Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework.

The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence is linked to several factors, including the use of injection drugs, poor sterilization practices in medical facilities, and low public awareness. This paper introduces a hyOPTGB model, which employs an optimized gradient boosting (GB) classifier to predict HCV disease in Egypt. The model's accuracy is enhanced by optimizing hyperparameters with the OPTUNA framework. Min-Max normalization is used as a preprocessing step for scaling the dataset values and using the forward selection (FS) wrapped method to identify essential features. The dataset used in the study contains 1385 instances and 29 features and is available at the UCI machine learning repository. The authors compare the performance of five machine learning models, including decision tree (DT), support vector machine (SVM), dummy classifier (DC), ridge classifier (RC), and bagging classifier (BC), with the hyOPTGB model. The system's efficacy is assessed using various metrics, including accuracy, recall, precision, and F1-score. The hyOPTGB model outperformed the other machine learning models, achieving a 95.3% accuracy rate. The authors also compared the hyOPTGB model against other models proposed by authors who used the same dataset.

Optimizing Image Classification: Automated Deep Learning Architecture Crafting with Network and Learning Hyperparameter Tuning.

This study introduces ETLBOCBL-CNN, an automated approach for optimizing convolutional neural network (CNN) architectures to address classification tasks of varying complexities. ETLBOCBL-CNN employs an effective encoding scheme to optimize network and learning hyperparameters, enabling the discovery of innovative CNN structures. To enhance the search process, it incorporates a competency-based learning concept inspired by mixed-ability classrooms during the teacher phase. This categorizes learners into competency-based groups, guiding each learner's search process by utilizing the knowledge of the predominant peers, the teacher solution, and the population mean. This approach fosters diversity within the population and promotes the discovery of innovative network architectures. During the learner phase, ETLBOCBL-CNN integrates a stochastic peer interaction scheme that encourages collaborative learning among learners, enhancing the optimization of CNN architectures. To preserve valuable network information and promote long-term population quality improvement, ETLBOCBL-CNN introduces a tri-criterion selection scheme that considers fitness, diversity, and learners' improvement rates. The performance of ETLBOCBL-CNN is evaluated on nine different image datasets and compared to state-of-the-art methods. Notably, ELTLBOCBL-CNN achieves outstanding accuracies on various datasets, including MNIST (99.72%), MNIST-RD (96.67%), MNIST-RB (98.28%), MNIST-BI (97.22%), MNST-RD + BI (83.45%), Rectangles (99.99%), Rectangles-I (97.41%), Convex (98.35%), and MNIST-Fashion (93.70%). These results highlight the remarkable classification accuracy of ETLBOCBL-CNN, underscoring its potential for advancing smart device infrastructure development.

An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction.

The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction.

Enhanced Dual Convolutional Neural Network Model Using Explainable Artificial Intelligence of Fault Prioritization for Industrial 4.0.

Artificial intelligence (AI) systems are increasingly used in corporate security measures to predict the status of assets and suggest appropriate procedures. These programs are also designed to reduce repair time. One way to create an efficient system is to integrate physical repair agents with a computerized management system to develop an intelligent system. To address this, there is a need for a new technique to assist operators in interacting with a predictive system using natural language. The system also uses double neural network convolutional models to analyze device data. For fault prioritization, a technique utilizing fuzzy logic is presented. This strategy ranks the flaws based on the harm or expense they produce. However, the method's success relies on ongoing improvement in spoken language comprehension through language modification and query processing. To carry out this technique, a conversation-driven design is necessary. This type of learning relies on actual experiences with the assistants to provide efficient learning data for language and interaction models. These models can be trained to have more natural conversations. To improve accuracy, academics should construct and maintain publicly usable training sets to update word vectors. We proposed the model dataset (DS) with the Adam (AD) optimizer, Ridge Regression (RR) and Feature Mapping (FP). Our proposed algorithm has been coined with an appropriate acronym DSADRRFP. The same proposed approach aims to leverage each component's benefits to enhance the predictive model's overall performance and precision. This ensures the model is up-to-date and accurate. In conclusion, an AI system integrated with physical repair agents is a useful tool in corporate security measures. However, it needs to be refined to extract data from the operating system and to interact with users in a natural language. The system also needs to be constantly updated to improve accuracy.

Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection.

This study aims to develop a predictive model for SARS-CoV-2 using machine-learning techniques and to explore various feature selection methods to enhance the accuracy of predictions. A precise forecast of the SARS-CoV-2 respiratory infections spread can help with efficient planning and resource allocation. The proposed model utilizes stochastic regression to capture the virus transmission's stochastic nature, considering data uncertainties. Feature selection techniques are employed to identify the most relevant and informative features contributing to prediction accuracy. Furthermore, the study explores the use of neighbor embedding and Sammon mapping algorithms to visualize high-dimensional SARS-CoV-2 respiratory infection data in a lower-dimensional space, enabling better interpretation and understanding of the underlying patterns. The application of machine-learning techniques for predicting SARS-CoV-2 respiratory infections, the use of statistical measures in healthcare, including confirmed cases, deaths, and recoveries, and an analysis of country-wise dynamics of the pandemic using machine-learning models are used. Our analysis involves the performance of various algorithms, including neural networks (NN), decision trees (DT), random forests (RF), the Adam optimizer (AD), hyperparameters (HP), stochastic regression (SR), neighbor embedding (NE), and Sammon mapping (SM). A pre-processed and feature-extracted SARS-CoV-2 respiratory infection dataset is combined with ADHPSRNESM to form a new orchestration in the proposed model for a perfect prediction to increase the precision of accuracy. The findings of this research can contribute to public health efforts by enabling policymakers and healthcare professionals to make informed decisions based on accurate predictions, ultimately aiding in managing and controlling the SARS-CoV-2 pandemic.

Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm.

The virus that causes monkeypox has been observed in Africa for several years, and it has been linked to the development of skin lesions. Public panic and anxiety have resulted from the deadly repercussions of virus infections following the COVID-19 pandemic. Rapid detection approaches are crucial since COVID-19 has reached a pandemic level. This study's overarching goal is to use metaheuristic optimization to boost the performance of feature selection and classification methods to identify skin lesions as indicators of monkeypox in the event of a pandemic. Deep learning and transfer learning approaches are used to extract the necessary features. The GoogLeNet network is the deep learning framework used for feature extraction. In addition, a binary implementation of the dipper throated optimization (DTO) algorithm is used for feature selection. The decision tree classifier is then used to label the selected set of features. The decision tree classifier is optimized using the continuous version of the DTO algorithm to improve the classification accuracy. Various evaluation methods are used to compare and contrast the proposed approach and the other competing methods using the following metrics: accuracy, sensitivity, specificity, p-Value, N-Value, and F1-score. Through feature selection and a decision tree classifier, the following results are achieved using the proposed approach; F1-score of 0.92, sensitivity of 0.95, specificity of 0.61, p-Value of 0.89, and N-Value of 0.79. The overall accuracy of the proposed methodology after optimizing the parameters of the decision tree classifier is 94.35%. Furthermore, the analysis of variation (ANOVA) and Wilcoxon signed rank test have been applied to the results to investigate the statistical distinction between the proposed methodology and the alternatives. This comparison verified the uniqueness and importance of the proposed approach to Monkeypox case detection.

A Novel Bio-Inspired Optimization Algorithm Design for Wind Power Engineering Applications Time-Series Forecasting.

