bluuebunny/crossref_metadata_2025 huggingface dataset:
Model updating of engineering structures based on modified subset simulation methods and measured data
Authors: Xia Zhiyuan , Tang Baijian , Shi Huiyuan , Chen Peng , Chen Xin , Gu Sheng
Published: December 2024

Subset simulation (SuS) as a reliability method is used to solve optimization issues in areas of civil engineering for relatively simple engineering structures, and several advancements in the generation approaches of samples in the subset to alleviate the generation of repeat samples have been made. However, the application of the SuS method to real, complicated engineering structures is rare. In this paper, firstly, the feasibility of four typical SuS-based methods — namely, SuS-modified Metropolis-Hasting (SuS-MMH), SuS-MMH with delayed rejection (SuS-MMHDR), SuS-adaptive conditional sampling (SuS-ACS) and SuS-Hamiltonian Monte Carlo — was assessed based on the updating of a damaged simulated cantilever beam. The differences in the first 10 frequencies of the beam between the updated finite element model (FEM) and the real FEM were all reduced from above 5% to within 1%, with the methods of SuS-MMHDR and SuS-ACS showing better efficiency with fewer iteration steps. Then, the application of SuS-MMHDR and SuS-ACS to the model updating of a real steel corridor structure was analyzed. The results show that the differences in the first five vertical vibration frequencies are reduced from approximately 20% to within 10%, and the result of SuS-MMHDR was around 5%. Finally, the updated model under human-induced vibration for the steel corridor structure was verified. Based on the experiment, the difference in the largest acceleration between the updated model and real structure was controlled at around 10%, which shows relatively good agreement with each other. The above model updating applications show the feasibility of the SuS-based optimization method for real engineering optimization, and SuS-MMHDR shows the strongest performance.
A target recognition algorithm using machine learning based on millimeter wave radar on intelligent connected vehicles
Authors: Fan Likang , Liu Haichao , Wei Hongqian , He Zhuoyu , Chen Xu
Published: December 2024

Recognition technology based on millimeter wave radar (MMW) can operate in all-weather conditions and has received much attention in the field of intelligent connected vehicles (ICVs). However, the label information of the targets cannot be directly obtained from the original radar point clouds, making it necessary to develop advanced recognition algorithms. This paper proposes a target recognition algorithm based on machine learning that utilizes radar point clouds and leverages the radar reflection intensity to improve target recognition accuracy. Firstly, regional division and density clustering techniques are employed to preprocess the original point clouds from the MMW and segment them into meaningful regions, thereby reducing the computational burden; Secondly, relevant features are extracted from the processed radar point cloud, including radar scattering cross section and its related features. Finally, to improve target recognition accuracy, this paper proposes a grid search optimization principal component analysis support vector machine (GS-PCA-SVM) classification algorithm. The algorithm uses PCA to reduce the dimensionality of the data while preserving key information; then, it optimizes the parameters and kernel function of SVM by using the GS method to improve the performance of the classifier. The experimental results indicate that the recognition algorithm proposed in this paper achieves accuracies of 80%, 93%, and 95% on static, dynamic, and mixed datasets, respectively. Real vehicle experiments also prove that this algorithm has high accuracy and reliability when applied to ICV.
Surface defect detection of steel strip at low resolution based on SAC-YOLOv5
Authors: Rui Changxin , Wu Zhantao , Liu Chenzhen , Li Baoqing , Cheng Junsheng
Published: December 2024

Surface defects are common occurrences in the production process of strip steel. The development of automatic and efficient intelligent detection algorithms for strip steel is crucial for enhancing product quality and operational safety. While deep learning-based defect detection techniques have achieved satisfactory accuracy when applied to high-resolution images, obtaining a sufficient number of high-resolution images in practical engineering scenarios is challenging. The degradation of image quality often results in a significant decline in the performance of existing detection techniques. To address these challenges, this paper proposes SAC-you only look once (YOLO)v5-based surface defect detection model specifically designed for low-resolution strip steel images. SAC, SIoU based K-Means++, asymmetric convolutional neural networks (ACNNs) and composite down-sampling feature fusion block (CDFFB). Firstly, we introduce an anchor boxes clustering algorithm called Shape-IoU based K-Means++ (SIoU based K-Means++) to enhance the efficiency of anchor boxes regression. Secondly, we construct an ACNNs for multi-level feature extraction. Utilizing reparameterization techniques, we reduce the inference resources required by the model. Additionally, we propose a CDFFB which enhances key texture information and improves the model’s nonlinear fitting ability. Experimental analysis using NEU-DET surface defect data demonstrates the superiority of the proposed model in processing low-resolution strip steel surface defect images. In comparison to YOLOv8, YOLOv7, YOLOX, YOLOv3SPP, CenterNet and Faster RCNN, SAC-YOLOv5 outperforms all other models in terms of both speed and accuracy.
Electrospun poly(vinylidene fluoride) nanofibers: a review on its utilization in energy harvesting devices
Authors: Fadzallah Iman Aris , Sabran Nuur Syahidah , Hassan Hazirah Che , Ono Takahito , Sabri Mohd Faizul Mohd
Published: December 2024

This review provides a comprehensive exploration of small-scale energy harvesting (EH) for low-power devices, covering various ambient energy sources such as human activities, solar, thermal, mechanical vibration, radio frequency (RF), magnetism, and temperature differentials. It explains the use of conversion mechanisms like piezoelectric, thermoelectric, pyroelectric, and triboelectric. The focus is on piezoelectric materials, particularly pyroelectric materials, delving into the fundamental principles and equations governing their operation. The mechanisms of piezoelectric and pyroelectric effects under mechanical loadings and temperature changes are also explained. The review addresses material selection for small-scale EH, discussing both inorganic and organic piezoelectric materials. It justifies the preference for lead-free materials like poly(vinylidene fluoride) (PVDF) due to its biocompatibility, mechanical flexibility, ease of thin film production, and cost-effective implementation, replacing toxic lead-based materials. The various polymorphs within PVDF are explained, emphasizing the β-phase as the one responsible for its highest piezoelectric property. Different methods to enhance β-phase content in PVDF are reviewed, with electrospinning highlighted as a one-step process eliminating the need for post-treatment steps. The research effort to fabricate PVDF-based EH devices with various techniques, dimensions, mechanical loadings, and excitations is thoroughly examined. Recent advancements in the Internet of Things and low-power devices have driven interest in device miniaturization and complex circuit module fabrication using microelectromechanical systems (MEMS) technologies. The review explores approaches for fabricating PVDF-based EH devices using MEMS techniques and discusses hybrid systems combining piezoelectric and pyroelectric effects, with PVDF as the conversion medium.
One trick to treat them all: SuperEasy linear response for any hot dark matter in N-body simulations
Authors: Pierobon Giovanni , Mosbech Markus R. , Upadhye Amol , Wong Yvonne Y.Y.
Published: December 2024

We generalise the SuperEasy linear response method, originally developed to describe
massive neutrinos in cosmological N-body simulations, to any subdominant hot dark matter (HDM)
species with arbitrary momentum distributions. The method uses analytical solutions of the HDM
phase space perturbations in various limits and constructs from them a modification factor to the
gravitational potential that tricks the cold particles into trajectories as if HDM particles were
present in the simulation box. The modification factor is algebraic in the cosmological
parameters and requires no fitting. Implementing the method in a Particle-Mesh simulation code
and testing it on subdominant HDM cosmologies up to the equivalent effect of ∑ m

ν
=
0.315 eV-mass neutrinos, we find that the generalised SuperEasy approach is able to predict the
total matter and cold matter power spectra to ≲ 0.1% relative to other linear response
methods and to ≲ 0.25% relative to particle HDM simulations. Applying the method to
cosmologies with mixed neutrinos+thermal QCD axions and
neutrinos+generic thermal bosons, we find that non-standard subdominant
HDM cosmologies have no intrinsically different non-linear signature in the total matter power
spectrum from standard neutrino cosmologies. However, because they predict different time
dependencies even at the linear level and the differences are augmented by non-linear evolution,
it remains a possibility that observations at multiple redshifts may help distinguish
between them.
An NDVI analysis is conducted to determine the arrangement of green open space in the riparian zone of Sungai Kahayan, Palangka Raya City
Authors: Sutrisno Herwin , Susi Theresia , Hartanto Singgih , Perkasa Petrisly , Supriadi Benong , Mantana Handri
Published: December 2024

The configuration of riparian zones is predominantly characterized by moderate vegetation, comprising 43.4%. Deforestation in the riparian zone of the Kahayan River diminishes habitat quality and exacerbates soil erosion. Human activities, such deforestation and mining, can intensify soil erosion along riverbanks, hence increasing flood risks and harming river ecosystems. Riverside communities are susceptible to inundation, particularly during the monsoon season. Floods can impair infrastructure, devastate residences, and result in economic detriment. This study employs a quantitative methodology utilizing a descriptive technique, revealing that the research site comprises water bodies covering 16.6%, low vegetation at 12.2%, medium vegetation at 43.4%, and high vegetation at 27.7%. Based on these findings, the riparian zone of the Kahayan River is characterized by moderate vegetation; therefore, to preserve the ecosystem and hydrological functions, it is essential to plant large tree seedlings to enhance soil protection and augment biodiversity. This study aims to investigate the configuration of riparian tree habitat (RTH) along the Kahayan River in Palangka Raya City, utilizing Geographic Information System (GIS) analysis alongside the Normalized Difference Vegetation Index (NDVI) for assessing vegetation density and the spatial distribution of settlements in the area. The findings of this analysis influence the management of flood risk, environmental degradation, and improved RTH planning in Palangka Raya.
Sustainable construction materials: a bibliometric analysis of research on bauxite residues utilization in artificial aggregate production
Authors: Meilasari Fitriana , Warmadewanthi IDAA , Trihadiningrum Yulinah , Ekaputri Januarti Jaya
Published: December 2024

The significant increase in aggregate demand drives the development of sustainable alternatives from waste materials to address environmental concerns and construction material availability. The complexity of artificial aggregates and advancements in construction materials necessitates a systematic mapping of scientific developments, exploration of state of the art, and projection of research trends to avoid research replication and repetition, optimizing the allocation of research resources. Bibliometric analysis emerges as one of the effective methods to map the evolution of research in this field, revealing the intellectual structure and collaboration networks within the scientific community. This study aims to map and analyze the development of artificial aggregate research, particularly the utilization of bauxite residue, through bibliometric analysis. The research examined Scopus-indexed documents over the past two decades using bibliometric methods. Analysis results revealed rapid development in artificial aggregate research, with a significant increase in publications and diversification of topics. However, studies on utilizing bauxite residue as artificial aggregate remain limited, comprising only about 5% of artificial aggregate research data collected. This finding underscores the need for further research into bauxite waste utilization because of its substantial development potential research. Thematic mapping indicated the dominance of construction materials and sustainability, reflecting a paradigm shift towards environmentally friendly construction practices. Geographical analysis revealed artificial aggregates research globally distributed, encompassing 64 countries, with a significant concentration in China and India. International research collaboration is crucial for driving innovation in sustainable construction, particularly utilizing bauxite waste as artificial aggregates.
Chitosan-Titanium Dioxide Nanoadsorbent for Cd(II), Cu(II), Ni(II) and Pb(II) Ions: Adsorption Isotherm and Characterization Studies
Authors: Abdulrasool Mustafa M. , Kamari Azlan , Wong Siew Tin Susana , Abdullah Nurul Nabila Aini , Bakar Norlaili Abu , Ahmad Wan Haslinda Wan , Yusof Norjan , Wayan Sutapa I.
Published: December 2024

