Green Synthesis of Silver Nanoparticles Induced by the Fungus Penicillium citrinum

Purpose: To evaluate a green process for the extracellular production of silver (Ag) nanoparticles synthesized and stabilized using Penicillium citrinum isolated from soil.Methods: The pure colonies of Penicillium citrinum were cultured in Czapek dox broth. The supernatant of the broth was examined for the ability to produce silver nanoparticles. The reactions were performed in a dark compartment at 28 oC. After 24 h, the synthesized silver nanoparticles were filtered through a membrane filter (0.45 ƒÊ) and characterized by UV-visible spectroscopy, fluorescence spectroscopy, photon correlation spectroscopy (PCS), scanning electron microscopy (SEM) and Fourier transformed infrared spectroscopy (FTIR) for particle size, shape and the presence of different functional groups in the nanoparticles.Results: The silver nanoparticles formed were fairly uniform in size with a spherical shape and a Zaverage diameter of 109 nm. FTIR spectra revealed the presence of amide linkage groups which were also found in the fungal extract itself.Conclusion: The current approach suggests that rapid synthesis of nanoparticles of silver nitrate would be suitable for developing a biological process for mass scale production of formulations.Keywords: Green synthesis, Penicillium citrinum, silver nanoparticles

The Granted Effects of Agricultural Bank Credits on Total Factor Productivity in Agriculture Production

The present study examined the effect of agricultural bank credit on the productivity of production factors in the agricultural sector over the period 1971-2012 using the Auto Regression pattern with wide interruptions. Solow residual model has been used to calculate the growth rate of total factor productivity of agricultural sector. The results showed that credit variable in the both long-term and short-term has a positive effect on total production factors productivity in the agricultural sectors of Iran. Therefore, an increase in credits granted to the agricultural sector has caused to enhance the growth of these sectors and increase total productivity of production factors in the agricultural sector. The effect of energy consumption, exports of agricultural sector, research and development expenditures in the agricultural sector are also positive on total productivity of production factor in the short and long term. But, in the long run, impact of liquidity and oil income on total productivity production factor in the agricultural sector is negative. Therefore, planning in this regard is important

Determination of heavy metal content of processed fruit products from Tehran's market using ICP- OES: A risk assessment study

Abstract In this study, the levels of Cd, Hg, Sn, Al, Pb and As of 72 samples (36 samples for fruits juices and 36 samples for fruits canned) of three different brands including of Peach, Orange, Cherry, and Pineapple (18 samples of each fruits) marketed in Tehran, Iran (2015) were evaluated using Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) technique. Also, Probabilistic risk assessment (non-carcinogenic and carcinogenic risks) was estimated by models include target hazard quotient (THQ) and cancer risk (CR) in the Monte Carlo Simulation (MCS) model. However, all samples were contaminated with the heavy metals investigated, most of them not surpassed established standards. The range of concentration for Al, Sn, As, Cd, Hg, and Pb as average in fruit juices were reported as 340.62 (65.17–1039.2), 72.33 (49.76–119.4), 3.76 (1.137–18.36), 2.12 (0.89–3.44), 0.351 and 40.86 (27.87–66.1) μg/kg, respectively. The level of heavy metals measured in different kinds of fruit juices was ranked as Al > Sn > Pb > As > Cd > Hg, and for fruits canned this rank was Pb > Al > Sn > As > Cd > Hg. The range of concentration for Al, Sn, As, Cd, Hg, and Pb in fruits canned were reported as 361.23 (43.15–1121.2), 101.42 (71.45–141.61), 3.92 (1.279–19.50), 2.78 (1.09–5.56), 0.35 and 690.54 (470.56–910.14) μg/kg, respectively. The lead (Pb) concentration in 97.22% (35 out of 36 samples) of fruit juices samples surpassed Codex limit (0.05 mg/kg) and in all samples of FC was lower than the legal limit of Codex limit (1 mg/kg). All of the samples had Tin (Sn) lower than the legal limit of Codex (fruit juices 100 mg/kg and FC 250 mg/kg). The MCS indicated that the rank order of heavy metals in both adults and children based on THQ was Al > Sn > As > Pb > Cd > Hg. The THQ of Al and Sn in the FJ and FC, for both adults, and children, was considerably higher than 1 value. Also, CR of As in both adults and children were higher than 1E-6 value. Although the mean concentration of heavy metal in the FJ and FC was lower than the standard limit, the MCS indicated that adults and children are at considerable non-carcinogenic and carcinogenic risks. Keywords: Heavy metals Fruits juice Health risk assessment Monte Carlo simulation Fruit canned Food safety ICPOES

