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This study identifies Portuguese residents’ behavioural intention towards Chinese electric vehicles from the perspective of the Country-of-Origin Effect (COE) and uses the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) as a guide to design interview questions. According to the existing literature, UTAUT-2, a comprehensive technology acceptance model, has not been used to assess the Portuguese electric vehicle market, and no relevant research has been found to apply the method of combining the COE into the UTAUT-2 framework. The purpose of this study is exploratory in nature, it uses qualitative methods to identify the behavioural intention of Portuguese residents towards the acceptance of Chinese electric vehicles. Data from 16 Portuguese residents was collected through semi-structured interviews and analyzed with qualitative methodology. The study found that factors such as performance expectations, environmental concerns, effort expectations, hedonic motivation, and social influence have a positive impact on Portuguese residents' purchase of electric vehicles, while price value, habits, and convenience conditions have a neutral or negative impact. Regarding COE, apart from social influence, it has no impact on other factors. To increase the popularity and sales of Chinese electric vehicles in Portugal, it is recommended that advertising and marketing efforts focus on price, charging services, after-sales service, and design
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Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.
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Ligand peptides that have high affinity for ion channels are critical for regulating ion flux across the plasma membrane. These peptides are now being considered as potential drug candidates for many diseases, such as cardiovascular disease and cancers. In this work, we developed Multi-Branch-CNN, a CNN method with multiple input branches for identifying three types of ion channel peptide binders (sodium, potassium, and calcium) from intra- and inter-feature types. As for its real-world applications, prediction models that are able to recognize novel sequences having high or low similarities to training sequences are required. To this end, we tested our models on two test sets: a general test set including sequences spanning different similarity levels to those of the training set, and a novel-test set consisting of only sequences that bear little resemblance to sequences from the training set. Our experiments showed that the Multi-Branch-CNN method performs better than thirteen traditional ML algorithms (TML13), yielding an improvement in accuracy of 3.2%, 1.2%, and 2.3% on the test sets as well as 8.8%, 14.3%, and 14.6% on the novel-test sets for sodium, potassium, and calcium ion channels, respectively. We confirmed the effectiveness of Multi-Branch-CNN by comparing it to the standard CNN method with one input branch (Single-Branch-CNN) and an ensemble method (TML13-Stack). The data sets, script files to reproduce the experiments, and the final predictive models are freely available at https://github.com/jieluyan/Multi-Branch-CNN.
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Nowadays, the increasing number of medical diagnostic data and clinical data provide more complementary references for doctors to make diagnosis to patients. For example, with medical data, such as electrocardiography (ECG), machine learning algorithms can be used to identify and diagnose heart disease to reduce the workload of doctors. However, ECG data is always exposed to various kinds of noise and interference in reality, and medical diagnostics only based on one-dimensional ECG data is not trustable enough. By extracting new features from other types of medical data, we can implement enhanced recognition methods, called multimodal learning. Multimodal learning helps models to process data from a range of different sources, eliminate the requirement for training each single learning modality, and improve the robustness of models with the diversity of data. Growing number of articles in recent years have been devoted to investigating how to extract data from different sources and build accurate multimodal machine learning models, or deep learning models for medical diagnostics. This paper reviews and summarizes several recent papers that dealing with multimodal machine learning in disease detection, and identify topics for future research.
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As an emergent tourism sector, driving tourism connects car use and touristic activities intimately. Following the notion of the ‘inhabited car’, this article explores how and why Chinese tourists inhabit a travelling car for drivers/passengers in the leisure automobility and driving tourism context. Through three different road trips and ‘mobile methods’, it was found that Chinese tourists inhabit the car in four ways: driving, gazing, listening, and communicating. Through this embodied habitation, the car is turned into a ‘touristic inhabitation’ space for protecting the tourists generating touristic emotions、social interactions, and tourism meanings. The study contributes to automobility and tourism literature and provides implications for driving tourism development in China.