The application of nanotechnology in water treatment is predicted to continue to grow in coming years, worldwide. Environmental scientists are currently focusing on development of cost-effective and eco-friendly nanoadsorbents, nanomembranes, nanofilters and nanophotocatalysts to treat contaminated water. In this research work, chitosan-titanium dioxide (Cs-TiO2) nanocomposite was prepared using a simple one-step method. The physicochemical properties of nanocomposite were studied using spectroscopy and microscopy techniques. The consequences of experimental parameters on adsorption performance of Cs-TiO2 for Cd(II), Cu(II), Ni(II) and Pb(II) metal ions were studied in details. The Langmuir model fitted the adsorption isotherm very well with correlation coefficient values of R
2 > 0.99. The separation factor (R
L) values were in the range of 0-1.0 for all metal ions studied, suggesting the adsorption activity was favorable. The maximum adsorption capacities (Q
max) of Cs-TiO2 for Cd(II), Cu(II), Ni(II) and Pb(II) metal ions were determined as 133.33, 416.67, 151.51 and 277.78 mg/g, respectively. From Fourier Transform Infrared (FTIR) spectra, the appearance and wavenumber of absorption bands for functional groups of Cs-TiO2 changed and shifted after adsorption of metal ions. The X-ray Diffraction (XRD) spectroscopy and Scanning Electron Microscopy (SEM) analyses revealed the change in crystallinity and surface morphology after interaction of Cs-TiO2 with metal ions studied. Overall, findings obtained from this research work highlight the feasibility of Cs-TiO2 as a nanoadsorbent for the treatment of water contaminated by toxic Cd(II), Cu(II), Ni(II) and Pb(II).
Observational constraint on momentum flux-gradient relationships reduces modeling biases of PBL mixing of particles in urban area
Authors: Yang Qike , Zhao Chun , Yuan Renmin , Yang Zining , Du Qiuyan , Xu Mingyue , Li Yubin , Xia Kaihui
Published: December 2024

The momentum flux-gradient relationships within the urban roughness sublayer deviate from the classical relationships proposed by the Monin-Obukhov similarity theory. Despite this discrepancy, prevailing atmospheric models persistently rely on these classical relationships, which were originally derived from natural, homogeneous surfaces. This practice may introduce significant errors when simulating urban atmospheric pollution. This study utilized meteorological parameters and turbulent fluxes obtained from field measurements in the urban roughness sublayer to re-establish momentum flux-gradient relationships. Compared to non-urban areas, the flux-gradient relationships in the urban environments exhibit significant deviations under stable conditions. Consequently, we revised a planetary boundary layer parameterization to incorporate new urban flux-gradient relationships. The revised scheme was implemented in the WRF-Chem model, and the simulation results demonstrated a marked reduction in modeling biases under stable conditions, particularly for relatively developed cities in Eastern China.
Using AI to assess corporate climate transition disclosures
Authors: Colesanti Senni Chiara , Schimanski Tobias , Bingler Julia , Ni Jingwei , Leippold Markus
Published: December 2024

Company transition plans toward a low-carbon economy are key for effective capital allocation and risk management. This paper proposes a set of 64 indicators to comprehensively assess transition plans and develops a Large Language Model-based tool to automate the assessment of company disclosures. We evaluate our tool with experts from 26 institutions, including financial regulators, investors, and non-governmental organizations. We apply the tool to the sustainability reports from carbon-intensive Climate Action 100+ companies. Our results show that companies tend to disclose more information related to target setting (talk), but less information related to the concrete implementation of strategies (walk). In addition, companies that disclose more information tend to have lower emissions. Our results highlight the need for increased scrutiny of companies’ efforts and potential greenwashing risks. The complexity of transition activities presents a major challenge for comprehensive large-scale assessments. As shown in this paper, novel and flexible approaches using Large Language Models can serve as a remedy.
Spatial and temporal variations, and socioeconomic drivers of air quality: case study of the Beijing-Tianjin-Hebei urban agglomeration
Authors: Wang Weiqi , He Yufeng , Gao Jingran , Cao Lei , Ding Huanru , Liu Chenhui , Tang Hanqi , Chen Yanfei , Zhang Yunlu
Published: December 2024

