搜索结果：Journal of insect physiology

Rankings of scholarly journals based on citation data are often met with skepticism by the scientific community. Part of the skepticism is due to disparity between the common perception of journals' prestige and their ranking based on citation counts. A more serious concern is the inappropriate use of journal rankings to evaluate the scientific influence of authors. This paper focuses on analysis of the table of cross-citations among a selection of Statistics journals. Data are collected from the Web of Science database published by Thomson Reuters. Our results suggest that modelling the exchange of citations between journals is useful to highlight the most prestigious journals, but also that journal citation data are characterized by considerable heterogeneity, which needs to be properly summarized. Inferential conclusions require care in order to avoid potential over-interpretation of insignificant differences between journal ratings. Comparison with published ratings of institutions from the UK's Research Assessment Exercise shows strong correlation at aggregate level between assessed research quality and journal citation `export scores' within the discipline of Statistics.

An exploratory, descriptive analysis is presented of the national orientation of scientific, scholarly journals as reflected in the affiliations of publishing or citing authors. It calculates for journals covered in Scopus an Index of National Orientation (INO), and analyses the distribution of INO values across disciplines and countries, and the correlation between INO values and journal impact factors. The study did not find solid evidence that journal impact factors are good measures of journal internationality in terms of the geographical distribution of publishing or citing authors, as the relationship between a journal's national orientation and its citation impact is found to be inverse U-shaped. In addition, journals publishing in English are not necessarily internationally oriented in terms of the affiliations of publishing or citing authors; in social sciences and humanities also USA has their nationally oriented literatures. The paper examines the extent to which nationally oriented journals entering Scopus in earlier years, have become in recent years more international. It is found that in the study set about 40 per cent of such journals does reveal traces of internati

Automatic recognition of insect sound could help us understand changing biodiversity trends around the world -- but insect sounds are challenging to recognize even for deep learning. We present a new dataset comprised of 26399 audio files, from 459 species of Orthoptera and Cicadidae. It is the first large-scale dataset of insect sound that is easily applicable for developing novel deep-learning methods. Its recordings were made with a variety of audio recorders using varying sample rates to capture the extremely broad range of frequencies that insects produce. We benchmark performance with two state-of-the-art deep learning classifiers, demonstrating good performance but also significant room for improvement in acoustic insect classification. This dataset can serve as a realistic test case for implementing insect monitoring workflows, and as a challenging basis for the development of audio representation methods that can handle highly variable frequencies and/or sample rates.

This study examines the social media uptake of scientific journals on two different platforms - X and WeChat - by comparing the adoption of X among journals indexed in the Science Citation Index-Expanded (SCIE) with the adoption of WeChat among journals indexed in the Chinese Science Citation Database (CSCD). The findings reveal substantial differences in platform adoption and user engagement, shaped by local contexts. While only 22.7% of SCIE journals maintain an X account, 84.4% of CSCD journals have a WeChat official account. Journals in Life Sciences & Biomedicine lead in uptake on both platforms, whereas those in Technology and Physical Sciences show high WeChat uptake but comparatively lower presence on X. User engagement on both platforms is dominated by low-effort interactions rather than more conversational behaviors. Correlation analyses indicate weak-to-moderate relationships between bibliometric indicators and social media metrics, confirming that online engagement reflects a distinct dimension of journal impact, whether on an international or a local platform. These findings underscore the need for broader social media metric frameworks that incorporate locally dom

Overlay journals are characterised by their articles being published on open access repositories, often already starting in their initial preprint form as a prerequisite for submission to the journal prior to initiating the peer-review process. In this study we aimed to identify currently active overlay journals and examine their characteristics. We utilised an explorative web search and contacted key service providers for additional information. The final sample consisted of 34 overlay journals. While the results show that new overlay journals have been actively established within recent years, the current presence of overlay journals remains diminutive compared to the overall number of open access journals. Most overlay journals publish articles in natural sciences, mathematics or computer sciences, and are commonly published by groups of academics rather than formal organisations. They may also rank highly within the traditional journal citation metrics. None of the investigated journals required fees from authors, which is likely related to the cost-effective aspects of the overlay publishing model. Both the growth in adoption of open access preprint repositories and researcher

