Rationale:Dengue fever is a leading cause of death in tropical and subtropical countries.Although most patients have a self-limited febrile illness,the viral infection can induce virus-mediated host changes,making imm...Rationale:Dengue fever is a leading cause of death in tropical and subtropical countries.Although most patients have a self-limited febrile illness,the viral infection can induce virus-mediated host changes,making immunocompetent persons susceptible to deadly fungal infections.However,there are only a few reports of such an association.Here we present a case of this deadly co-infection.Patient’s Concern:A 17-year-old male patient was diagnosed with dengue fever.He presented to us with facial swelling,periorbital edema,and black discoloration over the palate during the second week of his illness.Diagnosis:Diagnostic tests confirmed the presence of fungal hyphae.A diagnosis of post-dengue mucormycosis was made.No other comorbidity or underlying immune deficit was detected.Interventions:The patient underwent surgical debridement and antifungal treatment.Outcomes:The patient recovered and showed signs of palatal healing with an advancing mucosal edge.Lessons:Dengue virus and mucor co-infection has brought to light a new pathogenic paradigm.Clinicians need to be aware of this emerging medical condition and maintain a high index of suspicion for mucor co-infections while treating dengue patients.展开更多
Sexually transmitted infections (STIs) are the infections that can be transmitted from one sex partner, who already has such infection, to another. The causes of STIs in human are very well elucidated and their causat...Sexually transmitted infections (STIs) are the infections that can be transmitted from one sex partner, who already has such infection, to another. The causes of STIs in human are very well elucidated and their causative agents are identified as bacteria, parasites and viruses. The worldwide epidemiology of more than 20 types of STIs has been established, which includes diseases like Chlamydia, Gonorrhea, Genital herpes, HIV/ AIDS, HPV, Syphilis and Trichomoniasis. Though STIs affect both men and women indiscriminately, however, the pathophysiology of disease is more obvious among women. Other than abstinence, the most effective way to prevent the transmission or acquisition of STIs is to use a condom during sexual intercourse. Condoms are effective in decreasing the transmission of HIV. However, once contacted, STIs caused by bacteria or parasites can be treated with antibiotics. STIs caused by a virus may not virtually have any cure though the medicines may keep the disease under control. Correct usage of preventive measures greatly reduces but does not completely eliminate the risk of catching or spreading STIs.展开更多
This research service provides an original perspective on how artificial intelligence(AI)is making its way into the retail sector.Retail has entered a new era where ECommerce and technology bellwethers like Alibaba,Am...This research service provides an original perspective on how artificial intelligence(AI)is making its way into the retail sector.Retail has entered a new era where ECommerce and technology bellwethers like Alibaba,Amazon,Apple,Baidu,Facebook,Google,Microsoft,and Tencent have raised consumers’expectations.AI is enabling automated decision-making with accuracy and speed,based on data analytics,coupled with selflearning abilities.The retail sector has witnessed the dramatic evolution with the rapid digitalization of communication(i.e.Internet)and;smart phones and devices.Customer is no longer the same as they became more empowered by smart devices which has entirely prevailed their expectation,habits,style of shopping and investigating the shops.This article outlines the Significant innovation done in retails which helped them to evolve such as Artificial Intelligence(AI),Big data and Internet of Things(IoT),Chatbots,Robots.This article further also discusses the ideology of various author on how AI become more profitable and a close asset to customers and retailers.展开更多
Today,the customer’s requirements are entirely transformed.Many big retail organizations are facing sudden decline in the sales and revenues caused due to indecisive and erratic purchasing habits of recent generation...Today,the customer’s requirements are entirely transformed.Many big retail organizations are facing sudden decline in the sales and revenues caused due to indecisive and erratic purchasing habits of recent generation of users,as they get abundant preferred information such as cheaper rates,amazing offers,discounts,comparison of similar products,etc.over their smartphones or laptops hence they straightaway place order instead of walking down to showroom.As a result,large companies such as Tesco,Wal-Mart,Target,etc.have realized that it is requisite to shake hands with startup firms which already supports platform to retain customers either via deep exploration of transactional data or by offering lucrative offers in the benefit of customer and to promote market basket.The data which are generated from consumer purchase pattern,Big Data is a concern for companies as a result various big retail organizations are applying advanced and scalable data mining algorithms to precisely store and evaluate data in real-time manner to boost market basket analysis.This research work discusses various improved association rule mining(ARM)algorithms.The objective of this study is to identify gaps,providing opportunities for new research,to recognize expansion of Big Data analytics with retail environment and its future directions.This paper assimilates various aspects of parallel ARM algorithm for market basket analysis against sequential and distributed nature which are further escalated to Hadoop and MapReduce computing platform.Further various use cases highlighting the need of‘Big Data Retail Analytics’are discussed for emerging trends to promote sales and revenues,to keep check on competitor’s websites,comparison of various brands,enticing new customers.展开更多
