1,644 research outputs found

    Organización criminal de robo y hurto de vehículos en El Callao-2021

    Get PDF
    La presente investigación es manifestada, con los resultados que se ha podido recoger y verificar la información de lo que acontece en estos días sobre el la organización del crimen organizado del hurto, robo de autopartes y automóviles, Distrito del callao durante el periodo 2021, infringiendo la ley donde se encuentra la modalidad en las acciones jurídicas que se realiza, lo que para la presente investigación se recurrió a muchos medios donde se proporción la información correcta de las cantidad de y modalidades que utilizan el crimen organizado dentro de la jurisdicción del Callao. El índice de denuncia hasta la fecha que se da en la comisaria el callao y la unidad de robo de vehículos, indican ha aumentado por la misma razón que no hay personal para patrullar en las noches y están limitados del crimen organizado, realice los actos delincuenciales, donde la población del callao en el periodo 2021, esta perjudicada con los robos, hurtos, robo de auto partes, estas que tiene cargo el crimen organizado. De acuerdo a los encuestados las redes sociales en muy importante para ver la incidencia de todo que sucede en el distrito del callao en el periodo 2021, sobre el robo y hurto de vehículos de vehículo, ayuda para denunciar donde se está realizando los robos de vehículos y ver así donde son los lugares más frecuentes de dichos actos, Algunos encuestados indicaron que no denunciaron su robo de vehículo, por el mismo hecho de tener desconfianza en las autoridades y sobre todo a la policía. Si realmente nos enfocamos dentro del contexto del hurto robo de vehículo por las organizaciones criminales, vemos que el impacto económico para recuperar los vehículos robados, la encuestada recuperación y gastos para el arreglo del vehículo al final tiene un costo de 2500 y 4000.The present investigation is manifested, with the results that it has been possible to collect and verify the information of what is happening these days about the organized crime organization of theft, theft of auto parts and automobiles, Callao District during the period 2021, infringing the law where the modality in the legal actions that is carried out is found, which for the present investigation was resorted to many means where the correct information was provided on the amount of and modalities used by organized crime within the jurisdiction of Callao. The rate of complaints to date that occurs in the Callao police station and the vehicle theft unit, indicate that it has increased for the same reason that there are no personnel to patrol at night and they are limited to organized crime, carry out criminal acts , where the population of Callao in the period 2021, is harmed by robberies, thefts, theft of auto parts, these are in charge of organized crime. According to those surveyed, social networks are very important to see the incidence of everything that happens in the Callao district in the period 2021, about the theft and theft of vehicles, help to report where vehicle thefts are being carried out and to see where the most frequent places of such acts are, Some respondents indicated that they did not report their vehicle theft, due to the very fact of distrusting the authorities and especially the police. If we really focus within the context of vehicle theft by criminal organizations, we see that the economic impact to recover stolen vehicles, the respondent recovery and expenses for the repair of the vehicle in the end has a cost of 2500 and 4000

    Airframe Integrity Based on Bayesian Approach

    Get PDF
    Aircraft aging has become an immense challenge in terms of ensuring the safety of the fleet while controlling life cycle costs. One of the major concerns in aircraft structures is the development of fatigue cracks in the fastener holes. A probabilistic-based method has been proposed to manage this problem. In this research, the Bayes' theorem is used to assess airframe integrity by updating generic data with airframe inspection data while such data are compiled. This research discusses the methodology developed for assessment of loss of airframe integrity due to fatigue cracking in the fastener holes of an aging platform. The methodology requires a probability density function (pdf) at the end of SAFE life. Subsequently, a crack growth regime begins. As the Bayesian analysis requires information of a prior initial crack size pdf, such a pdf is assumed and verified to be lognormally distributed. The prior distribution of crack size as cracks grow is modeled through a combined Inverse Power Law (IPL) model and lognormal relationships. The first set of inspections is used as the evidence for updating the crack size distribution at the various stages of aircraft life. Moreover, the materials used in the structural part of the aircrafts have variations in their properties due to their calibration errors and machine alignment. A Matlab routine (PCGROW) is developed to calculate the crack distribution growth through three different crack growth models. As the first step, the material properties and the initial crack size are sampled. A standard Monte Carlo simulation is employed for this sampling process. At the corresponding aircraft age, the crack observed during the inspections, is used to update the crack size distribution and proceed in time. After the updating, it is possible to estimate the probability of structural failure as a function of flight hours for a given aircraft in the future. The results show very accurate and useful values related to the reliability and integrity of airframes in aging aircrafts. Inspection data shown in this dissertation are not the actual data from known aircrafts and are only used to demonstrate the methodologies

    Endophthalmitis following penetrating eye injuries

    Get PDF
    Postinjury endophthalmitis is the eye infection with the worst prognosis. A retrospective 9-year study was made of penetrating eye injuries, with an analysis of the incidence of infection and its relation to the type of wound and the presence of intraocular foreign bodies. There were 403 cases of penetrating eye injury; of these, 233 affected the cornea and 170 involved the posterior pole. Intraocular foreign bodies were present in 40 cases. Endophthalmitis developed in 4.2% of cases (17/403), and was more common in patients with posterior pole involvement (7%) than in purely corneal trauma (2.1%) (p = 0.03, Chi-square). Infection was in turn more frequent in the presence of intraocular foreign bodies (15%) (p = 0.17, Chi-square). Staphylococcus epidermidis was the most common cause (23.4%), while in three cases (17.6%) mixed infection was detected. The visual results were evisceration or non-perception of light in 82.3% of cases

