A novel AI-based approach for modelling the fate, transportation and prediction of chromium in rivers and agricultural crops: A case study in Iran

Chromium (Cr) pollution caused by the discharge of industrial wastewater into rivers poses a significant threat to the environment, aquatic and human life, as well as agricultural crops irrigated by these rivers. This paper employs artificial intelligence (AI) to introduce a new framework for modeling the fate, transport, and estimation of Cr from its point of discharge into the river until it is absorbed by agricultural products. The framework is demonstrated through its application to the case study River, which serves as the primary water resource for tomato production irrigation in Mashhad city, Iran. Measurements of Cr concentration are taken at three different river depths and in tomato leaves from agricultural lands irrigated by the river, allowing for the identification of bioaccumulation effects. By employing boundary conditions and smart algorithms, various aspects of control systems are evaluated. The concentration of Cr in crops exhibits an accumulative trend, reaching up to 1.29 µg/g by the time of harvest. Using data collected from the case study and exploring different scenarios, AI models are developed to estimate the Cr concentration in tomato leaves. The tested AI models include linear regression (LR), neural network (NN) classifier, and NN regressor, yielding goodness-of-fit values (R2) of 0.931, 0.874, and 0.946, respectively. These results indicate that the NN regressor is the most accurate model, followed by the LR, for estimating Cr levels in tomato leaves

Evaluation of the Prevalence of Congenital Cytomegalovirus Infection and its Clinical Outcomes in Neonates Born in Vali-e-Asr Hospital of Birjand, Iran

Background Cytomegalovirus (CMV) has been known as the most common cause for congenital infections worldwide which can lead to death in fetus and neonates as well as neuropsychiatric deficits. The aim of this study was to determine the prevalence of congenital CMV infection in newly born neonates and to evaluate the medical outcomes. Materials and Methods In this cross-sectional study, 868 neonates were selected using unconditional random sampling in 2017. Neonatal saliva was given on the first or second day of birth using a Dacron swab and tested by PCR for the presence of CMV DNA. All infants with positive CMV infection went through further tests and examinations to evaluate the clinical outcomes. Results: 787 (90.67%) and 81 (9.33%) births were term and preterm respectively. The PCR test was positive results only in 14 term neonates (1.61%). Thus, the prevalence of CMV infection in term neonates (n=14, 1.61%) was higher than that of preterm infants (n=0), although there was no statistically significant difference (P>0.05). The most common abnormalities were neutropenia (50%, n=4) followed by anemia (37.5%, n=3). Conclusion The prevalence of CMV infection in this study (1.61%) was within the global range and there was no association between CMV infection and birth weight, infant gender, and as well ae neonatal type. The frequency of symptomatic neonates at birth in this study was higher than the average global range, but almost the same as in developing countries

Developing a smart and clean technology for bioremediation of antibiotic contamination in arable lands

This study presents a smart technological framework to efficiently remove azithromycin from natural soil resources using bioremediation techniques. The framework consists of several modules, each with different models such as Penicillium Simplicissimum (PS) bioactivity, soft computing models, statistical optimisation, Machine Learning (ML) algorithms, and Decision Tree (DT) control system based on Removal Percentage (RP). The first module involves designing experiments using a literature review and the Taguchi Orthogonal design method for cultural conditions. The RP is predicted as a function of cultural parameters using Response Surface Methodology (RSM) and three ML algorithms: Instance-Based K (IBK), KStar, and Locally Weighted Learning (LWL). The sensitivity analysis shows that pH is the most important factor among all parameters, including pH, Aeration Intensity (AI), Temperature, Microbial/Food (M/F) ratio, and Retention Time (RT), with a p-value of < 0.0001. AI is the next most significant parameter, also with a p-value of < 0.0001. The optimal biological conditions for removing azithromycin from soil resources are a temperature of 32°C, pH of 5.5, M/F ratio of 1.59 mg/g, and AI of 8.59 m3/h. During the 100-day bioremediation process, RP was found to be an insignificant factor for more than 25 days, which simplifies the conditions. Among the ML algorithms, the IBK model provided the most accurate prediction of RT, with a correlation coefficient of over 95%

Epidemiology of Pediatric Acute Poisoning in Iran: A Systematic Review and Meta-Analysis

Background: The epidemiology of pediatric poisoning differs from one country to another. Due to the scarcity of reviews on this issue in Iran, we performed a systematic review and meta-analysis of studies providing data on Iranian pediatric poisoning epidemiology. Methods: PubMed, Web of Sciences, Science direct, Embase, Scopus and the Persian databases Magiran, Scientific Information Database (SID), and Iranmedex were searched. Twenty-seven studies published between 2002 and 2019 were included, based on the inclusion and exclusion criteria. Results: 54.7% of the participants in the reviewed studies were male, and 88.1% of them were unintentional. Most of the children were in the age range of 3-5 years. Non-pharmaceutical agents were the most common causes of poisonings (n=7175, 59.2%) and among them, illicit drugs (19.3%) followed by hydrocarbons (16.4%) constituted the most common non-pharmaceutical poisonings. Illicit drugs, especially opioids, showed an upward trend from 2002 to 2019. Among pharmaceuticals, central nervous system (CNS) drugs (50.4%), especially benzodiazepines (BZDs) (25.8%) and analgesics (14.5%), were the most frequent agents implicated. CNS complaints (51.8%), followed by gastrointestinal complaints (27.6%), were the most common symptoms. Ingestion was the most common route of poisoning (22.1%). Most of the poisoning cases occurred in summer (28.2%). 21.7% of the cases were hospitalized and the mortality rate was 0.8%. A remarkable downward trend in both hospitalization and death rates occurred over time. Conclusion: Overall, non-pharmaceutical toxicity was found to be the most common cause of poisoning. However, considering the agents separately, pharmaceuticals, illicit drugs, and hydrocarbons were the most common causes of poisoning, respectively. In contrast to the decreasing trend in hydrocarbons, pesticides, and pharmaceutical poisonings, we found an increase in opioid poisoning during our study period

A new empirical approach for modelling fate and transport of Chromium bioaccumulation in irrigated crops: A water-food-pollution nexus

Discharge of chromium (Cr) into receiving water bodies is a serious problem in water resources worldwide that inevitably gets taken up by agricultural crops and hence threatens both the environment and human health. This study investigates the fate and transport modelling of Cr discharged into the Kashaf River by leather industries in Mashhad city, Iran and the bio magnification effects on agricultural crops irrigated by the river. The accumulative concentration of Cr in tomato in the present case study from the time of planting until harvest day shows an increasing trend of up to 126 μg/L. The sensitivity analysis illustrates that the accumulated chromium ions in tomato are affected by time in growth cycle, chromium dosage in water, and total hardness of water more than any other factors. This study adopts an empirical approach by developing statistical modelling for bio-accumulated Cr in tomato during the growth period and evaluates different 3D mathematical distribution such as Polynomial, Interpolant, and Lowest models. The results demonstrate Polynomial with x and y more than four-degree model has the best efficiency for the measurement of accumulated chromium ion in tomato as per qualitative factors. The outputs in this study can be viewed in the context of water-food-pollution nexus and how the pollution discharged from the industry into the water resources can have a major impact on the safety of food that is dependent on irrigation from freshwater resources

A new integrated agent-based framework for designing building emergency evacuation: a BIM approach

Today, safety control is considered one of the most important pillars of building construction processes due to maintaining security in major incidents such as fire, earthquake, and flood, and placing a basis of mutual trust between builders and residents for building design and construction. The evacuation process is a key aspect of safety control in case of an emergency such as a fire. This study develops a new integrated agent-based framework for designing building emergency evacuation by using Building Information Model (BIM). Three main steps of the framework include data collection, building model development, and evacuation simulation with a combination of Revit-MassMotion. The methodology is demonstrated through its application to a real case of a multi-story commercial building located in Iran. The building model is simulated through three scenarios with a different number of floors (i.e., one, two, and three floors). In each scenario, the safety of evacuation is evaluated for three designs of stairs in the building. The results show the best performance of the building evacuation in all scenarios can be achieved when two individual stairs are designed for each floor. Other influential factors including the maximum density, vision time, and agent count are more acceptable compared to other design factors. These parameters can also be used to design a control system by using smart conceptual models based on both decision tree and auto-work break structure methods

An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls

This study presents an intelligent Decision Support System (DSS) aimed at bridging the theoretical-practical gap in groundwater management. The ongoing demand for sophisticated systems capable of interpreting extensive data to inform sustainable groundwater decision- making underscores the critical nature of this research. To meet this challenge, telemetry data from six randomly selected wells were used to establish a comprehensive database of groundwater pumping parameters, including flow rate, pressure, and current intensity. Statistical analysis of these parameters led to the determination of threshold values for critical factors such as water pressure and electrical current. Additionally, a soft sensor was developed using a Random Forest (RF) machine learning algorithm, enabling real-time forecasting of key variables. This was achieved by continuously comparing live telemetry data to pump design specifications and results from regular field testing. The proposed machine learning model ensures robust empirical monitoring of well and pump health. Furthermore, expert operational knowledge from water management professionals, gathered through a Classical Delphi (CD) technique, was seamlessly integrated. This collective expertise culminated in a data-driven framework for sustainable groundwater facilities monitoring. In conclusion, this innovative DSS not only addresses the theory-application gap but also leverages the power of data analytics and expert knowledge to provide high-precision online insights, thereby optimizing groundwater management practices

Evaluation Of Mechanical and Biocompatibility Properties of Hydroxyapatite/Manganese Dioxide Nanocomposite Scaffolds for Bone Tissue Engineering Application

The aim of this research was to evaluate the mechanical properties, biocompatibility, and degradation behavior of scaffolds made of pure hydroxyapatite (HA) and HA‐modified by MnO2 for bone tissue engineering applications. HA and MnO2 were developed using sol‐gel and precipitation methods, respectively. The scaffolds properties were characterized using X‐ray diffraction (XRD), Fourier transform spectroscopy (FTIR), scanning electron microcopy (SEM), energy dispersive spectroscopy (EDS), and transmission electron microscopy (TEM). The interaction of scaffold with cells was assessed using in vitro cell proliferation and alkaline phosphatase (ALP) assays. The obtained results indicate that the HA/ MnO2 scaffolds possess higher compressive strength, toughness, hardness, and density when compared to the pure HA scaffolds. After immersing the scaffold in the SBF solution, more deposited apatite appeared on the HA/MnO2, which results in the rougher surface on this scaffold compared to the pure HA scaffold. Finally, the in vitro biological analysis using human osteoblast cells reveals that scaffolds are biocompatible with adequate ALP activit

An accurate DFT study within conformational survey of the d-form serine−alanine protected dipeptide

Abstract The conformational analysis of n-formyl-d-serine-d-alanine-NH2 dipeptide was studied using density functional theory methods at B3LYP, B3LYP‒D3, and M06‒2X levels using 6‒311 + G (d,p) basis set in the gas and water phases. 87 conformers of 243 stable ones were located and the rest of them were migrated to the more stable geometries. Migration pattern suggests the more stable dipeptide model bears serine in β L , γ D , γ L and the alanine in γ L  and γ D  configurations. The investigation of side‒chain‒backbone interactions revealed that the most stable conformer, γ D – γ L , is in the β‒turn region of Ramachandran map; therefore, serine-alanine dipeptide model should be adopted with a β‒turn conformation. Intramolecular hydrogen bonding in β‒turns consideration by QTAIM disclosed γ D – γ L includes three hydrogen bonds. The computed UV‒Vis spectrum alongside of NBO calculation showed the five main electronic transition bands derived of n → n* of intra‒ligand alanine moiety of dipeptide structure