[sumber: ShutterStock]

Bahasa pemrograman semakin banyak dipelajari oleh banyak orang. Hal ini terkait dengan kemajuan zaman yang menjadikan teknologi sebagai hal penting untuk menunjang kemajuan. Bagi pembaca yang ingin mempelajari bahasa pemrograman, hal dasar yang harus dipahami adalah algoritma pemrograman tersebut. Untuk mengerti apa itu algoritma pemrograman, silahkan simak pembahasan di bawah ini.
Dalam matematika dan ilmu komputer, algoritma adalah urutan atau langkah-langkah untuk penghitungan atau untuk menyelesaikan suatu masalah yang ditulis secara berurutan. Sehingga, algoritma pemrograman adalah urutan atau langkah-langkah untuk menyelesaikan masalah pemrograman komputer.
Dalam pemrograman, hal yang penting untuk dipahami adalah logika kita dalam berpikir bagaimana cara untuk memecahkan masalah pemrograman yang akan dibuat. Sebagai contoh, banyak permasalahan matematika yang mudah jika diselesaikan secara tertulis, tetapi cukup sulit jika kita terjemahkan ke dalam pemrograman. Dalam hal ini, algoritma dan logika pemrograman akan sangat penting dalam pemecahan masalah.
Untuk contoh algoritma dalam matematika seperti di bawah ini:

Algoritma untuk menghitung nilai y dari persamaan y = 3x + 8
Algoritmanya adalah:

• Mulai
• Tentukan nilai x
• Hitung nilai y = 3x + 8
• Cetak nilai x dan y
• Selesai

Walaupun algoritma bisa dibilang jantung ilmu komputer atau informatika, tetapi jangan beranggapan bahwa algoritma selalu identik dengan ilmu komputer saja. Dalam kehidupan sehari-hari, terdapat banyak proses yang dinyatakan dalam suatu algoritma. Misal cara memasak mie, cara membuat kue, dan lainnya.
Jika kita buat algoritma memasak mie akan seperti di bawah ini:

• Siapkan 1 bungkus mie instan, 400 ml air (2 gelas), panci, mangkok, sendok, dan garpu
• Masukkan 400 ml air kedalam panci
• Masak air
• Tunggu hingga mendidih
• Masukkan mie kedalam panci yang sudah berisi air mendidih
• Tunggu dan aduk hingga 3 menit
• Jika sudah matang masukkan bumbu
• Aduk hingga rata
• Sajikan mie

Penyajian algoritma secara garis besar dapat dibagi dalam dua bentuk penyajian yaitu tulisan dan gambar. Algoritma yang disajikan dengan tulisan yaitu dengan struktur bahasa tertentu (misalnya bahasa Indonesia atau bahasa Inggris) dan pseudocode. Pseudocode adalah kode yang mirip dengan kode pemrograman yang sebenarnya seperti Pascal, atau C, sehingga tepat digunakan dalam menggambarkan algoritma yang akan dikomunikasikan kepada programmer.
Sedangkan untuk algoritma yang disajikan dengan gambar adalah dengan flowchart. Flowcart adalah bagan (chart) yang menunjukkan alir (flow) di dalam program atau merupakan prosedur sistem secara logika. Flowcart digunakan untuk alat bantu komunikasi dan untuk dokumentasi.


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Hard Science

This Molecule Could Be the Key to Regenerating Human Tissue

FUTU

In Brief

• Researchers have discovered that IL6, a molecule produced by tissue damage, plays a critical role in reverting cells back to their embryonic state during cellular reprogramming.
• Scientists can now explore ways to use IL6 to enhance the efficiency of cellular reprogramming and harness our bodies' regenerative power to fight disease and aging.

