High salinity restricts biological hydrogen production, but the factors associated with performance differences among microbial consortia under saline food-waste fermentation remain insufficiently understood. In this study, three hydrogen-producing consortia, classified as high-performance (HP), medium-performance (MP), and lowperformance (LP), were compared under thermophilic conditions using food waste with 3.5 % NaCl. Modified Gompertz modeling showed that HP achieved a hydrogen production potential of 1408 mL center dot L- 1 and a maximum production rate of 100 mL center dot L- 1 center dot h- 1, exceeding MP and LP. Taxonomic analysis based on 16S rRNA gene sequencing indicated that genus-level community composition alone did not fully explain the observed performance differences, particularly between MP and LP. PICRUSt2-based functional prediction suggested that HP showed higher predicted abundances of KOs annotated to carbohydrate degradation, acetate- and butyrate-type fermentation, and hydrogenase-related functions. In contrast, MP showed partial enrichment of predicted KOs associated with upstream carbohydrate metabolism, while LP showed weaker predicted representation of hydrogen-related functional categories. These findings suggest that, under the tested food waste composition and thermophilic high-salinity condition, hydrogen production performance was associated with pathway-level predicted functional potential that was not fully captured by taxonomic composition alone. Because functional profiles were inferred from 16S rRNA gene sequences, these associations should be interpreted as hypothesis-generating rather than mechanistic evidence. Further metabolite-level and multi-omics validation is required to confirm whether these putative functional associations reflect active hydrogen-producing pathways under the tested conditions.
Construction sites pose a high risk of struck-by accidents due to limited situational awareness. This study investigates the feasibility of using a neckband-style wearable 360 degrees camera for proximity warning in construction environments. The proposed framework consists of a data preprocessing module-handling motion blur detection and distortion correction-and a hazard detection module, which integrates object detection with depth estimation. By identifying hazardous equipment and estimating its distance, the system provides personalized warnings to workers at risk. Field tests conducted at actual construction sites demonstrated an F1-score of 0.81 in hazard detection. Unlike fixed cameras or sensor-based systems, this approach enables omnidirectional, individualized hazard monitoring without additional site infrastructure. The wearable camera also minimizes interference with tasks, supporting stable image capture. The findings highlight the potential of combining wearable computer vision and deep learning to improve safety in complex and dynamic construction settings.
