bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transformer fusion-based scale-aware attention network for multispectral victim detection
Authors: Chen Yunfan , Li Yuting , Zheng Wenqi , Wan Xiangkui 
Published: June 2024

AbstractThe aftermath of a natural disaster leaves victims trapped in rubble which is challenging to detect by smart drones due to the victims in low visibility under the adverse disaster environments and victims in various sizes. To overcome the above challenges, a transformer fusion-based scale-aware attention network (TFSANet) is proposed to overcome adverse environmental impacts in disaster areas by robustly integrating the latent interactions between RGB and thermal images and to address the problem of various-sized victim detection. Firstly, a transformer fusion model is developed to incorporate a two-stream backbone network to effectively fuse the complementary characteristics between RGB and thermal images. This aims to solve the problem that the victims cannot be seen clearly due to the adverse disaster area, such as smog and heavy rain. In addition, a scale-aware attention mechanism is designed to be embedded into the head network to adaptively adjust the size of receptive fields aiming to capture victims with different scales. Extensive experiments on two challenging datasets indicate that our TFSANet achieves superior results. The proposed method achieves 86.56% average precision (AP) on the National Institute of Informatics—Chiba University (NII-CU) multispectral aerial person detection dataset, outperforming the state-of-the-art approach by 4.38%. On the drone-captured RGBT person detection (RGBTDronePerson) dataset, the proposed method significantly improves the AP of the state-of-the-art approach by 4.33%.
Joint entity and relation extraction combined with multi-module feature information enhancement
Authors: Li Yao , Yan He , Zhang Ye , Wang Xu 
Published: June 2024

AbstractThe proposed method for joint entity and relation extraction integrates the tasks of entity extraction and relation classification by sharing the encoding layer. However, the method faces challenges due to incongruities in the contextual information captured by these subtasks, resulting in potential feature conflicts and adverse effects on model performance. To address this, we introduced a novel joint entity and relation extraction method that incorporates multi-module feature information enhancement (MFIE) (https://github.com/liyao345496280/Relation-extraction). We employ a relation awareness enhancement module for the entity extraction task, which directs the model’s focus towards extracting entities closely related to potential relations using a potential relation extraction module and an attention mechanism. For the relation extraction task, we implement an entity information enhancement module that uses entity extraction results to augment the original feature information through a gating mechanism, thereby enhancing relation classification performance. Experiments on the NYT and WebNLG datasets demonstrate that our method performs well. Compared to the state-of-the-art method, the F1 score on the NYT dataset improved by 0.7%.
Minimal resolutions of Iwasawa modules
Authors: Kataoka Takenori , Kurihara Masato 
Published: June 2024

AbstractIn this paper, we study the module-theoretic structure of classical Iwasawa modules. More precisely, for a finite abelianp-extensionK/kof totally real fields and the cyclotomic$$\mathbb {Z}_p$$Zp-extension$$K_{\infty }/K$$K∞/K, we consider$$X_{K_{\infty },S}={{\,\textrm{Gal}\,}}(M_{K_{\infty },S}/K_{\infty })$$XK∞,S=Gal(MK∞,S/K∞)whereSis a finite set of places ofkcontaining all ramifying places in$$K_{\infty }$$K∞and archimedean places, and$$M_{K_{\infty },S}$$MK∞,Sis the maximal abelian pro-p-extension of$$K_{\infty }$$K∞unramified outsideS. We give lower and upper bounds of the minimal numbers of generators and of relations of$$X_{K_{\infty },S}$$XK∞,Sas a$$\mathbb {Z}_p[[{{\,\textrm{Gal}\,}}(K_{\infty }/k)]]$$Zp[[Gal(K∞/k)]]-module, using thep-rank of$${{\,\textrm{Gal}\,}}(K/k)$$Gal(K/k). This result explains the complexity of$$X_{K_{\infty },S}$$XK∞,Sas a$$\mathbb {Z}_p[[{{\,\textrm{Gal}\,}}(K_{\infty }/k)]]$$Zp[[Gal(K∞/k)]]-module when thep-rank of$${{\,\textrm{Gal}\,}}(K/k)$$Gal(K/k)is large. Moreover, we prove an analogous theorem in the setting thatK/kis non-abelian. We also study the Iwasawa adjoint of$$X_{K_{\infty },S}$$XK∞,S, and the minus part of the unramified Iwasawa module for a CM-extension. In order to prove these theorems, we systematically study the minimal resolutions of$$X_{K_{\infty },S}$$XK∞,S.
RMSxAI: arginine methylation sites prediction from protein sequences using machine learning algorithms and explainable artificial intelligence
Authors: Dwivedi Gaurav , Khandelwal Monika , Rout Ranjeet Kumar , Umer Saiyed , Mallik Saurav , Qin Hong 
Published: June 2024

