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This paper reports on a novel load cell and a novel torque transducer having stiffness and potential overload capability some ten times that of existing load cells and torque transducers based on the resistance strain gauges.

Describes the practical capabilities and technology – the design, construction and characterisation.

Both the load cell and the torque transducer use recently developed metallic triple beam resonators with thick‐film lead zirconate titanate (PZT) drives and pickups. The advantages of this technology are frequency output, high overload capability, high sensitivity, high resolution, and low‐cost manufacture. Both the load cell and torque transducer output large changes in frequency (>500 Hz for relatively low changes in strain level i.e. <200 microstrain for the load cell and <400 microstrain for the torque transducer), providing high sensitivity and high overload capability.

Load cells and torque transducers employing the new metallic resonators are expected to be far more robust than those using metallic resistance strain gauges.

Focuses on an instrument with important features of use in many applications.

This chapter is an introduction to the English version of Karl Kautsky’s essay “Theories of Crises,” which in turn is a review of Michael von Tugan-Baranowsky, Studien zur Theorie und Geschichte der Handelskrisen in England (Studies on the Theory and History of Commercial Crises in England), published in 1901. The chapter contextualizes Kautsky’s essay in the framework of the ongoing revisionist controversy within the Social Democratic Party of Germany and the Second International, and well as of the debate between Marxists and Populists in Russia.

The universal sensor interface chip (USIC) represents a complete signal processing capability for data acquisition systems designed to support a wide range of sensor applications. Offers high performance with flexibility and requires only a small number of external components for many applications.

The finite element method (FEM) for 3D models needs heavy computational cost. The computational cost for FE analysis of moving objects, e.g. Vibration energy harvester, must be reduced to exploit the simulation of the dynamic system in its design. The paper aims to discuss these issues.

To reduce the computational time of FEM, the model order reduction (MOR) based on proper orthogonal decomposition has been proposed. For the moving systems, MOR is modified.

It is shown that proposed MOR makes it possible to drastically reduce the coupling analysis of the energy harvester in which the equations of motion, magnetostatics, and circuit are repeatedly solved.

To reduce the computational time of FEM, block-MOR is presented, in which the whole domain is subdivided into N-blocks. As a result computational cost for MOR can be reduced.

For decades, continuous research work is going on to maximize the power harvested from the sun; however, there is only a limited analysis on exploiting the microwatt output power from indoor lightings. Microelectronic system has power demand in the µW range, and therefore, indoor photovoltaics would be appropriate for micro-energy harvesting appliances. “Energy harvesting is defined as the transfer process by which energy source is acquired from the ambient energy, stored in energy storage element and powered to the target systems”. The theory of energy harvesting is: gathering energy from surroundings and offering technological solutions such as solar energy harvesting, wind energy collection and vibration energy harvesting. “The solar cell or photovoltaic cell (PV), is a device that converts light into electric current using the photoelectric effect”. Factors such as light source, temperature, circuit connection, light intensity, angle and height can manipulate the functions of PV cells. Among these, the most noticeable factor is the light intensity that has a major impact on the operations of solar panels.

This paper aims to design an enhanced prediction model on illuminance or irradiance by an optimized artificial neural network (ANN). The input attributes or the features considered here are temperatures, maxim, TSL, VI, short circuit current, open-circuit voltage, maximum power point (MPP) voltage, MPP current and MPP power, respectively. To enhance the performance of the prediction model, the weights of ANN are optimally tuned by a new self-improved brain storm optimization (SI-BSO) model.

