TL;DR
A real-time status monitoring and traceability system based on the edge nodes of the Internet of Things is constructed, providing a replicable technical paradigm for the digital protection of intangible cultural heritage processes.
This community service activity aims to address the manual monitoring of temperature and humidity in PT. MINORI's training rooms, which results in slow response times and a lack of historical data. The solution implemented is an Internet of Things (IoT) based environmental monitoring system using an ESP32 microcontroller and DHT22 sensor integrated with a web dashboard for real-time monitoring and data storage. The stages of the activity included a partner needs survey, device design and testing, system installation, and operational and maintenance training for technical staff. The results of the implementation showed that the system achieved 95% data delivery success with an average latency of 2 seconds, as well as a 95% accuracy rate for temperature and humidity measurements compared to the reference device. The outputs of the activity include installed monitoring devices, access to the monitoring dashboard, and operational and maintenance guidelines for the system. This activity has had an impact in the form of increased efficiency in monitoring the training room environment, and is planned to be developed in other training rooms as part of the ongoing implementation of IoT-based smart buildings.
Liquified petroleum gas are more essentioal in all the domestic puporses and utilizes in many applcations, The research work is about to automatically identify LPG gas leakage, Consumption, Temperature and Controlling of the source using sensors. The LPG MQ-2 gas detection sensor is the main component of this device's which is integrated with the design. It measures the amount of LPG while consuming and. The sensor's analogue signal is converted to digital by the ADC and processed by an MCU unit to activate the buzzer and SMS (short message service) fig-5. The sensor system can also display the amount of LPG gas along with the temperature, which as measured by temperature sensor and displayed in LCD, in parts per million standard and The research was carried out in three distinct spatial conditions: semi-open, closed, and open, according to the distance detection and characterization wih different enivroment. According to the data analysis, the module operates best when it is 2.5 centimetres closer to the area. The simulated results are given better results while taking the datas in realtime.
Industry 4.0 has accelerated the adoption of Internet of Things (IoT) technologies for intelligent industrial automation, enabling continuous monitoring of critical environmental and operational parameters. This paper presents an IoT-based Industrial Parameters Monitoring System developed using the STM32F446RE microcontroller for real-time monitoring of industrial conditions. The proposed system integrates multiple sensors, including the MQ135 gas sensor, BMP280 pressure and temperature sensor, DHT11 humidity sensor, KY-026 flame sensor, and SW-420 vibration sensor, to collect and process data related to industrial safety and environmental conditions. Sensor data are acquired through ADC, GPIO, and I²C interfaces and displayed locally on a 16×2 LCD. A WE10 Wi-Fi module enables wireless transmission of processed data to the Right Tech Cloud platform through UART communication, facilitating remote monitoring and visualization. The system also incorporates an audible alert mechanism using a buzzer to notify users when abnormal conditions such as gas leakage, fire, or excessive vibration are detected. Experimental evaluation demonstrates reliable real-time data acquisition, cloud connectivity, and continuous monitoring performance. The proposed solution provides a low-cost, scalable, and efficient approach for improving industrial safety, minimizing manual intervention, and supporting predictive monitoring applications in smart manufacturing environments.
The global issue of increasing amounts of waste in water areas has been very detrimental to the environment and has become a serious concern due to its impacts, especially on water quality, which can disrupt aquatic ecosystems and cause public health problems. Current conventional cleaning methods still rely on manual labor, which is time-consuming and inefficient. This study proposes a solar cell-based and Internet of Things (IoT)-enabled waste transport vessel and utilizes sensors in its design. This prototype was built using an ESP32 microcontroller as the main control unit, two HC-SR04 ultrasonic sensors for waste detection and container level monitoring, servo-based waste pickup, and a DC motor-driven conveyor. Remote monitoring and supervision are implemented through the Blynk IoT platform, enabling actual and real-time system observation. The prototype was built and evaluated, showing an average response time of 4.0–5.2 seconds across various types of waste samples; sensor calibration yielded good linearity (R² = 0.9987) and an average distance error of only 2.08 mm. Stable buoyancy testing where the prototype was able to hold a total mass of 1.73 kg. The solar cell successfully charged a parallel Li-ion 18650 battery bank from 3.40 V to 3.75 V over a period of 5 hours, resulting in a near-neutral daily energy output (~3.43 Wh consumption vs. ~3.38 Wh generation). IoT telemetry successfully monitors and stores data in the cloud efficiently. These results enhance operational reliability, energy efficiency, and control systems that benefit the environment, making this technology a promising solution for sustainability and addressing environmental issues, particularly in aquatic environments. Further research is expected to focus on real-world, dynamic field applications by integrating multi-sensor data and waste classification.
Operating room air cleanliness is critical for infection control, yet conventional static monitoring cannot capture the dynamic release of textile fibers from surgical drapes and medical linens during operations, limiting timely prevention. In this study, an IoT-based real-time monitoring network was designed and deployed with 12 high-precision nodes to continuously collect particulate matter concentration, microbial concentration, temperature, humidity, pressure difference, and personnel activity. Considering the dense medical-equipment environment of operating rooms, the wireless sensing architecture was configured for stable radio-frequency data transmission and electromagnetic-compatible operation, providing a practical sensing scenario related to electromagnetic wave propagation in indoor medical spaces. A dynamic infection-risk assessment model was then established through data fusion, and a long short-term memory algorithm was introduced to predict short-term risk trends. A graded intelligent warning mechanism was connected to the air-conditioning and purification system to support on-demand control. By tracking particulate matter and fiber shedding associated with medical textiles, the system achieved real-time perception, risk prediction, and closed-loop intervention for air cleanliness, offering a reliable technical approach for reducing surgical site infection risks in digitally monitored operating-room environments.
The server room at the Class I Climatology Station of East Java requires reliable monitoring of temperature, humidity, flame detection, and electrical voltage; however, monitoring is still conducted manually without automatic electrical protection. This study aims to design and implement an Internet of Things (IoT)-based environmental condition monitoring and automatic electrical protection system using an ESP32 integrated with BME280, KY-026, and ZMPT101B sensors. The system applies a two-level threshold structure—warning and critical—to trigger notifications as well as automatic power disconnection via relays. The research methodology includes system design, sensor calibration of the BME280 and ZMPT101B using a comparative method, functional testing of the KY-026, and a 14-day field test. The test results demonstrated a temperature correction of -0.21 °C, a humidity correction of -0.80% RH, and an average voltage error of 1.05%. The system recorded two warning-status readings for humidity without reaching critical conditions and successfully transmitted and stored 114,903 data points during the testing period. The reading interval was designed to be 5 seconds, whereas the actual transmission interval was influenced by network conditions. The results indicate that the system can effectively support environmental monitoring and automatic electrical protection for server rooms.
MIT affiliates engage with the MIT-IBM Computing Research Lab to bring rigorous theory to production systems.
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
