University of Anbar

BACKGROUND: Antibiotic resistance is currently the most serious global threat to the effective treatment of bacterial infections. Antibiotic resistance has been established to adversely affect both clinical and therapeutic outcomes, with consequences ranging from treatment failures and the need for expensive and safer alternative drugs to the cost of higher rates of morbidity and mortality, longer hospitalization, and high-healthcare costs. The search for new antibiotics and other antimicrobials continues to be a pressing need in humanity's battle against bacterial infections. Antibiotic resistance appears inevitable, and there is a continuous lack of interest in investing in new antibiotic research by pharmaceutical industries. This review summarized some new strategies for tackling antibiotic resistance in bacteria. METHODS: To provide an overview of the recent research, we look at some new strategies for preventing resistance and/or reviving bacteria's susceptibility to already existing antibiotics. RESULTS: Substantial pieces of evidence suggest that antimicrobials interact with host immunity, leading to potent indirect effects that improve antibacterial activities and may result in more swift and complete bactericidal effects. A new class of antibiotics referred to as immuno-antibiotics and the targeting of some biochemical resistance pathway components including inhibition of SOS response and hydrogen sulfide as biochemical underlying networks of bacteria can be considered as new emerging strategies to combat antibiotic resistance in bacteria. CONCLUSION: This review highlighted and discussed immuno-antibiotics and inhibition of SOS response and hydrogen sulfide as biochemical underlying networks of bacteria as new weapons against antibiotic resistance in bacteria.

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.

Using gestures can help people with certain disabilities in communicating with other people. This paper proposes a lightweight model based on YOLO (You Only Look Once) v3 and DarkNet-53 convolutional neural networks for gesture recognition without additional preprocessing, image filtering, and enhancement of images. The proposed model achieved high accuracy even in a complex environment, and it successfully detected gestures even in low-resolution picture mode. The proposed model was evaluated on a labeled dataset of hand gestures in both Pascal VOC and YOLO format. We achieved better results by extracting features from the hand and recognized hand gestures of our proposed YOLOv3 based model with accuracy, precision, recall, and an F-1 score of 97.68, 94.88, 98.66, and 96.70%, respectively. Further, we compared our model with Single Shot Detector (SSD) and Visual Geometry Group (VGG16), which achieved an accuracy between 82 and 85%. The trained model can be used for real-time detection, both for static hand images and dynamic gestures recorded on a video.

Coconut (Cocos nucifera) shell was chemically treated with sulfuric acid (H2SO4) to produce acid- factionalized biosorbent for methylene blue (MB) dye removal from aqueous environment. Various analytical techniques were utilized to investigate the surface area, surface morphology, crystallinity, elemental composition, and functional group of the sulfuric acid-treated coconut shell (SATCS). The adsorption parameters such as adsorbent dosage (0.02–0.20 g), solution pH (3–10), contact time (0–360 min), and initial MB dye concentration (25–200 mg/L) were studied. The adsorption results were illustrated by pseudo-second order kinetic and Freundlich isotherm models. It was found that SATCS has a maximum adsorption capacity (qmax) of 50.6 mg/g at 303 K. The adsorption mechanism of MB dye on the SATCS surface can be assigned to the various types of interactions such as electrostatic attractions, H-bonding interaction, and π-π interaction. This work shows SATCS as promising acid- factionalized biosorbent for removal MB dye.

Harvested rainwater is an alternative source of water in arid and semi-arid regions (ASARs) around the world. Many researchers have developed and applied various methodologies and criteria to identify suitable sites and techniques for rainwater harvesting (RWH). Determining the best method or guidelines for site selection, however, is difficult. The main objective of this study was to define a general method for selecting suitable RWH sites in ASARs by assembling an inventory of the main methods and criteria developed during the last three decades. We categorised and compared four main methodologies of site selection from 48 studies published in scientific journals, reports of international organisations, or sources of information obtained from practitioners. We then identified three main sets of criteria for selecting RWH locations and the main characteristics of the most common RWH techniques used in ASARs. The methods were diverse, ranging from those based only on biophysical criteria to more integrated approaches including socio-economic criteria, especially after 2000. The most important criteria for the selection of suitable sites for RWH were slope, land use/cover, soil type, rainfall, distance to settlements/streams, and cost. The success rate of RWH projects tended to increase when these criteria were considered, but an objective evaluation of these selection methods is still lacking. Most studies now select RHW sites using geographic information systems in combination with hydrological models and multi-criteria analysis.