The superiority of the implemented work is compared and proved over the conventional models in terms of error analysis and prediction analysis. Accordingly, the presented approach was analysed and its superiority was proved over other conventional schemes such as ANN, ANN-Levenberg–Marquardt (LM), adaptive-network-based fuzzy inference system (ANFIS) and brainstorm optimization (BSO). In addition, analysis was held with respect to error measures such as mean absolute relative error (MARE), mean square root error (MSRE), mean absolute error and mean absolute percentage error. Moreover, prediction analysis was also performed that revealed the betterment of the presented model. More particularly, the proposed ANN + SI-BSO model has attained minimal error for all measures when compared to the existing schemes. More particularly, on considering the MARE, the adopted model for data set 1 was 23.61%, 48.12%, 79.39% and 90.86% better than ANN, ANN-LM, ANFIS and BSO models, respectively. Similarly, on considering data set 2, the MSRE of the implemented model was 99.87%, 70.69%, 99.57% and 94.74% better than ANN, ANN-LM, ANFIS and BSO models, respectively. Thus, the enhancement of the presented ANN + SI-BSO scheme has been validated effectively.

This work has established an improved illuminance/irradiance prediction model using the optimization concept. Here, the attributes, namely, temperature, maxim, TSL, VI, Isc, Voc, Vmpp, Impp and Pmpp were given as input to ANN, in which the weights were chosen optimally. For the optimal selection of weights, a novel ANN + SI-BSO model was established, which was an improved version of the BSO model.

Various approaches have been made to alter the vibration sensing properties of zinc oxide (ZnO) films to achieve high sensitivity. This paper aims to report the experimental study of the fabrication of precursor molar ratio concentration varied ZnO nanostructures grown on rigid substrates using the refresh hydrothermal method. The effect of these fabricated ZnO nanostructures-based vibration sensors was experimentally investigated using a vibration sensing setup.

ZnO nanostructures have been grown using low temperature assisted refresh hydrothermal method with different precursor molar concentrations 0.025 M (R1), 0.075 M (R2) and 0.125 M (R3). Poly 3,4-ethylenedioxythiophene polystyrene sulfonate, a p-type material is spun coated on the grown ZnO nanostructures. Structural analysis reveals the increased intensity of the (002) plane and better c-axis orientation of the R2 and R3 sample comparatively. Morphological examination shows the changes in the grown nanostructures upon increasing the precursor molar concentration. The optical band gap value decreases from 3.11 eV to 3.08 eV as the precursor molar concentration is increased. Photoconductivity study confirms the formation of a p-n junction with less turn-on voltage for all the fabricated devices. A less internal resistance of 0.37 kΩ was obtained from Nyquist analysis for R2 compared with the other two fabricated samples. Vibration testing experimentation showed an improved output voltage of the R2 sample (2.61 V at 9 Hz resonant frequency and 2.90 V for 1 g acceleration) comparatively. This also gave an increased sensitivity of 4.68 V/g confirming its better performance when compared to the other fabricated two samples.

Photoconductivity study confirms the formation of a p-n junction with less turn-on voltage for all the fabricated devices. A less internal resistance of 0.37 kΩ was calculated from the Nyquist plot. Vibration testing experimentation proves an increased sensitivity of 4.68 V/g confirming its better performance when compared to the other fabricated two samples.

Vibration testing experimentation proves an increased sensitivity of 4.68 V/g for R2 confirming its better performance when compared to the other fabricated two samples.

The purpose of this paper is to demonstrate the feasibility of using kinetic energy harvesting to power wireless condition monitoring sensors.

The system presented duty cycles its operation depending upon the energy being harvested. The harvested energy is stored on a supercapacitor and the system samples sufficient vibration data to enable an FFT to be performed at the receiver.

The results of this study show it is perfectly feasible to power practical wireless condition monitoring sensors entirely from the vibrations of the machines being monitored.

Energy harvesting techniques can be used to power wireless sensors in a range of applications. Removing the need for a battery power supply presents obvious environmental benefits and avoids the need to periodically replace batteries.

Autism spectrum disorder is a kind of neurodevelopmental disorder characterized by persistent deficits in social communication and interaction across multiple contexts, and restricted, repetitive patterns of behavior, interests or activities. The purpose of this paper is to determine the effectiveness of sensory integration intervention on emotional-behavioral problems in children with autism spectrum disorder.

