This statistic presents the share of children wearing school uniforms in the United States from 2008 to 2018. In 2015, **** percent of respondents stated that their children were required to wear a school uniform.

This dataset contains data for 2014-2019 sourced from the State of Connecticut Uniform Chart of Accounts (UCOA). This system enables the annual collection of unaudited municipal trial balance data at the local account level, along with a mapping, or “cross walk” of that data to a standard, uniform chart of accounts for comparative purposes. This data is collected and stored in a system of databases developed for the Office of Policy and Management (OPM), including a Data Warehouse of financial balances. These balances are recorded at the account level and contain both local and uniform account metadata. Additional details can be found here: http://www.ct.gov/opm/cwp/view.asp?a=2984&q=576636 Some Municipalities are not included in this release. They can be found here: https://ucoa.ct.gov/benchmarking/#/

Unlock School Uniforms trends 2025: Track sales velocity, growth patterns & top-performing tags through interactive analytics. Discover data-proven opportunities with our dual-axis charts comparing product sales vs. keyword demand acceleration - your ultimate toolkit for winning eCommerce assortment strategies.

Municipal Fiscal Indicators is an annual compendium of information compiled by the Office of Policy and Management, Office of Finance, Municipal Finance Services Unit (MFS). Municipal Fiscal Indicators contains the most current financial data available for each of Connecticut's 169 municipalities. The data contained in Indicators provides key financial and demographic information on municipalities in Connecticut. The data includes selected demographic and economic data relating to, or having an impact upon, a municipality’s financial condition. The majority of this data was compiled from the audited financial statements that are filed annually with the State of Connecticut, Office of Policy and Management, Office of Finance. Unlike prior years' where the audited financial information was compiled by OPM, the FY 2021 information in this edition was based upon the self-reporting by municipalities of their own audited data. The most recent edition is for the Fiscal Years Ended 2018-2022 published in September 2024. Note: For 2020, this dataset does not include data for the following municipalities, which did not complete the UCOA reporting process in Ansonia, Bloomfield, Bozrah, Danbury, Derby, East Haven, East Lyme, Easton, Hamden, Hebron, Meriden, New Canaan, Norfolk, North Branford, North Canaan, North Haven, Prospect, Seymour, Southington, Stonington, West Haven, Westbrook, Wethersfield, Willington, and Wilton. For 2021, this dataset does not include data for: Andover, Bolton, Bozrah, Bridgewater, Colebrook, Danbury, East Haven, East Lyme, Easton, Enfield, Mansfield, New Britain, New Haven, Norfolk, North Branford, North Canaan, North Haven, Seymour, Southington, Warren, West Hartford, West Haven, Willington, Wilton, and Woodbury. For 2022, this dataset does not include data for: Andover, Colchester, Danbury, East Lyme, Marlborough, Norfolk, North Canaan, Prospect, Westbrook, and Wethersfield. The most recent data on the Municipal Fiscal Indicators is included in the following datasets: Municipal-Fiscal-Indicators: Financial Statement Information, 2020-2022 https://data.ct.gov/d/d6pe-dw46 Municipal-Fiscal-Indicators: Uniform Chart of Accounts, 2020-2022 https://data.ct.gov/d/e2qt-k238 Municipal Fiscal Indicators: Pension Funding Information for Defined Benefit Pension Plans, 2020-2022 https://data.ct.gov/d/73q3-sgr8 Municipal Fiscal Indicators: Type and Number of Pension Plans, 2020-2022 https://data.ct.gov/d/i84g-vvfb Municipal Fiscal Indicators: Other Post-Employment Benefits (OPEB), 2020-2022 https://data.ct.gov/d/ei7n-pnn9 Municipal Fiscal Indicators: Economic and Grand List Data, 2019-2024 https://data.ct.gov/d/xgef-f6jp Municipal Fiscal Indicators: Benchmark Labor Data, 2020-2024 https://data.ct.gov/d/5ijb-j6bn Municipal Fiscal Indicators: Bond Ratings, 2019-2022 https://data.ct.gov/d/a65i-iag5 Municipal Fiscal Indicators: Individual Town Data, 2014-2022 https://data.ct.gov/d/ej6f-y2wf Municipal Fiscal Indicators: Totals and Averages, 2014-2022 https://data.ct.gov/d/ryvc-y5rf

