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The document compares the accuracy of forward, backward, and central difference methods for approximating the derivative of a function. It provides the exact derivative, calculates the approximate derivatives using h=0.1, and determines the error compared to the exact value. The central difference method provides the most accurate approximation, with an error of 0.003, while the forward and backward differences have errors of 0.015 and 0.009 respectively.
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compiladores6Benjamin133467.pdf
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Compiladores5_Benjamin133467.pdf
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133467 compiladores 4.pdf
The document provides pseudocode for parsing arithmetic expressions using a bottom-up recursive descent parser based on a given grammar. It includes pseudocode for evaluating expressions, handling operators, and matching expected tokens. It also shows the parsing actions for three example arithmetic expressions using a given LL(1) parsing table.
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Logica proposicional
(1) La probabilidad de que dos personas sean hermanos antes de saber su apellido es 0.1.
(2) Si son hermanos, la probabilidad de que tengan el mismo apellido es 0.9. Si no son hermanos, es 0.1.
(3) Usando la fórmula de Bayes, la probabilidad de que sean hermanos dado que tienen el mismo apellido (P(h|s)) es 0.9.
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Algoritmo de INGRES
Índice
Algoritmo de INGRES…………................................................................................................ 1
Algoritmo de System R……………………………………………………………………………………………………… 3
Algoritmo R* ……………………………………………………………………………………………………………………. 5
Fragmentación
La fragmentación consiste en dividir una relación global en subconjuntos o fragmentos, donde cada fragmento contiene tuplas con propiedades comunes. Existen dos tipos de fragmentación: horizontal, donde la relación se divide en fragmentos basados en una condición de selección; y vertical, donde los atributos se dividen en grupos y los fragmentos se obtienen proyectando la relación global sobre cada grupo de atributos.
Modelo cliente servidor
Un sistema cliente/servidor tiene dos partes principales: el servidor o backend que ejecuta el SGBD, y el cliente o frontend que son las aplicaciones que interactúan con el usuario. Los clientes pueden ser aplicaciones creadas por los usuarios o herramientas provistas por el proveedor. El servidor soporta funciones básicas de base de datos y puede ser iterativo o concurrente, procesando consultas de clientes de forma secuencial o simultánea respectivamente. Las arquitecturas cliente/servidor pueden tener dos o tres capas.
Arquitectura de bases de datos distribuidas
La arquitectura de bases de datos distribuidas define la estructura de un sistema que ofrece transparencia de datos, independencia lógica y física a través de la distribución de datos en múltiples sitios. Estos sistemas distribuidos pueden ser homogéneos, con una sola colección de datos, o heterogéneos, con múltiples colecciones de datos y usuarios locales y globales que acceden a datos almacenados en diferentes sitios.
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The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理
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（详情请加一下 文凭顾问+微信:95270640）欢迎咨询！子小伍玩小伍比山娃小一岁虎头虎脑的很霸气父亲让山娃跟小伍去夏令营听课山娃很高兴夏令营就设在附近一所小学山娃发现那所小学比自己的学校更大更美操场上还铺有塑胶跑道呢里面很多小朋友一班一班的快快乐乐原来城里娃都藏这儿来了怪不得平时见不到他们山娃恍然大悟起来吹拉弹唱琴棋书画山娃都不懂却什么都想学山娃怨自己太笨什么都不会斟酌再三山娃终于选定了学美术当听说每月要交元时父亲犹豫了山娃也说爸算了吧咱学校一学期才转
学校原版美国波士顿大学毕业证学历学位证书原版一模一样
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本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
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Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Hematology Analyzer Machine - Complete Blood Count
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
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Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
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133467 p3a1
- 1. Aproxima el valor de la derivada de la siguiente función cuando x=1 usando diferencias progresivas, regresivas y centrales. Utiliza en todos los casos h=0.1.Calcula el error cometido comparando con el valor exacto al sustituir en la derivada de la función que se te indica Valor real de la derivada = −18+50𝑥+18𝑥2 −18𝑥3 (𝑥2+3)3 −18 + 50(1)+ 18(1)2 − 18(1)3 ((1)2 + 3)3 −18 + 50 + 18 − 18 (1 + 3)3 −18 + 50 (4)3 32 64 = 0.5
- 2. Diferencias progresivas : f’(1)=( f(1+0.1) – f(1) ) / 0.1 f(1.1) = ( 3𝑥−1 𝑥2 +3 )2 = ( 3(1.1)−1 1.12+3 )2 = ( 2.3 4.21 )2 = 𝟎.𝟐𝟗𝟖𝟒𝟔𝟑𝟔𝟕𝟑𝟕𝟓𝟒𝟗𝟒𝟑𝟖𝟕 f(1)= ( 3(1)−1 12+3 )2 = ( 2 4 )2 = 0.25 f’(1)=( 0.29846367375494387–0.25) / 0.1 = 0.4846367375494387 Diferencias regresivas f’(1)=( f(1) - f(1- 0.1) ) / 0.1 = ( f(1) - f(0.9) ) / 0.1 f(0.9) = ( 3𝑥−1 𝑥2 +3 )2 = ( 3(0.9)−1 0.92 +3 )2 = ( 1.7 3.81 )2 = 0.19908928706746304 f’(1)=( 0.25 - 0.19908928706746304)/ 0.1 = 0.5091071293253696
- 3. Diferencias centrales f’(1)= ( f(1.1) – f(0.9) )/ 2h = ( 0.29846367375494387 - 0.19908928706746304 ) / 2*0.1 = 0.09937438668748083/0.2 = 0.49687193343740416 Valor real de la derivada = 0 .5 Error Progresivas 0.4846367375494387 0.0153632624505613 Regresivas 0.5091071293253696 0.009107129325369612 Centrales 0.49687193343740416 0.0031280665625958437