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Abstract Fused deposition modeling 3D printing provides a cost-effective and streamlined method for producing electrochemical sensors, overcoming the challenges associated with material selection, complex fabrication processes, and reproducibility issues. This study introduces an innovative approach utilizing a dual-printer setup to simplify the manufacturing of sensor electrodes. A critical enhancement in this process is the surface modification with reduced graphene oxide (rGO), which not only improves the electrochemical characteristics but also induces a wrinkled structure on the 3D printed surface. These wrinkles significantly increase the surface area, directly boosting the electrode’s electrochemical performance. Comprehensive characterization of the electrode surfaces, both before and after rGO modification, demonstrates a substantial increase in sensitivity, with a fortyfold improvement observed in hydrogen peroxide (H₂O₂) amperometric measurements. This breakthrough paves the way for advanced applications in 3D printed electrochemical sensors. 

Central to the navigation of an ever-changing environment is the ability to form positive associations with places and conspecifics. The functions of location and social conditioned preferences are often studied independently, limiting our understanding of their interplay. Furthermore, a de-emphasis on natural functions of conditioned preferences has led to neurobiological interpretations separated from ecological context. By adopting a naturalistic and ethological perspective, we uncover complexities underlying the expression of conditioned preferences. Development of conditioned preferences is a combination of motivation, reward, associative learning, and context, including for social and spatial environments. Both social- and location-dependent reward-responsive behaviors and their conditioning rely on internal state-gating mechanisms that include neuroendocrine and hormone systems such as opioids, dopamine, testosterone, estradiol, and oxytocin. Such reinforced behavior emerges from mechanisms integrating past experience and current social and environmental conditions. Moreover, social context, environmental stimuli, and internal state gate and modulate motivation and learning via associative reward, shaping the conditioning process. We highlight research incorporating these concepts, focusing on the integration of social neuroendocrine mechanisms and behavioral conditioning. We explore three paradigms: 1) conditioned place preference, 2) conditioned social preference, and 3) social conditioned place preference. We highlight nonclassical species to emphasize the naturalistic applications of these conditioned preferences. To fully appreciate the complex integration of spatial and social information, future research must identify neural networks where endocrine systems exert influence on such behaviors. Such research promises to provide valuable insights into conditioned preferences within a broader naturalistic context. 

The NUMA architecture accommodates the hardware trend of an increasing number of CPU cores. It requires the coop- eration of memory allocators to achieve good performance for multithreaded applications. Unfortunately, existing allo- cators do not support NUMA architecture well. This paper presents a novel memory allocator – NUMAlloc , that is de- signed for the NUMA architecture. NUMAlloc is centered on a binding-based memory management. On top of it, NUMAl- loc proposes an “origin-aware memory management” to ensure the locality of memory allocations and deallocations, as well as a method called “incremental sharing” to balance the performance benefits and memory overhead of using transparent huge pages. According to our extensive evalua- tion, NUMAlloc hasthebestperformanceamongallevaluated allocators, running 15.7% faster than the second-best allo- cator (mimalloc), and 20.9% faster than the default Linux allocator with reasonable memory overhead. NUMAlloc is also scalable to 128 threads and is ready for deployment. 

The NUMA architecture accommodates the hardware trend of an increasing number of CPU cores. It requires the cooperation of memory allocators to achieve good performance for multithreaded applications. Unfortunately, existing allocators do not support NUMA architecture well. This paper presents a novel memory allocator – NUMAlloc, that is designed for the NUMA architecture. is centered on a binding-based memory management. On top of it, proposes an “origin-aware memory management” to ensure the locality of memory allocations and deallocations, as well as a method called “incremental sharing” to balance the performance benefits and memory overhead of using transparent huge pages. According to our extensive evaluation, NUMAlloc has the best performance among all evaluated allocators, running 15.7% faster than the second-best allocator (mimalloc), and 20.9% faster than the default Linux allocator with reasonable memory overhead. NUMAlloc is also scalable to 128 threads and is ready for deployment.