Planes are perhaps the most marvelous engineering invention of the modern age. The ability to transport hundreds of people across vast distances at high altitudes is a testament to the incredible progress in aerospace engineering. From the days of riding on camels to the era of global air travel, human civilization has undergone a remarkable transformation enabled by advancements in flight technology. In this article, we will explore the design considerations of an airplane. As a generation born in the era where airplanes existed long before us, we may sometimes fail to appreciate the sheer marvel of these engineering achievements. Did you know that over 130,000 flights are taken per day, with roughly 6 million people traveling by air around the world? Understanding Aerodynamics The design of airplanes is a complex and fascinating field worthy of exploration. Aerospace design is driven by aerodynamics which is a field that allows us to understand how an object (airplane) moves through air by studying the forces acting on the object (airplane). It is what enables us to understand the different forces acting on the airplane, whether an airplane will fly, and how the design of an airplane can be optimized such that it can fly the furthest and the fastest. Forces on an Airplane Before getting more into the design, let us understand the fundamentals of how an airplane works. In flight, an airplane is being acted on by four forces: lift, drag, weight, and thrust which are shown below. Figure 1: Aerodynamic forces on an airplane Let us summarize each force as follows:
Among these four forces, there are two desirable forces and two undesirable forces. Can you guess which is which? Think of which forces speeds up the plane and which slows down the plane. Drag (backwards force) and weight (downwards force) slows down an airplane and bring it down and are hence undesirable. Airplanes are thus strategically designed to minimize drag and weight. Whereas lift (upwards force) and thrust (forwards force) are desirable since they propel and enable airplanes to fly. Thus, the airplane is strategically designed to optimize lift and the engines are designed to generate maximum thrust. The Core Elements of an Airplane An airplane can be broken into several parts where each part is designed as aerodynamically efficient as possible. The core parts of an airplane are the fuselage, wings, tails, landing gears and nacelle which are shown below. Figure 2: Airplane The fuselage, which is the circular tube-like structure, can be considered as the body of an airplane. It is what houses the passengers and the wings. Fuselages are circular in shape not due to aesthetics, but rather for aerodynamic reasons. The smooth rounded surface reduces air resistance and offers lower drag compared to other shapes, proving to be more aerodynamically efficient. It also distributes the internal and external forces evenly to reduce stress concentrations. Generation of Lift by the Wings Moving on to the wing, which is the heart of an airplane, as it provides most of the lift generated by the airplane. Below is a cross-section of a wing called an airfoil. Figure 3: Airfoil Airfoils are used to manipulate the movement or reaction of the air when the airplane passes through the air. For an airplane, an airfoil is what enables heavier-than-air flight due to the generation of lift. Airfoils are strategically designed such that the top surface of the airfoil has a greater curvature than the lower surface. Physics plays a huge role here. Due to the increased curvature of the upper surface, as the air flows over the airfoil, it speeds up. As the speed increases above the airfoil, the pressure decreases as per Bernoulli’s principle. Hence, a net upward force (lift) is created as the pressure above the airfoil is lower than the pressure below the airfoil. Taking Inspiration from Nature Some airplanes, such as the widely known and loved A380, have winglets, which are extensions of the wingtips that as shown below. Figure 4: Winglets They are used to reduce drag to improve the aerodynamic efficiency of the wing. The inventor of winglets took inspiration from nature, specifically the shape of bird’s wing. Some birds such as eagles, as shown in the picture below, have upturned tips which helps to reduce drag during flying. After studying the effect of these upturned tips in wind tunnels, engineers developed winglets for planes to mimic the upturned tips of the birds to improve the aerodynamics of the wings. Figure 5: Wings of an eagle The Importance of Retracting the Landing Gear Another crucial aerodynamic design feature is the retraction of landing gear upon take-off. The landing gear is retracted to reduce drag. Since every exposed part on the airplane is subject to drag, engineers aim to reduce the number of exposed parts of the airplane. A way to achieve this is to retract the landing gears upon take-off as they are not used during cruise. Evolution of Airplane WindowsThe design of airplanes is everchanging. In fact, the windows of airplanes were not always the oval windows we see today. The world’s first commercial jetliner, de Havilland Comet, had great success upon entering the market in the 1950’s. However, it soon faced two fatal crashes which resulted in a loss of 56 lives. The culprit for the crashes was found to be the square windows, which lead to stress concentrations at the corners and hence resulted in structural weaknesses in the fuselage. This prompted de Havilland to modify the design of the windows, and soon later, oval windows were introduced to ensure even distribution of stress. These oval windows are still used today many years later, and have proven to be a safe and effective design. Figure 6: Oval windows ConclusionThe design of airplanes is a result of a complex interplay between aerodynamics, structural engineering, materials science, avionics, human factors, regulatory requirements, and a continuous drive for innovation and improvement in aviation technology. So, the next time you fly, remember to take a minute to appreciate all the hard work that go behind designing an airplane. From the streamlined fuselage to the ingenious wing design, each component represents the culmination of decades of research, experimentation, and innovation in the field of aerospace engineering. Image Credits: Picture credit of airfoil is to Wikipedia Commons. All other images are taken from freepik. By Iqra Bibi
Iqra is an aerospace engineering student at HKUST. She is interested in the world of aviation and beyond. She is passionate bringing a change and being a part of a community that strives for the betterment of the society.
