Al-Khwarizmi Engineering Journal

The process for preparing activated carbon (AC) made from tea residue was described in this paper. Investigated were the physicochemical characteristics and adsorption efficiency of the produced AC. Activation with potassium hydroxide (KOH) and carbonization at 350 °C are the two key steps in the manufacturing of AC. The activated carbon was used to adsorb Tetracycline (TC). Different parameters were studied at room temperature to show their effects on the adsorption efficiency of TC. These parameters are the initial concentration of adsorbate TC, solution acidity pH, time of adsorption, and adsorbent dosage. The prepared active carbon was characterized using Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray diffraction (XRD), and Brunauer-Emmett-Teller (BET). The equilibrium of TC adsorption on the tea-activated carbon TAC is effectively represented by the Langmuir model. Tetracycline could be adsorbed onto the prepared activated carbon with a maximum capacity of 45.662 mg g-1. Adsorption kinetics are well represented by pseudo-second-order. The investigation of adsorption thermodynamics demonstrates that TC adsorption on TAC is endothermic and spontaneous.

This investigation aims to explore the potential of waterworks sludge (WS), low-cost byproduct of water treatment processes, as a sorbent for removing Congo Red (CR) dyes. This will be achieved by precipitating nano-sized (MgAl-LDH)-layered double hydroxide onto the surface of the sludge. The efficiency of utilizing MgAl-LDH to modify waterworks sludge (MWS) for use in permeable reactive barrier technology was confirmed through analysis with Fourier transform infrared and X-ray diffraction. The isotherm model was employed to elucidate the adsorption mechanisms involved in the process. Furthermore, the COMSOL model was utilized to establish a continuous testing model for the analysis of contaminant transport under diverse conditions. A strong correlation was indicated, with a coefficient of determination (R2) ≥ 0.97, when the model\'s predictions were compared to experimental values, indicating the accuracy of the model. Continuous transport exhibited earlier breakthrough points when the bed depth decreased and the initial concentration and flow rate increased.

The purpose of the current investigation is to distinguish between working memory (WM) in five patients with vascular dementia (VD), fifteen post-stroke patients with mild cognitive impairment (SMCI), and fifteen healthy control individuals (HC) based on background electroencephalography (EEG) activity. The elimination of EEG artifacts using wavelet (WT) pre-processing denoising is demonstrated in this study. In the current study, spectral entropy (SpecEn), permutation entropy (PerEn), and approximation entropy (ApEn) were all explored. To improve the WM classification using the k-nearest neighbors (kNN) classifier scheme, a comparative study of using fuzzy neighbourhood preserving analysis with QR-decomposition (FNPAQR) as a dimensionality reduction technique and the improved binary gravitation search (IBGSA) optimization algorithm as a channel selection method has been conducted. The kNN classification accuracy was increased from 86.67% to 88.09% and 90.52% using the FNPAQR dimensionality reduction technique and the IBGSA channel selection algorithm, respectively. According to the findings, IBGSA reliably enhances WM discrimination of HC, SMCI, and VD participants. Therefore, WT, entropy features, IBGSA and kNN classifiers provide a valid dementia index for looking at EEG background activity in patients with VD and SMCI

In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test AVSS systems. In its basic form, this review aims to highlight the growing importance of AVSS in improving the quality of audio signals.

Worldwide, shipping documents are still primarily created and handled in the traditional paper manner. Processes taking place in shipping ports as a result are time-consuming and heavily dependent on paper. Shipping documents are particularly susceptible to paperwork fraud because they involve numerous parties with competing interests. With the aid of smart contracts, a distributed, shared, and append-only ledger provided by blockchain technology allows for the addition of new records. In order to increase maritime transport and port efficiency and promote economic development, this paper examines current maritime sector developments in Iraq and offers a paradigm to secure the management system based on a hyper-ledger fabric blockchain platform. The performance evaluation of the proposed system implies two scenarios: one organization and two organizations by examining throughput and latency. High transaction transmission rates on large block sizes produced positive results. Similarly, employing a small block size and higher send rates results in poor performance. Additionally, it was noted that throughput will decrease and latency will rise as the number of organizations increases. Block size and block timeouts should be high in applications with many concurrent transactions in order to maintain good throughput.

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software\'s capacity to forecast the color of the code that belongs to an object under detection is one of the results of the proposed classifier. The work demanded the collection of about 5000 color codes which in turn were subjected to algorithms for training and testing. The open-source platform TensorFlow for ML and the open-source neural network library Keras were used to construct the algorithm for the study. The results showed an acceptable efficiency of the built classifier represented by an accuracy of 90% which can be considered applicable, especially after some improvements in the future to makes it more effective as a trusted colorimeter.

Recently, wireless communication environments with high speeds and low complexity have become increasingly essential. Free-space optics (FSO) has emerged as a promising solution for providing direct connections between devices in such high-spectrum wireless setups. However, FSO communications are susceptible to weather-induced signal fluctuations, leading to fading and signal weakness at the receiver. To mitigate the effects of these challenges, several mathematical models have been proposed to describe the transition from weak to strong atmospheric turbulence, including Rayleigh, lognormal, Málaga, Nakagami-m, K-distribution, Weibull, Negative-Exponential, Inverse-Gaussian, G-G, and Fisher-Snedecor F distributions. This paper extensively studies and analyses different probability density functions (PDFs) that govern the FSO channel, considering various channel models. This paper aims to comprehensively understand how FSO channels can be effectively modeled using different PDFs. Accurate modeling is crucial for designing FSO systems that can operate optimally under potential environmental conditions. Selecting the appropriate PDF model plays a crucial role in determining the FSO channel\'s performance during system design. With a multitude of PDF models available, this study aims to identify the most effective PDF model to be employed in FSO channel modeling.