AbstractProtein methylation is a vital regulator of many biological processes at the post-translational level, and accurate prediction of protein methylation sites is essential for research and drug discovery. In this paper, we present a new method, namely RMSxAI, to predict the arginine methylation sites from primary sequences using machine learning algorithms and describe the predictions using explainable artificial intelligence (XAI) techniques. Leveraging experimentally validated methylated and unmethylated protein sequences from diverse organisms, we deduced several sequence features, encompassing physicochemical properties, amino acid composition, and evolutionary insights. Our results show that the proposed RMSxAI can predict protein methylation sites with high accuracy, bringing the F1 score up to 0.88 and overall accuracy up to 88.4%. We use various XAI methods to explain the output results. These include key features, partial occupancy maps, and local variation models that provide insight into key features and interactions that lead to predictions. Overall, our approach is relevant to research and drug discovery, and our results demonstrate the potential of machine learning algorithms and XAI methods to provide accurate and meaningful prediction of arginine methylation sites.
Efficiency optimization methods for stochastic configuration networks
Authors: Yan Aijun , He Shixiao 
Published: June 2024

AbstractTo solve the problems of the low efficiency of parameter allocation and time-consuming computation of output weights in the hidden layers of stochastic configuration networks (SCNs), an optimization method is proposed to improve the SCNs construction efficiency. Firstly, with the increase in the number of hidden layer nodes, the key parameters that determine the strictness of the supervision mechanism are reconstructed to speed up the configuration efficiency of hidden layer input weights and biases. Then, the incremental mechanism of the SCNs are combined with the QR decomposition method, and the output weights are calculated by iteratively updating the transformation matrix, thus reducing the computational complexity of training the SCNs. Finally, the proposed method is validated on four standard datasets and historical data of a municipal solid waste incineration process. The experimental results show that the proposed method improves the efficiency of SCN construction while guaranteeing the prediction accuracy of SCNs model.
Secluding the vegetation of India in retaliation to tropospheric ozone: a mechanistic approach
Authors: Singh Harshita , Gupta Akanksha , Yadav Durgesh Singh , Singh Priyanka , Singh Pallavi , Agrawal Shashi Bhushan , Agrawal Madhoolika 
Published: June 2024

AbstractRapid urban development and population outbursts in India have led to a tremendous increase in pollutant emissions and their transboundary dispersion. Hence, the increase in tropospheric ozone (O3) formation is further amplified depending on the meteorology of the area. This review attempts to compile the studies reporting tropospheric O3-induced loss of yield and biomass in the vegetation of the Indian sub-continent from 2012 to 2023, with a mechanistic approach. The response of vegetation (agricultural, horticultural, perennial, medicinal and grassland) to O3 have been collated and their order of sensitivity has been established. The vegetation displayed two significant strategies to cope with the O3 induced stress- stomatal flux regulation and shifting the photoassimilates towards either defense or reproduction (trade-off), which decides the plant's ability to respond towards O3. Considering the varying responses of plants, it was observed that plants that adopted both stomatal regulation and trade-off strategies to endure the stress were least sensitive to O3 than those focusing on one of them.
Delineation and Fine‐Scale Structure of Fault Zones Activated During the 2014–2024 Unrest at the Campi Flegrei Caldera (Southern Italy) From High‐Precision Earthquake Locations
Authors: Scotto di Uccio Francesco , Lomax Anthony , Natale Jacopo , Muzellec Titouan , Festa Gaetano , Nazeri Sahar , Convertito Vincenzo , Bobbio Antonella , Strumia Claudio , Zollo Aldo 
Published: June 2024

