Search Results (63)

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms, with tremors being one of the most prominent. Traditional assessment methods, such as the Unified Parkinson’s Disease Rating Scale (UPDRS), rely on subjective, intermittent evaluations, which can miss symptom fluctuations. This study presents the development and validation of the ELENA system, an IoT-based wearable device designed for the continuous monitoring of tremors in PD patients and medication tracking in PD patients. Named in honor of a 67-year-old woman who has lived with Parkinson’s since 2011 and inspired the project, the ELENA system integrates an MPU6050 accelerometer, an ESP32 microcontroller, and cloud-based data analysis and MATLAB. The ELENA system was calibrated and validated against an Apple Watch, demonstrating high accuracy with frequency deviations under 0.5% and an average percentage error of −0.37%. Unlike commercial devices, ELENA offers a clinical-grade solution with customizable data access and visualization tailored for healthcare providers. Participants, including PD patients and a non-PD control group, completed a series of clinical tasks to evaluate tremor monitoring capabilities. The results showed that the system effectively captured tremor frequency and amplitude, enabling the analysis of resting, action, and postural tremors. This study highlights the ELENA system’s potential to enhance PD management by providing real-time, remote monitoring of tremors. The scalable, cost-effective solution supports healthcare professionals in tracking disease progression and optimizing treatment plans, paving the way for improved patient outcomes. Full article

A STEM education provides students with a friendly and efficient environment for learning science, technology, engineering, and math. According to the needs of STEM programs and activities, humanoid, biped, and quadruped robots have been developed. Those robots are used as a learning tool supporting students in exploring the principles and theory of robotics and their related applications. In addition, those robots adapt open sources to provide free instructions for the students to build their own low-cost robots. To enhance the effects, a low-cost, two-wheel robot was created in this study. Unlike other robots, two-wheel robots usually require a gyroscope sensor and a motion controller to keep them balanced. The developed robot is an integrated system including hardware and software. Its hardware consists of an ESP32 microcontroller, a pair of DC motors, a gyroscope sensor MPU6050, and a driver for DC motors. The robot receives signals “angle” from the gyroscope, and then depends on the PID approach to drive the DC motors precisely in order to achieve balanced and smooth motions. The results of this study present the design of the robot, sensor calibration methods, and proportional-integral-derivative tuning. Full article

A plasma-assisted electrochemical deposition (PAECD) technology was introduced to coat a cast iron brake disc for the possible reduction of brake wear and brake wear particle (BWP) emission. The majority of the coating consisted of alumina (Al₂O₃), determined by energy dispersive X-ray (EDX) analysis and X-ray diffraction (XRD) analysis. To validate the above strategy of the coating technology for automotive brake corners, one brake stock rotor was replaced by a PAECD-coated rotor for a vehicle road test. After the road test, weight loss of the brake components (rotors and pads) was measured, showing that the alumina coating can reduce the brake wear by more than 70%. BWPs were also collected from wheel barrels, spokes, and brake friction rings of the coated and uncoated rotors during the road test. A morphology and chemical composition analysis of the collected BWPs indicated that the coating could reduce BWP generation from the original sources and avoid a metal pick-up (MPU) issue, leading to less metallic content in BWPs. This alumina coating may provide the auto sector with a sustainable approach to overcome the brake dust emission problem, evidenced by less wear of the brake pads, minimal wear of the coated brake rotor, less MPUs, and a clean wheel rim on the coated brake corner. Full article

Current technological progress in automation and robotics allows human kinematics to be used to control any device. As part of this study, a sensory glove was developed that allows for a delta robot to be controlled using hand movements. The process of controlling an actuator can often be problematic due to its complexity. The proposed system solves this problem using human–machine interactions. The sensory glove allows for easy control of the robot by detecting the rotation of the hand and pressing the control buttons. Conventional buttons have been replaced with SMART materials such as conductive thread and conductive fabric. The ESP32 microcontroller placed on the control glove collects data read from the MPU6050 sensor. It also facilitates wireless communication with the Raspberry Pi microcontroller supporting the Modbus TCP/IP protocol, which controls the robot’s movement. Due to the noise of the data read from the gyroscope, the signals were subjected to a filtering process using basic recursive filters and an advanced algorithm with Kalman filters. Full article

