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🔗 Biography 🔗 Philosophy 🔗 Biography/science and academia 🔗 Philosophy/Philosophers 🔗 Norway 🔗 Philosophy/Ethics

Arne Dekke Eide Næss ( AR-nə NESS; Norwegian: [ˈnɛsː]; 27 January 1912 – 12 January 2009) was a Norwegian philosopher who coined the term "deep ecology" and was an important intellectual and inspirational figure within the environmental movement of the late twentieth century. Næss cited Rachel Carson's 1962 book Silent Spring as being a key influence in his vision of deep ecology. Næss combined his ecological vision with Gandhian nonviolence and on several occasions participated in direct action.

Næss averred that while western environmental groups of the early post-war period had raised public awareness of the environmental issues of the time, they had largely failed to have insight into and address what he argued were the underlying cultural and philosophical background to these problems. Naess believed that the environmental crisis of the twentieth century had arisen due to certain unspoken philosophical presuppositions and attitudes within modern western developed societies which remained unacknowledged.

He thereby distinguished between what he called deep and shallow ecological thinking. In contrast to the prevailing utilitarian pragmatism of western businesses and governments, he advocated that a true understanding of nature would give rise to a point of view that appreciates the value of biological diversity, understanding that each living thing is dependent on the existence of other creatures in the complex web of interrelationships that is the natural world.

🔗 Biography 🔗 Syria

Bassel Khartabil (Arabic: باسل خرطبيل‎), also known as Bassel Safadi (Arabic: باسل صفدي‎), (22 May 1981, Damascus – 3 October 2015) was a Palestinian Syrian open-source software developer. On 15 March 2012, the one-year anniversary of the Syrian uprising, he was detained by the Syrian government at Adra Prison in Damascus. Between then and 3 October 2015, he had been transferred to an unknown location, probably to be judged by a military court. On 7 October 2015, Human Rights Watch and 30 other human rights organizations issued a letter demanding that Khartabil's whereabouts be disclosed. On 11 November 2015, rumors surfaced that Khartabil had been secretly sentenced to death. In August 2017, his wife made public that Khartabil had been executed by the Syrian regime shortly after his disappearance in 2015.

Khartabil was born in Damascus and raised in Syria, where he specialized in open source software development. He was chief technology officer (CTO) and co-founder of collaborative research company Aiki Lab and was CTO of Al-Aous, a publishing and research institution dedicated to archaeological sciences and arts in Syria. He has served as project lead and public affiliate for Creative Commons Syria, and has contributed to Mozilla Firefox, Wikipedia, Openclipart, Fabricatorz, and Sharism. He "is credited with opening up the Internet in Syria and vastly extending online access and knowledge to the Syrian people."

His last work included an open, 3D virtual reconstruction of the ancient city of Palmyra in Syria, real time visualization, and development with Fabricatorz for the web programming framework Aiki Framework. This was later created and displayed in his honor.

On February 7, 2018, the Bassel Khartabil Free Culture Fellowship was announced in Bassel's memory. The fellowship awards $50,000, including additional support, to outstanding individuals developing open culture in their communities. The fellowship was created by Creative Commons, Fabricatorz Foundation, Jimmy Wales Foundation, Mozilla, #NEWPALMYRA, and Wikimedia.

🔗 Biography 🔗 Military history 🔗 Military history/Military biography 🔗 Military history/World War II

Sir Basil Zaharoff, GCB, GBE, born Vasileios Zacharias (Greek: Βασίλειος Zαχαρίας Ζαχάρωφ; October 6, 1849 – November 27, 1936), was a Greek arms dealer and industrialist. One of the richest men in the world during his lifetime, Zaharoff was described as a "merchant of death" and "mystery man of Europe". His success was forged through his cunning, often aggressive and sharp, business tactics. These included the sale of arms to opposing sides in conflicts, sometimes delivering fake or faulty machinery and skilfully using the press to attack business rivals.

Zaharoff maintained close contacts with many powerful political leaders, including British Prime Minister David Lloyd George, Greek Prime Minister Eleftherios Venizelos and Ottoman Sultan Abdul Hamid II; he served as a primary inspiration for Ian Fleming's fictional James Bond villain Ernst Stavro Blofeld.

