Elemental Analysis and Forensic Comparison of Soils by Laser-Induced Breakdown Spectroscopy (LIBS) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS)

The elemental analysis of soil is useful in forensic and environmental sciences. Methods were developed and optimized for two laser-based multi-element analysis techniques: laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and laser-induced breakdown spectroscopy (LIBS). This work represents the first use of a 266 nm laser for forensic soil analysis by LIBS. Sample preparation methods were developed and optimized for a variety of sample types, including pellets for large bulk soil specimens (470 mg) and sediment-laden filters (47 mg), and tape-mounting for small transfer evidence specimens (10 mg). Analytical performance for sediment filter pellets and tape-mounted soils was similar to that achieved with bulk pellets. An inter-laboratory comparison exercise was designed to evaluate the performance of the LA-ICP-MS and LIBS methods, as well as for micro X-ray fluorescence (μXRF), across multiple laboratories. Limits of detection (LODs) were 0.01-23 ppm for LA-ICP-MS, 0.25-574 ppm for LIBS, 16-4400 ppm for µXRF, and well below the levels normally seen in soils. Good intra-laboratory precision (≤ 6 % relative standard deviation (RSD) for LA-ICP-MS; ≤ 8 % for µXRF; ≤ 17 % for LIBS) and inter-laboratory precision (≤ 19 % for LA-ICP-MS; ≤ 25 % for µXRF) were achieved for most elements, which is encouraging for a first inter-laboratory exercise. While LIBS generally has higher LODs and RSDs than LA-ICP-MS, both were capable of generating good quality multi-element data sufficient for discrimination purposes. Multivariate methods using principal components analysis (PCA) and linear discriminant analysis (LDA) were developed for discriminations of soils from different sources. Specimens from different sites that were indistinguishable by color alone were discriminated by elemental analysis. Correct classification rates of 94.5 % or better were achieved in a simulated forensic discrimination of three similar sites for both LIBS and LA-ICP-MS. Results for tape-mounted specimens were nearly identical to those achieved with pellets. Methods were tested on soils from USA, Canada and Tanzania. Within-site heterogeneity was site-specific. Elemental differences were greatest for specimens separated by large distances, even within the same lithology. Elemental profiles can be used to discriminate soils from different locations and narrow down locations even when mineralogy is similar.

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support Vector Machines (SVMs) are widely used classifiers for detecting physiological patterns in Human-Computer Interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the application of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables, and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

Using multiple GPUs to accelerate string searching for digital forensic analysis

String searching within a large corpus of data is an important component of digital forensic (DF) analysis techniques such as file carving. The continuing increase in capacity of consumer storage devices requires corresponding im-provements to the performance of string searching techniques. As string search-ing is a trivially-parallelisable problem, GPGPU approaches are a natural fit – but previous studies have found that local storage presents an insurmountable performance bottleneck. We show that this need not be the case with modern hardware, and demonstrate substantial performance improvements from the use of single and multiple GPUs when searching for strings within a typical forensic disk image.

Meaningful insights: explainability techniques for black-box models on tabular data

Artificial Intelligence (AI) and Machine Learning (ML) are novel data analysis techniques providing very accurate prediction results. They are widely adopted in a variety of industries to improve efficiency and decision-making, but they are also being used to develop intelligent systems. Their success grounds upon complex mathematical models, whose decisions and rationale are usually difficult to comprehend for human users to the point of being dubbed as black-boxes. This is particularly relevant in sensitive and highly regulated domains. To mitigate and possibly solve this issue, the Explainable AI (XAI) field became prominent in recent years. XAI consists of models and techniques to enable understanding of the intricated patterns discovered by black-box models. In this thesis, we consider model-agnostic XAI techniques, which can be applied to Tabular data, with a particular focus on the Credit Scoring domain. Special attention is dedicated to the LIME framework, for which we propose several modifications to the vanilla algorithm, in particular: a pair of complementary Stability Indices that accurately measure LIME stability, and the OptiLIME policy which helps the practitioner finding the proper balance among explanations' stability and reliability. We subsequently put forward GLEAMS a model-agnostic surrogate interpretable model which requires to be trained only once, while providing both Local and Global explanations of the black-box model. GLEAMS produces feature attributions and what-if scenarios, from both dataset and model perspective. Eventually, we argue that synthetic data are an emerging trend in AI, being more and more used to train complex models instead of original data. To be able to explain the outcomes of such models, we must guarantee that synthetic data are reliable enough to be able to translate their explanations to real-world individuals. To this end we propose DAISYnt, a suite of tests to measure synthetic tabular data quality and privacy.