An Important Molecule

Back in 2006, stem cell researcher Shinya Yamanaka figured out how to use a series of four genes (OCT4, SOX2, KLF4, and MYC, or OSKM) to reprogram adult cells into pluripotent cells. These “master” stem cells are the precursor to all types of cells and give the body the ability to heal itself using its own cells. Figuring out how to induce them in adult cells opened up innumerable new doors for the field of regenerative medicine.
However, Yamanaka’s reprogramming process came with several limitations. Not only did it have a low efficiency rate, some trials even showed the emergence of teratoma tumors, making the process unreliable for clinical use. Now, a new study published in Science takes this research one step further, providing new insight into how the reprogramming mechanism works and ways we could potentially harness it for practical usage.
Researchers at the Spanish National Cancer Research Centre (CNIO) have demonstrated that damage in the cells plays a critical role in reverting the cells back to their embryonic state. Study author Manuel Serrano and his colleagues noted that exposure to the OSKM genes causes damage to the cells. That damage causes the cells to secrete a molecule called interleukin-6 (IL6), and it’s this molecule that promotes the reprogramming into pluripotent cells.

Credit: Spanish National Cancer Research Centre (CNIO)

The Power of Self-Healing

Cell reprogramming literally takes old cells and makes them new again, so figuring out how to take advantage of this ability of the body to heal itself could be the key to curing many diseases, including degenerative conditions related to aging. By furthering our understanding of how this process works, the researchers open doors for scientists to target ways to manipulate IL6 to enhance the efficiency of cellular reprogramming.
This promising field of medicine has spurred several other studies. The successful reprogramming of connective tissue into cardiac tissue could lead to a cure for heart failure. Fibroblast scar cells have been manipulated to become lining in blood vessels, and doctors have been able to restore a patient’s vision using skin cells that were converted into pluripotent cells and then into eye cells.
There’s a long way to go before we fully understand the wonders of cell reprogramming, but continued studies may soon give us the ability to fully harness the regenerative power of our own bodies.

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 Ilmu kerasMolekul ini Bisa Jadi Kunci Regenerasi Jaringan Manusiafutu
Secara singkat

    Para peneliti telah menemukan bahwa IL6, molekul yang diproduksi oleh kerusakan jaringan, memainkan peran penting dalam mengembalikan sel-sel kembali ke keadaan embrio mereka selama pemrograman ulang sel.
    Para ilmuwan sekarang dapat mengeksplorasi cara untuk menggunakan IL6 untuk meningkatkan efisiensi pemrograman ulang sel dan memanfaatkan kekuatan regeneratif tubuh kita untuk melawan penyakit dan penuaan.
Sebuah Molekul Penting
Kembali pada tahun 2006, batang peneliti sel Shinya Yamanaka menemukan cara untuk menggunakan serangkaian empat gen (OCT4, Sox2, KLF4, dan MYC, atau OSKM) untuk memprogram ulang sel-sel dewasa menjadi sel pluripoten. Ini "master" sel induk adalah pendahulu untuk semua jenis sel dan memberikan tubuh kemampuan untuk menyembuhkan dirinya sendiri menggunakan sel sendiri. Mencari tahu bagaimana untuk mendorong mereka di sel dewasa membuka pintu baru yang tak terhitung untuk bidang kedokteran regeneratif.
Namun, proses pemrograman ulang Yamanaka datang dengan beberapa keterbatasan. Tidak hanya itu memiliki tingkat efisiensi yang rendah, beberapa percobaan bahkan menunjukkan munculnya tumor teratoma, membuat proses tidak dapat diandalkan untuk penggunaan klinis. Sekarang, sebuah studi baru yang diterbitkan di Science mengambil penelitian ini satu langkah lebih jauh, memberikan wawasan baru bagaimana mekanisme pemrograman ulang bekerja dan cara kita berpotensi memanfaatkan itu untuk penggunaan praktis.
Para peneliti di National Research Centre Kanker Spanyol (CNIO) telah menunjukkan bahwa kerusakan pada sel-sel memainkan peran penting dalam mengembalikan sel-sel kembali ke keadaan embrio mereka. penulis studi Manuel Serrano dan rekan-rekannya mencatat bahwa paparan gen OSKM menyebabkan kerusakan pada sel-sel. kerusakan yang menyebabkan sel untuk mengeluarkan molekul yang disebut interleukin-6 (IL6), dan itu molekul ini yang mempromosikan pemrograman ulang ke dalam sel pluripoten.
Kredit: Spanyol National Cancer Research Centre (CNIO)Kredit: Spanyol National Cancer Research Centre (CNIO)
The Power of Self-Healing
Sel pemrograman ulang harfiah mengambil sel-sel lama dan membuat mereka baru lagi, jadi mencari tahu bagaimana untuk mengambil keuntungan dari kemampuan ini tubuh untuk menyembuhkan dirinya sendiri bisa menjadi kunci untuk menyembuhkan berbagai penyakit, termasuk kondisi degeneratif yang berhubungan dengan penuaan. Dengan memajukan pemahaman kita tentang bagaimana proses ini bekerja, para peneliti pintu terbuka bagi para ilmuwan untuk menargetkan cara untuk memanipulasi IL6 untuk meningkatkan efisiensi pemrograman ulang sel.
bidang ini menjanjikan pengobatan telah mendorong beberapa penelitian lainnya. Pemrograman ulang sukses dari jaringan ikat dalam jaringan jantung dapat menyebabkan obat untuk gagal jantung. sel fibroblast bekas luka telah dimanipulasi menjadi lapisan dalam pembuluh darah, dan dokter telah mampu mengembalikan visi pasien menggunakan sel-sel kulit yang diubah menjadi sel pluripoten dan kemudian menjadi sel mata.
Ada jalan panjang untuk pergi sebelum kita memahami keajaiban pemrograman ulang sel, namun studi lanjut akan segera memberikan kita kemampuan untuk sepenuhnya memanfaatkan kekuatan regeneratif dari tubuh kita sendiri.
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Hard Science

