Performance analysis and working fluid selection for geothermal energy-powered organic Rankine-vapor compression air conditioning

AbstractBackgroundTo utilize geothermal energy from hot springs, an organic Rankine cycle/vapor compression cycle (ORC/VCC) system was employed for air conditioning and a thermodynamic model was developed.MethodsSix working fluids, R123, R134a, R245fa, R600a, R600 and R290, were selected and compared in order to identify suitable working fluids which may yield high system efficiencies.ResultsThe calculated results show that because of high system pressure for R290 and R134a, R600a is the more suitable working fluid for ORC in terms of expander size parameter, system efficiency and system pressure. In addition, R600a is also the most appropriate working fluid for VCC in terms of pressure ratio and coefficient of performance. R600 and R600a are more suitable working fluids for ORC/VCC in terms of overall coefficient of performance, refrigerating capacity per unit mass flow rate and chilled water yield from per ton hot water.ConclusionsIn sum, R600a is the most suitable working fluid for ORC/VCC through comprehensive comparison of ORC efficiency, expander size parameter, pressure ratio, coefficient of performance, system pressure and chilled water yield from per ton hot water for six different working fluids. However, the flammability of R600a should attract enough attention

Optically-controlled long-term storage and release of thermal energy in phase-change materials

Thermal energy storage offers enormous potential for a wide range of energy technologies. Phase-change materials offer state-of-the-art thermal storage due to high latent heat. However, spontaneous heat loss from thermally charged phase-change materials to cooler surroundings occurs due to the absence of a significant energy barrier for the liquid-solid transition. This prevents control over the thermal storage, and developing effective methods to address this problem has remained an elusive goal. Herein, we report a combination of photo-switching dopants and organic phase-change materials as a way to introduce an activation energy barrier for phase-change materials solidification and to conserve thermal energy in the materials, allowing them to be triggered optically to release their stored latent heat. This approach enables the retention of thermal energy (about 200 J g⁻¹) in the materials for at least 10 h at temperatures lower than the original crystallization point, unlocking opportunities for portable thermal energy storage systems

TSST: A Benchmark and Evaluation Models for Text Speech-Style Transfer

Text style is highly abstract, as it encompasses various aspects of a speaker's characteristics, habits, logical thinking, and the content they express. However, previous text-style transfer tasks have primarily focused on data-driven approaches, lacking in-depth analysis and research from the perspectives of linguistics and cognitive science. In this paper, we introduce a novel task called Text Speech-Style Transfer (TSST). The main objective is to further explore topics related to human cognition, such as personality and emotion, based on the capabilities of existing LLMs. Considering the objective of our task and the distinctive characteristics of oral speech in real-life scenarios, we trained multi-dimension (i.e. filler words, vividness, interactivity, emotionality) evaluation models for the TSST and validated their correlation with human assessments. We thoroughly analyze the performance of several large language models (LLMs) and identify areas where further improvement is needed. Moreover, driven by our evaluation models, we have released a new corpus that improves the capabilities of LLMs in generating text with speech-style characteristics. In summary, we present the TSST task, a new benchmark for style transfer and emphasizing human-oriented evaluation, exploring and advancing the performance of current LLMs.Comment: Working in progres

Multiscale Motion-Aware and Spatial-Temporal-Channel Contextual Coding Network for Learned Video Compression

Recently, learned video compression has achieved exciting performance. Following the traditional hybrid prediction coding framework, most learned methods generally adopt the motion estimation motion compensation (MEMC) method to remove inter-frame redundancy. However, inaccurate motion vector (MV) usually lead to the distortion of reconstructed frame. In addition, most approaches ignore the spatial and channel redundancy. To solve above problems, we propose a motion-aware and spatial-temporal-channel contextual coding based video compression network (MASTC-VC), which learns the latent representation and uses variational autoencoders (VAEs) to capture the characteristics of intra-frame pixels and inter-frame motion. Specifically, we design a multiscale motion-aware module (MS-MAM) to estimate spatial-temporal-channel consistent motion vector by utilizing the multiscale motion prediction information in a coarse-to-fine way. On the top of it, we further propose a spatial-temporal-channel contextual module (STCCM), which explores the correlation of latent representation to reduce the bit consumption from spatial, temporal and channel aspects respectively. Comprehensive experiments show that our proposed MASTC-VC is surprior to previous state-of-the-art (SOTA) methods on three public benchmark datasets. More specifically, our method brings average 10.15\% BD-rate savings against H.265/HEVC (HM-16.20) in PSNR metric and average 23.93\% BD-rate savings against H.266/VVC (VTM-13.2) in MS-SSIM metric.Comment: 12pages,12 figure

MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications

Large Language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence. While general artificial intelligence is leveraged by developing increasingly large-scale models, there could be another branch to develop lightweight custom models that better serve certain domains, taking into account the high cost of training and deploying LLMs and the scarcity of resources. In this paper, we present MindLLM, a novel series of bilingual lightweight large language models, trained from scratch, alleviating such burdens by offering models with 1.3 billion and 3 billion parameters. A thorough account of experiences accrued during large model development is given, covering every step of the process, including data construction, model architecture, evaluation, and applications. Such insights are hopefully valuable for fellow academics and developers. MindLLM consistently matches or surpasses the performance of other open-source larger models on some public benchmarks. We also introduce an innovative instruction tuning framework tailored for smaller models to enhance their capabilities efficiently. Moreover, we explore the application of MindLLM in specific vertical domains such as law and finance, underscoring the agility and adaptability of our lightweight models.Comment: Working in progres

Citation

Multifunctional silicon inspired by a wing of male Papilio ulysse Appl. Phys. Lett. 100, 033109 (2012) Nonlinear behavior of photoluminescence from silicon particles under two-photon excitation Appl. Phys. Lett. 99, 251105 (2011) About the internal pressure in cavities derived from implantation-induced blistering in semi-conductors J. Appl. Phys. 110, 114903 (2011) Structural evolution of implanted vicinal Si(111) during annealing via analysis of the dipole contribution J. Appl. Phys. 110, 103520 (2011) Positive or negative gain: Role of thermal capture cross sections in impurity photovoltaic effect J. Appl. Phys. 110, 104508 (2011) Additional information on J. Chem. Phys. Transition state analyses have been carried out within a density functional theory setting to explain and quantify the distinctly different ways in which hydrogen and methyl terminations serve to protect silicon surfaces from the earliest onset of oxidation. We find that oxidation occurs via direct dissociative adsorption, without any energy barrier, on Si(111) and reconstructed Si(001) that have been hydrogen terminated; oxidation initiates with a barrier of only 0.05 eV on unreconstructed Si(001). The commonly measured protection afforded by hydrogen is shown to derive from a coverage-dependent dissociation rate combined with barriers to the hopping of adsorbed oxygen atoms. Methyl termination, in contrast, offers an additional level of protection because oxygen must first undergo interactions with these ligands in a three-step process with significant energy barriers: adsorption of O 2 into a C-H bond to form a C-O-O-H intermediate; decomposition of C-O-O-H into C-O-H and C=O intermediates; and, finally, hopping of oxygen atoms from ligands to the substrate

Re-evaluating supply chain integration and firm performance: linking operations strategy to supply chain strategy

This paper aims to explore the performance implications of supply chain integration (SCI) taking a strategic perspective. Thus, this paper is set to provide answers to the following research questions: Does a higher degree of SCI always lead to greater firm performance improvements? As the answer to this question is likely to be no, the authors explore the performance implications from a strategic perspective: Is the SCI–performance relationship contingent on a company’s competitive priorities (i.e. operations strategy)? The authors explore their questions through multiple quasi-independent data sets to test the impact of SCI on firm performance. Furthermore, the authors provide a more nuanced conceptual and empirical view to explore the previously uncovered contradictory results and contingent relationship challenging the “more integration equals higher firm performance” proposition. The results only provide partial support for the proposition that more integration is always beneficial in the supply chain context. The authors also identified that the impact of SCI on financial performance is contingent on a company’s competitive priorities. This study provides a much-needed comprehensive assessment of the SCI–performance relationship through critically re-evaluating one of the most popular propositions in the field of supply chain management. The results can be extrapolated beyond the dyad, as the authors conceptualise integration simultaneously from an upstream and downstream perspective.N/

Atomic Structure and Dynamics of Single Platinum Atom Interactions with Monolayer MoS

We have studied atomic level interactions between single Pt atoms and the surface of monolayer MoS₂ using aberration-corrected annular dark field scanning transmission electron microscopy at an accelerating voltage of 60 kV. Strong contrast from single Pt atoms on the atomically resolved monolayer MoS₂ lattice enables their exact position to be determined with respect to the MoS₂ lattice, revealing stable binding sites. In regions of MoS₂ free from surface contamination, the Pt atoms are localized in S vacancy sites and exhibit dynamic hopping to nearby vacancy sites driven by the energy supplied by the electron beam. However, in areas of MoS₂ contaminated with carbon surface layers, the Pt atoms appear at various positions with respect to the underlying MoS₂ lattice, including on top of Mo and in off-axis positions. These variations are due to the Pt bonding with the surrounding amorphous carbon layer, which disrupts the intrinsic Pt-MoS₂ interactions, leading to more varied positions. Density functional theory (DFT) calculations reveal that Pt atoms on the surface of MoS₂ have a small barrier for migration and are stabilized when bound to either a single or double sulfur vacancies. DFT calculations have been used to understand how the catalytic activity of the MoS₂ basal plane for hydrogen evolution reaction is influenced by Pt dopants by variation of the hydrogen adsorption free energy. This strong dependence of catalytic effect on interfacial configurations is shown to be common for a series of dopants, which may provide a means to create and optimize reaction centers