Synthesis and characterization of novel curcumin based polyurethanes varying diisocyanates structure

In this research work, novel polyurethanes (PUs) based on blends of curcumin/1,4-butane diol (BDO) by varying the structure of diisocyanates were prepared following step growth polymerization. Structural study of blends and various diisocyanates based PU through Fourier Transform Infrared (FTIR) spectroscopy confirmed the incorporation of curcumin into the backbone of the PU. The scanning electron microscopic (SEM) study confirmed the well dispersion of incorporated curcumin and homogeneity of surface of synthesized samples. The SEM results also indicated that surface morphology of synthesized samples much dependent on diisocynates structure. Moreover SEM images inferred that phase separation is more pronounced in aromatic diisocyanate based PU. The anti-bacterial and anti-fungal tests were performed against different strains in order to determine the biocompatibility of the curcumin based PU. The antimicrobial activity results revealed that the material having aromatic diisocyanate are more biocompatible than the aliphatic diisocyanates in the PU structure. On the whole, this work is actually a step towards the generation of novel biocompatible materials preferably useful for biomedical applications.     

High-efficiency remediation of cadmium (Cd2+) from aqueous solution using poultry manure– and farmyard manure–derived biochars

Farmyard manure (FYM-BC) and poultry manure (PM-BC) derived biochars were applied as adsorbents to remove Cd²⁺ from water. Results indicated that PM-BC was a more efficient adsorbent than FYM-BC at all experimental conditions. Maximum Cd²⁺ adsorption was observed at pH 4, temperature 318 K and contact time 1 h, regardless of biochar type. The Langmuir model predicted maximum adsorption capacity of 90.09 mg g^?1 for PM-BC. The data fitting to pseudo-second-order model proposed chemisorption of Cd²⁺ onto biochars. Thermodynamics indicated that adsorption was spontaneous and endothermic. Post-adsorption analysis provided evidences of strong chemical interactions between biochars’ functional groups and Cd²⁺ ions.     

Role of quaternary ammonium salts in improving the fastness properties of anionic dyes on cellulose fibres

The object of this study was to review the developments taking place during 1990–2005 regarding the use of quaternary ammonium salts as dye fixing agents for improving the fastness properties of anionic dyes on cellulose fibres. As far as fastness properties are concerned, this review is restricted only to fastness to light, washing and water treatments.     

Essential oil composition of commercial black tea (Camellia sinensis)

The essential oil components of different tea brands were investigated by gas chromatography. The oil yields of dried tea samples were ranged from 0.09% to 0.63%. Twenty-five compounds from Supreme and Lipton Yellow Label tea brands representing 98.0% and 88.0% of the Camellia sinensis oil were identified, respectively. The main ones were β-pinene (51.2%) and α-pinene (30.2%). Nineteen components from Tapal tea brand representing 76.7% of the C. sinensis oil were determined with high contents of muurol-5-en-4-a-ol (10.5%) and muurol-5-en-4-b-ol (31.3%). Fifteen components from Deer and Diana tea brands were identified, accounting for 83.3% and 78.2% of the oil containing α-cadinol and β-pinene. Seventeen components from non-branded teas were determined with high contents of muurol-5-en-4-a-ol and muurol-5-en-4-b-ol. Twenty-one compounds from non-branded Bangladeshi Shezan and Indian teas were also identified. All oils consisted of monoterpenic hydrocarbons, oxygenated monoterpenes and sesquiterpenes.     

An Energy Efficient Message Scheduling Algorithm Considering Node Failure in IoT Environment

Advancements in the area of computing and the networking gave birth to a new concept Internet of Things (IoT). This can be thought as “network of future” connecting diverse objects/things together. The focus is on scheduling the messages in an IoT environment where things/sensors are clustered into IoT subgroups, each subgroup has a message broker that delivers the messages originated from the group to the ultimate receiver of the sensed data. The message scheduler works at the broker level to decide which message to be transmitted first. This scheduling improves the overall IoT system efficiency. Furthermore to keep the flow of services provided by these things/sensors continuous and non-disruptive, the optimal tackling of the faulty or failed nodes has become the salient feature of the proposed scheduling algorithm. The faults or failures identified on time help to initiate recovery or replacement procedures. To find the right level of replacement nodes deployed for the sensor network, we consider the energy a scarce resource and the cost of deployment of the backup nodes as per failure of the node occurring in the underlying environment. In this work we propose an energy efficient recovery and backup node selection for IoT systems followed with energy efficient message scheduling. Simulation results show the effectiveness and efficiency of the proposed message scheduling considering the node failure with recovery and replacement technique.     

Current scenario and potential of biodiesel production from waste cooking oil in Pakistan: An overview

Biodiesel utilization has been rapidly growing worldwide as the prime alternative to petrodiesel due to a global rise in diesel fuel demand along with hazardous emissions during its thermochemical conversion. Although, several debatable issues including feedstock availability and price, fuel and food competition, changes in land use and greenhouse gas emission have been raised by using edible as well as inedible feedstocks for the production of biodiesel. However, non-crop feedstocks could be a promising alternative. In this article, waste cooking oils have been recommended as a suitable option for biodiesel production bearing in mind the current national situation. The important factors such as the quantity of waste cooking oil produced, crude oil and vegetable oil import expenses, high-speed diesel imports, waste management issues and environmental hazards are considered. Moreover, process simulation and operating cost evaluation of an acid catalyzed biodiesel production unit are also conducted. The simulation results show that the production cost of waste cooking oil-based biodiesel is about 0.66USD·L^-1. We believe that the present overview would open new pathways and ideas for the development of biofuels from waste to energy approach in Pakistan.     

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear

Malaria is a serious worldwide disease, caused by a bite of a female Anopheles mosquito. The parasite transferred into complex life round in which it is grown and reproduces into the human body. The detection and recognition of Plasmodium species are possible and efficient through a process called staining (Giemsa). The staining process slightly colorizes the red blood cells (RBCs) but highlights Plasmodium parasites, white blood cells and artifacts. Giemsa stains nuclei, chromatin in blue tone and RBCs in pink color. It has been reported in numerous studies that manual microscopy is not a trustworthy screening technique when performed by nonexperts. Malaria parasites host in RBCs when it enters the bloodstream. This paper presents segmentation of Plasmodium parasite from the thin blood smear points on region growing and dynamic convolution based filtering algorithm. After segmentation, malaria parasite classified into four Plasmodium species: Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, and Plasmodium malaria. The random forest and K‐nearest neighbor are used for classification base on local binary pattern and hue saturation value features. The sensitivity for malaria parasitemia (MP) is 96.75% on training and testing of the proposed approach while specificity is 94.59%. Beside these, the comparisons of the two features are added to the proposed work for classification having sensitivity is 83.60% while having specificity is 94.90% through random forest classifier based on local binary pattern feature.