Earth’s climate changes rapidly due to the increases in human demands and rapid economic growth. These changes will affect the entire biosphere, mostly in negative ways. Predicting future changes will put us in a better position to minimize their catastrophic effects and to understand how humans can cope with the new changes beforehand. In this research, previous global climate data set observations from 1961-1990 have been used to predict the future climate change scenario for 2010-2039. The data were processed with Idrisi Andes software and the final Köppen-Geiger map was created with ArcGIS software. Based on Köppen climate classification, it was found that areas of Equator, Arid Steppes, and Snow will decrease by 3.9 %, 2.96%, an

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi

  The current research discussed biophysics data as a theoretical and applied knowledge base linking industrial design with the natural sciences at the level of applied strategies through which we can enrich the knowledge base of industrial design. The research focused on two main aspects of the scientific references for biophysics, namely: electromagnetism, and biomechanics. According to the performance and functional applications in designing the functions of industrial products at the electromagnetic level, it was found that remote sensing applications: such as fire sensors that were adopted from the insect (Black Beetle) and that their metaphors enable them to hear fire, and collision sensors, which were adopted from the insect

Isolation and identification fungi of Emericella nidulans and Aspergillus flavus from a pinkish and yellowish artificial clay, by using potato dextrose agar (PDA). Results revealed that E. nidulans was the best for degrading anthracene (92.3%) with maximum biomass production (3.7gm/l), compared to A. flavus with the rate of degradation (89%) and biomass production of (1.2gm/l), when methylene blue was used as redox indicator after incubating in a shaker incubator 120rpm at 30Co for 8days. Results indicated that E. nidulans has a high ability of anthracene degradation with the rate of (84%), while A. flavus showed the lower level with (77%) by using HPLC.

In "historical" fiction, characters that never really existed, give expression to the impact of historical events on the people who really did live through them. The result is not history, as an accurate record of actual events, but fiction in which an earlier age is rendered through the personal joys and sufferings of characters. This paper
aims at investigating the historical realities presented in Dickens’s A Tale of Two Cities.

In this paper, we establish the conditions of the occurrence of the local bifurcations, such as saddle node, transcritical and pitchfork, of all equilibrium points of an eco-epidemiological model consisting of a prey-predator model with SI (susceptible-infected) epidemic diseases in prey population only and a refuge-stage structure in the predators. It is observed that there is a transcritical bifurcation near the axial and free predator equilibrium points, near disease-free equilibrium point is a saddle-node bifurcation and near positive (coexistence) equilibrium point is a saddle-node bifurcation, a transcritical bifurcation and a pitchfork bifurcation. Further investigations for Hopf bifurcation near coexistence equilibrium point