Lithium batteries are the heart of every electronic device, from laptops and smartphones to electric vehicles or renewable power stations. They introduced a plethora of benefits like high energy density and durability without the need for maintenance that other technologies lead acid batteries required. However, battery operation must be constantly monitored, as it suffers from increased temperatures caused by increased charging/ discharging currents, high voltage and peak loads. These operating conditions lead to lithium deposition and partial electrolyte decomposition, limiting the total capacity (State of Health) or even leading to a possible breakdown. Hence, consistent monitoring of power, temperature and operating voltage is essential to ensure maximum lifespan without risking safety. In this paper a compact module consisting of 3 lithium batteries at 3.7V is introduced. Each battery has its own NTC thermistor and voltage sensor installed at the negative electrode, while the total Bus voltage, current and load voltage are monitored as well. The output power and other parameters are depicted through the Grafana Dashboard for quick access and graphical representation. The goal of this paper is to check how the variations of voltage and cell temperature, depending on the power required, affect battery health ensuring battery operating conditions stay within manufacturer limits to avoid damage.

IoT-based garden automation “Green Connect” is a system that automates and tracks numerous gardening-related operations using internet-connected sensors and devices. Benefits of this technology include enhanced plant growth, improved watering schedules, and remote monitoring and management of plants. The system is made up of numerous parts, including irrigation sys-tems, temperature sensors, humidity sensors, and soil moisture sensors, all of which are linked to a central hub. In order to automate the watering, lighting, and other environmental conditions of the garden, the microcontroller gathers and analyses the data from the sensors. The development of an IoT-based garden automation system is covered in this article, along with the design of the system architecture, component selection, optical sensor and device integration. The experiment's findings demonstrate that the system was able to improve the garden's growth conditions, leading to better plant health and yield. According to the study, IoT-based garden automation has the ability to completely change how we think about gardening by making it simpler and more effective to grow plants in a range of settings. By remotely managing the water pump and keeping track of the soil moisture in the garden, this study integrates the IoT into the irrigation system for gardens.

Glaucoma, a primary cause of irreversible blindness, necessitates early detection to prevent significant vision loss. In the literature, fundus imaging is identified as a key tool in diagnosing glaucoma, which captures detailed retina images. However, manual analysis of these images can be time-consuming and subjective. Thus, this paper presents an automated system for glaucoma detection using fundus images, combining diverse feature extraction methods with advanced classifiers, specifically Support Vector Machine (SVM) and AdaBoost (ADB). The pre-processing step incorporates Image Enhancement via Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance image quality and feature extraction. This work investigates individual features such as Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Chip Histogram Features, and Gray Level Co-occurrence Matrix (GLCM), as well as their various combinations, including HOG + LBP + Chip Histogram + GLCM, HOG + LBP + Chip Histogram, and others. These features are utilized with SVM and ADB classifiers to improve classification performance. For validation, the ACRIMA dataset, a public fundus image collection comprising 369 glaucoma-affected and 309 normal images is used in this work, with 80% of the data allocated for training and 20% for testing. The results of the proposed study show that different feature sets yield varying accuracies with SVM and ADB classifiers. For instance, the combination of LBP + Chip Histogram achieved the highest accuracy of 99.29% with ADB, while the same combination yielded 65.25% accuracy with SVM. The individual feature LBP alone achieved 97.87% with ADB and 98.58% with SVM. Furthermore, the combination of GLCM + LBP provided 98.58% accuracy with ADB and 97.87% with SVM. The results demonstrate that CLAHE and combined feature sets significantly enhance detection accuracy, providing a reliable tool for early and precise glaucoma diagnosis, thus facilitating timely intervention and improved patient outcomes.

In today's engineering applications, model-based fault diagnosis methods are widely used to reduce costs. This study continues previous work[1,2] on model-based fault diagnosis by integrating the residual value structure of tire pressure into an existing Bayesian network, aiming for more accurate fault detection.A residual value is modeled using the pressure values of a vehicle's four tires, and the Bayesian network is updated accordingly, enabling a stochastic rather than deterministic approach. The updated method is first modeled and tested in the Matlab/Simulink environment. Following this, the algorithm and resolution procedures for obtaining tire pressure values from the vehicle are updated in the ROS environment. The method is then validated through real vehicle tests.During these tests, the tire pressures are deliberately reduced to create a fault scenario. A car lighter pump is used to lower the tire pressure, and the updated Bayesian network is tasked with detecting and identifying the faulty tires. The detected faults are displayed on the Human-Machine Interface (HMI) in real-time, providing feedback on tire pressure status.This integration of the Bayesian network with the residual value structure allows for more accurate and reliable tire fault detection, enhancing both safety and efficiency. The study highlights the importance of combining model-based methods with practical testing to validate diagnostic algorithms. The successful verification of the designed method through real vehicle tests marks a significant advancement in automotive fault diagnosis.

[1] T.Bodrumlu, M.M.Gozum, and Batıkan Kavak, “Enhanced Fault Detection of Vehicle Lateral Dynamics Using a Dynamically Adjustable Bayesian Network Structure and Extended Kalman Filter”, ASME International Mechanical Engineering Congress and Exposition, 2023, V009T14A024.

[2] M.F Yalcin, T. Bodrumlu; M. M. Gozum ; E. Ates. Dinamik Bayes Ağ Yapısı ve Genişletilmiş Kalman Filtresi Kullanılarak Gerçek Zamanlı ROS Uygulaması ile Otonom Bir Araçtaki Yanal Dinamiklerdeki Arıza Tespitinin Gerçeklenmesi

Personalized medicine is an emerging field in healthcare which aims to tailor the drug and its delivery in an individualized approach aiming to improve the treatments efficiency. Drug delivery systems are used to regulate the medication dosage in time to optimize its therapeutic outcomes while minimizing its side effects. Several techniques have been developed to achieve controlled drug release, these include passive devices engineered to respond to stimuli such as temperature, pH or enzymes. Passive drug delivery devices include functionalized micro particles, hydrogels, liposomes etc. The main disadvantage of passive delivery devices is that although they can release drug at specific time intervals, they lack of controllability. However active drug delivery devices can be used as wearables and can be activated via external stimuli representing a step forward in the quest for the development of advanced drug delivery systems. In this paper, we present the development of externally triggered active wearable drug delivery systems. Our design is based on a cost-effective 3D printing-based method and the development of magnetically porous triggerable elastomers containing the patient’s medication. Magnetic fields in the range of 100 to 350 mT are used to control the compression of the triggerable elastomer. Such magnetic fields are used to dynamically adjust the drug release patterns, ensuring optimal delivery at the target site. The devices compliance is tested showing consistent flexibility and recovery of its original shape even after multiple rounds of deformation upon the presence of the control magnetic fields. This resulted in a precise and repeatable drug delivery dosage demonstrating that our approach can enhance the precision and efficiency of drug delivery. This research advances the development of wearable drug delivery technologies, paving the way for personalized treatments for achieving improved patient treatment outcomes.

The paper considers the development of a low-cost acoustic sensor based on an
import-substituting element base as a cheap alternative to professional sensors or
analog sensors.
The following components were used in the design: an Arduino board for easy
prototyping; a MAX9812 microphone module sensing frequencies 20 - 20000 Hz;
a microSD card module, an RTC sensor, a 32 GB microSDHC card, an IP65
enclosure, a printed breadboard for soldering, a battery compartment, a
microphone diaphragm and a key switch were selected because of their
availability; the NB-IoT module - SIM7080G was chosen for data exchange with
the server; 3 Li-ion Cosmos batteries at 1800 mAh were selected for autonomous
operation of the sensor.
After checking the sensor performance on the breadboard, it was assembled into
the case, drilling and machining a hole for the microphone and key switch output.
To improve the accuracy of the data taken from the microphone, it was calibrated
using a reference noise meter, using a regression method to calculate the sound
level in decibels, and obtained an equation for converting the obtained ADC values
into decibels.
The code of the program executed by the sensor was developed and sewn into the
device. The data captured from the microphone is stored in a text file with the date
and time of the captured data.
A prototype of an import-substituting noise sensor was designed for use in
production and the workplace. All available components have been selected for it
and the IP65 dust and moisture protection level has been achieved.
The sensor is ready for further improvement, namely the addition of a user
interface, easier access to data, the development of its own housing, and eventually
mass production.

Falls pose a significant threat to the elderly population, often resulting in severe health complications such as fractures and other adverse outcomes, which can drastically lower their quality of life. Early detection of fall risks is crucial in mitigating the impact of such events. Various technologies have been developed to address this issue, including alert systems that notify users of imminent dangers due to environmental factors or physiological changes. However, accurately detecting and distinguishing between normal activities, imminent risk of falling, and actual falls remains challenging. This study proposes a machine learning approach using the XGBoost algorithm to improve fall detection accuracy among the elderly. A dataset comprising 2,039 samples, categorized into normal, imminent risk of fall, and fall classes, was utilized to train and test the model. The model was trained on 70% of the data, with 30% allocated for testing. Hyperparameter optimization was performed using a randomized search with cross-validation. The optimal parameters were then employed to train the model, achieving an overall accuracy of 96.67%. The confusion matrix demonstrates the model's robust ability to distinguish between the three classes with minimal false positives. Additionally, sensitivity tests were conducted by varying training sample sizes and randomizing data splits, confirming the model’s robustness in different conditions. These results show that the proposed method outperforms previous studies in detecting fall-related events, reducing the likelihood of false alarms and enhancing resource allocation for elderly care.

