Synthesis of Sulfur‐Based Biocompatible Nonionic Surfactants and Their Nano‐Vesicle Drug Delivery

Nonionic surfactants are capable of forming nano‐range vesicles upon self‐assembling in an aqueous medium. These vesicles are highly stable, low in toxicity, and cost‐effective. Owing to their ability to solubilize both hydrophilic and hydrophobic substances, they are of great interest for drug solubilization and delivery. This study describes the synthesis and characterization of two new nonionic surfactants and their screening for biocompatibility and drug loading potentials in nano‐scale niosomal vesicles. They were characterized through mass spectroscopy, ¹HNMR, and FT‐IR. Their critical micelle concentration (CMC) was investigated using UV–vis spectrophotometry. The biocompatibility study was carried out through blood hemolysis and in vitro cytotoxicity assays. The surfactants have very low CMC values, are highly hemo‐compatible, and were nontoxic when tested against a cell culture. They were able to form nano‐range niosomal vesicles with large variation in their size. Both new surfactants were able to encapsulate increased amounts of the drug, in this case clarithromycin. The chemical nature of the drug remained intact in the niosomal vesicles. The results suggest that these nonionic surfactants could be promising drug delivery vehicles.     

Porcine Small Intestinal Submucosa (SIS) as a Suitable Scaffold for the Creation of a Tissue-Engineered Urinary Conduit: Decellularization,Biomechanical and Biocompatibility Characterization Using New Approaches

Bladder cancer (BC) is among the most common malignancies in the world and a relevant cause of cancer mortality. BC is one of the most frequent causes for bladder removal through radical cystectomy, the gold-standard treatment for localized muscle-invasive and some cases of high-risk, non-muscle-invasive bladder cancer. In order to restore urinary functionality, an autologous intestinal segment has to be used to create a urinary diversion. However, several complications are associated with bowel-tract removal, affecting patients’ quality of life. The present study project aims to develop a bio-engineered material to simplify this surgical procedure, avoiding related surgical complications and improving patients’ quality of life. The main novelty of such a therapeutic approach is the decellularization of a porcine small intestinal submucosa (SIS) conduit to replace the autologous intestinal segment currently used as urinary diversion after radical cystectomy, while avoiding an immune rejection. Here, we performed a preliminary evaluation of this acellular product by developing a novel decellularization process based on an environmentally friendly, mild detergent, i.e., Tergitol, to replace the recently declared toxic Triton X-100. Treatment efficacy was evaluated through histology, DNA, hydroxyproline and elastin quantification, mechanical and insufflation tests, two-photon microscopy, FTIR analysis, and cytocompatibility tests. The optimized decellularization protocol is effective in removing cells, including DNA content, from the porcine SIS, while preserving the integrity of the extracellular matrix despite an increase in stiffness. An effective sterilization protocol was found, and cytocompatibility of treated SIS was demonstrated from day 1 to day 7, during which human fibroblasts were able to increase in number and strongly organize along tissue fibres. Taken together, this in vitro study suggests that SIS is a suitable candidate for use in urinary diversions in place of autologous intestinal segments, considering the optimal results of decellularization and cell proliferation. Further efforts should be undertaken in order to improve SIS conduit patency and impermeability to realize a future viable substitute.     

An adaptive and efficient buffer management scheme for resource-constrained delay tolerant networks

Provisioning buffer management mechanism is especially crucial in resource-constrained delay tolerant networks (DTNs) as maximum data delivery ratio with minimum overhead is expected in highly congested environments. However, most DTN protocols do not consider resource limitations (e.g., buffer, bandwidth) and hence, results in performance degradation. To strangle and mitigate the impact of frequent buffer overflows, this paper presents an adaptive and efficient buffer management scheme called size-aware drop (SAD) that strives to improve buffer utilization and avoid unnecessary message drops. To improve data delivery ratio, SAD exactly determines the requirement based on differential of newly arrived message(s) and available space. To vacate inevitable space from a congested buffer, SAD strives to avoid redundant message drops and deliberate to pick and discard most appropriate message(s) to minimize overhead. The performance of SAD is validated through extensive simulations in realistic environments (i.e., resource-constrained and congested) with different mobility models (i.e., Random Waypoint and disaster). Simulation results demonstrate the performance supremacy of SAD in terms of delivery probability and overhead ratio besides other metrics when compared to contemporary schemes based on Epidemic (DOA and DLA) and PRoPHET (SHLI and MOFO).     