    IN VITRO EQUIVALENCE STUDY OF DIFFERENT DOSES OF CARBAMAZEPINE REFERENCE TABLETS USING USP APPARATUSES 2 AND 4

    Get PDF
    Objective: To perform an in vitro equivalence study of two doses of carbamazepine reference tablets sold in the local market under hydrodynamic conditions of USP Apparatus 4, a dissolution apparatus that better simulates the human gastrointestinal tract. Results were compared with dissolution official conditions using USP Apparatus 2. Methods: Dissolution profiles of both formulations were carried out with an automated USP Apparatus 2 at 75 rpm and 900 ml of dissolution medium. USP Apparatus 4 with laminar flow at 16 ml/min and 22.6 mm cells were used. 1% lauryl sulfate aqueous solution at 37.0±0.5 °C was used as dissolution medium. Spectrophotometric determination of drug at 285 nm was carried out during 60 min. Dissolution profiles were compared with model-independent and-dependent approaches. Results: When comparing dissolution profiles of low vs. high dose similar profiles were found (f2>50) in each dissolution apparatus, however, when the same dose was compared, USP 2 vs. USP 4, opposite results were obtained. Comparison of mean dissolution time and dissolution efficiency data corroborates these results. Weibull function was the best mathematical model that described the in vitro dissolution performance of carbamazepine. No significant differences were found in Td values (low vs. high dose) but opposite results were also found with USP 2 vs. USP 4. Conclusion: Equivalent dissolution performance of two doses of carbamazepine reference tablets were found in each USP dissolution apparatus. The main problem identified in this comparative study is the low dissolution rate and extent found with USP Apparatus 4. More research on this field is necessary for all available doses of reference drug products since the quality of generic formulations depends on the quality of references

    INFLUENCE OF DOSE AND USP DISSOLUTION APPARATUS IN THE RELEASE PERFORMANCE OF REFERENCE TABLETS: PROPRANOLOL-HCl AND RANITIDINE-HCl CASES

    Get PDF
    Objective: Due to quality of generic formulations depends on available information of reference drug products the aim of this work was to perform an in vitro dissolution study of two doses of propranolol-HCl and ranitidine-HCl reference tablets using USP basket or paddle apparatus and flow-through cell method. Methods: Two doses of propranolol-HCl (10-mg and 80-mg) and ranitidine-HCl (150-mg and 300-mg) of Mexican reference products were used. Dissolution profiles of propranolol-HCl were obtained with USP basket apparatus at 100 rpm and 1000 ml of 1% hydrochloric acid. Profiles of ranitidine-HCl were determined with USP paddle apparatus at 50 rpm and 900 ml of distilled water. All formulations were also studied with the flow-through cell method using laminar flow at 16 ml/min. Dissolution profiles were compared by model-independent (f2 similarity factor, mean dissolution time and dissolution efficiency) and model-dependent methods (dissolution data adjusted to some mathematical equations). Time data, derived from these adjustments, as t50%, t63.25%, and t85% were used to compare dissolution profiles. Results: With all approaches used and being high solubility drugs significant differences were found between low and high doses and between USP dissolution apparatuses (*P<0.05). Conclusion: In vitro dissolution performance of two doses of propranolol-HCl and ranitidine-HCl was not expected. Considering the same USP dissolution apparatus, the reference tablets did not allow the simultaneous release of the used doses. The results could be of interest for pharmaceutical laboratories or health authorities that classify some drug products as a reference to be used in dissolution and bioequivalence studies

    Aplicación del lean office para mejorar la satisfacción del cliente en la empresa Comphill S.A., Trujillo 2023

    Get PDF
    En la presente investigación busca mejorar la satisfacción del cliente por la atención de las áreas administrativas de la empresa COMPHILL S.A mediante la implementación de la metodología lean office y sus herramientas de mejora del VSM y las 5S,El estudio fue tipo aplicado, con un enfoque cuantitativo ,con una población de 500 clientes que adquieren los servicios pertinentes de acorde a sus requerimientos dentro de la empresa, como resultados se obtuvo que la satisfacción de los clientes por la atención de las áreas administrativas, mejoro de forma significativa en los tramites documentarios, evidenciando la implementación de esta metodología donde la muestra conformada por 50 clientes encuestados, se identificó como la satisfacción aumento desde un 20% a un 92%, esto adecuado al tiempo de aplicación, la razón de cambio fue significativa, porque establece una mejora de 72% en criterios de aceptación sobre el servicio brindado. Así mismo logramos identificar que las buenas prácticas laborales hacen fortalecer la estructura de la empresa COMPHILL S.A. y a su vez se mejora la satisfacción de los clientes según sus necesidades diarias

