Mars Plus is a 1994 science fiction novel by American writer Frederik Pohl and Thomas T. Thomas. It is the sequel to Pohl's 1976 novel Man Plus, which is about a cyborg, Roger Torraway, who is designed to operate in the harsh Martian environment, so that humans can start to colonize Mars. Mars Plus is set fifty years after the first novel. Young Demeter Coghlan travels to Mars, now settled by humans and cyborgs, and finds herself amidst a rebellion by the colonists. == Plot == In Man Plus, set in the not-too-distant future, with threat of the Cold War becoming a fighting war, people plan for the colonization of Mars to escape the seemingly-inevitable Armageddon. The American government begins a cyborg program to create a being capable of surviving the harsh Martian environment: a "Man Plus" called Roger Torraway who is converted from man to cyborg. While his cyborg body is adapted to Mars, he feels strange at first. As more nations develop cyborgs, the computer networks of Earth become sentient. Mars Plus is set fifty years after the first novel, when Mars is settled by humans and cyborgs. The cyborg Torroway is in the novel, but he is not the main character. The protagonist is Demeter Coghlan, a young woman from Earth who travels to Mars. Demeter is seeking information about a canyon that she believes may be significant if the colonists begin to convert Mars to an Earth-like planet. Amidst a backdrop of spies and newly dispatched Earth diplomats, the inexperienced Demeter senses that tensions are rising on the planet. She is further disoriented due to recovering from an accident. Despite the risks in the region, Demeter has intense sexual encounters with some of the local colonists. When the locals rebel against the surveillance set up by the computer network, Demeter is kidnapped by the computer network. == Reception == The reviewer from SFBook Reviews criticizes the book, saying "nothing really happens" and stating that there is no linkage to Man Plus apart from the presence of the cyborg Torraway; moreover, the reviewer states that the questions posed in the first novel are not answered. SF Reviews calls Mars Plus "...not as good as Man Plus but...not bad", and it is praised for "...some nice touches: Demeter continuously forgetting to think about geology; her careless dictation to the computer and her irresistible urges for wild sex." SF Reviews criticizes the writing in Mars Plus for being "...a little careless in places" and in need of more "...more crafting and pruning."
Shell Control Box
Shell Control Box (SCB) is a network security appliance that controls privileged access to remote IT systems, records activities in replayable audit trails, and prevents malicious actions. For example, it records as a system administrator updates a file server or a third-party network operator configures a router. The recorded audit trails can be replayed like a movie to review the events as they occurred. The content of the audit trails is indexed to make searching for events and automatic reporting possible. SCB is a Linux-based device developed by Balabit. It is an application level proxy gateway. In 2017, Balabit changed the name of the product to Privileged Session Management (PSM) and repositioned it as the core module of its Privileged Access Management solution. == Main Features == Balabit’s Privileged Session Management (PSM), Shell Control Box (SCB) is a device that controls, monitors, and audits remote administrative access to servers and network devices. It is a tool to oversee system administrators by controlling the encrypted connections used for administration. PSM (SCB) has full control over the SSH, RDP, Telnet, TN3270, TN5250, Citrix ICA, and VNC connections, providing a framework (with solid boundaries) for the work of the administrators. === Gateway Authentication === PSM (SCB) acts as an authentication gateway, enforcing strong authentication before users access IT assets. PSM can also integrate to user directories (for example, a Microsoft Active Directory) to resolve the group memberships of the users who access the protected servers. Credentials for accessing the server are retrieved transparently from PSM’s credential store or a third-party password management system by PSM impersonating the authenticated user. This automatic password retrieval protects the confidentiality of passwords as users can never access them. === Access Control === PSM controls and audits privileged access over the most wide-spread protocols such as SSH, RDP, or HTTP(s). The detailed access management helps to control who can access what and when on servers. It is also possible to control advanced features of the protocols, like the type of channels permitted. For example, unneeded channels like file transfer or file sharing can be disabled, reducing the security risk on the server. With PSM policies for privileged access can be enforced in one single system. === 4-eyes Authorization === To avoid accidental misconfiguration and other human errors, PSM supports the 4-eyes authorization principle. This is achieved by requiring an authorizer to allow administrators to access the server. The authorizer also has the possibility to monitor – and terminate - the session of the administrator in real-time, as if they were watching the same screen. === Real-time Monitoring and Session Termination === PSM can monitor the network traffic in real time, and execute various actions if a certain pattern (for example, a suspicious command, window title or text) appears on the screen. PSM can also detect specific patterns such as credit card numbers. In case of detecting a suspicious user action, PSM can send an e-mail alert or immediately terminate the connection. For example, PSM can block the connection before a destructive administrator command, such as the „rm” comes into effect. === Session Recording === PSM makes user activities traceable by recording them in tamper-proof and confidential audit trails. It records the selected sessions into encrypted, timestamped, and digitally signed audit trails. Audit trails can be browsed online, or followed real-time to monitor the activities of the users. PSM replays the recorded sessions just like a movie – actions of the users can be seen exactly as they appeared on their monitor. The Balabit Desktop Player enables fast forwarding during replays, searching for events (for example, typed commands or pressing Enter) and texts seen by the user. In the case of any problems (database manipulation, unexpected shutdown, etc.) the circumstances of the event are readily available in the trails, thus the cause of the incident can be identified. In addition to recording audit trails, transferred files can be also recorded and extracted for further analysis.
