Efficiency of the Combined Chemical Precipitation -Reduction Process to Remove Dye and Chromium from Industry Wastewater of Home Appliance

Background: Industrial wastewater is one the most important pollutants of environment. This study aimed to evaluate the efficiency of the combined chemical precipitation-reduction process for removal of dye and chromium from wastewater of home appliance factories. Methods: This experimental study was performed in laboratory scale on wastewater from the dying unit of the home appliance factory. The process used was a combination of the chemical precipitation-reduction process. Combine sampling was done and 214 samples were analyzed. COD, dye, and chromium were measured in samples. MgCl2, Polyaluminium Chloride (PAC), and FeCl3, cationic polymer and bentonite were used for chemical precipitation; and sodium meta bi sulfite was used for chemical reduction. Data were analysed by ANOVA and Tukey tests and by SPSS 16 software. Results: The results show that PAC had the highest color removal efficiency (90.92%). Also, the highest COD removal was related to the combination of magnesium chloride (1.4 mg/l), poly aluminum chloride (0.6 mg/l), and the coagulant aid cationic polymer (0.4 mg/l) with an efficiency of 89.11%. Moreover, total efficiency of the combined chemical precipitation and reduction process in chromium removal was 94%. Conclusion: The chemical precipitation- reduction process as a pre-treatment method has high efficiency in removal of COD, dye and chrome from wastewater of home appliance factories

A Mathematical Model of Breast Tumor Progression Based on Immune Infiltration

Breast cancer is the most prominent type of cancer among women. Understanding the microenvironment of breast cancer and the interactions between cells and cytokines will lead to better treatment approaches for patients. In this study, we developed a data-driven mathematical model to investigate the dynamics of key cells and cytokines involved in breast cancer development. We used gene expression profiles of tumors to estimate the relative abundance of each immune cell and group patients based on their immune patterns. Dynamical results show the complex interplay between cells and molecules, and sensitivity analysis emphasizes the direct effects of macrophages and adipocytes on cancer cell growth. In addition, we observed the dual effect of IFN-γ role= presentation style= box-sizing: border-box; max-height: none; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative; \u3eγ on cancer proliferation, either through direct inhibition of cancer cells or by increasing the cytotoxicity of CD8+ T-cells

The effects of mother corm size, organic fertilizers and micronutrient foliar application on corm yield and phosphorus uptake of saffron (Crocus sativus L.)

Saffron flowering can be mainly affected by mother corm size and phosphorus content of corm. In order to investigate the effects of mother corms size, organic fertilizers and foliar application on corm yield and phosphorus uptake of saffron (Crocus sativus L.) under control conditions, an experiment was conducted in the growing years of 2012-2013 at Faculty of Agriculture, Ferdowsi University of Mashhad, Iran, by using a complete randomized design with 24 treatments and three replications. The mother corms size (0.1-4 g (small), 4.1 – 8 g (medium) and 8-12 g (large), organic fertilizers (cow manure 25 t. ha-1, vermicompost 10 t. ha-1,compost 10 t. ha-1 and control) and micro nutrient (Fe-EDTA and Zn-EDTA) in two levels (foliar application and no application) were the first, second and third experimental factors, respectively. Based on the results, the highest number and yield of replacement corms were observed by using the large (8.1-12g) mother corms. The effect of cow manure on replacement corm yield was significantly more than other organic fertilizers. The effect of foliar application on replacement corms yield were also significant. In addition, the highest concentration and content of phosphorus replacement corms was observed by using the large (8.1- 12g) mother corms. The content of phosphorus in replacement corms was significantly decreased by reducing the size of the mother corms

Hierarchical cluster analysis to identify the homogeneous desertification management units.

