Recent advancements in Artificial Intelligence (AI), Deep Learning (DL), and computer vision have revolutionized various industrial processes through image classification and object detection. State-of-the-art Optical Character Recognition (OCR) and Object Detection (OD) technologies, such as YOLO and PaddleOCR, have emerged as powerful solutions for addressing challenges in recognizing textual and non-textual information on printed stickers. However, a well-established framework integrating these cutting-edge technologies for industrial applications still needs to be discovered. In this paper, we propose an innovative framework that combines advanced OCR and OD techniques to automate visual inspection processes in an industrial context. Our primary contribution is a comprehensive framework adept at detecting and recognizing textual and non-textual information on printed stickers within a company, harnessing the latest AI tools and technologies for sticker information recognition. Our experiments reveal an overall macro accuracy of 0.88 for the sticker OCR across three distinct patterns. Furthermore, the proposed system goes beyond traditional Printed Character Recognition (PCR) by extracting supplementary information, such as barcodes and QR codes present in the image, significantly streamlining industrial workflows and minimizing manual labor demands.

Stand Up Paddleboard (SUP) surfing entails riding breaking waves and maneuvering the board on the wave face in a similar manner to traditional surfing. Despite some scientific investigations on SUP, little is known about SUP surfing. The aim of this study was to investigate the physiological response during SUP surfing sessions and to determine how various environmental conditions can influence this response. Heart rate (HR) of an experienced male SUP surfer aged 43 was recorded for 14.9 hours during ten surfing session and synced with on board video footage to enable the examination of the effect of different surfing modes and weather conditions on exercise intensity. Results indicated the SUP surfer's HR was above 70% of HR_max during 85% of each session, with the greatest heart rates were found during falls off the board (~85%HR_max) and while paddling back to the peak (~83%HR_max). Total time surfing a wave was less than 5% with the majority of time spend paddling back into position. Wind speed positively correlated with HR (r² = 0.062, p = 0.012) and wave height negatively correlated wave caching frequency (r² = 0.54, p = 0.025). The results highlight the aerobic fitness for SUP surfing, that wave riding, paddling back to the peak and falls appear to be associated with the greatest cardiovascular demand and that environmental conditions can have an effect of physiological response during SUP surfing sessions.

Visits to coastal and inland-waterways have strong associations with many health and economic benefits but are also opportunities for exposure to environmental pollution. In England, few balanced survey samples exist which can facilitate accurate identification of correlates with recreation in blue spaces. This information could support risk assessment for likelihood of exposure and message targeting in case of environmental threats. We process data from a recent (2022-2023) and large (n = 177,551) survey of English residents to describe demographic, seasonal and regional correlates with any participation in water-based recreational activities in previous year (logistic regression model), and in the previous 28 days (zero-inflated negative binomial regression). Activities studied were: surfing and paddleboarding; open water swimming; scuba-diving and snorkelling; canoeing and kayaking; angling. Summer was associated with most frequent participation, winter with fewest sessions except for rowing. Anglers were group most likely to be socially vulnerable: less likely to be university educated, more likely to be disabled and especially likely to be male. People resident in rural locations were more likely to participate at least once in prior year, but did not have higher participation rates in the previous 28 days. Regional variations in specific activities may suggest where demand for water recreation is unmet. The results provide a reference description of outdoor water-recreationalists doing specific types of activity in England and may be useful when designing emergency messages and otherwise setting policy with regard to management of microbiological, radiological or chemical hazards in coastal and inland waters.

A paddle wheel aerator is developed in this research, which includes a wireless sensor network for measuring the water quality in the aquaculture of marine shrimp ponds. It can also move to the desired position to fill oxygen and measure water quality. A basic requirement is good standard water quality to prevent shrimp from epidemics and improve production. The water quality paddle wheel aerator applies a microcontroller and sensors to measure eight parameters of water quality. These water quality parameters are observed on the web application via an IoT module. The movement system of the water-quality paddle wheel aerator consists of a LiDAR, GPS, and remote RF signal. Each item of data is recorded immediately on the cloud server while the water-quality paddle wheel moves in the marine shrimp farm. The controlled paddle wheel aerator is harnessed automatically to enhance precisely the spatial monitoring resolution of the measurement system installed, which is needless for a multiple measurement system with high cost of investment. Also, farmers can access real data through the Line application. Hence, they are able to plan and control a good environment for aquaculture, preventing the occurrence of various epidemics and decomposing organic matter in the pond.