Higher Final Scores Research Articles

How Creative Economy Development Plays a Role as a Driving Force in Community-Based Tourism Village Development? An Empirical Study in Central Java Province

This study aims to develop a Creative Economy Development Strategy as a Driving Force for Community-Based Tourism Village Development. This study uses quantitative and qualitative approaches. This study uses primary and secondary data. Data collection methods in this study include observation, interviews, documentation, and questionnaires. The questionnaire in this study was addressed to key persons consisting of several elements including academics, government, society, NGOs, Communities, and also the private sector. The results of this study indicate that Infrastructure and Accessibility and Policy and Regulatory Support have the highest weights, which means that supporting factors such as infrastructure and government policies are very important in the development of creative economy-based tourism villages. Policy and Regulatory Support and Local Human Resource Empowerment are the next priority criteria. Provision of Grants and Subsidies for Creative MSMEs has the highest final score, which means that these elements are alternative strategies that are the main priority for driving the creative economy. Improving Road Connectivity to Villages, Training in Crafts, Fine Arts, and Local Culinary, and Adding Tourism Support Facilities are important strategies to increase the attractiveness and accessibility of tourism villages. The development of galleries or showrooms for MSME products and digital campaigns through social media are also considered strategic in supporting the promotion of local creative products.

Use of a novel donor lung scoring system as a tool for increasing lung recovery for transplantation

BackgroundThere is a critical shortage of donor lungs for transplantation. We previously developed a parsimonious, highly discriminatory 9-variable Lung Donor (LUNDON) acceptability score. Here we assessed the utility of this score as a tool for improving lung recovery rates for transplantation. MethodsWe examined all brain-dead donors between 2014 and 2020 from 3 US organ procurement organizations and validated the score’s predictive performance. We examined the trajectory of donors with low (<40) and high (>60) initial LUNDON scores, their corresponding lung recovery rates, factors contributing to score improvement using multivariable regression models, and 1-year post-transplant recipient survival. ResultsOverall lung recovery was 32.4% (1410 of 4351). Validation of the LUNDON score in our cohort revealed a C statistic of 0.904 and required intercept calibration. Low initial LUNDON donors that improved to a high final score had an increase in lung recovery rate from 29.3% (1100 of 3765) to 86.8% (441 of 508), associated with lower body mass index, management in a specialized donor care facility (SDCF), and more bronchoscopies. Donors with high initial and final LUNDON scores had a lung recovery rate of 85.2% (98 of 115), associated with shorter length of hospital stay. One-year survival was similar in recipients of low-to-high versus high-to-high LUNDON score donors (0.89 vs 0.84; P = .2). ConclusionsThe LUNDON score performs well as a predictor of lung recovery in a contemporary cohort but may require organ procurement organization–specific calibration. SDCF care, increasing use of bronchoscopy, and decreasing the time from brain death to organ procurement may improve lung utilization. The LUNDON score can be used to guide donor management to expand the donor pool.

Estimation of the shear strength of UHPC beams via interpretable deep learning models: Comparison of different optimization techniques