Failing Antibiotics Could Kill 300 Million by 2050. Now, We May Have a Way to Fight Back

Jun MT/Shutterstock

In Brief

• A team of Australian researchers have proposed a new method for tracking antibiotic resistance, focusing on the resistance genes that give the pathogens their deadly boost.
• While this new method of tracking is more complicated, it would provide doctors with valuable insights that could result in more effective treatment options.

Tracking the Source

The problem of antibiotic resistance has been slowly but steadily moving into the limelight. World leaders have talked about it at the United Nations General Assembly, and the UN has even declared it a “crisis,” but the resources and methods currently used to combat it are proving inadequate. However, a newly proposed tracking protocol may offer hope for battling this growing threat.
Scientists from the University of Technology Sydney (UTS) and La Trobe University have proposed taking a wholly difference approach to how we define and track antibiotic-resistant pathogens. Instead of tracking antibiotic resistance by counting the number of pathogens that have developed said resistance like we currently do, these scientists propose we start tracking the resistance genes that give the pathogens their deadly boost.
Shifting from tracking resistant pathogens to resistant genes doesn’t sound so hard, but it’s actually fairly complicated. Microbes are capable of something called horizontal gene transfer, which allows them to transfer their genes between different species of microbes, including those that don’t affect us. This means that the new system of tracking would require us to not just count the number of infections with resistant pathogens, but also test our sewage, hospitals, and general environment to figure out what kinds of resistant genes they host.

Knowledge is power

Once we do that, scientists will have a better understanding of which resistances thrive in particular locales, which will give doctors a lot of valuable information about their enemy. If they know which resistances are present in their area, they know which antibiotics to prescribe and which ones to “rest” until a later date.
Not only would this new system of tracking for antibiotic resistance allow doctors to provide their patients with the most effective treatment, it would also ensure they don’t blindly give out stronger antibiotics than necessary. Limiting the microbes’ exposure to these drugs means they will have less of a chance to develop stronger resistances.
If we don’t take action, antibiotic resistance is expected to cause an estimated 300 million preventable deaths and a cumulative loss of $100 trillion USD by 2050. We need to start taking this threat seriously as the devastation it could cause is on par with climate change, and like climate change, better tracking and defining of the problem will give us a better shot at stopping it.

source

Off World

Cassini Is Preparing for Its Dramatic Death-Dive Into Saturn

NASA/JPL-Caltech/Space Science Institute

In Brief

• Twelve years after reaching Saturn, NASA's Cassini mission will begin its final phase this week, exploring the planet's outer rings before crashing to its surface in April.
• The mission has taught us so much about our neighboring planets, identifying the first off-Earth lakes and hurricanes and making the first landing in our outer Solar System.

