Журнал компьютерных наук и системной биологии

An industrial warehouse is the area of a factory, machine shop, etc. for storing and retrieval of parts/objects used in production and sales. At this juncture, the current storage and retrieval system having difficulties with respective tracking the parts/objects information and space information in real time. The part/object information is usually retrieved by pulling a manual report in backend and this information is needed every moment, and also there is no availability of space information in the current system. This paper proposing a solution to overcome the problems which are faced by the operators and officers with the current system. In this approach Raspberry Pi used as predix device for sending sensor data to predix cloud, the sensors IR, Sonar and RFID used for gathering parts and space information in real time. Next, it uses the predix services for security and storage of data and predix user interface is for showing results to the users. This system also provides single window information about warehouses present around the globe, which reduces the human effort and waiting time to getting the information about single warehouse. The benefits get multiplied for total number of warehouses across the globe which is a big boost to the industry and at higher level they take decisions quickly and easily.

A Myelodysplastic syndrome (MDS) is a disorder characterized by active but ineffective hematopoiesis that leads to pancytopenia. MDS, also termed as myeloid neoplasms, is described by different level of cytopenia that is a different level of blood cells in the body. Various genes mutations have been reported to associate with MDS. To investigate the mechanisms at molecular level underlying MDS patients carrying genetic mutations, the gene expression profiles of MDS the patients were compared to that of healthy individuals and analyzed by bioinformatics tools. In biological networks, genes having important functional roles can be identified by a measure of the node. Networks of genes an in co-expression, candidate hubs also called extremely associated genes have been connected with the key disease-related pathway. Thus, this technique was used to discover the MDS related genes hub. Affymetrix Human Genome U133 plus 2.0 gene expression dataset of microarray GSE58831 was retrieved from GEO (Gene Expression Omnibus) database that contained four 159 diseased samples and 17 samples of control. Based on statistical method and co-expression networking, DEGs gene was detected. DAVID an online tool was employed for Gene ontology (GO) function and KEGG pathway enrichment analysis of DEGs. Besides, PPI (Protein-protein interaction) networks were developed by mapping the DEGs with respect to protein-protein interaction set available in databases for the identification of the pathways involving DEGs. PPI interaction networks were divided into subnetworks via MCODE algorithm and were examined by Cytoscape. Interferon Signaling Pathway, cellular response to zinc ions and negative growth regulation. Immune response, negative regulation of transcription from RNA polymerase II promotor, positive regulation of smooth muscle cell proliferation and cellular response to Dexamethasone stimulus, extracellular matrix, extracellular space, and extracellular region were the main enriched processes and pathways in these DEGs and many of the hub genes’ (UBC, TP53, EGFR, GADPH, CREBBP, HDAC1, STAT1, IL6, ESR1, SMAD4) reported in this study were purposed as novel therapeutic targets against MDS disease.

Heart Rate is an important physiological parameter for health monitoring. Heart Rate measurement with smart phone is used by many people all over the world and different applications are developed. But, there are few issues behind the proper measurement like motion, baseline drift, power line interference, low amplitude PPG, and premature ventricular contraction and noise in the signal. While capturing red contact video of fingertips, miss touch errors can produce significant variation in real result as noise gets incorporated. Similarly, high pressure on camera and low pressure on camera can produce incorrect PPG signal and therefore, mislead to incorrect result. They can be treated as noise and needs to be removed up to a level to keep up the originality of a signal to give correct BPM rate. Here, in our study we have introduced an algorithm to get rid of certain percent of miss touch errors and thereby calculate heart rate from noise free signal, accurately. Here in our study, we have focused on Non-Invasive PPG signal based Heart Rate monitoring from skin blood flow using IR light at 900 nm wavelength. We have captured contact unfocused video to capture PPG using smart phone and developed algorithm to remove some percent of touch errors and followed by noise removal with 2^nd Order Butterworth (IIR) band pass filter with frequency domain analysis and Hann Windowing for leakage reduction. We have also completed a comparative study in between Butterworth filter and Savitzky-Golay filter. The PPG is obtained from RED channel of the captured live video of smart phone camera.

This paper deals with tree cutting real-world problem, causing significant damages to forests. The sensing and classification of acoustic signal emitted during tree cutting, is used to extract information of tree cutting events using sensors. Detecting the acoustic signal due to saw scratching power level in presence of ambiance noise and the other choral noise sources is a major issue in a forest environment. An acoustic sensor experimental setup is established for capturing the acoustic signal generated due to cross cut sawing with varying distances. Based on the experimental analysis, saw scratching acoustic signal is found with appropriate for tree cutting detection. The acoustic signal pre-processing is performed with the help of a SNR algorithm. The extraction of features in frequency space is done by using modified MFCC and spectral features extraction. Modified MFCC feature based dynamic time warping (MDTW) and spectral feature based Gauss-Bayesian classifier (SGBC) are used and compared.