This paper reports a fiber Bragg grating (FBG) as a biosensor. The FBGs were etched using a chemical agent,namely,hydrofluoric acid (HF). This implies the removal of some part of the cladding layer. Consequently, the evanescent field propagating out of the core will be closer to the environment and become more sensitive to the change in the surrounding. The proposed FBG sensor was utilized to detect toxic heavy metal ions aqueous medium namely, copper ions (Cu2+). Two FBG sensors were etched with 20 and 40 μm diameters and fabricated. The sensors were studied towards Cu2+ with different concentrations using wavelength shift as a result of the interaction between the evanescent field and copper ions. The FBG sensors showed

 The gas sensing properties of undoped Co₃O₄ and doped with Y₂O₃ nanostructures were investigated. The films were synthesized using the hydrothermal method on a seeded layer. The XRD, SEM analysis and gas sensing properties were investigated for the prepared thin films. XRD analysis showed that all films were polycrystalline, of a cubic structure with crystallite size of (12.6) nm for cobalt oxide and (12.3) nm for the Co₃O₄:6% Y₂O₃. The SEM analysis of thin films indicated that all films undoped Co₃O₄ and doped possessed a nanosphere-like structure.

The sensi

Classifying butterfly species is crucial in biodiversity studies and environmental monitoring. However, manual classification is often a laborious process that requires specialized expertise and is prone to error, especially when species have similar visual characteristics. To address these drawbacks, this paper presents a hybrid approach that combines machine learning with deep learning for feature extraction. To enhance the visibility of important features, preprocessing techniques such as background removal and binarization are applied to butterfly images. Feature extraction was performed using the SqueezeNet convolutional neural network, pretrained on the ImageNet dataset. By discarding the final classification layer, the networ

This paper proposes a new strategy to enhance the performance and accuracy of the Spiral dynamic algorithm (SDA) for use in solving real-world problems by hybridizing the SDA with the Bacterial Foraging optimization algorithm (BFA). The dynamic step size of SDA makes it a useful exploitation approach. However, it has limited exploration throughout the diversification phase, which results in getting trapped at local optima. The optimal initialization position for the SDA algorithm has been determined with the help of the chemotactic strategy of the BFA optimization algorithm, which has been utilized to improve the exploration approach of the SDA. The proposed Hybrid Adaptive Spiral Dynamic Bacterial Foraging (HASDBF)

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci

Construction is a complicated process that takes place in an almost uncontrollable environment. Although projects can be carefully planned in advance in principle, there is a chance that unforeseen events and crises can disrupt these plans, affecting project development. Because the initial investment expenditures in construction projects are so large, they may be quickly influenced by crises, resulting in significant financial losses. The 2014 financial crisis was one of the most prominent crises that Iraq faced, which significantly impacted various activities in general and the construction industry in particular. Despite the importance of crisis management systems, the researchers found a great lack of local studies looking at cr