YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor

IntroductionDrought stress has become an important factor affecting global food production. Screening and breeding new varieties of peas (Pisum sativum L.) for drought-tolerant is of critical importance to ensure sustainable agricultural production and global food security. Germination rate and germination index are important indicators of seed germination vigor, and the level of germination vigor of pea seeds directly affects their yield and quality. The traditional manual germination detection can hardly meet the demand of full-time sequence nondestructive detection. We propose YOLOv8-Peas, an improved YOLOv8-n based method for the detection of pea germination vigor.MethodsWe constructed a pea germination dataset and used multiple data augmentation methods to improve the robustness of the model in real-world scenarios. By introducing the C2f-Ghost structure and depth-separable convolution, the model computational complexity is reduced and the model size is compressed. In addition, the original detector head is replaced by the self-designed PDetect detector head, which significantly improves the computational efficiency of the model. The Coordinate Attention (CA) mechanism is added to the backbone network to enhance the model's ability to localize and extract features from critical regions. The neck used a lightweight Content-Aware ReAssembly of FEatures (CARAFE) upsampling operator to capture and retain detailed features at low levels. The Adam optimizer is used to improve the model's learning ability in complex parameter spaces, thus improving the model's detection performance.ResultsThe experimental results showed that the Params, FLOPs, and Weight Size of YOLOv8-Peas were 1.17M, 3.2G, and 2.7MB, respectively, which decreased by 61.2%, 61%, and 56.5% compared with the original YOLOv8-n. The mAP of YOLOv8-Peas was on par with that of YOLOv8-n, reaching 98.7%, and achieved a detection speed of 116.2FPS. We used PEG6000 to simulate different drought environments and YOLOv8-Peas to analyze and quantify the germination vigor of different genotypes of peas, and screened for the best drought-resistant pea varieties.DiscussionOur model effectively reduces deployment costs, improves detection efficiency, and provides a scientific theoretical basis for drought-resistant genotype screening in pea

Cross-Modal Distillation for Speaker Recognition

Speaker recognition achieved great progress recently, however, it is not easy or efficient to further improve its performance via traditional solutions: collecting more data and designing new neural networks. Aiming at the fundamental challenge of speech data, i.e. low information density, multimodal learning can mitigate this challenge by introducing richer and more discriminative information as input for identity recognition. Specifically, since the face image is more discriminative than the speech for identity recognition, we conduct multimodal learning by introducing a face recognition model (teacher) to transfer discriminative knowledge to a speaker recognition model (student) during training. However, this knowledge transfer via distillation is not trivial because the big domain gap between face and speech can easily lead to overfitting. In this work, we introduce a multimodal learning framework, VGSR (Vision-Guided Speaker Recognition). Specifically, we propose a MKD (Margin-based Knowledge Distillation) strategy for cross-modality distillation by introducing a loose constrain to align the teacher and student, greatly reducing overfitting. Our MKD strategy can easily adapt to various existing knowledge distillation methods. In addition, we propose a QAW (Quality-based Adaptive Weights) module to weight input samples via quantified data quality, leading to a robust model training. Experimental results on the VoxCeleb1 and CN-Celeb datasets show our proposed strategies can effectively improve the accuracy of speaker recognition by a margin of 10% ∼ 15%, and our methods are very robust to different noises

Influence of Chloride in Mortar Made of Portland Cement Types II, III, and V on the Near-Field Microwave Reflection Properties

Corrosion of steel rebar in reinforced concrete structures, can be induced by the presence of chloride in the structure. Corrosion of steel rebar is a problematic issue in the construction industry as it compromises the strength and integrity of the structure. Although techniques exist for chloride detection and its migration into a structure, they are destructive, time consuming and cannot be used for the interrogation of large surfaces. In this investigation three different portland cement types; namely, ASTM types II, III and V were used, and six cubic (8\u27 X 8\u27 X 8\u27) mortar specimens were produced all with water-to-cement (w/c) ratio of 0.6 and sand-to-cement (s/c) ratio of 1.5. Tap water was used when producing three of these specimens (one of each cement type). For the other three specimens calcium chloride was added to the mixing tap water resulting in a salinity of 2.5%. These specimens were placed in a hydration room for one day and thereafter left it the room temperature with low humidity. The reflection properties of these specimens, using an open-ended rectangular waveguide probe, were monitored daily at 3 GHz (S-band) and 10 GHz (X-band). The results show the influence of cement type on the reflection coefficient as well as the influence of chloride on the curing process and setting time

Effect of the Internal Humidity of Concrete on Frost Resistance and Air Void Structure under Different Low Temperature Conditions

From the perspective of combining macroscopic and microscopic properties, this paper simulates the freeze–thaw cycle process at different freezing low temperatures based on the climate simulation equipment and by setting the curing conditions with different temperatures and relative humidity to produce different moisture conditions in concrete. The frost resistance properties and microscopic air void performance of concrete with different internal water content under different freezing low temperatures in freeze–thaw cycles were systematically studied. The results show that the higher the internal water content of concrete, the more obvious the mass loss rate and dynamic elastic modulus loss of concrete in the freeze–thaw process, and the more serious the deterioration of the air void parameter performance of the air-entraining agent introduced into concrete, which is manifested as the average bubble diameter and bubble spacing factor become larger and the bubble specific surface area decreases. In addition, in the case of the same internal moisture content of concrete, the freezing temperature used in the freeze–thaw cycle also has an important impact on the frost resistance of concrete and air void parameters; the lower the freezing temperature used, the more significant the decline in the frost resistance of concrete, the more obvious the deterioration of air void parameters

DataSheet_1_YOLOv8-Peas: a lightweight drought tolerance method for peas based on seed germination vigor.pdf

IntroductionDrought stress has become an important factor affecting global food production. Screening and breeding new varieties of peas (Pisum sativum L.) for drought-tolerant is of critical importance to ensure sustainable agricultural production and global food security. Germination rate and germination index are important indicators of seed germination vigor, and the level of germination vigor of pea seeds directly affects their yield and quality. The traditional manual germination detection can hardly meet the demand of full-time sequence nondestructive detection. We propose YOLOv8-Peas, an improved YOLOv8-n based method for the detection of pea germination vigor.MethodsWe constructed a pea germination dataset and used multiple data augmentation methods to improve the robustness of the model in real-world scenarios. By introducing the C2f-Ghost structure and depth-separable convolution, the model computational complexity is reduced and the model size is compressed. In addition, the original detector head is replaced by the self-designed PDetect detector head, which significantly improves the computational efficiency of the model. The Coordinate Attention (CA) mechanism is added to the backbone network to enhance the model's ability to localize and extract features from critical regions. The neck used a lightweight Content-Aware ReAssembly of FEatures (CARAFE) upsampling operator to capture and retain detailed features at low levels. The Adam optimizer is used to improve the model's learning ability in complex parameter spaces, thus improving the model's detection performance.ResultsThe experimental results showed that the Params, FLOPs, and Weight Size of YOLOv8-Peas were 1.17M, 3.2G, and 2.7MB, respectively, which decreased by 61.2%, 61%, and 56.5% compared with the original YOLOv8-n. The mAP of YOLOv8-Peas was on par with that of YOLOv8-n, reaching 98.7%, and achieved a detection speed of 116.2FPS. We used PEG6000 to simulate different drought environments and YOLOv8-Peas to analyze and quantify the germination vigor of different genotypes of peas, and screened for the best drought-resistant pea varieties.DiscussionOur model effectively reduces deployment costs, improves detection efficiency, and provides a scientific theoretical basis for drought-resistant genotype screening in pea.</p

Data_Sheet_1_Cost-effectiveness of two screening strategies based on Chinese diabetes risk score for pre-diabetes in China.docx

