Algorithms are an integral part of coding and computer programming. They form the foundation of how software and applications operate, ensuring that tasks and processes are carried out efficiently and accurately. In the world of coding, an algorithm can be defined as a step-by-step procedure or set of rules designed to solve a specific problem or accomplish a particular task.
When you think about algorithms, imagine a recipe for your favorite dish. Just like a recipe provides a precise set of instructions for preparing a meal, an algorithm provides a clear roadmap for the computer to follow in order to perform a specific task. Without algorithms, software and applications would be unable to function effectively or deliver the desired results.
The significance of algorithms in coding cannot be overstated. They enable programmers to create efficient and optimized solutions to complex problems. By breaking down tasks into smaller, manageable steps, algorithms allow programmers to identify and address potential issues and enhance the overall performance of a program.
Furthermore, algorithms provide developers with a systematic approach to problem-solving. They help in simplifying complex problems and organizing the implementation process. With well-designed algorithms, programmers can save time, resources, and effort, ultimately delivering high-quality software products.
In the world of coding, algorithms possess certain characteristics that make them reliable and effective. Firstly, algorithms should be unambiguous, meaning that every step and instruction should be clear and well-defined. This ensures that programmers can accurately implement the algorithm without any confusion.
Secondly, algorithms should be deterministic, meaning that they produce the same results for a given set of inputs. This allows developers to trust that their algorithms will consistently deliver the same output, which is crucial for ensuring the reliability and stability of a program.
Additionally, algorithms should be efficient and have a finite number of steps. This means that they should offer optimal solutions within a reasonable timeframe, without unnecessary repetitions or delays. Efficiency is vital in coding as it contributes to faster execution and improved performance of software applications.
In the next sections, we will explore different types of algorithms, examples of popular algorithms in coding, algorithm design techniques, and how algorithms contribute to problem-solving in the coding realm. Through understanding algorithms and their importance, developers can take their coding skills to new heights and develop innovative solutions to various challenges.
Definition of an Algorithm
An algorithm is a specific set of step-by-step instructions or rules that are designed to solve a particular problem or achieve a specific task. It can be thought of as a well-defined process that takes an input, performs a series of operations or transformations, and produces an output. In the context of coding and computer programming, algorithms serve as the building blocks for creating software applications and systems.
At its core, an algorithm is a sequence of logical and mathematical operations that guide the computer in executing a task. It provides a clear and structured roadmap for solving problems and performing complex computations. By breaking down a problem into smaller subtasks and defining the steps required to solve each subtask, algorithms offer a systematic approach to problem-solving.
One important aspect of algorithms is their universality. In other words, an algorithm can be implemented on different programming languages and platforms, as long as the underlying logic and operations remain the same. This universality allows algorithms to be reusable and adaptable, making them valuable tools for programmers in various domains.
Moreover, algorithms can be classified based on their level of abstraction. Some algorithms are general-purpose, meaning they can be applied to a wide range of problems and scenarios. These algorithms are fundamental and form the basis of many computational tasks. On the other hand, some algorithms are designed to solve specific problems or address particular domains or industries.
Algorithms can also be classified based on their complexity. Some algorithms have a fixed number of steps and can be executed in a predictable and efficient manner. These are known as deterministic algorithms. Conversely, there are also non-deterministic algorithms that involve some level of randomness or unpredictability in their execution.
Overall, algorithms are crucial in the field of coding and computer programming. They provide a structured approach to problem-solving, allowing programmers to develop efficient and optimized solutions. Understanding algorithms and their various classifications is essential for every developer, as it forms the foundation for building robust and functional software applications.
Importance of Algorithms in Coding
Algorithms play a pivotal role in coding and computer programming. They are essential for designing and creating efficient, reliable, and scalable software applications. Here are some of the key reasons why algorithms are important in coding:
1. Efficiency: Algorithms allow developers to optimize the performance of their code by providing a systematic approach to problem-solving. By breaking down complex tasks into smaller, manageable steps, algorithms enable programmers to identify potential bottlenecks or inefficiencies and improve the overall efficiency of their code. Efficient algorithms can significantly reduce the execution time of a program, resulting in faster and more responsive software applications.
2. Reusability: Algorithms are reusable components that can be applied to multiple problems or scenarios. Once a programmer has designed and implemented an algorithm for a specific task, they can reuse it in different projects or adapt it to solve similar problems. This reusability not only saves time and effort but also promotes consistency and standardization across software development projects.
3. Scalability: When developing software applications, it is crucial to consider their scalability, especially as the project grows in complexity and size. Algorithms provide a foundation for building scalable applications by enabling developers to design code structures that can handle increasing data loads and perform efficiently regardless of the size of the input. Scalable algorithms are essential for accommodating growing user bases and expanding functionalities without sacrificing performance.
4. Problem-solving: Algorithms are powerful tools for problem-solving in coding. They provide a structured approach to breaking down complex problems into smaller, more manageable subproblems. By tackling each subproblem individually and designing algorithms to solve them, programmers can develop effective solutions to even the most challenging tasks. Algorithms help in analyzing problem requirements, identifying dependencies, and implementing appropriate logic to achieve the desired outcome.
5. Optimization: Optimization is a critical aspect of software development. Algorithms allow developers to optimize their code by identifying areas for improvement, reducing unnecessary operations, and enhancing the overall efficiency of their solutions. Skilled algorithm design and implementation can result in significant performance gains and resource savings, making the code more robust and cost-effective.
6. Innovation: Algorithms drive innovation by enabling developers to create new and groundbreaking solutions to complex problems. By utilizing algorithms creatively, programmers can come up with novel approaches and functionalities that push the boundaries of what is currently possible. Algorithms form the backbone of many advanced technologies, such as artificial intelligence, machine learning, and data analytics, driving innovation and progress in various industries.
Overall, algorithms are the building blocks of coding and play a vital role in the development of efficient, scalable, and reliable software applications. They promote problem-solving, optimization, reusability, and innovation, ultimately enhancing the quality and performance of the code.
Characteristics of an Algorithm
Algorithms possess certain key characteristics that make them effective and reliable tools for solving problems in coding. Understanding these characteristics is crucial for developing well-structured and efficient algorithms. Here are some of the main characteristics of an algorithm:
1. Unambiguous: An algorithm must be unambiguous, meaning that each step and instruction should be clear and well-defined. There should be no room for interpretation or confusion. This clarity ensures that programmers can accurately implement the algorithm without any ambiguity, leading to consistent and predictable results.
2. Deterministic: Determinism is another important characteristic of an algorithm. It means that given the same input, an algorithm should always produce the same output. This determinism is crucial for validating and verifying the correctness and reliability of the algorithm. It allows developers to trust that the algorithm will consistently deliver the expected results, enabling them to build robust and predictable software applications.
3. Finite: An algorithm should have a finite number of steps. It means that the algorithm should eventually terminate after a certain number of operations, regardless of the input or the size of the problem. Having a finite nature ensures that the algorithm does not get stuck in an infinite loop or run indefinitely, which could lead to poor performance or crashes in software applications.
