Ecotourism, past, current and future perspectives : a bibliometric review between 2001 to 2018

Abstract: Ecotourism is natural based travel that conserves the environment, sustains the well-being of the local communities, and involves environmental interpretation and education. A number of literature reviews have been published focusing on specific aspects of the ecotourism market segmentation, ecological impacts of wildlife viewing, and community-based ecotourism, but there has been minimal attention to critical areas such as quality control, the industry, external environments or institutions. In order to further promote related studies, it is important to conduct a comprehensive review on ecotourism so that recent research progresses can be summarized and future research directions can be identified. Accordungly, this paper aims to conduct a bibliometric review on ecotourism to glean the past, current and future perspectives on ecotourism. Based on 1,889 articles published from 2001 to 2018 and searched from Web of Science, a systematic method combining bibliometric analysis and network analysis is applied to uncover the dynamic trends, academic collaboration and research hotspots. Results show that the overall publication quantity had been gradually improved. The key journals include Journal of Hospitality and Tourism Management, Annals of Tourism Research, Conservation Biology and Biological Conservation. Authors from USA have the most publications and international co-authorships, followed by Australia and England, while the most influential institution is the Chinese Academy of Science followed by Griffith University. Moreover, research keywords have been identified, including ecotourism, management, biodiversity, national park, sustainability and sustainable tourism. In order to further improve research in this field, it is crucial to combine different methods so that more innovative perspectives can be presented. Research findings from this study will provide limitations, and suggestions for future research

SEPT: Towards Scalable and Efficient Visual Pre-Training

Recently, the self-supervised pre-training paradigm has shown great potential in leveraging large-scale unlabeled data to improve downstream task performance. However, increasing the scale of unlabeled pre-training data in real-world scenarios requires prohibitive computational costs and faces the challenge of uncurated samples. To address these issues, we build a task-specific self-supervised pre-training framework from a data selection perspective based on a simple hypothesis that pre-training on the unlabeled samples with similar distribution to the target task can bring substantial performance gains. Buttressed by the hypothesis, we propose the first yet novel framework for Scalable and Efficient visual Pre-Training (SEPT) by introducing a retrieval pipeline for data selection. SEPT first leverage a self-supervised pre-trained model to extract the features of the entire unlabeled dataset for retrieval pipeline initialization. Then, for a specific target task, SEPT retrievals the most similar samples from the unlabeled dataset based on feature similarity for each target instance for pre-training. Finally, SEPT pre-trains the target model with the selected unlabeled samples in a self-supervised manner for target data finetuning. By decoupling the scale of pre-training and available upstream data for a target task, SEPT achieves high scalability of the upstream dataset and high efficiency of pre-training, resulting in high model architecture flexibility. Results on various downstream tasks demonstrate that SEPT can achieve competitive or even better performance compared with ImageNet pre-training while reducing the size of training samples by one magnitude without resorting to any extra annotations.Comment: Accepted by AAAI 202

Genetic sources and diversity of the paddy field carp in the Pearl River basin inferred from two mitochondrial loci

Paddy field carp (PF-carp) is an economically important fish cocultured with rice in traditional agricultural systems. Several distinctive strains of PF-carp have been formed through years of artificial and cross breeding. There is a concern about the status of germplasm resources among PF-carp, since little is known about the genetic sources, diversity, or differentiation. In this study we collected 17 PF-carp populations covering Daotian carp (DTL), Ru Yuan No. 1 (RY), Jinbian carp (JBL), Shaijiang carp (SJL), and Wu carp (WL) along the Pearl River basin to explore their genetic sources and diversity using concatenated sequences of the mitochondrial cytochrome b gene and the D-Loop region. According to the haplotype network analyses, 1, 9, and 57 haplotypes originated from Cyprinus carpio carpio, Cyprinus carpio haematopterus and Cyprinus carpio rubrofuscus, respectively, confirming that genetic introgression has occurred in Pearl River PF-carp populations and Cyprinus carpio carpio was the most common species for genetic origin. The results showed that RY exhibited the lowest level of nucleotide diversity (π = 0.0011) due to high-intensity breeding and was significantly differentiated from the other four strains. PF-carp strains in these remote traditional systems tended to experience artificial selection and a lack of farmer connection that gradually increased genetic differentiation among strains. Notably, three populations of JBL exhibited significant high-level differentiation, since they originated from mountainous areas hindering farmers from fry exchange. In contrast, no significant differentiation was uncovered in the WL populations, since this strain is the most popular cultured strain and has undergone artificial exchange of parents and fry in many cultured regions. This study helps us to understand the status of germplasm resources among PF-carp and to trace their genetic origin before being introduced for local cultivation

Gate stress polarity dependence of AC bias temperature instability in silicon carbide MOSFETs

As the silicon carbide (SiC) power metal-oxide-semiconductor field-effect transistor (MOSFET) develops, increasing efforts are placed on ac bias temperature instability (AC BTI). It was reported that AC BTI becomes significant when and only when the gate stress is bipolar. A detailed study is made in this article to reveal the underpinning mechanism. A physical model is proposed to explain on how and why the gate stress bipolar affects the threshold drift. It is found that the gate stress polarity has to be carefully defined. As the model shows, it is the bipolar electric field, rather than the gate voltage itself, that speeds up the threshold voltage drift. It is hoped that this study provides a stepping stone toward the eventual understanding and management of AC BTI

VIGC: Visual Instruction Generation and Correction

The integration of visual encoders and large language models (LLMs) has driven recent progress in multimodal large language models (MLLMs). However, the scarcity of high-quality instruction-tuning data for vision-language tasks remains a challenge. The current leading paradigm, such as LLaVA, relies on language-only GPT-4 to generate data, which requires pre-annotated image captions and detection bounding boxes, suffering from understanding image details. A practical solution to this problem would be to utilize the available multimodal large language models (MLLMs) to generate instruction data for vision-language tasks. However, it's worth noting that the currently accessible MLLMs are not as powerful as their LLM counterparts, as they tend to produce inadequate responses and generate false information. As a solution for addressing the current issue, this paper proposes the Visual Instruction Generation and Correction (VIGC) framework that enables multimodal large language models to generate instruction-tuning data and progressively enhance its quality on-the-fly. Specifically, Visual Instruction Generation (VIG) guides the vision-language model to generate diverse instruction-tuning data. To ensure generation quality, Visual Instruction Correction (VIC) adopts an iterative update mechanism to correct any inaccuracies in data produced by VIG, effectively reducing the risk of hallucination. Leveraging the diverse, high-quality data generated by VIGC, we finetune mainstream models and validate data quality based on various evaluations. Experimental results demonstrate that VIGC not only compensates for the shortcomings of language-only data generation methods, but also effectively enhances the benchmark performance. The models, datasets, and code will be made publicly available

Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs

Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a list of non-discrete attributes for each entity. Intuitively, these attributes such as height, price or population count are able to richly characterize entities in knowledge graphs. This additional source of information may help to alleviate the inherent sparsity and incompleteness problem that are prevalent in knowledge graphs. Unfortunately, many state-of-the-art relational learning models ignore this information due to the challenging nature of dealing with non-discrete data types in the inherently binary-natured knowledge graphs. In this paper, we propose a novel multi-task neural network approach for both encoding and prediction of non-discrete attribute information in a relational setting. Specifically, we train a neural network for triplet prediction along with a separate network for attribute value regression. Via multi-task learning, we are able to learn representations of entities, relations and attributes that encode information about both tasks. Moreover, such attributes are not only central to many predictive tasks as an information source but also as a prediction target. Therefore, models that are able to encode, incorporate and predict such information in a relational learning context are highly attractive as well. We show that our approach outperforms many state-of-the-art methods for the tasks of relational triplet classification and attribute value prediction.Comment: Accepted at CIKM 201

Reversible splenial lesion syndrome in children: a retrospective study of 130 cases

BackgroundReversible splenial lesion syndrome (RESLES) is a new clinico-radiological syndrome. We retrospectively analyzed the clinical features of 130 children with RESLES in China, which is the largest case series available in the literature.MethodsThe clinical data of children diagnosed as RESLES in Jiangxi Provincial Children's Hospital between 2017 and 2023 were retrospectively analyzed. The 130 cases were divided into two groups: ≤ 3 years old group (group A) (n = 83) and > 3 years old group (group B) (n = 47). The chi-squared test or Fisher's test was used to evaluate the data.ResultsThe vast majority of patients (127/130 cases, 97.7%) had prodromal symptoms of infection. Preceding infections of the gastrointestinal tract were statistically more significant in group A (60/83, 72.3%) than in group B (11/47, 23.4%) (P < 0.05). Preceding infections of the respiratory tract were statistically more significant in group B (33/47, 70.2%) than in group A (17/83, 20.5%) (P < 0.05). Seizures were statistically more significant in group A (82/83, 98.8%) than in group B (24/47,51.1%) (P < 0.05). The disturbance of consciousness and headache/dizziness were statistically more significant in group B (27/47, 57.4%; 37/47, 78.7%) than in group A (3/83, 3.6%; 1/83, 1.2%), respectively (P < 0.05). Convulsions with mild gastroenteritis (CwG) were statistically more significant in group A (50/83, 60.2%) than in group B (8/47, 17.0%) (P < 0.05). However, encephalitis/encephalopathy was statistically more significant in group B (20/47, 42.6%) than in group A (10/83, 12.0%) (P < 0.05). MRI showed cytotoxic edema in typical locations (RESLES type-1 limited to the splenium of the corpus callosum and RESLES type-2 spread to the entire corpus callosum, adjacent white matter, or both). There was full recovery of the lesions of MRI in all cases from 3 days to 50 days after the initial examinations. All the children showed normal neurodevelopment.ConclusionInfection was the most common cause of RESLES. Infections of the gastrointestinal tract are common in ≤ 3 years old children, while infections of the respiratory tract are common in >3 years old children. Younger patients are more likely to develop convulsions, and older children were more likely to have symptoms with disturbance of consciousness and headache/dizziness. RESLES has characteristic MRI manifestations and a good prognosis

Transition metals on the (0001) surface of graphite: Fundamental aspects of adsorption, diffusion, and morphology

In this article, we review basic information about the interaction of transition metal atoms with the (0001) surface of graphite, especially fundamental phenomena related to growth. Those phenomena involve adatom-surface bonding, diffusion, morphology of metal clusters, interactions with steps and sputter-induced defects, condensation, and desorption. General traits emerge which have not been summarized previously. Some of these features are rather surprising when compared with metal-on-metal adsorption and growth. Opportunities for future work are pointed out