How transformation expectation leads consumers to immediate gratification - A PLS-SEM approach

This study explores the mechanism which triggers consumer's immediate gratification behavior. It is proposed that consumer's expectation of meaningful life transformation by acquisition of a product causes her perception of product hedonic and utilitarian value, which can further predict immediate gratification. The positive impact of perception of hedonic value on immediate gratification can be mediated by price sensitivity and moderated by materialism level. The structural model is established for further empirical analysis with PLSSEM approach. The model suggests different domain of transformation expectation may have conflicting impact on immediate gratificatio

Medium-Term Earthquake Forecast Using Gravity Monitoring Data: Evidence from the Yutian and Wenchuan Earthquakes in China

Gravity changes derived from regional gravity monitoring data in China from 1998 to 2005 exhibited noticeable variations before the occurrence of two large earthquakes in 2008 in China—the 2008 Yutian (Xinjiang) Ms=7.3 earthquake and the 2008 Wenchuan (Sichuan) Ms=8.0 earthquake. Based on these gravity variations, a group of researchers at the Second Crust Monitoring and Application Center of China Earthquake Administration made a suggestion in December of 2006 that the possibility for the Yutian (Xinjiang) and Wenchuan (Sichuan) areas to experience a large earthquake in either 2007 or 2008 was high. We review the gravity monitoring data and methods upon which the researchers reached these medium-term earthquake forecasts. Experience related to the medium-term forecasts of the Yutian and Wenchuan earthquakes suggests that gravity changes derived from regional gravity monitoring data could potentially be a useful medium-term precursor of large earthquakes, but significant additional research is needed to validate and evaluate this hypothesis

Classificação das carteiras na Blockchain Ethereum usando machine learning

Mestrado Bolonha em Métodos Quantitativos para a Decisão Económica e EmpresarialO interesse em compreender o tipo de carteiras por trás das transações registadas na blockchain Ethereum tem crescido consideravelmente. Isso deve-se ao facto desta análise permitir perceber os comportamentos das transações e obter informações antecipadas sobre os movimentos dos grandes detentores da Ether, fornecendo uma visão valiosa do comportamento do mercado e tornando-se uma fonte de informações estratégicas cruciais para investidores e observadores do ecossistema de criptomoedas. O objetivo deste trabalho é aplicar métodos de análise de dados e Machine Learning que permitam classificar o tipo de carteiras através das características das transações. Sendo uma área recente, a maioria da literatura concentra-se na deteção dos endereços de anomalia. A análise de tipo de carteiras por grau de segurança é uma área de pesquisa académia limitada, pois a própria definição das carteiras é subjetiva. Assim, com esta dissertação pretende-se realizar uma classificação das carteiras, caraterizando os grupos de carteiras com rótulos publicamente estabelecidos e os grupos de carteiras com rótulos definidas através das características essenciais recorrendo a técnicas de análise de dados. A análise dos dados passa pela extração dos dados brutos até à aplicação de algoritmos de Machine Learning. Assim, foram considerados vários modelos para fazer a classificação de tipos de carteiras, como Regressão Logística, Random Forest, AdaBoost e GradientBoosting. A validação e comparação dos modelos elaborados foi feita de acordo com várias medidas como accuracy, precisão, sensibilidade, especificidade, F_Score, e AUC. A validação cruzada é o método escolhido para a avaliação dos modelos. Dos resultados obtidos para dados de transações de Ethereum entre 2016 e 2023, conclui-se que as metodologias aqui proposta constituem uma ferramenta importante na classificação de carteiras Ethereum.The interest in comprehending the various wallet types associated with transactions on the Blockchain Ethereum has seen substantial growth. This is primarily because such analysis yields invaluable insights into transaction patterns, furnishing early data on the actions of prominent Ether holders. Consequently, it provides a vantage point into market behavior, establishing itself as a pivotal source of strategic information for both investors and observers within the cryptocurrency ecosystem. The objective of this work is to apply data analysis and Machine Learning methods to classify wallet types based on transaction characteristics. In the relatively recent field of cryptocurrency analysis, most research efforts have focused on anomaly address detection, while the analysis of wallet types by security level remains a limited area of academic research. This is partly due to the inherent subjectivity in defining wallet types. In this study, the goal is to classify wallets from various perspectives, characterizing groups of wallets with publicly established labels and groups of wallets with labels defined through essential characteristics using data analysis techniques. For the data analysis various tasks are considered, from data extraction to the use of Machine Learning algorithms for predictions. Several models are used to predict wallet types, including Logistic Regression, Random Forest, AdaBoost, and GradientBoosting. The validation and comparison of models are conducted utilizing a diverse set of metrics, including accuracy, precision, recall, specificity, F-value, and AUC. The chosen approach for model evaluation was cross-validation. From de results, obtained using data from 2016 to 2023, we conclude that the methodologies proposed in this work constitute on important tool in classifying Ethereum wallets.info:eu-repo/semantics/publishedVersio

