Genotoxic Effect of Atrazine, Arsenic, Cadmium and Nitrate, Individually and in Mixtures at Maximum Contaminant Levels on mammalian Breast Cell Lines

There is strong evidence that hormonally active agents (HAAs) such as Atrazine (ATZ), Cadmium (Cd), Arsenic (As) and Nitrate (NO3) have both estrogenic activity and carcinogenic potential. Atrazine has clastogenic effects and may also act as tumor promoter as it induces the aromatase enzyme. Arsenic and Cadmium have been implicated in the etiology of skin, lung, prostate and liver cancers. Nitrate in drinking water has been found to increase the risk of bladder cancer.This study examined the genotoxicity of the aforementioned HAAs alone and in mixtures using mammalian breast cell lines, MCF-7 and MCF-10A, which are estrogen receptorpositive (ER+) and estrogen receptor-negative (ER-), respectively. To study the clastogenic potential by whole cell and flow karyotype damage, cells were exposed to environmentally relevant concentrations of ATZ, Cd, As and NO3 for 4 and 7 days.Results indicated that all treatments induced whole cell clastogenicity in MCF-7 cells; except Cd and NO3 after 4 and 7 days as well as the 10% quaternary As mixture after 1 week. In MCF-10A cells, all treatments except the 10% mixture induced whole cell clastogenicity after 4 days, where flow karyotype damage was detected in all treatments except for the 10% mixture after 1 week. Estrogen caused whole cell damage but not flow karyotype damage in MCF-7. On the other hand, estrogen caused flow karyotype damage and not whole cell damage in MCF-10A cells, suggesting that estrogen receptor modulated the genotoxicity of estrogen. Cd caused flow karyotype damage but not whole cell damage in MCF-7 indicating that Cd’s gentoxicity is not related to its estrogenic activity.Keywords: HAAs, clastogenicity, flow-karyotype, genotoxicity, MCF-7, MCF-10

An improved optimization technique for estimation of solar photovoltaic parameters

The nonlinear current vs voltage (I-V) characteristics of solar PV make its modelling difficult. Optimization techniques are the best tool for identifying the parameters of nonlinear models. Even though, there are different optimization techniques used for parameter estimation of solar PV, still the best optimized results are not achieved to date. In this paper, Wind Driven Optimization (WDO) technique is proposed as the new method for identifying the parameters of solar PV. The accuracy and convergence time of the proposed method is compared with results of Pattern Search (PS), Genetic Algorithm (GA), and Simulated Annealing (SA) for single diode and double diode models of solar PV. Furthermore, for performance validation, the parameters obtained through WDO are compared with hybrid Bee Pollinator Flower Pollination Algorithm (BPFPA), Flower Pollination Algorithm (FPA), Generalized Oppositional Teaching Learning Based Optimization (GOTLBO), Artificial Bee Swarm Optimization (ABSO), and Harmony Search (HS). The obtained results clearly reveal that WDO algorithm can provide accurate optimized values with less number of iterations at different environmental conditions. Therefore, the WDO can be recommended as the best optimization algorithm for parameter estimation of solar PV

