University of Technology - Iraq

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Titanium dioxides (TiO2) have been widely studied, due to its interesting general properties in a wide range of fields including catalysis, photocatalysis, and antibacterial agents and in civil as nano-paint (self-cleaning) that affect the quality of life. Therefore, TiO2 and doped with noble metal are good candidates in the performance these applications. The fascinating physical and chemical features of TiO2 depend on the crystal phase, size and shape of particles. For example, varying phases of crystalline TiO2 have different band gaps that rutile TiO2 of 3.0 eV and anatase TiO2 of 3.2 eV, determine the photocatalytic performance of TiO2. This chapter explains some applications and theoretical concepts of nanostructure of TiO2 nanoparticles. Also, it demonstrates electrical, optical and morphological properties which make TiO2 preferable for environmental applications.

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.

Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined.

Natural fibres (NFRs) composite materials are acquiring popularity in the modern world due to their eco-friendliness and superior mechanical properties. Although it has been shown that determining this is a herculean endeavour in the literature, the water absorption (WA) qualities of the natural fibre (NFR) are crucial in the progressive degradation of the features of the resulting composites. This article seeks to report exhaustively on studies pertaining to the WA attributes of polymer composites reinforced with NFRs. This article provides an overview of NFR, its characterization, and the issues related to its addition to the matrix. The primary purpose of this research study is to investigate existing studies on the problems associated with the creation of cellulosic fibre hybrid composites, water absorption, and its impact on the tensile (TS), flexural (FS), and impact strength (IS) of NFR reinforced composites. We reviewed various surface treatments (ST) applied to NFR, including alkali treatment, silane treatment, acetylation, as well as recent advancements aimed at mitigating WA, enhancing hydrophobicity, and improving the interfacial bonding (IB) between NFR and the polymer matrix (PM). Additionally, we assessed the effectiveness of utilizing nanoparticles (NAPs) in specific ST of NFR to minimize water absorption.

Currently, biological method has been utilized in the treatment of wastewater -containing synthetic dyes used by textile industries in Iraq. The present work was devoted to study the operating feasibility using reverse osmosis (RO) and nanofiltration (NF) membrane systems as an alternative treatment method of wastewater discharged from Iraqi textile mills. Acid red, reactive black and reactive blue dyes were selected, based on the usage rate in Iraq. Effects of dye concentration, pH of solution, feed temperature, dissolved salts and operating pressure on permeate flux and dye rejection were studied. Results at operating conditions of dye concentration = 65 mg/L, feed temperature = 39°C and pressure = 8 bar showed the final dye removal with RO membrane as 97.2%, 99.58% and 99.9% for acid red, reactive black and reactive blue dyes, respectively. With NF membrane, the final dye removal were as 93.77%, 95.67%, and 97% for red, black and blue dyes, respectively. The presence of salt (particularly NaCl) in the dye solution resulted in a higher color removal with a permeate flux decline. It was confirmed that pH of solution had a positive impact on dye removal while feed temperature showed a different image. A comparison was made between the results of dye removal in biological and membrane methods. The results showed that membrane method had higher removal potential with lower effective cost. The present study indicates that the use of NF membrane in dye removal from the effluent of Iraqi textile mills is promising.

This study aims at preparing thin layers of (TiO2) with a high photocatalytic activity and antibacterial properties for use as a self- cleaning transparent coatings for windows in outdoors applications. Titanium dioxide (TiO2) nanoparticles were prepared by sol-gel process using Titanium Tetrachloride (TiCl4) as a precursor, and calcined at different calcination temperatures (400, 600, 800, and 1000) °C. The synthesized nanoparticles were characterized by X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), Ultraviolet spectroscopy (Uv-Vis), Atomic Force Microscopy (AFM). Self-cleaning properties were studies through two important tests; hydrophilicity by measuring the Water Contact Angle (WCA) and photocatalytic activity by using potassium permanganate (KMnO4) as a model organic pollutant. Secondly, a thin film coating of TiO2 nanoparticles was deposited by spin coating. The antimicrobial activity of TiO2 nanoparticles was assessed quantitatively against two types of bacteria, (Pseudomonas aeruginosa), and (Staphylococcus aurous).

Water Quality Index (WQI) was applied in Dokan Lake, Kurdistan region, Iraq using ten water quality parameters (pH, Dissolved Oxygen, Turbidity, Conductivity, Hardness, Alkalinity, Sodium, Biochemical Oxygen Demand, Nitrate and Nitrite). The relative weight assigned to each parameter ranged from 1 to 4 based on the importance of the parameter for aquatic life. The results indicated that water quality of Dokan Lake declined from Good in the years 1978, 1979, 1980, 1999, 2000 and 2008 to Poor in 2009. The impact of various anthropogenic activities was evident on some parameters such as the EC and BOD. It is suggested that monitoring of the lake is necessary for proper management. Application of the WQI is also suggested as a very helpful tool that enables the public and decision makers to evaluate water quality of lakes in Iraq.

