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Green mold ( Penicillium digitatum ) and blue mold ( Penicillium italicum ) are among the most economically impactful post-harvest diseases of citrus fruit worldwide. Post-harvest citrus diseases are largely controlled with synthetic fungicides such as pyrimethanil, imazalil, fludioxonil, and thiabendazole. Due to their toxic effects, prolonged and excessive application of these fungicides is gradually restricted in favor of safe and more eco-friendly alternatives. This review comprehensively describes alternative methods for the control of P. digitatum and P. italicum : (a) antagonistic micro-organisms, (b) plant extracts and essential oils, (c) biofungicides, (d) chitosan and chitosan-based citrus coatings, (e) heat treatments, (f) ionizing and non-ionizing irradiations, (g) food additives, and (h) synthetic elicitors. Integrating multiple approaches such as the application of biocontrol agents with food additives or heat treatments have overcome some drawbacks to single treatments. In addition, integrating treatment approaches could produce an additive or synergistic effect on controlling both molds for a satisfactory level of disease reduction in post-harvest citrus. Further research is warranted on plant resistance and fruit-pathogen interactions to develop safer strategies for the sustainable control of P. digitatum and P. italicum in citrus.

Citrus are vulnerable to the postharvest decay caused by Penicillium digitatum, Penicillium italicum, and Geotrichum citri-aurantii, which are responsible for the green mold, blue mold, and sour rot post-harvest disease, respectively. The widespread economic losses in citriculture caused by these phytopathogens are minimized with the use of synthetic fungicides such as imazalil, thiabendazole, pyrimethanil, and fludioxonil, which are mainly employed as control agents and may have harmful effects on human health and environment. To date, numerous non-chemical postharvest treatments have been investigated for the control of these pathogens. Several studies demonstrated that biological control using microbial antagonists and natural products can be effective in controlling postharvest diseases in citrus, as well as the most used commercial fungicides. Therefore, microbial agents represent a considerably safer and low toxicity alternative to synthetic fungicides. In the present review, these biological control strategies as alternative to the chemical fungicides are summarized here and new challenges regarding the development of shelf-stable formulated biocontrol products are also discussed.

Summary Pathogenic fungi cause major postharvest losses. During storage and ripening, fruit becomes highly susceptible to fungi that cause postharvest disease. Fungicides are effective treatments to limit disease. However, due to increased public concern for their possible side effects, there is a need to develop new strategies to control postharvest fungal pathogens. Botrytis cinerea, a common postharvest pathogen, was shown to uptake small double‐stranded RNA (dsRNA) molecules from the host plant. Such dsRNA can regulate gene expression through the RNA interference system. This work aimed to develop a synthetic dsRNA simultaneously targeting three essential transcripts active in the fungal ergosterol biosynthesis pathway (dsRNA‐ERG). Our results show initial uptake of dsRNA in the emergence zone of the germination tube that spreads throughout the fungus and results in down‐regulation of all three targeted transcripts. Application of dsRNA‐ERG decreased B. cinerea germination and growth in in vitro conditions and various fruits, leading to reduce grey‐mould decay. The inhibition of growth or decay was reversed by the addition of ergosterol. While dual treatment with dsRNA‐ERG and ergosterol‐inhibitor fungicide reduced by 100‐fold the required amount of fungicide to achieve the same protection rate. The application of dsRNA‐ERG induced systemic protection as shown by decreased decay development at inoculation points distant from the treatment point in tomato and pepper fruits. Overall, this study suggests that dsRNA‐ERG can effectively control B. cinerea growth and grey‐mould development suggesting its efficacy as a future method for postharvest control of fungal pathogens.

