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Acceleration-based sensors are widely used in indicating the severity of damage caused to structural buildings during dynamic events. The force rate of change is of interest when investigating the effect of seismic waves on structural elements, and hence the calculation of the jerk is necessary. For most sensors, the technique used for measuring the jerk (m/s3) is based on differentiating the time–acceleration signal. However, this technique is prone to errors especially in small amplitude and low frequency signals, and is deemed not suitable when online feedback is required. Here, we show that direct measurement of the jerk can be achieved using a metal cantilever and a gyroscope. In addition, we focus on the development of the jerk sensor for seismic vibrations. The adopted methodology optimized the dimensions of an austenitic stainless steel cantilever and enhanced the performance in terms of sensitivity and the jerk measurable range. We found, after several analytical and FE analyses, that an L-35 cantilever model with dimensions 35 × 20 × 0.5 (mm3) and a natural frequency of 139 (Hz) has a remarkable performance for seismic measurements. Our theoretical and experimental results show that the L-35 jerk sensor has a constant sensitivity value of 0.05 ((deg/s)/(G/s)) with ±2% error in the seismic frequency bandwidth of 0.1~40 (Hz) and for amplitudes in between 0.1 and 2 (G). Furthermore, the theoretical and experimental calibration curves show linear trends with a high correlation factor of 0.99 and 0.98, respectively. These findings demonstrate the enhanced sensitivity of the jerk sensor, which surpasses previously reported sensitivities in the literature.

High surface roughness hinders the flow of fluids in microchannels leading to low accuracy and poor-quality products. In this work, the adaptive neuro-fuzzy inference system (ANFIS) was used to examine surface roughness in CO2 laser fabrication of microchannels on polymethyl methacrylate (PMMA). The PMMA substrates were coated with a 500 nm layer of 99.95% pure aluminium. The inputs were speed (10, 15, and 20 mm/s), power (1.5, 3.0, and 4.5 W), and pulse rate (800, 900, and 1000 pules per inch) while the output was surface roughness. A 3-level full factorial design of experiments was used, and 27 experiments were conducted. Using the gaussian membership function (gaussmf), the ANFIS model was developed using the ANFIS toolbox in MATLAB R2022a. Analysis of variance was performed to examine the significance of the inputs. Power is the most significant followed by speed and pulse rate. The mean relative error (MRE), mean absolute error (MAE), and the correlation coefficient (R) were used to examine the accuracy and viability of the model. MRE, MAE, and R were found to be 0.257, 0.899, and 0.9957 (R2 = 0.9914) respectively. The root mean square error (RMSE) was 0.0022 and 3.6099 for the training data and checking data respectively. Hence, the developed model can predict the values of the average surface roughness with high accuracy.

Temperature control is a critical factor in PCR for efficient DNA amplification. The main aim is to achieve tight control and high rate of heating and cooling for a portable, cost-effective PCR device. This speed depends on reduction of the thermal mass of the PCR heating part. The common methods used to decrease the device's thermal mass or heating/cooling time are to improve desirable device structural design and to choose a better heating and cooling mechanism with robust controller. Increasing the thermal mass provides a good temperature distribution on the heater surface, but it delays the heat transfer. Therefore, removing thermal mass makes the controller struggle to provide a high temperature uniformity distribution on Peltier surface. In this paper, we provide a cost-effective PCR heating/cooling system using Peltier element. This system is controlled using adaptive FLC with bang-bang as a hybrid controller to provide good accuracy with maximum available temperature changing rate. The results show that in cooling, the adaptive FLC with bang-bang controller is faster by 20 % than the normal PD-like FLC, however in heating it is faster by 5 to 10 %. The adaptive FLC provided steady state error 3 % and 1.5 % less than the normal FLC at denaturation and annealing steps, respectively. Temperature distribution is tested using thermal camera. The device is validated by performing conventional PCR. The amplification product was analyzed by electrophoresis on a 1.5 % agarose gel then stained with ethidium bromide and the products show successfully amplified.

