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This statistic shows the results of a survey on the share of users of selected smartphone operating systems in Finland in 2016. According to the survey, 44 percent used the Android operating system developed by Google. Meanwhile, 13 percent of respondents stated that their smartphone ran on an iOS operating system. iOS, developed by Apple and mainly used in the brand's mobile devices, is considered a competitor to Google's Android operating system. 23 percent of the respondents had a smartphone running on a Windows operating system while 3 percent were Symbian users. 17 percent of the respondents stated that their smartphone ran on another operating system or were unable to tell.

Statista provides additional statistical information on smartphone ownership in Finland. Owners of smartphones can be further observed looking at genders and age groups.

For the evaluation of OS fingerprinting methods, we need a dataset with the following requirements:

• First, the dataset needs to be big enough to capture the variability of the data. In this case, we need many connections from different operating systems.
• Second, the dataset needs to be annotated, which means that the corresponding operating system needs to be known for each network connection captured in the dataset. Therefore, we cannot just capture any network traffic for our dataset; we need to be able to determine the OS reliably.

To overcome these issues, we have decided to create the dataset from the traffic of several web servers at our university. This allows us to address the first issue by collecting traces from thousands of devices ranging from user computers and mobile phones to web crawlers and other servers. The ground truth values are obtained from the HTTP User-Agent, which resolves the second of the presented issues. Even though most traffic is encrypted, the User-Agent can be recovered from the web server logs that record every connection’s details. By correlating the IP address and timestamp of each log record to the captured traffic, we can add the ground truth to the dataset.

For this dataset, we have selected a cluster of five web servers that host 475 unique university domains for public websites. The monitoring point recording the traffic was placed at the backbone network connecting the university to the Internet.

The dataset used in this paper was collected from approximately 8 hours of university web traffic throughout a single workday. The logs were collected from Microsoft IIS web servers and converted from W3C extended logging format to JSON. The logs are referred to as web logs and are used to annotate the records generated from packet capture obtained by using a network probe tapped into the link to the Internet.

The entire dataset creation process consists of seven steps:

1. The packet capture was processed by the Flowmon flow exporter (https://www.flowmon.com) to obtain primary flow data containing information from TLS and HTTP protocols.
2. Additional statistical features were extracted using GoFlows flow exporter (https://github.com/CN-TU/go-flows).
3. The primary flows were filtered to remove incomplete records and network scans.
4. The flows from both exporters were merged together into records containing fields from both sources.
5. Web logs were filtered to cover the same time frame as the flow records.
6. Web logs were paired with the flow records based on shared properties (IP address, port, time).
7. The last step was to convert the User-Agent values into the operating system using a Python version of the open-source tool ua-parser (https://github.com/ua-parser/uap-python). We replaced the unstructured User-Agent string in the records with the resulting OS.

The collected and enriched flows contain 111 data fields that can be used as features for OS fingerprinting or any other data analyses. The fields grouped by their area are listed below:

