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Best LIFE Environment Projects 2015
RONM VI EN N T E P E R O F I J L E C T T S S E B Best LIFE Environment projects 2015 LIFE Environment Environment LIFE ENVIRONMENT | BEST LIFE ENVIRONMENT PROJECTS 2015 EUROPEAN COMMISSION ENVIRONMENT DIRECTORATE-GENERAL LIFE (“The Financial Instrument for the Environment”) is a programme launched by the European Commission and coordinated by the Environment and Climate Action Directorates-General. The Commission has delegated the im- plementation of many components of the LIFE programme to the Executive Agency for Small and Medium-sized Enterprises (EASME). The contents of the publication “Best LIFE Environment Projects 2015” do not necessarily reflect the opinions of the institutions of the European Union. Authors: Gabriella Camarsa (Environment expert), Justin Toland, Jon Eldridge, Wendy Jones, Joanne Potter, Stephen Jones, Stephen Nottingham, Marianne Geater, Isabelle Rogerson, Derek McGlynn, Carlos de la Paz (NEEMO EEIG), Eva Martínez (NEEMO EEIG, Communications Team Coordinator). Managing Editor: Hervé Martin (European Commission, Environment DG, LIFE D.4). LIFE Focus series coordination: Simon Goss (LIFE Communications Coordinator), Valerie O’Brien (Environment DG, Publications Coordinator). Technical assistance: Chris People, Luule Sinnisov, Cristóbal Gines (NEEMO EEIG). The following people also worked on this issue: Davide Messina, François Delceuillerie (Environment DG, LIFE Unit), Giulia Carboni (EASME, LIFE Unit). Production: Monique Braem (NEEMO EEIG). Graphic design: Daniel Renders, Anita Cortés (NEEMO EEIG). Photos database: Sophie Brynart (NEEMO EEIG). Acknowledgements: Thanks to all LIFE project beneficiaries who contributed comments, photos and other useful material for this report. Photos: Unless otherwise specified; photos are from the respective pro- jects. For reproduction or use of these photos, permission must be sought directly from the copyright holders. -
Dacia, Still Dacia
PRESS RELEASE 01/14/2021 MORE DACIA, STILL DACIA • Building on a strong and efficient all-weather business model, the Dacia saga continues with a new chapter • The creation of the new Dacia-Lada business unit will boost Dacia’s competitiveness through leveraged engineering & manufacturing synergies with Lada • The New Dacia Bigster Concept embodies the Dacia brand evolution, breaking the C-segment glass-ceiling • Dacia stays Dacia, as affordable as ever, with a touch of coolness Boulogne-Billancourt – 14th January 2021. As part of Groupe Renault’s Renaulution strategy, Dacia unveiled its 5-year plan. With the creation of the Dacia-Lada business unit, Dacia will boost its efficiency and competitiveness, while going beyond its perimeter in terms of products. The presentation of the Bigster Concept paves the way for new horizons for Dacia in the C-Segment. “Dacia will stay Dacia, always offering a trustworthy, authentic, best value-for money proposition to smart buyers. With the creation of the Dacia-Lada business unit, we’ll leverage to the full the CMF-B modular platform, boost our efficiency and further increase our products competitiveness, quality and attractiveness. We’ll have everything we need to bring the brands to higher lands, with the Bigster Concept leading the way.” (Denis Le Vot, CEO Dacia and Lada brands, during the unveiling of Renaulution) An all-weather business model, as unique as it is efficient For the past 15 years, Dacia has consistently rolled out contemporary, simple, and appealing vehicles. Relying on an unrivalled know-how, Dacia leverages the best proven and amortized technical solutions from Groupe Renault and Renault Nissan Mitsubishi Alliance Thanks to a lean distribution model, Dacia has since carved out a name for itself in 44 countries, with 7 million vehicles sold so far and many best-sellers. -
ATF Quick Reference Conversion Chart
