In this paper, a new algorithm is presented to reduce the uncertainty effects of wind farms power generation (WFPG) and photo-voltaic generation (PVG) in both day-ahead energy and ancillary services markets. Firstly, this research tries to predict the uncertainty of short-term WFPG with acceptable accuracy. Indeed, it uses the hybrid method of wavelet transform (WT) in order to reduce the fluctuations in the input historical data along with the improved artificial neural network (ANN) based on the nonlinear structure for better training and learning. Furthermore, the imperialist competitive algorithm (ICA) is adopted to find the best weights and biases for minimizing the mean square error of predictions. In addition, regarding the high-level penetration of wind farms (WFS) on the power system, cascaded hydro units (CHUs) and pump-storage units (PSUs) are taken for the first time as supplementary units. Therefore, they are coordinated with WFS and photo-voltaic (PV) operations. Considering uncertainties of energy price, spinning and non-spinning reserves in the electricity market, WFPG, PVG and the availability of WFS, PV, CHUs and PSUs along with their effects on energy supply reliability lead to a scenario-based stochastic optimization problem. The aim of this problem is to increase the profit and decrease the financial risk (FR) of all of the units. The proposed method is implemented on WFS, PV, CHUs and PSUs of IEEE 118-bus standard system. Studying the results of profit and FR in the coordinated operation (CO) and the independent operation (IO) confirms that the profit is increased and the FR is reduced in the CO. Hence, the ability and merit of hybrid method of WT-ANN-ICA is verified.

Type 2 diabetes mellitus (T2DM)is one of the most common multifactorial disorders in Iran. Recent genome wide association studies (GWASs) and functional studies have suggested thatWFS1 may predispose individuals to T2DM. However, to date, the possible association of such variants with T2DM in Iranians remained unknown.Here, we investigated the association of the two polymorphisms ofWFS1 (rs1801214 a CpG-SNP, and rs1046320 a 3’UTR-SNP) with T2DM in an Iranian population. The study population comprised 432 unrelated Iranian individuals including 220 patients with T2DM, and 211 unrelated healthy control subjects. Genotyping was performed using PCR-RFLP, and confirmed with sequencing. In a logistic regression analysis, the rs1801214-T allele was associated with a significantly lower risk of T2DM assuming the log-additive model (OR: 0.68, 95% CI: 0.52-0.91, P=0.007539). Moreover, the G allele of rs1046320 was associated with a lower risk of T2DM assuming the log-additive model (OR: 0.68, 95% CI: 0.50- 0.91, P=0.008313). Haplotype analysis revealed that haplotypes that carry at least one protective allele are associated with a lower risk of T2DM. This is a first evidence for the association ofWFS1 rs1801214, and rs1046320 with T2DM in an Iranian population.

The development of models that predict power production of wind farms (WFS) by considering the interacting wakes is important; because wakes of the turbines exert a significant influence on power production of turbines, and hence on the layout of wind turbines in WFS. Thus, the purpose of present study was to provide an innovative analytical method for the prediction of power generation of the WFS that have a flat terrain and are consisted of horizontal-axis wind turbines (HAWTs) with the same hub height. The methodology employed utilized an analytical Gaussian model of HAWT wake to develop an analytical model that calculates the effective wind velocity acting on the downstream HAWT(s), which is further used for reading its generated power from the turbine’s catalog; thus, providing the generated power of the WF as the output. The results of presented model were validated by the field measurements data of Horns Rev WF and also were compared to two analytical models for predicting the generated power. The results were compared with two numerical simulations of the literature, and the output data of three commercial software. Moreover, the error analysis revealed that the presented model mostly showed superior accuracy in predicting the field measurements data.

Background: Welding is an extensively utilized industrial technique, which involves the fusion of fumes containing various heavy metals. To protect exposed workers, it is crucial to investigate the subsequent adverse effects. This study aimed to carry out the welding process within a regulated welding chamber and examine the effects of glutathione peroxidase (GPX) and superoxide dismutase (SOD) as antioxidant enzymes and pathological damage on rat testicular tissue. Methods: Male adult Wistar rats (n = 7/experiment group) were exposed to an average concentration of (44.48 mg/m3) welding fumes (WFS) for 30 min/d × 8 days through shielded metal arc welding (SMAW). The control group (n = 7) was in similar conditions without direct exposure to WFS. Results: The metal composition of WFS was found to contain iron (Fe), manganese (Mn), copper (Cu), aluminum (Al), lead (Pb), and chromium (Cr). Among these metals, Fe had the highest average concentration (12.06 mg/m3), while Cu displayed the lowest one (0.019 mg/m3). The majority of the produced particles were found within the micron size range (0.25 to 0.5 μm). The mean activity of GPX and SOD was determined 15.16777 (mU/mg protein) and 81.955 (U/mg protein) in the exposed group, respectively. In the microscopic examination of testis tissue, some complications were observed. Conclusion: Oxidative damage occurred in the testicular tissue of experimental rats by decreasing the mean activity of antioxidant enzymes. Besides, the exposed group showed reduced sperm quality and quantity indexes.

Wolfram syndrome (WFS) is a rare disease inherited as an autosomal dominant trait. Type I diabetes mellitus and optic atrophy are the main symptoms of the disease. It is also known as DIDMOAD syndrome due to the association of diabetes insipidus, diabetes mellitus, optic atrophy, and deafness. WFS may be associated with other disorders such as kidney failure, gonadal atrophy, and mental and behavioral disorders. This report is about a 14-year-old teenager who had suffered from vision loss and cataracts when he was 4 years old. At the age of 7 he has been diagnosed with type I diabetes mellitus due to polyuria and polydipsia. At the age of 12 he developed diabetes insipidus, neural hearing loss, urinary incontinence and bilateral hydronephrosis, neurogenic bladder, and increased blood pressure. Physicians should think of this disease and recommend genetic counselling before marriage.

Background: Welding fumes (WFS) contain heavy metals that can induce oxidative stress and health issues in welders. This review investigated the relationship between oxidative stress biomarkers and trace metals in welders’ bodily fluids. Methods: Online databases such as Scopus, PubMed, Web of Science, Science Direct, EMBASE, and Google Scholar were reviewed, with a specific emphasis on the effects of metal fume exposure during welding. Specific keywords such as “welding fumes”, “metal fumes”, “antioxidant enzymes”, “biomarkers”, and similar terms were employed to search for articles published between 2004 and 2023. After the evaluation of article titles and abstracts, this study reviewed a total of 19 articles. Results: Studies suggest that welders experience oxidative stress due to changes in trace metals in their body fluids, affecting antioxidant enzymes and oxidative stress biomarkers. Elevated heavy metals in welders’ biological samples lead to oxidative stress and inflammation, even at low levels. Certain metals in blood and urine, such as lead (Pb), manganese (Mn), iron (Fe), chromium (Cr), and cadmium (Cd), positively correlate with serum superoxide dismutase (SOD) and glutathione peroxidase (GPx) levels. There is also a significant positive correlation between serum/EBC MDA and blood/urine Pb, Mn, Cd, Cr, and Fe, indicating cellular damage, lipid peroxidation, and reduced antioxidant capacity. Additionally, welders may experience more DNA damage compared to non-welders. Conclusion: Exposure to WFS significantly altered oxidative stress biomarkers in bodily fluids, underscoring the importance of the relationship between oxidative stress and trace metal imbalances in WF-related injuries. These factors could serve as valuable biomarkers for monitoring workers exposed to WFS.

This paper presents an effective approach for determining optimal integration of renewable energy distributed generator (RE-DGs) of solar farms (SFs) and wind farms (WFS) in IEEE 69-node power distribution network (PDN) with target of minimizing (1) the single objective function of total active power loss and (2) multi-objective function including a) total active power loss, b) total reactive power loss, c) the voltage deviation and d) imported energy from the main power gird. Intelligent and adaptive meta-heuristic optimization algorithm called bonobo optimizer (BO) is introduced to address optimization problem considering the changing four seasons of winter, spring, summer and autumn from both generation and consumption. The obtained results from BO show its outstanding performance in determining the suitable installation of SFs and WFS compared with many published methods and implemented methods for two cases of single and multi-objective functions.