Biophotonic Tools in Cell and Tissue Diagnostics.

In order to maintain the rapid advance of biophotonics in the U.S. and enhance our competitiveness worldwide, key measurement tools must be in place. As part of a wide-reaching effort to improve the U.S. technology base, the National Institute of Standards and Technology sponsored a workshop titled "Biophotonic tools for cell and tissue diagnostics." The workshop focused on diagnostic techniques involving the interaction between biological systems and photons. Through invited presentations by industry representatives and panel discussion, near- and far-term measurement needs were evaluated. As a result of this workshop, this document has been prepared on the measurement tools needed for biophotonic cell and tissue diagnostics. This will become a part of the larger measurement road-mapping effort to be presented to the Nation as an assessment of the U.S. Measurement System. The information will be used to highlight measurement needs to the community and to facilitate solutions

Characterization of spatio-temporal epidural event-related potentials for mouse models of psychiatric disorders.

Distinctive features in sensory event-related potentials (ERPs) are endophenotypic biomarkers of psychiatric disorders, widely studied using electroencephalographic (EEG) methods in humans and model animals. Despite the popularity and unique significance of the mouse as a model species in basic research, existing EEG methods applicable to mice are far less powerful than those available for humans and large animals. We developed a new method for multi-channel epidural ERP characterization in behaving mice with high precision, reliability and convenience and report an application to time-domain ERP feature characterization of the Sp4 hypomorphic mouse model for schizophrenia. Compared to previous methods, our spatio-temporal ERP measurement robustly improved the resolving power of key signatures characteristic of the disease model. The high performance and low cost of this technique makes it suitable for high-throughput behavioral and pharmacological studies

Laser powder bed additive manufacturing: A review on the four drivers for an online control

Online control of Additive Manufacturing (AM) processes appears to be the next challenge in the transition toward Industry 4.0 (I4.0). Although many efforts have been dedicated by industry and research in the last decades, there remains substantial room for improvement. Additionally, the existing scientific literature lacks a wide-ranging identification and classification of the primary drivers that enable online control of AM processes. This article focuses on online control of one of the most industrially widespread AM processes: metal Laser Powder Bed Fusion (L-PBF), with particular emphasis on two subcategories, namely Selective Laser Sintering (SLS) and Selective Laser Melting (SLM). Through a systematic literature review, this article initially identified over 200 manuscripts. The search was conducted utilizing a defined research query within the Scopus database, double checked on Scholar. The results were refined through multiple phases of inclusion/exclusion criteria, culminating in the selection of 95 pertinent papers. This article aims to provide a systematic and comprehensive review of four identified drivers i) Online controllable input parameters, ii) Online observable output signatures, iii) Online sensing techniques, iv) Online feedback strategies, adopted from the general Deming control loop Plan-Do-Check-Act (PDCA). Ultimately, this article delves into the challenges and prospects inherent in the online control of metal L-PBF

ASIME 2018 White Paper. In-Space Utilisation of Asteroids: Asteroid Composition -- Answers to Questions from the Asteroid Miners

In keeping with the Luxembourg government's initiative to support the future use of space resources, ASIME 2018 was held in Belval, Luxembourg on April 16-17, 2018. The goal of ASIME 2018: Asteroid Intersections with Mine Engineering, was to focus on asteroid composition for advancing the asteroid in-space resource utilisation domain. What do we know about asteroid composition from remote-sensing observations? What are the potential caveats in the interpretation of Earth-based spectral observations? What are the next steps to improve our knowledge on asteroid composition by means of ground-based and space-based observations and asteroid rendez-vous and sample return missions? How can asteroid mining companies use this knowledge? ASIME 2018 was a two-day workshop of almost 70 scientists and engineers in the context of the engineering needs of space missions with in-space asteroid utilisation. The 21 Questions from the asteroid mining companies were sorted into the four asteroid science themes: 1) Potential Targets, 2) Asteroid-Meteorite Links, 3) In-Situ Measurements and 4) Laboratory Measurements. The Answers to those Questions were provided by the scientists with their conference presentations and collected by A. Graps or edited directly into an open-access collaborative Google document or inserted by A. Graps using additional reference materials. During the ASIME 2018, first day and second day Wrap-Ups, the answers to the questions were discussed further. New readers to the asteroid mining topic may find the Conversation boxes and the Mission Design discussions especially interesting.Comment: Outcome from the ASIME 2018: Asteroid Intersections with Mine Engineering, Luxembourg. April 16-17, 2018. 65 Pages. arXiv admin note: substantial text overlap with arXiv:1612.0070

