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An increasing number of projects require the precise knowledge and control of thermal conditions within the study system and their temporal evolution. This is particularly critical for equipment such as laboratory ovens and microbiological incubators, which are essential in various fields of chemistry and microbiology areas. These devices allow and facilitate the execution of experimental work in controlled environments, leading to reproducible experiments. This work presents a methodology for assembling and calibrating a highly accessible and low-cost data logger equipped with multiple temperature sensors. The final calibrated dispositive is straightforward to construct and allows the simultaneous and independent temperature measurement from multiple positions within the same system, which is then applied to the study, characterization, control, and monitoring of the internal thermal behavior of a laboratory oven dedicated to microbiological agents' cultivation. This approach ensures, through a robust methodology, a precise characterization by quantitative methods that allows objective decision making in the management and control of the temperature inside the system. Additionally, the device is suitable for extension and application in diverse research environments by modifying the sensor calibration to achieve a desired temperature range or number of measurement units, representing a potential work tool for laboratory systems. [ABSTRACT FROM AUTHOR]

Data acquisition is the process of collecting information from various sources through sensors or instruments. After obtaining the data, it is usually converted from the analogue format to a digital format. In this paper an application that gathers room-related information is presented. The purpose of such a system would be to be able to monitor certain parameters of interest in a living space. Afterwards this data can be used in various manners. One of the most important results of this data is being able to identify the habitability of a room. It can point out potential health hazards before they cause actual health problems. An additional objective is for the system to be on an accessible budget and to be easy to understand and implement. The hardware system using Arduino is presented in detail, as well as the graphical user interface created in Visual Studio. The system works as intended for the purposes stated above. It measures: temperature, humidity, light intensity, sound, and air quality. It works on its own if it is supplied with a 9-volt power source. The Graphical User Interface can be used to visualise and analyse the collected data. The system can still be improved by adding wireless communication such as Wi-Fi and allowing it to be integrated into Internet of Things systems. The power consumption could be improved if needed for higher sampling frequency demands. [ABSTRACT FROM AUTHOR]

In recent years, the development of robotic vehicles in agriculture has made it possible to reduce human intervention and fatigue in carrying out arduous or repetitive tasks, as well as helping to promote sustainable agriculture to address climate change. However, the great diversity of agricultural tasks and the varied production systems and crops demand a wide range of solutions that can be adapted to robotic vehicles as a power source. These alternatives must be affordable and user-friendly for some users, although more sophisticated solutions must also be developed for others, depending on their specific needs. For this, the present work focuses on the development of two maize seeders with different metering systems coupled to an agricultural robot. The first seeder has a conventional mechanically driven seed metering system with a drive wheel and chain gear, while the second one has an electronically driven metering system based on a DC motor and a digital encoder controlled by a microcontroller. Both seeders were coupled to a remote-controlled robotic vehicle and evaluated on real farmland. Seed distribution in the seed rows was contrasting; the results indicated that the mechanical system performed better in the field than the electronic system. For both seeders, the working capacity was approximately 0.135 ha/h at an average speed of 2.0 km/h. The proposed robot–seeder assembly could help farmers automate and reduce the workload associated with planting, as well as attract young people to the field. [ABSTRACT FROM AUTHOR]