The journal structure in the China Scientific and Technical Papers and Citations Database (CSTPCD) is analysed from three perspectives: the database level, the specialty level and the institutional level (i.e., university journals versus journals issued by the Chinese Academy of Sciences). The results are compared with those for (Chinese) journals included in the Science Citation Index. The frequency of journal-journal citation relations in the CSTPCD is an order of magnitude lower than in the SCI. Chinese journals, especially high-quality journals, prefer to cite international journals rather than domestic ones. However, Chinese journals do not get an equivalent reception from their international counterparts. The international visibility of Chinese journals is low, but varies among fields of science. Journals of the Chinese Academy of Sciences (CAS) have a better reception in the international scientific community than university journals.

Using the Scopus dataset (1996-2007) a grand matrix of aggregated journal-journal citations was constructed. This matrix can be compared in terms of the network structures with the matrix contained in the Journal Citation Reports (JCR) of the Institute of Scientific Information (ISI). Since the Scopus database contains a larger number of journals and covers also the humanities, one would expect richer maps. However, the matrix is in this case sparser than in the case of the ISI data. This is due to (i) the larger number of journals covered by Scopus and (ii) the historical record of citations older than ten years contained in the ISI database. When the data is highly structured, as in the case of large journals, the maps are comparable, although one may have to vary a threshold (because of the differences in densities). In the case of interdisciplinary journals and journals in the social sciences and humanities, the new database does not add a lot to what is possible with the ISI databases.

In precision agriculture, the detection and recognition of insects play an essential role in the ability of crops to grow healthy and produce a high-quality yield. The current machine vision model requires a large volume of data to achieve high performance. However, there are approximately 5.5 million different insect species in the world. None of the existing insect datasets can cover even a fraction of them due to varying geographic locations and acquisition costs. In this paper, we introduce a novel "Insect-1M" dataset, a game-changing resource poised to revolutionize insect-related foundation model training. Covering a vast spectrum of insect species, our dataset, including 1 million images with dense identification labels of taxonomy hierarchy and insect descriptions, offers a panoramic view of entomology, enabling foundation models to comprehend visual and semantic information about insects like never before. Then, to efficiently establish an Insect Foundation Model, we develop a micro-feature self-supervised learning method with a Patch-wise Relevant Attention mechanism capable of discerning the subtle differences among insect images. In addition, we introduce Description Co

Multimodal conversational generative AI has shown impressive capabilities in various vision and language understanding through learning massive text-image data. However, current conversational models still lack knowledge about visual insects since they are often trained on the general knowledge of vision-language data. Meanwhile, understanding insects is a fundamental problem in precision agriculture, helping to promote sustainable development in agriculture. Therefore, this paper proposes a novel multimodal conversational model, Insect-LLaVA, to promote visual understanding in insect-domain knowledge. In particular, we first introduce a new large-scale Multimodal Insect Dataset with Visual Insect Instruction Data that enables the capability of learning the multimodal foundation models. Our proposed dataset enables conversational models to comprehend the visual and semantic features of the insects. Second, we propose a new Insect-LLaVA model, a new general Large Language and Vision Assistant in Visual Insect Understanding. Then, to enhance the capability of learning insect features, we develop an Insect Foundation Model by introducing a new micro-feature self-supervised learning wi

The question of whether insects experience pain has long been debated in neuroscience and animal behavior research. Increasing evidence suggests that insects possess the ability to detect and respond to noxious stimuli, exhibiting behaviors indicative of pain perception. This study investigates the relationship between pain stimuli and physiological responses in crickets (Gryllidae), focusing on heart rate (ECG) and brain wave (EEG) patterns. We applied a range of mechanical, chemical, thermal, and electrical stimuli to crickets, recording ECG and EEG data while employing a deep learning-based model to classify pain levels. Our findings revealed significant heart rate changes and EEG fluctuations in response to various stimuli, with the highest intensity stimuli inducing marked physiological stress. The AI-based analysis, utilizing AlexNet for EEG signal classification, achieved 90% accuracy in distinguishing between resting, low-pain, and high-pain states. While no social sharing of pain was observed through ECG measurements, these results contribute to the growing body of evidence supporting insect nociception and offer new insights into their physiological responses to external

