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CAMeL-View TestRig

CAMeL-View is a software application, which is used for the model based design of mechatronic systems (multi-body simulation, block diagrams, pneumatic systems, hydraulic systems, general simulation, linear analysis and Hardware-in-the-Loop). CAMeL-View enables object-oriented model creation of mechatronic systems through the use of graphic blocks. The basic elements of multi-body system dynamics, control technology, hydraulics and hardware connectivity support the modeling process. The user’s proprietary C-Code can also be integrated into the models, which allows CAMeL-View TestRig to be implemented in all phases of the model based design process ( modeling, physical testing and prototyping), and lends itself especially well to mechatronic system design. The model’s structure is described and displayed with the help of directional connectors. Physical connections (such as mechanical or hydraulic linkages) as well as input and output connections (signal flow) are also available. The input of equations is done via mathematical expressions, e.g. the input of constitutive differential equations in vector and matrix form. Based on the model’s structure, the descriptive equations are converted into non-linear state space representations and converted into executable C-Code. CAMeL-View supports the simulation process with a configurable “experiment environment” (for simulator and instrumentation components) which allows the user to apply simulation models to supported targets (MPC5200, TriCore, X86, etc.) without the need for additional software tools for Hardware-in-the-Loop applications. In addition, the generation of so-called S-Functions for use in Simulink and the generation of ANSI C-Code for use in stand-alone simulators is also supported. A particularly noteworthy feature in CAMeL-View TestRig is the way in which the descriptive equations for multi-body system models are created. All multi-body simulation formalisms used for code generation create their equations in the form of typical explicit differential equations (ODE). This is especially important in Hardware-in-the-Loop applications where the calculation of simulation results within a specific, defined time frame must be assured. Only then is it possible to implement complex multi-body simulation models for Hardware-in-the-Loop applications under stringent real-time conditions. These constraints cannot be met when using DAE-based methods. Additional Toolboxes are available for linear analysis (Eigenvalues, pole-zero analysis, frequency response, etc.) of VRML-based animation. Development of CAMeL-View began in 1991 in the Paderborn Mechatronic Laboratory of Professor Dr. Ing. J. Lückel. The software was based on predecessors that had been developed there since 1986. The name stands for Computer Aided Mechatronic Laboratory – Virtual Engineering Workbench and describes the basic intent of one of the specific demands placed on development engineers in the computer lab.
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Pray.com

Pray.com is a Christian social networking service and mobile application designed to facilitate religious communities. Launched in 2016, it was founded by Steve Gatena, Michael Lynn, Ryan Beck and Matthew Potter. The platform offers features for social networking, daily prayers, sermons, biblical content, and podcasts. The COVID-19 pandemic significantly increased Pray.com's user base, with downloads surging by 955%. During this period, the platform collaborated with churches to support virtual ministry services as in-person gatherings were restricted. The Federal Election Commission issued an opinion in 2021 that allows the platform to feature members of the United States Congress. Pray.com serves as a specialized social media platform for religious groups. Congregations can establish their own groups where members and leaders can participate in discussions, livestream services, and manage donations. Additionally, users can join "prayer communities" to post and respond to prayer requests. For those who subscribe to premium services, the platform provides access to biblically-inspired meditations and bedtime stories, and Bible stories for children. Pray.com also produces Radio drama-style productions with notable actors such as Kristen Bell and Blair Underwood narrating biblical stories. == History == === Funding and development === Pray.com has secured significant funding to support its development and growth. In 2017, the platform raised $2 million in seed funding from Science Inc., Greylock Partners, and Spark Capital. This was followed by a Series A funding round in March 2018, in which the company secured an additional $14 million from TPG Growth, Science Inc., and Greylock Partners. Founder Steve Gatena has highlighted difficulties in securing funding, noting some venture capitalists' negative attitudes towards faith-based technology. === Clinical studies === There have been clinical studies on Pray.com. In one study, the app was found to be acceptable and easy to use among racial and ethnic minority groups, with participants reporting improved mental health and well-being. Greater app use was associated with better outcomes, though low and variable usage suggests the need for further research to fully understand its impact. Another study examined Pray.com's impact on mental health by assigning 192 participants to use the app freely, use its meditative prayer function, or not use it at all. Over two months, participants reported overall improvements in mental health and well-being. Although no significant differences were found between groups, greater app usage correlated with better mental health outcomes. This suggests that religiously based mobile apps may help improve mental health and well-being. Another study of pray.com had similar findings. === National Day of Prayer === Pray first hosted a National Day of Prayer event in 2020 when it streamed to nearly one million viewers on Facebook. In 2021, Pray hosted a virtual event for the National Day of Prayer in the United States. The event featured remarks from public figures including United States President Joe Biden and former Vice President Mike Pence. President Biden spoke of his faith and prayed for an end to the COVID-19 pandemic. Biden remarked: "It means the world to me to know that there are people across the country who include Jill and me in their prayers. And I hope you know that you and your families are in our prayers as well. Today I am praying for the end of this great COVID crisis." The event featured musical performances from Gary Valenciano, Brooke Ligertwood from the Christian band Hillsong Worship, Lecrae, Heather Headley and Michael Neale. Other notable speakers included Ronnie Floyd, Ed Young, Mark Driscoll, and Samuel Rodriguez. Pray.com partnered with Sirius XM, DirecTV and Facebook to stream the event across multiple platforms. Pray.com was featured as a pop-up channel on Sirius XM, channel 154, to host the prayer event and celebrate people of all faith. === Partnerships and sponsorships === In 2024, Pray.com partnered with Sting Ray Robb as the primary sponsor for his No. 41 Chevrolet in the 2024 NTT IndyCar Series. The partnership, highlighting Robb's Christian faith, aims to engage younger audiences with faith-based content. The car, featuring Pray.com's branding, was set to debut at the Firestone Grand Prix of St. Petersburg. A partnership with Palantir Technologies for use of its AI systems was also announced in 2024. === Censorship in China === The app was removed from Apple's App Store in China as part of the country's broader efforts to restrict access to religious content. The app was targeted due to China's stringent regulations on religious material, particularly content distributed through digital platforms. The removal aligns with China's ongoing campaign to control online religious expression and maintain state-approved religious activities.
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Big data

Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing software. Data with many entries (rows) offers greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data sources. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data that have only volume, velocity, and variety can pose challenges in sampling. A fourth concept, veracity, which refers to the level of reliability of data, was thus added. Without sufficient investment in expertise to ensure big data veracity, the volume and variety of data can produce costs and risks that exceed an organization's capacity to create and capture value from big data. Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from big data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that's not the most relevant characteristic of this new data ecosystem." Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on". Scientists, business executives, medical practitioners, advertising and governments alike regularly meet difficulties with large datasets in areas including Internet searches, fintech, healthcare analytics, geographic information systems, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology, and environmental research. The size and number of available data sets have grown rapidly as data is collected by devices such as mobile devices, cheap and numerous information-sensing Internet of things devices, aerial (remote sensing) equipment, software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks. The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 exabytes (2.17×260 bytes) of data are generated. Based on an IDC report prediction, the global data volume was predicted to grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. By 2025, IDC predicts there will be 163 zettabytes of data. According to IDC, global spending on big data and business analytics (BDA) solutions is estimated to reach $215.7 billion in 2021. Statista reported that the global big data market is forecasted to grow to $103 billion by 2027. In 2011 McKinsey & Company reported, if US healthcare were to use big data creatively and effectively to drive efficiency and quality, the sector could create more than $300 billion in value every year. In the developed economies of Europe, government administrators could save more than €100 billion ($149 billion) in operational efficiency improvements alone by using big data. And users of services enabled by personal-location data could capture $600 billion in consumer surplus. One question for large enterprises is determining who should own big-data initiatives that affect the entire organization. Relational database management systems and desktop statistical software packages used to visualize data often have difficulty processing and analyzing big data. The processing and analysis of big data may require "massively parallel software running on tens, hundreds, or even thousands of servers". What qualifies as "big data" varies depending on the capabilities of those analyzing it and their tools. Furthermore, expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration." == Definition == The term big data has been in use since the 1990s, with some giving credit to John Mashey for popularizing the term. Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big data philosophy encompasses unstructured, semi-structured and structured data; however, the main focus is on unstructured data. Big data "size" is a constantly moving target; as of 2012 ranging from a few dozen terabytes to many zettabytes of data. Big data requires a set of techniques and technologies with new forms of integration to reveal insights from datasets that are diverse, complex, and of a massive scale. Variability is often included as an additional quality of big data. A 2018 definition states "Big data is where parallel computing tools are needed to handle data", and notes, "This represents a distinct and clearly defined change in the computer science used, via parallel programming theories, and losses of some of the guarantees and capabilities made by Codd's relational model." In a comparative study of big datasets, Kitchin and McArdle found that none of the commonly considered characteristics of big data appear consistently across all of the analyzed cases. For this reason, other studies identified the redefinition of power dynamics in knowledge discovery as the defining trait. Instead of focusing on the intrinsic characteristics of big data, this alternative perspective pushes forward a relational understanding of the object claiming that what matters is the way in which data is collected, stored, made available and analyzed. === Big data vs. business intelligence === The growing maturity of the concept more starkly delineates the difference between "big data" and "business intelligence": Business intelligence uses applied mathematics tools and descriptive statistics with data with high information density to measure things, detect trends, etc. Big data uses mathematical analysis, optimization, inductive statistics, and concepts from nonlinear system identification to infer laws (regressions, nonlinear relationships, and causal effects) from large sets of data with low information density to reveal relationships and dependencies, or to perform predictions of outcomes and behaviors. == Characteristics == Big data can be described by the following characteristics: Volume The quantity of generated and stored data. The size of the data determines the value and potential insight, and whether it can be considered big data or not. The size of big data is usually larger than terabytes and petabytes. Variety The type and nature of the data. Earlier technologies like RDBMSs were capable to handle structured data efficiently and effectively. However, the change in type and nature from structured to semi-structured or unstructured challenged the existing tools and technologies. Big data technologies evolved with the prime intention to capture, store, and process the semi-structured and unstructured (variety) data generated with high speed (velocity), and huge in size (volume). Later, these tools and technologies were explored and used for handling structured data also but preferable for storage. Eventually, the processing of structured data was still kept as optional, either using big data or traditional RDBMSs. This helps in analyzing data towards effective usage of the hidden insights exposed from the data collected via social media, log files, sensors, etc. Big data draws from text, images, audio, video; plus it completes missing pieces through data fusion. Velocity The speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development. Big data is often available in real-time. Compared to small data, big data is produced more continually. Two kinds of velocity related to big data are the frequency of generation and the frequency of handling, recording, and publishing. Veracity The truthfulness or reliability of the data, which refers to the data quality and the data value. Big data must not only be large in size, but also must be reliable in order to achieve value in the analysis of it. The data quality of captured data can vary greatly, affecting an accurate analysis. Value The worth in information that can be achieved by the processing and analysis of large datasets. Value also can be measured by an assessment of the other qualities of big data. Value may also represent the profitability of information that is retrieved from the analysis of big data. Variability The characteristic of the changing formats, structure, or sources of big data. Big data can include structured, unstructured,
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List of security-focused operating systems