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IntroductionThe fast growth of technology has created an era in which the boundaries of innovation are constantly being tested. One of the frontier is the field of hack electronics, which provides a diverse technique to creating custom systems and improves the convenience for hardware engineers. Hack Electronics involves hands-on tinkering to modify and create electronic projects without needing complex theories or formal training. Emphasizing "just do it," it encourages practical learning in soldering, wiring, and programming, letting beginners use tools like Arduino for smart home projects and more, allowing customization and enhancement of electronic devices. This article delves into the role of hack electronics in revolutionizing smart home automation and streamlining processes for hardware engineers, focusing on practical applications such as custom smart lighting, indoor environment control, and security systems. Smart Home Systems and Devices - Custom Smart Home Automation Enthusiasts can create their own smart lighting, climate control, and security systems using low-cost microcontrollers such as Arduino or Raspberry Pi. For example, a homeowner may develop a smart lighting system that adapts based on ambient light levels and occupancy, using sensors and microcontrollers to create an intelligent home environment. Examples of smart home device and system tailored to specific user needs: 1. The Smart Lighting System: Components: Arduino, light sensors, PIR (passive infrared) motion sensors, and LEDs. Implementation:
Arduino Uno SMD R3 is a versatile microcontroller board, ideal for a wide range of DIY electronics projects. 2. Climate Control: Components: Raspberry Pi, camera module, and PIR sensors. Implementation:
3. Security System: Components: Raspberry Pi, DHT22 sensor (a temperature and humidity sensor), and relays. Implementation:
The Raspberry Pi Pico is perfect for both beginners and experts, offering flexibility and powerful features in a compact microcontroller board. Smart Home Systems and Devices - Centralized home appliance system and server Furthermore, hack electronics allow for the integration of diverse smart home gadgets into a single system. Instead of relying on preconfigured systems, homeowners may set up their devices to connect effortlessly. The centralized system enables the creation of custom voice commands, gesture controls, and mobile app interfaces based on user preferences. By optimizing open-source hardware and software, homeowners can attain a level of freedom that allows customised solutions. Here are a few practical examples of how homeowners can personalize their smart home systems for greater convenience:
Managing your entire home with just one device Convenience for Testers and Hardware Engineers – Custom tools and instruments Custom Tools and Instruments Hardware engineers frequently encounter the difficulty of requiring specific tools and instrument to streamline their workflows. Hack electronics provide a solution by allowing users to create unique gadgets tailored to their specific needs. 1. Personalized Testing Setups: Components: Use microcontrollers, sensors, and motors to construct custom testing platforms Implementation:
A Conventional Test Setup for hardware engineers 2. Boosting Test Instruments: Components: Test equipment and extra sensors. Implementation:
Convenience for Testers and Hardware Engineers – Rapid prototyping and validation Hack electronics enable rapid prototyping and validation of novel hardware designs, allowing engineers to iterate more quickly on their projects. Engineers can experiment with different circuit designs and combinations using breadboards and FPGAs (Field Programmable Gate Arrays), eliminating the need for complex and time-consuming manufacturing processes. 1. Prototype Development:
A simple breadboard prototype 2. Custom Development Boards:
FPGAs are customizable hardware chips, has a greater flexibility than fixed-function microcontrollers. Getting Started With Hack Electronics Diving into hack electronics, "Hacking Electronics" by Simon Monk is a fantastic piece to refer. The intuitive guideline teaches you the fundamentals of electronics through hands-on projects, which is ideal for beginners. Readers can obtain practical experience in designing their own electronic innovations by working on projects ranging from building a noxious gas detector to developing an accelerometer-based game. Unleash your inner innovator, you will definitely be satisfied with bringing your electronic ideas to life. ConclusionHacking electronics create potential for improving smart home systems and simplifying the job of test and hardware engineers. Individuals can create customizable, adaptive, and secure solutions tailored to their own needs by utilizing low-cost microcontrollers, sensors, and open-source platforms. This technique enables anyone to tackle problems imaginatively, whether by developing custom home automation systems or unique testing tools. The potential for hack electronics is enormous, offering a future of innovation and personalization in both smart home technologies and hardware development. By Clayton Tsoi