🔗 Television 🔗 Telecommunications 🔗 Amateur radio

Slow-scan television (SSTV) is a picture transmission method, used mainly by amateur radio operators, to transmit and receive static pictures via radio in monochrome or color.

A literal term for SSTV is narrowband television. Analog broadcast television requires at least 6 MHz wide channels, because it transmits 25 or 30 picture frames per second (see ITU analog broadcast standards), but SSTV usually only takes up to a maximum of 3 kHz of bandwidth. It is a much slower method of still picture transmission, usually taking from about eight seconds to a couple of minutes, depending on the mode used, to transmit one image frame.

Since SSTV systems operate on voice frequencies, amateurs use it on shortwave (also known as HF by amateur radio operators), VHF and UHF radio.

🔗 Canada

The Inco Superstack in Sudbury, Ontario, with a height of 381 metres (1,250 ft), is the tallest chimney in Canada and the Western hemisphere, and the second tallest freestanding chimney in the world after the GRES-2 Power Station in Kazakhstan. It is also the second tallest freestanding structure of any type in Canada, behind the CN Tower but ahead of First Canadian Place. It is the 51st tallest freestanding structure in the world. The Superstack is located on top of the largest nickel smelting operation in the world at Vale's Copper Cliff processing facility in the city of Greater Sudbury.

In 2018, Vale announced that the stack would be decommissioned and dismantled beginning in 2020. Two new, smaller stacks were constructed under the company's Clean Atmospheric Emissions Reduction Project. In July 2020, Vale announced that the Superstack had been officially taken out of service, but would remain operational in standby mode for two more months as a backup in the event of a malfunction in the new system, following which the dismantling of the Superstack would begin. As of August 2021, however, Vale has not yet announced the awarding of a demolition contract on the Superstack, and it remains unknown when demolition will actually begin.

In addition to further reducing sulphur dioxide emissions by 85 per cent, the decommissioning of the stack is expected to cut the complex's natural gas consumption in half.

🔗 Telecommunications

The Squarial (a portmanteau of the words square and aerial) was a satellite antenna used for reception of the now defunct British Satellite Broadcasting television service (BSB). The Squarial was a flat plate satellite antenna, built to be unobtrusive and unique. BSB were counting on the form factor of the antenna to clearly differentiate themselves from their competitors at the time. At the time of development, satellite installations usually required a 90 cm dish in order to receive a clear signal from the transmitting satellite. The smaller antenna was BSB's unique selling point and was heavily advertised in order to attract customers to their service.

🔗 Computing 🔗 Telecommunications 🔗 Lists

This list ranks the countries of the world by the number of mobile phone numbers in use. Note that it is not the number of phone devices that are being given here, but the number of phone numbers in a country. In some countries, one person might have two mobile phones. Also, some mobile phone numbers may be used by machines as a modem (examples: intrusion detection systems, home automation, leak detection).

🔗 Computer science

In machine learning, the kernel embedding of distributions (also called the kernel mean or mean map) comprises a class of nonparametric methods in which a probability distribution is represented as an element of a reproducing kernel Hilbert space (RKHS). A generalization of the individual data-point feature mapping done in classical kernel methods, the embedding of distributions into infinite-dimensional feature spaces can preserve all of the statistical features of arbitrary distributions, while allowing one to compare and manipulate distributions using Hilbert space operations such as inner products, distances, projections, linear transformations, and spectral analysis. This learning framework is very general and can be applied to distributions over any space ${\displaystyle \Omega }$ on which a sensible kernel function (measuring similarity between elements of ${\displaystyle \Omega }$) may be defined. For example, various kernels have been proposed for learning from data which are: vectors in ${\displaystyle \mathbb {R} ^{d}}$, discrete classes/categories, strings, graphs/networks, images, time series, manifolds, dynamical systems, and other structured objects. The theory behind kernel embeddings of distributions has been primarily developed by Alex Smola, Le Song , Arthur Gretton, and Bernhard Schölkopf. A review of recent works on kernel embedding of distributions can be found in.

