The management of Mediterranean mountains need to know whether or not the flora is adapted to respond to fire and, if so, through what mechanisms. Serpentine outcrops constitute particular ecosystems in the Mediterranean Basin, and plants need to make an additional adaptive effort. The objective of this study is to know the response to fire of the main members of the group of serpentine plants, which habit the Spanish Mediterranean ultramafic mountain, to help in their management. For this purpose, monitoring plots were established on a burned ultramafic outcrop, which was affected by fire in August 2012.They were located in the Mediterranean south of the Iberian Peninsula, Andalusia region. The dominant vegetation of this serpentine ecosystem had been studied previously to fire; it was a shrubland composed of endemic serpentinophytes (small shrubs and perennial herbs) included in Digitali laciniatae-Halimietum atriplicifolii plant association (Cisto-Lavanduletea class) in an opened pine forest. The post-fire response of the plants was studied in the stablished burned plots by field works through permanent 200 x 10 m transect methods, consisting on checking whether they were resprouters, seeders, both of them or if they showed no survival response. Additional information about fire related functional traits is provided for the studied taxa from other studies. Of the total of plants studied (23 taxa), 74% acted as resprouters, 30% as seeders, some of which also had the capacity to resprout (13%), and only 9% of the plants did not show any survival strategy. The presence of a resprouting burl was not high (17%), although serpentine small shrubs such as Bupleurum acutifolium and the generalist Teucrium haenseleri had this kind of organ. The herbaceous taxa Sanguisorba verrucosa, Galium boissieranum and Linum carratracense were seen to be resprouters and seeders. The serpentine obligated Ni-accumulator, Alyssum serpyllifolium subsp. malacitanum, did not show any survival strategy in the face of fire and therefore their populations need monitoring after fires. In the studied ecosystems no species had traits that would protect the aerial part of the plant against fire, although most of the species are capable of post-fire generation by below ground buds. Our results show that the ecosystem studied, composed of taxa with a high degree of endemism and some of them threatened, is predominantly adapted to survival after a fire, although their response capacity may be decreased by environmental factors.
Lead sulfide (PbS) is an important IV-VI semiconductor material with narrow bandwidth and wide wave width, which attracts people's attention. Nano-level PbS has many novel optoelectronic properties and has a wide range of applications in the field of optoelectronics, such as infrared optoelectronic devices, photovoltaic devices, light-emitting devices and display devices. In this paper, Pbs is produced by solvent thermal method by using lead acetate as lead source, sulfur power as sulfur source, ethylene glycol as solvent, and acetic acid to provide acidic environment. The reaction acidity, type of lead source, amount of sulfur source and other aspects will be explored. The products obtained under different conditions were characterized by X-ray diffraction (XRD), optical microscopy and scanning electron microscopy (SEM). The results showed that PbS produced at 140°C for 24 hours, using 14mL ethylene glycol and 1.2mL acetic acid has the best morphology. It has a non-planar six-arm symmetrical structure. Finally, we prepare the lead sulfide composite Ni/PbS, and characterized it.
In this paper, we will provide an extensive analysis of how Generative Artificial Intelligence (GenAI) could be applied when handling Supply Chain Management (SCM). The paper focuses on how GenAI is more relevant in industries, and for instance, SCM where it is employed in tasks such as predicting when machines are due for a check-up, man-robot collaboration, and responsiveness. The study aims to answer two main questions: (1) What prospects can be identified when the tools of GenAI are applied in SCM? Secondly, it aims to examine the following question: (2) what difficulties may be encountered when implementing GenAI in SCM? This paper assesses studies published in academic databases and applies a structured analytical framework to explore GenAI technology in SCM. It looks at how GenAI is deployed within SCM and the challenges that have been encountered, in addition to the ethics. Moreover, this paper also discusses the problems that AI can pose once used in SCM, for instance, the quality of data used, and the ethical concerns that come with, the use of AI in SCM. A grasp of the specifics of how GenAI operates as well as how to implement it successfully in the supply chain is essential in assessing the performance of this relatively new technology as well as prognosticating the future of generation AI in supply chain planning.
Modelling and simulation have now become standard methods that serve to cut the economic costs of R&D for novel advanced systems. This paper introduces the study of modelling and simulation of the infrared thermography process to detect defects in the hydroelectric penstock. A 3-D penstock model was built in ANSYS version 19.2.0. Flat bottom holes of different sizes and depths were created on the inner surface of the model as an optimal scenario to represent the subsurface defect in the penstock. The FEM was applied to mimic the heat transfer in the proposed model. The model’s outer surface was excited at multiple excitation frequencies by a sinusoidal heat flux, and the thermal response of the model was presented in the form of thermal images to show the temperature contrast due to the presence of defects. The harmonic approximation method was applied to calculate the phase angle, and its relationship with respect to defect depth and defect size was also studied. The results confirmed that the FEM model has led to a better understanding of lock-in infrared thermography and can be used to detect subsurface defects in the hydroelectric penstock.
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