In today's era, personal security is a significant issue, and research is being conducted worldwide for better solutions to protect us in times of danger. Other than learning self-protection, people need to have a specialized gadget that allows them to find support the soonest while they are in a difficult situation. However, admittance to a particular gadget or application may not be feasible for them immediately after the assault. As a proposed arrangement, we have built up a framework in the form of a band, including an electromyography sensor (EMG) sensor and a heartbeat sensor named FemmeBand. This group of devices is connected using wireless body area network (WBAN)'s on-body communication. The conjugation of the sensor nodes will convey SOS messages in the wake of deciphering a signal pattern from the EMG sensor brought about by gestures of hand along with the combination of a pulse rate from the subject. FemmeBand deals with Arduino Uno, ESP8266 WiFi module, EMG muscle sensor, and heartbeat sensor, all of which are powered by a lithium-ion rechargeable battery. In a time of danger, FemmeBand can send signals to the cell phone, sending the person's coordinates to contacts pre-saved in safelists. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative improvement to impressed current cathodic protection (ICCP) systems by integrating a pulse-feeding technique designed to address metal protection degradation during off-potential periods, a common issue in conventional systems. The proposed system enhances the overall effectiveness and reliability of ICCP, providing consistent corrosion protection for critical metal structures. A notable advantage of this method is its simplicity, utilizing a cost-effective microcontroller for pulse feeding. This approach simplifies integration processes and enhances cost-effectiveness, making it an attractive solution for improving cathodic protection system performance without substantial additional costs. The method addresses conventional ICCP weaknesses by applying a high-frequency pulse current during off-potential periods. This reduces excessive negative charge buildup on metal surfaces during interruptions, boosting the system's effectiveness and stability. Research laboratory experiments were conducted using pulse width modulation (PWM) on an ATmega328P microcontroller to demonstrate the method's effectiveness. Additionally, an IoT-monitored ICCP system was developed using an ESP32 microcontroller and the Blynk application. Results highlight the superiority of a 50 kHz pulse feeding frequency in preventing corrosion compared to lower frequencies. Overall, this advancement significantly enhances ICCP systems, providing improved corrosion protection and durability in harsh environments. [ABSTRACT FROM AUTHOR]

IoT-based Metal Oxide Semiconductor (MOS) gas sensors have potential applications in health, industrial, and agriculture sectors. MQ gas sensors are easy to use, have a large detection range, high sensitivity, and efficiency, and can be interfaced with Arduino for effective use. However, like other any other sensors, the MQ gas sensors are also not able to escape the effect of noise that affects the sensitivity and selectivity causing the unwanted wrong classification and identification of gases, etc finally leading to a wrong justification of results and data. Hence, the study of noise and its removal is very crucial for greater accuracy in its analysis. The main aim of our work is to check whether one can classify different types of noises or noisy signals accurately using even simple ANNs so that after testing the different filtration techniques for the signals we can obtain an independent system specifically for the MQ gas sensors, that can classify as well as filter the noisy signals based on the category to which they are classified. The best and most effective noise filtration method is then obtained. During the study Narrow Neural Network model and Medium Neural Network demonstrated high accuracy in validation and testing, with ROC curves indicating their efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

This study deals with the design and construction of a device that enhances driver comfort and safety by automatically adjusting the microclimate in the vehicle cabin based on real-time skin resistance measurements. Using electrodes attached to driver's skin and an Arduino microcontroller, the system monitors and evaluates skin conductivity and adjusts heating, ventilation, and air conditioning (HVAC) settings accordingly. Experimental verification in laboratory conditions demonstrated device's functionality in changing microclimate parameters. Preliminary results suggest a potential correlation between baseline skin resistance values and the magnitude of observed changes in response to ambient conditions. Subjects with lower baseline skin resistance (≤100,000 Ω) showed smaller changes compared to those with higher baseline resistance (≥100,000 Ω). The current results are graphically processed as the course of skin resistance changes depending on the changing parameters of microclimate. [ABSTRACT FROM AUTHOR]

Background: Climate change and the growing demand for agricultural water threaten global food security. Understanding water use characteristics of major crops from leaf to field scale is critical, particularly for identifying crop varieties with enhanced water-use efficiency (WUE) and stress tolerance. Traditional methods to assess WUE are either by gas exchange measurements at the leaf level or labor-intensive manual pot weighing at the whole-plant level, both of which have limited throughput. Results: Here, we developed a microcontroller-based low-cost system that integrates pot weighing, automated water supply, and real-time monitoring of plant water consumption via Wi-Fi. We validated the system using major crops (rice soybean, maize) under diverse stress conditions (salt, waterlogging, drought). Salt-tolerant rice maintained higher water consumption and growth under salinity than salt-sensitive rice. Waterlogged soybean exhibited reduced water use and growth. Long-term experiments revealed significant WUE differences between rice varieties and morphological adaptations represented by altered shoot-to-root ratios under constant drought conditions in maize. Conclusions: We demonstrate that the system can be used for varietal differences between major crops in their response to drought, waterlogging, and salinity stress. This system enables high-throughput, long-term evaluation of water use characteristics, facilitating the selection and development of water-saving and stress-tolerant crop varieties. [ABSTRACT FROM AUTHOR]

