Teknoloji
Gülnihal Mert
02/01/2017 - 17:26

Projeler İş Başında

Bir bilim fuarı projesi hazırlamak için öncelikle “Bilimsel Yöntem Nedir?” sorusunun cevabını bilmemiz gerekir.

Bilim fuarlarıyla çevremize bilimsel gözle bakabilir, yaratıcılığımızın farkına varabilir, yeni beceriler geliştirebilir, merak ettiğimiz konular hakkında daha çok bilgi edinebilir ve ortaya çıkardığımız eserleri sergileyebiliriz.

Bilimin hayattaki önemini kavramamıza ve derslerimize daha yüksek motivasyonla devam etmemize yardımcı olan bu fuarlar, ilgi duyduğumuz alanlarda bize keşif yapma olanağı da sunar. Böylece ders, kitap ve laboratuvar uygulamalarından öğrendiklerimizi kendi projemiz içinde pekiştirebiliriz.

Bir bilim fuarı projesi hazırlamak için öncelikle “Bilimsel Yöntem Nedir?” sorusunun cevabını bilmemiz gerekir.

BİLİMSEL YÖNTEM

“Bilimsel yöntem”, araştırma projelerinde kullandığımız, birbirini takip eden bilimsel adımlardan oluşan ve sorduğumuz sorulara cevaplar bulduran yöntemdir. “Soru, araştırma, hipotez, deney, sonuç ve değerlendirme” basamaklarından oluşur. Bilimsel yöntem basamaklarını adım adım geçtiğimizde projemizi tamamlamış oluruz. Bu nedenle bilimsel yöntem, proje boyunca bize yardımcı olacak, gerekli bir araçtır.

 

1. Proje Konusu Bulalım

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Projemize, ilgi duyduğumuz alanlar içinden bir konu belirleyerek başlayalım. Konuyu belirlerken araştırma sorularını da belirlememiz gerekir. Araştırma sorusu olarak şu örnekler verilebilir: İnci nasıl oluşur? Kalp nasıl çalışır? Neden bazı kuşlar göç eder? Küresel ısınma nasıl oluşur? Limon neden küflenir? Mantarlar bitki midir? Neden arabaların şekilleri birbirine benzer? Neden bazı ağaçların yaprakları sonbahar geldiğinde dökülürken bazılarınınki dökülmez? Orta Anadolu Bölgesi’nde yetişen bitkilerdeki çinko eksikliğinin nedeni nedir? Günümüzde kanser hastalığına yakalanan insanların sayısındaki artışın olası sebepleri nelerdir? Pestisitler zirai mücadelede nasıl ve neden kullanılır? vb. Araştırma soruları, konuyu araştırırken ortaya çıkabileceği gibi en başta da belirlenebilir.

Konumuzu kitaplardan, çeşitli kaynaklardan ve internetten araştırabiliriz. Konu ile ilgili uzmanlardan da bilgi ve destek alabiliriz. Seçtiğimiz konuyla ilgili merak ettiğimiz bir soru belirleyip sonraki adıma geçebiliriz.

 

2. Araştırma Yapalım

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Sorumuzu cevaplamak için öncelikle konuyla ilgili mevcut bilgileri araştıralım. Bir araştırma planı yaparak daha sistemli bir şekilde ilerleyebiliriz. Konuyla ilgili yazılı, sözlü ya da görsel her türlü materyali  (kitap, dergi, ansiklopedi, broşür, internet, film, ses kaydı, fotoğraf, resim, afiş vb.) kaynak olarak kullanabiliriz. Araştırmamız sırasında konuyla ilgili uzmanlarla görüşebilir; üniversiteler, müzeler, laboratuvarlar, hayvanat bahçeleri, tıp merkezleri, botanik bahçeleri gibi yerleri ziyaret edebilir; fen ve teknoloji, teknoloji ve tasarım gibi derslerin öğretmenlerinden destek isteyebiliriz.

 

3. Hipotez Kuralım

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Belirlediğimiz konuyla ilgili çeşitli kaynaklardan yararlanarak araştırma yaptık ve bilgi edindik. Şimdi, bu bilgiler ışığında ne yapmak istediğimizi yani projemizin amacını belirleyelim. Amaç, proje tamamlandığında elde edilmek istenen sonucun tanımlanmasıdır. Projelerin genelde tek bir amacı vardır. Amacı belirlemek ise hipotezi kurmayı sağlar. Örneğin “Neden bazı ağaçların yaprakları sonbahar geldiğinde dökülürken bazılarınınki dökülmez?” sorusunu cevaplamak için yapacağımız projenin amacını “ağaçların yapraklarını dökmesine sebep olan faktörleri bulmak” olarak tanımlayabiliriz. “Orta Anadolu Bölgesi’nde yetişen bitkilerdeki çinko eksikliğinin nedeni nedir?” sorusunun cevabını araştırırken, amacımız “bazı bitkilerde çinko eksikliğine sebep olan faktörleri incelemek”tir.

Hipotez “araştırma sorumuzun cevabına dair yaptığımız tahmindir.” Diğer bir deyişle, “deney sonucunda ortaya çıkması muhtemel durum ya da durumlardır.”  Bu yönüyle hipotez; gözlem, test ve deneylerde bize rehberlik edecektir.

“Eğer hava soğuk olursa ağaçlar yapraklarını döker” gibi bir hipotezimiz varsa deneyimizi bu düşünceyi ispatlamak üzerine kurarız. “Eğer yeterince yağmur yağmazsa bitkilerde çinko eksikliği olur” gibi bir hipotezin doğruluğunu çeşitli deneylerle test etmemiz gerekir.

 

4. Deney ve Gözlem Zamanı

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Hipotezimizi sınamak ve tahminlerimizin doğru olup olmadığını anlamak için bir deney tasarlamamız, gözlem ve analizler yapmamız gerekir.

Tasarladığımız deneyi “kontrollü olarak” yapmalıyız. Sonucu etkileyecek koşullardan birini değiştirip diğerlerini sabit tutarak yapılan deneylere “kontrollü deney” denir. Bir hipotezi test etmeye başlamadan önce “deney grubu”, “kontrol grubu”, “bağımlı değişken” ve “bağımsız değişken” kavramlarını anlamamız gerekir.

Örneğin mıknatısların bitkilerin büyümesinde ne kadar etkili olduğunu araştırıyorsak, bir grup bitkiyi mıknatısla birlikte incelerken bir grup bitkiyi mıknatıssız bir ortamda incelemeliyiz. Böylece mıknatıs kullanılan grup “deney grubu”, diğeri “kontrol grubu” olur. Aynı süre içinde iki grubun bitki gelişimi incelendiğinde, mıknatısların bitki gelişimini ne derece etkilediği anlaşılabilir.

Deney grubunda değiştirilebilen ve etkisi olduğu düşünülen değişken, “bağımsız değişken”dir. Bağımsız değişkeni istediğimiz şekilde seçebilir veya istediğimiz zaman değiştirebiliriz. Örneğin bitki deneyinde mıknatıs bağımsız değişkendir.

“Bağımlı değişken” ise, deneylerde bağımsız değişkenlere bağlı olarak değişen materyaldir ve ölçülebilir. Hipotezimiz, “Mıknatıslı ortamda bitkiler daha hızlı gelişir” şeklinde kurulursa yapacağımız deneyle mıknatısa bağlı olarak bitkilerdeki gelişimi ölçmeye çalışırız. Burada bitkilerin gelişimini nasıl ölçeceğimizi düşünmeliyiz. Eğer gelişimin ölçütü olarak bitki boyunu belirlersek bağımlı değişken “bitkinin boyu” olacaktır.

Deneye başlamadan önce amacı, hipotezi, bağımsız değişkeni ve diğer değişkenleri bir kere daha gözden geçirmeliyiz. Her şey tamam mı? Deney tüm kurallara uygun mu? İhtiyacımız olan tüm araç gereç ve malzemelere ulaşabiliyor muyuz? Deneyin süresi fuara yetişecek şekilde mi veya yetişecek şekilde ayarlanabilir mi? Deney için yukarıdaki soruları ve benzerlerini cevaplayabiliyorsak deney zamanı gelmiş demektir. Zamanı en etkin şekilde kullanarak deneye başlayabiliriz.

Deneyimizi uygun ve geçerli bir şekilde yapmak için bir faktörü değiştirirken diğer tüm koşulları sabit tutmalıyız. Deneyde elde ettiğimiz sonuçların kesinliğinden emin olmak için deneyimizi birkaç defa tekrarlamalıyız.

