Ranking patients on the kidney transplant waiting list based on fuzzy inference system

Background Kidney transplantation is the best treatment for people with End-Stage Renal Disease (ESRD). Kidney allocation is the most important challenge in kidney transplantation process. In this study, a Fuzzy Inference System (FIS) was developed to rank the patients based on kidney allocation factors. The main objective was to develop an expert system, which would mimic the expert intuitive thinking and decision-making process in the face of the complexity of kidney allocation. Methods In the first stage, kidney allocation factors were identified. Next, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) has been used to weigh them. The purpose of this stage is to develop a point scoring system for kidney allocation. Fuzzy if-then rules were extracted from the United Network for Organ Sharing (UNOS) dataset by constructing the decision tree, in the second stage. Then, a Multi-Input Single-Output (MISO) Mamdani fuzzy inference system was developed for ranking the patients on the waiting list. Results To evaluate the performance of the developed Fuzzy Inference System for Kidney Allocation (FISKA), it was compared with a point scoring system and a filtering system as two common approaches for kidney allocation. The results indicated that FISKA is more acceptable to the experts than the mentioned common methods. Conclusion Given the scarcity of donated kidneys and the importance of optimal use of existing kidneys, FISKA can be very useful for improving kidney allocation systems. Countries that decide to change or improve the kidney allocation system can simply use the proposed model. Furthermore, this model is applicable to other organs, including lung, liver, and heart.

Novel Strategy of Adaptive Predictive Control Based on a MIMO-ARX Model

Many industrial processes include MIMO (multiple-input, multiple-output) systems that are difficult to control by standard commercial controllers. This paper describes a MIMO case of a class of SISO-APC (single-input, single-output adaptive predictive controller) based upon an ARX (autoregressive with exogenous variable) model. This class of SISO-APC based on ARX models has been successfully and extensively used in many industrial applications. This approach aims to minimize the barriers between the theory of predictive adaptive control and its application in the industrial environment. The proposed MIMO-APC (MIMO adaptive predictive controller) performance is validated with two simulated processes: a quadrotor drone and the quadruple tank process. In the first experiment the proposed MIMO APC shows ISE-IAE-ITAE performance indices improvements of up to 25%, 25.4% and 38.9%, respectively. For the quadruple tank process the water levels in the lower tanks follow closely the set points, with the exception of a 13% overshoot in tank 1 for the minimum phase behavior response. The controller responses show significant performance improvements when compared with previously published MIMO control strategies.

Multimodal Optimization with the Local Optimum Ranking 2 Algorithm

In optimization tasks, it is interesting to achieve a set of efficient solutions instead of one single output, in the case the best solution is not suitable. Many niching methods offer a diversified response, yet some important problems are common: (1) The most interesting solutions of each local optimum are not identified. Thus, the output is the overall population of solutions, which increases the work of the designer in verifying which solution is the most interesting. (2) Existing niching algorithms tend to distribute the solutions on the most promising regions, over-populating some local optima and sub-populating others, which leads to poor optimization.To solve these challenges, a novel niching method is presented, named local optimum ranking 2 (LOR2). This sorting methodology favors the exploration of a defined number of local optima and ranks each local population by objective value within each local optimum. Thus, is performed a multi-focus exploration, with an equalized number of solutions on each local optimum, while identifying which solutions are the local apices. To exemplify its application, the LOR2 algorithm is applied in the design optimization of a metallic cantilever beam. It achieves a set of efficient and diverse design configurations, offering both performance and diversity for structural design challenges.In addition, a second experiment describes how the algorithm can be applied to segment the domain of any function, into a mesh of similar sized or custom-sized elements. Thus, it can significantly simplify metamodels and reduce their computation time.

River flow forecasting by comparative analysis of multiple input and multiple output models form using ANN

ANN was used to create a storage-based concurrent flow forecasting model. River flow parameters in an unsteady flow must be modeled using a model formulation based on learning storage change variable and instantaneous storage rate change. Multiple input-multiple output (MIMO) and multiple input-single output (MISO models in three variants were used to anticipate flow rates in the Tar River Basin in the United States. Gamma memory neural networks, as well as MLP and TDNNs models, are used in this study. When issuing a forecast, storage variables for river flow must be considered, which is why this study includes them. While considering mass balance flow, the proposed model can provide real-time flow forecasting. Results obtained are validated using various statistical criteria such as RMS error and coefficient of correlation. For the models, a coefficient of correlation value of more than 0.96 indicates good results. While considering the mass balance flow, the results show flow fluctuations corresponding to expressly and implicitly provided storage variations.

