RESUMEN:

 Pretendemos hacer con este trabajo un breve recorrido sobre lo que han dicho algunos gramáticos de las construcciones condicionales, a la par señalaremos los criterios que los mismos establecen y siguen para diferenciar entre distintos tipos de estructuras condicionales o entre las construcciones que ellos entienden o consideran como tales y las que no, atendiendo fundamentalmente a dos criterios; formal y semántico-pragmático. El presente trabajo tiene como objetivo la clasificación y la descripción de las construcciones condicionales, demostrando que no todas las condicionales son hipotéticas ni todas las hipotéticas son condicionales.

Abstract

We intend to make this work

Six isolates of Bacillus thuringiensis were isolated from Iraqi soil characterized as non- insecticidal and non- hemolytic parasporal inclusion proteins. Bacterial isolates were propagated on nutrient broth. Then, the parasporal inclusion proteins were extracted and processed with proteinase K and trypsin. The major protein segments produced of 64KDa were characterized and tested for cytocidal activity against human leukemic T- cells (CLL) (Chronic lymphoid leukemia). Results indicated that the treated parasporal proteins of four isolates (Bt2, Bt3, Bt4 and Bt6) showed strong cytotoxicity with no significant differences between normal lymphocytes and leukemic lymphocytes. Two isolates BtA1 and BtA5 show discriminative cytotoxicity between n

            In this study, pure SnO₂ Nanoparticles doped with Cu were synthesized by a chemical precipitation method. Using SnCl₂.2H₂O, CuCl₂.2H₂O as raw materials, the materials were annealed at 550°C for 3 hours in order to improve crystallization. The XRD results showed that the samples crystallized in the tetragonal rutile type SnO₂ stage. As the average SnO₂ crystal size is pure 9nm and varies with the change of Cu doping (0.5%, 1%, 1.5%, 2%, 2.5%, 3%),( 8.35, 8.36, 8.67, 9 ,7, 8.86)nm respectively an increase in crystal size to 2.5% decreases at this rate and that the crystal of SnO₂ does not change with the introduction of Cu, and S

The kinetics of nickel removal from aqueous solutions using a bio-electrochemical reactor with a packed bed rotating cylinder cathode was investigated. The effects of applied voltage, initial nickel concentration, the rotation speed of the cathode, and pH on the reaction rate constant (k) were studied. The results showed that the cathodic deposition occurred under mass transfer control for all values of the applied voltage used in this research. Accordingly, the relationship between concentration and time can be represented by a first-order equation. The rate constant was found to be dependent on the applied voltage, initial nickel concentration, pH, and rotation speed. It was increased as the applied voltage increased and decreased as t

The use of deep learning.

In this work semi–empirical method (PM3) calculations are carried out by (MOPAC) computational packages have been employed to calculate the molecular orbital's energies for some organic pollutants. The long– chain quaternary ammonium cations called Iraqi Clays (Bentonite – modified) are used to remove these organic pollutants from water, by adding a small cationic surfactant so as to result in floes which are agglomerates of organobentonite to remove organic pollutants. This calculation which suggests the best surface active material, can be used to modify the adsorption efficiency of aniline , phenol, phenol deriviatives, Tri methyl glycine, ester and pecticides , on Iraqi Clay (bentonite) by comparing the theoretical results w

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