Computer Vision Technology in the Development of an Ultrasonic Repeller

OMICS: A Journal of Integrative BiologyVol. 24, No. 5 EditorialsCOVID-19 (Coronavirus Disease 2019): Opportunities and Challenges for Digital Health and the Internet of Medical Things in ChinaBiaoyang Lin and ShengJun WuBiaoyang LinAddress correspondence to: Biaoyang Lin, PhD, Department of Urology, University of Washington School of Medicine, Seattle, WA 98195, USA E-mail Address: biaoylin@gmail.comZhejiang-California International Nanosystems Institute (ZCNI) Proprium Research Center, Zhejiang University, Hangzhou, China.Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.Department of Urology, University of Washington School of Medicine, Seattle, Washington, USA.Search for more papers by this author and ShengJun WuDepartment of Clinical Laboratories, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.Search for more papers by this authorPublished Online:7 May 2020https://doi.org/10.1089/omi.2020.0047AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"COVID-19 (Coronavirus Disease 2019): Opportunities and Challenges for Digital Health and the Internet of Medical Things in China." OMICS: A Journal of Integrative Biology, 24(5), pp. 231–232FiguresReferencesRelatedDetailsCited byEmerging technologies for COVID (ET-CoV) detection and diagnosis: Recent advancements, applications, challenges, and future perspectivesBiomedical Signal Processing and Control, Vol. 83Integration of cloud computing with the Internet of things for the treatment and management of the COVID-19 pandemic15 November 2022 | Information Systems and e-Business Management, Vol. 26A novel e-healthcare diagnosing system for COVID-19 via whale optimization algorithm24 September 2022 | Journal of Experimental & Theoretical Artificial Intelligence, Vol. 6Call for Papers: COVID-19 Systems Biology, Multi-Omics Integration, and Digital Technologies in Ecology, Health, and Society Vural Özdemir7 September 2022 | OMICS: A Journal of Integrative Biology, Vol. 26, No. 9From Personalized Medicine to Population Health: A Survey of mHealth Sensing TechniquesIEEE Internet of Things Journal, Vol. 9, No. 17The role of contemporary digital tools and technologies in COVID‐19 crisis: An exploratory analysis6 October 2021 | Expert Systems, Vol. 39, No. 6Coronavirus Disease (COVID-19): Challenges and Opportunities10 September 2020 | Disaster Medicine and Public Health Preparedness, Vol. 16, No. 2Anthropomorphic Robotic Eyes: Structural Design and Non-Verbal Communication Effectiveness15 April 2022 | Sensors, Vol. 22, No. 8Digital Transformation in Personalized Medicine with Artificial Intelligence and the Internet of Medical Things Biaoyang Lin and Shengjun Wu7 February 2022 | OMICS: A Journal of Integrative Biology, Vol. 26, No. 2A Review About the Main Technologies to Fight COVID-1915 April 2022Identifying and addressing digital health risks associated with emergency pandemic response: Problem identification, scoping review, and directions toward evidence-based evaluationInternational Journal of Medical Informatics, Vol. 157Unlocking the Public Perception of COVID-19 Vaccination Process on Social MediaToward Combatting COVID-19: A Risk Assessment SystemIEEE Internet of Things Journal, Vol. 8, No. 21An online COVID-19 self-assessment framework supported by IoMT technology6 August 2021 | Journal of Intelligent Systems, Vol. 30, No. 1A Security Management Framework for Big Data in Smart HealthcareBig Data Research, Vol. 25Connected Medical Kiosks to Counter COVID-19: Needs, Architecture & Design GuidelinesThe perspectives of biomarker-based electrochemical immunosensors, artificial intelligence and the Internet of Medical Things toward COVID-19 diagnosis and managementMaterials Today Chemistry, Vol. 20Internet of Things (IoT) Enabled Architecture for Social Distancing During Pandemic15 April 2021 | Frontiers in Communications and Networks, Vol. 2A Comprehensive Review of Applications of Internet of Things for Covid-19 PandemicIOP Conference Series: Materials Science and Engineering, Vol. 1055, No. 1Robotics and Drone-Based Solution for the Impact of COVID-19 Worldwide Using AI and IoT16 February 2021Digital Transformation and Emerging Technologies for COVID-19 Pandemic: Social, Global, and Industry Perspectives20 February 2021Telemedicine maybe an effective solution for management of chronic disease during the COVID-19 epidemic29 September 2021 | Primary Health Care Research & Development, Vol. 22Uma análise sobre o desenvolvimento de tecnologias digitais em saúde para o enfrentamento da COVID-19 no Brasil e no mundo1 January 2021 | Cadernos de Saúde Pública, Vol. 37, No. 3Enabled IoT Applications for Covid-191 May 2021Swarm Intelligence based Hierarchical Routing Protocols Study in WSNsCOVID-19 Digital Health Innovation Policy: A Portal to Alternative Futures in the Making Mustafa Bayram, Simon Springer, Colin K. Garvey, and Vural Özdemir3 August 2020 | OMICS: A Journal of Integrative Biology, Vol. 24, No. 8Digital Technologies-Enabled Smart Manufacturing and Industry 4.0 in the Post-COVID-19 Era: Lessons Learnt from a Pandemic3 July 2020 | International Journal of Environmental Research and Public Health, Vol. 17, No. 13Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature6 November 2020 | Journal of Medical Internet Research, Vol. 22, No. 11 Volume 24Issue 5May 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:Biaoyang Lin and ShengJun Wu.COVID-19 (Coronavirus Disease 2019): Opportunities and Challenges for Digital Health and the Internet of Medical Things in China.OMICS: A Journal of Integrative Biology.May 2020.231-232.http://doi.org/10.1089/omi.2020.0047Published in Volume: 24 Issue 5: May 7, 2020Online Ahead of Print:April 20, 2020PDF download

Digitalization penetrates all areas of economic activity, including the banking industry. Therefore, the purpose of the article is to analyze the transformation of banking services in the digital era based on an integrated approach. In the course of conducting scientific research, the authors used a set of scientific methods of cognition: the method of system analysis; the bibliographical method; the method of extrapolation and forecasting, which made it possible to identify patterns and trends in the development of digital transformation in banking. The digital transformation of banking leads to the introduction of innovative technologies to protect personal data, transaction data, the array of collected data, convenience and ease of use of mobile banking applications, speed of payments, etc. Such innovative technologies in the banking and financial sector are: mobile banking, AI, Blockchain, Machine Learning, Internet of Things, Big Data and open banking, etc. Each of the above areas of technological change contains the following characteristics: mobile banking – for digital interaction of banks with their clients; financial consultants based on artificial intelligence – to help in using the main functions of banking services, making informed management decisions; Blockchain – to reduce banking costs, the number of employees and increase the security of transactions; Machine Learning – to obtain a positive customer experience; Internet of Things – to integrate disparate types of equipment, devices for data exchange; Big Data – for collecting, processing, analyzing large data sets and making forecasts based on them and Open Banking – for the free exchange of financial data between different financial providers. The digital technology market is dominated by companies from the USA (Oracle, Microsoft, IBM, Amazon, etc.), as well as the UK and the EU. Forecasts of well-known consulting companies indicate the growth of digital technologies and the gradual transition to a virtual environment, where there is no need for the physical presence of the client in a bank. Keywords: digital transformation, banking structures, banking services, digital technologies, artificial intelligence, mobile banking, Blockchain technologies, Internet of Things, machine learning, big data, open banking.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

The Internet of Things (IoT) continues to attract attention in the context of computational resource growth. Various disciplines and fields have begun to employ IoT integration technologies in order to enable smart applications. The main difficulty in supporting industrial development in this scenario involves potential risk or malicious activities occurring in the network. However, there are tensions that are difficult to overcome at this stage in the development of IoT technology. In this situation, the future of security architecture development will involve enabling automatic and smart protection systems. Due to the vulnerability of current IoT devices, it is insufficient to ensure system security by implementing only traditional security tools such as encryption and access control. Deep learning and blockchain technology has now become crucial, as it provides distinct and secure approaches to IoT network security. The aim of this survey paper is to elaborate on the application of deep learning and blockchain technology in the IoT to ensure secure utility. We first provide an introduction to the IoT, deep learning, and blockchain technology, as well as a discussion of their respective security features. We then outline the main obstacles and problems of trusted IoT and how blockchain and deep learning may be able to help. Next, we present the future challenges in integrating deep learning and blockchain technology into the IoT. Finally, as a demonstration of the value of blockchain in establishing trust, we provide a comparison between conventional trust management methods and those based on blockchain.

With the advent of the digital era in the middle of the 20th century and the German government's introduction of Industry 4.0 in 2011, conventional industries shifted into an economy built on massive Internet and Information and Communication Technologies (ICT). The ICTs such as big data, cloud computing, the Internet of Things (IoT), mobile devices, artificial intelligence, 5G network, etc., are not only being used in a variety of businesses but also by an increasing number of people globally. There is no doubt that the use of digital technology in a variety of sectors has effectively increased productivity and decreased costs. Digital agriculture is one of the logical conclusions of Industry 4.0 growth. However, implementing ICT in agriculture may be severely hampered by the risks associated with digital agriculture. One of the main obstacles is unclear Digital Agricultural Education (DAE), a market gap formed by the explosive rise of digital agriculture but with little research. In this exploratory study, we used content analysis as a research methodology, aimed to comprehend Hungary's efforts in digital agriculture education and investigate the challenges by presenting practical examples.

The paper considers external and internal growth factors of the agricultural industry within the framework of the Russian Federation. The evolution of digitalization and its stages are considered in detail. Digital technologies in crop production, agricultural machinery, and animal husbandry are also considered. Examples of successful implementation of Russian digital platforms and services in the field of agriculture at enterprises are shown. The necessity of digitalization of the Russian economy is substantiated, given that not only European countries have entered the era of digital globalization. The tasks and prospects of digitalization of agriculture are considered. It is concluded that by developing robotics and production automation systems, the digital transformation of the agro-industrial complex should contribute to the development of all areas of activity of agribusiness enterprises, and all this together will significantly increase the productivity of specialists and the quality of the decision made, and this leads to a significant increase in the efficiency of the regional economy.KeywordsResource and energy savingAgricultural machinery and mechanizationClean fuelsFuel efficiency

Smart farming leverages Artificial Intelligence (AI) to address modern agricultural sustainability challenges. This study investigates the application of machine learning (ML), deep learning (DL), and time series analysis in agriculture through a systematic literature review following the PRISMA methodology. The review highlights the critical roles of ML and DL techniques in optimizing agricultural processes, such as crop selection, yield prediction, soil compatibility classification, and water management. ML algorithms facilitate tasks like crop selection and soil fertility classification, while DL techniques contribute to forecasting crop production and commodity prices. Additionally, time series analysis is employed for demand forecasting of crops, commodity price prediction, and forecasting crop yield production. The focus of this article is to provide a comprehensive overview of ML and DL techniques within the farming industry. Utilizing crop datasets, ML algorithms are instrumental in classifying soil fertility, crop selection, and various other aspects. DL algorithms, when applied to farming data, enable effective time series analysis and crop selection. By synthesizing the integration of these technologies, this review underscores their potential to enhance decision-making in agriculture and mitigate food scarcity challenges in the future.

Multidisciplinary single-cell optical cytometry.

Biopharma Is Going Digital … Bit by Bit

Agriculture Technology (AgTech) integrates various technologies, devices, protocols, and computational paradigms to improve agricultural processes. Big data, artificial intelligence, the Internet of Things (IoT), cloud and edge computing are applied to provide capabilities for collecting, transmitting, storing, processing, and analyzing agricultural data. Large amounts of data are gathered from multiple sources and processed in real time, enabling more effective decision-making. Data can be used to automate processes, resulting in savings on farm labor. To obtain the benefits of AgTech, data trustworthiness is essential and cyber resilience is required. Security flaws may result in farming equipment and processes being interrupted or inoperable, with significant revenue and capital losses. It is important for farmers to identify and respond to cyber incidents. Several existing works have attempted to classify AgTech technologies. However, most existing classifications are based on a limited set of characteristics and only focus on communication technologies in use or provide generic adversarial scenarios. There are real risks to data trustworthiness that impact on digital agriculture, and many of these risks are yet to be addressed. It is critical to understand the building blocks and possible components to be considered in a future security framework and their capabilities for satisfying the data confidentiality, quality, authenticity, and integrity requirements. This paper proposes a taxonomy to enable a consistent means of classifying the technologies that form the backbone of AgTech and outline the building blocks of data trustworthiness. This taxonomy consists of seven main criteria that recognize the technologies and systems (used to enable the movement of data at different stages) in terms of how it operates, its features, its benefits, and its limitations. Understanding the taxonomy enables effective implementation of anomaly detection and cryptographic methods to ensure data trustworthiness. Efficient AgTech with no security represents a serious risk to long-term sustainable smart farming and food security. Further, this paper outlines the integration of post-quantum cryptography with AgTech. The usefulness of the proposed taxonomy is demonstrated using two different types of case study related to the design and implementation of a modular security framework in real-world scenarios. The paper also identifies and discusses other important factors that impact data trustworthiness.

Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions

A inovação tecnológica se mantém como relevante fator de crescimento econômico no contexto capitalista dinâmico, marcado pelo avanço das Tecnologias de Informação e Comunicação (TIC), no qual as organizações articulam capacidades dinâmicas na busca de vantagem competitiva sustentável, principalmente através de competências individuais. Nesse cenário, destacam-se a Indústria 4.0 (I4.0) e a Transformação Digital (TD), que inclui Big Data, Internet of Things (IoT) e Artificial Intelligence (AI), com ferramentas baseadas em algoritmos complexos, machine learning e deep learning. Podem servir à mineração de dados, análises preditivas e interação com pessoas através de Agentes Virtuais Inteligentes (AVI) que utilizam linguagem natural e aprendizado contínuo, formas de emular habilidades humanas complexas. Deste modo, os elementos da TD se apresentam como novas tecnologias que impactam a Organização do Trabalho (OT) e intensificam os desafios relacionados à integração da função estratégica da Gestão de Recursos Humanos (GRH), suas práticas e instrumentos, com exemplos de produtos (como HR analytics, people analytics e Artificial Intelligence, oferecidos por startups/spin-offs de HR Tech. Sob essas perspectivas, é proposto discutir o uso de IA (Computação Cognitiva) a partir do caso de um banco brasileiro, inicialmente através da percepção de seus funcionáros, atendidos por um AVI que presta informações sobre os produtos e serviços desta empresa, atividade normalmente atribuída aos atendentes do departamento de contact center. Como complemento, buscou-se observar se o uso dessa nova tecnologia, voltada a processos bancários, também inclui processos de RH.

The Fourth Industrial Revolution (FIR) is radically changing the way people live, work and interact through the introduction of digital technologies that integrate into all spheres of society. ChPR is based on a complex of innovations, such as artificial intelligence (AI), the Internet of Things (IoT), blockchain, robotics, virtual and augmented reality, cloud computing, big data and 5G. These technologies make it possible to achieve new levels of automation, individualization and efficiency of production and business processes, as well as significantly increase the speed of information exchange and processing. Artificial intelligence opens up possibilities for the creation of intelligent systems capable of making decisions based on the analysis of large amounts of data, which are used in the financial sector, medicine, logistics, agriculture and the entertainment industry. The Internet of Things (IoT) creates "smart" ecosystems where everyday objects interact with each other over the network, providing users with new conveniences, from automated "smart" homes to "smart" cities. Blockchain, in turn, ensures the reliability and transparency of transactions and finds its application not only in the financial sphere, but also in logistics, legal transactions and even in voting systems. In addition, digital technologies contribute to the growth of manufacturing flexibility, which allows companies to quickly adapt to changes in demand, and robotics and automation create new production models where human labor is replaced by high-precision machines. With cloud computing, businesses have the ability to access powerful computing resources without significant capital investment, making innovation more accessible to small and medium-sized businesses. However, with the development of CSPR, new challenges arise, in particular, the threat of cyber security, the need to adapt personnel to new technologies, and the need for legislative regulation that protects users' personal data. The labor market is also changing: routine work is being replaced by automated systems, which requires improving the skills of employees and promotes the development of new professions. Digital CNC technologies change all aspects of our lives, transforming not only the economic model, but also the social structure, relations between countries, as well as developing a new level of technological interaction that can affect all spheres of society.

The rise and development of the Internet of Things (IoT) have given birth to the frontier technology of the agricultural IoT, which marks the future trend in agriculture and the IoT. The agricultural IoT can be combined with Zigbee, a short-range wireless network technology for monitoring systems, to solve the excessively large planting area and other defects in agricultural production. Meanwhile, the modernization of planting and harvesting has set the stage for deep learning. Unlike the artificial neural network, the deep learning is an important intelligent algorithm, capable of solving many real-world problems. Therefore, this paper probes into the problems of modern automatic agriculture. First, the routing allocation technology and transmission mode were optimized to solve the energy consumption problem. Second, the classification model based on deep learning algorithm was put forward according to the application of the Wireless Sensor Network (WSN) in continuous monitoring of soil temperature and humidity. Despite the lack of upper soil humidity sensor in agriculture, the model can still classify the soil moisture of the nodes, and derive the main soil moisture content. Finally, a solution was presented based on agricultural ZigBee WSN technology. In addition to cheap cost and low power consumption, the solution has the functions of reminding and recognition due to the adoption of artificial intelligence algorithm. Suffice it to say that the solution is a successful attempt to integrate artificial intelligence and sensor technology into agricultural modernization.

Understanding the potential of emerging digital technologies for improving road safety