The sustainable management of biowaste, mainly food and pruning waste, is currently a challenge due to the increase in its production. The CaMPuSTAJE program, which has been implemented on the campus of the Public University of Navarre (UPNA) since 2019, is an excellent example of how the institution is addressing its strategic interests in sustainable waste management. The principal aim of this program is to manage the biowastes generated by the campus canteens through a simple community composting facility, involving UPNA students and graduates. This program aims to promote experiential learning and applied research in sustainability and circular economy, managing their own waste in a circular and local way. Thus, four composting sets of the CaMPuSTAJE program were evaluated by monitoring the process and the main chemical properties of the composting samples. Also, final composts were fully characterized to ensure the process reproducibility and efficiency and the absence of any hazard in the end-products. The final composts showed a significant agronomic quality, had low content of potentially toxic elements, and were free from phytotoxicity, thus being able to be reintroduced as an organic amendment at the university campus itself. Full article

Swimming is a well-rounded, highly efficient physical activity that provides significant contributions to a healthy lifestyle. Therefore, it is frequently chosen as a form of exercise, even later in life, by many individuals with no prior swimming experience. However, mastering swimming as an adult can be challenging, due to the required time needed to be invested at a swimming facility under the guidance of an instructor. This paper proposes a method of dryland training suitable for swimmers of all levels, with the aid of embedded solutions. The solution is composed of pairs of MPU6050 accelerometer sensors and ESP32 development boards within a multi-device system. These pairs are affixed onto strategic points on the human body to analyze swimming movements performed by the user. The system records the data and generates accuracy assessments based on a reference dataset. Full article

The paper presents the research results related to the operational parameters of a needle grinder for biological materials. The study focused particularly on monitoring the wear of the grinder’s working elements, the pins. During the operation of the device, the level and nature of vibrations in the grinding system were examined. In the next stage, after the grinding of 600 tons of biological material, an analysis of the wear of the metal pins was conducted. The degradation process of the pins was observed based on elements made of both untreated steel and heat-treated steel. The apparatus used to measure the grinder’s operational parameters consisted of three IEPE KS 80C piezoelectric accelerometers. The applied research methods enabled the identification of vibration components resulting from an improper grinder operation related to pin wear. Based on the conclusions from the conducted research, a low-budget device (prototype) was proposed for continuous machine monitoring, made using an ESP32 system and a capacitive three-axis accelerometer in the MPU6050 system. The applied monitoring method opens new possibilities for quality control and production efficiency in industries that use grinding. 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

Kinematic assessment of ball kicking may require significant human effort (e.g., traditional vision-based tracking systems). Wearables offer a potential solution to reduce processing time. This systematic review collated measurement properties (validity, reliability, and/or accuracy) of wearable kinematic-based technology systems used to evaluate soccer kicking. Seven databases were searched for studies published on or before April 2024. The protocol was previously published and followed the PRISMA 2020 statement. The data items included any validity, reliability, and/or accuracy measurements extracted from the selected articles. Twelve articles (1011 participants) were included in the qualitative synthesis, showing generally (92%) moderate methodological quality. The authors claimed validity (e.g., concurrent) in seven of the eight studies found on the topic, reliability in two of three, and accuracy (event detection) in three of three studies. The synthesis method indicated moderate evidence for the concurrent validity of the MPU-9150/ICM-20649 InvenSense and PlayerMaker™ devices. However, limited to no evidence was identified across studies when considering wearable devices/systems, measurement properties, and specific outcome variables. To conclude, there is a knowledge base that may support the implementation of wearables to assess ball kicking in soccer practice, while future research should further evaluate the measurement properties to attempt to reach a strong evidence level. Full article

Cropland is a fundamental basis for agricultural development and a prerequisite for ensuring food security. The segmentation and extraction of croplands using remote sensing images are important measures and prerequisites for detecting and protecting farmland. This study addresses the challenges of diverse image sources, multi-scale representations of cropland, and the confusion of features between croplands and other land types in large-area remote sensing image information extraction. To this end, a multi-source self-annotated dataset was developed using satellite images from GaoFen-2, GaoFen-7, and WorldView, which was integrated with public datasets GID and LoveDA to create the CRMS dataset. A novel semantic segmentation network, the Global–Local Context Aggregation Network (GLCANet), was proposed. This method integrates the Bilateral Feature Encoder (BFE) of CNNs and Transformers with a global–local information mining module (GLM) to enhance global context extraction and improve cropland separability. It also employs a multi-scale progressive upsampling structure (MPUS) to refine the accuracy of diverse arable land representations from multi-source imagery. To tackle the issue of inconsistent features within the cropland class, a loss function based on hard sample mining and multi-scale features was constructed. The experimental results demonstrate that GLCANet improves OA and mIoU by 3.2% and 2.6%, respectively, compared to the existing advanced networks on the CRMS dataset. Additionally, the proposed method also demonstrated high precision and practicality in segmenting large-area croplands in Chongzhou City, Sichuan Province, China. Full article

