This study aims to analyze the reasons that motivate Brazilian companies to implement Quality Management Systems, according to ISO 9001:2015 requirements. Based on information collected in the literature, a questionna...
详细信息
Photocatalytic degradation is a promising way to treat emerging pollutants in wastewater. Recently, metal-free photocatalysts such as carbon nitride- and graphene-based materials have attracted much interest in the ph...
Photocatalytic degradation is a promising way to treat emerging pollutants in wastewater. Recently, metal-free photocatalysts such as carbon nitride- and graphene-based materials have attracted much interest in the photocatalytic degradation of emerging water pollutants owing to their visible light activity and unique electrical properties, respectively. Graphitic carbon nitride (GCN) is considered a superior visible light–active photocatalyst because of its suitable bandgap (2.7 eV). Moreover, the facile synthesis process and the high chemical and thermal stability of GCN make it one of the research hotspots in photocatalytic wastewater treatment. Besides GCN, graphene and its derivatives are utilized to support main photocatalysts by enhancing their light absorption, pollutant adsorption, and photogenerated charge separation. Furthermore, the vast modification of these materials has promoted various outstanding performances in carbon nitride- and graphene-based photocatalysts in the application of pollutant degradation. In this review, we highlight recent developments in carbon nitride- and graphene-based photocatalysts (2018–2023), focusing on the strategies to improve the activity of GCN as a visible light–active photocatalyst and the role of graphene and its derivatives as supporting materials in wastewater pollutant remediation applications.
Context: Problem-Based Learning (PBL) and Experiential Learning Theory (ELT) are convergent active learning approaches widely known for their competent integration between theory and practice. Problem/Objective: Howev...
Context: Problem-Based Learning (PBL) and Experiential Learning Theory (ELT) are convergent active learning approaches widely known for their competent integration between theory and practice. Problem/Objective: However, the usual implementation of PBL leaves out the final active experimentation stage of the experiential learning cycle. In this article, we intend to systematically investigate the impacts of this last stage on the learning outcomes of software engineering students. Methods: A quasi-experiment was designed and applied in three software engineering courses of an undergraduate course, in Rio Branco-Acre / Brazil. Results: students who participated in two of the three treatment groups scored significantly higher on measures of motivation, experience and learning, which means that the PBL method contains gaps that can be significantly improved with the help of ELT, benefiting the learning outcomes of software engineering students.
The idea of a “smart city” is one in which cities may effectively and efficiently manage their resources to raise the standard of living of its residents. Cities are already capable of performing sensing, understand...
The idea of a “smart city” is one in which cities may effectively and efficiently manage their resources to raise the standard of living of its residents. Cities are already capable of performing sensing, understanding, and acting at a certain degree because to the existence of information technology as an enabler. Several studies have created various physical sensors for sensing in order to produce data. However, urban data is not limited to factual information on the environment and infrastructure. In order to collect data from the digital world, including social media, online news, and other digital social sensors, this research aims to create an urban data collecting system. The creation of digital urban sensor data can enhance the accuracy of the information that can help decision-makers.
Education to the public on green products has been widely carried out. Consumers who buy green products can be profiled by segmenting green consumers. In green marketing, consumer education on sustainable consumption ...
详细信息
The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those network...
详细信息
The Internet of Things (IoT) and Artificial Intelligence (AI) have reshaped educational technology, increasing the chances of developing more effective and engaging learning environments. This study proposes an archit...
The Internet of Things (IoT) and Artificial Intelligence (AI) have reshaped educational technology, increasing the chances of developing more effective and engaging learning environments. This study proposes an architecture for asynchronous data communication with real-time face recognition, a virtual and remote labs confluence. The architecture uses IoT hardware and software to comprise critical components such as IoT cameras, message brokers, FTP servers, and AI processing servers. The aim is to optimize resource usage and maximize response time efficiency. The architecture employs message queuing to manage high volumes of face image data, facilitating immediate verification and rapid access. Our findings demonstrate that the system can achieve face recognition with 91.7% accuracy and an average system communication latency of 2.631 seconds, effectively leveraging asynchronous communication to optimize resource usage and computational efficiency. The research comprehensively measures the data communication metrics and face recognition metrics. It also explains how asynchronous data communication can be strategically deployed in a hybrid lab environment to enhance educational and operational efficacy.
This study investigates trends in deep learning research on microscopic images to identify and classify plankton. We performed a bibliometric analysis using VOSviewer to display the visualizations. The first step is t...
详细信息
In the field of computer science, expert systems have attracted many computer scientists. In recent years, expert systems have been widely used in the medical field, particularly for diagnosing and treating various di...
In the field of computer science, expert systems have attracted many computer scientists. In recent years, expert systems have been widely used in the medical field, particularly for diagnosing and treating various diseases, especially prostate cancer. Reality has shown that expert systems hold great promise for accurate diagnosis and treatment of prostate cancer. This research summarizes several studies related to the application of artificial intelligence in the field of prostate cancer through bibliometric analysis and explores potential future research. Articles and reviews on the application of expert systems in prostate cancer from 2015 to 2022 were selected from the PubMed, Embase, and Web of Science databases. Microsoft Excel 2019 and GraphPad Prism 8 were used for target variable analysis. A total of 37 articles were selected for this research. Research on artificial intelligence in prostate cancer has grown exponentially in recent years, particularly in Indonesia. Image data usage is more predominant (62%) than numerical data types in prostate cancer research. Artificial intelligence research in prostate cancer, especially in the field of treatment cases, and the use of Deep Neural Network (DNN), Human Neural Network (HNN), Fully Convolutional Network (FCN), QT Environment for C++, Artificial Neural Network (ANN) algorithms, are highly feasible for further research.
Flood in Jakarta is occurred almost every year due to inadequate flood control systems to the flood prediction, inadequate drainage system, and trash that clog the drainage. The increase of rainfall is also one of the...
详细信息
暂无评论