Search Results (800)

The research introduces a revolutionary Internet of Things (IoT)-based system for fish farming, designed to significantly enhance efficiency and cost-effectiveness. By integrating the NodeMcu12E ESP8266 microcontroller, this system automates the management of critical water quality parameters such as pH, temperature, and oxygen levels, essential for fostering optimal fish growth conditions and minimizing mortality rates. The core of this innovation lies in its intelligent monitoring and control mechanism, which not only supports accelerated fish development but also ensures the robustness of the farming process through automated adjustments whenever the monitored parameters deviate from desired thresholds. This smart fish farming solution features an Arduino IoT cloud-based framework, offering a user-friendly web interface that enables fish farmers to remotely monitor and manage their operations from any global location. This aspect of the system emphasizes the importance of efficient information management and the transformation of sensor data into actionable insights, thereby reducing the need for constant human oversight and significantly increasing operational reliability. The autonomous functionality of the system is a key highlight, designed to persist in adjusting the environmental conditions within the fish farm until the optimal parameters are restored. This capability greatly diminishes the risks associated with manual monitoring and adjustments, allowing even those with limited expertise in aquaculture to achieve high levels of production efficiency and sustainability. By leveraging data-driven technologies and IoT innovations, this study not only addresses the immediate needs of the fish farming industry but also contributes to solving the broader global challenge of protein production. It presents a scalable and accessible approach to modern aquaculture, empowering stakeholders to maximize output and minimize risks associated with fish farming, thereby paving the way for a more sustainable and efficient future in the global food supply. Full article

Listeriosis is a rare but severe foodborne disease caused by Listeria Monocytogenes (LM), a small facultative intracellular bacillus. When occurring in pregnant women, it can be vertically transmitted to the fetus and the newborn. Infected women usually display aspecific and mild symptoms, and rarely develop the severe forms of the disease (such as neurolisteriosis). On the contrary, fetal and neonatal listeriosis can lead to complications such as fetal loss, preterm birth, neonatal sepsis, and respiratory distress syndrome (RDS). Prompt diagnosis is one of the main challenges because of the aspecific presentation of the disease; therapy relies on antibiotics that reach high intracellular concentration and can penetrate and pass the placenta reaching the fetus. Herein we report an infrequent case of LM infection involving a woman with monochorionic diamniotic twin pregnancy, followed by a comprehensive review of the available literature on listeriosis in pregnancy. Full article

This study presents an air quality monitoring system that employs the Arduino Uno microcontroller. The system is augmented with a moving average filter and data fusion techniques from BME680 and CCS811 sensors, which are designed to process and combine data from these sensors. The system was tested and analyzed empirically across a range of residential environments in order to validate its efficacy. The findings indicated that the typical IAQ level in a bedroom was approximately 20 units. However, this level increased significantly, reaching 140 units, within minutes after the introduction of a 17% perfume spray. In contrast, the use of an aromatic diffuser resulted in a smaller increase in IAQ to 40 units, which returned to normal levels after ventilation. Moreover, the analysis demonstrated that the kitchen and bathroom exhibited inferior air quality in comparison to the bedroom. This was evidenced by elevated VOC and humidity levels, which were observed to be 10–20% higher due to the combined effects of household activities and inadequate ventilation. This study makes a significant contribution to the field of air quality monitoring by proposing a solution that employs sensor technology and data processing methods to enhance the quality of life within residential spaces. Full article

Microfluidics are crucial for managing small-volume analytical solutions for various applications, such as disease diagnostics, drug efficacy testing, chemical analysis, and water quality monitoring. The precise control of flow control devices can generate diverse flow patterns using pneumatic control with solenoid valves and a microcontroller. This system enables the active modulation of the pneumatic pressure through Arduino programming of the solenoid valves connected to the pressure source. Additionally, the incorporation of solenoid valve sets allows for multichannel control, enabling simultaneous creation and manipulation of various microflows at a low cost. The proposed microfluidic flow controller facilitates accurate flow regulation, especially through periodic flow modulation beneficial for droplet generation and continuous production of microdroplets of different sizes. Overall, we expect the proposed microfluidic flow controller to drive innovative advancements in technology and medicine owing to its engineering precision and versatility. Full article

Background: Inherited epidermolysis bullosa (EB) is a group of genetic disorders with skin fragility and blistering. The use of Cord Blood Platelet Gel (CBPG) in combination with laser photobiomodulation (PBM) leads to a reduction in lesions. The aim of this study is to evaluate clinical and morphometric changes with Optical Coherence Tomography (OCT) during GPC-PBM therapy. Methods: OCT scanning before the first session (T0), with relative measurement of the thicknesses of the epithelium (EP) and lamina propria (LP), and three consecutive sessions (once daily for 3 days) of CBPG and PBM applications were performed. A new OCT scan at the end of the three sessions (T1) and a week after (T2) were performed. All OCT scans were compared with the values of healthy reference tissues of the same site. Results: A statistically confirmed increase in EP thickness and a decrease in LP thickness with a progressive reduction in inflammatory content were highlighted. This case series did not have recurrences in the treated sites, or adverse reactions to therapy. Conclusions: This study shows the advantages of OCT monitoring in evaluating the effects of therapy at an ultrastructural level with a possibility of obtaining objective, precise, and repeatable measurements with an atraumatic device. Full article