4. Input and Output: Every algorithm has an input, which represents the data or information on which the algorithm operates. It also has an output, which represents the result or the transformed data after the algorithm has executed. An algorithm should clearly define both the input and output specifications to ensure that developers understand the requirements and can utilize the algorithm correctly.
5. Efficiency: Efficiency is a crucial characteristic of an algorithm. An efficient algorithm accomplishes the task at hand using the fewest possible resources, such as time and computational power. Efficient algorithms ensure that software applications perform optimally and can handle larger data sets or complex operations without slowing down or experiencing performance issues.
6. Generic and Reusable: An algorithm should be designed in a way that it can be applied to multiple instances of a problem or similar problems. It should provide a generic solution that can be easily adapted and reused in various contexts. This characteristic promotes code reusability, reduces duplication of efforts, and encourages standardization in software development.
7. Well-structured: A well-structured algorithm follows a logical and organized flow of steps. It avoids unnecessary repetition, ambiguity, or complexity. Well-structured algorithms are easier to understand, debug, and maintain, ensuring that developers can effectively work with the algorithm and make necessary modifications as required.
By encompassing these characteristics, an algorithm becomes a reliable and valuable tool in coding. These traits not only make algorithms easier to work with but also contribute to increased efficiency, maintainability, and scalability of software applications.
Types of Algorithms
Algorithms come in different types, each tailored to address specific types of problems or scenarios. Understanding the various types of algorithms is essential for programmers to choose the most appropriate approach for solving a particular task. Here are some common types of algorithms:
1. Sorting Algorithms: Sorting algorithms are used to arrange data in a specific order, such as ascending or descending. Examples of sorting algorithms include bubble sort, insertion sort, selection sort, merge sort, and quicksort. These algorithms are essential for tasks that involve organizing data for efficient searching or presenting the information in a structured manner.
2. Searching Algorithms: Searching algorithms are utilized to find a specific element or value within a collection of data. Common searching algorithms include linear search, binary search, and hash-based search algorithms. These algorithms greatly assist in quickly locating data within large datasets, making them invaluable in tasks such as data retrieval and filtering.
3. Graph Algorithms: Graph algorithms are designed to solve problems related to graphs, which consist of nodes (vertices) connected by edges. Examples of graph algorithms include breadth-first search (BFS), depth-first search (DFS), Dijkstra’s algorithm, and Kruskal’s algorithm. Graph algorithms are widely used in various domains, such as network routing, social network analysis, and recommendation systems.
4. Dynamic Programming Algorithms: Dynamic programming algorithms focus on dividing a complex problem into smaller overlapping subproblems and solving them recursively. These algorithms store the intermediate results in a table or cache, reducing redundant computations and optimizing performance. Dynamic programming is particularly useful in tasks such as optimizing resource allocation, sequence alignment, and solving optimization problems.
5. Recursive Algorithms: Recursive algorithms involve solving a problem by breaking it down into smaller instances of the same problem until a base case is reached. The algorithm then combines the results of the smaller problems to obtain the final solution. Recursive algorithms are often used in tasks like tree traversal, recursive backtracking, and calculating factorial or Fibonacci series.
6. Greedy Algorithms: Greedy algorithms make locally optimal choices at each step, with the hope that the overall result will be globally optimal. These algorithms are efficient but may not always produce the optimal solution. Examples of greedy algorithms include the knapsack problem, Huffman coding, and the traveling salesman problem.
7. Backtracking Algorithms: Backtracking algorithms explore potential solutions by incrementally building them and backtracking when a solution is found to be invalid or unsatisfactory. They are commonly used in tasks such as generating permutations or combinations, solving puzzles, and constraint satisfaction problems.
These are just a few examples of the many types of algorithms that exist. Each type serves different purposes and has specific advantages and disadvantages. By understanding the characteristics and appropriate applications of these algorithms, programmers can choose the right algorithmic approach to tackle a wide range of problems and optimize the performance and efficiency of their code.
Examples of Algorithms
Algorithms are used in various domains and have countless applications in coding. They enable programmers to solve complex problems efficiently and optimize the performance of software applications. Let’s explore some examples of commonly used algorithms:
1. Binary Search: Binary search is a searching algorithm used to find a specific value within a sorted array or list. It follows a divide-and-conquer approach by repeatedly dividing the search space in half until the target value is found or the search space is empty. Binary search has a time complexity of O(log n), making it a very efficient algorithm for searching tasks.
2. Bubble Sort: Bubble sort is a simple sorting algorithm that repeatedly compares adjacent elements and swaps them if they are in the wrong order. It iterates through the entire list until the list is sorted. Although bubble sort is not the most efficient sorting algorithm, it is easy to understand and implement, making it a good choice for small datasets.
3. Dijkstra’s Algorithm: Dijkstra’s algorithm is a graph algorithm used to find the shortest path between two nodes in a weighted graph. It starts from a source node and iteratively explores the neighboring nodes, updating the distances from the source as it progresses. Dijkstra’s algorithm is widely used in various applications, such as network routing and route planning.
4. Merge Sort: Merge sort is a divide-and-conquer sorting algorithm that divides the input array into smaller subarrays, recursively sorts them, and then merges the sorted subarrays to obtain the final sorted array. Merge sort has a time complexity of O(n log n) and is known for its stability and efficiency in sorting large datasets.
5. Depth-First Search (DFS): DFS is a graph traversal algorithm that explores as far as possible along each branch before backtracking. It starts from a selected node and systematically visits all reachable nodes until all vertices in the graph are visited. DFS is used for tasks such as determining connectivity in a graph, maze traversal, and topological sorting.
6. QuickSort: QuickSort is a highly efficient sorting algorithm that uses a divide-and-conquer approach. It selects a pivot element from the array and partitions the other elements into two subarrays, according to whether they are less than or greater than the pivot. QuickSort recursively sorts the subarrays until the entire array is sorted. It has an average time complexity of O(n log n) and is widely used in practice.
7. Knapsack Problem: The knapsack problem is an optimization problem that involves selecting a subset of items from a set, with the constraint that the total weight of the selected items should not exceed a given capacity. Various algorithms, such as dynamic programming and branch and bound, can be used to solve the knapsack problem efficiently.
These are just a few examples of the many algorithms used in coding. Each algorithm serves a specific purpose and has different strengths and weaknesses. By understanding these algorithms and their applications, programmers can make informed decisions on which algorithm to use based on the problem at hand, optimizing the performance and efficiency of their code.
Common Algorithms in Coding
Coding involves the use of various algorithms to solve problems and optimize the performance of software applications. Here are some common algorithms that are widely used in coding:
1. Linear Search: Linear search is a simple algorithm used to find a specific element within an array or list. It iterates through the elements one by one until it finds the target element or reaches the end of the list. Linear search has a time complexity of O(n), where n is the size of the input, making it suitable for small datasets or unsorted lists.
2. Binary Search: Binary search is a more efficient algorithm for finding an element in a sorted array or list. It starts by comparing the target value with the middle element of the array and eliminates half of the remaining elements at each step. Binary search has a time complexity of O(log n), making it ideal for large datasets or sorted lists.
3. Bubble Sort: Bubble sort is a simple sorting algorithm that repeatedly compares adjacent elements and swaps them if they are in the wrong order. It continues this process until the entire list is sorted. Bubble sort has a time complexity of O(n^2), making it suitable for small datasets but less efficient for larger ones.