AbstractThe electrical characterization of materials, particularly superconductors, semiconductors, and those undergoing metal-insulator transitions (MITs), relies significantly on resistivity and Hall measurements as a function of temperature. The van der Pauw four-point probe method is commonly used for such measurements, which involves resistance measurements for different circuit configurations connected to sample contacts. However, repeating these measurements at different temperatures is challenging and time consuming. This study introduces a novel approach utilizing a switching circuit controlled by an Arduino device and a LabVIEW program to automate resistance measurements for different electrical configurations. The effectiveness of this setup was demonstrated by testing V2O3 thin film samples deposited on Al2O3 (001) substrates using DC magnetron sputtering. The MIT temperature of the sample was 115 K during heating and 100 K during cooling. The sample exhibited p-type charge carriers with a Hall coefficient of 1.3 ± 0.1 x 10−4 cm3/C and Hall mobility of 1.7 x 10−1 cm2/V∙s, which is consistent with findings from other studies employing commercial equipment. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to the automatic control of a DC motor has been presented through the application of advanced identification and adaptive control techniques. A combination of online and offline identification methods, including least squares and projection algorithms, has been explored to establish accurate models for the motor. Based on these identified models, PID and generalized polynomial integral-acting controllers have been specifically tailored and implemented. A distinctive feature of this work is the integration of adaptive control strategies with these controllers. The adaptive framework has enabled the controllers to autonomously adjust in response to varying DC motor conditions, thereby ensuring robust performance across different operating scenarios. The efficacy of the proposed methodology has been rigorously evaluated through comprehensive simulations and practical experiments on a physical DC motor setup. Utilizing an Arduino Due and an H-bridge for motor control, disturbances have been assessed by coupling an additional motor to the main setup. This approach has facilitated a thorough comparison of disturbance responses across all implemented models and controllers. In summary, a novel methodology combining advanced identification techniques with adaptive control strategies has been contributed by this research, demonstrating significant advancements in the automatic control of DC motors under varying operational conditions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering & Sustainable Development is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The work reports an analysis of vital signs of human beings through a smart heart beat and pulse monitoring system based on Arduino device. Vital signs include heart rate, pulse monitoring and oxygen level of a human being. The system contribute improvements in individual’s healthcare out-comes. AD8232 ECG sensor is used through Arduino Mega which amplify and filters the ECG signals obtained from the electrodes. Arduino Mega is powered by 5 V supply voltage. ECG sensor uses 3 cables which has disposable patches and connected to the chest of human being to capture bioelectric signals. Hence, heart rate and pulse of a human being has been monitored. An additional capacitive filter circuit is used Furthermore, MAX30102 sensor have been used to determine the oxygen level of a person. The results obtained from the study provides a good accuracy. The heart rate varies as per the age of human being. The oxygen accuracy level falls in the range of 95% to 100%. [ABSTRACT FROM AUTHOR]

Arduino, a widely used tool for physical computing, is favored for its affordability and easy availability. However, a drawback for beginners is the requirement of prior knowledge of C programming language and circuit theory for effectively utilizing Arduino. In this research, we address this issue by developing a Graphical Arduino IDE system that allows users to control Arduino without the need for prior knowledge of C language and circuit theory. Users can create node graph‐based scripts in the Wiring Tab of the Graphical Arduino IDE and develop flowchart‐based scripts in the Algorithm Tab. The scripts created in the Wiring Tab serve as guidelines for wiring, thus preventing users from making wiring mistakes. Additionally, users without knowledge of C language can control Arduino by creating flowchart‐based scripts in the Algorithm Tab. The finalized scripts are converted into Arduino code and uploaded to the Arduino board using the built‐in Code Upload feature. Finally, a paired t test was conducted between the Graphical Arduino IDE and Scratch for Arduino, confirming that the Graphical Arduino IDE required fewer user inputs. [ABSTRACT FROM AUTHOR]