Deep learning requires a large amount of data to perform well. However, the field of medical image analysis suffers from a lack of sufficient data for training deep learning models. Moreover, medical images require manual labeling, usually provided by human annotators coming from various backgrounds. More importantly, the annotation process is time-consuming, expensive, and prone to errors. Transfer learning was introduced to reduce the need for the annotation process by transferring the deep learning models with knowledge from a previous task and then by fine-tuning them on a relatively small dataset of the current task. Most of the methods of medical image classification employ transfer learning from pretrained models, e.g., ImageNet, which has been proven to be ineffective. This is due to the mismatch in learned features between the natural image, e.g., ImageNet, and medical images. Additionally, it results in the utilization of deeply elaborated models. In this paper, we propose a novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deep learning model on the small amount of labeled medical images. Additionally, we propose a new deep convolutional neural network (DCNN) model that combines recent advancements in the field. We conducted several experiments on two challenging medical imaging scenarios dealing with skin and breast cancer classification tasks. According to the reported results, it has been empirically proven that the proposed approach can significantly improve the performance of both classification scenarios. In terms of skin cancer, the proposed model achieved an F1-score value of 89.09% when trained from scratch and 98.53% with the proposed approach. Secondly, it achieved an accuracy value of 85.29% and 97.51%, respectively, when trained from scratch and using the proposed approach in the case of the breast cancer scenario. Finally, we concluded that our method can possibly be applied to many medical imaging problems in which a substantial amount of unlabeled image data is available and the labeled image data is limited. Moreover, it can be utilized to improve the performance of medical imaging tasks in the same domain. To do so, we used the pretrained skin cancer model to train on feet skin to classify them into two classes-either normal or abnormal (diabetic foot ulcer (DFU)). It achieved an F1-score value of 86.0% when trained from scratch, 96.25% using transfer learning, and 99.25% using double-transfer learning.

In the modern world, zero-carbon society has become a new buzzword of the era. Many projects have been initiated to develop alternatives not only to the environmental crisis but also to the shortage of fossil fuels. With successful projects in automobile technology, hydrogen fuel is now being tested and utilized as a sustainable green fuel in the aviation sector which will lead to zero carbon emission in the future. From the mid-20th century to the early 21st numerous countries and companies have funded multimillion projects to develop hydrogen-fueled aircraft. Empirical data show positive results for various projects. Consequently, large companies are investing in various innovations undertaken by researchers under their supervision. Over time, the efficiency of hydrogen-fueled aircraft has improved but the lack of refueling stations, large production cost, and consolidated carbon market share have impeded the path of hydrogen fuel being commercialized. In addition, the Unmanned Aerial Vehicle (UAV) is another important element of the Aviation industry, Hydrogen started to be commonly used as an alternative fuel for heavy-duty drones using fuel cell technology. The purpose of this paper is to provide an overview of the chronological development of hydrogen-powered aircraft technology and potential aviation applications for hydrogen and fuel cell technology. Furthermore, the major barriers to widespread adoption of hydrogen technology in aviation are identified, as are future research opportunities.

OBJECTIVE: To synthesize silver nanopaticles from leaves extract of Eucalyptus chapmaniana (E. chapmaniana) and test the antimicrobial of the nanoparticles against different pathogenic bacteria, yeast and its toxicity against human acute promyelocytic leukemia (HL-60) cell line. METHODS: Ten milliliter of leaves extract was mixed with 90 mL of 0.01 mmol/mL or 0.02 mmol/mL aqueous AgNO3 and exposed to sun light for 1 h. A change from yellowish to reddish brown color was observed. Characterization using UV-vis spectrophotometery and X-ray diffraction analysis were performed. Antimicrobial activity against six microorganisms was tested using well diffusion method and cytoxicity test using 3-(4, 5-Dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide, a yellow tetrazole was obtained on the human leukemia cell line (HL-60). RESULTS: UV-vis spectral analysis showed silver surface plasmon resonance band at 413 nm. X-ray diffraction showed that the particles were crystalline in nature with face centered cubic structure of the bulk silver with broad beaks at 38.50° and 44.76°. The synthesized silver nanoparticles efficiently inhibited various pathogenic organisms and reduced viability of the HL-60 cells in a dose-dependent manner. CONCLUSIONS: It has been demonstrated that the extract of E. chapmaniana leaves are capable of producing silver nanoparticles extracellularly and the Ag nanoparticles are quite stable in solution. Further studies are needed to fully characterize the toxicity and the mechanisms involved with the antimicrobial and anticancer activity of these particles.

