Search Results (1,306)

Cotton is an important economic crop, and its weed management directly affects yield and quality. In actual cotton fields, detection accuracy still faces challenges due to the complex types of weeds, variable morphologies, and environmental factors. Most existing models rely on the attention mechanism to improve performance, but channel attention tends to ignore spatial information, while full spatial attention brings high computational costs. Therefore, this paper proposes a grouped enhanced fusion attention mechanism (GEFA), which combines grouped convolution and local spatial attention to reduce complexity and parameter quantity while effectively enhancing feature expression ability. The GEFAY detection model constructed based on GEFA achieves good balance in efficiency, accuracy, and complexity on the CottonWeedDet12, VOC, and COCO datasets. Compared with classic attention methods, this model has the smallest increase in parameters and computational costs while significantly improving accuracy. It is more suitable for deployment on edge devices. The further designed end-to-end intelligent weed detection system and edge device deployment can achieve image detection on local maps and real-time cameras, with good practicality and scalability, providing effective technical support for intelligent visual applications in precision agriculture. Full article

In this study, to reveal the changes in explosion pressure and flame propagation characteristic, a 12 L cylindrical explosion device was used to conduct experiments on the explosions of two-phase mixtures of paper powder and volatile organic compounds (VOCs) at varying concentrations. The findings indicate that, at a constant paper powder concentration, increasing the VOCs concentration initially causes minor fluctuations in the maximum explosion pressure (P_max), followed by an increase. At a constant VOCs concentration, as the paper powder concentration rises, the P_max also increases, while the time to reach peak explosion pressure initially decreases before increasing. Additionally, under the two-phase concentration range produced in the production process, higher concentrations of paper powder and VOCs significantly enhance flame brightness, combustion intensity, heat release rate, and flame duration. These insights provide data support for determining the alarm limit values of VOCs concentration detection, provide a scientific basis for evaluating and predicting explosion risks associated with paper powder and VOCs, offering significant practical implications for fire and explosion prevention in the printing industry. Full article

Kargı Tulum cheese differs from other Tulum cheeses with its unique production and ripening method. No systematic study has yet explored the change in the volatile compounds and fatty acids during the ripening process of Kargı Tulum cheese. The objective of this study was to monitor the change in the fatty acids and volatile compounds of Kargı Tulum cheese at different time points during the production and ripening stages. Fatty acid profile, lipid quality parameters and volatile compound profiles were determined. A principal component analysis (PCA) was performed to determine how the volatile profiles differed across production and ripening stages. During the ripening, short- and medium-chain fatty acids (FAs) increased with notably high levels of butyric acid. Lipid quality parameters, including total saturated FAs (SFAs), atherogenicity index (AI), and thrombogenicity index (TI), remained unchanged throughout ripening. A total of 62 volatile compounds (VOC) were detected. Esters and ketones were the most abundant groups in fresh curds, while carboxylic acids became the dominant group by the end of the ripening process. The total concentration of most VOC increased over time. Butyric acid, hexanoic acid, ethyl hexanoate and acetic acid were the dominant compounds contributing the flavor of the Kargı Tulum cheese. This study presents data on what flavor compounds form and how they change during ripening and can be useful for comparative purposes in future studies on ripened raw milk cheeses. Full article

Background/Objectives: Volatile organic compounds (VOCs) are emerging as non-invasive biomarkers of metabolic and disease-related processes, yet their reliable detection from complex biological matrices such as urine remains analytically challenging. This study aimed to establish a robust, non-targeted headspace solid-phase microextraction gas chromatography–mass spectrometry (HS–SPME GC–MS) workflow optimized for very small-volume urinary samples. Methods: We systematically evaluated the effects of pH adjustment and NaCl addition on VOC extraction efficiency using a 75 µm CAR/PDMS fiber and a sample volume of only 0.75 mL. Method performance was further assessed using concentration-dependent experiments with representative VOC standards and by application to real human urine samples analyzed in technical triplicates. Results: Acidification to pH 3 markedly improved extraction performance, increasing both total signal intensity and the number of detectable VOCs, whereas alkaline conditions and additional NaCl produced only minor effects. Representative VOC standards showed compound-specific linear dynamic ranges with minimal carry-over within the relevant analytical range. Application to real urine samples confirmed high analytical reproducibility, with triplicates clustering tightly in principal component analysis and most metabolites exhibiting relative standard deviations below 25%. Conclusions: The optimized HS–SPME GC–MS method enables comprehensive, non-targeted urinary VOC profiling from limited sample volumes. This workflow provides a robust analytical foundation for exploratory volatilomics studies under sample-limited conditions and supports subsequent targeted method refinement once specific compounds or chemical classes have been prioritized. Full article

