Cross-sectional Analysis of Sound Levels in the Neonatal Intensive Care Unit (NICU) at Thomas Jefferson University Hospital

Introduction . Infants in the NICU are considered at greater risk of developmental delay. It is now known that excessively loud noise can have a negative impact on parameters such as blood pressure, breathing, heart beat and oxygen saturation. Previous research has concluded that the optimal decibel (dB) level for proper growth of neonate hair cells rests around 45dB. Consequently, the American Academy of Pediatrics recommends that noise levels in the NICU be maintained to a maximum of 45dBA. However, little research has focused on designing new noise-altering products and their impact on neonatal outcomes. Methods. This was a cross sectional study. The NICU at Thomas Jefferson University Hospital was observed for room arrangements and general workflow. Additionally, decibel levels around empty neonatal incubators were measured. A decibel analyzer (REED Instruments SD-4023, Wilmington, NC) was used to record sound levels, both inside and outside of isolettes during various routine activities, including patient rounds, provider-parent conversations and vital monitoring alarms. Results. 30 discrete data points were surveyed, in addition to a 24-hour continuous decibel recording. Across all discrete data points, decibel levels had a mean of 65.6dB (SD ± 10.3). Ambient noise alone in a patient room was measured at 50dB. Noise levels in an open and closed isolette were measured at 58 and 57dB, respectively. Isolette side door opening and closing had a mean of 80.2dB (SD ± 7.60). With medical devices active in the patient room, noise levels had a mean of 62.7dB (SD ± 7.74). Conclusions. All data points were above the recommended safe noise level of 45dB. This data supports our development of a noise reduction product for use within neonatal isolettes. Our design will incorporate sterilizable, sound-absorbent materials and diffusion technologies to decrease ambient noise within neonatal incubators

Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

Neural networks are among the most accurate supervised learning methods in use today, but their opacity makes them difficult to trust in critical applications, especially when conditions in training differ from those in test. Recent work on explanations for black-box models has produced tools (e.g. LIME) to show the implicit rules behind predictions, which can help us identify when models are right for the wrong reasons. However, these methods do not scale to explaining entire datasets and cannot correct the problems they reveal. We introduce a method for efficiently explaining and regularizing differentiable models by examining and selectively penalizing their input gradients, which provide a normal to the decision boundary. We apply these penalties both based on expert annotation and in an unsupervised fashion that encourages diverse models with qualitatively different decision boundaries for the same classification problem. On multiple datasets, we show our approach generates faithful explanations and models that generalize much better when conditions differ between training and test

Beyond Sparsity: Tree Regularization of Deep Models for Interpretability

The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step through the process behind their predictions in little time. Specifically, we train deep time-series models so their class-probability predictions have high accuracy while being closely modeled by decision trees with few nodes. Using intuitive toy examples as well as medical tasks for treating sepsis and HIV, we demonstrate that this new tree regularization yields models that are easier for humans to simulate than simpler L1 or L2 penalties without sacrificing predictive power.Comment: To appear in AAAI 2018. Contains 9-page main paper and appendix with supplementary materia

A new paracolletine bee from Colombia (Hymenoptera: Colletidae), with an updated checklist of the tropical Andean bee fauna

A new species of the paracolletine genus Lonchopria Vachal, Lonchopria (Biglossa) comforti Gonzalez & Engel, new species, from high elevations in the Central Andes of Colombia is described and figured.  A preliminary key to the species of the Lonchopria subgenus Biglossa Friese is presented.  Recent records of bees occurring at elevations above 2500 m in Colombia and other Andean countries are also summarized

Observations on the urban ecology of the Neotropical stingless bee Tetragonisca angustula (Hymenoptera: Apidae: Meliponini)

This is the publisher's version, also available electronically from https://journals.ku.edu/index.php/melittology/indexTetragonisca angustula (Latreille) is a small, docile, cavity-nestingstingless bee that is widely distributed in the Neotropical region. This speciesis particularly abundant in disturbed environments, including humansettlements. Between August 2005 and March 2006, we located and followed duringeight months 59 nests of this species in Medellín, the second most populatedcity in Colombia. Herein, we document their foraging behavior, mortality, andincidence of predators and natural enemies. Also, to determine if higherambient temperature and light intensity in urban environments affect the dailyforaging activity of T. angustula, wecompared the daily foraging activity of bees from nests found in open areas in thecity and bees from nests from a nearby covered, forested area. Likewise, todetermine if urban nests of T. angustulaare largely undetected and undisturbed by people, we experimentally made themvisible by adding a ring color (white, red, or black) around the nest entrance tube.Our observations indicate that higher ambient temperature and light intensity inurban environments do not significantly affect the daily foraging activity of T. angustula. Nearly half of the markednests disappeared, thus suggesting that nests of T. angustula are often undetected by people in Medellín. We discussbriefly some features of the biology of T.angustula that might contribute to its success in urban environments

Gestión del talento humano y desempeño laboral en el Hospital Regional Ayacucho en tiempos de COVID-19, 2021

A propósito, la investigación tuvo como objetivo determinar la relación de la Gestión del talento humano y Desempeño laboral en el Hospital Regional Ayacucho en tiempos de COVID-19, 2021, La investigación alcanzó un enfoque cuantitativo, tipo aplicada y la técnica empleada fue la encuesta, nivel descriptivo correlacional, diseño no experimental transversal. Donde se obtuvieron una muestra de 132 trabajadores tanto asistenciales como administrativos indistintamente de su condición laboral, aplicando encuestas para la variable gestión del talento humano y desempeño laboral; En cuanto a la confiabilidad de Alpha de Cronbach para ambas variables cuyo resultado fue 0.956 y 0.812, respectivamente, y el procesamiento de datos se realizó con el software SPSS (versión 26), obteniendo un resultado de la gestión del talento humano se relaciona directa con el desempeño laboral con rs = 0.390 estableciéndose como una correlación positiva baja y significativa con p= 0.000 < 0.05, es decir si mejora la gestión del talento humano mejora el desempeño laboral, por lo tanto, se rechazó, la hipótesis nula, es decir: Existe relación significativa entre la Gestión del talento humano y Desempeño laboral en el hospital regional Ayacucho en tiempos de COVID-19, 202

Gestión del conocimiento y productividad laboral según los funcionarios de la Dirección Regional de Salud de Ica, 2018

La investigación titulada “Gestión del conocimiento y productividad laboral según los funcionarios de la Dirección Regional de Salud de Ica, 2018” tuvo como objetivo general de determinar la relación que existe entre la gestión del conocimiento y la productividad laboral según los funcionarios de la Dirección Regional de Salud de Ica, 2018. La investigación se desarrolló bajo el enfoque cuantitativo; la investigación fue de tipo básica con un nivel descriptivo y correlacional; diseño no experimental con corte transversal; la muestra estuvo conformada por 136 funcionarios de la Dirección Regional de Salud de Ica, 2018; los instrumentos de medición fueron sometidos a validez (Suficiente para su aplicación para ambos instrumentos) y fiabilidad (fuerte confiabilidad en ambos instrumentos de medición). Se aplicó el estadístico Rho de Spearman para determinar la relación entre las variables, donde el coeficiente de correlacion fue de 0,322 lo cual indicó una correlación positiva débil entre las variables y p = 0.000 < 0.01, por lo que se concluye que existe una relación positiva y significativa entre las variables