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Carcass and meat composition and eating quality of m. longissimus lumborum (LI) and leg joints were compared in entire male (ram), castrated male (wether) and female (ewe) Dorset Down-cross and Suffolk-cross lambs at about 20 weeks old. Rams grew faster than wethers and ewes particularly in the earlier maturing Suffolk crosses. Carcass weights ranged from 13 to 24 kg and at the mean carcass weight of 17-7 kg ram carcasses yielded larger shoulder joints. Ram carcasses were assessed visually leaner than those from the other sexes with similar conformation scores. Dorset Down carcasses tended to be fatter than Suffolk carcasses and the leanest carcasses were from Suffolk rams. Intramuscular fatness in LI was similar in all sexes. Intramuscular collagen contents were higher in ram LI than in other sexes. There were no differences in protein content, pH or colour of raw or roast LI. In assessments of eating quality of LI by triangle tests, male and female assessors differentiated twin rams and wethers equally. Category scaling showed LI from ewes to be slightly tougher than those from rams and wethers. Consumer evaluation of leg joints did not detect any differences in odour due to sex and ram meat was assessed better than that from wethers or ewes.

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As data processing volume increases, the limitations of traditional computers and the need for more efficient computing methods become evident. Neuromorphic computing mimics the brain's low‐power and high‐speed computations, making it crucial in the era of big data and artificial intelligence. One significant development in this field is the memristor, a device that exhibits neuromorphic tendencies. The performance of memristive devices and circuits relies on the materials used, with graphene being a promising candidate due to its unique properties. Researchers are investigating graphene‐based memristors for large‐scale, sustainable fabrication. Herein, progress in the development of graphene‐based memristive neuromorphic devices and circuits is highlighted. Graphene and its common fabrication methods are discussed. The fabrication and production of graphene‐based memristive devices are reviewed and comparisons are provided among graphene‐ and nongraphene‐based memristive devices. Next, a detailed synthesis of the devices utilizing graphene‐based memristors is provided to implement the basic building blocks of neuromorphic architectures, that is, synapses, and neurons. This is followed by reviewing studies building graphene memristive spiking neural networks (SNNs). Finally, insights on the prospects of graphene‐based neuromorphic memristive systems including their device‐ and network‐level challenges and opportunities are given.