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Geotechnologies applied to the analysis of buildings involved in the production of poultry and swine to the integrated food safety system and environment
Get PDFInitiatives to promote food security in relation to reducing food waste are of relevant for sustainability on a global scale. Rural buildings involved in the production of poultry and swine, in an integrated system tend to comprise a variety of forms with complex characteristics. Studying the impacts of an integrated system on sustainability and food production becomes opportune. This study aims to analyze parameters in different buildings involved in the production of poultry and swine; considering economic, social and environmental factors with the use of remote sensing geotechnologies related to food safety. Methodologically, a K-means cluster analysis of the buildings was carried out to understand the impacts of raising poultry and pigs in relation to the distance from forests, roads, water bodies, distances from local and total buildings, based on the use of geotechnologies. Unpublished images from the Landsat TM-8 satellite were used, made available by the United States Geological Survey, in partnership with the United States National Aeronautics and Space Administration, modeled in QGIS. The results present eight production clusters, and a variation of distances between the studied buildings was identified, along with distances close to water resources. The importance of an integrated poultry and swine production system, which contributes to global food security, was highlighted.Dal Moro, Leila-will be generated-orcid-0000-0003-0456-4260-600Stolfo Maculan, LaércioNeckel, Alcindo-will be generated-orcid-0000-0001-5435-3096-600de Vargas Mores, Giana-will be generated-orcid-0000-0003-3733-2220-600Pivoto, Dieisson-will be generated-orcid-0000-0002-6759-8946-600BODAH, ELIANE-will be generated-orcid-0000-0001-9204-3080-600William Bodah, BrianSilva Oliveira, Marcos Leandr
Rare earth elements study of cretaceous coals from benue trough basin, Nigeria: modes of occurrence for greater sustainability of mining
Get PDFThe rare earth elements (REE) possess a beneficial combination of chemical and physical properties, making them valuable for most advanced branches of engineering and technology. Alternative sources of REE are desirable due to limited reserves of conventional REE containing minerals over the world combined with disproportionate supply over demand in the commodity markets. This study investigated the occurrence of REE and carbon nanotubes (CNTs) in Cretaceous Nigerian coals for prospective industrial applications. Results show that the coals’ crystalline mineral matter comprises quartz, kaolinite, and illite with minor quantities of feldspar, hematite, magnetite, calcite, dolomite, which indicate detrital mineral origins. Elemental relationships (such as Al2O3/TiO2, Cr/Th vs. Sc/Th, and Co/Th vs. La/Sc) suggest sediment-source regions with mafic, intermediate or felsic compositions. REE are either strongly fractionated or characterized by light-enrichment along with outlook coefficient (Coutl) values that suggest the coals are prospective substitute sources for REE and yttrium (REY) recovery. Several minerals including jarosite, goethite, epsomite, ferrohexahydrite, natrojarosite, rozenite, and gypsum were detected in trace amounts. REE mineral phases were not identified but only amorphous phases containing Ce, La, Nd, Th, Pr, Sm, Gd, Tb, Dy, Ho, and Hf. Maceral composition (high vitrinite), presence of iron-containing minerals (hematite and magnetite), high carbon contents, reduced volatile matter and low ash content favoured the formation of naturally occurring multi-walled carbon nanotube (MWCNTs) structures in Maiganga (MGA) coal. Hence, the present study is the first scientific report on the naturally occurring REEs and MWC nanophases in Cretaceous coals from the Benue Trough. © 2021Akinyemi, Segun Ajayi-will be generated-orcid-0000-0001-6363-3698-600Nyakuma, Bemgba Bevan-will be generated-orcid-0000-0001-5388-7950-600Jauro, Aliyu-will be generated-orcid-0000-0002-8159-1644-600Olanipekun, Timileyin-will be generated-orcid-0000-0003-2819-8920-600Mudzielwana, Rabelani-will be generated-orcid-0000-0002-7744-4561-600Gitari, Wilson-will be generated-orcid-0000-0002-6387-0682-600Saikia, Binoy-will be generated-orcid-0000-0002-3382-6218-600Dotto, Guilherme Luiz-will be generated-orcid-0000-0002-4413-8138-600Hower, J. CSilva, Luis F.
