Sustainable Procurement in British Dairy Supply Chain

Purpose – Dairy industry has several negative environmental impacts while continuous decline of number of British farmers in the supply chain questions the overall sustainability of the UK dairy supply chain. The research aims to explore the promotion of sustainable development in the British dairy sector and its supply chain through specific objectives, which are: to identify the current penetration level of different sustainability practices on dairy farming and milk purchasing; to investigate drivers, barriers and benefits of implementing sustainability practices in dairy sector; to identify different supply chain types existing in dairy sector and their implications to sustainability performance. Design / methodology / approach – questionnaire survey was used to collect data from dairy producers and dairy processors. 43 and 53 valid questionnaires returned from dairy producers and dairy processors, respectively, were used in the analysis. Findings – Social sustainability requirements received highest penetration level in sustainable procurement practices, while GHG emission requirements received lowest level of penetration. The most important driver for processor implementing sustainable procurement practices is company’s reputation and brand image, barrier is economic reasons, benefit is the creation of competitive advantage. The research also identified two major types of SC structure operating in the British dairy sector, Type A (farmer – processor - customer) and Type D (farmer and processor –- customer). Type D SC is advantageous to implement sustainability practices and achieved high sustainability performance. Practical implications –Improving sustainability performance throughout dairy supply chain needs continuous financial inputs. It would be very helpful to establish dairy sustainability accreditation and labelling scheme. Originality / value – This work is the first research so far which examined the penetration level of 15 environmental and social sustainability practices in dairy farming and milk sourcing, also identified drivers, barriers and benefits of implementing these practices. Financial incentive, information transparency and lead firm pressure can affect the coupling / decoupling of primary and secondary agency role in dairy supply chain

Synaptic transistor with multiple biological functions based on metal-organic frameworks combined with the LIF model of a spiking neural network to recognize temporal information

Spiking neural networks (SNNs) have immense potential due to their utilization of synaptic plasticity and ability to take advantage of temporal correlation and low power consumption. The leaky integration and firing (LIF) model and spike-timing-dependent plasticity (STDP) are the fundamental components of SNNs. Here, a neural device is first demonstrated by zeolitic imidazolate frameworks (ZIFs) as an essential part of the synaptic transistor to simulate SNNs. Significantly, three kinds of typical functions between neurons, the memory function achieved through the hippocampus, synaptic weight regulation and membrane potential triggered by ion migration, are effectively described through short-term memory/long-term memory (STM/LTM), long-term depression/long-term potentiation (LTD/LTP) and LIF, respectively. Furthermore, the update rule of iteration weight in the backpropagation based on the time interval between presynaptic and postsynaptic pulses is extracted and fitted from the STDP. In addition, the postsynaptic currents of the channel directly connect to the very large scale integration (VLSI) implementation of the LIF mode that can convert high-frequency information into spare pulses based on the threshold of membrane potential. The leaky integrator block, firing/detector block and frequency adaptation block instantaneously release the accumulated voltage to form pulses. Finally, we recode the steady-state visual evoked potentials (SSVEPs) belonging to the electroencephalogram (EEG) with filter characteristics of LIF. SNNs deeply fused by synaptic transistors are designed to recognize the 40 different frequencies of EEG and improve accuracy to 95.1%. This work represents an advanced contribution to brain-like chips and promotes the systematization and diversification of artificial intelligence

Solid‐State Electrolyte Gate Transistor with Ion Doping for Biosignal Classification of Neuromorphic Computing

As the core component of an intelligent neuromorphic computer system, reliable synaptic devices process vast amounts of data with high computing speed and low energy consumption. In this work, the ion-doped eco-friendly solution-processed indium oxide (InOx)/aluminum oxide (AlOx) electrolyte gate transistors (EGTs) with typical and reliable synaptic behavior are proposed. The lithium ions doped into the AlOx solid-state layer to facilitate the generation of electrical double layers and doped into InOx to improve the stability of long-term potentiation/depression cyclic update and enhance the synaptic plasticity. Finally, an artificial neural network simulator is well designed to electrocardiogram signal recognition based on the Gmax/Gmin ratio and nonlinearity of weight update curve. According to the results, the device possesses tremendous potential for biosignal prediction and neural intervention. Moreover, for the first time, the recognition accuracy of the abnormality of the cardiovascular can reach over 94.8% obtained from the confusion matrix. Consequently, this research article presents a stable and robust neuromorphic device for biosignal recognition based on solid-state EGTs via the synaptic long-term plasticity.</p