2017 ATF Quick Reference Conversion Chart OEM FLUID CONVERSION FLUID OEM FLUID CONVERSION FLUID Automatic Transmission Automatic Transmission Fluid (ATF) Automatic Transmission Automatic Transmission Fluid (ATF) Fluid (ATF) called for equivalent.Use these ATF’s below plus Fluid (ATF) called for equivalent.Use these ATF’s below plus in owners manuals ONE of the appropriate LUBEGARD products in owners manuals ONE of the appropriate LUBEGARD products LUBEGARDS’s ATF Use Use LUBEGARD’S LUBEGARDS’s ATF Use Use LUBEGARD’S Protectant #60902 is LUBEGARD’S Platinum High Protectant #60902 is LUBEGARD’S Platinum High compatible in all makes HFM #61910 Performance compatible in all makes HFM #61910 Performance and models. Platinum or MV ATF Multi-Vehicle and models. Platinum or MV ATF Multi-Vehicle ATF Protectant #62005 ATF Protectant ATF Protectant #62005 ATF Protectant #63010 may also be Supplement #63010 with the #63010 may also be Supplement #63010 with the used as a premium with the base base ATF as recom- used as a premium with the base base ATF as recom- substitute. Not for use ATF as mended below, for a substitute. Not for use ATF as mended below, for a in Ford Type-F, CVT, recommended premium conversion in Ford Type-F, CVT, recommended premium conversion or DCT below product or DCT below product Aisan Warner GM/ GMC/ Buick/ Cadillac/ Hummer/ Aisin Warner AW-1 Synthetic D/M ATF Synthetic D/M ATF Oldsmobile/ Pontiac/ Geo ATF Type A, Type A-Suffix A Aisin Warner M315 Type A-1 *D/M ATF* *D/M ATF* Toyota TIV (Jaso 315 Spec) DEXRON, DEXRON II, -
Document Type Communiqué De Presse Groupe
PRESS RELEASE #RenaultResults REVENUES OF €11.3 BILLION IN THE THIRD QUARTER OF 2019 • Group revenues reached €11,296 million (-1.6%) in the quarter. At constant exchange rates and perimeter1, the decrease would have been -1.4%. • The Group sold 852,198 vehicles in the quarter, down -4.4% in a global market down -3.2%2. Excluding Iran, the decrease would have been -1.8% in a market down -2.3%. • The Group is pursuing its pricing policy in the third quarter. Boulogne-Billancourt, 10/25/2019 COMMERCIAL RESULTS: THIRD QUARTER HIGHLIGHTS In the third quarter, Groupe Renault sold 852,198 vehicles, down -4.4% in a market that fell by -3.2%. Excluding Iran, the decrease would have been -1.8% in a market down -2.3%. In Europe, the Group recorded a -3.4% decline in sales in a market up +2.4%. This decrease is partly due to a high comparison basis related to the introduction of the WLTP3 for passenger cars in September 2018 and the awaiting of the full availability of New Clio in Europe. In regions outside Europe, the Group over-performed the market. In a market down -6.2%, the Group recorded a -5.4% decrease in sales, mainly due to the decline in markets in Turkey (-21.7%), Argentina (-30.0%), and the end of sales in Iran since August 2018 (23,649 vehicles sold in the third quarter 2018). Excluding Iran, sales would have been down -0.3%. 1 In order to analyze the change in consolidated revenues at constant perimeter and exchange rates, Groupe Renault recalculates revenues for the current year by applying the average annual exchange rates of the previous year, and excluding significant changes in perimeter that occurred during the year. -
NTGAN: Learning Blind Image Denoising Without Clean Reference
RUI ZHAO, DANIEL P.K. LUN, KIN-MAN LAM: NTGAN 1 NTGAN: Learning Blind Image Denoising without Clean Reference Rui Zhao Department of Electronic and [email protected] Information Engineering, Daniel P.K. Lun The Hong Kong Polytechnic University, [email protected] Hong Kong, China Kin-Man Lam [email protected] Abstract Recent studies on learning-based image denoising have achieved promising perfor- mance on various noise reduction tasks. Most of these deep denoisers are trained ei- ther under the supervision of clean references, or unsupervised on synthetic noise. The assumption with the synthetic noise leads to poor generalization when facing real pho- tographs. To address this issue, we propose a novel deep unsupervised image-denoising method by regarding the noise reduction task as a special case of the noise transference task. Learning noise transference enables the network to acquire the denoising ability by only observing the corrupted samples. The results on real-world denoising benchmarks demonstrate that our proposed method achieves state-of-the-art performance on remov- ing realistic noises, making it a potential solution to practical noise reduction problems. 1 Introduction Convolutional neural networks (CNNs) have been widely studied over the past decade in various computer vision applications, including image classification [13], object detection [29], and image restoration [35]. In spite of high effectiveness, CNN models always require a large-scale dataset with reliable reference for training. However, in terms of low-level vision tasks, such as image denoising and image super-resolution, the reliable reference data is generally hard to access, which makes these tasks more challenging and difficult. -
