Current Signal Transduction Therapy - Volume 16, Issue 1, 2021

Background: Networked control systems (NCSs) are used to control industrial and medical plants via data communication networks. These systems have many wide applications in a broad range of areas such as remote surgery, industrial and space sciences. Two important challenging problems in these systems are stochastic time delays and packet dropouts. Classic proportional- integral controllers due to their simple inherent design and implementation have many applications in controlling industrial and medical plants. However, these simple controllers do not have high performance in NCS because of communication networks induced time-varying delays and so this causes instability in NCS. In this paper, an adaptive proportional-integral controller is proposed using an online estimation of network time delay technique in a node application layer. The coefficients of this new controller change according to the values of estimated time delays online. Therefore, the proposed controller causes stability in NCS loop. The performance of the proposed method is simulated for a DC motor that can be used in remote surgery. The simulation results show the proposed controller is better at least about 1000 times according to IAE performance index rather than a classic proportional-integral controller. Also, the results of practical implementation show that the proposed controller causes the stability of NCS. Objective: The study intends to analyze and design an online adaptive approach that can stabilize networked control systems having important applications such as remote surgery. Materials and Methods: The article proposes an online adaptive proportional-integral controller that can be used in NCS and has applications such as remote surgery. The coefficients of the newly proposed controller are changed online based on the estimation of network time delay. In the proposed controller firstly, the variable time delay value is estimated online, then the coefficients of the PI controller is updated based on this estimated value. Results: The proposed method causes the controller to generate and transmit the suitable control signal according to different and random conditions of the network. Adaption of coefficients compensates time-varying delay effect on system performance and causes increasing stability that is necessary for medical applications. Conclusion: The proposed system performs better than the traditional approach in terms of measuring the average value of the error, recall, and ITAE. According to simulation and practical results, when the network average time delay is about 40ms, the performance index for an online adaptive PI controller is equal to 4.4236, and value for a classic PI controller is 4409. Thus, the performance of an online adaptive PI controller has been improved about 1000 times rather than a classic one. Therefore, the proposed controller in real network time delay has proper performance and keeps the stability of the control loop.

Background: Health economics is amongst the academic fields which can aid in ameliorating conditions so as to make better decisions related to the economy such as determining cash prices. The prediction of ending cash value is fundamental for internal and external users and can come quite handy in health economics. The most important purpose of financial reporting is the presentation of information to predict ending cash value. Therefore, the aim of this research is to predict ending cash value using feature selection and multiple linear regression (MLR) method from 2010-2012. Methods: Feature selection algorithm (Best-First, Greedy-Stepwise, and Ranker) was employed in this research to nominate relevant data that affect ending cash value. Results: According to results, to determine ending cash value, the most relevant features include: interest payments for loans, dividends received from short and long term deposits, total net flow of investment activities, net increase (decrease) in cash and beginning cash based on best-first (CFSSubset- Evaluation) and Greedy-Stepwise (CFS-Subset-Evaluation). Net out flow, dividends, dividends paid, interest payments for loans and dividends received deposits for short and long term were the most important data as indicated by the Ranker (Info-Gain-Attribute-Evaluation, Gain- Ratio-Attribute-Evaluation and Symmetricer-Attribute-Evaluation). According to Ranker (Principal- Components and Relief-FAttribute-Evaluation), the best data for determining ending cash include beginning cash, interest payments for loans, dividends, net increase (decrease) in cash and dividends received from short and long term deposits. The findings were also indicative of a positive and highly significant correlation between dividends received from short and long term deposits and beginning cash (1.00**), with a significance level of 0.01, whereas the observed correlation between interest payments for loans and ending cash (0.999**), at a significance level of 0.01 was negatively significant. Conclusion: The present research attempted to reduce the volume of data required for predicting end cash by means of employing a feature selection so as to save both precious money and time.

