Pub Date : 2020-11-24DOI: 10.2174/1574362415999201124224606
D. Yadav, S. Yadav, K. Veer
This article provides a comprehensive review of the recent trends and applications of BCIs. This review also provides future directions towards the acceleration and maturation of BCI technology. Based on a methodical search strategy, major technical databases were searched in quest of research papers of average and outstanding interest. A total of 188 research works were contained within this review due to their suitability and state-of-the-art achievements. This review identifies various eminent applications of BCIs in medical and non-medical domains. The findings of this review reveal the need of further exploration of BCI devices outside the laboratory-based settings for their development and seamless integration. In addition, applications of BCIs, including neuromarketing, neurorehabilitation, and neuroergonomics, require additional investigations for further validation and fruition of BCI technology. Based on this review, it is concluded that BCIs are in their embryonic stage and seek further research and investigation for their maturation.
{"title":"Trends and Applications of Brain Computer Interfaces","authors":"D. Yadav, S. Yadav, K. Veer","doi":"10.2174/1574362415999201124224606","DOIUrl":"https://doi.org/10.2174/1574362415999201124224606","url":null,"abstract":"\u0000\u0000This article provides a comprehensive review of the recent trends and applications of BCIs. This review also provides future directions towards the acceleration and maturation of BCI technology. Based on a methodical search strategy, major technical databases were searched in quest of research papers of average and outstanding interest. A total of 188 research works were contained within this review due to their suitability and state-of-the-art achievements. This review identifies various eminent applications of BCIs in medical and non-medical domains. The findings of this review reveal the need of further exploration of BCI devices outside the laboratory-based settings for their development and seamless integration. In addition, applications of BCIs, including neuromarketing, neurorehabilitation, and neuroergonomics, require additional investigations for further validation and fruition of BCI technology. Based on this review, it is concluded that BCIs are in their embryonic stage and seek further research and investigation for their maturation.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41833673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-10DOI: 10.2174/1574362415999201110101418
Bhumika Chauhan, S. Nandi
The world is connected by the internet. It is very useful because we use Google to find out any new topic, to search new places, to quest updated research, and to get knowledge for learnng. The person around the world can communicate with each other through the Google video conference talk. Internet is frequently used in smartphones, laptops, desktop, and tablet. Excessive affinity towards internet-based online data collection, downloading pictures, videos, cyber relationships, and social media may produce addiction disorders followed by different symptoms such as behaviors change, mind disturbance, depression, anxiety, loss of appetite hyperactivity, sleeping disorder, headache, visual fatigueness, trafficking of memory, attention-deficit, loss of efficiency in work and social detachment which may be caused by an imbalance of neurotransmitters. This is very difficult to control because of abnormal signal transduction in the brain. The present study is an attempt to discuss internet addiction disorder (IAD), internet gaming disorder (IGD), and give awareness to society to get rid of this addiction.
{"title":"Exploring the abnormal signal transduction mediated internet addiction and gaming disorders","authors":"Bhumika Chauhan, S. Nandi","doi":"10.2174/1574362415999201110101418","DOIUrl":"https://doi.org/10.2174/1574362415999201110101418","url":null,"abstract":"\u0000\u0000The world is connected by the internet. It is very useful because we use Google to find out any new topic, to\u0000search new places, to quest updated research, and to get knowledge for learnng. The person around the world can\u0000communicate with each other through the Google video conference talk. Internet is frequently used in smartphones, laptops,\u0000desktop, and tablet. Excessive affinity towards internet-based online data collection, downloading pictures, videos, cyber\u0000relationships, and social media may produce addiction disorders followed by different symptoms such as behaviors change,\u0000mind disturbance, depression, anxiety, loss of appetite hyperactivity, sleeping disorder, headache, visual fatigueness,\u0000trafficking of memory, attention-deficit, loss of efficiency in work and social detachment which may be caused by an\u0000imbalance of neurotransmitters. This is very difficult to control because of abnormal signal transduction in the brain. The\u0000present study is an attempt to discuss internet addiction disorder (IAD), internet gaming disorder (IGD), and give awareness\u0000to society to get rid of this addiction. \u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46741673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-19DOI: 10.2174/1574362415999200819202218
Neelambika B. Hiremath, Dayananda P
The advent of Next Generation Sequencing (NGS) has created a high throughput platform, to identify disease traits and phenotypic characteristics using RNASeq Sequencing analysis in humans. Non-small cell lung cancer (NSCLC), a lethal disease accounts for 85 percent of most lung cancers with very small window ofsurvival rate. The decision of tumour image bio marker impression can be improved by gene profile. Hence there is a need to characterise the variants in the disease manifestation. To understand the SNP’s in the major genes responsible for NSCLC, RNASeq data of patients aged above 50 years, were downloaded from SRA database. The quality matrix analysis is mapped to Genome reference consortium human build 38 (GRCh38) to call the variants and identify SNP’s with the tuxedo protocol. The SNP’s and the patterns of variants were analysed to see the comparison between healthy individual and NSCLC patients, and in between patients of different age. Oncogenes commonly associated with the NSCLC like KRAS, EGFR, ALK, BRAF and HER2 were mainly analysed to see the SNP’s and their characterisations with respect to the functional change was done. The SNP’s with the greater quality scores belonging to the above said genes were identified which gives us a baseline to understand the NSCLC at the Genomic level. Further fold change of these genes to the frequency of variant can be mapped to understand the NSCLC at a greater depth.
