University of Kufa

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

Rolling bearing fault detection is critical for improving production efficiency and lowering accident rates in complicated mechanical systems, as well as huge monitoring data, posing significant challenges to present fault diagnostic technology. Deep Learning is now an extraordinarily popular research topic in the field and a promising approach for detecting intelligent bearing faults. This paper aims to give a comprehensive overview of Deep Learning (DL) based on bearing fault diagnosis. The most widely used DL algorithms for detecting bearing faults include Convolutional Neural Network, Recurrent neural network, Autoencoder, and Generative Adversarial Network. It discusses a variety of transfer learning architectures and relevant theories while summarises, classifies, and explains several publications on the subject. The research area’s applications and problems are also addressed.

Different biological methods are gaining recognition for the production of silver nanoparticles (Ag-NPs) due to their multiple applications. The use of plants in the green synthesis of nanoparticles emerges as a cost effective and eco-friendly approach. In this study the green biosynthesis of silver nanoparticles using Callicarpa maingayi stem bark extract has been reported. Characterizations of nanoparticles were done using different methods, which include; ultraviolet-visible spectroscopy (UV-Vis), powder X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy dispersive X-ray fluorescence (EDXF) spectrometry, zeta potential measurements and Fourier transform infrared (FT-IR) spectroscopy. UV-visible spectrum of the aqueous medium containing silver nanoparticles showed absorption peak at around 456 nm. The TEM study showed that mean diameter and standard deviation for the formation of silver nanoparticles were 12.40 ± 3.27 nm. The XRD study showed that the particles are crystalline in nature, with a face centered cubic (fcc) structure. The most needed outcome of this work will be the development of value added products from Callicarpa maingayi for biomedical and nanotechnology based industries.

BACKGROUND: Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. METHODS: GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. FINDINGS: The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6-47·0) in 1990 to 63·4 years (63·1-63·7) in 2023. For males, mean age increased from 45·4 years (45·1-45·7) to 61·2 years (60·7-61·6), and for females it increased from 48·5 years (48·1-48·8) to 65·9 years (65·5-66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9-81·0) and for males 74·8 years (74·8-74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5-38·4) for females and 35·6 years (35·2-35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. INTERPRETATION: We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. FUNDING: Gates Foundation.

Rheumatoid arthritis (RA) is considered a chronic systemic, multi-factorial, inflammatory, and progressive autoimmune disease affecting many people worldwide. While patients show very individual courses of disease, with RA focusing on the musculoskeletal system, joints are often severely affected, leading to local inflammation, cartilage destruction, and bone erosion. To prevent joint damage and physical disability as one of many symptoms of RA, early diagnosis is critical. Auto-antibodies play a pivotal clinical role in patients with systemic RA. As biomarkers, they could help to make a more efficient diagnosis, prognosis, and treatment decision. Besides auto-antibodies, several other factors are involved in the progression of RA, such as epigenetic alterations, post-translational modifications, glycosylation, autophagy, and T-cells. Understanding the interplay between these factors would contribute to a deeper insight into the causes, mechanisms, progression, and treatment of the disease. In this review, the latest RA research findings are discussed to better understand the pathogenesis, and finally, treatment strategies for RA therapy are presented, including both conventional approaches and new methods that have been developed in recent years or are currently under investigation.

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.

In this paper, we describe a periodically-forced oscillator with spatially-periodic damping. This system has an infinite number of coexisting nested attractors, including limit cycles, attracting tori, and strange attractors. We are aware of no similar example in the literature.