Introduction: Optical imaging in the underwater environment to monitor marine objects is now a hot topic of research which can be used for environmental healthcare systems through the underwater ecosystem. Among different areas of research, image coding techniques are widely applied to compress data for reliable communications. One of the challenges faced during the underwater communications is having a low bit rate in acoustic links, particularly while doing imaging in deep waters (in this condition, light needed for imaging is provided by a power supply). Materials and Methods: Two Dimensional-Discrete Cosine Transform (2D-DCT) is the main technique that we want to use for image compression, to test two different patch sizes in 2D-DCT to study the patch size effect on the quality of compression, execution time and preservation ability of high-frequency information in edges. Results: The results clearly show that a larger patch size can always be better in terms of computational complexity, quality of coded images and also edge preservation when we use DCT for the compression process. Discussion and Conclusion: Although this research approves the approach of JPEG codec once again for using the largest sub-image block in image compression (in terms of similarity and complexity), however, the use of an edge preservation factor is a new finding for our research. On the other hand, using the largest patch size is not a general approach for all image processing applications, because some studies have shown that smaller patch may be more effective for some other applications.

Background: Notch is a cell signaling pathway that is highly conserved in all metazoans and is the master responsible for cell differentiation and cross communication with other signaling pathways such as Wingless and Hedgehog. In most cancers, the Notch signaling pathway is altered, causing normal controls of vital processes such as cell cycle, differentiation and apoptosis to be compromised, leading the cell to a carcinogenic state. However, the inherent nature of the Notch signaling pathway of regulating the activation or repression of other signaling pathways related to differentiation and cell cycle has caused the strategies against breast cancer to be even more specialized and to know more efficiently the suitable method againts the disease. Methods: Different bibliographies and articles related to the Notch pathway and the biology of cancer were reviewed, especially with breast cancer, as well as the participation of other signaling pathways directly and indirectly related to the constitutive activation of the Notch pathway and relating the threads loose to create a general conceptual map of the wide effects of the malfunction of the Notch pathway in this disease so that we can understand from another point of view how this flow of information can be controlled. Results: The study of Notch signaling during more than a decade has gave us new clues to elucidate the mechanisms of their finely orchestrated molecular functioning and how this signaling path interacts with another signaling pathways in differentiation, survival, cell cycle and apoptosis. Notch Signaling is a professional in the process of differentiation and cross-talk communication with another signaling pathways, especially with the WNT and Hh pathways. The responsibility of WNT pathway is to maintain the survival and growth controls, having direct effects on the cell cycle and the Hh pathway is to maintain a state of diferentiation trought the life of the cell and the cell migration instead, cell cycle and the response to growth stimuli of angiogenesis, exacerbated characteristics of many breast cancer. Conclusion: This review highlights the complex communication network between the WNT, Hh and Notch Signaling and the therapies in the field of cancer that are not entirely effective, however, most of the therapies that are currently being developed are including the Notch Signaling as a target quite promising and is for its regulatory effect with other signaling pathways.

Introduction: A progressive and flourishing technological advancement occurs across the communities working on a domain that needs clinical training and Technology Transfer. There is an essentiality for the evolution of advanced concepts in the Classification of healthcare, particularly in relation to arrhythmia detection towards clinical operations. Being the forerunner among the emerging areas in science and technology, this field demands an extensive practical and verification research. These innovative technological progress has significantly contributed to highquality, on-time, acceptable and affordable healthcare. Materials & Methods: This paper approaches a novel method of Detecting and classifying the cardiac arrhythmias using deep learning model for classification of electrocardiogram (ECG) signals. This method is based on using Cubic Wavelet Transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmias. Results & Discussion: In these parameters are used as input to these classifier, five most important types of ECG signals they are Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Pre- Ventricular Contraction (PVC), Ventricular Fibrillation (VF), and Ventricular Flutter (VFLU). By using the deep learning algorithm to recognition and classification capabilities across a broad area of biomedical engineering. The performance of the deep learning model was evaluated in terms of training performance and classification accuracies. The classification accuracy of 99.24% is achieved. Good accuracy of ECG patterns is achievable only over a large number of files. Conclusion: These difficulties have necessitated us to develop a new detection scheme, which gives a high level of accuracy, low false positive and low false-negative statistics.

Aims: The work aims to implement a soft switched high frequency ac link universal power converter controlled BLDC motor for medical applications. The universal power converter is an ac link converter, with the parallel combination of an inductor and a capacitor which offers improved power factor, low power consumption and more efficiency. Materials and Methods: SVPWM technique is utilized as a control technique for the proposed converter. AC link converter controlled with PDM technique requires an additional control procedure or an algorithm for regulating the output power of the PDM converters. SVPWM technique was adopted to turn ON and OFF the input and output switches in sequence to get the regulated power at the output. Result and Conclusion: These types of inverters could be used in medical field as an alternate to conventional inverters during power shut downs. The inverter proposed in this work has both the capability to step up and step down the input voltage based on the load requirements. The soft switched AC link inverter may be incorporated in a variety of applications including medial applications, industrial needs, renewable energy resources, variable frequency drives and electric and hybrid vehicles.

Introduction: In the recent past, the requirement of backup sources has increased exponentially and necessitated massive energy storage devices like lead-acid batteries, and supercapacitors. Materials and Methods: Especially these power backup resources become inevitable, while installing critical diagnostic and lifesaving equipment. The storage system, usually lead-acid battery bank, needs to be connected to the electrical appliances through a converter station where the energy conversion takes place. Such practices are involved in medical emergency appliances of uninterrupted power supply. The devices like monitoring instruments, patient’s artificial respiratory systems, light loads in operation theaters, motor loads and other electronic devices of laptop & desktop computers act as a source of harmonics. Apart from these, the sine wave / square wave inverter also introduces harmonics as well as micro ripples on the battery side. These micro frequency components have certain impacts on the storage system performance, efficiency, and utilization of electrical energy. Results and Conclusion: This work investigates the impact of micro frequency components on the lead-acid storage battery when it is operating with a sine wave inverter supplying Resistive load and RLE load. In order to perform the investigation, a hardware test bench was created. The results of laboratory tests on a lead-acid battery show that batteries lose capacity and the utilization gets reduced by the frequent use. This manuscript opens up a new direction of research in the medical emergency backup system operating with a 12V DC system.

Background: Diagnosis of ophthalmologic and cardiovascular systems most often rely on the prerequisite step of segmentation of retinal blood vessels. Analysis of vascular structures in the retinal fundus images can aid in the early screening or detection of many ophthalmological diseases like glaucoma, diabetic retinopathy, vein occlusions, hemorrhages etc. In most cases, optic nerve gets damaged causing a blind spot. In this paper, a method of blood vessel segmentation using improved SOM (iSOM) and ANN classifier is presented. Methods: Morphological operations are carried out to enhance the input image. Clustering of pixels is done using improved Kohonen Self- Organizing Map (SOM) based on texture feature wherein a new node is introduced and new learning methodology is adopted using constrained weight updation. Finally, modified Otsu method is designed to label the output neuron class as vessel and non -vessel. Results: Segmentation is tested on public image sets, High Resolution Fundus (HRF) images and DRIONS-DB databases for Accuracy, Recall rate, Precision, F-Score, AUC and JC. The results achieve an appreciable level of accuracy (∽97%) as compared to other similar methods of classification. The average time taken is less in estimating the neuron class and is about 12.1 sec per image when evaluated on Intel Core i5 CPU running at 2.30 GHz coupled with 4 GB RAM. The mean squared error for the segmented images is found to be in the range of 4-5%. Conclusion: Segmentation of retinal blood vessels based on artificial neural networks employing iSOM preserves the topology consuming less time for constrained weight updation achieving better results than SOM. A new model to detect vessels can be developed by concatenating iSOMs in parallel for multi class functions.