{"title":"Identification and Characterization of SNP Mutation in Genes related to Non-small cell lung cancer","authors":"Neelambika B. Hiremath, Dayananda P","doi":"10.2174/1574362415999200819202218","DOIUrl":"https://doi.org/10.2174/1574362415999200819202218","url":null,"abstract":"\u0000\u0000 The advent of Next Generation Sequencing (NGS) has created a high throughput\u0000platform, to identify disease traits and phenotypic characteristics using RNASeq Sequencing analysis in humans. Non-small\u0000cell lung cancer (NSCLC), a lethal disease accounts for 85 percent of most lung cancers with very small window ofsurvival\u0000rate. The decision of tumour image bio marker impression can be improved by gene profile. Hence there is a need to\u0000characterise the variants in the disease manifestation.\u0000\u0000\u0000\u0000To understand the SNP’s in the major genes responsible for NSCLC, RNASeq data of patients aged above 50\u0000years, were downloaded from SRA database. The quality matrix analysis is mapped to Genome reference consortium human\u0000build 38 (GRCh38) to call the variants and identify SNP’s with the tuxedo protocol.\u0000\u0000\u0000\u0000 The SNP’s and the patterns of variants were analysed to see the comparison between healthy individual and NSCLC\u0000patients, and in between patients of different age. Oncogenes commonly associated with the NSCLC like KRAS, EGFR,\u0000ALK, BRAF and HER2 were mainly analysed to see the SNP’s and their characterisations with respect to the functional\u0000change was done.\u0000\u0000\u0000\u0000 The SNP’s with the greater quality scores belonging to the above said genes were identified which gives us a\u0000baseline to understand the NSCLC at the Genomic level. Further fold change of these genes to the frequency of variant can\u0000be mapped to understand the NSCLC at a greater depth.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43885274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-08-07DOI: 10.2174/1574362415999200807154418
P. Sharma, K. Veer
It was 11 March 2020 when the World Health Organization (WHO) declared the name COVID-19 for coronavirus disease and also described it as a pandemic. Till that day 118,000 cases were confirmed of pneumonia with breathing problem throughout the world. At the start of New Year when COVID-19 came into knowledge a few days later, the gene sequencing of the virus was revealed. Today the number of confirmed cases is scary, i.e. 9,472,473 in the whole world and 484,236 deaths have been recorded by WHO till 26 June 2020. WHO's global risk assessment is very high [1]. The report is enlightening the lessons learned by India from the highly affected countries.
{"title":"How India Fights with COVID-19 with Learning from Highly Affected Countries","authors":"P. Sharma, K. Veer","doi":"10.2174/1574362415999200807154418","DOIUrl":"https://doi.org/10.2174/1574362415999200807154418","url":null,"abstract":"\u0000\u0000 It was 11 March 2020 when the World Health Organization (WHO) declared the name COVID-19 for coronavirus\u0000disease and also described it as a pandemic. Till that day 118,000 cases were confirmed of pneumonia with breathing\u0000problem throughout the world. At the start of New Year when COVID-19 came into knowledge a few days later, the gene\u0000sequencing of the virus was revealed. Today the number of confirmed cases is scary, i.e. 9,472,473 in the whole world and\u0000484,236 deaths have been recorded by WHO till 26 June 2020. WHO's global risk assessment is very high [1]. The report\u0000is enlightening the lessons learned by India from the highly affected countries.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41471894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-31DOI: 10.2174/1574362413666180713092948
N. Hemalatha, S. Nageswari
Position sensorless control technique for Permanent Magnets-Brush Less Direct Current (PM-BLDC) motor drive is considered in this paper. A new estimation based on sensorless technique is proposed for PMBLDC motor. Artificial Neural Network (ANN) is aided for the purpose. The inputs to the ANN are the voltages of PM-BLDC motor and it estimates the sample signals to feed Zero Crossing Point (ZCP) detection circuit. The ZCP detection circuit provides ZCP signals for commutation logic which gives the commutation sequence to power switches. In order to provide the correct sample signal to ZCP detection circuit, the ANN is well trained by Genetic Algorithm (GA). The proposed sensor less control model is implemented in MATLAB/SIMULINK working platform. Field Programmable Gate Array (FPGA) is used to implement the proposed method. Experimental results verify the analysis and demonstrate the advantages of the proposed method.