Going In For A Kiss

On November 30, after dancing with Saturn for 12 years, NASA’s Cassini spacecraft is swooping in for a kiss, plunging very close to the planet’s unexplored F ring to collect samples of ring particles and faint gas molecules while maintaining a distance safe enough from the debris — over 4,850 miles (7,800 kilometers).
A gravitational nudge from one of Saturn’s moons, Titan, will propel Cassini along as it enters the first phase of what NASA is calling “the mission’s dramatic endgame.” From tomorrow through April 22, the craft will plunge through the unexplored area in the planet’s outer rings every seven days for a total of 20 times.

The Sun produces a glowing spot as it is positioned behind Saturn’s B ring. NASA/JPL.

“We’re calling this phase of the mission Cassini’s Ring-Grazing Orbits, because we’ll be skimming past the outer edge of the rings,” said Linda Spilker, Cassini project scientist at NASA’s Jet Propulsion Laboratory in Pasadena, California. “In addition, we have two instruments that can sample particles and gases as we cross the ringplane, so in a sense Cassini is also ‘grazing’ on the rings.”

An Epic Finale

Launched in 1997, the Cassini spacecraft’s mission was to probe and image Saturn, its moons, and its rings, beginning with its arrival in 2004 until its expected end in 2008. Since then, however, the mission has been extended twice, and it has been responsible for some of the biggest breakthroughs in space exploration: its landing on Titan was the first ever successfully completed in the outer solar system, it captured the first off-Earth hurricane, and it identified the first lakes anywhere beyond Earth.
This new phase of Cassini’s mission will provide in-depth, high-quality, high-resolution views of Saturn’s moons, main rings, and the small moonlets mixed in with them. It might also be able to capture images of dust clouds as positions become favorable, such as when the Sun backlights the planet in Cassini’s view in March.

Cassini’s final phase. NASA/JPL-Caltech.

No doubt Cassini’s ring-grazing phase will be phenomenal, but so will the way the spacecraft intends to take its final bow. To make sure it doesn’t collide with Saturn’s moons and potentially infect them with Earth microbes when it runs out of fuel, Cassini will instead repeatedly dive through the narrow gap between Saturn and its rings, over and over as it slowly runs out of fuel and eventually falls down into Saturn’s embrace.
With Cassini’s mission complete and the craft making its final resting place on the planet it has studied for a dozen years, NASA will focus on its next generation of spacecrafts, including Orion, which will send astronauts on missions beyond the Moon. We’ve learned a lot from Cassini, and now it’s time to look to the future of space exploration.

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Future Society

HP’s Successful Test Heralds A New Computing System That’s 8,000 Times Faster

Richard Lewington/Hewlett-Packard

In Brief

• Hewlett Packard Enterprise's successfully tested Memory-Driven Computer relies on photonic/optical communication links and is faster than traditional computers by leaps and bounds.
• While this was just a proof-of-concept, we can start to see real progress with innovations in memory coming in the next few years.

Memory Over Processing Power

Hewlett Packard Enterprise (HPE) successfully tested an ambitious new model that challenges how computing is done today. In a press release, HPE announced that it successfully tested a proof-of-concept computing architecture that’s memory-driven. As HPE explains, it is  “a concept that puts memory, not processing, at the center of the computing platform to realize performance and efficiency gains not possible today.”
The prototype was developed as part of The Machine, HP’s computer of the future, leading the company’s efforts to revolutionize the fundamental architecture by which all computers have been built in the past 60 years.
The proof-of-concept prototype was able demonstrate how computer nodes sharing a pool of Fabric-Attached Memory, speedy photonics/optical-based communication data links, and a customized software can make it all work. Simulations suggested that a memory-driven computing (MDC) system can be “multiple orders of magnitude” faster than conventional PCs — faster by 8,000 times, in some cases.

