The number of persons described by survey year (2013) reported in OMH Region-specific totals (Region of Provider) and three demographic characteristics of the client served during the week of the survey: gender (Male, Female,Transgender Male, Transgender Female), age (below 5,5–12, 13–17, 18–20, 21–34, 35–44, 45–64, 65–74, 75 and above, and unknown age) and race (White only, Black/ African American Only, Multi-racial, Other and unknown race) and ethnicity (Non-Hispanic, Hispanic, and Unknown). Persons with Hispanic ethnicity are grouped as “Hispanic,” regardless of race or races reported.

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The number of persons described by survey year (2015) reported in OMH Region‐specific totals (Region of Provider) and three demographic characteristics of the client served during the week of the survey: sex (Male, Female, and Unknown), Transgender (No, Not Transgender; Yes, Transgender and Unknown), age (below 17 (Child), 18 and above(Adult) and unknown age) and race (White only, Black Only, Multi‐racial, Other and Unknown race) and ethnicity (Non‐Hispanic, Hispanic, Client Did Not Answer and Unknown). Persons with Hispanic ethnicity are grouped as “Hispanic,” regardless of race or races reported.

The number of persons described by survey year (2017) reported in OMH Region‐specific totals (Region of Provider) and three demographic characteristics of the client served during the week of the survey: sex (Male, Female, and Unknown), Transgender (No, Not Transgender; Yes, Transgender and Unknown), age (below 17 (Child), 18 and above(Adult) and unknown age) and race (White only, Black Only, Multi‐racial, Other and Unknown race) and ethnicity (Non‐Hispanic, Hispanic, Client Did Not Answer and Unknown). Persons with Hispanic ethnicity are grouped as “Hispanic,” regardless of race or races reported.

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United States CUI: sa: Special Aggregates: PCs, Communication Eq & Semiconductor data was reported at 63.791 % in Oct 2002. This records a decrease from the previous number of 64.042 % for Sep 2002. United States CUI: sa: Special Aggregates: PCs, Communication Eq & Semiconductor data is updated monthly, averaging 80.456 % from Jan 1967 (Median) to Oct 2002, with 430 observations. The data reached an all-time high of 93.347 % in Jan 1967 and a record low of 60.598 % in Dec 2001. United States CUI: sa: Special Aggregates: PCs, Communication Eq & Semiconductor data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.B061: Industrial Capacity Utilization Rate: By SIC System.

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The data are organized by OMH Region‐specific (Region of Provider), program type, and by the following demographic characteristics of the clients served during the week of the survey: sex (Male, Female, X (Non-binary), and Unknown), Transgender (No, Not Transgender; Yes, Transgender and Unknown), age (below 17 (Child), 18 and above(Adult) and unknown age) and race (White only, Black Only, Multi‐racial, Other and Unknown race) and ethnicity (Non‐Hispanic, Hispanic, Client Did Not Answer and Unknown). Persons with Hispanic ethnicity are grouped as “Hispanic,” regardless of race or races reported.

Japan PCS: Tohoku: Others: Year to Date data was reported at 58.339 JPY bn in Oct 2018. This records an increase from the previous number of 52.928 JPY bn for Sep 2018. Japan PCS: Tohoku: Others: Year to Date data is updated monthly, averaging 39.080 JPY bn from Oct 2008 (Median) to Oct 2018, with 121 observations. The data reached an all-time high of 125.298 JPY bn in Mar 2015 and a record low of 2.839 JPY bn in Apr 2010. Japan PCS: Tohoku: Others: Year to Date data remains active status in CEIC and is reported by East Japan Construction Surety Co. Ltd. The data is categorized under Global Database’s Japan – Table JP.EA009: Public Construction Work Statistics.

Background: Increasing numbers of variables in surveys and administrative databases are created. Principal component analysis (PCA) is important to summarize data or reduce dimensionality. However, one disadvantage of using PCA is the interpretability of the principal components (PCs), especially in a high-dimensional database. By analyzing the variance distribution according to PCA loadings and approximating PCs with input variables, we aim to demonstrate the importance of variables based on the proportions of total variances contributed or explained by input variables.Methods: There were five data sets of various sizes used to understand the performance of PC approximation: Hitters, SF-12v2 subset of the 2004–2011 Medical Expenditure Panel Survey (MEPS), and the full set of 1996–2011 MEPS data, along with two data sets derived from the Canadian Health Measures Survey (CHMS): a spirometry subset with the measures from the first trial of spirometry and a full data set that contained non-redundant variables. The variables in data sets were first centered and scaled before PCA. PCs were approximated through two approaches. First, the PC loadings were squared to estimate the variance contribution by variables to PCs. The other method was to use forward-stepwise regression to approximate PCs with all input variables.Results: The first few PCs had large variances in each data set. Approximating PCs using stepwise regression could efficiently identify the input variables that explain large portions of PC variances than approximating according to PCA loadings in the data sets. It required fewer numbers of variables to explain more than 80% of the PC variances through stepwise regression.Conclusion: Approximating and interpreting PCs with stepwise regression is highly feasible.PC approximation is useful to (1) interpret PCs with input variables, (2) understand the major sources of variances in data sets, (3) select unique sources of information, and (4) search and rank input variables according to the proportions of PC variance explained. This can be an approach to systematically understand databases and search for variables that are important to databases.