4. Merge Sort: Merge sort is an efficient divide-and-conquer sorting algorithm. It divides the input array into smaller subarrays, sorts them recursively, and then merges them to obtain the final sorted array. Merge sort has a time complexity of O(n log n) and is known for its stability and effectiveness in sorting large datasets.
5. QuickSort: QuickSort is another popular sorting algorithm that uses a divide-and-conquer approach. It selects a pivot element from the array and partitions the other elements into two subarrays based on whether they are less than or greater than the pivot. QuickSort then recursively sorts the subarrays. On average, QuickSort has a time complexity of O(n log n), making it suitable for a wide range of applications.
6. Depth-First Search (DFS): DFS is a graph traversal algorithm that explores as far as possible along each branch before backtracking. It starts from a selected node and systematically visits all reachable nodes until all vertices in the graph are visited. DFS is commonly used in tasks such as graph connectivity, cycle detection, and maze solving.
7. Breadth-First Search (BFS): BFS is another graph traversal algorithm that explores all the vertices of a graph in breadth-first order. It starts from a selected node and visits all its immediate neighbors before moving on to the next level of neighbors. BFS is often used in applications such as shortest path finding, network analysis, and social network algorithms.
These are just a few examples of the common algorithms used in coding. Each algorithm has its own advantages and is suitable for different scenarios. As a programmer, understanding and utilizing these algorithms can greatly enhance your problem-solving abilities and improve the efficiency of your code.
Algorithm Design Techniques
When it comes to designing algorithms, several techniques and methodologies can be employed to create efficient and optimized solutions. These techniques help programmers approach problems with a structured mindset, ensuring that the resulting algorithms are scalable, reusable, and reliable. Here are some common algorithm design techniques:
1. Divide and Conquer: The divide and conquer technique involves breaking down a complex problem into smaller, more manageable subproblems. Each subproblem is then solved independently, and the solutions are combined to obtain the final result. This technique is often used in sorting and searching algorithms, such as merge sort and binary search.
2. Backtracking: Backtracking is a technique used to find solutions to problems by systematically exploring all possible options. It involves making choices at each step and backtracking when a chosen path leads to a dead end or an unsatisfactory solution. Backtracking algorithms are commonly used in tasks like generating permutations, solving puzzles, and constraint satisfaction problems.
3. Greedy Approach: The greedy approach involves making locally optimal choices at each step, with the hope that these choices will lead to a globally optimal solution. Greedy algorithms are efficient and easy to implement but may not always guarantee the optimal solution. They are commonly used in tasks such as scheduling, minimum spanning trees, and the knapsack problem.
4. Dynamic Programming: Dynamic programming is a technique that breaks down a problem into smaller overlapping subproblems and solves them recursively. It uses memoization or caching to store and reuse intermediate results, reducing redundant computations and improving efficiency. Dynamic programming is commonly used in tasks such as optimizing resource allocation, sequence alignment, and solving optimization problems.
5. Branch and Bound: Branch and bound is a technique used to solve optimization problems by systematically exploring the space of possible solutions. It involves branching at each decision point and bounding the search space based on certain criteria. By pruning unpromising branches, this technique can significantly reduce the search space and improve the efficiency of the algorithm.
6. Randomized Algorithms: Randomized algorithms involve introducing an element of randomness into the algorithm’s design or execution. These algorithms use randomization to achieve efficiency or improve the likelihood of finding optimal solutions. Randomized algorithms are commonly used in tasks such as randomized sorting, graph algorithms, and Monte Carlo simulations.
These are just a few examples of the algorithm design techniques used in coding. Each technique has its own advantages and is suitable for different types of problems. By employing these techniques in your algorithm designs, you can create efficient, scalable, and robust solutions to a wide range of coding challenges.
Algorithmic Problem Solving
Algorithmic problem solving is at the core of coding and computer programming. It involves breaking down complex problems into smaller, more manageable components and designing algorithms to solve them efficiently. Here are some key aspects of algorithmic problem solving:
Understanding the Problem: The first step in algorithmic problem solving is gaining a thorough understanding of the problem at hand. It requires identifying the inputs, desired outputs, constraints, and any specific requirements or conditions. By dissecting the problem and clarifying its requirements, you can lay a solid foundation for designing an effective algorithm.
Breaking Down the Problem: Once you understand the problem, the next step is breaking it down into smaller, more manageable subproblems. The idea is to decompose the original problem into simpler components that can be solved independently. This allows you to focus on solving each subproblem individually before combining the solutions to obtain the final result.
Choosing the Right Data Structures: Selecting appropriate data structures is crucial for efficient algorithmic problem solving. Different types of problems require different data structures, such as arrays, linked lists, stacks, queues, trees, or graphs. The choice of data structure impacts the efficiency and effectiveness of the algorithm, as it determines how data is organized and accessed during the problem-solving process.
Designing the Algorithm: Once you have broken down the problem and identified the relevant data structures, the next step is designing the algorithm. This involves defining the steps and logic required to solve each subproblem. Consider factors like efficiency, simplicity, and maintainability when designing the algorithm. Strive to find the most optimal and elegant solution to the problem.
Testing and Debugging: After designing the algorithm, it is crucial to thoroughly test and debug it. Test the algorithm with various input scenarios, including edge cases, to ensure that it produces the correct output. Debug any errors or unexpected behavior that may arise during the testing phase. This iterative process helps refine the algorithm and ensures its accuracy and reliability.
Optimization: Algorithmic problem solving also involves optimizing the algorithm for performance and efficiency. Look for opportunities to reduce time complexity, minimize redundant operations, and improve resource utilization. Techniques like memoization, pruning, and choosing efficient algorithms can significantly enhance the efficiency of the solution.
Documentation and Refinement: Proper documentation and refinement are essential for effective problem-solving. Document the algorithm’s logic, assumptions, and complexities for future reference. Refine the algorithm by seeking feedback, reviewing code, and incorporating improvements. Continuously update the documentation to ensure the algorithm is well-documented and easily maintainable.
Algorithmic problem solving is an iterative and creative process that requires logical thinking and a systematic approach. By following these steps and utilizing efficient problem-solving techniques, you can develop robust and optimized algorithms to tackle a wide range of coding challenges.
Algorithms are the driving force behind coding and computer programming. They provide step-by-step instructions for solving problems and performing tasks efficiently. Understanding the characteristics, types, and design techniques of algorithms is crucial for developers to create optimized and scalable software applications.
By employing algorithmic problem-solving techniques, programmers can break down complex problems into smaller, manageable subproblems and design efficient algorithms to solve them. Techniques such as divide and conquer, dynamic programming, and greedy algorithms help in creating effective solutions.
Common algorithms, such as linear search, binary search, bubble sort, merge sort, and DFS, have widespread applications in various domains. These algorithms improve search and sorting operations, graph traversal, and optimization. Choosing the appropriate algorithm for a specific problem and implementing it correctly can lead to improved performance and better user experiences.
Moreover, algorithm design techniques like dividing problems into subproblems, backtracking, and using dynamic programming foster creativity and innovation in solving complex tasks. These techniques enable programmers to devise efficient solutions and optimize algorithms further.
In conclusion, algorithms are the building blocks of coding, providing structure and efficiency in problem-solving. By leveraging different types of algorithms alongside appropriate design techniques, developers can tackle diverse challenges and create robust, scalable, and optimized software applications. Staying abreast of algorithmic advancements and continually enhancing algorithmic problem-solving skills is essential for success in the ever-evolving field of coding.


