The aim of the present study was to evaluate the antifungal activity of fatty acids against phytopathogenic fungi. Two pot experiments were conducted by mixing palmitic and oleic acids in the soil in which poor plant growth was observed. In addition, the antifungal activities of nine fatty acids (butyric acid, caproic acid, caprylic acid, capric acid, lauric acid, myristic acid, palmitic acid, oleic acid, and linoleic acid) against four phytopathogenic fungi: Alternaria solani, Colletotrichum lagenarium, Fusarium oxysporum f. sp. Cucumerinum, and Fusarium oxysporum f. sp. lycopersici, were assessed by measuring mycelial growth and spore germination via Petri dish assay. The results of the pot experiments showed that the mixture of palmitic and oleic acids enhanced the growth of the seedlings of continuous-tomato and continuous-cucumber. Except for oleic acid, in the Petri dish assay, the fatty acids tested were observed to inhibit the mycelial growth of one or more tested fungi. In addition to the suppression of mycelial growth, butyric acid, caproic acid, caprylic acid, capric acid, lauric acid, and palmitic acid showed an inhibitory effect against spore germination and the extent of inhibition varied with both the type of fatty acids, and the fungi. In particular, capric acid displayed strong inhibitory effect against C. lagenarium on the mycelial growth and spore germination. The saturated fatty acids, i.e. palmitic acids, showed stronger antifungal activity than the unsaturated fatty acids, i.e. oleic acid. It suggests that fatty acids might be applicable to exploring for alternative approaches to integrated control of phytopathogens.

R&D professionals are the impetus behind technological innovation, and their competitiveness and capability drive the growth of a company. However, high-tech industries have a chronic shortage of such indispensable professionals. Accordingly, reducing R&D personnel turnover has become a major human resource management challenge facing innovative companies. This study combined importance–performance analysis (IPA) with the decision-making trial and evaluation laboratory (DEMATEL) method to propose an IPA–DEMATEL model. Establishing this model involved three steps. First, an IPA was conducted to measure the importance of and satisfaction gained from job satisfaction criteria. Second, the DEMATEL method was used to determine the causal relationships of and interactive influence among the criteria. Third, a criteria model was constructed to evaluate job satisfaction of high-tech R&D personnel. On the basis of the findings, managerial suggestions are proposed.

Bottom-up cracking may be due to CSL surface raveling or fatigue of the CSL, as follows:(a) Bottom-up cracking due to CSL surface raveling Based on accelerated pavement testing, studies show that the surface of a stabilized base layer can ravel, creating a layer of loose material between the HMA and base CSL (Figure A-8). Raveling of the base increases the strain level at the bottom of the HMA, which can result in alligator cracking. In addition, pumping was observed in these cases. The pumping is caused by the loss of fines in the loose material layer. This phenomenon may be linked to the erodibility of stabilized materials, which often happens when relatively fine raw materials are treated (De Beer 1985). reported the loose layer is about 0.8 in. thick and is believed to be due to shear failure within the CSL as a result of horizontal loading, which in turn causes alligator cracking.

With the rise of labor costs and the advancement of automation in the textile industry, fabric defect detection has become a hot research field in recent years. We proposed a learning-based framework for automatic detection of fabric defects. Firstly, we use a fixed-size square slider to crop the original image to a certain step and regularity. Then an improved histogram equalization is used to enhance each cropped image. Furthermore, the Inception-V1 model is employed to predict the existence of defects in the local area. Finally, we apply the LeNet-5 model, which plays the role of a voting model, to recognize the type of the defect in the fabric. In brief, the proposed framework mainly consists of two steps, namely local defect prediction and global defect recognition. Experiments on the dataset have demonstrated the superior performance in fabric defect detection.

Significance The detection of low-abundance molecular biomarkers is key to the liquid–biopsy-based disease diagnosis. Existing methods are limited by the affinity and specificity of recognition probes and the mass transportation of analyte molecules onto the sensor surfaces, resulting in insufficient sensitivity and long assay time. This work establishes a rapid and ultrasensitive approach by actively tuning binding kinetics and accelerating the mass transportation via nanoparticle micromanipulations. This is significant because it permits extremely sensitive measurements within clinically acceptable assay time. It is incubation-free, washing-free, and compatible with low- and high-affinity probes.

The unique capability of surface plasmon resonance microscopy (SPRM) in single nanoparticle analysis has found use in various chemical and biological applications. While SPRM offers exceptional sensitivity, the statistical analysis of numerous nanoparticles has been extremely laborious and time-consuming. Herein, we presented an image processing software package for nanoparticle analysis in SPRM, which is empowered by a deep learning algorithm. This package enabled fully automated nanoparticle identification, digital counting, three-dimensional tracking of particle locations, and quantification of dwell time and Brownian motion properties. With a built-in image filtering process to improve the contrast, robust identification and analysis have been achieved from SPRM images of low refractive index nanoparticles. This software tool would largely promote the translation of SPRM technology into the digital sensing platform for high throughput sample screening.

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