Clayton is an Electronic Engineer. He is passionate about problem-solving and improving API and software skills and aims to take on increasingly challenging projects while delivering effective solutions. Linkedin: https://www.linkedin.com/in/claytontsoi/ Artificial Intelligence is one of the powerful drivers of many scientific disciplines today and is really changing the way science is done. Biological research has been characterized by high complexity and large volumes of data. In the case of conventional methods, the discovery and understanding of complicated biological structures are slow and full of errors. For example, the problem of determining the structure of proteins—something very basic to an understanding of biological processes—has traditionally been a time-consuming operation, requiring vast resources. With AI, such complications will be done away with when researchers process huge amounts of information at an unprecedentedly fast speed and high accuracy. The impact of AI in biological research is revolutionary, notably on the key breakthroughs that have been made in protein folding. AI and Biological Research AI is capable of doing much more than a simple processing of information. It can uncover really complex patterns, make outcome predictions, and model a number of biological processes. One of the most important applications of AI in biology is related to protein folding. Proteins are key molecules that execute a broad variety of functions within organisms whose roles are often linked to their three-dimensional structure. Knowing the mechanism of folding of a protein into its functional form has been one of the prime focuses of researchers for many decades. The role of knowing protein folding is very important because proteins are considered to be the building blocks of life. They build enzymes, cells, tissues, and in fact, almost every biological function is linked with a protein or another. Traditional techniques of protein structure determination like X-ray crystallography are very time-consuming and expensive since crystallization of proteins is necessary, followed by the requirement of huge equipment. While these methods have revealed only a few structures, AI is moving into this space, promising faster and more accurate predictions of protein structures, which otherwise can hugely progress biology and medicine. DeepMind's AlphaFold In November 2020, DeepMind, a London-based AI firm, published a breakthrough in protein folding research. Their AI system, AlphaFold, had the chance to identify a protein's complex shape with close-to-experimental accuracy, all by prediction. Years of research and development have borne fruit. The success of AlphaFold lies in techniques dealing with deep learning to analyze extensive data on proteins. It was trained on a very wide and deep set of protein structures, thus allowing extremely good predictions. The AlphaFold breakthrough answered a scientific challenge that had not been met in nearly half a century. Since the early '70s, it was known that the sequence of amino acids in a protein should uniquely determine its final structure, yet predicting this structure from the sequence alone remained elusive. Until now, laborious and costly experimental methods were the only ones researchers could resort to. AlphaFold was something that broke ground: it surpassed other methods in CASP14—that is, the 14th Critical Assessment of Protein Structure Prediction—reaching effectiveness comparable to experimental information and considerably bending the arc of protein folding studies. Q8I3H7: May protect the malaria parasite against attack by the immune system. Mean pLDDT 85.57. — IMAGE COURTESY OF DEEPMIND Recent DevelopmentsBuilding on the success of AlphaFold, DeepMind published a statement in July 2022 announcing that AlphaFold had predicted nearly all known proteins, surpassing 200 million. This step will change the face of biological research and drug discovery forever. Proteins are at the core of all biological processes; to further advance our knowledge regarding diseases and set up treatments against them, knowledge of protein structures is very important. For example, AlphaFold's predictions have already contributed to research into diseases such as Alzheimer's and cancer, providing new insights into their mechanisms and possible avenues for treatment. Besides, AlphaFold can also provide fast and accurate protein structure prediction, which may assist in accelerating drug discovery. The traditional process of drug development is time-consuming and financially exhaustive; it takes years or even billions of dollars. With AlphaFold's detailed information on protein structure, designing drugs would be more effective, hence reducing the time and cost associated with drug development. It is important because it is not only going to improve our understanding of protein folding but will also give rise to future technologies and applications that can be applied in biological research, to which we turn now. Future ProspectsThe future of AI in biological research is incredibly promising. As AI technology continues to advance, its applications in this field are expected to grow exponentially. Here are some key areas where AI is poised to make a significant impact: 1. Personalized MedicineAI has the potential to revolutionize personalized medicine by analyzing individual genetic data to create customized treatment plans. This approach considers a person's unique genetic makeup, allowing AI to predict their response to specific treatments. For instance, AI algorithms can analyze genetic mutations and biomarkers to identify the most effective therapies for cancer patients. This can lead to more effective and tailored healthcare, reducing adverse reactions and increasing treatment success rates. Personalized medicine can also extend to managing chronic diseases, where AI can help optimize medication dosages and lifestyle recommendations, improving the overall quality of life for patients. Arianna Huffington, CEO and founder of Thrive Global, a company which develop an AI health coach to give personalized medicine and treatments. 