Field Environment Research Articles

Teaching and learning about race, culture, and environment in a predominately white institution

The environment and natural resource fields have traditionally centered western science, the scholarship of white men, and land conservation strategies that neglect historical inhabitants. These tenets have led to a narrow view of how conservation is defined and created challenges for BIPOC students and professionals to see themselves as full and equal participants in the environmental sciences. The Rubenstein School of Environment and Natural Resources has worked to address these shortcomings through courses designed to address issues of systemic racism and exclusion in the environmental field. In our student’s first year, we pair a fall course focused on communication skills with a spring course that addresses issues of racism and social justice in the environmental fields. We use the fall semester to create a learning community where students build relationships of trust, mutual regard, and care and develop a deeper understanding of their relationship with the environment. In the spring, we present students with a variety of frameworks to think critically about equity, inclusion, positionality, privilege, racism, and diversity. A key learning outcome is to help students consider how historical and present-day dynamics of race and racism have shaped the environmental field. Importantly, we focus on the voices and messages of environmental leaders who have historically been left out of popular environmental narratives. We outline lessons learned in the integration of diversity, equity, and inclusion into our environment and natural resources curriculum and ways to further enhance our centering of equity and inclusion in the curriculum.

Path planning of PRM based on artificial potential field in radiation environments

The radiation environment in nuclear facility work sites is complex. To ensure the safety of personnel working in these environments, it is essential to develop an optimal path planning method based on the criterion of personnel radiation dose. In this paper an improved probabilistic roadmap algorithm (APF-PRM) is proposed based on the combination of artificial potential fields (APF) and probabilistic roadmap algorithm (PRM) for path planning in complex static radiation environments. The entire two-dimensional dose field model of the radiation environment is constructed using bilinear interpolation based on simulation data from Geant4. Using this dose field model and the personnel radiation dose calculation model, the new algorithm is integrated with the A* algorithm to perform path planning in the radiation environment. To assess the performance of the APF-PRM algorithm, a comparison with other algorithms is made in four cases. In the first case, the computation speed of APF-PRM algorithm is significantly faster than that of the conventional A* algorithm in the same environment. In case 2, a reduction of 16.25% in cumulative personnel radiation dose is observed when compared with the traditional PRM algorithm, and the results from multiple repetitions of the experiment display higher stability. In case 3 a comparison between the APF-PRM algorithm and the Voronoi diagram is made, revealing a 36.92% reduction in cumulative dose, along with a 16.49% decrease in path length. In case 4, the four algorithms, A*, APF-PRM, PRM, and Voronoi, are comprehensively compared under the same circumstances, and the parameters of the APF-PRM algorithm that meets the requirements of minimum dose and optimal path are found.

Automatic Identification of Sea Rice Grains in Complex Field Environment Based on Deep Learning

The number of grains per sea rice panicle is an important parameter directly related to rice yield, and it is also a very important agronomic trait in research related to sea rice breeding. However, the grain number per sea rice panicle still mainly relies on manual calculation, which has the disadvantages of being time-consuming, error-prone, and labor-intensive. In this study, a novel method was developed for the automatic calculation of the grain number per rice panicle based on a deep convolutional neural network. Firstly, some sea rice panicle images were collected in complex field environment and annotated to establish the sea rice panicle image data set. Then, a sea grain detection model was developed using the Faster R-CNN embedded with a feature pyramid network (FPN) for grain identification and location. Also, ROI Align was used to replace ROI pooling to solve the problem of relatively large deviations in the prediction frame when the model detected small grains. Finally, the mAP (mean Average Precision) and accuracy of the sea grain detection model were 90.1% and 94.9%, demonstrating that the proposed method had high accuracy in identifying and locating sea grains. The sea rice grain detection model can quickly and accurately predict the number of grains per panicle, providing an effective, convenient, and low-cost tool for yield evaluation, crop breeding, and genetic research. It also has great potential in assisting phenotypic research.

An ultrasensitive dual-mode stagey for 17β-estradiol assay: Photoelectrochemical and colorimetric biosensor based on a WSe2/TiO2-modified electrode coupled with nucleic acid amplification

BackgroundThe abuse of 17β-estradiol(E2) has aroused wide concern in environmental and biomedical fields, which severely affects the endocrine function of human and animals. Therefore, an ultrasensitive and accurate assay of E2 is critically important. Traditional chromatography or immunoassay techniques exhibited good sensitivity and selectivity, but expensive instruments and antibodies may pose cost and stability issues, as well as difficulties in meeting on-site detection requirements. Ultrasensitive, reliable, and on-site detection of E2 at trace level remains a challenge. Hence, developing a simple, ultrasensitive assay to simultaneously achieve accurate detection and rapid visual analysis of E2 is extremely crucial. ResultsWe developed a versatile dual-mode photoelectrochemical (PEC) and colorimetric biosensor based on isothermal nucleic acid amplification strategy for the ultrasensitive and accurate detection of E2. The method modified titanium dioxide (TiO2) with tungsten selenide (WSe2) nanoflowers to synthesize WSe2/TiO2 heterostructures as a substrate for signal amplification and nanoprobe modification. Isothermal nucleic acid amplification strategy has been proven to be a powerful tool for strong signal amplification. The presence of a target triggered the nucleic acid amplification reaction, and produced a large amount of tDNA that competed with G-quadruplex immobilized on the electrode surface. The remaining G-quadruplex/hemin catalyzed the 4-chloro-1-naphthol (4-CN) to form biocatalytic precipitation (BCP) and ABTS-H2O2 chromogenic reaction, thus, the dual-mode platform was capable of achieving PEC-colorimetric ultrasensitive detection based on the catalytic activity of G-quadruplex/hemin DNAzyme. Within optimal conditions, the dual-mode biosensor exhibited a remarkable detection limit as low as 0.026 pM. SignificanceBenefiting from the superior performance of WSe2/TiO2 and the power signal amplification of isothermal nucleic acid amplification strategy, this aptasensor achieved the ultrasensitive detection of E2. The independent transmission paths of photoelectrochemical and colorimetric provide mutual support and flexible switching, significantly enhancing the overall sensitivity and accuracy of the detection strategy, which can meet the needs for E2 precise quantification and rapid on-site detection.

Enhanced Cataluminescence Sensor Based on SiO2/MIL-53(Al) for Detecting Isobutylaldehyde.

A simple, rapid, and reliable method for detecting harmful gases is urgently required in environmental security fields. In this study, a highly effective cataluminescence sensor based on SiO2/MIL-53(Al) composites was developed to detect trace isobutylaldehyde. The sensor was designed using isobutylaldehyde to generate an interesting cataluminescence phenomenon in SiO2/MIL-53(Al). Under optimized conditions, a positive linear relationship was observed between the signal intensity of the cataluminescence and isobutylaldehyde concentration. The isobutylaldehyde concentration range of 1.55-310 ppm responded well to the sensing test, with an excellent correlation coefficient of 0.9996. The minimum detectable concentration signal-to-noise ratio (S/N = 3) was found to be 0.49 ppm. In addition, the sensor was effectively utilized for analyzing trace isobutylaldehyde; the analysis resulted in recoveries ranging from 83.4% to 105%, with relative standard deviations (RSDs) of 4.8% to 9.4%. Furthermore, the mechanism of cataluminescence between SiO2/MIL-53(Al) and isobutylaldehyde was explored using GC-MS analysis and density functional theory. We expect that this cataluminescence methodology will provide an approach for the environmental monitoring of isobutylaldehyde.

Recognition Method of Cabbage Heads at Harvest Stage under Complex Background Based on Improved YOLOv8n

To address the problems of low recognition accuracy and slow processing speed when identifying harvest-stage cabbage heads in complex environments, this study proposes a lightweight harvesting period cabbage head recognition algorithm that improves upon YOLOv8n. We propose a YOLOv8n-Cabbage model, integrating an enhanced backbone network, the DyHead (Dynamic Head) module insertion, loss function optimization, and model light-weighting. To assess the proposed method, a comparison with extant mainstream object detection models is conducted. The experimental results indicate that the improved cabbage head recognition model proposed in this study can adapt cabbage head recognition under different lighting conditions and complex backgrounds. With a compact size of 4.8 MB, this model achieves 91% precision, 87.2% recall, and a mAP@50 of 94.5%—the model volume has been reduced while the evaluation metrics have all been improved over the baseline model. The results demonstrate that this model can be applied to the real-time recognition of harvest-stage cabbage heads under complex field environments.