The Future of Computing

“With this prototype, we have demonstrated the potential of Memory-Driven Computing and also opened the door to immediate innovation,” said Antonio Neri, Executive Vice President and General Manager of the Enterprise Group at HPE. And the potential is indeed huge. The technology can greatly enhance the performance of servers and other higher-end computers, as HPE intends to use it. But it can also trickle down into versions that can even improve Internet of Things (IoT) devices.
Improving the IoT has been one of the objectives of developing The Machine. However, as exciting as it is,  the tech still needs to be made practical. Non-volatile memory, which is crucial for an MDC system, is still to expected to come sometime in 2018 or 2019.
Still, the proof-of-concept works, and that is what’s important. Everything else, at this stage, will simply be a matter of making it real. Soon, when we consider buying computers, we may be looking at the memory specs of devices more than their processing power.

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Hard Science

Free Science: A New Company is Making Peer Review Science Free For Everyone

Ben Broomfield / The Guardian

In Brief

• ScienceMatters is a new journal seeking to democratize scientific knowledge by taking away some of the pretense associated with the most prestigious journals.
• Greater access by more thinkers to more knowledge is a wonderful ingredient to boost scientific discovery.

The Science World’s Culture of Prestige

Access to knowledge is the best tool we have to solve all of the world’s greatest and most mysterious questions. In the scientific academe, everyone covets a publication in prominent journals like Cell, Nature, or Science. However, prestige is hard to come by—these journals typically accept only 5 to 10 percent of submitted work.
ScienceMatters is a science publishing company that aims to change the perspective on scientific research by providing a more democratized platform.
The founders criticize top journals for only taking more alluring work. Lawrence Rajendran, founder and CEO of ScienceMatters, said that this criteria for acceptance makes “competition for space is extremely high so there needs to be that wow factor.” Many scientists craft their work specifically to please journal editors, in order to attain the most citations. Stacking of citations is often the basis of hiring and promoting researchers, which developers consider to be unjust.

Science for Everybody

The journal’s acceptance criteria is more general. As long as the topic has a solid foundation in science and concrete evidence, their anonymous panel of editors take in the work. The developers behind the publication desire to drive scientific research back to a joy of curiosity and discovery, instead of the mere glorification of big names and citations.
“[The] publish or perish culture instills a hostile scientific environment, pressuring young researchers to outperform their peers, which can lead to data fraud,” Rajendran explained.
Other open-access peer review platforms also aim to deviate from a culture of prestige in the science world, including eLife and Frontiers. These open-access platforms are arms reaching out encouraging more minds to keep learning and innovating. There’s always something new to discover, always another problem to solve, all we have to do is reach for the right tools.

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Hard Science

Unlocking the Physics of Our Universe: Unusual Numbers Found in Particle Collisions

CERN

In Brief

• Values computed from particle physics experiments seem to correspond with periods, a specific set of unusual values found in a branch of mathematics.
• If physicists are able to understand this connection, they could use it to simplify their prediction process and gain insight into the messy world of quantum mechanics.

Finding Patterns

Mathematicians and physicists have noticed a strange coincidence occurring between their respective fields: the values computed from particle physics experiments seem to correspond with a specific set of values found in a branch of mathematics called algebraic geometry.
Particle physicists conduct some of their most advanced experiments at the Large Hadron Collider in Geneva, and many of those experiments generate gigabytes of data. To make sense of that information, the physicists use Feynman diagrams, simple representations of the particles and outputs connected to their collisions.  Lines and squiggly lines in the diagrams represent the particles and their interactions from the collision. When details like mass, momentum, and direction are added to the diagram, the physicists can calculate the Feynman probability, the likelihood that a collision will occur according to their diagram.
While making these calculations, they noticed that the numbers emerging from their diagrams were the same as a class of numbers from pure math: periods. These values describe motives, which are basically the building blocks of polynomial functions. When you get two polynomials with the same period, you know that the motives will be the same. One example of a period is pi. Because that period appears in both the integral defining the function of a sphere and the one defining the function of a circle, a mathematician can know that the motives for a sphere and circle are the same.