Concentration of sucrose solutions via vacuum membrane distillation
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The potential use of vacuum membrane distillation (VMD) process for the concentration of sucrose solution was examined. The effect of several parameters, including feed temperature, flow rate, and initial sucrose concentration on the flux quality and quantity was studied. VMD process was found effective in concentrating aqueous sucrose solutions as the permeate was absolutely pure water. The feed temperature significantly affected the flux. The increase in feed flow rate increased the permeate flux and this increase was more pronounced in the laminar than in the turbulent region. The increase in the initial sucrose concentration resulted in a slight decrease in the flux without affecting the quality of the permeate. A mathematical model describing the variation of sucrose concentration with time was developed and validated with the experimental results. Concentration and temperature polarization effects were also considered in this work.
- Chemical Engineering [1105 items ]


Liker App Apk
Liker App offers you a very simple and intuitive graphics. With the Dashboard, you can keep track of the number of “likes” received and, through the dedicated section.
The Yapp! team, constantly working, has developed a UNIQUE product in its category, SAFE and EASY to use. Our goal is to create honest solutions that can help people in their intent.
Yapp! That a group loves to create!
|Published||Jun 19, 2017|


911 Emergency? This campaign was created so smart phone users could experience how confusing and frightening accidental 911 calls can be for emergency operators. It generated over 4000 scans in the Vancouver area.
Kind of like the 60’s. Only legal. Body Copy: This is Earth’s finest atmospheric show — the Aurora Borealis — an intense, color inducing blend of oxygen, nitrogen, solar wind and magnetic fields. It dances across the sky and unleashes up to 10 million megawatts of mind-bending power. Dream on, man.


AI UX Strategy
Effectively integrate AI research and modelling into your UX strategy plan.
Traditional strategy techniques can give powerful insights on your customers and users and drive product strategy. Emerging developments in AI and data science have the potential to greatly enhance your products. Our strategic approach ensures that you have a holistic product strategy that includes AI and data science without sacrificing traditional proven product definition methods.


We recently released Gravity PDF 6.5.5, which has better compatibility with the Gravity Forms ecosystem, and a few bug fixes.
This update sees PDF Conditional Logic now getting correctly saved, which could cause problems when the logic contained symbols. We also resolved an issue when displaying Gravity Wiz Populate Anything’s live merge tags in custom PDF templates. Please update your plugin to avoid issues.
- Ensure PDF conditional logic is run through the correct sanitization function upon save
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Like a sleeping giant, Internet radio is quietly attracting more and more listeners and advertising dollars, leading some experts to predict that some day soon it will eclipse the popularity of satellite radio and iPods. ¶ Already, ratings company Arbitron Inc. ... says, some 37 million Americans tune into Internet radio at least once a month, up from 11 million four years ago. ¶ With its growing audience, it could start to take a bigger bite of the $11 billion spent annually on online advertising. Nevertheless, But Internet radio faces hurdles, say proponents, because digital copyright laws make it less viable than rivals. ... Link: Reuters.


1 article published in JoVE
A Flexible Low Cost Hydroponic System for Assessing Plant Responses to Small Molecules in Sterile Conditions Carolina C. Monte-Bello*1,2, Elias F. Araujo*1,3, Marina C.M. Martins1, Valeria Mafra4, Viviane C.H. da Silva1,2, Viviane Celente1, Camila Caldana1,5 1Brazilian Bioethanol Science and Technology Laboratory (CTBE), Brazilian Center for Research in energy and materials (CNPEM), 2University of Campinas (UNICAMP), 3University of Viçosa (UFV), 4CTBE, CNPEM, 5Brazilian Bioethanol Science and Technology Laboratory (CTBE/CNPEM), Max Planck Partner Group A simple, versatile, and low-cost in vitro hydroponic system was successfully optimized, enabling large-scale experiments under sterile conditions. This system facilitates the application of chemicals in a solution and their efficient absorption by roots for molecular, biochemical, and physiological studies.


BOISE -- As the countdown to the college football season gets closer and fan excitement builds, Boise State released a rendering of what its new video board for Bronco Stadium will look like once it is installed.
The foundation is set for the new Double R Vision video board built by Daktronics. It will measure 37.2 feet tall and be 58.8 wide, for a total of 2,187.4 square feet. The 15mm HD LED display panel will provide a state-of-the-art viewing experience for Bronco fans.
Double R Vision also features an upgraded sound system that will provide crisp and clear sound for both music and PA announcements across the entire stadium.
The new video board is expected to be completed by the Broncos third home game against Southern Mississippi on September 28.


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Generative AI Horizons: Unleashing Digital Marketing's Future
In this community lead event we will be discussing where people are at with generative AI and their predictions for where we will be in 5-years time.
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What it is?
A clock made with JS and CSS. For the digital clock I worked with momentjs.
This was my third project, but the first one that was based on a tutorial. After I done the tutorial from Wes Bos - CSS +JS clock and because the use of time in JS was daunting for me I decided that I had to give it a go. In the middle of the project I learn about the library momentjs and also end up using it in the digital clock.
What I learned?
- Time Variables


Coin Tiles Beta
Introducing the Coin Tiles Crypto Analyzer Beta release Coin Tiles This web app currently loads 2000 coins and refreshes the page in real time so it may be a bit CPU intensive. Future version will probably load 500 coins at a time and provide links to relevant coin data for each coin. This is Beta! Enjoy!


The majority of patients diagnosed with neuroblastoma present with aggressive disease. Improved detection of neuroblastoma cancer cells following initial therapy may help in stratifying patient outcome and monitoring for relapse. To identify potential plasma biomarkers, we utilised a liquid chromatography-tandem mass spectrometry-based proteomics approach to detect differentially-expressed proteins in serum from TH-MYCN mice. TH-MYCN mice carry multiple copies of the human MYCN oncogene in the germline and homozygous mice for the transgene develop neuroblastoma in a manner resembling the human disease. The abundance of plasma proteins was measured over the course of disease initiation and progression. A list of 86 candidate plasma biomarkers was generated. Pathway analysis identified significant association of these proteins with genes involved in the complement system. One candidate, complement C3 protein, was significantly enriched in the plasma of TH-MYCN+/+ mice at both 4 and 6weeks of age, and was found to be elevated in a cohort of human neuroblastoma plasma samples, compared to healthy subjects. In conclusion, we have demonstrated the suitability of the TH-MYCN+/+ mouse model of neuroblastoma for identification of novel disease biomarkers in humans, and have identified Complement C3 as a candidate plasma biomarker for measuring disease state in neuroblastoma patients. Biological significance: This study has utilised a unique murine model which develops neuroblastoma tumours that are biologically indistinguishable from human neuroblastoma. This animal model has effectively allowed the identification of plasma proteins which may serve as potential biomarkers of neuroblastoma. Furthermore, the label-free ion count quantitation technique which was used displays significant benefits as it is less labour intensive, feasible and accurate. We have been able to successfully validate this approach by confirming the differential abundance of two different plasma proteins. In addition, we have been able to confirm that the candidate biomarker Complement C3, is more abundant in the plasma of human neuroblastoma patient plasma samples when compared to healthy counterparts. Overall we have demonstrated that this approach can be potentially useful in the identification of biomarker candidates, and that further validation of the candidates may lead to the discovery of novel, clinically useful diagnostic tools in the detection of sub-clinical neuroblastoma.
Bibliographical noteVersion archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
- Complement C3
- Complement system