Snapshot img

Mini PCs Market Size 2024-2028

The mini PCs market size is forecast to increase by USD 17.4 billion at a CAGR of 4.5% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing use of these compact devices in educational institutions. With the rapid penetration of internet-enabled devices in educational institutes, mini PCs have become an essential tool for delivering digital learning experiences. However, this trend also brings challenges, particularly inadequate cybersecurity measures. As more schools and universities adopt mini PCs for remote learning and digital classrooms, ensuring strong security protocols becomes crucial to protect sensitive student data.
Additionally, mini PCs offer cost-effective solutions for businesses and individuals seeking powerful yet compact computing devices, further fueling market growth. In healthcare, mini PCs are being used in telemedicine and digital health technologies, enabling data transfer and patient care in space-constrained environments such as patient rooms and mobile healthcare units. Overall, the mini PC market is poised for continued expansion, driven by educational sector adoption and the need for portable, efficient computing solutions.

What will the Mini Pcs Market Size During the Forecast Period?

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The market is witnessing significant growth due to the increasing demand for compact computing devices that offer high data processing capabilities. These devices, often smaller than a standard desktop computer, come equipped with advanced features such as AI and IoT integration, 5G connection, and machine learning capabilities. Mini PCs are becoming increasingly popular for on-the-go computing, remote working, and digital signage technologies. Mini PCs are available in various form factors, from sticks to small boxes, making them highly portable. They come with CPUs, solid-state drives, and communication ports that enable seamless connectivity to monitors, keyboards, and other peripherals.



Operating systems like Windows, Linux, and Chrome OS power these devices, providing users with a familiar computing experience. Mini PCs are popular among millennials, the educational sector, healthcare professionals, and smart city projects. With the ability to deliver VR experiences, gaming, and high-speed data processing, mini PCs are becoming an essential tool for those who require powerful computing on the go. Portable computer devices are available in bags, making it convenient for users to carry them around. The mini PC market is expected to continue its growth trajectory, driven by the increasing need for portable and powerful computing devices.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Healthcare
  Retail
  Education and training
  Banking
  Others


Application

  Home Entertainment
  Gaming
  Digital Signage
  Industrial Automation
  Others


Component

  Processor
  Memory
  Storage
  GPU
  Others


Geography

  APAC

    China
    India
    Japan


  North America

    US


  Europe

    UK


  South America



  Middle East and Africa

By End-user Insights

The healthcare segment is estimated to witness significant growth during the forecast period. Mini PCs, a category of compact and energy-efficient computers, are gaining significant traction in various sectors due to their portability, versatility, and advanced features. In the realm of consumer electronics, mini PCs are increasingly being used for home entertainment, office work, and digital media consumption. They come with powerful processors, ample memory, and storage, enabling seamless data processing, machine learning, and AI capabilities. Moreover, mini PCs are revolutionizing industries such as healthcare, retail, manufacturing, education, and digital signage, among others. In healthcare, they facilitate the storage and accessibility of electronic health records (EHRs), enhancing the quality of patient care.

In retail, they power digital signage solutions and self-checkout systems. In manufacturing, they are utilized for industrial automation and quality control. Mini PCs are also integral to the smart home ecosystem, enabling IoT technologies to connect various smart home devices such as thermostats, lighting systems, and security cameras. With the advent of 5G connection, mini PCs offer uninterrupted streaming services, making them an essential component of the corporate and budget-conscious individual's digital transformation journey. With connectivity options galore, mini PCs are the future of hardware components in an increasingly digital world.

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Thailand Agricultural Production Price: Livestocks: Chicken Egg: 100 Pcs data was reported at 254.475 THB/Unit in Oct 2018. This records a decrease from the previous number of 281.875 THB/Unit for Sep 2018. Thailand Agricultural Production Price: Livestocks: Chicken Egg: 100 Pcs data is updated monthly, averaging 262.486 THB/Unit from Jan 2005 (Median) to Oct 2018, with 166 observations. The data reached an all-time high of 345.000 THB/Unit in Sep 2013 and a record low of 182.000 THB/Unit in Oct 2006. Thailand Agricultural Production Price: Livestocks: Chicken Egg: 100 Pcs data remains active status in CEIC and is reported by Office of Agricultural Economics. The data is categorized under Global Database’s Thailand – Table TH.P004: Agricultural Production Price.