Presently,retailing has changed its face from unordered stacked traditional stores to beautifully decorated and appropriately managed merchandise stores or shopping malls with excellent ambiance and comfort.Therefore,...Presently,retailing has changed its face from unordered stacked traditional stores to beautifully decorated and appropriately managed merchandise stores or shopping malls with excellent ambiance and comfort.Therefore,these stores try to accommodate all needed items for daily use or rarely required items under the same roof.However,the primary challenge for today’s retailer is that the modern customer is quality and brands conscious as well as compare for services provided to them by different outlets at the comfort of home with a single click.Therefore,customers prefer to purchase from E-Commerce websites instead of physically visiting a retail store,which leads to the downfall in the sales of retailers which become a serious threat to them.Therefore,retailers are required to work sincerely towards their customer expectations by providing all their needed goods under the same roof.Therefore,the objective of this paper is to assist retail business owners to recognize the purchasing needs of their customers and hence to entice customers to physical retail stores away from competitor E-Commerce websites.This paper employs a systematic research methodology based on association rule mining deployed over Map-Reduce based Apriori association mining and Hadoop based intelligent cloud architecture to determine useful buying patterns from purchase history of previous customers,in order to assist retail business owners.The finding acknowledges that the traditional mining algorithms have not progressed to support big data analysis as required by current retail businesses owners.The job of finding unknown association rules from big data requires a lot of resources such as memory and processing engines.Moreover,traditional mining systems are inadequate to provide support for partial failure support,extensibility,scalability etc.Therefore,this study aims to implement and develop MapReduce based Apriori(MR-Apriori)algorithm in the form of Intelligent Retail Mining Tool i.e.IRM Tool to recognize all these concerns in an efficient manner.The proposed system adequately satisfy all significant requisites anticipated from modern Big Data processing systems such as scalability,fault tolerance,partial failure support etc.Finally,this study experimentally verifies the effectiveness of the proposed algorithm i.e.MR-Apriori by speed-up,size-up,and scale-up evaluation parameters.展开更多
文摘Rationale:Dengue fever is a leading cause of death in tropical and subtropical countries.Although most patients have a self-limited febrile illness,the viral infection can induce virus-mediated host changes,making immunocompetent persons susceptible to deadly fungal infections.However,there are only a few reports of such an association.Here we present a case of this deadly co-infection.Patient’s Concern:A 17-year-old male patient was diagnosed with dengue fever.He presented to us with facial swelling,periorbital edema,and black discoloration over the palate during the second week of his illness.Diagnosis:Diagnostic tests confirmed the presence of fungal hyphae.A diagnosis of post-dengue mucormycosis was made.No other comorbidity or underlying immune deficit was detected.Interventions:The patient underwent surgical debridement and antifungal treatment.Outcomes:The patient recovered and showed signs of palatal healing with an advancing mucosal edge.Lessons:Dengue virus and mucor co-infection has brought to light a new pathogenic paradigm.Clinicians need to be aware of this emerging medical condition and maintain a high index of suspicion for mucor co-infections while treating dengue patients.
文摘Sexually transmitted infections (STIs) are the infections that can be transmitted from one sex partner, who already has such infection, to another. The causes of STIs in human are very well elucidated and their causative agents are identified as bacteria, parasites and viruses. The worldwide epidemiology of more than 20 types of STIs has been established, which includes diseases like Chlamydia, Gonorrhea, Genital herpes, HIV/ AIDS, HPV, Syphilis and Trichomoniasis. Though STIs affect both men and women indiscriminately, however, the pathophysiology of disease is more obvious among women. Other than abstinence, the most effective way to prevent the transmission or acquisition of STIs is to use a condom during sexual intercourse. Condoms are effective in decreasing the transmission of HIV. However, once contacted, STIs caused by bacteria or parasites can be treated with antibiotics. STIs caused by a virus may not virtually have any cure though the medicines may keep the disease under control. Correct usage of preventive measures greatly reduces but does not completely eliminate the risk of catching or spreading STIs.