Analysis of the predictors of unemployment of venezuelan migrants in the city of Barranquilla through a logistic regression model
No full textThis research sought to analyze the predictors of unemployment of Venezuelan migrants in Barranquilla through a logistic regression model. A correlational research is presented, in which a sample of 931 Venezuelan migrants in Barranquilla is taken, to whom an instrument aimed at sociodemographic elements linked to living conditions and employment status is applied. Regarding the statistical treatment, a logistic regression analysis is applied, which sought to determine which elements are predictors of unemployment of the migrant population. These findings show as outstanding elements: being active within the informal economy, performing economic activities recognized as trades and having owned a business in Venezuela. It is concluded that there is a condition of vulnerability in Venezuelan migrants as a result of being inserted in the informal sector, being the predictor factors those established in the regression model, with a level of significance
IDRM: Brain tumor image segmentation with boosted RIME optimization
No full textTimely diagnosis of medical conditions can significantly mitigate the risks they pose to human life. Consequently, there is an urgent demand for an effective auxiliary model that assists physicians in accurately diagnosing medical conditions based on imaging data. While multi-threshold image segmentation models have garnered considerable attention due to their simplicity and ease of implementation, the selection of threshold combinations greatly influences the segmentation performance. Traditional optimization algorithms often require substantial time to address multi-threshold image segmentation problems, and their segmentation accuracy is frequently unsatisfactory. As a result, metaheuristic algorithms have been employed in this domain. However, several algorithms suffer from drawbacks such as premature convergence and inadequate exploration of the solution space when it comes to threshold selection. For instance, the recently proposed optimization algorithm RIME, inspired by the physical phenomenon of rime-ice, falls short in terms of avoiding local optima and fully exploring the solution space. Therefore, this study introduces an enhanced version of RIME, called IDRM, which incorporates an interactive mechanism and Gaussian diffusion strategy. The interactive mechanism facilitates information exchange among agents, enabling them to evolve towards more promising directions and increasing the likelihood of discovering the optimal solution. Additionally, the Gaussian diffusion strategy enhances the agents' local exploration capabilities and expands their search within the solution space, effectively preventing them from becoming trapped in local optima. Experimental results on 30 benchmark test functions demonstrate that IDRM exhibits favorable optimization performance across various optimization functions, showcasing its robustness and convergence properties. Furthermore, the algorithm is applied to select threshold combinations for brain tumor image segmentation, and the results are evaluated using metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The overall findings consistently highlight the exceptional performance of this approach, further validating the effectiveness of IDRM in addressing image segmentation problems
Credit risk scoring model based on the discriminant analysis technique
No full textCredit risk models are vitally important for organizations whose corporate purpose is to operate profitably in the loan or credit business. Technological developments have enabled the application of different statistical techniques to create functions that assist in measuring, and consequently in managing, exposure to credit risk; however, these models must be periodically reassessed and optimized to ensure that they fulfill their objectives. This study addresses problems that have been observed in the model for reading the credit history of customers of a company in the real sector, contributing to the design of a risk-scoring model using the discriminant analysis technique
Global, regional, and national burden of allergic disorders and their risk factors in 204 countries and territories, from 1990 to 2019: a systematic analysis for the Global Burden of Disease Study 2019
No full textBackground: Asthma and atopic dermatitis (AD) are chronic allergic conditions, along with allergic rhinitis and food allergy and cause high morbidity and mortality both in children and adults. This study aims to evaluate the global, regional, national, and temporal trends of the burden of asthma and AD from 1990 to 2019 and analyze their associations with geographic, demographic, social, and clinical factors. Methods: Using data from the Global Burden of Diseases (GBD), Injuries, and Risk Factors Study 2019, we assessed the age-standardized prevalence, incidence, mortality, and disability-adjusted life years (DALYs) of both asthma and AD from 1990 to 2019, stratified by geographic region, age, sex, and socio-demographic index (SDI). DALYs were calculated as the sum of years lived with disability and years of life lost to premature mortality. Additionally, the disease burden of asthma attributable to high body mass index, occupational asthmagens, and smoking was described. Results: In 2019, there were a total of 262 million [95% uncertainty interval (UI): 224–309 million] cases of asthma and 171 million [95% UI: 165–178 million] total cases of AD globally; age-standardized prevalence rates were 3416 [95% UI: 2899–4066] and 2277 [95% UI: 2192–2369] per 100,000 population for asthma and AD, respectively, a 24.1% [95% UI: −27.2 to −20.8] decrease for asthma and a 4.3% [95% UI: 3.8–4.8] decrease for AD compared to baseline