Prediction Techniques in Internet of Things (IoT) Environment: A Comparative Study

Socialization and Personalization in Internet of Things (IOT) environment are the current trends in computing research. Most of the research work stresses the importance of predicting the service & providing socialized and personalized services. This paper presents a survey report on different techniques used for predicting user intention in wide variety of IOT based applications like smart mobile, smart television, web mining, weather forecasting, health-care/medical, robotics, road-traffic, educational data mining, natural calamities, retail banking, e-commerce, wireless networks & social networking. As per the survey made the prediction techniques are used for: predicting the application that can be accessed by the mobile user, predicting the next page to be accessed by web user, predicting the users favorite TV program, predicting user navigational patterns and usage needs on websites & also to extract the users browsing behavior, predicting future climate conditions, predicting whether a patient is suffering from a disease, predicting user intention to make implicit and human-like interactions possible by accepting implicit commands, predicting the amount of traffic occurring at a particular location, predicting student performance in schools & colleges, predicting & estimating the frequency of natural calamities occurrences like floods, earthquakes over a long period of time & also to take precautionary measures, predicting & detecting false user trying to make transaction in the name of genuine user, predicting the actions performed by the user to improve the business, predicting & detecting the intruder acting in the network, predicting the mood transition information of the user by using context history, etc. This paper also discusses different techniques like Decision Tree algorithm, Artificial Intelligence and Data Mining based Machine learning techniques, Content and Collaborative based Recommender algorithms used for prediction

Phenotypic trait association studies in brinjal upon drought stress

Eggplant is popularly known as poor man’s vegetable. With respect to present situation of climatic challenges, fruit yield of eggplant is reduced due to drought or moisture stresses. In view of this condition, an experiment was aimed to study character association between yield and yield components in eggplant. The resultant outcome from correlation analysis computed among nine eggplant characters indicated that traits like plant height and total plant length at harvesting, fruit length and number of fruits per plant significantly correlated with fruit yield per plant. Whereas, traits like plant height and total plant length observed at harvesting stage, number of days for flower initiation, number of primary branches, fruit length and average fruit weight were significantly associated with fruit yield per plant under moisture stressed condition

On the Importance of MC&A to Nuclear Security

Over the past fifty years, the threats posed by nuclear material and nuclear weapons have changed. These changes demand a new response. During the Cold War, the primary concern was that more States might establish programs to develop nuclear weapons. This is still a possibility, however, the concern of State proliferation of nuclear weapons has been joined by a new concern, namely the concern that a non-State actor might acquire a nuclear weapon or misuse nuclear or other radioactive material to create a disruptive nuclear security event. Because the threat has changed, international and national approaches to nuclear security need to change. Measures should be adopted world-wide that respond to the potential for a non-State actor to acquire and misuse nuclear material. (The primary subject of this paper is containing nuclear material threats. However, the same concepts that apply to nuclear material apply to other radioactive material, and from this point forward “nuclear material” could be interchanged with “nuclear and other radioactive material.”) The first step in preventing a non-State actor from acquiring nuclear material is for States to require nuclear facilities (i.e. organizations that possess nuclear material) to establish programs to maintain control over and account for the nuclear material that they possess. Most States already require a program of accounting for and control of nuclear material as part of their international nuclear safeguards programs. Enhancing existing nuclear material control and accounting (MC&A) programs could help to address the evolved threat to nuclear security, in addition to improving safeguards. This paper addresses the need to enhance existing MC&A programs to accommodate the needs of nuclear security. If you know what nuclear material you have, if you know where it is, and if you would recognize if it had gone missing, then you have taken the first step toward protecting people and the environment from misuse of it—one of the primary goals of nuclear security

Dissection of genetic diversity present in eggplant populations using simple sequence repeat markers

Eggplant (Solanum melongena L.) is the third most important solanaceous vegetable and most diversified within species spread across the world-geographical area. A study was conducted to assess the genetic diversity among the selected fifty-four eggplant genotypes (sub-categorized into five sub-population) using twenty-three SSR markers. The Analysis of Molecular Variance among the five sub-population of eggplant revealed the existence of 90.67% variation within populations and 9.34% variation among populations. The SSR markers analysis revealed important locus-wise information like mean Observed-Heterozygosity (0.216), mean Expected-Heterozygosity (0.496), Shannon’s Information Index (0.879), mean number of different alleles (3.209), mean number of effective alleles (2.535), Fixation-Index (0.649). Further, Phylogenetic-analysis clearly categorize genetically distinct individuals in which the most diversified clusters was cluster-1 (C1) out of total of five clusters and especially, wild cultivars were grouped into cluster-5 (C5). The obtained results can be used in eggplant breeding and germplasm conservation in a resourceful manner

Integrating artificial intelligence for knowledge management systems – synergy among people and technology: a systematic review of the evidence

This paper analyses Artificial Intelligence (AI) and Knowledge Management (KM) and focuses primarily on examining to what degree AI can help companies in their efforts to handle information and manage knowledge effectively. A search was carried out for relevant electronic bibliographic databases and reference lists of relevant review articles. Articles were screened and the eligibility was based on participants, procedures, comparisons, outcomes (PICO) model, and criteria for PRISMA (Preferred Reporting Items for Systematic Reviews). The results reveal that knowledge management and AI are interrelated fields as both are intensely connected to knowledge; the difference reflects in how – while AI offers machines the ability to learn, KM offers a platform to better understand knowledge. The research findings further point out that communication, trust, information systems, incentives or rewards, and the structure of an organization; are related to knowledge sharing in organizations. This systematic literature review is the first to throw light on KM practices & the knowledge cycle and how the integration of AI aids knowledge management systems, enterprise performance & distribution of knowledge within the organization. The outcomes offer a better understanding of efficient and effective knowledge resource management for organizational advantage. Future research is necessary on smart assistant systems thus providing social benefits that strengthen competitive advantage. This study indicates that organizations must take note of definite KM leadership traits and organizational arrangements to achieve stable performance through KM