Canonical explicit B\"{a}cklund transformations with spectrality for constrained flows of soliton hierarchies

It is shown that explicit B\"{a}cklund transformations (BTs) for the high-order constrained flows of soliton hierarchy can be constructed via their Darboux transformations and Lax representation, and these BTs are canonical transformations including B\"{a}cklund parameter $\eta$ and possess a spectrality property with respect to $\eta$ and the 'conjugated' variable $\mu$ for which the pair $(\eta, \mu)$ lies on the spectral curve. As model we present the canonical explicit BTs with the spectrality for high-order constrained flows of the Kaup-Newell hierarchy and the KdV hierarchy.Comment: 21 pages, Latex, to be published in "PHYSICA A

Personalized Prompt for Sequential Recommendation

Pre-training models have shown their power in sequential recommendation. Recently, prompt has been widely explored and verified for tuning in NLP pre-training, which could help to more effectively and efficiently extract useful knowledge from pre-training models for downstream tasks, especially in cold-start scenarios. However, it is challenging to bring prompt-tuning from NLP to recommendation, since the tokens in recommendation (i.e., items) do not have explicit explainable semantics, and the sequence modeling should be personalized. In this work, we first introduces prompt to recommendation and propose a novel Personalized prompt-based recommendation (PPR) framework for cold-start recommendation. Specifically, we build the personalized soft prefix prompt via a prompt generator based on user profiles and enable a sufficient training of prompts via a prompt-oriented contrastive learning with both prompt- and behavior-based augmentations. We conduct extensive evaluations on various tasks. In both few-shot and zero-shot recommendation, PPR models achieve significant improvements over baselines on various metrics in three large-scale open datasets. We also conduct ablation tests and sparsity analysis for a better understanding of PPR. Moreover, We further verify PPR's universality on different pre-training models, and conduct explorations on PPR's other promising downstream tasks including cross-domain recommendation and user profile prediction

Intakes of magnesium, calcium and risk of fatty liver disease and prediabetes

Objective Obesity and insulin resistance play important roles in the pathogenesis of non-alcoholic fatty liver disease (NAFLD). Mg intake is linked to a reduced risk of metabolic syndrome and insulin resistance; people with NAFLD or alcoholic liver disease are at high risk of Mg deficiency. The present study aimed to investigate whether Mg and Ca intakes were associated with risk of fatty liver disease and prediabetes by alcohol drinking status. Design We analysed the association between Ca or Mg intake and fatty liver disease, prediabetes or both prediabetes and fatty liver disease in cross-sectional analyses. Setting Third National Health and Nutrition Examination Survey (NHANES III) follow-up cohort of US adults. Subjects Nationally representative sample of US adults in NHANES (n 13 489). Results After adjusting for potential confounders, Mg intake was associated with approximately 30 % reduced odds of fatty liver disease and prediabetes, comparing the highest intake quartile v. the lowest. Mg intake may only be related to reduced odds of fatty liver disease and prediabetes in those whose Ca intake is less than 1200 mg/d. Mg intake may also only be associated with reduced odds of fatty liver disease among alcohol drinkers. Conclusions The study suggests that high intake of Mg may be associated with reduced risks of fatty liver disease and prediabetes. Further large studies, particularly prospective cohort studies, are warranted to confirm the findings