AbstractIn the past two decades, the central portion of Campi Flegrei caldera has experienced ground uplift up to 15 mm/month, with an increase of rate, magnitude and extent of the seismicity. In this work, we perform multi‐scale precise earthquake relocation of the 2014–2024 seismicity, mapping in detail activated fault zones. We relate the geometry, extent, and depth of these zones with up‐to‐date structural reconstructions of the caldera. The current seismicity is mainly driven by ground‐uplift‐induced stress concentration on pre‐existing, weaker fault zones, some of which identified for the first time. These structures are not only related to the inner caldera and dome resurgence but also to volcano‐tectonic events of the last 10 ka. The extent of imaged fault segments suggests they can accommodate ruptures up to a moment magnitude 5.1, significantly increasing seismic hazard in the area.
Emission Characteristics of Greenhouse Gases and Air Pollutants in Northern Hemisphere Cities: Comprehensive Assessment Using Ground‐Based Fourier Transform Spectrometers
Authors: Lee Jonghyuk , Jeong Sujong , Park Hayoung , Hong Jaemin , Kim Jueun , Frey Matthias Max , Morino Isamu , Ohyama Hirofumi , Hase Frank , Mermigkas Marios , Zhou Minqiang , Té Yao , Roehl Coleen M. 
Published: June 2024

AbstractDespite the importance of understanding the urban emission characteristics of greenhouse gases (GHGs) and air pollutants, few studies have conducted integrated assessments across diverse urban environments. Herein, we conducted a comprehensive evaluation of the emission characteristics of GHGs and air pollutants in seven cities in the Northern Hemisphere using ground‐based Fourier transform spectrometers. Our analysis primarily focused on emission ratios of excess column‐averaged dry‐air mole fractions of carbon monoxide (CO) to carbon dioxide (CO2) (∆XCO:∆XCO2) and those of methane (CH4) to CO2 (∆XCH4:∆XCO2). We found that the emission ratios varied significantly across cities. Xianghe (China) and Pasadena (USA), known for severe air pollution, showed the highest emission ratios. Notably, Seoul (South Korea) showed lower ∆XCO:∆XCO2 (3.32 ± 0.10 ppb/ppm) but relatively higher ∆XCH4:∆XCO2 (4.85 ± 0.04 ppb/ppm), which was comparable to the ∆XCH4:∆XCO2 value of Xianghe (5.15 ± 0.10 ppb/ppm), suggesting that targeted CH4 reduction strategies may be required for climate change mitigation in Seoul.
Explicit Consideration of Plant Xylem Hydraulic Transport Improves the Simulation of Crop Response to Atmospheric Dryness in the U.S. Corn Belt
Authors: Yang Yi , Guan Kaiyu , Peng Bin , Liu Yanlan , Pan Ming 
Published: June 2024

AbstractAtmospheric dryness (i.e., high vapor pressure deficit, VPD), together with soil moisture stress, limits plant photosynthesis and threatens ecosystem functioning. Regions where rainfall and soil moisture are relatively sufficient, such as the rainfed part of the U.S. Corn Belt, are especially prone to high VPD stress. With globally projected rising VPD under climate change, it is crucial to understand, simulate, and manage its negative impacts on agricultural ecosystems. However, most existing models simulating crop response to VPD are highly empirical and insufficient in capturing plant response to high VPD, and improved modeling approaches are urgently required. In this study, by leveraging recent advances in plant hydraulic theory, we demonstrate that the VPD constraints in the widely used coupled photosynthesis‐stomatal conductance models alone are inadequate to fully capture VPD stress effects. Incorporating plant xylem hydraulic transport significantly improves the simulation of transpiration under high VPD, even when soil moisture is sufficient. Our results indicate that the limited water transport capability from the plant root to the leaf stoma could be a major mechanism of plant response to high VPD stress. We then introduce a Demand‐side Hydraulic Limitation Factor (DHLF) that simplifies the xylem and the leaf segments of the plant hydraulic model to only one parameter yet captures the effect of plant hydraulic transport on transpiration response to high VPD with similar accuracy. We expect the improved understanding and modeling of crop response to high VPD to help contribute to better management and adaptation of agricultural systems in a changing climate.
Dominant Sources of Uncertainty for Downscaled Climate: A Military Installation Perspective
Authors: Kim Taereem , Villarini Gabriele , Done James M. , Johnson David R. , Prein Andreas F. , Wang Chao 
Published: June 2024

