A copula-based multivariate flood frequency analysis under climate change effects.

Floods are among the most severe natural hazards, causing substantial damage and affecting millions of lives. These events are inherently multi-dimensional, requiring analysis across multiple factors. Traditional research often uses a bivariate framework relying on historical data, but climate change is expected to influence flood frequency analysis and flood system design in the future.

Backward induction-based multi-layer approach for watershed flood management in arid regions.

Managing floods in interconnected nonurban and urban areas of arid regions prone to flash floods requires a more dynamic and integrated approach than traditional linear methods. In this regard, this study proposes a novel multi-stage decision-making framework which reshapes the traditional cascade approach to flood management by addressing the interdependencies between upstream and downstream regions. The proposed cyclic decision-making process involves five main steps: First, the Hydraulic and Hydrological (H/H) conditions in urban and nonurban areas were modeled using the SWMM.

Estimating the vertical profile of water quality variables in reservoirs: Application of remotely sensed data and machine learning techniques.

Water quality assessment and management of reservoirs depend on accurate, large-scale, and continuous monitoring of the vertical profile of Water Quality Variables (WQVs). Remote sensing data have been widely used to retrieve high spatiotemporal water quality data; however, their application has practically been limited to evaluating surface WQVs. In this paper, a novel and efficient approach is introduced for assessing the profile of WQVs in reservoirs that depend on stratification, by taking into account the shape of profile as prior knowledge.

The Effect of Foot Reflexology on Fatigue, Sleep Quality, Physiological Indices, and Electrocardiogram Changes in Patients with Acute Myocardial Infarction: A Randomized Clinical Trial.

Background: Limited evidence is available regarding the effect of reflexology on Acute Myocardial Infarction (AMI). The present study evaluated the effect of foot reflexology on fatigue, sleep quality, physiological indices, and electrocardiogram changes in AMI.

Materials And Methods: This clinical trial was conducted on 80 subjects with AMI.

Near future flash flood prediction in an arid region under climate change.

Flash floods represent a significant threat, triggering severe natural disasters and leading to extensive damage to properties and infrastructure, which in turn results in the loss of lives and significant economic damages. In this study, a comprehensive statistical approach was applied to future flood predictions in the coastal basin of North Al-Abatinah, Oman. In this context, the initial step involves analyzing eighteen General Circulation Models (GCMs) to identify the most suitable one.

Enhancing flood risk assessment in urban areas by integrating hydrodynamic models and machine learning techniques.

Urban flood risks have intensified due to climate change and dense infrastructural development, necessitating innovative assessment approaches. This study aimed to integrate advanced hydrodynamic models with machine learning (ML) techniques to improve urban flood prediction and hazard analysis. Integrating 1D and 2D hydrodynamic models calibrated with precise parameters demonstrated exceptional predictive accuracy for flood dynamics.

The association between GJB2 gene (producing Cx26 protein) and the ventricular storm: A case report.

Background: A structural heart disease or functional electrical abnormalities can cause an electrical storm.

Case Presentation: We present a young boy with an electrical storm who had no cardiac risk factors and a positive family history of sudden cardiac death. The stepwise diagnostic approach was ineffective in determining previously known causes as the origin of the electrical storm.

Enhancing groundwater quality assessment in coastal area: A hybrid modeling approach.

Monitoring of groundwater (GW) resources in coastal areas is vital for human needs, agriculture, ecosystems, securing water supply, biodiversity, and environmental sustainability. Although the utilization of water quality index (WQI) models has proven effective in monitoring GW resources, it has faced substantial criticism due to its inconsistent outcomes, prompting the need for more reliable assessment methods. Therefore, this study addressed this concern by employing the data-driven root mean squared (RMS) models to evaluate groundwater quality (GWQ) in the coastal Bhola district near the Bay of Bengal, Bangladesh.

Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy.

In regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting the demand for ample, superior water downstream proves to be a formidable task. Thus, accurately estimating and mapping water quality indicators (WQIs) is paramount for sustainable planning of inland in the study area.

Long-term resilience in wastewater management: Optimizing treated wastewater allocation with a dynamic multi-agent approach.

Water scarcity poses a significant challenge to sustainable development, necessitating innovative approaches to manage limited resources efficiently. Effective water resource management involves not just the conservation and distribution of freshwater supplies but also the strategic reuse of treated wastewater (TWW). This study proposes a novel approach for the optimal allocation of treated wastewater among three key sectors (user agents): agriculture, industry, and urban green space.

Performance evaluation of a new sponge-based moving bed biofilm reactor for the removal of pharmaceutical pollutants from real wastewater.

Pharmaceutical pollutants, a group of emerging contaminants, have attracted outstanding attention in recent years, and their removal from aquatic environments has been addressed. In the current study, a new sponge-based moving bed biofilm reactor (MBBR) was developed to remove chemical oxygen demand (COD) and the pharmaceutical compound Ibuprofen (IBU). A 30-L pilot scale MBBR was constructed, which was continuously fed from the effluent of the first clarifier of the Southern Tehran wastewater treatment plant.

Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis.

Machine learning methodology has recently been considered a smart and reliable way to monitor water quality parameters in aquatic environments like reservoirs and lakes. This study employs both individual and hybrid-based techniques to boost the accuracy of dissolved oxygen (DO) and chlorophyll-a (Chl-a) predictions in the Wadi Dayqah Dam located in Oman. At first, an AAQ-RINKO device (CTD sensor) was used to collect water quality parameters from different locations and varying depths in the reservoir.

The effect of extracorporeal shock-wave therapy on pain in patients with various tendinopathies: a systematic review and meta-analysis of randomized control trials.

Objectives: Tendinopathy is a common condition that affects the body's tendon structures, causing discomfort, restricted movement, and reduced functionality. In this study, we looked at how extracorporeal shock wave therapy (ESWT) affected pain levels in individuals with various forms of tendinopathy around the world.

Design: This study is a comprehensive review and meta-analysis of previously published randomized controlled trials.

Hybrid WT-CNN-GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features.

Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administration of rivers, lakes, and reservoirs. This research utilized two unique DL algorithms-namely, convolutional neural network (CNNs) and gated recurrent units (GRUs)-alongside their amalgamation, CNN-GRU, to precisely gauge the concentration of these indicators within a reservoir.

Efficient implicit constraint handling approaches for constrained optimization problems.

Many real-world optimization problems, particularly engineering ones, involve constraints that make finding a feasible solution challenging. Numerous researchers have investigated this challenge for constrained single- and multi-objective optimization problems. In particular, this work extends the boundary update (BU) method proposed by Gandomi and Deb (Comput.

Global Longitudinal Strain May Be the One that Appropriately Identifies Candidates of ICD Implantation.

Hypertrophic cardiomyopathy (HCM) significantly contributes to an elevated risk of sudden cardiac death. Primary prevention is implemented by using an implantable cardioverter defibrillator (ICD). However, all of the HCM patients do not really need ICD therapy.

The effect of virtual interactive nurse-led support group intervention on fatigue, shock anxiety, and acceptance of implantable cardioverter defibrillator patients: a randomized trial.

Background: Implantable cardioverter defibrillators (ICD), as a gold and standard treatment for fatal cardiac arrhythmia, may lead to some physical and psychological problems for the patients. Therefore, performing some interventions to reduce or eliminate these issues is crucial. This study aimed to determine the effect of virtual interactive nurse-led support group intervention on fatigue, shock anxiety, and acceptance of ICD patients.

Correlates of malnutrition in patients with heart failure: the role of social support.

Aims: Heart failure (HF) is a major public health challenge. Malnutrition has a significant effect on HF prognosis. Understanding the impact of social and clinical factors on the risk of malnutrition is necessary because it may aid in improving the health status of HF patients.

Methanol poisoning during the COVID-19 pandemic in Iran: A retrospective cross-sectional study of clinical, laboratory, and brain imaging characteristics and outcomes.

Objective: To evaluate the frequency and significance of brain imaging findings in methanol poisoning patients and to propose a criterion for prioritizing brain imaging.

Methods: We retrospectively reviewed the data of 306 patients (286 men and 34 women, mean age 32.10 ± 9.

Optimal waste load allocation in river systems based on a new multi-objective cuckoo optimization algorithm.

Water pollution escalates with rising waste discharge in river systems, as the rivers' limited pollution tolerance and constrained self-cleaning capacity compel the release of treated pollutants. Although several studies have shown that the non-dominated sorting genetic algorithm-II (NSGA-II) is an effective algorithm regarding the management of river water quality to reach water quality standards, to our knowledge, the literature lacks using a new optimization model, namely, the multi-objective cuckoo optimization algorithm (MOCOA). Therefore, this research introduces a new optimization framework, including non-dominated sorting and ranking selection using the comparison operator densely populated towards the best Pareto front and a trade-off estimation between the goals of discharges and environmental protection authorities.

Forecasting water quality variable using deep learning and weighted averaging ensemble models.

Water quality variables, including chlorophyll-a (Chl-a), play a pivotal role in comprehending and evaluating the condition of aquatic ecosystems. Chl-a, a pigment present in diverse aquatic organisms, notably algae and cyanobacteria, serves as a valuable indicator of water quality. Thus, the objectives of this study encompass: (1) the assessment of the predictive capabilities of four deep learning (DL) models - namely, recurrent neural network (RNN), long short-term memory (LSTM), gated recurrence unit (GRU), and temporal convolutional network (TCN) - in forecasting Chl-a concentrations; (2) the incorporation of these DL models into ensemble models (EMs) employing genetic algorithm (GA) and non-dominated sorting genetic algorithm (NSGA-II) to harness the strengths of each standalone model; and (3) the evaluation of the efficacy of the developed EMs.

Assessing vulnerability of coastal aquifer to seawater intrusion using Convolutional Neural Network.

This study examined coastal aquifer vulnerability to seawater intrusion (SWI) in the Shiramin area in northwest Iran. Here, six types of hydrogeological data layers existing in the traditional GALDIT framework (TGF) were used to build one vulnerability map. Moreover, a modified traditional GALDIT framework (mod-TGF) was prepared by eliminating the data layer of aquifer type from the GALDIT model and adding the data layers of aquifer media and well density.

Machine learning model optimization for removal of steroid hormones from wastewater.

In the past few decades, there has been a significant focus on detecting steroid hormones in aquatic environments due to their influence on the endocrine system. Most compounds of these pollutants are the natural steroidal estrogens, i.e.

Comparison of The Effectiveness of Exercise Therapy and Electroacupuncture in Patients with Forward Head Abnormalities and Myofascial Pain Syndrome: A Randomized Clinical Trial.

Background: Forward Head Posture (FHP), which refers to the head being more forward than the shoulder, is one of the most common postural defects of all ages. Therefore, in this study, we aimed to compare the effectiveness of exercise therapy and electroacupuncture in patients with FHP and myofascial pain syndrome (MPS).

Methods: The present study was an open-label randomized clinical trial.