55° 48′ 0″ North, 38° 27′ 0″ East
Distance (in kilometers) between Elektrostal and the biggest cities of Russia.
Locate simply the city of Elektrostal through the card, map and satellite image of the city.
Weather forecast for the next coming days and current time of Elektrostal.
Find below the times of sunrise and sunset calculated 7 days to Elektrostal.
Day | Sunrise and sunset | Twilight | Nautical twilight | Astronomical twilight |
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8 June | 02:43 - 11:25 - 20:07 | 01:43 - 21:07 | 01:00 - 01:00 | 01:00 - 01:00 |
9 June | 02:42 - 11:25 - 20:08 | 01:42 - 21:08 | 01:00 - 01:00 | 01:00 - 01:00 |
10 June | 02:42 - 11:25 - 20:09 | 01:41 - 21:09 | 01:00 - 01:00 | 01:00 - 01:00 |
11 June | 02:41 - 11:25 - 20:10 | 01:41 - 21:10 | 01:00 - 01:00 | 01:00 - 01:00 |
12 June | 02:41 - 11:26 - 20:11 | 01:40 - 21:11 | 01:00 - 01:00 | 01:00 - 01:00 |
13 June | 02:40 - 11:26 - 20:11 | 01:40 - 21:12 | 01:00 - 01:00 | 01:00 - 01:00 |
14 June | 02:40 - 11:26 - 20:12 | 01:39 - 21:13 | 01:00 - 01:00 | 01:00 - 01:00 |
Our team has selected for you a list of hotel in Elektrostal classified by value for money. Book your hotel room at the best price.
Located next to Noginskoye Highway in Electrostal, Apelsin Hotel offers comfortable rooms with free Wi-Fi. Free parking is available. The elegant rooms are air conditioned and feature a flat-screen satellite TV and fridge... | from | |
Located in the green area Yamskiye Woods, 5 km from Elektrostal city centre, this hotel features a sauna and a restaurant. It offers rooms with a kitchen... | from | |
Ekotel Bogorodsk Hotel is located in a picturesque park near Chernogolovsky Pond. It features an indoor swimming pool and a wellness centre. Free Wi-Fi and private parking are provided... | from | |
Surrounded by 420,000 m² of parkland and overlooking Kovershi Lake, this hotel outside Moscow offers spa and fitness facilities, and a private beach area with volleyball court and loungers... | from | |
Surrounded by green parklands, this hotel in the Moscow region features 2 restaurants, a bowling alley with bar, and several spa and fitness facilities. Moscow Ring Road is 17 km away... | from | |
Below is a list of activities and point of interest in Elektrostal and its surroundings.
Direct link | |
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DB-City.com | Elektrostal /5 (2021-10-07 13:22:50) |
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Coordinates of elektrostal in degrees and decimal minutes, utm coordinates of elektrostal, geographic coordinate systems.
WGS 84 coordinate reference system is the latest revision of the World Geodetic System, which is used in mapping and navigation, including GPS satellite navigation system (the Global Positioning System).
Geographic coordinates (latitude and longitude) define a position on the Earth’s surface. Coordinates are angular units. The canonical form of latitude and longitude representation uses degrees (°), minutes (′), and seconds (″). GPS systems widely use coordinates in degrees and decimal minutes, or in decimal degrees.
Latitude varies from −90° to 90°. The latitude of the Equator is 0°; the latitude of the South Pole is −90°; the latitude of the North Pole is 90°. Positive latitude values correspond to the geographic locations north of the Equator (abbrev. N). Negative latitude values correspond to the geographic locations south of the Equator (abbrev. S).
Longitude is counted from the prime meridian ( IERS Reference Meridian for WGS 84) and varies from −180° to 180°. Positive longitude values correspond to the geographic locations east of the prime meridian (abbrev. E). Negative longitude values correspond to the geographic locations west of the prime meridian (abbrev. W).