Code and supplementary information for the speed of neutral evolution on graphs https://doi.org/10.5061/dryad.0p2ngf27x ## Description of the data and code * Data 1. graphs.RData: This file contains the adjacency matrices of 112 undirected graphs with 6 nodes. These adjacency matrices represent the connectivity patterns of the graphs and can be utilized for generating an Atlas of all the undirected graphs of size 6. For generating Atlases of graphs with larger node sizes, please refer to NetworkX or other graph analysis libraries. 2. all_state_112_graph.zip The file contains visual representations of the 64 states for each of the 112 graphs. These states comprise 2 absorbing states and 62 transient states. * code 1. DB_six_nodes_random_initial.R: This script file is designed to compute simulation results for Death-Birth process under neutral selection for each graph included in the graphs.RData file. Specifically, it calculates the fixation probability, fixation time, extinction time, and absorption time under uniform initialization. 2. DB_six_nodes_temp.R: This script file is intended to compute simulation results for Death-Birth process under neutral selection for each graph included in the graphs.RData file. It calculates the fixation probability, fixation time, extinction time, and absorption time under temperature-dependent mutation. 3. BD_six_nodes_random_initial.R: This script file is designed to compute simulation results for Birth-Death process under neutral selection for each graph included in the graphs.RData file. Specifically, it calculates the fixation probability, fixation time, extinction time, and absorption time under uniform initialization. ## The speed of evolution on structured populations is crucial for biological and social systems. The likelihood of invasion is key for evolutionary stability, but it makes little sense if it takes long. It is far from known what population structure slows down evolution. We investigate the absorption time of a single neutral mutant for all the 112 non-isomorphic undirected graphs of size 6. We find that about three-quarters of the graphs have an absorption time close to that of the complete graph, less than one-third are accelerators, and more than two-thirds are decelerators. Surprisingly, determining whether a graph has a long absorption time is too complicated to be captured by the joint degree distribution. Via the largest sojourn time, we find that echo-chamber-like graphs, which consist of two homogeneous graphs connected by few sparse links, are likely to slow down absorption. These results are robust for large graphs, mutation patterns as well as evolutionary processes. This work serves as a benchmark for timing evolution with complex interactions and fosters the understanding of polarization in opinion formation.

The JK clothing market, encompassing school uniforms, Japanese-style uniforms, and apparel for both adults and minors, is experiencing robust growth. While the exact market size for 2025 is not provided, considering the global popularity of Japanese fashion and the consistent demand for school uniforms, a reasonable estimate for the 2025 market size would be $2.5 billion USD. This is a conservative estimate, factoring in potential variations in regional market penetration and fluctuating economic conditions. Assuming a compound annual growth rate (CAGR) of 5% (a figure attainable based on observed growth in similar fashion segments), the market is projected to reach approximately $3.3 billion by 2033. Key drivers for this growth include the rising popularity of Japanese street style and anime culture globally, influencing fashion choices beyond just school uniforms. The increasing disposable income in emerging economies also fuels demand, particularly for higher-quality, branded JK clothing. Trends such as personalized customization options and collaborations between brands and influencers are further propelling market expansion. However, potential restraints include fluctuating raw material prices and the emergence of substitute products. Market segmentation by type (school uniforms, Japanese-style uniforms) and application (adult, minor) provides opportunities for targeted marketing strategies focusing on distinct customer preferences. Leading brands like Kuri-ori, Conomi, and Eastboy are currently shaping the market landscape, though the growing popularity of niche brands and online retailers presents both opportunities and challenges. The geographic distribution of the JK clothing market reflects a blend of established and emerging markets. North America and Asia-Pacific currently represent significant market shares, driven by strong consumer interest in Japanese fashion and well-established retail networks. However, the market holds substantial growth potential in other regions like South America and parts of Africa, where exposure to Japanese pop culture is increasing. This expansion will likely be driven by online retail channels, reducing geographical barriers to market entry for both consumers and brands. To sustain growth, brands need to focus on innovative designs, sustainable production practices, and effective marketing campaigns that resonate with the target audience's values and cultural preferences. Competitive pricing strategies and the ability to adapt to changing fashion trends will also play a vital role in achieving long-term success within this dynamic market.

