When an electron moves from one atom or molecule to another, a charge-transfer complex is formed. The other objects must be able to accept these electrons, and one entity must have free electrons or a tendency to donate them. This resembles an internal oxidation-reduction reaction more. This research aims to shed light on charge transfer complexes formed by polyenes and carotenes, which act as electron-donating molecules due to their alternating double and single bonds. This allows them to create such complexes when interacting with organic molecules that lack electrons. These complexes exhibited distinctive optical and physicochemical properties, enabling them to be adapted for a wide range of applications. In addition, the researchers demonstrated significant interest in the study of the ability of some of these compounds to form self-internal charge-transfer complexes, also known as intramolecular charge-transfer complexes, and their precise electronic and dynamic states.
Alizarin is one of the popularly used and wide separated compounds with a chemical name (1,2- dihydroxy-9,10-anthraquinone) which belong to the anthraquinones family that contain three aromatic conjugated rings and in the central rings it contains two ketonic groups.1
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Background: Inflammation of the brain parenchyma brought on by a virus is known as viral encephalitis. It coexists frequently with viral meningitis and is the most prevalent kind of encephalitis. Objectives: To throw light on viral encephalitis, its types, epidemiology, symptoms and complications. Results: Although it can affect people of all ages, viral infections are the most prevalent cause of viral encephalitis, which is typically seen in young children and old people. Arboviruses, rhabdoviruses, enteroviruses, herpesviruses, retroviruses, orthomyxoviruses, orthopneumoviruses, and coronaviruses are just a few of the viruses that have been known to cause encephalitis. Conclusion: As new viruses emerge, diagnostic techniques advan
... Show MoreDeep learning techniques are applied in many different industries for a variety of purposes. Deep learning-based item detection from aerial or terrestrial photographs has become a significant research area in recent years. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed. A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles and classification probabilities for an image. In layman's terms, it is a technique for instantly identifying and recognizing
... Show MoreMany water supplies are now contaminated by anthropogenic sources such as domestic and agricultural waste, as well as manufacturing activities, the public's concern about the environmental effects of wastewater contamination has grown. Several traditional wastewater treatment methods, such as chemical coagulation, adsorption, and activated sludge, have been used to eliminate pollution; however, there are several drawbacks, most notably high operating costs, because of its low operating and repair costs, the usage of aerobic waste water treatment as a reductive medium is gaining popularity. Furthermore, it is simple to produce and has a high efficacy and potential to degrade pollu
... Show MoreDeep learning techniques are used across a wide range of fields for several applications. In recent years, deep learning-based object detection from aerial or terrestrial photos has gained popularity as a study topic. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed. A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles andclassification probabilities for an image. In layman's terms, it is a technique for instantly identifying and rec
... Show MoreA field study aimed at identifying the sources of mutual complaints among the Directorates of Education staff of their departments and management and run it in their daily dealings with principals in the province of Baghdad, and adopt approach. It was determined the research consists of (2357) male and female employees and 305 randomly stratified simple by the rate of (7%) from the research community as the number of sample reached (167) male and female employees, and selected sample was randomly stratified simple by the rate of (39.67%) of the research community, as the number of sample was (121) principals. It was constructed two questionnaires, the first included (28) items and the second contained (28) items. And the two researchers
... Show MoreThe aim of this research is to calculate mass transfer coefficient, kd, during cathodic protection of low carbon steel in neutral seawater (3.5% W/V NaCl in distilled water with pH = 7). Two types of cathodic protection were used:
First: Sacrificial anode cathodic protection (SACP) were a pipeline of steel carrying seawater using zinc as a sacrificial anode and with variable temperatures ranged (0 – 45oC) and volumetric flow rate ranged (5 – 900 lit/hr). It was found that the kd increases with increasing temperature and volumetric flow rate of seawater, where kd ranged (0.24×10-6 – 41.6×10-6 m/s).
Second: Impressed current cathodic pr
... Show MoreClinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision b