Diluted-Average-Shares Time Series for Telephone and Data Systems Inc. Telephone and Data Systems, Inc., a telecommunications company, provides communications services to consumer, business, and government in the United States. It operates through three segments: UScellular Wireless, UScellular Towers, and TDS Telecom. The company offers wireless solutions, including a suite of connected Internet of things (IoT) solutions, and software applications for monitor and control, business automation/operations, communication, fleet/asset/video management solutions, security solutions, private cellular networks, and custom and bespoke end-to-end IoT solutions, as well as professional and managed services, such as staff augmentation, IPX services, and SIM management; and critical connectivity solutions comprising wireless priority services and quality priority and preemption options. It also provides devices, such as smartphones and other handsets, tablets, wearables, mobile hotspots, fixed wireless home internet, and IoT devices; accessories, including wireless essentials which include cases, screen protectors, cables, chargers, memory cards, as well as consumer electronics, comprising bluetooth audio, wi-fi enabled cameras, and networking products. In addition, the company offers replace and repair services; Trade-In program through which it buys customers' used equipment; internet connections and all-home Wi-Fi services; TDS TV+, an integrated cloud television platform; local and long-distance telephone service, voice over internet protocol, and enhanced services; broadband, IP-based services, and hosted voice and video collaboration services; and communication services in underserved areas. The company sells and distributes its products through third-party direct sales, retail stores, sales agents, and an online platform to sell services and products. Telephone and Data Systems, Inc. was incorporated in 1968 and is based in Chicago, Illinois.

In 2022, smartphone vendors sold around 1.39 billion smartphones were sold worldwide, with this number forecast to drop to 1.34 billion in 2023.

Smartphone penetration rate still on the rise

Less than half of the world’s total population owned a smart device in 2016, but the smartphone penetration rate has continued climbing, reaching 78.05 percent in 2020. By 2025, it is forecast that almost 87 percent of all mobile users in the United States will own a smartphone, an increase from the 27 percent of mobile users in 2010.

Smartphone end user sales

In the United States alone, sales of smartphones were projected to be worth around 73 billion U.S. dollars in 2021, an increase from 18 billion dollars in 2010. Global sales of smartphones are expected to increase from 2020 to 2021 in every major region, as the market starts to recover from the initial impact of the coronavirus (COVID-19) pandemic.

The number of smartphone users in the Philippines was forecast to increase between 2024 and 2029 by in total 5.6 million users (+7.29 percent). This overall increase does not happen continuously, notably not in 2026, 2027, 2028 and 2029. The smartphone user base is estimated to amount to 82.33 million users in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

**SUBF Dataset v1.0: Bearing Fault Diagnosis using Vibration Signals **

Description The SUBF dataset v1.0 has been designed for the analysis and diagnosis of mechanical bearing faults. The mechanical setup consists of a motor, a frame/base, bearings, and a shaft, simulating different machine conditions such as a healthy state, inner race fault, and outer race fault. This dataset aims to facilitate reproducibility and support research in mechanical fault diagnosis and machine condition monitoring.

The dataset is part of the research paper "Aziz, S., Khan, M. U., Faraz, M., & Montes, G. A. (2023). Intelligent bearing faults diagnosis featuring automated relative energy-based empirical mode decomposition and novel cepstral autoregressive features. Measurement, 216, 112871." DOI: https://doi.org/10.1016/j.measurement.2023.112871

The dataset can be used with MATLAB and Python.

Experimental Setup Motor: A 3-phase AC motor, 0.25 HP, operating at 1440 RPM, 50 Hz frequency, and 440 Volts. Target Bearings: The left-side bearing was replaced to represent three categories: - Normal Bearings - Inner Race Fault Bearings - Outer Race Fault Bearings

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Instrumentation - Sensor: BeanDevice 2.4 GHz AX-3D, a wireless vibration sensor, was used to record vibration data. - Recording: Data collected via BeanGateway and stored on a PC. - Sampling: 1000 Hz.

Data Acquisition - Duration: 18 hours of data collection (6 hours per class). - Segmenting: Signals were divided into 10-second segments, resulting in 2160 signals for each fault category. - Classes: Healthy state, inner race fault, and outer race fault.

Dataset Organization The dataset is structured as follows: Main Folder: Contains two subfolders for .mat and .csv file formats to accommodate different user preferences.

• Subfolder 1: .mat Files Healthy: Contains .mat files representing vibration signals for the healthy state. Inner Race Fault: Contains .mat files representing vibration signals for bearings with an inner race fault. Outer Race Fault: Contains .mat files representing vibration signals for bearings with an outer race fault.

• Subfolder 2: .csv Files Healthy: Contains .csv files representing vibration signals for the healthy state. Inner Race Fault: Contains .csv files representing vibration signals for bearings with an inner race fault. Outer Race Fault: Contains .csv files representing vibration signals for bearings with an outer race fault.