This paper focuses on the novel and unique training provision of acoustics relevant for noise, vibration, and harshness (NVH), focused on the ISO-362 standard highlighting important design aspects for electric vehicles. A case study of the practical implementation of off-site vehicle testing supporting an acoustics module is described, detailing a time-constrained test for automotive pass-by-noise and tyre-radiated noise with speed. Industrial test standards are discussed, with education as a primary motivation. The connections between low-cost, accessible equipment and future electric vehicle acoustics are made. The paper contains a full equipment breakdown to demonstrate the ability to link digital data transfer, analogue-to-digital communication, telemetry, and acquisition skills. The benchmark results of novel pass-by-noise and tyre testing are framed around discussion points for assessments. Inexpensive Arduino Uno boards provide data acquisition with class 1 sound pressure meters, XBee radios provide telemetry to a vehicle, and a vehicle datalogger provides GPS position with CANBUS data. Data acquisition is triggered through the implementation of light gate sensors on the test track, with the whole test lasting 90 minutes. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Muscles are an important organ in the movement of the body's skeleton to carry out sports activities. Measurement of muscle activity during the exercise process can be done using electromyography (EMG). This research uses muscle sensor (AT-04-001) which is integrated with ATMEGA 328 SMD. This project presents a wearable, real-time muscle strain mapping system using Arduino and IoT technologies. The system utilizes electromyography (EMG) sensors to detect muscle activity and strain levels. Data is transmitted wirelessly to a cloud-based platform for analysis and visualization. Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise. Moreover, it is widely utilizing for preventing injuries of athletes during a practice session and in few cases, it leads to muscle fatigue. At present, emerging technology like the internet of things (IoT) and sensors is empowering to monitor and visualize the physical data from any remote location through internet connectivity. In this study, an IoT-enabled wearable device is proposing for monitoring and identifying the muscle fatigue condition using a surface electromyogram (sEMG) sensor. Normally, the EMG signal is utilized to display muscle activity. Arduino controller, Wi-Fi module, and EMG sensor are utilized in developing the wearable device. The Time-frequency domain spectrum technique is employed for classifying the three muscle fatigue conditions including mean RMS, mean frequency, etc. A real-time experiment is realized on six different individuals with developed wearable devices and the average RMS value assists to determine the average threshold of recorded data. [ABSTRACT FROM AUTHOR]

The soil moisture sensor provides numerical measurements to detect changes in soil moisture using an analog voltage output. This research aims to develop a capacitive sensor based on a statistical model to detect soil moisture for plant watering, leveraging the Internet of Things (IoT). The analysis was conducted using polynomial and linear regression models. The modeling process was based on primary gravimetric test results from dried soil. The best model coefficients, selected based on the highest adjusted R-squared value, were used for sensor recalibration. A watering system was then developed using an Arduino and a model-driven capacitive soil moisture sensor integrated into an internet-connected smart plant pot, enabling remote control via a mobile phone. The research findings indicate that the 8th-order polynomial model, with the highest adjusted R-squared value of 0.9583, is the most accurate. The smart watering system using the model-driven capacitive sensor achieved soil moisture prediction outcomes ranging from 0.08 to 1.01 for 150 to 418 sensor data points. The internet-connected smart plant pot allows precise and real-time control, delivering notifications and enabling actions when plants require watering.