Integrated Report 2020
INTEGRATED REPORT 2020 For the year ended March 31, 2020 Contents Message from the CEO . 2 Contribution to Local Economy Message from the CFO . 4 through Business Activities . 31 New Mid-Term Business Plan. 6 Business and Financial Condition . 32 Introducing Our New Models . 10 Overview of Operations by Region . 32 Mitsubishi Motors’ History . 12 Consolidated Financial Summary . 36 Major Successive Models . 14 Operational Review . 37 Sales and Production Data . 16 Business-related risks . 38 Sustainability Management . 18 Consolidated Financial Statements . 42 Corporate Governance . 20 Consolidated Subsidiaries and Affiliates . 48 Management . 24 Principal Production Facilities . 50 The New Environmental Plan Package . 27 Investor Information . 51 Safety and Quality . 30 System for Disclosing Information Extremely high Extremely This z Integrated Report Report • Financial and non-financial information with a direct connection to the Company’s management strategy ・Focus on information that is integral and concise Stakeholders’ Concern Stakeholders’ z Sustainability Report • Sustainability (ESG) information • Focus on information that is comprehensive and continuous y Sustainability Report High https://www.mitsubishi-motors.com/en/sustainability/report/ High Impact on Management Extremely high y Global Website: “Investors” https://www.mitsubishi-motors.com/en/investors/ Forward-looking Statements Mitsubishi Motors Corporation’s current plans, strategies, beliefs, performance outlook and other statements in this annual report that are not historical facts are forward-looking statements. These forward-looking statements are based on management’s beliefs and assumptions drawn from current expectations, estimates, forecasts and projections. These expectations, estimates, forecasts and projections are subject to a number of risks, uncertainties and assumptions that may cause actual results to differ materially from those indicated in any forward-looking statement. -
High-Quality Self-Supervised Deep Image Denoising
High-Quality Self-Supervised Deep Image Denoising Samuli Laine Tero Karras Jaakko Lehtinen Timo Aila NVIDIA∗ NVIDIA NVIDIA, Aalto University NVIDIA Abstract We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images. The training does not need access to clean reference images, or explicit pairs of corrupted images, and can thus be applied in situations where such data is unacceptably expensive or im- possible to acquire. We build on a recent technique that removes the need for reference data by employing networks with a “blind spot” in the receptive field, and significantly improve two key aspects: image quality and training efficiency. Our result quality is on par with state-of-the-art neural network denoisers in the case of i.i.d. additive Gaussian noise, and not far behind with Poisson and impulse noise. We also successfully handle cases where parameters of the noise model are variable and/or unknown in both training and evaluation data. 1 Introduction Denoising, the removal of noise from images, is a major application of deep learning. Several architectures have been proposed for general-purpose image restoration tasks, e.g., U-Nets [23], hierarchical residual networks [20], and residual dense networks [31]. Traditionally, the models are trained in a supervised fashion with corrupted images as inputs and clean images as targets, so that the network learns to remove the corruption. Lehtinen et al. [17] introduced NOISE2NOISE training, where pairs of corrupted images are used as training data. They observe that when certain statistical conditions are met, a network faced with the impossible task of mapping corrupted images to corrupted images learns, loosely speaking, to output the “average” image. -
HSBC Bank Plc Annual Report and Accounts 2006
