Reclamation of reactive metal oxides from complex minerals using alkali roasting and leaching- an improved approach to process engineering

In nature, the commonly occurring reactive metal oxides of titanium, chromium, aluminium, and vanadium often chemically combine with the transition metal oxides such as iron oxides and form complex minerals. Physico-chemical separation of transition metal oxides from the remaining reactive metal oxides is therefore an important step in the purification of reactive oxide constituents. Each purification step has quite a high energy requirement at present. Current practice in industry yields sulphate and neutralized chloride waste from titanium dioxide enrichment, red mud from bauxite refining, slag and leach residues from vanadium extraction and chromite ore process residue (COPR) from chromate processes. In this review article, a novel alkali-based oxidative roasting and aqueous leaching for the extraction of mineral oxides is explained in the context of the original work of Le Chatelier in 1850, which was unsuccessful in the industrialization of bauxite processing for alumina extraction. However, much later in the 19th century the alkali-based oxidative mineral roasting was successfully developed for industrial scale manufacturing of chromate chemicals, which yields COPR. The crystal chemistry of mineral oxides, namely alumina, titanium dioxide, and chromium oxide in naturally occurring minerals is briefly reviewed in the context of chemical extraction, which is then developed as a model for developing thermodynamic chemical equilibrium principles for analyzing the physical separation and enrichment of such reactive metal oxides by forming water-soluble and water-insoluble alkali complexes. The involvement of the alkali roasting chemistry of non-magnetic titaniferous mineral waste is also reported in the initial separation of rare-earth oxide mixtures for subsequent separation of individual oxides. The paper concludes with a generic approach to process chemistry which minimizes waste generation and therefore helps in reducing the overall process and energy costs. Examples of recovering alkali from high pH solution using carbon dioxide are also demonstrated

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Dual-channel Geometric Registration of a Multispectral-augmented Endoscopic Prototype

International audienceMultispectral measurement and analysis have proven to be useful to detect and monitor gastric pathologies at early stages. We developed a multispectral-augmented endoscopic prototype which allows exploration in the visible and near infrared range (400-1000 nm), increasing the common number of bands under analysis. The prototype comprises a fiberscope connected to two multispectral snapshot cameras which is inserted through the instrument channel of a commercial endoscope. However, due to aseptic practices, the system must be sterilized between exams, forcing physicians to remove and reintroduce it on each examination and leading to different relative positions between modalities. In the present work, we introduce an axial displacement correction function for dual-channel registration (i.e., RGB and multispectral) based on the insertion depth of the fiberscope. The performance was assessed using a chessboard pattern and its corner coordinates as ground truth. The mean RMSE error of the control points after registration using our method was 2.3 ± 0.7 pixels, whereas the RMSE error using a frame by frame homographic registration was 1.2 ± 0.4 pixels. In addition, the technique was tested on mouth exploration samples to simulate in-vivo acquisition. The results reveal that our method provides similar results when compared to a homographic transformation which would be impossible to perform in-vivo

Low complexity FPGA based background subtraction technique for thermal imagery

Embedded smart camera systems are gaining popularity for a number of real world surveillance applications. However, there are still challenges, i.e. variation in illumination, shadows, occlusion, and weather conditions while employing the vision algorithms in outdoor environments. For safety-critical surveillance applications, the visual sensors can be complemented with beyond-visual-range sensors. This in turn requires analysis, development and modification of existing imaging techniques. In this work, a low complexity background modelling and subtraction technique has been proposed for thermal imagery. The proposed technique has been implemented on Field Programmable Gate Arrays (FPGAs) after in-depth analysis of different sets of images, characterizing poor signal-to-noise ratio challenges, e.g. motion of high frequency background objects, temperature variation and camera jitter etc. The proposed technique dynamically updates the background on pixel level and requires a single frame storage as opposed to existing techniques. The comparison of this approach with two other approaches show that this approach performs better in different environmental conditions. The proposed technique has been modelled in Register Transfer Logic (RTL) and implementation on the latest FPGAs shows that the design requires less than 1 percent logics, 47 percent block RAMs, and consumes 91 mW power consumption on Artix-7 100T FPGA