Carboxylate anions are analytical targets with environmental and biological relevance, whose detection is often challenging in aqueous solutions. We describe a method for discrimination and ...quantitation of carboxylates in water buffered to pH 7.4 based on their differential interaction with a supramolecular fluorescent sensor, self-assembled from readily available building blocks. A fifth-generation poly(amidoamine) dendrimer (PAMAM G5), bound to organic fluorophores (calcein or pyranine) through noncovalent interactions, forms a dye•PAMAM complex responsive to interaction with carboxylates. The observed changes in absorbance, and in fluorescence emission and anisotropy, were interpreted through linear discriminant analysis (LDA) and principal component analysis (PCA) to differentiate 10 structurally similar carboxylates with a limit of discrimination around 100 μM. The relationship between the analytes’ chemical structures and the system’s response was also elucidated. This insight allowed us to extend the system’s capabilities to the simultaneous identification of the nature and concentration of unknown analytes, with excellent structural identification results and good concentration recovery, an uncommon feat for a pattern-based sensing system.
Penicillins and cephalosporins belong to the β-lactam antibiotic family, which accounts for more than half of the world market for antibiotics. Misuse of antibiotics harms human health and the ...environment. Here, we describe an easy, fast, and sensitive optical method for the sensing and discrimination of two penicillin and five cephalosporin antibiotics in buffered water at pH 7.4, using fifth-generation poly (amidoamine) (PAMAM) dendrimers and calcein, a commercially available macromolecular polyelectrolyte and a fluorescent dye, respectively. In aqueous solution at pH 7.4, the dendrimer and dye self-assemble to form a sensor that interacts with carboxylate-containing antibiotics through electrostatic interaction, monitored through changes in the dye’s spectroscopic properties. This response was captured through absorbance, fluorescence emission, and fluorescence anisotropy. The resulting data set was processed through linear discriminant analysis (LDA), a common pattern-base recognition method, for the differentiation of cephalosporins and penicillins. By pre-hydrolysis of the β-lactam rings under basic conditions, we were able to increase the charge density of the analytes, allowing us to discriminate the seven analytes at a concentration of 5 mM, with a limit of discrimination of 1 mM.
•Formation mechanism of variability of FRAM is formulated.•N-K model is used to calculate the coupling risk intensity.•Coupling risk intensity is used to evaluate the variability.
In this paper, we ...formulate the formation mechanism of variability of Functional Resonance Analysis Method (FRAM) based on the risk pulse theory for the first time. The coupling and interactions among human errors, mechanical failures, terrible environment and organization factors might cause the system state change, and cause the variability during the operation processes of the system. Based on the collected historical occurrence frequency of each risk factor and the determined coupling and interaction relationships among the risk factors, the N-K model is used to calculate the coupling risk intensity, which provides a quantitative method to evaluate the variability of the functional module. The more frequent the coupling times, the greater the probability of the coupling. The greater the coupling intensity, the greater the risk and the greater the occurrence probability of accidents. Compared with previous improvement works, the historical statistical frequency of each risk factor is need during the calculation based on N-K model, the results are not influenced by experts. A case study is conducted by taking a railway dangerous goods transportation accident in 2001 as the background. The results show that N-K model is able to calculate the variability of the functional module, quantitatively and effectively.
Weeds are among the major factors that could harm crop yield. Site-specific weed management has become an effective tool to control weed and machine vision combined with image processing is an ...effective approach for weed detection. In this work, an encoder-decoder deep learning network was investigated for pixel-wise semantic segmentation of crop and weed. Different input representations including different color space transformations and color indices were compared to optimize the input of the network. Three image enhancement methods were investigated to improve model robustness against different lighting conditions. The results show that for images without enhancement, color space transformation and vegetation indices without NIR (Near Infrared) information did not improve the segmentation results, while inclusion of NIR information significantly improved the segmentation accuracy, indicating the effectiveness of NIR information for precise segmentation under weak lighting condition. Image enhancement improved the image quality and consequently the robustness of segmentation models against different lighting conditions. The best MIoU value for pixel-wise segmentation was 88.91% and the best mean accuracy of object-wise segmentation was 96.12%. The deep network and image enhancement methods applied in this work provided promising segmentation results for weed detection and did not need large amount of data for model training, which is suitable for site-specific weed management.
Abstract This study proposes a novel method to prevent fatigue cracks at the root of fillet welds in steel bridge supports by inserting epoxy resin as an adhesive material. A total of 36 specimens, ...categorized into welded‐only and bonding‐assisted types, were subjected to a series of four‐point bending fatigue tests to simulate cyclic tensile stress conditions. Additionally, finite element analysis was employed to investigate the impact of epoxy insertion on stress distribution near the weld root. The results demonstrated that bonding‐assisted specimens exhibited significantly improved fatigue life compared to welded‐only specimens, with a notable reduction in tensile stress at the weld root. Furthermore, a displacement‐based method was employed to evaluate weld root fatigue performance, yielding consistent results. These findings highlight the potential of integrating adhesive bonding in fillet welds to improve the durability and service life of steel bridge structures by effectively mitigating fatigue‐related issues.