{"title":"A New Approach of Position Sensorless Control for Brushless DC Motor","authors":"N. Hemalatha, S. Nageswari","doi":"10.2174/1574362413666180713092948","DOIUrl":"https://doi.org/10.2174/1574362413666180713092948","url":null,"abstract":"\u0000\u0000Position sensorless control technique for Permanent Magnets-Brush Less\u0000Direct Current (PM-BLDC) motor drive is considered in this paper.\u0000\u0000\u0000\u0000A new estimation based on sensorless technique is proposed for PMBLDC\u0000motor. Artificial Neural Network (ANN) is aided for the purpose.\u0000\u0000\u0000\u0000The inputs to the ANN are the voltages of PM-BLDC motor and it estimates the sample\u0000signals to feed Zero Crossing Point (ZCP) detection circuit. The ZCP detection circuit provides\u0000ZCP signals for commutation logic which gives the commutation sequence to power switches. In\u0000order to provide the correct sample signal to ZCP detection circuit, the ANN is well trained by\u0000Genetic Algorithm (GA). The proposed sensor less control model is implemented in\u0000MATLAB/SIMULINK working platform.\u0000\u0000\u0000\u0000Field Programmable Gate Array (FPGA) is used to implement the proposed method.\u0000Experimental results verify the analysis and demonstrate the advantages of the proposed method.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"15 1","pages":"65-76"},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46981400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-31DOI: 10.2174/1574362413666180831105203
C. Senthamarai, N. Malmurugan
Due to the huge development of wireless devices and mobile data traffic had gained attention towards identifying accurate solutions for more proficient utilization of the wireless spectrum. An essential issue confronting the future in wireless systems is to identify the appropriate spectrum bands to satisfy the request of future administrations. While the greater part of the radio spectrum is allocated to various services, applications and users show that spectrum usage is quite low. The spectrum sensing is performed at the start of each time slot before the data transmission. As a promising framework to improve the spectrum utilization, Cognitive Radio (CR) technique has the immense potential to meet such a necessity by permitting unlicensed users to exist together in licensed bands. In this paper Cognitive radio and Full-Duplex (FD) based two-way relay communications are developed to enhance spectrum utilization for multichannel and to decrease the false alarm rate. To solve the optimization problems in spectral efficiency, soft computing techniques is proposed to minimize the self-interference and delay to the licensed users. In this proposed work the kurtosis parameter is used for channel detection to determine whether the signal is present or not. The performance results of the proposed method are evaluated in terms of spectral allocation and outage probability which achieves better performance than the existing Multi- Objective Genetic Algorithm (MOGA) optimization.
{"title":"Efficient Spectral Allocation for Cognitive Full Duplex Relay Network Systems Based Soft Computing Technique","authors":"C. Senthamarai, N. Malmurugan","doi":"10.2174/1574362413666180831105203","DOIUrl":"https://doi.org/10.2174/1574362413666180831105203","url":null,"abstract":"\u0000\u0000Due to the huge development of wireless devices and mobile data traffic\u0000had gained attention towards identifying accurate solutions for more proficient utilization of the\u0000wireless spectrum. An essential issue confronting the future in wireless systems is to identify the\u0000appropriate spectrum bands to satisfy the request of future administrations. While the greater part\u0000of the radio spectrum is allocated to various services, applications and users show that spectrum\u0000usage is quite low.\u0000\u0000\u0000\u0000The spectrum sensing is performed at the start of each time slot before\u0000the data transmission. As a promising framework to improve the spectrum utilization, Cognitive\u0000Radio (CR) technique has the immense potential to meet such a necessity by permitting unlicensed\u0000users to exist together in licensed bands. In this paper Cognitive radio and Full-Duplex\u0000(FD) based two-way relay communications are developed to enhance spectrum utilization for multichannel\u0000and to decrease the false alarm rate.\u0000\u0000\u0000\u0000To solve the optimization problems in spectral efficiency, soft computing techniques is\u0000proposed to minimize the self-interference and delay to the licensed users. In this proposed work\u0000the kurtosis parameter is used for channel detection to determine whether the signal is present or\u0000not.\u0000\u0000\u0000\u0000The performance results of the proposed method are evaluated in terms of spectral\u0000allocation and outage probability which achieves better performance than the existing Multi-\u0000Objective Genetic Algorithm (MOGA) optimization.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":" ","pages":"1-1"},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45649404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-31DOI: 10.2174/1574362413666180705123733
A. Sunder, A. Shanmugam
Wireless Sensor Networks (WSNs) are self-configured infrastructure-less networks are comprising of a number of sensing devices used to monitor physical or environmental quantities such as temperature, sound, vibration, pressure, motion etc. They collectively transmit data through the network to a sink where it is observed and analyzed. The major issues in WSN are interference, delay and attacks that degrade their performance due to their distributed nature and operation. Timely detection of attacks is imperative for various real time applications like healthcare, military etc. To improve the Black hole attack detection in WSN, Projected Independent Component Analysis (PICA) technique is proposed herewith, which detects black hole attack by analyzing collected physiological data from biomedical sensors. The PICA technique performs attack detection through Mutual information to measure the dependence in the joint distribution. The dependence among the nodes is identified based on the independent probability distribution functions and mutual probability function. The black hole attack isolation is then performed through the distribution of the attack separation message. This supports to improve Packet Delivery Ratio (PDR) with minimum delay. The simulation is carried out based on parameters such as black hole attack detection rate (BHADR), Black Hole Attack Detection Time (BHADT), False Positive Rate (FPR), PDR and delay.
