Una empresa presupuest� ventas unitarias de 204 000 unidades para en.pdf

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Una empresa presupuest ventas unitarias de 204 000 unidades para enero de 2017 y 240 000 unidades para febrero de 2017. La empresa tiene la poltica de tener un inventario de unidades disponible al final de cada mes equivalente al 30 % de las ventas unitarias presupuestadas del prximo mes. Si haba 61 200 unidades de inventario disponibles el 31 de diciembre de 2016, cuntas unidades se deben producir en enero de 2017 para que la empresa alcance sus metas?.

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Turner, Roth y Lowe son socios que comparten ingresos y prdidas en una proporcin de 2:3:5. Despus de largos desacuerdos entre los socios y varios perodos improductivos, los socios deciden liquidar la sociedad. Inmediatamente antes de la liquidacin, el balance de la sociedad muestra activos totales, $164,400; pasivo total, $110,000; Turner, Capital, $5,700; Roth, Capital, $15,600; y Lowe, Capital, $33,100. Los ingresos en efectivo de la venta de los activos fueron suficientes para pagar a los acreedores todo menos $44,000. Requerido: a. Calcule la prdida por la venta de los activos. b. Distribuya la prdida de la parte a a los socios. C. Determinar cunto, en su caso, cada socio debe contribuir a la sociedad para cubrir cualquier deficiencia de capital restante..

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Uber - Bir Startup'n Kkenleri ve lk Gnleri Dava: Mterileri eletirmek. Ksa deien iiler. Saldrgan dzenleyiciler. Aldatc yetkililer. "Her ne pahasna olursa olsun kazan" iyeri kltrnde kural ihlali ve acmaszln vurgulanmas. Sadece yedi ylda balangtan 70 milyar dolarlk bir iletmeye gemek iin gereken bu mu? Yoksa bu nitelendirmeler, kskan rakiplerin eletirileri yanl m? Uber ya da dnyann nde gelen ulam hizmetlerinden biri olan Uber Technologies Inc. olarak bilinen dk maliyetli ara arma hizmetinin heyecanl yolculuuna uzunca bir gz atalm. Balarken 2008 ylnda Paris'te bir konferansa katlan teknoloji giriimcileri Garrett Camp ve Travis Kalanick, karl bir gecede taksi bulmann zorluundan ikayet ediyorlard. Bu dei tokutan, bir dmeye dokunarak talep zerine bir araba aracak yeni teknoloji yaratma fikri geldi. The Upstarts'ta yazar Brad Stone, en azndan irketin resmi kken efsanesinin bu olduuna dikkat ekiyor. Aslnda bu fikri ortaya atan Camp, bir sredir arkada Kalanick ile akll telefon tabanl bir ehir arabas (lks araba veya siyah araba) paylam hizmeti kavramn tartyordu. Camp, yelerin favori web sitelerini dier insanlarla paylamalarna olanak tanyan keif ve neri arama motoru StumbleUpon'un kurucu ortayd. Kalanick ("cal-a-nick" olarak telaffuz edilir) ilk iini 18 yanda kurdu, bu bir SAT-hazrlk zel ders hizmetiydi. Bir dosya paylam ve hizmet ii olan Scour, onun ikinci i macerasyd, ancak Kalanick'in byk abasna ve srarna ramen baarsz oldu. Bir sonraki giriimi, bir video ak giriimi olan Red Swoosh'du. 2007'de 23 milyon dolara satld. Mart 2009'da Camp, Calgary'den eski yksek lisans rencileri Oscar Salazar ve Conrad Whelan ile birlikte Kalanick'in "mega danman" olarak grev yapt yeni ehir arabas fikrinin denenmesinde ba ekti. . Bunlardan biri, ilk olarak Haziran 2007'de Apple tarafndan piyasaya srlen ve yalnzca akll telefon endstrisinde devrim yaratmakla kalmayp ayn zamanda genellikle "paylam ekonomisi" olarak adlandrlan modern ibirliki ekonomiyi yaratmasyla tannan iPhone iin bir mobil uygulamann prototipiydi. Mart 2009'da UberCab olarak kurulan irket, mobil uygulama konseptini ilk olarak New York'ta test etti, ardndan 2011'de San Francisco'da kullanclarn siyah lks bir araba talep etmek iin iPhone'larnda bir dmeye basmalarna veya bir metin gndermelerine olanak tanyan bir hizmet olarak resmi olarak lanse edildi. (limuzin), geleneksel bir taksinin fiyatnn sadece 1,5 kat. Ancak o zamanki taksi irketlerinden farkl olarak, UberCab'in ayrt edici zellii, bir ulam a irketi olarak adlandrlmasyd; bu irkette yolcular, mobil uygulama veya web sitesi araclyla hizmet talep edebilir ve srcnn konumunu (GPS araclyla) takip edebilirdi. telefonlarnda) ve arabann tam olarak ne zaman geleceini biliyorlar. Birka arabas olmasna ve ok az kiinin bunu bilmesine ramen, giriim byk bir hit oldu. 2011 ylnda, ehir ve eyalet dzenleyicilerinin srar zerine, irket adn UberCab'den (nk bir ehir arabas veya taksi firmasnn ruhsat gerekliliklerini karlamad iin) "sper" anlamna gelen Uber'e (Almanca ber kelimesi.

Uber - Bir Startupn K�kenleri ve lk G�nleriDava M�terileri ele.pdf

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Trilogy Enterprises Inc. de Austin, Texas, es una empresa de software de rpido crecimiento y proporciona soluciones de software a empresas globales gigantes para mejorar las ventas y el rendimiento. Se enorgullece de su cultura nica y poco ortodoxa. Muchos de sus enfoques de la prctica comercial son inusuales, pero en el entorno altamente competitivo y cambiante de Trilogy, parecen funcionar. No existe un cdigo de vestimenta y los empleados establecen sus propios horarios, a menudo muy extensos. Tienden a socializar juntos (la edad promedio es de 26 aos), tanto en la cocina bien surtida de la oficina como en eventos patrocinados por la empresa y viajes a lugares como clubes de baile locales y retiros en Las Vegas y Hawi. Se ha desarrollado una jerga interna y la historia compartida de la firma ha adquirido el estatus de leyenda. La responsabilidad es pesada y llega temprano, con una actitud de "hazlo ahora" que prescinde de largos aprendizajes. Los nuevos reclutas reciben unas pocas semanas de capacitacin intensiva, conocida como "Universidad de Triloga" y los participantes la describen como "ms como un campo de entrenamiento que una escuela de negocios". La informacin se entrega como si fuera una manguera contra incendios y se espera que los nuevos empleados comprometan su experiencia y vitalidad en todo lo que hacen. Jeff Daniel, director de reclutamiento universitario, admite que la empresa intensa y poco convencional no es el empleador para todos. Pero definitivamente es un entorno donde las personas apasionadas por lo que hacen pueden prosperar. La empresa emplea a unas 700 personas tan apasionadas. Los gerentes de Trilogy saben que el rpido crecimiento que buscan depende de tener un personal de las mejores personas que puedan encontrar, capacitadas rpidamente y con amplia responsabilidad y libertad lo antes posible. El CEO Joe Liemandt dice: En una empresa de software, las personas lo son todo. No puedes construir la prxima gran empresa de software, que es lo que estamos tratando de hacer aqu, a menos que ests totalmente comprometido con eso. Por supuesto, los lderes de todas las empresas dicen: "La gente lo es todo". Pero no actan en consecuencia. Trilogy hace que encontrar a las personas adecuadas (las llama "grandes personas") sea una misin de toda la empresa. Los reclutadores buscan activamente a las personas ms frescas, si al menos con experiencia, en el mercado laboral, buscando ferias de carreras universitarias y departamentos de ciencias de la computacin para personas talentosas con ambicin e instinto empresarial. Los altos directivos llevan a cabo las primeras rondas de entrevistas y les informan a los prospectos que se vern presionados para lograr logros pero que sern bien recompensados. Los empleados llevan a los reclutas y a sus seres queridos a la ciudad cuando vuelan a Austin para la visita preliminar estndar de 3 das. Un da tpico puede comenzar con entrevistas agotadoras, pero terminar con ciclismo de montaa, patinaje sob.

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Transforming Data - Creating a Data Set Creating a JOIN between two tables is one of several ways to consolidate data. Which of the following is NOT true about table JOINS in Tableau? Select one. JOINs effectively create one physical table of combined data. All JOIN types combine data in the same way. To JOIN two tables in Tableau, you must first double-click on the primary table, to reveal the physical layer. After performing a JOIN, we should hide one of the JOIN keys to ensure we do not have duplicate columns..

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Using the location quotients(LQ) for year 2020 for 10 provinces analyze what province has the most relational importance in the following sectors. (i)Agriculture (ii)Forestry, fishing, mining, quarrying, oil, and gas (iii)Manufacturing In this answer can you please mention the names of the provinces like Manitoba, Alberta and in the answer its mentions like province A and province B which don't indicate the province clearly instead please provide the names for them..

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using IRAC method Joseph was a team manager of an excursion in the Okavango Delta. Two of Joseph' s team were attacked by elephants and left, claiming that it has become too dangerous. With that Joseph promised the other colleague who remained, Karabo, that if she stayed and all of the excursion work was completed, Karabo would get monies of one of the colleagues who left without completing work. Karabo went on and agreed, but after the excursion was done, to Karabo's surprise Joseph refuses to pay the money to Karabo. in 250-350 words ,advice Karabo whether she is entitled to the extra payment.

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*USING 'https://raw.githubusercontent.com/BeeDrHU/Introduction-to-Python-CSPC-323- /main/sales_forecast.csv' please fix the errors for the RNN using python so the code can run without errors, Thank you* ##Defining Function to Build Multivariate Data Structure def multivariate_data(dataset, target, start_index, end_index, history_size, target_size, step, single_step=False): data = [] labels = [] start_index = start_index + history_size if end_index is None: end_index = len(dataset) - target_size for i in range(start_index, end_index): indices = range(i-history_size, i, step) data.append(dataset[indices]) if single_step: labels.append(target[i+target_size]) else: labels.append(target[i:i+target_size]) return np.array(data), np.array(labels) ##Define the Multi-step Plotting Structure def multi_step_plot(history, true_future, prediction): plt.figure(figsize=(12, 6)) num_in = create_time_steps(len(history)) num_out = len(true_future) plt.plot(num_in, np.array(history[:, 1]), label='History') plt.plot(np.arange(num_out)/STEP, np.array(true_future), 'bo', label='True Future') if prediction.any(): plt.plot(np.arange(num_out)/STEP, np.array(prediction), 'ro', label='Predicted Future') plt.legend(loc='upper left') plt.show() ##Define Function to Plot training and Validation over Time def plot_train_history(history, title): loss = history.history['loss'] val_loss = history.history['val_loss'] epochs = range(len(loss)) plt.figure() plt.plot(epochs, loss, 'b', label='Training loss') plt.plot(epochs, val_loss, 'r', label='Validation loss') plt.title(title) plt.legend() plt.show() ##How far into the past do you want the model to see? past_history= ##How far into the future do you want the model to see? future_target= ## STEP= df_val = len(dataset) - past_history - future_target x_train_multi, y_train_multi = multivariate_data(dataset, dataset[:, 1], 0, df_train, past_history, future_target, STEP) x_val_multi, y_val_multi = multivariate_data(dataset, dataset[:, 1], df_train, df_val, past_history, future_target, STEP) ##Define Constants BATCH_SIZE = BUFFER_SIZE = EPOCHS = EVALUATION_INTERVAL = ##Adding Layers Due to Complexity train_data_multi = tf.data.Dataset.from_tensor_slices(()) train_data_multi = train_data_multi.cache().shuffle().batch().repeat() val_data_multi = tf.data.Dataset.from_tensor_slices(()) val_data_multi = val_data_multi.batch().repeat() multi_step_model = tf.keras.models.Sequential() multi_step_model.add(tf.keras.layers.LSTM()) multi_step_model.add(tf.keras.layers.LSTM()) multi_step_model.add(tf.keras.layers.Dense(72)) multi_step_model.compile(optimizer=tf.keras.optimizers.RMSprop(clipvalue=1.0), loss='mae') ##Train the Model multi_step_history = multi_step_model.fit(train_data_multi, epochs=, steps_per_epoch=, validation_data=, validation_steps=50) ##Predict Future Data for x, y in .take(3): multi_step_plot(x[0], y[0], .predict(x)[0]) #Evaluate the model on the test data test_data_multi = tf.data.Dataset.from_tensor_slices()) test_data_mu.

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Using facial recognition software in shelters for victims of domestic violence raises several ethical concerns that must be carefully considered before pursuing such a project. Firstly, using this technology in such a sensitive and vulnerable setting raises serious concerns about privacy, safety, and the potential abuse of power by staff. It is essential to obtain informed consent from guests and staff and ensure that their personal information is securely stored and not misused. Additionally, there is a risk that this technology could be used to track and monitor guests' movements, which could violate their rights and compromise their safety. Secondly, there is the issue of potential bias in the technology itself, as facial recognition software has been shown to be less accurate when identifying people of colour and women, which could result in false accusations and wrongful identification. To address these ethical concerns, engaging in a thorough ethical review process that involves input from diverse stakeholders, including legal experts, privacy advocates, and survivors of domestic violence is crucial. This process should prioritize transparency, accountability, and the protection of guests and staff's rights. Other ethical issues that designers and developers of such an application would need to address include the potential for hacking and data breaches, the impact on civil liberties and human rights, and the potential for unintended consequences and harm. Response to this article is required, please provide In-Text Citations and credible references with the response. Thanks!.

Using facial recognition software in shelters for victims of domesti.pdf

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Use the info that os provided. I just need basic sketch I H.I. H..1..pdf

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Tory and Cathy entered into a contract under which Cathy was supposed to provide the floral arrangements for Torys wedding on May 23. The day of the wedding arrives, but Cathy does not deliver the floral arrangements. Cory is devastated and feels that her wedding was ruined by the lack of floral arrangements. She sues Cathy successfully for breach of contract and then asks for punitive damages because her wedding was ruined. The evidence presented at court only shows that Cathy did not deliver the arrangements because she had accidentally written the wrong delivery date on her calendar. Answer following the IRAC method..

Tory and Cathy entered into a contract under which Cathy was suppose.pdf

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use Selenium to write a Java code that will Scrape data from a podcast site, such as episode titles, descriptions, and ratings. Java program should do the following: Open the website in a web browser using Selenium. Find and interact with various elements on the page (e.g., links, buttons, text boxes) using Selenium commands. Extract data from the page using Selenium commands, such as finding and storing text, images, or other content. Save the scraped data in a CSV file or other format of podcast site..

use Selenium to write a Java code that will Scrape data from a podca.pdf

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