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This paper presents a new method for quantifying and allocating the effects of distributed generation (DG) in distribution systems. The basic concept is the identification, accounting and sharing of what is referred to functions in the proposed methodology, which represent chargeable properties and are considered fair when investigating DG's remuneration. In this work, the functions that will be evaluated represent the impact of DG on the feeder usage, electrical losses, and peak demand of the distribution network. The allocation among local generating sources is performed using the Shapley value from the cooperative game theory. The proposed method is applied to the IEEE 33 bus test system and the results are fully discussed.

Gamification has been discussed as a standout approach to improve user experience, with different studies showing that users can have different preferences over game elements according to their user types. However, relatively less is known how different kinds of users may react to different types of gamification. Therefore, in this study ($$N=331$$ N = 331 ) we investigate how user orientation (Achiever, Disruptor, Free Spirit, Philanthropist, Player, and Socializer) is associated with the preference for and perceived sense of accomplishment from different gamification designs. Beyond singular associations between the user orientation and the gamification designs, the findings indicate no comprehensive and consistent patterns of associations. From the six user orientations, five presented significant associations: Socializer orientation was positively associated with Social, Fictional, and Personal designs, while negatively associated with Performance design; Player orientation was positively associated with Social (Accomplishment), Personal, and Ecological designs, while negatively associated with the Social design (Preference); Disruptor orientation was positively associated with Social design; Achiever orientation was positively associated with Performance and Social designs; and Free Spirit orientation was negatively associated with Social design. Based on the results, we provide recommendations on how to personalize gamified systems and set further research trajectories on personalized gamification.

Teachers and learners who search for learning materials in open educational resources (OER) repositories greatly benefit from feedback and reviews left by peers who have activated these resources in their class. Such feedback can also fuel social-based ranking algorithms and recommendation systems. However, while educational users appreciate the recommendations made by other teachers, they are not highly motivated to provide such feedback by themselves. This situation is common in many consumer applications that rely on users’ opinions for personalisation. A possible solution that was successfully applied in several other domains to incentivise active participation is gamification. This paper describes for the first time the application of a comprehensive cutting-edge gamification taxonomy, in a user-centred participatory-design process of an OER system for Physics, PeTeL, used throughout Israel. Physics teachers were first involved in designing gamification features based on their preferences, helping shape the gamification mechanisms likely to enhance their motivation to provide reviews. The results informed directly the implementation of two gamification elements that were implemented in the learning environment, with a second experiment evaluating their actual effect on teachers’ behaviour. After a long-term, real-life pilot of two months, teachers’ response rate was measured and compared to the prior state. The results showed a statistically significant effect, with a 4X increase in the total amount of recommendations per month, even when taking into account the ‘Covid-pandemic effect’.

The 22q11.2 deletion syndrome (22q11DS) is associated with a 20-25% risk of schizophrenia. In a cohort of 962 individuals with 22q11DS, we examined the shared genetic basis between schizophrenia and schizophrenia-related early trajectory phenotypes: sub-threshold symptoms of psychosis, low baseline intellectual functioning and cognitive decline. We studied the association of these phenotypes with two polygenic scores, derived for schizophrenia and intelligence, and evaluated their use for individual risk prediction in 22q11DS. Polygenic scores were not only associated with schizophrenia and baseline intelligence quotient (IQ), respectively, but schizophrenia polygenic score was also significantly associated with cognitive (verbal IQ) decline and nominally associated with sub-threshold psychosis. Furthermore, in comparing the tail-end deciles of the schizophrenia and IQ polygenic score distributions, 33% versus 9% of individuals with 22q11DS had schizophrenia, and 63% versus 24% of individuals had intellectual disability. Collectively, these data show a shared genetic basis for schizophrenia and schizophrenia-related phenotypes and also highlight the future potential of polygenic scores for risk stratification among individuals with highly, but incompletely, penetrant genetic variants.Polygenic risk scores are nearing a level of differentiation required for their clinical utility in risk prediction in populations with high-risk rare pathogenic genetic variants.

Background Neurosurgical management of traumatic brain injury (TBI) is challenging, with only low-quality evidence. We aimed to explore differences in neurosurgical strategies for TBI across Europe. Methods A survey was sent to 68 centers participating in the Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. The questionnaire contained 21 questions, including the decision when to operate (or not) on traumatic acute subdural hematoma (ASDH) and intracerebral hematoma (ICH), and when to perform a decompressive craniectomy (DC) in raised intracranial pressure (ICP). Results The survey was completed by 68 centers (100%). On average, 10 neurosurgeons work in each trauma center. In all centers, a neurosurgeon was available within 30 min. Forty percent of responders reported a thickness or volume threshold for evacuation of an ASDH. Most responders (78%) decide on a primary DC in evacuating an ASDH during the operation, when swelling is present. For ICH, 3% would perform an evacuation directly to prevent secondary deterioration and 66% only in case of clinical deterioration. Most respondents (91%) reported to consider a DC for refractory high ICP. The reported cut-off ICP for DC in refractory high ICP, however, differed: 60% uses 25 mmHg, 18% 30 mmHg, and 17% 20 mmHg. Treatment strategies varied substantially between regions, specifically for the threshold for ASDH surgery and DC for refractory raised ICP. Also within center variation was present: 31% reported variation within the hospital for inserting an ICP monitor and 43% for evacuating mass lesions. Conclusion Despite a homogeneous organization, considerable practice variation exists of neurosurgical strategies for TBI in Europe. These results provide an incentive for comparative effectiveness research to determine elements of effective neurosurgical care. © The Author(s) 2018 This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Gamification design has benefited from data-driven approaches to creating strategies based on students characteristics. However, these strategies need further validation to verify their effectiveness in e-learning environments. The exploratory study presented in this paper thus aims at verifying how data-driven gamified strategies are perceived by the students, i.e., the users of e-learning environments. In this study, we conducted a survey presenting 25 predefined strategies, based on a previous study, to students and analysed each strategys perceived relevance, instanced in an e-learning environment. Our results show that students perceive Acknowledgement, Objective and Progression as important elements in a gamified e-learning environment. We also provide new insights about existing elements and design recommendations for domain specialists.