This is the dataset for our paper

What does it mean when your URIs are redirected? Examining identity and redirection in the LOD cloud

Redirection of URIs is widely used in the LOD cloud, and is even part of the best practice guidelines as an approach to the ``curation problem'' on the semantic web (i.e. how to repair imperfections). When dereferencing, one URI is redirected to another URI. Such a redirection could be the result of an update of the namespace, a different encoding scheme, or some other reasons. In this paper, we study the semantics of redirection and examine if redirection indicates how entities in the LOD cloud evolve. More specifically, we focus on entities in the identity graphs: subgraphs in the semantic web restricted to identity links. The entities we study are from sameAs.cc, an identity graph extracted from a crawl of the semantic web in 2015. Our analytical results include an examination of edges and chains of redirection as well as a statistical analysis of the redirection behavior of sampled entities. Additionally, we present properties of the graphs formed by redirection relations.

The dataset contains the redirect relations of four sets of sampled entities. These sampled files are:

• ite_uniform... the edges of redirection graph corresponding to uniform samplings
• cc_sample_2... the sampling regarding connected components of size 2, 3-10, 10+, respectively.

The Python scripts are open source online at:

https://github.com/shuaiwangvu/redirection

The paper is attached. In case of any questions, please contact Shuai Wang at shuai.wang@vu.nl.

United States - Producer Price Index by Commodity: Textile Products and Apparel: Team Sport Uniforms, Women's and Girls', Made from Purchased Fabrics was 101.10000 Index Dec 2011=100 in July of 2020, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Textile Products and Apparel: Team Sport Uniforms, Women's and Girls', Made from Purchased Fabrics reached a record high of 108.40000 in June of 2015 and a record low of 100.00000 in March of 2012. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Textile Products and Apparel: Team Sport Uniforms, Women's and Girls', Made from Purchased Fabrics - last updated from the United States Federal Reserve on June of 2025.

Contains data for 2014-2017. The Connecticut Uniform Chart of Accounts project enables the annual collection of municipal trial balance data at the local account level, along with a mapping, or “cross walk” of that data to a standard, uniform chart of accounts for comparative purposes. This data is collected and stored in a system of databases developed for the Office of Policy and Management (OPM), including a Data Warehouse of all approved and certified financial balances. These balances are recorded at the account level and contain both local and uniform account metadata. The Following "UniformFund" values are included in this raw data release, but are not reflected in the benchmarking tool: Permanent Fund Library Fund Internal Service Fund Medical & Health Insurance Other Internal Service Funds Trust Fund Scholarship Funds Agency Funds Student Activities Fund Other Agency Funds Pooled Cash/Treasury Governmental Fixed Assets

Additional details can be found here: http://www.ct.gov/opm/cwp/view.asp?a=2984&q=576636

Some Municipalities are not included in this release. They can be found here: https://ucoa.ct.gov/benchmarking/#/

Classification of small graphs with uniform initialization.

Hypergraphs have gained increasing attention in the machine learning community lately due to their superiority over graphs in capturing super-dyadic interactions among entities. In this work, we propose a novel approach for the partitioning of k-uniform hypergraphs. Most of the existing methods work by reducing the hypergraph to a graph followed by applying standard graph partitioning algorithms. The reduction step restricts the algorithms to capturing only some weighted pairwise interactions and hence loses essential information about the original hypergraph. We overcome this issue by utilizing tensor-based representation of hypergraphs, which enables us to capture actual super-dyadic interactions. We extend the notion of minimum ratio-cut and normalized-cut from graphs to hypergraphs and show that the relaxed optimization problem can be solved using eigenvalue decomposition of the Laplacian tensor. This novel formulation also enables us to remove a hyperedge completely by using the “hyperedge score” metric proposed by us, unlike the existing reduction approaches. We propose a hypergraph partitioning algorithm inspired from spectral graph theory and also derive a tighter upper bound on the minimum positive eigenvalue of even-order hypergraph Laplacian tensor in terms of its conductance, which is utilized in the partitioning algorithm to approximate the normalized cut. The efficacy of the proposed method is demonstrated numerically on synthetic hypergraphs generated by stochastic block model. We also show improvement for the min-cut solution on 2-uniform hypergraphs (graphs) over the standard spectral partitioning algorithm.

Hungary - Harmonised index of consumer prices (HICP): Clothes washing machines, clothes drying machines and dish washing machines was 133.19 points in May of 2025, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Hungary - Harmonised index of consumer prices (HICP): Clothes washing machines, clothes drying machines and dish washing machines - last updated from the EUROSTAT on July of 2025. Historically, Hungary - Harmonised index of consumer prices (HICP): Clothes washing machines, clothes drying machines and dish washing machines reached a record high of 134.04 points in April of 2025 and a record low of 98.54 points in October of 2017.

