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Insights on global rangeland ecosystem services shaped by grazing and fertilization
Get PDFRangelands are crucial to human well‐being, but their ability to provide ecosystem services is threatened. We (1) quantified key ecosystem services provided by rangelands, (2) assessed short‐ and long‐term impacts of fertilization (nutrient addition) and the exclusion of large grazing herbivores with fences (herbivore exclusion) on services, and (3) identified synergies and trade‐offs among services. We measured indicators of ecosystem services and plant diversity at 79 sites across six continents in the global Nutrient Network. Short‐term herbivore exclusion increased forage quantity and soil fertility, but longer‐term herbivore exclusion decreased both along with plant richness and pollination. Nutrient addition improved forage provisioning, soil stability, climate regulation, and control of soil erosion but lowered plant diversity and impeded delivery of related services, especially after prolonged application. We found synergies between plant diversity and pollination, as well as between soil fertility, soil stability, and climate regulation. Trade‐offs between forage stability and quality persisted after nutrient addition but disappeared with herbivore exclusion. Our results suggest that alternative management actions may sustain livestock production while maintaining rangeland ecosystem services
Confidence intervals and point estimates for treatment effects in adaptive enrichment designs
Get PDFAdaptive enrichment designs allow subgroup selection of the patient population within a confirmatory trial via an interim analysis. However, this design complicates treatment effect estimation and uncertainty quantification. This paper introduces a -value inversion method using various sample space orderings to construct confidence intervals either unconditionally or conditional on the subgroup selected for a general class of two-stage two-group designs. In addition, the p-value functions can be used to derive median-unbiased estimators and conditional moment estimators. Through simulation it is shown that the proposed intervals have close to nominal coverage, in contrast to naive confidence intervals based on the maximum likelihood estimator. Moreover, the median-unbiased estimators and conditional moment estimators have good performance with respect to median and mean bias, respectively. The method is illustrated by a re-analysis of a trial investigating treatment interactions with KRAS mutation type in patients with metastatic colorectal cancer
Prompt-guided selective frequency network for real-world scene text image super-Resolution
No full textReal-world scene text image super-resolution is challenging due to complex writing strokes, random text distribution, and diverse scene degradations. Existing text super-resolution methods focus on pure text images or fixed-size single-line text, which limits their practical utility. To address that, we propose a Prompt-Guided Selective Frequency super-resolution Network (PGSFNet). Our unique bicephalous neural model comprises a super-resolution branch and a prompt guidance branch. The latter specifically helps in leveraging text content-aware information priors. To that end, we propose a Text Information Enhancement module. To exploit selective frequency information present in the image, PGSFNet employs a proposed Adaptive Frequency Modulator fused with multi-attention structures. Considering the criticality of text edges in our task, we also propose a tailored text edge perception loss. Extensive experiments on the standard open real-world scene text image datasets demonstrate remarkable performance of our method, achieving up to 8.75% PNSR gain for × 2 and 2.28% SSIM gain for × 4 super-resolution on the Real-CE dataset. Our code will be made public at https://github.com/holastq/PGSFNet
Hearing the Neuro-Voice in the International Narrative Neurology Network (INNN)
No full textClaire Jeantils, Rong Huang, and Benjamin Dalton unravel the stories behind the International Narrative Neurology Network (INNN), a cross-sectoral network that investigates Narrative Neurology from a practical and theoretical perspective
Dual Event-Triggered Polynomial Dynamic Output Control for Positive Fuzzy Systems via an IT2 Membership Function Relaxation Method
Get PDFThe co-design problem of dual event-triggered (DET) mechanism and polynomial dynamic output-feedback (PDOF) controller is investigated for positive polynomial fuzzy systems (PPFSs) with uncertainty and disturbance constraints. Specifically, a 1-norm DET mechanism compatible with the positivity of PPFSs is proposed to asynchronously update measurement outputs and PDOF control signals. However, synthesizing this DET-PDOF controller proves challenging due to the coupling of multiple unknown PDOF controller gain matrices within the positivity and stability conditions, which results in complex nonconvex terms. By introducing auxiliary variables and constraints, sufficient conditions for DET-PDOF controller solution are given to ensure both the L1 -gain performance and strict positivity of PPFSs with uncertainty and disturbance. Moreover, existing stability analysis results that ignore membership functions (MFs) tend to be conservative, implying that the obtained DET-PDOF controller is effective only within a limited triggered threshold range, leading to worse transmission performance. Therefore, a multivariate optimization method based on an improved genetic algorithm (IGA), which accounts for the system states and PDOF controller variables, is developed to substantially expand the admissible DET threshold range while effectively suppressing dual-triggering frequencies. Finally, a numerical example and a two-linked tank system with parameter uncertainty are provided to validate the feasibility of the proposed scheme
Emotive content and sleep enhance memory for metaphorical language
Get PDFMemory for emotional information is greater than for non-emotional information, and is enhanced by sleep-related consolidation. Previous studies have focused on emotional arousal and valence of established stimuli, but what is the effect of sleep on newly acquired emotional information? Figurative expressions, which are pervasive in everyday communication, are often rated as higher in emotionality than their literal counterparts, but the effect of emotionality on the learning of metaphors, and the effect of sleep on newly acquired emotionally negative, positive, and neutral language is as yet poorly understood. In this study, participants were asked to memorise conventional (e.g., “sunny disposition”) and novel (e.g., “cloudy disposition”) metaphorical word pairs varying in valence, accompanied by their definitions. After a 12-hour period of sleep or wake, participants were tested on their recognition of word pairs and recall of definitions. We found higher arousal ratings were related to increased recognition and recall performance. Furthermore, sleep increased accurate recognition of all word pairs compared to wake, but also reduced the valence of word pairs. The results indicate better memory for newly acquired emotional stimuli, a benefit of sleep for memory, but also a reduction of emotional arousal as a consequence of sleep consolidation