In this article, optimized deep learning (DL) models with different algorithms are adopted to estimate the shear strength of rectangular ultra-high performance concrete beams (UHPC-Bs) in order to overcome the challenges in traditional mechanics-based approaches. Long short-term memory (LSTM) and gated recurrent unit (GRU) are chosen as the DL models, whereas the recent popular optimization algorithms are phasor particle swarm optimization (PPSO), dwarf mongoose optimization (DMO), mountain gazelle optimizer (MGO), and atom search optimization (ASO). A thorough and reliable dataset of 244 UHPC-Bs test results with ten input features has been used to construct the hybrid DL models. The performance of the optimized hybrid LSTM and GRU models with different algorithms is extensively assessed and compared based on various statistical metrics, error, and score analyses. Then, the model with the best estimation performance is determined and compared with the mechanics-based formulas in the current international design codes. Additionally, Shapley additive explanations (SHAP) analysis is used to assist in the interpretability of DL models and to reveal the effects of input features that contribute to the model's estimation. According to the results of the present work, all DL models successfully estimate the shear strength of UHPC-Bs. Among these models, the MGO-LSTM model stands out compared to the other models in terms of several performance measures for both the training and testing phases, like a higher R2 value, lower RMSE, MAPE, and MAE values, as well as a smaller error ratio and a higher final score. The performance of the algorithms applied to optimize the hyper-parameters of the LSTM and GRU models can be ranked as follows: MGO > DMO > PPSO > ASO. Moreover, a graphical user interface (GUI) was constructed based on the best estimation model that was built so that the shear strength of UHPC-Bs could be estimated in real-world situations without the need for any extra software or tools. This enables more users to quickly and easily estimate the shear strength of UHPC-Bs, optimize design processes, and decrease experimental testing costs.

Investigation of optimized machine learning models with PSO for forecasting the shear capacity of steel fiber-reinforced SCC beams with/out stirrups

This article presents a comprehensive investigation of the applicability of optimized machine learning (ML) models with particle swarm optimization (PSO) for forecasting the shear strength of steel fiber-reinforced self-compacting concrete (SFR-SCC) beams with/without stirrups in engineering applications. Firstly, a database containing the results of 101 specimens with nine input features is adopted to train the models. As ML models such as random forest (RF), adaptive boosting regression (AdaBoost), extreme gradient boosting (XGBoost), support vector regression (SVR), and K-nearest neighbors regression (KNN) are considered, whereas the hyper-parameters of these models are set as default by the sklearn module. On the other hand, PSO-ML models (PSO-RF, PSO-AdaBoost, PSO-XGBoost, PSO-SVR, and PSO–KNN) are constructed using particle swarm optimization to find the optimal combination of the hyper-parameters of these default ML models. Afterwards, the forecasting ability of each model is extensively assessed using various performance metrics, error analysis, and score analysis, and the model with the best forecasting ability is determined and compared with existing empirical models. Moreover, Shapley additive explanation (SHAP) analysis is also utilized to ensure the interpretability of the forecasting models and to overcome the “black box” problem of ML methods. Lastly, based on the best forecasting model developed in this study, a graphical user interface (GUI) has been developed to easily forecast the shear strength of SFR-SCC beams in practical applications. The results of the study clearly illustrate that PSO-ML models exhibit better forecasting capabilities than default models. It can be emphasized from here that the PSO algorithm can be an effective tool to improve the performance of ML models. It should also be pointed out that the use of PSO in simpler algorithms instead of tree-based models can further improve forecasting efficiency. On the other hand, the PSO-RF model has the best performance, with a lower error value and a high final score. And this makes it a more reliable option for predicting the shear strength of the SFR-SCC beams compared to empirical equations. In addition, according to the results of SHAP feature importance analysis, the most important input parameters affecting the shear strength of SFR-SCC beams are stirrup rebar ratio (ρv), stirrup yield strength (fyv) and longitudinal rebar ratio (ρt). This information can assist engineers in paying special attention to these features in their design and assessment processes.

A Two-Stage Approach of DEA and AHP in Selecting Optimal Wind Power Plants

Sustainable development has become a global tendency in the growth orientation of governments. Renewable energies such as wind and solar have attracted many concerns in reducing the level of fossil fuels dependence. One of the most significant stages in such a transmission process is determining the most suitable location. This article aims to propose a novelty approach combining two multiple criteria decision making models, namely data envelopment analysis (DEA) and analysis hierarchy process (AHP) to recommend the most optimal site for wind power plants. Vietnam has been one of the most successful ASEAN nations in attracting renewable energy investments; hence, the article considers it as a case study. In the first stage, there are five inputs and outputs conducted by DEA models to choose the five most potential alternatives among 12 locations for the next stage. In the second stage, the AHP model is implemented on five main criteria and 20 subcriteria that are referred to the options of experienced experts. The findings show that Ninh Thuan province, which is obtained with the highest final score (0.374), is the most suitable location for wind power plants construction. The article has contributed to renewable literature in terms of methodologies as well as to practice for both public and private stakeholders.