The current work concentrated on the green synthesis of silver nanoparticles (AgNPs) through the use of aqueous Citruslimon zest extract, optimizing the different experimental factors required for the formation and stability of AgNPs. The preparation of nanoparticles was confirmed by the observation of the color change of the mixture of silver nitrate, after the addition of the plant extract, from yellow to a reddish-brown colloidal suspension and was established by detecting the surface plasmon resonance band at 535.5 nm, utilizing UV-Visible analysis. The optimum conditions were found to be 1 mM of silver nitrate concentration, a 1:9 ratio extract of the mixture, and a 4 h incubation period. Fourier transform infrared spectroscopy spectrum indicated that the phytochemicals compounds present in Citrus limon zest extract had a fundamental effect on the production of AgNPs as a bio-reducing agent. The morphology, size, and elemental composition of AgNPs were investigated by zeta potential (ZP), dynamic light scattering (DLS), SEM, EDX, X-ray diffraction (XRD), and transmission electron microscopy (TEM) analysis, which showed crystalline spherical silver nanoparticles. In addition, the antimicrobial and antioxidant properties of this bioactive silver nanoparticle were also investigated. The AgNPs showed excellent antibacterial activity against one Gram-negative pathogens bacteria, Escherichia coli, and one Gram-positive bacteria, Staphylococcus aureus, as well as antifungal activity against Candida albicans. The obtained results indicate that the antioxidant activity of this nanoparticle is significant. This bioactive silver nanoparticle can be used in biomedical and pharmacological fields.

Hesperidin is a flavonoid glycoside with proven therapeutic activities for various diseases, including cancer. However, its poor solubility and bioavailability render it only slightly absorbed, requiring a delivery system to reach its therapeutic target. Hesperidin loaded on gold nanoparticles (Hsp-AuNPs) was prepared by a chemical synthesis method. Various characterization techniques such as UV-VIS spectroscopy, FTIR, XRD, FESEM, TEM and EDX, Zeta potential analysis, particle size analysis, were used to confirm the synthesis of Hsp-AuNPs. The cytotoxic effect of Hsp-AuNPs on human breast cancer cell line (MDA-MB-231) was assessed using MTT and crystal violet assays. The results revealed significant decrease in proliferation and inhibition of growth of the treated cells when compared with human normal breast epithelial cell line (HBL-100). Determination of apoptosis by fluorescence microscope was also performed using acridine orange-propidium iodide dual staining assay. The in vivo study was designed to evaluate the toxicity of Hsp-AuNPs in mice. The levels of hepatic and kidney functionality markers were assessed. No significant statistical differences were found for the tested indicators. Histological images of liver, spleen, lung and kidney showed no apparent damages and histopathological abnormalities after treatment with Hsp-AuNPs. Hsp-AuNPs ameliorated the functional activity of macrophages against Ehrlich ascites tumor cells-bearing mice. The production of the pro-inflammatory cytokines was also assessed in bone marrow-derived macrophage cells treated with Hsp-AuNPs. The results obviously demonstrated that Hsp-AuNPs treatment significantly inhibited the secretion of IL-1β, IL-6 and TNF.

Abstract The purpose of this study was to investigate the possibility of the limestone as an adsorbed media and low-cost adsorbent. Batch adsorption studies were conducted to examine the effects of the parameters such as initial metal ion concentration C 0 , particle size of limestone D L , adsorbent dosage and equilibrium concentration of heavy metal C e on the removal of the heavy metal (Cu) from synthetic water solution by limestone. The removal efficiency is increased with the increase in the volume of limestone (influenced by the media specific area). It has been noted that the limestone with diameter of 3.75 is the most effective size for removal of copper from synthetic solution. The adsorption data were analyzed by the Langmuir and Freundlich isotherm model. The average values of the empirical constant and adsorption constant (saturation coefficient) for the Langmuir equation were a = 0.022 mg/g and b = 1.46 l/mg, respectively. The average values of the Freundlich adsorption constant and empirical coefficient were K f = 0.010 mg/g and n = 1.58 l/mg, respectively. It was observed that the Freundlich isotherm model described the adsorption process with high coefficient of determination R 2 , better than the Langmuir isotherm model and for low initial concentration of heavy metal. Also, when the values of amount of heavy metal removal from solution are predicted by the Freundlich isotherm model, it showed best fits the batch study. It is clear from the results that heavy metal (Cu) removal with the limestone adsorbent appears to be technically feasible and with high efficiency.

