Explore the vast range of titles available.

\"A comprehensive description of technologies available for converting old landfills to energy producers, and capturing the green house gases emitting from them. Its key assets are the case studies of successful landfill gas (LFG) recovery for energy projects around the world, and that it highlights why this has not been done in many more landfills around the world. Technical, financial, and social challenges facing the conversion of landfills to energy producers will be detailed, and solutions offered to either remine the landfill for recovering useful land (as is planned in dense urban areas of India) or close them properly while recovering the methane for energy use. Intended as a guide with background information and instructive tools to educate, guide and establish a basis for decision-making, technical feasibility assessment, economic assessment, and market evaluation of all aspects necessary for developing successful LFG management projects. \"-- Provided by publisher.

Biosorbent of rice husk was utilized to evaluate the removing of paracetamol from aqueous medium by sorption process (batch-concept); studying the influence of several experimental parameters as biosorbent dose, contact time, change of temperature; also studying the behaviour of the equilibrium isotherm of paracetamol into the rice husks and comparing the data with different isotherm models. Pseudo-second order equation and Langmuir model were best suited fordata experience. With increasing temperature the sorption process increased; suggesting that the process is endothermic in nature. FTIR test was tested before and after sorption for the purpose of showingthe presence and number of the functional groups ofparacetamolbinding on to the tested sorbent.

\"Artificial Intelligence (AI) in general and machine learning (ML) and deep learning (DL) in particular and related digital technologies are a couple of fledging paradigms that the next generation healthcare services are sprouting towards. These digital technologies can transform various aspects of healthcare, leveraging advances in computing and communication power. With a new spectrum of business opportunities, AI-powered healthcare services would improve the lives of patients, their families, and societies. However, the application of AI in the healthcare field requires special attention given the direct implication with human life and well-being. Rapid progress in AI leads to the possibility of exploiting healthcare data for designing practical tools for automated diagnosis of chronic diseases such as dementia and diabetes. This book highlights the current research trends in applying AI models in various disease diagnoses and prognoses to provide enhanced healthcare solutions. The primary audience of the book will be postgraduate students and researchers in the broad domain of healthcare technologies\"-- Provided by publisher.

Abstract Objective Our New York City Municipal Public Health System-based multisite ambulatory and school-based Gotham Health clinics offer waived point-of-care tests and provider-performed microscopy to the local communities. Our Gotham Health laboratory service conducts system-wide centralized implementation, monitoring, and oversight of the POCT operations. Laboratory staffing has always been an issue for us as there is a decades-long shortage of laboratory staff, primarily licensed medical technologists and technicians, in New York, like many other states. Our clinical laboratory operations team struggled to hire qualified people even before the COVID-19 pandemic onset. It has faced more significant challenges with the emergence of SARS-CoV-2 pandemic cases in New York City and across the country since mid-March 2020. Methods As staffing continues to be a struggle, it directly affected the POCT performances and a system-wide reduction in the test numbers during the pandemic. We investigated to identify the factors that made staffing more challenging. Results The impact on our POCT started after laboratory staff relocated to the acute care hospital laboratories to provide testing support during the pandemic’s peak. That caused significant delays or complete cessation of POCT operations in the clinics due to a lack of oversight support. We also experienced the risk of more vacated positions where staff already feel overworked, overwhelmed, and emotionally drained, causing professional burnout. The significant challenges identified are noncompliance with vaccine mandates resulting in job dismissal and voluntary resignations in exchange for higher-paying laboratories. Finally, the other challenges identified were frequent sick calls due to mental fatigue, retirement of seasoned staff, and inability to attract qualified technologists to meet the demands of increasing test-ordering patterns. Conclusions Determining the factors that culminated in the staffing issues becoming more challenging during the COVID-19 pandemic in our ambulatory care clinic laboratory operations will help us in future crisis planning and mitigation.

Hypertension (HTN), a major global health concern, is a leading cause of cardiovascular disease, premature death and disability, worldwide. It is important to develop an automated system to diagnose HTN at an early stage. Therefore, this study devised a machine learning (ML) system for predicting patients with the risk of developing HTN in Ethiopia. The HTN data was taken from Ethiopia, which included 612 respondents with 27 factors. We employed Boruta-based feature selection method to identify the important risk factors of HTN. The four well-known models [logistics regression, artificial neural network, random forest, and extreme gradient boosting (XGB)] were developed to predict HTN patients on the training set using the selected risk factors. The performances of the models were evaluated by accuracy, precision, recall, F1-score, and area under the curve (AUC) on the testing set. Additionally, the SHapley Additive exPlanations (SHAP) method is one of the explainable artificial intelligences (XAI) methods, was used to investigate the associated predictive risk factors of HTN. The overall prevalence of HTN patients is 21.2%. This study showed that XGB-based model was the most appropriate model for predicting patients with the risk of HTN and achieved the accuracy of 88.81%, precision of 89.62%, recall of 97.04%, F1-score of 93.18%, and AUC of 0. 894. The XBG with SHAP analysis reveal that age, weight, fat, income, body mass index, diabetes mulitas, salt, history of HTN, drinking, and smoking were the associated risk factors of developing HTN. The proposed framework provides an effective tool for accurately predicting individuals in Ethiopia who are at risk for developing HTN at an early stage and may help with early prevention and individualized treatment.

Artificial intelligence (AI) development across the health sector has recently been the most crucial. Early medical information, identification, diagnosis, classification, then analysis, along with viable remedies, are always beneficial developments. Precise and consistent image classification has critical in diagnosing and tactical decisions for healthcare. The core issue with image classification has become the semantic gap. Conventional machine learning algorithms for classification rely mainly on low-level but rather high-level characteristics, employ some handmade features to close the gap, but force intense feature extraction as well as classification approaches. Deep learning is a powerful tool with considerable advances in recent years, with deep convolution neural networks (CNNs) succeeding in image classification. The main goal is to bridge the semantic gap and enhance the classification performance of multi-modal medical images based on the deep learning-based model ResNet50. The data set included 28378 multi-modal medical images to train and validate the model. Overall accuracy, precision, recall, and F1-score evaluation parameters have been calculated. The proposed model classifies medical images more accurately than other state-of-the-art methods. The intended research experiment attained an accuracy level of 98.61%. The suggested study directly benefits the health service.

Background and Objectives: Burn injuries, particularly partial-thickness burns, often require advanced therapies to improve re-epithelialization and scar quality. This study aims to evaluate the efficacy of autologous skin cell suspensions, such as Recell, compared to standard treatments in promoting faster and better-quality skin healing. Our goal is to provide evidence-based conclusions on the effectiveness of these regenerative approaches in burn treatment. Materials and Methods: During our comprehensive investigation, we systematically examined several databases for the period to November 2024, including PubMed, Scopus, Web of Science, and the Cochrane Central Register of Controlled Trials. Our primary objective was to assess the efficacy of autologous cell suspension in treatment for burn injuries. We employed the ROB2 method to assess the quality of evidence to ensure the validity of the conclusions derived from these studies. The gathered data were systematically organized in a standardized online format and analyzed with RevMan 5.4. Results: Our systematic literature search identified nine studies (n = 358 patients) evaluating the efficacy of autologous skin cell suspensions in promoting re-epithelialization in burn injuries. The meta-analysis revealed a statistically significant reduction in time to re-epithelialization in the autologous skin cell suspension group compared to the control group (MD = −1.71 days, 95% CI [−2.73, −0.70], p = 0.001), with moderate heterogeneity among the studies (I2 = 58%). However, no significant differences were found in secondary outcomes, including postoperative pain (SMD = −0.71, 95% CI [−2.42, 1.00], p = 0.42), POSAS scores (MD = −0.35, 95% CI [−2.12, 1.42], p = 0.69), Vancouver Scar Scale (MD = −0.76, 95% CI [−2.86, 1.33], p = 0.48), or the incidence of complete healing by the 4th week (RR = 0.98, 95% CI [0.94, 1.02], p = 0.24). Similarly, no significant differences were found in postoperative infection rates (RR = 0.85, 95% CI [0.28, 2.60], p = 0.78) or the need for further interventions (RR = 0.15, 95% CI [0.02, 1.16], p = 0.07). Conclusions: autologous skin cell suspension significantly reduces the time to re-epithelialization in burn injuries compared to standard treatments. However, no significant differences were found in secondary outcomes, such as postoperative pain, scar quality (POSAS, Vancouver Scar Scale), complete healing rates, infection rates, or the need for additional interventions. While autologous skin cell suspension shows promise in accelerating re-epithelialization, it does not provide significant advantages over conventional methods in other clinical aspects. The results underscore the need for further research with larger, more robust studies to assess the long-term benefits of autologous skin cell suspension in burns carefully.

Malnutrition is a major health issue among Bangladeshi under-five (U5) children. Children are malnourished if the calories and proteins they take through their diet are not sufficient for their growth and maintenance. The goal of the research was to use machine learning (ML) algorithms to detect the risk factors of malnutrition (stunted, wasted, and underweight) as well as their prediction. This work utilized malnutrition data that was derived from Bangladesh Demographic and Health Survey which was conducted in 2014. The selected dataset consisted of 7079 children with 13 factors. The potential risks of malnutrition have been identified by logistic regression (LR). Moreover, 3 ML classifiers (support vector machine (SVM), random forest (RF), and LR) have been implemented for predicting malnutrition and the performance of these ML algorithms were assessed on the basis of accuracy. The average prevalence of stunted, wasted, and underweight was 35.4%, 15.4%, and 32.8%, respectively. It was noted that LR identified five risk factors for stunting and underweight, as well as four factors for wasting. Results illustrated that RF can be accurately classified as stunted, wasted, and underweight children and obtained the highest accuracy of 88.3% for stunted, 87.7% for wasted, and 85.7% for underweight. This research focused on the identification and prediction of major risk factors for stunting, wasting, and underweight using ML algorithms which will aid policymakers in reducing malnutrition among Bangladesh's U5 children.

BackgroundCardiovascular and arrhythmic events have been reported in hospitalized COVID-19 patients. However, arrhythmia manifestations and treatment strategies used in these patients have not been well-described. We sought to better understand the cardiac arrhythmic manifestations and treatment strategies in hospitalized COVID-19 patients through a worldwide cross-sectional survey.MethodsThe Heart Rhythm Society (HRS) sent an online survey (via SurveyMonkey) to electrophysiology (EP) professionals (physicians, scientists, and allied professionals) across the globe. The survey was active from March 27 to April 13, 2020.ResultsA total of 1197 respondents completed the survey with 50% of respondents from outside the USA, representing 76 countries and 6 continents. Of respondents, 905 (76%) reported having COVID-19-positive patients in their hospital. Atrial fibrillation was the most commonly reported tachyarrhythmia whereas severe sinus bradycardia and complete heart block were the most common bradyarrhythmias. Ventricular tachycardia/ventricular fibrillation arrest and pulseless electrical activity were reported by 4.8% and 5.6% of respondents, respectively. There were 140 of 631 (22.2%) respondents who reported using anticoagulation therapy in all COVID-19-positive patients who did not otherwise have an indication. One hundred fifty-five of 498 (31%) reported regular use of hydroxychloroquine/chloroquine (HCQ) + azithromycin (AZM); concomitant use of AZM was more common in the USA. Sixty of 489 respondents (12.3%) reported having to discontinue therapy with HCQ + AZM due to significant QTc prolongation and 20 (4.1%) reported cases of Torsade de Pointes in patients on HCQ/chloroquine and AZM. Amiodarone was the most common antiarrhythmic drug used for ventricular arrhythmia management.ConclusionsIn this global survey of > 1100 EP professionals regarding hospitalized COVID-19 patients, a variety of arrhythmic manifestations were observed, ranging from benign to potentially life-threatening. Observed adverse events related to use of HCQ + AZM included prolonged QTc requiring drug discontinuation as well as Torsade de Pointes. Large prospective studies to better define arrhythmic manifestations as well as the safety of treatment strategies in COVID-19 patients are warranted.