The Philippines is famous for its beautiful islands and beaches. The proposal came after a task force on infectious diseases in the Philippines asked stakeholders to learn about the process of receiving international guests who have received the Covid-19 vaccine. If the “green corridor” is approved, eligible international visitors will be allowed to enter the Philippines for tourism and resort purposes, in the context of quarantine rules being relaxed in many other countries. DOT is monitoring neighboring markets on how to reopen international travel. Specifically, for fully vaccinated people, the quarantine period will be reduced from 10 days to 7 days in Thailand, or from 21 days to 7 days in Hong Kong (China). Meanwhile, the Philippines is requiring travelers to quarantine for 10 days and 4 days at home, along with an RT-PCR test on the seventh day of isolation. “We must catch up with our neighbors and the rest of the world in gradually reopening tourist attractions. The Philippines should be ready, before international tourism in the world resumes,” Ms. Bernadette said. Romulo-Puyat added.

VinFast is building electric car charging stations The Ministry of Finance has just sent a document to the Ministry of Industry and Trade, the Ministry of Justice, the Ministry of Natural Resources and Environment and the Ministry of Transport, to respond to tax incentives for battery electric cars. Proposals for tax incentives only mention battery electric vehicles (BEVs). Currently, according to statistics, battery electric vehicles account for about 1% of electrified vehicles in Vietnam. Electric vehicles include many types of vehicles. In addition to gasoline-powered vehicles combined with electric energy (including HEV, PHEV) and Hydrogen-fueled cars (FCEV), there are currently BEV (pure electric) battery electric cars. Completely electrically charged from an external source. The two types of hybrid vehicles move mainly with internal combustion engines, in parallel taking advantage of the benefits of an electric powertrain consisting of an electric motor with a battery. FCEVs have an advantage over BEVs when the charging time is short, but the production of water makes hydrogen fuel-powered electric vehicles only suitable for island nations, arid areas, and unsuitable for urban areas. big. Meanwhile, BEV battery electric vehicles have a minimalist structure, run short distances, can consume electricity everywhere, have great advantages in urban areas. With three types of vehicles HEV, PHEV and FCEV all use gasoline and gas, so they have more impact on the environment than BEV models. Electric cars are becoming an advantageous choice to replace vehicles using internal combustion engines, especially battery electric cars (BEVs). Many countries around the world have had preferential policies and tax support to prioritize the development and production of battery electric vehicles. According to the Ministry of Finance, Vietnam has a number of preferential policies for electric vehicles. Accordingly, electric cars carrying passengers with less than 9 seats are entitled to the special consumption tax rate of 15% (ie 10% reduction compared to cars of the same type using fossil fuels; 10-16 seats are allowed to be used for cars of the same type using fossil fuels. tax rate of 10% (5% reduction compared to cars of the same type running on gasoline) and the tax rate of 16 to less than 24 seats is applied at the tax rate of 5% (5% reduction compared to cars of the same type running on petrol). At the same time, the Law on Special Consumption Tax No. 27/2008/QH12 also stipulates the special consumption tax rate for environmentally friendly vehicles. Specifically, vehicles running on gasoline combined with electric energy and bioenergy, in which the proportion of gasoline used does not exceed 70% of the energy used by the vehicle, is entitled to a tax rate of 70% for vehicles of the same capacity. species. For cars running on bio-energy, the special consumption tax rate is only 50% compared to cars of the same type. Meanwhile, about the current registration fee, there are only preferential rates for buses using clean energy while there is no policy applicable to other environmentally friendly vehicles. To encourage production and stimulate consumers to use BEVs, contributing to reducing the emissions released into the environment by vehicles, contributing to the protection of the ecological environment and based on the outstanding advantages of vehicles. BEV, the Ministry of Finance is planning to propose new incentives for this model. An electric vehicle charging station in Ho Chi Minh City Specifically, incentives related to excise tax fall under the jurisdiction of the National Assembly. Therefore, the National Assembly must amend the excise tax law. The Government has also assigned the Ministry of Finance to preside over the overall assessment of the implementation of the excise tax policy on automobiles in the past time to study and amend the excise tax law accordingly. Regarding the registration fee under the authority of the Government, when there is a need to change, it will amend and supplement Decree No. 14/20216/ND-CP on registration fee. The Ministry of Finance said that in the past time, it has also received recommendations from a number of associations on adjusting registration fee rates to remove difficulties for businesses. Therefore, the Ministry of Finance is also evaluating the overall policy on registration fees and is expected to submit it to the Government in October. The Ministry of Finance also asked agencies to propose specific content on special consumption tax incentives and registration fees related to battery electric vehicles to summarize and report to the Government. Battery-powered electric vehicles currently account for a very small percentage in Vietnam. According to data from the Vietnam Automobile Manufacturers Association (VAMA), the number of electrified vehicles registered in Vietnam is still very modest, accumulating just over 1,000 units by the end of 2020. of which BEVs only account for about 1%, the rest are HEV and PHEV models. According to the Ministry of Finance, currently only VinFast invests in producing electric vehicles, according to reports, VinFast has now launched the first battery-powered electric car model. The factory has a capacity of 250,000 vehicles per year and the brand is focusing on battery electric vehicles with 9 seats or less and electric buses with 24 seats or more. Also according to the Ministry of Finance’s assessment, the biggest obstacle facing Vietnam today is the lack of charging station infrastructure, the main source of power for power supply is using fuel sources with high CO2 emissions, accounting for nearly half of the total mobilized electricity output, while the electricity generated from renewable energy accounts for a small proportion, about 4.3% and this is considered an unstable power source.