    Automatic classification of human facial features based on their appearance

    Full text link
    [EN] Classification or typology systems used to categorize different human body parts have existed for many years. Nevertheless, there are very few taxonomies of facial features. Ergonomics, forensic anthropology, crime prevention or new human-machine interaction systems and online activities, like e-commerce, e-learning, games, dating or social networks, are fields in which classifications of facial features are useful, for example, to create digital interlocutors that optimize the interactions between human and machines. However, classifying isolated facial features is difficult for human observers. Previous works reported low inter-observer and intra-observer agreement in the evaluation of facial features. This work presents a computer-based procedure to automatically classify facial features based on their global appearance. This procedure deals with the difficulties associated with classifying features using judgements from human observers, and facilitates the development of taxonomies of facial features. Taxonomies obtained through this procedure are presented for eyes, mouths and noses.Fuentes-Hurtado, F.; Diego-Mas, JA.; Naranjo Ornedo, V.; Alcañiz Raya, ML. (2019). Automatic classification of human facial features based on their appearance. PLoS ONE. 14(1):1-20. https://doi.org/10.1371/journal.pone.0211314S120141Damasio, A. R. (1985). Prosopagnosia. Trends in Neurosciences, 8, 132-135. doi:10.1016/0166-2236(85)90051-7Bruce, V., & Young, A. (1986). Understanding face recognition. British Journal of Psychology, 77(3), 305-327. doi:10.1111/j.2044-8295.1986.tb02199.xTodorov, A. (2011). Evaluating Faces on Social Dimensions. Social Neuroscience, 54-76. doi:10.1093/acprof:oso/9780195316872.003.0004Little, A. C., Burriss, R. P., Jones, B. C., & Roberts, S. C. (2007). Facial appearance affects voting decisions. Evolution and Human Behavior, 28(1), 18-27. doi:10.1016/j.evolhumbehav.2006.09.002Porter, J. P., & Olson, K. L. (2001). Anthropometric Facial Analysis of the African American Woman. Archives of Facial Plastic Surgery, 3(3), 191-197. doi:10.1001/archfaci.3.3.191Gündüz Arslan, S., Genç, C., Odabaş, B., & Devecioğlu Kama, J. (2007). Comparison of Facial Proportions and Anthropometric Norms Among Turkish Young Adults With Different Face Types. Aesthetic Plastic Surgery, 32(2), 234-242. doi:10.1007/s00266-007-9049-yFerring, V., & Pancherz, H. (2008). Divine proportions in the growing face. American Journal of Orthodontics and Dentofacial Orthopedics, 134(4), 472-479. doi:10.1016/j.ajodo.2007.03.027Mane, D. R., Kale, A. D., Bhai, M. B., & Hallikerimath, S. (2010). Anthropometric and anthroposcopic analysis of different shapes of faces in group of Indian population: A pilot study. Journal of Forensic and Legal Medicine, 17(8), 421-425. doi:10.1016/j.jflm.2010.09.001Ritz-Timme, S., Gabriel, P., Tutkuviene, J., Poppa, P., Obertová, Z., Gibelli, D., … Cattaneo, C. (2011). Metric and morphological assessment of facial features: A study on three European populations. Forensic Science International, 207(1-3), 239.e1-239.e8. doi:10.1016/j.forsciint.2011.01.035Ritz-Timme, S., Gabriel, P., Obertovà, Z., Boguslawski, M., Mayer, F., Drabik, A., … Cattaneo, C. (2010). A new atlas for the evaluation of facial features: advantages, limits, and applicability. International Journal of Legal Medicine, 125(2), 301-306. doi:10.1007/s00414-010-0446-4Kong, S. G., Heo, J., Abidi, B. R., Paik, J., & Abidi, M. A. (2005). Recent advances in visual and infrared face recognition—a review. Computer Vision and Image Understanding, 97(1), 103-135. doi:10.1016/j.cviu.2004.04.001Tavares, G., Mourão, A., & Magalhães, J. (2016). Crowdsourcing facial expressions for affective-interaction. Computer Vision and Image Understanding, 147, 102-113. doi:10.1016/j.cviu.2016.02.001Buckingham, G., DeBruine, L. M., Little, A. C., Welling, L. L. M., Conway, C. A., Tiddeman, B. P., & Jones, B. C. (2006). Visual adaptation to masculine and feminine faces influences generalized preferences and perceptions of trustworthiness. Evolution and Human Behavior, 27(5), 381-389. doi:10.1016/j.evolhumbehav.2006.03.001Boberg M, Piippo P, Ollila E. Designing Avatars. DIMEA ‘08 Proc 3rd Int Conf Digit Interact Media Entertain Arts. ACM; 2008; 232–239. doi: https://doi.org/10.1145/1413634.1413679Rojas Q., M., Masip, D., Todorov, A., & Vitria, J. (2011). Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models. PLoS ONE, 6(8), e23323. doi:10.1371/journal.pone.0023323Laurentini, A., & Bottino, A. (2014). Computer analysis of face beauty: A survey. Computer Vision and Image Understanding, 125, 184-199. doi:10.1016/j.cviu.2014.04.006Alemany S, Gonzalez J, Nacher B, Soriano C, Arnaiz C, Heras H. Anthropometric survey of the Spanish female population aimed at the apparel industry. Proceedings of the 2010 Intl Conference on 3D Body scanning Technologies. 2010. pp. 307–315.Vinué, G., Epifanio, I., & Alemany, S. (2015). Archetypoids: A new approach to define representative archetypal data. Computational Statistics & Data Analysis, 87, 102-115. doi:10.1016/j.csda.2015.01.018Jee, S., & Yun, M. H. (2016). An anthropometric survey of Korean hand and hand shape types. International Journal of Industrial Ergonomics, 53, 10-18. doi:10.1016/j.ergon.2015.10.004Kim, N.-S., & Do, W.-H. (2014). Classification of Elderly Women’s Foot Type. Journal of the Korean Society of Clothing and Textiles, 38(3), 305-320. doi:10.5850/jksct.2014.38.3.305Sarakon P, Charoenpong T, Charoensiriwath S. Face shape classification from 3D human data by using SVM. The 7th 2014 Biomedical Engineering International Conference. IEEE; 2014. pp. 1–5. doi: https://doi.org/10.1109/BMEiCON.2014.7017382PRESTON, T. A., & SINGH, M. (1972). Redintegrated Somatotyping. Ergonomics, 15(6), 693-700. doi:10.1080/00140137208924469Lin, Y.-L., & Lee, K.