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.
AltStore
AltStore is an alternative app store for the iOS and iPadOS[1] mobile operating systems, which allows users to download applications that are not available on the App Store, most commonly tweaked apps, jailbreak apps, and apps including paid apps on the app store. It was publicly announced on September 25, 2019, and launched on September 28. == History == Riley Testut is an American developer who began to work on AltStore after Apple declined to allow his Nintendo emulator Delta on the App Store. Since Xcode allowed him to temporarily install his Delta app to his iOS device for 7 days of testing, he created AltStore in 2019 to replicate this functionality, which could be extended to other .ipa files. As of 2022, AltStore had been downloaded 1.5 million times. In the following years, AltStore expanded beyond its initial sideloading functionality. The platform was founded by Testut, with Shane Gill later joining as co-founder. AltStore was initially supported through Patreon contributions from its user community, and later saw increased adoption following regulatory developments in the European Union that enabled broader third-party app distribution. The project has also been involved in notable industry collaborations, including a partnership with Epic Games. == Features == AltStore exploits a loophole in the Xcode developer platform, which allows developers to sideload their own apps which they are working on without needing to jailbreak. Sideloaded apps are signed like a developer project for testing and will expire after 7 days with a free account or one year with a paid developer account, by which they will need to be refreshed or reinstalled.
Stevens Award
The Stevens Award is a software engineering lecture award given by the Reengineering Forum, an industry association. The international Stevens Award was created to recognize outstanding contributions to the literature or practice of methods for software and systems development. The first award was given in 1995. The presentations focus on the current state of software methods and their direction for the future. This award lecture is named in memory of Wayne Stevens (1944-1993), a consultant, author, pioneer, and advocate of the practical application of software methods and tools. The Stevens Award and lecture is managed by the Reengineering Forum. The award was founded by International Workshop on Computer Aided Software Engineering (IWCASE), an international workshop association of users and developers of computer-aided software engineering (CASE) technology, which merged into The Reengineering Forum. Wayne Stevens was a charter member of the IWCASE executive board. == Recipients == 1995: Tony Wasserman 1996: David Harel 1997: Michael Jackson 1998: Thomas McCabe 1999: Tom DeMarco 2000: Gerald Weinberg 2001: Peter Chen 2002: Cordell Green 2003: Manny Lehman 2004: François Bodart 2005: Mary Shaw, Jim Highsmith 2006: Grady Booch 2007: Nicholas Zvegintzov 2008: Harry Sneed 2009: Larry Constantine 2010: Peter Aiken 2011: Jared Spool, Barry Boehm 2012: Philip Newcomb 2013: Jean-Luc Hainaut 2014: François Coallier 2015: Pierre Bourque
Mobile DevOps
Mobile DevOps is a set of practices that applies the principles of DevOps specifically to the development of mobile applications. Traditional DevOps focuses on streamlining the software development process in general, but mobile development has its own unique challenges that require a tailored approach. Mobile DevOps is not simply as a branch of DevOps specific to mobile app development, instead an extension and reinterpretation of the DevOps philosophy due to very specific requirements of the mobile world. == Rationale == Traditional DevOps approach has been formed around 2007-2008, close to the dates when iOS and Android mobile operating systems were released to the public. The traditional DevOps approach primarily evolved to meet the changing needs of the software development world with the paradigm shift towards continuous and rapid development and deployment (such as in web development, where interpreted languages are more prevalent than compiled languages). While traditional DevOps embraced agility and flexibility, mobile operating system providers steered towards a walled-garden approach with compiled apps with tight controls over how they can be distributed and installed on a mobile device. This difference in the mobile development mindset compared to what the traditional DevOps approach is advocating, is augmented further with the mobile applications to be deployed on a high number of varying devices and operating systems. Eventually, the concept of Mobile DevOps took off as a trend around 2014-2015, in line with the fast growth of the number of applications in mobile app stores. As individuals and corporations alike are developing and publishing more and more mobile applications, the need for efficiency and shorter release cycles increased, which is addressed by the continuous feedback and continuous development approach within the concept of DevOps, while requiring a significant level of adaptation and extension of the traditional DevOps practices. == Mindset shift from traditional DevOps to mobile DevOps == Mobile DevOps has a unique set of challenges and constraints, which solidifies the fact that it needs to be approached as a separate discipline. These challenges can be outlined as follows: Platform-specific requirements and tight controls of mobile operating system providers, where for instance a macOS device is mandatory for iOS application development and release. The walled-garden approach of distributing mobile apps, specifically applying to iOS applications, which comes with app review and app release delays that would not be needed in web development, for instance. Code signing requirements that come with the walled-garden approach, which introduce additional processes in the mobile application build pipeline along with new security concerns. An entire deployment cycle is re-run even in the slightest code change due to how applications are compiled and delivered to the users. The final product is to be deployed to a wide variety of mobile