Ultrafine-grained and heterostructured materials are currently of high interest due to their superior mechanical and functional properties. Severe plastic deformation (SPD) is one of the most effective methods to produce such materials with unique microstructure-property relationships. In this review paper, after summarizing the recent progress in developing various SPD methods for processing bulk, surface and powder of materials, the main structural and microstructural features of SPD-processed materials are explained including lattice defects, grain boundaries and phase transformations. The properties and potential applications of SPD-processed materials are then reviewed in detail including tensile properties, creep, superplasticity, hydrogen embrittlement resistance, electrical conductivity, magnetic properties, optical properties, solar energy harvesting, photocatalysis, electrocatalysis, hydrolysis, hydrogen storage, hydrogen production, CO2 conversion, corrosion resistance and biocompatibility. It is shown that achieving such properties is not currently limited to pure metals and conventional metallic alloys, and a wide range of materials are processed by SPD, including high-entropy alloys, glasses, semiconductors, ceramics and polymers. It is particularly emphasized that SPD has moved from a simple metal processing tool to a powerful means for the discovery and synthesis of new superfunctional metallic and nonmetallic materials. The article ends by declaring that the borders of SPD have been extended from materials science and it has become an interdisciplinary tool to address scientific questions such as the mechanism of geological and astronomical phenomena and the origin of life.

Printed, flexible and wearable dry electrodes for monitoring electrocardiogram (ECG) signals, without any skin preparation and use of wet gel, has been developed. Silver (Ag) flake ink was screen printed on a flexible polyethylene terephthalate (PET) substrate to fabricate the dry ECG electrode. Multi-walled carbon nanotube (MWCNT)/polydimethylsiloxane (PDMS) composite, as a conductive polymer, was then deposited on the printed Ag electrode by using a bar coating technique. The performance of the printed electrodes was investigated by testing the MWCNT/PDMS composite conductivity and measuring the electrode-skin impedance for electrode radii varying from 8 mm to 16 mm. It was observed that the dry ECG electrode, with the largest area, demonstrated better performance, in terms of MWCNT/PDMS composite conductivity, ECG signal intensity and correlation when compared to a commercial wet silver/silver chloride (Ag/AgCl) electrode. In addition, the capability of the dry ECG electrodes for monitoring ECG signals in both the relaxed sitting position and while the subject is in motion, was also investigated and the results were compared with a wet Ag/AgCl ECG electrode (T716). While the subject is in motion, the printed dry electrodes were less noisy and were able to better identify the typical ECG characteristics in the signals due to its better conformal contact at the electrode-skin interface. The results obtained demonstrated the feasibility of employing conventional screen printing process for the development of flexible dry ECG electrodes for applications in the biomedical industry. Keywords: Dry ECG electrode, ECG monitoring, MWCNT, PDMS, Screen printing, Wearable biomedical sensors

Negative effects of using traditional engine fuels on climate change and global warning have produced a scenario of high competition to find an alternative fuel more friendly and unharmful to the environment. Alcohol fuel was found very practical to be mixed with engine designed fuel. In the present work, ethanol was mixed with gasoline in different proportions (10% ethanol + 90% gasoline, 20% ethanol + 80% gasoline, 30% ethanol +70% gasoline, 40% ethanol + 60% gasoline) by utilizing ultrasonic bath to ensure perfect mixing which in turn will increase the fuel energy content. A one-cylinder, four-stroke and spark ignition engine was used to study and analyze the effect of ethanol's/gasoline blend on power, efficiency and exhaust gases. Results showed that power, brake specific fuel consumption and thermal efficiency are improved with the increase of ethanol concentration. On the other hand, ethanol was found to have negative effect on volumetric efficiency. Furthermore, adding ethanol reduce harmful exhaust gasses. It is found that more ethanol is an accompanied with less exhaust gasses. Finally, research octane number and motor octane number are found to be much with ethanol blends. Although lower heating value was found to be higher for pure gasoline, it's found that all other parameters are enhanced with adding ethanol to the engine fuel.

Greece has reached the height of its greatness and its reputation has spread throughout the world to this day. Every intellectual and philosophical product throughout history has been studied and drank from the wellspring of Greek civilization, as it is the fountain of ideas and the center of global thought to this day. This does not mean that it was not preceded by another civilization. Mesopotamia and the Chinese, Indian and Persian civilization preceded it by many centuries, and Greece took a lot from them, and this was not a defect, but rather a universal norm and a law that governs humanity, as civilizational and cultural exchange and the interaction and integration of ideas are part of this human system within the title of “Integration of Civilizations”.

A new adsorbent formed from pistachio shell powder that was coated with ZnO nanoparticles (CPS) was examined in terms of simultaneous adsorption of tetracycline (TEC), amoxicillin (AMO), and ciprofloxacin (CIP) from an aqueous solution. Initially, the characterization properties of a CPS-like surface morphology, functional groups, and structure were obtained using advanced analysis of TEM, SEM, XRD, EDS, and FT-IR. Post coating with ZnO nanoparticles, several surface and structural characteristics relating to the adsorption ability of the pistachio shell were significantly improved. The correlation of the kinetic data by a pseudo second-order model was successful for three antibiotics. High compatibility resulted between the TEC and CIP isotherm data and the Fruendlich model. However, the Langmuir model produced a better fit to the AMO isotherm curves. In addition, its spontaneous and exothermic nature was the main feature for the adsorption process of the three antibiotics onto CPS. Through the results, the chemical adsorption has been governed by the AMO, CIP, and TEC reaction onto the homogeneous and heterogeneous sites of CPS surfaces. The CPS exhibited a highest adsorption capacity for AMO (132.240 mg/g), then for TEC (98.717 mg/g), and CIP (92.450 mg/g). These results place CPS one among the highly efficient adsorbents that can be used to eradicate wastewater containing antibiotics.

COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.

The ventilation systems are inadequate, the increase in the amount of carbon dioxide and carbon monoxide in the inner environment can damage human health. In addition, obtaining the level of external environment with excessive ventilation can lead to unnecessary energy consumption. In this study, a system that absorbs air from the environment using carbon dioxide, oxygen and carbon monoxide values in ambient air and controls the operation of the ventilation system that gives fresh air to the environment is designed with fuzzy logic method. The overall purpose of the system is to achieve the ideal ventilation level. Two separate ventilation valves were controlled at the same time with the fuzzy control system designed in the study. The fuzzy control system was created with MATLAB Fuzzy Logic Toolbox and the operating structure of the control system had analyzed.

Abstract Most of the third world countries having rivers passing through them suffer from the water contaminant problem. This problem is considered so difficult to get the water quality within the standard allowable limits for drinking, as well as for industrial and agricultural purposes. This research aims to assess the water quality of the Tigris River using the water quality index method and GIS software. Twelve parameters (Ca, Mg, Na, K, Cl, SO 4 , HCO 3 , TH, TDS, BOD 5 , NO 3 , and EC) were taken from 14 stations along the river. The weighted arithmetic method was applied to compute the water quality index (WQI). The interpolation method (IDW) was applied in ArcGIS 10.5 to produce the prediction maps for 12 parameters at 11 stations along the Tigris River during the wet and dry seasons in 2016. The regression prediction was applied on three stations in the Tigris River between observed values and predicted values, from the prediction maps, in both seasons. The results showed that the regression prediction for all parameters was given the acceptable values of the determination coefficient ( R 2 ). Furthermore, the state of water quality for the Tigris River was degraded downstream of the Tigris River, especially at the station (8) in Aziziyah in the wet and dry seasons and increase degradation clearly at Qurnah (Basrah province) in the south of Iraq. This paper considers the whole length of the Tigris River for the study. This is important to give comprehensive knowledge about the contamination reality of the river. Such that it becomes easier to understand the problem of contamination, analyze it, and then find the suitable treatments and solutions.

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.

The border gateway protocol (BGP) is the Internet's default inter-domain routing protocol that manages connectivity among autonomous systems (ASes). Over the past two decades many anomalies of BGP have been identified that threaten its stability and reliability. This survey discusses and classifies these anomalies and discusses the 20 most significant techniques used to identify them. Our classification is based on the broad category of approach, BGP features used to identify the anomaly, effectiveness in identifying the anomaly and effectiveness in identifying which AS was the location of the event that caused the anomaly. We also discuss a number of key requirements for the next generation of BGP anomaly detection techniques.

BACKGROUND: Chronic kidney disease (CKD) is common and ranks among the leading causes of mortality and morbidity. This analysis aimed to present global CKD estimates using the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 to inform evidence-based policies for CKD identification and treatment. METHODS: This analysis focused on adults aged 20 years and older over the period 1990 to 2023, from 204 countries and territories. Data sources used were published literature, vital registration systems, kidney failure treatment registries, and household surveys. Estimates of CKD burden, including deaths, incidence, prevalence, and disability-adjusted life-years (DALYs), were produced using a Cause of Death Ensemble model and a Bayesian meta-regression analytical tool. A comparative risk assessment approach estimated the proportion of cardiovascular deaths attributable to impaired kidney function and estimated risk factors for CKD. FINDINGS: Globally, in 2023, 788 million (95% uncertainty interval 743-843) people aged 20 years and older were estimated to have CKD, up from 378 million (354-407) in 1990. The global age-standardised prevalence of CKD in adults was 14·2% (13·4-15·2), a relative rise of 3·5% (2·7-4·1) from 1990. The region with the highest age-standardised prevalence was north Africa and the Middle East (18·0%; 16·9-19·4). Most people had stage 1-3 CKD, with a combined prevalence of 13·9% (13·1-15·0). In 2023, CKD was the ninth leading cause of death globally, accounting for 1·48 million (1·30-1·65) deaths, and the 12th leading cause of DALYs, with an age-standardised DALY rate of 769·2 (691·8-857·4) per 100 000. Impaired kidney function as a risk factor accounted for 11·5% (8·4-14·5) of cardiovascular deaths. High fasting plasma glucose, body-mass index, and systolic blood pressure were all leading risk factors for CKD DALYs. INTERPRETATION: CKD is a major global health issue, with rising prevalence and increasing importance as a cause of death and as a risk factor for cardiovascular death. A better understating of aetiology, appropriate screening, and implementation programmes are needed to translate advances in CKD treatment into improved patient outcomes. FUNDING: Gates Foundation, Wellcome, US National Kidney Foundation, and US National Institute of Diabetes and Digestive and Kidney Diseases.

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.

SARS-CoV-2 is a new generation of coronavirus, which was first determined in Wuhan, China, in December 2019. So far, however, there no effective treatment has been found to stop this new generation of coronavirus but discovering of the crystal structure of SARS-CoV-2 main protease (SARS-CoV-2 Mpro) may facilitate searching for new therapies for SARS-COV-2. The aim was to assess the effectiveness of available FDA approved drugs which can construct a covalent bond with Cys145 inside binding site SARS-CoV-2 main protease by using covalent docking screening. We conducted the covdock module MMGBSA module in the Schrodinger suite 2020-1, to examine the covalent bonding utilizing. Besides, we submitted the top three drugs to molecular dynamics simulations via Gromacs 2018.1. The covalent docking showed that saquinavir, ritonavir, remdesivir, delavirdine, cefuroxime axetil, oseltamivir and prevacid have the highest binding energies MMGBSA of -72.17, -72.02, -65.19, -57.65, -54.25, -51.8, and -51.14 kcal/mol, respectively. The 50 ns molecular dynamics simulation was conducted for saquinavir, ritonavir and remdesivir to evaluate the stability of these drugs inside the binding pocket of SARS-CoV-2 main protease. The current study provides a powerful in silico results, means for rapid screening of drugs as anti-protease medications and recommend that the above-mentioned drugs can be used in the treatment of SARS-CoV-2 in combined or sole therapy.Communicated by Ramaswamy H. Sarma.