WHO Director-General Tedros Adhanom Ghebreyesus. Photo: Reuters As suggested by WHO, the COVID-19 mutation is called “British strain” or B.1.1.7 should be called “Alpha”, while the common mutant strain in South Africa is renamed “Beta” . The two Brazilian strains, called P.1 and P.2, became Gamma and Zeta, respectively, while the two subtypes of the “Indian variant”, B.1.617.1 and B.1.617.2, were called “Kappa” and “Delta”. Two other strains, first reported by the US in March of this year, were assigned the most difficult names to remember, “Epsilon” and “Iota”. Maria Van Kerkhove, head of WHO’s COVID-19 expert team, stressed that the new names will not replace the scientific name of the strain, because the scientific name is a complex combination of letters and numbers. Instead, the new name will make it easier for people to name variables in public discussions. At the same time, this will help remove the stigma against the country that first discovered the mutation. “No country should be discriminated against for detecting and reporting mutations,” and Van Kerkhove said. WHO List of VOCs of concern. List of Variants of Interest (VOI). In which, the first column is the new name proposed by WHO. The WHO’s new move comes in the context of India’s growing anger when the media calls the B.1.617 virus strain an “Indian variant”. India’s Health Ministry argued last month that the name freely used in the media was “misleading” because it was not a name designated by the WHO. Earlier, in February, Professor Salim Abdool Karim, then co-chair of South Africa’s Ministerial Advisory Committee on COVID-19, told CNN in an interview to stop calling the virus spreading. in his country is the “South African variant.” Instead, call it by its scientific name: 501Y.V2. The politically correct idea of ​​WHO is supported by many countries. But it also confuses many people. Some observers argue that Greek letters are actually not that easy to remember. While others point out that the WHO is in danger of “running out of names” at some point, since the Greek alphabet has only 24 letters.

The EU proposes to strictly restrict the use of facial recognition The draft law adds that feature face recognition “Real-time”, using live tracking instead of past shots or photos in public spaces by authorities should only be used for a limited amount of time and it should be prior consent from a judge or a national authority. The document comes as privacy advocates, politicians and European citizens increasingly speak up about the use of direct facial recognition. Currently, there is no clear regulation on how and where technology can be used by the public, so the proposed law would be the first law to codify these restrictions into law. The introduction of stricter restrictions on the use of facial recognition technology will likely rekindle debate over whether this activity should be banned entirely, as scene experts report that it’s still risky. In a landmark ruling last August, the UK Court of Appeals found the use of facial recognition to be illegal by the South Wales Police and that it violates privacy, data protection law and equality law. The draft EU law also addresses a range of related issues such as algorithmic interference, the argument that technology used in recruitment and financial operations should be played out. development so as not to repeat “historical forms of discrimination” against ethnic minority groups. EU regulators have proposed a hefty fine of up to 6% of a company’s global revenue if found to abuse artificial intelligence in this way or with no prejudice detected when hiring workers or Service Provider. They added that so-called social scoring, which measures a person’s trustworthiness from behavioral data gathered about them, should also be banned. In China, for example, a system is being developed to calculate a person’s credit score using information about their online habits. “The social score obtained … can lead to the adverse or unfavorable treatment of a person or group of people … irrespective of the context in which the data was originally generated”, said the leak. These proposals will now be discussed by the European Parliament and member states until at least 2023 before becoming a formal law. Phan Van Hoa (According to Arstechnica)

Smart things (public account: zhidxcom)

Compilation | Qu Wangmiao

Edit | Jiang Xinbai

Wise things April 22 news, according to foreign media reports, this Wednesday, the European Union’s executive body proposed a bill to restrict the use of some artificial intelligence technologies, and will create a so-called “artificial intelligence high-risk uses list” “Regarding related industries, companies that severely violate the regulations may be fined 6% of their total annual revenue. This is one of the biggest efforts to regulate high-risk applications of artificial intelligence so far.

However, for this proposal to become law, it will take several years to obtain the approval of the European Council and the Parliament, and in the process to make adjustments to its prescribed scope and specific regulatory measures.

In 2018, the new privacy law “General Data Protection Regulation (GDPR)” promulgated by the European Union provides a template for widely applicable data supervision rules and is supported by high fines. However, according to the predictions of relevant people, this time The bill does not necessarily have the same effect.

1. It is forbidden for police to use face recognition, but judges can be exempted widely

   The bill directly prohibits some behaviors. As a complement to the social credit system, the proposal will also prohibit the use of “subliminal technology” or artificial intelligence systems that insult the disabled, “distort the behavior of others,” and may cause physical or psychological harm.

   EU officials hope to use the bill to restrict the police from using facial recognition technology.

   Although the police usually cannot use the so-called “remote biometric system” in public places, such as face recognition, the judge can approve immunity, such as child kidnapping, terrorist threats, etc., can also be used to lock out fraud and murder Waiting for the suspect of the crime.

   “The scope of the exemption is incredible,” said Sarah Chander, a senior policy adviser for European Digital Rights, a non-governmental organization network. Such a list “a little bit contrary to the claim of facial recognition. Is the purpose of the ban.”

2. The high-risk artificial intelligence provider “hands in homework”, the bank takes the lead

Providers of artificial intelligence systems used for high-risk purposes need to submit relevant documents to the regulatory agency, in which they specify how their systems work. Margrethe Vestager, the executive vice-chairman of the European Commission (European Commission), the executive body of the European Union, believes that this type of system also needs to be in the design of the system, the way it is used, and the quality of the data used to train artificial intelligence. , Showing “appropriate human supervision.”

Large banks took the lead in introducing their artificial intelligence algorithms to regulators, and the application of these algorithms can help prevent the global credit crisis. According to Andre Franca, former director of Goldman Sachs’ model risk management team and current data science director of artificial intelligence startup causaLens, more companies will eventually adopt the same approach.

For example, Dr. Franka said that in the past 10 years, banks have had to hire teams to help provide regulators with the mathematical code behind their artificial intelligence models, sometimes with more than 100 pages of code per model.

Dr. Franca said that the EU can also send a supervisory team to the company to personally review whether its artificial intelligence algorithms fall into the high-risk categories specified in the proposal, such as systems that recognize faces or fingerprints, and algorithms that may affect personal safety. He also said that the European Central Bank’s regulators often review the codes submitted by banks through several days of seminars.

The EU also stated that the new bill will not establish new regulations for most artificial intelligence applications such as video games and spam filtering. But some low-risk artificial intelligence systems, such as chatbots, need to inform users that they are not real people.

“We have to clearly tell users that they are interacting with a machine,” Westger said.

Deep forgery, that is, software that performs face-changing in the video, also needs to indicate this. Ukraine-based NeoCortext has developed a popular face-changing app, Reface. The company said it is marking it up and will work hard to follow EU guidelines. “For fast-growing start-ups, there is now a challenge that is to regulate the rules.” Neocortext CEO Dima Shvets (Dima Shvets) said.

1. There are divergent opinions, and it will take time for the new bill to come into effect

   Although some digital rights activists appreciate some of the content of this proposal, they say that there seem to be too many loopholes in other content. Others in the industry believe that this proposal will bring advantages to Chinese companies that do not have these restrictions.

   “This will make building artificial intelligence in Europe very expensive and even technically infeasible,” said Benjamin Mueller, a senior policy analyst at the Center for Data Innovation of a technology think tank. The United States and China will watch with interest the European Union destroying their start-ups.”

   However, some lobbyists in the technology industry believe that the bill is not too strict. It merely imposes strict supervision on certain so-called “high-risk artificial intelligence applications”. These applications include software used in critical infrastructure and police use to predict crime. algorithm.

   Christian Borggreen, vice president and head of the Brussels office of the Computer & Communications Industry Association, believes that “it is a good thing for the European Commission to adopt this risk-based approach.” Computer & Communications Industry associations represent many large technology companies such as Amazon, Facebook, and Google.

   Julien Cornebise, Honorary Associate Professor of Computer Science at University College London and a former Google research scientist, said that the new regulations will not necessarily have the same impact as the GDPR, which was introduced by the European Union in 2018. The General Data Protection Regulation (General Data Protection Regulation), which restricts a large number of organizations that collect, transmit, retain, or process personal information related to all member states of the European Union.

   “Artificial intelligence is a mobile benchmark,” he said. “Our mobile phones are doing things that were considered’artificial intelligence’ 20 years ago. One risk of the new bill is that it regulates the’artificial intelligence’ The definition is changing, and it may become inapplicable or be eliminated soon.”

   Conclusion: High-risk applications of artificial intelligence urgently need supervision

   In recent years, the European Union has been trying to take the lead in drafting and enforcing new regulations designed to curb the so-called “excessive behavior” of large technology companies and curb the potential dangers of new technologies, covering areas ranging from digital competition to online content review And so on, very extensive. Previously, the EU’s GDPR provided a template for widely applicable regulations, so other countries and some states in the United States have adopted similar practices.

   As Westger said, “Our regulations are designed to address the human and social risks associated with specific uses of artificial intelligence. We think this is urgent. The European Union is the first to propose such a legal framework.” In the future, With the development and wider application of artificial intelligence technology, there may be more

Heart of the Machine Editorial Department Yuncong Technology Speech Group proposed a semantic error correction technical solution based on the BART pre-trained model. It can not only correct common spelling errors in ASR data, but also correct common sense errors, grammatical errors, and even some that require reasoning. Errors are corrected. In recent years, with the development of automatic speech recognition (ASR) technology, the recognition accuracy has been greatly improved. However, there are still some errors that are very obvious to humans in the ASR transcribing results. We don’t need to listen to the audio, we can find out only by observing the transcribed text. The correction of such errors often requires some common sense and grammatical knowledge, and even the ability of reasoning. Thanks to the recent development of unsupervised pre-training language model technology, error correction models based on plain text features can effectively solve such problems. The semantic error correction system proposed in this paper is divided into two modules: encoder and decoder. The encoder focuses on understanding the semantics of the output text of the ASR system, and the design of the decoder focuses on the use of standardized vocabulary to re-express. Link to the paper: https://arxiv.org/abs/2104.05507 introduction Text error correction is an important method to improve the accuracy of ASR recognition. Common text error correction includes grammatical error correction and spelling error correction. Because the error distribution of the ASR system transfer is quite different from the above error distribution, these models are often not suitable for direct use in the ASR system.Here, Yuncong Technology Speech Group proposed a pre-training model based on BART [1] The Semantic Error Correction (SC) technical solution can not only correct common spelling errors in ASR data, but also correct some common sense errors, grammatical errors, and even some errors that require reasoning. In our experiments with 10,000 hours of data, the error correction model can relatively reduce the error rate (CER) based on 3gram decoding results by 21.7%, achieving an effect similar to RNN rescoring. Using error correction on the basis of RNN re-scoring, a further 6.1% relative reduction in CER can be achieved. The error analysis results show that the actual error correction effect is better than the CER indicator shows. model 1) ASR semantic error correction system design The ASR semantic error correction process is shown in Figure 1. The semantic error correction module can be directly applied to the first pass of the decoding result as an alternative to the re-scoring module. In addition, it can also be connected to the re-scoring model to further improve the recognition accuracy. Figure1 ASR system with integrated semantic error correction model 2) Baseline ASR system The baseline acoustic model structure selected by the author is pyramidal FSMN[2], Training on 10,000 hours of Mandarin audio data. The WFST used in the first pass of decoding is composed of 3gram language model, pronunciation dictionary, dual phoneme structure and HMM structure. 4grams and RNNs are used in the rescoring, and the training data is the reference text corresponding to these audios.Acoustic model and language model use Kaldi tools [3] training. 3) Semantic error correction model structure The semantic error correction model proposed by the researcher is based on Transformer [4]Structure, it contains 6 layers of encoder layer and 6 layers of decoder layer, and the modeling unit is token. In the training process using the Teacher forcing method, the text output by the ASR is input to the input side of the model, and the corresponding reference text is input to the output side of the model. The input embedding matrix and the output embedding matrix are used for encoding, and the cross entropy is used as the loss function. In the semantic error correction model inference process, beam search is used for decoding, and the beam width is set to 3. Figure 2 Semantic error correction model based on Transformer experiment 1. Error correction training data preparation The training set of our baseline ASR model is 10,000 hours of Mandarin speech data, which contains about 800 transliterated texts. The side test set consists of 5 hours of mixed voice data, including Aishell, Thchs30 and other side test sets. In order to fully sample the error distribution identified by the ASR system, we adopted the following techniques when constructing the error correction model training data set: Use a weak acoustic model to generate error correction training data. Here, 10% of the speech data is used to train a small acoustic model separately to generate training data; Add disturbance to the MFCC feature, and randomly multiply the MFCC feature by a coefficient between 0.8 and 1.2; Input the noisy features into the weak acoustic model, take the first 20 results of beam search, and filter the samples according to the typos rate threshold. Finally, we pair the filtered decoding results with their corresponding reference texts as the error correction model training data. By decoding the full audio data and setting the threshold at 0.3, we obtained about 30 million error correction sample pairs. 2. Input and output presentation layer In the semantic error correction model, the input and output text use the same dictionary. But the typo in the input text contains more semantics than its standard usage, while the output text only uses the standard words to express. Therefore, the independent representation of the tokens on the input and output sides is more in line with the needs of error correction tasks. The results in Table 1 prove our inference. The experimental results show that when the input and output embedded matrix share the weight, the error correction model will bring negative effects. When the input and output tokens are represented independently, the CER of the system can be reduced by 5.01%. 3. BART vs BERT initialization Here, the researcher pre-trains the language model technology, and transfers the semantic knowledge learned from the large-scale corpus to the error correction scene, so that the error correction model obtains better robustness and universality on a relatively small training set.化性. We compare random initialization, BERT[5]Initialization and BART[1]Initialization method. During the initialization process, because the BART pre-training task and model structure are the same as Transformer, the parameters can be reused directly.In BERT initialization, both the encoder and decoder of Transformer are applicable to the first 6-layer network parameters of BERT[6]. The results in Table 2 show that BART initialization can reduce the typo rate of the baseline ASR by 21.7%, but the improvement of the BERT-initialized model relative to the random initialization model is very limited. We push this may be because the structure of the BERT and the semantic error correction model and the training target are too different, and the knowledge has not been effectively transferred. In addition, the error correction model corrects the output of the language model after re-scoring, and the recognition rate can be further improved. Compared with 4grams, RNN re-scoring results, CER can be relatively reduced by 21.1% and 6.1%, respectively. 4. Error correction model vs large language model Generally speaking, the ASR system uses a larger language model to obtain better recognition results, but it also consumes more memory resources and reduces decoding efficiency. Here, we add a large number of crawlers or open source plain text corpus on the basis of the speech data reference text, and newly train 3gram, 4gram and RNN language models, and call them the big language model. The one used in the baseline ASR system is called a small model. By comparison, it is found that the recognition accuracy of adding error correction on the basis of a small model surpasses the effect of using a large model alone. In addition, by using semantic error correction on the basis of a large model, the recognition rate can be further improved. Some examples of error correction are as follows: 5. Error analysis In error analysis of 300 examples of failed corrections, we found that the actual effect of semantic error correction is significantly better than the CER indicator evaluation. About 40% of errors hardly affect semantics, for example, some transliterated foreign names or place names There are many ways of expression, some personal pronouns lack context, which will cause the mixed use of “her, other, and other”, and some are substitutions of modal particles that do not affect semantics. In addition, 30% of errors are not suitable for correction based on pure text features due to insufficient contextual information. The remaining 30% of errors are caused by insufficient semantic understanding or expressive ability of the semantic error correction model. to sum up This paper proposes a BART-based semantic error correction model, which has good generalization and consistently improves the recognition results of multiple ASR systems. In addition, the researchers verified the importance of independent representation of input and output in the task of text error correction through experiments. In order to more fully sample the ASR system identification error distribution, this paper proposes a simple and effective error correction data generation strategy. Finally, although the semantic error correction method we proposed has achieved certain benefits, there is still room for optimization, such as: 1. The introduction of acoustic features helps the model identify whether there are errors in the text and reduces the false touch rate. 2. Introducing more contextual information can eliminate some semantic ambiguities or missing information in the text. 3. Adapt to vertical business scenarios to improve the recognition accuracy of some professional terms. references [1]M. Lewis et al., “BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension,” 2019, [Online]. Available: http://arxiv.org/abs/1910.13461. [2]X. Yang, J. Li, and X. Zhou, “A novel pyramidal-FSMN architecture with lattice-free MMI for speech recognition,” vol. 1, Oct. 2018, [Online]. Available: http://arxiv.org/abs/1810.11352. [3]D. Povey et al., “The Kaldi Speech Recognition Toolkit,” IEEE Signal Process. Soc., vol. 35, no. 4, p. 140, 2011. [5]J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” 2018, [Online]. Available: http://arxiv.org/abs/1810.04805. [6]O. Hrinchuk, M. Popova, and B. Ginsburg, “Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model,” ICASSP 2020-2020 IEEE Int. Conf. Acoust. Speech Signal Process., pp. 7074C7078, Oct . 2019, [Online]. Available: http://arxiv.org/abs/1910.10697. Build new, see wisdom – 2021 Amazon Cloud Technology AI Online conference April 22, 14:00-18:00 Why do so many machine learning loads choose Amazon Cloud Technology? How to achieve large-scale machine learning and enterprise digital transformation? “Building New · Seeing Wisdom-2021 Amazon Cloud Technology AI Online Conference” is led by Alex Smola, vice president of global artificial intelligence technology and outstanding scientist of Amazon Cloud Technology, and Gu Fan, general manager of Amazon Cloud Technology Greater China Product Department, and more than 40 heavyweights Guests will give you an in-depth analysis of the innovation culture of Amazon cloud technology in the keynote speech and 6 major conferences, and reveal how AI/ML can help companies accelerate innovation. Session 1: Amazon Machine Learning Practice Revealed Session 2: Artificial Intelligence Empowers Digital Transformation of Enterprises Session 3: The Way to Realize Large-scale Machine Learning Session 4: AI services help the Internet to innovate rapidly Session 5: Open Source and Frontier Trends Sub-venue 6: Intelligent ecology of win-win cooperation Which topic are you more interested in in the 6 major conference venues?

At the meeting, Mr. Nguyen Huu Trung – Deputy Director of Binh Thuan Department of Transport said: Over the past time, the province has stepped up the implementation of site clearance work as well as issues related to the project, especially especially the work of solving the demand for construction materials of the expressway project. Up to now, the number of households and organizations has handed over 2,657 / 2,684 dossiers, reaching the rate of 99%; The compensated clean land area was 1,204.89 ha, reaching the rate of 98.6%. 27 households have not received compensation money and handed over the premises. In addition, at the locations where the ground has been handed over, there are 25 cases (received money and signed the handover) but have not moved or handed over the site to construction contractors. Currently, the province is actively directing the People’s Committees of relevant localities to completely settle the compensation and site clearance work in May 2021.

The Economic Committee of the National Assembly’s supervisory delegation works with Binh Thuan province.

According to the report of Project Management Unit (PMU) 7 and PMU Thang Long, the total demand for construction materials for the project of the Vinh Hao – Phan Thiet section and the Phan Thiet – Dau Giay section is nearly 16 million blocks.

Deputy Director of Binh Thuan Department of Transport added: For the demand for construction sand and stone, the construction material mines in the province can meet the needs of the project. As for embankment materials, according to the approved technical design documents of the approved Vinh Hao – Phan Thiet and Phan Thiet – Dau Giay sections, the reserves of the mines in the province meet the quality and demand of the project. project. However, through the field survey at the mines, the volume of soil and soil cover mentioned above is preliminarily classified into 3 categories by PMU 7, PMU Thang Long, including: Soil, rock mixed with weathered rock and rock.

According to the assessment of the two boards mentioned above, the soil mixed with rock and weathered rock does not meet the requirements for making backfill materials according to the project’s technical requirements. Based on the proposals of the Thang Long Project Management Board and PMU 7, the Provincial Working Group is now speeding up the determination of the mines being exploited that need to increase capacity, expand the scale, or push To speed up the mining licensing procedures to guide the Miners in order procedures, shorten the time but ensure compliance with the provisions of law. At the same time, invite the Miners to work to guide the procedures for capacity increase, scaling up and mining licensing. Expected to complete the mining licensing procedures in the second quarter of 2021.

Mr. Vu Hong Thanh – Member of the Party Central Committee – Chairman of the Economic Committee of the National Assembly spoke at the meeting.

At the meeting, Chairman of Binh Thuan Provincial People’s Committee – Le Tuan Phong said: The province has drastically participated in solving the needs of building materials for the expressway project, step by step solving problems, Gradually came to agreement on issues related to the provision of soil filling materials for the project. In particular, the province has instructed the Department of Natural Resources and Environment and related departments and sectors to give priority to processing investment, environmental, land, and mining permits within 3 working days upon receipt. are business records.

To ensure the demand for building materials for the project, the Chairman of the Provincial People’s Committee suggested that the Project Management Units: Thang Long, 7, 85 coordinate closely with relevant departments and branches of the province and mine owners. to survey, evaluate and determine the reserves and quality of each field of embankment materials to unify data, propose ground-filling requirements for each specific construction and installation package, in which clearly identify which mines Exploiting operations need to increase capacity, expand the scale, which mines need to speed up the mining licensing procedures.

The Chairman of the Provincial People’s Committee also requested the Ministry of Natural Resources and Environment to guide the province to shorten the time for implementing mining licensing procedures. At the same time, it is recommended that the Ministry of Transport soon consider and approve the plan to use 1.1 million cubic meters of salvaged rock in package No. 01, Vinh Hao – Phan Thiet section to crush into expressway embankment as proposed. proposal of PMU 7; Consider and approve for the use of rock and wind mixed in mines.

The Chairman of the Economic Committee of the National Assembly – Vu Hong Thanh highly appreciated the efforts of Binh Thuan province in implementing and implementing the North-South Expressway project in the East, the section passing through the province, especially the coordination. solving the need for building materials to implement the project. Mr. Vu Hong Thanh also suggested that the province continue to direct the completion of site clearance in May 2021 according to the province’s progress; Early completion of related procedures to ensure a dumping site for project construction. Regarding the handling of materials for construction, the Chairman of the Economic Committee of the National Assembly requested the Ministry of Transport to review the material needs of the entire route, directing the Project Management Boards to: Thang Long, 7, 85 urgently coordinate with the province to remove problems.