This work presents a new probabilistic methodology for cost allocation of transmission systems, considering the intermittency of the wind power source. The proposed algorithm inserts a nodal transmission pricing scheme in a chronological simulation environment, which allows analyzing the behavior of transmission charges against the variable power output of a wind power plant. The aim is to calculate an equivalent tariff for each market participant taking into account the systems operational reality. The proposed method is applied to the IEEE RTS considering a modified configuration with insertion of a wind power plant.

Greetings to all ENT Fellows! When I became a member of the Board of Trustees during the Presidency of Dr. Cesar Anthony Yabut in 2012, I surmised how concerned and dedicated the Board was in supporting and funding researches of fellows-- whether they were descriptive, analytical, surgical innovations and instrumentations -- through the PSO-HNS research committee. Amendments to the research paper contest guidelines were made to give way to the advent of information technology in synchronization with the electronic journal management system of the Philippine Journal of Otolaryngology Head and Neck Surgery (PJOHNS). Online review and corrections were implemented to maximize time and efforts of the judges and the authors. This was given emphasis during the administration of Dr. Wilfredo Batol in 2013. Well, these are all done for academic excellence. Under Dr. Howard Enriquez’ administration in 2014, research still remained one of the priorities of PSO-HNS. Each and every paper submitted passed through the eye of the needle of the editorial board of PJOHNS. It may be a lot of hard work and effort for the researchers, judges and editors but it is worth the hard work. PERSEVERANCE is the key! Just choose a “worth-knowing” topic and write a “worth-publishing” paper. If it is rejected, just try again. Be patient with the corrections and revisions-- in the end, the fruit of your labor is a sweet success. An ORL-HNS specialist should be equipped with this trait to really cope with the ever changing needs of our society whether it is surgically or research oriented as evidenced by our very own PJOHNS. It is with great pride and honor that I render a message in our prestigious journal. I would like to take this opportunity to express my sincere gratitude and congratulations to my colleague, Dr. Joey Lapena, and the same is true for the editorial staff and members for their never ending dedication and hard work in making a journal that is equally competitive and accepted in the 21st Century.

The impact of gamification has been typically evaluated via self-report assessments (questionnaires, surveys, etc.). In this work, we analise the use of gamification elements as parameters, to predict whether students are going to fail or not in a programming course. Additionally, unlike prior research, we verify how usage of gamification features can predict student performance not only as a discrete, but as a continuous measure as well, via classification and regression, respectively. Moreover, we apply our approach onto two programming courses from two different universities and involve three gamification features, i.e., ranking, score, and attempts. Our results for both predictions are notable: by using data from only the first quarter of the course, we obtain 89% accuracy for the binary classification task, and explain 78% of the students’ final grade variance, with a mean absolute error of 1.05, for regression. Additionally and interestingly, initial observations point also to gamification elements used in the online judge encouraging competition and collaboration. For the former, students that solved more problems, with fewer attempts, achieved higher scores and ranking. For the latter, students formed groups to generate ideas, then implemented their own solution.

Palicourea species has been used in folk medicine in the treatment of some diseases including cancer and inflammatory disorders. This work aimed to evaluate the in vitro antiproliferative and in vivo topical anti-inflammatory activities of the methanolic extract, fractions and two major alkaloids isolated from Palicourea minutiflora. Methanolic extract, non-alkaloidal and alkaloidal fractions exhibited strong growth inhibition for ovarian cell lines (OVCAR-3, GI50 = 3.8 at 16.3 µg mL−1) and the vincosamide alkaloid revealed selective effect on the growth of glioma cell lines (U251, GI50 = 33.0 µg mL−1) compared with doxorubicin (DOX, GI50 = 0.42 and 0.025 µg mL−1, respectively) anticancer drug. Methanolic extract, fractions and strictosidinic acid showed significant inhibitory effect with 62.7% at 77.5% (p < 0.05) to ear edema induced by croton oil and 81% at 100% (p < 0.05) to myeloperoxidase assay compared with indomethacin (positive control) 68.4% and 91.3% (p < 0.05), respectively.

This work proposes an efficient method for solving the generation maintenance scheduling (GMS) problem, defined through an interactive process carried out between the independent system operator (ISO) and generation companies (GENCOs). In this problem, starting from a schedule previously informed by GENCOs, it is desired to minimize the expected costs of production and system load shedding for the period of analysis from the ISO point of view. The Evolution Strategy metaheuristic is used to solve the resulting optimization problem. The non-sequential Monte Carlo simulation and Cross-Entropy methods are combined to efficiently assess the maintenance schedules searched during the solving process. Uncertainties related to the behavior of load, unavailability of generation equipment, and variability of renewable energy sources are taken into account in the modeling and solution of the GMS problem. The performance of the proposed method is tested with the IEEE-RTS modified with the inclusion of renewable sources.

This work presents a new method to evaluate generation reserve margins in systems with renewable sources. In assessing the adequacy of generation reserve amounts, besides failures in generating units, their capacity intermittencies, unavailability, and capacity limits of the transmission system are duly considered. Risk indices are evaluated using quasi-sequential Monte Carlo simulation techniques. The cross-entropy method is used to treat rare events and also to identify critical equipment for operation in each scenario. The proposed method is applied to the original IEEE RTS system and to a modified configuration with insertion of wind power plants. A subsystem of the Brazilian interconnected network is also used to illustrate the practicality of the proposed method.

