#table1

[oru-tree]

DON'T STEP ON THE TABLE1!!! People are eating there rn!!!!!!

[linguisticsizfun]

Words for Rice in Marma in the STEDT Database

Introduction

Marma is the name given to Arakanese/ Rakhine as spoken in Bangladesh and depending on the resource, Marma, Arakanese and Standard Burmese are stated as 3 closely related languages or just dialects of one another. There is an online database called STEDT (Sino-Tibetan etymology and thesaurus) and it is quite decent. As Marma data is available, for my first search I decided to search up “rice” and see how many words can be compared to standard Burmese. The etymologies of the Burmese terms are taken solely from Wiktionary as the Burmese etymologies are quite decent, or these terms are common enough to warrant etymologies for them on a popular site.

Figure 1: Basically how the STEDT database looks like when searching for the terms

Results and Discussion

Table1: results of searching for “rice” in “Marma” language, compared with the standard Burmese terms with IPA pronunciation and etymologies according to Wiktionary given.

Burmese words from the table so you can search them yourselves if you want: ဆန် စပါး ကောက်ညှင်း လယ် ထမင်

More discussion of Etymology

Here, basically some of the individual terms I found interesting will be explored, these explorations are done via Wiktionary and I prefer to copy paste from Wiktionary giving some commentary here are there.

ဆန်

"Cognate with Nuosu ꍯ (che), Lolopo cei. Luce compares Old Chinese 粲 (OC *sʰaːns, “polished white rice”).". The Chinese entry mentions Burmese again, stating "Luce, 1981 considers the "polished white rice" sense to be related to Burmese ဆန် (hcan, “uncooked white rice”), perhaps referring to the "clear and bright" nature of uncooked rice." and the reference used is "Luce, G. H. (1981) A Comparative Word-List of Old Burmese, Chinese and Tibetan, University of London, SOAS". The Chinese term is stated to be "From Proto-Sino-Tibetan *g-sal (“clear, bright, pleasant”) (Unger, 1990; Schuessler, 2007; STEDT). Cognate with Tibetan སལ་བ། (sal ba, “clear, distinct, bright”)."

So where exactly are Nuosu and Lolopo on the ST family tree? The Lolo-Burmese branch of Sino-Tibetan is divided into Burmish and Loloish. Both Nuosu and Lolopo are Loloish languages.

ကောက်ညှင်း

"Shan ၶဝ်ႈၼဵဝ် (khāo nǎeo) or Northern Thai ข้าวนึ่ง". Northern Thai entry gives: "ข้าว (“rice”) +‎ นึ่ง (“steamed”)". Northern Thai entry for rice says it is from Proto-Tai, borrowed from Austroasiatic, and cognate to Burmese ကောက် kauk /kaʊʔ/. But the Burmese entry states: From Proto-Sino-Tibetan *kuk (“rice; grain”); compare Old Chinese 穀 (OC *kloːɡ, “grain”) (Schuessler (2007), Hill (2019)). The Chinese entry states:

"From Austroasiatic or an area word (Schuessler, 2007); compare:

Proto-Vietic *r-koːʔ (“husked rice”), Khmu [script needed] (rŋkoʔ, “husked rice”)

Jingpho n-gu (n³³ ku³³, “rice (uncooked)”) (from Austroasiatic)

Proto-Tai *C̬.qawꟲ (“rice”) (borrowed from Austroasiatic, whence Thai ข้าว (kâao, “rice”))

Burmese ကောက် (kauk, “rice; paddy”) (supported by Hill, 2019)

STEDT compares this to provisional Proto-Tibeto-Burman *kuk (“rice, grain (crop)”)."

So is the etymology Sino-Tibetan or some other Asian language family like Austroasiatic? The Sino-Tibetan term at the proto-level could be borrowed from Austroasiatic. There for sure is research out there about borrowings at the proto level but this post is supposed to be brief and not going into the full iceberg of etymology.

Abbreviations

WB = written Burmese

OC = Old Chinese

Skt. = Sanskrit

cf. = confer, Latin for “compare”

References

[atplblog]

Price: [price_with_discount] (as of [price_update_date] - Details) [ad_1] Feature: This console table rustic and simplistic. Easy to compliment to your home decor and highlight taste. What is more, it will be a good decoration in your home, kitchen and office! With a beautifully simplistic look. Specifications: 1. Product Dimension: (42 x 11 x 30)" (L x W x H) 2. Color: Black, Marble Sticker 3. Weight Capacity: 88lbs / 40kg 4. Material: MDF, Metal Package Includes: 1 x Console Table 1 x Accessory Kit 1 x Instructions Notes: If there are any product quality problems during the using within 6 months after receiving the item, please feel free to contact us, we will try our best to help you deal with the issue.Thanks! It is recommended that the installation should not be too tight to adjust easily. ???Elegant Tabletop and Metal Leg Visual?A durable, laminate faux marble table top sits atop the metal frame designed with a smooth finish to add life to any setting; ???Sturdy structure? Sofa table is very stable and not easy to be deformed. Even if the sofa table is bumped by pets or people, the things on the table or shelf are not easy to fall down. And you can use the console table for long time ???Multi-purpose Slim Desk?Works great as a console table, entry way table, Hall table, display table, a bookcase, a plant flower sand or just a practical storage rack ???Use To?This is a good entryway table for behind the couch to keep your phone chargers, remotes, keys and other items you need close by or behind a corner bed to sit lamps on ??? Easy To Clean And Assemble ? The smooth surface of entry table makes it easier for you to clean with a damp or dry cloth. The stains on the table can be cleaned quickly Item Shape: Rectangular; Special Features: Foldable; Base Type: Legs [ad_2]

[computerhardwareguide]

In today’s rapidly evolving technological landscape, the role of a programmer is undergoing significant transformation. The integration of artificial intelligence (AI) and machine learning (ML) into software development is reshaping traditional practices, creating new opportunities and challenges for developers. While AI tools like GitHub Copilot and TensorFlow are enhancing productivity, they do not diminish the creative problem-solving capabilities that human programmers bring to the table1. https://computerhardwareguide.co.uk/what-technology-will-be-required-for-computer-programmer-essential-tools-and-skills/

[umifani]

