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Articles from Springer a leading global scientific publisher of scientific books and journals. - dna forensic @ Mon, 18 Mar 2019 at 07:34 AM
Forensic Science - Encyclopedia of Law and Economics @ 2021-01-01
Forensic science applies natural, physical, and social sciences to resolve legal matters. The term forensics has been attached to many different fields: economics, anthropology, dentistry, pathology, toxicology, entomology, psychology, accounting, engineering, and computer forensics. Forensic evidence is gathered, examined, evaluated, interpreted, and presented to make sense of an event and provide investigatory leads. Various classification schemes exist for forensic evidence, with some forms of evidence falling under more than one scheme. Rules of evidence differ between jurisdictions, even between countries that share similar legal traditions. This makes the sharing of evidence between countries particularly problematic, at times rendering this evidence inadmissible in national courts. Several measures have been proposed and organizations created to strengthen forensic science and promote best practices for practitioners, researchers, and academicians in the field.
 
Genetics and Tropical Forests - Tropical Forestry Handbook @ 2021-01-01
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Genetics and Tropical Forests - Tropical Forestry Handbook @ 2021-01-01
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Fluorescence Lifetime Imaging - Handbook of Photonics for Biomedical Engineering @ 2021-01-01
Fluorescence lifetime imaging (FLIM) is a key fluorescence microscopy technique to map the environment and interaction of fluorescent probes. It can report on photophysical events that are difficult or impossible to observe by fluorescence intensity imaging, because FLIM is largely independent of the local fluorophore concentration and excitation intensity. Many FLIM applications relevant for biology concern the identification of Förster resonance energy transfer (FRET) to study protein interactions and conformational changes. In addition, FLIM has been used to image viscosity, temperature, pH, refractive index, and ion and oxygen concentrations, all at the cellular level. The basic principles and recent advances in the application of FLIM, FLIM instrumentation, molecular probe, and FLIM detector development will be discussed.
 
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Role of Robotic Process Automation in Pharmaceutical Industries - The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) @ 2020-01-01
Robotic Process Automation (RPA) is a technological revolution in the offing and is aimed at taking up the mundane and repetitive tasks from people’s daily workload. It throws up a new vista of research to the research community and lot many types of research are going on in this domain. It is not Robotics but is different technology altogether. RPA is a recent and fast-growing sub-domain of Robotics. The healthcare and pharmaceutics domain generate a lot of data or we may call it medical big data, and it is all the more pertinent to analyze & evaluate such data coming from varied sources. New drug discovery, drug formulation process, drug delivery mechanisms or in-patient and out-patient activities are some of the key processes in the Healthcare and Pharmaceutical industries generating a tremendous amount of data. Therefore, data science and RPA provides handy tools to work with such huge data volumes. In this paper, the authors highlight the key aspects of RPA and review its usage in the all-important healthcare and pharmaceutics domain. RPA is proving to be the technology of future and its goal is to provide a sustainable solution that reduces costs and delivery time, improves quality, speed and operational efficiency of a business process. The application of Machine Learning (ML) technologies in the healthcare domain are proving to be beneficial and effective in gaining new insights. The author also proposes a generic RPA/ML-based framework to ensure the standardization and quality of Bhasma – an end product obtained after multiple activities in the traditional Indian System of Medicine – Ayurveda.
 
Smart and Incremental Model to Build Clustered Trending Topics of Web Documents - The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) @ 2020-01-01
The abstract Social media trends, which have become more popular nowadays, introduce a rich hub of a broad spectrum of topics. It is of great importance to track emerging related topics when major events occur. The source of such information would be available not only through social portals but also through news, articles and web portals. All this information is aggregated together, by the proposed news aggregator model, to be useful for retrieving the recent popular trends of a certain category or country. The proposed model addresses the identification of semantically related topics from user preferences and favorites that are added manually by the user. Their textual contexts are acquired from the news search and then a clustering technique is applied followed by tracking of trending topics in term space. By quantitative experiments on manually annotated trends, we compared the model with two other well-known algorithms, using three different online datasets. The presented results demonstrate that the model reliably achieves a better entropy and F-measure, and so outperforms the two other mentioned algorithms.
 
Interactive Aggregate Message Authentication Scheme with Detecting Functionality - Advanced Information Networking and Applications @ 2020-01-01
In this paper, we newly propose a formal model and a construction methodology of interactive aggregate MAC schemes with detecting functionality (IAMD). The IAMD is an interactive aggregate MAC protocol which can identify invalid messages with a small amount of tag-size. Several aggregate MAC schemes that can specify invalid messages have been proposed so far by using non-adaptive group testing in the prior work. Instead, we utilize adaptive group testing to construct IAMD scheme and the resulting IAMD scheme can identify invalid messages with a small amount of tag-size compared to the previous schemes. In this paper, we propose a generic construction of IAMD starting from any adaptive group testing protocol and any aggregate MAC scheme, and we apply several concrete constructions of adaptive group testing protocols and aggregate MAC schemes. In addition, we compare and analyze those IAMD constructions in terms of efficiency and security.
 
Analysis of environmental DNA (eDNA) offers an unprecedented ability to accurately survey biodiversity from aquatic ecosystems. Although eDNA methods have been applied to myriad taxa, scientists are now moving away from proof-of-concept work, ultimately evaluating the limits and opportunities of this technology to detect and quantify abundance across organisms and environments. Important considerations enabling such methodology to be used for aquatic conservation contexts includes understanding both the effects of (1) the amount of eDNA released from focal taxa—sources, and (2) the removal of eDNA in the environment—sinks. I review publications on aquatic macroorganism eDNA that have evaluated or considered the effect of sources on signal detection (or quantification) and find few studies acknowledge, and fewer still evaluate, the impact of eDNA production on genomic signal recovery. In this review, I encourage readers to carefully consider source dynamics, and using previously published literature, dissect what roles biotic (e.g. life-history traits, species interactions including stressors) and abiotic (e.g. temperature, salinity) factors likely play in eDNA deposition and recovery, and how this impacts detection, abundance, biomass estimation, and ultimately informed signal interpretation. I further explore the physical sources of eDNA and propose other methods (spatial and temporal) and markers to assist in identifying eDNA origins in aquatic systems. Understanding how these parameters influence variation in eDNA sources will allow for a more comprehensive survey tool, and potentially give insights into environment-population responses.
 
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