Estimation of blast-induced peak response of concrete-filled double-skin tube columns by intelligence-based technique

This paper aims to predict the peak response of concrete-filled steel tubes (CFSTs) under blast loads using regression-based machine learning (ML) algorithms considering the variability of input parameters. Twelve regression metrics that are commonly used are utilized to evaluate and compare the efficiency and effectiveness of the proposed ML models. The Monte Carlo approach is used to propagate the variability in the input space to the predicted output. The results showed that the optimal method varies with perspectives. The XGBoost algorithm has the highest final score, and the SVM algorithm exhibits the highest total score for standard deviation. Furthermore, the confidence interval increases with the peak response, and the single model presents a higher accuracy in predicting large displacements. However, ensemble models do the opposite. Sensitivity and uncertainty analyses are performed, indicating that the scaled distance, section size and ultimate moment capacity exhibit the highest contribution to the peak response of the CFST. Finally, a simple formula is developed based on the MLP model, which can cover three input parameters. The findings of this paper can be used for computer-aided dynamic response design of CFST columns subjected to blast loads.

Analisis Sistem Pendukung Keputusan Dalam Rekomendasi Kenaikan Pangkat PNS Menggunakan Kombinasi Metode TOPSIS dan SAW

PNS promotion is an award given to Civil Servants who have met the specified requirements as well as for work performance and service to the country. a Civil Servant is required to continue to be able to apply for promotions periodically to the highest level, so that within a certain period of time every civil servant gets the opportunity to apply for promotions. Similar to the scope of the East Kotawaringin Regional Secretariat, civil servants have the right to apply for a promotion every 4 years. The Staffing of the Regional Secretariat of the Kotim has difficulties in selecting the file for promotion proposals because of the large number of employees who propose promotions and the files and provisions gathered by civil servants must be managed immediately so that they are submitted to the BKPSDM. Researchers offer a decision support system with TOPSIS and SAW methods. The combination of the two methods serves to produce optimal decisions in accordance with predetermined criteria. This study uses 5 alternatives and 4 criteria. In this study, the alternatives consisted of Kurniawan Wibowo, Idris Sugiono, Nuringsih Sujati, Meuthia Rakhmasari, and Maulana. Of the five alternatives, those with the highest final score were Idris Sugiono, Nuringsih Sujati and Meuthia Rakhmasari with a value of 0.427 or 42.7%.

Research on network cloud equipment anomaly and root cause analysis

With the development of 5G communication technology, a cloud computing system has become a trend. However, with the expansion of the scale of deployment and the increase in framework complexity, ensuring the security and stability of cloud-based systems has become a serious challenge. In a real business environment, existing algorithms are powerless in the face of current problems, such as complex types of abnormal logs, inaccurate time information, and the lack of key information. This paper proposes network cloud equipment anomaly detection and a root cause analysis scheme based on large-scale logs in distributed cluster systems. The scheme uses unsupervised integrated learning, keyword search, and root cause generalization to analyze logs, accurately find anomalies and locate root causes. The F1 score in log anomaly detection is 0.962, and the accuracy in root cause location of anomalies is 0.849. In the ITU AI/ML in 5G Challenge 2021, the solution got the highest final score 93.904 in the China Mobile problem statement of network cloud equipment anomaly and root cause analysis. Furthermore, the scheme has been deployed on China Mobile's 5G network management system and achieves the detection and location of anomalies under intelligent operation.