Antitussive efficacy of the current treatment protocol for refractory chronic cough: our real-world experience in a retrospective cohort study

BACKGROUND: The management of refractory chronic cough (RCC) is a great challenge. Neuromodulators have long been used for RCC with imperfect efficacy. OBJECTIVES: We summarized the outcomes of the current treatments used at our specialist cough clinic, which provides a guideline-led service and real-world experience for the future management of RCC. DESIGN: This is a single-centre retrospective observational cohort study. METHODS: Consecutive RCC patients (the first clinic visit between January 2016 and May 2021) were included into this observational cohort study. Medical records in the Chronic Cough Clinical Research Database were fully reviewed using uniform criteria. The included subjects were followed-up for at least 6 months after the final clinic visit via instant messages with the link to self-scaled cough-associated questionnaires. RESULTS: Overall, 369 RCC patients were analysed with a median age of 46.6 years and a cough duration of 24.0 months. A total of 10 different treatments were offered. However, 96.2% of patients had been prescribed at least one neuromodulator. One-third of patients had alternative treatments prescribed given the poor response to the initial therapy and 71.3% favourably responded to at least one of the treatments. Gabapentin, deanxit, and baclofen had comparable therapeutic efficacy (56.0%, 56.0%, and 62.5% respectively; p = 0.88) and overall incidences of adverse effects (28.3%, 22.0%, and 32.3% respectively; p = 0.76). However, 19.1 (7.7-41.8) months after the last clinic visit, 65.0% reported improvement (24.9%) or control of their cough (40.1%); 3.8% reported a spontaneous remission and 31.2% still had a severe cough. Both HARQ (n = 97; p < 0.001) and LCQ (n = 58; p < 0.001) demonstrated marked improvement. CONCLUSION: Trying different neuromodulators is a pragmatic strategy for RCC, which helped around two-thirds of patients. Relapse is common on withdrawal or reduction of dosage. Novel medication for RCC is an urgent clinical need. PLAIN LANGUAGE SUMMARY: This is the first report that fully represented a guideline-led treatment protocol for refractory chronic cough (RCC) based on a large series of patients, which evaluated the short- and long-term effects of the currently available treatments for RCC. We found that the therapeutic trial of different neuromodulators is a pragmatic strategy, which helped around two-thirds of patients. Gabapentin, deanxit (flupentixol/melitracen), and baclofen had similar therapeutic outcomes. This study may offer real-world experience for the future management of RCC

Exchange renormalized crystal field excitation in a quantum Ising magnet KTmSe2_2

Rare-earth delafossite compounds, ARCh$_2$ (A = alkali or monovalent ion, R = rare earth, Ch = chalcogen), have been proposed for a range of novel quantum phenomena. Particularly, the Tm series, ATmCh$_2$, featuring Tm ions on a triangular lattice, serves as a representative group of compounds to illustrate the interplay and competition between spin-orbit coupling, crystal fields, and exchange couplings in the presence of geometric frustration. Here we report the thermodynamic and inelastic neutron scattering studies on the newly discovered triangular-lattice magnet KTmSe$_2$. Both heat capacity and neutron diffraction reveal the absence of long-range magnetic order. Magnetic susceptibility shows strong Ising-like interactions with antiferromagnetic correlations. Furthermore, inelastic neutron scattering measurements reveal a branch of dispersive crystal field excitations. To analyze these observations, we employ both the transverse field Ising model and the full crystal field scheme, along with exchange interactions. Our results suggest a strong competition between spin exchange interactions and crystal field effects. This work is expected to offer a valuable framework for understanding low-temperature magnetism in KTmSe$_2$ and similar materials.Comment: 9 pages, 4 figures. Submitted on the behalf of Shiyi Zhen