devices worldwide, which requires extensive testing and user feedback. Monitoring mobile applications require additional tools and approaches to be able to get data from an application running on a mobile device while respecting user privacy. Frequent operating system updates by mobile platforms can require rapid adaptation of apps, introducing further complexity to the development and maintenance cycles. == Benefits of mobile DevOps == Mobile DevOps is not an abstract concept and offers a range of benefits that can help improve the efficiency and effectiveness of the mobile app development process. These benefits can even be quantified by collecting the data within the mobile application development lifecycle. The benefits can be categorized into the following areas: Faster Release Cycles: By automating tasks and streamlining the development process, mobile DevOps enables teams to deliver new features and updates more frequently. Improved Quality: Automated testing and continuous monitoring help to identify and fix bugs earlier in the development cycle, leading to higher quality apps. Optimized Resource Utilization: Mobile DevOps promotes optimized resource utilization by automating tasks and streamlining workflows. Furthermore, mobile DevOps practices like containerization can help to create more efficient and scalable development environments. Increased Agility: Mobile DevOps allows teams to be more responsive to changes in the market and user feedback. == List of Dedicated Mobile DevOps Platforms == Even though it is possible to run a mobile DevOps cycle with most of the CI/CD platforms, they may require significant effort compared to non-mobile CI/CD (e.g. you need to bring your own infrastructure or it may require "reinventing the wheel" for commonly used platforms like Jenkins). To overcome the mobile-specific challenges specified, there are certain platforms that are dedicated to the lifecycle of mobile applications. These platforms exclusively focus on DevOps processes for mobile app development and are also referred as mobile CI/CD platforms. Appcircle (Multiplatform | Cloud-based & On-premise) Visual Studio App Center (Multiplatform | Cloud-based) Xcode Cloud (Apple platforms only | Cloud-based)
Mojito (framework)
Mojito is an environment agnostic, Model-View-Controller (MVC) web application framework. It was designed by Yahoo. == Features == Mojito supports agile development of web applications. Mojito has built-in support for unit testing, Internationalization, syntax and coding convention checks. Both server and client components are written in JavaScript. Mojito allows developers designing web applications to leverage the utilities of both configuration and MVC framework. Mojito is capable of running on both JavaScript-enabled web browsers and servers using Node.js because they both utilize JavaScript. Mojito applications mainly consist of two components: JSON Configuration files: these define relationships between code components, assets, routing paths, and framework defaults and are available at the application and mojit level. Directories: these reflect MVC architecture and are used to separate resources such as assets, libraries, middleware, etc. == Architecture == In Mojito, both server and "client" side scripting is done in JavaScript, allowing it to run on both client and server thereby breaking the "front-end back-end barrier." It has both client and server runtimes. === Server runtime === This block houses operations needed by server side components. Services include: Routing rules, HTTP Server, config loader and disk-based loader. === Client runtime === This block houses operations called upon while running client sides components. Services include local storage/cache access and JSON based /URL based loader === Core === Core function can be accessed on client or server. Services include Registry, Dispatcher, Front controller, Resource store. === Container === mojit object comes into the picture. This container also include the services used by mojits. API and Mojito services are the blocks which caters to services needed for execution of mojits. === API (Action Context) === Mojito services are a customizable service block. It offers mojits a range of services which might be needed by mojit to carry out certain actions. These services can be availed at both client and server side. Reusable services can be created and aggregated to the core here. == Mojits == Mojits are the modules of a Mojito application. An application consists of one or more mojits. A mojit encompasses a Model, Views and a Controller defined by JSON configuration files. It includes a View factory where views are created according to the model and a View cache that holds frequently requested views to aid performance. === Application Architecture === A Mojito application is a set of mojits facilitated by configurable JSON files which define the code for model, view and controller. This MVC structure works with API block and Mojito services, and can be deployed at both client and server side. While the application is deployed at client side, it can call server-side modules using binders. Binders are mojit codes that let mojits request services from each other. Mojit Proxy acts as an intermediary between binders and mojit's API (application context) block and other mojits. Controllers are command-issuing units of mojits. Models mirror the core logic and hold data. Applications can have multiple models. They can be centrally accessed from controllers. View files are created in accordance with controllers and models, and are marked-up before they are sent to users as output. === Application Directory Structure === Directory structure of a Mojito application with one mojit: [mojito_app]/ |-- application.json |-- assets/ | `-- favicon.icon |-- yui_modules/ | `-- .{affinity}.js |-- index.js |-- mojits/ | `-- [mojit_name | |-- assets/ | |-- yui_modules/ | | `-- .{affinity}.js | |-- binders/ | | `-- {view_name}.js | |-- controller.{affinity}.js | |-- defaults.json | |-- definition.json | |-- lang/ | | `-- {mojit_name}_{lang}.js | |-- models/ | | `-- {model_name}.{affinity}.js | |-- tests/ | | |-- yui_modules/ | | | `-- {module_name}.