The use of Adobe Connect in synchronous online teaching

The COVID-19 pandemic is a huge challenge to the world. It has interrupted many aspects of lives, especially education settings. Emergency remote education was put into practice to ensure the continuity of education while learners are physically out of universities, schools and institutes. The purpose of the present study was to investigate the effect of learners’ attitude using Adobe Connect on their final course scores in synchronous online teaching. 75 participants from Qazvin University of Medical Sciences and Health Services participated in this study. Data were gathered by means of a questionnaire and final course exam. A one-way ANOVA was used in order to analyze the data. Results revealed that the attitude level significantly affected the participants’ final scores (F = 18.503, p &lt; .01). The participants who had higher attitude levels had higher final scores.

IMMUNOHISTOCHEMICAL EXPRESSION LEVELS OF CYCLIN E1 IN THREE HISTOLOGICAL GRADES OF ORAL SQUAMOUS CELL CARCINOMA

Objective: The objective of this study is to assess and compare the immunohistochemical expression levels of Cyclin E1 in three histological grades of oral squamous cell carcinoma. Material and Methods: A descriptive cross-sectional study was designed. Sample size was 90 and categorised into three groups as i.e., well, moderate and poorly differentiated oral squamous cell carcinoma (OSCC) with 30 cases in each group. Specimens were collected from Histopathology Laboratories at Pakistan Institute of Medical Sciences (PIMS) and Foundation University Dental College and Hospital (FUDC&amp;H) Islamabad, to evaluate the expression levels of Cyclin E1 (CCNE1) with the help of immunohistochemical (IHC) staining. Statistical analysis was performed using the statistical analysis software, SPSS version 25. Results: In OSCC cases, average age of patients was 61.1±10.2 years. Over all age range of patients was 40-79 years (min – max) which was statistically significant (p=0.02). Male to female ratio was 1.1:1 (p=0.003). The most common site of development of OSCC was Buccal mucosa (p= &lt; 0.001). According to immunoreactive scoring system (IRS) value of CCNE1 expression levels was low in 42.2% of cases and high in 57.7% of cases. The high final score of 6 and 9 was found in 80% cases of poorly differentiated OSCC cases and the results were found statistically significant (p&lt;0.001) Conclusion: It is concluded that increased expression levels of Cyclin E1 significantly correlated with lack of differentiation in OSCC. Key Words: Cyclin E1, Immunohistochemistry, Oral squamous cell carcinoma.

Comparing the Effect of Contextualized Versus Generic Automated Feedback on Students' Scientific Argumentation

This study uses a computerized formative assessment system that provides automated scoring and feedback to help students write scientific arguments in a climate change curriculum. We compared the effect of contextualized versus generic automated feedback on students' explanations of scientific claims and attributions of uncertainty to those claims. Classes were randomly assigned to the contextualized feedback condition (227 students from 11 classes) or to the generic feedback condition (138 students from 9 classes). The results indicate that the formative assessment helped students improve their scores in both explanation and uncertainty scores, but larger score gains were found in the uncertainty attribution scores. Although the contextualized feedback was associated with higher final scores, this effect was moderated by the number of revisions made, the initial score, and gender. We discuss how the results might be related to students' familiarity with writing scientific explanations versus uncertainty attributions at school.