文摘This research service provides an original perspective on how artificial intelligence(AI)is making its way into the retail sector.Retail has entered a new era where ECommerce and technology bellwethers like Alibaba,Amazon,Apple,Baidu,Facebook,Google,Microsoft,and Tencent have raised consumers’expectations.AI is enabling automated decision-making with accuracy and speed,based on data analytics,coupled with selflearning abilities.The retail sector has witnessed the dramatic evolution with the rapid digitalization of communication(i.e.Internet)and;smart phones and devices.Customer is no longer the same as they became more empowered by smart devices which has entirely prevailed their expectation,habits,style of shopping and investigating the shops.This article outlines the Significant innovation done in retails which helped them to evolve such as Artificial Intelligence(AI),Big data and Internet of Things(IoT),Chatbots,Robots.This article further also discusses the ideology of various author on how AI become more profitable and a close asset to customers and retailers.
文摘Today,the customer’s requirements are entirely transformed.Many big retail organizations are facing sudden decline in the sales and revenues caused due to indecisive and erratic purchasing habits of recent generation of users,as they get abundant preferred information such as cheaper rates,amazing offers,discounts,comparison of similar products,etc.over their smartphones or laptops hence they straightaway place order instead of walking down to showroom.As a result,large companies such as Tesco,Wal-Mart,Target,etc.have realized that it is requisite to shake hands with startup firms which already supports platform to retain customers either via deep exploration of transactional data or by offering lucrative offers in the benefit of customer and to promote market basket.The data which are generated from consumer purchase pattern,Big Data is a concern for companies as a result various big retail organizations are applying advanced and scalable data mining algorithms to precisely store and evaluate data in real-time manner to boost market basket analysis.This research work discusses various improved association rule mining(ARM)algorithms.The objective of this study is to identify gaps,providing opportunities for new research,to recognize expansion of Big Data analytics with retail environment and its future directions.This paper assimilates various aspects of parallel ARM algorithm for market basket analysis against sequential and distributed nature which are further escalated to Hadoop and MapReduce computing platform.Further various use cases highlighting the need of‘Big Data Retail Analytics’are discussed for emerging trends to promote sales and revenues,to keep check on competitor’s websites,comparison of various brands,enticing new customers.
文摘Presently,retailing has changed its face from unordered stacked traditional stores to beautifully decorated and appropriately managed merchandise stores or shopping malls with excellent ambiance and comfort.Therefore,these stores try to accommodate all needed items for daily use or rarely required items under the same roof.However,the primary challenge for today’s retailer is that the modern customer is quality and brands conscious as well as compare for services provided to them by different outlets at the comfort of home with a single click.Therefore,customers prefer to purchase from E-Commerce websites instead of physically visiting a retail store,which leads to the downfall in the sales of retailers which become a serious threat to them.Therefore,retailers are required to work sincerely towards their customer expectations by providing all their needed goods under the same roof.Therefore,the objective of this paper is to assist retail business owners to recognize the purchasing needs of their customers and hence to entice customers to physical retail stores away from competitor E-Commerce websites.This paper employs a systematic research methodology based on association rule mining deployed over Map-Reduce based Apriori association mining and Hadoop based intelligent cloud architecture to determine useful buying patterns from purchase history of previous customers,in order to assist retail business owners.The finding acknowledges that the traditional mining algorithms have not progressed to support big data analysis as required by current retail businesses owners.The job of finding unknown association rules from big data requires a lot of resources such as memory and processing engines.Moreover,traditional mining systems are inadequate to provide support for partial failure support,extensibility,scalability etc.Therefore,this study aims to implement and develop MapReduce based Apriori(MR-Apriori)algorithm in the form of Intelligent Retail Mining Tool i.e.IRM Tool to recognize all these concerns in an efficient manner.The proposed system adequately satisfy all significant requisites anticipated from modern Big Data processing systems such as scalability,fault tolerance,partial failure support etc.Finally,this study experimentally verifies the effectiveness of the proposed algorithm i.e.MR-Apriori by speed-up,size-up,and scale-up evaluation parameters.