Since in most mapping models geometric mean of different criteria are used to determine the desertification intensity, one of the most important issues in desertification studies is understanding the similar areas, which require similar management after determining the desertification intensity map. Two similar classes of desertification intensity may require different management due to differences in the criteria that affect its desertification severity. Therefore, after determining the geomorphological facies as the working units in Sistan plain, we used hierarchical cluster analysis to identify the homogeneous environmental management units (HEMUs) based on indices of MEDALUS model. According to the MEDALUS model, the studied area was divided into two categories namely medium and high desertification classes. Working units (geomorphological facies) are classified into five clusters according to HEMUs analysis based on climate, soil, vegetation, and wind erosion criteria. The first cluster (C11) include six facies with moderate and severe desertification; in all of these units the main effective factor was wind erosion, so they need the same management decisions controlling wind erosion. Two working units (1 and 4) with the same desertification severity were placed in two different clusters due to the main factors affecting each other. The results of the Mann-Whitney test showed that the value of the test statistics was 79. Also, the value of Asymp.Sig was obtained to be 0.018, which is less than 0.025 (two-tailed test), and it can be concluded that the classification of work units in the two models, clustering and desertification, is not equal (P<0.05). So It seems that using cluster analysis to identify the same units, which need the same management decision after preparing the desertification intensity, is necessary

Evolution of mechanical properties, microstructure and texture and of various brass alloys processed by multi-directional forging

This study investigated mechanical properties and texture evolution of Cu-xZn alloys (x = 5, 15, 20, 30) processed by multi-directional forging (MDF) up to 6 passes at ambient temperature. Mechanical properties were examined via shear punch and hardness tests. Optical microscopy and high-resolution scanning electron microscopy (HR-SEM), equipped with electron backscattered diffraction (EBSD) detector were also utilized to characterize the detailed microstructures and texture evolutions. The MDFed Cu-xZn alloys after 6 passes showed superior mechanical properties without the reduction of electrical conductivity. Hardness, ultimate shear strength and shear yield strength of Cu-30Zn alloy improved by 213%, 83% and 85%, respectively after 6 passes of MDF. Enhancement of mechanical properties can be attributed to grain refinement. The MDFed Cu-5Zn alloy presented a predominantly Copper-type texture due to the higher activity of shear bands. By increasing Zn to Cu-15Zn and Cu-20Zn, Brass and Goss texture components were observed. Finally, Cu-30Zn alloy possessed a Brass texture which could be attributed to a higher amount of twinings

A PDE Model of Breast Tumor Progression in MMTV-PyMT Mice

The evolution of breast tumors greatly depends on the interaction network among different cell types, including immune cells and cancer cells in the tumor. This study takes advantage of newly collected rich spatio-temporal mouse data to develop a data-driven mathematical model of breast tumors that considers cells&rsquo; location and key interactions in the tumor. The results show that cancer cells have a minor presence in the area with the most overall immune cells, and the number of activated immune cells in the tumor is depleted over time when there is no influx of immune cells. Interestingly, in the case of the influx of immune cells, the highest concentrations of both T cells and cancer cells are in the boundary of the tumor, as we use the Robin boundary condition to model the influx of immune cells. In other words, the influx of immune cells causes a dominant outward advection for cancer cells. We also investigate the effect of cells&rsquo; diffusion and immune cells&rsquo; influx rates in the dynamics of cells in the tumor micro-environment. Sensitivity analyses indicate that cancer cells and adipocytes&rsquo; diffusion rates are the most sensitive parameters, followed by influx and diffusion rates of cytotoxic T cells, implying that targeting them is a possible treatment strategy for breast cancer

Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation.

We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict cancer prevention, diagnosis, and treatment in individual patients. This will be realized based on advances in high performance computing, computational modeling, and an expanding repertoire of observational data across multiple scales and modalities. In 2020, the US National Cancer Institute, and the US Department of Energy, through a trans-disciplinary research community at the intersection of advanced computing and cancer research, initiated team science collaborative projects to explore the development and implementation of predictive Cancer Patient Digital Twins. Several diverse pilot projects were launched to provide key insights into important features of this emerging landscape and to determine the requirements for the development and adoption of cancer patient digital twins. Projects included exploring approaches to using a large cohort of digital twins to perform deep phenotyping and plan treatments at the individual level, prototyping self-learning digital twin platforms, using adaptive digital twin approaches to monitor treatment response and resistance, developing methods to integrate and fuse data and observations across multiple scales, and personalizing treatment based on cancer type. Collectively these efforts have yielded increased insights into the opportunities and challenges facing cancer patient digital twin approaches and helped define a path forward. Given the rapidly growing interest in patient digital twins, this manuscript provides a valuable early progress report of several CPDT pilot projects commenced in common, their overall aims, early progress, lessons learned and future directions that will increasingly involve the broader research community