Search Results (851)

Microfluidics is a technology that manipulates liquids on the scales ranging from microliters to femtoliters. Such low volumes require precise control over pressures that drive their flow into the microfluidic chips. This article describes a custom-built pressure controller for driving microfluidic chips. The pressure controller features piezoelectrically controlled pressure regulation valves. As an open-source system, it offers high customizability and allows users to modify almost every aspect. The cost is roughly a third of what similar, alternative, commercially available piezoelectrically controlled pressure regulators could be purchased for. The measured output pressure values of the device vary less than 0.7% from the device’s reported pressure values when the requested pressure is between −380 and 380 mbar. Importantly, the output pressure the device creates fluctuates only ±0.2 mbar when the pressure is cycled between 10 and 500 mbar. The pressure reading accuracy and stability validation suggest that the device is highly feasible for many advanced (low-pressure) microfluidic applications. Here, we compare the main features of our device to commercially and non-commercially available alternatives and further demonstrate the device’s performance and accessibility in successful microfluidic hydrodynamic focusing (MHF)-based synthesis of large unilamellar vesicles (LUVs). Full article

Background/Objectives: Determining whether dietary fatty acids and the use of fat spreads are associated with cardiovascular risk factors is difficult due to the multicollinearity of fatty acids and the consumption of multiple spread types. Methods: We applied clustering methodologies using data on 31 different fatty acids and 5 different types of fat spreads (high fat: butter, blended butters, and margarines; lower fat: polyunsaturated and monounsaturated) and investigated associations with blood pressure, serum lipid patterns and insulin resistance in the Raine Study Gen2 participants in Western Australia, at 20 and 22 years of age. Results: Amongst n = 785 participants, there were eight distinct clusters formed from the fatty acid data and ten distinct clusters formed from the fat spread data. Male participants had higher systolic blood pressure than females (122.2 ± 11.6 mmHg versus 111.7 ± 10.3, p < 0.001 at age 20 and 123.4 ± 10.6 versus 113.9 ± 9.8, p < 0.001 at age 22). Males consuming exclusively butter as a fat spread had significantly higher SBP (+4.3 mmHg) compared with males not using spreads. Males consuming a high intake of margarine had significantly higher SBP (+6.6 mmHg), higher DBP (+3.4 mmHg) and higher triglycerides (+30.5%). Amongst females, four patterns of fatty acid intake were associated with lower levels of HDL cholesterol compared with the low-saturated-fat/high n-3 reference group (p = 0.017 after adjustment for relevant confounders, range = −10.1% to −16.0%, p = 0.017). There were no associations between clusters and HOMA-IR or other serum lipids for males or females. Conclusions: Compared to using no fat spreads, amongst males, a high intake of margarine was characterised by higher systolic and diastolic blood pressure and higher serum triglycerides, whilst the use of butter also was associated with higher SBP. Diets low in n-3s or high in trans fats were associated with sub-optimal HDL levels amongst females. Full article

This paper is the first to offer a digital-twin of the Energy Rebound Testing Device, which is specified by the National Collegiate Athletic Association for the sport of basketball. This digital-twin replicates the physical ERTD, which was previously studied empirically. This paper merges the original finite element analysis of a basketball rim and backboard with the finite element analysis of the Energy Rebound Testing Device, using the ANSYS Workbench 2024R2, student edition. The first modal model was of the ERTD in isolation in the Workbench Modal Analysis system, and the natural frequency modeled via finite element analysis, 12.776 Hz, compared favorably with the empirical modal analysis value of 12.72 Hz. The second modal model, also in the Workbench Modal Analysis system, was of the ERTD rotatably attached to a basketball rim and backboard. This second model was then imported into the Transient Structural Analysis system and first used to confirm the hypothesis that the ERTD did indeed transfer kinetic energy from its drop-mass to the basketball rim and backboard. Then, an energy transfer surface was used to confirm the hypothesis that this kinetic energy transfer was responsive to changes in rim and backboard stiffness via changes in the respective Young’s moduli. Finally, a second-generation ERTD was proposed, where the control box transmits its energy readings to “the cloud” via the WiFi capabilities of the Arduino UNO R4 WiFi. Full article

