Topology optimisation of lattice telecommunication tower and performance-based design considering wind and ice loads

With increasing demand of infrastructure to support power transmission and telecommunication systems, the need of erecting more towers has also been rising significantly. For many years, these towers were designed by using a conservative approach and the opportunities lying in the design optimisation of the towers were not leveraged. This paper presents the application of structural topology optimisation to lattice self-supported telecommunication towers in developing an improved solution in terms of weight-to-stiffness ratio. 2D and 3D topology optimisation studies were performed with highly optimised bracing systems reducing the amount of steel material used, thus its carbon footprint. The new exoskeleton structure is representing a lattice tower composed of ‘high-waisted’ bracing type and elliptical hollow sections (EHS). Comparative modal analyses demonstrated the structural performance of the optimised tower models. In addition, a research-led design was carried out for optimising the geometric cross-sectional properties of the optimised lattice tower subjected to quasi-static analysis followed by regression analysis. The cross-sectional parameters were progressively changed; explicitly the diameter and thickness of the members. The performance-based analysis and design of a topologically optimised lattice tower present alternatives to onerous approaches such as wind tunnel testing or finite element modelling. The results were further analysed to understand their viability in different loading design cases and the effect of cross-sections. Conclusions highlighted the benefits gained by introducing the structural topology optimisation process in the design of slender support structures

Understanding Melanocyte Stem Cells for Disease Modeling and Regenerative Medicine Applications

Melanocytes in the skin play an indispensable role in the pigmentation of skin and its appendages. It is well known that the embryonic origin of melanocytes is neural crest cells. In adult skin, functional melanocytes are continuously repopulated by the differentiation of melanocyte stem cells (McSCs) residing in the epidermis of the skin. Many preceding studies have led to significant discoveries regarding the cellular and molecular characteristics of this unique stem cell population. The alteration of McSCs has been also implicated in several skin abnormalities and disease conditions. To date, our knowledge of McSCs largely comes from studying the stem cell niche of mouse hair follicles. Suggested by several anatomical differences between mouse and human skin, there could be distinct features associated with mouse and human McSCs as well as their niches in the skin. Recent advances in human pluripotent stem cell (hPSC) research have provided us with useful tools to potentially acquire a substantial amount of human McSCs and functional melanocytes for research and regenerative medicine applications. This review highlights recent studies and progress involved in understanding the development of cutaneous melanocytes and the regulation of McSCs

Identification of drought in Dhalai river watershed using MCDM and ANN Models

An innovative approach for drought identification is developed using Multi-Criteria Decision-Making (MCDM) and Artificial Neural Network (ANN) model from surveyed drought parameters data around the Dhalai river watershed in Tripura hinterlands, India. Total eight drought parameters i.e. precipitation, soil moisture, evapotranspiration, vegetation canopy, cropping pattern, temperature, cultivated land, and groundwater level were obtained from experts, literature and cultivators survey. Then, the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used for weighting of parameters and Drought Index Identification (DII). Field data of weighted parameters in the meso scale Dhalai river watershed were collected and used to train the ANN model. The trained ANN model has been tested in the same watershed for its calibration. Results indicate that the Limited Memory - Quasi Newton algorithm was better than the commonly used training method. Based on obtained results from ANN model drought index 0.30 to 0.75 were generated for present study area. Overall analysis revealed that, with appropriate training, the ANN model could be used in the areas where the model is calibrated, or other areas where range of input parameters is similar to the calibrated region.by Sainath Aher, Sambhaji Shinde, Shantamoy Guha and Mrinmoy Majumde