Nero Burning ROM 2016
- Trial version
Nero Burning ROM ...writing speed is easily configurable Nero ...software Powerful software to burn CDs / DVDs / Blu-rays In the field of burning, Nero ...free ...
- PROS: Excellent burning quality , Various advanced tools , Supports SecurDisc
- CONS: Installation phase a bit long


Smartphone holder with safety elastic strings.
Adjustable anti-vandal seat for multiple sizes.
Ultra-bright no-friction dynamo light.
Easy 3 speed gear shifting with grip shifter.
Basket case with safety elastic strings.
Mudguard with integrated light and reflector.
24” wheels with a reflective stripe integrated on tire.
Drum brakes with cooling disc and anti-vandal nuts.
Area for advertising on front and back mudguards.
Easy adjustment of seat height with comfortable lever and vandal securing mechanism.
Battery free LED lights powered by magnets.
Internal 3 speed gear solution with low maintenance.
Lightweight, dual piece aluminium frame with internal cable routing and low step in.
Novel innovation all weather locking device with RFID and magnet insert.
Aluminium chain guard.


Discover how to build, deploy and run applications in production on Koyeb. The fastest way to deploy applications globally.
Discover how to use Remix and Tailwind to build a reactive frontend. This guide covers how to build a Hacker News clone application and deploy it on the Koyeb Serverless Platform for native global load balancing, autoscaling, autohealing, auto HTTPS (SSL) encryption, and more with zero configuration.
Discover the MERN stack by learning how to build a blog application using MongoDB Atlas, Express, React, and Node. Then, learn how to deploy your MERN stack on the Koyeb Serverless Platform. This guide is the first in a mini-series on the MERN stack.


The discovery, made in rodents, may lead to advances in understanding Alzheimer’s disease, autism and age-related memory loss, and could point to potential treatments for these and other neurological conditions, said senior study investigator Michael Ehlers, M.D., Ph.D., an associate professor of neurobiology and a Howard Hughes Medical Institute investigator at Duke.
The researchers published the findings in the Dec. 7, 2006, issue of the journal Neuron.
The research was funded by the National Institutes of Health, the American Health Assistance Foundation and the Raymond and Beverley Sackler Foundation.
The team focused on specific structures in brain nerve cells, or neurons, called dendritic spines. These are tiny bumps that form on the surface of dendrites, which extend off neurons like tree branches and receive chemical signals from other neurons. Each dendritic spine "talks" with its counterpart on a nearby neuron, and collectively the two structures comprise the "synapse" that links the neurons.
The brain stores new information by changing the structure of the synapses, Ehlers said. “If we need to remember a name, directions to a location or how to perform certain motor tasks -- anything involving learning or memory, really -- our brain does it by changing the properties of synapses,” he said.
During learning, synapses change in ways that make it easier for connected neurons to communicate with each other. This “plasticity” can occur in two ways. One way is structural, in which a synapse changes in size or shape; the other way is functional, in which connections between the synapses are strengthened by increasing the chemical signals sent or received by connected neurons.
In previous studies, Ehlers and colleagues at Duke found that certain cellular structures called recycling endosomes, which recycle used proteins within the cell, play an important role in controlling the functional type of plasticity. In the current study, the researchers sought to determine if recycling endosomes are involved in the structural type of plasticity as well.
To create a study model, the researchers transplanted neurons from rats into cell culture dishes. They then stimulated the neurons with chemicals and examined the cultures using a technique called live-cell imaging, in which a camera attached to a powerful microscope recorded the dendritic spines as they grew. This technique, Ehlers said, enabled the team to glimpse inside the internal world of the neuron to see how the recycling endosomes responded when neurons were stimulated.
When the scientists triggered the neurons, they saw the recycling endosomes, labeled with a green dye, streaming up and down the neurons, dipping in and out of the dendritic spines. Inside the dendritic spines, the recycling endosomes deposited pieces of recycled proteins that grew new spines or changed the shape and size of existing spines, Ehlers said.
The finding supported the team's theory that recycling endosomes transport the cargo that dendritic spines need to grow, Ehlers said.
Ehlers added that by providing a better understanding of how cells develop new synapses or strengthen existing synapses, the study may give researchers new ideas for developing drugs that target these critical cellular processes. A variety of neurological disorders, including Alzheimer’s disease, autism and early forms of age-related memory loss, are characterized by the loss of synapses or by the abnormal structural development of dendritic spines, he said.
A video will be available starting December 6, 2006 at 12 noon. To view, visit http://dukehealth.org/news/brain_remodeling.
Marla Vacek Broadfoot | EurekAlert!
Could this protein protect people against coronary artery disease?
17.11.2017 | University of North Carolina Health Care
Microbial resident enables beetles to feed on a leafy diet
17.11.2017 | Max-Planck-Institut für chemische Ökologie
The formation of stars in distant galaxies is still largely unexplored. For the first time, astron-omers at the University of Geneva have now been able to closely observe a star system six billion light-years away. In doing so, they are confirming earlier simulations made by the University of Zurich. One special effect is made possible by the multiple reflections of images that run through the cosmos like a snake.
Today, astronomers have a pretty accurate idea of how stars were formed in the recent cosmic past. But do these laws also apply to older galaxies? For around a...
Just because someone is smart and well-motivated doesn't mean he or she can learn the visual skills needed to excel at tasks like matching fingerprints, interpreting medical X-rays, keeping track of aircraft on radar displays or forensic face matching.
That is the implication of a new study which shows for the first time that there is a broad range of differences in people's visual ability and that these...
Computer Tomography (CT) is a standard procedure in hospitals, but so far, the technology has not been suitable for imaging extremely small objects. In PNAS, a team from the Technical University of Munich (TUM) describes a Nano-CT device that creates three-dimensional x-ray images at resolutions up to 100 nanometers. The first test application: Together with colleagues from the University of Kassel and Helmholtz-Zentrum Geesthacht the researchers analyzed the locomotory system of a velvet worm.
During a CT analysis, the object under investigation is x-rayed and a detector measures the respective amount of radiation absorbed from various angles....
The quantum world is fragile; error correction codes are needed to protect the information stored in a quantum object from the deteriorating effects of noise. Quantum physicists in Innsbruck have developed a protocol to pass quantum information between differently encoded building blocks of a future quantum computer, such as processors and memories. Scientists may use this protocol in the future to build a data bus for quantum computers. The researchers have published their work in the journal Nature Communications.
Future quantum computers will be able to solve problems where conventional computers fail today. We are still far away from any large-scale implementation,...
Pillared graphene would transfer heat better if the theoretical material had a few asymmetric junctions that caused wrinkles, according to Rice University...
15.11.2017 | Event News
15.11.2017 | Event News
30.10.2017 | Event News
17.11.2017 | Physics and Astronomy
17.11.2017 | Health and Medicine
17.11.2017 | Studies and Analyses


