Multi-split configuration design for fluid-based thermal management systems

High power density systems require efficient cooling to maintain their thermal performance. Despite this, as systems get larger and more complex, human practice and insight may not suffice to determine the desired thermal management system designs. To this end, a framework for automatic architecture exploration is presented in this article for a class of single-phase, multi-split cooling systems. For this class of systems, heat generation devices are clustered based on their spatial information, and flow-split are added only when required and at the location of heat devices. To generate different architectures, candidate architectures are represented as graphs. From these graphs, dynamic physics models are created automatically using a graph-based thermal modeling framework. Then, an optimal fluid flow distribution problem is solved by addressing temperature constraints in the presence of exogenous heat loads to achieve optimal performance. The focus in this work is on the design of general multi-split heat management systems. The architectures discussed here can be used for various applications in the domain of configuration design. The multi-split algorithm can produce configurations where splitting can occur at any of the vertices. The results presented include 3 categories of cases and are discussed in detail.Comment: 11 pages, 18 figure

Reactivity of neodymium carriers in deep sea sediments: Implications for boundary exchange and paleoceanography

The dissolved neodymium (Nd) isotopic distribution in the deep oceans is determined by continental weathering inputs, water mass advection, and boundary exchange between particulate and dissolved fractions. Reconstructions of past Nd isotopic variability may therefore provide evidence on temporal changes in continental weathering inputs and/or ocean circulation patterns over a range of timescales. However, such an approach is limited by uncertainty in the mechanisms and importance of the boundary exchange process, and the challenge in reliably recovering past seawater Nd isotopic composition (εNd) from deep sea sediments. This study addresses these questions by investigating the processes involved in particulate–solution interactions and their impact on Nd isotopes. A better understanding of boundary exchange also has wider implications for the oceanic cycling and budgets of other particle-reactive elements. Sequential acid-reductive leaching experiments at pH ∼2–5 on deep sea sediments from the western Indian Ocean enable us to investigate natural boundary exchange processes over a timescale appropriate to laboratory experiments. We provide evidence that both the dissolution of solid phases and exchange processes influence the εNd of leachates, which suggests that both processes may contribute to boundary exchange. We use major element and rare earth element (REE) data to investigate the pools of Nd that are accessed and demonstrate that sediment leachate εNd values cannot always be explained by admixture between an authigenic component and the bulk detrital component. For example, in core WIND 24B, acid-reductive leaching generates εNd values between −11 and −6 as a function of solution/solid ratios and leaching times, whereas the authigenic components have εNd ≈ −11 and the bulk detrital component has εNd ≈ −15. We infer that leaching in the Mascarene Basin accesses authigenic components and a minor radiogenic volcanic component that is more reactive than Madagascan-derived clays. The preferential mobilisation of such a minor component demonstrates that the Nd released by boundary exchange could often have a significantly different εNd composition than the bulk detrital sediment. These experiments further demonstrate certain limitations on the use of acid-reductive leaching to extract the εNd composition of the authigenic fraction of bulk deep sea sediments. For example, the detrital component may contain a reactive fraction which is also acid-extractible, while the incongruent nature of this dissolution suggests that it is often inappropriate to use the bulk detrital sediment elemental chemistry and/or εNd composition when assessing possible detrital contamination of leachates. Based on the highly systematic controls observed, and evidence from REE patterns on the phases extracted, we suggest two approaches that lead to the most reliable extraction of the authigenic εNd component and good agreement with foraminiferal-based approaches; either (i) leaching of sediments without a prior decarbonation step, or (ii) the use of short leaching times and low solution/solid ratios throughout

Auditory training changes temporal lobe connectivity in Wernicke's aphasia: a randomised trial

