was developed with a temperature sensor that activates a fan when excessive heat is detected. Third, a mobile application was created to provide real-time weather forecasts, alerts, and safety recommendations using linear regression. Users can check forecasts and receive recommendations based on the weather conditions. In achieving these objectives, the protective outbox cover was designed tin Tinkercard and constructed from water- and heat-resistant materials. The cooling system incorporated Arduino components, including an Arduino UNO R3, and LM35 temperature sensor, a 12V cooling fan, and a 9V battery. The mobile application was built using the Expo framework and React Native, integrating the Tomorrow.io API for real-time forecasts and historical data set and implementing linear regression in Java. All objectives were achieved, resulting in an integrated system that enhances mobile phone usability while outdoors, protects devices from environmental factors, and improves safety for outdoor users, specifically motorcycle riders.
526			_aF
655			_aacademic writing
942			_2lcc _cMS
999			_c37308 _d37308