• basic flow properties - flow_ID;start;end;L3 PROTO;L4 PROTO;BYTES A;PACKETS A;SRC IP;DST IP;TCP flags A;SRC port;DST port;packetTotalCountforward;packetTotalCountbackward;flowDirection;flowEndReason;
• IP parameters - IP ToS;maximumTTLforward;maximumTTLbackward;IPv4DontFragmentforward;IPv4DontFragmentbackward;
• TCP parameters - TCP SYN Size;TCP Win Size;TCP SYN TTL;tcpTimestampFirstPacketbackward;tcpOptionWindowScaleforward;tcpOptionWindowScalebackward;tcpOptionSelectiveAckPermittedforward;tcpOptionSelectiveAckPermittedbackward;tcpOptionMaximumSegmentSizeforward;tcpOptionMaximumSegmentSizebackward;tcpOptionNoOperationforward;tcpOptionNoOperationbackward;synAckFlag;tcpTimestampFirstPacketforward;
• HTTP - HTTP Request Host;URL;
• User-agent - UA OS family;UA OS major;UA OS minor;UA OS patch;UA OS patch minor;
• TLS - TLS_CONTENT_TYPE;TLS_HANDSHAKE_TYPE;TLS_SETUP_TIME;TLS_SERVER_VERSION;TLS_SERVER_RANDOM;TLS_SERVER_SESSION_ID;TLS_CIPHER_SUITE;TLS_ALPN;TLS_SNI;TLS_SNI_LENGTH;TLS_CLIENT_VERSION;TLS_CIPHER_SUITES;TLS_CLIENT_RANDOM;TLS_CLIENT_SESSION_ID;TLS_EXTENSION_TYPES;TLS_EXTENSION_LENGTHS;TLS_ELLIPTIC_CURVES;TLS_EC_POINT_FORMATS;TLS_CLIENT_KEY_LENGTH;TLS_ISSUER_CN;TLS_SUBJECT_CN;TLS_SUBJECT_ON;TLS_VALIDITY_NOT_BEFORE;TLS_VALIDITY_NOT_AFTER;TLS_SIGNATURE_ALG;TLS_PUBLIC_KEY_ALG;TLS_PUBLIC_KEY_LENGTH;TLS_JA3_FINGERPRINT;
• Packet timings - NPM_CLIENT_NETWORK_TIME;NPM_SERVER_NETWORK_TIME;NPM_SERVER_RESPONSE_TIME;NPM_ROUND_TRIP_TIME;NPM_RESPONSE_TIMEOUTS_A;NPM_RESPONSE_TIMEOUTS_B;NPM_TCP_RETRANSMISSION_A;NPM_TCP_RETRANSMISSION_B;NPM_TCP_OUT_OF_ORDER_A;NPM_TCP_OUT_OF_ORDER_B;NPM_JITTER_DEV_A;NPM_JITTER_AVG_A;NPM_JITTER_MIN_A;NPM_JITTER_MAX_A;NPM_DELAY_DEV_A;NPM_DELAY_AVG_A;NPM_DELAY_MIN_A;NPM_DELAY_MAX_A;NPM_DELAY_HISTOGRAM_1_A;NPM_DELAY_HISTOGRAM_2_A;NPM_DELAY_HISTOGRAM_3_A;NPM_DELAY_HISTOGRAM_4_A;NPM_DELAY_HISTOGRAM_5_A;NPM_DELAY_HISTOGRAM_6_A;NPM_DELAY_HISTOGRAM_7_A;NPM_JITTER_DEV_B;NPM_JITTER_AVG_B;NPM_JITTER_MIN_B;NPM_JITTER_MAX_B;NPM_DELAY_DEV_B;NPM_DELAY_AVG_B;NPM_DELAY_MIN_B;NPM_DELAY_MAX_B;NPM_DELAY_HISTOGRAM_1_B;NPM_DELAY_HISTOGRAM_2_B;NPM_DELAY_HISTOGRAM_3_B;NPM_DELAY_HISTOGRAM_4_B;NPM_DELAY_HISTOGRAM_5_B;NPM_DELAY_HISTOGRAM_6_B;NPM_DELAY_HISTOGRAM_7_B;
• ICMP - ICMP TYPE;

The details of OS distribution grouped by the OS family are summarized in the table below. The Other OS family contains records generated by web crawling bots that do not include OS information in the User-Agent.

Snapshot img { margin: 10px !important; } Below are some of the key findings from this computing device operating system market analysis report

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Global computing device operating system industry overview

As smart cities integrate multiple technologies to enable the smart economy, investments in smart cities will increase the deployment of connected systems and solutions such as smart lighting, smart meters, and smart parking. The growing positioning of connected systems and IoT devices will increase the adoption of operating systems to be installed in these devices. Moreover, the deployment of connected systems and solutions will also generate huge amount of data, which will require its storing and management, resulting in a high need for servers. This will further propel the demand for operating systems to be installed in servers to improve their efficiency. Such factors will boost computing device operating systems market growth during the forecast period.

To increase their customer base, operating system providers are focusing on forming strategic investments and partnerships with OEMs. This also enables operating system vendors to generate revenue through the sale of their mobile applications supported by their operating system platforms. For instance, Google generates revenue from the licensing of Google apps. Similarly, Google and Huawei technologies formed a partnership where Google would provide Android messages and RCS messaging for smartphones developed by Huawei technologies. Strategic investments and partnerships between computing device operating system and OEM providers will accelerate the popularity and deployment of operating systems, positively impacting market growth. The computing device OS market will register a CAGR of more than 2% during the forecast period. However, the market’s momentum will decelerate in the coming years because of the decrease in year-over-year growth.

Top computing device operating system companies covered in this report

The global computing device operating system market is highly concentrated. To help clients improve their revenue share, this research report provides an analysis of the market’s competitive landscape and offers information on the products offered by various companies. Key insights provided by this computing device operating system market analysis report will help companies make informed business decisions.