Objectives: To assess the number of new cases of Medication-Related Osteonecrosis of the Jaw (MRONJ) among patients with osteoporosis and other non-malignant bone diseases in Northwestern Italy between 2007 and 2022. Methods: MRONJ cases were collected from referral centers in a population of 4.5 million. We analysed the number of new MRONJ cases per year, type of disease, administered drugs, duration of therapy (when available), and onset time of disease. Results: We analysed 198 cases (out of 1071 total MRONJ cases); diseases included osteoporosis (87%), rheumatoid arthritis (5%), their association (4%), Paget’s disease, and other various diseases (4%). Patients received bisphosphonates alone (74%), or denosumab alone (6%), or a sequence of different drugs (20%). The number of new cases increased over five years from 2 (2003–2007) to 51 (2008–2012), 65 (2013–2017), and 79 (2018–2022), and the percentage increased from 1% to 14%, 20%, and 29% of the total cases. Conclusions: The number of new MRONJ cases per year among patients with non-malignant diseases is rapidly increasing all around the world, though underestimation cannot be excluded. In this study, we describe epidemiological and clinical characteristics of patients, and the drug most frequently involved in MRONJ cases in our region over a long period, allowing a comprehensive view of the progression of the disease. Greater collaboration among specialists is needed for correct and early diagnosis to improve measures potentially reducing disease incidence and to limit quality of life deterioration in patients with osteoporosis and other non-malignant diseases. Full article

The accuracy and unit cost of sensors are important factors for a continuous soil moisture monitoring system. This study compares the accuracy of four soil moisture sensors differing in unit costs in coarse-, fine-, and medium-textured soils. The sensor outputs were recorded for the VWC, ranging from 0% to 50%. Low-cost capacitive and resistive sensors were evaluated with and without the external 16-bit analog-to-digital converter ADS1115 to improve their performances without adding much cost. Without ADS1115, using only Arduino’s built-in analog-to-digital converter, the low-cost sensors had a maximum RMSE of 4.79% (v/v) for resistive sensors and 3.78% for capacitive sensors in medium-textured soil. The addition of ADS1115 showed improved performance of the low-cost sensors, with a maximum RMSE of 2.64% for resistive sensors and 1.87% for capacitive sensors. The higher-end sensors had an RMSE of up to 1.8% for VH400 and up to 0.95% for the 5TM sensor. The RMSE differences between higher-end and low-cost sensors with the use of ADS1115 were not statistically significant. Full article

In this article, a novel electromagnetic diaphragm pump design controlled by an Arduino NANO microcontroller is proposed to pump liquid inside the pumping chamber completely separated from mechanical and transmission parts. The prototype is primarily based on alternating the polarity of two electromagnets that attract or repel a permanent magnet located on a flexible diaphragm. The system hardware layout is completed by electronic components:. an Arduino NANO microcontroller created by Atmel, Headquarters San Jose, California. and display within the cabinet to control the polarization of the electromagnets and exhibit the temperature inside the pump. The electromagnetic pump and control system consist of innovative approaches as a solution for the treatment of unclean water and integration with solar panel systems. In addition, the measurement tests of the electromagnetic pump, including the temperatures of electromagnets and the quantity of the pumped liquid within the chamber, indicate a dependence on the selected speed of the electromagnet’s polarization. The electromagnetic pump achieves high efficiency as a combination of the temperature and the amount of liquid that can be regulated and controlled by the switching speed of the electromagnet’s polarization. Full article

This work presents a comparative study of a step-down converter controlled through the algorithm MPPT Perturb and Observe (P&O) with or without a fuzzy logic controller supplied by a photovoltaic system. This study aimed at increasing the quantity of electric energy taken over from the photovoltaic systems by the load through the DC-DC convertor. To follow up the maximum power point where the transfer is performed from the photovoltaic system to the load at maximum power, the Perturb and Observe (P&O) method was used. Two programs were elaborated in MATLAB-Simulink R2018a to control the buck convertor commanded through the P&O algorithm with or without a fuzzy logic controller. The analysis of the results showed that a higher quantity of energy is transferred from the source to the receptor circuit in the case of the buck convertor controlled through the P&O algorithm with a fuzzy logic controller. The P&O algorithm was implemented on an experimental stand at the Laboratory of Electrical Machinery and Drives of the Engineering Faculty in Bacau, with the help of a program issued in the Arduino IDE programming environment. The analysis of the results showed that for an increase in the power conveyed to the receptor circuit, there will also be an increase in the filling factor of the PWM signal that controls the power transistor in the structure of the DC-DC convertor. The P&O algorithm with a fuzzy logic controller may also be implemented in the DC-DC converters in the structure of the driving systems of electric vehicles. Full article