-L. (1999). Investigation of anthropometry basis grouping technique for subject classification. Ergonomics, 42(10), 1311-1316. doi:10.1080/001401399184965Malousaris, G. G., Bergeles, N. K., Barzouka, K. G., Bayios, I. A., Nassis, G. P., & Koskolou, M. D. (2008). Somatotype, size and body composition of competitive female volleyball players. Journal of Science and Medicine in Sport, 11(3), 337-344. doi:10.1016/j.jsams.2006.11.008Carvalho, P. V. R., dos Santos, I. L., Gomes, J. O., Borges, M. R. S., & Guerlain, S. (2008). Human factors approach for evaluation and redesign of human–system interfaces of a nuclear power plant simulator. Displays, 29(3), 273-284. doi:10.1016/j.displa.2007.08.010Fabri M, Moore D. The use of emotionally expressive avatars in Collaborative Virtual Environments. AISB’05 Convention:Proceedings of the Joint Symposium on Virtual Social Agents: Social Presence Cues for Virtual Humanoids Empathic Interaction with Synthetic Characters Mind Minding Agents. 2005. pp. 88–94. doi:citeulike-article-id:790934Sukhija, P., Behal, S., & Singh, P. (2016). Face Recognition System Using Genetic Algorithm. Procedia Computer Science, 85, 410-417. doi:10.1016/j.procs.2016.05.183Trescak T, Bogdanovych A, Simoff S, Rodriguez I. Generating diverse ethnic groups with genetic algorithms. Proceedings of the 18th ACM symposium on Virtual reality software and technology—VRST ‘12. New York, New York, USA: ACM Press; 2012. p. 1. doi: https://doi.org/10.1145/2407336.2407338Vanezis, P., Lu, D., Cockburn, J., Gonzalez, A., McCombe, G., Trujillo, O., & Vanezis, M. (1996). Morphological Classification of Facial Features in Adult Caucasian Males Based on an Assessment of Photographs of 50 Subjects. Journal of Forensic Sciences, 41(5), 13998J. doi:10.1520/jfs13998jTamir, A. (2011). Numerical Survey of the Different Shapes of the Human Nose. Journal of Craniofacial Surgery, 22(3), 1104-1107. doi:10.1097/scs.0b013e3182108eb3Tamir, A. (2013). Numerical Survey of the Different Shapes of Human Chin. Journal of Craniofacial Surgery, 24(5), 1657-1659. doi:10.1097/scs.0b013e3182942b77Richler, J. J., Cheung, O. S., & Gauthier, I. (2011). Holistic Processing Predicts Face Recognition. Psychological Science, 22(4), 464-471. doi:10.1177/0956797611401753Taubert, J., Apthorp, D., Aagten-Murphy, D., & Alais, D. (2011). The role of holistic processing in face perception: Evidence from the face inversion effect. Vision Research, 51(11), 1273-1278. doi:10.1016/j.visres.2011.04.002Donnelly, N., & Davidoff, J. (1999). The Mental Representations of Faces and Houses: Issues Concerning Parts and Wholes. Visual Cognition, 6(3-4), 319-343. doi:10.1080/135062899395000Davidoff, J., & Donnelly, N. (1990). Object superiority: A comparison of complete and part probes. Acta Psychologica, 73(3), 225-243. doi:10.1016/0001-6918(90)90024-aTanaka, J. W., & Farah, M. J. (1993). Parts and Wholes in Face Recognition. The Quarterly Journal of Experimental Psychology Section A, 46(2), 225-245. doi:10.1080/14640749308401045Wang, R., Li, J., Fang, H., Tian, M., & Liu, J. (2012). Individual Differences in Holistic Processing Predict Face Recognition Ability. Psychological Science, 23(2), 169-177. doi:10.1177/0956797611420575Rhodes, G., Ewing, L., Hayward, W. G., Maurer, D., Mondloch, C. J., & Tanaka, J. W. (2009). Contact and other-race effects in configural and component processing of faces. British Journal of Psychology, 100(4), 717-728. doi:10.1348/000712608x396503Miller, G. A. (1994). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 101(2), 343-352. doi:10.1037/0033-295x.101.2.343Scharff, A., Palmer, J., & Moore, C. M. (2011). Evidence of fixed capacity in visual object categorization. Psychonomic Bulletin & Review, 18(4), 713-721. doi:10.3758/s13423-011-0101-1Meyers, E., & Wolf, L. (2007). Using Biologically Inspired Features for Face Processing. International Journal of Computer Vision, 76(1), 93-104. doi:10.1007/s11263-007-0058-8Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001). Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 681-685. doi:10.1109/34.927467Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037-2041. doi:10.1109/tpami.2006.244Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711-720. doi:10.1109/34.598228Turk, M., & Pentland, A. (1991). Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1), 71-86. doi:10.1162/jocn.1991.3.1.71Klare B, Jain AK. On a taxonomy of facial features. IEEE 4th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2010. IEEE; 2010. pp. 1–8. doi: https://doi.org/10.1109/BTAS.2010.5634533Chihaoui, M., Elkefi, A., Bellil, W., & Ben Amar, C. (2016). A Survey of 2D Face Recognition Techniques. Computers, 5(4), 21. doi:10.3390/computers5040021Ma, D. S., Correll, J., & Wittenbrink, B. (2015). The Chicago face database: A free stimulus set of faces and norming data. Behavior Research Methods, 47(4), 1122-1135. doi:10.3758/s13428-014-0532-5Asthana A, Zafeiriou S, Cheng S, Pantic M. Incremental face alignment in the wild. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2014. pp. 1859–1866. doi: https://doi.org/10.1109/CVPR.2014.240Bag S, Barik S, Sen P, Sanyal G. A statistical nonparametric approach of face recognition: combination of eigenface & modified k-means clustering. Proceedings Second International Conference on Information Processing. 2008. p. 198.Doukas, C., & Maglogiannis, I. (2010). A Fast Mobile Face Recognition System for Android OS Based on Eigenfaces Decomposition. Artificial Intelligence Applications and Innovations, 295-302. doi:10.1007/978-3-642-16239-8_39Huang P, Huang Y, Wang W, Wang L. Deep embedding network for clustering. Proceedings—International Conference on Pattern Recognition. 2014. pp. 1532–1537. doi: https://doi.org/10.1109/ICPR.2014.272Dizaji KG, Herandi A, Deng C, Cai W, Huang H. Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization. Proceedings of the IEEE International Conference on Computer Vision. 2017. doi: https://doi.org/10.1109/ICCV.2017.612Xie J, Girshick R, Farhadi A. Unsupervised deep embedding for clustering analysis [Internet]. Proceedings of the 33rd International Conference on International Conference on Machine Learning—Volume 48. JMLR.org; 2016. pp. 478–487. Available: https://dl.acm.org/citation.cfm?id=3045442Nousi, P., & Tefas, A. (2017). Discriminatively Trained Autoencoders for Fast and Accurate Face Recognition. Communications in Computer and Information Science, 205-215. doi:10.1007/978-3-319-65172-9_18Sirovich, L., & Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A, 4(3), 519. doi:10.1364/josaa.4.00051