The report offers a detailed analysis of several leading computing device operating system companies, including:

Alphabet
Apple Inc.
Canonical Ltd. 
Microsoft
Red Hat, Inc.

Computing device operating system market segmentation based on type

Mobile OS
Client OS
Server OS

Mobile OS segment held the largest computing device operating system market share in 2018. The market share of this segment will increase in the coming years because of the rising use of smartphones and mobile related applications such as Facebook Messenger, WhatsApp, and INSTAGRAM. Mobile OS segment will continue to dominate the market during the next five years.

Computing device operating system market segmentation based on region

APAC
Europe
MEA
North America
South America

APAC accounted for the largest computing device operating system market share in 2018 because of the presence of several operating system vendors in the region. APAC’s contribution to the growth of the computing device operating system market size will increase, and the region will account for the largest market share throughout the forecast period.

Key highlights of the global computing device operating system market for the forecast years 2019-2023:

CAGR of the market during the forecast period 2019-2023
Detailed information on factors that will accelerate the growth of the computing device operating system market during the next five years
Precise estimation of the global computing device operating system market size and its contribution to the parent market
Accurate predictions on upcoming trends and changes in consumer behavior
The growth of the computing device operating system industry across APAC, Europe, MEA, North America, and South America
A thorough analysis of the market’s competitive landscape and detailed information on several vendors
Comprehensive details on factors that will challenge the growth of computing device operating system companies 

We can help! Our analysts can customize this market research report to meet your requirements. Get in touch

United States PPI: Svcs: SC: PO: EM: PS: SG: OS: Medicare data was reported at 96.700 Jun2014=100 in Mar 2018. This stayed constant from the previous number of 96.700 Jun2014=100 for Feb 2018. United States PPI: Svcs: SC: PO: EM: PS: SG: OS: Medicare data is updated monthly, averaging 97.200 Jun2014=100 from Jun 2014 (Median) to Mar 2018, with 46 observations. The data reached an all-time high of 100.000 Jun2014=100 in Dec 2015 and a record low of 96.600 Jun2014=100 in May 2016. United States PPI: Svcs: SC: PO: EM: PS: SG: OS: Medicare data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I: Producer Price Index: by Industry: Services: Selected Health Care Industries.

This study examines the siting scenarios for renewable energy infrastructure (REI) in Switzerland, incorporating the external costs of ecosystem services and, innovatively, social preferences. This approach challenges the prevalent techno-economic siting paradigm, which often overlooks these externalities. To minimize the external costs of the scenarios while maximizing energy yield, Marxan, an optimization software, was employed. MARXAN was run for 2 versions: a) without ground-mounted open space PV infrastructure (excl. OS) and b) with ground-mounted open space PV infrastructure (incl. OS). In each version optimization was done using ecological costs (ECUess) or social costs (ECUsoc) in a regular grid of 4x4km (planning unit).

File: PU_data.shp: compressed Shapefile (ESRI) from the MARXAN optimization with 2216 rows (objects) and 18 columns (variables) with sf (simple feature) and data frame classes.

Headers are described below: X01 = Planning_units (PUs)

X02 = incl.OS. number of times pu was selected in MARXAN when optimized for ecological costs X03 = incl.OS. number of times pu was selected in MARXAN when optimized for social costs X04 = incl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized) X05 = incl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized) X06 = incl.OS. total energy (MWh/a) X07 = incl.OS. energy from roof-mounted PV infrastructure (MWh/a) X08 = incl.OS. energy from wind turbines (MWh/a) X09 = incl.OS. energy from ground-mounted PV infrastructure (MWh/a)

X10 = excl.OS. number of times pu was selected in MARXAN when optimized for ecological costs X11 = excl.OS. number of times pu was selected in MARXAN when optimized for social costs X12 = excl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized) X13 = excl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized) X14 = excl.OS. total energy (MWh/a) X15 = excl.OS. energy from roof-mounted PV infrastructure (MWh/a) X16 = excl.OS. energy from wind turbines (MWh/a) X17 = excl.OS. energy from ground-mounted PV infrastructure (MWh/a)

geometry = Simple feature XY geometry (SFC_POINT) each representing the center of a 4x4km planning unit (PU) in EPSG 21781 (CRS CH1903 / LV03).