France - Harmonised index of consumer prices (HICP): Clothes washing machines, clothes drying machines and dish washing machines was 89.25 points in May of 2025, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for France - Harmonised index of consumer prices (HICP): Clothes washing machines, clothes drying machines and dish washing machines - last updated from the EUROSTAT on July of 2025. Historically, France - Harmonised index of consumer prices (HICP): Clothes washing machines, clothes drying machines and dish washing machines reached a record high of 177.81 points in February of 1996 and a record low of 87.58 points in April of 2020.

Graph and download economic data for Employment for Other Services (Except Public Administration): Linen and Uniform Supply (NAICS 81233) in the United States (IPUUN81233W200000000) from 1987 to 2024 about textiles, supplies, NAICS, services, employment, and USA.

** Please note this dataset will be removed as it has been consolidated into a single dataset for all years here: https://data.ct.gov/Local-Government/Uniform-Chart-of-Accounts-Municipal-Spending-Data/fdjf-7z7f

Note: This is an initial release of the 80 municipalities that have submitted data thus far. Additional municipalities will be added as they submit. For a simplified view please visit: https://ucoa.ct.gov/benchmarking/#/ The Connecticut Uniform Chart of Accounts project enables the annual collection of municipal trial balance data at the local account level, along with a mapping, or “cross walk” of that data to a standard, uniform chart of accounts for comparative purposes. This data is collected and stored in a system of databases developed for the Office of Policy and Management (OPM), including a Data Warehouse of all approved and certified financial balances. These balances are recorded at the account level and contain both local and uniform account metadata. The Following "UniformFund" values are included in this raw data release, but are not reflected in the benchmarking tool: Permanent Fund Library Fund Internal Service Fund Medical & Health Insurance Other Internal Service Funds Trust Fund Scholarship Funds Agency Funds Student Activities Fund Other Agency Funds Pooled Cash/Treasury Governmental Fixed Assets

Additional details can be found here: http://www.ct.gov/opm/cwp/view.asp?a=2984&q=576636

Some Municipalities are not included in this release. They can be found here: https://ucoa.ct.gov/benchmarking/#/

This dissertation is involved in Hamiltonian decomposition of two families of hypergraphs. We found the Hamiltonian decompositions of the prism over a complete 3-uniform hypergraph Prism (K(3) n for nε{4, 5, 8} and the Hamiltonian decompositions of the complete tripartite 3-uniform hypergraph K(3) m,m,m for all positive integer such that 3 .

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Custom Apparel Market Size 2025-2029

The custom apparel market size is forecast to increase by USD 2.45 billion, at a CAGR of 8.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing trend of internet penetration and smartphone adoption. These technological advancements enable consumers to easily access custom apparel designs and order personalized clothing online. Moreover, the rise of the DIY culture and maker movement adds to the market's momentum, as individuals seek unique, handcrafted garments. However, this burgeoning market faces challenges, including the proliferation of counterfeit products. As consumers become more discerning, authenticity and quality become essential differentiators. Companies must focus on building strong brand reputations and ensuring product authenticity to maintain customer trust and loyalty.
Additionally, embracing innovative technologies, such as 3D modeling and virtual fitting rooms, can help businesses cater to the evolving demands of tech-savvy consumers. By staying attuned to these trends and challenges, custom apparel businesses can effectively capitalize on market opportunities and navigate the competitive landscape.

What will be the Size of the Custom Apparel Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by dynamic market trends and shifting consumer preferences. Embroidered logos and garments remain popular choices for team apparel and corporate uniforms, with design templates offering endless possibilities for personalization. Fabric weights and care instructions are crucial considerations for ensuring a superior customer experience. Bulk orders require efficient order fulfillment and precise garment construction for uniform regulations. Sublimation printing and custom accessories add value to promotional products, while cut and sew techniques cater to unique designs. Delivery times and customer support are essential for maintaining strong business relationships. Quality control and color matching are critical aspects of apparel manufacturing, with digital printing and heat transfer vinyl offering advanced solutions.

Event merchandise and brand identity are key areas of focus for marketing strategies, while sizing charts and graphic design services ensure a perfect fit. Fabric sourcing is a continuous process, with an increasing emphasis on recycled materials, sustainable apparel, and ethical sourcing. E-commerce platforms streamline the ordering process, while embroidery machines and sewing equipment enable customization at scale. Social media marketing and fair trade practices further enhance brand reputation and customer loyalty. Inventory management and order tracking systems ensure seamless operations, enabling businesses to adapt to the ever-changing market landscape.

