The purpose of water treatment through ultraviolet (UV) radiation is to interrupt the development and multiplication of microorganisms by preventing their growth and reproduction.Ultraviolet treatment has become a very affordable solution to produce clean water of optimal quality in regions where access to drinking water is essential. The experimental water treatment unit is powered by a photovoltaic (PV) source. It incorporates a discharge lamp used to generate ultraviolet radiation, which is harnessed to address bacteriological issues related to water quality. This lamp is continuously powered by a photovoltaic (PV) source through an inverter and an electronic ballast. The water treatment system also includes a motor pump designed to draw contaminated water from the input reservoir, a flow control valve to adjust flow rates as needed, and a filter aimed at enhancing the quality of the contaminated water. The water circulates between the input reservoir (contaminated water) and the output reservoir (treated water) through the UV lamp. The relationship between the intensity of the UV lamp and the water flow rate can ensure an adequate water quality.

Ultraviolet (UV) disinfection is widely used in water treatment; however, its effectiveness strongly depends on water optical quality (e.g., turbidity, total dissolved solids, and UV transmittance, UVT). This study investigates an integrated RO–UV scheme in which reverse osmosis (RO) pretreatment improves UVT and thereby increases the effective UV dose available for microbial inactivation. First, UV-only reactor performance is characterized using literature data to fit an intensity-specific dose response relationship. The RO contribution is then incorporated at the process level through a UVT based coupling and evaluated using deterministic low/central/high scenarios (p05/p50/p95) constructed from assumed input ranges. Finally, a multi-objective optimization solved with the Grey Wolf Optimizer (GWO) is used to identify operating conditions that maximize predicted bacterial log-inactivation while limiting a UV-equivalent energy proxy based on nominal UV dose. Across the investigated flow-rate and intensity ranges, RO pretreatment yields a systematic increase in effective dose (median gain $\approx 6.8\%$) and a corresponding improvement in predicted inactivation, with the marginal benefit depending on the dose response regime.

Date palm harvesting requires reliable localization of anatomically appropriate cutting sites under cluttered orchard conditions, variable illumination, and frequent occlusion. This study presents an embedded two-stage detect-to-cut vision pipeline for date palm branch analysis on NVIDIA Jetson Orin NX. In the first stage, a YOLOv8-based detector localizes candidate fruit-bearing branches in the full image. In the second stage, a YOLOv8-Pose model operates on cropped regions of interest to estimate four harvest-relevant anatomical keypoints, namely the peduncle base, cut point, rachis base, and rachis tip. Under the reported evaluation setting, the detection stage achieved a precision of 0.893, a recall of 0.881, an mAP@0.5 of 0.942, and an mAP@0.5:0.95 of 0.669. For keypoint localization, the pose stage achieved a Pose mAP@0.5 of 0.952 and a Pose mAP@0.5:0.95 of 0.840, while cut-point accuracy, assessed using peduncle-normalized PCK, reached 0.605, 0.801, and 0.871 at $\alpha =0.1$, $0.2$, and $0.5$, respectively. On-device evaluation further showed a mean end-to-end latency of 380.34 ms per frame (4.00 FPS) across multi-ROI scenes and 135.01 ms per frame (7.79 FPS) in a continuous single-bunch video scenario. Taken together, these results support the feasibility of the proposed pipeline as a leakage-controlled within-domain embedded perception prototype for cut-point localization in the studied Deglet Nour orchard setting, rather than as a broadly validated field-ready harvesting solution.