For MARXAN optimizations (for meaning of standard files in MARXAN see https://scholar.google.ch/scholar_url?url=https://courses.washington.edu/cfr590/software/Marxan1810/marxan_manual_1_8_2.pdf&hl=de&sa=X&ei=NAWHZI6JJ8PFmAGMo47oDQ&scisig=AGlGAw-DBrsV4kUzkR6GkN1jtG66&oi=scholarr), pu_data.shp was used to generate MARXAN files input.dat, pu.dat, puvfeat.dat, spec.dat. Coordinate Reference System: CH1903 / LV03. EPSG: 21781.

United States PPI: Svcs: SC: PO: EM: PS: SG: OS: Others data was reported at 108.300 Jun2014=100 in Mar 2018. This records an increase from the previous number of 108.100 Jun2014=100 for Feb 2018. United States PPI: Svcs: SC: PO: EM: PS: SG: OS: Others data is updated monthly, averaging 102.150 Jun2014=100 from Jun 2014 (Median) to Mar 2018, with 46 observations. The data reached an all-time high of 108.300 Jun2014=100 in Mar 2018 and a record low of 100.000 Jun2014=100 in Dec 2014. United States PPI: Svcs: SC: PO: EM: PS: SG: OS: Others data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I: Producer Price Index: by Industry: Services: Selected Health Care Industries.

United States PPI: Svcs: SC: PO: EM: PS: SG: OS: Private Insurance data was reported at 103.200 Jun2014=100 in Mar 2018. This stayed constant from the previous number of 103.200 Jun2014=100 for Feb 2018. United States PPI: Svcs: SC: PO: EM: PS: SG: OS: Private Insurance data is updated monthly, averaging 100.800 Jun2014=100 from Jun 2014 (Median) to Mar 2018, with 46 observations. The data reached an all-time high of 103.200 Jun2014=100 in Mar 2018 and a record low of 100.000 Jun2014=100 in Jun 2014. United States PPI: Svcs: SC: PO: EM: PS: SG: OS: Private Insurance data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I: Producer Price Index: by Industry: Services: Selected Health Care Industries.

Bananas Bounding Boxes

How to load the mxnet dataset: ```

import the mxnet Image module

from mxnet import image

prepare the path to the files:

base_path = '../input/bananas-bounding-boxes/bananas/' path_imgrec = os.path.join(base_path, 'train.rec') path_imgidx = os.path.join(base_path, 'train.idx')

select the image size height x width

edge_size = 256

prepare the batch iterator

you can also add shuffle=True and rand_crop=1 (probability to random crop)

image.ImageDetIter(path_imgrec=path_imgrec, path_imgidx=path_imgidx, batch_size=1, data_shape=(3, edge_size, edge_size)) ```

BackgroundGastrointestinal neuroendocrine tumor (GI-net) is a rare heterogeneous tumor, and there is a lack of models to predict its prognosis. Our study aims to develop and validate two new nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of GI-net patients and investigate their application value.MethodsSEER*Stat 8.4.4 software was used to download clinicopathological information of GI-net patients between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly divided into a training group (n=3007) and an internal-validation group (n=1289) at a 7:3 ratio. Patients from the Fourth Hospital of Hebei Medical University were enrolled in this study to form the external-validation group (n=86). Univariate and multivariate Cox analyses were performed to explore the independent prognostic factors and establish two nomograms. The concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomograms. X-tile was used to divide GI-net patients into high-, medium-, and low-risk groups. Kaplan–Meier (KM) curves and log-rank tests were used to compare survival differences among the three groups.ResultsSeven variables (age, site, size, grade, M stage, surgery, and chemotherapy) were selected to establish the nomogram for OS, and 6 variables (age, size, grade, M stage, surgery, and chemotherapy) were selected for CSS. The C indices (0.785, 0.813, and 0.936 in the training, internal-validation, and external-validation groups for OS; 0.888, 0.893, and 0.930 for CSS, respectively) and AUCs (≥0.7) indicated that the nomograms had satisfactory discriminative ability. Calibration curve analysis and DCA revealed that the nomogram had a satisfactory ability to predict OS and CSS. KM curves indicated that each of the two nomograms clearly differentiated the high-, medium-, and low-risk groups. In addition, two online risk calculators were developed to predict the OS and CSS of these patients visually.ConclusionsOur nomograms may play an important role in predicting 3- and 5-year OS and CSS for GI-net patients. Risk stratification systems and online risk calculators can be utilized in clinical practice to help doctors create personalized treatment plans.

