Aplicación de la metodología 5s para incrementar la productividad en el área de producción en la empresa panificadora Mary E.I.R.L., Independencia, 2021

El presente trabajo de investigación “Aplicación de la metodología 5s para incrementar la productividad en el área de producción en la empresa panificadora Mary E.I.R.L., Independencia, 2021”, cuyo objetivo general es determinar cómo la aplicación de la metodología 5S incrementa la productividad en el área de producción. En esta investigación se tomó como variable dependiente la productividad y como variable independiente la metodología 5S. Esta investigación es tipo aplicada y diseño no experimental de nivel propositivo, La población estuvo conformada por los tipos panes que se producen en la panificadora Mary, la muestra está dada por la producción de pan francés estos datos serán recolectados durante 30 días calendario, donde se usa la técnica de observación y fichaje para el recojo de la información; siendo que estos instrumentos fueron sometidos a validez y confiabilidad. Entre las principales conclusiones se tiene que: La aplicación de la metodología 5S incrementa la productividad en el área de producción de la empresa panificadora Mary E.I.R.L, Independencia, 2021; implicando que mejora el índice de la eficiencia y eficacia, lo que se evidencia en el escenario actual considerando la mejora refleja un incremento del 22.26% en su productividad

Characterization of the practicing center´s tutor surpassing of Primary Education Degree in Pinar del Río

In the article it approaches the theme related with the tutor´s preparation in the practicing center, context in which the student of Primary Education Degree develops an important part of his formation as a future professional. It has as an objective to socialize the results of the study-diagnostic that allowed the characterization performed about the tutor’s surpassing of the practicing center in Pinar del Rio Province. To its realization it has applied analysis research methods and information gathering, among there are some like systematization, observation, the interview, the questionnaire and the methodological triangulation. To the diagnostic development it has worked with a sample of tutors, students in formation who are attended by those tutors, executives of the Primary Schools where the tutors work and teachers of the Primary Education Department pertaining to the University of Pinar del Rio, including career´s coordinators at the CUM. As a result, it has obtained a group of strengths and weaknesses which characterized the actual state of the tutor´s surpassing process of the students’ practicing center of the Primary Education Degree, necessaries to the solutions searching that permit to improve the tutor´s surpassing process

A data mining approach to guide students through the enrollment process based on academic performance

Student academic performance at universities is crucial for education management systems. Many actions and decisions are made based on it, specifically the enrollment process. During enrollment, students have to decide which courses to sign up for. This research presents the rationale behind the design of a recommender system to support the enrollment process using the students’ academic performance record. To build this system, the CRISP-DM methodology was applied to data from students of the Computer Science Department at University of Lima, Perú. One of the main contributions of this work is the use of two synthetic attributes to improve the relevance of the recommendations made. The first attribute estimates the inherent difficulty of a given course. The second attribute, named potential, is a measure of the competence of a student for a given course based on the grades obtained in relatedcourses. Data was mined using C4.5, KNN (K-nearest neighbor), Naïve Bayes, Bagging and Boosting, and a set of experiments was developed in order to determine the best algorithm for this application domain. Results indicate that Bagging is the best method regarding predictive accuracy. Based on these results, the “Student Performance Recommender System” (SPRS) was developed, including a learning engine. SPRS was tested with a sample group of 39 students during the enrollment process. Results showed that the system had a very good performance under real-life conditions

A Case Study: Data Mining Applied to Student Enrollment

One of the main problems faced by university students is deciding the right learning path based on available information such as courses, schedules and professors. In this context, this paper presents a recommender system based on data mining. This recommender system intends to create awareness of the difficulty and amount of workload entailed by a chosen set of courses. For the purpose of building the underlying model, this paper describes the generation of domain specific variables that are capable of representing students’ past performance. The objective is to improve students’ performance in general, by reducing the rate of misguided enrollment decisions

Characterization of Thermophilic Lignocellulolytic Microorganisms in Composting

Composting involves the selection of a microbiota capable of resisting the high temperatures generated during the process and degrading the lignocellulose. A deep understanding of the thermophilic microbial community involved in such biotransformation is valuable to improve composting efficiency and to provide thermostable biomass-degrading enzymes for biorefinery. This study investigated the lignocellulose-degrading thermophilic microbial culturome at all the stages of plant waste composting, focusing on the dynamics, enzymes, and thermotolerance of each member of such a community. The results revealed that 58% of holocellulose (cellulose plus hemicellulose) and 7% of lignin were degraded at the end of composting. The whole fungal thermophilic population exhibited lignocellulose-degrading activity, whereas roughly 8–10% of thermophilic bacteria had this trait, although exclusively for hemicellulose degradation (xylan-degrading). Because of the prevalence of both groups, their enzymatic activity, and the wide spectrum of thermotolerance, they play a key role in the breakdown of hemicellulose during the entire process, whereas the degradation of cellulose and lignin is restricted to the activity of a few thermophilic fungi that persists at the end of the process. The xylanolytic bacterial isolates (159 strains) included mostly members of Firmicutes (96%) as well as a few representatives of Actinobacteria (2%) and Proteobacteria (2%). The most prevalent species were Bacillus licheniformis and Aeribacillus pallidus. Thermophilic fungi (27 strains) comprised only four species, namely Thermomyces lanuginosus, Talaromyces thermophilus, Aspergillus fumigatus, and Gibellulopsis nigrescens, of whom A. fumigatus and T. lanuginosus dominated. Several strains of the same species evolved distinctly at the stages of composting showing phenotypes with different thermotolerance and new enzyme expression, even not previously described for the species, as a response to the changing composting environment. Strains of Bacillus thermoamylovorans, Geobacillus thermodenitrificans, T. lanuginosus, and A. fumigatus exhibiting considerable enzyme activities were selected as potential candidates for the production of thermozymes. This study lays a foundation to further investigate the mechanisms of adaptation and acquisition of new traits among thermophilic lignocellulolytic microorganisms during composting as well as their potential utility in biotechnological processing