    The Influence of Each Facial Feature on How We Perceive and Interpret Human Faces

    Full text link
    [EN] Facial information is processed by our brain in such a way that we immediately make judgments about, for example, attractiveness or masculinity or interpret personality traits or moods of other people. The appearance of each facial feature has an effect on our perception of facial traits. This research addresses the problem of measuring the size of these effects for five facial features (eyes, eyebrows, nose, mouth, and jaw). Our proposal is a mixed feature-based and image-based approach that allows judgments to be made on complete real faces in the categorization tasks, more than on synthetic, noisy, or partial faces that can influence the assessment. Each facial feature of the faces is automatically classified considering their global appearance using principal component analysis. Using this procedure, we establish a reduced set of relevant specific attributes (each one describing a complete facial feature) to characterize faces. In this way, a more direct link can be established between perceived facial traits and what people intuitively consider an eye, an eyebrow, a nose, a mouth, or a jaw. A set of 92 male faces were classified using this procedure, and the results were related to their scores in 15 perceived facial traits. We show that the relevant features greatly depend on what we are trying to judge. Globally, the eyes have the greatest effect. However, other facial features are more relevant for some judgments like the mouth for happiness and femininity or the nose for dominance.This study was carried out using the Chicago Face Database developed at the University of Chicago by Debbie S. Ma, Joshua Correll, and Bernd Wittenbrink.Diego-Mas, JA.; Fuentes-Hurtado, FJ.; Naranjo Ornedo, V.; Alcañiz Raya, ML. (2020). The Influence of Each Facial Feature on How We Perceive and Interpret Human Faces. i-Perception. 11(5):1-18. https://doi.org/10.1177/2041669520961123S118115Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037-2041. doi:10.1109/tpami.2006.244Axelrod, V., & Yovel, G. (2010). External facial features modify the representation of internal facial features in the fusiform face area. NeuroImage, 52(2), 720-725. doi:10.1016/j.neuroimage.2010.04.027Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711-720. doi:10.1109/34.598228Biederman, I. (1987). Recognition-by-components: A theory of human image understanding. Psychological Review, 94(2), 115-147. doi:10.1037/0033-295x.94.2.115Blais, C., Roy, C., Fiset, D., Arguin, M., & Gosselin, F. (2012). The eyes are not the window to basic emotions. Neuropsychologia, 50(12), 2830-2838. doi:10.1016/j.neuropsychologia.2012.08.010Bovet, J., Barthes, J., Durand, V., Raymond, M., & Alvergne, A. (2012). Men’s Preference for Women’s Facial Features: Testing Homogamy and the Paternity Uncertainty Hypothesis. PLoS ONE, 7(11), e49791. doi:10.1371/journal.pone.0049791Brahnam, S., & Nanni, L. (2010). Predicting trait impressions of faces using local face recognition techniques. Expert Systems with Applications, 37(7), 5086-5093. doi:10.1016/j.eswa.2009.12.002Bruce, V., & Young, A. (1986). Understanding face recognition. British Journal of Psychology, 77(3), 305-327. doi:10.1111/j.2044-8295.1986.tb02199.xCabeza, R., & Kato, T. (2000). Features are Also Important: Contributions of Featural and Configural Processing to Face Recognition. Psychological Science, 11(5), 429-433. doi:10.1111/1467-9280.00283Chihaoui, M., Elkefi, A., Bellil, W., & Ben Amar, C. (2016). A Survey of 2D Face Recognition Techniques. Computers, 5(4), 21. doi:10.3390/computers5040021Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001). Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 681-685. doi:10.1109/34.927467Diamond, R., & Carey, S. (1986). Why faces are and are not special: An effect of expertise. Journal of Experimental Psychology: General, 115(2), 107-117. doi:10.1037/0096-3445.115.2.107Dixson, B. J. W., Sulikowski, D., Gouda‐Vossos, A., Rantala, M. J., & Brooks, R. C. (2016). The masculinity paradox: facial masculinity and beardedness interact to determine women’s ratings of men’s facial attractiveness. Journal of Evolutionary Biology, 29(11), 2311-2320. doi:10.1111/jeb.12958Dunn†, J. C. (1974). Well-Separated Clusters and Optimal Fuzzy Partitions. Journal of Cybernetics, 4(1), 95-104. doi:10.1080/01969727408546059Eberhardt, J. L., Davies, P. G., Purdie-Vaughns, V. J., & Johnson, S. L. (2006). Looking Deathworthy. Psychological Science, 17(5), 383-386. doi:10.1111/j.1467-9280.2006.01716.xFink, B., Neave, N., Manning, J. T., & Grammer, K. (2006). Facial symmetry and judgements of attractiveness, health and