2. Synthetic BiologyAI is set to play a pivotal role in synthetic biology, enabling the design of synthetic organisms and biomolecules with desired functions. This technology has far-reaching applications in agriculture, energy production, and environmental protection. For example, AI-designed enzymes could break down plastic waste, offering a sustainable solution to the global plastic pollution crisis. In agriculture, AI can help create genetically modified crops that are more resistant to pests and diseases, increasing food security. Additionally, AI-driven synthetic biology can lead to the production of biofuels, reducing reliance on fossil fuels and mitigating climate change. The ability to engineer biological systems with precision opens up new possibilities for addressing some of the world's most pressing challenges. 3. Disease Prediction and PreventionAI's capability to analyze vast datasets for patterns and predict disease outbreaks is transformative for public health. By processing data from sources such as social media, healthcare records, and climate information, AI can provide early warnings of potential outbreaks. This enables timely intervention and better control of infectious diseases. For instance, during the COVID-19 pandemic, AI models were used to track the spread of the virus and predict hotspots, aiding in resource allocation and containment strategies. In the future, AI could help monitor emerging diseases and provide real-time surveillance, ultimately saving lives and reducing the economic impact of pandemics. Another example is an artificial intelligence tool named Sybil to revolutionize cancer diagnosis. Sybil, a deep learning model that is leveraged by Medical professionals and technologists from Massachusetts General Hospital and MIT, can predict lung cancer risk using data from a single low-dose chest CT scan. According to their research, Sybil can accurately predict whether an individual will develop lung cancer within the next one to six years, with an accuracy rate of up to 94% for one-year predictions. This tool does not rely on clinical data or radiologist annotations, making it a powerful aid in early cancer detection and potentially improving patient outcomes significantly. Sybil, The AI lung cancer system. Ultromics, can give very early warning of the disease. 4. Understanding Complex Biological SystemsAI's ability to simulate and model intricate biological systems offers insights that traditional methods cannot achieve. This helps researchers understand complex processes like cellular signaling pathways and gene regulation networks, leading to new discoveries in biology. For instance, AI can model how cells communicate and respond to external stimuli, providing a deeper understanding of immune responses and disease mechanisms. These insights can drive the development of novel therapies and interventions. Furthermore, AI can assist in deciphering the human microbiome's role in health and disease, opening up new avenues for probiotic treatments and personalized nutrition. 5. Research AccelerationAI can significantly accelerate scientific research by automating repetitive tasks and analyzing large datasets. This allows scientists to focus on more creative and complex aspects of their work, fostering innovation across various biological fields. For example, AI can streamline the process of drug discovery by identifying potential drug candidates and predicting their efficacy and safety. This reduces the time and cost associated with bringing new drugs to market. Additionally, AI can assist in literature reviews, data mining, and experimental design, making research more efficient and productive. By handling data-intensive tasks, AI empowers researchers to explore new hypotheses and push the boundaries of scientific knowledge. ConclusionAI is rapidly revolutionizing biological research by offering solutions to the most intractable problems in this field. DeepMind's AlphaFold has gone ahead to show this by hitting almost unimaginable success in the prediction of protein structures with a high degree of accuracy. Now, it can open totally new avenues into mechanisms of disease and treatments by letting researchers understand the tortuous shape of proteins. With the advancing technology in AI, it is expected to have more extended applications in biological studies that will bring with it personalized medicine, synthetic biology, diseases prediction, and an understanding of various complicated biological systems. The future of biological research goes without saying; it's anything but completely intertwined with developments in AI, promising a whole new frontier of scientific exploration and innovation. By Hon Ming To James