In combat sports, precise technique evaluation is crucial for performance optimization; however, traditional systems for evaluating kick performance are frequently unreasonably complicated and costly. This study offers a useful and accessible substitute by introducing a contact mat-based tool that measures the roundhouse kick’s execution time during both the attack and recovery phases and by demonstrating its reliability. The experimental sessions involved 16 male Shotokan karate athletes (age: 25.6 ± 7.1 years; height: 1.74 ± 0.05 m; body mass: 71.5 ± 8.7 kg; body fat percentage: 14.7 ± 6.7%; training experience: 11.0 ± 4.9 years). The protocol included four sessions, starting with a familiarization phase followed by three testing sessions (test, retest, and retest two), during which a standardized warm-up was performed along with the roundhouse kick test. The intraclass coefficient correlation (ICC) used indicated high reliability for the at-tack (ICC = 0.85, 95% CI [0.64, 0.94]), recovery (ICC = 0.89, 95% CI [0.75, 0.96]), and total time (ICC = 0.90, 95% CI [0.76, 0.96]). The Friedman test revealed no significant difference between testing sessions (p > 0.31), demonstrating high reliability and no significant differences between sessions. This study confirms the system as a simple and reliability tool for measuring roundhouse-kick timing in combat sports. Full article

Ensuring synchronization between real-world sensor data and industrial robotic simulations remains a critical challenge in digital twin and virtual commissioning applications. This study proposes an innovative method for integrating real sensor signals into RoboDK simulations, bridging the gap between virtual models and real-world dynamics. The proposed system utilizes an Arduino-based data acquisition module and a custom Python script to establish real-time communication between physical sensors and RoboDK’s simulation environment. Unlike traditional simulations that rely on predefined simulated signals or manually triggered virtual inputs, our approach enables dynamic real-time interactions based on live sensor data. The system supports both analog and digital signals and is validated through latency measurements, demonstrating an average end-to-end delay of 23.97 ms. These results confirm the feasibility of real sensor integration into RoboDK, making the system adaptable to various industrial applications. This framework provides a scalable foundation for researchers and engineers to develop enhanced simulation environments that more accurately reflect real industrial conditions. Full article

A 3D-printable, ARDUINO-based multipurpose X-ray stage of compact dimensions enabling in situ electric field and temperature-dependent measurements is put into practice and tested here. It can be routinely applied in combination with a technique of structural characterization of materials. Using high-performance X-ray laboratory equipment, two investigations were conducted to illustrate the device’s performance. The lattice characteristics and microstructure evolution of piezoelectric ceramics of barium titanate, BaTiO₃ (BT), and barium calcium zirconate titanate, with compositions of (Ba_0.92Ca_0.08) (Ti_0.95Zr_0.05)O₃ (BC8TZ5), were studied as a function of the applied electric field and temperature. The X-ray stage is amenable as an off-the-shelf device for a diffraction line in a synchrotron. It provides valuable information for poling piezoceramics and subsequent optimization of their performance. Full article

Early diagnosis of subclinical ketosis is fundamental in the production management of dairy cattle. Without evident clinical signs, this pathological condition causes important economic losses for the farmer and significant health repercussions for the cattle that could develop an altered immune function. Laboratory techniques, although accurate, are expensive, invasive, and cannot be used for real-time monitoring of the entire herd. On the contrary, the analysis of volatile organic compounds (VOCs) contained in the breath of dairy cattle affected by ketosis could represent a key biomarker of the ketogenic process. For this reason, we developed a sensory device, tested in the laboratory, to detect acetone concentrations ranging from 1 to 10 ppm (concentrations typically detected in the cow’s breath), and we look to verify the electronic nose’s potential as a non-invasive diagnostic tool for ketosis. Experimental results show the high sensitivity of the instrument in differentiating acetone solutions. Principal component analysis (PCA) showed a clear separation of samples in the score plot, while classification using linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) achieved accuracy rates above 70% and 85%, respectively. These findings suggest the potential application of the electronic nose as a non-invasive diagnostic tool in veterinary diagnostic studies. In particular, its ability to detect and discriminate low acetone concentrations could help the farmer to improve the overall management of the herd, optimising monitoring strategies and ketosis diagnosis before the appearance of the clinical signs of the disease. Full article

