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AbstractEmergence of antibiotic‐resistance pathogens has caused serious health issues and if the current trend is to continue, treatment of the infection will become complicated and even unsuccessful due to new antimicrobial resistance (AMR). Therefore, there is a global drive to identify new methods to treat infection and develop better antibacterial materials and therapy. Although new and more potent antibiotics have aided the fight against microbes, they only offer a temporary solution because future bacteria strains may become resistant to these antibiotics and drugs. Recently, application of non‐biological methods such as, electrical currents and photothermal/dynamic therapies to kill bacteria, reveal new approaches to design antimicrobial biomaterials, as complications stemming from drug‐resistant bacteria can be obviated. Furthermore, recent research has focused on mimicking the surface patterns on plants and insects such as lotus leaves and dragonfly wings. Bio‐inspired micro/nano patterns have been replicated on a variety of biomaterials to improve the bacterial resistance and other properties with good success. This is an exciting research area with immense practical and clinical potentials. In this review, recent advances in the application of chemical/biological approaches to combat bacterial infection and AMR are summarized and the related mechanisms are discussed.

The accurate simulation of a photovoltaic solar cell requires the precise determination of modelling parameters specific to the device under study. For the case of the single diode model, five parameters must be determined; Iph, I0, Rs, Rsh, and n. Generally speaking these values may be calculated either by analytical or numerical methods. Although analytical approaches are simple and fast to carry out, the assumptions and simplifications they introduce in order to deal with the non-linear characteristics of a solar cell may result in modelling inaccuracies. In this study a new approach is presented to calculate all five parameter values numerically minimising assumptions and simplifications. The method proposed is based on solving the single diode current–voltage equation expressed using the Lambert W-function at five experimentally obtained points along the current–voltage curve. To solve the system of non-linear equations, the multi-dimensional variant of the Newton–Raphson method is applied. All necessary first order partial differential equations are provided in closed form. Experimental validation of the proposed method revealed an improvement in modelling accuracy over one commonly used analytical approach. Furthermore, using TRNSYS software to simulate the annual energy output we show that modelling photovoltaic systems with small variations in solar cell parameters can result in non-trivial variations in annual energy output highlighting the importance of their calculation.

A fuzzy controller has been designed and implemented to enhance the overall power system stability. To achieve good damping characteristics over a wide range of operating conditions, speed deviation and acceleration of a synchronous generator are chosen as the input signals to the fuzzy controller. The stabilizing signal is determined according to the nonlinear fuzzy membership functions depending on the speed and acceleration states of the generator. The design and digital simulation studies are carried out on a one-machine infinite bus power system. The design is implemented on a 5 kVA generator in the Power System Laboratory with an IBM-486 computer associated with A/D and D/A converter acting as the real time controller. The implementation study is carried out on both single-machine and two-machine infinite bus power systems respectively. Studies of simulation and implementation show that the proposed fuzzy controller is very effective. >

Abstract Discounting the future is essential to inform long-term decisions, but the future of humanity is being put in jeopardy by using the same discount rate for all capital types. Different types of capital assets (built, human, social, natural) have inherently different characteristics and contribute differently to the production of all goods and services. They will behave and depreciate differently and will thus require different discount rates and different approaches to discounting. Here, we estimate the net present value (NPV) of global ES recognizing that ecosystem services are the product of the interaction of the four different types of capital that each have different characteristics. We combine a range of different discount rates for each of the 4 types of capital according to their relative contributions to the production and value of each of 17 global ecosystem services. We estimate that the NPV of global ES ranges from $5.7 to $9.1 × 1015 (quadrillion 2011$USD). For comparison, the NPV of global GDP estimated in the same way would be about $2.9 to $4.8 × 1015. This more nuanced approach to discounting can improve information for long-term project appraisal and decision making and help build a more sustainable and desirable future.

Abstract A new converter consisting of an elastically mounted circular cylinder and a free-to-rate pentagram impeller is proposed to harness hydrokinetic energy from water currents. The vibration energy of the cylinder and the rotation energy of the impeller are harvested simultaneously. The two-way fluid-structure interaction simulations are employed to investigate the vibration and rotation response of the converter. The simulated Reynolds number range is 14,000

Recent innovations in Information and Communication Technologies (ICT) provide new opportunities and challenges for integration of distributed energy resources (DERs) into the energy supply system as active market players. By increasing integration of DERs, novel market platform should be designed for these new market players. The designed electricity market should maximize market surplus for consumers and suppliers and provide correct incentives for them to join the market and follow market rules. In this paper, a feeder-based market is proposed for local energy trading among prosumers and consumers in the distribution system. In this market, market players are allowed to share energy with other players in the local market and with neighborhood areas. A Two-StepMarket Clearing (2SMC) mechanism is proposed for market clearing, in which in the first step, each local market is cleared independently to determine the market clearing price and in the second step, players can trade energy with neighborhood areas. In comparison to a centralized market, the proposed method is scalable and reduces computation overheads, because instead of clearing market for a large number of players, the market is cleared for a fewer number of players. Also, by applying distributed method and Lagrangian multipliers for market clearing, there is no need for a central computation centre and private information of market players. Case studies demonstrate the efficiency and effectiveness of the proposed market clearing method in increasing social welfare and reducing computation time. 6 pages

Abstract This paper is a survey of research on how oil prices affect stock returns. In the last couple of decades there has been an upsurge in such research, suggesting that a stock take is timely. The sheer volume of research on the interaction between oil markets and stock markets has meant that we have lost track of the key findings from the literature. The danger, in the absence of a stock take, is that we will produce a large volume of studies on how oil prices interact with stock returns without them having any real impact on the profession. This paper is a response to this concern. It highlights the key themes researched, main findings and, equally importantly, identifies key challenges and suggests an agenda for future research on the interaction between oil prices and stock returns and oil prices and the financial sector more generally.

The advancements in distributed generation (DG) technologies such as solar panels have led to a widespread integration of renewable power generation in modern power systems. However, the intermittent nature of renewable energy poses new challenges to the network operational planning with underlying uncertainties. This paper proposes a novel probabilistic scheme for renewable solar power generation forecasting by addressing data and model parameter uncertainties using Bayesian bidirectional long short-term memory (BiLSTM) neural networks, while handling the high dimensionality in weight parameters using variational auto-encoders (VAE). The forecasting performance of the proposed method is evaluated using various deterministic and probabilistic evaluation metrics such as root-mean square error (RMSE), Pinball loss, etc. Furthermore, reconstruction error and computational time are also monitored to evaluate the dimensionality reduction using the VAE component. When compared with benchmark methods, the proposed method leads to significant improvements in weight reduction, i.e., from 76,4224 to 2,022 number of weight parameters, quantifying to 97.35% improvement in weight parameters reduction and 37.93% improvement in computational time for 6 months of solar power generation data.