Observability (software)
In software engineering, more specifically in distributed computing, observability is the ability to collect data about programs' execution, modules' internal states, and the communication among components. To improve observability, software engineers use a wide range of logging and tracing techniques to gather telemetry information, and tools to analyze and use it. Observability is foundational to site reliability engineering, as it is the first step in triaging a service outage. One of the goals of observability is to minimize the amount of prior knowledge needed to debug an issue. == Etymology, terminology and definition == The term is borrowed from control theory, where the "observability" of a system measures how well its state can be determined from its outputs. Similarly, software observability measures how well a system's state can be understood from the obtained telemetry (metrics, logs, traces, profiling). The definition of observability varies by vendor: Observability is the process of making a system’s internal state more transparent. Systems are made observable by the data they produce, which in turn helps you to determine if your infrastructure or application is healthy and functioning normally. a measure of how well you can understand and explain any state your system can get into, no matter how novel or bizarre [...] without needing to ship new code software tools and practices for aggregating, correlating and analyzing a steady stream of performance data from a distributed application along with the hardware and network it runs onobservability starts by shipping all your raw data to central service before you begin analysisthe ability to measure a system’s current state based on the data it generates, such as logs, metrics, and traces Observability is tooling or a technical solution that allows teams to actively debug their system. Observability is based on exploring properties and patterns not defined in advance. proactively collecting, visualizing, and applying intelligence to all of your metrics, events, logs, and traces—so you can understand the behavior of your complex digital system The term is frequently referred to as its numeronym o11y (where 11 stands for the number of letters between the first letter and the last letter of the word). This is similar to other computer science abbreviations such as i18n and l10n and k8s. === Observability vs. monitoring === Observability and monitoring are sometimes used interchangeably. As tooling, commercial offerings and practices evolved in complexity, "monitoring" was re-branded as observability in order to differentiate new tools from the old. The terms are commonly contrasted in that systems are monitored using predefined sets of telemetry, and monitored systems may be observable. Majors et al. suggest that engineering teams that only have monitoring tools end up relying on expert foreknowledge (seniority), whereas teams that have observability tools rely on exploratory analysis (curiosity). == Telemetry types == Observability relies on three main types of telemetry data: metrics, logs and traces. Those are often referred to as "pillars of observability". === Metrics === A metric is a point in time measurement (scalar) that represents some system state. Examples of common metrics include: number of HTTP requests per second; total number of query failures; database size in bytes; time in seconds since last garbage collection. Monitoring tools are typically configured to emit alerts when certain metric values exceed set thresholds. Thresholds are set based on knowledge about normal operating conditions and experience. Metrics are typically tagged to facilitate grouping and searchability. Application developers choose what kind of metrics to instrument their software with, before it is released. As a result, when a previously unknown issue is encountered, it is impossible to add new metrics without shipping new code. Furthermore, their cardinality can quickly make the storage size of telemetry data prohibitively expensive. Since metrics are cardinality-limited, they are often used to represent aggregate values (for example: average page load time, or 5-second average of the request rate). Without external context, it is impossible to correlate between events (such as user requests) and distinct metric values. === Logs === Logs, or log lines, are generally free-form, unstructured text blobs that are intended to be human readable. Modern logging is structured to enable machine parsability. As with metrics, an application developer must instrument the application upfront and ship new code if different logging information is required. Logs typically include a timestamp and severity level. An event (such as a user request) may be fragmented across multiple log lines and interweave with logs from concurrent events. === Traces === ==== Distributed traces ==== A cloud native application is typically made up of distributed services which together fulfill a single request. A distributed trace is an interrelated series of discrete events (also called spans) that track the progression of a single user request. A trace shows the causal and