Automatic analysis of bronchus-artery dimensions to diagnose and monitor airways disease in cystic fibrosis

Background:Cystic fibrosis (CF) lung disease is characterised by progressive airway wall thickening and widening. We aimed to validate an artificial intelligence-based algorithm to assess dimensions of all visible bronchus-artery (BA) pairs on chest CT scans from patients with CF.Methods:The algorithm fully automatically segments the bronchial tree; identifies bronchial generations; matches bronchi with the adjacent arteries; measures for each BA-pair bronchial outer diameter (Bout), bronchial lumen diameter (Bin), bronchial wall thickness (Bwt) and adjacent artery diameter (A); and computes Bout/A, Bin/A and Bwt/A for each BA pair from the segmental bronchi to the last visible generation. Three datasets were used to validate the automatic BA analysis. First BA analysis was executed on 23 manually annotated CT scans (11 CF, 12 control subjects) to compare automatic with manual BA-analysis outcomes. Furthermore, the BA analysis was executed on two longitudinal datasets (Copenhagen 111 CTs, ataluren 347 CTs) to assess longitudinal BA changes and compare them with manual scoring results.Results:The automatic and manual BA analysis showed no significant differences in quantifying bronchi. For the longitudinal datasets the automatic BA analysis detected 247 and 347 BA pairs/CT in the Copenhagen and ataluren dataset, respectively. A significant increase of 0.02 of Bout/A and Bin/A was detected for Copenhagen dataset over an interval of 2 years, and 0.03 of Bout/A and 0.02 of Bin/A for ataluren dataset over an interval of 48 weeks (all p<0.001). The progression of 0.01 of Bwt/A was detected only in the ataluren dataset (p<0.001). BA-analysis outcomes showed weak to strong correlations (correlation coefficient from 0.29 to 0.84) with manual scoring results for airway disease.Conclusion:The BA analysis can fully automatically analyse a large number of BA pairs on chest CTs to detect and monitor progression of bronchial wall thickening and bronchial widening in patients with CF

Mitigation of phytotoxic effect of compost by application of optimized aqueous extraction protocols

The abuse of chemical fertilizers in recent decades has led the promotion of less harmful alternatives, such as compost or aqueous extracts obtained from it. Therefore, it is essential to develop liquid biofertilizers, which in addition of being stable and useful for fertigation and foliar application in intensive agriculture had a remarkable phytostimulant extracts. For this purpose, a collection of aqueous extracts was obtained by applying four different Compost Extraction Protocols (CEP1, CEP2, CEP3, CEP4) in terms of incubation time, temperature and agitation of compost samples from agri-food waste, olive mill waste, sewage sludge and vegetable waste. Subsequently, a physicochemical characterization of the obtained set was performed in which pH, electrical conductivity and Total Organic Carbon (TOC) were measured. In addition, a biological characterization was also carried out by calculating the Germination Index (GI) and determining the Biological Oxygen Demand (BOD5). Furthermore, functional diversity was studied using the Biolog EcoPlates technique. The results obtained confirmed the great heterogeneity of the selected raw materials. However, it was observed that the less aggressive treatments in terms of temperature and incubation time, such as CEP1 (48 h, room temperature (RT)) or CEP4 (14 days, RT), provided aqueous compost extracts with better phytostimulant characteristics than the starting composts. It was even possible to find a compost extraction protocol that maximize the beneficial effects of compost. This was the case of CEP1, which improved the GI and reduced the phytotoxicity in most of the raw materials analyzed. Therefore, the use of this type of liquid organic amendment could mitigate the phytotoxic effect of several composts being a good alternative to the use of chemical fertilizers

Biopriming of cucumber seeds using actinobacterial formulas as a novel protection strategy against Botrytis cinerea

This work highlights the ability of various actinobacterial formulas, to control the incidence of gray-mold caused by Botrytis cinerea in cucumber seedlings. Protocols applied aimed at the preliminary characterization of the actinobacterial collection and the biopolymers used as carriers were very useful for predicting their phytotoxic, phytostimulating and biopesticidal capacity. First, the phytostimulatory or phytotoxic potential of 3 biopolymers at 3 different concentrations and a collection of 10 actinobacteria were analyzed by calculating the germination index in cucumber seeds by seed dipping (biopriming). In general, two-member consortia and independent actinobacteria previously selected reached a phytostimulant effect on cucumber seedlings after their application by biopriming. Likewise, the selected actinobacteria were characterized, sole and in co-cultures, according to its ability to inhibit the growth of B. cinerea by dual culture bioassays. Finally, after selecting the most effective actinobacterial formulas, a preventive gray-mold bioassay was performed based on cucumber seed biopriming. The strains A5 and A7, in axenic and co-culture, showed to be the most efficient strains against the in vitro growth of B. cinerea. Seed biopriming strategy with actinobacterial formulas revealed a remarkable promoter effect in the early stages of plant development and after the infection with the phytopathogen fungus was remained. Definitely, the microbial formulas used in this work showed a phytostimulant and biopesticide character, laying the foundations for subsequent studies that allow a deeper scrutiny of the mechanisms of action that grant the specialization of the effect that occurs between beneficial microorganisms and specific plant hosts