{"title":"Black Hole Attack Detection in Healthcare Wireless Sensor Networks Using Independent Component Analysis Machine Learning Technique","authors":"A. Sunder, A. Shanmugam","doi":"10.2174/1574362413666180705123733","DOIUrl":"https://doi.org/10.2174/1574362413666180705123733","url":null,"abstract":"\u0000\u0000 Wireless Sensor Networks (WSNs) are self-configured infrastructure-less\u0000networks are comprising of a number of sensing devices used to monitor physical or environmental\u0000quantities such as temperature, sound, vibration, pressure, motion etc. They collectively transmit\u0000data through the network to a sink where it is observed and analyzed.\u0000\u0000\u0000\u0000The major issues in WSN are interference, delay and attacks that degrade\u0000their performance due to their distributed nature and operation. Timely detection of attacks is imperative\u0000for various real time applications like healthcare, military etc. To improve the Black hole\u0000attack detection in WSN, Projected Independent Component Analysis (PICA) technique is proposed\u0000herewith, which detects black hole attack by analyzing collected physiological data from\u0000biomedical sensors.\u0000\u0000\u0000\u0000The PICA technique performs attack detection through Mutual information to measure\u0000the dependence in the joint distribution. The dependence among the nodes is identified based on\u0000the independent probability distribution functions and mutual probability function.\u0000\u0000\u0000\u0000The black hole attack isolation is then performed through the distribution of the\u0000attack separation message. This supports to improve Packet Delivery Ratio (PDR) with minimum\u0000delay. The simulation is carried out based on parameters such as black hole attack detection rate\u0000(BHADR), Black Hole Attack Detection Time (BHADT), False Positive Rate (FPR), PDR and delay.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"15 1","pages":"56-64"},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45931324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-31DOI: 10.2174/1574362413666180830105740
Mansour Rached
In this paper, we present an approach to evaluate the information sharing in the supply chain. We propose a study of four scenarios of sharing upstream and downstream information simultaneously. Replenishment lead time is the upstream information studied in this work and demand information is the downstream one. We treat in this context the case of two-echelon (a warehouse and several retailers) and multi-products supply chain. We focus our approach on the centralised decision, in which, the warehouse is the decision maker and his goal is to minimise the system cost independently. In our formulation, we consider a system cost composed of holding, ordering, penalty and transportation costs. Then, we use a Genetic Algorithm in order to approximate the optimal echelon inventory position at the warehouse and optimal allocation quantity of each item from the warehouse to the respective retailer, which minimises the system cost. Our approach is illustrated by some numerical experiments.
{"title":"Genetic Algorithm to Evaluate Downstream and Upstream Information Sharing","authors":"Mansour Rached","doi":"10.2174/1574362413666180830105740","DOIUrl":"https://doi.org/10.2174/1574362413666180830105740","url":null,"abstract":"\u0000\u0000In this paper, we present an approach to evaluate the information sharing\u0000in the supply chain.\u0000\u0000\u0000\u0000We propose a study of four scenarios of sharing upstream and downstream\u0000information simultaneously. Replenishment lead time is the upstream information studied\u0000in this work and demand information is the downstream one. We treat in this context the case of\u0000two-echelon (a warehouse and several retailers) and multi-products supply chain.\u0000\u0000\u0000\u0000We focus our approach on the centralised decision, in which, the warehouse is the decision\u0000maker and his goal is to minimise the system cost independently. In our formulation, we consider\u0000a system cost composed of holding, ordering, penalty and transportation costs. Then, we use\u0000a Genetic Algorithm in order to approximate the optimal echelon inventory position at the\u0000warehouse and optimal allocation quantity of each item from the warehouse to the respective retailer,\u0000which minimises the system cost.\u0000\u0000\u0000\u0000Our approach is illustrated by some numerical experiments.\u0000","PeriodicalId":10868,"journal":{"name":"Current Signal Transduction Therapy","volume":"15 1","pages":"24-33"},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45684806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：