Necrotizing gingivitis (NG) is an acute inflammatory process with an estimated prevalence of less than 1%. The treatment of choice is usually antibiotics in addition to periodontal treatment. This scoping review aims to detail extent and type of proof related to NG in paediatric patient; moreover, a decision tree protocol was developed to define NG management in paediatric patients based on the presence or absence of systemic compromission. In addition, we also propose the use of ozone treatment as an adjuvant therapy. Seven papers (3 case reports, 2 guidelines, and 2 reviews) were selected for evaluation by reading the full texts. This review outlines the lack of research on the treatment of NG in paediatric patients; we, however, demonstrate the efficacy of the decision tree protocol by describing two case reports in which patients were treated with antibiotics according to the presence or absence of systemic involvement through the implementation of an individualized therapeutic approach, with periodontal ozone therapy. Moreover, the supportive use of this molecule in the management of NG can be a valuable tool in the healing of gingival tissues. Full article

Data on the soil nutrient content are required to calculate fertilizer rate recommendations. The soil laboratory determines these soil properties, yet the measurement is time-consuming and costly. Meanwhile, portable devices to assess the soil nutrient content in real-time are limited. However, a proprietary and low-cost NPK sensor is available and commonly used in IoT for agriculture. This study aimed to assemble and test a portable, NPK sensor-based device in irrigated and rainfed paddy soils from Java, Indonesia. The device development followed a prototyping approach. The device building included market surveys and opted for an inexpensive, light, and compact soil sensor, power storage, monitor, and wire connectors. Arduino programming language was used to write scripts for data display and sub-device communication. The result is a real-time, portable soil nutrient detector that measures the soil temperature, moisture, pH, electrical conductivity, and N, P, and K contents. Field tests show that the device is sensitive to soil properties and location. The portable soil nutrient detector may be an alternative tool for the real-time measurement of soil nutrients in paddy fields in Indonesia. Full article

This study presents a cost-effective IoT-based Supervisory Control and Data Acquisition system for the real-time monitoring and control of photovoltaic systems in a rural Pakistani community. The system utilizes the Blynk platform with Arduino Nano, GSM SIM800L, and ESP-32 microcontrollers. The key components include a ZMPT101B voltage sensor, ACS712 current sensors, and a Maximum Power Point Tracking module for optimizing power output. The system operates over both Global System for Mobile Communications and Wi-Fi networks, employing universal asynchronous receiver–transmitter serial communication and using the transmission control protocol/Internet protocol and hypertext transfer protocol for data exchange. Testing showed that the system consumes only 3.462 W of power, making it highly efficient. With an implementation cost of CAD 35.52, it offers an affordable solution for rural areas. The system achieved an average data transmission latency of less than 2 s over Wi-Fi and less than 5 s over GSM, ensuring timely data updates and control. The Blynk 2.0 app provides data retention capabilities, allowing users to access historical data for performance analysis and optimization. This open-source SCADA system demonstrates significant potential for improving efficiency and user engagement in renewable energy management, offering a scalable solution for global applications. Full article

During adaptive laboratory evolution experiments, any unexpected interruption in data monitoring or control could lead to the loss of valuable experimental data and compromise the integrity of the entire experiment. Most homemade mini-bioreactors are built employing microcontrollers such as Arduino. Although affordable, these platforms lack the robustness of the programmable logic controller (PLC), which enhances the safety and robustness of the control process. Here, we describe the design and validation of a PLC-controlled morbidostat, an innovative automated continuous-culture mini-bioreactor specifically created to study the evolutionary pathways to drug resistance in microorganisms. This morbidostat includes several improvements, both at the hardware and software level, for better online monitoring and a more robust operation. The device was validated employing Escherichia coli, exploring its adaptive evolution in the presence of didecyldimethylammonium chloride (DDAC), a quaternary ammonium compound widely used for its antimicrobial properties. E. coli was subjected to increasing concentrations of DDAC over 3 days. Our results demonstrated a significant increase in DDAC susceptibility, with evolved populations exhibiting substantial changes in their growth after exposure. Full article

Visually Impaired People (VIP) face significant challenges in their daily lives, relying on others or trained dogs for assistance when navigating outdoors. Researchers have developed the Smart Stick (SS) system as a more effective aid than traditional ones to address these challenges. Developing and utilizing the SS systems for VIP improves mobility, reliability, safety, and accessibility. These systems help users by identifying obstacles and hazards, keeping VIP safe and efficient. This paper presents the design and real-world implementation of an SS using an Arduino Nano microcontroller, GPS, GSM module, heart rate sensor, ultrasonic sensor, moisture sensor, vibration motor, and Buzzer. Based on sensor data, the SS can provide warning signals to VIP about the presence of obstacles and hazards around them. Several Machine Learning (ML) algorithms were used to improve the SS alert decision accuracy. Therefore, this paper used sensor data to train and test ten ML algorithms to find the most effective alert decision accuracy. Based on the ML algorithms, the alert decision, including the presence of obstacles, environmental conditions, and user health conditions, was examined using several performance metrics. Results showed that the AdaBoost, Gradient boosting, and Random Forest ML algorithms outperformed others and achieved an AUC and specificity of 100%, with 99.9% accuracy, F1-score, precision, recall, and MCC in the cross-validation phase. Integrating sensor data with ML algorithms revealed that the SS enables VIP to live independently and move safely without assistance. Full article

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. Full article