Odontology & artificial intelligence

Neste trabalho avaliam-se os três fatores que fizeram da inteligência artificial uma tecnologia essencial hoje em dia, nomeadamente para a odontologia: o desempenho do computador, Big Data e avanços algorítmicos. Esta revisão da literatura avaliou todos os artigos publicados na PubMed até Abril de 2019 sobre inteligência artificial e odontologia. Ajudado com inteligência artificial, este artigo analisou 1511 artigos. Uma árvore de decisão (If/Then) foi executada para selecionar os artigos mais relevantes (217), e um algoritmo de cluster k-means para resumir e identificar oportunidades de inovação. O autor discute os artigos mais interessantes revistos e compara o que foi feito em inovação durante o International Dentistry Show, 2019 em Colónia. Concluiu, assim, de forma crítica que há uma lacuna entre tecnologia e aplicação clínica desta, sendo que a inteligência artificial fornecida pela indústria de hoje pode ser considerada um atraso para o clínico de amanhã, indicando-se um possível rumo para a aplicação clínica da inteligência artificial.There are three factors that have made artificial intelligence (AI) an essential technology today: the computer performance, Big Data and algorithmic advances. This study reviews the literature on AI and Odontology based on articles retrieved from PubMed. With the help of AI, this article analyses a large number of articles (a total of 1511). A decision tree (If/Then) was run to select the 217 most relevant articles-. Ak-means cluster algorithm was then used to summarize and identify innovation opportunities. The author discusses the most interesting articles on AI research and compares them to the innovation presented during the International Dentistry Show 2019 in Cologne. Three technologies available now are evaluated and three suggested options are been developed. The author concludes that AI provided by the industry today is a hold-up for the praticioner of tomorrow. The author gives his opinion on how to use AI for the profit of patients

Broadband stimulated Raman scattering with Fourier-transform detection

We propose a new approach to broadband Stimulated Raman Scattering (SRS) spectroscopy and microscopy based on time-domain Fourier transform (FT) detection of the stimulated Raman gain (SRG) spectrum. We generate two phase-locked replicas of the Stokes pulse after the sample using a passive birefringent interferometer and measure by the FT technique both the Stokes and the SRG spectra. Our approach blends the very high sensitivity of single-channel lock-in balanced detection with the spectral coverage and resolution afforded by FT spectroscopy. We demonstrate our method by measuring the SRG spectra of different compounds and performing broadband SRS imaging on inorganic blends

SMART ADDITIVE MANUFACTURING: IN-PROCESS SENSING AND DATA ANALYTICS FOR ONLINE DEFECT DETECTION IN METAL ADDITIVE MANUFACTURING PROCESSES

The goal of this dissertation is to detect the incipient flaws in metal parts made using additive manufacturing processes (3D printing). The key idea is to embed sensors inside a 3D printing machine and conclude whether there are defects in the part as it is being built by analyzing the sensor data using artificial intelligence (machine learning). This is an important area of research, because, despite their revolutionary potential, additive manufacturing processes are yet to find wider acceptance in safety-critical industries, such as aerospace and biomedical, given their propensity to form defects. The presence of defects, such as porosity, can afflict as much as 20% of additive manufactured parts. This poor process consistency necessitates an approach wherein flaws are not only detected but also promptly corrected inside the machine. This dissertation takes the critical step in addressing the first of the above, i.e., detection of flaws using in-process sensor signatures. Accordingly, the objective of this work is to develop and apply a new class of machine learning algorithms motivated from the domain of spectral graph theory to analyze the in-process sensor data, and subsequently, detect the formation of part defects. Defects in additive manufacturing originate due to four main reasons, namely, material, process parameters, part design, and machine kinematics. In this work, the efficacy of the graph theoretic approach is determined to detect defects that occur in all the above four contexts. As an example, in Chapter 4, flaws such as lack-of-fusion porosity due to poor choice of process parameters in additive manufacturing are identified with statistical accuracy exceeding 80%. As a comparison, the accuracy of existing conventional statistical methods is less than 65%. Advisor: Prahalada Ra

SMART ADDITIVE MANUFACTURING: IN-PROCESS SENSING AND DATA ANALYTICS FOR ONLINE DEFECT DETECTION IN METAL ADDITIVE MANUFACTURING PROCESSES

The goal of this dissertation is to detect the incipient flaws in metal parts made using additive manufacturing processes (3D printing). The key idea is to embed sensors inside a 3D printing machine and conclude whether there are defects in the part as it is being built by analyzing the sensor data using artificial intelligence (machine learning). This is an important area of research, because, despite their revolutionary potential, additive manufacturing processes are yet to find wider acceptance in safety-critical industries, such as aerospace and biomedical, given their propensity to form defects. The presence of defects, such as porosity, can afflict as much as 20% of additive manufactured parts. This poor process consistency necessitates an approach wherein flaws are not only detected but also promptly corrected inside the machine. This dissertation takes the critical step in addressing the first of the above, i.e., detection of flaws using in-process sensor signatures. Accordingly, the objective of this work is to develop and apply a new class of machine learning algorithms motivated from the domain of spectral graph theory to analyze the in-process sensor data, and subsequently, detect the formation of part defects. Defects in additive manufacturing originate due to four main reasons, namely, material, process parameters, part design, and machine kinematics. In this work, the efficacy of the graph theoretic approach is determined to detect defects that occur in all the above four contexts. As an example, in Chapter 4, flaws such as lack-of-fusion porosity due to poor choice of process parameters in additive manufacturing are identified with statistical accuracy exceeding 80%. As a comparison, the accuracy of existing conventional statistical methods is less than 65%. Advisor: Prahalada Ra