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ObjectiveThis study aimed to evaluate the health risks of cadmium exposure in the local rice of Wenzhou residents by analyzing the pollution level and spatial distribution of cadmium in locally produced Wenzhou rice.MethodsThe monitoring data of local rice in Wenzhou from 2016 to 2021 were collected， and the spatial distribution of the cadmium concentration of local rice was displayed visually by ARCGIS 10.2. The Ordinary Kriging interpolation analysis was used to analyze spatial interpolation. The cadmium exposure was estimated by combining the concentration of cadmium in local rice with food consumption data. The health risk was assessed using a simple distribution model.ResultsThe median of cadmium in 740 local rice samples was 0.069 mg/kg. The detection rate and over-limit rate were 95.68% and 11.49%， respectively. The areas with high cadmium concentrations in local rice were mainly distributed in the northwest of Lucheng City and the central area of Rui’an City. The average exposure and the exposure of high-consumption groups （P95） to cadmium through eating local rice in Wenzhou adult residents were 12.04 and 21.08 μg/kg·BW， respectively. The average and high-consumption group exposure to cadmium in children were 17.02 and 32.04 μg/kg·BW， respectively. The average exposure and the exposure of high-consumption groups to cadmium by eating local rice in Lucheng were greater than the provisional tolerable monthly intake （PTMI）.ConclusionThe cadmium exposure levels of 1.16% of all adult residents in Wenzhou by eating local rice were higher than the PTMI. Children with high rice consumption had a higher cadmium exposure level from local rice， and residents had a higher health risk of cadmium exposure by eating local rice in Lucheng.

ObjectiveTo provide basis for further ensuring the safety of student meals， the microbial contamination status in kindergarten， primary and secondary school canteens in Wenzhou in the past five years were investigated.MethodsFood samples collected in Wenzhou from 2016 to 2020 were detected for hygienic target bacteria （Aerobic Plate count and Escherichia coli） and foodborne pathogens （Staphylococcus aureus， Bacillus cereus， Salmonella and Listeria monocytogenes）， and data were analyzed with SPSS 18.0.ResultsThe microbial contamination of student meal in 2016 was serious， with a unqualified rate as high as 23.58%. While the unqualified rates of student meal decreased by 6.27%， 6.80%， 9.06%， and 3.82% from 2017 to 2020 （χ2 = 60.852， P

In recent years, increased species extinction and habitat loss have significantly reduced biodiversity, posing a serious threat to both nature and human survival. Environmental factors strongly influence bird distribution and diversity. The potential distribution patterns and species richness offer a conservation modeling framework for policymakers to assess the effectiveness of natural protected areas (PAs) and optimize their existing ones. Very few such studies have been published that cover a large and complete taxonomic group with fine resolution at regional scale. Here, using birds as a study group, the maximum entropy model (MaxEnt) was used to analyze the pattern of bird species richness in Jiangsu Province. Using an unparalleled amount of occurrence data, we created species distribution models (SDMs) for 312 bird species to explore emerging diversity patterns at a resolution of 1 km2. The gradient of species richness is steep, decreasing sharply away from water bodies, particularly in the northern part of Jiangsu Province. The migratory status and feeding habits of birds also significantly influence the spatial distribution of avian species richness. This study reveals that the regions with high potential bird species richness are primarily distributed in three areas: the eastern coastal region, the surrounding area of the lower reaches of the Yangtze River, and the surrounding area of Taihu Lake. Compared with species richness hotspots and existing PAs, we found that the majority of hotspots are well‐protected. However, only a small portion of the regions, such as coastal areas of Sheyang County in Yancheng City, as well as some regions along the Yangtze River in Nanjing and Zhenjiang, currently have relatively weak protection. Using stacked SDMs, our study reveals effective insights into diversity patterns, directly informing conservation policies and contributing to macroecological research advancements. [ABSTRACT FROM AUTHOR]

The research on electron transfer coupled spin transition regulating the valence state and spin state transition of metal ions is promising and challenging. Herein, we report a cyano-bridged {Fe III2 Fe II2 } molecular square complex, {[Fe(Tp)(CN)3]2 [Fe(bnbpen)]2}(ClO4)2·8CH3OH (1·8CH3OH, bnbpen = N,N′-bis-(2-naphthylmethyl)-N,N′-bis(2-picolayl)-ethylenediamine), and its free of solvents form (1). Combined single-crystal X-ray diffraction, temperature-dependent infrared (IR) spectra, magnetic measurements, and Mössbauer spectra reveal that 1·8CH3OH and 1 exhibit reversible one-step and two-step electron transfer coupled spin transition (ETCST) with temperature change, between the low-temperature state {FeII,LS(μ-CN)FeIII,LS}2 (LS = low spin, HS = high spin) and the high-temperature state {FeIII,LS(μ-CN)FeII,HS}2, respectively. [ABSTRACT FROM AUTHOR]

In the realm of smart agriculture technology's rapid advancement, the integration of various sensors and Internet of Things (IoT) devices has become prevalent in the agricultural sector. Within this context, the precise identification of pests and diseases using unmanned robotic systems assumes a crucial role in ensuring food security, advancing agricultural production, and maintaining food reserves. Nevertheless, existing recognition models encounter inherent limitations such as suboptimal accuracy and excessive computational efforts when dealing with similar pests and diseases in real agricultural scenarios. Consequently, this research introduces the lightweight cross-layer aggregation neural network (LCA-Net). To address the intricate challenge of fine-grained pest identification in agricultural environments, our approach initially enhances the high-performance large-scale network through lightweight adaptation, concurrently incorporating a channel space attention mechanism. This enhancement culminates in the development of a cross-layer feature aggregation (CFA) module, meticulously engineered for seamless mobile deployment while upholding performance integrity. Furthermore, we devised the Cut-Max module, which optimizes the accuracy of crop pest and disease recognition via maximum response region pruning. Thorough experimentation on comprehensive pests and disease datasets substantiated the exceptional fine-grained performance of LCA-Net, achieving an impressive accuracy rate of 83.8%. Additional ablation experiments validated the proposed approach, showcasing a harmonious balance between performance and model parameters, rendering it suitable for practical applications in smart agricultural supervision. [ABSTRACT FROM AUTHOR]

In summary, we have developed a decarboxylative oxidation reaction of carboxylic acids through visible-light-induced photocatalysis. [26] For examples, Itami and co-workers developed a [Ru(bpy) SB 3 sb ]Cl SB 2 sb (bpy = 2,2'-bipyridyl)-catalyzed decarboxylative oxidation reaction of arylacetic acids. Graph Decarboxylative oxidation is an important and powerful reaction because it can provide one-carbon-shorter ketones or aldehydes directly from readily available and bench-stable carboxylic acids. [34] Various carboxylic acids were successfully employed as substrates, including phenylacetic acid-type substrates and aliphatic carboxylic acids. [Extracted from the article]