Highlights Preventing fatigue cracks in fillet weld root through epoxy insertion. Inserted rubber prevents welding defects caused by epoxy burning during welding. The combination of welding and bonding reduces the opening displacement of the weld root. Displacement‐based method can effectively evaluate weld root fatigue life.
In this work, we studied four types of cosmological models with different mechanisms driving the accelerated expansion of the universe, include Braneworld models, Chaplygin Gas models, Emergent Dark ...Energy models, and cosmological torsion models. Considering that the dynamics of these models at low redshifts are very similar and difficult to distinguish, we used the latest and largest UV and X-ray measurements of quasars (QSOs) observations covering the range of redshift
0.009
<
z
<
7.5
. However, the high intrinsic dispersion of this sample and the degeneracy between cosmological model parameters, we added 2D-BAO and 3D-BAO datasets to help us constrain the parameters of these cosmological models. Our results suggest that standard cold dark matter scenario may not be the best cosmological model preferred by the high-redshift observations. The Generalized Chaplygin Gas (GCG) and cosmological constant plus torsion (named Case II) models perform best by Akaike Information Criterion (AIC), but the
Λ
CDM is the best cosmological model preferred by Bayesian Information Criterion (BIC). Our work also supports that the Phenomenologically Emergent Dark Energy and cosmological torsion models may alleviate the Hubble tension, the reported value of the Hubble constant obtained from QSO+BAO datasets combination lies between Planck 2018 observations and local measurements from the SH0ES collaboration, while other cosmological models all support that the Hubble constant tends to be closer to recent Planck 2018 results, but these model are penalized by information criterion.
A novel coronavirus has caused thousands of human infections in China since December 2019, raising a global public health concern. Recent studies (Huang et al., Chan et al., and Zhou et al.) have ...provided timely insights into its origin and ability to spread among humans, informing infection prevention and control practices.
•Steam-Water Condensing-Injector (SI)’s pressure elevation mechanism was investigated through experiment•Experiments were carried out to obtain axial pressure and temperature profiles of ...central-water jet SI.•SI's heat transfer capability under steam-water direct contact condensation was assessed.•One-dimensional analytical model was implemented to assess SI's axial pressure distribution.
The steam-water condensing injector (SI) is a device capable of producing high-pressure subcooled liquid streams by combining steam and subcooled liquid jet at a higher pressure than the inlet streams. The present study investigated the SI's pressure elevation mechanism using supersonic steam and a subcooled water jet. The experiment was carried out using central-water jet type SI equipped with an overflow port at the inlet steam pressures of 0.3 to 0.7 MPa and inlet liquid mass flow rates of 0.4 to 0.7 kg/s. Axial pressure and temperature distributions were measured at the mixing nozzle and diffuser sections to study the pressure elevation mechanism. Based on the analytical foundation laid by Grolmes (1968), the one-dimensional analytical model was implemented by treating SI's two-phase flow regimes as inverted annular and dispersed flows. For the current SI geometry and operating conditions, the present one-dimensional model could predict the axial pressure distribution of the SI and the maximum discharging pressure with reasonable accuracy for sufficiently high inlet flowrate ratio conditions.
Development of weed and crop detection algorithms provides theoretical support for weed control and becomes an effective tool for the site-specific weed management. For weed and crop object detection ...tasks in the field, there is often a large difference between the number of weed and crop, resulting in an unbalanced distribution of samples and further posing difficulties for the detection task. In addition, most developed models tend to miss the small weed objects, leading to unsatisfied detection results. To overcome these issues, we proposed a pixel-level synthesization data augmentation method and a TIA-YOLOv5 network for weed and crop detection in the complex field environment.
The pixel-level synthesization data augmentation method generated synthetic images by pasting weed pixels into original images. In the TIA-YOLOv5, a transformer encoder block was added to the backbone to improve the sensitivity of the model to weeds, a channel feature fusion with involution (CFFI) strategy was proposed for channel feature fusion while reducing information loss, and adaptive spatial feature fusion (ASFF) was introduced for feature fusion of different scales in the prediction head.
Test results with a publicly available sugarbeet dataset showed that the proposed TIA-YOLOv5 network yielded an F1-scoreweed, APweed and mAP@0.5 of 70.0%, 80.8% and 90.0%, respectively, which was 11.8%, 11.3% and 5.9% higher than the baseline YOLOv5 model. And the detection speed reached 20.8 FPS.
In this paper, a fast and accurate workflow including a pixel-level synthesization data augmentation method and a TIA-YOLOv5 network was proposed for real-time weed and crop detection in the field. The proposed method improved the detection accuracy and speed, providing very promising detection results.
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