У роботі охарактеризовано методичні особливості використання програмно-апаратного комплексу Arduino у середовищі навчання вчителів на платформі Tinkercad Circuits. Окреслено функціональні особливості, переваги і недоліки цієї платформи, що роблять доцільним її запровадження у процес неформальної освіти вчителів. Формулювання проблеми. Необхідність дослідження обумовлена потребою підвищення рівня ІКТ компетентності вчителів інформатики та фізики, викладачів закладів педагогічної освіти в аспекті розвитку навичок з робототехніки з використанням програмно-апаратного комплексу Arduino. Особливої актуальності набуває проблема підвищення кваліфікації вже працюючих вчителів, щоб привести її у відповідність останнім досягненням науково-технічного прогресу у сфері розвитку технологій. У зв’язку з цим, важливо розглянути найсучасніші рішення щодо організації середовища навчання, зокрема на базі хмарних технологій, потреба в якимх особливо гостро постає в умовах неформальної освіти. Матеріали та методи. Для досягнення мети роботи були використані загальнонаукові методи: а) теоретичні – аналіз технічної та психолого-педагогічної літератури з проблеми дослідження; узагальнення вітчизняного і зарубіжного досвіду; теоретичний аналіз, систематизація та узагальнення наукових фактів і закономірностей б) емпіричні –бесіди з учасниками освітньо-наукового середовища; педагогічні спостереження. Результати. У роботі обґрунтовано, що методично виважене та доцільне використання апаратно-програмного комплексу Arduino сприятиме більш активному засвоєнню знань, вмінь та навичок з робототехніки, запровадженню інноваційних форм та методів навчання в процесі неформальної освіти вчителів; підвищенню рівня їх ІКТ-компетентності. Висновки. Використання програмно-апаратного комплексу Arduino та робота в онлайн платформі Tinkercad - методично доцільний складник у процесі неформальної освіти вчителів, запровадження даного комплексу відповідно до спеціально розробленої методики спрятиме підвищенню ІКТ компетентності вчителів, ширшому запровадженню актуального освітнього контенту і найсучасніших технологій у процес навчання.

Abstract Background Climate change and the growing demand for agricultural water threaten global food security. Understanding water use characteristics of major crops from leaf to field scale is critical, particularly for identifying crop varieties with enhanced water-use efficiency (WUE) and stress tolerance. Traditional methods to assess WUE are either by gas exchange measurements at the leaf level or labor-intensive manual pot weighing at the whole-plant level, both of which have limited throughput. Results Here, we developed a microcontroller-based low-cost system that integrates pot weighing, automated water supply, and real-time monitoring of plant water consumption via Wi-Fi. We validated the system using major crops (rice soybean, maize) under diverse stress conditions (salt, waterlogging, drought). Salt-tolerant rice maintained higher water consumption and growth under salinity than salt-sensitive rice. Waterlogged soybean exhibited reduced water use and growth. Long-term experiments revealed significant WUE differences between rice varieties and morphological adaptations represented by altered shoot-to-root ratios under constant drought conditions in maize. Conclusions We demonstrate that the system can be used for varietal differences between major crops in their response to drought, waterlogging, and salinity stress. This system enables high-throughput, long-term evaluation of water use characteristics, facilitating the selection and development of water-saving and stress-tolerant crop varieties.

Background: The adoption of sixth generation (6G) networks is expected to revolutionize various sectors like health care by providing unprecedented data transfer capabilities and reliability. This transition is particularly applicable to real-time health monitoring where the speed and consistency of data transmission matters. Objective: The article explores the integration of Arduino Bluetooth modules, specifically the Bluetooth HC-05 module, with 6G technologies for betterment in remote health monitoring systems. By merging the ultra-reliable, low-latency communication (URLLC) characteristics of 6G with the relatively cheap and user-friendly nature of Arduino platforms, a more intelligent health monitoring system could be established. Methodology: The article proposes Arduino sensor modules with a 6G-based real-time uplink to transmit high-resolution biometric data. The study also uses machine learning and edge computing methodologies to improve data processing for patient diagnosis. Factors such as power usage, scalability, and network integration are thoroughly explored to reach a holistic strategy. Results: The results suggest that the use of Arduino Bluetooth and 6G together can enable much better monitoring of many categories in health metrics to meet very different patient profiles. In this collaboration, we are also going to include the two main things that need everyone's attention, data privacy and security. Moreover, the utilization of artificial intelligence (AI) and edge computing led to improved diagnoses and quicker responses. Conclusions: The study demonstrates the promise of Arduino Bluetooth for future healthcare monitoring over 6G networks, and serves as a benchmark toward such ends. It paves the way for cheaper, more available, and innovative healthcare options in the era of the Internet of Things (IoT) and beyond, transforming patient treatment.