This is a list of operating systems specifically focused on security. Similar concepts include security-evaluated operating systems that have achieved certification from an auditing organization, and trusted operating systems that provide sufficient support for multilevel security and evidence of correctness to meet a particular set of requirements. == Linux == === Android-based === GrapheneOS is a security-focused, Android-based mobile OS that uses a hardened kernel, C library, custom memory allocator (hardened_malloc), and a hardened Chromium-based browser named Vanadium. It also offers privacy/security features, such as Duress PIN/Password or disabling the USB-C port at a driver/hardware level to avoid exploitation. It deploys exploit mitigations such as hardware-based memory tagging, secure app spawning, restricted dynamic code loading, and more. === Debian-based === Linux Kodachi is a security-focused operating system. Tails is aimed at preserving privacy and anonymity. KickSecure is a security-focused Linux distribution that aims to be "hardened by default". It uses network hardening, kernel hardening, Strong Linux User Account Isolation, better randomness, root access restrictions, and app-specific hardening. Whonix is an anonymity focused operating system based on KickSecure. It consists of two virtual machines, And all communications are routed through Tor. === Other Linux distributions === Alpine Linux is designed to be small, simple, and secure. It uses musl, BusyBox, and OpenRC instead of the more commonly used glibc, GNU Core Utilities, and systemd. Owl - Openwall GNU/Linux, a security-enhanced Linux distribution for servers. Secureblue, a Fedora Silverblue based distro that uses a hardened kernel, custom memory allocator (hardened_malloc), Trivalent, a security-focused, Chromium-based browser inspired by Vanadium, and many other exploit mitigations. == BSD == OpenBSD is a Unix-like operating system that emphasizes portability, standardization, correctness, proactive security, and integrated cryptography. == Xen == Qubes OS aims to provide security through isolation. Isolation is provided through the use of virtualization technology. This allows the segmentation of applications into secure virtual machines.
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Kleene star

In formal language theory, the Kleene star (or Kleene operator or Kleene closure) refers to two related unary operations, that can be applied either to an alphabet of symbols or to a formal language, a set of strings (finite sequences of symbols). The Kleene star operator on an alphabet V generates the set V of all finite-length strings over V, that is, finite sequences whose elements belong to V; in mathematics, it is more commonly known as the free monoid construction. The Kleene star operator on a language L generates another language L, the set of all strings that can be obtained as a concatenation of zero or more members of L. In both cases, repetitions are allowed. The Kleene star operators are named after American mathematician Stephen Cole Kleene, who first introduced and widely used it to characterize automata for regular expressions. == Of an alphabet == Given an alphabet V {\displaystyle V} , define V 0 = { ε } {\displaystyle V^{0}=\{\varepsilon \}} (the set consists only of the empty string), V 1 = V , {\displaystyle V^{1}=V,} and define recursively the set V i + 1 = { w v : w ∈ V i and v ∈ V } {\displaystyle V^{i+1}=\{wv:w\in V^{i}{\text{ and }}v\in V\}} for each i > 0 , {\displaystyle i>0,} where w v {\displaystyle wv} denotes the string obtained by appending the single character v {\displaystyle v} to the end of w {\displaystyle w} . Here, V i {\displaystyle V^{i}} can be understood to be the set of all strings of length exactly i {\displaystyle i} , with characters from V {\displaystyle V} . The definition of Kleene star on V {\displaystyle V} is V ∗ = ⋃ i ≥ 0 V i = V 0 ∪ V 1 ∪ V 2 ∪ V 3 ∪ V 4 ∪ ⋯ . {\displaystyle V^{}=\bigcup _{i\geq 0}V^{i}=V^{0}\cup V^{1}\cup V^{2}\cup V^{3}\cup V^{4}\cup \cdots .} == Of a language == Given a language L {\displaystyle L} (any finite or infinite set of strings), define L 0 = { ε } {\displaystyle L^{0}=\{\varepsilon \}} (the language consisting only of the empty string), L 1 = L , {\displaystyle L^{1}=L,} and define recursively the set L i + 1 = { w v : w ∈ L i and v ∈ L } {\displaystyle L^{i+1}=\{wv:w\in L^{i}{\text{ and }}v\in L\}} for each i > 0 , {\displaystyle i>0,} where w v {\displaystyle wv} denotes the string obtained by concatenating w {\displaystyle w} and v {\displaystyle v} . Here, L i {\displaystyle L^{i}} can be understood to be the set of all strings that can be obtained by concatenating exactly i {\displaystyle i} strings from L {\displaystyle L} , allowing repetitions. The definition of Kleene star on L {\displaystyle L} is L ∗ = ⋃ i ≥ 0 L i = L 0 ∪ L 1 ∪ L 2 ∪ L 3 ∪ L 4 ∪ ⋯ . {\displaystyle L^{}=\bigcup _{i\geq 0}L^{i}=L^{0}\cup L^{1}\cup L^{2}\cup L^{3}\cup L^{4}\cup \cdots .} == Kleene plus == In some formal language studies, (e.g. AFL theory) a variation on the Kleene star operation called the Kleene plus is used. The Kleene plus omits the V 0 {\displaystyle V^{0}} or L 0 {\displaystyle L^{0}} term in the above unions. In other words, the Kleene plus on V {\displaystyle V} is V + = ⋃ i ≥ 1 V i = V 1 ∪ V 2 ∪ V 3 ∪ ⋯ , {\displaystyle V^{+}=\bigcup _{i\geq 1}V^{i}=V^{1}\cup V^{2}\cup V^{3}\cup \cdots ,} or V + = V ∗ V . {\displaystyle V^{+}=V^{}V.} == Examples == Example of Kleene star applied to a set of strings: {"ab","c"} = { ε, "ab", "c", "abab", "abc", "cab", "cc", "ababab", "ababc", "abcab", "abcc", "cabab", "cabc", "ccab", "ccc", ...}. Example of Kleene star applied to a set of strings without the prefix property: {"a","ab","b"} = { ε, "a", "ab", "b", "aa", "aab", "aba", "abab", "abb", "ba", "bab", "bb", ...};In this example, the string "aab" can be obtained in two different ways. The Sardinas-Patterson algorithm can be used to check for a given V whether any member of V can be obtained in more than one way. Example of Kleene and Kleene plus applied to a set of characters (following the C programming language convention where a character is denoted by single quotes and a string is denoted by double quotes): {'a', 'b', 'c'} = { ε, "a", "b", "c", "aa", "ab", "ac", "ba", "bb", "bc", "ca", "cb", "cc", "aaa", "aab", ...}. {'a', 'b', 'c'}+ = { "a", "b", "c", "aa", "ab", "ac", "ba", "bb", "bc", "ca", "cb", "cc", "aaa", "aab", ...}. == Properties == If V {\displaystyle V} is any finite or countably infinite set of characters, then V ∗ {\displaystyle V^{}} is a countably infinite set. As a result, each formal language over a finite or countably infinite alphabet Σ {\displaystyle \Sigma } is countable, since it is a subset of the countably infinite set Σ ∗ {\displaystyle \Sigma ^{}} . ( L ∗ ) ∗ = L ∗ {\displaystyle (L^{})^{}=L^{}} , which means that the Kleene star operator is an idempotent unary operator, as ( L ∗ ) i = L ∗ {\displaystyle (L^{})^{i}=L^{}} for every i ≥ 1 {\displaystyle i\geq 1} . V ∗ = { ε } {\displaystyle V^{}=\{\varepsilon \}} , if V {\displaystyle V} is the empty set ∅. For the version of the Kleene star operator on languages, L ∗ = { ε } {\displaystyle L^{}=\{\varepsilon \}} when L {\displaystyle L} is either the empty set ∅ or the singleton set { ε } {\displaystyle \{\varepsilon \}} . == Generalization == Strings form a monoid with concatenation as the binary operation and ε the identity element. In addition to strings, the Kleene star is defined for any monoid. More precisely, let (M, ⋅) be a monoid, and S ⊆ M. Then S is the smallest submonoid of M containing S; that is, S contains the neutral element of M, the set S, and is such that if x,y ∈ S, then x⋅y ∈ S. Furthermore, the Kleene star is generalized by including the -operation (and the union) in the algebraic structure itself by the notion of complete star semiring.
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Himmat (app)

