Assessment of antifungal effects of copper nanoparticles on the growth of the fungus Saprolegnia sp. on white fish (Rutilus frisii kutum) eggs

This study was conducted to evaluate the in-vitro effects of copper nanoparticles on the growth of the fungus Saprolegnia sp. isolated from white fish (Rutilus frisii kutum) eggs. The antifungal effects were measured by determining the minimum lethal concentration of copper nanoparticles on Saprolegnia sp. in yeast extract glucose chloramphenicol (YGC) agar at 25 °C. Saprolegnia grown in YGC agar without added copper nanoparticles served as negative controls. Our study showed that copper nanoparticles at a minimum concentration of 10 ppm have antifungal effects on Saprolegnia sp. The antifungal effects of copper nanoparticles are positively correlated to both concentration and time of exposure. This study showed that the antifungal properties of copper nanoparticles make it a good alternative to malachite green, which is carcinogenic

A mathematical model for the capacitated location-arc routing problem with deadlines and heterogeneous fleet

This paper considers a Capacitated Location-Arc Routing Problem (CLARP) with Deadlines (CLARPD) and a fleet of capacitated heterogeneous vehicles. The proposed mixed integer programming model determines a subset of potential depots to be opened, the served roads within predefined deadlines, and the vehicles assigned to each open depot. In addition, efficient routing plans are determined to minimize total establishment and traveling costs. Since the CLARP is NP-hard, a Genetic Algorithm (GA) is presented to consider proposed operators, and a constructive heuristic to generate initial solutions. In addition, a Simulated Annealing (SA) algorithm is investigated to compare the performance of the GA. Computational experiments are carried out for several test instances. The computational results show that the proposed GA is promising. Finally, sensitivity analysis confirms that the developed model can meet arc routing timing requirements more precisely compared to the classical Capacitated Arc Routing Problem (CARP). First published online 26 September 201

A ROBUST OPTIMIZATION MODEL FOR A LOCATION-ARC ROUTING PROBLEM WITH DEMAND UNCERTAINTY

The present article considers a location-arc routing problem (LARP) where the demands are on the edges rather than nodes on an undirected network. A mixed integer programming model is developed for an LARP with vehicle and depot capacity constraints and a fleet of heterogeneous vehicles. To adapt with reality, it is assumed that the demand of each road is an uncertain value that belongs to a bounded uncertainty set. In order to have a less conservative decision, we employ the robust optimization model proposed by Bertsimas and Sim (2003) to handle uncertainty. The proposed robust model determines a subset of potential depots to be opened along with their allocated roads in order to have an efficient location-routing decision which is immune to different realization of uncertainties. The proposed robust model is less sensitive to demand variations and is validated through Monte-Carlo simulation and relative extra cost (REC) measure with promising results. The results of sensitivity analysis showed that by increasing the degrees of conservatism, planners may employ more vehicles. Also, more depots may be opened to service all required roads

Early detection of alzheimer’s disease with convolutional neural network

The main purpose of this study is to provide a method for early diagnosis of Alzheimer's disease. This disease reduces memory function by destroying neurons in the nervous system and reducing connections and neural interactions. Alzheimer's disease is on the rise and there is no cure for it. With the help of medical image processing, Alzheimer's disease is determined and the similarity of the characteristics of brain signals with medical images is determined. Then, by presenting the characteristics of effective brain signals, the mild Alzheimer's group is determined. The level of this disease should be diagnosed according to the relationship between this disease and different features in the brain signal and medical images. First, with appropriate preprocessing, nonlinear properties such as phase diagram, correlation dimension, entropy and Lyapunov exponential are extracted and classification is done using convolutional neural network. The use of deep learning methods, including channel neural network, can have more appropriate and accurate results among other classification methods. The accuracy of the results in the reminder period is 97.5% for the brain signal and 99% for the MRI images, which is an acceptable result

A Cultural-Legal Analysis of Spiritual Damage Inflicted on the Family by the Media

The family, as a social institution, is entitled to some rights vis-à-vis other social phenomena such as the media. Some of the family's spiritual rights have been mentioned in the statute and some in Islamic sources. Expounding on these rights when affected by the media, and determining instances of their being neglected which cause spiritual damage to the family institution, is a necessity. This research has picked up the element of loss from civil liability and spiritual damage compensation, in order to analyze the spiritual damage inflicted on the family. This is also an introduction to provide the way for future set up of the regime on spiritual damage compensation as caused by the media in the Iranian legal system, so that judges could benefit from it in their media-related cases, and no damage of the kind could escape unpunished

Explaining the challenges of pre-hospital emergency healthcare workers in providing care at the scene

Background and Purpose: Pre-hospital emergency healthcare workers face various problems because of the complexities of providing care during emergencies. These challenges can affect their successful performance in achieving their professional goals. To identify these challenges, the present study was conducted to explain the challenges of pre-hospital emergency workers in providing care at the scene. Materials and Methods: The present qualitative study was conducted at Shahrekord University of Medical Sciences in 2022 using the contractual content analysis method. Twenty pre-hospital emergency workers were selected purposefully, and data were collected using individual in-depth semistructured interviews and analyzed using Granheim and Lundman’s approach. Results: The findings include three categories (systemic obstacles, society’s cultural ignorance, and religious obstacles) and seven subcategories (manpower-related obstacles, inappropriate and insufficient equipment, lack of attention and support, inconsistency between organizations, disruptive measures of care, wrong attitude and wrong belief, and gender-related barriers) of obstacles that cause emergency healthcare workers to face challenges in providing care at the scene. Conclusion: The factors that need consideration in strengthening pre-hospital emergency healthcare include improving the training process of students, planning for in-service training of employees, increasing their motivation levels, noticing their psychological issues, developing inter-organizational protocols and policies, and educating, training, and employing women in pre-hospital emergency car

Early stage evaluation of cancer stem cells using platinum nanoparticles/CD133 + enhanced nanobiocomposite

Abstract Background Cancer stem cells (CSCs) are of great diagnostic importance due to their involvement in tumorigenesis, therapeutic resistance, metastasis and relapse. Method In this work, a sensitive electrochemical cytosensor was successfully established to detect HT-29 colorectal cancer stem cells based on a nanocomposite composed of mesoporous silica nanoparticles (MSNs) and platinum nanoparticles (PtNPs) using a simple and fast electrodeposition technique on a glassy carbon electrode (GCE). Results According to SEM images, the PtNPs nanoparticles formed on the MSNs substrate are about 100 nm. As expected, high-rate porosity, increased surface-to-volume ratio, provides appropriate local electron transfer rate and suitable platform for the efficient formation of PtNPs. These features allow direct and stable binding of biotinylated monoclonal antibody of CD133 to streptavidin (Strep) and the subsequent availability of active sites for CSCs identification. Differential pulse voltammetry (DPV) results show that close interaction of CD133 + cells with monoclonal antibodies reduces charge transfer and electrical current, as confirmed by square wave voltammogram (SWV). Based on the recorded current versus number of CSCs, we noted that our developed system can sense CSCs from 5 to 20 cells/5 μL. Conclusions As a proof of concept, the designed nanobiocomposite was able to specifically detect CD133 + cells compared to whole HT-29 cells before magnetic activated cell sorting (MACS) process