Al al-Bayt University

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.

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.

This study aimed at examining the impact of E-HRM on organizational health. It focused on telecommunications companies operating in Jordan. Data were primarily gathered through self-reported questionnaires created in Google Forms and distributed to a purposive sample of senior managers via email. AMOSv24 was used to test the study hypotheses. The results of the study show that E-HRM has a positive impact on organizational health. Based on the obtained results, the researchers recommend managers and decision-makers of the telecommunications companies in Jordan to invest in electronic human resources systems, which can help them fully implement human resources practices electronically, to obtain economic savings and to be able to attract talents. The study also highlights the importance of focusing more on the electronic training and development process in order to raise individuals’ practical capabilities, which is reflected in their creativity.

INTRODUCTION: In the spot of the new emerging COVID-19 pandemic and its major impact worldwide on day-to-day activities many rules had to be changed in order to fight this pandemic. Lockdown started in Jordan and around the globe affecting several aspects of life including economy, education, entertainment, and government policies. Regarding education, the priority was to ensure the safety and progress of the educational process. Thus, new methods of teaching had to be applied using the online learning at Jordan University of Science and Technology (JUST), Faculty of Medicine. This study was done to assess (1) Class Experience (2) Students and Lecturers' Interaction (3) Online Learning Advantages & Disadvantages (4) Students' Preference. METHODS: A cross sectional study was conducted Convenience sampling technique was used to collect the data from the participants using a survey composed of 18 questions on Google Forms platform. A link was sent to the undergraduate medical students at the Jordan University of Science & Technology via their e-learning accounts (n = 3700). The form was available from May 22nd, 2020 to May 30th, 2020 for 8 days long. Data analysis was done using SPSS V 23. RESULTS: 2212 out of 3700 students responded, (55.8%) of them were in the basic years and (44.2%) of them were in the clinical years. (55.8%) of students started to take online lectures after 3 weeks. (45.7%) used the hybrid teaching method (asynchronous and synchronous), (31.4%) used live classes, and 22.8% recorded classes. Zoom was the most used platform. (48.7%) and (57%) of clinical students and basic students express their interaction as bad, while the others had good and excellent interaction. Maintaining social distance was the most advantage of online teaching, while poor technical setup and no direct contact were the most disadvantage, furthermore inability to have real clinical access was a significant problem for clinical students (p < .001). With reference to students' preferences 75% of students were not pleased with their experience and 42% of students prefer to integrate online learning with traditional learning. CONCLUSION: Most medical students at JUST preferred the traditional face-to-face teaching method over the solo online teaching methods with recommendations to convert to a more integrated educational system. Also, a well-established infrastructure should be done in involving online teaching.

The changes in the business environment and the increase in competition have led organizations to focus greatly on improving their organizational performance in order to achieve a sustainable competitive advantage by relying on keeping pace with these changes and developing their innovation capability to meet their customers’ desires. Therefore, this research paper aims to explore the relationship between strategic agility and organizational performance through the mediating role of innovation capability. The research population consisted of senior managers in industrial corporations, and the sample comprised 224 senior managers. Structural equation modeling (SEM) was used as a statistical method for testing hypotheses. The results showed that there is a significant influence of strategic agility on organizational performance and innovation capability. Furthermore, innovation capability plays a mediating role in improving the relationship between strategic agility and organizational performance. Accordingly, a set of recommendations are provided to corporations’ senior managers for supporting the organizational activities that lead to the creation of new products and services that are appropriate to the general context of the development of customer desires, realizing the importance of the corporation acquiring flexible re-sources that can be reallocated to meet the changes in the business environment, and adopting modern business models based on stimulating collaborative work and adopting creative ideas.

The aim of the study was to examine the impact of digital marketing capabilities on organizational ambidexterity by focusing on the Information Technology Sector in UAE. Data were primarily gathered through self-reported questionnaires created by Google Forms which were distributed to a purposive sample of managers at different levels via email. This study was conducted structural equation modeling (SEM) to test the hypotheses, which represents a contemporary statistical technique for testing and estimating the relationship between factors and variables. The results showed that the highest impact on organizational ambidexterity was for strategic approach and data content infrastructure, followed by integrating customers with employees, and finally the lowest impact belonged to the process of improving performance. Based on the study findings, the researcher hopes that the decision-makers and managers define all tasks, roles and work procedures in companies through digital marketing systems to improve their organizational ambidexterity and enhance their performance.

BACKGROUND: Electronic health records (EHRs) are increasingly being implemented in healthcare organizations but little attention has been paid to the degree to which nurses as end-users will accept these systems and subsequently use them. OBJECTIVES: To explore nurses' perceptions of usefulness and ease-of-use of EHRs. The relationship between these constructs was examined, and its predictors were studied. METHOD: A national exploratory study was conducted with 1539 nurses from 15 randomly selected hospitals, representative of different regions and healthcare sectors in Jordan. Data were collected using a self-administered questionnaire, which was based on the Technology Acceptance Model. Correlations and linear multiple regression were utilized to analyze the data. RESULTS: Jordanian nurses demonstrated a positive perception of the usefulness and ease-of-use of EHRs, and subsequently accepted the technology. Significant positive correlations were found between these two constructs. The variables that predict usefulness were the gender, professional rank, EHR experience, and computer skills of the nurses. The perceived ease-of-use was affected by nursing and EHR experience, and computers skills. CONCLUSION: This study adds to the growing body of knowledge on issues related to the acceptance of technology in the health informatics field, focusing on nurses' acceptance of EHRs.

Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users' needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.

Big data analytics (BDA) adoption has gained attention in both practical and theoretical circles owing to the opportunities and advantages that can be reaped from it. In theory, the majority of researchers have evidenced the benefits of BDA, although barriers to its adoption have also been mentioned. This study draws upon the technology-organisation-environment framework and resource-based view theory to propose an integrated model that examines the drivers and impact of BDA adoption in the retail industry in Jordan. The proposed single model encapsulates the aspects of BDA adoption and performance. The study makes use of an online questionnaire survey to collect the required data, and the research model is eventually validated based on 132 responses gathered from the retail industry in Jordan. The findings highlight two major observations. The first is that relative advantage, organisational readiness, top management support, government support, data variety and data velocity all have a significant influence over BDA adoption. The second observation is that a significant association exists between BDA adoption and firm performance, providing information on the way firms can enhance their BDA adoption for enhanced performance. This study contributes to literature dedicated to examining BDA in terms of its drivers and impact on performance and can be used as a reference in developing nations.

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.

Depression and anxiety are prevalent mental illnesses among young people. Crisis like the Coronavirus Disease 2019 (COVID-19) pandemic may increase the current prevalence of these illnesses. A cross-sectional, descriptive design was used to (1) explore the prevalence of depression, anxiety, and stress among youth and (2) identify to what extent certain variables related to COVID-19 could predict depression, anxiety, and stress (DAS) among young people in six different countries. Participants were requested to complete an online survey including demographics and the DAS scale. A total of 1,057 participants from Oman (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>155</mml:mn></mml:math>), Saudi Arabia (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>121</mml:mn></mml:math>), Jordan (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>332</mml:mn></mml:math>), Iraq (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>117</mml:mn></mml:math>), United Arab Emirates (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>147</mml:mn></mml:math>), and Egypt (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>182</mml:mn></mml:math>) completed the study. The total prevalence of depression, anxiety, and stress was 57%, 40.5%, and 38.1%, respectively, with no significant differences between countries. Significant predictors of stress, anxiety, and depression were being female, being in contact with a friend and/or a family member with mental illness, being quarantined for 14 days, and using the internet. In conclusion, COVID-19 is an epidemiological crisis that is casting a shadow on youths’ DAS. The restrictions and prolonged lockdowns imposed by COVID-19 are negatively impacting their level of DAS. Healthcare organisations, in collaboration with various sectors, are recommended to apply psychological first aid and design appropriate educational programmes to improve the mental health of youth.

To assess the relationship between serum C3 or C4 levels and lupus renal flare, C3 and C4 levels were measured bimonthly in 71 lupus nephritis patients for a mean of 35 months, during which time 70 renal flares were identified. Comparing baseline, pre-flare, and at-flare values indicated that neither C3 nor C4 levels decreased pre-flare, but both decreased on average significantly at flare. However, sensitivity/specificity for C3 (75%/71%) and C4 (48%/71%) were low. To account for other influencing factors, multiple regression was performed that included bimonthly values of C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR), and genotype data on C3 (S/F), CRP (1846G > A), and the complement regulator factor H (Y402H). This analysis revealed that reduced levels of C4, but not C3, were independently associated with the two-month pre-flare period. Conversely, reduced levels of C3, but not C4, were independently associated with the flare visit. Significant pro-flare interactions included low C3 levels with the factor H 402HH-encoding genotype, and low CRP levels with the C3 F allele. Together these data suggest that C4 activation is critical for initiating renal flare while C3 activation is involved in the actual tissue damage, and that these effects are influenced by genetic variability in complement activation and regulation.

The structural, optical and magnetic properties of Mn doped ZnO nanocrystalline particles, Zn1-xMnxO, with different percentages of Mn content have been studied. XRD and XPS measurements showed that all samples with Mn doping up to x = 0.1 possess typical wurtzite structure and have no other impurity phases. The incorporation of Mn ions into the ZnO lattice was also confirmed by FTIR and UV–Vis. spectroscopy results. Both XRD and SEM results indicated a slight decrease in the grain size with increasing the Mn doping level. The XPS results indicated an increase in the oxygen vacancies concentration with increasing the Mn doping level. The magnetization measurements revealed a weak ferromagnetic behavior at room temperature and a clear ferromagnetic behavior with relatively large coercive fields at low temperature. The ferromagnetic order is improved by increasing the Mn doping. In addition, we observed an increase in the concentration of oxygen vacancies, which is also induced by increasing the Mn doping level. A ferromagnetic coupling of the local moment of Mn dopants through the sp-d exchange interaction and oxygen vacancies, in addition to different magnetic contributions due to different forms of Mn ions that coexist in the Mn doped nanoparticles were presented in order to interpret the observed magnetic behavior. We observed a clear red shift in the direct band gap and an increase in the coercive field and saturation magnetization values with increasing the Mn doping level.

The effects of interactions (dipolar and exchange) on the magnetic behavior of granular solid systems are examined using a Monte Carlo model capable of predicting the temperature and time dependence of the magnetic properties. Using this model the interaction effects on the magnetization and the magnetoresistance are studied. The results show that these properties depend critically on the strength and nature of the interactions. Magnetostatic interactions are found to decrease both remanence and coercivity and Hc is predicted to decrease linearly with concentration. It is shown that spatial disorder may be responsible for an increase of coercivity with exchange coupling which has been observed in some experimental studies. In systems with no hysteresis, magnetostatic interaction effects are found to increase the superparamagnetic transition temperature, in agreement with experimental data and previous analytical treatments. Calculations of the giant magnetoresistance (GMR) show that magnetostatic interaction effects give rise to a finite positive resistivity at zero field which increases with concentration. This causes the value of the maximum change in resistivity, which occurs near the coercivity, to be greater than the value at zero field. These calculations are in agreement with experimental observations of GMR in granular solids. It is predicted that the GMR is strongly dependent on the spin diffusion length via the local spin–spin correlation function.

Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted plants, into valuable insights, like the identification of specific crop diseases. This innovation has spurred the development of cutting-edge techniques for early detection and diagnosis of crop diseases, leveraging tools such as convolutional neural networks (CNN), K-nearest neighbour (KNN), support vector machines (SVM), and artificial neural networks (ANN). This paper offers an all-encompassing exploration of the contemporary literature on methods for diagnosing, categorizing, and gauging the severity of crop diseases. The review examines the performance analysis of the latest machine learning (ML) and DL techniques outlined in these studies. It also scrutinizes the methodologies and datasets and outlines the prevalent recommendations and identified gaps within different research investigations. As a conclusion, the review offers insights into potential solutions and outlines the direction for future research in this field. The review underscores that while most studies have concentrated on traditional ML algorithms and CNN, there has been a noticeable dearth of focus on emerging DL algorithms like capsule neural networks and vision transformers. Furthermore, it sheds light on the fact that several datasets employed for training and evaluating DL models have been tailored to suit specific crop types, emphasizing the pressing need for a comprehensive and expansive image dataset encompassing a wider array of crop varieties. Moreover, the survey draws attention to the prevailing trend where the majority of research endeavours have concentrated on individual plant diseases, ML, or DL algorithms. In light of this, it advocates for the development of a unified framework that harnesses an ensemble of ML and DL algorithms to address the complexities of multiple plant diseases effectively.

Co-Ti and Ru-Ti substituted barium ferrite nanocrystalline particles BaFe12−2xCoxTixO19 with (0≤x≤1) and BaFe12−2xRuxTixO19 with (0≤x≤0.6) were prepared by ball milling method, and their magnetic properties and their temperature dependencies were studied. The zero-field-cooled (ZFC) and field-cooled (FC) processes were recorded at low magnetic fields and the ZFC curves displayed a broad peak at a temperature TM. In all samples under investigation, a clear irreversibility between the ZFC and FC curves was observed below room temperature, and this irreversibility disappeared above room temperature. These results were discussed within the framework of random particle assembly model and associated with the magnetic domain wall motion. The resistivity data showed some kind of a transition from insulator to perfect insulator around TM. At 2 K, the saturation magnetization slightly decreased and the coercivity dropped dramatically with increasing the Co-Ti concentration x. With Ru-Ti substitution, the saturation magnetization showed small variations, while the coercivity decreased monotonically, recording a reduction of about 73% at x = 0.6. These results were discussed in light of the single ion anisotropy model and the cationic distributions based on previously reported neutron diffraction data for the CoTi substituted system, and the results of our Mössbauer spectroscopy data for the RuTi substituted system.

This study came with the aim of identifying the impact of the green supply chain on sustainability. This research targeted managers at the senior and middle levels of the Pharmaceutical Industry in Jordan, as they were formulating the company's strategies and determining its policies. A purposive sample consisting of 258 managers was selected. To gather the data needed for the analysis, a self-report questionnaire was used formulated electronically through Google Forms. AMOS software was used to examine the research hypotheses. The study concluded that there was an impact of the green supply chain with its dimensions (Eco-Design, Green Distribution, Green Purchasing, Green Manufacturing, and Green Reverse Logistics) on sustainability. Based on this result, the researcher recommends pharmaceutical companies in Jordan to take green initiatives and the trends towards implementing a green supply chain approach that reduces the consumption of non-renewable resources and waste, and to establish special laws and regulations in the company that oblige employees to apply the green approach in their practices within the work.

Abstract Floods are one of the most destructive natural disasters causing financial damages and casualties every year worldwide. Recently, the combination of data‐driven techniques with remote sensing (RS) and geographical information systems (GIS) has been widely used by researchers for flood susceptibility mapping. This study presents a novel hybrid model combining the multilayer perceptron (MLP) and autoencoder models to produce the susceptibility maps for two study areas located in Iran and India. For two cases, nine, and twelve factors were considered as the predictor variables for flood susceptibility mapping, respectively. The prediction capability of the proposed hybrid model was compared with that of the traditional MLP model through the area under the receiver operating characteristic (AUROC) criterion. The AUROC curve for the MLP and autoencoder‐MLP models were, respectively, 75 and 90, 74 and 93% in the training phase and 60 and 91, 81 and 97% in the testing phase, for Iran and India cases, respectively. The results suggested that the hybrid autoencoder‐MLP model outperformed the MLP model and, therefore, can be used as a powerful model in other studies for flood susceptibility mapping.

The purpose of this paper is to examine the impact of the constructs of service quality and customer satisfaction in commercial banks operating in Jordan. The study finds that the order of importance of the dimensions of service quality tested here is: assurance; reliability; tangibles; empathy; and responsiveness. Customers' satisfactions are mostly influenced by the service quality. Customers indicated high satisfaction with the five dimensions of service quality. This finding reinforces the need for banks managers to place an emphasis on the underlying dimensions of service quality, especially on assurance, and should start with improving service quality in order to raise customer satisfaction. Managers should be aware that, among the various dimensions of service quality, assurance was especially significant in fostering satisfaction for the customers of Jordanian commercial banks. It is apparent that focusing on delivering high quality services, and improve service quality effectively is critical for customer satisfaction.

The Central Platte River often experiences high water temperatures during sunny, hot summer days. A 128-km reach of the Platte River downstream of two hydropower dams (Kingsley Dam and North Platte/Keystone Diversion Dam) was studied to determine the relationship between river summer water temperatures and river flow-rate, and the impacts of in-stream flow requirements upon peak water temperatures. This reach serves as a habitat for eight federally listed or endangered species, as well as over 300 species of migratory birds, including 500,000 sandhill cranes and 7-9 million ducks and geese. Hourly water temperatures were simulated using a dynamic numerical model (MNSTREM) with and without in-stream flow requirements. It was found that a clear relationship exists between river water temperatures and river flow-rate. In addition, it was found that the occurrence of high water temperatures can be attributed to low river flow-rate and can be reduced, but not eliminated, with minimum in-stream flow requirements.