Multihop routing with static and distributed clustering in WSNs

Wireless Networks - Nowadays, Wireless Sensor Networks are one of the fundamental infrastructures for IoT technology. Although WSN has been researched for a decade, providing energy efficiency for...     

Poly(lactide)/cellulose nanocrystal nanocomposites by high-shear mixing

There is currently considerable interest in developing stiff, strong, tough, and heat resistant poly(lactide) (PLA) based materials with improved melt elasticity in response to the increasing demand for sustainable plastics. However, simultaneous optimization of stiffness, strength, and toughness is a challenge for any material, and commercial PLA is well-known to be inherently brittle and temperature-sensitive and to show poor melt elasticity. In this study, we report that high-shear mixing with cellulose nanocrystals (CNC) leads to significant improvements in the toughness, heat resistance, and melt elasticity of PLA while further enhancing its already outstanding room temperature stiffness and strength. This is evidenced by (i) one-fold increase in the elastic modulus (6.48 GPa), (ii) 43% increase in the tensile strength (87.1 MPa), (iii) one-fold increase in the strain at break (∼6%), (iv) two-fold increase in the impact strength (44.2 kJ/m²), (v) 113-fold increase in the storage modulus at 90°C (787.8 MPa), and (vi) 10³-fold increase in the melt elasticity at 190°C and 1 rad/s (∼10⁵ Pa) via the addition of 30 wt% CNC. It is hence possible to produce industrially viable, stiff, strong, tough, and heat resistant green materials with improved melt elasticity through high-shear mixing.     

Strategies To Design Selective Histone Deacetylase Inhibitors

This review classifies drug-design strategies successfully implemented in the development of histone deacetylase (HDAC) inhibitors, which have many applications including cancer treatment. Our focus is on especially demanded selective HDAC inhibitors and their structure-activity relationships in relation to corresponding protein structures. The main part of the paper is divided into six subsections each narrating how optimization of one of six structural features can influence inhibitor selectivity. It starts with the impact of the zinc binding group on selectivity, continues with the optimization of the linker placed in the substrate binding tunnel as well as the adjustment of the cap group interacting with the surface of the protein, and ends with the addition of groups targeting class-specific sub-pockets: the side-pocket-, lower-pocket- and foot-pocket-targeting groups. The review is rounded off with a conclusion and an outlook on the future of HDAC inhibitor design.     

Prediction of fish quality level with machine learning

In this study, sea bream, sea bass, anchovy and trout were captured and recorded using a digital camera during refrigerated storage for 7 days. In addition, their total viable counts (TVC) were determined on a daily basis. Based on the TVC, each fish was classified as ‘fresh’ when it was <5 log cfu per g, and as ‘not fresh’ when it was >7 log cfu per g. They were uploaded on a web-based machine learning software called Teachable Machine (TM), which was trained about the pupils and heads of the fish. In addition, images of each species from different angles were uploaded to the software in order to ensure the recognition of fish species by TM. The data of the study indicated that the TM was able to distinguish fish species with high accuracy rates and achieved over 86% success in estimating the freshness of the fish species tested.     

The Nucleation Effect of PbSe Additive on Bi2Sr2CaCu2Oδ Glass Ceramics

Glass and Ceramics - The crystallisation kinetics and effects of doping on Bi2Sr2CaCu2Oδ (BSCCO) glass ceramic system with 0.0%, 0.1%, 0.3% and 0.5% PbSe were investigated in this study....