To strengthen the safety management and control of explosives in underwater blasting construction sites, this study proposes an improved YOLOv11-based network named CRS-Y, designed to enhance the detection accuracy of explosives in complex underwater environments and improve the recognition capability of multi-scale targets. To address the limitations of traditional object detection methods in handling complex backgrounds and low-resolution targets, a lightweight re-parameterized vision transformer was integrated into the C3K module, forming a novel CSP structure (C3K-RepViT) that enhances feature extraction under small receptive fields. In combination with the Efficient Multi-Scale Attention (EMSA) mechanism, the model’s spatial feature representation is further strengthened, enabling a more effective understanding of objects in complex scenes. Furthermore, to reduce the computational cost of the P2 feature layer, a new convolutional structure named SPD-DSConv (Space-to-Depth Depthwise Separable Convolution) is proposed, which integrates downsampling and channel expansion within depthwise separable convolution. This design achieves a balance between parameter reduction and multidimensional feature learning. Finally, the Inner-IoU loss function is introduced to dynamically adjust auxiliary bounding box scales, accelerating regression convergence for both high-IoU and low-IoU samples, thereby optimizing bounding box shapes and localization accuracy while improving overall detection performance and robustness. Experimental results demonstrate that the proposed CRS-Y model achieved superior performance on the VOC2012, URPC2020, and self-constructed underwater blasting datasets, effectively meeting the real-time detection requirements of underwater blasting construction scenarios while exhibiting strong generalization ability and practical value. Full article

Bounding box regression (BBR) loss plays a critical role in object detection within computer vision. Existing BBR loss functions are typically based on the Intersection over Union (IoU) between predicted and ground truth boxes. However, these methods neither account for the effect of predicted box scale on regression nor effectively address the drift problem inherent in BBR. To overcome these limitations, this paper introduces a novel BBR loss function, termed Gaussian Adaptive Ochiai BBR loss (GAOC), which combines the Ochiai Coefficient (OC) with a Gaussian Adaptive (GA) distribution. The OC component normalizes by the square root of the product of bounding box dimensions, ensuring scale invariance. Meanwhile, the GA distribution models the distance between the top-left and bottom-right corners (TL/BR) coordinates of predicted and ground truth boxes, enabling a similarity measure that reduces sensitivity to positional deviations. This design enhances detection robustness and accuracy. GAOC was integrated into YOLOv5 and RT-DETR and evaluated on the PASCAL VOC and MS COCO 2017 benchmarks. Experimental results demonstrate that GAOC consistently outperforms existing BBR loss functions, offering a more effective solution. Full article

Oriented object detection in remote sensing images rapidly evolves as a pivotal technique, driving transformative advancements across geospatial analytics, intelligent transportation systems, and urban infrastructure planning. However, the inherent characteristics of remote sensing objects, including complex background interference, multi-scale variations, and high-density distribution, pose critical challenges to balance detection accuracy and computational efficiency. This paper presents an anchor-free framework that eliminates the intrinsic constraints of anchor-based detectors, specifically the positive–negative sample imbalance and the computationally expensive non-maximum suppression (NMS) process. By effectively integrating adaptive kernel module with boundary refinement network, we achieved lightweight and efficient detection. Our method adaptively generates convolutional kernels tailored for multi-scale objects to extract discriminative features, while utilizing a boundary refinement network to precisely capture oriented bounding boxes. Experiments were carried out on the widely recognized HRSC2016 and DOTA datasets for the oriented bounding box (OBB) task. The proposed approach achieves 90.13% mAP (VOC07 metric) on HRSC2016 with 61.60 M parameters and 158.84 GFLOPS. For the DOTA benchmark, we attain 75.84% mAP with 45.96 M parameters and 131.39 GFLOPs. Our work highlights a lightweight yet powerful architecture that effectively balances accuracy and efficiency, making it particularly suitable for resource-constrained edge platforms. Full article