Air pollution poses a serious threat to both economic development and public health in China’s heavily polluted Beijing-Tianjin-Hebei region.Realizing a win-win situation for social development and environmental well-being is a pressing issue.This study analyzed panel data from 199 districts in Beijing-Tianjin-Hebei from 2013 to 2020 using various regression models and found that air quality index values decreased by 48.2%. Spatially, the Beijing-Tianjin-Hebei Air Quality Index had a significantly positively correlated spatial correlation that increased over time. Second, energy consumption per unit of gross domestic product and the proportion of construction land had a positive effect on the AQI values of Beijing-Tianjin-Hebei. This implies that increasing energy consumption exacerbates air pollution, particularly in Baoding. Influenced by the urbanization process, the increase in the construction-land ratio was the most significant positive factor, notably in Baoding and Zhangjiakou. Third, there was a negative influence of Research and Development investment and total social retail sales on the Beijing-Tianjin-Hebei Air Quality Index. This finding indicated that increasing Research and Development investment will alleviate air pollution, with most of the counties in the study area benefitting. The collective findings indicate that more prosperous economic areas (such as Beijing and Tangshan) need to pay more attention to air pollution control and that air pollution in the Hebei cluster of heavy industries will be aggravated by the increase in total social retail sales. Based on these results, long-term measures for air pollution control in Beijing-Tianjin-Hebei should focus on reducing energy consumption, slowing urban expansion, and industrial restructuring.
Towards high loading cesium lead halide nanocomposites for radiation detection
Authors: Král Jan , Děcká Kateřina , Zabloudil Vojtěch , Liška Petr , Hájek František , Horák Michal , Čuba Václav , Mihóková Eva , Auffray Etiennette
Published: December 2024

Cesium lead halide nanocrystals (NCs) and their nanocomposites have attracted a lot of attention in the field of radiation detection thanks to their excellent luminescent properties, especially their potential for fast timing. However, most research on their nanocomposites focuses on low loadings (around 1 wt%) which is insufficient for detecting high-energy X-rays or γ-rays. There have been only few reports exploring materials with higher loadings but all with limited success in terms of transparency of the final material. In our work, we present nanocomposites of cesium lead halide NCs with loading up to 40 wt%. We employ innovative surface functionalization of the NCs to enhance their dispersion within the matrix, thereby improving the final material transparency. We display the NC dispersion within the matrix using confocal photoluminescence microscopy and we then characterise the radioluminescent properties of nanocomposites of cesium lead bromide (CPB) and cesium lead bromochloride NCs with varying chloride content. To asses their performance as radiation detectors, we measure their timing capabilities under X-rays. Our findings reveal a significant improvement in time resolution under X-rays from previously published 300 ps for CPB polymer nanocomposites to 215 ps. When combined with increased stopping power of high NC content, this advancement holds great promise for practical applications, e.g. in time-of-flight positron emission tomography and computed tomography or high energy physics.
Assessment of computational fluid dynamics simulation approach for the aerodynamic performance of small unmanned aerial vehicle propeller
Authors: Arafat Mohammad , Ishak Izuan Amin , Mohd Maruai Nurshafinaz
Published: December 2024