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Applications This dataset is suitable for tasks such as: Fault detection and diagnosis Signal processing and feature extraction research Development and benchmarking of machine learning and deep learning models

Usage This dataset can be used for academic research, industrial fault diagnosis applications, and algorithm development. Please cite the following reference when using this dataset: Aziz, S., Khan, M. U., Faraz, M., & Montes, G. A. (2023). Intelligent bearing faults diagnosis featuring automated relative energy based empirical mode decomposition and novel cepstral autoregressive features. Measurement, 216, 112871." DOI: https://doi.org/10.1016/j.measurement.2023.112871

Licence This dataset is made publicly available for research purposes. Ensure appropriate citation and credit when using the data.https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F7973470%2F1a76f7de5a0ca312ddc2d9ed0caf99a5%2Fs2.png?generation=1732113357938012&alt=media" alt="">

The number of Apple iPhone unit sales dramatically increased between 2007 and 2023. Indeed, in 2007, when the iPhone was first introduced, Apple shipped around *** million smartphones. By 2023, this number reached over *** million units. The newest models and iPhone’s lasting popularity Apple has ventured into its 17th smartphone generation with its Phone ** lineup, which, released in September 2023, includes the **, ** Plus, ** Pro and Pro Max. Powered by the A16 bionic chip and running on iOS **, these models present improved displays, cameras, and functionalities. On the one hand, such features come, however, with hefty price tags, namely, an average of ***** U.S. dollars. On the other hand, they contribute to making Apple among the leading smartphone vendors worldwide, along with Samsung and Xiaomi. In the first quarter of 2024, Samsung shipped over ** million smartphones, while Apple recorded shipments of roughly ** million units. Success of Apple’s other products Apart from the iPhone, which is Apple’s most profitable product, Apple is also the inventor of other heavy-weight players in the consumer electronics market. The Mac computer and the iPad, like the iPhone, are both pioneers in their respective markets and have helped popularize the use of PCs and tablets. The iPad is especially successful, having remained as the largest vendor in the tablet market ever since its debut. The hottest new Apple gadget is undoubtedly the Apple Watch, which is a line of smartwatches that has fitness tracking capabilities and can be integrated via iOS with other Apple products and services. The Apple Watch has also been staying ahead of other smart watch vendors since its initial release and secures around ** percent of the market share as of the latest quarter.

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. To reduce the energy burden on income-qualified households within New York State, NYSERDA offers the EmPower New York (EmPower) program, a retrofit program that provides cost-effective electric reduction measures (i.e., primarily lighting and refrigerator replacements), and cost-effective home performance measures (i.e., insulation air sealing, heating system repair and replacments, and health and safety measures) to income qualified homeowners and renters. Home assessments and implementation services are provided by Building Performance Institute (BPI) Goldstar contractors to reduce energy use for low income households. This data set includes energy efficiency projects completed since January 2018 for households with income up to 60% area (county) median income.

D I S C L A I M E R: Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and First Year Energy Savings $ Estimate represent contractor reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the impact analysis indicate that, on average, actual savings amount to 54 percent of the Estimated Annual kWh Savings and 70 percent of the Estimated Annual MMBtu Savings. The analysis did not evaluate every single project, but rather a sample of projects from 2007 and 2008, so the results are applicable to the population on average but not necessarily to any individual project which could have over or under achieved in comparison to the evaluated savings. The results from the impact analysis will be updated when more recent information is available. Some reasons individual households may realize savings different from those projected include, but are not limited to, changes in the number or needs of household members, changes in occupancy schedules, changes in energy usage behaviors, changes to appliances and electronics installed in the home, and beginning or ending a home business. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: https://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-EmPower-New-York-Impact-Report.pdf.

This dataset includes the following data points for projects completed after January 1, 2018: Reporting Period, Project ID, Project County, Project City, Project ZIP, Gas Utility, Electric Utility, Project Completion Date, Total Project Cost (USD), Pre-Retrofit Home Heating Fuel Type, Year Home Built, Size of Home, Number of Units, Job Type, Type of Dwelling, Measure Type, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, First Year Modeled Energy Savings $ Estimate (USD).