METHOD OF INCREASING MEAT PRODUCTIVITY AND ENVIRONMENTAL SAFETY OF BROILER MEAT BY FEEDING ADSORBENT AND ANTIOXIDANT

In recent years, the use of adsorbent and antioxidant preparations in the formulation of feed with a high content of heavy metals is a promising direction in the industrial production of broiler meat. The study aims to study the consumer and hygienic properties of broiler meat when including adsorbent Ferrotsin and antioxidant Selenopyran in diets with high heavy metals and aflatoxin B 1 content. The authors experimentally established the necessity of containing the adsorbent Ferrotsin in the dose of 300 g/t feed and the antioxidant Selenopyran in the quantity of 300 g/t feed into the composition of mixed fodder with excessive concentration of heavy metals. This inclusion of adsorbents and antioxidants in the compound feed composition is necessary to increase the nutritional properties and protective properties of poultry meat. Group 3 broiler chickens significantly exceeded (P &lt; 0.05) the control counterparts by 18.88 % by weight of half-gutted carcass and 18.26 % by weight of the gutted carcass. Also, broiler chickens of the 3rd experimental group exceeded the content of solids in the thoracic and thigh muscles by 1,40 and 0,60, and protein by 0,67 % and 0,67 %. Group 3 broilers significantly (P &lt; 0.05) exceeded the control in protein quality index (PQI) of meat by 18.86 %. In broilers of experimental group 3, there was a reliable (P &lt; 0.05) decrease in the level of zinc by 3.07, cadmium by 2.25 and lead by 3.24 times in the samples of breast muscle relative to the control counterparts. The highest final score was obtained by boiled white poultry meat of the 3rd experimental group. This sample of meat reliably (P &lt; 0.05) exceeded the total sum of organoleptic parameters of the control sample by 0.81 points. In broilers of experimental group 3 in examples of breast, thigh muscles and meat homogenate, the level of selenium was significantly higher than in control due to the presence of this substance in Selenopyran.

Quality of mental health services and rights of people receiving treatment in inpatient services in Finland: a cross-sectional observational survey with the WHO QualityRights Tool Kit

BackgroundThis article aims to review the quality of mental health services and the rights of the people receiving treatment in inpatient hospital care in Finland using the World Health Organization’s QualityRights Tool Kit as a part of a randomized controlled trial VIOLIN. So far, reports on the QualityRights Tool Kit have mainly been from low- and middle-income countries or countries lacking resources for health services. Reports from countries with well-resourced health care systems, such as the Nordic countries, are still quite few.MethodsA cross-sectional observational survey was conducted on 13 closed inpatient psychiatric wards (acute, rehabilitation, forensic psychiatric) at eight hospitals in Finland. The data for the survey were gathered through a document review, observations, and group interviews among staff members, service users and family members. The STROBE checklist for cross-sectional studies was followed in the reporting.ResultsFinnish mental health services are partially or fully achieving the standards set by the WHO QualityRights Tool Kit (final scores: 2.5–2.9 out of 3). The highest final score out of the five themes (2.9/3) was achieved under Freedom from torture or cruel, inhuman or degrading treatment or punishment and from exploitation, violence and abuse. The lowest final score out of the five themes (2.5/3) was achieved under the right to exercise legal capacity and the right to personal liberty and the security of person.ConclusionsAccording to the findings, Finnish mental health services appear to be of high quality. However, we have identified some gaps in quality, which we have addressed in a national randomized controlled trial VIOLIN. Improvements can be realized through shared decision making and relaying information to service users.

Model Penguatan Pendidikan Karakter pada Sekolah Perjumpaan di Nusa Tenggara Barat

The character of students should be able to describe the implementation of the educational process. However, so far, the educational process has emphasized more knowledge orientation than character building. It is reflected in the pride of almost every educational institution if its students succeed in achieving a high National Exam or UAN Final Score. This orientation is supported by the growth and development of tutoring institutions, private lessons, study groups, or teacher rooms. This condition triggers a real thought and action to restore the primary function of education as character building. One model of strengthening character education is through the community of Sekolah Perjumpaan in West Nusa Tenggara. This research is qualitative research with a case study approach. The subjects of this study were student participants in the Sekolah Perjumpaan in West Nusa Tenggara. This research was conducted at the community point of Sekolah Perjumpaan in Bangket Bilong Village, Midang, Mantang, and at the initiator of the Sekolah Perjumpaan. This study found that the Sekolah Perjumpaan is a model of strengthening character education, positioning students as subject to subject, and awakening students to fulfill each participant's obligations as complementary beings collectively. Thus, the character education model of the Sekolah Perjumpaan can be applied in other educational institutions.

