Sensitivity of the global carbonate weathering carbon-sink flux to climate and land-use changes

The response of carbonate weathering carbon-sink flux (CCSF) to its environmental drivers is still not well understood on the global scale. This hinders understanding of the terrestrial carbon cycle. Here, we show that there is likely to be a widespread and consistent increase in the global CCSF (ranging from + 9.8% (RCP4.5) to + 17.1% (RCP8.5)) over the period 1950–2100. In the coming years the increasing temperature might be expected to have a negative impact on carbonate weathering. However, the increasing rainfall and anticipated land-use changes will counteract this, leading to a greater CCSF. This finding has been obtained by using long-term historical (1950–2005) and modeled future (2006–2100) data for two scenarios (RCP4.5 and RCP8.5) for climate and land-use change in our CCSF equilibrium model. This study stresses the potential role that carbonate weathering may play in the evolution of the global carbon cycle over this century

Sweet Sulfamethazine Acesulfamate Crystals With Improved Compaction Property

University of Minnesota M.S. thesis. July 2019. Major: Pharmaceutics. Advisor: Changquan Sun. 1 computer file (PDF); vii, 52 pages.Sulfamethazine (SMT) is a sulfonamide antibacterial drug used to treat or prevent infections in both humans and animals. However, SMT has an unfavorable taste and poor compaction behavior. To overcome these problems, a 1:1 complex with an artificial sweetener, acesulfame (Acs), was prepared and characterized. The single crystal structure suggests that the new complex, SMT-Acs, is a salt. This was confirmed by analysis of C-N bond length and comparison to multicomponent SMT crystals with known ionization states of SMT and Fourier transformation infrared spectroscopy. The applicability of the ΔpKa rule in multicomponent crystals of SMT is discussed. SMT-Acs exhibits better tabletability than SMT, which is attributed to its greater plasticity as shown by Heckel and Kuentz – Leuenberger analysis. The greater plasticity of SMT-Acs is consistent with the presence of slip planes identified by combined energy framework and topological analysis of the crystal structure

Performance persistence of institutional investors in IPO market : evidence from China

Using a dataset consisting of complete bid information for 477 bookbuilt IPOs that took place during Nov 2010 to Oct 2012 in China, I examine whether the performance of institutional investors demonstrates persistence in the IPO market. Building on the adverse selection model as developed by Rock (1986) and a twoperiod analysis, I develop three hypotheses and obtain empirical results that are consistent with the hypotheses. Firstly, I find that the performance of institutional investors continues into the next period. Secondly, I find that the performance persistence exists only for the investors with good past performance but not for investors with bad past performance. Finally, an index capturing the past performance of institutional investors is shown to be informative about the IPO’s initial and medium-term post-market returns. Overall, the results are consistent with the existence of performance persistence among the institutional investors. I conduct additional tests to trace the roots of the observed performance persistence. Results support the hypothesis that institutional investors with good past performance are relatively more informed than those with bad past performance. Specifically, investors with good past performance are more likely to participate in issues with high underpricing, exhibit stronger bid shaving ability, provide more information in terms of high elasticity of demand curve, and show a weaker tendency of naïve reinforcement learning. The results are robust after controlling for the influence of underwriters and after ruling out different alternative explanations. Taking all the results together, my study provides the first systematic evidence on the performance persistence of institutional investors in the IPO market. The results provide important insights for understanding the role of institutional investors in the IPO process and have implications for the design of IPO methods

PRSim: Sublinear Time SimRank Computation on Large Power-Law Graphs

{\it SimRank} is a classic measure of the similarities of nodes in a graph. Given a node $u$ in graph $G =(V, E)$, a {\em single-source SimRank query} returns the SimRank similarities $s(u, v)$ between node $u$ and each node $v \in V$. This type of queries has numerous applications in web search and social networks analysis, such as link prediction, web mining, and spam detection. Existing methods for single-source SimRank queries, however, incur query cost at least linear to the number of nodes $n$, which renders them inapplicable for real-time and interactive analysis. { This paper proposes \prsim, an algorithm that exploits the structure of graphs to efficiently answer single-source SimRank queries. \prsim uses an index of size $O(m)$, where $m$ is the number of edges in the graph, and guarantees a query time that depends on the {\em reverse PageRank} distribution of the input graph. In particular, we prove that \prsim runs in sub-linear time if the degree distribution of the input graph follows the power-law distribution, a property possessed by many real-world graphs. Based on the theoretical analysis, we show that the empirical query time of all existing SimRank algorithms also depends on the reverse PageRank distribution of the graph.} Finally, we present the first experimental study that evaluates the absolute errors of various SimRank algorithms on large graphs, and we show that \prsim outperforms the state of the art in terms of query time, accuracy, index size, and scalability.Comment: ACM SIGMOD 201

Efficient deep data assimilation with sparse observations and time-varying sensors

Variational Data Assimilation (DA) has been broadly used in engineering problems for field reconstruction and prediction by performing a weighted combination of multiple sources of noisy data. In recent years, the integration of deep learning (DL) techniques in DA has shown promise in improving the efficiency and accuracy in high-dimensional dynamical systems. Nevertheless, existing deep DA approaches face difficulties in dealing with unstructured observation data, especially when the placement and number of sensors are dynamic over time. We introduce a novel variational DA scheme, named Voronoi-tessellation Inverse operator for VariatIonal Data assimilation (VIVID), that incorporates a DL inverse operator into the assimilation objective function. By leveraging the capabilities of the Voronoi-tessellation and convolutional neural networks, VIVID is adept at handling sparse, unstructured, and time-varying sensor data. Furthermore, the incorporation of the DL inverse operator establishes a direct link between observation and state space, leading to a reduction in the number of minimization steps required for DA. Additionally, VIVID can be seamlessly integrated with Proper Orthogonal Decomposition (POD) to develop an end-to-end reduced-order DA scheme, which can further expedite field reconstruction. Numerical experiments in a fluid dynamics system demonstrate that VIVID can significantly outperform existing DA and DL algorithms. The robustness of VIVID is also accessed through the application of various levels of prior error, the utilization of varying numbers of sensors, and the misspecification of error covariance in DA

The Role of Mastery Goal on Life Satisfaction Using PERMA as A Mediator for College Students

This study examined the relationship between mastery goals, including task-based and self-based competence, and positive emotions, engagement, relationship, meaning, and accomplishment (PERMA), which may affect life satisfaction. Mastery goals, PERMA, and life satisfaction were examined using a relationship study model. The current study involved 260 English education programs, with 81 (31.2%) male students and 179 (68.8%) female students. AMOS 18 was used to conduct a confirmatory factor analysis (CFA). The results of the current study demonstrate that task-based competence influences life satisfaction. In contrast, self-based competence was found not to affect life satisfaction. Analysis of SEM revealed significant influences of task-based competence on PERMA and no significant relationships between self-based competence and PERMA. PERMA partially mediates the influence of task-related competence on life satisfaction. The indirect effects of self-based competence on life satisfaction were observed through PERMA as a complete mediator. The novelty of the current research lies in its focus on mastery goals, the target population of college students, and the mediating role of PERMA. These contributions are critical, as teachers or instructors are responsible for developing student well-being and life satisfaction. Doi: 10.28991/ESJ-2023-SIED2-018 Full Text: PD

IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Recent years have witnessed the strong power of large text-to-image diffusion models for the impressive generative capability to create high-fidelity images. However, it is very tricky to generate desired images using only text prompt as it often involves complex prompt engineering. An alternative to text prompt is image prompt, as the saying goes: "an image is worth a thousand words". Although existing methods of direct fine-tuning from pretrained models are effective, they require large computing resources and are not compatible with other base models, text prompt, and structural controls. In this paper, we present IP-Adapter, an effective and lightweight adapter to achieve image prompt capability for the pretrained text-to-image diffusion models. The key design of our IP-Adapter is decoupled cross-attention mechanism that separates cross-attention layers for text features and image features. Despite the simplicity of our method, an IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fully fine-tuned image prompt model. As we freeze the pretrained diffusion model, the proposed IP-Adapter can be generalized not only to other custom models fine-tuned from the same base model, but also to controllable generation using existing controllable tools. With the benefit of the decoupled cross-attention strategy, the image prompt can also work well with the text prompt to achieve multimodal image generation. The project page is available at \url{https://ip-adapter.github.io}

An Organocobalt–Carbon Nanotube Chemiresistive Carbon Monoxide Detector

A chemiresistive detector for carbon monoxide was created from single-walled carbon nanotubes (SWCNTs) by noncovalent modification with diiodo(η⁵:η¹-1-[2-(N,N-dimethylamino)ethyl]-2,3,4,5-tetramethylcyclopentadienyl)-cobalt(III) ([Cp[superscript ∧]CoI₂]), an organocobalt complex with an intramolecular amino ligand coordinated to the metal center that is displaced upon CO binding. The unbound amino group can subsequently be transduced chemiresistively by the SWCNT network. The resulting device was shown to have a ppm-level limit of detection and unprecedented selectivity for CO gas among CNT-based chemiresistors. This work, the first molecular-level mechanistic elucidation for a CNT-based chemiresistive detector for CO, demonstrates the efficacy of using an analyte’s reactivity to produce another chemical moiety that is readily transduced as a strategy for the rational design of chemiresistive CNT-based detectors.National Science Foundation (U.S.) (DMR-1410718)National Science Foundation (U.S.) (1122374

ETP: Learning Transferable ECG Representations via ECG-Text Pre-training

In the domain of cardiovascular healthcare, the Electrocardiogram (ECG) serves as a critical, non-invasive diagnostic tool. Although recent strides in self-supervised learning (SSL) have been promising for ECG representation learning, these techniques often require annotated samples and struggle with classes not present in the fine-tuning stages. To address these limitations, we introduce ECG-Text Pre-training (ETP), an innovative framework designed to learn cross-modal representations that link ECG signals with textual reports. For the first time, this framework leverages the zero-shot classification task in the ECG domain. ETP employs an ECG encoder along with a pre-trained language model to align ECG signals with their corresponding textual reports. The proposed framework excels in both linear evaluation and zero-shot classification tasks, as demonstrated on the PTB-XL and CPSC2018 datasets, showcasing its ability for robust and generalizable cross-modal ECG feature learning.Comment: under revie