The statistic illustrates the operating system (OS) market share in selected EU countries (Germany, France, Italy, Spain and UK) in August 2011, by mobile device platform*. After a three-month average ending August 2011, Google Android ranked third with **** percent of market share.

The OS Widgets extension for CKAN enhances data portals with map-based search and preview capabilities, primarily developed by Ordnance Survey for data.gov.uk. This extension provides widgets, an API for preview lists and spatial datastore for spatial data previews. It integrates mapping functionality into CKAN to allow users to discover and visualize geospatial datasets and to show a shopping basket preview list to give users an idea on what data to preview. Key Features: Map-Based Search Widget: Enables users to search for datasets based on geographic location, improving dataset discoverability in specific areas of interest. Map Preview Widget: Allows users to visualize geospatial datasets directly within CKAN, providing an immediate understanding of the data's spatial extent and content. Preview List API: Provides an API to store & manage a list of selected datasets for previewing, working as a "shopping basket" to keep a list of packages to preview. It supports adding to, removing from, and listing the packages available in the preview list. Spatial Database Integration: Supports the preview of data with geo-references (latitude/longitude coordinates, postcodes, etc.) through a spatial database (PostGIS). Spatial Ingester Wrapper: Includes a wrapper to call the Spatial Ingester, a Java tool that converts data (typically CSV/XLS) and stores it in a PostGIS database. This converted data can then be served in WFS format for display in the Map Preview tool. Configurable Server Settings: Enables customization of server URLs and API keys for the widgets, allowing configuration of the geoserver, gazetteer and libraries used in the widgets. Proxy Configuration Support: Provides guidance on configuring an Apache proxy to improve the performance of GeoServer WFS calls, ensuring quick retrieval of boundary information. Technical Integration: The extension integrates with CKAN through plugins (ossearch, ospreview, oswfsserver) that are enabled in the CKAN configuration file. Configuration involves adding plugin names to the ckan.plugins setting and adjusting server URLs, spatial database connection details, and API keys according to the specific environment, ensuring seamless integration with existing CKAN deployments. In addition to the PostGIS dependency that has to be created, it is also dependent on other external libraries such as, Jquery, underscore, backbone, etc. Benefits & Impact: Enhanced Data Discovery: Map-based search significantly improves the discovery of geospatial datasets within CKAN, allowing users to easily find data relevant to their geographic area of interest. Improved Data Understanding: Map previews provide immediate visual context for geospatial datasets, leading to a better understanding of the data's spatial characteristics.

This statistic shows the number of apps available to access video-on-demand (VoD) in Spain in 2017, ranked by type of device and operating systems (OS). Owners of Adroid and iOS operating systems could both choose among more than 150 apps to access video-on-demand content in Spain during this year.

This statistic shows the results of a survey on the share of users of the Apple iOS smartphone operating system in Finland in 2016, by age group. ** percent of the respondents aged 15 to 24 years had the Apple iOS operating system on their smartphone. The corresponding figure for the respondents aged 25 to 34 years was ** percent. The lowest share of iOS operating system users had the age group of 55 to 64 years, * percent of respondents in that age range having an iOS-based smartphone.

According to a survey on the share of users of selected smartphone systems in Finland, the share of iOS users was exceeded by Android and Windows users. In addition, Statista provides further statistical information on smartphone ownership in Finland.

OS Open Names is a geographic directory that contains basic information about identifiable places (Named Place). The content of the Product is divided into themes based on their type and local type classification values. The primary use of the product is to provide the location for a named place to support discovery or identification and visualisation, geocoding, routing and navigation and linking diverse information about a place (for example, statistics or descriptions). The name of the place will be the key property used for querying. It is also recognised that a place may have multiple names; an official name, which may be defined in multiple languages (English/Welsh or English/Gaelic), for example, Cardiff (English) and Caerdydd (Welsh). Names are not unique so additional location information is provided to enable users to refine their query to select the Named Place they are interested in. These include: postcode district, populated place, district/borough, county/unitary authority, European region and country. The OS Open Names specification will extend the Infrastructure for Spatial Information in the European Community (INSPIRE) Geographical Names theme to ensure that it is compliant with European open data initiatives.