Goal-oriented mobile robot navigation is significant for exploration, transport, and telerobotics ubiquitously. Among the existing mobile robot configurations, the omnidirectional platforms offer enhanced maneuverability and flexibility in navigation. Also, Fuzzy Logic (FL) is favorable regarding intelligible control rules and robustness to uncertainties among the existing control schemes. However, there exists a gap in the control of omnidirectional robots using FL, especially tackling stagnation and local-minima in goal-oriented navigation. This study presents a methodological framework, first of its kind to the best of our knowledge, combining the benefits of a behavioral FL and Differential Evolution for stagnation-free and goal-oriented omnidirectional robot navigation. Whereas FL considers a behavioral modulation between goal-seeking and obstacle avoidance, relevant classes of Differential Evolution (DE), which comprise exploration, exploitation, and self-adaptation modalities, tackle FL membership functions' search space. Through rigorous computational experiments, we demonstrate that Rank-Based Differential Evolution (RBDE)'s exploitative features show favorable performance in learning and tackling stagnation in the context of a small number of function evaluations (up to 1000) and unknown/partially known environments, both of which are relevant for quick adaptation to dynamic environments. After explaining the reason for learning efficiency and navigation performance in training-testing scenarios, we discuss the potential of rank-based learning algorithms.

Recent years have seen a rapid increase in the level of sophistication in modern day devices which has given rise to the demand for better performance in all of their components, one of which is the heating element. Two excellent approaches to this need would be to improve the materials from which these Joule’s heating elements are made and the other to design improved heater geometries for best temperature distribution. In this paper, we discuss a high performance electrothermal heater prepared from laser reduced graphene oxide (LrGO) deposited on Polyethylene Terephthalate (PET) flexible Substrate. The surface morphology and structural properties of the prepared LrGO films were investigated by means of Scanning Electron Microcopy (SEM), X-Ray Diffraction (XRD) and Raman Spectroscopy (RS). Electrothermal (ET) responses of the fabricated electrothermal heaters to different driving DC voltages were studied by Infrared thermal Imagery. An electrothermal heater with a low Sheet resistance of ∼52 O/square was fabricated and it can attained a steady state temperature of up to 135 °C in only 10 s when a low voltage of 9 V was applied. A finite Element model (FEM) was prepared for this heater which agreed well with the experimental results. Power consumption for this heater is as low as 0.389 W/cm2, making it a suitable candidate for energy-saving applications such as wearable electronic.

We propose a polarization rotator using partially-etched tilted Bragg grating on a strip waveguide. Our structure features a compact device footprint of $\boldsymbol{25.2 \mu \mathrm{m}}$ with insertion loss and crosstalk of −1 dB and −20 dB, respectively, while switching the polarization state among fundamental modes (TM/TE).

The kinematic model of a skid-steering mobile robot (SSMR) is manipulated using signed polar transformation which represents a discontinuous state transformation. The influence of relative position...

In this work, the design and simulation of a novel way to harvest energy from blood flow in superior vena cava (SVC) vein to power impeded medical devices connected with wires like the pacemaker is introduced. The concept depends on the pulsatile blood flow inside SVC to deform micro-piezoelectric beams integrated into the medical device wires. This deformation induced by blood flow generates electrical charges in the piezoelectric beams which could be stored to power or recharge the medical device's battery. The harvesting process is simulated using finite element and MATLAB software. The first part of the simulation is done by a finite element package with fluid-solid interaction (FSI) module to simulate the deformation of the piezoelectric beams due to fluid forces (in this case the blood pressure and velocity profile). In the second part, the deformation of the piezoelectric beams is fed to MATLAB to translate this deformation into electrical charge. The shape and number of the harvester beams are selected so that the blood pressure drop is within the allowed range. Results show that a power of 9 μW can be generated with 8 harvesters with an area of 2 × 8 mm2 and arranged at 120° spacing between harvesters. The harvested energy is enough to power the implanted devices and avoid additional battery replacement surgery and infection problems.