Procedimentos pedagogicos para o ensino das lutas : contextos e possibilidades

Universidade Estadual de Campinas . Faculdade de Educação Física

O papel da propriocepção no desenvolvimento da força muscular e da flexibilidade

Universidade Estadual de Campinas . Faculdade de Educação Física

Incapacidades fisicas em hanseniase e atividade fisica : coisa do passado ou problema do futuro

Universidade Estadual de Campinas . Faculdade de Educação Física

O Taekwondo como modalidade paradesportiva

Universidade Estadual de Campinas . Faculdade de Educação Física

Desempenho da força explosiva durante uma temporada em futebolistas púberes

Universidade Estadual de Campinas . Faculdade de Educação Física

Lei n.º 11.889/2008: avanço ou retrocesso nas competências do técnico em saúde bucal?

O objetivo deste estudo foi analisar as competências que, desde a aprovação da lei n.º 11.889/08, incumbem ao técnico em saúde bucal (TSB) no Brasil, incluindo os termos definidos para sua supervisão. Foi realizada análise documental, comparando-se as competências definidas no referido instrumento legal com as previstas no parecer n.º 460/75 do Conselho Federal de Educação e na resolução n.º 63/2005 do Conselho Federal de Odontologia. Foram empregadas técnicas de análise temática considerando-se as habilidades em termos de ações diretas e indiretas distribuídas em quatro áreas de competência: planejamento e administração em saúde, promoção da saúde, prevenção de doenças e de assistência individual. Embora as competências aprovadas na lei tenham sido distribuídas em um número menor de itens, comparado aos dois outros documentos, do ponto de vista qualitativo, os resultados da análise permitiram concluir que vários avanços foram obtidos com a regulamentação da profissão, nos termos aprovados, em todas as áreas de competência. Houve impacto positivo para o processo de trabalho em saúde, tanto com relação à cooperação interprofissional quanto à supervisão técnica das atividades, representando uma conquista relevante dos trabalhadores da área e também uma contribuição significativa para avançar na ampliação do acesso aos serviços odontológicos

Utilização de técnicas estatísticas em duas revistas de fruticultura

Neste trabalho são descritas as técnicas de análise estatística utilizadas e a acessibilidade estatística em uma amostra dos artigos originais publicados no período 1996-2006 em duas revistas de pesquisa na área de fruticultura: a Revista Brasileira de Fruticultura (RBF) e a revista francesa Fruits. No total foram classificados 986 artigos em 16 categorias de análise estatística, ordenadas em grau ascendente de complexidade. No período analisado, foi constatado um aumento no uso de análises mais sofisticadas ao longo do tempo em ambos as revistas. Os trabalhos publicados pela RBF aplicaram com maior freqüência técnicas estatísticas mais complexas, com maior utilização de delineamentos em blocos aleatorizados, arranjos fatoriais, parcelas subdivididas e modelos hierárquicos, e do teste de Tukey para comparações múltiplas de médias. Nos trabalhos publicados pela revista Fruits, predominou o uso de outros testes paramétricos e do teste de Duncan. O pacote estatístico SAS foi o mais utilizado nos artigos publicados em ambas as revistas. Os leitores da revista RBF precisaram de um nível de conhecimento estatístico mais elevado para ter acesso à maior parte dos artigos publicados no período, em comparação com os leitores da revista francesa.