Electromagnetic compatibility of active cardiovascular implants to occupational magnetic field environments: impact of the field direction

Electromagnetic compatibility of active cardiovascular implants to occupational magnetic field environments: impact of the field direction

Worldview, Conflicting Spiritualities and Religious Ecology in Nigeria

Abstract This article contributes to the religion and environment field by analyzing the environmental impact of two divergent ethical responses within Nigerian Christianity. Based on different interpretations of a specific aspect of African spiritual cosmologies, African Indigenous churches (AIC s) and African independent Pentecostals (AIP s) in Nigeria have formulated conflicting spiritualities that shape their ecotheologies. Based on this same cosmology, the ecotheology of the AIC s shows continuity with the indigenous notion of sacred space which also encourages the preservation of the ecosystem especially trees and wet forests. The AIP s, on the other hand, interpret the same notion differently by segregating the public space into holy and evil ecologies. To rid these habitats of perceived evil spirits in the ecosystem, some AIP s have constructed a theology of evangelism and spiritual warfare to expel these spirits by destroying their habitats and converting such habitats into holy use such as building churches and prayer camps. Based on data obtained through observation and literature, the proliferation of prayer camps along the Lagos-Ibadan Expressway is analysed as a product of these notions of holy and evil ecologies.

A novel formulation of wind velocity spectrum incorporating rainfall influence

Wind-rain interaction constitutes a common physical phenomenon in nature. Nevertheless, in fluid mechanics and wind engineering, whether and how rainfall influences wind remains an unresolved challenge. This study relies on a substantial dataset obtained through on-site measurement. Initially, three distinct rainfall categories—light rain, moderate rain, and heavy rain—are established based on the magnitude of vertical wind velocity. Notably, significant disparities in the high-frequency constituents of the fluctuating wind velocity spectrum are discerned across various rainfall levels. Subsequently, a rigorous quantitative analysis explores the relationship between the high-frequency cumulative energy proportion of the fluctuating wind velocity spectrum and rainfall intensity, leading to formulations that delineate the high-frequency cumulative energy proportion in the alongwind, acrosswind, and vertical wind directions as explicit functions of rainfall intensity. Moreover, a comprehensive unified expression models the three-dimensional fluctuating wind velocity spectrum. This expression incorporates parameters encompassing the relative scales of fluctuation and mean components, the extent of conversion between high and low-frequency energy, and the scaling laws governing the inertial subrange. Additionally, explicit functional dependencies for these parameters concerning variations in rainfall intensity are provided. The novel formulation of the fluctuating wind velocity spectrum proposed in this study bears substantial scientific significance, as it facilitates a deeper understanding of wind-rain interaction and offers valuable insights for conducting intricate environmental flow field simulations.

UAV imaging hyperspectral for barnyard identification and spatial distribution in paddy fields