Our client faced an unprecedented situation and immediately had to deal with event data from hundreds of sources. They were forced to work quickly to find and create information to assess the situation and guide the response. Due to the immediate nature of the situation, a huge amount of data was created with varying structure. Our client needed a Master Data Management (MDM) solution to correlate, search, and understand the entire data set collected during the response.
As none of the current out-of-the-box MDM solutions met the requirements, a decision was made to develop a custom MDM solution. The solution extended the Microsoft analytics technology stack already in use within the organization.
A series of Dimensional Models were developed for each of the 14 types of entities (people, organizations, vendors, etc.) included in the scope. A common Extract, Transform, and Load (ETL) layer was developed to orchestrate the flow of data from the different sources of information into the MDM repository.
A Fuzzy Logic Match and Merge capability was developed, using SSIS native components. This feature was critical to ensure data governance, allowing the Data Stewards to quickly identify related and duplicated data among domains.
In the wake of this unprecedented situation, we empowered our client with an accurate and consistent set of event data to analyze the situation and guide their response. This solution was implemented successfully and included multiple domains. We were able to provide the client with a consistent, consolidated view of their data. After the MDM repository was completed, it was possible to link related data across domains, enabling the Search component of the project. The Data-stewards from each domain were able to review proposed duplicate and matched data to allow them to make an informed decision over data validity. This information was published to SharePoint, allowing it to be consumed internally and externally.


My background is that of a meteorologist. I have 28 years of experience in meteorology at Environment Canada, 16 years as an operational weather forecaster (where I analyzed NWP models every day), and seven years as a research meteorologist. I have a physics degree from the University of Toronto and a Masters’ degree from York University in meteorology.
In my research project I will defend the position of scientific realism, the belief that our best scientific theories try to correctly describe the mind-independent world as faithfully as possible. Realists believe that the ability of our scientific theories to generate predictions is directly proportional to how faithfully they represent the large-scale order in the universe. This conclusion will be derived from an modified version of Putnam’s no-miracles argument or NMA. I will introduce a variation on the NMA that I call the Reverse No-Miracles Argument (Reverse NMA).


Clock, Clock In Grass Clock In French: Clock In
Published at March 21st, 2019 17:56:45 PM by justin
Tagged as web based time clock discussion or clock in calculator topic along with simple employee time clock subject as well as open time clock 2008 free area of interest also clock in clock out discussion with clock in the wall object also Clock, So don't forget to check out the main article in Clock In
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can autopainting be made to work like an effect. so it can be applied to more than one image that changes shape and position. I know about using a script but the script doesnt account for the changing position of the object


Audio Channel Mismap
"Audio Channel Mismap" is the incorrect implementation of the audio track or audio channel order, which dictates the desired speaker assignment or channel arrangement of an audio mix.
Audio Channel Mismap impacts both Content and Technical Quality of the asset. The member experience is negatively impacted by audio playback occurring out of the wrong speaker assignment.
The majority, if not all audio appears to be mis-mapped and routes to the wrong speaker assignment
How to Prevent:
Use templates with correct audio mapping.
Review channel mapping on timeline prior to exporting.
Please refer to the technical specifications for the correct Audio Channel Mapping order, reorder the channels accordingly, and redeliver. Check the M&E for the same issue, and redeliver M&E as well if necessary.


Home > News > Taking the NanoPulse -- What's for dinner? Nanotechnology, of course.
September 11th, 2007
Taking the NanoPulse -- What's for dinner? Nanotechnology, of course.
Nanotechnology is now officially more than just food for thought. It's actual, stick-to-your ribs, tasty food -- on the shelves now with more on the shopping list.
Check the numbers. Estimates put the current global nano-food market at $2.6 billion right. By 2010, experts say it could top $20 billion. Who's setting the menu? Major corporations around the world and a number of smaller companies, as well. One estimate suggests more the 200 companies have current research projects, including five of the top 10 food giants
Interviews/Book Reviews/Essays/Reports/Podcasts/Journals/White papers
Atom-thick CCD could capture images: Rice University scientists develop two-dimensional, light-sensitive material December 20th, 2014
Oregon researchers glimpse pathway of sunlight to electricity: Collaboration with Lund University uses modified UO spectroscopy equipment to study 'maze' of connections in photoactive quantum dots December 19th, 2014
Instant-start computers possible with new breakthrough December 19th, 2014
Iranian Scientists Use Nanotechnology to Increase Power, Energy of Supercapacitors December 18th, 2014
First Home-Made Edible Herbal Nanodrug Presented to Pharmacies across Iran December 17th, 2014
Scientists trace nanoparticles from plants to caterpillars: Rice University study examines how nanoparticles behave in food chain December 16th, 2014
Iranian Scientists Convert Curcumin Existing in Turmeric into Edible Nanodrug December 15th, 2014
Nanoparticles Prove Effective in Removing Phosphor from Calcareous Soil December 10th, 2014


The Faculty of Science is a research powerhouse. Our researchers attract funding from a broad array of funding agencies, including NSERC, CIHR, SSHRC, Alberta Inovates, and many others.
UAlberta scientists view carbon’s impact on the environment in real time
New research examines 1200-year old volcanic ash from Alaska to northern Europe
In this section


