Growth responses and accumulation of heavy metals by fungus Agaricus bisporus

Ectomycorrhizal fungi are able to form symbiotic associations with tree roots, and therefore, plants gain different benefits. On metal-contaminated soils, these fungi may improve plant fitness through an enhanced nutrition or by reducing toxicity of the metals. Agaricus bisporus, an edible fungus has been noted to grow in large numbers under Pistacia vera plantations in orchards of Kerman, Iran, indicating that it may form ectomycorrhiza with the tree. This research describes the responses of this fungus to heavy metals in solid and liquid MMN media. The fungus was grown in vitro in liquid and solid cultures for 3 weeks on five different concentrations (0, 15, 30, 45, 60 ppm) of five heavy metals (Cu, Zn, Ni, Co, Mn) as sulphate and the effect of these metal on radial growth, biomass production and metal content of fungal biomass were determined. The result showed there was a strong variation in metal tolerance, so that Agaricus bisporus was more tolerant to Mn than other metals, while the reverse was true for Ni, so that the fungus had an increased growth in the presence of low concentrations of Co, Mn, and Zn, but Ni greatly inhibited increase in biomass and colony diameter even at concentrations as low as 15 mg/l

The modified Adomian decomposition method for solving Chaotic Lü system

In this paper, a numerical scheme based on adaption of standard adomian decomposition method (ADM) is applied to thechaotic Lü system. Then, the standard ADM is converted into a hybrid numeric-analytic method called the modified ADM (MADM). Numerical comparisons with the standard ADM and the fourth-order Runge-Kutta method (RK4) is made in order to prove that MADM is the reliable method for nonlinear problems

Performance Analysis of Adaptive Rate Scheduling Scheme for 3G WCDMA Wireless Networks with Multi-Operators

Sharing of 3G network infrastructure among operators offers an alternative solution to reducing the investment in the coverage phase of WCDMA. For radio access network (RAN) sharing method each operator has its own core network and only the RAN is shared. Without an efficient RRM, one operator can exhausts the capacity of others. This paper proposes and analyzes an efficient uplink-scheduling scheme in case of RAN sharing method. We refer to this new scheme as Multi-operators Code Division Generalized Processor sharing scheme (M-CDGPS). It employs both adaptive rate allocation to maximize the resource utilization and GPS techniques to provide fair services for each operator. The performance analysis of this scheme is derived using the GPS performance model. Also, it is compared with static rate M-CDGPS scheme. Numerical and simulation results show that the proposed adaptive rate MCDGPS scheduling scheme improves both system throughput and average delays

Performance Analysis of Adaptive Rate Scheduling Scheme for 3G WCDMA Wireless Networks with Multi-Operators

Sharing of 3G network infrastructure among operators offers an alternative solution to reducing the investment in the coverage phase of WCDMA. For radio access network (RAN) sharing method each operator has its own core network and only the RAN is shared. Without an efficient RRM, one operator can exhausts the capacity of others. This paper proposes and analyzes an efficient uplink-scheduling scheme in case of RAN sharing method. We refer to this new scheme as Multi-operators Code Division Generalized Processor sharing scheme (M-CDGPS). It employs both adaptive rate allocation to maximize the resource utilization and GPS techniques to provide fair services for each operator. The performance analysis of this scheme is derived using the GPS performance model. Also, it is compared with static rate M-CDGPS scheme. Numerical and simulation results show that the proposed adaptive rate MCDGPS scheduling scheme improves both system throughput and average delays

Evaluation of The Quality of E-Learning Platforms Used in Educating Kindergarten Children Distantly During the Coronavirus Pandemic

During the coronavirus pandemic, and for the first time, ministries of education in many countries have resorted to electronic education platforms for kindergarten children as a safe alternative to face-to-face education. This shift to distance education is still used partly in the education process, even after the end of the pandemic. The current study aimed to consider this unique experience by evaluating the quality of the Rawdaty platform, which is one of the e-learning platforms launched by the Saudi Ministry of Education during the coronavirus pandemic, to ensure the continuation of the educational process in kindergartens. The analytical descriptive approach was used, and a scale derived from the Saudi National E-Learning Center (NELC) standards was designed to achieve the studys objectives. The scale includes four main dimensions: Design, Interaction, Equity and accessibility, Measurement and Evaluation. The research sample consisted of (94) mothers and (67) kindergarten teachers. The results of the study were as follows: The quality standards of the Rawdaty platform were achieved to a high degree from the point of view of mothers, with an arithmetic mean of (55.93) and an average weight of (2.664). It also showed the high-quality standards of the kindergarten platform from the point of view of the teachers, with an arithmetic mean of (58.35) and an average weight of (2.78). The study recommended the possibility of adopting e-learning platforms to teach kindergarten children distantly during exceptional circumstances

Prevalence of drug and alcohol use in urban Afghan istan: epidemiological data from the Afghanistan National Urban Drug Use Study (ANUDUS)

Background Previous attempts to assess the prevalence of drug use in Afghanistan have focused on subgroups that are not generalisable. In the Afghanistan National Urban Drug Use Study, we assessed risk factors and drug use in Afghanistan through self-report questionnaires that we validated with laboratory test confi rmation using analysis of hair, urine, and saliva. Methods The study took place between July 13, 2010, to April 25, 2012, in 11 Afghan provinces . 2187 randomly selected households completed a survey, representing 19 025 ho usehold members. We completed surveys with the female head of the household about past and current drug use among members of their household . We also obtained hair, urine, and saliva samples from 5236 people in these households and tested them for metabolites of 13 drugs. Find ings Of 2170 households with biological samples tested, 247 (11·4%) tested positive for any drug. Overall, opioids were the most prevale nt drug in the biological samples (5·6%), although prescription drugs (prescription pain pills, sedatives, and tranquilliser) were the most commonly reported in the past 30 days in the questionnaires (7·6%). Of individuals testing positive for at least one substance, opioids accounted for more than 50% of substance use in women and children, but only a third of substances in men, who predominantly tested positive for cannabinoids. After controlling for age with direct standardisation, individual prevalence of substance use (from laboratory tests) was 7·2% (95% CI 6·1–8·3) in men and 3·1% (2·5–3·7) in women—with a national prevalence of 5·1% (4·4–5·8) and a prevalence of 5·0% (4·1–5·8) in Kabul. Concordance between laboratory test results and self-reports was high. Interpretation These data suggest the female head of household to be a knowledgeable informant for household substance use. They also might provide insight into new avenues for targeted behavioural interventions and prevention messages

A novel approach for communicating with patients suffering from completely locked-in-syndrome (CLIS) via thoughts: brain computer interface system using EEG signals and artificial intelligence

This paper investigates the development of an intelligent system method to address completely locked-in-syndrome (CLIS) that is caused by some illnesses such as Amyotrophic Lateral Sclerosis (ALS) as the most predominant type of Motor Neuron Disease (MND). In the last stages of ALS and despite the limitations in body movements, patients however will have a fully functional brain and cognitive capabilities and able to feel pain but fail to communicate. This paper aims to address the CLIS problem by utilizing EEG signals that human brain generates when thinking about a specific feeling or imagination as a way to communicate. The aim is to develop a low-cost and affordable system for patients to use to communicate with carers and family members. In this paper, the novel implementation of the ASPS (Automated Sensor and Signal Processing Selection) approach for feature extraction of EEG is presented to select the most suitable Sensory Characteristic Features (SCFs) to detect human thoughts and imaginations. Artificial Neural Networks (ANN) are used to verify the results. The findings show that EEG signals are able to capture imagination information that can be used as a means of communication; and the ASPS approach allows the selection of the most important features for reliable communication. This paper explains the implementation and validation of ASPS approach in brain signal classification for bespoke arrangement. Hence, future work will present the results of relatively high number of volunteers, sensors and signal processing methods