Himmat is a women's safety mobile application of Delhi Police. It was launched by Home Minister Rajnath Singh on 1 January 2015. The app is freely available for Android mobile phones and can be downloaded from Delhi Police website. Delhi Police plans to launch app for other platforms in future. Low registrations and other problems resulted in a parliamentary panel calling the app a failure in 2018. Himmat has gone on to be called as one of India's best safety apps for women.
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MultiValue database

A MultiValue database is a type of NoSQL and multidimensional database. It is typically considered synonymous with PICK, a database originally developed as the Pick operating system. MultiValue databases include commercial products from Rocket Software, Revelation, InterSystems, Northgate Information Solutions, ONgroup, and other companies. These databases differ from a relational database in that they have features that support and encourage the use of attributes which can take a list of values, rather than all attributes being single-valued. They are often categorized with MUMPS within the category of post-relational databases, although the data model actually pre-dates the relational model. Unlike SQL-DBMS tools, most MultiValue databases can be accessed both with or without SQL. == History == Don Nelson designed the MultiValue data model in the early to mid-1960s. Dick Pick, a developer at TRW, worked on the first implementation of this model for the US Army in 1965. Pick considered the software to be in the public domain because it was written for the military, this was but the first dispute regarding MultiValue databases that was addressed by the courts. Ken Simms wrote DataBASIC, sometimes known as S-BASIC, in the mid-1970s. It was based on Dartmouth BASIC, but had enhanced features for data management. Simms played a lot of Star Trek (a text-based early computer game originally written in Dartmouth BASIC) while developing the language, to ensure that DataBASIC functioned to his satisfaction. Three of the implementations of MultiValue - PICK version R77, Microdata Reality 3.x, and Prime Information 1.0 - were very similar. In spite of attempts to standardize, particularly by International Spectrum and the Spectrum Manufacturers Association, who designed a logo for all to use, there are no standards across MultiValue implementations. Subsequently, these flavors diverged, although with some cross-over. These streams of MultiValue database development could be classified as one stemming from PICK R83, one from Microdata Reality, and one from Prime Information. Because of the differences, some implementations have provisions for supporting several flavors of the languages. An attempt to document the similarities and differences can be found at the Post-Relational Database Reference (PRDB). One reasonable hypothesis for this data model lasting 50 years, with new database implementations of the model even in the 21st century is that it provides inexpensive database solutions. == Data model example == In a MultiValue database system: a database or schema is called an "account" a table or collection is called a "file" a column or field is called a field or an "attribute", which is composed of "multi-value attributes" and "sub-value attributes" to store multiple values in the same attribute. a row or document is called a "record" or "item" Data is stored using two separate files: a "file" to store raw data and a "dictionary" to store the format for displaying the raw data. For example, assume there's a file (table) called "PERSON". In this file, there is an attribute called "eMailAddress". The eMailAddress field can store a variable number of email address values in a single record. The list [[email protected], [email protected], [email protected]] can be stored and accessed via a single query when accessing the associated record. Achieving the same (one-to-many) relationship within a traditional relational database system would include creating an additional table to store the variable number of email addresses associated with a single "PERSON" record. However, modern relational database systems support this multi-value data model too. For example, in PostgreSQL, a column can be an array of any base type. == MultiValue Basic Language == Multivalue Basic (now commonly styled as mvBasic) is a family of programming languages more or less common (and portable) to all the multivalue databases derived from the original Pick Operating System. The variations between implementations are known as flavours. The language originates from Dartmouth Basic and the earliest implementation of PickBASIC (now D3 FlashBasic). Over time various customisations and extensions have been added to take advantage of capabilities added to the different flavours while staying mainly in sync. mvBasic statements and functions are designed to access and take advantage of the multivalue database model and providing the usual capabilities of most modern languages. For example, cryptography and communications. mvBasic is typeless and lends itself to structured programming techniques. Example code is available but limited. Whilst there are commercial applications and tools available, the multivalue database community has not embraced the open source library/package model to the degree seen with other languages. The typical mvBasic compiler compiles program source to a P-code executable object and runs in an interpreter, with D3 FlashBasic and jBASE being notable exceptions. == MultiValue Query Language == Known as ENGLISH, ACCESS, AQL, UniQuery, Retrieve, CMQL, and by many other names over the years, corresponding to the different MultiValue implementations, the MultiValue query language differs from SQL in several respects. Each query is issued against a single dictionary within the schema, which could be understood as a virtual file or a portal to the database through which to view the data. LIST PEOPLE LAST_NAME FIRST_NAME EMAIL_ADDRESSES WITH LAST_NAME LIKE "Van..." The above statement would list all e-mail addresses for each person whose last name starts with "Van". A single entry would be output for each person, with multiple lines showing the multiple e-mail addresses (without repeating other data about the person).
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MyRadar