 

5. Veri Toplayalım ve Değerlendirelim

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Deney sırasında kesin bilgiler toplanmalıdır. Bu bilgilere “veri” denir. Pek çok deneyde veri, sayılardan oluşur ve bağımlı değişkenin değişimini yansıtır. Örneğin bir deneyde suyun sıcaklığının her on dakikada bir okunup değerlerin kaydedilmesi ya da mıknatıs deneyinde bitkinin boyunda oluşan değişimin eşit zaman aralıklarında ölçülerek kaydedilmesi veri toplamaktır.

Ne kadar çok veri elde edersek hipotezimizi o denli iyi destekleyebilir veya çürütebiliriz.  Hipotezimizin doğru kurulup kurulmadığını belirlemek için deney sırasında ve sonunda, kaydettiğimiz verileri analiz etmeliyiz. Araştırma sonucunda edindiğimiz bilgiler doğrultusunda bazı kararlara varabiliriz. Sonuçlar hipotezi doğrulamıyorsa, bu, deneyimizin yanlış olduğu anlamına gelmez, hipotezimizi gözden geçirmemiz gerektiğini gösterir. Örneğin “Mıknatıslı ortamda bitkiler daha hızlı gelişir” hipotezini test etmek için yaptığımız deneyler sonucunda mıknatısın bitkiler üzerinde bir etkisi olmadığı sonucuna ulaşırsak, bu sonuç hipotezimizin yanlış kurulduğu anlamına gelir. Bu durumda bitkilerin gelişimine yönelik “Rüzgârlı ortamda bitkiler daha hızlı gelişir” şeklinde yeni bir hipotez belirleyebiliriz. Bilim insanları çoğunlukla hipotezlerinin yanlış kurulduğu sonucuna ulaşır. Böyle durumlarda araştırmaya baştan başlayarak yeni bir hipotez kurarlar. Hipotezlerinin doğru kurulduğu sonucuna ulaşırlarsa, bu defa başka bir yoldan bu sonucu sınamaları gerekebilir.

Yaptığımız analizlerle elde ettiğimiz sonuçları rapor haline getirip bilim fuarlarında sunabiliriz. Şekil, grafik, çizim ve tablo kullanarak raporumuzun daha kolay anlaşılmasını sağlayabiliriz.

 

6. Proje Posteri Hazırlayalım

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Posterimiz bütün projeyi tanıttığı için iyi düzenlenmeli, ziyaretçileri projeyi okumaya teşvik etmelidir. Karışık ve özen gösterilmemiş posterler, projenin de anlaşılamamasına neden olacaktır. Posterimiz düzenli ve anlaşılır olursa projemiz de ziyaretçiler tarafından beğenilecektir.

Posterin şekli nasıl olursa olsun, proje çalışma sürecini yansıtmalıdır. Posterimizi deney öncesi hazırlıklar,  deney süreci ve deney sonuçları olmak üzere üç bölüme ayırarak hazırlayabiliriz.

Örnek posterde görüldüğü gibi ilk panele proje özetini, araştırma sorusu veya problemi, hipotezimizi ve yaptığımız araştırmaları yazabiliriz. İkinci panelde proje adı, kullandığımız malzemeler, uyguladığımız işlemler ve analizlerimiz yer almalıdır. Son kısımda ise bulduğumuz sonuçlar ve değerlendirmelerimiz bulunmalıdır. Ayrıca buraya, gelecek çalışmalar için önerilerimizi ve yapılabilecek çalışmaları da yazabiliriz.

Projemizin adı projeyi en iyi şekilde anlatmalı ve projemiz hakkında bir fikir vermelidir. Yazı karakteri de ara başlık ve içerik yazılarından daha büyük olmalıdır. Projenin yazı karakterini de kolay okunacak büyüklükte ve renkte seçmeliyiz. Siyah karakter diğerlerine nazaran daha kolay okunur. Posterde kullandığımız fotoğrafların boyutları çok büyük veya çok küçük olmamalıdır.