AbstractWhile the Department of Defense (DoD) infrastructure is no stranger to extremes, recent events have been unprecedented, with climate change acting as a growing risk multiplier. To assess the level of exposure of DoD installations to extreme weather and climate events, site‐specific climate information is needed. One way to bridge the scale gap between outputs from existing global climate models (GCMs) and sites is climate downscaling. This makes the information more relevant for impact assessment at the DoD installation and facility scale. However, downscaling GCMs is beset by a myriad of challenges and sources of uncertainty, and downscaling methods were not designed with specific infrastructure planning and design needs in mind. Here, we evaluate state‐of‐the‐science dynamical downscaling and statistical downscaling and bias correction for climate variables (i.e., temperature and precipitation) at the daily scale. We also combine downscaling approaches in novel ways to optimize computational efficiency and reduce uncertainty. Furthermore, we examine the sensitivity of the downscaled outputs to the choice of reference data and quantify the relative uncertainty related to downscaling approach, reference data, and other factors across the climate variables and aggregation scales. Results show that empirical quantile mapping (EQM), a statistical downscaling, consistently performs well and has less sensitivity to the choice of reference data. Moreover, the hybrid downscaling that leverages EQM improves the performance of dynamical downscaling. Our findings highlight that the choice of reference data dominates uncertainties in temperature downscaling, while their role is more muted for precipitation but still non‐negligible.
Quantifying the Impacts of an Urban Area on Clouds and Precipitation Patterns: A Modeling Perspective
Authors: Moraglia G. , Pryor S. C. , Crippa P. 
Published: June 2024

AbstractOver the past century, rapid urbanization has greatly altered landscapes and affected atmospheric conditions causing impacts across a wide range of sectors including human welfare, infrastructure, and ecosystems. As a result, there is a growing imperative to improve understanding of urbanization impacts on local and regional weather events and hydroclimates. This study investigates how urbanization affects precipitation and cloud fraction (CF) in the vicinity of Indianapolis. We employ multi‐month simulations using the Weather Research and Forecasting model to: (a) assess the impact of an urban area on precipitation and CF, (b) quantify how this impact varies with urban growth, and (c) examine the main mechanisms through which the city alters the local hydroclimate. Specifically, two perturbed simulations, where the urban land cover is either replaced by croplands or is increased in size, are also performed for the two rainiest months. Comparisons of the control run with no city against the perturbed runs indicate statistically significant impacts of the urban area in enhancing precipitation amounts, frequency and low‐level CF, particularly within the first 100 km radius downwind of the city boarder. The urban environment is found to increase precipitation efficiency over the city and in downwind regions. Temperature at 2 m height, planetary boundary layer height, turbulent kinetic energy, and convective available potential energy, are also enhanced in the perturbed runs and drive changes in vertical mixing, downwind precipitation amounts, frequency and low‐level height CF. All these changes appear to be a non‐linear function of the city size.
Numerical Investigation of Observational Flux Partitioning Methods for Water Vapor and Carbon Dioxide
Authors: Zahn Einara , Ghannam Khaled , Chamecki Marcelo , Moene Arnold F. , Kustas William P. , Good Stephen , Bou‐Zeid Elie 
Published: June 2024

AbstractWhile yearly budgets of CO2 flux (Fc) and evapotranspiration (ET) above vegetation can be readily obtained from eddy‐covariance measurements, the separate quantification of their soil (respiration and evaporation) and canopy (photosynthesis and transpiration) components remains an elusive yet critical research objective. In this work, we investigate four methods to partition observed total fluxes into soil and plant sources: two new and two existing approaches that are based solely on analysis of conventional high frequency eddy‐covariance (EC) data. The physical validity of the assumptions of all four methods, as well as their performance under different scenarios, are tested with the aid of large‐eddy simulations, which are used to replicate eddy‐covariance field experiments. Our results indicate that canopies with large, exposed soil patches increase the mixing and correlation of scalars; this negatively impacts the performance of the partitioning methods, all of which require some degree of uncorrelatedness between CO2 and water vapor. In addition, best performances for all partitioning methods were found when all four flux components are non‐negligible, and measurements are collected close to the canopy top. Methods relying on the water‐use efficiency (W) perform better when W is known a priori, but are shown to be very sensitive to uncertainties in this input variable especially when canopy fluxes dominate. We conclude by showing how the correlation coefficient between CO2 and water vapor can be used to infer the reliability of different W parameterizations.
Light People: Professor Che Ting Chan, curiosity drives to create the impossibilities
Authors: Sun Tingting , Xiong Ying 
Published: June 2024

Editorial“When something is said to be impossible, there are two points for researchers to initially clarify: whether it really is forbidden by the laws of nature; or whether it is simply that no material that currently exists in nature can do that.” Metamaterials are such magical beings, which have physical properties like invisibility, negative refraction, super-resolution, and perfect absorption that are absent from natural materials. It has been rated by Science as one of the top ten scientific and technological breakthroughs affecting human beings in the 21st century.In this issue of Light People, we spoke with a “magic” creator, Professor Che Ting Chan, the Associate Vice-President (Research & Development) of the Hong Kong University of Science and Technology (HKUST), Member of the Hong Kong Academy of Sciences and Fellow of the American Physical Society. He has researched a number of theoretical problems in material physics, investigated the theory behind what they seek to achieve, and modulated light (electromagnetism) and acoustic waves through metamaterials. In the following, let’s take a closer look at Professor Che Ting Chan’s research life, and appreciate his style and the background of his accomplishment.
Downfolding from ab initio to interacting model Hamiltonians: comprehensive analysis and benchmarking of the DFT+cRPA approach
Authors: Chang Yueqing , van Loon Erik G. C. P. , Eskridge Brandon , Busemeyer Brian , Morales Miguel A. , Dreyer Cyrus E. , Millis Andrew J. , Zhang Shiwei , Wehling Tim O. , Wagner Lucas K. , Rösner Malte 
Published: June 2024

AbstractModel Hamiltonians are regularly derived from first principles to describe correlated matter. However, the standard methods for this contain a number of largely unexplored approximations. For a strongly correlated impurity model system, here we carefully compare a standard downfolding technique with the best possible ground-truth estimates for charge-neutral excited-state energies and wave functions using state-of-the-art first-principles many-body wave function approaches. To this end, we use the vanadocene molecule and analyze all downfolding aspects, including the Hamiltonian form, target basis, double-counting correction, and Coulomb interaction screening models. We find that the choice of target-space basis functions emerges as a key factor for the quality of the downfolded results, while orbital-dependent double-counting corrections diminish the quality. Background screening of the Coulomb interaction matrix elements primarily affects crystal-field excitations. Our benchmark uncovers the relative importance of each downfolding step and offers insights into the potential accuracy of minimal downfolded model Hamiltonians.
Orpinolide disrupts a leukemic dependency on cholesterol transport by inhibiting OSBP
Authors: Cigler Marko , Imrichova Hana , Frommelt Fabian , Caramelle Lucie , Depta Laura , Rukavina Andrea , Kagiou Chrysanthi , Hannich J. Thomas , Mayor-Ruiz Cristina , Superti-Furga Giulio , Sievers Sonja , Forrester Alison , Laraia Luca , Waldmann Herbert , Winter Georg E. 
Published: June 2024

Abstract
Metabolic alterations in cancer precipitate in associated dependencies that can be therapeutically exploited. To meet this goal, natural product-inspired small molecules can provide a resource of invaluable chemotypes. Here, we identify orpinolide, a synthetic withanolide analog with pronounced antileukemic properties, via orthogonal chemical screening. Through multiomics profiling and genome-scale CRISPR–Cas9 screens, we identify that orpinolide disrupts Golgi homeostasis via a mechanism that requires active phosphatidylinositol 4-phosphate signaling at the endoplasmic reticulum–Golgi membrane interface. Thermal proteome profiling and genetic validation studies reveal the oxysterol-binding protein OSBP as the direct and phenotypically relevant target of orpinolide. Collectively, these data reaffirm sterol transport as a therapeutically actionable dependency in leukemia and motivate ensuing translational investigation via the probe-like compound orpinolide.