Starting a programming journey can be exciting and overwhelming, especially with the rapidly evolving technology landscape. As a student, focusing on the right technologies is crucial to lay a strong foundation for your programming career.
This blog post will explore the leading technologies students should consider in 2023 to kickstart their programming journey.
Python course continues to dominate the programming world, making it an ideal language for students to start their coding journey. Known for its simplicity, readability, and versatility, Python Training is the go-to choice for beginners and seasoned developers. Its clean syntax and an extensive standard library make learning and working easy.
Python is widely used in various domains, such as web development, data analysis, machine learning, and game development. With frameworks like Django and Flask, students can build web applications effortlessly. Python’s robust data processing libraries, including NumPy and Pandas, make it an excellent choice for data analysis and manipulation. Additionally, the popularity of machine learning has soared in recent years, and Python course’s machine learning libraries, like Scikit-learn, provide a solid foundation for building predictive models.
When it comes to Java programming, Core Java serves as the fundamental building block. It is an essential language for students starting their coding journey. Known for its platform independence, robustness, and versatility, Core Java is widely used to develop various applications.
Java’s syntax is similar to other popular programming languages, making it easier for beginners to grasp. Its vast standard library provides a rich set of classes and APIs, enabling students to develop various desktop, web, and mobile applications. With Java’s object-oriented nature, students can practice essential concepts like encapsulation, inheritance, and polymorphism.
Moreover, Core Java offers robust input/output handling, exception handling, and multithreading features, making it suitable for building reliable and efficient applications. By understanding the Core Java tutorial, students gain a strong foundation that can be applied to other advanced Java technologies.
Once students have mastered the Core Java course for beginners’ training, delving into Advanced Java opens up a new world of possibilities. Advanced Java developer certification focuses on specialized areas of Java development, providing students with additional tools and frameworks to build complex and scalable applications.
A critical aspect of Advanced Java training is Java Enterprise Edition (Java EE), which offers a set of APIs and protocols for developing enterprise-level applications. Java EE includes technologies like JavaServer Pages (JSP), Servlets, Enterprise JavaBeans (EJB), and Java Persistence API (JPA). Students can leverage these technologies to build robust web applications that handle high traffic and integrate with databases.
Furthermore, students can explore frameworks like Spring and Hibernate. Spring provides a comprehensive ecosystem for building enterprise applications, offering features like dependency injection, aspect-oriented programming, and MVC architecture. Hibernate, on the other hand, simplifies database interaction by providing an object-relational mapping (ORM) framework, allowing students to work with databases seamlessly.
Additionally, students can venture into web development using JavaServer Faces (JSF) or build RESTful APIs using Java API for RESTful Web Services (JAX-RS). These technologies enable students to create dynamic and interactive web interfaces and develop scalable and secure web services.
Git And Version Control
While learning to code, it’s crucial to understand the importance of version control and collaborative workflows. Git, a distributed version control system, is a fundamental technology for students to grasp. Using Git, students can track changes, collaborate with teammates, and efficiently manage their codebase.
Platforms like GitHub and GitLab seamlessly integrate with Git, allowing students to showcase their projects and collaborate with the open-source community. With Git, students can work on projects with multiple contributors, keep track of changes, and quickly revert to previous versions if needed. Understanding Git and version control is a skill development highly valued in the programming industry, as it promotes efficient collaboration and ensures project stability.
Machine Learning And Data Science
Machine learning and Data science have become indispensable in today’s tech-driven world. As a student, diving into these fields can open up many opportunities. With libraries like NumPy, Pandas, and Scikit-learn, Python training provides a solid foundation for data analysis and machine learning.
Data science involves collecting, cleaning, and analyzing data to extract valuable insights. Python’s data processing libraries enable students to perform complex data manipulations, statistical analysis, and visualization. With Pandas, students can easily handle large datasets and derive meaningful information.
Machine learning, on the other hand, focuses on developing algorithms that allow computers to learn from data and make predictions or decisions. Python’s machine learning libraries, such as Scikit-learn, TensorFlow, and PyTorch, provide many tools and algorithms for tasks like image recognition, natural language processing, and predictive analytics.
In an era of rapid digital transformation, understanding cloud computing is essential for students venturing into the programming world. Platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud offer a wide range of services that enable students to deploy and scale their applications quickly.
Cloud computing eliminates physical infrastructure and allows students to access computing resources on demand. They can deploy their applications to the cloud, which provides scalability, reliability, and cost-effectiveness. Learning cloud computing concepts like virtual machines, containers, and serverless architectures can give students a competitive edge and a deeper understanding of modern application development.
Begin your journey towards success today with LearNowX, where you can dive into comprehensive courses and resources to enhance your programming skills. Take the first step and unlock opportunities to grow and excel in your programming endeavors.
At LearNowX, we’re here to elevate your learning journey. Our comprehensive platform allows you to acquire practical skill development courses, master new knowledge, and unlock exciting career opportunities. Contact us, and let’s embark on a transformative adventure together.


Electronics Expo offers the iHome Alarm Clock Radio w/ iPod Dock
, model no. IP90B, for $87.86. Coupon code "STBLU" chops it to $71.13
. With $7.99
for shipping, that's the lowest total price we could find by $6, although it was $20 less last month (since expired). It features an iPod dock, FM radio tuner, dual alarm, remote control, and more.


Westville NJ ZIP Code
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Westville city is located in New Jersey State, Gloucester County and has a unique zip code assigned by the US Postal Service Office.
You can search for the Westville NJ zip code by its streets and block numbers if we have some of the Westville streets loaded in our website
Westville Area Code is 856 and Time Zone is Eastern (GMT -05:00).
Westville Zip Code - New Jersey
The US ZIP Code basic format consists of five decimal numerical digits assigned to the Westville City. An extended ZIP+4 code, introduced in the 1980s, includes the five digits of the Westville ZIP code, a hyphen, and four more digits that determine a more specific location within a given ZIP code in Westville, New Jersey.
The Westville Zip Code is 08093.
If you want to send a mail to Westville City, you should use the 08093 Zip Code.
In 1983, the U.S. Postal Service introduced an expanded Westville ZIP code system that it called ZIP+4, often called "plus-four codes", "add-on codes", or "add ons". A ZIP+4 code uses the basic 08093 Zip code plus four additional digits to identify a geographic segment within the five-digit delivery area, such as a Westville city block, a group of apartments, an individual high-volume receiver of mail or any other unit that could use an extra identifier to aid in efficient mail sorting and delivery in New Jersey.


The field of 3D printing and additive manufacturing has seen tremendous growth in recent years. This technology has revolutionized the way we design and manufacture products, allowing for faster production times, greater customization, and reduced waste. However, the success of 3D printing relies heavily on the materials used in the process. This is where materials science comes in.
Materials science is the study of the properties and behavior of materials, including metals, ceramics, polymers, and composites. In the context of 3D printing, materials science plays a crucial role in developing new materials that are suitable for use in additive manufacturing. These materials must be able to withstand the high temperatures and pressures involved in the printing process, as well as meet the specific requirements of the final product.
One of the most significant advancements in materials science for 3D printing has been the development of metal powders. These powders are used in metal additive manufacturing, where layers of metal powder are fused together using a laser or electron beam. The resulting parts are incredibly strong and durable, making them ideal for use in aerospace, automotive, and medical applications.
Another area of materials science that has seen significant progress is the development of polymers for use in 3D printing. Polymers are widely used in additive manufacturing due to their low cost, ease of use, and versatility. However, not all polymers are suitable for 3D printing. Researchers have been working to develop new polymers that have the necessary properties, such as strength, flexibility, and heat resistance, to be used in additive manufacturing.
In addition to developing new materials, materials science is also essential in optimizing the printing process itself. For example, researchers have been studying the effect of different printing parameters, such as temperature and speed, on the final product. By understanding how these parameters affect the properties of the printed part, researchers can optimize the printing process to produce parts with the desired properties.
Materials science is also critical in ensuring the safety and reliability of 3D printed products. As with any manufacturing process, there are potential risks associated with 3D printing, such as the release of harmful fumes or the use of materials that are not safe for human contact. Materials scientists are working to develop new materials that are safe for use in additive manufacturing and to identify potential risks associated with the process.
In conclusion, materials science plays a crucial role in the success of 3D printing and additive manufacturing. Without the development of new materials and the optimization of the printing process, the full potential of this technology cannot be realized. As the field of materials science continues to advance, we can expect to see even more exciting developments in 3D printing and additive manufacturing in the years to come.