This study explores the complexities of UAV propeller aerodynamics by examining the influence of various parameters, including mesh resolutions, timestep sizes, iterations per timestep, and different Reynolds-Averaged Navier–Stokes (RANS) turbulence models, using Delayed Detached Eddy Simulation (DDES). Through extensive computational fluid dynamics (CFD) simulations, the aerodynamic performance of the propeller is analyzed under different conditions. Mesh resolutions from coarse to fine are evaluated, with Grid Convergence Index (GCI) and Richardson Extrapolation applied to ensure mesh-independent results. Timestep sizes ranging from 0.5° to 10° are also analyzed, revealing a trade-off between accuracy and computational efficiency. Furthermore, the study assesses the impact of different numbers of iterations per timestep and compares three distinct RANS turbulence models: k-ω SST, Spalart-Allmaras, and Realizable k-ε. The results show that finer mesh resolutions and smaller timesteps enhance accuracy in thrust and torque predictions. The k-ω SST model demonstrates the best balance between thrust generation and torque minimization, while the Spalart-Allmaras model underperforms in both categories. In addition, fewer iterations per timestep were found to be sufficient for convergence, reducing computational cost. Overall, this study provides a comprehensive evaluation of UAV propeller performance using CFD simulations.
Error source analysis and modeling of gear involute template processing device
Authors: Li Li , Xin Huang , Hongtao Yang , Ruikang Jin
Published: December 2024

Involute gear template is a standard measuring instrument for gear measurement. In order to ensure the high-precision calibration of gear measuring instruments, based on the structure of gear involute template processing device, the error source is analyzed in detail, and the error transmission model between each single error and the total deviation of template tooth surface is accurately established. On this basis, the simulation experiment is carried out, the primary and secondary influencing factors of the total deviation of the sample tooth surface are analyzed, and the comprehensive model of the total deviation of the sample tooth surface is established. In order to verify the correctness of the established error model, the numerical simulation experiments such as eccentricity error and guide rail error of the template are carried out by MATLAB, and the total deviation of gear involute template tooth surface is measured by inductance micrometer. The experimental results show that the eccentricity error of template installation, the deviation angle error of template, the assembly eccentricity error of double basic circular wheels, the ovality error of the working surface of basic circular wheels, and the micro-deformation error after wheel-rail contact are the main factors affecting the total deviation of template tooth surface. The precision of gear measuring instruments can be improved by accurately compensating each single error, which provides a theoretical basis for the precision improvement of gear involute template processing device.
Numerical investigation of glass laminate aluminium reinforced epoxy (GLARE) composite to impact loading
Authors: Shrivastava Kanupriya , Mishra Ashish
Published: December 2024

The aerospace industry is consistently in persistent need of lightweight materials for which, Fiber Metal Laminates (FMLs) have arisen as an innovative solution by combining the ductility of metals and strength of composites. Glass Fiber Aluminum Laminate (GLARE), a commonly used fiber metal laminate in aircraft structures, is often exposed to low-velocity impact incidents. A study of the dynamic response of GLARE under repeated low-velocity impacts is presented in the paper. The studied fiber metal laminate consists of two layers of glass fiber-reinforced plastics alternately sandwiched between three layers of aluminum 2024 alloy. A finite element (FE) model is developed, incorporating interface modeling, contact modeling, and failure considerations, validated against published experimental results. Four distinct impact energy scenarios are examined, comparing the damage patterns of GLARE composites concerning the precision and efficiency of the numerical model. The validated model is then utilized to explore the effect of impact of energy on energy absorption characteristics of GLARE. The analysis, conducted on ANSYS—AUTODYN software, contributes valuable insights for optimizing the design and structural considerations of GLARE in aerospace applications, particularly in situations involving repeated low-velocity impacts.
Dynamical systems and complex networks: a Koopman operator perspective
Authors: Klus Stefan , Djurdjevac Conrad Nataša
Published: December 2024

The Koopman operator has entered and transformed many research areas over the last years. Although the underlying concept—representing highly nonlinear dynamical systems by infinite-dimensional linear operators—has been known for a long time, the availability of large data sets and efficient machine learning algorithms for estimating the Koopman operator from data make this framework extremely powerful and popular. Koopman operator theory allows us to gain insights into the characteristic global properties of a system without requiring detailed mathematical models. We will show how these methods can also be used to analyze complex networks and highlight relationships between Koopman operators and graph Laplacians.
D-SELD: Dataset-Scalable Exemplar LCA-Decoder
Authors: Mahmoodi Takaghaj Sanaz , Sampson Jack
Published: December 2024

Neuromorphic computing has recently gained significant attention as a promising approach for developing energy-efficient, massively parallel computing systems inspired by the spiking behavior of the human brain and natively mapping spiking neural networks (SNNs). Effective training algorithms for SNNs are imperative for increased adoption of neuromorphic platforms; however, SNN training continues to lag behind advances in other classes of ANN. In this paper, we reduce this gap by proposing an innovative encoder–decoder technique that leverages sparse coding and the locally competitive algorithm (LCA) to provide an algorithm specifically designed for neuromorphic platforms. Using our proposed Dataset-Scalable Exemplar LCA-Decoder we reduce the computational demands and memory requirements associated with training SNNs using error backpropagation methods on increasingly larger training sets. We offer a solution that can be scalably applied to datasets of any size. Our results show the highest reported top-1 test accuracy using SNNs on the ImageNet and CIFAR100 datasets, surpassing previous benchmarks. Specifically, we achieved a record top-1 accuracy of 80.75% on ImageNet (ILSVRC2012 validation set) and 79.32% on CIFAR100 using SNNs.
Quasiregular distortion of dimensions
Authors: Chrontsios-Garitsis Efstathios-K.
Published: December 2024

We investigate the distortion of the Assouad dimension and (regularized) spectrum of sets under planar quasiregular maps. The respective results for the Hausdorff and upper box-counting dimension follow immediately from their quasiconformal counterparts by employing elementary properties of these dimension notions (e.g. countable stability and Lipschitz stability). However, the Assouad dimension and spectrum do not share such properties. We obtain upper bounds on the Assouad dimension and spectrum of images of compact sets under holomorphic and planar quasiregular maps by studying their behavior around their critical points. As an application, the invariance of porosity of compact subsets of the plane under quasiregular maps is established.
The Market in Global International Society
Authors: Buzan Barry , Falkner Robert
Published: December 2024

This book examines the idea and practice of the market as a primary institution of global international society. Viewing the market as a political ideology in support of a liberal system of governance, and not just as an economic practice or economy-wide structural feature, draws attention to the market’s role as part of the normative structure of International Relations. The book tracks the emergence of the market as an international norm in the nineteenth century and explores its evolution up to the early twenty-first century. It explores the market’s international institutionalization and examines its wide-ranging interplay with other social and political institutions of international society. It also traces early forms of markets, from the fourth millennium bc up to the late eighteenth century ad, highlighting similarities and differences between premodern and modern conceptions of the market. With the help of this historical account, the book brings into close contact two areas of study that have for much too long stood back-to-back: the English School of International Relations (ES), and International Political Economy (IPE). For the ES, the book fills in the large economic gap in the understanding and portrayal of the primary institutions of international society. For IPE, the book develops a new and detailed approach to understanding the relationship between the market and other social and political institutions of global international society. The book thus builds a much-needed bridge between the ES and IPE and shows how each would benefit from paying more attention to the other.
International Political Economy and the Market in Premodern Societies
Authors: Buzan Barry , Falkner Robert
Published: December 2024

This chapter covers International Political Economy from the fourth millennium bc up to the late eighteenth century ad, stretching from the beginnings of urban civilization and empire to the transition towards global modernity. This was a period in which, until the last few centuries, there was no global international society and many of the primary institutions of international society were quite different from current ones. The chapter highlights the continuities and differences between this era, and the period since the nineteenth century. It seeks to clarify the precursors to the modern market, which were mainly about economic practice. It identifies the structures and dynamics of premodern political economy, and the distinctive ways in which the economic and political elites made mutually supporting bargains with each other. The discussion of norms and practices relating to the market is structured around the four economic functions of production, trade, finance, and labour.
The Long Nineteenth Century
Authors: Buzan Barry , Falkner Robert
Published: December 2024