エレガントなダイニングテーブル　円形テーブル　北欧スタイル　クラシックかつ現代的な丸形一本脚

[cleverhottubmiracle]

The global trade of HS-560312 (nonwoven fabrics made from man-made filaments) plays a pivotal role in the US textile industry, catering to diverse applications such as automotive, medical, filtration, hygiene products, and industrial textiles. As a key importer, the United States offers free market access for these products under an MFN (Most Favoured Nation) tariff of zero per cent, making it an attractive destination for global exporters. Table1: Key Observations and Trade Statistics on HS-560312- Nonwoven Fabrics (Man-Made Filaments) in CY 2024Germany leads global nonwoven fabric exports with high-value products and strong logistics. India and Israel are expanding, with India offering cost-effective alternatives and Israel specialising in premium fabrics. China remains a major player but faces a 20 per cent US tariff, slightly affecting its low-cost advantage. Trade policies and logistics efficiency will shape future market dynamics. Source: TradeMap and F2F Analysis; *Effective from 4th MarchNote: RCA - Revealed Comparative Advantage; UVR - Unit Value Realisation; LPI - Logistic Performance Index GermanyGermany holds the top position in the export market for nonwoven fabrics of man-made filaments, with a significant export value of $68.678 million to the US. Its Revealed Comparative Advantage (RCA) of 2.78 indicates a strong specialisation in this segment, demonstrating that Germany exports these fabrics at a much higher rate than the global average. This high RCA underscores Germany’s competitive edge, positioning it as a leader in the trade of this product to the US.Despite offering high-priced products with a Unit Value Realisation (UVR) of $6.30 per kg, Germany maintains its market leadership in the US market. This premium pricing is a result of advanced manufacturing technology, superior quality standards, and stringent regulatory compliance. German manufacturers prioritise innovation, durability, and performance, making their nonwoven fabrics suitable for high-end applications in industries such as automotive, medical textiles, filtration, and geotextiles.Furthermore, Germany’s dominance is driven by its strong research and development (R&D) ecosystem, which fosters continuous product improvement. Investments in automation, precision engineering, and sustainability initiatives also contribute to its competitive positioning. While higher prices may limit cost-sensitive buyers, Germany’s reputation for reliability and excellence ensures continued demand from premium markets that prioritise quality over cost.ChinaChina’s position in the nonwoven fabrics of man-made filaments market is a mix of strength and challenge in the US market. With an export value of $56.263 million, the country showcases its large-scale manufacturing capacity, a key factor in maintaining its global presence. However, its RCA of 0.81 per cent suggests that China is losing its competitive edge in this category compared to other leading exporters to the US. An RCA value below 1 indicates that China is less specialised in this segment relative to global trade patterns.Despite this, China’s low Unit Value Realisation (UVR) of $1.21 per kg allows it to remain a formidable player. A lower UVR implies that China’s pricing strategy is highly competitive, making its products more attractive to cost-sensitive buyers.Tariff Impact: With the first tariff imposition on February 4th, 2025, the tariff rate increased to 10 per cent. This rise in the tariff burden would lead to an increase in the UVR as production and export costs escalate. As a result, the UVR would likely increase to around $1.3/kg, reflecting the growing challenges posed by the higher tariffs. The increase in the UVR shows that the products are becoming more expensive, which could make them less competitive for price-sensitive US consumers. In the second tariff imposition, effective from March 4th, 2025, the tariff rate rose further to 20 per cent. This substantial increase would push the UVR to approximately $1.5/per kg or higher. The higher tariff burden will continue to raise costs, further diminishing the cost-effectiveness of the products. However, it is important to note that despite the tariff imposition, China does not entirely lose its cost advantage. India, which has the capacity to compete with China, is still behind in terms of UVR.IndiaIndia stands as a strong contender in the nonwoven fabrics of man-made filaments market, with exports totalling $55.174 million to the US. Its RCA of 4.01 underscores its significant specialisation in this category, making it one of the most competitive global players. This high RCA value indicates that India’s share in the global nonwoven fabrics trade is much larger than its overall participation in world exports, reflecting a strong foothold in the industry.India’s growing textile manufacturing ecosystem, supported by robust government policies, infrastructure development, and increasing investments, plays a key role in strengthening its presence in nonwoven fabric exports.India’s moderate UVR of $2.48 per kg positions it as an affordable supplier in the global market. Unlike Germany, which focuses on premium, high-tech products, India competes on a balance between price and quality, making it attractive for buyers looking for cost-effective solutions without