The detection of faults in electronic circuits is crucial to ensure the proper performance and reliability of electronic applications that utilize these devices. This work discovers, for the first time, that a direct tester board for fault diagnosis can be used not only for the intended measurement of current and voltage but also for studying the potential development of these magnitudes in inaccessible locations, as it detects register transfer level signals through oscilloscopes with low acquisition speeds. The experimental analysis carried out combines the use of commercial software with spatial distribution tracking and the exploitation of the sizes of network links in their computer graphical representation. The proper detection of malfunctions in electronic systems is crucial for enhancing their performance and reliability. We intend to explore the troubleshooting of analog electronic systems, for which we use wide-band direct tester boards. To evaluate its performance in routine practice, we perform experimentation using two different analog circuits designed. They consist of conventional operational amplifiers and element modeling based on equivalent resistance-capacitance networks. Given the procedure followed, commercial programs were used. Special mention should be made of the conclusion matrix, which is interesting when selecting suitable diagnostic parameters. The effectiveness of direct measurement based on integrated probes in the two projects, which allowed for fault insertion, was also confirmed. The results and discussions were enriched by the summarized experimental test report. The work concludes with a reflection on the relationship between this work and the existing state of the art, as well as the new challenges posed by international researchers.
Advanced prosthetics are a crucial aspect of rehabilitation technology and are receiving increased attention globally. Approximately 2 million people require prosthetic limbs, presenting opportunities for enhancing their quality of life. State-of-the-art technologies such as realistic arms and myoelectric prostheses are gaining popularity. Progress in sensor technology, artificial intelligence, and materials has driven the field forward. Various types of controllers, including direct, pattern recognition, and proportional-derivative, have been developed. Integration of material science, computer science, artificial intelligence, and neurology has facilitated controller advancements. Techniques like targeted muscle reinnervation and Osseo integrated prostheses offer improved surgical options. Gesture recognition technologies and intelligent sensors are enhancing hand control. Future advancements will involve machine learning, artificial intelligence, and sensing techniques, while ethical concerns must be addressed. Advanced myoelectric prostheses, also known as myocontrolled or lower-limb micromod investigative prostheses, have a patient acceptance rate of 75% to 80%. However, while these methods offer advantages, there are also drawbacks. Integrating different types of controllers for these smart prostheses and enhancing the overall device's strength and robustness will have a significant impact. This discussion focuses on various types of smart prosthetic controllers, dividing muscle activity into extracellular myoelectric potential and EEG signals
In recent decades, functionally graded porous structures have been utilized due to their light weight and excellent energy absorption. They have various applications in the aerospace, biomedical, and engineering fields. Therefore, the balance between material strength and light weight is the goal of the researchers to decrease the cost. Samples of PLA material were designed and manufactured using a 3D printer according to international standard specifications to study the effect of porosity gradient through thickness. An experimental three-point bending test was performed, and then simulations were performed using ANSYS 2022 R1 software on samples with functionally gradient different porosity layers to verify the experimental results. The results from the experiment and the numerical values were in excellent alignment with an error rate of no more than 13%. The maximum bending load and maximum deflection of the beam were specified experimentally and compared with the numerical solution. The maximum bending and the maximum deflection When the porosity layer in the middle of the beam, matched the ideal maximum bending load (190,194) N experimentally and numerically, respectively. The maximum deflection (5.9,6.4) mm experimentally and numerically, respectively was obtained in samples with varying porous layers.
The porous Titanium is characterized by high permeability which can assure the ingrowth of bone tissues, and consequently results in a good bonding between the metallic implant and the bone. In this work, Silicon element was added to the Commercially Pure Titanium at different weight percent of (2, 4, 6, 8 and 10) to investigate its effect on the porosity percentage, mechanical properties of the resulted samples. XRD analysis stated that at (Si) content lower than (2 wt%) the alloy is single phase (α- Ti alloy), as the Silicon content increased, in addition to (αphase), (Ti5Si3) intermetallic compound developed in the alloy. Porosity measurement results showed that the porosity percentage increases with the increase in Silicon content. Wear results stated that the wear rate increases with the increase in silicon content due to the increase in porosity percentage while the hardness results stated that there is no significant effect for Ti5Si3 intermetallic compound on improving the hardness of the samples. This is attributed to its low percent and the major effect of porosity on hardness which declined the effect of Ti5Si3 by reducing the hardness of the alloy compared with the master sample. The obtained results of the (yield strength, ultimate compressive strength and Young’s modulus) were within the values that match bone’s properties. This means these materials are suitable for biomedical application
Obesity is the excess of body weight relative to height above the desired level as a result of excessive increase in the ratio of body fat mass to lean mass. It causes many health problems due to its negative effects on body systems (cardiovascular system, musculoskeletal system, gastrointestinal system, respiratory system, skin, endocrine system, genitourinary system) and psychosocial status. In this study is aimed to effective detection of the eating and physical condition-based obesity stages using machine learning algorithms. The dataset contains data for the estimation of obesity stages in individuals from Mexico, Peru, and Colombia and is available as open source. There are 2111 records and 17 attributes in the dataset. In the records, obesity stages were categorized as insufficient weight, normal weight, overweight level I, overweight level II, obesity type I, obesity type II and obesity type III. The 10-fold cross-validation method was used to validate the model and the performances of the Support Vector Machine (SVM), Random Forest (RF), and Multilayer Perceptron (MLP) classification algorithms were compared. It has been determined that the highest performance among the algorithms whose performances are compared belongs to the RF Algorithm (95.78%). This paper’s abstract has been presented at the International Conference on Computational Mathematics and Engineering Sciences held in Ordu (Turkey), / 20-22 May. 2022.