Introduction Aphasia is one of the most disabling sequelae after stroke, occurring in 25%–40% of stroke survivors. However, there remains a lack of good evidence for the efficacy or mechanisms of speech comprehension rehabilitation. Trial Design This within-subjects trial tested two concurrent interventions in 20 patients with chronic aphasia with speech comprehension impairment following left hemisphere stroke: (1) phonological training using ‘Earobics’ software and (2) a pharmacological intervention using donepezil, an acetylcholinesterase inhibitor. Donepezil was tested in a double-blind, placebo-controlled, cross-over design using block randomisation with bias minimisation. Methods The primary outcome measure was speech comprehension score on the comprehensive aphasia test. Magnetoencephalography (MEG) with an established index of auditory perception, the mismatch negativity response, tested whether the therapies altered effective connectivity at the lower (primary) or higher (secondary) level of the auditory network. Results Phonological training improved speech comprehension abilities and was particularly effective for patients with severe deficits. No major adverse effects of donepezil were observed, but it had an unpredicted negative effect on speech comprehension. The MEG analysis demonstrated that phonological training increased synaptic gain in the left superior temporal gyrus (STG). Patients with more severe speech comprehension impairments also showed strengthening of bidirectional connections between the left and right STG. Conclusions Phonological training resulted in a small but significant improvement in speech comprehension, whereas donepezil had a negative effect. The connectivity results indicated that training reshaped higher order phonological representations in the left STG and (in more severe patients) induced stronger interhemispheric transfer of information between higher levels of auditory cortex

Non-monotonic variation with salt concentration of the second virial coefficient in protein solutions

The osmotic virial coefficient $B_2$ of globular protein solutions is calculated as a function of added salt concentration at fixed pH by computer simulations of the ``primitive model''. The salt and counter-ions as well as a discrete charge pattern on the protein surface are explicitly incorporated. For parameters roughly corresponding to lysozyme, we find that $B_2$ first decreases with added salt concentration up to a threshold concentration, then increases to a maximum, and then decreases again upon further raising the ionic strength. Our studies demonstrate that the existence of a discrete charge pattern on the protein surface profoundly influences the effective interactions and that non-linear Poisson Boltzmann and Derjaguin-Landau-Verwey-Overbeek (DLVO) theory fail for large ionic strength. The observed non-monotonicity of $B_2$ is compared to experiments. Implications for protein crystallization are discussed.Comment: 43 pages, including 17 figure

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented

Investigating the human—environment relationship of early intensive salt production: a case study from the Upper Seille Valley, Lorraine, northeast France

This paper presents the latest findings of multi-disciplinary research into the human—environment relationship of intensive Iron Age salt production in the Upper Seille Valley, Lorraine, northeast France. Investigations focus on the early Iron Age workshop “La Digue” (~ 625–500 cal BCE; Hallstatt D1–2), where high-resolution borehole sampling has been coupled with conventional excavation and geophysical surveying to establish direct linkages between intensive occupation and the alluvial environment of this site. Detailed insights into human—river interactions have been identified, enhancing current understanding of the environmental context and impact of this important early industry. The workshop's palaeogeographic setting has been reconstructed and new evidence for briquetage disposal practices has been identified, confirming that a close relationship existed between salt-making and the local hydrological regime. A large volume of briquetage waste (broken clay-fired salt-making equipment, ash and charcoal) was dumped into the river at La Digue, causing rapid and deliberate channel blockage, increasing the distance between the workshop and the river. This probably contributed to a localised increase in channel mobility and/or flooding whilst the workshop was active, producing challenging conditions for salt production. The workshop was abandoned following an intense flood event in ~ 500 cal BCE, coinciding with a major hydrological shift towards wetter floodplain conditions, likely arising from a combination of natural and anthropogenic factors. This study demonstrates the importance of understanding the environmental context of salt production and the roles of water management and briquetage disposal practices, which have been largely overlooked at other intensive salt making sites that employed the “briquetage technique”

The Tripartite Motif Protein MADD-2 Functions with the Receptor UNC-40 (DCC) in Netrin-Mediated Axon Attraction and Branching