Investigating the Effect of COVID-19 Infection on P300 Based BCI Application Performance

The COVID-19, which has rapidly spread and infected millions of people from all over the world, causes various problems including psychiatric, economic, educational as well as health. Many studies have been reported that COVID-19 can be characterized by vascular damage predominantly involving micro vessels. In this study, we proposed a method to examine whether COVID-19 effects on brain computer interface (BCI) performance or not. We collected P300 based electroencephalogram (EEG) signals from six subjects before and after the COVID-19 infection. For classifying the P300 and non-P300 EEG signals, single output and two-layer artificial neural network was utilized. Based on the t-test analysis, it was observed that there was a significant difference between the before and after COVID-19 infection test groups especially on Oz channel in occipital region for alpha=0.05 percent while that of for alpha=0.01 percent shows no statistical difference for P300 classification results. The latency values, on the other hand, before and after COVID-19 infection did not represent any difference for both significance levels. It is clearly understood from the literature that COVID-19 negatively effects to the microvascular bed. Therefore, it might be expected that it could cause to reduce the P300 based BCI performance. This was the first study to investigate the impact of COVID-19 on P300-based BCI performance, taking into account the EEG signals of the COVID-19 infection. The obtained results showed that although the COVID-19 infection did not generally effected P300 based BCI application performance and latency values, the performance of the occipital region electrodes slightly effected.

Development of multi-input multi-output virtual tuned mass damper controls for the vibration suppression of structures

This study is concerned with the development of multi-input multi-output control algorithms for the active vibration suppression of structures using accelerometer signals and force-type actuators. The concept of the single-input single-output virtual tuned mass damper control algorithm developed in the previous study was extended to cope with multiple natural modes of structure equipped with a limited number of sensors and actuators. Two control algorithms were developed based on the assumption of collocated control. One is the decentralized virtual tuned mass damper control that produces the actuator signal using only the accelerometer signal of that actuator position. The other is the centralized virtual tuned mass damper control that is designed in modal-space, and produces the modal control force using the modal coordinate. Both the theoretical and experimental results show that the proposed control algorithms are effective in suppressing multiple natural modes with a lesser number of sensors and actuators. However, the decentralized virtual tuned mass damper control can be designed and implemented more easily than the centralized virtual tuned mass damper control.

Electronically Tunable TISO Voltage-Mode Universal Filter Using Two LT1228s

This study aims to design an electronically tunable voltage-mode (VM) universal filter utilizing commercially available LT1228 integrated circuits (ICs) with three-input and single-output (TISO) configuration. With the procedure based on two integrator loop filtering structures, the proposed filter consists of two LT1228s, four resistors, and two grounded capacitors. It realizes five filter output responses: low-pass, all-pass, band-reject, band-pass, and high-pass functions. By selecting input voltage signals, each output responses can be achieved without changing the circuit architecture. The natural angular frequency can be controlled electronically. The input voltage nodes Vin1 and Vin3 possess high impedance. The output node has low impedance, so it can be cascaded to other circuits. The performance of the proposed filter is corroborated by PSpice simulation and hardware implementation which support the theoretical assumptions. The result shows that the range of total harmonic distortion (THD) is lower than 1%, and that the higher the temperature is, the lower the natural angular frequency is.

A Modified Multi-Winding DC–DC Flyback Converter for Photovoltaic Applications

DC–DC power converters have generated much interest, as they can be used in a wide range of applications. In micro-inverter applications, flyback topologies are a relevant research topic due to their efficiency and simplicity. On the other hand, solar photovoltaic (PV) systems are one of the fastest growing and most promising renewable energy sources in the world. A power electronic converter (either DC/DC or DC/AC) is needed to interface the PV array with the load/grid. In this paper, a modified interleaved-type step-up DC–DC flyback converter is presented for a PV application. The topology is based on a multi-winding flyback converter with N parallel connected inputs and a single output. Each input is supplied by an independent PV module, and a maximum power point tracking algorithm is implemented in each module to maximize solar energy harvesting. A single flyback transformer is used, and it manages only 1/N of the converter rated power, reducing the size of the magnetic core compared to other similar topologies. The design of the magnetic core is also presented in this work. Moreover, the proposed converter includes active snubber networks to increase the efficiency, consisting of a capacitor connected in series with a power switch, to protect the main switches from damaging dv/dt when returning part of the commutation energy back to the source. In this work, the operating principle of the topology is fully described on a mathematical basis, and an efficiency analysis is also included. The converter is simulated and experimentally validated with a 1 kW prototype considering three PV panels. The experimental results are in agreement with the simulations, verifying the feasibility of the proposal.

Online Estimation of Terminal Airspace Sector Capacity from ATC Workload

Neural Partial Differentiation (NPD) approach is applied to estimate terminal airspace sector capacity in real-time from the ATC (Air Traffic Controller) dynamical neural model with permissible safe separation and affordable workload. A neural model of a multi-input-single-output (MISO) ATC dynamical system is primarily established and used to estimate parameters from the experimental data using NPD. Since the relative standard deviations of these estimated parameters are lesser, the predicted neural model response is well matched with the intervention of ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters that are unknown in practice.