To analyze the progression of Leidenfrost evaporation, traditional experiments were conducted manually to generate a complete evaporation curve. However, physical constraints render Leidenfrost evaporation experiments inherently time-consuming and susceptible to uncertainty. To address these challenges, this study aimed to develop an automated system using webcams for real-time image acquisition and processing, as well as a syringe pump constructed using an Arduino microcontroller, a stepper motor, and 3D-printed components. In the domain of real-time image processing, the radii of levitated droplets were determined using circular detection techniques. By fitting the droplet radii over hundreds of consecutive frames, it was concluded that the shrinking rate of levitated droplet radii remain constant when the radius exceeds 0.6 mm, and the evaporation time is accurately derived. A moving average algorithm was employed to identify the heat transfer area as well as the evaporation time between the boiling droplet and the hot surface, enabling simultaneous calculation of the heat flux. The automated system was then used to perform Leidenfrost experiments under varying experimental parameters, and was compared to manual methods to demonstrate its superior precision in both the film boiling and nucleate boiling regimes. For example, the automated system was utilized to perform a series of experiments as the Weber number increased from 7.01 to 23.18. The detected Leidenfrost temperature rose from 154 °C to 192 °C, while the evaporation time decreased from 85.2 s to 78.9 s. These findings were consistent with previous studies and aligned with physical expectations, reinforcing the reliability of the system and its results. Full article

Solar energy is one of the promising renewable energies; it is clean, green, and accepted worldwide for targeting sustainable development through applications such as power generation, desalination, food preservation, etc. Solar-powered desalination has received more attention in recent times to meet the demand of pure water in the rural places of many countries where solar energy is abundant. In the present work, a double-slope passive solar desalination system was fabricated with readily available materials that can be installed and used in rural places, either for domestic purposes or in small-scale industries. The capacity of the desalination system fabricated to be filled with saline water is ~15 L. The performance of the desalination system is continuously monitored by recording the temperatures at various locations around the system, such as the outer surface of the glass, the inner surface of the glass, inside the basin, and outside the basin, through DHT11 sensors controlled by Arduino programming fed in the Arduino UNO board. The influence of solar radiation intensity and temperatures at various locations on the solar still on the thermal performance and production of desalination unit is analyzed by the data recorded by the Arduino program. A cumulative yield of fresh water of around 0.7–0.9 L is recorded every day, and the lowest yield of around 0.55 L was obtained on the third day of experimentation. Full article

The growing demand for innovative solutions to accurately measure variables in dewatering processes has driven the development of advanced technologies. This study focuses on the evaluation of a measurement system in a rotary dryer used to dehydrate carrots at an operating temperature of 70 °C. The system uses the Arduino platform, strain gauges, and LM35 temperature sensors. Experimental tests were designed to evaluate the performance of the dryer, using initial quantities of carrots of 1.5 kg, 1.0 kg, and 0.5 kg. The novelty of this study lies in the application of fuzzy logic for signal conditioning in real time, in order to improve the precision of measurements, designed in MATLAB (version 9.5) and programmed in Arduino. The dryer reduces the water content of the product to a final average of 10%. The research offers a novel solution for the integration of an intelligent measurement system that optimizes dewatering efficiency. The manuscript is organized as follows: in the methodology section, the design of the measurement system is described; subsequently, the experimental results and the analysis of the dryer efficiency are presented, and finally, in the conclusions, the implications of the system and its possible applications in other processes are discussed. Full article

This study details the design and implementation of a real-time river monitoring station established on the Sakarya River, capable of instantaneously tracking water levels and flow rates. The system comprises an ultrasonic distance sensor, a GSM module (Global System for Mobile Communications), which enables real-time wireless data transmission to a server via cellular networks, a solar panel, a battery, and a microcontroller board. The river monitoring station operates by transmitting water level data collected by the ultrasonic distance sensor to a server via a communication module developed on a microcontroller board using an Arduino program, and then sharing these data through a web interface. The developed system performs regular and continuous water level readings without the need for human intervention. During the installation and calibration of the monitoring station, laboratory and field tests were conducted, and the obtained data were validated by comparison with data from the hydropower plant located upstream. This system, mounted on a bridge, measures water levels twice per minute and sends these data to the relevant server via the GSM module. During this process, precipitation data were utilized as a critical reference point for validating measurement data for the 2023 hydrological year, with changes in precipitation directly correlated with river water levels and calculated flow values, which were analyzed accordingly. The real-time river monitoring station allows for instantaneous monitoring of the river, achieving a measurement accuracy of within 0.1%. The discharge values recorded by the system showed a high correlation (r² = 0.92) with data from the hydropower plant located upstream of the system, providing an accurate and comprehensive database for water resource management, natural disaster preparedness, and environmental sustainability. Additionally, the system incorporates early warning mechanisms that activate when critical water levels are reached, enabling rapid response to potential flood risks. By combining energy-independent operation with IoT (Internet Of Things)-based communication infrastructure, the developed system offers a sustainable solution for real-time environmental monitoring. The system demonstrates strong applicability in field conditions and contributes to advancing technologies in flood risk management and water resource monitoring. Full article

