Laser texturing to control the wettability of materials

Many applications of different materials are related to the properties of their surface. Wettability is a key property affecting applications in all fields: adhesives, lubricants, detergents, all types of coatings, implant integration, heat transmission, corrosion, etc. Laser texturing has been demonstrated to be an excellent technique to modify surface wettability of many different materials: polymers, metals, ceramics, or even natural stones. The relative simplicity and robustness of the results, together with the widespread availability of affordable industrial laser sources made laser texturing a very promising tool for modifying the surface of parts in manufacturing plants. In this paper we introduce the basics of the technique and show some examples of applications. On one hand, treating the surface of different polymers for biomedical applications. And on the other hand, the production of surfaces with extreme wettability properties is shown: superhydrophilic, superhydrophobic and omniphobic surfaces were obtained by laser texturing

Epidemiological Algorithm and Early Molecular Testing to Prevent COVID-19 Outbreaks in a Mexican Oncologic Center

Introduction: Prevention strategies and detection of latent COVID-19 infections in oncology staff and oncologic patients are essential to prevent outbreaks in a cancer center. In this study, we used two statistical predictive models in oncology staff and patients from the radiotherapy area to prevent outbreaks and detect COVID-19 cases. Methods: Staff and patients answered a questionnaire (electronic and paper surveys, respectively) with clinical and epidemiological information. The data was collected through two online survey tools: Real-Time Tracking (R-Track) and Summary of Factors (S-Facts). According to the algorithm\u27s models, cut-off values were established. SARS-CoV-2 qRT-PCR tests confirmed the algorithm\u27s positive individuals. Results: Oncology staff members (n=142) were tested, and 14% (n=20) were positives for the R-Track algorithm; 75% (n=15) were qRT-PCR positive. The S-Facts algorithm identified 7.75% (n=11) positive oncology staff members, and 81.82% (n=9) were qRT-PCR positive. Oncology patients (n=369) were evaluated, and 1.36% (n=5) were positive for the algorithms. The 5 patients (100%) were confirmed by qRT-PCR at a very early stage. Conclusions: The proposed algorithms could prove to become an essential prevention tool in countries where qRT-PCR tests and vaccines are insufficient for the population

Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection

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What makes a task a problem in early childhood education?

This article begins with a theoretical discussion of the characteristics that a task should feature to be regarded as a mathematics problem suitable for pre-primary students. Those considerations are followed by a report of a classroom experience in which three problems involving quotative or partitive division were posed to pre-primary school pupils to determine the presence of otherwise of the respective characteristics. The findings show that the characteristics of pre-primary education problems depend on two factors: mathematical activity that engages pupils and a structure that favours both their understanding of the problem and the application and verification of the solutions

Broadband access in complex environments: LTE on railway

This paper assesses the main challenges associated with the propagation and channel modeling of broadband radio systems in a complex environment of high speed and metropolitan railways. These challenges comprise practical simulation, modeling interferences, radio planning, test trials and performance evaluation in different railway scenarios using Long Term Evolution (LTE) as test case. This approach requires several steps; the first is the use of a radio propagation simulator based on ray-tracing techniques to accurately predict propagation. Besides the radio propagation simulator, a complete test bed has been constructed to assess LTE performance, channel propagation conditions and interference with other systems in real-world environments by means of standard-compliant LTE transmissions. Such measurement results allowed us to evaluate the propagation and performance of broadband signals and to test the suitability of LTE radio technology for complex railway scenarios

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area

Review: Strategies for enteric methane mitigation in cattle fed tropical forages

Methane (CH4) is a greenhouse gas (GHG) produced and released by eructation to the atmosphere in large volumes by ruminants. Enteric CH4 contributes significantly to global GHG emissions arising from animal agriculture. It has been contended that tropical grasses produce higher emissions of enteric CH4 than temperate grasses, when they are fed to ruminants. A number of experiments have been performed in respiration chambers and head-boxes to assess the enteric CH4 mitigation potential of foliage and pods of tropical plants, as well as nitrates (NO3−) and vegetable oils in practical rations for cattle. On the basis of individual determinations of enteric CH4 carried out in respiration chambers, the average CH4 yield for cattle fed low-quality tropical grasses (>70% ration DM) was 17.0 g CH4/kg DM intake. Results showed that when foliage and ground pods of tropical trees and shrubs were incorporated in cattle rations, methane yield (g CH4/kg DM intake) was decreased by 10% to 25%, depending on plant species and level of intake of the ration. Incorporation of nitrates and vegetable oils in the ration decreased enteric CH4 yield by ∼6% to ∼20%, respectively. Condensed tannins, saponins and starch contained in foliages, pods and seeds of tropical trees and shrubs, as well as nitrates and vegetable oils, can be fed to cattle to mitigate enteric CH4 emissions under smallholder conditions. Strategies for enteric CH4 mitigation in cattle grazing low-quality tropical forages can effectively increase productivity while decreasing enteric CH4 emissions in absolute terms and per unit of product (e.g. meat, milk), thus reducing the contribution of ruminants to GHG emissions and therefore to climate change