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fish farmers struggle with the problem of fish mortality due to climate change, poisoning, and bad water quality. Estimates on the mortality of fishes ranges from 60% to 80% depending on the type of fish. Traditional approaches in monitoring the quality of water such as observation or laboratory testing prove to be costly and time-consuming. This study aims to develop a portable water quality monitoring kit for fish farms. The device allows users to monitor the water's dissolved oxygen, nitrogen, total dissolved solids, turbidity, temperature, and pH level. It is powered by a rechargeable battery, uses an Arduino microcontroller, and has a real-time alert system. The device's limitations include the battery life and the sensors will need to be recalibrated at least monthly to ensure the accuracy of the readings. However, the results show that the implementation of the device garnered high acceptability in all parameters in the evaluation with a total mean rating of 4.66. This implies that the device can be used in monitoring the water quality in aquaculture sectors as corroborated by the respondent's evaluation. It is recommended to enhance the device by adding more water quality sensors and integrating Internet of Things (IoT) capabilities to further improve its functionality and reliability.

To address the challenge of improving the understanding of chemistry concepts, particularly pH, this study explored the impact of integrating educational robotics on students’ motivation and learning outcomes. A total of 160 3rd-year middle school students from a public school in Meknes, Morocco, participated in the study, which was divided into an experimental group (n = 82) and a control group (n = 78). Student performance was evaluated through standardized tests, while motivation levels were gauged using a questionnaire. The findings indicated a significant increase in motivation among students in the experimental group compared to those in the control group. However, no significant differences in overall academic performance were detected between the two groups. These findings provide valuable insights for educators and curriculum developers, underscoring the importance of integrating innovative pedagogical approaches with traditional methods to enhance science education.

Lithium-ion batteries have a wide range of usage. Electric vehicles, portable electronic devices, energy storage systems and medical devices are some of the topics in this wide range. In such applications, it is important to monitor the safety and performance of lithium-ion batteries, increase their lifespan and detect potential problems in advance. Our study was designed using 18650 3.7V lithium-ion battery to meet this need. In this article, an interface is presented that collects instantaneous voltage, current, power and temperature values of lithium-ion batteries under load through sensors and provides visualization using the Arduino microcontroller and C# programming language. The purpose of this study is to monitor the performance of lithium-ion batteries and detect potential overload or overheating situations. The data obtained through the sensors is processed on the microcontroller in the circuit and transferred to the computer environment using the C# programming language. This data is visually presented to the user through radial indicators. Additionally, regression models were used with the Python programming language to predict long charging and discharging times. In the following sections of the article, the design of the study, the hardware and software components used, and the results obtained are explained in detail. The results demonstrate the effectiveness of such an approach for real-time monitoring and ensuring the safety of lithium-ion batteries. [ABSTRACT FROM AUTHOR]

This methodology paper introduces an illuminated culture flask-based 3D-printed bioreactor system (FlaskLED) based on Arduino, Grove ecosystem and LED sticks. The Arduino microcontroller allows extensibility and future upgrades via programming and other modifications. We provide the 3D printer files, video presentation of assembly instructions and a detailed description of the INO code and wiring scheme. The INO code presented can be used to produce different light colors and patterns of varying intensity and durations (intermittent/flashing/pulsing light). The system was validated by examining the toxicity of Au3+ ions on Phaeodactylum tricornutum growth. The capabilities of the system offer unique applications for laboratory and industrial research. Our aim is to provide a low-cost and open-source tool to promote and improve cultivation of microalgae and other photosynthetic organisms under different laboratory and experimental setups. [ABSTRACT FROM AUTHOR]