A substantial body of evidence suggests that indoor mold exposure is a cause of allergic and respiratory diseases in humans. While models exist for assessing the risk of mold growth on building materials, few study the characteristics of mold growth after germination. This study conducted mold growth experiments in a constant temperature chamber, using four temperature settings of 15, 20, 25 and 30 °C, and three relative humidities of 56 to 61%, 75 to 76% and 83 to 86%. A mold growth prediction model was established using temperature and relative humidity. The accuracy of the model was verified by comparing the sampling and the predicted values in a laboratory environment. The results indicated that reducing the environmental temperature and relative humidity could significantly inhibit the growth of mold, although the inhibitory effects varied. Temperature might play a more critical role. At higher temperatures (25 °C and 30 °C), the growth rate and lag time of mold tended to be consistent and there were differences in the maximum diameter. In the predictive model, the polynomial secondary model for the maximum growth rate and lag time and the Arrhenius–Davey secondary model for the maximum diameter (A) had good predictive effects (Adj.R2 > 0.850). It is speculated that temperature is the key factor affecting the maximum growth diameter of mold. The mold growth prediction model could better predict the growth of mold in actual environments without wind Adj.R2 > 0.800), but the accuracy of the model decreased under windy conditions (wind velocity < 1 m/s). The mold growth predictive model we established could be used to predict the growth characteristics of mold in windless environments. It also provides control suggestions for the regulation of temperature and relative humidity in indoor environments, supporting indoor thermal environment management and pollutant control, and ensuring indoor human health.

The currently growing demand for metallic and polymeric products has undoubtedly changed the rules of manufacturing, enabling customers to more functionally define their products based on their needs. Nowadays, a new technique for rapid tooling, Additive Manufacturing (AM), can create customized products with more complex geometries and short life cycles (flexibility) in order to keep up with the new variables imposed by the manufacturing environment. In the last two decades, the migration from subtractive manufacturing to AM has materialized such products with reduced costs and cycle times. AM has been recently promoted to develop polymer molds for product manufacturing. This paper reviews the main findings in the literature concerning polymer molds created by AM compared to conventional (metal) molds obtained by subtractive manufacturing. Information about specific topics is scarce or nonexistent, for example, about the characterization of the most commonly injected materials and molds used in this type of technology, their mechanical properties (part and mold), designs for all types of geometries, and costs. These aspects are addressed in this literature review, highlighting the advantages of this alternative manufacturing process, which is considered a desirable technology worldwide.

Micro/nanostructured components play an important role in micro-optics and optical engineering, tribology and surface engineering, and biological and biomedical engineering, among other fields. Precision glass molding technology is the most efficient method of manufacturing micro/nanostructured glass components, the premise of which is meld manufacturing with complementary micro/nanostructures. Numerous mold manufacturing methods have been developed to fabricate extremely small and high-quality micro/nanostructures to satisfy the demands of functional micro/nanostructured glass components for various applications. Moreover, the service performance of the mold should also be carefully considered. This paper reviews a variety of technologies for manufacturing micro/nanostructured molds. The authors begin with an introduction of the extreme requirements of mold materials. The following section provides a detailed survey of the existing micro/nanostructured mold manufacturing techniques and their corresponding mold materials, including nonmechanical and mechanical methods. This paper concludes with a detailed discussion of the authors recent research on nickel-phosphorus (Ni-P) mold manufacturing and its service performance.

Complex behaviors are typically associated with animals, but the capacity to integrate information and function as a coordinated individual is also a ubiquitous but poorly understood feature of organisms such as slime molds and fungi. Plasmodial slime molds grow as networks and use flexible, undifferentiated body plans to forage for food. How an individual communicates across its network remains a puzzle, but Physarum polycephalum has emerged as a novel model used to explore emergent dynamics. Within P. polycephalum, cytoplasm is shuttled in a peristaltic wave driven by cross-sectional contractions of tubes. We first track P. polycephalum’s response to a localized nutrient stimulus and observe a front of increased contraction. The front propagates with a velocity comparable to the flow-driven dispersion of particles. We build a mathematical model based on these data and in the aggregate experiments and model identify the mechanism of signal propagation across a body: The nutrient stimulus triggers the release of a signaling molecule. The molecule is advected by fluid flows but simultaneously hijacks flow generation by causing local increases in contraction amplitude as it travels. The molecule is initiating a feedback loop to enable its own movement. This mechanism explains previously puzzling phenomena, including the adaptation of the peristaltic wave to organism size and P. polycephalum’s ability to find the shortest route between food sources. A simple feedback seems to give rise to P. polycephalum’s complex behaviors, and the same mechanism is likely to function in the thousands of additional species with similar behaviors.