MyRadar is a free weather forecasting application developed by Andy Green and his Orlando, Florida-based company ACME AtronOmatic (ACME). The app began operations in 2008 and ran on government-provided weather and radar data for its first decade. In 2019, ACME launched personal satellites to improve predictions of ongoing weather. The app received funding to improve its radar and imaging from the Federal Communications Commission (FCC), National Oceanic and Atmospheric Administration (NOAA), and the Office of Naval Research (ONR). ACME created a weather data satellite constellation named "Hyperspectral Orbital Remote Imaging Spectrometer" (HORIS), which utilizes machine learning and artificial intelligence (AI) to create a current weather map. With the introduction of additional features, including the detection of wildfires and illegal fishing, the app has more broadly become an environmental intelligence app since 2022. In 2024, the app partnered with the Total Traffic and Weather Network (TTWN) to provide traffic flow and incident data for users with paying subscriptions via CarPlay and Android Auto. == History == The app's creator, Andy Green, had created internet tech since the 1980s. His first major project was the development of a public access internet service company based in Rhode Island, which he later sold to finance the creation of ACME AtronOmatic ("ACME" for short), based in Orlando, Florida. The first major app created by ACME was called "Flightwise", which provided users with flight tracking information. In summer 2008, Green had the idea to use the animated location tracker already built-in to Flightwise to make a stand-alone weather forecasting app after wondering if a meal he was eating outdoors would get rained out. MyRadar was launched in 2012 out of an office in Orlando. Despite running solely off of free government-provided weather and radar data for the first decade after launch, Green said the app "took off like wildfire" in downloads. In December 2017, the app partnered with "TripIt" to provide users with information about flight delays and gate changes, eliminating the need for a separate app like Flightwise. In 2019, ACME launched their first personal satellite for the app, a small prototype from New Zealand, as part of an effort to provide detailed imagery and improved predictions of ongoing weather unique to the app. More satellites were eventually launched by ACME to create a weather data satellite constellation named "Hyperspectral Orbital Remote Imaging Spectrometer" (HORIS), monitored by ground stations maintained by Kongsberg Satellite Services. HORIS operates MyRadar by taking the environmental data and imagery it collects and pairing it with machine learning and artificial intelligence (AI) to create a real-time weather map. In 2022, HORIS was expanded upon after ACME won approval from the Federal Communications Commission (FCC) to improve their satellite constellation to include 250 satellites or more. The main batch of satellites were PocketQubes, which entered the atmosphere on May 2, 2022, by Rocket Lab Electron launched from New Zealand, with the additional purpose to test and validate the existing satellites in orbit. In October 2022, ACME received a US$150,000 Small Business Innovation Research (SBIR) grant from the National Oceanic and Atmospheric Administration (NOAA) to improve the app's wildfire detection and air quality measurement technology to better detect smoke, aerosols, fire hotspots using satellites and aerial drones. On August 18, 2023, phase two of the NOAA grant was approved, providing an additional US$650,000 to aid in the app's aforementioned goals by launching a pair of CubeSat satellites to provide high-definition infrared imagery. On September 8, 2023, ACME secured another US$1,200,000 in crowd funding to aid accomplishing the goals of the NOAA grant by expanding the app's workforce from 35 to 100 employees by the end of 2024. In January 2024, MyRadar partnered with Total Traffic and Weather Network (TTWN) to provide traffic data overlaid with its pre-existing weather graphics for users in the United States. The partnership allowed for the app to additionally become a tool for navigation. This officially became a feature days later on January 8, 2024, when the app was made compatible with Apple's CarPlay. On February 7, 2024, the Android equivalent Android Auto also gained the ability to display the app on car interfaces. In March 2024, the app launched a "meteorological wedding planning service" in the United States and Canada for prices between US$1,000 and US$5,000, in which users can request a personal meteorologist to provide an in-person meeting about the best dates for a wedding, and on-call local weather updates the day of. Scheduled for February 2025, four more satellites to help with the NOAA-sponsored wildfire detection are to be launched, and the first by ACME to have AI processing in the satellites themself and not computers on the ground, allowing for quicker transfer of information. == Features and general information == The app's primary function is to provide weather forecasting and prediction to users. The app includes toggleable options to track and send alerts to users for rain, wind patterns, earthquakes, tornadoes, tropical cyclones, wildfires, and more. In early 2020, a feature was added to track orbital objects such as the International Space Station. In May 2022, with the imagery improvement of HORIS, the app gained the secondary abilities to better monitor algae blooms, coral reefs, illegal fishing, and wildfires. In January and February 2024, the ability to display traffic flow and incident data in a feature called "RouteCast" was added, and can be displayed in video and 3D options via CarPlay and Android Auto for users with paying subscriptions. The app also provides annual tropical storm and tornado outlooks for their respective seasons, gathered through satellite and aerial drone data, as well as through on the ground storm chasers.
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JDoodle

