Study on micro-patterning process of vertically aligned carbon nanotubes (VACNTs)

Vertically aligned carbon nanotubes (VACNTs) have drawn significant attention by the researchers because of their nanometric size and favorable material properties. Patterning of CNT forests in the micrometric domain is very important for their application in the area of microelectromechanical system (MEMS). For the first time this paper reports, detailed experimental investigation on a post growth m-patterning process of VACNT forests. The micromechanical bending (M2B) process was locally applied at the targeted area in order to change the alignment of VACNT forests. Interestingly, the VACNT forest was transformed from typical black body absorber to reflective mirror as the M2B process was applied. Several parameters were identified that govern the resultant patterns such as rotational spindle speed, lateral bending speed, step size, tool morphology, and total depth of bend. Optimization of the parameters was carried out experimentally to obtain the best surface roughness and integrity of the microstructure. A minimum average surface roughness of Ra D 15 nm was achieved with 2000 rpm spindle speed, 1 mm/min bending speed and 1 mm step size

Enhancement of reflectance of densified vertically aligned carbon nanotube forests

Vertically aligned carbon nanotubes (VACNTs), also known as a carbon nanotube (CNT) forest, are a porous material that is well known for its exceptional optical absorbance property. The reflectance from a VACNT forest has been reported to be as low as 0.045% [1,2]. It is known as the darkest material on Earth. Because of its remarkable material properties, it has various other applications as gas sensors [3], pressure sensors [4], temperature sensors [5], and strain sensors [6]. Recently, various efforts have been made to mechanically manipulate the vertical structure of the nanotubes in the CNT forest and to conduct their optical characterization [7,8]. Optical reflection from bare VACNTs has also been investigated at different wavelengths by Wąsik et al. [9]. Controlled densification by wetting of the CNT forest is another post processing technique that has been reported by other researchers [10]. A densification process is necessary to make the CNT forest useful as a future electronics interconnect [10]. However, no study has been done so far on the optical behavior of CNT forests densified by a wetting process. In this letter, for the first time, we investigate and explain the nature of the optical reflectance of densified VACNTs. Fig. 1 illustrates how the CNT forest is able to absorb most incident light. It was reported elsewhere that VACNT arrays are highly porous [11]. As a result, when incident light enters the bare CNT forest, it goes through several internal reflection-absorption cycles via individual nanotubes and finally makes its way out of the CNT forest as shown in Fig. 1b. Hence, a very low amount of light bounces back (approximately 0.045%) [1,2]. Mathematically, a simple model can be developed to estimate the final amount of light coming out of a CNT forest after several internal reflections; this process is explained by eq (1)

Forecast of the demand for hourly electric energy by artificial neural networks

Obtaining an accurate forecast of the energy demand is fundamental to support the several decision processes of the electricity service agents in a country. For market operators, a greater precision in the short-term load forecasting implies a more efficient programming of the electricity generation resources, which means a reduction in costs. In the long term, it constitutes a main indicator for the generation of investment signals for future installed capacity. This research proposes a prognostic model for the demand of electrical energy in Bogota, Colombia at hourly level in a full week, through Artificial Neural Network

Student performance assessment using clustering techniques

The application of informatics in the university system management allows managers to count with a great amount of data which, rationally treated, can offer significant help for the student programming monitoring. This research proposes the use of clustering techniques as a useful tool of management strategy to evaluate the progression of the students’ behavior by dividing the population into homogeneous groups according to their characteristics and skills. These applications can help both the teacher and the student to improve the quality of education. The selected method is the data grouping analysis by means of fuzzy logic using the Fuzzy C-means algorithm to achieve a standard indicator called Grade, through an expert system to enable segmentation.Universidad de la Costa, 2 Universidad Nacional Experimental Politécnica “Antonio José de Sucre”, Universidad Simón Bolívar, Corporación Universitaria Latinoamericana, Corporación Universitaria Minuto de Dios

Prevalence of chronic cough, its risk factors and population attributable risk in the Burden of Obstructive Lung Disease (BOLD) study: a multinational cross-sectional study

Background: Chronic cough is a common respiratory symptom with an impact on daily activities and quality of life. Global prevalence data are scarce and derive mainly from European and Asian countries and studies with outcomes other than chronic cough. In this study, we aimed to estimate the prevalence of chronic cough across a large number of study sites as well as to identify its main risk factors using a standardized protocol and definition. Methods: We analyzed cross-sectional data from 33,983 adults (≥40 years), recruited between Jan 2, 2003 and Dec 26, 2016, in 41 sites (34 countries) from the Burden of Obstructive Lung Disease (BOLD) study. We estimated the prevalence of chronic cough for each site accounting for sampling design. To identify risk factors, we conducted multivariable logistic regression analysis within each site and then pooled estimates using random-effects meta-analysis. We also calculated the population-attributable risk (PAR) associated with each of the identified risk factors. Findings: The prevalence of chronic cough varied from 3% in India (rural Pune) to 24% in the United States of America (Lexington, KY). Chronic cough was more common among females, both current and passive smokers, those working in a dusty job, those with a history of tuberculosis, those who were obese, those with a low level of education, and those with hypertension or airflow limitation. The most influential risk factors were current smoking and working in a dusty job. Interpretation: Our findings suggested that the prevalence of chronic cough varies widely across sites in different world regions. Cigarette smoking and exposure to dust in the workplace are its major risk factors.info:eu-repo/semantics/publishedVersio