This study discusses the optimization of municipal solid waste management through the implementation of automated waste sorting systems, comparing two advanced artificial intelligence methodologies: Vertex AI and convolutional neural network (CNN) architectures, developed using TensorFlow. Automated solid waste classification is presented as an innovative technological approach that leverages advanced algorithms to accurately identify and segregate materials, addressing the inherent limitations of conventional sorting methods, such as high labor dependency, inaccuracies in material separation, and constrained scalability for processing large waste volumes. A system was designed for the classification of paper, plastic, and metal waste, integrating an Arduino Uno microcontroller, a Raspberry Pi, a high-resolution camera, and a robotic manipulator. The system was evaluated based on performance metrics including classification accuracy, response time, scalability, and implementation cost. The findings revealed that Xception achieved a flawless classification accuracy of 100% with an average processing time of 0.25 s, whereas Vertex AI, with an accuracy of 90% and a response time of 2 s, exceled in cloud scalability, making it ideal for resource-constrained environments. The findings highlight Xception’s superiority in high-precision applications and Vertex AI’s adaptability in scenarios demanding flexible deployment, advancing efficient and sustainable waste management solutions. Full article

Recent years have witnessed the development of human-unmanned aerial vehicle (UAV) interfaces to meet the growing demand for intuitive and efficient solutions in UAV piloting. In this paper, we propose a novel Smart Glove v 1.0 prototype for advanced drone gesture control, leveraging key low-cost components such as Arduino Nano to process data, MPU6050 to detect hand movements, flexible sensors for easy throttle control, and the nRF24L01 module for wireless communication. The proposed research highlights the design methodology of reporting flight tests associated with simulation findings to demonstrate the characteristics of Smart Glove v1.0 in terms of intuitive, responsive, and hands-free piloting gesture interface. We aim to make the drone piloting experience more enjoyable and leverage ergonomics by adapting to the pilot’s preferred position. The overall research project points to a seedbed for future solutions, eventually extending its applications to medicine, space, and the metaverse. Full article

Studying the interactions between biological organisms and their environment provides engineers with valuable insights for developing complex mechanical systems and fostering the creation of novel technological innovations. In this study, we introduce a novel bio-inspired three degrees of freedom (DOF) spherical robotic manipulator (SRM), designed to emulate the biomechanical properties observed in nature. The design utilizes the transformation of spherical Complex Spatial Kinematic Pairs (CSKPs) to synthesize bio-inspired robotic manipulators. Additionally, the use of screw theory and the Levenberg–Marquardt algorithm for kinematic parameter computation supports further advancements in human–robot interactions and simplifies control processes. The platform directly transmits motion from the motors to replicate the ball-and-socket mobility of biological joints, minimizing mechanical losses, and optimizing energy efficiency for superior spatial mobility. The proposed 3DOF SRM provides advantages including an expanded workspace, enhanced dexterity, and a lightweight, compact design. Experimental validation, conducted through SolidWorks, MATLAB, Python, and Arduino, demonstrates the versatility and broad application potential of the novel bio-inspired 3DOF SRM, positioning it as a robust solution for a wide range of robotic applications. Full article

The need for safe and healthy air quality has become critical as urbanization and industrialization increase, leading to health risks and environmental concerns. Gas leaks, particularly of gases like carbon monoxide, methane, and liquefied petroleum gas (LPG), pose significant dangers due to their flammability and toxicity. LPG, widely used in residential and industrial settings, is especially hazardous because it is colorless, odorless, and highly flammable, making undetected leaks an explosion risk. To mitigate these dangers, modern gas detection systems employ sensors, microcontrollers, and real-time monitoring to quickly identify dangerous gas levels. This study introduces an IoT-based system designed for comprehensive environmental monitoring, with a focus on detecting LPG and butane leaks. Using sensors like the MQ6 for gas detection, MQ135 for air quality, and DHT11 for temperature and humidity, the system, managed by an Arduino Mega, collects data and sends these to the ThingSpeak platform for analysis and visualization. In cases of elevated gas levels, it triggers an alarm and notifies the user through IFTTT. Additionally, the system includes a microphone and a CNN model for analyzing audio data, enabling a thorough environmental assessment by identifying specific sounds related to ongoing activities, reaching an accuracy of 96%. Full article