Optimal distribution grid allocation of reactive power with a focus on the particle swarm optimization technique and voltage stability.

A structured approach to managing reactive power is imperative within the context of power systems. Among the restructuring initiatives in the electrical sector, power systems have undergone delineation into three principal categories: generation, transmission, and distribution entities, each of which is overseen by an independent system operator. Notably, active power emerges as the predominant commodity transacted within the electrical market, with the autonomous grid operator assuming the responsibility of ensuring conducive conditions for the execution of energy contracts across the transmission infrastructure. Ancillary services, comprising essential frameworks for energy generation and delivery to end-users, encompass reactive power services pivotal in the regulation of bus voltage. Of particular significance among the array of ancillary services requisite in a competitive market milieu is the provision of adequate reactive power to uphold grid safety and voltage stability. A salient impediment to the realization of energy contracts lies in the inadequacy of reactive power within the grid, which poses potential risks to its operational safety and voltage equilibrium. The optimal allocation of the reactive power load is predicated upon presumptions of consistent outcomes within the active power market. Under this conceptual framework, generators are afforded continual compensation for the provision of reactive power indispensable for sustaining their active energy production endeavors.

An artificial intelligence approach to predict infants' health status at birth.

BACKGROUND: Machine learning could be used for prognosis/diagnosis of maternal and neonates' diseases by analyzing the data sets and profiles obtained from a pregnant mother. PURPOSE: We aimed to develop a prediction model based on machine learning algorithms to determine important maternal characteristics and neonates' anthropometric profiles as the predictors of neonates' health status. METHODS: This study was conducted among 1280 pregnant women referred to healthcare centers to receive antenatal care. We evaluated several machine learning methods, including support vector machine (SVM), Ensemble, K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Decision tree classifiers, to predict newborn health state. RESULTS: The minimum redundancy-maximum relevance (MRMR) algorithm revealed that variables, including head circumference of neonates, pregnancy intention, and drug consumption history during pregnancy, were top-scored features for classifying normal and unhealthy infants. Among the different classification methods, the SVM classifier had the best performance. The average values of accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) in the test group were 75%, 75%, 76%, 76%, and 65%, respectively, for SVM model. CONCLUSION: Machine learning methods can efficiently forecast the neonate's health status among pregnant women. This study proposed a new approach toward the integration of maternal data and neonate profiles to facilitate the prediction of neonates' health status.

Relationship between body mass index and mortality of burns patients: A systematic review and meta-analysis.

This systematic review and meta-analysis aimed to evaluate the relationship between body mass index (BMI) and mortality of burn patients. A comprehensive, systematic search was conducted in different international electronic databases, such as Scopus, PubMed, Web of Science and Persian electronic databases such as Iranmedex, and Scientific Information Database (SID) using keywords extracted from Medical Subject Headings such as "Body mass index", "Burns" and "Mortality" from the earliest to the April 1, 2023. The quality of the studies included in this systematic review was evaluated using the appraisal tool for cross-sectional studies (AXIS tool). Finally, six articles were included in this systematic review and meta-analysis. A total of 16 154 burn patients participated in six studies. Their mean age was 46.32 (SD = 1.99). Of the participants, 71.7% were males. The mean length of hospitalization was 18.80 (SD = 8.08) days, and the average TBSA in burn patients was 38.32 (SD = 2.79) %. Also, the average BMI in burn patients was 27.10 (SD = 1.75). Results found mortality in patients with abnormal BMI (overweight to morbidity BMI) was 0.19 more than normal BMI (ES: 1.19, 95%CI: 0.76-1.87, Z = 0.75, I2 : 71.8%, p = 0.45). Results of linear dose-response showed each 5 kg/m2 increase in BMI was associated with a 5% increase in mortality that was marginally significant (ES: 1.05, 95%CI: 1.00-1.11, Z = 1.99, I2 : 22.2%, p = 0.047). There was a non-linear relationship between levels of BMI and mortality (Prob > χ2 = 0.02). There was an increase in mortality from percentile 10 to 50, although it was not significant (Correlational coefficient: 0.01, p = 0.85). Also, there was an increase in mortality rate from percentile 50 to 90 that was statistically significant (correlational coefficient: 0.06, p = 0.047). Finally, the results of the study indicated BMI can increase the chance of mortality by 0.19, although it was not significant. As a result, more studies are needed to better judge the relationship between BMI and mortality in burn victims.