A number of journal classification systems have been developed in bibliometrics since the launch of the Citation Indices by the Institute of Scientific Information (ISI) in the 1960s. These systems are used to normalize citation counts with respect to field-specific citation patterns. The best known system is the so-called "Web-of-Science Subject Categories" (WCs). In other systems papers are classified by algorithmic solutions. Using the Journal Citation Reports 2014 of the Science Citation Index and the Social Science Citation Index (n of journals = 11,149), we examine options for developing a new system based on journal classifications into subject categories using aggregated journal-journal citation data. Combining routines in VOSviewer and Pajek, a tree-like classification is developed. At each level one can generate a map of science for all the journals subsumed under a category. Nine major fields are distinguished at the top level. Further decomposition of the social sciences is pursued for the sake of example with a focus on journals in information science (LIS) and science studies (STS). The new classification system improves on alternative options by avoiding the problem

Approximately half of the existing winged-insect species are of very small size (wing length about 0.3-4 mm); they are referred to as miniature insects. Yet until recently, much of what we know about the mechanics of insect flight was derived from studies on relatively large insects, such as hoverflies, honey bees and hawkmoths. Because of their very small size, many miniature insects fly at a Reynolds number (Re) on the order of 10 or less. At such a low Re, the viscous effect of the air is very large: A miniature insect moves through the air as would a bumble bee move through mineral oil. Miniature insects must use new flapping mode and new aerodynamic mechanisms to fly. Over the past decade, much work has been done in the study of the mechanics of flight in miniature insects: novel flapping modes have been discovered and new mechanisms of aerodynamic force generation have been revealed; progress has also been made on the fluid-mechanics related flight problems, such as flight power requirements and flight dynamic stability. This article reviews these developments and discusses potential future directions.

Using three years of the Journal Citation Reports (2011, 2012, and 2013), indicators of transitions in 2012 (between 2011 and 2013) are studied using methodologies based on entropy statistics. Changes can be indicated at the level of journals using the margin totals of entropy production along the row or column vectors, but also at the level of links among journals by importing the transition matrices into network analysis and visualization programs (and using community-finding algorithms). Seventy-four journals are flagged in terms of discontinuous changes in their citations; but 3,114 journals are involved in "hot" links. Most of these links are embedded in a main component; 78 clusters (containing 172 journals) are flagged as potential "hot spots" emerging at the network level. An additional finding is that PLoS ONE introduced a new communication dynamics into the database. The limitations of the methodology are elaborated using an example. The results of the study indicate where developments in the citation dynamics can be considered as significantly unexpected. This can be used as heuristic information; but what a "hot spot" in terms of the entropy statistics of aggregated cit

Traditionally, studies in experimental physiology have been conducted in small groups of human participants, animal models or cell lines. Identifying optimal study designs that achieve sufficient power for drawing proper statistical inferences to detect group level effects with small sample sizes has been challenging. Moreover, average effects derived from traditional group-level inference do not necessarily apply to individual participants. Here, we introduce N-of-1 trials as an innovative study design that can be used to draw valid statistical inference about the effects of interventions on individual participants and can be aggregated across multiple study participants to provide population-level inferences more efficiently than standard group randomized trials. N-of-1 trials have been used in healthcare settings since the late 1980s, but without large-scale adoption and with few applications in experimental physiology research settings. In this manuscript, we introduce the key components and design features of N-of-1 trials, describe statistical analysis and interpretations of the results, and describe some available digital tools to facilitate their use using examples from exp