{affinity}-tests.js | | |-- controller.{affinity}-tests.js | | `-- models/ | | `-- {model_name}.{affinity}-tests.js | `-- views/ | |-- {view_name}.{view_engine}.html | `-- {view_name}.{device}.{view_engine}.html |-- package.json |-- routes.json (deprecated) |-- server.js == Model, View and Controller == The Model hosts data, which is accessed by the Controller and presented to the View. Controller also handles any client requests for data, in which case controller fetches data from the model and passes the data to the client. All three components are clustered in the mojit. Mojits are physically illustrated by directory structures and an application can have multiple mojits. Every mojit can have one controller, one or more views and zero or more models. === Model === The model it represents the application data and is independent of view or controller. Model contains code to manipulate the data. They are found in the models directory of each mojit. Functions include: Storing information for access by controller. Validation and error handling. Metadata required by the view === Controller === The controller acts like a connecting agent between model and view. It supplies input to Model and after fetching data from model, passes it to View. Functions include Redirection Monitors authentication Web safety Encoding === View === The view acts as a presentation filter by highlighting some model attributes and suppressing others. A view can be understood as a visual permutation of the model. The view renders data received from controller and displays it to the end user.
Manual override
A manual override (MO) or manual analog override (MAO) is a mechanism where control is taken from an automated system and given to the user. For example, a manual override in photography refers to the ability for the human photographer to turn off the automatic aperture sizing, automatic focusing, or any other automated system on the camera. Some manual overrides can be used to veto an automated system's judgment when the system is in error. An example of this is a printer's ink level detection: in one case, a researcher found that when he overrode the system, up to 38% more pages could be printed at good quality by the printer than the automated system would have allowed. Automated systems are becoming increasingly common and integrated into everyday objects such as automobiles and domestic appliances. This development of ubiquitous computing raises general issues of policy and law about the need for manual overrides for matters of great importance such as life-threatening situations and major economic decisions. The loyalty of such autonomous devices then becomes an issue. If they follow rules installed by the manufacturer or required by law and refuse to cede control in some situations then the owners of the devices may feel disempowered, alienated and lacking true ownership. == Major incidents == China Airlines Flight 140 crashed, causing many deaths, due to a misunderstanding about the manual overrides for the autopilot. The Take-Off/Go Around system had been activated to abort a landing. It was programmed to ignore manual controls in this situation but the human pilots tried to continue the landing. The conflicting control signals from the pilots and autopilot then resulted in the aircraft stalling and crashing. The autopilot for this aircraft type was then reprogrammed so that it would never ignore a manual override.
Biomedical data science
Biomedical data science is a multidisciplinary field which leverages large volumes of data to promote biomedical innovation and discovery. Biomedical data science draws from various fields including Biostatistics, Biomedical informatics, and machine learning, with the goal of understanding biological and medical data. It can be viewed as the study and application of data science to solve biomedical problems. Modern biomedical datasets often have specific features which make their analyses difficult, including: Large numbers of feature (sometimes billions), typically far larger than the number of samples (typically tens or hundreds) Noisy and missing data Privacy concerns (e.g., electronic health record confidentiality) Requirement of interpretability from decision makers and regulatory bodies Many biomedical data science projects apply machine learning to such datasets. These characteristics, while also present in many data science applications more generally, make biomedical data science a specific field. Examples of biomedical data science research include: Computational genomics Computational imaging Electronic health records data mining Biomedical network science Clinical Natural Language Processing (NLP) == Computational Imaging and Deep Learning == Computational imaging is a cornerstone of biomedical data science, focusing on the development of algorithms to enhance, analyze, and interpret medical imagery. In recent years, the field has been transformed by the integration of deep learning, particularly through the use of Convolutional Neural Networks. Deep learning started from researchers manually defining characteristics like edge detection or texture representation learning. In a more modern approach of computational imaging, models automatically learn a hierarchy of features directly from raw pixel data. This overlap between data science and deep learning is applied across several key tasks: Classification: Identifying the presence of specific diseases, such as distinguishing between benign and malignant tumors in histopathology slides or detecting pneumonia in chest X-rays. Segmentation: The precise delineation of anatomical structures or lesions. A notable example is the U-Net architecture, which is widely used for biomedical image segmentation to help clinicians quantify organ volume or track tumor growth. Detection: Automating the localization of small objects, such as identifying microcalcifications in mammograms or polyps during colonoscopies. Registration: The process of aligning multiple images to provide a comprehensive view of the patient's anatomy. Even with all of these enhancements, the application of deep learning in medical imaging requires accomplishing vigorous challenges. An example of