UTM or Universal Transverse Mercator coordinate system divides the Earth’s surface into 60 longitudinal zones. The coordinates of a location within each zone are defined as a planar coordinate pair related to the intersection of the equator and the zone’s central meridian, and measured in meters.
Elevation above sea level is a measure of a geographic location’s height. We are using the global digital elevation model GTOPO30 .
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Date | Supplier | Customer | Details | 43 more fields |
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2022-07-28 | Mercatus Nova Co. | |||
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2022-01-07 | Mercatus Nova Co. |
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It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .
With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business for distributed digital and AI innovation.
QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.
Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.
Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.
Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.
Let’s deliver on the promise of technology from strategy to scale.
Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.
The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.
To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.
Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.
Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.
The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.
Join our colleagues Jessica Lamb and Gayatri Shenai on April 8, as they discuss how companies can navigate the ever-changing world of gen AI.
By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.
The following are examples of new skills needed for the successful deployment of generative AI tools:
The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).
It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.
While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.
To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.
While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built. They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).
For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.
Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.
Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:
The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture are needed to maximize the future strategic benefits of gen AI:
Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.
One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.
Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.
Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.
While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.
Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.
In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.
The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.
This article was edited by Barr Seitz, an editorial director in the New York office.
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This final copy of the thesis must be submitted to Academic section (Assistant Registrar, Academics) through the M. Tech. program Coordinator/Advisor. A soft copy of the thesis on a Compact Disk must also be submitted along with the hard copy of the thesis. 1.2. Title of the Thesis and Title page format.
GUIDELINES FOR PREPARATIONOF M.E./M.TECH PHASE I REPORT & PHASE II THESIS (Prescribed Format and Specification) 1. GENERAL: The broad guidelines to the preparation of M.E./M.Tech report/thesis are outlined below. In general, the thesis shall report, in an organized fashion, an account of original
13. The report submitted for examination has to be softbound and printed on both sides. The reports should have, on their spines, the abbreviated title of the report, the name of the student, and the year of submission of the report. FORMAT FOR THE REPORT After the text of the report is written, it is to be formatted in an appropriate manner
M.Pharm. candidates for the preparation of the thesis. It lists the general and specific requirements governing the thesis preparation including guidelines for structuring the contents. The candidates are advised to have thoroughly gone through the up-to-date Ph.D. or M.Tech Ordnances, and other relevant announcements brought out from time
M.Tech. DISSERTATION REPORT Preamble While utmost attention must be paid to the content of the dissertation report, which is being ... DISSERTATION REPORT FORMAT 2.1 Paper 2.1.1 Quality: The Dissertation report shall be printed / photo copied on white bond paper, whiteness 95% or above, weight 70 gram or more per square meter. ...
3.2.1.1 An M Tech. thesis should contain an abstract not exceeding 300 words (about one page), and a Ph.D. thesis should contain an abstract/synopsis not exceeding 1000 words (about four pages) in double spacing. 3.2.1.2 Ph.D. students shall submit a copy of the synopsis/abstract for transmission to examiners.
2.1.2 The thesis must be printed or photocopied on both sides of white paper. All copies of thesis pages must be clear, sharp and even, with uniform size and uniformly spaced characters, lines and margins on every page of good quality white paper of 75 gsm or more. 2.1.3 Thesis should be free from typographical errors.
a template for the synopsis a template for the thesis a simple tutorial for reference Changelog - 17/03/22: added an option to format the table of contents; minor formatting fixes 22/03/22: added some formatting options for the synopsis; re-structured the 'References' section for the synopsis; set some default styles in math mode
submitted is 4 (including thesis of supervisors) for an M.Tech/MPhil./MSc Degree student, and (b) the number of thesis to be submitted for a Ph.D. degree student are 5 copies (for single supervisor) and 6 copies (for co-supervisor). Besides various existing requirements for thesis submission such as submission of a list of
Laser Printed manuscripts on A4 (or 8.5x11 inch) Sheets with 12‐point letters, you may prepare your manuscript directly. Standard character spacing and a line spacing of 13‐point will result as you see in this sheet. The side margins of 30 mm each. The top margin should be 30 mm for the title page only and 22 mm on all subsequent pages.
