Güneş kremleri ve SPF1000Kitap
Genel olarak Sunscreen diye adlandırılan güneş ışınlarını bloke edici maddeler içeren kremler, güneş ışınlarının yakıcı etkilerine karşı koruma sağlarken vücudun en önemli D vitamini üretim kaynağı olan ciltteki üretimi de engelleyebiliyorlar. Bu ürünlerin özellikle son 20 yılda kulla nımları oldukça yaygınlaştı. En pahalı markalardan marketlerde satılan en ucuz kozmetiklere kadar, neredeyse SPF içermeyen gündüz kremi bulmak imkânsız gibi...
Ne yapmalıyız, güneşe korumasız mı çıkmalıyız? Elbette hayır! Günes ışınlarının deri kanseri görülme sıklığını artırdığına ve erken yaşlanmayə neden olduğuna dair neredeyse hiçbir bilimsel şüphe yok. Öncelikle en kolay ve en garantili yöntem olan öğle saatlerinde güneşlenmemeyi seçmek en akıllıca yol. Bu saatlerde bronzlaşsanız da zaten vücut o 'yanmış' deriyi bir şekilde atıyor. Saat 12-16 arası güneşlenmek cilde yapılabilecek en büyük kötülük! Şemsiye, şapka, ince beyaz giysiler ve gözlük yazın ihmal edilmemesi gereken fiziksel koruyucular. Kozmetik koruyucular ise kimyasal SPF içermemeli. Doğal bitkisel yağlar arasında doğal güneş koruyucu özelliği olan birçok farklı yağ var. Bunların kullanıldığı güneş yağları ve gündüz kremleri, cildin doğal D vitamini üretimini engellemeden sizi güneşin zararlı ışınlarından korur.
There are many other models to forecast time series, such as weighted
moving average models or autoregressive integrated moving average
(ARIMA) models. Some of them require you to first remove the trend
and seasonality. For example, if you are studying the number of active
users on your website, and it is growing by 10% every month, you
would have to remove this trend from the time series. Once the model
is trained and starts making predictions, you would have to add the
trend back to get the final predictions. Similarly, if you are trying to
predict the amount of sunscreen lotion sold every month, you will
probably observe strong seasonality: since it sells well every summer,
a similar pattern will be repeated every year. You would have to
remove this seasonality from the time series, for example by
computing the difference between the value at each time step and the
value one year earlier (this technique is called differencing). Again,
after the model is trained and makes predictions, you would have to
add the seasonal pattern back to get the final predictions.
When using RNNs, it is generally not necessary to do all this, but it
may improve performance in some cases, since the model will not
have to learn the trend or the seasonality.
TREND AND SEASONALITY, satış tahminleri!
Reklam
“I like you very much, Mara. I like talking to you. I like watching you do yoga. I like the way you always smell like sunscreen. I like how you manage to say pretty much whatever you want while still being unbelievably kind. I like being in this house with you, and everything we do in here.” His throat bobs. “I don’t think it’s a surprise that I really, really like the idea of fucking you.”
Anh, you are the building block of this relationship
“Why?” Anh blinked at her innocently. “I put sunscreen on Jeremy all the time. Look”—she squirted lotion on her hand and haphazardly slapped it across Jeremy’s face— “I am putting sunscreen on my boyfriend. Because I don’t want him to get melanoma. Am I ‘inappropriate’?”
“Is the sunscreen going in the Title IX complaint?”
His mouth twitched. “Right on the first page. Nonconsensual sunblock application.”
“Oh, come on. I saved you from basal cell carcinoma.”
"Groped under SPF pretense.”
“Is the sunscreen going in the Title IX complaint?”
His mouth twitched. “Right on the first page. Nonconsensual sunblock application.”
“Oh, come on. I saved you from basal cell carcinoma.”
“Groped under SPF pretense.”
Reklam