Life Cycle Assessment of Municipal Waste Management System (Case Study: Karaj, Iran)

LCA has been defined as a tool for evaluating the environmental burdens and potential impacts that can be applied to municipal solid waste management systems for determine the optimum municipal solid waste (MSW) management strategy.To investigate the Waste Management system strategyof Karaj City we used LCA method. Three scenarios were defined and compared based on environmental burden include water pollution, air pollution, consumed energy and waste residues.. For each of these scenarios, an ecological indicator was achieved from checklist values. From the environmental point of view, results show that recycling is one of the best alternatives for Waste Management. Furthermore, composting has an important role in alleviating the load of pollutants and energy usage in the Waste Management system. ©JASEMKeywords: Waste Management system, LCA, Kara

Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification

Identifying seizure activities in non-stationary electroencephalography (EEG) is a challenging task, since it is time-consuming, burdensome, and dependent on expensive human resources and subject to error and bias. A computerized seizure identification scheme can eradicate the above problems, assist clinicians and benefit epilepsy research. So far, several attempts were made to develop automatic systems to help neurophysiologists accurately identify epileptic seizures. In this research, a fully automated system is presented to automatically detect the various states of the epileptic seizure. The proposed method is based on sparse representation-based classification (SRC) theory and the proposed dictionary learning using electroencephalogram (EEG) signals. Furthermore, the proposed method does not require additional preprocessing and extraction of features which is common in the existing methods. The proposed method reached the sensitivity, specificity and accuracy of 100% in 8 out of 9 scenarios. It is also robust to the measurement noise of level as much as 0 dB. Compared to state-of-the-art algorithms and other common methods, the proposed method outperformed them in terms of sensitivity, specificity and accuracy. Moreover, it includes the most comprehensive scenarios for epileptic seizure detection, including different combinations of 2 to 5 class scenarios. The proposed automatic identification of epileptic seizures method can reduce the burden on medical professionals in analyzing large data through visual inspection as well as in deprived societies suffering from a shortage of functional magnetic resonance imaging (fMRI) equipment and specialized physician

Therapeutic potentials of curcumin in the treatment of glioblstoma

Glioblastoma multiforme (GBM), a greatly aggressive malignancy of the brain, is correlated with a poor prognosis and low rate of survival. Up to now, chemotherapy and radiation therapy after surgical approaches have been the treatments increasing the survival rates. The low efficacy of mentioned therapies as well as their side-effects has forced researchers to explore an appropriate alternative or complementary treatment for glioblastoma. In experimental models, it has been shown that curcumin has therapeutic potentials to fight against GBM. Given that curcumin has pharmacological effects against cancer stem cells, as major causes of resistance to therapy in glioblastoma cells. Moreover, it has been showed that curcumin exerts its therapeutic effects on GBM cells via affecting on apoptosis, oxidant system, and inflammatory pathways. Curcumin would possess a synergistic impact with chemotherapeutic agents. Herein, we summarized the current findings on curcumin as therapeutic agent in the treatment of GBM. © 2020 Elsevier Masson SA

Effects of subcutaneous injection MnO2 micro- and nanoparticles on blood glucose level and lipid profile in rat

Background: The use of nanotechnology has led to rapid growth in various areas. Thus, health and safety issues of nanoparticles (NPs) should be promptly addressed. Manganese oxide (MnO2) nanoparticles (NPs) are typically used for biomedical and industrial applications. However, characterizing the potential human health effects of MnO2 NPs is required before fully exploiting these materials. The aim of this study was to investigate the toxicity of MnO2 micro- and nanoparticles on blood glucose level and lipid profile in male Wistar rats. Methods: A total of 105 rats were divided into one control and two experimental groups. Each experimental group received a single subcutaneous injection of MnO2 micro- and nanoparticles (100 μg/kg), respectively, every two weeks for 14 weeks. Their blood glucose, cholesterol, triglycerides, LDL, and HDL levels were then measured. The data presented as mean±SEM and compared with the repeated measures using the Prism statistical software (version 6.0). Results: Biochemical assessment in plasma samples showed that MnO2 micro- and nanoparticles injection significantly (P<0.01) increased the plasma glucose and cholesterol levels in all and few weeks, respectively. MnO2 nanoparticles significantly (P<0.01) decreased the HDL level in weeks 6, 12, and 14, but MnO2 microparticles decreased the HDL level only in week 12. In both MnO2 micro- and nanoparticles groups, LDL alterations were near to the control group, except for week 10. However, the same treatment had no effect on triglycerides concentrations compared to the control group. Conclusion: Our results show that exposure to nanosized particles at subchronic doses caused adverse changes in animal biochemical profiles, especially in glucose level. It seems that the high oxidative power of these particles is the main reason for these disturbances. © 2016 Shiraz University of Medical Sciences. All rights reserved