The color and scent of flowers are vital ornamental attributes influenced by a complex interaction of metabolic and transcriptional mechanisms. Comparative analyses were performed to determine the molecular rationale for these features in Hydrangea arborescens, between the white-flowered variety ‘Annabelle’ (An) and its pink-flowered variant ‘Pink Annabelle’ (PA). Gas chromatography–mass spectrometry (GC–MS) detected 25 volatile organic compounds (VOCs) in ‘An’ and 21 in ‘PA’, with 18 chemicals common to both types. ‘An’ exhibited somewhat higher VOC diversity, whereas ‘PA’ emitted much bigger quantities of benzenoid and phenylpropanoid volatiles, including benzaldehyde, benzyl alcohol, and phenylethyl alcohol, resulting in a more pronounced floral scent. UPLC–MS/MS metabolomic analysis demonstrated obvious clustering of the two varieties and underscored the enrichment of phenylpropanoid biosynthesis pathways in ‘PA’. Transcriptomic analysis revealed 11,653 differentially expressed genes (DEGs), of which 7633 were elevated and linked to secondary metabolism. Key biosynthetic genes, including PAL, 4CL, CHS, DFR, and ANS, alongside transcription factors such as MYB—specifically TRINITY_DN5277_c0_g1, which is downregulated in ‘PA’ (homologous to AtMYB4, a negative regulator of flavonoid biosynthesis)—and TRINITY_DN23167_c0_g1, which is significantly upregulated in ‘PA’ (homologous to AtMYB90, a positive regulator of anthocyanin synthesis), as well as bHLH, ERF, and WRKY (notably TRINITY_DN25903_c0_g1, highly upregulated in ‘PA’ and homologous to AtWRKY75, associated with jasmonate pathway), demonstrating a coordinated activation of color and fragrance pathways. The integration of metabolomic and transcriptome data indicates that the pink-flowered ‘PA’ variety attains its superior coloring and aroma via the synchronized transcriptional regulation of the phenylpropanoid and flavonoid pathways. These findings offer novel molecular insights into the genetic and metabolic interplay of floral characteristics in Hydrangea. Full article

Volatile organic compounds (VOCs) released from automotive interior materials and exchanged with external air seriously compromise cabin air quality and pose health risks to occupants. Electronic noses (E-noses) based on metal oxide semiconductor (MOS) micro-electro-mechanical system (MEMS) sensor arrays provide an efficient, real-time solution for in-vehicle gas monitoring. This review examines the use of SnO₂-, ZnO-, and TiO₂-based MEMS sensor arrays for this purpose. The sensing mechanisms, performance characteristics, and current limitations of these core materials are critically analyzed. Key MEMS fabrication techniques, including magnetron sputtering, chemical vapor deposition, and atomic layer deposition, are presented. Commonly employed pattern recognition algorithms—principal component analysis (PCA), support vector machines (SVM), and artificial neural networks (ANN)—are evaluated in terms of principle and effectiveness. Recent advances in low-power, portable E-nose systems for detecting formaldehyde, benzene, toluene, and other target analytes inside vehicles are highlighted. Future directions, including circuit–algorithm co-optimization, enhanced portability, and neuromorphic computing integration, are discussed. MOS MEMS E-noses effectively overcome the drawbacks of conventional analytical methods and are poised for widespread adoption in automotive air-quality management. Full article

