Iraqi University

The sol‐gel process is a more chemical method (wet chemical method) for the synthesis of various nanostructures, especially metal oxide nanoparticles. In this method, the molecular precursor (usually metal alkoxide) is dissolved in water or alcohol and converted to gel by heating and stirring by hydrolysis/alcoholysis. Since the gel obtained from the hydrolysis/alcoholysis process is wet or damp, it should be dried using appropriate methods depending on the desired properties and application of the gel. For example, if it is an alcoholic solution, the drying process is done by burning alcohol. After the drying stage, the produced gels are powdered and then calcined. The sol‐gel method is a cost‐effective method and due to the low reaction temperature there is good control over the chemical composition of the products. The sol‐gel method can be used in the process of making ceramics as a molding material and can be used as an intermediate between thin films of metal oxides in various applications. The materials obtained from the sol‐gel method are used in various optical, electronic, energy, surface engineering, biosensors, and pharmaceutical and separation technologies (such as chromatography). The sol‐gel method is a conventional and industrial method for the synthesis of nanoparticles with different chemical composition. The basis of the sol‐gel method is the production of a homogeneous sol from the precursors and its conversion into a gel. The solvent in the gel is then removed from the gel structure and the remaining gel is dried. The properties of the dried gel depend significantly on the drying method. In other words, the “removing solvent method” is selected according to the application in which the gel will be used. Dried gels in various ways are used in industries such as surface coating, building insulation, and the production of special clothing. It is worth mentioning that, by grinding the gel by special mills, it is possible to achieve nanoparticles.

A climate change workshop for the Middle East brought together scientists and data for the region to produce the first area‐wide analysis of climate extremes for the region. This paper reports trends in extreme precipitation and temperature indices that were computed during the workshop and additional indices data that became available after the workshop. Trends in these indices were examined for 1950–2003 at 52 stations covering 15 countries, including Armenia, Azerbaijan, Bahrain, Cyprus, Georgia, Iran, Iraq, Israel, Jordan, Kuwait, Oman, Qatar, Saudi Arabia, Syria, and Turkey. Results indicate that there have been statistically significant, spatially coherent trends in temperature indices that are related to temperature increases in the region. Significant, increasing trends have been found in the annual maximum of daily maximum and minimum temperature, the annual minimum of daily maximum and minimum temperature, the number of summer nights, and the number of days where daily temperature has exceeded its 90th percentile. Significant negative trends have been found in the number of days when daily temperature is below its 10th percentile and daily temperature range. Trends in precipitation indices, including the number of days with precipitation, the average precipitation intensity, and maximum daily precipitation events, are weak in general and do not show spatial coherence. The workshop attendees have generously made the indices data available for the international research community.

Aqueous-phase electrochemical reduction of carbon dioxide requires an active, earth-abundant electrocatalyst, as well as highly efficient mass transport. Here we report the design of a porous hollow fibre copper electrode with a compact three-dimensional geometry, which provides a large area, three-phase boundary for gas-liquid reactions. The performance of the copper electrode is significantly enhanced; at overpotentials between 200 and 400 mV, faradaic efficiencies for carbon dioxide reduction up to 85% are obtained. Moreover, the carbon monoxide formation rate is at least one order of magnitude larger when compared with state-of-the-art nanocrystalline copper electrodes. Copper hollow fibre electrodes can be prepared via a facile method that is compatible with existing large-scale production processes. The results of this study may inspire the development of new types of microtubular electrodes for electrochemical processes in which at least one gas-phase reactant is involved, such as in fuel cell technology.