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Hydrothermal activities on ultraslow-spreading ridges exhibit diverse characteristics, long histories with multiple participants, and might form large-scale, high-grade sulfide deposits. The Duanqiao hydrothermal field (DHF) is located at the segment with the thickest oceanic crust and a large axial magma chamber on the Southwest Indian Ridge, providing unique perspective of sulfide metallogenesis on ultraslow-spreading ridges. Previous studies revealed that DHF sulfide exhibits distinct features of enrichment of ore-forming elements in comparison with those of hydrothermal fields on sediment-starved mid-ocean ridges. However, the genesis and processes responsible for such differences remain poorly constrained. In this study, mineralogical, geochemical and S and Pb isotopic analyses were performed on relict sulfide mound samples to characterize DHF formation. The samples show clear concentric mineral zonation from the interior to the exterior wall. Assemblages of chalcopyrite, sphalerite, and pyrite are distributed mainly in the interior wall, whereas pyrite and marcasite are distributed mainly in the exterior wall. The low Cu content and Pb isotopic composition of the sulfide indicate that the metals are derived mainly from basement basalts. The δ34S values exhibit positive values distributed over a reasonably narrow range (2.42‰–7.97‰), which suggests approximately 62.1%–88.5% of S with basaltic origin. Compared with most hydrothermal fields along the sediment starved mid-ocean ridges, the DHF sulfide shows particularly high contents of Pb (263–2630 ppm), As (234–726 ppm), Sb (7.32–44.3 ppm), and Ag (35.2 to >100 ppm). The δ34S values exhibit an increasing tendency from the sample exterior to the interior. We propose that these features probably reflect the existence of a subsurface zone refining process. Our results provide new insight into the sulfide formation process and contribute to understanding the metallogenic mechanism of hydrothermal sulfides on ultraslow-spreading ridges.
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Medical classification is affected by many factors, and the traditional medical classification is usually restricted by factors such as too long text, numerous categories and so on. In order to solve these problems, this paper uses word vector and word vector to mine the text deeply, considering the problem of scattered key features of medical text, introducing long-term and short-term memory network to effectively retain the features of historical information in long text sequence, and using the structure of CNN to extract local features of text, through attention mechanism to obtain key features, considering the problems of many diseases, by using hierarchical classification. To stratify the disease. Combined with the above ideas, a deep DLCF model suitable for long text and multi-classification is designed. This model has obvious advantages in CMDD and other datasets. Compared with the baseline models, this model is superior to the baseline model in accuracy, recall and other indicators.
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With the fifth generation (5G) communication technology, the mobile multiuser networks have developed rapidly. In this paper, the performance analysis of mobile multiuser networks which utilize decode-and-forward (DF) relaying is considered. We derive novel outage probability (OP) expressions. To improve the OP performance, we study the power allocation optimization problem. To solve the optimization problem, we propose an intelligent power allocation optimization algorithm based on grey wolf optimization (GWO). We compare the proposed GWO approach with three existing algorithms. The experimental results reveal that the proposed GWO algorithm can achieve a smaller OP, thus improving system efficiency. Also, compared with other channel models, the OP values of the 2-Rayleigh model are increased by 81.2% and 66.6%, respectively.
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Seafloor massive sulfide (SMS) deposits are important deep-sea mineral resources expected to occur predominantly on slow- and ultraslow-spreading mid-ocean ridges. Resource estimates are already available for some of the largest SMS deposits on slow-spreading ridges but not on ultraslow-spreading ridges. Based on geological mapping and sampling, this study investigates the distribution and content of sulfide-rich deposits in the Yuhuang-1 hydrothermal field (YHF), located on the ultraslow-spreading Southwest Indian Ridge. The sulfide-rich deposits in the YHF are composed of two areas ∼500 m apart: the southwest sulfide area (SWS) and the northeast sulfide area (NES). We calculated the volume of sulfide-rich mounds in the YHF and arrived at a total accumulation of ∼10.6 × 106 tons, including at least ∼7.5 × 105 tons of copper and zinc and ∼18 tons of gold. Furthermore, considering the coverage of layered hydrothermal sediment mixed with sulfide-rich breccias, which may have underlying massive sulfide deposits, the maximum total mass was estimated at ∼45.1 × 106 tons. This suggests that the YHF is one of the largest SMS deposits worldwide and confirm that ultraslow-spreading ridges have the greatest potential to form large-scale SMS deposits.
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In the light of the many kinds of journeys that have been considered pilgrimages, this book uses phenomenology as a method to examine the claim that pilgrimage is a journey to the ‘center’ during which pilgrims seek meaning s for themselves. First, by analyzing a phenomenology of Christian pilgrimage, this work attempts to identify what commonalities, as well as differences, exist between Christian pilgrimage and secular pilgrimage in terms of ‘natural attitude’. Next, by using a phenomenological method, such as transcendental reduction, the distinction between these two types of pilgrimage could be clarified that the happiness sought in Christian pilgrimage is both intentionally spiritual and sustainable, while primarily intellectual or sensory in secular pilgrimage. Lastly, this work seeks to establish whether or not ‘being at leisure’ is the primary element for pilgrims whose aim is to attain an understanding of happiness during a pilgrimage
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The Dayingezhuang gold deposit in the Jiaodong district, eastern margin of the North China Craton is hosted in Mesozoic granitic rocks and consists of quartz-sulfide veins/veinlets and sulfide disseminations in alteration envelopes. Previous studies mainly focused on the geochronology, sources of ore-forming fluids and metals to investigate the ore genesis. However, enrichment mechanism of Au and other associated trace metals remain unclear. In this study, we present detailed textures and in-situ LA-ICP-MS trace-element compositions of different generations of pyrite, as well as EMP analysis of Au-bearing minerals to discuss the occurrence and enrichment mechanism of Au at this deposit. Three generations of pyrite (Py1, Py2 and Py3) formed during three hydrothermal ore stages (I, II, and III) at Dayingezhuang. Py1 occurs as disseminations in sericitic alteration assemblages and is characterized by low Au (mean 0.15 ppm), Ag, As and Te contents. The time-resolved depth-concentration profiles indicate that Au in Py1 mainly occurs as nanoparticles and/or micron-sized inclusions. Py2 can be further divided into the early undeformed Py2a and later Py2b, which is the product of deformed Py2a with different degrees of brittle to plastic deformation and recrystallization. Py2a in pyrite-siderite-quartz veins is relatively enriched in invisible Au (mean 0.41 ppm), Ag, As, and Te compared to Py1, and contains numerous micron-sized Au inclusions. In contrast, Py2b contains lesser invisible Au (0.21 ppm) and host abundant gold minerals along the grain boundaries and microfractures. Py3 in polymetallic sulfide veins has little Au. As a whole, Au in pyrite is positively correlated with Ag and Te, which is consistent with the results of EMP analysis showing the occurrence of Au as electrum, native gold and minor petzite in pyrite. Such evidences show that the deformation and recrystallization of auriferous Py2a potentially caused local remobilization of Au (mainly as micron-sized inclusion Au) via solid-state ductile flow and subsequent reconcentration of Au in microfractures of Py2b. The pyrite deformation and Au remobilization events were suggested to be related to the continuous reactivation of the regional Zhaoping Fault contemporaneous with gold mineralization. Our study highlights the importance of remobilization and reconcentration of Au triggered by syn-ore tectonic activities at Dayingezhuang and possibly other Au deposits in the Jiaodong district.
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This research aims to evaluate a Macau tea brand's social media advertising effectiveness with neuromarketing tools, including physiological monitoring that can measure emotional arousal. This research bridges the gap of social media marketing on Instagram for brands through the neuromarketing method. Data from 40 respondents were collected with iMotions software using neuroscientific tools. This research uses the stimuli of Guanding Teahouse, a newly established Macau tea brand, to evaluate social media advertising effectiveness. The neuroscientific tools – Galvanic Skin Response (GSR) sensors, Eye-tracking, Facial Expression Analysis (FEA) and emotion analysis are used to do the experiment. The data analysis was drawn from one representative respondent to measure the emotions and attention on the Instagram advertisements. Video 1 recorded 9 GSR peaks and Video 2 recorded 12 GSR peaks, both videos attention is ranging between 96-98 indexes. Results show that advertising videos should focus more on the products than the model. Moreover, the participant is more interested in Video 2, but the effectiveness of advertising is showing a lower focus on the brand and the tea. Future studies should consider comparing the video advertising effectiveness of Instagram stories and Instagram reels to prevent disruption of video on the stories ad.
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The invention of neuroscience has benefited medical practitioners and businesses in improving their management and leadership. Neuromarketing, a field that combines neuroscience and marketing, helps businesses understand consumer behaviour and how they respond to advertising stimuli. This study aims to investigate the consumer purchase intention and preferences to improve the marketing management of the brand, based on neuroscientific tools such as emotional arousal using Galvanic Skin Response (GSR) sensors, eye-tracking, and emotion analysis through facial expressions classification. The stimuli for the experiment are two advertisement videos from the Macau tea brand “Guanding Teahouse” followed by a survey. The experiment was conducted on 40 participants. 76.2% of participants that chose the same product in the first survey responded with the same choice of products in the second survey. The GSR peaks in video ad 1 measured a total of 60. On the other hand, video ad 2 counted a total of 55 GSR peaks. The emotions in ad1 and ad2 have similar responses, with an attention percentage of 76%. The results showed that ad1 has a higher engagement time of 11.1% and ad2 has 9.6%, but only 19 of the respondent’s conducted engagement in video ad1, and 31 showed engagement in video ad2. The results demonstrated that although ad 1 has higher engagement rates, the respondents are more attracted to video ad 2. Therefore, ad2 has better marketing power than ad 1. Overall, this study bridges the gap of no previous research on measuring tea brand advertisements with the neuroscientific method. The results provide valuable insights for marketers to develop better advertisements and marketing campaigns and understand consumer preferences by personalising and targeting advertisements based on consumers' emotional responses and behaviour of consumers' purchase intentions. Future research could explore advertisements targeting different demographics.
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