Two different approaches, PLS regression and neural networks, were compared for monitoring the quality of grapes using sugar content predictions based on hyperspectral imaging. The present work expands the result analysis and updates the state-of-the-art published in a conference article of the authors which concern the prediction of sugar content for vintages not used in model creation when the measured samples are composed of a small number of whole berries. This is highly innovative. The prediction models were established upon training under each approach and the generalization ability of both methodologies was determined through using n-fold-Cross-Validation and test sets. Sugar content was estimated using a model trained with spectra from samples of 2012. The test sets were composed of samples with six whole berries of 2012 or 2013 vintages. The results for PLS regression and Neural Networks for a test set with 2012 samples, were 0.94 °Brix and 0.96 °Brix for the root mean square error (RMSE), and 0.93 and 0.92 for squared correlation coefficients (R2), respectively, for each approach. When using test data containing 2013 samples, the RMSE values were 1.34 °Brix and 1.35 °Brix, and the R2 values were 0.95 and 0.92. These errors are competitive with those of works from other authors executed under less demanding conditions. The results obtained suggest that when combining hyperspectral imaging with appropriate chemometric techniques or machine learning algorithms, it is possible to have a satisfactory generalization for vintages not employed in model creation.

Elbow joint incongruity is recognized as an important factor in the development, treatment, and prognosis of canine elbow dysplasia. Elbow incongruity has been measured based on radiographic joint space widths, however these values can be affected by the degree of elbow joint flexion. Recent studies have reported radiographic curvature radii as more precise measures of humeroulnar congruity in dogs. The aim of this prospective observational study was to describe radiographic curvature radii measured from flexed and extended elbow radiographs for a sample of dogs representing a medium breed (Portuguese Pointing Dog) and a large breed (Estrela Mountain Dog). The curvature radii from the ulnar trochlear notch and humeral trochlea were measured in 114 mediolateral elbow extended radiographic views (30 Portuguese Pointing Dog and 27 Estrela Mountain Dog), and 84 mediolateral flexed views (22 Portuguese Pointing Dog and 20 Estrela Mountain Dog). The sampled animals' ages ranged from 12 to 84 months (34.6 ± 17.8 months). Good agreement was observed between curvature radii measurements for flexed vs. extended views in both breed groups. Ulnar trochlear notch curvature radii measurements were greater than humeral trochlea curvature radii measurements in both breed groups. Both curvature radii were greater in the large-breed dog group vs. the medium-breed dog group. Both breed groups had ulnar and humeral curves with similar typology. However, the large breed group had greater intermediate differences between the humeroulnar surface curvature radii. Results from this study supported the use of curvature radii as measures of humeroulnar congruity in mediolateral flexed elbow radiographs of medium and large breed dogs.

Summary Objective: Assess the fit between the ulnar trochlear notch (UTN) and humeral trochlea (HT) in elbow radiographs and computed tomography (CT) images by measuring the curvature radii in normal joints. Methods: Mediolateral extended view (MLE) and CT were performed on 16 elbows from cadaveric dogs weighing over 20 kg. Curves were traced at the subchondral level from the UTN central ridge and the HT sagittal groove related to the UTN on ~132° joint extension. Curvature radii were achieved using appropriate computer software. Results: Intraclass correlation coefficient between MLE/CT measurements were statistically significant, with lower limits of a 95% confidence interval (CI) >0.75. The mean differences between MLE/CT measurements were -0.71 mm (95% CI: -0.97 to -0.45) for UTN and -1.04 mm (95% CI: -1.21 to -0.87) for HT. The UTN and HT curvature radii typology were similar, with maximum radius values at the starting point and two intermediate peaks. The UTN curvature radii were bigger than the HT radii, with the largest differences in the most proximal aspect of the joint and in the second intermediate peak. Clinical significance: To the authors’ knowledge, this is the first report on humeroulnar congruity through curvature radii evaluation. The software and methodology proposed enabled an adequate UTN and HT radii curvature assessment in MLE and CT images. Comparative studies in normal and dysplastic dogs of various breeds could better clarify the role of UTN and HT curvature radii in elbow incongruity and dysplasia.

Currently, the use of applications that requiring biometric facial authentication is increasing. However, the performance of these systems is compromised when the images do not present the quality required for their operation. Considering this, in the present work, a Naive Bayes classifier was developed for image quality assessment (IQA) dedicated to facial biometry systems. The metrics that characterize the image according to blur, sharpness and focus are used as input variables. A bibliographical study is carried out on the subject, deepening in the characteristics of the Bayesian classifiers. A public database is used to create the statistical data with which the Naive Bayes classifier is developed. Finally, the results of the classifier are presented for a set of conditions of the predictor variables.

Gamification has been widely employed in the educational domain over the past eight years when the term became a trend. However, the literature states that gamification still lacks formal definitions to support the design of gamified strategies. This paper aims to create a taxonomy for the game elements, based on gamification experts' opinions. After a brief review from existing work, we extract first the game elements from the current state of the art, and then evaluate them via a survey with 19 gamification and education experts. The resulting taxonomy taxonomy included the description of 21 game elements and their quantitative and qualitative evaluation by the experts. Overall, the proposed taxonomy was in general well accepted by most of the experts. They also suggested expanding it with the inclusion of Narrative and Storytelling game elements. Thus, the main contribution of this paper is proposing a new, confirmed taxonomy to standardise the terminology used to define the game elements as a mean to design and deploy gamification strategies in the educational domain.

Gamification applied to education studies are focusing to encourage students to perform specific tasks, however many of these studies are still inconclusive about how much gamification can influence engagement. Also, the frameworks used to apply gamification in those systems are mainly structural (e.g. scoring and ranking systems) rather than content frameworks (where the game elements are applied to the content). Therefore, this paper aims at creating a narrative definition exclusively for gamification purposes. First, we developed an empirical research starting with a literature review of the narrative concept in other medias, such as games. Then, we mapped these definitions into features and crossed the similarities and differences across them, to find a common ground that could be applied in gamification contexts. Our results show that the narrative element definition to use in gamification contexts best drifts from the games ones, however its definition is unique to the point it could be isolated and used in future frameworks. We also found that the characteristics of this concept resembles some of User Experience.

As maritime smuggling is being combatted more effectively, the criminal “modus operandi” consists more frequently of using small aircraft and drones for drug transport. To address this issue, we report our efforts to develop a system capable of accurately tracking suspicious flying objects and identifying them on video streams. Our solution consists in coupling classical computer vision with deep learning to perform tracking and object detection. A discrete Kalman filter is used to predict the location of each object being tracked while the Hungarian algorithm is used to match objects between successive frames. Whenever a potential target is considered suspicious the input images are zoomed and fed into a deep learning pipeline that separates images into the classes aircraft, drones, birds or clouds. A literature survey indicates that this problem with important applications is yet to be fully explored.

This paper presents an experience report concerning the use of a platformcalled “Storium” in the subject of “Interactive Fiction” for undergraduatestudents, from a Digital Design Course. The objective was to use the learningtheories of constructivism and multimedia learning to create an instructionalplan devised to teach the students how to create complex interactive narrativesand stories from a practical perspective. During the course, the students learnedthe subject’s theoretical concepts and applied them directly, creating their owninteractive fiction. The results from this research proposes a new approach, usingdigital tools whose resources provides an environment for the creation ofinteractive narratives. These narratives can be used to aid future designs ofinstructional plans for complex writing concepts.

Medicinal plants have been used for millennia by different cultures, such as those from China, India, Greece, and Rome. Over time, they have been used to treat various health conditions, such as diabetes mellitus, arthritis, high blood pressure, microbial infections, and cancer. Thanks to technological and scientific advances, the identification of their bioactive compounds has allowed in vitro, in vivo, and clinical studies to prove their efficacy and mechanism of action in the treatment of various diseases, including cancer. Losses in the battle against cancer, of modern medicine, have led some cancer patients to use various medicinal plants to increase their life expectancy or to reduce the side effects of chemotherapy. However, for the use and recommendation of any compound with biological activities, it is necessary to understand its effect and mechanisms of action. For this reason, this chapter aims to summarize the different anticancer activities, their mechanism of action, and possible side effects, of four of the main medicinal plants: Curcuma longa, Zingiber officinale, Moringa oleifera, and Azadirachta indica.

Bioactive compounds are essential and nonessential substances (e.g., vitamins and phytochemicals) that are found in nature or are created by the processing of foods or plants and are capable of exerting health benefits. Currently, there is a large and growing amount of scientific evidence demonstrating the utility of various active compounds in different health and disease situations. These compounds exert different beneficial effects, among them: antiinflammatory, antioxidant, lipid-lowering, antihypertensive, and regulating the expression of a great variety of genes. Thus, offering several health benefits, including: antiaging, prevention of cardiovascular diseases, and protection against chronic diseases such as diabetes mellitus, cancer, and neurodegenerative diseases. This chapter aims to summarize part of the biological activity of compounds of multiple compounds, in the precise situations mentioned.

The metabolic syndrome (MS) is considered an important worldwide topic since it is a complex disease. The MS is a disease set related with proinflammatory process that directly increases the risk of type 2 diabetes mellitus and cardiovascular diseases. Chronic inflammation has been associated with insulin resistance, endothelial dysfunction, visceral obesity, which is characterized by abnormal production of proinflammatory cytokines. Gut microbiota exerts a role in the MS. Changes in the diet and lifestyle affect the composition and functions and disrupt the gut microbiota balance. These changes have been associated with systemic inflammation and insulin resistance, characterized in MS. Modulation of the gut microbiota through the consumption of probiotic beverages is an alternative to reduce intestinal inflammation and improve the gut barrier integrity. Several studies have demonstrated that probiotic, in particular Lactobacillus and Bifidobacterium, exert multiple beneficial effects on subjects with MS. There are currently several probiotic products on the market that offer the consumer an alternative to prevent disease and promote health. The main purpose of this chapter is to give a review of the gut microbiota modifications on people with MS, and know the role of probiotic beverages in the chronic inflammatory process, insulin resistance, and the microbiome balance reestablishment. Likewise, probiotic beverages have an important contribution to the dietetic treatment of MS.

Gamification planning has been a topic of discussion in the last years since it can be used to increase performance, engagement, and motivation of end users. When properly applied in educational settings, gamification can lead to better learning. Furthermore, it can be boosted when tied to social networks. However, according to the literature, there are three main concerns regarding this topic: (a) instructors and teachers does not have the resources to plan and develop gamification strategies into their classes; (b) gamification needs a systematic approach to achieve the desired positive results; and (c) inexistence of systematic approaches that connect and help in the design of gamification and social network tasks within these contexts. Thus, this work proposes a solution to help instructors and teachers to plan and deploy gamification concepts with social network features in learning environments. In this paper, we detailed our approach depicting the set of items to analyze and compare it with other solutions that are focused on education. Then, it was conducted a case study over a programming course (N = 40) to analyze the planning and deployment phases. Our results demonstrated that our approach is the first to consider the stakeholders (i.e. instructors and teachers) as part of the process. Moreover, even though there are still some obstacles to overcome, the gamified strategies that were created achieved positive acceptance among the students and professor.

