Common ground as (inter)cultural in-betweenness in human-machine communication: A literary pragmatic perspective

Advances in Artificial Intelligence (AI) technologies and Autonomous Unmanned Vehicles are shaping our daily lives, society, and will continue to transform how we will fight future wars. Advances in AI technologies have fueled an explosion of interest in the military and political domain. As AI technologies evolve, there will be increased reliance on these systems to maintain global security. For the individual and society, AI presents challenges related to surveillance, personal freedom, and privacy. For the military, we will need to exploit advances in AI technologies to support the warfighter and ensure global security. The integration of AI technologies in the battlespace presents advantages, costs, and risks in the future battlespace. This chapter will examine the issues related to advances in AI technologies, as we examine the benefits, costs, and risks associated with integrating AI and autonomous systems in society and in the future battlespace.

The continuous goal of discovering extraterrestrial life has driven scientific interest in exoplanets—celestial bodies orbiting stars outside our solar system. Traditional methods of exoplanet detection, reliant on manual analysis and prone to human error, presented unavoidable challenges given the vastness of the universe. This paper aims to discuss the benefits and limitations of AI within the exoplanet detection field, and determine whether the highly-regarded artificial intelligence is as beneficial to astronomical fields as we think. With the introduction of the first ever fully-robotic exoplanet detector which takes high-precision radial velocity measurements to measure the gravitational reflex motion, and advancing computer algorithms that avoid human errors in data analysis, modern advancements in artificial intelligence (AI) technology have not only transformed the efficiency and accuracy of exoplanet detection, but also extended our understanding of these distant worlds. While the use of AI does have its benefits, there are several drawbacks that could potentially hinder further advancement in the field of exoplanet detection.

The Taihu Lake plain is a highly urbanized region in China with many water-related environmental problems. Although point-source pollution has been effectively controlled by government legislation, urban surface runoff pollution is still a major issue. Different types of urban communities were selected for rainfall runoff experiments. According to the monitoring data of rainfall events, multiple methods were used to analyze the characteristics of surface runoff pollution and estimate the pollution load for different types of communities. The results indicated that surface runoff from urban communities reduced the river water quality. Certain degrees of the 'first flush' effect occurred in different types of urban communities. The surface runoff pollution in the commercial residential community was weaker than that in commercial and private residential communities; however, the first flush occurred more frequently in the commercial residential community. Holding back 30% of the surface runoff could effectively improve the runoff water quality in commercial and private residential communities as well as the commercial residential community with restaurants. In the commercial residential community, 25% of surface runoff should be held to improve runoff water quality effectively. The loads of pollutants, especially nitrogen and phosphorus, in urban communities in the Taihu Lake basin were higher than those in other regions in China. This research can assist with the reduction of surface runoff pollution in highly urbanized communities.

Space–time fixity and flexibility of daily activities and the built environment: A case study of different types of communities in Beijing suburbs

Being involved in the country: Productive ageing in different types of rural communities