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The automotive infotainment OS market size has the potential to grow by USD 247.84 million during 2021-2025, and the market’s growth momentum will accelerate at a CAGR of 9.70%.

This report provides a detailed analysis of the market by product (QNX, Linux, Microsoft Windows Embedded, and others), geography (APAC, Europe, MEA, North America, and South America), and key vendors.

Market Overview

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Market Competitive Analysis

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Alibaba Group Holding Ltd. Alphabet Inc. Apple Inc. Baidu Inc. BlackBerry Ltd. BMW AG Green Hills Software LLC Microsoft Corp. The Linux Foundation Wind River Systems Inc.

The automotive infotainment OS market is concentrated and the vendors are deploying various organic and inorganic growth strategies to compete in the market. Click here to uncover other successful business strategies deployed by the vendors.

The market players also significantly leverage external market drivers such as increasing use of automotive infotainment OS as a differentiator and medium to reduce the cost of additional features to achieve growth opportunities. However, factors such as rising adoption of smartphone-based automotive infotainment system as a cost-effective solution will challenge the growth of the market participants. To make the most of the opportunities and recover from post COVID-19 impact, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

Download a free sample of the automotive infotainment OS market forecast report for insights on complete key vendor profiles. The profiles include information on the production, sustainability, and prospects of the leading companies,

This automotive infotainment OS market analysis report also provides detailed information on the upcoming trends and challenges that will influence market growth. This will help companies create strategies to make the most of future growth opportunities.

Automotive Infotainment OS Market: Key Drivers and Trends

The use of automotive infotainment OS as a differentiator and medium to reduce the cost of additional features will drive the market growth during the forecast period. Several manufacturers in the automotive industry are actively invoked in the development of the infotainment system. Manufacturers of automotive infotainment systems are shifting toward the adoption of PC-like architectural concept. Herein, the functionality of the system is dependent on the main central processing unit (CPU). Consequently, the OS or software used in the system acts as the product differentiator among the brands. Android Automotive offers plentiful features to the vehicle infotainment system, including application support, touch, and notifications. Moreover, the automotive manufacturers look to control the OS on which their products run on and use it as a product differentiator to gain a competitive edge over their competitors.

The growing demand for automotive infotainment from entry-level and mid-segment vehicles will also fuel the growth of the automotive infotainment OS market size. The automotive infotainment OSs are penetrating extensively into the automotive market, with automotive OEMs developing low-cost solutions for entry-level vehicles. With increasing awareness and rising demand for telematics and built-in connectivity, automotive manufacturers have started to equip their mid-range vehicles with embedded infotainment OSs as standard features. In addition, a few of the leading automotive manufacturing companies have collaborated with infotainment OSs and software manufacturers to provide infotainment solutions in their mid-range vehicles. The demand for applications such as navigation systems and hands-free calling in mid-segment and entry-level vehicles is expected to boost the adoption of automotive infotainment OS significantly, in turn, driving the market growth.

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Automotive Infotainment OS Market: Segmentation by Geography

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33% of the market’s growth will originate from Europe during the forecast period. Germany is a key market for automotive infotainment OS in Europe. Market growth in this region will be faster than the growth of the market in MEA, North America, and South America.

The increasing demand for advanced infotainment OS is one of the prime factors that will facilitate the automotive infotainment OS market growth in Europe over the f

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Server Operating System Market Size 2024-2028

The server operating system market size is estimated to increase by USD 12.19 billion and grow at a CAGR of 10.87% between 2023 and 2028. The market is experiencing significant growth, driven by several key factors. Firstly, the increasing investments in the construction of hyper-scale data centers are fueling the demand for advanced server operating systems that can efficiently manage large-scale infrastructure. Secondly, technological advancements in server operating systems, such as containerization and virtualization, are enabling organizations to optimize their IT resources, data center, and improve application performance. However, the market is also facing challenges, including the rising number of security issues, which require server operating systems to provide robust security features to protect against cyber threats. Additionally, the growing complexity of IT environments is necessitating the need for server operating systems that can seamlessly integrate with various applications and tools. Overall, the server operating system market is expected to continue its growth trajectory, driven by these market trends and challenges.

What will be the Size of the Market During the Forecast Period?

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Market Segmentation

By Deployment

The market share growth by the on-premises segment will be significant during the forecast period. On-premises solutions in the global market are popular among companies looking to manage their IT infrastructure. On-premises solutions give companies complete control over the hardware and software, allowing them to adapt the system to their individual needs. One of the main benefits of on-premises solutions is increased security and privacy. Businesses can keep data and applications behind firewalls and other security measures, reducing the risk of cyberattacks and data breaches. This is especially important for software companies that handle sensitive data such as financial or medical information.

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The on-premises segment was valued at USD 7.57 billion in 2018. On-premises solutions allow companies to choose their hardware and software companies, design their systems to meet their specific needs and make changes and upgrades as needed. This gives software companies more control over their IT infrastructure, helping them achieve the best possible performance. On-premises solutions also offer improved performance and reliability compared to cloud-based solutions. However, on-premises solutions can be expensive up-front, as software companies must invest in the hardware, software, and staff required to maintain and manage the operating system.

By Region

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North America is estimated to contribute 40% to the growth of the global market during the forecast period. Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. One of the reasons why North America has a strong position in the market is the high acceptance of cloud-based services by enterprises. These cloud-based services require advanced systems for optimal performance and security. North America is home to major cloud service providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. Another factor contributing to the growth of the North American market is the increasing demand for data centers.

The North American region is home to some of the world's largest data centers. Companies such as Microsoft Corp (Microsoft), IBM Corp (IBM), and Oracle Corp (Oracle) have a strong presence in North America and invest heavily in research and development (R&D) to innovate and gain a competitive advantage over other companies. For example, Google LLC (Google) announced its USD 750 million new data center in Nebraska to meet its goal of spending USD 9.5 billion on new Google data centers and offices in 2022. Such expansion plans drive the growth of the market in North America during the forecast period.

Market Dynamics and Customer Landscape

The market is a significant segment of the IT industry, focusing on software that manages and operates servers in data centers and cloud platforms. Server OS includes various types such as Application Servers, File Servers, Database Servers, Mail Servers, Web Servers, and others. These operating systems are essential for Client Server Infrastructure and Client Machinery to function effectively in Network environments. Cloud computing has been a major driver in the growth of the Server OS market, with hybrid cloud environments and 5G networking technologies playing a pivotal role. Server OS software is integral to enterprise migration and digital transformation, as businesses

The conversion of natural environments into agricultural land has profound effects on the composition of the landscape, often resulting in a mosaic of crop fields, pastures and remnant patches of natural vegetation. It is thought that an increase in structural complexity of a habitat mosaic may improve the availability of ecological niches for animals, potentially increasing species diversity. Bats are highly vagile, and many species require the use of distinct habitats to fulfil their daily and seasonal needs. However, their distribution throughout a landscape may reflect a response to landscape structure and spatial and seasonal dynamics of resource distribution, as well as preferences for some habitats relative to others, determined by species eco-morphological traits. Therefore, the way bats select a habitat is an aggregative response to both landscape and local features. We investigated the spatial and seasonal patterns of bat diversity and activity within a heterogeneous landscape in Portugal, constituted by a mosaic of natural, semi-natural and human-altered terrestrial, freshwater and brackish habitats. Furthermore, we investigated which landscape features determine those patterns, across four distinct focal scales. We sampled bats acoustically, while simultaneously sampling insects with light traps, across 24 sampling sites representative of the main habitat types that shape the landscape. We found bat assemblages of the different habitats to be relatively similar, and that bat activity hardly differed among them. However, we found seasonal variation in bat activity within habitats. Additionally, our results revealed both scale- and guild-dependent responses of bats to landscape and local features. Overall, our results suggest that bats exploit all habitats of this heterogeneous area, and that the mosaic landscape provides them several opportunities, which results in strong seasonal and spatial dynamics. On the other hand, we found these dynamics to be influenced by broad-scale landscape features, as well as by weather conditions, and local resource availability and distribution. Lastly, our results indicate that forest and Bocage habitats are potential keystone structures for bats within this heterogeneous landscape. / A conversão de ambientes naturais em terrenos agrícolas tem efeitos profundos na composição da paisagem, frequentemente resultando em mosaicos de campos de cultivo, pastagens e restantes fragmentos de vegetação natural. Pensa-se que um aumento na complexidade estrutural de um mosaico de habitats pode favorecer a disponibilidade de nichos ecológicos para os animais, potencialmente aumentando a diversidade de espécies. Os morcegos são muito móveis, e muitas espécies requerem o uso de diferentes habitats de forma a cumprir as suas necessidades diárias e sazonais. No entanto, a sua distribuição ao longo de uma paisagem pode refletir uma resposta à estrutura da mesma, e às dinâmicas de distribuição espacial e temporal dos recursos, assim como refletir as preferências de alguns habitats em detrimento de outros, determinadas pelas características eco-morfológicas da espécie. Desta forma, a seleção de habitat por parte dos morcegos é uma resposta conjunta a características locais e de paisagem. Neste estudo foram investigados os padrões espaciais e sazonais de atividade e diversidade de morcegos numa paisagem heterogénea em Portugal, constituída por um mosaico de habitats naturais, semi-naturais e alterados pelo Homem, tanto em ambientes terrestres, como sob a influência de água-doce ou salobra. Além disso, foram investigadas quais as características da paisagem que determinam esses padrões, ao longo de quatro escalas focais distintas. A amostragem de morcegos foi feita acusticamente, enquanto em simultâneo se amostraram insetos usando armadilhas de luz, em 24 pontos representativos dos principais tipos de habitat que caracterizam a paisagem. Foi descoberto que as assemblages de morcegos dos diferentes habitats eram relativamente semelhantes entre si, e que a atividade de morcegos praticamente não diferia entre habitats. No entanto, verificou-se a existência de uma forte variação sazonal dos níveis de atividade de morcegos nos vários habitats. Além do mais, os resultados obtidos revelaram que a resposta dada pelos morcegos às características locais e de paisagem é dependente da escala e da guild. De uma forma geral, os resultados obtidos sugerem que os morcegos exploram todos os habitats que constituem esta paisagem heterogénea, e que o mosaico de habitats lhes fornece diversas oportunidades, o que resulta em fortes dinâmicas espaciais e sazonais. Por outro lado, foi descoberto que estas dinâmicas são influenciadas por características da paisagem a uma larga escala, assim como por condições meteorológicas, e pela disponibilidade e distribuição locais de recursos. Por último, os resultados indicam que as zonas florestais e o Bocage são potencialmente os habitats mais importantes para os morcegos nesta paisagem heterogénea.

