Solar-driven CO2-to-fuels production through photocatalysis is a promising approach to address the worsening energy crisis and environmental concerns associated with CO2 emissions. A significant ...effort has been devoted to developing robust photocatalytic materials to convert CO2 into fuels. However, the performances of obtained materials are still far from the desirable targets rooted in the restriction of the employed catalysts such as inefficient light-harvesting, poor charge separation, and limited active sites. This review aims to provide a roadmap to support the development of superior photocatalysts for CO2-to-fuels conversion by presenting the fundamentals of photocatalytic CO2 reduction and the critical challenges restricting catalytic efficiency, followed by a discussion of leading research and developments in this field. We will explore state-of-the-art CO2-to-fuels production processes and the mechanisms involved before presenting the latest materials that have been developed to convert CO2 into chemical fuels via photocatalysis. Catalysts that possess features that improve light absorption, charge separation, and CO2 reduction, will be highlighted. Finally, a summary of, and perspective on, the obstacles and opportunities associated with this technology will be provided. We expect that this review will assist the production of superior catalysts for solar-driven CO2-to-fuels conversion.
•Solar-driven CO2-to-fuels is a promising approach to address the worsening energy crisis and environmental concerns.•This review provides a roadmap to support the development of superior photocatalysts for CO2-to-fuels conversion.•A summary of the obstacles and opportunities associated with this technology are provided.
Hybrid composite TiO
2
/reduced graphene oxide (TiO
2
/rGO) is considered as a potential photoanode for dye-sensitized solar cells (DSSC) due to their excellent charge transport. This work aimed to ...prepare the composite TiO
2
/rGO from TiO
2
and GO via a facile reduction step by ascorbic acid and annealing step at 450 °C. Incorporation 1%wt rGO into TiO
2
photoanode can boost the overall performance of DSSC with the photocurrent of 13.5 mA/cm
2
and the efficiency of 6.1% which is 37% enhanced than the bare TiO
2
photoanode. The XPS, Raman spectroscopy and FT–IR results revealed the formation of the chemical bonding titania–carbon in composites TiO
2
/rGO-1% by ex-situ preparation. The interfacial charge transfer through chemical bonds significantly improves the photoelectrons transport and suppresses the recombination of electron holes in TiO
2
structure.
The electrocatalytic nitrate-to-ammonia (NO3 ̄ to NH3) production has been raised as a potential alternative pathway to produce green ammonia in the context of the increasing demand of ammonia and ...decontaminate the nitrate pollutions. However, the poor electrocatalytic NH3 production performance, originated from sluggish kinetics and competitive reactions, has been considered as the primary obstacles for the beyond lab-scale application. To this point, the development of robust catalysts has become the heart of whole process, addressing the mentioned issues. Indeed, numerous attempts have been devoted to develop efficient electrocatalytic materials for this purpose. Nevertheless, such investigations, deployed to various classes of materials with engineered active sites, have been sporadic. Also, the recent explorations have witnessed novel materials, which could be the game-changers in the near future. Accordingly, this review attempts to provide a comprehensive perspective and sufficient roadmap to the development of outstanding electrocatalysts for NO3̶ to NH3 reduction. The resulted outcomes are expected to pave the way for the exploration of superior electrocatalysts, which could bridge the research gap in taking this process from lab to market.
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•This review focuses on electrocatalytic NO3- to NH3 conversion for sustainable NH3 production and nitrate pollution control.•Highlight strategies to solve kinetic challenges and competitive reactions, vital for scaling up electrocatalytic processes.•Explore varied materials with tailored active sites to advance efficient electrocatalysts, vital for boosting NH3 production.•Provide roadmap to bridge research and commerce, boosting electrocatalytic NO3- to NH3 from lab to real-world applications.
The typical intrusion detection system (IDS) based on machine learning classifies normal and attack network traffic by extracting and analyzing network features. However, several extracted features ...are irrelevant and may degrade the classification accuracy. In addition, they also increase the training time and model size. Therefore, feature selection is an essential process in building an IDS system. In this paper, we propose a feature selection method for IDS by employing a Deep Neural Network model to search for and select the most crucial features. The proposal is evaluated with two datasets: UNSW-NB15 and CIC-IDS2017, and archives superior results compared with other feature selection algorithms with accuracy up to 99.96% for UNSW-NB15, 99.88% for CIC-IDS2017 while combining with LSTM-based IDS. It also reduces significant data size and time for training.
Traditional wet markets are the main source of fresh food and the largest source of daily nutrient intake for citizens of Hanoi. Nevertheless, due to the lack of traceability and sales registration ...systems, food flows within these markets remain largely invisible. This makes it challenging to assess the impact of shocks, such as pandemics, on these markets. In this paper, we characterize the impact of COVID-19 by analyzing data from 25 Wi-Fi access points installed in five formally established wet markets. The study timeframe covers a pre-pandemic period from July 2019 to the end of the initial stage of the pandemic in November 2020. While providing free Internet access, data were continuously collected about devices in close vicinity to the access points. Based on this information, we tested five hypotheses about the number, frequency, time, and duration of visits to the markets as well as changes in inter-market activities. The results show that during the shock (February to mid-April 2020) and aftershock (mid-April to July 2020) periods, market actors significantly decreased the total number of market visits (-26% P < 0.001) and the frequency of market visits (up to -47% for very frequent market users, P < 0.001). The number of inter-market visits dropped sharply during the shock period (66%
±
17% of the baseline level, P < 0.001), and the peak time for market shopping shifted significantly by 90 min later in the day, P < 0.001. No change was observed in visit duration. Several factors identified in existing literature as affecting consumer behaviors provide possible explanations for the changes observed. We present a set of recommendations to limit the negative impact of the pandemic in terms of food security and livelihoods in Hanoi and to mitigate consumers’ negative perception of wet markets in terms of food safety.
Bài báo trình bày một giải pháp thiết kế máy cắt bột tự động cho các làng nghề làm bột gạo nguyên liệu ở Việt Nam. Gạo sau khi ngâm và xay ra thành nước bột sẽ được tách nước, nén và cắt với kích ...thước định trước. Để phơi mau khô và tạo tính đồng đều cho bột nguyên liệu, cơ cấu cắt bột tự động được đề xuất. Từ kinh nghiệm của các hộ sản xuất bột tại các làng nghề và thử nghiệm thực tế, máy nén bột với cơ cấu vít-me được dùng trong nghiên cứu này. Kết quả thử nghiệm thực tế tại làng nghề huyện Mỹ Tú cho thấy, cơ cấu nén bột dùng vít-me cho bột đầu ra đồng đều và tự nhiên hơn. Năng suất trung bình của máy đạt xấp xỉ 400 kg/ngày. Ngoài ra để tạo tính linh hoạt cho máy cắt bột tự động này, một cơ cấu tạo các viên trân châu được tích hợp như một lựa chọn. Nếu muốn tạo viên trân châu, khung dao tạo khối bột hình trụ được sử dụng. Khối bột hình trụ này được cắt và tạo viên nhờ vào cơ cấu vo viên tự động sử dụng hai ru-lô quay cùng chiều nhưng khác tốc độ. Với kết quả đạt được, máy cắt bột tự động nên được đưa vào sử dụng tại các làng nghề làm bột ở Đồng bằng sông Cửu Long nói riêng và ở Việt Nam nói chung.
Libraries usually subscribe to digital data sources. Their members (students, teachers, researchers, ...) gain access to resources stored in these external services via proxy servers managed by the ...local libraries. Library managers have the need to know the usage of subscribed services by members so that they can make decisions, for example, whether to renew a subscription or not. Proxy servers access logs store valuable information for them, however, it's difficult to get the answers directly from log files because the raw data is unstructured and massive. In this paper, we describe a two-step technique to support library managers in viewing and understanding usage information more effectively using the data provided by proxy servers' log files. The first step consists of preprocessing access logs from proxy servers and constructing multiple time series from the data. A modified version of horizon graphs was developed in the second step to visualize many time series on one screen. The screen space is divided unequally to different time series based on their relative importance with each other. In addition, an interaction method and a dynamic legend are also introduced to give a better understanding of the graphic representation. The proposed method is implemented as an application running in a web browser. Experiment results with real world data gathered in one year show that the proposed technique can improve users' performance on data exploration and analysis tasks notably.
Text-based person search that aims to associate pedestrian images with free-form natural language descriptions has recently emerged thanks to its wide range of applications, such as searching for ...missing people and tracking criminals. While the majority of existing methods focus on English and obtain promising results, text-based person in other languages, including Vietnamese, is still in its infancy due to the different characteristics of the languages. In this paper, we propose a method for person search through queries in Vietnamese. In this method, to tackle the specific characteristics of Vietnamese, a Vietnamese language parser and textual feature extractor have been integrated into a model based on the correlation filtering named SRCF that has been proposed for text-based person search in English 1 which named Correlation Filter For Vietnamese Language (CFFVL). Extensive experiments have been conducted on VNPersonSearch3000, a large-scale dataset for person search in Vietnam, to evaluate the effectiveness of different Vietnamese language parsers and textual feature extractors. The experimental results show that the proposed method outperforms the state-of-the-art method by 18.42% in top-1 and 23.10% in top-5 on the VNPersonSearch3000 dataset.
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