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

Household Travel Survey (HTS) is the most comprehensive source of personal travel data for the Sydney Greater Metropolitan Area (GMA). This data explores average weekday travel patterns for residents in Sydney GMA. The Household Travel Survey (HTS) collects information on personal travel behaviour. The study area for the survey is the Sydney Greater Metropolitan Area (GMA) which includes Sydney Greater Capital City Statistical Area (GCCSA), parts of Illawarra and Hunter regions. All residents of occupied private dwellings within the Sydney GMA are considered within scope of the survey and are randomly selected to participate. The HTS has been running continuously since 1997/981 and collects data for all days through the year – including during school and public holidays. Typically, approximately 2,000-3,000 households participate in the survey annually. Data is collected on all trips made over a 24-hour period by all members of the participating households. Annual estimates from the HTS are usually produced on a rolling basis using multiple years of pooled data for each reporting year2. All estimates are weighted to the Australian Bureau of Statistics’ Estimated Resident Population, corresponding to the year of collection3. Unless otherwise stated, all reported estimates are for an average weekday. Due to disruptions in data collection resulting from the lockdowns during the COVID-19 pandemic, post-COVID releases of HTS data are based on a lower sample size than previous HTS releases. To ensure integrity of the results and mitigate risk of sampling errors some post-COVID results have been reported differently to previous years. Please see below for more information on changes to HTS post-COVID (2020/21 onwards). Data collection for the HTS was suspended during lock-down periods announced by the NSW Government due to COVID-19. Exceptions apply to the estimates for 2020/21 which are based on a single year of sample as it was decided not to pool the sample with data collected pre-COVID-19. HTS population estimates are also slightly lower than those reported in the ABS census as the survey excludes overseas visitors and those in non-private dwellings. Changes to HTS post-COVID (2020/21 onwards) HTS was suspended from late March 2020 to early October 2020 due to the impact and restrictions of COVID-19, and again from July 2021 to October 2021 following the Delta wave of COVID-19. Consequently, both the 2020/21 and 2021/22 releases are based on a reduced data collection period and smaller samples. Due to the impact of changed travel behaviours resulting from COVID-19 breaking previous trends, HTS releases since 2020/21 have been separated from pre-COVID-19 samples when pooled. As a result, HTS 2020/21 was based on a single wave of data collection which limited the breadth of geography available for release. Subsequent releases are based on pooled post-COVID samples to expand the geographies included with reliable estimates. Disruption to the data collection during, and post-COVID has led to some adjustments being made to the HTS estimates released post-COVID: SA3 level data has not been released for 2020/21 and 2021/22 due to low sample collection. LGA level data for 2021/22 has been released for selected LGAs when robust Relative Standard Error (RSE) for total trips are achieved Mode categories for all geographies are aggregated differently to the pre-COVID categories Purpose categories for some geographies are aggregated differently across 2020/21 and 2021/22. A new data release – for six cities as defined by the Greater Sydney Commission - is included since 2021/22. Please refer to the Data Document for 2022/23 (PDF, 262.54 KB) for further details. RELEASE NOTE The latest release of HTS data is 15 May 2025. This release includes Region, LGA, SA3 and Six Cities data for 2023/24. Please see 2023/24 Data Document for details. A revised dataset for LGAs and Six Cities for HTS 2022/23 data has also been included in this release on 15 May 2025. If you have downloaded HTS 2022/23 data by LGA and/or Six Cities from this link prior to 15/05/2025, we advise you replace it with the revised tables. If you have been supplied bespoke data tables for 2022/23 LGAs and/or Six Cities, please request updated tables. Revisions to HTS data may be made on previously published data as new sample data is appended to improve reliability of results. Please check this page for release dates to ensure you are using the most current version or create a subscription (https://opendata.transport.nsw.gov.au/subscriptions) to be notified of revisions and future releases.

ABSTRACT This paper contributes by encouraging discussions about the public policy of setting tariffs for public services based on the value of the investment made by the providers of these services. The purpose of this study was, in an unprecedented way and by combining theories of equity valuation and finance, to identify the asset valuation method that can lead to a fair value and balance between an affordable price for the consumer and an adequate return on investment for the concessionaires. The value assigned to these assets affects the tariff in two ways: (i) via depreciation/amortization, which affects the cost of service; (ii) via the return on investment, which is the portion that corresponds to the investor’s profit. We analyzed the Brazilian electricity sector, in which the rates set by the Brazilian Electricity Regulatory Agency (ANEEL) currently use the new replacement value (NRV) approach. We carried out empirical tests using data available on the ANEEL website from the second cycle periodic tariff review and information obtained in financial statements from 1995 onwards. The analysis included the NVR and restated historical cost (RHC) methods, the latter being updated by the extended consumer price index (IPCA). After the descriptive and statistical analyses, we used the test of means to verify the differences between the variables in terms of NRV vs. RHC. The first conclusion was the absence of a significant difference between the NRV and RHC methods; that is, on average, the replacement price showed no significant difference to what would be the pure and simple restatement of assets. But this was found to hide something relevant, the fact that this average is derived from two main groups: that of the consumers who are paying more for energy services than they should, which constitutes a visible benefit to investors and loss for these consumers, and that of the consumers who are paying less than they should, which benefits them but harms investors.