Copyright of Packaging & Food Machinery is the property of Packaging & Food Machinery Magazine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper focused ona smart, cloud-enabled internet of things system that optimizes home energy distribution, safety, and consumption. The materials used in this paper include Servo motor, lamp holder, energy bulbs, 13A socket, rectifier, jumper wires, electric cables, Blynk interface, motion sensor, temperature/humidity sensor, Esp32, and smoker sensor. The development of the system's code, which was essential for optimizing the system's operations, was carried out using the Arduino Integrated Development Environment. A testbed comprising of the designed work was done in house located in Anambra State, Nigeria over a six-month period, where eight appliances were used to gather used energy by deploying smart plugs, and a smart meter that measures the major energy load of the house. Results show that both implementations collected, stored, and controlled the energy according to the smart, cloud-enabled internet of things system. This work showed that energy providers, and technology developers implement a cloud-enabled Internet of Things system for the best possible household energy usage, safety, and distribution. This technology can significantly increase energy efficiency, save costs, and improve home safety to 100%. [ABSTRACT FROM AUTHOR]

Currently, engine remapping is widely utilized to enhance the performance of diesel and petrol engines. Given that a substantial amount of data must be collected during the optimization process, a data acquisition system is necessary to organize and retrieve data automatically. This research focuses on the design, development, and testing of a data acquisition system for controlling and measuring the throttle pedal in diesel vehicles equipped with a common rail system. The system is designed to manage the Throttle Position Sensor (TPS) and monitor vehicle operational parameters in real-time. The proposed device employs Arduino as the primary hardware, with TechStream software for vehicle data acquisition, and a web-based application developed using the Python programming language, and displays data in real-time in the form of graphs and tables. From the test results, it can be concluded that the developed acquisition system produces precise and sensitive throttle position settings. Its operation is also straightforward, safe, and requires a relatively short amount of time. [ABSTRACT FROM AUTHOR]

Carbon dioxide is a colorless, odorless, and non-flammable gas and is claimed to be the fourth most abundant in the earth's atmosphere. Carbon dioxide emission is mainly generated by human and animal exhalation, decomposition of organic matter, and forest fires. Moreover, human activities in the industrial sector emit high levels of carbon dioxide gas, such as through fossil fuel burning, transportation, and deforestation. It is also an asphyxiant and high exposure to it may lead to health effects in humans such as headaches, breathing difficulty, tiredness, coma, and elevated blood pressure. Therefore, in this paper, a carbon dioxide gas sensor with IoT using TiO2 is proposed to observe varying concentrations of carbon dioxide gas at room temperature. Three similar gas sensors were fabricated via screen-printing technology to compare their performance towards carbon dioxide. The hardware development consisted of an Arduino Uno R3 with ESP 8266 Wi-Fi module, wires, LCD display, red and green LEDs, and a 5V power supply. The ThingSpeak application was integrated with the gas sensor and hardware parts to monitor the carbon dioxide concentration in a real-time system. Gas sensor G1 produced the highest response and highest sensitivity with values of 2.120 and 0.245, respectively. [ABSTRACT FROM AUTHOR]

The rapid advancement of the Internet of Things (IoT) has transformed our lives by connecting objects and creating an intelligent environment that drives automation and massive data collection. IoT has overcome challenges through remote monitoring, robotics, and sensors, with powerful wireless technologies opening up possibilities in various fields, including anatomical techniques. The objective of this study is to demonstrate the feasibility and benefits of integrating IoT technology into the body maceration process by developing a low-cost, accessible, automated maceration control device compatible with theArduino IDE programming environment. Using an open-source NodeMCU system based on the ESP-12E chip, which employs the LUA programming language and is compatible with Arduino IDE, we incorporated a microSD card reader module for data storage, a digital temperature sensor (DS18B20), a clock module (DS3231), a magnetic stirrer and an aquarium heater controlled by a digital thermostat with relay (W1209). To validate the device, we conducted maceration tests on C57BL mice, comparing two groups: Control (n=5; weight=17g) subjected to maceration in 4 liters of water at 10°C without agitation; Experimental (n=5; weight=17g) exposed to maceration in 4 liters of water at 35°C, with agitation at 1500 r/min and water flow renewal at a rate of 900 ml/h. Specimens were evaluated for their degree of maceration, cleaned, weighed, and photographed. The maceration time for the control and experimental groups was 730 hours and 60 hours, respectively. In the experimental group, no foul odors, damage, or bone demineralization were detected. The device cost was 60 USD, and programming through the Arduino IDE environment was straightforward. The focus on hardware contributed to accessibility and time savings. The system operated with a conventional power supply and required a water supply, making it adaptable to various configurations with greater capacity. Under this configuration, we effectively controlled the variables (temperature, agitation, and water flow). The NodeMCU-based bone maceration system improved the performance and adaptability of existing systems and increased accessibility by reducing costs, complexity, and foul odors. [ABSTRACT FROM AUTHOR]