Earthquakes are one of the most life-threatening natural phenomena, and their prediction is of constant concern among scientists. The study proposes that abrupt weather parameter value fluctuations may influence the occurrence of shallow seismic events by focusing on developing an innovative concept that combines historical meteorological and seismic data collection to predict potential earthquakes. A machine learning (ML) model utilizing the ML.NET framework was designed and implemented. An analysis was undertaken to identify which modeling approach, value prediction, or data classification performs better in forecasting seismic events. The model was trained on a dataset of 8766 records corresponding to the period from 1 January 2001 to 5 October 2024. The achieved accuracy of the model was 95.65% for earthquake prediction based on weather conditions in the Vrancea region, Romania. The authors proposed a unique alerting algorithm and conducted a case study that evaluates multiple predictive models, varying parameters, and methods to identify the most effective model for seismic event prediction in specific meteorological conditions. The findings demonstrate the potential of combining Internet of Things (IoT)-based environmental monitoring with AI to improve earthquake prediction accuracy and preparedness. An IoT-based application was developed using C# with ASP.NET framework to enhance earthquake prediction and public warning capabilities, leveraging Azure cloud infrastructure. The authors also created a hardware prototype for real-time earthquake alerting, integrating the M5Stack platform with ESP32 and MPU-6050 sensors for validation. The testing phase and results describe the proposed methodology and various scenarios. Full article

Multiparametric ultrasound (mpUS) enhances prostate cancer (PCa) diagnosis by using multiple imaging modalities. Tissue-mimicking materials (TMM) phantoms, favoured over animal models for ethical and consistency reasons, were created using polyvinyl alcohol (PVA) with varying molecular weights (Mw). Methods: Four PVA samples, varying in Mw with constant concertation, were mixed with glycerol, silicon carbide (SiC), and aluminium oxide (Al₂O₃). Phantoms with varying depth and inclusion sizes were created and tested using shear-wave elastography (SWE). An mpUS phantom was developed to mimic prostate tissue, including isoechoic and hypoechoic inclusions and vessels. The phantom was scanned using supersonic ultrasound, strain elastography, and Doppler ultrasound. Validation was performed using radical prostatectomy data and shear-wave elastography. Results: The acoustic properties varied with enhancers like glycerol and Al₂O₃. Low Mw PVA samples had a speed of sound ranging from 1547.50 ± 2 to 1553.70 ± 2.2 m/s and attenuation of 0.61 ± 0.062 to 0.63 ± 0.05 dB/cm/MHz. High Mw PVA samples ranged from 1555 ± 2.82 to 1566 ± 4.5 m/s and 0.71 ± 0.02 to 0.73 ± 0.046 dB/cm/MHz. Young’s modulus ranged from 11 ± 2 to 82.3 ± 0.5 kPa across 1 to 10 freeze-thaw cycles. Inclusion size, depth, and interaction statistically affect the SWE measurements with p-value = 0.056327, p-value = 8.0039 × 10⁻⁸, and p-value = 0.057089, respectively. SWE showed isoechoic inclusions, prostate tissue, and surrounding tissue only. The Doppler velocity was measured in three different inner diameters. Conclusion: PVA mixed with enhancer materials creates an mpUS phantom with properties that mimic normal and abnormal prostate tissue, blood vessels, and soft tissue, facilitating advanced diagnostic training and validation. Full article

