Improved indoor environment through optimised ventilator and furniture positioning: A case of slum rehabilitation housing,Mumbai, India

This study optimized the ventilator and furniture location of a tenement unit in a low-income urban habitat to obtain maximum experiential indoor environmental quality (e-IEQ) over the breathing zone. Hypothetical interior layouts using a combination of the two design parameters of ventilator location and bed position were generated for optimizing the design layout. This layout could promote maximum indoor airflow and minimum indoor air temperature and contaminant concentration. In this study, an improved indoor environment is hypothesized to be attainable through improved natural ventilation and thermal performance in the occupied zones. A sequential methodology involving “parametric design modeling–computational simulation–multiobjective optimization–multicriteria decision making”-based framework was selected. Results exhibited that the currently designed tenement unit had a poor indoor environment, whereas the hypothesized iterated layout “optimized design layout, scenario 3 (ODL 3)” derived from the optimization and decision-making algorithm performed effectively in providing e-IEQ. An increase in experiential indoor air velocity by 0.2 m/s and a decrease in temperature by 2 °C were observed over the monitoring point in the ODL 3 considering the existing scenario. Therefore, this study can find a way toward the development of sustainable habitat design guidelines under upcoming slum redevelopment policies across the nation.     

Density-Dependence of Surface Transport in Tellurium-Enriched Nanograined Bulk Bi2Te3

Three-dimensional topological insulators (3D TI) exhibit conventional parabolic bulk bands and protected Dirac surface states. A thorough investigation of the different transport channels provided by the bulk and surface carriers using macroscopic samples may provide a path toward accessing superior surface transport properties. Bi₂Te₃ materials make promising 3D TI models; however, due to their complicated defect chemistry, these materials have a high number of charge carriers in the bulk that dominate the transport, even as nanograined structures. To partially control the bulk charge carrier density, herein the synthesis of Te-enriched Bi₂Te₃ nanoparticles is reported. The resulting nanoparticles are compacted into nanograined pellets of varying porosity to tailor the surface-to-volume ratio, thereby emphasizing the surface transport channels. The nanograined pellets are characterized by a combination of resistivity, Hall- and magneto-conductance measurements together with (THz) time-domain reflectivity measurements. Using the Hikami-Larkin-Nagaoka (HLN) model, a characteristic coherence length of ≈200 nm is reported that is considerably larger than the diameter of the nanograins. The different contributions from the bulk and surface carriers are disentangled by THz spectroscopy, thus emphasizing the dominant role of the surface carriers. The results strongly suggest that the surface transport carriers have overcome the hindrance imposed by nanoparticle boundaries.     

Wavelet enabled convolutional autoencoder based deep neural network for hyperspectral image denoising

Denoising of hyperspectral images (HSIs) is an important preprocessing step to enhance the performance of its analysis and interpretation. In reality, a remotely sensed HSI experiences disturbance from different sources and therefore gets affected by multiple noise types. However, most of the existing denoising methods concentrates in removal of a single noise type ignoring their mixed effect. Therefore, a method developed for a particular noise type doesn’t perform satisfactorily for other noise types. To address this limitation, a denoising method is proposed here, that effectively removes multiple frequently encountered noise patterns from HSI including their combinations. The proposed dual branch deep neural network based architecture works on wavelet transformed bands. The first branch of the network uses deep convolutional skip connected layers with residual learning for extracting local and global noise features. The second branch includes layered autoencoder together with subpixel upsampling that performs repeated convolution in each layer to extract prominent noise features from the image. Two hyperspectral datasets are used in the experiment to evaluate the performance of the proposed method for denoising of Gaussian, stripe and mixed noises. Experimental results demonstrate the superior performance of the proposed network compared to other state-of-the-art denoising methods with PSNR 36.74, SSIM 0.97 and overall accuracy 94.03?%.

     

Rethinking water policy in India with the scope of metering towards sustainable water future

Clean Technologies and Environmental Policy - The water sector in India has undergone several institutional and structural reforms through different policies and legislation changes over the past...