Lamb muscle discrimination is important for the meat industry due to the different pricing of each type of muscle. In this paper, we combine hyperspectral imaging, operating in the wavelength range 380–1028 nm, with several machine learning algorithms to deal automatically with the classification of lamb muscles. More specifically, we study the discrimination of four different lamb muscles, namely, Longissimus dorsi, Psoas major, Semimembranosus and Semitendinosus from thirty lambs of Churra Galega Mirandesa breed. The objective of the paper is to determine the best method for muscle classification. In the experimental study we report an analysis of the performance of seven classifiers. We study their behavior when they are applied over the original data as well as over the data pre-processed using Principal Component Analysis (PCA) to reduce the dimensionality of the problem. The seven classifiers used to differentiate the muscle types are two Artificial Neural Networks, namely the linear Least Mean Squares (LMS) classifier and the Multilayer Perceptron with Scaled Conjugate Gradient (MLP-SCG), two Support Vector Machines (SVM), namely the ν SVM and the SVM trained with Sequential Minimal Optimization (SMO), the Logistic Regression (LR), the Center Based Nearest Neighbor classifier and the Linear Discriminant Analysis. The best result, determined using a leave-one-animal-out scheme, is provided by the linear LMS classifier using the original data, since it correctly classifies 96.67% of the samples. The LR, the MLP-SCG using original data and the SVM trained with SMO on data preprocessed with PCA are also suitable techniques to tackle the lamb muscle classification problem.

This paper proposes a methodology to evaluate the spinning reserve requirements of generating systems, taking into consideration the loss of load risk due to failures in the generation and transmission network. In order to provide a more flexible and robust tool to assess the risk indices for bulk power systems, a Monte Carlo simulation based tool is proposed. Due to the rarity of the events associated with this particular problem, an unbiased estimator based on importance sampling is applied, and the set of optimal parameters is obtained using a cross-entropy method. The proposed methodology is capable of assessing the operating reserve requirements, especially the one that is synchronized, to cope with failures and capacity restrictions of both generating and transmission systems. The IEEE-RTS (reliability test system) is used to test the proposed methodology and also some modifications of this system are created to fully verify the ability of the proposed approach to satisfactorily respond to the transmission constraints.

Objectives: Axial flap surgery is associated with numerous complications. The purpose of this study is to determine the frequency of these complications, and identify possible factors contributory to their occurrence. Methods: Study design: Cross-sectional study Setting: Tertiary public university hospital Subjects: Records of all patients who underwent axial pedicled flap reconstruction at the otorhinolaryngology ward of our tertiary public university hospital from January 2013 to July 2015 were retrospectively reviewed, and data consisting of age, sex, diagnosis, disease stage, smoking history, alcohol intake, co-morbidities, past operations, pre-operative hemoglobin and albumin, total operative time, total blood loss, location and total area of the surgical defect, and length of hospitalization were tabulated. All complications were listed. Data were analyzed for any potential trends. Results: A total of 38 patients underwent axial pedicled flap reconstruction in the study period. Nineteen out of 38 (50%) cases involved complications. The most common complication was infection. Most of the complications occurred in males with history of alcohol intake, advanced cancer stage, significant blood loss, recurrent tumors, low pre-operative hemoglobin and albumin levels, and a large area of surgical defect. Conclusions: The complication rate for axial flap surgery in our series was significant at 50%. Potential risk factors identified were male gender, advanced cancer stage, tumor recurrence, alcohol intake, low pre-operative hemoglobin and albumin levels, significant blood loss, longer operative time, and a larger surgical defect. Keywords: surgical flaps, myocutaneous flap, axial flap, complications, risk factors

This paper proposes a new and practical probabilistic method for calculating reliability indices and costs associated with distribution substations that share spare transformers of a same group. Based on Monte Carlo simulation, the proposed algorithm considers the chronological representation of failure occurrences, and enhances previous methods by modeling emergency actions, such as transferring load to neighboring feeders and connecting a mobile unit substation to ensure electricity supply to customers during the replacement of failed transformers. Thus, spare transformer requirements with new acquisitions to replenish the stock or even to expand the system are probabilistically assessed. All waiting times associated to these actions can be duly represented by any probability distribution, including the usual exponential model. A more accurate calculation of reliability indices and costs associated with the interruption of consumers is achieved. The corresponding computational algorithm is used in several numerical applications, considering a class of transformers of a Brazilian electricity utility. The results of these applications are deeply discussed to demonstrate the practical relevance of the proposed method.

In the present work, the artificial intelligence is used, through neural networks, in the diagnosis of power transformers for the interpretation of the results obtained in the frequency response analysis test. The results of the classification of three types of failures in this technique are exposed. The characteristics of the statistical indicators that function as input variables of the neural network and the reason for implementing a multilayer network with backpropagation algorithm, in the training are exposed. The results of the discrimination of acceptable or not acceptable state of the transformer are presented and if it has any of the following faults: open winding, short circuit winding and winding with a point to ground. The response of the neural network is determined in case of study of real transformers.

The role of computing in society is fundamental for the political, economic and cultural development of countries, especially in a globalized context. Computational thinking is an extremely important skill in the education of young students, since the ability of intuitively abstracting information and knowledge promotes an expansion of the learning dimension. This study aims to gather approaches used for teaching computational thinking exclusively to high school students, highlighting their performance and motivation resulting from the applications of such approaches. In order to answer our research question, a systematic mapping of the analysis of 15 studies was conducted. The use of computer programming proved to be the most used way to disseminate computational thinking. The students who participated in approaches regarding the insertion of computational thinking in the classroom showed positive impact on the development of classroom activities, as well as motivation to learn.