JDoodle is a cloud-based online integrated development environment and compiler platform that supports execution of source code in 70+ programming languages including Java, Python, C/C++, PHP, Ruby, Perl, HTML, and more. It provides zero‑setup code for compilation, execution, and sharing via a web browser interface. == Features == Provides real‑time collaboration and code embedding via shareable URLs and APIs Offers an integrated terminal interface supporting database engines such as MySQL and MongoDB. JDroid — AI‑assistant to generate code snippets, optimize code, and assist debugging. == Languages and frameworks supported ==
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Digital supply chain security

Digital supply chain security refers to efforts to enhance cyber security within the supply chain. It is a subset of supply chain security and is focused on the management of cyber security requirements for information technology systems, software and networks, which are driven by threats such as cyber-terrorism, malware, data theft and the advanced persistent threat (APT). Typical supply chain cyber security activities for minimizing risks include buying only from trusted vendors, disconnecting critical machines from outside networks, and educating users on the threats and protective measures they can take. The acting deputy undersecretary for the National Protection and Programs Directorate for the United States Department of Homeland Security, Greg Schaffer, stated at a hearing that he is aware that there are instances where malware has been found on imported electronic and computer devices sold within the United States. == Examples of supply chain cyber security threats == Network or computer hardware that is delivered with malware installed on it already. Malware that is inserted into software or hardware (by various means) Vulnerabilities in software applications and networks within the supply chain that are discovered by malicious hackers Counterfeit computer hardware == Related U.S. government efforts == Comprehensive National Cyber Initiative Defense Procurement Regulations: Noted in section 806 of the National Defense Authorization Act International Strategy for Cyberspace: White House lays out for the first time the U.S.’s vision for a secure and open Internet. The strategy outlines three main themes: diplomacy, development and defense. Diplomacy: The strategy sets out to “promote an open, interoperable, secure and reliable information and communication infrastructure” by establishing norms of acceptable state behavior built through consensus among nations. Development: Through this strategy the government seeks to “facilitate cybersecurity capacity-building abroad, bilaterally and through multilateral organizations.” The objective is to protect the global IT infrastructure and to build closer international partnerships to sustain open and secure networks. Defense: The strategy calls out that the government “will ensure that the risks associated with attacking or exploiting our networks vastly outweigh the potential benefits” and calls for all nations to investigate, apprehend and prosecute criminals and non-state actors who intrude and disrupt network systems. == Related government efforts around the world == Common Criteria offers with Evaluation Assurance Level(EAL) 4 an opportunity to evaluate all relevant aspects of the digital supply chain security like the product, the development environment, IT systems security, the processes in human resource, physical security and with the module ALC_FLR.3 (Systematic Flaw Remediation) also security update processes and methods even by physical site visits. EAL 4 is mutually recognized in countries that signed the SOGIS-MRA and up to ELA 2 in countries the signed the CCRA but including ALC_FRL.3. Russia: Russia has had non-disclosed functionality certification requirements for several years and has recently initiated the National Software Platform effort based on open-source software. This reflects the apparent desire for national autonomy, reducing dependence on foreign suppliers. India: Recognition of supply chain risk in its draft National Cybersecurity Strategy. Rather than targeting specific products for exclusion, it is considering Indigenous Innovation policies, giving preferences to domestic ITC suppliers in order to create a robust, globally competitive national presence in the sector. China: Deriving from goals in the 11th Five Year Plan (2006–2010), China introduced and pursued a mix of security-focused and aggressive Indigenous Innovation policies. China is requiring an indigenous innovation product catalog be used for its government procurement and implementing a Multi-level Protection Scheme (MLPS) which requires (among other things) product developers and manufacturers to be Chinese citizens or legal persons, and product core technology and key components must have independent Chinese or indigenous intellectual property rights. == Private sector efforts == SLSA (Supply-chain Levels for Software Artifacts) is an end-to-end framework for ensuring the integrity of software artifacts throughout the software supply chain. The requirements are inspired by Google’s internal "Binary Authorization for Borg" that has been in use for the past 8+ years and that is mandatory for all of Google's production workloads. The goal of SLSA is to improve the state of the industry, particularly open source, to defend against the most pressing integrity threats. With SLSA, consumers can make informed choices about the security posture of the software they consume. == Other references == Financial Sector Information Sharing and Analysis Center International Strategy for Cyberspace (from the White House) NSTIC SafeCode Whitepaper Archived 2013-10-21 at the Wayback Machine Trusted Technology Forum and the Open Trusted Technology Provider Standard (O-TTPS) Archived 2012-01-03 at the Wayback Machine Cyber Supply Chain Security Solution Malware Implants in Firmware Supply Chain in the Software Era INFORMATION AND COMMUNICATIONS TECHNOLOGY SUPPLY CHAIN RISK MANAGEMENT TASK FORCE: INTERIM REPORT
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Catalog server