personality. Personality and Individual Differences, 41(3), 491-499. doi:10.1016/j.paid.2006.01.017Fox, E., & Damjanovic, L. (2006). The eyes are sufficient to produce a threat superiority effect. Emotion, 6(3), 534-539. doi:10.1037/1528-3542.6.3.534Fuentes-Hurtado, F., Diego-Mas, J. A., Naranjo, V., & Alcañiz, M. (2019). Automatic classification of human facial features based on their appearance. PLOS ONE, 14(1), e0211314. doi:10.1371/journal.pone.0211314Gill, D. (2017). Women and men integrate facial information differently in appraising the beauty of a face. Evolution and Human Behavior, 38(6), 756-760. doi:10.1016/j.evolhumbehav.2017.07.001Gosselin, F., & Schyns, P. G. (2001). Bubbles: a technique to reveal the use of information in recognition tasks. Vision Research, 41(17), 2261-2271. doi:10.1016/s0042-6989(01)00097-9Hagiwara, N., Kashy, D. A., & Cesario, J. (2012). The independent effects of skin tone and facial features on Whites’ affective reactions to Blacks. Journal of Experimental Social Psychology, 48(4), 892-898. doi:10.1016/j.jesp.2012.02.001Hayward, W. G., Rhodes, G., & Schwaninger, A. (2008). An own-race advantage for components as well as configurations in face recognition. Cognition, 106(2), 1017-1027. doi:10.1016/j.cognition.2007.04.002Jack, R. E., & Schyns, P. G. (2015). The Human Face as a Dynamic Tool for Social Communication. Current Biology, 25(14), R621-R634. doi:10.1016/j.cub.2015.05.052Jones, B. C., Little, A. C., Burt, D. M., & Perrett, D. I. (2004). When Facial Attractiveness is Only Skin Deep. Perception, 33(5), 569-576. doi:10.1068/p3463Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception. The Journal of Neuroscience, 17(11), 4302-4311. doi:10.1523/jneurosci.17-11-04302.1997Keating, C. F., & Doyle, J. (2002). The faces of desirable mates and dates contain mixed social status cues. Journal of Experimental Social Psychology, 38(4), 414-424. doi:10.1016/s0022-1031(02)00007-0Keil, M. S. (2009). «I Look in Your Eyes, Honey»: Internal Face Features Induce Spatial Frequency Preference for Human Face Processing. PLoS Computational Biology, 5(3), e1000329. doi:10.1371/journal.pcbi.1000329Kwart, D. G., Foulsham, T., & Kingstone, A. (2012). Age and Beauty are in the Eye of the Beholder. Perception, 41(8), 925-938. doi:10.1068/p7136Langlois, J. H., Kalakanis, L., Rubenstein, A. J., Larson, A., Hallam, M., & Smoot, M. (2000). Maxims or myths of beauty? A meta-analytic and theoretical review. Psychological Bulletin, 126(3), 390-423. doi:10.1037/0033-2909.126.3.390Levine, T. R., & Hullett, C. R. (2002). Eta Squared, Partial Eta Squared, and Misreporting of Effect Size in Communication Research. Human Communication Research, 28(4), 612-625. doi:10.1111/j.1468-2958.2002.tb00828.xLittle, A. C., Burriss, R. P., Jones, B. C., & Roberts, S. C. (2007). Facial appearance affects voting decisions. Evolution and Human Behavior, 28(1), 18-27. doi:10.1016/j.evolhumbehav.2006.09.002Lundqvist, D., Esteves, F., & Ohman, A. (1999). The Face of Wrath: Critical Features for Conveying Facial Threat. Cognition & Emotion, 13(6), 691-711. doi:10.1080/026999399379041Ma, D. S., Correll, J., & Wittenbrink, B. (2015). The Chicago face database: A free stimulus set of faces and norming data. Behavior Research Methods, 47(4), 1122-1135. doi:10.3758/s13428-014-0532-5Maloney, L. T., & Dal Martello, M. F. (2006). Kin recognition and the perceived facial similarity of children. Journal of Vision, 6(10), 4. doi:10.1167/6.10.4McKone, E., & Yovel, G. (2009). Why does picture-plane inversion sometimes dissociate perception of features and spacing in faces, and sometimes not? Toward a new theory of holistic processing. Psychonomic Bulletin & Review, 16(5), 778-797. doi:10.3758/pbr.16.5.778Meyers, E., & Wolf, L. (2007). Using Biologically Inspired Features for Face Processing. International Journal of Computer Vision, 76(1), 93-104. doi:10.1007/s11263-007-0058-8Miller, G. A. (1994). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 101(2), 343-352. doi:10.1037/0033-295x.101.2.343Pallett, P. M., Link, S., & Lee, K. (2010). New «golden» ratios for facial beauty. Vision Research, 50(2), 149-154. doi:10.1016/j.visres.2009.11.003Paunonen, S. V., Ewan, K., Earthy, J., Lefave, S., & Goldberg, H. (1999). Facial Features as Personality Cues. Journal of Personality, 67(3), 555-583. doi:10.1111/1467-6494.00065Petrican, R., Todorov, A., & Grady, C. (2014). Personality at Face Value: Facial Appearance Predicts Self and Other Personality Judgments among Strangers and Spouses. Journal of Nonverbal Behavior, 38(2), 259-277. doi:10.1007/s10919-014-0175-3Piepers, D. W., & Robbins, R. A. (2012). A Review and Clarification of the Terms «holistic,» «configural,» and «relational» in