these changes is building large, annotated datasets and creating the imperative for model interpretability in clinical decision-making. == Electronic Health Records == Electronic Health Records (EHRs) are a digital alternative to patient paper charts, usually including individual records or population health information. EHRs can be used in a wide variety of applications, including research and analysation as they often include demographics, diagnoses, medications, test results, and personal statistics. === History === ==== 1960s ==== The earliest precursor is considered Dr. Lawrence Weed's problem-oriented medical record (POMR) published in the 1968 which sorts and groups medical records by medical diagnoses and symptoms. The POMR was the first system to organize based off of patient information rather than the source (doctors, nurses, attendings, etc.). In 1969, the Regenstrief Institute developed and published the Regenstrief Medical Record System which established electronic writing, storage, and retrieval of records which served as the basis for modern EHR systems. ==== 2000s ==== In 2009, the Health Information Technology for Economic and Clinical Health Act (HITECH Act) was passed in the United States. This act standardized privacy and distribution of EHRs and increased the acceptance and utilization of EHRs within medical and academic settings. == Artificial Intelligence and Machine Learning Applications == Machine Learning and Artificial Intelligence have become central tools in biomedical data science. Recent advances in large language models (LLMs) have expanded their role beyond text, with models trained directly on genomic sequences enabling tasks such as gene function prediction, variant effect analysis, and drug discovery. In clinical settings, Natural Language Processing (NLP) models are applied to electronic health records to extract structured insights from unstructured clinical notes and data, supporting diagnosis and treatment planning. Beyond genomics, AI models have been applied to protein structure prediction. AlphaFold, developed by Google DeepMind, uses deep learning to predict three-dimensional protein structures from amino acid sequences with high accuracy. These predictions have been used to support drug target identification and the study of disease mechanisms. == Knowledge Graphs == Knowledge graphs (KGs) are widely used in biomedical data science to represent and analyze complex relationships among biological and medical entities. By structuring data as nodes (e.g., genes, diseases, drugs) and edges (relationships), KGs enable computational methods to extract insights and support decision-making. These biomedical relationships can be efficiently modeled and queried using technologies such as Neo4j. === Biomedical Research Applications === KGs provide biomedical researchers with a way to model complex biological systems. They have been used to identify the relationships between diseases and biomolecules, support drug repurposing, and to uncover new biological insights. Additional applications include: Identification of novel antibiotic resistance genes through graph-based link prediction. Finding associations between miRNA and diseases. Prediction of protein-protein interactions. === Clinical Applications === In clinical settings, KGs can be used to make visual representations of a patient's electronic health records. The data obtained from these graphs can assist healthcare providers in improving patient diagnoses and prescribing more effective drugs. Additionally, embeddings derived from resources like the Unified Medical Language System (UMLS) enable natural language processing of clinical text and similarity analysis between medical concepts. === Limitations === Despite their advantages, knowledge graphs face several challenges. Some of these include: High algorithmic complexity and large biological datasets make the process computationally expensive. KG construction can be a time-consuming process that requires careful attention to assign appropriate node types and vocabularies. Using data from a wide range of datasets in one KG requires them to be effectively integrated. == Privacy == A primary challenge in biomedical data science is maintaining medical privacy. Conducting research requires that data be collected on a number of people for training and testing purposes and is stored within biomedical datasets. This poses a risk for violating patient confidentiality and may dissuade people from participating in studies. The main sources of health statistics are surveys administrative and medical records health care claims data, vital records surveillance disease registries grey literature and peer-reviewed literature. Large data collection is a useful tool for researching various medical conditions. Researchers use these large datasets of information to identify factors that may make people more susceptible to certain diseases. Large amounts of collected data can help researchers identify patterns for disease probabilities. The findings can show a person is more likely for a condition, or identify environmental, social, and personal habits that may lead to adverse health issues. Institutions researching using personal medical information come with a moral and legal responsibility to protect the use of that information. Protection of the collected information has become a big concern. Sophisticated and coordinated attacks on certain medical systems happen more frequently. Medical companies, medical insurance and private businesses have invested a great deal into the protection of personal data. Despite this, data breaches continue to be documented. The chart below shows the top healthcare breaches in 2025. For these reasons, many people have reservations about giving up their personal data. Aside from the legitimate use of personal data there have been instances where companies have found methods to profit from brokering medical information. Concerns exist regarding unauthorized use of sensitive information within these data companies. If a person is identified within a dataset, then sensitive data can be used to discriminate against them. For example, insurance companies may charge a hi