This study presents a systematic review of artificial intelligence (AI) techniques used in the detection and classification of coronavirus disease 2019 (COVID-19) medical images in terms of evaluation and benchmarking. Five reliable databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus were used to obtain relevant studies of the given topic. Several filtering and scanning stages were performed according to the inclusion/exclusion criteria to screen the 36 studies obtained; however, only 11 studies met the criteria. Taxonomy was performed, and the 11 studies were classified on the basis of two categories, namely, review and research studies. Then, a deep analysis and critical review were performed to highlight the challenges and critical gaps outlined in the academic literature of the given subject. Results showed that no relevant study evaluated and benchmarked AI techniques utilised in classification tasks (i.e. binary, multi-class, multi-labelled and hierarchical classifications) of COVID-19 medical images. In case evaluation and benchmarking will be conducted, three future challenges will be encountered, namely, multiple evaluation criteria within each classification task, trade-off amongst criteria and importance of these criteria. According to the discussed future challenges, the process of evaluation and benchmarking AI techniques used in the classification of COVID-19 medical images considered multi-complex attribute problems. Thus, adopting multi-criteria decision analysis (MCDA) is an essential and effective approach to tackle the problem complexity. Moreover, this study proposes a detailed methodology for the evaluation and benchmarking of AI techniques used in all classification tasks of COVID-19 medical images as future directions; such methodology is presented on the basis of three sequential phases. Firstly, the identification procedure for the construction of four decision matrices, namely, binary, multi-class, multi-labelled and hierarchical, is presented on the basis of the intersection of evaluation criteria of each classification task and AI classification techniques. Secondly, the development of the MCDA approach for benchmarking AI classification techniques is provided on the basis of the integrated analytic hierarchy process and VlseKriterijumska Optimizacija I Kompromisno Resenje methods. Lastly, objective and subjective validation procedures are described to validate the proposed benchmarking solutions.

Artificial intelligence (AI) holds significant promise for advancing natural disaster management through the use of predictive models that analyze extensive datasets, identify patterns, and forecast potential disasters. These models facilitate proactive measures such as early warning systems (EWSs), evacuation planning, and resource allocation, addressing the substantial challenges associated with natural disasters. This study offers a comprehensive exploration of trustworthy AI applications in natural disasters, encompassing disaster management, risk assessment, and disaster prediction. This research is underpinned by an extensive review of reputable sources, including Science Direct (SD), Scopus, IEEE Xplore (IEEE), and Web of Science (WoS). Three queries were formulated to retrieve 981 papers from the earliest documented scientific production until February 2024. After meticulous screening, deduplication, and application of the inclusion and exclusion criteria, 108 studies were included in the quantitative synthesis. This study provides a specific taxonomy of AI applications in natural disasters and explores the motivations, challenges, recommendations, and limitations of recent advancements. It also offers an overview of recent techniques and developments in disaster management using explainable artificial intelligence (XAI), data fusion, data mining, machine learning (ML), deep learning (DL), fuzzy logic, and multicriteria decision-making (MCDM). This systematic contribution addresses seven open issues and provides critical solutions through essential insights, laying the groundwork for various future works in trustworthiness AI-based natural disaster management. Despite the potential benefits, challenges persist in the application of AI to natural disaster management. In these contexts, this study identifies several unused and used areas in natural disaster-based AI theory, collects the disaster datasets, ML, and DL techniques, and offers a valuable XAI approach to unravel the complex relationships and dynamics involved and the utilization of data fusion techniques in decision-making processes related to natural disasters. Finally, the study extensively analyzed ethical considerations, bias, and consequences in natural disaster-based AI.

Abnormal vasculature is one of the most conspicuous traits of tumor tissue, largely contributing to tumor immune evasion. The deregulation mainly arises from the potentiated pro-angiogenic factors secretion and can also target immune cells' biological events, such as migration and activation. Owing to this fact, angiogenesis blockade therapy was established to fight cancer by eliminating the nutrient and oxygen supply to the malignant cells by impairing the vascular network. Given the dominant role of vascular-endothelium growth factor (VEGF) in the angiogenesis process, the well-known anti-angiogenic agents mainly depend on the targeting of its actions. However, cancer cells mainly show resistance to anti-angiogenic agents by several mechanisms, and also potentiated local invasiveness and also distant metastasis have been observed following their administration. Herein, we will focus on clinical developments of angiogenesis blockade therapy, more particular, in combination with other conventional treatments, such as immunotherapy, chemoradiotherapy, targeted therapy, and also cancer vaccines. Video abstract.

These days, the usage of machine-learning-enabled dynamic Internet of Medical Things (IoMT) systems with multiple technologies for digital healthcare applications has been growing progressively in practice. Machine learning plays a vital role in the IoMT system to balance the load between delay and energy. However, the traditional learning models fraud on the data in the distributed IoMT system for healthcare applications are still a critical research problem in practice. The study devises a federated learning-based blockchain-enabled task scheduling (FL-BETS) framework with different dynamic heuristics. The study considers the different healthcare applications that have both hard constraint (e.g., deadline) and resource energy consumption (e.g., soft constraint) during execution on the distributed fog and cloud nodes. The goal of FL-BETS is to identify and ensure the privacy preservation and fraud of data at various levels, such as local fog nodes and remote clouds, with minimum energy consumption and delay, and to satisfy the deadlines of healthcare workloads. The study introduces the mathematical model. In the performance evaluation, FL-BETS outperforms all existing machine learning and blockchain mechanisms in fraud analysis, data validation, energy and delay constraints for healthcare applications.

Today, in diverse medical and clinical fields, including cancer treatment, nanoscience has evolved and evolved. Cancer and its forms, on the other hand, have been rumored and inclusive, and many individuals suffer from this fatal and lethal condition. Actually, even with the medicinal effect, current therapeutic approaches, including chemotherapy, radiotherapy, etc., create symptoms that are inconvenient for patients. Scientists and scholars are also working to establish and, strengthen the options and methods of therapy to deal with this dangerous illness. Nanoscience and nanotechnology have been popular today, their different areas, including nanoparticles, are commonly used for a number of applications, especially for drug delivery and diagnostic products, and cases of imaging. Release mechanisms focused on nanotechnology have a profound effect on the release of cancer drugs. Biomaterials and bio-engineering developments are leading to novel approaches to nanoparticles that could offer a new way for cancer patients to improve. In the drug release method, Nano-technology has had a great effect on the selection of cancer cells, the release of a targeted drug, and the overcoming of traditional chemotherapy limitations. This article discusses the drug delivery to tumor tissue, a method that is more effective than traditional drug delivery methods, also many new nanoparticles have solved the problem of cell resistance to the drug, provided a new field in the treatment of cancer.

Malware is one of the most frequent cyberattacks, with its prevalence growing daily across the network. Malware traffic is always asymmetrical compared to benign traffic, which is always symmetrical. Fortunately, there are many artificial intelligence techniques that can be used to detect malware and distinguish it from normal activities. However, the problem of dealing with large and high-dimensional data has not been addressed enough. In this paper, a high-performance malware detection system using deep learning and feature selection methodologies is introduced. Two different malware datasets are used to detect malware and differentiate it from benign activities. The datasets are preprocessed, and then correlation-based feature selection is applied to produce different feature-selected datasets. The dense and LSTM-based deep learning models are then trained using these different versions of feature-selected datasets. The trained models are then evaluated using many performance metrics (accuracy, precision, recall, and F1-score). The results indicate that some feature-selected scenarios preserve almost the same original dataset performance. The different nature of the used datasets shows different levels of performance changes. For the first dataset, the feature reduction ratios range from 18.18% to 42.42%, with performance degradation of 0.07% to 5.84%, respectively. The second dataset reduction rate is between 81.77% and 93.5%, with performance degradation of 3.79% and 9.44%, respectively.

Skin cancer is one of the major types of cancer with an increasing incidence in recent decades. The source of skin cancer arises in various dermatologic disorders. Skin cancer is classified into various types based on texture, color, morphological features, and structure. The conventional approach for skin cancer identification needs time and money for the predicted results. Currently, medical science is utilizing various tools based on digital technology for the classification of skin cancer. The machine learning-based classification approach is the robust and dominant approach for automatic methods of classifying skin cancer. The various existing and proposed methods of deep neural network, support vector machine (SVM), neural network (NN), random forest (RF), and K-nearest neighbor are used for malignant and benign skin cancer identification. In this study, a method was proposed based on the stacking of classifiers with three folds towards the classification of melanoma and benign skin cancers. The system was trained with 1000 skin images with the categories of melanoma and benign. The training and testing were performed using 70 and 30 percent of the overall data set, respectively. The primary feature extraction was conducted using the Resnet50, Xception, and VGG16 methods. The accuracy, F1 scores, AUC, and sensitivity metrics were used for the overall performance evaluation. In the proposed Stacked CV method, the system was trained in three levels by deep learning, SVM, RF, NN, KNN, and logistic regression methods. The proposed method for Xception techniques of feature extraction achieved 90.9% accuracy and was stronger compared to ResNet50 and VGG 16 methods. The improvement and optimization of the proposed method with a large training dataset could provide a reliable and robust skin cancer classification system.

Abstract Topical treatments with structural equation modelling (SEM) and an artificial neural network (ANN), including a wide range of concepts, benefits, challenges and anxieties, have emerged in various fields and are becoming increasingly important. Although SEM can determine relationships amongst unobserved constructs (i.e. independent, mediator, moderator, control and dependent variables), it is insufficient for providing non-compensatory relationships amongst constructs. In contrast with previous studies, a newly proposed methodology that involves a dual-stage analysis of SEM and ANN was performed to provide linear and non-compensatory relationships amongst constructs. Consequently, numerous distinct types of studies in diverse sectors have conducted hybrid SEM–ANN analysis. Accordingly, the current work supplements the academic literature with a systematic review that includes all major SEM–ANN techniques used in 11 industries published in the past 6 years. This study presents a state-of-the-art SEM–ANN classification taxonomy based on industries and compares the effort in various domains to that classification. To achieve this objective, we examined the Web of Science, ScienceDirect, Scopus and IEEE Xplore ® databases to retrieve 239 articles from 2016 to 2021. The obtained articles were filtered on the basis of inclusion criteria, and 60 studies were selected and classified under 11 categories. This multi-field systematic study uncovered new research possibilities, motivations, challenges, limitations and recommendations that must be addressed for the synergistic integration of multidisciplinary studies. It contributed two points of potential future work resulting from the developed taxonomy. First, the importance of the determinants of play, musical and art therapy adoption amongst autistic children within the healthcare sector is the most important consideration for future investigations. In this context, the second potential future work can use SEM–ANN to determine the barriers to adopting sensing-enhanced therapy amongst autistic children to satisfy the recommendations provided by the healthcare sector. The analysis indicates that the manufacturing and technology sectors have conducted the most number of investigations, whereas the construction and small- and medium-sized enterprise sectors have conducted the least. This study will provide a helpful reference to academics and practitioners by providing guidance and insightful knowledge for future studies.

Water quality monitoring plays a significant part in the transition towards intelligent and smart agriculture and provides an easy transition to automated monitoring of crucial components of human daily needs as new technologies are continuously developed and adopted in agricultural and human daily life (water). For the monitoring and management of water quality, this effort, however, requires reliable models with accurate and thorough datasets. Analyzing water quality monitoring models by utilizing sensors that gather water properties during live experiments is possible due to the necessity for precision in modeling. To convey numerous conclusions regarding the concerns, issues, difficulties, and research gaps that have existed throughout the past five years (2018–2022), this review article thoroughly examines the water quality literature. To find trustworthy peer-reviewed publications, several digital databases were searched and examined, including IEEE Xplore®, ScienceDirect, Scopus, and Web of Science. Only 50 articles out of the 946 papers obtained, were used in the study of the water quality monitoring research area. There are more rules for article inclusion in the second stage of the filtration process. Utilizing a real-time data acquisition system, the criteria for inclusion for the second phase of filtration looked at the implementation of water quality monitoring and characterization procedures. Reviews and experimental studies comprised most of the articles, which were divided into three categories. To organize the literature into articles with similar types of experimental conditions, a taxonomy of the three literature was created. Topics for recommendations are also provided to facilitate and speed up the pace of advancement in this field of study. By conducting a thorough analysis of the earlier suggested methodologies, research gaps are made clear. The investigation largely pointed out the problems in the accuracy of the models, the development of data-gathering systems, and the types of data used in the proposed frameworks. Finally, by examining critical topics required for the development of this research area, research directions toward smart water quality are presented.

Shale oil reservoirs such as Bakken, Niobrara, and Eagle Ford have become the main target for oil and gas investors as conventional formations started to be depleted and diminished in number. These unconventional plays have a huge oil potential; however, the predicted primary oil recovery is still low as an average of 7.5%. Injecting carbon dioxide (CO2) to enhance oil recovery in these poor-quality formations is still a debatable issue among investigators. In this study, three steps of research have been integrated to investigate the parameters that control the success of CO2 huff-n-puff process in the field scale of shale oil reservoirs. First, a numerical simulation study was conducted to upscale the reported experimental studies outcomes to the field conditions. The second step was to validate these numerical models with the field data from some of CO2-EOR pilots, which were performed in Bakken formation, in North Dakota and Montana regions. Finally, statistical methods for Design of Experiments (DOE) have been used to rank the most important parameters affecting CO2-EOR performance in these unconventional reservoirs. The Design of Experiments approved that the intensity of natural fractures (the number of natural fractures per length unit in each direction, I-direction, J-direction, and K-direction) and the conductivity of oil pathways (the average conductivity for the entire oil molecules path, from its storage (matrix) to the wellbore) are the two main factors controlling CO2-EOR success in shale oil reservoirs. However, the fracture intensity has a positive effect on CO2-EOR, while the later has a negative effect. Furthermore, this study found that the porosity and the permeability of natural fractures in shale reservoirs are clearly changeable with the production time, which, in turn, led to a clear gap between CO2 performances in the lab conditions versus what happened in the field pilots. This work reported that the molecular diffusion mechanism is the key mechanism for CO2 to enhance oil recovery in shale oil reservoirs. However, the conditions of the candidate field and the production well criteria can enhance or downgrade this mechanism in the field scale. Accordingly, the operating parameters for managing CO2-EOR huff-n-puff process should be tuned according to the candidate reservoir and well conditions. Moreover, general guidelines have been provided from this work to perform successful CO2 projects in these complex plays. Finally, this Article provides a thorough idea about how CO2 performance is different between the field scale of shale oil reservoirs and the lab-scale conditions.

Mixed convection flow and heat transfer within various cavities including lid-driven walls has many engineering applications. Investigation of such a problem is important in enhancing the performance of the cooling of electric, electronic and nuclear devices and controlling the fluid flow and heat exchange of the solar thermal operations and thermal storage. The main aim of this fundamental investigation is to examine the influence of a two-phase hybrid nanofluid approach on mixed convection characteristics including the consequences of varying Richardson number, number of oscillations, nanoparticle volume fraction, and dimensionless length and dimensionless position of the solid obstacle. The migration of composite hybrid nanoparticles due to the nano-scale forces of the Brownian motion and thermophoresis was taken into account. There is an inner block near the middle of the enclosure, which contributes toward the flow, heat, and mass transfer. The top lid cover wall of the enclosure is allowed to move which induces a mixed convection flow. The impact of the migration of hybrid nanoparticles with regard to heat transfer is also conveyed in the conservation of energy. The governing equations are molded into the non-dimensional pattern and then explained using the finite element technique. The effect of various non-dimensional parameters such as the volume fraction of nanoparticles, the wave number of walls, and the Richardson number on the heat transfer and the concentration distribution of nanoparticles are examined. Various case studies for Al2O3-Cu/water hybrid nanofluids are performed. The results reveal that the temperature gradient could induce a notable concentration variation in the enclosure. The location of the solid block and undulation of surfaces are valuable in the control of the heat transfer and the concentration distribution of the composite nanoparticles.

Reproduction is fundamental for all living things as it ensures the continued existence of a species and an improved economy in animal husbandry. Reproduction has developed since history, and diverse processes, such as artificial insemination and in vitro fertilization, have been developed. Semen extenders were discovered and developed to protect sperm from harmful factors, such as freeze and osmotic shock, oxidative stress, and cell injury by ice crystals. Semen extenders preserve sperm by stabilizing its properties, including sperm morphology, motility, and viability and membrane, acrosomal, and DNA integrity. Therefore, semen extenders must provide a favorable pH, adenosine triphosphate, anti-cooling and anti-freeze shock, and antioxidant activity to improve semen quality for fertilization. Hence, this review provides precise data on different semen extenders, preservative mechanisms, and essential additives for semen extenders in different animals.

Accurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh). Daily data over 24 years for Kula Terengganu station were obtained from the Malaysia Meteorological Department. Results showed that MLP-NN performs well among the others in predicting daily T and Rh with R of 0.7132 and 0.633, respectively. However, in monthly prediction T also MLP-NN model provided closer standards deviation to actual value and can be used to predict monthly T with R 0.8462. Whereas in prediction monthly Rh, the RBF-NN model's efficiency was higher than other models with R of 0.7113. To validate the performance of the trained both artificial neural network (ANN) architectures MLP-NN and RBF-NN, both were applied to an unseen data set from observation data in the region. The results indicated that on either architecture of ANN, there is good potential to predict daily and monthly T and Rh values with an acceptable range of accuracy.

Abstract The performance of a solar photocatalysis reactor as pretreatment for the removal of total organic carbon (TOC) and turbidity from municipal wastewater was achieved by implementing an integrated system as tertiary treatment. The process consisted of ultraviolet (UV) sunlight, UV sunlight/H 2 O 2 , and UV sunlight/TiO 2 nanocatalysts as pretreatment steps to prevent ultrafiltration (UF) membrane fouling. The characterization of TiO 2 was conducted with X-ray diffraction spectroscopy, Fourier-transform infrared spectroscopy, scanning electron microscopy , and Brunauer–Emmett–Teller surface area analysis. This study investigated the effect of time and solar radiation using UV, UV/H 2 O 2 , and UV/TiO 2 to remove TOC and turbidity. The transmembrane pressure improvement was studied using a UF membrane system to pretreat wastewater with different UV doses of sunlight for 5 h and UV/H 2 O 2 and UV/TiO 2 . The results showed that the highest removal efficiency of the turbidity and TOC reached 95% and 31%, respectively. The highest removal efficiency of the turbidity reached 40, 75, and 95% using UV, UV/H 2 O 2 , and UV/TiO 2 , respectively, while the optimal removal efficiency of TOC reached 20%, 30%, and 50%, respectively.

One of the most significant nanobiotechnology and nanomaterial science areas today is the production of novel sensors and biosensors with applications in the food industry. Carbon quantum dots (CQDs) are a new generation of carbon nanoparticles with a lot of potential for food analysis. CQDs with robust physicochemical properties are one of the most recently researched carbon nanomaterials. This material has outstanding optical properties such as light persistence, photobleaching tolerance, photoluminescence, and the advantages of fast functionalization and strong biocompatibility, rendering it an excellent raw material for sensing devices. Thanks to its considerable features such as fast result outputs, low expense, ease of service, and high sensitivity, fluorescence analysis has tremendous potential for food protection. The aim of this article is to familiarise yourself with carbon points, their synthesis methods, and their optical properties. Finally, fluorescence sensors can be used to detect food additives, heavy metals, bacteria, insecticide residues, antibiotics, and nutritional components in food samples. CQDs' problems and opportunities in the area of food safety were also addressed.

Strontium antimony iodide (Sr 3 SbI 3 ) is one of the emerging absorbers materials owing to its intriguing structural, electronic, and optical properties for efficient and cost-effective solar cell applications.

Sixth-generation (6G) networks pose substantial security risks because confidential information is transmitted over wireless channels with a broadcast nature, and various attack vectors emerge. Physical layer security (PLS) exploits the dynamic characteristics of wireless environments to provide secure communications, while reconfigurable intelligent surfaces (RISs) can facilitate PLS by controlling wireless transmissions. With RIS-aided PLS, a lightweight security solution can be designed for low-end Internet of Things (IoT) devices, depending on the design scenario and communication objective. This article discusses RIS-aided PLS designs for 6G-IoT networks against eavesdropping and jamming attacks. The theoretical background and literature review of RIS-aided PLS are discussed, and design solutions related to resource allocation, beamforming, artificial noise, and cooperative communication are presented. We provide simulation results to show the effectiveness of RIS in terms of PLS. In addition, we examine the research issues and possible solutions for RIS modeling, channel modeling and estimation, optimization, and machine learning. Finally, we discuss recent advances, including simultaneous transmitting and reflecting-RIS and malicious RIS.