The use of Autonomous Surface Vehicles, equipped with water quality sensors and artificial vision systems, allows for a smart and adaptive deployment in water resources environmental monitoring. This paper presents a real implementation of a vehicle prototype that to address the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring. The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth. Furthermore, by means of a stereo-camera, it also can detect and locate macro-plastics in real environments by means of deep visual models, such as YOLOv5. In this paper, experimental results, carried out in Lago Mayor (Sevilla), has been presented as proof of the capabilities of the proposed architecture. The overall system, and the early results obtained, are expected to provide a solid example of a real platform useful for the water resource monitoring task, and to serve as a real case scenario for deploying Artificial Intelligence algorithms, such as path planning, artificial vision, etc.

Defined as the capability of a machine to mimic intelligent human behavior, AI encompasses a broad range of technologies, including machine learning, natural language processing, computer vision, and robotics. Its applications are far-reaching, impacting diverse fields such as healthcare, finance, manufacturing, retail, education, and more. The proliferation of data, advancements in computational power, and the development of sophisticated algorithms have accelerated the adoption of AI across industries. Businesses are leveraging AI not only to automate repetitive tasks but also to gain insights from data, improve customer experiences, and innovate in product and service offerings. In this paper, we provide an in-depth analysis of AI applications in four key industries: healthcare, finance, manufacturing, and retail. For each sector, we will examine the types of AI technologies being used, the problems they aim to solve, the benefits they bring, and the challenges associated with their deployment. We will also explore future trends and the broader implications of AI adoption.

In the era of Internet of Things, how to develop a smart sensor system with sustainable power supply, easy deployment and flexible use has become a difficult problem to be solved. The traditional power supply has problems such as frequent replacement or charging when in use, which limits the development of wearable devices. The contact-to-separate friction nanogenerator (TENG) was prepared by using polychotomy thy lene (PTFE) and aluminum (AI) foils. Human motion energy was collected by human body arrangement, and human motion posture was monitored according to the changes of output electrical signals. In 2012, Academician Wang Zhong lin and his team invented the triboelectric nanogenerator (TENG), which uses Maxwell displacement current as a driving force to directly convert mechanical stimuli into electrical signals, so it can be used as a self-driven sensor. Teng-based sensors have the advantages of simple structure and high instantaneous power density, which provides an important means for building intelligent sensor systems. At the same time, machine learning, as a technology with low cost, short development cycle, strong data processing ability and prediction ability, has a significant effect on the processing of a large number of electrical signals generated by TENG, and the combination with TENG sensors will promote the rapid development of intelligent sensor networks in the future. Therefore, this paper is based on the intelligent sound monitoring and recognition system of TENG, which has good sound recognition capability, and aims to evaluate the feasibility of the sound perception module architecture in ubiquitous sensor networks.

International Journal of Educational Technology in Higher Education is calling for submissions to our Collection on New advances in artificial intelligence applications in higher education. There has been growing interest in the educational potential of Artificial Intelligence (AI) applications within the field of educational technology for the past decade. Despite the recent peak of excitement towards advanced features and techniques of AI-driven language models and OpenAI's ChatGPT, their actual impact on higher education (HE) institutions and participants have been largely unknown. Thus, the discussions in the field have continuously remained, mainly consisting of overstated hype and untested hypotheses, either optimistic or pessimistic, about the impact of AI applications. About three years ago, the editors of the ETHE Special Issue "Can artificial intelligence transform higher education?" However, a lot has happened since then.

High Performance, Nonvolatile, Unlimited Endurance… Memory Element: MTJ (Magnetic Tunnel Junction) Information stored by magnetic polarization (nonvolatile) instead of charge MTJ bit state "1" (high resistance) and "0" (low resistance) is written by Spin Transfer Torque with a (polarized) current across MTJ Extremely Fast (as LL Cache/DRAM) Nonvolatile (Persistent) Unlimited endurance ( 1014) High Density (1T per cell) Scalable to 0x nm STT-MRAM cell: 1T MTJ 4 5. Avalanche Technology at Semicon Taiwan 2020 Stand Alone Applications STT-MRAM Broad Applications STT- MRAM Embedded Applications Unified eNVM (Flash like) eFlash, eOTP, eFuse LL Cache Memory (SRAM like) L3, eDRAM Slow SRAM (New Market Applications) (AI, IoT…) One single chip for both embedded storage and working memory nvSRAM market Memory buffers Persistent DRAM DRAM* New Market Applications* Storage Class Memory *with 3D stack MRAM High speed Unlimited endurance Low power consumption Low manufacturing cost Extended Temperature (150 oC) Y. Huai, Flash Summit 2015, Santa Clara, California, August 12, 2015.

From powering airplanes to generating electricity to cooking and much more, the world depends on a great deal of its energy in the form of "Fuel". The price of fuel fluctuates with revisions in crude oil prices or other global events and is also reflective of the political and economic state of a country. Predicting fuel prices remains a major bottleneck. So the question is: can artificial intelligence predict the fuel price? The answer is a big yes.

The increasing prevalence of web scraping has increased its usage areas considerably. There is a regular and uninterrupted flow of data from target websites to the data sets of artificial intelligence applications. Image processing is one of the most popular areas in artificial intelligence applications. Image processing is a field of computer science that focuses on enabling computers to identify and understand objects and people in images and videos. Like other types of artificial intelligence, image processing aims to perform and automate tasks that replicate human capabilities.

Author summary In this study we describe the use of artificial intelligence (AI) in the clinical response to COVID-19. AI has been variously predicted to play a key role during the pandemic or has been reported to have had little or no impact on patient care. Our findings support a balanced view. We identified 66 applications—specific AI products or tools—used in a variety of ways to diagnose, guide treatment, or prioritize patients during the pandemic response. Many were deployed early in 2020 and most were used in the U.S., other high-income countries, or China. Some were used to care for hundreds of thousands of patients though most were adopted at smaller scales. We found evaluation studies that supported the use of 39 of these applications, though few of these evaluations were written by independent authors, not affiliated with application developers. We found no clinical trials that evaluated the effect of using an AI application on patient health outcomes. Future research is needed to better understand the impact of using AI in clinical care.

The use of Artificial Intelligence is expanding in a variety of fields. In this article, you will learn about the Top 7 Artificial Intelligence Applications. The use of artificial intelligence (AI) is expanding in a variety of fields. AI has come a long way in the last few years and may be used in almost any field. AI is defined by its capacity for systemic or programmed learning and thought.

Artificial Intelligence technology is used to create recommendation engines through which you can engage better with your customers. These recommendations are made in accordance with their browsing history, preference, and interests. It helps in improving your relationship with your customers and their loyalty towards your brand. Virtual shopping assistants and chatbots help improve the user experience while shopping online. Natural Language Processing is used to make the conversation sound as human and personal as possible.