HSBCBankARAcover06 19/2/07 10:22 am Page 1 2006 Annual Report and Accounts HSBC Bank plc HSBC BANK PLC Annual Report and Accounts 2006 Contents Page Page Financial Highlights .............................................. 1 Consolidated statement of recognised income and expense for the year ended Board of Directors and Senior Management ...... 2 31 December 2006 .............................................. 29 Report of the Directors ......................................... 4 Consolidated cash flow statement for the year ended 31 December 2006 ............................ 30 Statement of Directors’ Responsibilities in Relation to the Directors’ Report and the HSBC Bank plc balance sheet at Financial Statements ........................................... 25 31 December 2006 .............................................. 31 Independent Auditors’ Report to the Member HSBC Bank plc statement of recognised income of HSBC Bank plc ............................................... 26 and expense for the year ended 31 December 2006 .............................................. 32 Financial Statements HSBC Bank plc cash flow statement for the year Consolidated income statement for the year ended 31 December 2006 .................................... 33 ended 31 December 2006 ................................27 Notes on the Financial Statements ............................ 34 Consolidated balance sheet at 31 December 2006 ......................................... 28 Presentation of Information This document comprises the Annual Report and Accounts -
Monitoring the Acoustic Performance of Low- Noise Pavements
Monitoring the acoustic performance of low- noise pavements Carlos Ribeiro Bruitparif, France. Fanny Mietlicki Bruitparif, France. Matthieu Sineau Bruitparif, France. Jérôme Lefebvre City of Paris, France. Kevin Ibtaten City of Paris, France. Summary In 2012, the City of Paris began an experiment on a 200 m section of the Paris ring road to test the use of low-noise pavement surfaces and their acoustic and mechanical durability over time, in a context of heavy road traffic. At the end of the HARMONICA project supported by the European LIFE project, Bruitparif maintained a permanent noise measurement station in order to monitor the acoustic efficiency of the pavement over several years. Similar follow-ups have recently been implemented by Bruitparif in the vicinity of dwellings near major road infrastructures crossing Ile- de-France territory, such as the A4 and A6 motorways. The operation of the permanent measurement stations will allow the acoustic performance of the new pavements to be monitored over time. Bruitparif is a partner in the European LIFE "COOL AND LOW NOISE ASPHALT" project led by the City of Paris. The aim of this project is to test three innovative asphalt pavement formulas to fight against noise pollution and global warming at three sites in Paris that are heavily exposed to road noise. Asphalt mixes combine sound, thermal and mechanical properties, in particular durability. 1. Introduction than 1.2 million vehicles with up to 270,000 vehicles per day in some places): Reducing noise generated by road traffic in urban x the publication by Bruitparif of the results of areas involves a combination of several actions. -
Securities Report Renault (E05907)
SECURITIES REPORT 1. This document is a printed copy, with table of contents and page numbers inserted, of the data of the Securities Report under Article 24, Paragraph 1 of the Financial Instruments and Exchange Law filed on May 23, 2012 through Electronic Disclosure for Investors’ Network (EDINET) provided for in Article 27-30-2 of such Law. 2. The documents attached to the Securities Report filed as stated above are not included herein. However, a copy of the audit report is attached at the end hereof. RENAULT (E05907) (TRANSLATION) Cover Page Document Name: Securities Report Based on: Article 24, Paragraph 1 of the Financial Instruments and Exchange Law Filed with: The Director General of Kanto Local Finance Bureau Filing Date: May 23, 2012 Fiscal Year: From January 1, 2011 to December 31, 2011 Corporate Name: Renault Name and Title of Representative: Carlos Ghosn Chairman and Chief Executive Officer Location of Head Office: 13-15, Quai Le Gallo, 92100 Boulogne-Billancourt France Name of Attorney-in-fact: Tsutomu Hashimoto, Attorney-at-law Address of Attorney-in-fact: Nagashima Ohno & Tsunematsu Kioicho Building, 3-12, Kioicho, Chiyoda-ku, Tokyo Telephone Number: 03-3288-7000 Name of Person to Contact: Akiko Tomiyama, Attorney-at-law Place to Contact: Nagashima Ohno & Tsunematsu Kioicho Building, 3-12, Kioicho, Chiyoda-ku, Tokyo Telephone Number: 03-3288-7000 Place(s) of Public Inspection: Not applicable TABLE OF CONTENTS PART I CORPORATE INFORMATION I. SUMMARY OF LAWS AND REGULATIONS IN THE COUNTRY TO WHICH THE COMPANY BELONGS ............................................ 1 1. Summary of Corporate System, etc. ............................................................................... 1 2. Foreign Exchange Control System .............................................................................. -
2007 Annual Report
2007 ANNUAL REPORT 2007 KEY FIGURES* GROUP SALES WORLDWIDE: 2,484,472 VEHICLES Revenues – RENAULT SHARE: €40,682 million OPERATING MARGIN: €1,354 million Net income — RENAULT SHARE: €2,669 MILLION DIVIDEND PER SHARE: €3.80** WORKFORCE: 130,179 EMPLOYEES * Published figures. ** As proposed at the Annual General Meeting on April 29, 2008. 2007 KEY FIGURES OPERATING MARGIN* WORKFORCE* DIVIDEND PER SHARE TOTAL INDUSTRY VOLUME – REGISTRATIONS – CARS + LCVs (€ MILLION) (IN UNITS) (€) (IN UNITS) 150,000 2,500 4.0 3.80** 2003 2004 2005 2006 2007 2,115 128,893 130,179 125,128 124,277 126,584 3.5 Europe + France 17,096,627 17,561,095 17,514,551 17,773,957 18,059,825 2,000 120,000 3.10 3.0 Euromed + Americas + Asia-Africa 21,994,091 24,571,894 27,022,655 29,353,333 31,984,185 1,402 1,354 90,000 2.5 2.40 1,500 1,323 Total 39 090 718 42,132,989 44,537,206 47,127,290 50,044,010 1,063 2.0 1.80 1,000 60,000 1.5 1.40 1.0 500 30,000 0.5 RENAULT GROUP – MARKET SHARE – CARS + LCVs 0 0 0 (%) 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 Europe + France 11.1% 10.8% 10.4% 9.4% 8.8% Euromed + Americas + Asia-Africa 2.1% 2.3% 2.5% 2.5% 2.7% NET INCOME – RENAULT SHARE REVENUES – RENAULT SHARE SIMPLIFIED STRUCTURE OF THE RENAULT (€ MILLION) (€ MILLION) GROUP AT DECEMBER 31, 2007 RENAULT GROUP – REGISTRATIONS - CARS + LCVs 3,500 3,367 (IN UNITS) 50,000 NISSAN 15 % RENAULT 3,000 2,836 2,869 2,669 41,338 41,528 40,682 44.3% 2003 2004 2005 2006 2007 2,500 2,480 40,000 37,525 40,292 2,000 Europe + France 1,894,262 1,895,703 1,823,479 1,666,032 1,593,789 30,000 AB VOLVO RENAULT Euromed + Americas + Asia-Africa 740,707 1,500 64.5 65.4 67.2 66 67.8 20% 100% TRUCKS 460,798 561,341 682,083 861,072 20,000 1,000 MACK Total 2,355,060 2,457,044 2,505,562 2,406,562 2,454,861 500 10,000 35.5 34.6 32.8 34 32.2 RENAULT DACIA 0 0 SAMSUNG 70.1% 99. -
AVTOVAZ Call with Financial Analysts
AVTOVAZ Call with Financial Analysts Nicolas MAURE / Dr. Stefan MAUERER CEO / CFO 16.01.2017 Disclaimer Information contained within this document may contain forward looking statements. Although the Company considers that such information and statements are based on reasonable assumptions taken on the date of this report, due to their nature, they can be risky and uncertain and can lead to a difference between the exact figures and those given or deduced from said information and statements. PJSC AVTOVAZ does not undertake to provide updates or revisions, should any new statements and information be available, should any new specific events occur or for any other reason. PJSC AVTOVAZ makes no representation, declaration or warranty as regards the accuracy, sufficiency, adequacy, effectiveness and genuineness of any statements and information contained in this report. Further information on PJSC AVTOVAZ can be found on AVTOVAZ’s web sites (www.lada.ru/en and http://info.avtovaz.ru). AVTOVAZ Call with Financial Analysts 16.01.2017 2 AVTOVAZ Overview Moscow International Automobile Salon 2016 AVTOVAZ 50-years History 1966/1970 VAZ 2101 2016 LADA XRAY AVTOVAZ Call with Financial Analysts 16.01.2017 4 AVTOVAZ Group: Key information 408 467 Cars & KDs produced 20.1% MOSCOW AVTOVAZ 331 Representative office sales points 30 IZHEVSK LADA-Izhevsk countries TOGLIATTI plant AVTOVAZ Head-office & 2015: 176.5 B-Rub (2.6 B-Euro) Togliatti plants 2016: estimated T/O > 2015 51 527 p. AVTOVAZ Call with Financial Analysts 16.01.2017 5 LADA product portfolio