Wind patterns can change due to climate change, causing more storms, hurricanes, and quiet spells. These changes can dramatically affect wind power system performance and predictability. Researchers and practitioners are creating more advanced wind power forecasting algorithms that combine more parameters and data sources. Advanced numerical weather prediction models, machine learning techniques, and real-time meteorological sensor and satellite data are used. This paper proposes a Recurrent Neural Network (RNN) forecasting model incorporating a Dynamic Fitness Al-Biruni Earth Radius (DFBER) algorithm to predict wind power data patterns. The performance of this model is compared with several other popular models, including BER, Jaya Algorithm (JAYA), Fire Hawk Optimizer (FHO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Particle Swarm Optimization (PSO)-based models. The evaluation is done using various metrics such as relative root mean squared error (RRMSE), Nash Sutcliffe Efficiency (NSE), mean absolute error (MAE), mean bias error (MBE), Pearson's correlation coefficient (r), coefficient of determination (R2), and determination agreement (WI). According to the evaluation metrics and analysis presented in the study, the proposed RNN-DFBER-based model outperforms the other models considered. This suggests that the RNN model, combined with the DFBER algorithm, predicts wind power data patterns more effectively than the alternative models. To support the findings, visualizations are provided to demonstrate the effectiveness of the RNN-DFBER model. Additionally, statistical analyses, such as the ANOVA test and the Wilcoxon Signed-Rank test, are conducted to assess the significance and reliability of the results.

Classification of Diabetes Using Feature Selection and Hybrid Al-Biruni Earth Radius and Dipper Throated Optimization.

INTRODUCTION: In public health, machine learning algorithms have been used to predict or diagnose chronic epidemiological disorders such as diabetes mellitus, which has reached epidemic proportions due to its widespread occurrence around the world. Diabetes is just one of several diseases for which machine learning techniques can be used in the diagnosis, prognosis, and assessment procedures. METHODOLOGY: In this paper, we propose a new approach for boosting the classification of diabetes based on a new metaheuristic optimization algorithm. The proposed approach proposes a new feature selection algorithm based on a dynamic Al-Biruni earth radius and dipper-throated optimization algorithm (DBERDTO). The selected features are then classified using a random forest classifier with its parameters optimized using the proposed DBERDTO. RESULTS: The proposed methodology is evaluated and compared with recent optimization methods and machine learning models to prove its efficiency and superiority. The overall accuracy of diabetes classification achieved by the proposed approach is 98.6%. On the other hand, statistical tests have been conducted to assess the significance and the statistical difference of the proposed approach based on the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. CONCLUSIONS: The results of these tests confirmed the superiority of the proposed approach compared to the other classification and optimization methods.

Breast Cancer Classification Depends on the Dynamic Dipper Throated Optimization Algorithm.

According to the American Cancer Society, breast cancer is the second largest cause of mortality among women after lung cancer. Women's death rates can be decreased if breast cancer is diagnosed and treated early. Due to the lengthy duration of manual breast cancer diagnosis, an automated approach is necessary for early cancer identification. This research proposes a novel framework integrating metaheuristic optimization with deep learning and feature selection for robustly classifying breast cancer from ultrasound images. The structure of the proposed methodology consists of five stages, namely, data augmentation to improve the learning of convolutional neural network (CNN) models, transfer learning using GoogleNet deep network for feature extraction, selection of the best set of features using a novel optimization algorithm based on a hybrid of dipper throated and particle swarm optimization algorithms, and classification of the selected features using CNN optimized using the proposed optimization algorithm. To prove the effectiveness of the proposed approach, a set of experiments were conducted on a breast cancer dataset, freely available on Kaggle, to evaluate the performance of the proposed feature selection method and the performance of the optimized CNN. In addition, statistical tests were established to study the stability and difference of the proposed approach compared to state-of-the-art approaches. The achieved results confirmed the superiority of the proposed approach with a classification accuracy of 98.1%, which is better than the other approaches considered in the conducted experiments.

Feature selection in wind speed forecasting systems based on meta-heuristic optimization.

