Gamification of mobile wallet as an unconventional innovation for promoting Fintech:An fsQCA approach

Although digitalisation brings important possibilities to banking &amp; finance service, implementing digital technologies in practices can be challenging. Indeed, the adoption of new innovative technology in the banking &amp; finance sector lags behind other business sectors. Many of the valuable banking &amp; finance-related technologies have not been adopted in relation to the strategic implications of decisions in domains such as the development of service innovation and personalization, value co-creation, and marketing strategies. In particular, there is a paucity of research in using gamification to explore ways of customising banking &amp; finance fintech offerings, improving customers’ experience, and developing efficient banking &amp; finance marketing tactics. Drawing on the UTAUT2 and Otcalysis gamification framework, this study develops a research model investigating what configurations of motivations, expectations and conditions can shape consumers’ behavioral intention to adopt a gamified mobile wallet system. Findings suggest that combining effort expectancy, facilitating conditions and perceived value leads to higher intention to use gamified mobile wallet. Accordingly, firms need to consider the three core conditions when design relevant gamifications.</p

Layer thickness crossover of type-II multiferroic magnetism in NiI2

The discovery of atomically thin van der Waals ferroelectric and magnetic materials encourages the exploration of 2D multiferroics, which holds the promise to understand fascinating magnetoelectric interactions and fabricate advanced spintronic devices. In addition to building a heterostructure consisting of ferroelectric and magnetic ingredients, thinning down layered multiferroics of spin origin such as NiI2 becomes a natural route to realize 2D multiferroicity. However, the layer-dependent behavior, widely known in the community of 2D materials, necessitates a rigorous scrutiny of the multiferroic order in the few-layer limit. Here, we interrogate the layer thickness crossover of helimagnetism in NiI2 that drives the ferroelectricity and thereby type-II multiferroicity. By using wavelength-dependent polarization-resolved optical second harmonic generation (SHG) to probe the ferroic symmetry, we find that the SHG arises from the inversion-symmetry-breaking magnetic order, not previously assumed ferroelectricity. This magnetism-induced SHG is only observed in bilayer or thicker layers, and vanishes in monolayer, suggesting the critical role of interlayer exchange interaction in breaking the degeneracy of geometrically frustrated spin structures in triangular lattice and stabilizing the type-II multiferroic magnetism in few-layers. While the helimagnetic transition temperature is layer dependent, the few-layer NiI2 exhibits another thickness evolution and reaches the bulk-like behavior in trilayer, indicated by the intermediate centrosymmetric antiferromagnetic state as revealed in Raman spectroscopy. Our work therefore highlights the magnetic contribution to SHG and Raman spectroscopy in reduced dimension and guides the optical study of 2D multiferroics.Comment: 23 pages, 4 figures, 6 supplementary figure

Cortical hierarchy disorganization in major depressive disorder and its association with suicidality

ObjectivesTo explore the suicide risk-specific disruption of cortical hierarchy in major depressive disorder (MDD) patients with diverse suicide risks.MethodsNinety-two MDD patients with diverse suicide risks and 38 matched controls underwent resting-state functional MRI. Connectome gradient analysis and stepwise functional connectivity (SFC) analysis were used to characterize the suicide risk-specific alterations of cortical hierarchy in MDD patients.ResultsRelative to controls, patients with suicide attempts (SA) had a prominent compression from the sensorimotor system; patients with suicide ideations (SI) had a prominent compression from the higher-level systems; non-suicide patients had a compression from both the sensorimotor system and higher-level systems, although it was less prominent relative to SA and SI patients. SFC analysis further validated this depolarization phenomenon.ConclusionThis study revealed MDD patients had suicide risk-specific disruptions of cortical hierarchy, which advance our understanding of the neuromechanisms of suicidality in MDD patients

Advanced Machine Learning and Water Quality Index (WQI) Assessment: Evaluating Groundwater Quality at the Yopurga Landfill

As industrial development and population growth continue, water pollution has become increasingly severe, particularly in rapidly industrializing regions like the area surrounding the Yopurga landfill. Ensuring water resource safety and environmental protection necessitates effective water quality monitoring and assessment. This paper explores the application of advanced machine learning technologies and the Water Quality Index (WQI) model as a comprehensive method for accurately assessing groundwater quality near the Yopurga landfill. The methodology involves selecting water quality indicators based on available data and the hydrochemical characteristics of the study area, comparing the performance of Decision Trees, Random Forest, and Xgboost algorithms in predicting water quality, and identifying the optimal algorithm to determine indicator weights. Indicators are scored using appropriate sub-index (SI) functions, and six different aggregation functions are compared to find the most suitable one. The study reveals that the Xgboost model surpasses Decision Trees and Random Forest models in water quality prediction. The top three indicator weights identified are pH, Manganese (Mn), and Nickel (Ni). The SWM model, with a 0% overestimation eclipsing rate and a 34% underestimation eclipsing rate, is chosen as the most appropriate WQI model for evaluating groundwater quality at the Yopurga landfill. According to the WQI results from the SWM aggregation function, the overall water quality in the area ranges from moderately polluted to slightly polluted. These assessment results provide a scientific basis for regional water environment protection

Near-Surface Defects Identification of Polyethylene Pipes Based on Synchro-Squeezing Transform and Deep Learning

To conduct the ultrasonic weld inspection of polyethylene pipes, it is necessary to use low-frequency transducers due to the high sound energy attenuation of polyethylene. However, one of the challenges in this process is that the blind zone of the ultrasonic transducer may cover a part of the workpiece being tested. This leads to a situation where if a defect appears near the surface of the workpiece, its signal will be buried by the blind zone signal. This hinders the early identification of defects, which is not favorable in such a scenario. To address this issue, we propose a new approach to detect and locate the near-surface defects. We begin by performing a synchro-squeezing transform on the original A-scan signal to obtain an accurate time-frequency distribution. While successful in detecting and localizing near-surface defects, the method alone fails to identify the specific type of defect directly: a limitation shared with other signal processing methods. Thus, an effective and lightweight defect identification model was established that combines depth-wise separable convolution and an attention mechanism. Finally, the performance of the proposed model was compared and visually analyzed with other models. This paper successfully achieves the detection, localization, and identification of near-surface defects through the synchro-squeezing transform and the defect identification model. The results show that our model can identify both general and near-surface defects with an accuracy of 99.50% while having a model size of only 1.14 MB

Ideography Leads Us to the Field of Cognition: A Radical-Guided Associative Model for Chinese Text Classification

Cognitive psychology research shows that humans have the instinct for abstract thinking, where association plays an essential role in language comprehension. Especially for Chinese, its ideographic writing system allows radicals to trigger semantic association without the need of phonetics. In fact, subconsciously using the associative information guided by radicals is a key for readers to ensure the robustness of semantic understanding. Fortunately, many basic and extended concepts related to radicals are systematically included in Chinese language dictionaries, which leaves a handy but unexplored way for improving Chinese text representation and classification. To this end, we draw inspirations from cognitive principles between ideography and human associative behavior to propose a novel Radical-guided Associative Model (RAM) for Chinese text classification. RAM comprises two coupled spaces, namely Literal Space and Associative Space, which imitates the real process in people's mind when understanding a Chinese text. To be specific, we first devise a serialized modeling structure in Literal Space to thoroughly capture the sequential information of Chinese text. Then, based on the authoritative information provided by Chinese language dictionaries, we design an association module and put forward a strategy called Radical-Word Association to use ideographic radicals as the medium to associate prior concept words in Associative Space. Afterwards, we design an attention module to imitate people's matching and decision between Literal Space and Associative Space, which can balance the importance of each associative words under specific contexts. Finally, extensive experiments on two real-world datasets prove the effectiveness and rationality of RAM, with good cognitive insights for future language modeling

Isolation and Characterization of a Deoxynivalenol-Degrading Bacterium Bacillus licheniformis YB9 with the Capability of Modulating Intestinal Microbial Flora of Mice

Deoxynivalenol (DON) is one of the most prevalent food- and feed-associated mycotoxins. It frequently contaminates agricultural commodities and poses serious threats to human and animal health and leads to tremendous economic losses globally. Much attention has been paid to using microorganisms to detoxify DON. In this study, a Bacillus licheniformis strain named YB9 with a strong ability to detoxify DON was isolated and characterized from a moldy soil sample. YB9 could degrade more than 82.67% of 1 mg/L DON within 48 h at 37 &deg;C and showed strong survival and DON degradation rate at simulated gastric fluid. The effects of YB9 on mice with DON intragastrical administration were further investigated by biochemical and histopathological examination and the gut microbiota was analyzed by 16S rRNA Illumina sequencing technology. The results showed that DON increased the levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and creatinine (Cr), decreased those of immunoglobulin G (IgG) and IgM in serum, and resulted in severe pathological damage of the liver, kidney, and spleen. By contrast, YB9 supplementation obviously inhibited or attenuated the damages caused by DON in mice. In addition, YB9 addition repaired the DON-induced dysbiosis of intestinal flora, characterized by recovering the balance of Firmicutes and Bacteroidetes to the normal level and decreasing the abundance of the potentially harmful bacterium Turicibacter and the excessive Lactobacillus caused by DON. Taken together, DON-degrading strain YB9 might be used as potential probiotic additive for improving food and feed safety and modulating the intestinal microbial flora of humans and animals

A predictable smoothing evolution model for computer-controlled polishing

Quantitative prediction of the smoothing of mid-spatial frequency errors (MSFE) is urgently needed to realize process guidance for computer controlled optical surfacing (CCOS) rather than a qualitative analysis of the processing results. Consequently, a predictable time-dependent model combining process parameters and an error decreasing factor (EDF) were presented in this paper. The basic smoothing theory, solution method and modification of this model were expounded separately and verified by experiments. The experimental results show that the theoretical predicted curve agrees well with the actual smoothing effect. The smoothing evolution model provides certain theoretical support and guidance for the quantitative prediction and parameter selection of the smoothing of MSFE