Uncooled infrared (IR) sensors Including bolometers, thermopiles, and pyroelectrics have traditionally dominated the market. Nevertheless, a new innovative technology, dubbed TMOS sensor, has emerged. It is based on CMOS-SOI-MEMS (complementary-metal-oxide-semiconductor silicon-on-insulator micro-electromechanical systems) fabrication. This pioneering technology utilizes a suspended, micro-machined floating transistor to directly convert absorbed infrared radiation into an electrical signal.

The miniaturization of IR sensors, including the TMOS, is crucial for seamless integration into wearable and mobile technologies. However, this presents a significant challenge: balancing size reduction with sensor sensitivity. Smaller sensor footprints can often lead to decreased signal capture and consequently, diminished performance.

Metamaterial advancements offer a promising solution to this challenge. These engineered materials exhibit unique electromagnetic properties that can potentially boost sensor sensitivity while enabling miniaturization. Strategic integration of metamaterials into sensor design offers a pathway towards compact, high-sensitivity IR systems with diverse applications.

This study explores the impact of electro-optical metal-insulator-metal (MIM) metamaterial absorbers on the thermal and electro-optical Characteristics of CMOS-SOI-MEMS sensors in the mid-IR region. We target key thermal properties critical to IR sensor performance: thermal conductance (G_th), thermal capacitance (C_th), and thermal time constant (τ_th). The study shows how material selection, layer thickness, and metamaterial geometry fill-factor affect the sensor's thermal performance. An analytical thermal model is employed alongside 3D finite element software for precise numerical simulations.

This research has addressed workflow efficiency of construction projects through the hybrid scheme of Lean Six Sigma and the use of monitoring sensors. The study showed how real-time data and automated monitoring was used to improve project timeframes through detection and elimination of wastes and inefficiencies. The research further utilised a virtual CiteOps software in combination with IoT sensors and soft robotics in the monitoring of important project parameters which allowed for dynamic modifications and increased efficiency.

The outputs obtained, showed that project delays were mostly caused by long lead times, delayed order placements, and job rework which resulted in a process control variation of 103.83%, which exceeded the acceptable threshold by 63.83%. The delays were dramatically reduced by installing a sensor-enhanced EOQ model and using CiteOps software, which includes real-time monitoring for better accountability and planning. The findings highlight the significance of continual technology integration in attaining long-term efficiency and sustainability in building projects.

This novel presentation of Lean Six Sigma methodologies, monitoring and IoT sensors solutions, have been successfully deployed to measure and manage project delays most optimally. The proposed research method has provided a new framework which supports real-time monitoring, automated task execution, and improved decision-making for the improvement of efficiency in construction works leveraging on features of 4IR for smart solutions.

Nowadays, some of the most severe problems faced by health institutions are related to people's mental health. According to the World Health Organization, approximately one billion people lived with some condition that affected their mental health in 2020, where depression, anxiety, and stress represent the most common examples. Furthermore, according to the American Psychological Association, stress aggravates the symptoms of depression and anxiety, besides having negative effects on the cardiovascular, respiratory, muscular, nervous, reproductive, endocrine, and gastrointestinal systems. It is estimated that, during the COVID-19 pandemic, the number of global cases of major depressive disorder and anxiety disorders increased by 53.2 million by 76.2 million respectively. Psychophysiology and other health disciplines such as psychology, neurology, psychiatry and physiotherapy provide quantitative data from physiological signals. These signals are acquired by specialized systems that are usually very expensive, and most are closed source hardware and software. In this work, the development of a low-cost prototype for acquisition and visualization of a patient’s heart rate, ECG, EMG, GSR, and body temperature biosignals using the MAX30102, ECG AD8232, EMG Muscle T084, Grove GSR sensor and LM35 AFEs breakout boards respectively is proposed. Signal acquisition tests were performed with each sensor without post-processing or filtering. Our test results show that the biosignals acquired by our prototype present usability, correct morphology, stability, and can operate without errors for up to 12 hours. This is expected to provide an affordable alternative to biosignal acquisition systems for educational and research institutions, which would give users a similar experience to that provided by high-cost equipment, thus benefiting the training of studies.