Barnyard grass (Echinochloa crus-galli), a noxious weed prevalent in rice fields, poses a significant threat to rice growth and development, ultimately leading to substantial yield reductions. Its morphological resemblance to rice plants complicates its identification and management. Consequently, accurately identifying and mapping barnyard grass in intricate rice field environments holds substantial research value and significance. Furthermore, most existing weed detection studies rely on single-day data, limiting the models’ generalizability. Therefore, incorporating multi-temporal data into modeling is highly desirable. This study utilized an unmanned aerial vehicle (UAV) to capture multi-temporal, low-altitude hyperspectral imagery of paddy fields, which were subsequently stitched, calibrated, and filtered using the SG convolution filter. The continuous projection algorithm (SPA) was employed to extract sensitive bands for discriminating between rice and barnyard grass. Initially, single-time data was utilized for barnyard grass identification, with models such as support vector machine (SVM), random forest (RF), 1D convolutional neural network (1DCNN), and 3D convolutional neural network (3DCNN) constructed using both full-spectrum and feature bands. The results indicated that the SPA-3DCNN model exhibited the highest performance in identifying rice (F1-score: 0.942) and barnyard grass (F1-score: 0.8936). Seven selected feature bands (482.5234, 546.5415, 675.0806, 709.1382, 762.0431, 922.0157, and 944.6371 nm) were found to be highly discriminative for distinguishing barnyard grass from rice. Subsequently, multi-temporal data modeling was employed to enhance the models’ generalization ability, resulting in improved overall accuracy (OA) and Kappa coefficients for each model, along with reduced misclassification rates. The spatial distribution maps also exhibited better performance with increased temporal data. The infestation trend of barnyard grass was visualized using multi-temporal difference maps, and a density map of barnyard grass was generated through image binarization. This study successfully demonstrated the feasibility of utilizing UAV-borne hyperspectral imagery for identifying barnyard grass in complex paddy field environments. By improving the model’s generalization ability through multi-temporal modeling, the study provided robust data support for managing and preventing barnyard grass infestations, including the creation of spatial distribution and density maps for barnyard grass.

Insights into the microbial assembly and metabolites associated with ginger (Zingiber officinale L. Roscoe) microbial niches and agricultural environments

Ginger, a vegetable export from China, is well-known for its spicy flavour and use in traditional Chinese medicine. By examining the interactions of ginger plants' microbiome and metabolome, we can gain insights to advance agriculture, the environment, and other fields. Our study used metataxonomic analysis to investigate ginger plants' prokaryotic and fungal microbiomes in open fields and greenhouses. We also conducted untargeted metabolomic analysis to identify specific metabolites closely associated with ginger microbiome assembly under both agricultural conditions. Various bacteria and fungi were classified as generalists or specialists based on their ability to thrive in different environments and microbial niches. Our results indicate that ginger plants grown in greenhouses have a greater prokaryotic diversity, while those grown in open fields exhibit a greater fungal diversity. We have identified specific co-occurring prokaryotic and fungal genera associated with ginger plant agroecosystems that can enhance the health and growth of ginger plants while maintaining a healthy environment. In the open field these genera include Sphingomonas, Methylobacterium-Methylorubrum, Bacillus, Acidovorax, Rhizobium, Microbacterium, unclassified_f_Comamonadaceae, Herbaspirillum, Klebsiella, Enterobacter, Chryseobacterium, Nocardioides, Subgroup_10, Enterococcus, Pseudomonas, Devosia, g_unclassified_f_Chaetomiaceae, Pseudaleuria, Mortierella, Cheilymenia, and Pseudogymnoascus. In the greenhouse, the enriched genera were Rhizobium, Stenotrophomonas, Aureimonas, Bacillus, Nocardioides, Pseudomonas, Enterobacter, Delftia, Trichoderma, Mortierella, Cheilymenia, Schizothecium, and Actinomucor. Our research has identified several previously unknown microbial genera for ginger plant agroecosystems. Furthermore, our study has important implications for understanding the correlation between ginger's microbiome and metabolome profiles in diverse environments and may pave the way for future research. Specific microbial genera in crop production environments are associated with essential metabolites, including Safingol, Docosatrienoic acid, P-acetaminophen, and Hypoglycin B.