Botrytis cinerea Pers., the causal agent of grey mould, causes major economic losses in viticulture by reducing grape and wine quality and yield. Antagonistic yeasts that release bioactive volatile organic compounds (VOCs) represent a sustainable alternative to synthetic fungicides. Here, VOCs produced by Pichia guilliermondii strain ZIM624 were identified and assessed for antifungal activity against B. cinerea. 65 VOCs—including higher alcohols, volatile phenols, esters, and terpenes—were detected using two newly developed and validated analytical methods combining automated headspace solid-phase microextraction with gas chromatography–mass spectrometry. A total of 13 VOCs were selected for the bioassays. Fumigation assays demonstrated that terpenes (citronellol, geraniol, nerol, α-terpineol, and linalool) were the most effective inhibitors of B. cinerea mycelial growth (EC₅₀ = 6.3–33.9 μL/L). Strong inhibition was also observed for 4-vinylphenol and isoamyl acetate. In vivo assays confirmed that exposing infected grape berries to P. guilliermondii VOCs significantly reduced grey mould incidence. These results highlight the potential of P. guilliermondii ZIM624 volatiles as natural biofumigants for the eco-friendly management of B. cinerea in grapes. Future research should focus on optimising VOC production, evaluating efficacy under field conditions, and developing formulations for practical application in vineyards and post-harvest storage. Additionally, investigating potential synergistic effects of VOC combinations could lead to more effective biocontrol strategies. Full article

Background/Objectives: Fungi produce a diverse array of metabolites, including various volatile organic compounds (VOCs) with known physiological functions and other biological activities. These metabolites hold significant potential for medical and industrial applications. Within the fungal domain, Penicillium species represent a particularly important group. Methods: This study characterized the VOC profiles of four Penicillium expansum strains (R11, R19, R21, and R27) and one Penicillium polonicum strain (RS1) using the solid-phase microextraction–gas chromatography–mass spectrometry technique. Results: The analysis revealed that the only compound in common among the five strains of Penicillium was phenyl ethanol. The high toxicity of P. polonicum RS1 to Drosophila larvae correlated with its diverse and abundant alkene production. Specifically, alkenes constituted 31.28% of its total VOCs, followed by alcohols at 29.13%. GC-MS analyses detected 22, 17, 22, and 18 specific VOCs from R11, R19, R21, and R27, respectively. Overall, alkenes dominated the R11 profile (17.03%), alcohols were most abundant in R19 (28.82%), and R21 showed the highest combined release of alcohols (23.2%) and alkenes (11.7%), while R27 produced a moderate abundance of alcohols (9.16%) and alkenes (4.19%). Among the P. expansum strains, R11, R21, and R27 exhibited substantially higher toxicity than R19 strain in our previous assessment; these findings are consistent with their respective VOC profiles. Conclusions: The distinct VOC compositions across Penicillium strains significantly influence their biological characteristics and ecological functions. These findings provide a basis for follow-up research into the mechanisms of fungal volatile-mediated toxicity and support the development of biocontrol strategies. Full article

To address the challenges of small fastener targets, complex backgrounds, and the low efficiency of traditional manual inspection in coating workshop scenarios, this paper proposes an improved Cascade R-CNN-based fastener detection method. A VOC-format dataset was constructed covering three target categories—Marking-painted fastener, Fastener, and Fallen off—which represents typical inspection scenarios of coating equipment under diverse operating conditions and enhances the adaptability of the model. Within the Cascade R-CNN framework, three improvements were introduced: the Convolutional Block Attention Module (CBAM) was integrated into the ResNet-101 backbone to enhance feature representation of small objects; anchor scales were reduced to better align with the actual size distribution of fasteners; and Soft-NMS was adopted in place of conventional NMS to effectively reduce missed detections in overlapping regions. Experimental results demonstrate that the proposed method achieves a mean Average Precision (mAP) of 96.60% on the self-constructed dataset, with both Precision and Recall exceeding 95%, significantly outperforming Faster R-CNN and the original Cascade R-CNN. The method enables accurate detection and missing-state recognition of fasteners in complex backgrounds and small-object scenarios, providing reliable technical support for the automation and intelligence of printing equipment inspection. Full article

Human milk is the benchmark for formulating infant formula, the latter serving as a substitute when breastfeeding is not possible. Nevertheless, the lipid composition and structure of commercially available infant formulas still differ from those of human milk fat. Accordingly, this paper employs a computational–experimental framework to optimize formulations of prepared lipid (PF). The quality of the optimized product was further validated by analyzing volatile organic compounds (VOCs), color, lipid oxidation indicators, and oxidative stability. The results show that a total of 43 fatty acids (FA) were detected in the base oil, and palmitic acid, oleic acid, and linoleic acid are the main types of FA. Through computer simulation, 6 of PF were obtained, which are superior to commercial products (SP) in the similarity score of the parsimonious model, and PF1 has the highest score (84.15). Multivariate statistical analysis indicates that PF may be more suitable for use in infant formula milk powder due to its lipid composition. Gas chromatography-ion mobility spectrometry was used to study the VOCs content of PF and SP, and a total of 35 VOCs were identified. It was found that alcohols and ketones accounted for the highest proportion in PF, while Nitriles, Aldehydes, and Esters were the most abundant in SP. In the comparison of the basic physical and chemical indices between PF and SP, the peroxide value and p-anisidine value of PF are lower, and the overall oxidation stability is stronger than that of SP. This study provides a reference for the preparation and multi-dimensional evaluation of human milk fat substitutes. Full article

Exhaled breath analysis has gained considerable interest as a noninvasive diagnostic tool capable of detecting volatile organic compounds (VOCs) and inorganic gases that serve as biomarkers for various diseases. Polymer-based gas sensors have garnered significant attention due to their high sensitivity, room-temperature operation, excellent flexibility, and tunable chemical properties. This review comprehensively summarized recent advancements in polymer-based gas sensors for the detection of disease biomarkers in exhaled breath. The gas-sensing mechanism of polymers, along with novel gas-sensitive materials such as conductive polymers, polymer composites, and functionalized polymers was examined in detail. Moreover, key applications in diagnosing diseases, including asthma, chronic kidney disease, lung cancer, and diabetes, were highlighted through detecting specific biomarkers. Furthermore, current challenges related to sensor selectivity, stability, and interference from environmental humidity were discussed, and potential solutions were proposed. Future perspectives were offered on the development of next-generation polymer-based sensors, including the integration of machine learning for data analysis and the design of electronic-nose (e-nose) sensor arrays. Full article

Colorectal cancer (CRC) remains a major cause of morbidity and mortality worldwide, with its incidence and biological behavior influenced by both genetic and environmental factors. Emerging evidence highlights notable sex differences in CRC, with men generally exhibiting higher incidence rates and poorer prognoses, while women often display stronger immune responses and distinct molecular profiles. Traditional screening tools, such as colonoscopy and fecal-based tests, have improved survival through early detection but are limited by invasiveness, cost, and adherence issues. In this context, biosensors have emerged as innovative diagnostic platforms capable of rapid, sensitive, and non-invasive detection of CRC-associated biomarkers, including genetic, epigenetic, and metabolic alterations. These technologies integrate biological recognition elements with nanomaterials, microfluidics, and digital systems, enabling the analysis of biomarkers such as proteins, nucleic acids, autoantibodies, epigenetic marks, and metabolic or VOC signatures from blood, stool, or breath and supporting point-of-care applications. Electrochemical, optical, piezoelectric, and FET platforms enable label-free or ultrasensitive multiplexed readouts and align with liquid biopsy workflows. Despite challenges related to standardization, robustness in complex matrices, and clinical validation, advances in nanotechnology, multi-analyte biosensing with artificial intelligence are enhancing biosensor performance. Integrating biosensor-based diagnostics with knowledge of sex-specific molecular and hormonal pathways may lead to more precise and equitable approaches in CRC detection, selection of therapeutic regimes and management. Full article