#Siemens
#MultiColumn Bullish Probable with O at same level (Resistance breakout with support)

#Investment
#Pyramid https://t.co/UZJsu8fd4i

Today is “Korean American Day”.

#OTD in 1903, the first large group of Korean immigrants arrived in US-conquered territory. [Thread]

102 Korean men, women, and children arrived in Honolulu aboard the SS Gaelic to work in Hawaii’s sugar plantations. By 1905, Koreans comprised 11% of Hawaii’s plantation workforce—around 7000 people.

This first wave of Korean immigrants arrived amid sharpening imperialist rivalry in the Pacific. Korea had slowly been losing its independence for decades, and by 1903 was just two years away from becoming a protectorate of Japan.

At the same time, the US was consolidating its position in the Pacific, annexing Hawaii, Guam, and the Philippines in 1898, and partitioning Samoa in 1899.

(Pictured: US troops in the Philippines)

US missionaries colluded with the Hawaiian Sugar Planters Association to secure a new, cheap labor force for Hawaii’s sugar fields. Many of the Koreans who arrived in Hawaii from 1903-1905 were Methodists.

So when it first appeared in Normandy, the Sherman Firefly and it's meaty 17 pounder represented an ideal combo of reliable chassis, great gun and devastating firepower right?

Ummm...

Truth is a bit more complex. /1
#WW2 #SWW #History

Deliveries of Firefly to units only commenced in April 1944,
just a few weeks before Overlord & left minimal time for familiarisation.

Armoured Regiments mostly utilised Fireflies on a basis of 1 per troop of 4 tanks. /2

Finally each armoured regiment troop had access to integral, meaty, anti-tank firepower capable of dealing serious damage to any German AFV likely to be encountered. /3

The dual purpose 75mm gun was quite capable of laying down a reasonable HE shell or AP round for dealing with Panzer III or IV.

Firefly's 17 Pounder had.... problems with HE, but excelled at tank killing. So Tigers and Panthers would need to be wary. /4

Firefly's design history dated back to March 1943 when the General Staff published "Policy on Tanks" which effectively stated the majority of tanks should have a good HE weapon and...

[See image below] /5

#ज्योतिष_विज्ञान #मंत्र_विज्ञान

ज्योतिषाचार्य अक्सर ग्रहों के दुष्प्रभाव के समाधान के लिए मंत्र जप, अनुष्ठान इत्यादि बताते हैं।

व्यक्ति के जन्म के समय ग्रहों की स्थिति ही उसकी कुंडली बन जाती है जैसे कि फ़ोटो खींच लिया हो और एडिट करना सम्भव नही है। इसे ही "लग्न" कुंडली कहते हैं।

लग्न के समय ग्रहों की इस स्थिति से ही जीवन भर आपको किस ग्रह की ऊर्जा कैसे प्रभावित करेगी का निर्धारिण होता है। साथ साथ दशाएँ, गोचर इत्यादि चलते हैं पर लग्न कुंडली का रोल सबसे महत्वपूर्ण है।

पृथ्वी से अरबों खरबों दूर ये ग्रह अपनी ऊर्जा से पृथ्वी/व्यक्ति को प्रभावित करते हैं जैसे हमारे सबसे निकट ग्रह चंद्रमा जोकि जल का कारक है पृथ्वी और शरीर के जलतत्व पर पूर्ण प्रभाव रखता है।
पूर्णिमा में उछाल मारता समुद्र का जल इसकी ऊर्जा के प्रभाव को दिखाता है।

अमावस्या में ऊर्जा का स्तर कम होने पर वही समुद्र शांत होकर पीछे चला जाता है। जिसे ज्वार-भाटा कहते हैं। इसी तरह अन्य ग्रहों की ऊर्जा के प्रभाव होते हैं जिन्हें यहां समझाना संभव नहीं।
चंद्रमा की ये ऊर्जा शरीर को (अगर खराब है) water retention, बैचेनी, नींद न आना आदि लक्षण दिखाती है

मंत्र क्या हैं-
मंत्र इन ऊर्जाओं के सटीक प्रयोग करने के पासवर्ड हैं। जिनके जप से संबंधित ग्रह की ऊर्जा को जातक की ऊर्जा से कनेक्ट करके उन ग्रहों के दुष्प्रभाव को कम किया और शुभ प्रभाव को बढ़ाया जाता है।

1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.