Reviews and syntheses: A scoping review evaluating the potential application of ecohydrological models for northern peatland restoration

Abstract. Peatland restoration and rehabilitation action has become more widely acknowledged as a necessary response to mitigating climate change risks and improving global carbon storage. Peatland ecosystems require restoration time spans of the order of decades and, thus, cannot be dependent upon the shorter-term monitoring often carried out in research projects. Hydrological assessments using geospatial tools provide the basis for planning restoration works as well as analysing associated environmental influences. “Restoration” encompasses applications to pre-restoration and post-restoration scenarios for both bogs and fens, across a range of environmental impact fields. The aim of this scoping review is to identify, describe, and categorize current process-based modelling uses in peatlands in order to investigate the applicability and appropriateness of ecohydrological and/or hydrological models for northern peatland restoration. Two literature searches were conducted using the entire Web of Science database in September 2022 and August 2023. Of the final 211 papers included in the review, models and their applications were categorized according to this review's research interests in seven distinct categories aggregating the papers' research themes and model outputs. Restoration site context was added by identifying 229 unique study site locations from the full database, which were catalogued and analysed against raster data for the Köppen–Geiger climate classification scheme. A majority of northern peatland sites were in temperate oceanic zones or humid continental zones that experienced snow. Over one in five models from the full database of papers were unnamed and likely intended for single use. Key themes emerging from topics covered by papers in the database included the following: modelling restoration development from a bog growth perspective, the prioritization of modelling greenhouse gas (GHG) emissions dynamics as a part of policymaking, the importance of spatial connectivity within or alongside process-based models to represent heterogeneous systems, and the increased prevalence of remote sensing and machine learning techniques to predict restoration progress with little physical site intervention. Models are presented according to their application to peatlands or broader ecosystem and organized from most to least complex. This review provides valuable context for the application of ecohydrological models in determining strategies for peatland restoration and evaluating post-intervention development over time.

SURE: A Visualized Failure Indexing Approach using Program Memory Spectrum

Failure indexing is a longstanding crux in software debugging, the goal of which is to automatically divide failures (e.g., failed test cases) into distinct groups according to the culprit root causes, as such multiple faults residing in a faulty program can be handled independently and simultaneously. The community of failure indexing has long been plagued by two challenges: 1) The effectiveness of division is still far from promising. Specifically, existing failure indexing techniques only employ a limited source of software run-time data, for example, code coverage, to be failure proximity and further divide them, which typically delivers unsatisfactory results. 2) The outcome can be hardly comprehensible. Specifically, a developer who receives the division result is just aware of how all failures are divided, without knowing why they should be divided the way they are. This leads to difficulties for developers to be convinced by the division result, which in turn affects the adoption of the results. To tackle these two problems, in this paper, we propose SURE, a vi SU alized failu R e ind E xing approach using the program memory spectrum. We first collect the run-time memory information (i.e., variables’ names and values, as well as the depth of the stack frame) at several preset breakpoints during the execution of a failed test case, and transform the gathered memory information into a human-friendly image (called program memory spectrum, PMS). Then, any pair of PMS images that serve as proxies for two failures is fed to a trained Siamese convolutional neural network, to predict the likelihood of them being triggered by the same fault. Last, a clustering algorithm is adopted to divide all failures based on the mentioned likelihood. In the experiments, we use 30% of the simulated faults to train the neural network, and use 70% of the simulated faults as well as real-world faults to test. Results demonstrate the effectiveness of SURE: It achieves 101.20% and 41.38% improvements in faults number estimation, as well as 105.20% and 35.53% improvements in clustering, compared with the state-of-the-art technique in this field, in simulated and real-world environments, respectively. Moreover, we carry out a human study to quantitatively evaluate the comprehensibility of PMS, revealing that this novel type of representation can help developers better comprehend failure indexing results.

Visual fluorescence detection kit for Cr (VI) based on biological matrix-derived carbon dots and assisted for AA

In this study, a novel green donkey-hide gelatin carbon dots (D-CDs) were synthesized by hydrothermal method and a portable detector based on Cr (VI) fluorescent test strips was prepared. The synthesized D-CDs exhibited bright blue fluorescence which was used for the detection of Cr (VI) and ascorbic acid (AA). It still exhibited high sensitivity and acceptable selectivity under high concentration of other interfering ions. The morphology and elemental characteristics of D-CDs were characterized. Meanwhile, the “on-off” quenching mechanism of Cr (VI) on D-CDs was studied by ultraviolet-visible absorption spectra, fourier transform infrared spectroscopy and fluorescence spectra. Then a D-CDs/Cr (VI) fluorescence sensing platform was constructed, and reopened by AA, thus eliciting an “on-off-on” fluorescence reaction. Under the optimal conditions, the fluorescence intensity was quenched significantly with the increase of Cr (VI) concentration and restored by the good reducibility of ascorbic acid (AA), with the linear ranges of 0.5–400 μM and 1–400 μM, respectively. The detection limits of Cr (VI) and AA can be as low as 0.08 μM and 0.14 μM. Consequently, it has low toxicity and can be successfully used as a fluorescent probe to determine Cr (VI) in river water and AA in fruits with satisfactory results. It not only lays a foundation for the development of fluorescence sensor in the field of environment and food, but also provides a good application prospect for its detection of heavy metal ions and AA.