James is a passionate biotechnology student who is captivated by the boundless possibilities of Artificial Intelligence in the realm of scientific research. He is intrigued by how AI can revolutionize understanding and innovation, particularly in areas such as protein dynamics, personalized medicine, and synthetic biology. LinkedIn: https://www.linkedin.com/in/hon-ming-to-9402172b4/ If you’re a graphic designer, computer programmer, or even just work on the internet in the modern era, you’ve probably heard the terms UX and UI, but do you know what they are? Have you ever gone to a website and gotten frustrated when you can’t find what you're looking for, a certain function doesn’t work like it should, or it is even just straining visually to look at? This brings us to the concept of UX and UI digital design, which stand for User Experience and User Interface respectively. It’s easy to recognize things that are drastically wrong, but the average internet user probably does not think much otherwise about the layout and design of the everyday websites and apps they use, and that’s because the whole purpose of good UX/UI design is to run so smoothly and seamlessly that users do not even think about it at all. What is UI/UX?UX and UI in digital design often work in tandem, but make no mistake, they are in fact entirely different concepts. User Experience is the design concept that takes a user-led approach, which means that the layout and design of a digital interface is guided by what is best for the user. User Interface, on the other hand, takes an aesthetics-led approach, meaning that the aesthetic look of the interface takes priority in the design. Despite the relative youth of UX/UI digital interface design, with the actual terms as we know them only having existed for the last two decades or so, UX/UI design has changed drastically time due to changes in our understanding of certain influential concepts like psychology and ergonomics as well as the actual purpose of the digital interfaces we interact. This then brings us to the primary questions of the article: where does UX/UI design come from and where might it be going? What then does this mean for us and our devices? History & Origins First, let’s take a look at where UX/UI design came from, and surprisingly it dates back to ancient China. Feng shui has its origins in 9th century BC China and revolves around the concept that people should feel in balance and at ease with their surrounding environment. This concept has been highly influential in Chinese interior design, where they use different colors, shapes, and objects to “maximize” the feng shui of the environment and give it a sense of balance and flow. Across the globe in the 4th century BC, the ancient Greeks similarly started developing concepts that we would later recognize as ergonomics, which looks at how tools and environmental features can be better used to improve efficiency specifically in the workplace. In the late 1800s, this idea was further refined through a more scientific lense by Frederick Winslow Taylor through Taylorism, and in the mid-1900s, Toyota took a more human-centered approach by creating assembly lines that included features where workers could suggest how to improve the process. Once the dawn of the computer came, many of these concepts transferred over into digital software and interface design, as people needed to be able to easily learn to interact with computers. This is where the term UX came from, with one of Apple’s early employees, Donald Norman, being given the title of User Experience Architect and coining the term “UX” design to encompass all of user experience design. All of this history led to the defining of UX/UI design and influenced its original core principles, but how has it changed since its establishment? Modern Developments and Trends Early UX/UI design in the 80s and 90s was far less complex than it is today, and the primary design principle was skeuomorphic design. Skeuomorphism was one of the first interface design concepts and is defined by design that mimics real-world objects and concepts. Some common examples of this could be the call icon being an old rotary phone, the save button displayed through a floppy disk, an envelope representing the email function, and the recording button for audio software resembling the red light on original audio recording devices. This was primarily to ease the transition between objects in the real world and digital software; if logos, icons, and functions looked like the closest real-world thing that shares their purpose, it would be easier for users to navigate them. Common digital symbols used for electronic devices: (from left to right) call, “save” function, email, and audio recording button. Credit: author. While skeuomorphic design was ideal for early internet and digital interface users, they slowly fell out of fashion as people adjusted to the new age of technology, no longer necessitating the ease of this highly intuitive design style. This led to a design shift in the early 2010s, where flat design became all the rage. Popularized by Windows 8, Google’s Material Design, and Apple’s iOS 7, flat design is a style that relies on simple, 2-D features and bright colors, and is often considered the antithesis of skeuomorphic design. A major benefit of flat design was that it's highly responsive, adapting easier to different screen sizes and interfaces and allowing programs to load faster, which was ideal as the internet started becoming more complex. 2017 saw the beginning of the rise of immersive technology, first with features like voice interfaces and later with technology like virtual and augmented reality. These provided entirely new interfaces for both users to connect with their devices and companies to create features. Certain UX/UI design trends also emerged during this period between 2017 and 2020, the most notable of which were Neomorphism, Glassmorphism (and its similar counterparts), and Animation/Motion UI. While these trends have their own unique features, they all share a similar sentiment: attempting to create medium between the realism of skeuomorphic design and the minimalism of flat design, helping technology to feel slightly more personal and bridge the digital gap. Three different types of UI design that were popularized in the 2010s and early 2020s: Neumorphism, a design style characterized by slightly 3-dimensional elements – Glassmorphism, characterized by a “frosted glass” appearance and dimensionality – and Motion UI, characterized by simple animated features. Credits (from left to right): “Neumorphism” by Le Paragone on Wikimedia Commons. “Health Tracker App on Glassmorphism” by Mark Vlasov on Dribble. “UI Animation Concept” by Alla Kudin on Dribble. By the current year of this article, 2024, many companies create software that uses a combination of these features and design styles. Each decade brings a new trend, and UX/UI designers learn from the response, and bring the best features into the next generation. What does this mean for UX/UI Design? So, where does this leave us now? What can we take away from knowing all of this? Many modern designers focus a lot on reinventing the wheel, always trying to do something unique, but there may be a lot of benefit to sticking to old traditions. Feng shui has recently come more into the public eye, and for good reason; a lot of feng shui practices are tried and true and could even be good inspiration for UX/UI designers in terms of color palettes and arrangements. While digital UX/UI design may be very recent, there are rules of design itself that span across multiple fields, such as interior design, graphics design, and more. Toyota’s human-centered approach may be an important idea to return to now more than ever, as feedback and user input is the forefront of UX design. Additionally, another thing these trends of UX/UI design can tell us is that users seem conflicted between this need for technology and their longing to be more separate from it. The trends of the late 2010s brought us into an era of something in between the sentiments of skeuomorphic design and flat design; technology that felt both human and somehow impersonal, a sort of technological “uncanny valley”. Voice interfaces like Siri blurred the barrier of technology by allowing users a type of communication that felt more human-like but was still obviously digital in nature. Even new trends like Neumorphism and Motion UI and the resurgence of skeuomorphism seem to reflect indecision between wanting something that feels real and something that feels digital. Many UX/UI designers now face this issue; how does a designer balance both the longing for traditional, non-digital objects with the necessity of minimalism and ease of use? This dilemma will undoubtably continue to shape future trends in interface design. The last important consideration to discuss when considering what this history may show us is how the purpose of UX/UI design has shifted over the years. Looking at feng shui, its original purpose was to give people a sense of peace with their environment, but UX/UI digital seems to primarily have financial incentives. As this purpose of user-to-surroundings interaction has shifted, it’s important to consider how this is both good and bad. On the positive side, financial incentive has encouraged more research and a greater understanding of how design impacts users, which has made UX/UI design more based in science and studies than its historical counterparts. It has also provided economies with an entirely new job market, as UX/UI designers are now required for almost any company that wishes to have a digital presence or even just market themselves. Historically, ergonomics, Taylorism, and other efficiency optimization tactics may not have been particularly useful or largely popular, but UX/UI design has been able to succeed due to its competitive advantage. On the other hand, however, financial compensation can lead to unethical practices. Many companies, especially social media platforms, use UX/UI design tactics to encourage addiction rather than prioritizing a positive user experience. Conclusions UX/UI design has been uniquely important to the internet and its users, as it is not just quintessential to a good user experience but also is mostly invisible. Looking at its history allows us to understand the context and foundation of UX/UI design, and it may be able to help designers in the future create better design. While digital trends may constantly be evolving, at their core, users will remain the same, and just as humans were to be the center of design approach centuries ago, they still are today. As society interacts more and more with devices and their interfaces, it's increasingly important to give them an enjoyable experience, and it will be interesting to see how the digital competition for user attention will continue to shape UX/UI trends. By Grace Whitfield
Grace is a graphic designer and multimedia artist. She is enthusiastic about up-and-coming art technologies and emerging creative fields, like immersive installation art, UX/UI design, and virtual and augmented reality. Linkedin: https://www.linkedin.com/in/grace-whitfield5/ In today's world, design and technology are increasingly intertwined, leading to more projects that combine the two. This convergence has created innovative fields that merge creativity with technical expertise, pushing the boundaries of what's possible. One field that showcases this blend is data visualisation, which has become a powerful tool for visually communicating complex information. However, to learn more about the topic, it's essential to understand the origins of data visualisation. Data visualisation has become a crucial skill within the broader field of data science, evolving from a complementary technique to an essential component of the data science toolkit. The growth of data visualisation in data science is supported by two key factors: the increasing quality and quantity of datasets and the advancement of technological platforms supporting visualisation. Although the history of data visualisation dates back centuries, early examples can be found in maps and astronomical charts. Then, a more modern form began in the 17th century when statistical data was first presented graphically. Since then, data visualisation has progressed significantly, adapting to new technologies and data types, specifically digital data, in recent years. As our digital world generates unprecedented volumes of data, effectively presenting this information has become critical across industries and disciplines. This has led to a surge in the popularity of data visualisation skills within the data science community, with professionals recognizing its importance in extracting and communicating meaningful insights from complex datasets. The Power of Visual Processing Besides the apparent supporting factors contributing to the growth of data visualisation, such as advancements in technology and the increasing quality of datasets, what makes visual data inherently a more exciting approach to presenting information? Visual information is significantly more straightforward to digest and consume than raw numbers or text. When data is presented visually, it often reveals more of the story hidden within the numbers. This is because our brains are wired to process visual information more quickly and efficiently according to MIT neuroscientists that found out that the brain can identify images seen for as little as 13 milliseconds. Data was being visualized by showing a list of connected items, their relationships, and the details within these connections. For example, the work of Barabási Lab in network science demonstrates how complex systems can be visualized by the “Hidden Patterns” exhibition. Their visualisations of social networks, biological systems, and technological networks have uncovered intricate relationships that were not apparent in the raw data. These visualisations allow researchers and viewers to grasp complex concepts and relationships at a glance. “150 years of Nature” by Barabási Lab in the Hidden Pattern Exhibition visualising the connection of papers’ co-citation network The Art of Creating Compelling Visualisations Creating compelling data visualisations requires careful consideration of the targeted response from the audience. Choosing the correct type of visualisation for the data and the story you want to tell is essential. This involves understanding your audience, selecting appropriate mediums, and ensuring the visualisation is accurate and easy to interpret. The goal is to create a visual representation that presents the data, engages the viewer, and guides them toward the intended insights. Edward Tufte, a pioneer in data visualisation, emphasizes the importance of "graphical excellence," which involves presenting complex ideas with clarity, precision, and efficiency. His principles have guided many data scientists and designers in creating visualisations that are not only informative but also aesthetically pleasing. At its core, data visualisation is a form of storytelling. Artists like Refik Anadol and Aaron Koblin have pushed the boundaries of data visualisation, creating immersive and interactive experiences that tell compelling stories through data. Their work demonstrates how data visualisation can be both informative and emotionally engaging, turning abstract numbers into narratives that resonate with viewers on a personal level. For instance, Refik Anadol's "Melting Memories" project uses brainwave data to create stunning visual art, exploring the intersection of memory and technology. Aaron Koblin's "Flight Patterns" visualizes air traffic data, transforming mundane flight paths into mesmerizing patterns that highlight the complexity and beauty of global travel. “Melting Memories” by Refik Anadol (2018) visualising the human neuro-mechanism “Flight Pattern” by Aaron Koblin (2005) visualising the air traffic over North America |
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