Competition in civil engineering markets usually imposes low-cost, low-density and environmentally resistant materials with minimum maintenance and extended service life features to withstand the undesired sever loading and aggressive environmental conditions. As a result, using advanced composite materials as reinforcing for many different structures has been developed acceptably in past decades through new construction and rehabilitation applications. “Fiber reinforced polymers” as composite materials are powerful strengthening technique for various structural applications and have been the main focus for many researchers in the latest years due to their aforementioned properties. FRPs technique has been successfully implemented for strengthening bridges, buildings, tunnels, silos, tanks, and underground infrastructures. FPRs have been conducted as high-performance materials owing to their advantages including light-weight, fatigue resistance, high tensile strength, anti-corrosion, and thermal insulation. This paper intends to review the design of FRP composites and the characteristics of their constituent materials. This review also provides a brief information about the potential of FRPs as an alternative to steel reinforcement in concrete structural members by providing evaluation of the mechanical properties of FRP composite materials in terms of compressive, shear, flexural and tensile strength against extreme loading and environmental conditions.

Many natural products present in our diet, including flavonoids, can prevent the progression of cancer and other diseases. Resveratrol, a natural polyphenol present in various fruits and vegetables, plays an important role as a therapeutic and chemopreventive agent used in the treatment of various illnesses. It exhibits effects against different types of cancer through different pathways. It additionally exerts antidiabetic, anti-inflammatory, and anti-oxidant effects in a variety of cell types. Furthermore, the cardiovascular protective capacities of resveratrol are associated with multiple molecular targets and may lead to the development of novel therapeutic strategies for atherosclerosis, ischemia/reperfusion, metabolic syndrome, and heart failure. Accordingly, this article presents an overview of recent developments in the use of resveratrol for the prevention and treatment of different diseases along with various mechanisms. In addition, the present review summarizes the most recent literature pertaining to resveratrol as a chemotherapeutic agent against multiple diseases and provides an assessment of the potential of this natural compound as a complementary or alternative medicine.

Determining project achievement indicators from the beginning of its establishment is one of the most important success factors that must be clearly documented for all parties involved in the project to achieve high fluidity during implementation, which is done through the application of project management as the most efficient method for implementing and completing construction projects to include the project life cycle (from the feasibility study stage to the close-up stage). The main objective of this study was to present a report on some important results resulting from an extensive study examining the role of critical factors determining the success of the construction project implementation. These results are supported by a lot of theoretical and survey work that was conducted on a group of fifty engineers distributed across ministries, companies and various positions on the role that these factors play in the success of the project. It was found that the answer depends on the stage of the life cycle in which the project falls. As a result, empirical evidence was provided to project managers indicating the need to pay attention to specific groups of critical factors at each of the four stages of the project life cycle. It has been proven that these factors have a strong impact on the success of the project, and in some cases represent up to 66% of the reasons for the success of the project implementation.

Zinc oxide nanoparticles (ZnO NPs) exhibit distinctive characteristics, making them highly sought-after in many sectors. Nevertheless, conventional techniques for producing ZnO-NPs are linked to environmental and health hazards due to toxic substances. In this review, we study zinc oxide nanoparticles (ZnO NPs) synthesized from plant extracts and their subsequent biomedical uses in detail. Research shows that several different plant extracts are employed in manufacturing ZnO nanoparticles. Leaves, fruits, seeds, roots, and complete plants are all included in these extracts. Phytochemicals such as phenolic compounds, alkaloids, flavonoids, and terpenoids are all a part of these biological matrices. compounds show bioreduction mechanism, act as stabilizing and reducing agent. The attributes of ZnO nanoparticles (NPs), including their size, shape, and crystallinity, may be altered by adjusting the plant extract variety, concentration, and synthesis conditions. Consequently, the formed nanoparticles display notable diversity in their physical and chemical characteristics, subsequently impacting their biological functionality. The biomedical uses of ZnO nanoparticles manufactured using green methods are extensive, including beneficial effects such as antibacterial activity against various pathogens, anti-inflammatory characteristics, and possible anticancer activities. Nanoparticles have been integrated into wound dressings, used as carriers for medication delivery, and utilized in biosensing and imaging applications. The enhanced biocompatibility and reduced toxicity of green-processed zinc oxide nanoparticles (ZnO NPs) techniques, in comparison to those made using conventional approaches, make them very appealing for use in biomedical contexts. Moreover, the paper examines the synthesis mechanisms, explicitly focusing on the involvement of phytochemicals in the processes of reduction and stabilization. Additionally, this study emphasizes the difficulties and potential future directions in optimizing synthesis processes, increasing manufacturing capacity, and facilitating the therapeutic use of these nanoparticles.

Many types of research have distinctly addressed the efficacy of natural plant metabolites used for human consumption both in cell culture and preclinical animal model systems. However, these in vitro and in vivo effects have not been able to be translated for clinical use because of several factors such as inefficient systemic delivery and bioavailability of promising agents that significantly contribute to this disconnection. Over the past decades, extraordinary advances have been made successfully on the development of novel drug delivery systems for encapsulation of plant active metabolites including organic, inorganic and hybrid nanoparticles. The advanced formulas are confirmed to have extraordinary benefits over conventional and previously used systems in the manner of solubility, bioavailability, toxicity, pharmacological activity, stability, distribution, sustained delivery, and both physical and chemical degradation. The current review highlights the development of novel nanocarrier for plant active compounds, their method of preparation, type of active ingredients, and their biomedical applications.

Systems based on fog computing produce massive amounts of data; accordingly, an increasing number of fog computing apps and services are emerging. In addition, machine learning (ML), which is an essential area, has gained considerable progress in various research domains, including robotics, neuromorphic computing, computer graphics, natural language processing (NLP), decision-making, and speech recognition. Several researches have been proposed that study how to employ ML to settle fog computing problems. In recent years, an increasing trend has been observed in adopting ML to enhance fog computing applications and provide fog services, like efficient resource management, security, mitigating latency and energy consumption, and traffic modeling. Based on our understanding and knowledge, there is no study has yet investigated the role of ML in the fog computing paradigm. Accordingly, the current research shed light on presenting an overview of the ML functions in fog computing area. The ML application for fog computing become strong end-user and high layers services to gain profound analytics and more smart responses for needed tasks. We present a comprehensive review to underline the latest improvements in ML techniques that are associated with three aspects of fog computing: management of resource, accuracy, and security. The role of ML in edge computing is also highlighted. Moreover, other perspectives related to the ML domain, such as types of application support, technique, and dataset are provided. Lastly, research challenges and open issues are discussed.

The aim of this study is to propose a model based on machine learning (ML) and Internet of Things (IoT) to diagnose patients with COVID-19 in smart hospitals. In this sense, it was emphasized that by the representation for the role of ML models and IoT relevant technologies in smart hospital environment. The accuracy rate of diagnosis (classification) based on laboratory findings can be improved via light ML models. Three ML models, namely, naive Bayes (NB), Random Forest (RF), and support vector machine (SVM), were trained and tested on the basis of laboratory datasets. Three main methodological scenarios of COVID-19 diagnoses, such as diagnoses based on original and normalized datasets and those based on feature selection, were presented. Compared with benchmark studies, our proposed SVM model obtained the most substantial diagnosis performance (up to 95%). The proposed model based on ML and IoT can be served as a clinical decision support system. Furthermore, the outcomes could reduce the workload for doctors, tackle the issue of patient overcrowding, and reduce mortality rate during the COVID-19 pandemic.

Voice pathology disorders can be effectively detected using computer-aided voice pathology classification tools. These tools can diagnose voice pathologies at an early stage and offering appropriate treatment. This study aims to develop a powerful feature extraction voice pathology detection tool based on Deep Learning. In this paper, a pre-trained Convolutional Neural Network (CNN) was applied to a dataset of voice pathology to maximize the classification accuracy. This study also proposes a distinguished training method combined with various training strategies in order to generalize the application of the proposed system on a wide range of problems related to voice disorders. The proposed system has tested using a voice database, namely the Saarbrücken voice database (SVD). The experimental results show the proposed CNN method for speech pathology detection achieves accuracy up to 95.41%. It also obtains 94.22% and 96.13% for F1-Score and Recall. The proposed system shows a high capability of the real-clinical application that offering a fast-automatic diagnosis and treatment solutions within 3 s to achieve the classification accuracy.

Nowadays, coronavirus (COVID-19) is getting international attention due it considered as a life-threatened epidemic disease that hard to control the spread of infection around the world. Machine learning (ML) is one of intelligent technique that able to automatically predict the event with reasonable accuracy based on the experience and learning process. In the meantime, a rapid number of ML models have been proposed for predicate the cases of COVID-19. Thus, there is need for an evaluation and benchmarking of COVID-19 ML models which considered the main challenge of this study. Furthermore, there is no single study have addressed the problem of evaluation and benchmarking of COVID diagnosis models. However, this study proposed an intelligent methodology is to help the health organisations in the selection COVID-19 diagnosis system. The benchmarking and evaluation of diagnostic models for COVID-19 is not a trivial process. There are multiple criteria requires to evaluate and some of the criteria are conflicting with each other. Our study is formulated as a decision matrix (DM) that embedded mix of ten evaluation criteria and twelve diagnostic models for COVID-19. The multi-criteria decision-making (MCDM) method is employed to evaluate and benchmarking the different diagnostic models for COVID19 with respect to the evaluation criteria. An integrated MCDM method are proposed where TOPSIS applied for the benchmarking and ranking purpose while Entropy used to calculate the weights of criteria. The study results revealed that the benchmarking and selection problems associated with COVID19 diagnosis models can be effectively solved using the integration of Entropy and TOPSIS. The SVM (linear) classifier is selected as the best diagnosis model for COVID19 with the closeness coefficient value of 0.9899 for our case study data. Furthermore, the proposed methodology has solved the significant variance for each criterion in terms of ideal best and worst best value, beside issue when specific diagnosis models have same ideal best value.

Globally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, but predominantly among the people living in remote areas where medical facilities are scarce. Diagnosis systems based on machine learning act as secondary readers and assist radiologists in the proper diagnosis of diseases, whereas cloud-based systems can support telehealth services and remote diagnostics. Techniques based on artificial neural networks (ANN) have attracted many researchers to explore their capability for disease diagnosis. Extreme learning machine (ELM) is one of the variants of ANN that has a huge potential for solving various classification problems. The framework proposed in this paper amalgamates three research domains: Firstly, ELM is applied for the diagnosis of breast cancer. Secondly, to eliminate insignificant features, the gain ratio feature selection method is employed. Lastly, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed. The performance of the cloud-based ELM is compared with some state-of-the-art technologies for disease diagnosis. The results achieved on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset indicate that the cloud-based ELM technique outperforms other results. The best performance results of ELM were found for both the standalone and cloud environments, which were compared. The important findings of the experimental results indicate that the accuracy achieved is 0.9868, the recall is 0.9130, the precision is 0.9054, and the F1-score is 0.8129.

Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.

The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.

Until now, an effective defense method against Distributed Denial of Service (DDoS) attacks is yet to be offered by security systems. Incidents of serious damage due to DDoS attacks have been increasing, thereby leading to an urgent need for new attack identification, mitigation, and prevention mechanisms. To prevent DDoS attacks, the basic features of the attacks need to be dynamically analyzed because their patterns, ports, and protocols or operation mechanisms are rapidly changed and manipulated. Most of the proposed DDoS defense methods have different types of drawbacks and limitations. Some of these methods have signature-based defense mechanisms that fail to identify new attacks and others have anomaly-based defense mechanisms that are limited to specific types of DDoS attacks and yet to be applied in open environments. Subsequently, extensive research on applying artificial intelligence and statistical techniques in the defense methods has been conducted in order to identify, mitigate, and prevent these attacks. However, the most appropriate and effective defense features, mechanisms, techniques, and methods for handling such attacks remain to be an open question. This review paper focuses on the most common defense methods against DDoS attacks that adopt artificial intelligence and statistical approaches. Additionally, the review classifies and illustrates the attack types, the testing properties, the evaluation methods and the testing datasets that are utilized in the methodology of the proposed defense methods. Finally, this review provides a guideline and possible points of encampments for developing improved solution models of defense methods against DDoS attacks.

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.

Abiotic stress is one of the main causes of crop reduction globally. Among the different abiotic stresses, cold is an essential factor that limits crop productivity worldwide. Low temperature affects the growth, development and distribution of agronomic species throughout the world. Cold stress is a serious threat to the sustainability of crop yields. Indeed, cold stress can cause major crop losses. A significant number of researches have been reviewed and discussed in this study in order to improve the understanding of the physiological and genetic nature and function of plant cold tolerance. Recent developments in determining the mechanism of genes with roles in freezing tolerance and the systems involved in low-temperature gene regulation and signal transduction are described. The roles of a family of Arabidopsis transcription factors, the CBF/DREB1 proteins, have been described and its role in controlling the expression of a regulon of cold-induced genes (COR) that increase plant freezing tolerance has been explained. Moreover, this study has reviewed the recent application applied to improve the cold tolerance of plants such as molybdenum. The use of infrared camera to study the process of plant injuries caused by low temperature has also been reviewed.