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Treatment of tunnel wash runoff water and road runoff water before it reaches the environment is recommended to limit the negative consequences of traffic-related pollution. The efficiency of existing water treatment systems to remove traffic-related microplastic (MP) has not been sufficiently documented. Expanding the knowledge about traffic-related MP and documenting the treatment efficiency of MP in road tunnel wash water (TWW) and road runoff (RRW) treatment systems were the objectives of the presented project. TWW from the Tåsen tunnel, Norway, and RRW from the Fossbekken sedimentation pond were investigated in summer and winter situations. Six commonly available polymer types, tire rubber tread particles (TRP), and road marking paints (RMP) were analyzed in the selected samples. About 0.12 and 0.26 μg/L of polymers were identified in winter and summer TWWs. Significantly higher tire rubber and road marking paint concentrations were identified in the winter sample compared to summer sample. Suspended particle concentration in the Fossbekken RRW treatment pond effluent was lower in the summer than in the winter sample. About 0.002 and 0.0008 μg/L polymer masses were identified in winter and summer samples, respectively. TRP in the winter and summer samples were 0.7 and 0.2 μg7/L, and 13.4 μg/L RMP was found in the winter sample, while it was only 0.008 μg/L in the summer sample. HIGHLIGHTS Microplastic contamination of road runoff water (RRW) and tunnel wash water (TWW) was investigated.; Heavy metal pollution and microplastic particles larger than 10 μm were measured.; Six commonly available polymer types, tire rubber tread particles (TRP), and road marking paint (RMP) in RRW and TWW samples were analyzed.; The treatment efficiencies of a roadside pond and a TWW sedimentation tank was evaluated.;

Diabetes is one of the most fatal and vastly heritable metabolic disorders across the globe primarily due to the associated long-term effects including uncontrolled blood sugar levels, blindness, renal and cardiac problems. One of the diabetes related genes peroxisome proliferated activated receptor gamma 2 (PPARγ2) plays significant role in insulin metabolism and is expressed predominantly in adipose tissues. Single nucleotide polymorphism (SNPs) like Pro12Ala, is known to be associated with PPARγ2 gene. In this study, we have made an effort to investigate the prevalence of Pro12Ala allele in the diabetic population of Guwahati, Assam. A total of 50 human subjects with control subjects were included in the study. The data obtained revealed that 49 out of 50 samples carried the Pro/Pro allele, while 1 individual carried the Pro/Ala allele. Results obtained in the study indicate the existence of insignificant link between the targeted SNP Pro12Ala and type 2 diabetes in the sampled population, whereas in contrast we found a strong association of the SNP Pro12Pro with diabetes. The present study shows that there is a need to carry out an extensive study with a large population, which would give us the better picture of occurrence of this SNP in Northeast Indian population.

We calculate the excess energy transferred into two-dot and three-dot quantum dot cellular automata systems during switching events. This is the energy that must eventually be dissipated as heat. The adiabaticity of a switching event is quantified using the adiabaticity parameter of Landau and Zener. For the logically reversible operations of WRITE or ERASE WITH COPY, the excess energy transferred to the system decreases exponentially with increasing adiabaticity. For the logically irreversible operation of ERASE WITHOUT COPY, considerable energy is transferred and so must be dissipated, in accordance with the Landauer Principle. The exponential decrease in energy dissipation with adiabaticity (e.g., switching time) distinguishes adiabatic quantum switching from the usual linear improvement in classical systems.

Image Processing, Machine Learning, and Deep Learning concepts were used to assist farmers. Our application includes features such as early detection of plant disease, which is accomplished through a variety of methods. Following evaluation, the results revealed that the Convolutional Neural Network performed better for plant disease detection with high accuracy. It also assists the farmer in forecasting the weather to determine the best time for agricultural activities such as harvesting and plucking. To prevent disease reoccurrence due to soil mineral loss, a crop specific fertiliser calculator is included, which can calculate the amount of urea, diammonium phosphate, and muriate of potash required for a given area. [ABSTRACT FROM AUTHOR]

We examine the impact of the intrinsic molecular reorganization energy on switching in two-state quantum-dot cellular automata cells. Switching a bit involves an electron transferring between charge centers within the molecule. This, in turn, causes the other atoms in the molecule to rearrange their positions in response. We capture this in a model that treats the electron motion quantum-mechanically but the motion of nuclei semiclassically. This results in a non-linear Hamiltonian for the electron system. Interaction with a thermal environment is included by solving the Lindblad equation for the time-dependent density matrix. The calculated response of a molecule to the local electric field shows hysteresis during switching when the sweep direction is reversed. The relaxation of neighboring nuclei increases the localization of the electron, which provides an intrinsic source of enhanced bistability and single-molecule memory. This comes at the cost of increased power dissipation. [ABSTRACT FROM AUTHOR]