The evaluation of hardened concrete quality became significantly important as a result of the continuous and increasing demand on concrete. Mechanical rebound hammer is a quick non-destructive evaluation method used to test the performance of hardened concrete onsite to assure the quality of newly casted concrete or asses the performance of old concrete elements. However, rebound hammer values have uncertainty problems in detecting concrete stiffness and are less reliable. In addition, the device performance is degraded with time due to the fatigue of the mechanical spring. Consequently, this paper presents a new methodology for developing the current measurement technique of Schmidt hammer by using a jerk sensor. Jerk sensors are used in the field of analytical dynamics which includes sensing of acceleration and rate of change of force. The sensor idea is based on using a gyroscope fixed at the free end of a metal cantilever. A cantilever (L-18) of dimensions 18x5x0.5 [mm] and natural frequency 202 [Hz] was designed and modeled using ANSYS finite element analysis (FEA) software. Modal and harmonic analyses were conducted to determine the sensor sensitivity for input jerk. The sensor was applied by constructing a finite element model (FEM) of the mass-spring system inside Schmidt hammer and attaching the sensor to the hammer mass. The correlation relationship between the sensor response and the stiffness modulus of concrete was examined by conducting a transient analysis simulating the Schmidt hammer test of 6 cubic concrete specimens of different grades 20, 30, 37, 45, 50 and 60 [MPa]. Each concrete grade has a different modulus of elasticity. FEA results showed that the L-18 cantilever model has a constant jerk sensitivity of 0.047 [(deg/s)/(G/s)] in the bandwidth 0.1~60 [Hz] and a maximum sensitivity of 1.0 [(deg/sec)/(G/s)] at impact (resonance). The predicted input jerk and output angular velocity are in phase. In addition, the results proved that the stiffness modulus of concrete can be evaluated more efficiently at the impact instant using the rate of change of impact force rather than the rebound distance.

The process of pressing mango fruits to check for their ripeness has caused mechanical damages. In order to reduce this damage, two varieties of mangoes namely Totapuri and Tommy Atkins were tested by an advanced universal testing machine (UTM). Each of these varieties had 5 samples of different ripeness levels to test for their elastic modulus. The mangoes were loaded radially and the results of the elastic modulus obtained, which falls between 0.5 and 3.5 MPa. This study aims at presenting a detailed design procedure for developing a tactile sensor that can differentiate between different ripeness levels in mango fruits. The tactile sensor is based on two springs configuration, with each of these springs having different stiffnesses. The performance of the tactile sensor was studied by developing a finite element model using the ANSYS Mechanical ANSYS Parametric Design Language (APDL) software. The simulation results gotten from the performance of the tactile sensor show that the sensor can differentiate between different mango fruits based on their elastic modulus within the specified range of the sensor. Finally, the fabricated sensor was tested with five specimens representing mangoes with known stiffness in the mango stiffness range, thus, the sensor differentiated between the five specimens by giving its own unique sensor output (force ratio).

An integrated optical mode converter, based on tilted Bragg surface gratings in silicon-on-insulator technology, is designed to achieve features needed to promote a refractive index integrated sensor. The tilted grating is optimized to minimize device footprint ( $60\ \mu \mathrm{m}$ ), and to obtain a narrow resonance full width at half maximum FWHM (7.3 nm) and a significant transmission dip (−10 dB). The proposed device achieves a linear response over a relative wide wavelength range, and a high sensitivity of 160 nm/RIU.

The mechanical properties for soft tissues become a new biomarker in the diseases diagnosing field and biomedical Robots. Measuring such properties can help surgeons in diagnosing during minimally invasive surgery (MIS). Furthermore, building a transducer capable of measuring viscoelastic properties could be used in industrial Robot's end effector for handling and grasping tasks. Here, we build a model for a new tactile sensor to measure the mechanical properties for the soft tissue. The proposed model for the sensor is simply consisting of a piezoelectric (PZT) benders, brass layer and prop. These PZT materials are easy to control, have a good frequency response, can be easily miniaturized and integrated into a MEMS system. Moreover, Jacobsen's approach is used to calculate the equivalent damping coefficient based on dissipated work. Three PDMS samples imitating human tissue are used to evaluate the sensor's model. The results show the sensor capability to measure the damping coefficient for soft materials.

This paper presents the control of an automotive air-spring suspension system using a Z-number based fuzzy inference system. One of automotive suspension functions is to isolate vehicle body from road disturbance which is described by transmissibility coefficient. There is a conflict between ride comfort and handling requirements. Lower transmissibility coefficient results in improving passengers’ comfort. In this study, an air-spring suspension system is connected to two additional volumes of different sizes via ON-OFF valves that are controlled by the proposed control strategy. The stiffness of the suspension system is the control parameter that is controlled by changing the enclosed pressure and the total air volume inside the air-spring within one of four chosen levels. The proposed Z-number control scheme is characterized by two components; constraint and reliability. The interpolating reasoning of the Z-number fuzzy is constructed and applied. Genetic Algorithm is used to estimate the optimal control gains and switching levels of the air volumes. The proposed control system is applied to a simulated quarter car model to evaluate system performance. Then, the proposed controller is compared to a conventional fuzzy controller and a fuzzy controller merged with discrete volume control. The results show that the proposed controller improves the air suspension performance compared to the other controllers with a maximum acceleration of 1.1 cm/s2.