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Background: Out of hospital cardiac arrest is one of the leading causes of death globally. This study aimed to identify the characteristics of out of hospital cardiac arrest patients who were attended and treated by the National Ambulance crew. A lot of studies reported the importance of implementing chain of survival to increase survival rates from cardiac arrest. To be implemented in United Arab Emirates (UAE), it required a detailed study of the community engagement. The study aimed to explore the demography of the incidences, location, age, gender epidemiology of the patients who had their cardiac arrest witnessed along with their Bystander cardiopulmonary resuscitation (CPR) performed prior to the arrival of National Ambulance and public access to an automated external defibrillator. The return of spontaneous circulation was also explored prior to their arrival to the emergency department. Methods: The research is a prospective descriptive cohort study of out of hospital cardiac arrest patients attended by National Ambulance between July 2017 and June 2018. The National Ambulance provides emergency medical services for public and private hospitals in the Emirates of Sharjah, Ajman, Ras-al-Khaimah, Fujairah, and Umm Al-Quwain and its clients in Abu Dhabi in UAE. Data for the study were collected by the National Ambulance crew attending the OHCA patients, using a structured questionnaire. Results: In this 1-year period, a total of 715 out of hospital cardiac arrest cases were attended by the National Ambulance with higher percentage (77%) of male patients. Resuscitation and transportation were attempted for 95% whereas 5% were pronounced dead on the spot. In this study, the median age of the patients was 50 years. Majority of the patients were Asians 55% (n = 395) followed by Arabs non-UAE citizens 19.4% (n = 139) and UAE citizens 16% (n = 113). Patients facing sudden cardiac arrest in their homes or residences represented 69.9% (n = 500), street and public places 22.5% (n = 161), and workplace 6.8% (n = 49). The percentage of patients who had witnessed cardiac arrest was 51.7% (n = 370) only 197 had CPR performed on them prior to the arrival of National Ambulance. Low public access to AED was found in this population that is 1.8% (n = 13). A majority of the participants in this study had nonshockable rhythms 84.3% (n = 603) whereas shockable rhythms presented on 11% (n = 80). The percentage of patients who had ROSC at the scene or en route to the hospitals was found 9.2% (n = 66). Conclusion: In this 1-year study, the result showed that cardiac arrest was recognized and witnessed in about half of the cases, but low bystander CPR was performed. Low public access and use of AED were found. Data on hospitalized and discharged OHCA patients were not available and required further linkage and corporation between ambulance services and hospitals to ensure data continuity of OHCA cases. This study is essential for the implementation of proper chain of survival and reduction in mortality rates in UAE.

This study aimed to assess the level of readiness among primary school teachers to handle sports injuries in Saudi Arabia.  A structured questionnaire was applied to collect information on teachers, training, knowledge, attitudes, and perceived barriers in managing common sports injuries. Descriptive analysis was performed for demographics and baseline information. Pearson's Chi-squared test and Fisher's exact test were used to assess the determinants of first-aid attitude. Multiple logistic regression was also used to evaluate the determinants of first-aid knowledge. A total number of 535 teachers participated in this study. Most teachers (64.3%) reported an occurrence of sports injury once per month. Only 373 (67.72%) perceived the first aid kit to be readily accessible. The majority (95.89%) of teachers reported familiarity with conducting first aid and 87.38% expressed interest and willingness to learn. Social media was the primary resource of first aid knowledge (57.94%). Logistic regression showed that male sex (OR: 0.51, 95% CI: 0.26, 0.95; p-value = 0.036) and experiencing sports injuries once per month (OR: 0.39, 95% CI: 0.16, 0.84; p-value = 0.024) were associated with negative attitude toward first aid. However, having 10-20 years of experience (OR: 2.46, 95% CI: 1.09, 5.62; p-value = 0.031) or more than 20 years of experience was associated with more positive attitude toward first aid (OR: 6.47, 95% CI: 2.18, 19.8; p-value ≤ 0.001). Furthermore, accessing first aid information from digital media and healthcare professionals compared to books was significantly associated with increased knowledge about first aid. Easy accessibility to first aid was also significantly associated with increased knowledge about first aid. While many teachers feel prepared, the primary source of their first aid knowledge is unattributed social media content rather than certified training. Certified first aid training programs are needed to help in ensuring the quality emergency management of sports injuries.

Solid wastes from domestic, industrial and agricultural sectors cause acute economic and environmental problems. These issues can be partly solved by anaerobic digestion of wastes, yet this process is incomplete and generates abundant byproducts as digestate. Therefore, cultivating mixotrophic algae on anaerobic digestate appears as a promising solution for nutrient recovery, pollutant removal and biofuel production. Here we review mixotrophic algal cultivation on anaerobic waste digestate with focus on digestate types and characterization, issues of recycling digestate in agriculture, removal of contaminants, and production of biofuels such as biogas, bioethanol, biodiesel and dihydrogen. We also discuss applications in cosmetics and economical aspects. Mixotrophic algal cultivation completely removes ammonium, phosphorus, 17β-estradiol from diluted digestate, and removes 62% of zinc, 84% of manganese, 74% of cadmium and 99% of copper.

The world is experiencing an energy crisis and environmental issues due to the depletion of fossil fuels and the continuous increase in carbon dioxide concentrations. Microalgal biofuels are produced using sunlight, water, and simple salt minerals. Their high growth rate, photosynthesis, and carbon dioxide sequestration capacity make them one of the most important biorefinery platforms. Furthermore, microalgae's ability to alter their metabolism in response to environmental stresses to produce relatively high levels of high-value compounds makes them a promising alternative to fossil fuels. As a result, microalgae can significantly contribute to long-term solutions to critical global issues such as the energy crisis and climate change. The environmental benefits of algal biofuel have been demonstrated by significant reductions in carbon dioxide, nitrogen oxide, and sulfur oxide emissions. Microalgae-derived biomass has the potential to generate a wide range of commercially important high-value compounds, novel materials, and feedstock for a variety of industries, including cosmetics, food, and feed. This review evaluates the potential of using microalgal biomass to produce a variety of bioenergy carriers, including biodiesel from stored lipids, alcohols from reserved carbohydrate fermentation, and hydrogen, syngas, methane, biochar and bio-oils via anaerobic digestion, pyrolysis, and gasification. Furthermore, the potential use of microalgal biomass in carbon sequestration routes as an atmospheric carbon removal approach is being evaluated. The cost of algal biofuel production is primarily determined by culturing (77%), harvesting (12%), and lipid extraction (7.9%). As a result, the choice of microalgal species and cultivation mode (autotrophic, heterotrophic, and mixotrophic) are important factors in controlling biomass and bioenergy production, as well as fuel properties. The simultaneous production of microalgal biomass in agricultural, municipal, or industrial wastewater is a low-cost option that could significantly reduce economic and environmental costs while also providing a valuable remediation service. Microalgae have also been proposed as a viable candidate for carbon dioxide capture from the atmosphere or an industrial point source. Microalgae can sequester 1.3 kg of carbon dioxide to produce 1 kg of biomass. Using potent microalgal strains in efficient design bioreactors for carbon dioxide sequestration is thus a challenge. Microalgae can theoretically use up to 9% of light energy to capture and convert 513 tons of carbon dioxide into 280 tons of dry biomass per hectare per year in open and closed cultures. Using an integrated microalgal bio-refinery to recover high-value-added products could reduce waste and create efficient biomass processing into bioenergy. To design an efficient atmospheric carbon removal system, algal biomass cultivation should be coupled with thermochemical technologies, such as pyrolysis.

The rising occurrence of emerging contaminants in sludges both inhibits the anaerobic digestion of sludges and induces health issues when sludges are recycled in agriculture, calling for methods to remove contaminants. Here we review emerging pollutants in wastewater treatment plants, before and after anaerobic digestion. We present their inhibitory effects and remediation methods to alleviate inhibition. Pharmaceuticals have been detected in about 50% of the sludge samples. Sewage sludge contaminants include 19% of diuretics, 16–21% of lipid-modifying agents, hydrochlorothiazide, diclofenac, furosemide, clarithromycin, atorvastatin, and carbamazepine. Levels of antibiotics, azithromycin, ciprofloxacin, and estrone range from 500 to 600 ng/g in sludges from wastewater treatment plants. Remediation methods comprise electrooxidation, ultrasonication, thermal hydrolysis, ozonation, and bioaugmentation. Fermenting the sludges with acidogenic bacteria reduces the level of emerging pollutants in the supernatant. Nonetheless, liquid digestates still contains emerging pollutants such as sunscreen octocrylene at 147 ug/L and acetaminophen at 58.6 ug/L. As a result, pretreatment of sludge containing emerging pollutants is required.

PurposeThe quality of higher education has become a topic of increasing interest to researchers in recent decades. This study, therefore, aims to investigate the comparative effectiveness of instructors at higher education institutions in Kuwait and the USA, while also investigating the parallel differences in student culture and gender.Design/methodology/approachThe researchers employed a quantitative research paradigm, using a questionnaire survey method to examine four dimensions of effective instructorship (teaching skills, human relations, ethics, and assessment). Descriptive statistics were used to analyze data from 254 college students (N = 254), comprising 132 students at Bemidji State University in the USA and 122 students at Kuwait University in Kuwait.FindingsThe findings showed that students ranked “human relations” as the most significant attribute of an effective university instructor. Study results also indicated that culture is an important influencer of student perceptions regarding effective instructor characteristics. Gender also played a role in student perceptions of teacher effectiveness. Cross-culturally, female participants ranked teacher effectiveness dimensions such as human relations, ethics, and assessment, significantly higher than did their male colleagues, while within each culture, male students at the American university showed significantly greater concern for ethics in comparison to their counterparts in Kuwait.Originality/valueThis study offers findings from a cross-cultural comparative perspective. It provides value to administrators, deans, and department chairs at higher educational institutions who are evaluating their current rank, tenure, and promotions criteria and processes for teaching faculty. Additionally, while K-12 education has received significant attention over the past few decades regarding the qualities and practices of effective teachers in that realm, this study extends such research significantly into higher education.

Biochar (BC) was prepared by carbonizing sludge from agricultural lignocellulosic waste fermentation and then used to adsorb lambda‐cyhalothrin (LM), malathion (MA), and oxamyl (OX) as potential pesticides in agrochemical industrial wastewater. Additionally, the photodegradation performance of ZnO and ZnO/Fe was evaluated using various catalyst doses in a constructed parabolic solar collector reactor. The optimal ZnO catalyst dose and reaction time was 1.0 g/L and 135 min. OX, MA, and LM removal increased from 38%, 30%, and 24% in pristine ZnO to 55%, 70%, and 46.9% in the case of the addition of BC with ZnO (ZnO/BC), respectively. The doping of ZnO with iron did not improve the photodegradation efficiency due to the reduction of crystallinity and catalyst affinity towards the pollutants after introducing those ions. The mechanism of degradation was proposed, and the by‐products generated were identified. The total cost was estimated for pure ZnO, the addition of BC with ZnO (ZnO/BC), and the addition of BC with iron‐doped ZnO (ZnO/Fe/BC). The highest binding energy of −44.74 was recorded for BC–OX complex, followed by BC–LM at −42.97. The adsorption of LM, MA, and OX by ZnO/BC is primarily due to the hydrophobic interaction, hydrogen bonding, and π–π interaction. After three cycles of recycling ZnO/BC, the degradation efficiency remained 55–52.5% for OX, 70–65% for MA, and 46.9–42.8% for LM, indicating excellent reusability and stability of the composite catalyst. The low cost of the solar‐light‐driven ZnO/BC process may improve the technique's feasibility for large‐scale implementation. Pesticide removal using cost‐effective materials is critical in industrial wastewater treatment. We conducted a comprehensive practical and computational study on pesticide removal in this manuscript, as well as an economic analysis of the process's cost. Herein, the performance of pure ZnO, ZnO loaded on biochar (BC; ZnO/BC), and ZnO doped with iron (ZnO/Fe) besides the addition of BC (ZnO/Fe/BC) for the degradation of three types of pesticides was evaluated.

The impact of partial and full COVID lockdowns in 2020 on vehicle miles traveled (VMT) in Kuwait was estimated using data extracted from the Directions API of Google Maps and a Python script running as a cronjob. This approach was validated by comparing the predictions based on the app to measuring traffic flows for 1 week across four road segments considered in this study. VMT during lockdown periods were compared to VMT for the same calendar weeks before the pandemic. NOx emissions were estimated based on VMT and were used to simulate the spatial patterns of NOx concentrations using an air quality model (AERMOD). Compared to pre-pandemic periods, VMT was reduced by up to 25.5% and 42.6% during the 2-week partial and full lockdown episodes, respectively. The largest reduction in the traffic flow rate occurred during the middle of these 2-week periods, when the traffic flow rate decreased by 35% and 49% during the partial and full lockdown periods, respectively. The AERMOD simulation results predicted a reduction in the average maximum concentration of emissions directly related to VMT across the region by up to 38%, with the maximum concentration shifting to less populous residential areas as a result of the lockdown. Graphical Abstract

This paper presents an integrated framework in which an air quality dispersion model is combined with an economic dispatch model to address the environmental tradeoffs of a cost-optimized fuel allocation strategy. A unit commitment dispatch model was developed to re-allocate fuel between power generation and desalination plants. Then, an air quality dispersion model was run for a 1-year period to simulate the spatiotemporal transport of pollutants and the possible formation of air pollution hotspots. The results showed that optimizing fuel allocation can reduce the associated fuel cost by as much as 16.5% of the total cost (1.08 billion USD). The optimized fuel allocation approach resulted in reducing the base case emissions of NOx, SO 2 , CO, and PM 10 by 25%, 4.6%, 3.1%, and 7.6%, respectively. However, the air quality impact of the optimized fuel allocation scheme was not as favorable. The 1-h-averaged maximum concentration of SO 2 increased, and NOx concentrations were slightly above the allowable limits. Although fewer pollutants were emitted over the study period in the optimized fuel allocation case, the variability in how fuel was allocated between power and desalination plants concentrated emissions near residential areas. As a result of this trend, the maximum 1-h concentrations of all pollutants increased, with increases ranging from 1% for CO to 29% for PM 10 . In addition, the total number of hourly SO 2 concentration violations increased dramatically, leading to additional hotspot areas. Therefore, the effectiveness of any environmental-economic fuel dispatch strategy should be tested based on additional indicators such as the allowable limits of pollutant concentrations and not exclusively the overall emissions of the system. This approach could promote the selection of the most economic fuel dispatch method while simultaneously considering regional air quality impacts.

Background Hospital readmission following renal transplantation significantly impacts patient outcomes and healthcare resources. While machine learning approaches offer promising solutions for risk prediction, their clinical application often lacks interpretability. We developed an explainable artificial intelligence (XAI) based supervised learning model to predict 30-day hospital readmission risk following renal transplantation. Methods We conducted a retrospective analysis of 588 renal transplant recipients at King Abdullah International Medical Research Center, with a predominance of living donor transplants (85.2%, n  = 500). Our methodology included a four-stage machine learning pipeline: data processing, feature preparation, model development using stratified 5-fold cross-validation, and clinical validation. Multiple algorithms were evaluated, with gradient boosting demonstrating superior performance. Model interpretability was achieved through dual-approach analysis using SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). Results The gradient boosting model demonstrated strong performance (AUC 0.837, 95% CI: 0.802–0.872) with accuracy of 0.796 ± 0.050 and sensitivity of 0.388 ± 0.129. Length of hospital stay (38.0% contribution) and post-transplant systolic blood pressure (30.0% contribution) emerged as primary predictors, with differences between living and deceased donor subgroups. Pre-transplant BMI showed a higher importance in deceased donor recipients (12.6% vs. 2.6%), while HbA1c and eGFR were more impacting in living donor outcomes. The readmission rate in our cohort (88.9%, n  = 523) was higher than previously reported ranges (18–47%), likely reflecting center-specific practices. Conclusions Our XAI-based machine learning model combines strong predictive performance with clinical interpretability, offering transplant physicians donor-specific risk stratification capabilities. The web-based implementation facilitates practical integration into clinical workflows. Given our single-center experience and high proportion of living donors, external validation across diverse transplant centers is essential before widespread implementation. Our approach establishes a framework for developing center-specific risk prediction tools in transplant medicine.