Study of the kinetic, equilibrium, and thermodynamic aspects of the removal and recovery of divalent lead heavy metal ions from industrial wastewaters contaminated by vesavin-enriched XAD-11600 amberlite resin.

In this study, we developed a unique adsorbent known as extractant-impregnated resin (EIR) by surface impregnation of XAD-11600 amberlite resin with the Vesavin ligand. This resin demonstrated exceptional selectivity for the absorption of lead (Pb2+) ions from aqueous solutions. The ability of EIR to remove lead from polluted water was studied as a function of experimental parameters, including the kinetics, equilibrium, and thermodynamics of the adsorption process. The experimental results provided the basis for the fitting of equilibrium adsorption isotherms with the Langmuir model, and the maximum adsorption capacity of EIR for Pb(II) ions was determined to be approximately 1662 mg/g. Kinetic and thermodynamic studies were also conducted to gain insight into the behavior of the adsorption process. It was found that the rate of penetration of lead ions into the particle was the primary factor controlling the absorption process of lead on the surface of the porous adsorbent. Additionally, the studies demonstrated that the EIR can be utilized for multiple absorption and desorption cycles.

Methanol extract of Iraqi Kurdistan Region Daphne mucronata as a potent source of antioxidant, antimicrobial, and anticancer agents for the synthesis of novel and bioactive polyvinylpyrrolidone nanofibers.

In this study, aqueous, ethanol, methanol, and hexane extracts from Iraqi Kurdistan Region Daphne mucronata were prepared due to the numerous applications and development of nanofibers in biological and medical fields, including food packaging, enzyme stabilization, and wound dressing. In the initial evaluation of the extracts, the antioxidant properties against DPPH, antimicrobial properties against 3-gram-positive bacterial species, 3-gram negative bacterial species, 3-common bacterial species between aquatic and human, and 3-fungal species, and anticancer properties against breast cancer cells were performed. The results proved that the methanol extract has the highest antimicrobial, antifungal, antioxidant, and anticancer properties. After identifying the compounds of prepared methanol extract using GC/MS, polyvinylpyrrolidone nanofibers containing methanol extract of Daphne mucronata were prepared. The structure and characteristics of prepared nanofibers were confirmed and determined using FTIR, TGA, BET, SEM, flexural strength, compressive strength, and hydrophilicity. Synthesized polyvinylpyrrolidone nanofibers containing methanol extract of D. mucronata were subjected to antimicrobial properties on the strains studied in methanol extract of D. mucronata. The antimicrobial properties of synthesized polyvinylpyrrolidone nanofibers containing methanol extract of D. mucronata were compared. The results showed that synthesized polyvinylpyrrolidone nanofibers containing methanol extract of D. mucronata have the potential to introduction bioactive natural synthesis nanoparticles.

A computational study on the mercaptopurine drug interaction with aluminum nitride nanostructures: analyzed by Marcus theory of electron-transfer.

We analyzed the mercaptopurine adsorption on AlN nanostructures consisting of zero-dimensional nanoclusters, one-dimensional nanotubes, and two-dimensional nanosheets using calculations based on density functional theory (DFT). The adsorption energy, energy band gap, fluctuations in the energy band gap, charge transfers, and types of interactions that take place after mercaptopurine is adsorbed on the AlN nanostructures have all been calculated using DFT. The results show MP adsorption energies on AlN nanoparticles are -4.22, -5.95, and -8.70 eV. In this situation, MP molecules have been drawn to the surface due to the higher adsorption energies available on the AlN nanosheet (a process known as chemisorption). The Atoms in Molecules inquiry was conducted to learn more about and better comprehend the binding properties of the investigated AlN nanostructures utilizing mercaptopurine. Our findings indicate the mercaptopurine/AlN nanosheet bonding's electrostatic properties. Additionally, the electrical conductivity of the AlN nanostructures increases whenever mercaptopurine is adsorbed on them. This shows that the AlN nanoparticles might function as chemical sensors and offer an electrical signal in mercaptopurine. The following is the order of sensitivity: AlN nanosheet > AlN nanotube > AlN nanocluster. The outcomes indicate that the nanosheet has the most potential for mercaptopurine detection among the AlN nanostructures.Communicated by Ramaswamy H. Sarma.