A classification methodology for the risk of weed infestation using fuzzy logic

Despite modern weed control practices, weeds continue to be a threat to agricultural production. Considering the variability of weeds, a classification methodology for the risk of infestation in agricultural zones using fuzzy logic is proposed. The inputs for the classification are attributes extracted from estimated maps for weed seed production and weed coverage using kriging and map analysis and from the percentage of surface infested by grass weeds, in order to account for the presence of weed species with a high rate of development and proliferation. The output for the classification predicts the risk of infestation of regions of the field for the next crop. The risk classification methodology described in this paper integrates analysis techniques which may help to reduce costs and improve weed control practices. Results for the risk classification of the infestation in a maize crop field are presented. To illustrate the effectiveness of the proposed system, the risk of infestation over the entire field is checked against the yield loss map estimated by kriging and also with the average yield loss estimated from a hyperbolic model.

Thermal properties and release of Lippia sidoides essential oil from gum arabic/maltodextrin microparticles

Microencapsulation of Lippia sidoides essential oil was carried out by spray drying. Blends of maltodextrin and gum arabic were used as carrier. Spray dried microparticles were characterized using conventional (thermogravimetry, evolved gas analysis) and combined (thermogravimetry-mass spectrometry analysis) thermal analysis techniques in order to evaluate the abilities of carriers with different compositions in retaining and in releasing the core vs. dynamic heating. Thermal analysis was useful to evaluate the physico-chemical interactions between the core and carriers and to determine the protective effect of the carriers on the evaporation of essential oil.

Returns to farm-level soil conservation on tropical steep slopes: The case of the Eritrean highlands

This study conducts an economic analysis of investment in simple soil conservation technologies in the highlands of Eritrea. The data used in the analysis were obtained from a farm survey and supplemented with data from secondary sources. Risk analysis techniques are used to take account of the uncertainties regarding the relationship between soil erosion and crop yield. The financial analysis reveals negative net present values (NPVs) and internal rates of return (IRRs) below 12 per cent for various slope categories. On the other hand, the economic analysis returns positive NPVs and IRRs of over 20 per cent. The results clearly indicate that in-vestment in soil conservation technology may not be a viable short-term proposition from the farmer's point of view and yet the net social benefits are positive. There is a strong case for government to provide incentives for soil conservation in view of the economic benefits.

EEG-based Brain-Computer Interfaces: An Overview of Basic Concepts and Clinical Applications in Neurorehabilitation

Some patients are no longer able to communicate effectively or even interact with the outside world in ways that most of us take for granted. In the most severe cases, tetraplegic or post-stroke patients are literally `locked in` their bodies, unable to exert any motor control after, for example, a spinal cord injury or a brainstem stroke, requiring alternative methods of communication and control. But we suggest that, in the near future, their brains may offer them a way out. Non-invasive electroencephalogram (EEG)-based brain-computer interfaces (BCD can be characterized by the technique used to measure brain activity and by the way that different brain signals are translated into commands that control an effector (e.g., controlling a computer cursor for word processing and accessing the internet). This review focuses on the basic concepts of EEG-based BC!, the main advances in communication, motor control restoration and the down-regulation of cortical activity, and the mirror neuron system (MNS) in the context of BCI. The latter appears to be relevant for clinical applications in the coming years, particularly for severely limited patients. Hypothetically, MNS could provide a robust way to map neural activity to behavior, representing the high-level information about goals and intentions of these patients. Non-invasive EEG-based BCIs allow brain-derived communication in patients with amyotrophic lateral sclerosis and motor control restoration in patients after spinal cord injury and stroke. Epilepsy and attention deficit and hyperactive disorder patients were able to down-regulate their cortical activity. Given the rapid progression of EEG-based BCI research over the last few years and the swift ascent of computer processing speeds and signal analysis techniques, we suggest that emerging ideas (e.g., MNS in the context of BC!) related to clinical neuro-rehabilitation of severely limited patients will generate viable clinical applications in the near future.