Smartphones running the Android operating system hold an ** percent share of the global market in 2019 and this is expected to increase over the forthcoming years. The mobile operating system developed by Apple (iOS) has a ** percent share of the market.

How Android became the market leader Android’s global success can in many ways be attributed to its open-source software that can be installed on all smartphone devices for free. Developed by Google, the open code provides manufacturers the freedom to choose which apps are pre-installed on their devices, and they can customize layouts to create unique experiences for users. The first commercial version of the Android software was released in 2008 and its rise to market leader was almost instant. The platform held a **** percent share of the global operating systems’ market in 2009, but this figure increased by around ** percent each year for the next three years.

The global smartphone market Annual sales of smartphones have increased to around **** billion units worldwide. They are now available to everyone and not just those with wealth. The cost of buying a smartphone has continued to fall each year, with the global average price now being around *** U.S. dollars. Fierce competition within the smartphone market could be one reason why prices are falling. Samsung (South Korea) and Apple (U.S.) have historically held large shares of global smartphone production, but Chinese brands Huawei, Xiaomi, Oppo, and Vivo are now offering alternative devices that are proving popular worldwide.

Android maintained its position as the leading mobile operating system worldwide in the first quarter of 2025 with a market share of about ***** percent. Android's closest rival, Apple's iOS, had a market share of approximately ***** percent during the same period. The leading mobile operating systems Both unveiled in 2007, Google’s Android and Apple’s iOS have evolved through incremental updates introducing new features and capabilities. The latest version of iOS, iOS 18, was released in September 2024, while the most recent Android iteration, Android 15, was made available in September 2023. A key difference between the two systems concerns hardware - iOS is only available on Apple devices, whereas Android ships with devices from a range of manufacturers such as Samsung, Google and OnePlus. In addition, Apple has had far greater success in bringing its users up to date. As of February 2024, ** percent of iOS users had iOS 17 installed, while in the same month only ** percent of Android users ran the latest version. The rise of the smartphone From around 2010, the touchscreen smartphone revolution had a major impact on sales of basic feature phones, as the sales of smartphones increased from *** million units in 2008 to **** billion units in 2023. In 2020, smartphone sales decreased to **** billion units due to the coronavirus (COVID-19) pandemic. Apple, Samsung, and lately also Xiaomi, were the big winners in this shift towards smartphones, with BlackBerry and Nokia among those unable to capitalize.