A New Species of Genus Microhyla (Amphibia: Anura: Microhylidae) from Zhejiang Province, China

We described a new species, Microhyla beilunensis sp. nov., from Zhejiang Province of China. Phylogenetic analyses based on the mitochondrial 12S, 16S and CO1 gene sequences suggested that the new taxon was distinctly separated from its congeners and closed to M. mixtura and M. okinavensis. Morphologically, the new species could be identified from its congeners except M. mixtura by several characters: (1) rudimentary webs on toe base; (2) absence of disks and dorsal median longitudinal grooves on finger tips; (3) presence of disks and dorsal median longitudinal grooves on toe tips. As well, the new species could be identified from topotype M. mixtura by the combination of characters: (1) apart from the stripes, bar-shaped and oval-shaped patterns, the rounded spots present on the dorsum of body and legs; (2) the outer metacarpal tubercles prominently larger than the inner one; (3) of males, the ratios of HW, IND, UEW and LAW to SVL of the new species were significantly larger than those of M. mixtura (P < 0.01), and the ratios of SL, IOD, LAHL, HLL, TL, TFL and FL to SVL of the new species were significantly less than those of M. mixtura (P < 0.05)

Evaluation Research on Green Operation Development of International Aviation

At present, with the rapid development of the global economy, the aviation industry, as a key part of the modern transportation system, is playing an increasingly important role. However, with the intensification of global warming and the continuous improvement of people's environmental protection awareness, the aviation industry is facing huge pressure for green transformation. Against this background, this paper conducts a study on the development of international aviation green operations. This paper first elaborates on the research background, the impact of greenhouse gas emissions from the aviation industry on the environment while promoting global economic development and communication, as well as the urgent global demand for the green transformation of the aviation industry. Then it explains the theoretical and practical significance of studying the green operation and development of international aviation, and introduces the research methods and ideas. Secondly, in the literature review section, The research status of green aviation operation at home and abroad was sorted out. The definition, connotation and related technical standards of green aviation operation were analyzed. The existing research was summarized and generalized. Then this paper selects two typical cases, Emirates Airlines and Southwest Airlines, and quantitatively evaluates their green operation levels by using AHP (Analytic Hierarchy Process). It analyzes the advantages and disadvantages of the two airlines in terms of environmental performance, energy efficiency and operation management, and puts forward corresponding improvement measures and development suggestions. Meanwhile, combined with SWOT analysis, The internal disadvantages and external threats of the two airlines were further explored, providing more targeted strategic guidance for their green transformation. Finally, in the conclusion and outlook section of this paper, the re-search results are summarized, the importance of the green transformation of the aviation industry is emphasized, and some constructive opinions and suggestions are put forward, hoping to contribute to the sustainable development of airlines of the same type

Physiologically based modelling of cerebral autoregulation

The human brain requires sufficient and continuous blood supply to maintain healthy function. Cerebral autoregulation (CA), a highly complex mechanism, plays a crucial role in achieving this function. Increasing evidence confirms the close association between cerebral haemodynamic abnormalities and the occurrence of cerebrovascular diseases, brain dysfunction, and cognitive impairment. To complement clinical trials and improve clinical outcomes, researchers use mathematical models to understand disease progression and test the feasibility of new therapies. However, existing cerebral autoregulation models are predominantly purely mathematical, making it challenging to relate simulation results to clinically measured parameters. Therefore, there is a need to develop CA models based on physiological mechanisms to enhance our understanding of brain physiology and diseases, and to facilitate the clinical translation of cerebral autoregulation. In this study, we first conducted a data-driven analysis of arterial blood pressure (ABP) and cerebral blood flow (CBF) under different physiological and pathological conditions, providing the latest insights into the human cerebral pressure-flow relationship and serving as a reference for developing new mathematical models. Subsequently, we proposed the first physiologically-based mathematical model capable of simulating both activation and autoregulatory responses. To ensure a solid physio- logical foundation, a single vessel model was first constructed, which was then scaled up to a whole-brain vasculature model using the analogy between cerebral vasculature and electrical circuits. This model not only replicated typical autoregulation curves but also the BOLD response. This CA model was subsequently coupled with a multi- compartment porous Finite Element model, successfully incorporates autoregulation mechanisms into the computational modelling of brain oedema and osmotherapy. The improved model demonstrated closer alignment with clinical data, particularly enhancing consistency in managing intracranial pressure during the rebound phase following osmotherapy. Overall, the combined approach adopted in this study offers crucial insights into the physiological pathways of cerebral autoregulation

Staying vigilant in the Age of AI: From content generation to content authentication

This paper presents the Yangtze Sea project, an initiative in the battle against Generative AI (GAI)-generated fake con-tent. Addressing a pressing issue in the digital age, we investigate public reactions to AI-created fabrications through a structured experiment on a simulated academic conference platform. Our findings indicate a profound public challenge in discerning such content, highlighted by GAI's capacity for realistic fabrications. To counter this, we introduce an innovative approach employing large language models like ChatGPT for truthfulness assess-ment. We detail a specific workflow for scrutinizing the authenticity of everyday digital content, aimed at boosting public awareness and capability in identifying fake mate-rials. We apply this workflow to an agent bot on Telegram to help users identify the authenticity of text content through conversations. Our project encapsulates a two-pronged strategy: generating fake content to understand its dynamics and developing assessment techniques to mitigate its impact. As part of that effort we propose the creation of speculative fact-checking wearables in the shape of reading glasses and a clip-on. As a computational media art initiative, this project under-scores the delicate interplay between technological progress, ethical consid-erations, and societal consciousness.Comment: ISEA 2024 full paper https://isea2024.isea-international.org/academic-program/ conference paper, 8 page

Use of X-ray to identify contaminants in pelleted seed lots for biosecurity : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science (Agricultural Science) at Massey University, Manawatū, New Zealand

The following Figures were removed for copyright reasons, but may be accessed via their respective source: Figs 16 & 17 (=Landis & Keane, 2010, Figs 1 & 3) and Figs 18-24 (=Blott & Pye, 2008, Figs 1, 8, 9, 14, 15, 16, 17).Thousands of tonnes of seed, of which around 10% is pelleted, comes into New Zealand through international trade every year. However, this trade also brings potential risks to New Zealand biosecurity. Pelleted seeds can contain contaminants, including seeds other than the crop species in the seed lot and inert matter; both may cause negative effects on crop growth or bring pests and diseases. A reliable method is necessary to inspect seed lots for the contaminants. The conventional way to inspect for contaminants in pelleted seeds is to separate the seeds from pellets and inspect visually. However, this is a time consuming and potentially health damaging procedure. A faster and safer non-invasive inspection method is needed urgently. X-ray imaging systems have the potential to non-invasively identify contaminants in seed lots. 2-D X-ray was firstly applied in this research to determine if the system could separate non-target seeds such as weed seed from naked crop “target” seeds, since if 2-D X-ray cannot separate non-target seeds from naked target seeds, there is little chance to separate seeds that are pelleted. In this research, three target species were used. These were beet (Rapistrum, Ranunculus and spinach as contaminants), carrot (Polygonum, Chenopodium and Solanum as contaminants) and lettuce (Sonchus and Lapsana as contaminants), because of their high contamination rates in imported seed lots. Seed shape parameters: dimensions, form, circularity, roughness and intensity, were used to characterize seeds for further comparison. The results showed Ranunulus can be separated from beet by dimensions and intensity; Rapistrum can be separated by elongation, circularity and intensity; spinach was hard to separate from beet. In the carrot group, Chenopodium and Solanum can be separated from carrot by either dimensions, elongation or circularity, while Polygonum cannot be separated from Carrot. For contaminants in lettuce, Sonchus can be separated from lettuce by dimensions and intensity; Lapsana can be separated by elongation and circularity. However, all the separation above was based on mean values, seeds with extreme sizes would limit the effects of shape parameters in seed separation. Determining if pelleting seeds can also be separated using the same parameters was the next important step for determining if 2-D X-ray can be used for pelleted seed inspection. However, little literature can be found regarding specific pelleting materials and pelleting procedures, as they are held by the seed companies. Therefore, protocols for pelleting the relatively small numbers of pelleted seed for research are needed. During several trials on seed pelleting, Methocel™ and gypsum was identified as suitable pelleting materials. The vortex mixer was identified as the best equipment for pelleting using a one-by-one adding method, which was feasible for pelleting both tiny-seeds and small-quantities seeds. The seeds pelleted showed a uniform and well-rounded appearance. However, when applying the same 2-D X-ray for seed separation, the seed projections were hard to be extracted for further analysis, because of the poor differentiation between seeds and pellets. This research explored the potential of using 2-D X-ray to separate naked non-target seed from naked target seeds by seed shape parameters. The outcomes confirmed that the mean values of shape parameters can separate contaminants from target seeds, however at the extreme ends of the range seed parameters overlap will limit the value of the shape parameters. Pelleting seeds under laboratory conditions can also be realized by using vortex mixer as equipment and using Methocel™ and gypsum as pelleting materials. Nonetheless, 2-D X-ray was not a reliable tool to detect pelleted seeds, since it is hard to separate seed projections from pellets with images only from a top view. 3-D X-ray could potentially be applied in future research because of its higher resolution than 2-D X-ray. In addition, 3-D X-ray images enable analysts to analyze seeds from different angles other than one fixed angle, which makes the analysis free from image overlap problems. Although research on 3-D X-ray for seed separation is at its beginning, it is potentially useful for pelleted seed analysis