Revisiting chloroform emissions from the pulp and paper sector : a brief communication
No full textHalogenated very short-lived substances (VSLS) represent a growing source of chlorine to the stratosphere where they may contribute to ozone layer depletion. Chloroform (CHCl3) is a prominent VSLS with poorly constrained anthropogenic sources that include its unintentional production when wood pulp is bleached for paper production. Recent assessments of the global CHCl3 budget have relied on emission factors (EFs) for the pulp/paper (PP) sector derived some 35 years ago when industrial practices were markedly different. Here, we analysed data from the Pollutant Release and Transfer Registers of the USA, Canada and Japan. Combined with data on the national number of pulp mills and bleached wood pulp production volume, we derive plausible lower and upper limit EFs. These factors show a downward trend since the early 2000s, which we attribute to a continued phase-down in the use of ‘elemental chlorine’ bleaching in favour of ‘elemental chlorine free’ bleaching. The derived mean EFs for the period 2000–2020, expressed as the mass of CHCl3 per air–dried tons (adt) of bleached pulp, are in close agreement for the regions considered: USA (39.8 ± 32 g/adt), Canada (38.6 ± 29.8 g/adt) and Japan (30.1 ± 8.6 g/adt). Assuming these factors are broadly representative of other world regions, a mean annual global CHCl3 source of 3 (1–6) Gg yr−1 from the PP sector is estimated for the approximate 2000–2020 period. We conclude that the sector’s contribution to the global CHCl3 budget has likely decreased considerably since the 1990s and that the use of older EFs to calculate present-day emissions should be avoided
A Lightweight and Privacy-preserving Distributed Multidimensional Data Trend Query Scheme with Fault-tolerant for Machine-as-a-Service
Get PDFIn the Machine-as-a-Service (MaaS) model, enterprises can significantly reduce production costs by leasing devices from original equipment manufacturers (OEM), while OEM can enhance device quality by utilizing device data shared by enterprises. As such, MaaS is emerging as a very promising paradigm in modern manufacturing. However, the multidimensional data trend formed by the multidimensional data may leak the private production data of enterprises, particularly when the OEM leases the same type of device to enterprises. Currently, there is no targeted and feasible solution to ensure the privacy, integrity, and fault-tolerant of multi-user and multidimensional data in the MaaS model. To address this challenge, we propose a lightweight, privacy-preserving and fault-tolerant distributed multidimensional data trend query scheme for MaaS. The proposed scheme ensures multidimensional data privacy through local differential privacy (LDP), and guarantees fault-tolerant and data integrity using Shamir secret sharing and hash-based message authentication code (mac). To protect the privacy of multidimensional data aggregation trend, we design a weighted noise injection query algorithm based on LDP. Additionally, the scheme mitigates the risk of data leakage by introducing the blockchain (BC) instead of cloud server (CS). We formally prove the security of our proposed scheme, and the experimental evaluation demonstrates that it outperforms existing schemes in terms of computation and communication overhead
BISE : Enhance data sharing security through consortium blockchain and IPFS
Get PDFData sharing is pivotal in sectors such as healthcare, finance, and social networking. Encrypting sensitive data, while essential for privacy protection, introduces complexity to data sharing and poses privacy risks when leveraging cloud servers. Blockchain-based searchable encryption offers a balance between privacy preservation and data availability; however, user anonymity remains a significant concern. Traditional storage systems, which rely on centralized servers, limit data stability and scalability. To address these challenges, we have introduced BISE, a solution that leverages the power of blockchain to achieve data integrity, using searchable encryption for secure searches and IPFS for decentralized storage. Constructed on Hyperledger Fabric and IPFS, our system demonstrates efficiency through simulations. This integrated approach ensures data privacy, integrity, and availability, with efficient updates and queries, making it a robust solution for sensitive data sharing in various domains
Euclid: Early Release Observations. Weak gravitational lensing analysis of Abell 2390
No full textThe Euclid space telescope of the European Space Agency (ESA) is designed to provide sensitive and accurate measurements of weak gravitational lensing distortions over wide areas on the sky. Here we present a weak gravitational lensing analysis of early Euclid observations obtained for the field around the massive galaxy cluster Abell 2390 as part of the Euclid Early Release Observations programme. We conduct galaxy shape measurements using three independent algorithms (LensMC, KSB+, and SourceXtractor++). Incorporating multi-band photometry from Euclid and Subaru/Suprime-Cam, we estimate photometric redshifts to preferentially select background sources from tomographic redshift bins, for which we calibrate the redshift distributions using the self-organising map approach and data from the Cosmic Evolution Survey (COSMOS). We quantify the residual cluster member contamination and correct for it in bins of photometric redshift and magnitude using their source density profiles, including corrections for source obscuration and magnification. We reconstruct the cluster mass distribution and jointly fit the tangential reduced shear profiles of the different tomographic bins with spherical Navarro--Frenk--White profile predictions to constrain the cluster mass, finding consistent results for the three shape catalogues and good agreement with earlier measurements. As an important validation test we compare these joint constraints to mass measurements obtained individually for the different tomographic bins, finding good consistency. More detailed constraints on the cluster properties are presented in a companion paper that additionally incorporates strong lensing measurements. Our analysis provides a first demonstration of the outstanding capabilities of Euclid for tomographic weak lensing measurements