A catalog server provides a single point of access that allows users to centrally search for information across a distributed network. In other words, it indexes databases, files and information across large network and allows keywords, Boolean and other searches. If you need to provide a comprehensive searching service for your intranet, extranet or even the Internet, a catalog server is a standard solution.
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Metadata repository

A metadata repository is a database created to store metadata. Metadata is information about the structures that contain the actual data. Metadata is often said to be "data about data", but this is misleading. Data profiles are an example of actual "data about data". Metadata adds one layer of abstraction to this definition– it is data about the structures that contain data. Metadata may describe the structure of any data, of any subject, stored in any format. A well-designed metadata repository typically contains data far beyond simple definitions of the various data structures. Typical repositories store dozens to hundreds of separate pieces of information about each data structure. Comparing the metadata of a couple data items - one digital and one physical - clarify what metadata is: First, digital: For data stored in a database one may have a table called "Patient" with many columns, each containing data which describes a different attribute of each patient. One of these columns may be named "Patient_Last_Name". What is some of the metadata about the column that contains the actual surnames of patients in the database? We have already used two items: the name of the column that contains the data (Patient_Last_Name) and the name of the table that contains the column (Patient). Other metadata might include the maximum length of last name that may be entered, whether or not last name is required (can we have a patient without Patient_Last_Name?), and whether the database converts any surnames entered in lower case to upper case. Metadata of a security nature may show the restrictions which limit who may view these names. Second, physical: For data stored in a brick and mortar library, one have many volumes and may have various media, including books. Metadata about books would include ISBN, Binding_Type, Page_Count, Author, etc. Within Binding_Type, metadata would include possible bindings, material, etc. This contextual information of business data include meaning and content, policies that govern, technical attributes, specifications that transform, and programs that manipulate. == Definition == The metadata repository is responsible for physically storing and cataloging metadata. Data in a metadata repository should be generic, integrated, current, and historical: Generic Meta model should store the metadata by generic terms instead of storing it by an applications-specific defined way, so that if your data base standard changes from one product to another the physical meta model of the metadata repository would not need to change. Integration of the metadata repository allows all business areas' metadata to be in an integrated fashion: Covering all domains and subject areas of the organization. current and historical The metadata repository should have accessible current and historical metadata. Metadata repositories used to be referred to as a data dictionary. With the transition of needs for the metadata usage for business intelligence has increased so is the scope of the metadata repository increased. Earlier data dictionaries are the closest place to interact technology with business. Data dictionaries are the universe of metadata repository in the initial stages but as the scope increased Business glossary and their tags to variety of status flags emerged in the business side while consumption of the technology metadata, their lineage and linkages made the repository, the source for valuable reports to bring business and technology together and helped data management decisions easier as well as assess the cost of the changes. Metadata repository explores the enterprise wide data governance, data quality and master data management (includes master data and reference data) and integrates this wealth of information with integrated metadata across the organization to provide decision support system for data structures, even though it only reflects the structures consumed from various systems. == Repository vs. registry == Repository has additional functionalities compared with registry. Metadata repository not only stores metadata like Metadata registry but also adds relationships with related metadata types. Metadata when related in a flow from its point of entry into organization up to the deliverables is considered as the lineage of that data point. Metadata when related across other related metadata types is called linkages. By providing the relationships to all the metadata points across the organization and maintaining its integrity with an architecture to handle the changes, metadata repository provides the basic material for understanding the complete data flow and their definitions and their impact. Also the important feature is to maintain the version control though this statement for contrasting is open for discussion. These definitions are still evolving, so the accuracy of the definitions needs refinement. The purpose of registry is to define the metadata element and maintained across the organization. And data models and other data management teams refer to the registry for any changes to follow. While Metadata repository sources metadata from various metadata systems in the organizations and reflects what is in the upstream. Repository never acts as an upstream while registry is used as an upstream for metadata changes. == Reason for use == Metadata repository enables all the structure of the organizations data containers to one integrated place. This opens plethora of resourceful information for making calculated business decisions. This tool uses one generic form of data model to integrate all the models thus brings all the applications and programs of the organization into one format. And on top of it applying the business definitions and business processes brings the business and technology closer that will help organizations make reliable roadmaps with definite goals. With one stop information, business will have more control on the changes, and can do impact analysis of the tool. Usually business spends much time and money to make decisions based on discovery and research on impacts to make changes or to add new data structures or remove structures in data management of the organization. With a structured and well maintained repository, moving the product from ideation to delivery takes the least amount of time (considering other variables are constant). To sum it up: Integration of the metadata across the organization Build relationship between various metadata types Build relationship between various disparate systems Define business golden copy of definitions Version control of the changes at structure level Interaction with Reference data Link view to master data Automatic synchronization with various authorized metadata source systems More control to business decisions Validate the structures by overlapping the models Discovering discrepancies, gaps, lineage, metrics at data structure level Each database management system (DBMS) and database tools have their own language for the metadata components within. Database applications already have their own repositories or registries that are expected to provide all of the necessary functionality to access the data stored within. Vendors do not want other companies to be capable of easily migrating data away from their products and into competitors products, so they are proprietary with the way they handle metadata. CASE tools, DBMS dictionaries, ETL tools, data cleansing tools, OLAP tools, and data mining tools all handle and store metadata differently. Only a metadata repository can be designed to store the metadata components from all of these tools. == Design == Metadata repositories should store metadata in four classifications: ownership, descriptive characteristics, rules and policies, and physical characteristics. Ownership, showing the data owner and the application owner. The descriptive characteristics, define the names, types and lengths, and definitions describing business data or business processes. Rules and policies, will define security, data cleanliness, timelines for data, and relationships. Physical characteristics define the origin or source, and physical location. Like building a logical data model for creating a database, a logical meta model can help identify the metadata requirements for business data. The metadata repository will be centralized, decentralized, or distributed. A centralized design means that there is one database for the metadata repository that stores metadata for all applications business wide. A centralized metadata repository has the same advantages and disadvantages of a centralized database. Easier to manage because all the data is in one database, but the disadvantage is that bottlenecks may occur. A decentralized metadata repository stores metadata in multiple databases, either separated by location and or departments of the business. This makes management of the repository more involved than a centraliz
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JAX (software)

JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. It is developed by Google with contributions from Nvidia and other community contributors. It is described as bringing together a modified version of the automatic differentiation system autograd and OpenXLA's XLA (Accelerated Linear Algebra). It is designed to follow the structure and workflow of NumPy as closely as possible and works with various existing frameworks such as TensorFlow and PyTorch. The primary features of JAX are: Providing a unified NumPy-like interface to computations that run on CPU, GPU, or TPU, in local or distributed settings. Built-in Just-In-Time (JIT) compilation via OpenXLA, an open-source machine learning compiler ecosystem. Efficient evaluation of gradients via its automatic differentiation transformations. Automatic vectorization to efficiently map functions over arrays representing batches of inputs. == Libraries using Jax == Flax Equinox Optax
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Vero (app)

Vero (stylized as VERO) is a social media platform and mobile app company. Vero markets itself as a social network free from advertisements, data mining and algorithms. == History == The app was founded by French-Lebanese billionaire Ayman Hariri who is the son of former Lebanese prime minister Rafic Hariri. The name is taken from the Italian word for true. The app launched officially in 2015 as an alternative to Facebook and their popular photo-blogging app Instagram. Within weeks of its release the app surged in popularity although users expressed mixed reports with some feeling confused about how the app worked. Cosplayers were early to adopt the app as their photo-sharing platform of choice, favouring the app's pinch and zoom magnification feature over Instagram's zoom feature. Other creative communities soon followed, and the app became popular with niche groups of makeup artists, tattoo artists, and skateboarders. In March 2018, Vero's popularity surged, partly helped by an exodus from Facebook and Instagram following the Cambridge Analytica data scandal. In the wake of the scandal, Vero devised an advertising campaign aimed at defected Facebook and Instagram users, hoping the app's policies and privacy settings would assuage concerns over sharing personal information on the internet. Within the space of one week, the app went from being a small service, akin to Ello or Peach, to being the most downloaded app in eighteen countries. In December 2020, Vero released its most significant update to date, Vero 2.0 which introduced new features including voice and video calls, game and app posts and bookmarks, and refinements to the UI. In October 2021, Vero introduced their Desktop app (beta) with multiple post options and a re-sizable multi-column feed. == Concept and funding == Vero's content feed resembles Instagram's although users can share a wider variety of content and the app has a chronological content feed whereas Facebook and Instagram's feeds are algorithm based. Vero's business plan is also distinct from similar social media apps. Whereas its competitors such as Facebook or Instagram make money from in-app advertising revenue and the sale of user data, Vero's business plan was to invite the first one million users to use the app for free then charge any subsequent users a subscription fee. The app was entirely funded by its founder and generated additional revenues by charging affiliate fees when someone buys a product they find on Vero. == Awards == Vero was recognized at the 2021 Webbys, being named as an Honoree in the Best Visual Design - Aesthetic Category. == Controversies == === Privacy === Vero has faced some criticism over the wording of their manifesto, in particular, the statement "Vero only collects the data we believe is necessary to provide users with a great experience and to ensure the security of their accounts." Because this policy does not explicitly state that the app will not sell data on to third parties some users fear that the need to monetise the app through data might prove too tempting. Users have also complained about not being able to delete their accounts. While this was never the case, the option was hidden deep in the app's settings. === Russian involvement === Although Vero remains transparent about the app's Russian development team, they have been caught up in concerns about Russian interference on social media platforms. The app's founder Ayman Hariri was quick to dismiss the remarks as xenophobic and defend the nationality of his employees, stating in an interview with Time Magazine; "At the end of the day, where people are from is really not how anybody should judge anyone". === Criticism of the app's founder === Until 2013, Vero's founder Ayman Harari was deputy CEO and chairman of Saudi Oger, the Saudi Arabian construction company which collapsed in 2017, mired by controversies over the welfare and treatment of their employees. However, Hariri is quick to point out that he divested from the firm in 2014 and the worker's rights violations occurred after he had left the company.
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Wargame (hacking)

In hacking, a wargame (or war game) is a cyber-security challenge and mind sport in which the competitors must exploit or defend a vulnerability in a system or application, and/or gain or prevent access to a computer system. A wargame usually involves a capture the flag logic, based on pentesting, semantic URL attacks, knowledge-based authentication, password cracking, reverse engineering of software (often JavaScript, C and assembly language), code injection, SQL injections, cross-site scripting, exploits, IP address spoofing, forensics, and other hacking techniques. == Wargames for preparedness == Wargames are also used as a method of cyberwarfare preparedness. The NATO Cooperative Cyber Defence Centre of Excellence (CCDCOE) organizes an annual event, Locked Shields, which is an international live-fire cyber exercise. The exercise challenges cyber security experts through real-time attacks in fictional scenarios and is used to develop skills in national IT defense strategies. == Additional applications == Wargames can be used to teach the basics of web attacks and web security, giving participants a better understanding of how attackers exploit security vulnerabilities. Wargames are also used as a way to "stress test" an organization's response plan and serve as a drill to identify gaps in cyber disaster preparedness.
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