This work presents a new method to evaluate generation reserve margins in systems with renewable sources. In assessing the adequacy of generation reserve amounts, besides failures in generating units, their capacity intermittencies, unavailability and capacity limits of the transmission system are properly considered. Risk indices are evaluated using quasi-sequential Monte Carlo simulation techniques. The cross-entropy method is used to treat rare events and also to rank critical equipment for operating reserve requirements in each scenario. The proposed method is applied to the IEEE RTS system considering a modified configuration with insertion of wind power plants.

Objective Although audio and visual information constitute relevant channels to communicate pain, it remains unclear to what extent observers combine and weight these sources of information when estimating others' pain. The present study aimed to examine this issue through the theoretical framework of the Information Integration Theory. The combination and weighting processes were addressed in view of familiarity with others' pain. Method Twenty-six participants familiar with pain (novice podiatry clinicians) and thirty non-specialists were asked to estimate the level of pain associated with different displayed locomotor behaviors. Audio and visual information (i.e., sound and gait kinematics) were combined across different intensities and implemented in animated human stick figures performing a walking task (from normal to pathological gaits). Results The novice clinicians and non-specialists relied significantly on gaits and sounds to estimate others' pain intensity. The combination of the two types of information obeyed an averaging rule for the majority of the novice clinicians and an additive rule for the non-specialists. The novice clinicians leaned more on gaits in the absence of limping, whereas they depended more on sounds in the presence of limping. The non-specialists relied more on gaits than on sounds. Overall, the novice clinicians attributed greater pain levels than the non-specialists did. Conclusion Depending on a person's clinical experience, the combination of audio and visual pain-related behavior can qualitatively change the processes related to the assessment of others' pain. Non-verbal pain-related behaviors as well as the clinical implications are discussed in view of the assessment of others' pain.

Food-derived proteins and peptides play an important role in functional foods because their ability to modulate the metabolism and act as regulatory compounds suggest their potential use as nutraceutical and functional food ingredients to prevent and treat diseases. Protein hydrolysates have been used to decrease allergies of certain native proteins, provide nutrients and produce peptides with specific biological effects: antihypertensive, anti-inflammatory, hypolipidemic, hypoglycaemic, antioxidant, antimicrobial and antithrombotic. Peptides with antioxidant, immunomodulatory and anticancer activity, are a dietary alternative to conventional approaches (e.g. chemotherapy, radiotherapy) used to prevent and treat cancer. The multigenic and environmental complexity of cancer leads to the realization that establishing food strategies to decrease disease cancer risk is both difficult and challenging, therefore the study of food as alternatives to conventional cancer treatments offers numerous pathways to advance the development of potential anticancer foods. Advances through research in cancer treatment are essential to improve the prognosis of patients affected by this disease. Efforts include the development of more effective and less toxic therapies. These include targeted therapies, immunotherapies and vaccines for cancer treatments, and the improvement of those that have existed for decades at the pharmacological and dietary level. The purpose of this chapter is to analyze the principal biopeptides that could have a positive effect on the dietary treatment of cancer and its principal alterations, inflammation and oxidative stress.

Nowadays, obesity is considered as an important worldwide topic since it is a complex disease in which environmental and genetic factors are involved and contribute to the inflammatory and chronic process. The intestinal microbiome plays a very important role for health and disease. Currently, obesity’s prevalence has been increasing due to some new factors, such as caloric ingest increment and the sedentary life-style. Recent research suggests that intestinal microbiome play an environmental factor, which affects obesity directly. The genetic composition of different kind of bacteria and the their variability suggest that they are an important key for the development of metabolic systems that are involved in the sugar and lipid metabolism. The evidence of the role that intestinal bacteria play on the regulation of lipid storage was first shown in animal model experiments. In 2006, Turnbaugh et al. showed, that a microbiome transplant made from genetically obese rats to free-microorganism rats produced an important increase of weight in comparison to the free microorganisms rats that were transplanted from slim rats. This proved that the phenotype is transmissible, and it could be transmitted by an obese microbiome transplant in healthy and slim donators. Although the cause-and-effect relation between the intestinal microbiome and obesity has not been established well yet, the intentional manipulation of the intestinal microbiome through diet has been proposed as a new tool to prevent or at least to modify the risk for obesity and all metabolic diseases related to this. The main purpose of this chapter is to give a review of the microbiome modifications on patients with obesity, how this is a risk factor for obesity, and finally the role that probiotics play as an important contribution to the dietetic treatment for obesity, weight management, and reestablishment of the microbiome balance.

BACterial Hosts for production of Bioactive phenolics from bERRY fruits (BacHBerry) was a 3-year project funded by the Seventh Framework Programme (FP7) of the European Union that ran between November 2013 and October 2016. The overall aim of the project was to establish a sustainable and economically-feasible strategy for the production of novel high-value phenolic compounds isolated from berry fruits using bacterial platforms. The project aimed at covering all stages of the discovery and pre-commercialization process, including berry collection, screening and characterization of their bioactive components, identification and functional characterization of the corresponding biosynthetic pathways, and construction of Gram-positive bacterial cell factories producing phenolic compounds. Further activities included optimization of polyphenol extraction methods from bacterial cultures, scale-up of production by fermentation up to pilot scale, as well as societal and economic analyses of the processes. This review article summarizes some of the key findings obtained throughout the duration of the project., The authors would like to thank the European Union’s Seventh Framework Programme (BacHBerry, Project No. FP7-613793, and FP7-PEOPLE2013-COFUND, Project No. FP7-609405) for the financial support. AD, RF, MHHN, PG, SGS, and JF would also like to acknowledge the Novo Nordisk Foundation. We express our gratitude to Dr. Martha Cyert (Stanford School of Medicine, EUA), Dr. Hitoshi Shimoi (National Research Institute of Brewing, Japan) and Dr. Yoshio Araki (Graduate School of Biosphere Science, Hiroshima University, Japan) for providing the yeast strain YAA5. We also thank Dr. Ian Macraedie, RMIT University, Australia) for providing the plasmid p416_GPDprGFP-A42., info:eu-repo/semantics/publishedVersion