A dual-modality sensing probe of fluorescent and colorimetric for detection of cobalt ion based on silver nanoparticles functionalized rhodamine 6G derivatives

The combination of fluorescent probe and colorimetric technique has become one of the most powerful analytical methods due to the advantages of visualization, minimal measurement errors and high sensitivity. Hence, a novel dual-modality sensing probe with both colorimetric and fluorescent capabilities was developed for detecting cobalt ions (Co2+) based on homocysteine mediated silver nanoparticles and rhodamine 6G derivatives probe (AgNPs-Hcy-Rh6G2). The fluorescence of the AgNPs-Hcy-Rh6G2 probe turned on due to the opening of the Rh6G2 spirolactam ring in the presence of Co2+ by a catalytic hydrolysis. The fluorescent intensity of probe is proportional to Co2+ concentration in the range of 0.10–50 μM with a detection limit of 0.05 μM (S/N = 3). More fascinatingly, the color of AgNPs-Hcy-Rh6G2 probe changed from colorless to pink with increasing Co2+ concentration, which allowing colorimetric determination of Co2+. The absorbance of AgNPs-Hcy-Rh6G2 probe is proportional to Co2+ concentration in the range from 0.10 to 25 μM with a detection limit of 0.04 μM (S/N = 3). This colorimetric and fluorescent dual-modal method exhibited good selectivity, and reproducibility and stability, holding great potential for real samples analysis in environmental and drug field.

PND-Net: plant nutrition deficiency and disease classification using graph convolutional network

Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. Furthermore, a GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), has been evaluated on two public datasets for nutrition deficiency, and two for disease classification using four backbone CNNs. The best classification performances of the proposed PND-Net are as follows: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document}: 95.50%, and BreakHis 100×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document}: 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, the proposed method has been evaluated using five-fold cross validation and achieved improved performances on these datasets. Clearly, the proposed PND-Net effectively boosts the performances of automated health analysis of various plants in real and intricate field environments, implying PND-Net’s aptness for agricultural growth as well as human cancer classification.

Improvement of the YOLOv8 Model in the Optimization of the Weed Recognition Algorithm in Cotton Field.

Due to the existence of cotton weeds in a complex cotton field environment with many different species, dense distribution, partial occlusion, and small target phenomena, the use of the YOLO algorithm is prone to problems such as low detection accuracy, serious misdetection, etc. In this study, we propose a YOLOv8-DMAS model for the detection of cotton weeds in complex environments based on the YOLOv8 detection algorithm. To enhance the ability of the model to capture multi-scale features of different weeds, all the BottleNeck are replaced by the Dilation-wise Residual Module (DWR) in the C2f network, and the Multi-Scale module (MSBlock) is added in the last layer of the backbone. Additionally, a small-target detection layer is added to the head structure to avoid the omission of small-target weed detection, and the Adaptively Spatial Feature Fusion mechanism (ASFF) is used to improve the detection head to solve the spatial inconsistency problem of feature fusion. Finally, the original Non-maximum suppression (NMS) method is replaced with SoftNMS to improve the accuracy under dense weed detection. In comparison to YOLO v8s, the experimental results show that the improved YOLOv8-DMAS improves accuracy, recall, mAP0.5, and mAP0.5:0.95 by 1.7%, 3.8%, 2.1%, and 3.7%, respectively. Furthermore, compared to the mature target detection algorithms YOLOv5s, YOLOv7, and SSD, it improves 4.8%, 4.5%, and 5.9% on mAP0.5:0.95, respectively. The results show that the improved model could accurately detect cotton weeds in complex field environments in real time and provide technical support for intelligent weeding research.