Technology for anticipating wind speed can improve the safety and stability of power networks with heavy wind penetration. Due to the unpredictability and instability of the wind, it is challenging to accurately forecast wind power and speed. Several approaches have been developed to improve this accuracy based on processing time series data. This work proposes a method for predicting wind speed with high accuracy based on a novel weighted ensemble model. The weight values in the proposed model are optimized using an adaptive dynamic grey wolf-dipper throated optimization (ADGWDTO) algorithm. The original GWO algorithm is redesigned to emulate the dynamic group-based cooperative to address the difficulty of establishing the balance between exploration and exploitation. Quick bowing movements and a white breast, which distinguish the dipper throated birds hunting method, are employed to improve the proposed algorithm exploration capability. The proposed ADGWDTO algorithm optimizes the hyperparameters of the multi-layer perceptron (MLP), K-nearest regressor (KNR), and Long Short-Term Memory (LSTM) regression models. A dataset from Kaggle entitled Global Energy Forecasting Competition 2012 is employed to assess the proposed algorithm. The findings confirm that the proposed ADGWDTO algorithm outperforms the literature's state-of-the-art wind speed forecasting algorithms. The proposed binary ADGWDTO algorithm achieved average fitness of 0.9209 with a standard deviation fitness of 0.7432 for feature selection, and the proposed weighted optimized ensemble model (Ensemble using ADGWDTO) achieved a root mean square error of 0.0035 compared to state-of-the-art algorithms. The proposed algorithm's stability and robustness are confirmed by statistical analysis of several tests, such as one-way analysis of variance (ANOVA) and Wilcoxon's rank-sum.

The Potential Role of MUC16 (CA125) Biomarker in Lung Cancer: A Magic Biomarker but with Adversity.

Lung cancer is the second most commonly diagnosed cancer in the world. In terms of the diagnosis of lung cancer, combination carcinoembryonic antigen (CEA) and cancer antigen 125 (CA125) detection had higher sensitivity, specificity, and diagnostic odds ratios than CEA detection alone. Most individuals with elevated serum CA125 levels had lung cancer that was either in stage 3 or stage 4. Serum CA125 levels were similarly elevated in lung cancer patients who also had pleural effusions or ascites. Furthermore, there is strong evidence that human lung cancer produces CA125 in vitro, which suggests that other clinical illnesses outside of ovarian cancer could also be responsible for the rise of CA125. MUC16 (CA125) is a natural killer cell inhibitor. As a screening test for lung and ovarian cancer diagnosis and prognosis in the early stages, CA125 has been widely used as a marker in three different clinical settings. MUC16 mRNA levels in lung cancer are increased regardless of gender. As well, increased expression of mutated MUC16 enhances lung cancer cells proliferation and growth. Additionally, the CA125 serum level is thought to be a key indicator for lung cancer metastasis to the liver. Further, CA125 could be a useful biomarker in other cancer types diagnoses like ovarian, breast, and pancreatic cancers. One of the important limitations of CA125 as a first step in such a screening technique is that up to 20% of ovarian tumors lack antigen expression. Each of the 10 possible serum markers was expressed in 29-100% of ovarian tumors with minimal or no CA125 expression. Therefore, there is a controversy regarding CA125 in the diagnosis and prognosis of lung cancer and other cancer types. In this state, preclinical and clinical studies are warranted to elucidate the clinical benefit of CA125 in the diagnosis and prognosis of lung cancer.

A comparative study between transcutaneous electrical nerve stimulation and pulsed electromagnetic field therapy in the management of post-herpetic neuralgia of the sciatic nerve.

OBJECTIVES: To compare the efficacy of pulsed electromagnetic field therapy (PEMFT) versus transcutaneous electrical nerve stimulation (TENS) in the treatment of post-herpetic neuralgia of the sciatic nerve. METHODS: A double-blinded randomized clinical study has included 56 patients (18 males and 38 females). Participants were randomly and equally assigned into 2 groups. Both groups received conventional physical therapy treatment. Moreover, group (A) has an additional TENS, and group (B) had PEMFT. Both modalities were applied once daily, 3 times a week for 20 minutes for 8 successive weeks. Visual analog scale (VAS) and carbamazepine intake (CMI) dose have been assessed before and after interventions. RESULTS: There was a significant decrease in VAS and CMI post-treatment in group A and B compared with that pretreatment (P > .001). The percent decrease in VAS and CMI in group A were 72.44% and 69.47% respectively and that for group B was 68.95% and 67.94% respectively. The findings revealed a non-significant difference in VAS and CMI (P > .05) between groups. The Means of VAS and CMI were (2.4 ±â€…0.78, 204.5 ±â€…16.76 and 2.67 ±â€…0.9, 210.57 ±â€…16.5) in group A and group B respectively. The mean difference for VAS and CMI was (-0.27 and -6.07) between groups post-treatment respectively. CONCLUSION: Both TENS and PEMFT were effective and nearly equivalent in improving the post-herpetic neuralgia of the sciatic nerve as measured by in VAS and CMI. Clinical recommendations should be highlighted to instigate the using of TENS and PEMFT in the management of post-herpetic neuralgia of the sciatic nerve.

An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease.

Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals in developed countries experiencing monkeypox. Such conditions often carry less obvious but no less devastating risks, including increased vulnerability to monkeypox, cancer, and low self-esteem. Due to the low visual resolution of monkeypox disease images, medical specialists with high-level tools are typically required for a proper diagnosis. The manual diagnosis of monkeypox disease is subjective, time-consuming, and labor-intensive. Therefore, it is necessary to create a computer-aided approach for the automated diagnosis of monkeypox disease. Most research articles on monkeypox disease relied on convolutional neural networks (CNNs) and using classical loss functions, allowing them to pick up discriminative elements in monkeypox images. To enhance this, a novel framework using Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) is proposed to fine-tune the deep CNN layers for classifying monkeypox disease from images. As a first step in the proposed approach, we use deep CNN-based models to learn the embedding of input images in Euclidean space. In the second step, we use an optimized classification model based on the triplet loss function to calculate the distance between pairs of images in Euclidean space and learn features that may be used to distinguish between different cases, including monkeypox cases. The proposed approach uses images of human skin diseases obtained from an African hospital. The experimental results of the study demonstrate the proposed framework's efficacy, as it outperforms numerous examples of prior research on skin disease problems. On the other hand, statistical experiments with Wilcoxon and analysis of variance (ANOVA) tests are conducted to evaluate the proposed approach in terms of effectiveness and stability. The recorded results confirm the superiority of the proposed method when compared with other optimization algorithms and machine learning models.

Effect of adding a supervised physical therapy exercise program to photobiomodulation therapy in the treatment of cervicogenic somatosensory tinnitus: A randomized controlled study.

OBJECTIVES: To evaluate the effectiveness of adding a supervised physical therapy exercise program to photobiomodulation therapy (PBMT) in the treatment of cervicogenic somatosensory tinnitus (CST). METHODS: Forty patients suffering from CST with age 45-55 years were included in the study. They were assigned randomly into 2 groups, 20 per each. (Study group) Group (A) received a supervised physical therapy exercise program in addition to 20 minutes PBMT with a 650-nanometer wavelength and a 5 milliWatt power output, spot size of 1 cm2, and energy density of 6 Joules, 3 sessions per week for 8 consecutive weeks, plus traditional medical treatment. While (control group), group (B) received the same PBMT protocol, 3 sessions per week for 8 consecutive weeks in addition to the traditional medical treatment. Tinnitus visual analog scaling (VAS), tinnitus handicap inventory (THI), and cervical range of motion (ROM) were measured at baseline and after 8 weeks. RESULTS: Mixed MANOVA showed a statistically significant reduction in tinnitus VAS, THI, and a significant improvement in cervical ROM (flexion, extension, right bending, left bending, right rotation, and left rotation) in favor of Group A (P < .05). There was a significant decrease in posttreatment VAS treatment (P > .001) MD [-2.05(-2.68:-1.41)], and THI relative to pretreatment mean difference [-5.35(-8.51: -2.19)] and a significant increase in posttreatment neck ROM in Groups A and B relative to pretreatment neck ROM (P > .001). Flexion range posttreatment MD[3.65(1.64:5.65)], Extension MD [6.55(1.35:11.75)], right bending MD[3.8(2.51:5.08)], left bending MD[1.75(0.19:3.3)], right rotation MD [3.5(1.28:5.71)] and left rotation [2.75(0.67:4.82)]. CONCLUSIONS: Adding a supervised physical therapy exercise program to PBMT showed positive and beneficial effects in the treatment of CST using VAS, THI, and Cervical ROM assessment tools.

Polarized Light Therapy in the Treatment of Wounds: A Review.

The most prevalent type of photo therapies are low-level laser therapy (LLLT) and ultraviolet (UV) treatments, which are distinguished by the physical properties of the light employed. However, in latest years, it has been suggested that polarization and an extensive light band including all light spectra are essential aspects in light treatment. Light waves are filtered to align and vibrate in a single plane, resulting in polarized light (PL). Light that has been polarized can penetrate tissues more deeply than light that has not been polarized. The visible light spectrum is very broad. PL varies from other types of light therapy in that it uses a considerably wider spectrum of wavelengths than LLLT or UV. As a result, PLT devices are often less expensive and simple to operate. Since the late 1960s, light therapy has been used to treat anything from neonatal jaundice to psoriasis and vitiligo. Fenyö created a PL source and found that it can stimulate wound healing in a similar way to the low-energy laser. In comparison to the laser, this source of light had numerous gains: lesser prices, fewer hazards, a greater area to be treated, and no sophisticated user expertise. Despite several findings from fundamental research (in vitro, in vivo, and animal trials), practitioners continue to have reservations regarding PL's potency and utility in treating musculoskeletal problems. It is even largely believed that the commercial use of these therapies is validated by a sufficient amount of scientific evidence based on reliable clinical papers. The major goal of this study is to gather information on the use of PL for treatment of various wound types in animal and human investigations.

Delayed versus early umbilical cord clamping for near-term infants born to preeclamptic mothers; a randomized controlled trial.

OBJECTIVE: This study aims to assess delayed versus early umbilical cord clamping in preeclamptic mothers undergoing scheduled caesarean delivery regarding the maternal intra-operative blood loss and neonatal outcomes. METHODS: A clinical trial was conducted on 62 near-term preeclamptic mothers (36-38+6 weeks) who were planned for caesarean delivery. They were randomly assigned into two groups. The first group was the early cord clamping (ECC) group (n= 31), in which clamping the umbilical cord was within 15 seconds, while the second group was the delayed cord clamping (DCC) group (n= 31), in which clamping the umbilical cord was at 60 seconds. All patients were assessed for intra-operative blood loss and incidence of primary postpartum haemorrhage (PPH). Otherwise, all neonates were assessed for APGAR scores, the need for the neonatal intensive care unit (NICU) admission due to jaundice, and blood tests (haemoglobin, haematocrit. and serum bilirubin). RESULTS: There was not any significant difference between the two groups regarding the maternal estimated blood loss (P=0.673), the rates of PPH (P=0.1), post-delivery haemoglobin (P=0.154), and haematocrit values (P=0.092). Neonatal outcomes also were showing no significant difference regarding APGAR scores at the first minute (P=1) and after 5 minutes (P=0.114), day 1 serum bilirubin (P=0.561), day 3 serum bilirubin (P=0.676), and the rate of NICU admission (P=0.671). However, haemoglobin and haematocrit values were significantly higher in the DCC group than in the ECC group (P<0.001). CONCLUSION: There is no significant difference between DCC and ECC regarding maternal blood loss. However, DCC has the advantage of significantly higher neonatal haemoglobin. TRIAL REGISTRATION: It was first registered at ClinicalTrials.gov on 10/12/2019 with registration number NCT04193345.

Improved weighted ensemble learning for predicting the daily reference evapotranspiration under the semi-arid climate conditions.

Evapotranspiration is an important quantity required in many applications, such as hydrology and agricultural and irrigation planning. Reference evapotranspiration is particularly important, and the prediction of its variations is beneficial for analyzing the needs and management of water resources. In this paper, we explore the predictive ability of hybrid ensemble learning to predict daily reference evapotranspiration (RET) under the semi-arid climate by using meteorological datasets at 12 locations in the Andalusia province in southern Spain. The datasets comprise mean, maximum, and minimum air temperatures and mean relative humidity and mean wind speed. A new modified variant of the grey wolf optimizer, named the PRSFGWO algorithm, is proposed to maximize the ensemble learning's prediction accuracy through optimal weight tuning and evaluate the proposed model's capacity when the climate data is limited. The performance of the proposed approach, based on weighted ensemble learning, is compared with various algorithms commonly adopted in relevant studies. A diverse set of statistical measurements alongside ANOVA tests was used to evaluate the predictive performance of the prediction models. The proposed model showed high-accuracy statistics, with relative root mean errors lower than 0.999% and a minimum R2 of 0.99. The model inputs were also reduced from six variables to only two for cost-effective predictions of daily RET. This shows that the PRSFGWO algorithm is a good RET prediction model for the semi-arid climate region in southern Spain. The results obtained from this research are very promising compared with existing models in the literature.

Inhalation injury in adult males: Evaluation of the short-term efficacy of transcutaneous electrical acupoint stimulation on pulmonary functions and diaphragmatic mobility after burn: A double-blind randomized controlled study.

BACKGROUND: Inhalation injuries can cause problems with diaphragmatic mobility and pulmonary function, which are accompanied by significant morbidity and mortality. No previous studies have determined the outcomes of acupoint transcutaneous electrical stimulation (Acu-TENS) in the treatment of inhalation burn injuries. The current study is therefore aimed at evaluating the influences of Acu-TENS on pulmonary functions and diaphragmatic mobility in adult-male patients experiencing after burn inhalation injury. METHODS: This randomized controlled study was double blinded in an inpatient setting and was conducted between June 2018 and July 2019. Forty-male participants with inhalation-injury (20-40 yrs.) were randomly allocated into two study and control groups equal in numbers; the same pulmonary rehabilitation program plus early mobility exercise was conducted in both groups. The study group (group A) received additional Acu-TENS while shame Acu-TENS was carried out on the control group (group B). The intervention program continued for four weeks, three sessions a week for 45 min bilaterally on the bilateral Ding-Chuan points (Ex-B1). Spirometry was used to assess pulmonary functions, the 5-points-Likert scale was used to assess dyspnea, and ultrasonography was used to assess diaphragmatic mobility (DM), and evaluations were performed before and after interventions. RESULTS: At baseline assessment, no significant differences were detected between the two study groups (p˃0.05). In the post-interventional program, a noteworthy difference was detected in all outcome measures in the two study groups (p˂0.05), supporting group A. After 4 weeks of intervention, the mean (SD) for FVC, FEV1, and DM was 83.7 ± 4.34, 86.75 ± 4.59, and 5.93 ± 1.13 in group A, 79.65 ± 5.14, 83.1 ± 4.44, and 5.08 ± 1.15, in-group B. The mean difference for FVC, FEV1, and DM was 4.05 (1: 7.09), 3.65 (0.75: 6.54), and 0.85 (0.11: 1.57) between groups after treatment, respectively. CONCLUSION: Depending on the study findings, Acu-TENS on bilateral Ding-Chuan points could be considered an effective approach for improving pulmonary functions and diaphragmatic mobility in patients with inhalation injuries after thermal burn. Future studies with a larger sample size and longer duration on different types of burn injuries are recommended.

An analysis of the COVID-19 vaccination campaigns in France, Israel, Italy and Spain and their impact on health and economic outcomes.

Objectives: This paper presents an overview of the vaccination campaigns in France, Israel, Italy and Spain during the first eleven months from the first COVID-19 vaccine approval (Dec 2020 - Nov 2021). These four countries were chosen as they share similar socioeconomic, and epidemiological profiles and adopted similar vaccination strategies. Methods: A rapid review of available primary data from each country was conducted. Data were collected from official government documents whenever possible, supplemented by information from international databases and local reports. The data were analysed via descriptive and graphical analysis to identify common patterns as well as significant divergences in the structural changes of countries' healthcare systems during the pandemic, outcomes of the vaccination roll-out, and their impact on contextual policies. Results: The four countries adopted similar interventions to protect and strengthen their healthcare systems. The effective coordination between the governance levels, ability to ensure a large supply of doses, and trust towards health authorities were amongst the determinants for more successful vaccination outcomes. The analysis reports a positive impact of the COVID-19 vaccines on epidemiological, political and economic outcomes. We observed some evidence of a negative association between increased vaccine coverage and fatalities and hospitalisation trends. Conclusions: The strengths and weaknesses of COVID-19 pandemic crisis management along with the various strategies surrounding the vaccination roll-out campaigns may yield lessons for policymakers amidst such decisions, including for future pandemics. Lay summary: This paper presents an overview of the vaccination campaigns in France, Israel, Italy and Spain during the first eleven months following approval of the first COVID-19 vaccine (Dec 2020 - Nov 2021). These four countries were chosen as they share similar demographic, socioeconomic, and epidemiological profiles, and adopted similar vaccinations strategies. Effective coordination between governance levels, ability to ensure a large supply of doses, and trust towards health authorities were amongst the determinants for successful outcomes of vaccination campaigns. The strengths and weaknesses of COVID-19 pandemic crisis management, along with the various strategies surrounding the vaccination roll-out campaigns may yield lessons for policymakers amidst such decisions, including for future pandemics.

Comparative effectiveness study of low versus high-intensity aerobic training with resistance training in community-dwelling older men with post-COVID 19 sarcopenia: A randomized controlled trial.

OBJECTIVE: To find and compare the clinical and psychological effects of low and high-intensity aerobic training combined with resistance training in community-dwelling older men with post-COVID-19 sarcopenia symptoms. DESIGN: Randomized control trial. SETTING: University physiotherapy clinic. PARTICIPANTS: Men in the age range of 60-80 years with post-COVID-19 Sarcopenia. INTERVENTION: All participants received resistance training for whatever time of the day that they received it, and that in addition they were randomized into two groups like low-intensity aerobic training group (n = 38) and high-intensity aerobic training group (n = 38) for 30 minutes/session, 1 session/day, 4 days/week for 8 weeks. OUTCOMES: Clinical (muscle strength and muscle mass) and psychological (kinesiophobia and quality of life scales) measures were measured at the baseline, fourth week, the eighth week, and at six months follow-up. RESULTS: The 2 × 4 group by time repeated measures MANOVA with corrected post-hoc tests for six dependent variables shows a significant difference between the groups (P < 0.001). At the end of six months follow up, the handgrip strength, -3.9 (95% CI -4.26 to -3.53), kinesiophobia level 4.7 (95% CI 4.24 to 5.15), and quality of life -10.4 (95% CI -10.81 to -9.9) shows more improvement (P < 0.001) in low-intensity aerobic training group than high-intensity aerobic training group, but in muscle mass both groups did not show any significant difference (P > 0.05). CONCLUSION: Low-intensity aerobic training exercises are more effective in improving the clinical (muscle strength) and psychological (kinesiophobia and quality of life) measures than high-intensity aerobic training in post-COVID 19 Sarcopenia.