This study harnesses EEG signals to enable the real-time control of servo motors, utilizing the OpenBCI Community Dataset to identify and assess brainwave patterns related to motor imagery tasks. Specifically, the dataset includes EEG data from 52 subjects, capturing electrical brain activity while participants imagined executing specific motor tasks. Each participant underwent multiple trials for each motor imagery task, ensuring a diverse and comprehensive dataset for model training and evaluation. A deep neural network model comprising convolutional and bidirectional long short-term memory (LSTM) layers was developed and trained using k-fold cross-validation, achieving a notable accuracy of 98%. The model's performance was further compared against recurrent neural networks (RNNs), multilayer perceptrons (MLPs), and Τransformer algorithms, demonstrating that the CNN-LSTM model provided the best performance due to its effective capture of both spatial and temporal features. The model was deployed on a Python script interfacing with an Arduino board, enabling communication with two servo motors. The Python script predicts actions from preprocessed EEG data to control the servo motors in real-time. Real-time performance metrics, including classification reports and confusion matrices, demonstrate the seamless integration of the LSTM model with the Arduino board for precise and responsive control. An Arduino program was implemented to receive commands from the Python script via serial communication and control the servo motors, enabling accurate and responsive control based on EEG predictions. Overall, this study presents a comprehensive approach that combines machine learning, real-time implementation, and hardware interfacing to enable the precise and real-time control of servo motors using EEG signals, with potential applications in the human–robot interaction and assistive technology domains. [ABSTRACT FROM AUTHOR]

Agriculture plays a pivotal role in the economy of most countries, serving as a primary source of livelihood and sustenance. In the case of India, it occupies a substantial portion of the nation's land. This article proposes the integration of IoT (Internet of Things) and an automated irrigation system with ML/DL (Machine Learning and Deep Learning) to revolutionize agriculture. The implementation of crop monitoring through sensors not only eases the burden on farmers but also enhances crop productivity. The system, at its core, monitors crucial field parameters such as soil moisture, temperature, and humidity. Given the increasing importance of efficient water management in agriculture, this study outlines an automated irrigation system that leverages cloud computing and IoT to curtail water consumption. Its primary objective is to gather and consolidate data from diverse sources, including data generated by sensors and IoT devices. This centralized data storage approach facilitates seamless data integration from various locations and devices. Through the application of algorithms and dataset analysis, the study determines that the cultivation of "Spider" plants is more favorable when compared to other plant species. Notably, the Random Forest classifier emerged as the most accurate, achieving an impressive 94.77% accuracy rate in this project. In essence, this research endeavors to propel agriculture into a technology-driven and sustainable future, optimizing water usage and improving crop yield. [ABSTRACT FROM AUTHOR]

This paper presents a stepper motor drive using the hybrid two-phase model. The stepper motor changes the pulse signals into angular displacement with some angles. Stepper motors are used to control the speed and are also more reliable for smooth operations. The stepper motor provides constant holding torque with controlled speed ranges from 0 to 6000 range. The closed-loop control technique with park transformation is used to control the speed torque variables in a design range. The simulation and hardware results were discussed in MATLAB\Simulink software. The verified simulation results are motor source voltage, motor source current, motor speed, and torque. The hardware results are also implemented in this paper, the implemented circuit by using Arduino microcontroller. [ABSTRACT FROM AUTHOR]