Catalytic hairpin-assembled and DNAzyme-driven DNA walker integrated hybridization chain reaction to construct a label-free electrochemical aptasensor for the detection of acetamiprid

As a new type of nicotine pesticide, the excessive residue of acetamiprid (ACE) in food and the environment seriously poses human health. Hence, this article developed an electrochemical aptasensor for detecting ACE based on a cascaded amplification circuit composed of catalytic hairpin-assembled and DNAzyme-driven DNA walker and hybridization chain reaction. Through the target triggered catalytic hairpin assembly (CHA), the free travelling strand of DNA walker and intact catalytic core of DNAzyme were formed, and with the assistance of Mg2+, DNAzyme was activated, driving the travelling strand to walk autonomously, repeatedly cutting hairpin DNA substrates on the electrode surface, and obtaining abundant DNA fragments. These fragments contained the initiators of hybridization chain reaction (HCR) could trigger HCR and produce the long double-strand DNA (dsDNA) for loading methylene blue (MB). Owing to the fact that Ce(Ⅲ, Ⅳ)-MOF material with electrocatalytic activity, the electrochemical reduction of MB could be effectively catalyzed, obtain significantly enhanced current signals, and ultimately achieve label-free and sensitive detection of ACE, with a detection limit of 16.38 fM (S/N = 3). Moreover, the aptasensor demonstrated excellent selectivity, stability, and reproducibility, and exhibited good reliability for detecting ACE in the vegetable soil and tomato samples, providing a new idea for the detection of pesticide residues in the fields of food and environment.

A simple numerical simulation model can elucidate the key factors for designing a miniaturized ion trap mass spectrometer

BackgroundMiniature ion trap mass spectrometer enables mass-to-charge ratio analysis of ions via quadrupole field in a low vacuum environment. It plays an important role in on-site detection due to its portability and specificity. In order to gain a deeper understanding of the analysis mechanism of miniature ion trap mass spectrometers, a quadrupole MS ion trajectory numerical simulation model (QITNS) is established in this paper for ions trajectory calculation under the action of quadrupole field, exciting field and neutral gas molecule collision. Compared with the existing methods, the model in this paper is simpler and more direct, which effectively explored the effects of dipole excitation and quadrupole excitation on ion manipulation under high background pressure. ResultsThe simulation results demonstrate that high RF amplitude, low auxiliary AC amplitude and quadrupole excitation can effectively improve the isolation resolution. Besides, it clarified the difference between the analysis mechanism of ion trap mass spectrometers under high background pressure (above 13.332 Pa) and absolute vacuum conditions. The relevant results are consistent with the conclusions of previous experiments and other theories, proving the applicability and accuracy of the proposed calculation model and solution method. SignificanceThis research bears the guiding significance for further understanding the mechanism of quadrupole mass spectrometry as well as designing and developing miniature mass spectrometers.

Suspected exertional heat stroke; A case study of worker cooling in a hot and humid field environment.

In the event of a severe occupational heat-related illness, paramedic assistance may not be immediately available. A worker's survival may depend on their co-workers access to efficacious field-based cooling modalities. One cooling method that has been claimed to be practical in field-based settings is the ice towel method. This case study assessed the practicality of the ice towel method in an industrial setting, where criteria for use include cost effectiveness, portability, scalability, and implementation by a single worker under the stress of an emergency. This case study describes the emergency application of the ice towel method while awaiting paramedics, for a worker suffering suspected exertional heat stroke on a remote job site. Ice towels were able to be transported to a remote field site and applied successfully by a single worker under the stress of a potentially life-threatening emergency. The ice towel method was cost effective, scalable, transportable, and rapidly applied in a field-based emergency. This case study demonstrates the importance of organizations assessing their heat-related risks, and determining controls based upon their efficacy and practicality for their unique setting.