In recent years, high-Si ductile cast irons (3–6% Si) have begun to be used more and more in the automotive and maritime industries, but also in wind energy technology and mechanical engineering. Si-alloyed ferrite has high strength, hardness and oxidation and corrosion resistance, but it has low ductility, toughness and thermal conductivity, with graphite as an important influencing factor. In this study, 4.5% Si uninoculated ductile iron solidified in thin wall castings (up to 15 mm section size) via a permanent (metal) mold versus a sand mold, was evaluated. Solidification in a metal mold led to small size, higher graphite particles (less dependent on the section size). The graphite particles’ real perimeter was 3–5% higher than the convex perimeter, while the values of these parameters were 41–43% higher in the sand mold. Increasing the casting section size led to an increased graphite perimeter, with it being much higher for sand mold. The graphite particles’ shape factors, involving the maximum and minimum size, were at a lower level for metal mold solidification, while by involving the difference between Pr and Pc, is higher for the metal mold. The shape factor, including the graphite area and maximum size, had higher values in the metal mold, sustaining a higher compactness degree of graphite particles and a higher nodularity regarding metal mold solidification (75.5% versus 67.4%). The higher was due to the graphite compactness degree level (shape factor increasing from 0.50 up to 0.80), while the lower was due to the graphite nodularity for both the metal mold (39.1% versus 88.5%) and the sand mold (32.3% versus 83.1%). The difference between the metal mold and sand mold as the average graphite nodularity increased favored the metal mold.

Tomato is a widely cultivated crop that is important for its nutritional value and genetic diversity. Tomato production is seriously challenged by pests and diseases, among which tomato gray mold and leaf mold are particularly serious. Biological control is one of the most preferred methods for disease management in tomato production. At present, the fungi used to control tomato gray mold are mainly Trichoderma and yeast. Bacillus and actinomycetes are the most effective microorganisms for controlling tomato leaf mold. Tomato gray mold and leaf mold often occur at the same time during the production process, yet there are fewer strains for controlling both diseases at the same time. Biocontrol bacteria Pseudomonas azotoformans WXCDD51, Bacillus sp. WXCDD105, Bacillus subtilis BS and Bacillus amyloliquefaciens BS WY-1, which were isolated and screened in the previous stage, can prevent both tomato gray mold and leaf mold. Here, we optimized liquid fermentation for the four biocontrol bacterial strains together. We obtained the best fermentation medium formula and fermentation conditions for the four biocontrol bacteria. The broad-spectrum properties of the four biocontrol bacteria were tested, and, on this basis, compound strains were constructed. The control effect of single and compound strains on tomato gray mold and leaf mold was evaluated. Their potential effects on the growth of tomato seeds and seedlings were also studied. This research provides a foundation for the development and use of compound bacteria for growth promotion and disease management in tomato production.

Gray mold disease is one of the most frequently occurring diseases in strawberries. Given that it spreads rapidly, rapid countermeasures are necessary through the development of early diagnosis technology. In this study, hyperspectral images of strawberry leaves that were inoculated with gray mold fungus to cause disease were taken; these images were classified into healthy and infected areas as seen by the naked eye. The areas where the infection spread after time elapsed were classified as the asymptomatic class. Square regions of interest (ROIs) with a dimensionality of 16 × 16 × 150 were acquired as training data, including infected, asymptomatic, and healthy areas. Then, 2D and 3D data were used in the development of a convolutional neural network (CNN) classification model. An effective wavelength analysis was performed before the development of the CNN model. Further, the classification model that was developed with 2D training data showed a classification accuracy of 0.74, while the model that used 3D data acquired an accuracy of 0.84; this indicated that the 3D data produced slightly better performance. When performing classification between healthy and asymptomatic areas for developing early diagnosis technology, the two CNN models showed a classification accuracy of 0.73 with regards to the asymptomatic ones. To increase accuracy in classifying asymptomatic areas, a model was developed by smoothing the spectrum data and expanding the first and second derivatives; the results showed that it was possible to increase the asymptomatic classification accuracy to 0.77 and reduce the misclassification of asymptomatic areas as healthy areas. Based on these results, it is concluded that the proposed 3D CNN classification model can be used as an early diagnosis sensor of gray mold diseases since it produces immediate on-site analysis results of hyperspectral images of leaves.