compromising too much on performance.Logistics Performance & Challenges: India has an above-average Logistics Performance Index (LPI) score of 3.4, highlighting its improving trade facilitation, infrastructure, and customs efficiency. However, it still lags behind Luxembourg, which has a slightly higher LPI of 3.6. While India has made significant progress in logistics efficiency, further improvements are needed in areas such as: Reducing port congestion and streamlining customs clearance for faster exports. Enhancing last-mile connectivity through better transportation networks. Investing in digitisation and automation to improve supply chain visibility and efficiency.By addressing these logistical bottlenecks, India can further strengthen its position in the global nonwoven fabrics market, boosting export volumes and competitiveness.Bottom of FormIsraelIsrael has carved out a strong position in the global nonwoven fabric market, with exports reaching $52.414 million to the US. Its exceptionally high RCA of 15.42 underscores its specialisation in this category, making it a key player in high-value, specialised textile products.A UVR of $4.73 per kg highlights the premium nature of Israel’s exports to the US, which are widely used in medical, industrial, defence, and high-tech applications. Unlike mass producers such as China and India, Israel focuses on quality, innovation, and advanced material development, ensuring strong demand in markets that prioritise performance over cost.Additionally, Israel’s efficient logistics performance and strategic trade agreements facilitate seamless exports, keeping it competitive against other leading nations. Going forward, the country’s success in this segment will depend on continued investment in R&D, market diversification, and sustainability initiatives to maintain its technological edge and premium market positioning.LuxembourgDespite being a relatively small player in the global textile export market, Luxembourg exhibits a staggering RCA of 341.86 in nonwoven fabric exports. This exceptionally high RCA signifies that Luxembourg’s share in global nonwoven fabric exports is disproportionately higher than its overall participation in world trade, highlighting the country’s strong specialisation in this segment.Targeted investments in high-value nonwoven fabrics: Luxembourg’s nonwoven fabric industry has increasingly focused on high-performance textiles, catering to specialised applications such as filtration, automotive, construction, and medical textiles. These sectors demand precision engineering and advanced material science, areas in which Luxembourg has developed a competitive edge.Premium positioning: Luxembourg’s UVR of $10.95 per kilogram significantly exceeds the global average for nonwoven fabrics to the US. This premium pricing reflects the export of high-tech, performance-driven nonwoven products rather than commodity-grade textiles. The country’s focus on low-volume, high-margin markets ensures profitability despite relatively smaller absolute export volumes.Leveraging European market integration & logistics strengths: As a centrally located European nation, Luxembourg benefits from efficient logistics and connectivity to major industrial hubs across the EU. This enables seamless supply chain operations and facilitates the export of high-value nonwoven fabrics to key European markets.Luxembourg’s remarkable RCA of 341.86 and UVR of $10.95/kg underscore its strategic positioning as a leader in specialised nonwoven fabrics. With continued investments in R&D, sustainability, and high-value manufacturing, the country is well-poised to maintain its competitive advantage in this dynamic segment of the textile industry.Outlook and conclusionGermany remains the undisputed leader in nonwoven fabric exports, backed by its high export value and superior trade infrastructure. The country’s Logistics Performance Index (LPI) of 4.10 further strengthens its competitive edge, ensuring seamless global trade.India and Israel are expanding their market presence, demonstrating consistent growth rates in exports. India, with a strong RCA of 4.01 and a UVR of $2.48/kg, balances volume with value-driven exports. Meanwhile, Israel stands out with an RCA of 15.42 and a UVR of $4.73/kg, indicating a specialisation in high-value nonwoven fabrics.China, despite being a major exporter, relies on high-volume, low-cost production, as reflected in its UVR of $1.21/kg. However, since an additional 20 per cent tariff is imposed by the US, China’s competitiveness could be only slightly affected, due to its significant lesser UVR, making it cost effective even in case of additional tariffs.As of January 1, 2025, all five countries discussed here benefited from a zero-tariff rate, ensuring unhindered international trade. Apart from Germany’s leading LPI, the other exporters—China, India, Israel, and Luxembourg—maintain competitive LPIs ranging from 3.40 to 3.70, highlighting their growing trade efficiency.Overall, the nonwoven fabric export landscape is evolving, with Germany maintaining dominance while India and Israel are gaining traction. Any shifts in trade policies, such as tariff hikes on China, would only result in a larger contribution of other countries volume-wise but they would have to level up in terms of prices to match China’s dominance. Fibre2Fashion News Desk (NS) Source link

[norajworld]

The global trade of HS-560312 (nonwoven fabrics made from man-made filaments) plays a pivotal role in the US textile industry, catering to diverse applications such as automotive, medical, filtration, hygiene products, and industrial textiles. As a key importer, the United States offers free market access for these products under an MFN (Most Favoured Nation) tariff of zero per cent, making it an attractive destination for global exporters. Table1: Key Observations and Trade Statistics on HS-560312- Nonwoven Fabrics (Man-Made Filaments) in CY 2024Germany leads global nonwoven fabric exports with high-value products and strong logistics. India and Israel are expanding, with India offering cost-effective alternatives and Israel specialising in premium fabrics. China remains a major player but faces a 20 per cent US tariff, slightly affecting its low-cost advantage. Trade policies and logistics efficiency will shape future market dynamics. Source: TradeMap and F2F Analysis; *Effective from 4th MarchNote: RCA - Revealed Comparative Advantage; UVR - Unit Value Realisation; LPI - Logistic Performance Index GermanyGermany holds the top position in the export market for nonwoven fabrics of man-made filaments, with a significant export value of $68.678 million to the US. Its Revealed Comparative Advantage (RCA) of 2.78 indicates a strong specialisation in this segment, demonstrating that Germany exports these fabrics at a much higher rate than the global average. This high RCA underscores Germany’s competitive edge, positioning it as a leader in the trade of this product to the US.Despite offering high-priced products with a Unit Value Realisation (UVR) of $6.30 per kg, Germany maintains its market leadership in the US market. This premium pricing is a result of advanced manufacturing technology, superior quality standards, and stringent regulatory compliance. German manufacturers prioritise innovation, durability, and performance, making their nonwoven fabrics suitable for high-end applications in industries such as automotive, medical textiles, filtration, and geotextiles.Furthermore, Germany’s dominance is driven by its strong research and development (R&D) ecosystem, which fosters continuous product improvement. Investments in automation, precision engineering, and sustainability initiatives also contribute to its competitive positioning. While higher prices may limit cost-sensitive buyers, Germany’s reputation for reliability and excellence ensures continued demand from premium markets that prioritise quality over cost.ChinaChina’s position in the nonwoven fabrics of man-made filaments market is a mix of strength and challenge in the US market. With an export value of $56.263 million, the country showcases its large-scale manufacturing capacity, a key factor in maintaining its global presence. However, its RCA of 0.81 per cent suggests that China is losing its competitive edge in this category compared to other leading exporters to the US. An RCA value below 1 indicates that China is less specialised in this segment relative to global trade patterns.Despite this, China’s low Unit Value Realisation (UVR) of $1.21 per kg allows it to remain a formidable player. A lower UVR implies that China’s pricing strategy is highly competitive, making its products more attractive to cost-sensitive buyers.Tariff Impact: With the first tariff imposition on February 4th, 2025, the tariff rate increased to 10 per cent. This rise in the tariff burden would lead to an increase in the UVR as production and export costs escalate. As a result, the UVR would likely increase to around $1.3/kg, reflecting the growing challenges posed by the higher tariffs. The increase in the UVR shows that the products are becoming more expensive, which could make them less competitive for price-sensitive US consumers. In the second tariff imposition, effective from March 4th, 2025, the tariff rate rose further to 20 per cent. This substantial increase would push the UVR to approximately $1.5/per kg or higher. The higher tariff burden will continue to raise costs, further diminishing the cost-effectiveness of the products. However, it is important to note that despite the tariff imposition, China does not entirely lose its cost advantage. India, which has the capacity to compete with China, is still behind in terms of UVR.IndiaIndia stands as a strong contender in the nonwoven fabrics of man-made filaments market, with exports totalling $55.174 million to the US. Its RCA of 4.01 underscores its significant specialisation in this category, making it one of the most competitive global players. This high RCA value indicates that India’s share in the global nonwoven fabrics trade is much larger than its overall participation in world exports, reflecting a strong foothold in the industry.India’s growing textile manufacturing ecosystem, supported by robust government policies, infrastructure development, and increasing investments, plays a key role in strengthening its presence in nonwoven fabric exports.India’s moderate UVR of $2.48 per kg positions it as an affordable supplier in the global market. Unlike Germany, which focuses on premium, high-tech products, India competes on a balance between price and quality, making it attractive for buyers looking for cost-effective solutions without compromising too much on performance.Logistics Performance & Challenges: India has an above-average Logistics Performance Index (LPI) score of 3.4, highlighting its improving trade facilitation, infrastructure, and customs efficiency. However, it still lags behind Luxembourg, which has a slightly higher LPI of 3.6. While India has made significant progress in logistics efficiency, further improvements are needed in areas such as: Reducing port congestion and streamlining customs clearance for faster exports. Enhancing last-mile connectivity through better transportation networks. Investing in digitisation and automation to improve supply chain visibility and efficiency.By addressing these logistical bottlenecks, India can further strengthen its position in the global nonwoven fabrics market, boosting export volumes and competitiveness.Bottom of FormIsraelIsrael has carved out a strong position in the global nonwoven fabric market, with exports reaching $52.414 million to the US. Its exceptionally high RCA of 15.42 underscores its specialisation in this category, making it a key player in high-value, specialised textile products.A UVR of $4.73 per kg highlights the premium nature of Israel’s exports to the US, which are widely used in medical, industrial, defence, and high-tech applications. Unlike mass producers such as China and India, Israel focuses on quality, innovation, and advanced material development, ensuring strong demand in markets that prioritise performance over cost.Additionally, Israel’s efficient logistics performance and strategic trade agreements facilitate seamless exports, keeping it competitive against other leading nations. Going forward, the country’s success in this segment will depend on continued investment in R&D, market diversification, and sustainability initiatives to maintain its technological edge and premium market positioning.LuxembourgDespite being a relatively small player in the global textile export market, Luxembourg exhibits a staggering RCA of 341.86 in nonwoven fabric exports. This exceptionally high RCA signifies that Luxembourg’s share in global nonwoven fabric exports is disproportionately higher than its overall participation in world trade, highlighting the country’s strong specialisation in this segment.Targeted investments in high-value nonwoven fabrics: Luxembourg’s nonwoven fabric industry has increasingly focused on high-performance textiles, catering to specialised applications such as filtration, automotive, construction, and medical textiles. These sectors demand precision engineering and advanced material science, areas in which Luxembourg has developed a competitive edge.Premium positioning: Luxembourg’s UVR of $10.95 per kilogram significantly exceeds the global average for nonwoven fabrics to the US. This premium pricing reflects the export of high-tech, performance-driven nonwoven products rather than commodity-grade textiles. The country’s focus on low-volume, high-margin markets ensures profitability despite relatively smaller absolute export volumes.Leveraging European market integration & logistics strengths: As a centrally located European nation, Luxembourg benefits from efficient logistics and connectivity to major industrial hubs across the EU. This enables seamless supply chain operations and facilitates the export of high-value nonwoven fabrics to key European markets.Luxembourg’s remarkable RCA of 341.86 and UVR of $10.95/kg underscore its strategic positioning as a leader in specialised nonwoven fabrics. With continued investments in R&D, sustainability, and high-value manufacturing, the country is well-poised to maintain its competitive advantage in this dynamic segment of the textile industry.Outlook and conclusionGermany remains the undisputed leader in nonwoven fabric exports, backed by its high export value and superior trade infrastructure. The country’s Logistics Performance Index (LPI) of 4.10 further strengthens its competitive edge, ensuring seamless global trade.India and Israel are expanding their market presence, demonstrating consistent growth rates in exports. India, with a strong RCA of 4.01 and a UVR of $2.48/kg, balances volume with value-driven exports. Meanwhile, Israel stands out with an RCA of 15.42 and a UVR of $4.73/kg, indicating a specialisation in high-value nonwoven fabrics.China, despite being a major exporter, relies on high-volume, low-cost production, as reflected in its UVR of $1.21/kg. However, since an additional 20 per cent tariff is imposed by the US, China’s competitiveness could be only slightly affected, due to its significant lesser UVR, making it cost effective even in case of additional tariffs.As of January 1, 2025, all five countries discussed here benefited from a zero-tariff rate, ensuring unhindered international trade. Apart from Germany’s leading LPI, the other exporters—China, India, Israel, and Luxembourg—maintain competitive LPIs ranging from 3.40 to 3.70, highlighting their growing trade efficiency.Overall, the nonwoven fabric export landscape is evolving, with Germany maintaining dominance while India and Israel are gaining traction. Any shifts in trade policies, such as tariff hikes on China, would only result in a larger contribution of other countries volume-wise but they would have to level up in terms of prices to match China’s dominance. Fibre2Fashion News Desk (NS) Source link

[chilimili212]

The global trade of HS-560312 (nonwoven fabrics made from man-made filaments) plays a pivotal role in the US textile industry, catering to diverse applications such as automotive, medical, filtration, hygiene products, and industrial textiles. As a key importer, the United States offers free market access for these products under an MFN (Most Favoured Nation) tariff of zero per cent, making it an attractive destination for global exporters. Table1: Key Observations and Trade Statistics on HS-560312- Nonwoven Fabrics (Man-Made Filaments) in CY 2024Germany leads global nonwoven fabric exports with high-value products and strong logistics. India and Israel are expanding, with India offering cost-effective alternatives and Israel specialising in premium fabrics. China remains a major player but faces a 20 per cent US tariff, slightly affecting its low-cost advantage. Trade policies and logistics efficiency will shape future market dynamics. Source: TradeMap and F2F Analysis; *Effective from 4th MarchNote: RCA - Revealed Comparative Advantage; UVR - Unit Value Realisation; LPI - Logistic Performance Index GermanyGermany holds the top position in the export market for nonwoven fabrics of man-made filaments, with a significant export value of $68.678 million to the US. Its Revealed Comparative Advantage (RCA) of 2.78 indicates a strong specialisation in this segment, demonstrating that Germany exports these fabrics at a much higher rate than the global average. This high RCA underscores Germany’s competitive edge, positioning it as a leader in the trade of this product to the US.Despite offering high-priced products with a Unit Value Realisation (UVR) of $6.30 per kg, Germany maintains its market leadership in the US market. This premium pricing is a result of advanced manufacturing technology, superior quality standards, and stringent regulatory compliance. German manufacturers prioritise innovation, durability, and performance, making their nonwoven fabrics suitable for high-end applications in industries such as automotive, medical textiles, filtration, and geotextiles.Furthermore, Germany’s dominance is driven by its strong research and development (R&D) ecosystem, which fosters continuous product improvement. Investments in automation, precision engineering, and sustainability initiatives also contribute to its competitive positioning. While higher prices may limit cost-sensitive buyers, Germany’s reputation for reliability and excellence ensures continued demand from premium markets that prioritise quality over cost.ChinaChina’s position in the nonwoven fabrics of man-made filaments market is a mix of strength and challenge in the US market. With an export value of $56.263 million, the country showcases its large-scale manufacturing capacity, a key factor in maintaining its global presence. However, its RCA of 0.81 per cent suggests that China is losing its competitive edge in this category compared to other leading exporters to the US. An RCA value below 1 indicates that China is less specialised in this segment relative to global trade patterns.Despite this, China’s low Unit Value Realisation (UVR) of $1.21 per kg allows it to remain a formidable player. A lower UVR implies that China’s pricing strategy is highly competitive, making its products more attractive to cost-sensitive buyers.Tariff Impact: With the first tariff imposition on February 4th, 2025, the tariff rate increased to 10 per cent. This rise in the tariff burden would lead to an increase in the UVR as production and export costs escalate. As a result, the UVR would likely increase to around $1.3/kg, reflecting the growing challenges posed by the higher tariffs. The increase in the UVR shows that the products are becoming more expensive, which could make them less competitive for price-sensitive US consumers. In the second tariff imposition, effective from March 4th, 2025, the tariff rate rose further to 20 per cent. This substantial increase would push the UVR to approximately $1.5/per kg or higher. The higher tariff burden will continue to raise costs, further diminishing the cost-effectiveness of the products. However, it is important to note that despite the tariff imposition, China does not entirely lose its cost advantage. India, which has the capacity to compete with China, is still behind in terms of UVR.IndiaIndia stands as a strong contender in the nonwoven fabrics of man-made filaments market, with exports totalling $55.174 million to the US. Its RCA of 4.01 underscores its significant specialisation in this category, making it one of the most competitive global players. This high RCA value indicates that India’s share in the global nonwoven fabrics trade is much larger than its overall participation in world exports, reflecting a strong foothold in the industry.India’s growing textile manufacturing ecosystem, supported by robust government policies, infrastructure development, and increasing investments, plays a key role in strengthening its presence in nonwoven fabric exports.India’s moderate UVR of $2.48 per kg positions it as an affordable supplier in the global market. Unlike Germany, which focuses on premium, high-tech products, India competes on a balance between price and quality, making it attractive for buyers looking for cost-effective solutions without compromising too much on performance.Logistics Performance & Challenges: India has an above-average Logistics Performance Index (LPI) score of 3.4, highlighting its improving trade facilitation, infrastructure, and customs efficiency. However, it still lags behind Luxembourg, which has a slightly higher LPI of 3.6. While India has made significant progress in logistics efficiency, further improvements are needed in areas such as: Reducing port congestion and streamlining customs clearance for faster exports. Enhancing last-mile connectivity through better transportation networks. Investing in digitisation and automation to improve supply chain visibility and efficiency.By addressing these logistical bottlenecks, India can further strengthen its position in the global nonwoven fabrics market, boosting export volumes and competitiveness.Bottom of FormIsraelIsrael has carved out a strong position in the global nonwoven fabric market, with exports reaching $52.414 million to the US. Its exceptionally high RCA of 15.42 underscores its specialisation in this category, making it a key player in high-value, specialised textile products.A UVR of $4.73 per kg highlights the premium nature of Israel’s exports to the US, which are widely used in medical, industrial, defence, and high-tech applications. Unlike mass producers such as China and India, Israel focuses on quality, innovation, and advanced material development, ensuring strong demand in markets that prioritise performance over cost.Additionally, Israel’s efficient logistics performance and strategic trade agreements facilitate seamless exports, keeping it competitive against other leading nations. Going forward, the country’s success in this segment will depend on continued investment in R&D, market diversification, and sustainability initiatives to maintain its technological edge and premium market positioning.LuxembourgDespite being a relatively small player in the global textile export market, Luxembourg exhibits a staggering RCA of 341.86 in nonwoven fabric exports. This exceptionally high RCA signifies that Luxembourg’s share in global nonwoven fabric exports is disproportionately higher than its overall participation in world trade, highlighting the country’s strong specialisation in this segment.Targeted investments in high-value nonwoven fabrics: Luxembourg’s nonwoven fabric industry has increasingly focused on high-performance textiles, catering to specialised applications such as filtration, automotive, construction, and medical textiles. These sectors demand precision engineering and advanced material science, areas in which Luxembourg has developed a competitive edge.Premium positioning: Luxembourg’s UVR of $10.95 per kilogram significantly exceeds the global average for nonwoven fabrics to the US. This premium pricing reflects the export of high-tech, performance-driven nonwoven products rather than commodity-grade textiles. The country’s focus on low-volume, high-margin markets ensures profitability despite relatively smaller absolute export volumes.Leveraging European market integration & logistics strengths: As a centrally located European nation, Luxembourg benefits from efficient logistics and connectivity to major industrial hubs across the EU. This enables seamless supply chain operations and facilitates the export of high-value nonwoven fabrics to key European markets.Luxembourg’s remarkable RCA of 341.86 and UVR of $10.95/kg underscore its strategic positioning as a leader in specialised nonwoven fabrics. With continued investments in R&D, sustainability, and high-value manufacturing, the country is well-poised to maintain its competitive advantage in this dynamic segment of the textile industry.Outlook and conclusionGermany remains the undisputed leader in nonwoven fabric exports, backed by its high export value and superior trade infrastructure. The country’s Logistics Performance Index (LPI) of 4.10 further strengthens its competitive edge, ensuring seamless global trade.India and Israel are expanding their market presence, demonstrating consistent growth rates in exports. India, with a strong RCA of 4.01 and a UVR of $2.48/kg, balances volume with value-driven exports. Meanwhile, Israel stands out with an RCA of 15.42 and a UVR of $4.73/kg, indicating a specialisation in high-value nonwoven fabrics.China, despite being a major exporter, relies on high-volume, low-cost production, as reflected in its UVR of $1.21/kg. However, since an additional 20 per cent tariff is imposed by the US, China’s competitiveness could be only slightly affected, due to its significant lesser UVR, making it cost effective even in case of additional tariffs.As of January 1, 2025, all five countries discussed here benefited from a zero-tariff rate, ensuring unhindered international trade. Apart from Germany’s leading LPI, the other exporters—China, India, Israel, and Luxembourg—maintain competitive LPIs ranging from 3.40 to 3.70, highlighting their growing trade efficiency.Overall, the nonwoven fabric export landscape is evolving, with Germany maintaining dominance while India and Israel are gaining traction. Any shifts in trade policies, such as tariff hikes on China, would only result in a larger contribution of other countries volume-wise but they would have to level up in terms of prices to match China’s dominance. Fibre2Fashion News Desk (NS) Source link

[jc-3dproject]

Continuing map adjustments:

Like the previous sign, I had to rotate and resize the sprite.

For this sign, all I had to do was resize the sprite and adjust its position.

I then returned to the inside of the facility to add another sign.

I then created a thin textured cube to cover up the corridor wall from inside the cargo bay. I then duplicated this cube and moved it over.

I made a smaller version of this cube to patch up the gap above the door. For some reason, the cubes became one object. I tried to use this to my advantage by duplicating it and moving them to cover up as much of the wall as possible.

I then undid this and managed to duplicate the smaller cubes for the wall.

To finish the wall up, I created another thin cube the was less wide to cover the last bit of the wall up.

When it came to the sprites, I found them to emit light. I solved this issue by changing the materials from unlit to lit.

Now that I had solved this issue with the signs, I moved this sign into a more shaded area as it won't look weird anymore.

I moved this sign from one of the corridors so that it was visible.

The Sci-Fi Lab tank model from the Fab store was imported in many pieces. I then decided to delete it.

The same thing happened with the Lab table1 model. I felt it would be pointless to test the second lab table as it is likely to have the same result.

Here, I downloaded a blender zip folder of the model seen below.

For these barrel models, I downloaded the 'fbx' zip folder of the model.

While attempting to import the barrels, UE5 crashed. I tried importing it again, which worked.

I opened up the imported material for the barrels and I deleted the colour parameter node.

After that, I connected texture sample nodes to create the barrel texture.

Here, I placed the barrels to the corner of the lab.

I then duplicated them to fill up more empty space.

As I knew that the doorframes were too big, I felt that the barrels would be too big against the player. I found it hard to get the scale right because of the door frames.

Here, I exported the tank model as an 'fbx' file.

For some reason, the model didn't come with a reflective material as shown on the fab store.

I substituted this with a pre-existing metal material.

I then added two of these tanks into the lab.

Next, I imported both letters that I made in photoshop.

I converted them into sprite to be placed across the map so the player can learn lore behind the facility in the game.

Like with the other sprites, this sprite too large. It is also using the unlit sprite material.

[umifani]

エレガントなダイニングテーブル　円形テーブル　北欧スタイル　クラシックかつ現代的な丸形一本脚

[mscdsmaheprojectassignment2]

Capstone Milestone Assignment 2: Methods

Full Code: https://github.com/sonarsujit/MSC-Data-Science_Project/blob/main/capstoneprojectcode.py

Methods

1. Sample: The data for this study was drawn from the World Bank's global dataset, which includes comprehensive economic and health indicators from various countries across the world. The final sample consists of N = 248 countries, and I am only looking at the 2012 data for this sample data.

Sample Description: The sample includes countries from various income levels, regions, and development stages. It encompasses both high-income countries with advanced healthcare systems and low-income countries where access to healthcare services might be limited. The sample is diverse in terms of geographic distribution, economic conditions, and public health outcomes, providing a comprehensive view of global health disparities.

2. Measures: The given dataset has 86 variables and form the perspective of answering the research question, I focused on life expectancy and access to healthcare services.  The objective is to look into these features statistically and narrow down to relevant and important features that will align to my research problem.

Here's a breakdown of the selected features and how they relate to my research:

Healthcare Access and Infrastructure: Access to electricity, Access to non-solid fuel,Fixed broadband subscriptions, Improved sanitation facilities, Improved water source, Internet users

Key Health and Demographic Indicators: Adolescent fertility rate, Birth rate, Cause of death by communicable diseases and maternal/prenatal/nutrition conditions, Fertility rate, Mortality rate, infant , Mortality rate, neonatal , Mortality rate, under-5, Population ages 0-14 ,Urban population growth

Socioeconomic Factors: Population ages 65 and above, Survival to age 65, female, Survival to age 65, male, Adjusted net national income per capita, Automated teller machines (ATMs), GDP per capita, Health expenditure per capita, Population ages 15-64, Urban population.

Variable Management:

The Life Expectancy variable was used as a continuous variable in the analysis.

All the independent variables were also used as continuous variables.

Out of the 84 in quantitative independent variables, I found that the above 26 features closely describe the health access and infrastructure, key health and demographic indicators and socioeconomic factors based on the literature review.

I run the Lasso regression to get insights on features that will align more closely with my research question

Table1: Lasso regression to find important features that support my research question.

Based on the result from lasso regression, I finalized 8 predictor variables which I believe will potentially help me answer my research question.

To further support the selection of these 8 features, I run Correlation Analysis for these 8 features and found to have both positive and negative correlations with the target variable (Life Expectancy at Birth, Total (Years))

Table 2: Pearson Correlation values and relative p values

The inclusion of both positive and negative correlations provides a balanced view of the factors affecting life expectancy, making these features suitable for your analysis.