Effect of educational intervention on clinical reasoning skills in nursing: A quasi-experimental study

This study evaluated the effect of an educational intervention based on virtual clinical simulation and problem-based learning using a mobile application in a clinical nursing education context as a tool to improve clinical reasoning skills of students on the second year of nursing graduation. A prospective quasi-experimental study was conducted in the year 2018, and assessments were performed before and after the educational intervention. A random convenience sample (n = 32) of nursing students in the second year of a public university in Brazil was divided equally into experimental and control groups. The experimental group underwent educational intervention about clinical reasoning skills and measured the quality of the Diagnostician Nurse software by LORI 2.0 instrument. The control group experienced the conventional class on clinical reasoning skills. Four clinical cases were used to assess reasoning skills before and after the educational intervention. The data were analyzed using descriptive and inferential statistics. The experimental group showed a statistically significant difference regarding the prioritization of nursing diagnoses (p = 0.014) and a higher final score. There was a statistically significant difference in performance between the pre- and post-test in the grades of the students who participated in the intervention (p = 0.003). The control group also showed statistical significance in the score attributed to the clinical reasoning process (p = 0.015). In addition, the Diagnostician Nurse software had excellent usability and quality evaluations (SUS 87.81 and LORI 4.66, respectively). It is concluded that educational intervention based on virtual clinical simulation and learning problems using the Diagnostician Nurse software is effective as a tool to improve clinical reasoning skills and can support early detection of patients. The educational intervention developed was of high quality and attractive and improved students' motivation for the teaching-learning process.

Supplier Selection Risk: A New Computer-Based Decision-Making System with Fuzzy Extended AHP

Supplier risks have attracted significant attention in the supply chain risk management literature. In this article, we propose a new computational system based on the ‘Fuzzy Extended Analytic Hierarchy Process (FEAHP)’ method for supplier selection while considering the relevant risks. We sought to evaluate the opportunities and limitations of using the FEAHP method in supplier selection and analyzed the support of the system developed through the real case of a Brazilian oil and natural gas company. The computational approach based on FEAHP automates supplier selection by determining a hierarchy of criteria, sub-criteria, and alternatives. First, the criteria and sub-criteria specific to the selection problem were identified by the experts taking the relevant literature as a starting point. Next, the experts performed a pair-wise comparison of the predefined requirements using a linguistic scale. This evaluation was then quantified by calculating the priority weights of criteria, sub-criteria, and alternatives. The best decision alternative is the one with the highest final score. Sensitivity analysis was performed to verify the results of the proposed model. The FEAHP computer approach automated the supplier selection process in a rational, flexible, and agile way, as perceived by the focal company. From this, we hypothesized that using this system can provide helpful insights in choosing the best suppliers in an environment of risk and uncertainty, thereby maximizing supply chain performance.

Valuation of a man-made dipterocarp forest as seed source for Shorea red meranti (Dipterocarpaceae)

Lack of good quality seeds has been one of the constraints in icreasing the success rate of rehabilitation, restoration and establishment of plantation forest. Establishing seed sources is a way to increase the availability of qualified seeds for various purposes. Gunung Dahu Research Forest (GDRF) has Shorea pinanga and Shorea platyclados (Dipterocarpaceae) stands that has potential to meet the standard of identified seed stands (ISS). We evaluate two stands of S. pinanga (plot 05 and 24) and four stands of S. platyclados (plots 04, 15, 20, and 21e) to be quantified for their potential as ISS. Assessment was carried out by evaluating the parameters related to number of mother trees, accessibility, stand quality, management, fruiting history, and origin. Among all assessed plots, highest final score was obtained by plot 20 (131.5), followed by plot 24 (112.5), plot 05 (105.5), plot 04 (87), plot 21e (85), and plot 15 (57). Based on the minimum score of 94.5, the potential plot that can be assigned as ISS are plot 05, plot 24 and plot 20.

The comparison of numerous machine learning algorithms performance in classifying rice growth stages based on Sentinel-2 to enhance crop monitoring in national level

The rice monitoring based on Sentinel-2 (SC-S2) has been developed for over nine months. It has been observed as the first and only system which generate rice growth stages maps in 10 m spatial resolution using machine learning in Indonesia. However, the SC-S2 use Support Vector Machine to separate the rice growth stages, which may have poor performances. The objective of this study is to investigate the performance of other classifiers to increase the performance of SC-S2. We used survey data from the field campaign in 2018 and synchronized with Sentinel-2 bands. The model dataset was trained using 61 machine learning algorithms to create 61 rice growth stages models. The models were applied to the Sentinel-2 image of part of Indramayu area. The accuracy, computational time and visual inspection score were collected, and the final score was calculated. The results are the highest final score is Shrinkage Discriminant Analysis, with overall accuracy 88.1% (p<0.001) and the average accuracy of all classifiers is 76.2% (p<0.05). The implication of this study is to propose some changes in the classification process into the SC-S2 for increasing the overall performance, which will provide better information for agricultural policymakers.

EVALUATION OF THE INTEGRATED TEACHING PROFESSIONAL PRACTICE (PPKT) PROGRAM FOR STUDENTS OF EDUCATIONAL MANAGEMENT, FITK UIN SYARIF HIDAYATULLAH JAKARTA

This study aims to describe the suitability and benefits of the Integrated Teaching Professional Practice (PPKT) Program for Educational Management (EM) students in developing their competencies. The approach used is program evaluation using the Context-Input-Process-Product (CIPP) model which aims to collect accurate and comprehensive information so that it can be used to assist leaders to make further decisions to redesign, improve, or possibly stop the implementation of the program. The results showed that the PPKT program for EM students was not on target and needed to be stopped immediately or replaced with another program whose value was equivalent because: (1) Theoretically contextual, the PPKT was intended for students from study-based study programs so that it was not suitable for EM students; (2) From the Input aspect, students face the constraints of planning, implementing and evaluating learning, the weak competency of supervisors because their educational background is not from an education degree, teaching staff lack competency; (3) Based on the Process aspect, the PPKT implementation is not appropriate expected especially in teaching and research activities because the supervisor and tutor are not able to carry out their duties and functions properly; and (4) From the Product aspect, PPKT has not been able to improve teaching competence, educational research, educational creation and innovation. The acquisition of a high final score does not reflect their competence because more assessment is based on the subjectivity of tutors and tutors. Based on the results of the study, it was suggested that the Head of the EM Department need to reformulate the vision, mission and competency standards of graduates who are more referring to the competence of education staff, encouraging faculty leaders to immediately issue a decision to terminate the PPKT program for EM students and replace it with other programs with the same weight as PPKT.Keywords: context, input, process, product, PPKT.

English

The processes of maturation, harvest and post-harvest coffee of are strictly related to the physical and chemical modifications that can affect the sensorial quality of the coffee, being decisive factors in the choice of the appropriate management to reach the desired quality standard. The objective of this research was to identify the sensorial attributes of the Coffee Association of American (SCAA) protocol of coffee cultivars grown in the same geographic space, with fruit collection on two faces of exposure to solar radiation from the same plants subsequent post-harvest processing by wet and dry route. The study was conducted in Minas Gerais, in the city of Monte Carmelo. Six cultivars of Coffea arabica L. species were evaluated. The fruit collection in two exhibitions faces the solar radiation from the same plants with subsequent post-harvest processing via wet and dry. In view of the results it can be concluded that there was a better discrimination of sensory attributes among postharvest cafes obtained in the sun face plant processes, the cultivar Obata had the highest final score between the years of assessment and all cultivars showed potential for the production of specialty coffees, with the lowest scores attributed to genetic material Iapar-59. Key words: Coffeea arabica L., specialty coffees, sensory evaluation, specialty coffee association of America, quality.