temporal relationships between the services that interoperate to fulfill a request. Instrumenting an application with traces means sending span information to a tracing backend. The tracing backend correlates the received spans to generate presentable traces. To be able to follow a request as it traverses multiple services, spans are labeled with unique identifiers that enable constructing a parent-child relationship between spans. Span information is typically shared in the HTTP headers of outbound requests. === Continuous profiling === Continuous profiling is another telemetry type used to precisely determine how an application consumes resources. === Instrumentation === To be able to observe an application, telemetry about the application's behavior needs to be collected or exported. Instrumentation means generating telemetry alongside the normal operation of the application. Telemetry is then collected by an independent backend for later analysis. In fast-changing systems, instrumentation itself is often the best possible documentation, since it combines intention (what are the dimensions that an engineer named and decided to collect?) with the real-time, up-to-date information of live status in production. Instrumentation can be automatic, or custom. Automatic instrumentation offers blanket coverage and immediate value; custom instrumentation brings higher value but requires more intimate involvement with the instrumented application. Instrumentation can be native - done in-code (modifying the code of the instrumented application) - or out-of-code (e.g. sidecar, eBPF). Verifying new features in production by shipping them together with custom instrumentation is a practice called "observability-driven development". == "Pillars of observability" == Metrics, logs and traces are most commonly listed as the pillars of observability. Majors et al. suggest that the pillars of observability are high cardinality, high-dimensionality, and explorability, arguing that runbooks and dashboards have little value because "modern systems rarely fail in precisely the same way twice." == Self monitoring == Self monitoring is a practice where observability stacks monitor each other, in order to reduce the risk of inconspicuous outages. Self monitoring may be put in place in addition to high availability and redundancy to further avoid correlated failures.
Apache Drill
Apache Drill is an open-source software framework that supports data-intensive distributed applications for interactive analysis of large-scale datasets. Built chiefly by contributions from developers from MapR, Drill is inspired by Google's Dremel system. Drill is an Apache top-level project. Drill supports a variety of NoSQL databases and file systems, including Alluxio, HBase, MongoDB, MapR-DB, HDFS, MapR-FS, Amazon S3, Azure Blob Storage, Google Cloud Storage, Swift, NAS and local files. A single query can join data from multiple datastores. Drill's datastore-aware optimizer automatically restructures a query plan to leverage the datastore's internal processing capabilities. In addition, Drill supports data locality, if Drill and the datastore are on the same nodes. Tom Shiran is the founder of the Apache Drill Project. It was designated an Apache Software Foundation top-level project in December 2016. == Features == One explicitly stated design goal is that Drill is able to scale to 10,000 servers or more and to be able to process petabytes of data and trillions of records in seconds. Schema-free JSON document model similar to MongoDB and Elasticsearch, without requiring a formal schema to be declared Industry-standard APIs: ANSI SQL, ODBC/JDBC, RESTful APIs Extremely user and developer friendly Pluggable architecture enables connectivity to multiple datastores Version 1.9 added dynamic user-defined functions Version 1.11 added cryptographic-related functions and PCAP file format support == Back-end support == Drill is primarily focused on non-relational datastores, including Apache Hadoop text files, NoSQL, and cloud storage. A notable feature also includes in situ querying of local JSON and Apache Parquet files. Some additional datastores that it supports include: All Hadoop distributions (HDFS API 2.3+), including Apache Hadoop, MapR, CDH and Amazon EMR NoSQL: MongoDB, Apache HBase, Apache Cassandra Online Analytical Processing: Apache Kudu, Apache Druid, OpenTSDB Cloud storage: Amazon S3, Google Cloud Storage, Azure Blob Storage, Swift, IBM Cloud Object Storage Diverse data formats, including Apache Avro, Apache Parquet and JSON RDBMs storage plugins (Using JDBC to connect to MySQL, PostgreSQL, and others) A new datastore can be added by developing a storage plugin. Drill's "schema-free" JSON data model enables it to query non-relational datastores in-situ . == Front-end support == Drill itself can be queried via JDBC, ODBC, or REST through a variety of methods and languages including Python and Java. The default install includes a web interface allowing end-users to execute ANSI SQL directly and export data tables as CSV files without any programming. The dashboard library, Apache Superset, is particularly well suited for visualization of data queried with Drill.
Tute Genomics
Tute Genomics was an American genomics startup that provided a cloud-based web application for rapid and accurate annotation of human genomic data. It was built on the expertise of ANNOVAR. Tute Genomics assisted researchers in identifying disease genes and biomarkers, and assisted clinicians/labs in performing genetic diagnosis. Based in Provo, Utah, Tute was co-founded by Dr. Kai Wang, an assistant professor at the University of Southern California (USC); and Dr. Reid J. Robison, a board-certified psychiatrist with fellowship training in both neurodevelopmental genetics and bioinformatics. Tute Genomics was acquired by PierianDX in 2016. == History == The word "tute" means "personal" in the Na’vi language created for the 2009 film Avatar by Paul Frommer, a linguist and communications professor at the USC Marshall School of Business. === Timeline === 2013 Tute Genomics launched in 2013 and entered the accelerator, BoomStartup. By "demo day" of BoomStartup, Tute had raised their seed round of funding and expanded the round to include angel investors from SLC Angels, Park City Angels, Life Science Angels. Tute was the tenth ever online syndicate for AngelList and in all raised a seed round of $1.5 million. 2014 In March 2014, the company announced that Affiliated Genetics, a Utah-based CLIA-certified laboratory, selected Tute Genomics for its next-generation sequencing (NGS) analytics pipeline. In May 2014, the company announced joining the Global Alliance for Genomics and Health. In June 2014, Advanced Biological Laboratories (ABL), S.A., announced a licensing and collaboration agreement with Tute Genomics and the commercial launch of OncoChek for managing and analysing genomics data in the field of oncology. In July 2014, the company announced an agreement with Lineagen, Inc., to provide next-generation sequencing analytics for Lineagen’s NextStepDx Plus assay. Also, Brigham Young University selected the Tute Genomics genome annotation and discovery platform for analysis and interpretation of 1,000 exomes and genomes. In November 2014, the company announced addition of the Tute platform to Illumina’s BaseSpace. The company announced a Series A1 funding round of $2.3 million in December 2014. The round was led by UK-based Eurovestech. Peak Ventures and a number of angel investors also participated in this round. 2015 Tute recruits David Mittelman, founder of Arpeggi, Inc. and former CSO at FamilyTreeDNA, to Tute Genomics as Chief Scientific Officer. Tute acquires Knome and integrates the KnoSys platform into its software product. 2016 Reid Robison, Tute CEO, launches a Kickstarter campaign to sell Tute interpreted whole genome and whole exome sequencing directly to consumers. The campaign was suspended within the same month after receiving a letter from the United States Food and Drug Administration. Tute is acquired by PierianDX.
Automated restaurant
An automated restaurant or robotic restaurant is a restaurant that uses robots to do tasks such as delivering food and drink to the tables or cooking the food. Restaurant automation means the use of a restaurant management system to automate some or occasionally all of the major operations of a restaurant establishment. More recently, restaurants are opening that have completely or partially automated their services. These may include: taking orders, preparing food, serving, and billing. A few fully automated restaurants operate without any human intervention whatsoever. Robots are designed to help and sometimes replace human labour (such as waiters and chefs). The automation of restaurants may also allow for the option for greater customization of an order. == History == === Vending machines === In the late 19th and early 20th century a number of restaurants served food solely through vending machines. These restaurants were called automats or, in Japan, shokkenki. Customers ordered their food directly through the machines. === Sushi conveyors === Yoshiaki Shiraishi is a Japanese innovator who is known for the creation of conveyor belt sushi. He had the idea following difficulty staffing his small sushi restaurant and managing the restaurant on his own. He was inspired seeing beer bottles on a conveyor belt in an Asahi brewery. Yoshiaki's restaurants are an early example of restaurant automation; they used a conveyor belt to distribute dishes around the restaurant, eliminating the need for waiters. This example of automation dates back to the Japanese economic miracle; the first of Yoshiaki's conveyor belt sushi restaurants was opened under the name Mawaru Genroku Sushi in 1958, in Osaka. === Partial automation === As of 2011, across Europe, McDonald's had already begun implementing 7,000 touch screen kiosks that could handle cashiering duties. From 2015 to 2020, Zume had an automated pizza parlor. Later companies would try to produce smaller, less ambitious devices, with one robotics company producing a machine that could automate the slowest and most repetitive parts of assembling a pizza, such as spreading pizza sauce or placing slices of pepperoni, while leaving other customizations to employees. In 2020, a restaurant in the Netherlands began trialling the use of a robot to serve guests. In September 2021, Karakuri's 'Semblr' food service robot served personalised lunches for the 4,000 employees of grocery technology solutions provider ocado Group's head offices in Hatfield, UK. 2,700 different combinations of dishes were on offer. Customers could specify in grams what hot and cold items, proteins, sauces and fresh toppings they wanted. In 2021, Columbia University School of Engineering and Applied Science engineers developed a method of cooking 3D printed chicken with software-controlled robotic lasers. The “Digital Food” team exposed raw 3D printed chicken structures to both blue and infrared light. They then assessed the cooking depth, colour development, moisture retention and flavour differences of the laser-cooked 3D printed samples in comparison to stove-cooked meat. In June 2022 a California nonprofit chain of residential communities, Front Porch, experimented with robots in dining rooms at two locations to supplement wait staff by carrying plated food and drink to tables, and removing dishes. 65% of residents found the robots helpful, with 51% saying they let the staff spend more quality time with diners. 51% of staff were "excited" and 58% said they enabled more quality time with diners. The chain has 19 senior living communities (and 35 affordable housing communities), so it has potential to expand robots to more dining rooms. It is shifting to memory care, which may affect plans. == Rationales == === Advantages === Efficiency: Automated restaurants can significantly enhance operational efficiency by minimizing human error and reducing service time. With automated ordering, payment, and food preparation systems, customers can enjoy faster service and reduced waiting times. Cost savings: By reducing the need for human staff, automated restaurants can potentially lower labor costs. This can be particularly beneficial in areas with high labor expenses, as it allows for better resource allocation and cost management. Consistency: Automation ensures consistency in food quality and presentation. With precise portion control and standardized cooking methods, customers can expect the same quality and taste in their meals every time they visit. Enhanced customer experience: Self-service kiosks and automated systems provide customers with control and convenience. They can customize their orders, browse through menu options, and pay seamlessly, creating a more interactive and satisfying dining experience. === Disadvantages === Lack of personal touch: Automated restaurants may lack the personal interaction and warmth that traditional restaurants provide. Some customers prefer the human touch, personalized recommendations, and the social aspect of dining out. Technical issues: Reliance on technology means that technical glitches and malfunctions can occur, resulting in service disruptions or delays. Maintenance and technical support become critical in ensuring smooth operations. Limited menu complexity: The automation process may be better suited for standardized menu items rather than complex or customized dishes. The ability to cater to unique dietary preferences or accommodate special requests may be limited. Employment implications: Automated restaurants may result in job losses for traditional restaurant staff, potentially impacting the local workforce. It is important to consider the social and economic implications of adopting such technology. == Locations == Automated restaurants have been opening in many countries. Examples include: Nala Restaurant in Naperville, Illinois Fritz's Railroad Restaurant in Kansas City, Kansas Výtopna, a Railway Restaurant using model trains: franchise of various restaurants and coffeehouses in the Czech Republic Bagger's Restaurant in Nuremberg, Germany FuA-Men Restaurant, a ramen restaurant located in Nagoya, Japan Fōster Nutrition in Buenos Aires, Argentina Dalu Robot Restaurant in Jinan, China Haohai Robot Restaurant in Harbin, China Robot Kitchen Restaurant in Hong Kong Robo-Chef restaurant in Tehran, Iran, started in 2017, is the first robotic and "waiterless" restaurant of the Middle East. MIT graduates opened Spyce Kitchens in downtown Boston, Massachusetts, in 2018 Foodom, under Country Garden Holdings, opened January 12, 2020, in Guangzhou, China Robot Chacha, the first robot restaurant of India, is planning to open in the capital city of New Delhi. Kura Revolving Sushi Bar, with a number of locations in the United States, uses a tablets at tables for ordering, a conveyor belt to deliver food, and robots to deliver drinks and condiments. Chipotle Mexican Grill is beginning to deploy the Hyphen Makeline, which assembles up to 350 bowls and salads automatically per hour, and Chippy, an automatic tortilla chip fryer made by Miso Robotics. Serious Dumplings in Boca Raton, Florida
Unit of work
A unit of work is a behavioral pattern in software development. Martin Fowler has defined it as everything one does during a business transaction which can affect the database. When the unit of work is finished, it will provide everything that needs to be done to change the database as a result of the work. A unit of work encapsulates one or more code repositories[de] and a list of actions to be performed which are necessary for the successful implementation of self-contained and consistent data change. A unit of work is also responsible for handling concurrency issues, and can be used for transactions and stability patterns.[de]