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Japan PCS: Tohoku: Prefectures: Year to Date data was reported at 254.697 JPY bn in Jun 2018. This records an increase from the previous number of 204.396 JPY bn for May 2018. Japan PCS: Tohoku: Prefectures: Year to Date data is updated monthly, averaging 309.920 JPY bn from Oct 2008 (Median) to Jun 2018, with 117 observations. The data reached an all-time high of 819.724 JPY bn in Mar 2015 and a record low of 31.229 JPY bn in Apr 2011. Japan PCS: Tohoku: Prefectures: Year to Date data remains active status in CEIC and is reported by East Japan Construction Surety Co. Ltd. The data is categorized under Global Database’s Japan – Table JP.EA009: Public Construction Work Statistics.

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Abstract

The State Ambulatory Surgery Databases (SASD), State Inpatient Databases (SID), and State Emergency Department Databases (SEDD) are part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP).

HCUP's state-specific databases can be used to investigate state-specific and multi-state trends in health care utilization, access, charges, quality, and outcomes. PHS has several years (2008-2011) and datasets (SASSD, SED and SIDD) for HCUP California available.

Usage

The State Ambulatory Surgery and Services Databases (SASD) are State-specific files that include data for ambulatory surgery and other outpatient services from hospital-owned facilities. In addition, some States provide ambulatory surgery and outpatient services from nonhospital-owned facilities. The uniform format of the SASD helps facilitate cross-State comparisons. The SASD are well suited for research that requires complete enumeration of hospital-based ambulatory surgeries within geographic areas or States.

The State Inpatient Databases (SID) are State-specific files that contain all inpatient care records in participating states. Together, the SID encompass more than 95 percent of all U.S. hospital discharges. The uniform format of the SID helps facilitate cross-state comparisons. In addition, the SID are well suited for research that requires complete enumeration of hospitals and discharges within geographic areas or states.

The State Emergency Department Databases (SEDD) are a set of longitudinal State-specific emergency department (ED) databases included in the HCUP family. The SEDD capture discharge information on all emergency department visits that do not result in an admission. Information on patients seen in the emergency room and then admitted to the hospital is included in the State Inpatient Databases (SID)

SASD, SID, and SEDD each have **Documentation **which includes:

• Description of the Database
• Restrictions on Use
• File Specifications and Load Program
• Data Elements
• Additional Resources for Data Elements
• ICD-10-CM/PCS Data Included in the Dataset Starting with 2015
• Known Data Issues
• HCUP Tools: Labels and Formats
• HCUP Supplemental Files
• Obtaining HCUP Data

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Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

phsdatacore@stanford.edu for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

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Documentation

The HCUP California inpatient files were constructed from the confidential files received from the Office of Statewide Health Planning and Development (OSHPD). OSHPD excluded inpatient stays that, after processing by OSHPD, did not contain a complete and “in-range” admission date or discharge date. California also excluded inpatient stays that had an unknown or missing date of birth. OSHPD removes ICD-9-CM and ICD-10-CM diagnoses codes for HIV test results. Beginning with 2009 data, OSHPD changed regulations to require hospitals to report all external cause of injury diagnosis codes including those specific to medical misadventures. Prior to 2009, OSHPD did not require collection of diagnosis codes identifying medical misadventures.

**Types of Facilities Included in the Files Provided to HCUP by the Partner **

California supplied discharge data for inpatient stays in general acute care hospitals, acute psychiatric hospitals, chemical dependency recovery hospitals, psychiatric health facilities, and state operated hospitals. A comparison of the number of hospitals included in the SID and the number of hospitals reported in the AHA Annual Survey is available starting in data year 2010. Hospitals do not always report data for a full calendar year. Some hospitals open or close during the year; other hospitals have technical problems that prevent them from reporting data for all months in a year.

**Inclusion of Stays in Special Units **

Included with the general acute care stays are stays in skilled nursing, intermediate care, rehabilitation, alcohol/chemical dependency treatment, and psychiatric units of hospitals in California. How the stays in these different types of units can be identified differs by data year. Beginning in 2006, the information is retained in the HCUP variable HOSPITALUNIT. Reliability of this indicator for the level of care depends on how it was assigned by the hospital. For data years 1998-2006, the information was retained in the HCUP variable LEVELCARE. Prior to 1998, the first

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