Using "Analyze Results" at the Web of Science, one can directly generate overlays onto global journal maps of science. The maps are based on the 10,000+ journals contained in the Journal Citation Reports (JCR) of the Science and Social Science Citation Indices (2011). The disciplinary diversity of the retrieval is measured in terms of Rao-Stirling's "quadratic entropy." Since this indicator of interdisciplinarity is normalized between zero and one, the interdisciplinarity can be compared among document sets and across years, cited or citing. The colors used for the overlays are based on Blondel et al.'s (2008) community-finding algorithms operating on the relations journals included in JCRs. The results can be exported from VOSViewer with different options such as proportional labels, heat maps, or cluster density maps. The maps can also be web-started and/or animated (e.g., using PowerPoint). The "citing" dimension of the aggregated journal-journal citation matrix was found to provide a more comprehensive description than the matrix based on the cited archive. The relations between local and global maps and their different functions in studying the sciences in terms of journal lit

Using Scopus data, we construct a global map of science based on aggregated journal-journal citations from 1996-2012 (N of journals = 20,554). This base map enables users to overlay downloads from Scopus interactively. Using a single year (e.g., 2012), results can be compared with mappings based on the Journal Citation Reports at the Web-of-Science (N = 10,936). The Scopus maps are more detailed at both the local and global levels because of their greater coverage, including, for example, the arts and humanities. The base maps can be interactively overlaid with journal distributions in sets downloaded from Scopus, for example, for the purpose of portfolio analysis. Rao-Stirling diversity can be used as a measure of interdisciplinarity in the sets under study. Maps at the global and the local level, however, can be very different because of the different levels of aggregation involved. Two journals, for example, can both belong to the humanities in the global map, but participate in different specialty structures locally. The base map and interactive tools are available online (with instructions) at http://www.leydesdorff.net/scopus_ovl.

This paper introduces the Unique Citing Documents Journal Impact Factor(Uniq-JIF) as a supplement to the traditional Journal Impact Factor(JIF). The Uniq-JIF counts each citing document only once, aiming to reduce the effects of citation manipulations. Analysis of 2023 Journal Citation Reports data shows that for most journals, the Uniq-JIF is less than 20% lower than the JIF, though some journals show a drop of over 75%. The Uniq-JIF also highlights significant reductions for journals suppressed due to citation issues, indicating its effectiveness in identifying problematic journals. The Uniq-JIF offers a more nuanced view of a journal's influence and can help reveal journals needing further scrutiny.

The purpose of the Insect Detection System for Crop and Plant Health is to keep an eye out for and identify insect infestations in farming areas. By utilizing cutting-edge technology like computer vision and machine learning, the system seeks to identify hazardous insects early and accurately. This would enable prompt response to save crops and maintain optimal plant health. The Method of this study includes Data Acquisition, Preprocessing, Data splitting, Model Implementation and Model evaluation. Different models like MobileNetV2, ResNet152V2, Xecption, Custom CNN was used in this study. In order to categorize insect photos, a Convolutional Neural Network (CNN) based on the ResNet152V2 architecture is constructed and evaluated in this work. Achieving 99% training accuracy and 97% testing accuracy, ResNet152V2 demonstrates superior performance among four implemented models. The results highlight its potential for real-world applications in insect classification and entomology studies, emphasizing efficiency and accuracy. To ensure food security and sustain agricultural output globally, finding insects is crucial. Cutting-edge technology, such as ResNet152V2 models, greatly influen

Insect population numbers and biodiversity have been rapidly declining with time, and monitoring these trends has become increasingly important for conservation measures to be effectively implemented. But monitoring methods are often invasive, time and resource intense, and prone to various biases. Many insect species produce characteristic sounds that can easily be detected and recorded without large cost or effort. Using deep learning methods, insect sounds from field recordings could be automatically detected and classified to monitor biodiversity and species distribution ranges. We implement this using recently published datasets of insect sounds (Orthoptera and Cicadidae) and machine learning methods and evaluate their potential for acoustic insect monitoring. We compare the performance of the conventional spectrogram-based audio representation against LEAF, a new adaptive and waveform-based frontend. LEAF achieved better classification performance than the mel-spectrogram frontend by adapting its feature extraction parameters during training. This result is encouraging for future implementations of deep learning technology for automatic insect sound recognition, especially as