Background and Objectives: Fibromyalgia syndrome (FM) is a chronic pain disorder that is ranked as one of the four most common rheumatological diseases in the world. This study aims to investigate the effects of an eight-week mat Pilates and electro-muscle stimulation (EMS) with combined mat Pilates exercises on pain, depression, anxiety, and strength in sedentary women. Methods: This study is a single-blind randomized controlled trial. A total of 30 sedentary female patients (Pilates (n = 15), EMS (n = 15)) diagnosed with FM were included in the study. The patients were subjected to Beck Depression (BDIs) and Anxiety Inventories (BAIs); a Fibromyalgia Impact Questionnaire (FIQ); five different Single-Leg Hop Tests (SLHTs); modified push-up (MPU), Handgrip Strength (HGS), Deep Squat (DSQ), V-Sit Flexor, bent-arm hang (BA), sit-up and Biering-Sørensen tests; and anthropometric tests before and after the 8-week exercise program. Results: The eight weeks of mat Pilates exercises combined with mat Pilates and EMS revealed significant results (p < 0.05) in anthropometric data (abdomen, lower abdomen, hips) (p < 0.05) except for the results of chest circumference measurements (p > 0.05). In addition, there were statistically significant positive results in BDIs, BAIs, FIQs, lower extremity (all SLHTs and DSQ), upper extremity (MPU, HGS, BA), and core (V-SIT, sit-up, Biering-Sørensen test) strength test findings (p < 0.05). Conclusions: Combining the mat Pilates exercises with EMS is an effective and reliable method to improve the pain, anxiety, depression, and strength of female patients diagnosed with FM. Full article

The nomenclatural status and typification of the names Arthrocaulon macrostachyum, Salicornia fruticosa, S. fruticosa var. deflexa, S. fruticosa var. glaucescens, S. fruticosa var. intermedia, S. fruticosa var. humilis, S. fruticosa var. pachystachya, S. fruticulosa, S. glauca, S. lignosa, S. macrostachya var. virescens, S. macrostachya var. glaucescens, S. perennis, S. radicans, S. radicans var. caespitosa, S. sarmentosa, S. sempervirens, and S. virginica, as well as an unnamed β-variety of S. fruticosa proposed by A. Bertoloni, are investigated. Concerning A. macrostachyum, we document that the type indicated in literature (G00177362) is not a holotype, and that lectotypification is necessary. A specimen from G (G00687638) is here designated as a lectotype. On the level of variety, Arthrocnemum fruticosum var. macrostachyum is an earlier legitimate name for Salicornia fruticosa var. pachystachya. Furthermore, Piirainen et al. are incorrect when citing Forsskål’s “Salicornia” from Alexandria as “S. virginica Forssk.”; it is not a new name and should be cited as S. virginica auct. non L., as published in Forsskål’s Flora Aegyptiaco-Arabica. Like with numerous other parallel cases in Flora Aegyptiaco-Arabica, Forsskål’s designation of “Salicornia virginica” for an Arabian plant is to be considered a misapplication of the earlier Linnaean name for an American plant. Arthrocnemum glaucum (a nomen illegitimum of Ungern-Sternberg), was listed as type species of Arthrocnemum by the Names in Current Use project; the basionym, Salicornia glauca Delile, is here lectotypified and identified as Arthrocaulon meridionale, published by Ramirez et al. Updated synonymies of Arthrocaulon macrostachyum, A. meridionale, Salicornia fruticosa, and S. perennis are proposed. Salicornia sempervirens is an invalid name according to Art. 36.1a of ICN. No original material was found for S. radicans var. caespitosa. This paper also refer to lecto- or neotypifications on specimens deposited at BM, G, LINN-HS, LY, MPU, NAP, and PAL, and their current taxonomic positions are suggested in a taxonomic part of the paper. Full article

Processing in Memory based on memristors is considered the most effective solution to overcome the Von Neumann bottleneck issue and has become a hot research topic. The execution efficiency of logical computation and in-memory data transmission is crucial for Processing in Memory. This paper presents a design scheme for data transmission and multi-bit multipliers within MAT (a data storage set in MPU) based on the memristive alternating crossbar array structure. Firstly, to improve the data transfer efficiency, we reserve the edge row and column of the array as assistant cells for OR AND (OA) and AND data transmission logic operations to reduce the data transfer steps. Furthermore, we convert the multipliers into multi-bit addition operations via Multiple Input Multiple Output (MIMO) logical operations, which effectively improves the execution efficiency of multipliers. PSpice simulation shows that the proposed data transmission and multi-bit multiplier solution has lower latency and power consumption and higher efficiency and flexibility. Full article