the Face Perception Literature. Frontiers in Psychology, 3. doi:10.3389/fpsyg.2012.00559Rakover, S. S. (2002). Featural vs. configurational information in faces: A conceptual and empirical analysis. British Journal of Psychology, 93(1), 1-30. doi:10.1348/000712602162427Rhodes, G., Ewing, L., Hayward, W. G., Maurer, D., Mondloch, C. J., & Tanaka, J. W. (2009). Contact and other-race effects in configural and component processing of faces. British Journal of Psychology, 100(4), 717-728. doi:10.1348/000712608x396503Richardson, J. T. E. (2011). Eta squared and partial eta squared as measures of effect size in educational research. Educational Research Review, 6(2), 135-147. doi:10.1016/j.edurev.2010.12.001Ritz-Timme, S., Gabriel, P., Obertovà, Z., Boguslawski, M., Mayer, F., Drabik, A., … Cattaneo, C. (2010). A new atlas for the evaluation of facial features: advantages, limits, and applicability. International Journal of Legal Medicine, 125(2), 301-306. doi:10.1007/s00414-010-0446-4Rojas Q., M., Masip, D., Todorov, A., & Vitria, J. (2011). Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models. PLoS ONE, 6(8), e23323. doi:10.1371/journal.pone.0023323Rossion, B. (2008). Picture-plane inversion leads to qualitative changes of face perception. Acta Psychologica, 128(2), 274-289. doi:10.1016/j.actpsy.2008.02.003Russell, R. (2003). Sex, Beauty, and the Relative Luminance of Facial Features. Perception, 32(9), 1093-1107. doi:10.1068/p5101Saavedra, C., Smith, P., & Peissig, J. (2013). The Relative Role of Eyes, Eyebrows, and Eye Region in Face Recognition. Journal of Vision, 13(9), 410-410. doi:10.1167/13.9.410Sadr, J., Jarudi, I., & Sinha, P. (2003). The Role of Eyebrows in Face Recognition. Perception, 32(3), 285-293. doi:10.1068/p5027Said, C., Sebe, N., & Todorov, A. (2009). «Structural resemblance to emotional expressions predicts evaluation of emotionally neutral faces»: Correction to Said, Sebe, and Todorov (2009). Emotion, 9(4), 509-509. doi:10.1037/a0016784Scharff, A., Palmer, J., & Moore, C. M. (2011). Evidence of fixed capacity in visual object categorization. Psychonomic Bulletin & Review, 18(4), 713-721. doi:10.3758/s13423-011-0101-1Schobert, A.-K., Corradi-Dell’Acqua, C., Frühholz, S., van der Zwaag, W., & Vuilleumier, P. (2017). Functional organization of face processing in the human superior temporal sulcus: a 7T high-resolution fMRI study. Social Cognitive and Affective Neuroscience, 13(1), 102-113. doi:10.1093/scan/nsx119Sirovich, L., & Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A, 4(3), 519. doi:10.1364/josaa.4.000519Tanaka, J. W., & Farah, M. J. (1993). Parts and Wholes in Face Recognition. The Quarterly Journal of Experimental Psychology Section A, 46(2), 225-245. doi:10.1080/14640749308401045Taubert, J., Apthorp, D., Aagten-Murphy, D., & Alais, D. (2011). The role of holistic processing in face perception: Evidence from the face inversion effect. Vision Research, 51(11), 1273-1278. doi:10.1016/j.visres.2011.04.002Terry, R. L. (1977). Further Evidence on Components of Facial Attractiveness. Perceptual and Motor Skills, 45(1), 130-130. doi:10.2466/pms.1977.45.1.130Todorov, A., Dotsch, R., Wigboldus, D. H. J., & Said, C. P. (2011). Data-driven Methods for Modeling Social Perception. Social and Personality Psychology Compass, 5(10), 775-791. doi:10.1111/j.1751-9004.2011.00389.xTodorov, A., Mandisodza, A. N., Goren, A., & Hall, C. C. (2005). Inferences of Competence from Faces Predict Election Outcomes. Science, 308(5728), 1623-1626. doi:10.1126/science.1110589Todorov, A., Said, C. P., Engell, A. D., & Oosterhof, N. N. (2008). Understanding evaluation of faces on social dimensions. Trends in Cognitive Sciences, 12(12), 455-460. doi:10.1016/j.tics.2008.10.001Tsankova, E., & Kappas, A. (2015). Facial Skin Smoothness as an Indicator of Perceived Trustworthiness and Related Traits. Perception, 45(4), 400-408. doi:10.1177/0301006615616748Turk, M., & Pentland, A. (1991). Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1), 71-86. doi:10.1162/jocn.1991.3.1.71Wang, R., Li, J., Fang, H., Tian, M., & Liu, J. (2012). Individual Differences in Holistic Processing Predict Face Recognition Ability. Psychological Science, 23(2), 169-177. doi:10.1177/0956797611420575Wilson, J. P., & Rule, N. O. (2015). Facial Trustworthiness Predicts Extreme Criminal-Sentencing Outcomes. Psychological Science, 26(8), 1325-1331. doi:10.1177/0956797615590992Yamaguchi, M. K., Hirukawa, T., & Kanazawa, S. (2013). Judgment of Gender through Facial Parts. Perception, 42(11), 1253-1265. doi:10.1068/p240563nMcArthur, L. Z., & Baron, R. M. (1983). Toward an ecological theory of social perception. Psychological Review, 90(3), 215-238. doi:10.1037/0033-295x.90.3.21