Last Updated. 3 years ago. License. Creative Commons CC BY 4.0. Abstract. This is the official report template for submission of MTech Project/Thesis at IIT Jodhpur. Tags. University Thesis. Find More Templates.
Tags. University Thesis Indian Institute of Technology Kharagpur. Find More Templates. This template will be useful for M.Tech students at IIT-Kgp to submit their thesis work.
By Year Wise M.Tech Project Reports 2019-2021 M.Tech Project Reports 2018-2020 M.Tech Project Reports 2017-2019 M.Tech Project Reports 2016-2018.
The Faculty of Engineering and Technology has come up with a LaTeX template that is useful to write a dissertation / thesis / report (or synopsis) in a format suitable for submission at SRM Institute of Science and Technology (formerly known as SRM University). The LaTeX class file provides options to format PhD, M.Tech. and B.Tech. project thesis.
of thesis/dissertation/report with the following color specification: Specimen Color Of Binding Material Color Of Lettering On The Specimen M.Tech / M.E. Sky Blue Black Blank Sheets In addition to the white sheets (binding requirement) two white sheets shall be put at the beginning and the end of the thesis. PART A
famous Ad-hoc routing protocol Ad Ho c On Demand Distance V ector Routing. Protocol (AODV).The broadcasting nature of the sensors presents a n umber of. security threats to this kind of netw ork ...
Visme Chatbot will suggest template styles; choose the most relevant for your presentation, and wait for the AI to create the design. Preview, regenerate or open your project in the Visme editor. Customize your project in Visme: Pick a color theme or create your own, edit text, and use assets from Visme's royalty-free library of photos ...
While proposing the external examiners, care should be taken to list the examiners who are working in the same field. The examiner may be drawn from other departments in case the work is of interdisciplinary type. M. Tech./ M.E. graduate from the industry with at least 05 years experience may be. the examiner.
IIT Kanpur PhD and MTech Thesis template 2022. This template adheres to the IIT Kanpur guidelines. The formatting of the thesis should not require any adjustments before being accepted by the Thesis Processing Cell. This template adheres to the IIT Kanpur guidelines. The formatting of the thesis should not require any adjustments before being ...
Elektrostal Geography. Geographic Information regarding City of Elektrostal. Elektrostal Geographical coordinates. Latitude: 55.8, Longitude: 38.45. 55° 48′ 0″ North, 38° 27′ 0″ East. Elektrostal Area. 4,951 hectares. 49.51 km² (19.12 sq mi) Elektrostal Altitude.
Geographic coordinates of Elektrostal, Moscow Oblast, Russia in WGS 84 coordinate system which is a standard in cartography, geodesy, and navigation, including Global Positioning System (GPS). Latitude of Elektrostal, longitude of Elektrostal, elevation above sea level of Elektrostal.
Sample Bill of Lading 183 shipment records available. Date. 2022-07-28 . Shipper Name "Mercatus Nova Company" Llc . Shipper Address. ELEKTROSTAL'SKOYE SHOSSE 1-A MOSCO NOGINSK 142410 RUSSIAN FEDERATION . Notify Party Name. Allied Customhouse Brokers Inc. Notify Party Address. 1600 LOWER RD., LINDEN,NEW JERSEY 07036,, UNITED STATES ...
FSSH-VOSTOK-ELEKTROSTAL vs FC-METALLIST-KOROLEV team performances, predictions and head to head team stats for goals, first half goals, corners, cards. RUSSIA MOSCOW-OBLAST-CUP
5 years ago. License. Other (as stated in the work) Abstract. Template for MTech theses at NIT Kurukshetra, created by Arvind Bakshi. This template was originally published on ShareLaTeX and subsequently moved to Overleaf in November 2019. Template for MTech theses at NIT Kurukshetra, created by Arvind Bakshi.
It's time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI's enormous potential value is harder than expected.. With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI ...