Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review

In Agriculture, orchards are the deciding factor in the country’s economy. There are many orchards, and citrus and sugarcane will cover 60 percent of them. These citrus orchards satisfy the necessity of citrus fruits and citrus products, and these citrus fruits contain more vitamin C. The citrus orchards have had some problems generating good yields and quality products. Pathogenic diseases, pests, and water shortages are the three main problems that plants face. Farmers can find these problems early on with the support of machine learning and deep learning, which may also change how they feel about technology.  By doing this in agriculture, the farmers can cut off the major issues of yield and quality losses. This review gives enormous methods for identifying and classifying plant pathogens, pests, and water stresses using image-based work. In this review, the researchers present detailed information about citrus pathogens, pests, and water deficits. Methods and techniques that are currently available will be used to validate the problem. These will include pre-processing for intensification, segmentation, feature extraction, and selection processes, machine learning-based classifiers, and deep learning models. In this work, researchers thoroughly examine and outline the various research opportunities in the field. This review provides a comprehensive analysis of citrus plants and orchards; Researchers used a systematic review to ensure comprehensive coverage of this topic

Frailty, delirium and hospital mortality of older adults admitted to intensive care : the Delirium (Deli) in ICU study

Background: Clinical frailty among older adults admitted to intensive care has been proposed as an important determinant of patient outcomes. Among this group of patients, an acute episode of delirium is also common, but its relationship to frailty and increased risk of mortality has not been extensively explored. Therefore, the aim of this study was to explore the relationship between clinical frailty, delirium and hospital mortality of older adults admitted to intensive care. Methods: This study is part of a Delirium in Intensive Care (Deli) Study. During the initial 6-month baseline period, clinical frailty status on admission to intensive care, among adults aged 50 years or more; acute episodes of delirium; and the outcomes of intensive care and hospital stay were explored. Results: During the 6-month baseline period, 997 patients, aged 50 years or more, were included in this study. The average age was 71 years (IQR, 63–79); 55% were male (n = 537). Among these patients, 39.2% (95% CI 36.1–42.3%, n = 396) had a Clinical Frailty Score (CFS) of 5 or more, and 13.0% (n = 127) had at least one acute episode of delirium. Frail patients were at greater risk of an episode of delirium (17% versus 10%, adjusted rate ratio (adjRR) = 1.71, 95% confidence interval (CI) 1.20–2.43, p = 0.003), had a longer hospital stay (2.6 days, 95% CI 1–7 days, p = 0.009) and had a higher risk of hospital mortality (19% versus 7%, adjRR = 2.54, 95% CI 1.72–3.75, p < 0.001), when compared to non-frail patients. Patients who were frail and experienced an acute episode of delirium in the intensive care had a 35% rate of hospital mortality versus 10% among non-frail patients who also experienced delirium in the ICU. Conclusion: Frailty and delirium significantly increase the risk of hospital mortality. Therefore, it is important to identify patients who are frail and institute measures to reduce the risk of adverse events in the ICU such as delirium and, importantly, to discuss these issues in an open and empathetic way with the patient and their families

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

QCNN_BaOpt: Multi-Dimensional Data-Based Traffic-Volume Prediction in Cyber–Physical Systems

The rapid growth of industry and the economy has contributed to a tremendous increase in traffic in all urban areas. People face the problem of traffic congestion frequently in their day-to-day life. To alleviate congestion and provide traffic guidance and control, several types of research have been carried out in the past to develop suitable computational models for short- and long-term traffic. This study developed an effective multi-dimensional dataset-based model in cyber–physical systems for more accurate traffic-volume prediction. The integration of quantum convolutional neural network and Bayesian optimization (QCNN_BaOpt) constituted the proposed model in this study. Furthermore, optimal tuning of hyperparameters was carried out using Bayesian optimization. The constructed model was evaluated using the US accident dataset records available in Kaggle, which comprise 1.5 million records. The dataset consists of 47 attributes describing spatial and temporal behavior, accidents, and weather characteristics. The efficiency of the proposed model was evaluated by calculating various metrics. The performance of the proposed model was assessed as having an accuracy of 99.3%. Furthermore, the proposed model was compared against the existing state-of-the-art models to demonstrate its superiority

QCNN_BaOpt: Multi-Dimensional Data-Based Traffic-Volume Prediction in Cyber–Physical Systems

The rapid growth of industry and the economy has contributed to a tremendous increase in traffic in all urban areas. People face the problem of traffic congestion frequently in their day-to-day life. To alleviate congestion and provide traffic guidance and control, several types of research have been carried out in the past to develop suitable computational models for short- and long-term traffic. This study developed an effective multi-dimensional dataset-based model in cyber–physical systems for more accurate traffic-volume prediction. The integration of quantum convolutional neural network and Bayesian optimization (QCNN_BaOpt) constituted the proposed model in this study. Furthermore, optimal tuning of hyperparameters was carried out using Bayesian optimization. The constructed model was evaluated using the US accident dataset records available in Kaggle, which comprise 1.5 million records. The dataset consists of 47 attributes describing spatial and temporal behavior, accidents, and weather characteristics. The efficiency of the proposed model was evaluated by calculating various metrics. The performance of the proposed model was assessed as having an accuracy of 99.3%. Furthermore, the proposed model was compared against the existing state-of-the-art models to demonstrate its superiority

Development and validation of a dissolution test with reversed-phase high performance liquid chromatographic analysis for Candesartan cilexetil in tablet dosage forms

A simple, rapid, selective and reproducible reversed-phase high performance liquid chromatographic (RP-HPLC) method has been developed and validated for the estimation of release of Candesartan cilexetil (CC) in tablets. Analysis was performed on an Agilent, Zorbax C8 column (150mm × 4.6mm, 5μm) with the mobile phase consisting of phosphate buffer (pH2.5)–acetonitrile (15:85, v/v) at a flow rate of 1.0mL/min. UV detection was performed at 215nm and the retention time for CC was 2.2. The calibration curve was linear (correlation coefficient = 1.000) in the selected range of analyte. The optimized dissolution conditions include the USP apparatus 2 at a paddle rotation rate of 50rpm and 900mL of phosphate buffer (pH7.2) with 0.03% of polysorbate 80 as dissolution medium, at 37.0 ± 0.5°C. The method was validated for precision, linearity, specificity, accuracy, limit of quantitation and ruggedness. The system suitability parameters, such as theoretical plate, tailing factor and relative standard deviation (RSD) between six standard replicates were well within the limits. The stability result shows that the drug is stable in the prescribed dissolution medium. Three different batches (A, B and C) of the formulation containing 8mg of Candesartan cilexetil was performed with the developed method and the results showed no significant differences among the batches