Chemiosmotic Energy Coupling
Some of the energy initially captured by the photosynthetic light reactions is used to power the vectorial movement of protons from the stroma to the thylakoid lumen, thereby forming a transmembrane proton gradient (3). This proton translocation causes a small pH increase of the stroma, and a much larger pH decrease in the lumen due to its relatively small volume. This leads to the non-equilibrium condition of a pH difference of 3 to 4 units across the thylakoid membrane during the photosynthetic light reactions. The rate that protons escape from the lumen to the stroma is relatively fast (t(½) of decay of the trans-membrane proton gradient is ~1 sec). Consequently, the instability of the proton gradient makes it unsuitable for long-term energy storage. Instead, the proton gradient is used by the FₒF₁ ATP synthase as the energy source to drive ATP synthesis. In this manner, the photosynthetic light reactions are coupled to the synthesis of ATP via the proton gradient. Reagents like ammonia that can transport protons across the membrane more rapidly than the FₒF₁ ATP synthase will inhibit ATP synthesis because they collapse the proton gradient. These reagents that uncouple ATP synthesis from electron transfer reactions, called uncouplers, typically increase the rate of the latter reactions by relieving back-pressure from the proton gradient.
Energy coupling occurs because Fₒ serves as an efficient conduit to move protons across the thylakoid membrane and back toward equilibrium with the stroma. Protons move to the stroma in response to the energy gradient that is derived from the concentration difference across the membrane (ΔpH). Because each proton also carries a positive charge, the charge difference across the membrane (ΔΨ) also contributes to this proton energy gradient. A proton concentration difference of about 1000 fold across the membrane (ΔpH = 3) provides sufficient energy for FₒF₁ to drive ATP synthesis. Even though FₒF₁ transports only protons across the membrane, nonequilibrium concentration gradients of other ions like K⁺ can contribute to the energy of the proton gradient by changing the ΔΨ if the ion is permeable to the membrane.
In this chemiosmotic coupling process the magnitude of the energy gradient, designated the proton-motive force (pmf or μ(H⁺)), is related to the trans-membrane concentration and charge differences in mV at 30°C by equation 1. The protonmotive force translates into more conventional energy terms because it is the sum of the free energy derived from the trans-membrane concentration difference (equation 2), and the electrical potential gradient generated by the trans-membrane concentration gradient of charged species (equation 3) where n is the charge on the ion (+1 for protons). This relationship simplifies to equation 4.


Electrical Risk Assessment Job Card (Australia) Mobile App
The Electrical Risk Assessment Job Card mobile app provides an electronic version of the typical risk assessment form used by electricians.
How does it work?
Using the electrician risk assessment form prior to starting electrical work can determine if there any risks or electrical hazards that may occur at a job site. The customizable app can be tailored to meet your needs and covers several different areas of electrical safety, including checking the scaffolding, ladders, in-ground conduits and more.
Instead of carrying around paper forms as part of your risk management program, the app for electrical work can be accessed from any smartphone or tablet. Simply open the app at the job site, and you'll be prompted to check all the different areas of an electrical installation for potential health and safety issues. The results of the hazard analysis can be used when taking steps in risk reduction, which is necessary when keeping electrical contractors, project managers and others safe from typical workplace hazards. Using the electrician risk assessment form enables easy hazard identification when performing work on all types of electrical equipment.


The results are out: women really do have better memories than men, proclaim scientists at Cambridge University.
As part of their study, the researchers tested cognitive and physical performance of almost 4,500 men and women currently aged between 48 and 90 years.
Results showed women making an average of 5.9 fewer errors on a specific test of memory than men.
"In this study, we used a measure of memory that is spatial and women consistently outperformed men. There are many possible explanations for this, including both neurobiological and environmental differences," The Telegraph quoted Dr Andrew Blackwell, Chief Scientific Officer at Cambridge Cognition, as saying.


December 14, 2009 in Utilities (F)
[prMac.com] Seoul, ROK - Blue Wind has released Battery LED Pro 2.1, its battery monitoring solution for iPhone and iPod touch. As an iPhone user, I have tried tons of battery applications to maximize and squeeze out the power of my iPhone while on the go. It is significant for me to track the current status of my battery, given that I do not own any battery packs and I do not want my iPhone to die on me in case of emergency, and calls upon certain circumstances. I consistently open this application whenever I charge my iPhone to ensure the correct battery charging information, plus the alarm feature is extremely a must-have.
The interface is very stunning. It has cool LED animations on every icon. On top of the screen, it displays the model of your unit, whether it is an iPod touch or iPhone. Just under the model, there is a sizable battery indicator that has small blinking boxes of squares, which eventually fades to keep track of the existing level of the battery's life. There is also a percentage on the middle of it, and besides the battery indicator, you can press on the small little "i" which of course stands for information. Inside the information, you can fine-tune the health of your battery for a more precise reading.
Another great feature is the alarm to remind you that your battery is fully charged. You can choose to set it to none, sound only, and sound plus alarm. The alarm facet of this app certainly boosts it's value. With the sound plus alarm on, you can now put your worries behind about overcharging your device, and you can easily hear the crisp sound/alarm when your iPhone became fully charged.
This application gives you a comprehensive and meticulous done representation of your remaining battery life in standby time, telephone icon for talk time in 3G and 2G, a global icon which stands for internet in 3G and WiFi, video, a headphone icon for audio, and a controller icon for game with accurate hours and minutes.
* iPhone or iPod touch 3.0 or later
Pricing and Availability:
Battery LED Pro 2.1 is only $0.99 (USD) and available worldwide exclusively through the App Store in the Utilities category. There is also a free version available.
Blue Wind has been established in 2005 as a monbile game company which is actively focusing on developing games and applications for iphone. We will continuously develop and service various applications for our customer's convenience and enjoyment. (C) 2005-2009 Blue Wind. All Rights Reserved. Apple, the Apple logo, iPhone and iPod are registered trademarks of Apple Inc. in the U.S. and/or other countries.


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Standard letter grades
Total contact hours
Introduces the language and process of the scientific method and scientific studies. Provides tools to evaluate scientific information, including identifying pseudoscience and unethical uses of science. Promotes critical evaluation of scientific data and effective science communication.
Course learning outcomes
1. Describe the scientific process.
2. Identify pseudoscience and types of bias.
3. Describe types of scientific studies and their limitations.
4. Evaluate scientific data, including graphs and images.
5. Examine impacts of unethical applications of scientific information.
1. Language of science; what science is and isn’t
2. Scientific method and science as a process
3. Identifying pseudoscience and types of bias in science using the fundamentals of information literacy
4. Types of scientific studies
5. Limitations of scientific studies, including non-representative samples, unrepeatability, and data collection errors
6. Evaluating and interpreting graphs and images
7. Applying critical scientific thinking to decision making
8. Identifying impacts of applying science unethically
9. Effective scientific communication
Required materials may include a course pack or textbook.
General education/Related instruction lists


ITM Web Conf.
Volume 35, 2020International Forum “IT-Technologies for Engineering Education: New Trends and Implementing Experience” (ITEE-2019)
|Number of page(s)||9|
|Section||Engineering Education Technology Based on Using Digital Resources|
|Published online||09 December 2020|
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