    Descripción de las medidas laborales adoptadas ante la pandemia Covid-19 en Colombia: características y alcance

    Get PDF
    With the declaration of a health emergency in the face of the global Covid-19 pandemic, the government began to implement a series of measures to mitigate the risk of contagion, with significant consequences in the economic sector, especially in labor matters. This article describes the labor regulations issued during the year 2020, characterizing their scope and affected actors. For this, a qualitative methodology was used through documentary analysis, with national regulations as the main unit of analysis, and research on labor measures at the international level as comparative sources. The results of the analysis allow us to arrive at a conceptual map represented by the regulations in the categories of labor and fiscal protection, from which classification by actors/beneficiaries and a comparison with the main measures in the central regions of the world are derived, identifying that the measures did not reach the expected effectiveness given the high rates of unemployment and informality that have been maintained since 2020 to date.Con la declaratoria de la emergencia sanitaria ante la pandemia mundial por Covid-19, el gobierno empezó a implementar una serie de medidas para mitigar el riesgo de contagio, con grandes consecuencias en el sector económico, especialmente en materia laboral. Este artículo describe la normatividad laboral expedida durante el año 2020, realizando una caracterización frente a su alcance y actores afectados. Para ello, se utilizó una metodología cualitativa mediante análisis documental, con la normatividad nacional como unidad de análisis principal, e investigaciones sobre medidas laborales a nivel internacional como fuentes comparativas. Los resultados del análisis permiten llegar a un mapa conceptual representado por la normatividad en las categorías de protección laboral y fiscal, de la que se derivan clasificación por actores/beneficiarios y un comparativo con las principales medidas en las principales regiones del mundo, identificando que las medidas no alcanzaron la efectividad esperada dadas las altas tasas de desempleo e informalidad que se mantienen desde el año 2020 a la fecha.Com a declaração de emergência sanitária face à pandemia global por Covid-19, o Governo passou a implementar um conjunto de medidas para mitigar o risco de contágio, com grandes consequências no setor económico, sobretudo em matéria laboral. Este artigo descreve os regulamentos trabalhistas emitidos durante o ano de 2020, fazendo uma caracterização quanto ao seu alcance e atores afetados. Para isso, utilizou-se uma metodologia qualitativa por meio da análise documental, tendo como principal unidade de análise as normativas nacionais, e a pesquisa sobre medidas trabalhistas em nível internacional como fontes comparativas. Os resultados da análise permitem chegar a um mapa conceitual representado pelas regulamentações nas categorias de proteção trabalhista e fiscal, do qual deriva a classificação por atores/beneficiários e uma comparação com as principais medidas nas principais regiões do mundo, identificando que as medidas não atingiram a efetividade esperada dados os altos índices de desemprego e informalidade que se mantêm desde 2020 até o momento

    When climate change couples social neglect: malaria dynamics in Panama

    Get PDF
    A major challenge of infectious disease elimination is the need to interrupt pathogen transmission across all vulnerable populations. Ethnic minorities are among the key vulnerable groups deserving special attention in disease elimination initiatives, especially because their lifestyle might be intrinsically linked to locations with high transmission risk. There has been a renewed interest in malaria elimination, which has ignited a quest to understand factors necessary for sustainable malaria elimination, highlighting the need for diverse approaches to address epidemiological heterogeneity across malaria transmission settings. An analysis of malaria incidence among the Guna Amerindians of Panama over the last 34 years showed that this ethnic minority was highly vulnerable to changes that were assumed to not impact malaria transmission. Epidemic outbreaks were linked with El Nino Southern Oscillations and were sensitive to political instability and policy changes that did not ensure adequate attention to the malaria control needs of the Gunas. Our results illustrate how the neglect of minorities poses a threat to the sustainable control and eventual elimination of malaria in Central America and other areas where ethnic minorities do not share the benefits of malaria control strategies intended for dominant ethnic groups
    corecore