How is this Custom Apparel Industry segmented?

The custom apparel industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Women
  Men
  Children


Distribution Channel

  Offline
  Online


Product Category

  T-Shirts
  Hoodies
  Uniforms


Technology Specificity

  Screen Printing
  Embroidery


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By End-user Insights

The women segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth due to the increasing demand for personalized clothing items. Production lines are optimized to meet the rising demand for custom patches, fit and style preferences, and team apparel. Customer experience is prioritized through efficient order fulfillment, timely delivery, and exceptional customer support. Apparel manufacturing processes incorporate quality control measures to ensure garment construction meets the highest standards. Garment care instructions are provided to maintain the longevity of embroidered logos and embroidered garments. Corporate uniforms and school uniforms are essential segments, with uniform regulations dictating specifications for bulk orders. Design software and templates enable easy customization of personalized gifts and promotional products.

Fabric weights and types cater to various applications, from lightweight performance fabrics to heavy-duty workwear. Marketing strategies focus on social media platforms, direct-to-garment printing, and custom branding to

Latvia - Harmonised index of consumer prices (HICP): Clothes washing machines, clothes drying machines and dish washing machines was 108.19 points in April of 2025, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Latvia - Harmonised index of consumer prices (HICP): Clothes washing machines, clothes drying machines and dish washing machines - last updated from the EUROSTAT on June of 2025. Historically, Latvia - Harmonised index of consumer prices (HICP): Clothes washing machines, clothes drying machines and dish washing machines reached a record high of 109.81 points in February of 2023 and a record low of 95.52 points in April of 2021.

Greece - Harmonised index of consumer prices (HICP): Clothes washing machines, clothes drying machines and dish washing machines was 83.35 points in May of 2025, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Greece - Harmonised index of consumer prices (HICP): Clothes washing machines, clothes drying machines and dish washing machines - last updated from the EUROSTAT on July of 2025. Historically, Greece - Harmonised index of consumer prices (HICP): Clothes washing machines, clothes drying machines and dish washing machines reached a record high of 102.43 points in December of 2014 and a record low of 73.78 points in August of 2021.

A {\em convex geometric hypergraph} or {\em cgh} is a hypergraph whose vertices form a convex polygon in the plane. It will be convenient to place the vertices in clockwise order on the vertices of a regular polygon $\vb{\gamma}_n$ (for $n \ge 3$), and to write $v_1 < v_2 < \dots < v_n < v_1$ to denote that the clockwise ordering of the vertices $\vb{\gamma}_n$ is $(v_1,v_2,\dots,v_n,v_1)$. We write cgg (cgh, cg $r$-graph) as abbreviation for convex geometric graph (convex geometric hypergraph, convex geometric $r$-graph), Given an $r$-uniform cgh $F$, let $\ex_{\circlearrowright}(n,F)$ be the maximum number of edges in an $r$-uniform cgh on $n$ vertices that does not contain $F$. In the case of graphs, this problem has a rich history with applications to a variety of problems in combinatorial geometry. Extremal questions for convex geometric graphs and hypergraphs are connected to a variety of topics in discrete geometry (see for instance Bra{\ss}, Rote, and Swanepoel~2001, %\cite{Brass-Rote-Swanepoel}, Pach and Pinchasi~2013, %\cite{Pach-Pinchasi}) and questions around the triangle removal problem (see Section 4.3 in Aronov, Dujmovi\v{c}, Morin, Ooms and da Silveira~2017, %\cite{Aronov}, Gowers and Long~2016 %\cite{Gowers-Long} and Loh~2016). %\cite{Loh}) We focus on the case that $F$ is a certain type of embedding of a tight path or matching. We obtain in many cases asymptotically sharp estimates for $\ex_{\circlearrowright}(n,F)$, generalizing classical results of Kupitz and Perles. As a consequence, we show that the number of edges in an $n$-vertex $r$-graph containing no tight $k$-edge path is at most [\frac{(k-1)(r - 1)}{r}{n \choose r - 1}.] The case $r = 2$ is the Erd\H{o}s-Gallai Theorem. For $r \geq 3$, this is the first non-trivial upper bound on the extremal number for tight paths in $r$-graphs which applies for all $k \ge 3$, and marks progress towards Kalai's tight tree conjecture. Author affiliation: Renyi Institute of Mathematics Unreviewed Non UBC Faculty