Corrosion is a major challenge in various industries and can cause significant damage to metal structures. Organic corrosion inhibitors are compounds that are used to reduce or prevent corrosion by forming a protective film on metal surfaces. The present review article focuses on natural and synthetic organic corrosion inhibitors and their classifications, active functional groups, and efficiency estimations. Furthermore, previous studies on the use of natural and synthetic organic inhibitors are discussed, along with adsorption isotherms and mechanisms of organic corrosion inhibitors. The kinetics of corrosion modeling are also discussed, providing insights into the effectiveness of organic inhibitors at reducing corrosion. This review aims to provide a comprehensive overview of the current knowledge on organic corrosion inhibitors, with the aim of promoting their wider use in corrosion protection.

Advanced oxidation processes (AOPs) have become increasingly more useful and necessary in the past few decades because they can degrade a wide range of organic and inorganic contaminants. The toxic and recalcitrant nature of many contaminants obstruct traditional biological processes, making treatment ineffective. Therefore, there is a pressing need to develop and apply more effective treatment processes for industrial wastewater, polluted by pharmaceuticals, dyes, and other industries. The AOPs have become successful technologies for treating industrial wastewater because they remove contaminants, reduce toxicity, and improve biodegradability, including using ultraviolet (UV) and visible light (VIS) combined with homogenous or heterogeneous catalysts. This review shows that using the Fenton process and semiconductor material greatly facilitates removing various organic pollutants. Additionally, heterogeneous photocatalysis research is ongoing because this method effectively removes emerging pollutants. In addition, challenges, research needs, and future uses are described to improve the performance and hasten the large-scale implementation of AOPs in water treatment.

Global warming is one of the most compelling environmental threats today, as the rise in energy consumption and CO2 emission caused a dreadful impact on our environment. The data centers, computing devices, network equipment, etc., consume vast amounts of energy that the thermal power plants mainly generate. Primarily fossil fuels like coal and oils are used for energy generation in these power plants that induce various environmental problems such as global warming ozone layer depletion, which can even become the cause of premature deaths of living beings. The recent research trend has shifted towards optimizing energy consumption and green fields since the world recognized the importance of these concepts. This paper aims to conduct a complete systematic mapping analysis on the impact of high energy consumption in cloud data centers and its effect on the environment. To answer the research questions identified in this paper, one hundred nineteen primary studies published until February 2022 were considered and further categorized. Some new developments in green cloud computing and the taxonomy of various energy efficiency techniques used in data centers have also been discussed. It includes techniques like VM Virtualization and Consolidation, Power-aware, Bio-inspired methods, Thermal-management techniques, and an effort to evaluate the cloud data center’s role in reducing energy consumption and CO2 footprints. Most of the researchers proposed software level techniques as with these techniques, massive infrastructures are not required as compared with hardware techniques, and it is less prone to failure and faults. Also, we disclose some dominant problems and provide suggestions for future enhancements in green computing.

Specialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015. Since its conception, MIBiG has been regularly updated to expand data coverage and remain up to date with innovations in natural product research. Here, we describe MIBiG version 4.0, an extensive update to the data repository and the underlying data standard. In a massive community annotation effort, 267 contributors performed 8304 edits, creating 557 new entries and modifying 590 existing entries, resulting in a new total of 3059 curated entries in MIBiG. Particular attention was paid to ensuring high data quality, with automated data validation using a newly developed custom submission portal prototype, paired with a novel peer-reviewing model. MIBiG 4.0 also takes steps towards a rolling release model and a broader involvement of the scientific community. MIBiG 4.0 is accessible online at https://mibig.secondarymetabolites.org/.

An experimental study was carried out to understand the electrical percolation thresholds of different carbon-based nano- and micro-scale materials in cementitious composites. Multi-walled carbon nanotubes (CNTs), graphene nanoplatelets (GNPs) and carbon black (CB) were selected as the nano-scale materials, while 6 and 12 mm long carbon fibers (CF6 and CF12) were used as the micro-scale carbon-based materials. After determining the percolation thresholds of different electrical conductive materials, mechanical properties and piezoresistive properties of specimens produced with the abovementioned conductive materials at percolation threshold were investigated under uniaxial compressive loading. Results demonstrate that regardless of initial curing age, the percolation thresholds of CNT, GNP, CB and CFs in ECC mortar specimens were around 0.55%, 2.00%, 2.00% and 1.00%, respectively. Including different carbon-based conductive materials did not harm compressive strength results; on the contrary, it improved overall values. All cementitious composites produced with carbon-based materials, with the exception of the control mixtures, exhibited piezoresistive behavior under compression, which is crucial for sensing capability. It is believed that incorporating the sensing attribute into cementitious composites will enhance benefits for sustainable civil infrastructures.