Neurons innervate multiple targets by sprouting axon branches from a primary axon shaft. We show here that the ventral guidance factor unc-6 (Netrin), its receptor unc-40 (DCC), and the gene madd-2 stimulate ventral axon branching in C. elegans chemosensory and mechanosensory neurons. madd-2 also promotes attractive axon guidance to UNC-6 and assists unc-6- and unc-40-dependent ventral recruitment of the actin regulator MIG-10 in nascent axons. MADD-2 is a tripartite motif protein related to MID-1, the causative gene for the human developmental disorder Opitz syndrome. MADD-2 and UNC-40 proteins preferentially localize to a ventral axon branch that requires their function; genetic results indicate that MADD-2 potentiates UNC-40 activity. Our results identify MADD-2 as an UNC-40 cofactor in axon attraction and branching, paralleling the role of UNC-5 in repulsion, and provide evidence that targeting of a guidance factor to specific axonal branches can confer differential responsiveness to guidance cues.National Institutes of Health (U.S.) (Grant number GM0680678

A classification system for teachers’ motivational behaviors recommended in self-determination theory interventions

Teachers’ behavior is a key factor that influences students’ motivation. Many theoretical models have tried to explain this influence, with one of the most thoroughly researched being self-determination theory (SDT). We used a Delphi method to create a classification of teacher behaviors consistent with SDT. This is useful because SDT-based interventions have been widely used to improve educational outcomes. However, these interventions contain many components. Reliably classifying and labeling those components is essential for implementation, reproducibility, and evidence synthesis. We used an international expert panel (N = 34) to develop this classification system. We started by identifying behaviors from existing literature, then refined labels, descriptions, and examples using the Delphi panel’s input. Next, the panel of experts iteratively rated the relevance of each behavior to SDT, the psychological need that each behavior influenced, and its likely effect on motivation. To create a mutually exclusive and collectively exhaustive list of behaviors, experts nominated overlapping behaviors that were redundant, and suggested new ones missing from the classification. After three rounds, the expert panel agreed upon 57 teacher motivational behaviors (TMBs) that were consistent with SDT. For most behaviors (77%), experts reached consensus on both the most relevant psychological need and influence on motivation. Our classification system provides a comprehensive list of TMBs and consistent terminology in how those behaviors are labeled. Researchers and practitioners designing interventions could use these behaviors to design interventions, to reproduce interventions, to assess whether these behaviors moderate intervention effects, and could focus new research on areas where experts disagreed. (PsycInfo Database Record (c) 2023 APA, all rights reserved

A classification system for teachers’ motivational behaviors recommended in self-determination theory interventions.

Teachers’ behavior is a key factor that influences students’ motivation. Many theoretical models have tried to explain this influence, with one of the most thoroughly researched being self-determination theory (SDT). We used a Delphi method to create a classification of teacher behaviors consistent with SDT. This is useful because SDT-based interventions have been widely used to improve educational outcomes. However, these interventions contain many components. Reliably classifying and labeling those components is essential for implementation, reproducibility, and evidence synthesis.We used an international expert panel (N = 34) to develop this classification system. We started by identifying behaviors from existing literature, then refined labels, descriptions, and examples using the Delphi panel’s input. Next, the panel of experts iteratively rated the relevance of each behavior to SDT, the psychological need that each behavior influenced, and its likely effect on motivation. To create a mutually exclusive and collectively exhaustive list of behaviors, experts nominated overlapping behaviors that were redundant, and suggested new ones missing from the classification. After three rounds, the expert panel agreed upon 57 teacher motivational behaviors (TMBs) that were consistent with SDT. For most behaviors (77%), experts reached consensus on both the most relevant psychological need and influence on motivation. Our classification system provides a comprehensive list of TMBs and consistent terminology in how those behaviors are labeled. Researchers and practitioners designing interventions could use these behaviors to design interventions, to reproduce interventions, to assess whether these behaviors moderate intervention effects, and could focus new research on areas where experts disagreed