Prevalence of chronic cough, its risk factors and population attributable risk in the Burden of Obstructive Lung Disease (BOLD) study: a multinational cross-sectional study

© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)Background: Chronic cough is a common respiratory symptom with an impact on daily activities and quality of life. Global prevalence data are scarce and derive mainly from European and Asian countries and studies with outcomes other than chronic cough. In this study, we aimed to estimate the prevalence of chronic cough across a large number of study sites as well as to identify its main risk factors using a standardised protocol and definition. Methods: We analysed cross-sectional data from 33,983 adults (≥40 years), recruited between Jan 2, 2003 and Dec 26, 2016, in 41 sites (34 countries) from the Burden of Obstructive Lung Disease (BOLD) study. We estimated the prevalence of chronic cough for each site accounting for sampling design. To identify risk factors, we conducted multivariable logistic regression analysis within each site and then pooled estimates using random-effects meta-analysis. We also calculated the population attributable risk (PAR) associated with each of the identifed risk factors. Findings: The prevalence of chronic cough varied from 3% in India (rural Pune) to 24% in the United States of America (Lexington,KY). Chronic cough was more common among females, both current and passive smokers, those working in a dusty job, those with a history of tuberculosis, those who were obese, those with a low level of education and those with hypertension or airflow limitation. The most influential risk factors were current smoking and working in a dusty job. Interpretation: Our findings suggested that the prevalence of chronic cough varies widely across sites in different world regions. Cigarette smoking and exposure to dust in the workplace are its major risk factors.info:eu-repo/semantics/publishedVersio

Association of respiratory symptoms and lung function with occupation in the multinational Burden of Obstructive Lung Disease (BOLD) study

Background Chronic obstructive pulmonary disease has been associated with exposures in the workplace. We aimed to assess the association of respiratory symptoms and lung function with occupation in the Burden of Obstructive Lung Disease study. Methods We analysed cross-sectional data from 28 823 adults (≥40 years) in 34 countries. We considered 11 occupations and grouped them by likelihood of exposure to organic dusts, inorganic dusts and fumes. The association of chronic cough, chronic phlegm, wheeze, dyspnoea, forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1)/FVC with occupation was assessed, per study site, using multivariable regression. These estimates were then meta-analysed. Sensitivity analyses explored differences between sexes and gross national income. Results Overall, working in settings with potentially high exposure to dusts or fumes was associated with respiratory symptoms but not lung function differences. The most common occupation was farming. Compared to people not working in any of the 11 considered occupations, those who were farmers for ≥20 years were more likely to have chronic cough (OR 1.52, 95% CI 1.19–1.94), wheeze (OR 1.37, 95% CI 1.16–1.63) and dyspnoea (OR 1.83, 95% CI 1.53–2.20), but not lower FVC (β=0.02 L, 95% CI −0.02–0.06 L) or lower FEV1/FVC (β=0.04%, 95% CI −0.49–0.58%). Some findings differed by sex and gross national income. Conclusion At a population level, the occupational exposures considered in this study do not appear to be major determinants of differences in lung function, although they are associated with more respiratory symptoms. Because not all work settings were included in this study, respiratory surveillance should still be encouraged among high-risk dusty and fume job workers, especially in low- and middle-income countries.publishedVersio

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

Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

To understand neurological complications of COVID-19 better both acutely and for recovery, we measured markers of brain injury, inflammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera (1–11 days post-admission) and 92 convalescent sera (56 with COVID-19-associated neurological diagnoses). Here we show that compared to 60 uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19 infection at acute timepoints and NfL and GFAP are significantly higher in participants with neurological complications. Inflammatory mediators (IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered consciousness and markers of brain injury. Autoantibodies are more common in COVID-19 than controls and some (including against MYL7, UCH-L1, and GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP and NfL, unrelated to attenuated systemic inflammatory mediators and to autoantibody responses. Overall, neurological complications of COVID-19 are associated with evidence of neuroglial injury in both acute and late disease and these correlate with dysregulated innate and adaptive immune responses acutely

Model pengembangan pengelolaan hasil tangkap ikan masyarakat pesisir kabupaten Pasuruan melalui pendekatan linear programming dan business model canvas dalam industri 4.0

Pasuruan Regency is one area that has the potential to manage fishing products because this area has a coastal area whose people work as fishermen. Based on the potential of natural resources possessed, the level of income and welfare of coastal communities can be better than other village areas. However, the level of income and welfare of coastal communities is still lacking, one of which is less than optimal in processing fishing products. The purpose of this study is to optimize the processing of coastal fishing products and formulate business strategies through the Linear Programming and Business Model Canvas methods. The final result obtained is a profit value of Rp. 84,642.33 per person, while the Model of the Development of Fishing Management of coastal communities in facing the industrial era 4.0 in order to create added value through the use of online media