Multi‐agent‐based sharing power economy for a smart community

In this paper, a multi‐agent‐based locally administrated power distribution hub (PDH) for social welfare is proposed that optimizes energy consumption, allocation, and management of battery energy storage systems (ESSs) for a smart community. Initially, formulation regarding optimum selection of a power storage system for a home (in terms of storage capacity) is presented. Afterwards, the concept of sharing economy is inducted in the community by demonstrating PDH. PDH is composed of multiple small‐scale battery ESSs (each owned by community users), which are connected together to form a unified‐ESS. Proposed PDH offers a localized switching mechanism that takes decision of whether to buy electricity from utility or use unified‐ESS. This decision is based on the price of electricity at ‘time of use’ and ‘state of charge’ of unified‐ESS. In response to power use or share, electricity bills are created for individual smart homes by incrementing or decrementing respective submeters. There is no buying or selling of power from PDH; there is power sharing with the concept of ‘no profit, no loss’. The objective of the proposed PDH is to limit the purchase of electricity on ‘high priced’ hours from the utility. This not only benefits the utility at crucial hours but also provides effective use of power at the demand side. The proposed multi‐agent system depicts the concept of sharing power economy within a community. Finally, the proposed model is analyzed analytically, considering on‐peak, off‐peak, and mid‐level (mid‐peak) prices of a real‐time price signal during 24 h of a day. Results clearly show vital financial benefits of ‘sharing power economy’ for end users and efficient use of power within the smart community. Copyright © 2017 John Wiley & Sons, Ltd.     

A hybrid lithium‐ion battery model for system‐level analyses

We describe an advanced lithium‐ion battery model for system‐level analyses such as electric vehicle fleet simulation or distributed energy storage applications. The model combines an empirical multi‐parameter model and an artificial neural network with particular emphasis on thermal effects such as battery internal heating. The model is fast and can accurately describe constant current charging and discharging of a battery cell at a variety of ambient temperatures. Comparison to a commonly used linear kilowatt‐hour counter battery model indicates that a linear model overestimates the usable capacity of a battery at low temperatures. We highlight the importance of including internal heating in a battery model at low temperatures, as more capacity is available when internal heating is taken into account. Copyright © 2016 John Wiley & Sons, Ltd.     

DOE applied to multi-response optimisation study on performance,combustion and emission characteristics of a VCR diesel engine

There are various parameters which may influence the IC engine performance. The trial and error method is a technique used to identify the best parameters. However, this method demands extensive experimental work and results in a great waste of time and resources. Thus, the design of experiments (DOEs) developed by Taguchi is employed. The multi-responses which need to be maximised are brake thermal efficiency, mechanical efficiency, cylinder pressure and cumulative heat release rate, whereas the responses which are to be minimised are specific fuel consumption, oxides of nitrogen and carbon monoxide. In the present study, an optimised DOE(L9) orthogonal array based on the Taguchi statistical method was formulated and a series of experiments were conducted under controlled supervision by considering three different injection operating pressures (IOPs) (200, 220 and 240 bar) and three different compression ratios (CRs) (16.5, 17.5 and 18.5). The main objective of this paper is to find the best suited IOP and CR values which obtain higher performance, better combustion and lower emissions.     

Integrated design of diesel hydrotreating processes

This work presents an integrated approach for the design of diesel hydrotreating processes employing a simulated annealing optimisation algorithm. The modelling of reactor, separation and heat recovery system for diesel hydrotreating processes is discussed, and a novel optimisation framework is developed for the design of complex refinery processes. A comparison with conventional approach to process design, i.e. sequential evolution of design, is given to illustrate the ability of proposed approach to obtain overall hydrotreating process designs with minimum total annualised costs. The proposed integrated approach takes into account the trade-offs between capital and operating costs, as well as interactions between the hydrotreater, distillation column, and the associated heat exchanger network.     

Strategies to Develop Warm Solutions for Real-Time Pump Scheduling for Water Distribution Systems

An optimal pump operation schedule that maintains satisfactory hydraulics conditions can generally reduce energy consumptions compared to the traditional trial and error based pump operation schedule. Linking an evolutionary based optimization algorithm with a hydraulic simulation model has gained attention for obtaining the optimal schedule. However, this technique requires significant computation time and thus has difficulty in real-time implementation. This paper presents several tactics to generate warm solutions that can be used in the initial population of the evolutionary algorithms to reduce the computation times. Strategies to generate warm solutions include the use of linear programming, surrogate model known as machine learning or meta-model, and historical pump schedule for similar demand pattern. Providing warm solutions from approximate methods or previous day’s results to stochastic search methods can improve solution convergence and offers significant computation time benefits. Results obtained from different strategies are compared.