Summary1. This article discusses the principles of comparative productivity and the net primary productivity of different types of plant community.2. Primary production is denned as the weight of new organic matter created by photosynthesis over a period; expressed as a rate it becomes productivity. Biomass is defined as the total weight of plant present at a particular time. Crop, yield and standing crop are comparable with production, productivity and biomass respectively, but refer to the parts of the plant normally harvested or sampled.3. Net production is that part of the gross photosynthetic production which is not respired by the plant, and hence becomes available for utilization.4. Ways of adjusting source data to a common form are examined at length, for meaningful comparisons are impossible if this is not done. Source data are published according to a great variety of criteria such as fresh weight, dry weight, oxygen production and carbon fixation. Standing crop or yield data need correction for omitted parts of the plant. The determination of productivity from changes in biomass may involve corrections for material accumulated from earlier periods and for losses due to death or grazing. Conversions from gross production to net production are usually required when photosynthetic determinations are made.5. Problems raised by the use of different units are discussed and selected factors for conversions to the recommended units are listed.6. The basis adopted for comparisons is the maximum average annual net productivity of organic (ash‐free) matter that can be attained over a large area. This facilitates the comparison of the productivity of different types of community by minimizing differences due to local site conditions and weather, and is the most useful measure for general ecological purposes. For some selected examples the productivity and biomass are expressed in a variety of other ways to facilitate direct comparisons with source data.7. Methods for determining productivity are only discussed in so far as the details affect the comparability of the results.8. The most productive temperate communities appear to be fertile reedswamps which may produce 30–45 metric tons per hectare in a year. Coniferous forests, and perennial plants under intensive cultivation, may produce 25–40 m.t./ha. Deciduous forests, uncultivated herbs and cultivated annual plants are less productive (10–25 m.t./ha.).9. The most productive communities of all appear to be found in the tropics. Rain forests and perennial plants under intensive cultivation may produce 50–80 m.t./ha. in a year and it is probable that swamps are similar. Cultivated annual plants only attain 25–35 m.t./ha.10. The phytoplankton of lakes and oceans are relatively poorly productive even on fertile sites, with an annual production of only 1–9 m.t./ha. Values greater than 3 m.t./ha. are only achieved in waters enriched by man's activities, or in the tropics. Submerged freshwater macrophytes are no more productive in the temperate region but may attain 13–21 m.t./ha. in warmer climates. Benthic marine plants in shallow waters may produce more; from 25–33 m.t./ha. in the temperate zone, rising to nearly 40 m.t./ha. by tropical coral reefs. Algae cultivated in sewage can produce up to 45 m.t./ha. and algae cultivated in mineral media, with carbon dioxide supplied artificially, may produce even more.11. If it were possible to devise cultivation techniques which would enable plants to grow all the year at the rate normally attained for only short periods in their seasonal cycle, much greater annual productivities, up to 150 m.t./ha. year, might be attained. Eichhornia crassipes might be a suitable plant for such cultivation.12. Assuming that the soil structure is good and that ample nutrients are available there appear to be three main ways of increasing yields; irrigating, using plants which maintain an active cover throughout the year, and developing techniques to obtain valuable products from plants, or parts of plants, not directly useable.

The rapid development and adoption of artificial intelligence (AI) technology has sparked debates about its implications for labor markets, yet the micro-level relationship between AI and labor share remains underexplored. Based on the theory of skill-biased technological change, this study aims to examine whether AI technology increases labor share by labor structure upgrading at the enterprise level. Using panel data for China’s listed companies from 2012 to 2022, this study tests this relationship using a two-way fixed effects model. The empirical results reveal that AI technology significantly increases labor share, with labor structure upgrading playing a mediating role in this relationship. Heterogeneity analysis reveals that the influence of AI technology on labor share is stronger for enterprises characterized by low labor market rigidity, high labor market supply, and talent policy support in external environments, as well as among labor-intensive, high-tech, and non-state-owned enterprises. Notably, this study finds that advancements in AI technology have achieved mutually beneficial outcomes of improving labor share and enhancing total factor productivity. Our research findings provide detailed empirical evidence for enterprises to formulate and implement AI strategies.

Fruits and vegetables preservation based on AI technology: Research progress and application prospects

Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities

The hardware implementation of advanced artificial intelligence (AI) technology based on complex deep learning and machine learning algorithms is constricted by the limitation of conventional Von–Neuman architecture. Emerging neuromorphic computing architecture based on the human brain with in‐memory computing capability could instigate unprecedented breakthroughs in AI technology. In this pursuit, 2D MoS2 optoelectronic artificial synapse imitating complex biological neuromorphic behavior such as short/long‐term memory, paired‐pulse facilitation, and long‐term depression‐potentiation is proposed and demonstrated. Furthermore, the broadband sensitivity of the device can be utilized to emulate Pavlov's classical conditioning for associative learning of the biological brain. More importantly, reconfigurable Boolean AND and OR logic gate operation is demonstrated within the same device by synergistically modulating the device conductance via the persistent photoconductivity and electrical gate stress. The linear response of the photocurrent to the optical stimulus can perform arithmetic operations such as counting, addition, and subtraction within a single device. This novel integration of memory, synaptic behavior, and processing within a single monolayer MoS2 device is believed to put forth a new horizon for the Non‐Von–Neuman type in‐memory computing architecture for highly advanced AI applications based on 2D materials.

Purpose/Rationale Sport officials operate within settings that dynamically change and shift. While they gather, synthesise, and store experiences related to task, performer, and environmental constraints, their internal mental models of judgement and decision-making individually evolve as they perform in different contexts. However, while a large body of work in psychology and behavioural economics has attempted to capture the way humans make decisions, there is a growing realisation among researchers, evaluators, and educational designers that quality improvement interventions cannot be understood outside of the context in which they occur [Ramaswamy, R., Reed, J., Livesley, N., Boguslavsky, V., Garcia-Elorrio, E., Sax, S., Houleymata D., Kimble L., Parry, G. (2018). Unpacking the black box of improvement. International Journal for Quality in Health Care, 30(suppl_1), 15–19. https://doi.org/10.1093/intqhc/mzy009]. Approach In this futuristic proposal, we put forward our vision of how artificial intelligence technologies can unpack and support the internal collections of cognitive knowledge, context, task goals, and on-field experiences that influence sport officiating development. Findings In what follows, we define what we mean by artificial intelligence and machine learning technologies, briefly highlighting their histories in sport analytics contexts. We outline how education is a promising field for the adoption of artificial intelligence/machine learning technologies and conclude by providing a theoretical case study scenario that describes a potential platform through which perspectives of environment, task and performer expertise might be developed for amateur and elite sport officials. Practical implications Using advanced AI technologies as the basis through which to examine on-field data provides tremendous potential to theoretically tackle the idiosyncrasies of officiating development in a range of sports as it can close the gap between a descriptive analysis (i.e. understanding the interactions undertaken by officials in the presence of others), and a more prescriptive one (i.e. suggesting the actions such officials should have executed). Research contribution We put forward that artificial intelligence technologies can offer sport organisations sophisticated, constructively based help with opening the “black box” of learning related to sport officiating development. By using ecological dynamics as the fundamental framework through which to filter the data collected, statistical information and qualitative ecological outcomes can be linked and managed into understandable and stable development content [Liu, A., Mahapatra, R. P., & Mayuri, A. V. R. (2021). Hybrid design for sports data visualization using AI and big data analytics. Complex & Intelligent Systems, https://doi.org/10.1007/s40747-021-00557-w], that strongly benefits the online development of amateur sport officials.

Advancements in machine learning (ML) and deep learning in sectors of science and technology related to data science contribute toward battling COVID-19. The present aim of artificial intelligence (AI) technology to practice warfare against COVID-19 upheaval in various aspects of handling the disease includes utilizing a disease-tracking system, sample collection, and providing required needs to infected persons. This chapter portrays different Internet of Things (IoT), 5G robotics, as well as prompt health-related tracking drones. The drones were helpful in saving several human lives. In addition, we offer a transitory examination of the different AI technology, such as IoT, 5G robots, smartphones, and deep learning, in tackling the COVID-19 pandemic and further recommend approaching research directions. Although the ongoing progress globally to combat the COVID-19 pandemic continues, several complementary efforts have been initiated with advanced AI technologies. The modern technology progression is a boon in improving people's lives. Until a permanent cure for such a pandemic disease, AI technology is the apt software to detect an infected persons at a robust rate. Thus, AI at present is the most significant technique in combating/identifying/preventing the disease.

While most contemporary researchers argue that Artificial Intelligence (AI) provides overwhelming and profound advancements in technology and beneficial tools used daily to advance human life on earth, there exist a host of other researchers who hold contrary opinions to this view, with regard to the rising adverse ontological and existential consequences which the products of super-intelligent technologies has on an alarming number of mankind in the 21st century. Scholars like Vardi, Tegmark and Greene likened this scenario to a time-bomb waiting to go off any moment. The recent endorsement of 23 AI principles by 1200 AI/Robotics researchers and over 2342 other researchers from diverse disciplines, in a just concluded Future of Life (FLI) Conference, adds credence to the worries which most researchers have about the presumed benefits of AI to mankind. The study draws from a combination of Marxian Alienation and ontological theories which basically holds that: rising advancements in AI technologies, continues to alienate mankind from his existential human nature. The ex-post facto research design in the social sciences and Deriders' deconstructive and critical reconstructive analytic method in philosophy, for interrogating the meaning of concepts, arguments and current debates on the relevance and risks of IA, were adopted for the study. The study identified justifiable grounds and reasons for the alarm raised over current innovations in the field of AI technologies. The study strengthen the resolve of current researchers to identify ways of reducing or avoiding the impending adverse consequences of evolving conscious AI and machines in the feature. The research proposes a radical legal enforcement and adoption of the 23 newly established AI principles as one of the pertinent measure for saving mankind from the impending threats posed by innovations powered by advances in AI technology.

Artificial intelligence (AI) stands at the forefront of modern scientific innovation. It has revolutionized our understanding of the world around us and its effects on Zoology also called as animal science have been profound. With continued advancements in AI technology, new opportunities to explore the unknown will continue to arise offering us unprecedented insight into this fascinating field. AI technology offers immense potential for animal scientists to improve their work. Automated tracking and predictive analytics allow data analysis on an unprecedented scale, enabling greater insight into complex biological systems than ever before. This can lead to improved animal welfare through better resource optimization, as well as more informed decision making in conservation efforts. AI also helps to reduce the workload of researchers by automating mundane tasks that would otherwise take a long time to complete. The good news is that it’s unlikely that AI will completely replace animal scientists any time soon; however, certain roles within the field may become automated by technology over time. So while it’s important to pay attention to advances in AI technology, it’s also possible to use them for advantage and secure the place in the workforce of tomorrow. Moreover, this technology continues to evolve, so too will its impact on our lives and careers.

Ensuring a comfortable and safe environment for the elderly is becoming an increasingly relevant task in the context of global population aging. Architectural design for elderly care homes in China is a critical aspect that affects the quality of their lives, health, safety, and happiness. Due to the specific needs of the elderly, designing such homes requires a higher level of professionalism and consideration of numerous factors, distinguishing it from traditional architectural design. Existing methods often fail to fully meet these specific needs, necessitating the search for new approaches and technological solutions. The key challenge lies in finding effective solutions to create an architectural environment that meets the complex and diverse needs of the elderly, ensuring their safety, comfort, and high quality of life. The integration of artificial intelligence (AI) technologies has the potential to significantly enhance the functionality and adaptability of such environments, requiring thorough research and practical implementation. This study aims to explore and analyze the auxiliary application of artificial intelligence technology in the architectural design of elderly care homes in China. The work focuses on identifying opportunities to improve the functionality and adaptability of living spaces for the elderly through the integration of AI technologies. Specifically, the study examines the impact of AI on enhancing safety, personalizing the living experience, managing health, and creating a comfortable psychological environment for the residents of elderly care homes. The implementation of artificial intelligence technologies in the architectural design of environments for elderly care homes can significantly increase their intelligence and better meet the needs of elderly individuals. Such environments, developed with the help of AI, can greatly enhance the efficiency and effectiveness of services provided in elderly care homes, making the care process more humane and refined. In the future, with the further advancement of AI technologies, the design of architectural environments for elderly care homes will become even more intelligent and sophisticated, providing safer, more comfortable, and more convenient living conditions for the elderly.