Incorporating these variables should allow us to capture the multifaceted relationship between healthcare access and life expectancy across different countries, and effectively address our research question.

Analyses:

The primary goal of the analysis was to explore and understand the factors that influence life expectancy across different countries. This involved using Lasso regression for feature selection and Pearson correlation for assessing the strength of relationships between life expectancy and various predictor variables.

The Lasso model revealed that factors such as survival rates to age 65, health expenditure, broadband access, and mortality rate under 5 were the most significant predictors of life expectancy.

The mean squared error (MSE)  = 1.2686 of the model was calculated to assess its predictive performance.

Survival to age 65 (both male and female) had strong positive correlations with life expectancy, indicating that populations with higher survival rates to age 65 tend to have higher overall life expectancy.

Health expenditure per capita showed a moderate positive correlation, suggesting that countries investing more in healthcare tend to have longer life expectancy.

Mortality rate, under-5 (per 1,000) had a strong negative correlation with life expectancy, highlighting that higher child mortality rates are associated with lower life expectancy.

Rural population (% of total) had a negative correlation, indicating that countries with a higher percentage of rural populations might face challenges that reduce life expectancy.

[adventuresinmazatlan]

Chamorro

Chamorro (pork shank) Méxican Chamorro via Instant Pot pressure cooker Instant Pot Pressure CookerSheet Pan, 1/4Spoon, slotted 2 ea Chamorro (Pork Shank) (without skin)1 tbsp Salt, table1 tbsp Oregano, Méxican4 cups Water Place the chamorro on the sheet pan and cover liberally with the salt and Méxican Oregano. Place the sheet pan in the refrigerator for a minimum of 30 minutes.Place 4 cups…

[vanyablog1]

1. Управління даними:

1.1 Кодування відсутніх даних:

У наборі даних NESARC пропущені значення позначені -99.

Заміню ці значення на NA (недоступно) у Python.

1.2 Кодування достовірних даних:

Деякі змінні, такі як DRNKQ1, містять нечислові значення, наприклад, 'Refused' або 'Don't Know'.

Заміню ці значення на відповідні коди, наприклад, 99 для 'Refused' та 98 для 'Don't Know'.

1.3 Перекодування змінних:

Деякі змінні, такі як GAD7 та PHQ9, мають шкали, які не відповідають моєму аналізу.

Перекодую ці змінні, щоб створити нові змінні, які відповідають моїм потребам.

1.4 Створення вторинних змінних:

За потреби створю нові змінні, які є комбінацією існуючих змінних.

1.5 Розбиття або групування змінних:

За потреби розбиваю або групую змінні на основі певних критеріїв.

import pandas as pd

#Завантаження набору даних NESARC

nesarc = pd.read_csv("NESARC_Public_Use_Dataset_2013_01_14.csv")

#Кодування відсутніх даних

nesarc.replace(-99, np.NA, inplace=True)

#Кодування достовірних даних

nesarc["DRNKQ1"].replace("Refused", 99, inplace=True) nesarc["DRNKQ1"].replace("Don't Know", 98, inplace=True)

#Перекодування змінних

nesarc["GAD7_binary"] = (nesarc["GAD7"] >= 10).astype(int) nesarc["PHQ9_binary"] = (nesarc["PHQ9"] >= 10).astype(int)

#Створення вторинних змінних

nesarc["overall_mental_health"] = nesarc["GAD7_binary"] + nesarc["PHQ9_binary"]

#Розбиття змінних

nesarc_men = nesarc[nesarc["SEX"] == 1] nesarc_women = nesarc[nesarc["SEX"] == 2]

#Запуск розподілів частот

table1 = pd.crosstab(nesarc["DRNKQ1"], nesarc["GAD7_binary"]) table2 = pd.crosstab(nesarc["DRNKQ1"], nesarc["PHQ9_binary"]) table3 = pd.crosstab(nesarc["DRNKQ1"], nesarc["overall_mental_health"]) table4 = pd.crosstab(nesarc["DRNKQ1"], [nesarc["GAD7_binary"], nesarc["PHQ9_binary"]])

#Відображення результатів

print(table1) print(table2) print(table3) print(table4)

#Аналіз результатів

#… (інтерпретація результатів)

[ibarrau]

[Python] PySpark to M, SQL or Pandas

Hace tiempo escribí un artículo sobre como escribir en pandas algunos códigos de referencia de SQL o M (power query). Si bien en su momento fue de gran utilidad, lo cierto es que hoy existe otro lenguaje que representa un fuerte pie en el análisis de datos.

Spark se convirtió en el jugar principal para lectura de datos en Lakes. Aunque sea cierto que existe SparkSQL, no quise dejar de traer estas analogías de código entre PySpark, M, SQL y Pandas para quienes estén familiarizados con un lenguaje, puedan ver como realizar una acción con el otro.

Lo primero es ponernos de acuerdo en la lectura del post.

Power Query corre en capas. Cada linea llama a la anterior (que devuelve una tabla) generando esta perspectiva o visión en capas. Por ello cuando leamos en el código #“Paso anterior” hablamos de una tabla.

En Python, asumiremos a "df" como un pandas dataframe (pandas.DataFrame) ya cargado y a "spark_frame" a un frame de pyspark cargado (spark.read)

Conozcamos los ejemplos que serán listados en el siguiente orden: SQL, PySpark, Pandas, Power Query.