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<br 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
e/+g6dbzl+7frOnb2B8PJJKU4qq2W4mk8m4maybzTluxnRcw7IN2zEd17Ad3bJ10wKecEUnlun+qDYwNStwcTlNxzkpMze5zBIuLiKXeDFcrPC9lR49ggdrPi6WSkTn5OJsnWkJF0G9KBXqxVlcrCRP46XhYumrrny9OOOVXMwLrCxP4zXiIgwjMBSAoI6bt6+0X2vruNHafr2t40Zbx/Xlr6sd19uv37jacb31antX1/0gDD1//SIEBaBAoLPr/uWrHa3t3r2qytW1X7/R3nGtveNGW/u1zrv3/AE/eIq8Gn43z4yLYJXnIvgVdsH8xbJcTE1x0bO8IXCC8NAIzhcpzxmVommG44DtjSBJvMBTNJXCsLGxsVGfr6+vr6ur6/r1G+fOXzx89PievQeadu5paGyub2iub9zZvGvvwUNHz7Scu3Llyo2bt7oePOofGIARJBFPEARB0TTHc5Isabqqm4ZqGIphKJqugIacqklg+y31Wpmt65/NRXBGNo2LSxhSKb/KcXERucQrXHxduVik42eTi2AadXh4eGhoaGh4eHh4ZGRkZGRkeLlreGRkeHhkZHhwcHB4aNg/6kPgwPPuo0JgxAYaGR0ZGhoaGBwcHa7GpY0MjwwPDw8PDw8NjYA/R0Ygr15c4WJFXCw9/lg2Fz2/Gy85g6IYmmYZhqZpMk2iSSwWjY2Ph5FgcGhk5OHDRx3Xrp9tubDv4JHtDU3rN21bW7txbe3G2vWbtmzf0bxrz6Ejx1vOX7p+8073075gMIRhKMvQkiRpuqYbmmboumHohq5pqqZp6oxNVdEkWVUKW67XqwNGqStcXGSXdWlc9KKJl83F0j7qXFx8Kfuoc+oXK+6jzsNF4OZV0C9WxEW/X5Ck7CvORXAtxRYyOIoLLv8NCQaDCFL4aggCw9Bz5yKYhoGhYMHAqDBRVZ03JIh4Fxf0hlRfm/NFb1RqkVwMhYLgkGJOLoLzxSVwMZ5Ek2AQD0thqVSKIAiSpEiSYWiGphmGpkgScJEg0olkYiw8Njg0cu/+wyttHYePnmratXfLth3rNmxeU7Nh9dratevWb9pa17Rzz7ETpy5dbuu82zU4PBwMjcWTSZIkWRYkbMiyrCiqCsSIvCTxsizIEphLLVrhCLIiyIqkKiKY1SjZWpWCih9o5EoHIRfm4hx91EVxscJVjT5q2Vzi+Q4zy1BzBtsXx8VicGU1uAiWl79YykVBFBeh0yjLRU9FNH/+YokL8MI5U3P6wFU+dxMaCwGLzBnzqMFCvRhCYKDlL8fF0lziV5mLsDd6A9QUEAzDYDZ1+QuGIQRBEBhCYKD9eDHzqAjsmQzAgUC1Lq2g+ICQQpJG0Uvc/4ro+o2y86jFcbP5uDgVTTzlFhsEE1azuVg6hlq+jzo1eQ5UjKlUisABCEkvcyoVjcWCwRDk9w0PDvZ0d9/p7Lxype3kqTP7DhzauXvfjoam7Tsa63Y01jc079q99+ChI8dPnb5wufX6zTsPHz8Z8fnD45F4MonjBJhL5XlBlCRJlmRF8SwwRUmSJFEUeV4A0cSCIAmiJMiSKEul4kXRW4o43Udsanct8eBUC2Y3IGpDM0zVNHXTVHXTmO2PahXnUStnil1ClgrXNNCYy/NHXVCnsYSad3FcND0uOtXgolrMJZ6C4iwuLlOnsWAuceVcXKY/6lg4XLR/nsbFEiu4UHAxXMy86joNoDYIeLs7VNQzLnPBEAwH4AAEQQgCB6AXk6cB0FW8AVfhuqYvCAb6D/BdkNdCvwiDCd7ZawYXQ0Gk8MIp4eJYGS6W90eNxmJTL/N4PJ5MjkcioWAIhuHBwcEnj5/cvHX74uW2w8dONjbvWb9p26dr1n26pnZVzYb1G7fW1TfuPXDoxKmz7R3XHz567PP5E/E4gadYlhZFT3UhCILACzzP87zA8wIHdjB+uq6/YHYjgMMjIGEUJFGc0rCVivqLuv7SOX9wuFjce0tLRs0wVd1UdBNs3apuaaY9s1icQ6dRYQ+yDG4WXsv0Da9cm/GsuGg8Gy5OQXEJ9eIrwsUy9eISufhK6xchCIFhPxSAEAhBkGAI9D6r02xESr4UHEReQL0IwUghEApBgMttVfuohQsEZehro+tfBBdBf+UZcBFF0aER3659B7fW1W/avH3d+s1r122sWb95w+a6xp17Dx49fv7ilc67XT29fX6ffyw8Ho0nkiiGgaEbmiZpmqTpNE3jYAyVJIFskaIZqmSzKpUwTnGR42ZK+yvgYmmqhlIycSPrhmZaumEZlmU7jpvJZjLZTCaXzeYcNyNruqKbpmnZ83JxQaZUiJsVLq5wcYWLFXIRjIoEAsMjI4ODg0ODQ8ODg0NDg8NDQ9VYg8NDQ0NDQ4ODAyMjI3DgxfijBqDAoHcvBgYHq3VpU2+DgwPDQ8PguQHBgVdj7ubl5mIskcAwbHBo+OCR46dOn73S2nbn7r1Hj58MDQ8HIDgcHo/F43gKpykK7DsURaVJKpVOEyRJpCmcSONEOkWQOEkSFIWTXi4xaJxSDE0xdJGLLDurXlwsFxVVVrViCrGqG2ArNizbsG1VN0VZ5XiBoikcx2PxBBIMDQ4Nd3U9fPyk27QsBcj6XwsuLvFsc/76tywXZ+kXQR9Vt5xK9ItV4eL8fVQW9FGLaKyEi0U0vkR91M8qFxEEDiLwpdarJ06dPXP23Omz5860XKjOOnfhTMuF0y3nTp5puXnzNgIFgs89TwPMDXdcv3HidMuZs+fPnrtwumpXd/7U2fOnW86fOH22tb3D5/d7w0WfMS4+iz5qDPiGx+OJeBzDMCyVItJpkqRIigambviUKAPHcRxsHThBgCQ7T8WYBm5xJElSwASOpGiARpDTCHatOblYLAN4XhAKXCx6v8maqmi6phsaGKIxTM0wdcvSDA+NLC/iBDUeS4wE4IePe1rbb5w8fW7vgUN1Dc0bNm9dU7P+k1Vrf/9vf2hs3u24mcpypl7WPiq4UXn+YuHKKsfkQmuaD5w3j7qAD9xiuDjTB26hedQCHTmWY1mOY1gWoHHBedR0miQI8Nyt5jxq5fXi7GHUOfM0yo+kvj5chGEIhnr7+p529/T09nX39HX3Vmf19Pb39Pb19PQ97e4ZGBwKIl5X0+cbwXHCyU2aTuZ5+MDBcP/AwNOnPb19/T1VurTu3r6ewuru6e3vHyg+MV6NPmqRjvNwcYF51MKxYnEkFUAxXHBYXD4XY/F4EkVRDEtiWBJNxhPTcomLqcRg3yBArCsOBBsk+ItIp9NEGgczOxRFkhRN0RQwSaUZhmZAygbNcAzLMizHsBzL8QzHcaLAi4Ioi7KiaLqq67phGoZpaoauaqqiKpIqc7xAMXQKwyKRyOio7979B1fa2o+fPLNz976tdfU1tZtWr12/ak3NqtU1a9bWrqvdtGnL9sbmPQePnGi5cOV2Z1dP/xAcGsdJyrAczbBmCvDKzaPOOdFSYek19yd6r6Dnw0XbdkzLftZcXMA3/BlzEeAQ/Lb1mnFxzt+XX08uQjACw8EgMhYMAnTNyGRe4kIQ4C0XCiLBIFJ83J43FyEYhqFgcOqHWJ2rCyIhZGqB58Zrw8UiGuflYkGnUSEXPf3i+CLOFz2RhqdfTJTnYqH/VOAiTuBEGic8KziCpAiSSlN02tuOaIpmaIZlOY7hQEEoybKsqKquaaqqyLIsijzHsWkyjaJoJBKBIGhgcPDh48c3b96+cqXtTMv5I8dO7t1/aOfuvfWNO7fVNdbVNzU07dq1e9/+Q0ePnzx97vzFtqvtdzrvdvf0joz6gqGxeDxBEGmO5SVZtUzLcV07m7Uc17As3bJN63Xn4qz3Vn7cuCguVpansXwuVqbrf85cXKZv+IJcnFPX/7pyEYYgT5NeUiVUcYE8jeJc6HPmIrBf8LQocJUvbcY3ep24WLyi2VwMlpijlvoMz+ZiuNQidXw8HA4vlouFVY6Lxa1jWgc1nU6nSSKdBjpHkmUZEKsoCoJ3NiQy/z977/3WRprt+/4Z99nPufc8J9y7z5w9e/ae2We63WE6OnXPdM9MJ++Z2bM7Tvd0cicHbMCAccDkZJKNcbZxThjjjDHYZCQkBEJZVaqcc5VC3R/ekiyijY1Tt3mWecpISCpUqk+t9a71/ZIEQuAYhsIROBgKjrndQ0O2zq6us+fOHTh4eFtjU0l51aaCotz8TZlZOZlZOVnZuTm56/M3FhQUllRV1+zave/wsZOXr7b39vaPjo2FwmEMxxiOFURRlERZVWRNkzVNNXRF11Vd13RD0XQZrIJJMi/KfLIrVZ7UkjrbXP9dt7DM+IsPiIsT7/DwuTitPirDCambkj+ZOV9MtizPxEULiiiOYbgFRQR5VLk4WR/1J50vJoExDPyY5mtGw550PbTbh212m33Ibrc9eC4Op3w8LPzP84xGevzI8sVZuHjn+eIEOo7eJRdvmy+mmvistUSgD44gOI7jOI6gWCgc9ni8Lteo3Wbv7e+/3tnVduHiyZNnDhw60rhzd1399srqmtKyysLisoLCkoKi0pKyyuqtdfXbd+zcs6/50JETp05fuHS5s+vG4ODg6Oio3x+AYRjFMJqhWZ4Hi4OyqkqKLMgSLwqcKLICz/I8KwiMwLOCwIo8K/CcKHPAkSo10S+BM/bE+cUfJxcVVVGUR5mLHC+wHM/ygiBJkqKC7ilVN0RZJhmaYoCrBpPkIj1NHZWgKJolSArDcYzA0/00gFA9gkxwdXkEuTjJB+Anu76YTKSs8T6nwzHtjt91pF1hDD+U9UUrG57wMuZzB1PAcPxk8sXU+uKcuAg+e3Ouo/pSH3x/IBgCEYKAnSKMIBFwronAERiCQqFQIBj0+33ucY9t2Nnd23/lasfJ02f3Hzxc39BYUlaxuaAoL39j1rq8zKzcrHV5efkbN28pLi2vqmvYse/goTNnz3V2dtpsNo/HA0MQhmEESbAsw/GcIAiiKIqSKCmSJMuiJPGCwPIcCJplKIamGIbmGJplGZ5jeJ7hOV7gecFq0OEliZNkXhBBgihIsiBJ1jSBoslToDgtF6cbhXjYXEzrR52lHSh97kKd8pom7cOM/aiKqimyqqoTb1Wn0Uedqy8xyws0x9MsJ8pSMBjYt//A9sam5uYjnV1dNMtKskwAZQeaphiWohmSZgiKJkgSJ0mKZgiCIDCMJEiO42maQZMDQCiGISjKMCzP8wiKQggawbAIEoEj0+vATe1HfZB11NGZ/Rd/cv2oYLrAbrcNDdlsQ3a7zWaZFc5PAC0Y8HgPXgdu2DKStAHdAod9aHie9stmt9ntdvutDSvVfmy4OB/+i1PNF0eS/oupK9AJ64vT9d0k0eiZ6qfhDwQgCEKQCBqJRCIRCILDEBQMhf2BgMfrdblcQzZ7d2/f+ctXj504vWfvgZrahqKyivUbNmety1uzdl1GZnZmdm7u+g2bNxeWlVc1bN9xqPnwudbW6zdu2oYd7nFPGIJwDKeAohvD0AzDAvEvlmNZnmE5imEZlmNZlmFYhmZTvsTJOY3JY4vpc/0cb030g3OvdTZOisMJ1oC/xIuyIN2pPqqiKrIi306I9A65OHmpEnwSVVVVNe3udOBmWUGcPabl4jQ3KaqmKFN2fj64yAki4KIRjZ48ffpXv/y3dTnrC7YUffzxJxkZa4LhsCCKFM1QjOW5SNFMamyRaU03PQAAIABJR

Teknoloji

ABD’nin Santa Cruz şehrinde bulunan Kaliforniya Üniversitesi ve Japonya’daki Ritsumeikan Üniversitesinden bir grup araştırmacı, üç boyutlu yazıcıları kullanarak insanlarınkine benzer biçimde çalışabilen robot parmak üretti.

 

Bir grup araştırmacı, tıpkı ayçiçekleri gibi Güneş’i takip eden bir malzeme geliştirdi.