Quanta Magazine

Order in Chaos

To get the probability that a specific outcome will arise from a collision, physicists need to take the associated integral of each possible Feynman diagram scenario and add it to all the other integrals to find the amplitude. Squaring the magnitude of that number will give them the probability. The problem comes when working with complicated collisions that cause loops (particles emitting and reabsorbing other particles in the middle of the collision process). Calculating amplitude is far harder with more loops, but adding in more increases the potential accuracy of the diagram.
If there is a connection between periods and Feynman diagrams, understanding it would help physicists be more accurate with their predictions. They could simply look at the structure of a Feynman diagram to get an idea of its amplitude, skipping over the potentially thousands of calculations that would otherwise be necessary. This would make creating and running particle physics experiments far less complicated and offer key insights into the quantum world, which, in turn, could lead to the quantum computers that would revolutionize the fields of engineering, gene processing, machine learning, and much more.

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 Ilmu kerasMembuka Fisika Universe kami: Bilangan biasa Ditemukan di Partikel TabrakanCERN
Secara singkat

    Nilai dihitung dari percobaan fisika partikel tampaknya sesuai dengan periode, satu set nilai-nilai tertentu yang tidak biasa ditemukan di cabang matematika.
    Jika fisikawan mampu memahami hubungan ini, mereka bisa menggunakannya untuk menyederhanakan proses prediksi mereka dan mendapatkan wawasan ke dalam dunia berantakan mekanika kuantum.
menemukan Pola
Matematikawan dan fisikawan telah melihat sebuah kebetulan yang aneh terjadi antara bidang masing-masing: nilai dihitung dari percobaan fisika partikel tampaknya sesuai dengan seperangkat nilai-nilai tertentu yang ditemukan dalam cabang matematika disebut aljabar geometri.
fisikawan partikel melakukan beberapa eksperimen yang paling canggih mereka di Large Hadron Collider di Jenewa, dan banyak dari mereka percobaan menghasilkan gigabyte data. Untuk memahami informasi itu, fisikawan menggunakan diagram Feynman, representasi sederhana dari partikel dan output terhubung ke tabrakan mereka. Garis dan berlekuk-lekuk garis di diagram mewakili partikel dan interaksi mereka dari tabrakan. Ketika rincian seperti massa, momentum, dan arah ditambahkan ke diagram, fisikawan dapat menghitung probabilitas Feynman, kemungkinan bahwa tabrakan akan terjadi sesuai dengan diagram mereka.
Sementara membuat perhitungan ini, mereka melihat bahwa angka yang muncul dari diagram mereka sama sebagai kelas nomor dari matematika murni: periode. Nilai-nilai ini menggambarkan motif, yang pada dasarnya adalah blok bangunan dari fungsi polinomial. Ketika Anda mendapatkan dua polinomial dengan periode yang sama, Anda tahu bahwa motif akan sama. Salah satu contoh dari periode adalah pi. Karena periode yang muncul di kedua integral mendefinisikan fungsi bola dan yang mendefinisikan fungsi lingkaran, matematikawan dapat mengetahui bahwa motif bola dan lingkaran adalah sama.
Majalah QuantaMajalah Quanta
Urutan Chaos
Untuk mendapatkan probabilitas bahwa suatu hasil tertentu akan muncul dari tabrakan, fisikawan perlu mengambil integral terkait setiap skenario yang mungkin diagram Feynman dan menambahkannya ke semua integral lainnya untuk menemukan amplitudo. Mengkuadratkan besarnya jumlah itu akan memberi mereka probabilitas. Masalahnya muncul ketika bekerja dengan tabrakan rumit yang menyebabkan loop (partikel memancarkan dan reabsorbing partikel lain di tengah-tengah proses tabrakan). Menghitung amplitudo jauh lebih sulit dengan lebih loop, tetapi menambahkan lebih meningkatkan akurasi potensi diagram.
Jika ada hubungan antara periode dan diagram Feynman, pemahaman itu akan membantu fisikawan lebih akurat dengan prediksi mereka. Mereka hanya bisa melihat struktur diagram Feynman untuk mendapatkan ide dari amplitudonya, melompati berpotensi ribuan perhitungan yang seharusnya diperlukan. Hal ini akan membuat menciptakan dan menjalankan eksperimen fisika partikel jauh lebih rumit dan menawarkan wawasan kunci ke dalam dunia kuantum, yang, pada gilirannya, dapat menyebabkan komputer kuantum yang akan merevolusi bidang teknik, pengolahan gen, pembelajaran mesin, dan banyak lagi.
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