The Antecedents and Consequences of Crowdfunding Investors’ Citizenship Behaviors – an Empirical Research on Motivations and Stickiness

This study investigates the antecedents (internal and external motivations) and consequences (stickiness intentions) of crowdfunding investors’ citizenship behavior. In addition, this study examines the moderating effects of investors’ perceived project novelty on the relationships between motivations and citizenship behavior. Based on a sample of 226 crowdfunding investors, results indicate that internal and external motivations significantly influence investors’ citizenship behavior, which further affect investors’ stickiness intentions. Furthermore, results show that investors’ perceived project novelty moderates the relationships between internal/ external motivation and citizenship behavior

Leveraging Label Information for Multimodal Emotion Recognition

Multimodal emotion recognition (MER) aims to detect the emotional status of a given expression by combining the speech and text information. Intuitively, label information should be capable of helping the model locate the salient tokens/frames relevant to the specific emotion, which finally facilitates the MER task. Inspired by this, we propose a novel approach for MER by leveraging label information. Specifically, we first obtain the representative label embeddings for both text and speech modalities, then learn the label-enhanced text/speech representations for each utterance via label-token and label-frame interactions. Finally, we devise a novel label-guided attentive fusion module to fuse the label-aware text and speech representations for emotion classification. Extensive experiments were conducted on the public IEMOCAP dataset, and experimental results demonstrate that our proposed approach outperforms existing baselines and achieves new state-of-the-art performance.Comment: Accepted by Interspeech 202

Signal Processing and Learning for Next Generation Multiple Access in 6G

Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed

FCS-HGNN: Flexible Multi-type Community Search in Heterogeneous Information Networks

Community Search (CS), a crucial task in network science, has attracted considerable interest owing to its prowess in unveiling personalized communities, thereby finding applications across diverse domains. Existing research primarily focuses on traditional homogeneous networks, which cannot be directly applied to heterogeneous information networks (HINs). However, existing research also has some limitations. For instance, either they solely focus on single-type or multi-type community search, which severely lacking flexibility, or they require users to specify meta-paths or predefined community structures, which poses significant challenges for users who are unfamiliar with community search and HINs. In this paper, we propose an innovative method, FCS-HGNN, that can flexibly identify either single-type or multi-type communities in HINs based on user preferences. We propose the heterogeneous information transformer to handle node heterogeneity, and the edge-semantic attention mechanism to address edge heterogeneity. This not only considers the varying contributions of edges when identifying different communities, but also expertly circumvents the challenges presented by meta-paths, thereby elegantly unifying the single-type and multi-type community search problems. Moreover, to enhance the applicability on large-scale graphs, we propose the neighbor sampling and depth-based heuristic search strategies, resulting in LS-FCS-HGNN. This algorithm significantly improves training and query efficiency while maintaining outstanding community effectiveness. We conducted extensive experiments on five real-world large-scale HINs, and the results demonstrated that the effectiveness and efficiency of our proposed method, which significantly outperforms state-of-the-art methods.Comment: 13 page

Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry for Identification of Microorganisms in Clinical Urine Specimens after Two Pretreatments

Rapid identification of microorganisms in urine is essential for patients with urinary tract infections (UTIs). Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been proposed as a method for the direct identification of urinary pathogens. Our purpose was to compare centrifugation-based MALDI-TOF MS and short-term culture combined with MALDI-TOF MS for the direct identification of pathogens in urine specimens. We collected 965 urine specimens from patients with suspected UTIs, 211/965 isolates were identified as positive by conventional urine culture. Compared with the conventional method, the results of centrifugation-based MALDI-TOF MS were consistent in 159/211 cases (75.4%), of which 135/159 (84.9%) had scores ≥ 2.00; 182/211 cases (86.3%) were detected using short-term culture combined with MALDI-TOF MS, of which 153/182 (84.1%) had scores ≥ 2.00. There were no apparent differences among the three methods (p = 0.135). MALDI-TOF MS appears to accelerate the microbial identification speed in urine and saves at least 24 to 48 hours compared with the routine urine culture. Centrifugation-based MALDI-TOF MS is characterized by faster identification speed; however, it is substantially affected by the number of bacterial colonies. In contrast, short-term culture combined with MALDI-TOF MS has a higher detection rate but a relatively slow identification speed. Combining these characteristics, the two methods may be effective and reliable alternatives to traditional urine culture

Gonadal atresia, estrogen-responsive, and apoptosis-specific mRNA expression in marine mussels from the East China coast: a preliminary study

This preliminary survey analysed mussel atresia incidences, estrogen-responsive and apoptotic-specific molecular end points, and aqueous and gonadal levels of selected estrogens from the East China coast. Estrogen levels were low (e.g. < LOD-28.36ng/L, < LOD-3.88ng/g wet weight of tissue for BPA) relative to worldwide freshwater environments, but high oocyte follicle atresia incidences (up to 26.6%) occurred at selected sites. Expression of estrogen-responsive ER2 was significantly increased in males relative to females at sites with high atresia incidences in females. A second estrogen-responsive gene, V9, was significantly increased at two sites in April in females relative to males; the opposite was true for the remaining two sites. Apoptosis-specific genes (Bcl-2, fas) showed elevated expression in males relative to females at the site with the highest atresia incidence. These results provide coastal estrogen levels and the utility of several estrogen-specific molecular-level markers for marine mussels

A web knowledge-driven multimodal retrieval method in computational social systems: unsupervised and robust graph convolutional hashing

Multimodal retrieval has received widespread consideration since it can commendably provide massive related data support for the development of computational social systems (CSSs). However, the existing works still face the following challenges: 1) rely on the tedious manual marking process when extended to CSS, which not only introduces subjective errors but also consumes abundant time and labor costs; 2) only using strongly aligned data for training, lacks concern for the adjacency information, which makes the poor robustness and semantic heterogeneity gap difficult to be effectively fit; and 3) mapping features into real-valued forms, which leads to the characteristics of high storage and low retrieval efficiency. To address these issues in turn, we have designed a multimodal retrieval framework based on web-knowledge-driven, called unsupervised and robust graph convolutional hashing (URGCH). The specific implementations are as follows: first, a &#x201C;secondary semantic self-fusion&#x201D; approach is proposed, which mainly extracts semantic-rich features through pretrained neural networks, constructs the joint semantic matrix through semantic fusion, and eliminates the process of manual marking; second, a &#x201C;adaptive computing&#x201D; approach is designed to construct enhanced semantic graph features through the knowledge-infused of neighborhoods and uses graph convolutional networks for knowledge fusion coding, which enables URGCH to sufficiently fit the semantic modality gap while obtaining satisfactory robustness features; Third, combined with hash learning, the multimodality data are mapped into the form of binary code, which reduces storage requirements and improves retrieval efficiency. Eventually, we perform plentiful experiments on the web dataset. The results evidence that URGCH exceeds other baselines about $1\%$ &#x2013; $3.7\%$ in mean average precisions (MAPs), displays superior performance in all the aspects, and can meaningfully provide multimodal data retrieval services to CSS

Alkali and alkaline earth metals in liquid salts for supercapattery

The full oxidation of lithium metal (4Li + O2 ⇌ 2Li2O) offers a mass normalised Gibbs energy change greater than that for the combustion of carbon (C + O2 ⇌ CO2) or any hydrocarbon fuel (CnH2n+2 + ((3n+1)/2)O2 ⇌ nCO2 + (n+1)H2O). This thermodynamic comparison promises a lithium-oxygen (air) battery with a petrol comparable energy density. Similar analyses apply to other abundant alkali and alkaline earth metals (AAEMs) which are all featured by their very high specific charge capacity and most negative electrode potentials. The success of lithium ion batteries (LIBs) in both research and commercial development confirms such thermodynamic predictions. However, the experimentally demonstrated energy capacities of all AAEM based batteries are only small fractions of the thermodynamic values. A main cause is that a satisfactory oxygen positive electrode (positrode) is still to be developed, whilst the very few options of AAEM storage positrodes still do not match with AAEM negative electrodes (negatrodes) in charge capacity. Another challenge results from the complicated interactions between AAEMs and the currently used organic carbonate electrolytes that not only reduce the negatrode capacity but also exert restriction on both electron and ion transfers. The flammability of currently used organic electrolytes is another major concern on the safety of AAEMs batteries. Herein, we introduce the concept and potential, and review the relevant practices of a promising ionic liquid supercapattery that couples an AAEM negatrode with a supercapacitor positrode to bypass the thermodynamic and kinetic difficulties of an oxygen or AAEM storage positrode. Further discussion aims at the selection of ionic liquid-based electrolytes that can enable the reversible anodic dissolution of AAEMs and a wide potential window for the supercapacitor positrode. The use of molten salt-based electrolytes is also postulated and analysed, not only because of their high ionic conductivity, low cost and unique applications, but also their high temperatures that eliminate dendritic growth on the negatrode and heat buildup in the cell

Electroacupuncture and human iPSC-derived small extracellular vesicles regulate the gut microbiota in ischemic stroke via the brain-gut axis

Electroacupuncture (EA) and induced pluripotent stem cell (iPSC)-derived small extracellular vesicles (iPSC-EVs) have substantial beneficial effects on ischemic stroke. However, the detailed mechanisms remain unclear. Here, we explored the mechanisms underlying the regulation of EA and iPSC-EVs in the microbiome-gut-brain axis (MGBA) after ischemic stroke. Ischemic stroke mice (C57BL/6) were subjected to middle cerebral artery occlusion (MCAO) or Sham surgery. EA and iPSC-EVs treatments significantly improved neurological function and neuronal and intestinal tract injury, downregulated the levels of IL-17 expression and upregulated IL-10 levels in brain and colon tissue after cerebral ischemia−reperfusion. EA and iPSC-EVs treatments also modulated the microbiota composition and diversity as well as the differential distribution of species in the intestines of the mice after cerebral ischemia−reperfusion. Our results demonstrated that EA and iPSC-EVs treatments regulated intestinal immunity through MGBA regulation of intestinal microbes, reducing brain and colon damage following cerebral ischemia and positively impacting the outcomes of ischemic stroke. Our findings provide new insights into the application of EA combined with iPSC-EVs as a treatment for ischemic stroke