This research was conducted in a pretest-posttest design with control group. The participants were 30 children with autism spectrum disorder (6–11 years old) who were selected through convenience sampling from among children with autism spectrum disorder in Zeinab center of Isfahan and were randomly divided into two groups of 15 subjects. The children of the experimental group received 14 sessions of sensory integration intervention while the control group did not receive this intervention. To measure emotional-behavioral problems, the Behavior Assessment System for Children-Second Edition was used. To analyze the data, ANCOVA and MANCOVA tests were used.

The results showed that sensory integration intervention improves emotional-behavioral problems and its subscales (hyperactivity, aggression, behavioral problems, anxiety, depression, somatization, attention problems, learning difficulties, atypicality and withdrawal) in children with autism spectrum disorder.

Therefore, it can be concluded that sensory integration intervention can be a suitable treatment for reducing sensory problems and improving emotional-behavioral problems in children with autism spectrum disorder.

The study of the effect of sensory integration on emotional-behavioral problems in children with autism spectrum disorder is necessary as a simple and non-side-effect educational and therapeutic method, both as a step to fill the research gap in this field, besides being a cheap and affordable way for improving the various skills of children with autism spectrum disorder for professionals, teachers, parents and educators.

The mid-1990s marked a paradigm shift in the way physical activity is promoted, and walking is now considered the most suitable type of physical activity for widespread promotion. Accurate measurement underpins public health practice, hence the aims of this chapter are to: (1) provide a typology for the measurement of walking; (2) review methods to assess walking; (3) present challenges in defining walking measures; (4) identify issues in selecting instruments for the evaluation of walking and (5) discuss current efforts to overcome measurement challenges and methodological limitations. The taxonomy of walking indicates that secondary purpose walking is a more complex set of behaviours than primary purpose walks. It has many purposes and no specific domain or intensity, may lack regularity, and therefore poses greater measurement challenges. Objective measurement methods, such as accelerometers, pedometers, smartphones and other electronic devices, have shown good approximation for walking energy expenditure, but are indirect methods of walking assessment. Global Positioning System technology, the ‘Smartmat’ and radio-frequency identification tags are potential objective methods that can distinguish walkers, but also require complex analysis, are costly, and still need their measurement properties corroborated. Subjective direct methods, such as questionnaires, diaries and direct observation, provide the richest information on walking, especially short-term diaries, such as trip records and time use records, and are particularly useful for assessing secondary purpose walking. A unifying measure for health research, surveillance and health promotion would strongly advance the understanding of the impact of walking on health.

Current technology uses large windmills that operate in remote regions and have complex generating mechanisms such as towers, blades gears, speed controls, magnets, and coils. In a city, wind energy that would otherwise be wasted can be claimed and stored for later use. The purpose of this paper is to introduce a small‐scale windmill that can work in urban areas.

The device uses a piezo‐composite generating element (PCGE) to generate the electric power. The PCGE is composed of layers of carbon/epoxy, lead zirconate titanate (PZT) ceramic, and glass/epoxy cured at an elevated temperature. Previous work by the authors had proved that the PCGE can produce high performance energy harvesting.

In the prototype, the PCGE performed as a secondary beam element. One end of the PCGE is attached to the frame of the device. Additionally, the fan blade rotates in the direction of the wind and hits the other end of the PCGE. When the PCGE is excited, the effects of the beam's deformation enable it to generate electric power. The power generation and battery charging capabilities of the proposed device were tested, and the results show that the prototype can harvest energy in urban regions using minor wind movement.

The paper presents a prototype that uses a PCGE for harvesting wind energy in urban areas. The PCGE has the potential of being used as a generator for harvesting energy from sources such as machine vibration, body motion, wind, and ocean waves. The PCGE design is flexible: the ply orientation and the size of the prepreg layers can be changed. Generating elements with a specific stacking sequence can be used for scavenging energy in a wide range of applications such as network sensors, portable electronics, and microelectromechanical systems.