Gamification strategies aligned with social network features increase student’s engagement and motivation, along its interactions. However, there is a lack of models or processes that aids the instructors to deploy gamification and social network features within their educational environments in an effective way, since most of these instructors do not have knowledge regarding gamification design nor resources and time to deploy it. The literature also lacks of studies that considers those instructors as variables in the implantation process, since their participation is essential in order to promote beneficial effects of gamification. Based on this premise we propose a meta-process to aid in gamification planning and implantation with social network concepts, called GAMIFY-SN. We conducted a case study to validate our approach, within a programming course with 40 students. We collected the student’s opinion through qualitative and quantitative approaches, and interviewed the instructor to get feedback from the meta-process. Our results demonstrated that the gamification within social network was well-accepted by the students and instructors, however there are still some obstacles to overcome.

Chronic obstructive pulmonary disease (COPD) is a life-threatening lung illness characterized by persistent and progressive airflow limitation. Exacerbations of COPD contribute to the severity of this pathology and accelerate disease progression. To date, pharmacological treatment of both stable COPD patients and patients experiencing exacerbations is mainly symptomatic with bronchodilators and steroids as the mainstay of therapy. Bacteria trigger such exacerbations in a number of cases; hence, antibiotics might be included in the treatment as well. Several respiratory viruses are frequently detected in sputum from patients during COPD exacerbations. These include influenza viruses, respiratory syncytial virus, and, most often, rhinoviruses. In this review, we discuss the potential use of an anti-rhinovirus drug for the treatment and prophylaxis of rhinovirus-induced COPD exacerbations and the path forward toward the development and use of such a drug. Copyright © 2015 John Wiley & Sons, Ltd.

Software testing is a relevant activity to provide evidence of qualifty of software products. However, there is a lack of qualified professionals in this area. This can be caused due to difficulty in teaching software testing through approaches that use only theoretical classes and traditional tools. In addition, there is a lack of motivation due to the work environment and the strategies of allocation and responsibility of these professionals in development and testing teams. To mitigate these problems, approaches have been used to support software testing education, such as: educational games, integrated teaching of software testing with programming, educational modules, among others. The objective of this paper was to develop an educational game named Testing Game, addressing the following topics: functional testing, structural testing and mutation testing. To support the development of the Testing Game, we performed a systematic mapping aiming at selecting a game engine. To evaluate the efficiency of the game, we conducted a feasibility study to evaluate the quality regarding motivation, user experience and learning from the point of view of the students. We also evaluate the usability of the Testing Game. Approximately 85.64% of people who participated in the study assessed the quality of the game in a positive perspective regarding motivation, user experience and learning from the point of view of the students. Regarding the usability of the game, students identified minor problems. Through this work, we realize that the game Testing Game can constitute as a complementary resource to support software testing education.

Spectroscopic techniques have shown great potential due to their quick response, cost-effective, non-destructive and non-invasive nature, and environmental friendliness. These characteristics make this technology very attractive for sustainable industry and research activities, being viticulture industry no exception. Spectroscopic techniques are an appealing alternative for ripeness assessment and harvest date determination as well as for plant variety and clone determination. Numerous recent works have clearly demonstrated that it is highly advantageous to process the high dimensionality spectroscopic data with soft computing or multivariate analysis techniques such as Partial Least Squares, Neural Networks or Support Vector Machines. In this review, focus will be given to two emergent subfields in viticulture where the combination of spectroscopy and soft computing is fundamental: (1) The difficult measurement of enological parameters, namely sugar content, pH and anthocyanin content, in samples containing a small number of grape berries, with the aim of assessing grapes’ ripeness; (2) The multiclass problem of identifying plant varieties and clones. The results of the various works in these subfields will be presented. The present article starts with a brief description of the spectroscopy principles and continues by making an overview of the scientific literature considering the number of berries per sample and the total number of samples in the various works. The use of different varieties, vintages and harvest locations in the same model will also be addressed. Special attention is given to the validation methods employed and to algorithm comparison. Some suggestions are presented in order to facilitate future comparison of published results.

This paper presents a new methodology for allocation of transmission system costs in electricity markets established in one single area or composed by the interconnection of multiple subsystems. Initially, the proposed method allows the decomposition of the total cost of transmission in two installments: the first, referring to the utilized capacity of the network (estimated at a predetermined operating point), and the second, to the transmission capacity still available in the system. A subsystem decomposition technique of the nodal tariffs also ensures the applicability of the method in interconnected markets. From the decomposition rates, it is possible to assess the responsibility of each participant on the cost of use of any specified set of transmission elements. The flexibility of the method enables an allocation via postage stamp costs (i.e., pro-rata), related to interconnection lines and/or high systemic importance circuits. After the presentation of the mathematical formulation, a procedure for the consideration of multiple operating points (dispatching scenarios) is proposed to evaluate nodal prices and also a new algorithm to determine zonal tariffs, i.e., applicable to all generators or loads at the zone. The proposed approach is described in detail and illustrated by numerical applications to the Brazilian interconnected system.

Biowaste is the large volume of solid waste generated daily by households, and it presents environmental and health hazards if not reused. Composting is one of the best options for such reuse. This study documents biowaste generation and collection from households and reuse through composting in Barangay Holy Spirit, Quezon City, Philippines. This barangay (the smallest administrative unit) has made pioneering efforts in composting, having developed it from a simple process to a mechanized process. The barangay’s composting has been integrated with its vegetable and flower urban garden, which serves as a demonstration and training center on how to provide livelihood opportunities and environmental benefits. However, to enhance the compost’s marketability, issues relating to quality, such as the high organic carbon, high carbon-to-nitrogen ratio, and low levels of nitrogen, phosphorus, and potassium, need to be addressed. The barangay intends to boost compost production and sales, thereby increasing income, which can further improve the solid-waste management and increase staff salaries. Barangay Holy Spirit is one of the most developed barangays in terms of solid-waste management, and it can serve as a model for other barangays.

This paper investigates and discusses the usefulness of the application of several valid adequacy evaluation protocols to both the IEEE Reliability Test System (IEEE RTS) and the entire Brazilian Power System (BPS). Three contributions are worth mentioning: 1) an in-depth evaluation of the IEEE RTS using several adequacy protocols, which may be compared with the set of results already available in the literature; 2) original results from an actual very large non-academic power system (BPS), also using a diverse set of adequacy protocols, which help to interpret the behavior of traditional reliability indices when evaluated in real generation and/or transmission systems; and 3) the establishment, evaluation, discussion, and comparison of a set of assumptions described through adequacy protocols used in both systems, which may be adapted to the systems of other countries.

In most practical cases, traditional methods for assessing the reliability of substations, separately from the electric power network, are not sufficient to a good understanding of the system performance and possible reinforcements. This paper presents a new methodology to evaluate the criticality of electrical substations taking into account their possible operating states, associate probabilities, and consequences on the static and dynamic performance of the electric power system. Two complementary reliability indices are proposed to express the dynamic security and static adequacy levels that a substation provides to the power grid. An application in a test system is presented and the substation criticality ranking is provided and discussed.

Grapevine variety identification is a matter of great interest in viticulture, which is currently addressed by visual ampelometry or wet chemistry genetic analysis. This paper reports the development of a simple and automatic method of classification of grapevine varieties from leaf spectroscopy. The method consists of a classifier based on partial least squares that discriminates among grapevine varieties using a hyperspectral image of a leaf measured in reflectance mode. Hyperspectral imaging was conducted with a camera with 1040 wavelength bands operating between 380. nm and 1028. nm. The classifier was created using 300 leaves, 100 of each of the varieties Vitis vinifera L., Tempranillo, Grenache and Cabernet Sauvignon. Monte-Carlo cross-validation confirmed the classifier's performance for the three varieties, which exceeded 92% in all cases. The proposed method has proven to satisfactory classify among grape varieties, but certainly a wider range of grapevine cultivars should be tested before it gets implemented for local sensing with the aim of providing the wine industry with a fast, automatic, environmentally friendly and accurate tool for grapevine variety identification. © 2013 Elsevier B.V., This work is supported by European Union Funds (FEDER/COMPETE – Operational Competitiveness Programme) and by national funds (FCT – Portuguese Foundation for Science and Technology) under the Project FCOMP-01-0124-FEDER-022692.

This paper proposes a new approach to evaluate loss of load indices in composite generation and transmission systems. The main idea is to combine a Cross-Entropy (CE)-based optimization process and nonsequential Monte Carlo Simulation (MCS) to obtain an auxiliary sampling distribution, which can minimize the variance of the reliability index estimators. This auxiliary sampling distribution will properly modify the original unavailabilities of both generation and transmission equipment, so that important failure events are sampled more often. As a result, the MCS algorithm can reach convergence faster and with fewer samples, leading to significant gains in computational performance, especially when dealing with very reliable system configurations. The proposed method is tested using several composite power systems, including the IEEE RTS 79, IEEE RTS 96, and a configuration of the Brazilian system.

The aim of this study was to quantitatively assess vegetable production and consumption in an urban fringe area in Metro Manila, the Philippines. We conducted field investigations into vegetable production at a farm within a subdivision, monitored customers for 1 week at the farm's vegetable stand, interviewed customers of the stand, and conducted household monitoring and interviews to identify purchasing behavior. We also conducted an object-based WorldView-2 image analysis of a larger area to identify current and potential future vegetable farming plots and a spatial analysis using GIS to visualize the vegetable production/consumption ratio. We found that about one-third of current vegetable demand could be supplied by greater promotion of vegetable farming in vacant lots and by setting up more vegetable stands at spatial intervals less than 1-km. Subdivision regulations also influenced vegetable farming because of the level of social acceptance of caretakers of vacant lots. The current planning and institutional framework does not consider social contexts in land use and planning. We suggested that a new framework is necessary; it should consider balancing the formation of social capital and formalize planning and institutional procedures to support sustainable local vegetable production.

Wind and solar power are well known intermittent power sources with high availability uncertainties. Hence, whenever they are integrated to distribution systems, these power sources can increase significantly the complexity of system operation. This paper presents an impact analysis of distributed energy resources integration on distribution systems, focusing mainly on reliability aspects. Therefore, an interesting algorithm to correctly determine the amount of capacity that may be transferred to other feeders is presented and discussed, taken into consideration the presence of distributed generation. The methodology is tested in a typical Brazilian distribution system, assuming the integration of a diesel-based combined heat and power unit, wind turbines, and solar panels. The results provide general insights regarding the benefits of applying distributed generation to alleviate load transfer restrictions.