En SQL:

SELECT TOP 5 * FROM table

En PySpark

spark_frame.limit(5)

En Pandas:

df.head()

En Power Query:

Table.FirstN(#"Paso Anterior",5)

Contar filas

SELECT COUNT(*) FROM table1

spark_frame.count()

df.shape()

Table.RowCount(#"Paso Anterior")

Seleccionar filas

SELECT column1, column2 FROM table1

spark_frame.select("column1", "column2")

df[["column1", "column2"]]

#"Paso Anterior"[[Columna1],[Columna2]] O podría ser: Table.SelectColumns(#"Paso Anterior", {"Columna1", "Columna2"} )

Filtrar filas

SELECT column1, column2 FROM table1 WHERE column1 = 2

spark_frame.filter("column1 = 2") # OR spark_frame.filter(spark_frame['column1'] == 2)

df[['column1', 'column2']].loc[df['column1'] == 2]

Table.SelectRows(#"Paso Anterior", each [column1] == 2 )

Varios filtros de filas

SELECT * FROM table1 WHERE column1 > 1 AND column2 < 25

spark_frame.filter((spark_frame['column1'] > 1) & (spark_frame['column2'] < 25)) O con operadores OR y NOT spark_frame.filter((spark_frame['column1'] > 1) | ~(spark_frame['column2'] < 25))

df.loc[(df['column1'] > 1) & (df['column2'] < 25)] O con operadores OR y NOT df.loc[(df['column1'] > 1) | ~(df['column2'] < 25)]

Table.SelectRows(#"Paso Anterior", each [column1] > 1 and column2 < 25 ) O con operadores OR y NOT Table.SelectRows(#"Paso Anterior", each [column1] > 1 or not ([column1] < 25 ) )

Filtros con operadores complejos

SELECT * FROM table1 WHERE column1 BETWEEN 1 and 5 AND column2 IN (20,30,40,50) AND column3 LIKE '%arcelona%'

from pyspark.sql.functions import col spark_frame.filter( (col('column1').between(1, 5)) & (col('column2').isin(20, 30, 40, 50)) & (col('column3').like('%arcelona%')) ) # O spark_frame.where( (col('column1').between(1, 5)) & (col('column2').isin(20, 30, 40, 50)) & (col('column3').contains('arcelona')) )

df.loc[(df['colum1'].between(1,5)) & (df['column2'].isin([20,30,40,50])) & (df['column3'].str.contains('arcelona'))]

Table.SelectRows(#"Paso Anterior", each ([column1] > 1 and [column1] < 5) and List.Contains({20,30,40,50}, [column2]) and Text.Contains([column3], "arcelona") )

Join tables

SELECT t1.column1, t2.column1 FROM table1 t1 LEFT JOIN table2 t2 ON t1.column_id = t2.column_id

Sería correcto cambiar el alias de columnas de mismo nombre así:

spark_frame1.join(spark_frame2, spark_frame1["column_id"] == spark_frame2["column_id"], "left").select(spark_frame1["column1"].alias("column1_df1"), spark_frame2["column1"].alias("column1_df2"))

Hay dos funciones que pueden ayudarnos en este proceso merge y join.

df_joined = df1.merge(df2, left_on='lkey', right_on='rkey', how='left') df_joined = df1.join(df2, on='column_id', how='left')Luego seleccionamos dos columnas df_joined.loc[['column1_df1', 'column1_df2']]

En Power Query vamos a ir eligiendo una columna de antemano y luego añadiendo la segunda.

#"Origen" = #"Paso Anterior"[[column1_t1]] #"Paso Join" = Table.NestedJoin(#"Origen", {"column_t1_id"}, table2, {"column_t2_id"}, "Prefijo", JoinKind.LeftOuter) #"Expansion" = Table.ExpandTableColumn(#"Paso Join", "Prefijo", {"column1_t2"}, {"Prefijo_column1_t2"})

Group By

SELECT column1, count(*) FROM table1 GROUP BY column1

from pyspark.sql.functions import count spark_frame.groupBy("column1").agg(count("*").alias("count"))

df.groupby('column1')['column1'].count()

Table.Group(#"Paso Anterior", {"column1"}, {{"Alias de count", each Table.RowCount(_), type number}})

Filtrando un agrupado

SELECT store, sum(sales) FROM table1 GROUP BY store HAVING sum(sales) > 1000

from pyspark.sql.functions import sum as spark_sum spark_frame.groupBy("store").agg(spark_sum("sales").alias("total_sales")).filter("total_sales > 1000")

df_grouped = df.groupby('store')['sales'].sum() df_grouped.loc[df_grouped > 1000]

#”Grouping” = Table.Group(#"Paso Anterior", {"store"}, {{"Alias de sum", each List.Sum([sales]), type number}}) #"Final" = Table.SelectRows( #"Grouping" , each [Alias de sum] > 1000 )

Ordenar descendente por columna

SELECT * FROM table1 ORDER BY column1 DESC

spark_frame.orderBy("column1", ascending=False)

df.sort_values(by=['column1'], ascending=False)

Table.Sort(#"Paso Anterior",{{"column1", Order.Descending}})

Unir una tabla con otra de la misma característica

SELECT * FROM table1 UNION SELECT * FROM table2

spark_frame1.union(spark_frame2)

En Pandas tenemos dos opciones conocidas, la función append y concat.

df.append(df2) pd.concat([df1, df2])

Table.Combine({table1, table2})

Transformaciones

Las siguientes transformaciones son directamente entre PySpark, Pandas y Power Query puesto que no son tan comunes en un lenguaje de consulta como SQL. Puede que su resultado no sea idéntico pero si similar para el caso a resolver.

Analizar el contenido de una tabla

spark_frame.summary()

df.describe()

Table.Profile(#"Paso Anterior")

Chequear valores únicos de las columnas

spark_frame.groupBy("column1").count().show()

df.value_counts("columna1")

Table.Profile(#"Paso Anterior")[[Column],[DistinctCount]]

Generar Tabla de prueba con datos cargados a mano

spark_frame = spark.createDataFrame([(1, "Boris Yeltsin"), (2, "Mikhail Gorbachev")], inferSchema=True)

df = pd.DataFrame([[1,2],["Boris Yeltsin", "Mikhail Gorbachev"]], columns=["CustomerID", "Name"])

Table.FromRecords({[CustomerID = 1, Name = "Bob", Phone = "123-4567"]})

Quitar una columna

spark_frame.drop("column1")

df.drop(columns=['column1']) df.drop(['column1'], axis=1)

Table.RemoveColumns(#"Paso Anterior",{"column1"})

Aplicar transformaciones sobre una columna

spark_frame.withColumn("column1", col("column1") + 1)

df.apply(lambda x : x['column1'] + 1 , axis = 1)

Table.TransformColumns(#"Paso Anterior", {{"column1", each _ + 1, type number}})

Hemos terminado el largo camino de consultas y transformaciones que nos ayudarían a tener un mejor tiempo a puro código con PySpark, SQL, Pandas y Power Query para que conociendo uno sepamos usar el otro.