The objective of this study is to design and develop a programmable electrocardiogram (ECG) generator with frequency domain characteristics of heart rate variability (HRV) which can be used to test the efficiency of ECG algorithms and to calibrate and maintain ECG equipment. We simplified and modified the three coupled ordinary differential equations in McSharry's model to a single differential equation to obtain the ECG signal. This system not only allows the signal amplitude, heart rate, QRS-complex slopes, and P- and T-wave position parameters to be adjusted, but can also be used to adjust the very low frequency, low frequency, and high frequency components of HRV frequency domain characteristics. The system can be tuned to function with HRV or not. When the HRV function is on, the average heart rate can be set to a value ranging from 20 to 122 beats per minute (BPM) with an adjustable variation of 1 BPM. When the HRV function is off, the heart rate can be set to a value ranging from 20 to 139 BPM with an adjustable variation of 1 BPM. The amplitude of the ECG signal can be set from 0.0 to 330 mV at a resolution of 0.005 mV. These parameters can be adjusted either via input through a keyboard or through a graphical user interface (GUI) control panel that was developed using LABVIEW. The GUI control panel depicts a preview of the ECG signal such that the user can adjust the parameters to establish a desired ECG morphology. A complete set of parameters can be stored in the flash memory of the system via a USB 2.0 interface. Our system can generate three different types of synthetic ECG signals for testing the efficiency of an ECG algorithm or calibrating and maintaining ECG equipment.

It is estimated that 1 billion waste tires are generated every year across the globe, yet only 10% are being processed, and much rubber waste is yielded during manufacturing. These waste tires and rubber scraps are poisonous to the environment when processed via incineration and landfill. Rubber circular manufacturing is an effective solution that reduces not only rubber waste but also raw material costs. In this paper we propose a two-line flowshop model for the circular rubber manufacturing problem (CRMP), where the job sequence of two production lines is appropriately aligned to obtain the shortest makespan while guaranteeing that sufficient rubber waste yielded in the first line is ready to be reused for circular production in the second line. A genetic algorithm (GA) is developed, and the design of its genetic operations is customized to the CRMP context to achieve efficient and effective evolution. The experimental results with both real and synthetic datasets show that the GA significantly surpasses two heuristics in the literature by delivering the minimum makespan, which is 3.4 to 11.2% shorter than those obtained by the two competing methods.

Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefly algorithm (FA), have been proposed. The two algorithms were inspired by the bioluminescence process that enables the light-mediated swarming behavior for mating or foraging. From our literature survey, we are convinced with much evidence that the EAs can be more effective if appropriate responsive strategies contained in the adaptive memory programming (AMP) domain are considered in the execution. This paper contemplates this line and proposes the Cyber Firefly Algorithm (CFA), which integrates key elements of the GSO and the FA and further proliferates the advantages by featuring the AMP-responsive strategies including multiple guiding solutions, pattern search, multi-start search, swarm rebuilding, and the objective landscape analysis. The robustness of the CFA has been compared against the GSO, FA, and several state-of-the-art metaheuristic methods. The experimental result based on intensive statistical analyses showed that the CFA performs better than the other algorithms for global optimization of benchmark functions.

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