WavSpA: Wavelet Space Attention for Boosting Transformers' Long Sequence Learning Ability

Transformer and its variants are fundamental neural architectures in deep learning. Recent works show that learning attention in the Fourier space can improve the long sequence learning capability of Transformers. We argue that wavelet transform shall be a better choice because it captures both position and frequency information with linear time complexity. Therefore, in this paper, we systematically study the synergy between wavelet transform and Transformers. We propose Wavelet Space Attention (WavSpA) that facilitates attention learning in a learnable wavelet coefficient space which replaces the attention in Transformers by (1) applying forward wavelet transform to project the input sequences to multi-resolution bases, (2) conducting attention learning in the wavelet coefficient space, and (3) reconstructing the representation in input space via backward wavelet transform. Extensive experiments on the Long Range Arena demonstrate that learning attention in the wavelet space using either fixed or adaptive wavelets can consistently improve Transformer's performance and also significantly outperform learning in Fourier space. We further show our method can enhance Transformer's reasoning extrapolation capability over distance on the LEGO chain-of-reasoning task

Distributed Invariant Kalman Filter for Cooperative Localization using Matrix Lie Groups

This paper studies the problem of Cooperative Localization (CL) for multi-robot systems, where a group of mobile robots jointly localize themselves by using measurements from onboard sensors and shared information from other robots. We propose a novel distributed invariant Kalman Filter (DInEKF) based on the Lie group theory, to solve the CL problem in a 3-D environment. Unlike the standard EKF which computes the Jacobians based on the linearization at the state estimate, DInEKF defines the robots' motion model on matrix Lie groups and offers the advantage of state estimate-independent Jacobians. This significantly improves the consistency of the estimator. Moreover, the proposed algorithm is fully distributed, relying solely on each robot's ego-motion measurements and information received from its one-hop communication neighbors. The effectiveness of the proposed algorithm is validated in both Monte-Carlo simulations and real-world experiments. The results show that the proposed DInEKF outperforms the standard distributed EKF in terms of both accuracy and consistency