ObjectiveStudies have shown that screening for pre-diabetes mellitus (pre-DM) is essential to prevent type 2 diabetes mellitus (T2DM). This study evaluates the cost-effectiveness of two screening strategies that apply the Chinese Diabetes Risk Score (CDRS) to screen for pre-DM in China.MethodsA Markov microsimulation model was conducted from a social perspective, and the input parameters were obtained from published literature or publicly available data. Two screening strategies for pre-DM based on CDRS were built and compared with the control group to determine the cost-effective strategy. The screening strategy of the control group was screening for pre-DM by fasting plasma glucose (FPG) test in adults undergoing annual health examination and no screening in adults without an annual health examination (status quo). Two screening strategies were strategy 1: screening for pre-DM using CDRS in all adults (including with or without an annual health examination); and strategy 2: supplemental self-screening for pre-DM using CDRS in adults without an annual health examination, based on the status quo. We focus on the cumulative prevalence of T2DM and the incremental cost-effectiveness ratio which signifies the cost per case of T2DM prevented. We also evaluated the cost-effectiveness from the health system perspective. One-way and probabilistic sensitivity analyses were conducted to verify the robustness of the results.ResultsThe costs a case of T2DM prevented for strategy 1 compared with the control group and strategy 2 were $299.67 (95$385.89 (95% CI 381.58, 390.20), respectively. In addition, compared with the control group, the cost of strategy 2 to prevent a case of T2DM was $272.23 (95% CI 271.50, 272.96).ConclusionsScreening for pre-DM using CDRS in all adults was the most cost-effective health policy. We suggest that medical institutions replace FPG with CDRS for pre-DM screening; at the same time, self-screening for pre-DM using CDRS is widely promoted among adults without an annual health examination. There were still some disputes about how CDRS is included in the health examination projects, so strategy 2 should be considered as an alternative screening strategy. Findings provide a reference for the application of the CDRS in pre-DM screening and contribute to T2DM prevention.</p

The Beginning of the rpoB Gene in Addition to the Rifampin Resistance Determination Region Might Be Needed for Identifying Rifampin/Rifabutin Cross-Resistance in Multidrug-Resistant Mycobacterium tuberculosis Isolates from Southern China

We aimed to study the distribution and contribution of mutations in the rpoB whole gene in rifampin-resistant/rifabutin-resistant (RIFr/Rfbr) (or RIF/Rfb cross-resistant) clinical Mycobacterium tuberculosis isolates. One standard M. tuberculosis strain (H37Rv) and 392 other clinical M. tuberculosis isolates mainly from Guangdong Province of China whose susceptibilities to rifampin (RIF), rifabutin (Rfb), streptomycin (SM), ethambutol (EMB), and isoniazid (INH) were previously determined were subjected to DNA sequencing of their rpoB whole genes. H37Rv and the 30 drug-susceptible clinical isolates had no mutations in rpoB whole genes. In 43 rifampin-resistant/rifabutin-susceptible (RIFr/Rfbs) isolates, the most frequent mutation codons were 516 (62.80%), 526 (14.0%), and 533 (6.98%), but codon 531 had no mutation. Twenty-one of the 43 isolates (48.84%) had single mutations of H526L, H526S, D516V, D516Y, and D516F. In 319 RIFr/Rfbr isolates, the most frequent mutation codons were 531 (73.7%) and 526 (18.8%); the mutation frequency for codon 516 was 2.5%, and that for codon 533 was only 0.31%. A total of 82.8% (264/319) of them had single mutations of S531L, S531W, H526D, H526Y, H526R, Q513K, Q513P, Q510H, V176F, P206TR, Y314TC, and H323TY (the superscript T indicates M. tuberculosis numbering; the remaining codons use the E. coli numbering), among which V176F, P206TR, Y314TC, and H323TY were located in the beginning of rpoB, and all of them were present in 1.9% (6/319) of RIFr/Rfbr isolates. The multiple mutations in RIFr/Rfbr isolates and in RIFr/Rfbs isolates were also different from each other either in mutation positions or in types of mutation combinations. In conclusion, the mutations of rpoB in RIF-R/Rfbs and in RIF-R/Rfb-R isolates differ significantly from each other not only in the most frequent mutation codons (516, 531, and 533) but also in the most frequent single mutations (S531L, H526L, D516V, D516Y, and D516F), and the beginning of rpoB may confer a RIF/Rfb cross-resistance phenotype in M. tuberculosis. Molecular assays for identifying RIF/Rfb cross-resistance in M. tuberculosis might be improved in terms of accuracy by including this region, in addition to the rifampin resistance determination region