in 1990. Both asthma and AD had similar trends according to age, with age-specific prevalence rates peaking at age 5–9 years and rising again in adulthood. The prevalence and incidence of asthma and AD were both higher for individuals with higher SDI; however, mortality and DALYs rates of individuals with asthma had a reverse trend, with higher mortality and DALYs rates in those in the lower SDI quintiles. Of the three risk factors, high body mass index contributed to the highest DALYs and deaths due to asthma, accounting for a total of 3.65 million [95% UI: 2.14–5.60 million] asthma DALYs and 75,377 [95% UI: 40,615–122,841] asthma deaths. Conclusions: Asthma and AD continue to cause significant morbidity worldwide, having increased in total prevalence and incidence cases worldwide, but having decreased in age-standardized prevalence rates from 1990 to 2019. Although both are more frequent at younger ages and more prevalent in high-SDI countries, each condition has distinct temporal and regional characteristics. Understanding the temporospatial trends in the disease burden of asthma and AD could guide future policies and interventions to better manage these diseases worldwide and achieve equity in prevention, diagnosis, and treatment
How the street network slope influences fuel consumption in urban freight routing
No full textAlthough there are many investigations on the problem of vehicle routing, few consider the real slope changes in the road networks of cities. The objective of this work is to calculate the effect that the slopes of a road network can have on fuel consumption, combining a metaheuristic solution algorithm, traffic characterization and an algorithm for the calculation of road slopes. The results of the model show that considering the slope in the urban merchandise distribution model can increase the estimates of fuel consumption by 28.67%
Detection of variables for the diagnosis of overweight and obesity in young chileans using machine learning techniques
No full textOverweight and obesity are considered epidemic problems. The number of factors involved in developing extra body fat makes harder the detection of this problem. Therefore, among the several variables and their levels presented in overweight and obese people, there is a need to improve the classification of people with these conditions. To this aim, in this paper, we conducted a variable analysis from biochemical and lipid profiles in young Chileans with normal weight, overweight, and obesity using machine learning techniques. XGBoost library was selected as the classifier. 21 variables (13 from biochemical and 8 from lipid profiles) were chosen as features. 100 iterations were conducted, and an 80% cross-validation was obtained. The variables with greater relevance in the classification task were total cholesterol, glycemia, LDH enzyme, bilirubin, and VLDL cholesterol. All of these, except bilirubin, are consistent with previous research in which these features have been used to assess risk factors for developing overweight or obesity. Then, further research must include a deep study regarding bilirubin's influence over these conditions
Factors that impact the innovation capability in MSMEs: case of Colombia's Atlántico Department
No full textSince there is no consensus in the previous literature on the factors that affect innovation capability in the context of Micro, Small, and Medium Enterprises (MSMEs), the objective of the article is to analyze the variables that affect this capability in industrial MSMEs in Colombia's Atlantico Department. For this, we develop a positivist, explanatory, and non-experimental cross-sectional study, using a survey applied to 400 industrial MSMEs. The results show that organizational learning, intellectual capital, and knowledge management directly and statistically significantly affect innovation capability. We concluded that in this type of company flexibility, informality, and control style, favor the development of strategies that allow the organization to face the environments demands, based on innovative and sustainable solutions
Impact of the duty cycle on the morphology and photocatalytic properties of S-TiO2 obtained by plasma electrolytic oxidation to treat real electroplating wastewater contaminated with Cr6+
No full textThis work reports S-TiO2 doped coatings to reduce Cr6+ to Cr3+ obtained from a Ti electrode through the Plasma Electrolytic Oxidation (PEO) process. The Ti sheets (20 × 20 × 1 mm) were submerged on 0.1 M H2SO4, and values of the duty cycle from 2% to 50% were applied to obtain various materials. SEM, XRD, AFM, XPS, and DRS techniques were used to characterize the resultant surfaces. It was observed that the duty cycle strongly in fluences the crystalline/amorphous ratio, anatase/rutile ratio, porosity density, pores size distribution, and surface roughness. Besides, it is explained that the introduction of SO4 2- into the TiO2 structure can take place either in the Ti or O places in the crystalline lattice. All materials showed photocatalytic properties to reduce Cr6+ to Cr3+ under UVC light (254 nm), decreasing the efficiency with the increase of the duty cycle. Addi tionally, the introduction of EDTA showed a positive synergy with the heterogeneous photocatalytic process when the material was obtained with the highest duty cycle. This last result was attributed to the relatively low bandgap and the high recombination rate; furthermore, EDTA acts as holes and hydroxyl radical scavenger. Thus, the photoelectrochemical system for the treatment of wastewater was evaluated, and again, the same materials showed the highest performance. In the same way, the re-use of the material obtained with the 2% duty cycle was tested, getting satisfactory results, obtaining 96.14 ± 2.77% Cr6+ reduction even after seven cycles of reuse