Antibiotic Resistance Bacteria in Tertiary Hospitals in Chittagong, Bangladesh

Nosocomial infections (HAI) are major cause for mortality and morbidity worldwide. In low income countries, data suggests 6.5% - 33% of patients have HAI with pneumonia being the most frequent..Antibiotic resistance is highly prevalent in developing countries due to self medication, easy availability and poor regulatory controls. Clinicians have been left with limited antibiotic drug options for the treatment of bacterial infections due to escalated rates of resistance. This comparative study aimed to identify microorganisms from hospital surfaces in two major tertiary care hospitals in Chittagong, Bangladesh. It also identifies antibiotic susceptibility of the samples to antibiotics commonly used in Bangladesh. Samples were collected by swabbing different environmental surface around patients in both hospitals. Identification of bacteria was done by culturing in nutrient media and various common biochemical techniques. Antibiotic sensitivity was determined by disk diffusion method. During the study, 27 samples were collected from different surfaces in different wards of the hospitals. The predominating organisms were Streptococcus, Staphylococcus, Bacillus, Pseudomonas and Serratia. The isolates of organisms showed high level of resistance to commonly used antibiotics especially a fourth generation cephalosporin, cefepime. In addition, antibiotic sensitivity tests showed small colonies or film of growth within zone of inhibition of some of the samples known as “satellite colonies”. The study identified bacterial isolates responsible for HAI in tertiary hospitals and their susceptibility to antibiotics. Further research is currently being conducted on understanding the satellite colonies some of the isolates from hospital surface swabs have exhibited

Non-invasive identification of turbogenerator parameters from actual transient network data

Synchronous machines are the most widely used form of generators in electrical power systems. Identifying the parameters of these generators in a non-invasive way is very challenging because of the inherent non-linearity of power station performance. This study proposes a parameter identification method using a stochastic optimisation algorithm that is capable of identifying generator, exciter and turbine parameters using actual network data. An eighth order generator/turbine model is used in conjunction with the measured data to develop the objective function for optimisation. The effectiveness of the proposed method for the identification of turbo-generator parameters is demonstrated using data from a recorded network transient on a 178 MVA steam turbine generator connected to the UK's national grid

Developing standard pedestrian-equivalent factors: passenger car–equivalent approach for dealing with pedestrian diversity

Similar to vehicular traffic, pedestrians, despite having diverse capabilities and body sizes, can be classified as heterogeneous. The use of vehicular traffic resolves the diversity issue with a conversion of heterogeneous vehicle flow into an equivalent flow with the use of passenger car–equivalent (PCE) factors. Analysis of pedestrian flow has yet to incorporate pedestrian diversity analysis implicitly into the design of pedestrian facilities, although some form of adjustment has been suggested. This paper introduces the concept of PCE-type factors for mixed pedestrian traffic called standard pedestrian-equivalent (SPE) factors. Estimates of SPE factors are made relative to the average commuter. The equivalent total travel time approach for PCE estimation was adapted to consider the effects of the differences in physical and operational characteristics of pedestrians, particularly walking speed and body size. Microsimulation of pedestrians was employed to evaluate hypothetical pedestrian proportions so as to generate corresponding flow relationships. Walking speeds and body sizes were varied across different flow conditions, walkway widths, and proportions of other pedestrian types. The first part of this paper explores how the two pedestrian characteristics (walking speed and body size) influence estimated SPE factors. The second part is a case study in which field-collected data illustrate SPE factors calculated for older adults, obese pedestrians, and their combination. An application of SPE factors demonstrates the robustness of the methodology in bridging the gap between pedestrian compositions and planning practice

A fast and accurate energy source emulator for wireless sensor networks

The capability to either minimize energy consumption in battery-operated devices, or to adequately exploit energy harvesting from various ambient sources, is central to the development and engineering of energy-neutral wireless sensor networks. However, the design of effective networked embedded systems targeting unlimited lifetime poses several challenges at different architectural levels. In particular, the heterogeneity, the variability, and the unpredictability of many energy sources, combined to changes in energy required by powered devices, make it difficult to obtain reproducible testing conditions, thus prompting the need of novel solutions addressing these issues. This paper introduces a novel embedded hardware-software solution aimed at emulating a wide spectrum of energy sources usually exploited to power sensor networks motes. The proposed system consists of a modular architecture featuring small factor form, low power requirements, and limited cost. An extensive experimental characterization confirms the validity of the embedded emulator in terms of flexibility, accuracy, and latency while a case study about the emulation of a lithium battery shows that the hardware-software platform does not introduce any measurable reduction of the accuracy of the model. The presented solution represents therefore a convenient solution for testing large-scale testbeds under realistic energy supply scenarios for wireless sensor networks