High-levelexpression of functional recombinant human coagulation factor VII in insect cells

Abstract: Recombinant coagulation factor VII (FVII) is used as a potential therapeutic intervention in hemophilia patients who produce antibodies against the coagulation factors. Mammalian cell lines provide low levels of expression, however, the Spodoptera frugiperda Sf9 cell line and baculovirus expression system are powerful systems for high-level expression of recombinant proteins, but due to the lack of endogenous vitamin K-dependent carboxylase, expression of functional FVII using this system is impossible. In the present study, we report a simple but versatile method to overcome the defect for high-level expression of the functional recombinant coagulation FVII in Sf9 cells. This method involves simultaneous expression of both human γ-carboxylase (hGC) and human FVII genes in the host. It may be possible to express other vitamin K-dependent coagulation factors using this method in the future. Keywords: Baculovirus; γ-carboxylase; Coagulation FVII; Factor VII; Insect cel

Microbial surfactants: fundamentals and applicability in the formulation of nano-sized drug delivery vectors

Microbial surfactants, so-called biosurfactants, comprise a wide variety of structurally distinct amphipathic molecules produced by several microorganisms. Besides exhibiting surface activity at the interfaces, these molecules present powerful characteristics including high biodegradability, low toxicity and special biological activities (e.g. antimicrobial, antiviral, anticancer, among others), that make them an alternative to their chemical counterparts. Several medical-related applications have been suggested for these molecules, including some reports on their potential use in the formulation of nano-sized drug delivery vectors. However, despite their promises, due to the generalized lack of knowledge on microbial surfactants phase behavior and stability under diverse physicochemical conditions, these applications remain largely unexplored, thus representing an exciting field of research. These nano-sized vectors are a powerful approach towards the current medical challenges regarding the development of efficient and targeted treatments for several diseases. In this review, a special emphasis will be given to nanoparticles and microemulsions. Nanoparticles are very auspicious as their size, shape and stability can be manipulated by changing the environmental conditions. On the other hand, the easiness of formulation, as well as the broad possibilities of administration justifies the recent popularity of the microemulsions. Notwithstanding, both vector types still require further developments to overcome some critical limitations related with toxicity and costs, among others. Such developments may include the search for other system components, as the microbial surfactants, that can display improved features.The author acknowledges the financial support from the Strategic Project PEst-OE/EQB/LA0023/2013 and project ref. RECI/BBB-EBI/0179/2012 (project number FCOMP-01-0124-FEDER-027462) funded by Fundacao para a Ciencia e a Tecnologia

Outlier Detection based on Robust Regression via Chance-Constrained Programming

Outlier detection is a critical step in data pre-processing to identify heterogeneous points in data. For high dimensional and extremely noisy data, many challenges are posed in outlier detection, including estimating the number of outliers, providing probabilistic confidence statement on identified outliers, fitting a model robust against outliers in the data set, and achieving high breakdown points with guarantee. In this paper, we propose a novel chance-constrained outlier detection (CCOD) model that not only finds a robust fit to the data set without guessing the proportion of outliers, but also automatically offers a diagnostic criteria (i.e., the relative outlying probabilities) to detect outliers with confidence. The main idea is to first model a probabilistic least quantile of squares (LQS) problem using chance-constrained optimization, then reformulate the problem using kernel density estimation. Since the resulting kernel-based LQS is nonlinear and nonconvex, we further propose a tractable convex approximation, the so-called CCOD model, and use its optimization to develop two outlier detection algorithms. Through numerical results, we show that our CCOD model outperforms the state-of-art LQS methodologies in terms of estimation accuracy, robustness, and computational time, and it provides robust fits to large-scale data that were otherwise intractable via other methodologies.Replaced with revised and approved PDF on 21-Apr-2022 per Graduate College and student request, Kimberl

Data-Driven Optimization under Uncertainty for Renewable Energy Integration and Management

High levels of clean renewable energy are being integrated into the power systems as a result of recent government incentives and technological advancements. The electricity generated from renewable resources such as wind and solar are highly intermittent and uncertain, significantly threatening the stability, reliability, and efficiency of the power grid. In particular, the electrical power system is known to be notoriously complex and small violations of the system limits can lead to systemwide catastrophic events. Therefore, the increasing level of uncertainty introduced into the network through renewable distributed generation units is further complicating the planning and operation of the network at various stages; from long-term planning decisions to the real-time operation decisions.As a result of recent computational advances alongside large-scale availability of data, data-driven distributionally robust and stochastic optimization methodologies have been extensively developed to find low cost and highly reliable solutions to large-scale complex problems under uncertainty. In this dissertation, we develop novel data-driven distributionally robust and stochastic optimization methodologies for addressing (i) long-term planning, (ii) short-term planning and (iii) real-time operational decisions of a power system under high penetration of uncertain renewable energy. First and to address the long-term planning of renewables, we propose a distributionally robust model for the optimal sizing of new renewable sites in an existing distribution system. In particular, we first propose a two-stage data-driven distributionally robust optimization model (O-DDSP) for the optimal planning of renewable distributed generation units (RDGs). The objective is to minimize the total cost of RDG installation plus the total operational cost on a planning horizon. Next, we introduce a tight approximation of O-DDSP based on principal component analysis (leading to a model denoted by P-DDSP), which reduces the original problem’s size by projecting the ambiguity set to lower dimensions. Finally, extensive numerical experiments demonstrate that our solution methodology significantly out-performs the state-of-art. Our optimal RDGs planning decisions lead to significant savings as well as increasing penetration of intermittent renewable energy in the distribution network. Next and to tackle the short-term challenges that are caused by high penetration of renewables in the power systems, we propose a mixed-integer stochastic optimization methodology for integrated transmission and distribution systems planning. In particular, through a careful and systematic analysis of the power system planning problems, we underline the necessity of developing methods that can tackle the planning of both the power transmission system (TS) and distribution system (DS) and realize the potential benefits of considering their planning in a coordinated mode. To that end, we introduce an integrated transmission and distribution system (InTDS) problem which minimizes both the unit commitment costs of the TS and the distributed energy resource (DER) management costs of the DSs, respectively, while respecting the technical constraints of both systems. We show that our integrated model achieves significant lower costs as compared to solving these problems separately. At last and to address the real-time operation of power system under high penetration of renewables, we propose a chance-constrained stochastic optimization methodology for the optimal power flow (OPF) problem. The increasing penetration of renewable energy in power systems calls for secure and reliable system operations under significant uncertainty. To that end, we introduce a fully two-sided chance-constrained ACOPF problem (TCC-ACOPF), in which the active and reactive generation, voltage, and power flow all remain within their upper and lower bounds simultaneously with a predefined probability. Instead of applying Bonferroni approximation or scenario-based approaches, we present an efficient second order conic (SOC) approximation of the TCCs under Gaussian Mixture (GM) distribution via a piecewise linear (PWL) approximation. We show that our SOCP provides consistently more robust solutions (about 60% reduction in constraint violation) without significant additional computational costs, as compared to other state-of-art ACOPF formulations

Sajjd Heidary.pmd

ABSTRACT This paper is devoted to an investigation carried out on a simple, rapid and sensitive method, which is proposed for selective determination of ultra trace amounts of silver from water and biological samples. The method is based on highly efficient separation and pre-concentration of silver by dispersive liquid-liquid microextraction (DLLME) and determination with graphite furnace atomic absorption spectrometry (GFAAS). 1-(2-pyridylazo)-2-naphtol (PAN) was used as a silver chelating agent prior to extraction. Parameters such as type and volume of extraction solvent, type and volume of dispersive solvent, pH, extraction time and concentration of the chelating agent have been optimized. Liner range of calibration curve, detection limit and relative standard deviation were 0.2-6.0 ng mL -1 , 0.02 ng mL -1 and 4.4 C/o, respectively. Silver determined successfully with this method in real samples

Investigating the relationship between security of crude oil exports demand and investment in the upstream oil industry in OPEC member countries

The efforts of oil importing countries to transfer from fossil fuels to non-fossil fuels and the feasibility of commercial exploitation of unconventional oil and gas reserves can jeopardize the security of demand for crude oil by exporting countries. . In this study, by calculating the oil demand risk index from OPEC member countries during the years 2000-2014, the relationship between this variable and the price of oil by investing in the upstream sector of the oil industry of these countries during the years (2000-2012) Using panel data model with random effects has been investigated. The results show that the effect of the index of risk of demand for oil exports on investment in the upstream sector of the oil industry is negative and this effect is significant. Therefore, given the coefficient of the risk index of demand for oil exports, it can be concluded that a percentage change in the risk of demand for oil exports makes up 0.51percent of investment in the upstream sector of the oil industry in the opposite direction. In addition, the effect of oil prices on investment in the upstream of the oil industry is positive, so that a 1percent change in oil prices changes 1،12percent of investment in the upstream sector of the oil industry in the same direction