GROUNDWATER LEVEL PREDICTION USING DEEP RECURRENT NEURAL NETWORKS AND UNCERTAINTY ASSESSMENT

Groundwater is one of the most important sources of regional water supply for humans. In recent years, several factors have contributed to a significant decline in groundwater levels (GWL) in certain regions. As a result of climate change, such as temperature increase, rainfall decrease, and changes in relative humidity, it is necessary to investigate and model the effects of these factors on GWL. Although a number of researches have been conducted on GWL modeling with machine learning (ML) and deep learning (DL) algorithms, only a limited number of studies have reported model uncertainty. In this paper, GWL modeling of some piezometric wells has been conducted by considering the effects of the meteorological parameters with Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. The models were trained on one piezometric well data and predictions were executed on six other wells. To perform an uncertainty assessment, the models were run 10 times and their means were calculated. Subsequently, their standard deviations were considered to evaluate the outcomes. In addition, the prediction power of the models was validated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and R-Squared (R2). Finally, for all the six wells that did not participate in the training phase, the prediction functions of the trained models were run 10 times and their accuracy was assessed. The results indicate that LSTM (R2=95.6895, RMSE=0.4744 m, NRMSE=0.0558, MAE=0.3383 m) had a better performance compared to that of GRU (R2=95.2433, RMSE=0.4984 m, NRMSE=0.0586, MAE=0.3658 m) on the GWL modeling

Resonant Visible Light Modulation with Graphene

Fast modulation and switching of light at visible and near-infrared (vis-NIR) frequencies is of utmost importance for optical signal processing and sensing technologies. No fundamental limit appears to prevent us from designing wavelength-sized devices capable of controlling the light phase and intensity at gigaherts (and even terahertz) speeds in those spectral ranges. However, this problem remains largely unsolved, despite recent advances in the use of quantum wells and phase-change materials for that purpose. Here, we explore an alternative solution based upon the remarkable electro-optical properties of graphene. In particular, we predict unity-order changes in the transmission and absorption of vis-NIR light produced upon electrical doping of graphene sheets coupled to realistically engineered optical cavities. The light intensity is enhanced at the graphene plane, and so is its absorption, which can be switched and modulated via Pauli blocking through varying the level of doping. Specifically, we explore dielectric planar cavities operating under either tunneling or Fabry-Perot resonant transmission conditions, as well as Mie modes in silicon nanospheres and lattice resonances in metal particle arrays. Our simulations reveal absolute variations in transmission exceeding 90% as well as an extinction ratio >15 dB with small insertion losses using feasible material parameters, thus supporting the application of graphene in fast electro-optics at vis-NIR frequencies.Comment: 17 pages, 13 figures, 54 reference

Photonic Analogue of Two-dimensional Topological Insulators and Helical One-Way Edge Transport in Bi-Anisotropic Metamaterials

Recent progress in understanding the topological properties of condensed matter has led to the discovery of time-reversal invariant topological insulators. Because of limitations imposed by nature, topologically non-trivial electronic order seems to be uncommon except in small-band-gap semiconductors with strong spin-orbit interactions. In this Article we show that artificial electromagnetic structures, known as metamaterials, provide an attractive platform for designing photonic analogues of topological insulators. We demonstrate that a judicious choice of the metamaterial parameters can create photonic phases that support a pair of helical edge states, and that these edge states enable one-way photonic transport that is robust against disorder.Comment: 13 pages, 3 figure

In vitro adherence of Lactobacillus strains isolated from the vaginas of healthy Iranian women

Background The lactobacilli are a part of the bacterial flora of the human vagina. Detection of normal Lactobacillus species in the vaginas of healthy women in different geographical locations, and evaluation of their specific properties, can aid in the selection of the best species for preventing sexually transmitted diseases in the future. This study was performed to isolate and identify the Lactobacillus species in the vaginas of healthy women and to evaluate the adherence of these lactobacilli to Vero and HeLa cell lines. Methods The study included 100 women. Bacteria were isolated from healthy women and purified. Phenotypic and biochemical tests were performed to identify the lactobacilli. The Lactobacillus species were detected by molecular methods using polymerase chain reaction amplification of the full length of the 16S rDNA of the isolated bacteria. Several isolates of each species were then selected to study their adherence to Vero and HeLa cell lines. Results Among the 50 samples taken from healthy women meeting the inclusion criteria, Lactobacillus species were identified in 33 (66) samples. Of these lactobacilli, 14 isolates were Lactobacillus crispatus, six (18.2) were Lactobacillus gasseri, nine (27) were Lactobacillus rhamnosus, and the rest were either Lactobacillus salivarius (6) or Lactobacillus plantarum (6). L. rhamnosus showed the greatest adhesion to the cells when compared to the other tested species. All the lactobacilli isolated in this study showed a smaller capacity for cell adherence when compared with control species. Conclusion L. crispatus, L. rhamnosus, and L. gasseri were the dominant Lactobacillus species in the vaginas of healthy women in Iran. L. rhamnosus attached more readily to the cells than did the other species; therefore, this isolate is a good candidate for further studies on the potential health benefits and application of lactobacilli as probiotics. © 201

Prediction of breast self-examination in a sample of Iranian women: an application of the Health Belief Model

<p>Abstract</p> <p>Background</p> <p>Iranian women, many of whom live in small cities, have limited access to mammography and clinical breast examinations. Thus, breast self examination (BSE) becomes an important and necessary approach to detecting this disease in its early stages in order to limit its resultant morbidity and mortality. This study examined constructs arising from the Health Belief Model as predictors of breast self examination behavior in a sample of women living in Bandar Abbas, Iran.</p> <p>Methods</p> <p>This study was conducted in eight health centers located in Bandar Abbas, Iran. The sample consisted of 240 eligible women who were selected from referrals to the centers. The inclusion criteria were as follows: aged 30 years and over; and able to read and write Farsi. Women with breast cancer, who were pregnant, or breast feeding, were excluded from the study. Data were collected by using a self administered questionnaire which included demographic characteristics and Champion's Health Belief Model Scale. This instrument measures the concepts of disease susceptibility (3 items), seriousness (6 items), benefits (4 items), barriers (8 items) and self-efficacy (10 items).</p> <p>Results</p> <p>The subjects' mean age was 37.2 (SD = 6.1) years. Just under a third of the subjects (31.7%) had performed BSE in the past and 7.1% of them performed it at least monthly. Perceived benefits and perceived self-efficacy of the women who performed BSE were significantly higher compared with women who did not practice BSE (p < 0.03). Furthermore, perceived barriers were lower among those who had performed BSE (p < 0.001). Logistic regression analysis indicated that women who perceived fewer barriers (OR: 0.70, 95% CI: 0.63-0.77, p < 0.001) and had higher self-efficacy (OR: 1.08, 95% CI: 1.02-1.13, p = 0.003) were more likely to perform BSE (R²= 0.52).</p> <p>Conclusion</p> <p>Findings from this study indicated that perceived barriers and perceived self-efficacy could be predictors of BSE behavior among the sample of women. Therefore, BSE training programs that emphasize self-efficacy and address perceived barriers are recommended.</p

Slip-rate on the Main Köpetdag (Kopeh Dagh) strike-slip fault, Turkmenistan, and the active tectonics of the South Caspian

We provide the first measurement of strike-slip and shortening rates across the 200-km-long right-lateral strike-slip Main Köpetdag Fault (MKDF) in Turkmenistan. Strike-slip and shortening components are accommodated on parallel structures separated by ∼10 km. Using Infra-red-stimulated luminescence and reconstruction of offset alluvial fans we find a right-lateral rate of 9.1 ± 1.3 mm/yr averaged over 100 ± 5 ka, and a shortening rate of only ∼0.3 mm/yr averaged over 35 ± 4 ka across the frontal thrust, though additional shortening is likely to be accommodated locally by folding and faulting, and regionally within the eastern Caspian lowlands to its south. The MKDF is estimated to have ∼35 km of cumulative right-lateral slip which, if these geological measurements are correct, would accumulate in only 3–5 Ma at the rate we have determined, suggesting that the present tectonic configuration started within that time period. We use the MKDF slip-rate to form a velocity triangle, from which we estimate the Iran-South Caspian and Eurasia-South Caspian shortening rates, and show that the South Caspian Basin moves at 10.4 ± 1.1 mm/yr in direction 333° ± 5 relative to Eurasia and at 4.8 ± 0.8 mm/yr in direction 236° ± 14 relative to Iran. In contrast to both the eastern Köpetdag and the Caspian lowlands the MKDF has little recent or historical seismicity. The rapid slip-rate estimated here suggests that it is a zone of high earthquake hazard