Bacterial Endo-Symbiont Inhabiting Tridax procumbens L. and Their Antimicrobial Potential

Bacterial symbionts inhabiting Tridax procumbens L. were screened for antimicrobial potential with the aim to isolate potent bacteria bearing significant activity against test pathogens. The selected isolate was subjected to large scale fermentation to extract antimicrobial metabolite. The organic phase was reduced under vacuum pressure and crude ethyl acetate extract (10 mg/mL) was evaluated for antimicrobial activity against panel of test pathogens. The antibacterial activity was measured as a zone of inhibition and compared with standard antibiotics, gentamicin and tetracycline. Similarly, antifungal activity was compared with miconazole and bavistin. Significant activity was conferred against Shigella flexneri (MTCC 731) with 27±1.5 mm zone across the disc. Partially, purification of antimicrobial metabolite with TLC-bioautography and HPLC resulted in active fraction bearing activity at Rf 0.65 and eluting between 4 and 5 retention times. The obtained results are promising enough for future purification and characterization of antimicrobial metabolite. Thus, the study attributes to the growing knowledge on endophytes as one of the rich sources of antimicrobial potentials

A Smart Home Application for Resident Activity Prediction

Predicting resident activity in smart home has become an emerging research trend in the field of Pervasive computing. Resident activity prediction by using contextual data in smart home provides a resident intended service thereby makes him more flexible. In this paper, an effort has been made to achieve resident activity prediction by developing an android based smart home application which takes the following contextual parameters: resident id, location & status of the devices as input and predicts the activity of the resident as the output. The proposed smart home application involves the following steps: 1) Constructing the training dataset with the contextual parameters such as resident id, location & status of the device 2) Storing the captured data in SQL database 3) Retrieving the activity from the database by using Python programming. This can be achieved by using the Bluetooth module that communicates between the Raspberry Pi & mobile

Biomimetic synthesis of silver nanoparticles using endosymbiotic bacterium inhabiting euphorbia hirtal. And their bactericidal potential

The present investigation aims to evaluate biomimetic synthesis of silver nanoparticles using endophytic bacterium EH 419 inhabiting Euphorbia hirta L. The synthesized nanoparticles were initially confirmed with change in color from the reaction mixture to brown indicating the synthesis of nanoparticles. Further confirmation was achieved with the characteristic absorption peak at 440 nm using UV-Visible spectroscopy. The synthesized silver nanoparticles were subjected to biophysical characterization using hyphenated techniques. The possible role of biomolecules in mediating the synthesis was depicted with FTIR analysis. Further crystalline nature of synthesized nanoparticles was confirmed using X-ray diffraction (XRD) with prominent diffraction peaks at 2θ which can be indexed to the (111), (200), (220), and (311) reflections of face centered cubic structure (fcc) of metallic silver. Transmission electron microscopy (TEM) revealed morphological characteristics of synthesized silver nanoparticles to be polydisperse in nature with size ranging from 10 to 60 nm and different morphological characteristics such as spherical, oval, hexagonal, and cubic shapes. Further silver nanoparticles exhibited bactericidal activity against panel of significant pathogenic bacteria among which Pseudomonas aeruginosa was most sensitive compared to other pathogens. To the best of our knowledge, present study forms first report of bacterial endophyte inhabiting Euphorbia hirta L. in mediating synthesizing silver nanoparticle

Immunization of pigs with replication-incompetent adenovirus-vectored African swine fever virus multi-antigens induced humoral immune responses but no protection following contact challenge

IntroductionAfrican swine fever virus (ASFV) is a pathogen of great economic importance given that continues to threaten the pork industry worldwide, but there is no safe vaccine or treatment available. Development of a vaccine is feasible as immunization of pigs with some live attenuated ASFV vaccine candidates can confer protection, but safety concerns and virus scalability are challenges that must to be addressed. Identification of protective ASFV antigens is needed to inform the development of efficacious subunit vaccines.MethodsIn this study, replication-incompetent adenovirus-vectored multicistronic ASFV antigen expression constructs that covered nearly 100% of the ASFV proteome were generated and validated using ASFV convalescent serum. Swine were immunized with a cocktail of the expression constructs, designated Ad5-ASFV, alone or formulated with either Montanide ISA-201™ (ASFV-ISA-201) or BioMize® adjuvant (ASFV-BioMize).ResultsThese constructs primed strong B cell responses as judged by anti-pp62-specific IgG responses. Notably, the Ad5-ASFV and the Ad5-ASFV ISA-201, but not the Ad5-ASFV BioMize®, immunogens primed significantly (p < 0.0001) higher anti-pp62-specific IgG responses compared with Ad5-Luciferase formulated with Montanide ISA-201™ adjuvant (Luc-ISA-201). The anti-pp62-specific IgG responses underwent significant (p < 0.0001) recall in all the vaccinees after boosting and the induced antibodies strongly recognized ASFV (Georgia 2007/1)-infected primary swine cells. However, following challenge by contact spreaders, only one pig nearly immunized with the Ad5-ASFV cocktail survived. The survivor had no typical clinical symptoms, but had viral loads and lesions consistent with chronic ASF.DiscussionBesides the limited sample size used, the outcome suggests that in vivo antigen expression, but not the antigen content, might be the limitation of this immunization approach as the replication-incompetent adenovirus does not amplify in vivo to effectively prime and expand protective immunity or directly mimic the gene transcription mechanisms of attenuated ASFV. Addressing the in vivo antigen delivery limitations may yield promising outcomes

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages