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With the emergence of new technologies, the design of urban infrastructure is constantly being innovated, and the lawn lamp as urban lighting infrastructure is an important part of urban infrastructure. For the current lawn lamp function, there are single, large power consumption, low light energy utilization and other shortcomings. Combined with deep learning and optical design, this paper constructs an adaptive lighting control system based on the technology of the Internet. Considering the nonlinear and time-varying characteristics of external factors, a fuzzy control model with ambient light level and pedestrian flow as input and dimming coefficient K (0 < K < 1) as output is proposed to adjust the brightness of the light source and achieve energy savings. In order to improve the light energy utilization of the luminaire and reduce the glare index of the luminaire, a free-form total internal reflection (TIR) lens was designed by finding the optimal curvature of the lens through the polycurved edge light principle. The light source of the lawn lamp was simulated by TracePro, and the results showed that the light energy utilization reached 90%. Finally, the ambient illumination and pedestrian flow data of Dalian ZT Park were measured for different time periods at the site, and the data were normalized using the min-max normalization algorithm. The adaptive dimming capability of the system was verified through simulation tests and field tests, and the results showed that the lighting energy efficiency under the control system was 38%.

Smart warehouses aim to increase the overall service quality, productivity, and efficiency of the warehouse while minimizing costs and failures. In recent years, several studies have proposed and discussed different types of smart warehouses, identified key challenges, and proposed several solution directions for coping with these challenges. The objective of this article is to identify, evaluate, and synthesize the relevant studies discussing the design of smart warehouses and the transition to these new types of warehouses. We applied a systematic literature review (SLR) protocol to select primary studies. The SLR resulted in the identification of the domains in which smart warehouses are applied, key motivations for adopting smart warehouses, current distinctive characteristics of smart warehouses, currently adopted technologies for realizing smart warehouses, and challenges and strategies for transitioning to smart warehouses. To the best of our knowledge, no SLR paper has been published yet on smart warehouses, and therefore, this is timely research as organizations are nowadays transitioning to smart warehouses.

This study aims to explore the use of force vs. time data obtained from an isometric handgrip test to match a frailty state based on the TFI score. BodyGrip, a novel prototype system, is used for handgrip strength over 10 s time interval tests. A cross-sectional study with a non-probabilistic sample of community-dwelling elderly women was conducted. The force/time data collected from the dominant handgrip strength test, together with the Tilburg Frailty Indicator (TFI) test results, were used to train artificial neural networks. Different models were tested, and the frailty matching of TFI scores reached a minimum accuracy of 75%. Despite the small sample size, the BodyGrip system appears to be a promising tool for exploring new frailty-related features. The adopted strategy foresees ultimately configuring the system to be used as an expedite mode for identifying individuals at risk, allowing an easy, quick, and frequent person-centered care approach. Additionally, it is suitable for following up of the elderly in particular, and it may assume a relevant role in the mitigation of the increase in frailty evolution during and after the imposed isolation of the COVID-19 pandemic. Further use of the system will improve the robustness of the artificial neural network algorithm.

Artificial intelligence applied to the educational field has a vast potential, especially after the effects worldwide of the COVID-19 pandemic. Online or blended educational modes are needed to respond to the health situation we are living in. The tutorial effort is higher than in the traditional face-to-face approach. Thus, educational systems are claiming smarter learning technologies that do not pretend to substitute the faculty but make their teaching activities easy. This Special Issue is oriented to present a collection of papers of original advances in educational applications and services propelled by artificial intelligence, big data, machine learning, and deep learning.

The cyber processing layer of smart systems based on a cognitive dynamic system (CDS) can be a good solution for better decision making and situation understanding in non-Gaussian and nonlinear environments (NGNLE). The NGNLE situation understanding means deciding between certain known situations in NGNLE to understand the current state condition. Here, we report on a cognitive decision-making (CDM) system inspired by the human brain decision-making. The simple low-complexity algorithmic design of the proposed CDM system can make it suitable for real-time applications. A case study of the implementation of the CDS on a long-haul fiber-optic orthogonal frequency division multiplexing (OFDM) link was performed. An improvement in Q-factor of ~7 dB and an enhancement in data rate efficiency ~43% were achieved using the proposed algorithms. Furthermore, an extra 20% data rate enhancement was obtained by guaranteeing to keep the CDM error automatically under the system threshold. The proposed system can be extended as a general software-based platform for brain-inspired decision making in smart systems in the presence of nonlinearity and non-Gaussian characteristics. Therefore, it can easily upgrade the conventional systems to a smart one for autonomic CDM applications.