The analysis of distributions is fundamental in machine learning and statistics, and many algorithms in these fields rely on information theoretic approaches such as entropy, mutual information, or Kullback–Leibler divergence. However, to estimate these quantities, one must first either perform density estimation, or employ sophisticated space-partitioning/bias-correction strategies which are typically infeasible for high-dimensional data. Commonly, methods for modeling complex distributions rely on parametric assumptions that may be unfounded or computationally challenging (e.g. Gaussian mixture models), while nonparametric methods like kernel density estimation (Note: the smoothing kernels in this context have a different interpretation than the kernels discussed here) or characteristic function representation (via the Fourier transform of the distribution) break down in high-dimensional settings.

Methods based on the kernel embedding of distributions sidestep these problems and also possess the following advantages:

1. Data may be modeled without restrictive assumptions about the form of the distributions and relationships between variables
2. Intermediate density estimation is not needed
3. Practitioners may specify the properties of a distribution most relevant for their problem (incorporating prior knowledge via choice of the kernel)
4. If a characteristic kernel is used, then the embedding can uniquely preserve all information about a distribution, while thanks to the kernel trick, computations on the potentially infinite-dimensional RKHS can be implemented in practice as simple Gram matrix operations
5. Dimensionality-independent rates of convergence for the empirical kernel mean (estimated using samples from the distribution) to the kernel embedding of the true underlying distribution can be proven.
6. Learning algorithms based on this framework exhibit good generalization ability and finite sample convergence, while often being simpler and more effective than information theoretic methods

Thus, learning via the kernel embedding of distributions offers a principled drop-in replacement for information theoretic approaches and is a framework which not only subsumes many popular methods in machine learning and statistics as special cases, but also can lead to entirely new learning algorithms.

🔗 Computer science 🔗 Mathematics

In mathematical logic and computer science, homotopy type theory (HoTT ) refers to various lines of development of intuitionistic type theory, based on the interpretation of types as objects to which the intuition of (abstract) homotopy theory applies.

This includes, among other lines of work, the construction of homotopical and higher-categorical models for such type theories; the use of type theory as a logic (or internal language) for abstract homotopy theory and higher category theory; the development of mathematics within a type-theoretic foundation (including both previously existing mathematics and new mathematics that homotopical types make possible); and the formalization of each of these in computer proof assistants.

There is a large overlap between the work referred to as homotopy type theory, and as the univalent foundations project. Although neither is precisely delineated, and the terms are sometimes used interchangeably, the choice of usage also sometimes corresponds to differences in viewpoint and emphasis. As such, this article may not represent the views of all researchers in the fields equally. This kind of variability is unavoidable when a field is in rapid flux.

🔗 Systems 🔗 Systems/Control theory

A proportional–integral–derivative (PID) controller, or three-term controller, is a feedback-based control loop mechanism commonly used to manage machines and processes that require continuous control and automatic adjustment. It is typically used in industrial control systems and various other applications where constant control through modulation is necessary without human intervention. The PID controller automatically compares the desired target value (setpoint or SP) with the actual value of the system (process variable or PV). The difference between these two values is called the error value, denoted as ${\displaystyle e(t)}$.

It then applies corrective actions automatically to bring the PV to the same value as the SP using three methods: The proportional (P) component responds to the current error value by producing an output that is directly proportional to the magnitude of the error. This provides immediate correction based on how far the system is from the desired setpoint. The integral (I) component, in turn, considers the cumulative sum of past errors to address any residual steady-state errors that persist over time, eliminating lingering discrepancies. Lastly, the derivative (D) component predicts future error by assessing the rate of change of the error, which helps to mitigate overshoot and enhance system stability, particularly when the system undergoes rapid changes. The PID output signal can directly control actuators through voltage, current, or other modulation methods, depending on the application. The PID controller reduces the likelihood of human error and improves automation.

A common example is a vehicle’s cruise control system. For instance, when a vehicle encounters a hill, its speed will decrease if the engine power output is kept constant. The PID controller adjusts the engine's power output to restore the vehicle to its desired speed, doing so efficiently with minimal delay and overshoot.

The theoretical foundation of PID controllers dates back to the early 1920s with the development of automatic steering systems for ships. This concept was later adopted for automatic process control in manufacturing, first appearing in pneumatic actuators and evolving into electronic controllers. PID controllers are widely used in numerous applications requiring accurate, stable, and optimized automatic control, such as temperature regulation, motor speed control, and industrial process management.