This chapter explores the rise of the market as a primary institution of global international society in the nineteenth century. It traces the overthrow of the mercantilist system of political economy that had supported the dynastic system in Europe, based on a mixture of emergent liberal belief, and intensifying global economic competition with Britain. The chapter demonstrates how this European drama was later imposed on Africa and Asia, which had remained mostly in the more open trading regime of agrarian empires. It discusses the intellectual origins of the rise of economic liberal ideology, especially the new ways of thinking about economics that began to emerge in the eighteenth century, first in France and Britain but also in other parts of Europe. And it discusses the many challenges that liberalism’s rise encountered, especially in the form of mercantilist and economic nationalist ideas among late developing economies.
1973–2007: A Second Golden Age of the Market
Authors: Buzan Barry , Falkner Robert
Published: December 2024

This chapter explores the fourth phase in the story of the market as an institution of global international society, the three-decade revival of market ideology from 1973 to 2007. The chapter discusses the origins of the neoliberal revolution in the United States and Britain and its spread to the rest of the West and much of the Third World, and eventually even to China and the former Soviet Union. It shows that was not the bottom-up global market of the late nineteenth century, but one driven both by transnational ideational shifts and their extensive embodiment in a range of secondary institutions. Its global triumph came after the collapse of communism and the ending of the Cold War in the early 1990s. The chapter shows how market ideology became global in scale, and comprehensive in scope, during the 1990s, though still unevenly applied across the world.
Expanding America’s Biological and Chemical Warfare Programs
Authors: Parasidis Efthimios
Published: December 2024

This chapter details America’s post-WWII programs in chemical and biological warfare. Although international treaties limited the situations under which a nation could deploy biological or chemical weapons, the agreements did not restrict research, development, or stockpiling. Coupled with the military’s biological and chemical warfare programs, the military and CIA collaborated with dozens of universities, hospitals, and research institutes on mind control projects with hallucinogenic drugs. As with atomic and radiation studies, haphazard adherence to research subject protections was a hallmark of these programs. In many instances, studies involving service members were deemed training exercises; in others, clandestine research was conducted on unsuspecting civilians. Projects sought to enhance military preparedness and garner knowledge on offensive and defensive measures: this included weaponization of pathogens, field tests with biological weapons, development of mind-altering drugs for use in military and intelligence operations, and formulation of noxious chemicals that could devastate enemy forces, land, and food supplies.
OpenVariant: a toolkit to parse and operate multiple input file formats
Authors: Martínez-Millán David , Brando Federica , L. Grau Miguel , Sánchez-Guixé Mònica , López-Elorduy Carlos , Reyes-Salazar Iker , Deu-Pons Jordi , López-Bigas Núria , González-Pérez Abel
Published: December 2024

Advances in high-throughput DNA sequencing technologies and decreasing costs have fueled the identification of small genetic variants (such as single nucleotide variants and indels) across tumors. Despite efforts to standardize variant formats and vocabularies, many sources of variability persist across databases and computational tools that annotate variants, hindering their integration within cancer genomic analyses. In this context, we present OpenVariant, an easily extendable Python package that facilitates seamless reading, parsing and refinement of diverse input file formats in a customizable structure, all within a single process.

Availability and implementation

OpenVariant is an open-source package available at https://github.com/bbglab/openvariant. Documentation may be found at https://openvariant.readthedocs.io.
A near-tight lower bound on the density of forward sampling schemes
Authors: Kille Bryce , Groot Koerkamp Ragnar , McAdams Drake , Liu Alan , Treangen Todd J
Published: December 2024

Motivation

Sampling k-mers is a ubiquitous task in sequence analysis algorithms. Sampling schemes such as the often-used random minimizer scheme are particularly appealing as they guarantee at least one k-mer is selected out of every w consecutive k-mers. Sampling fewer k-mers often leads to an increase in efficiency of downstream methods. Thus, developing schemes that have low density, i.e. have a small proportion of sampled k-mers, is an active area of research. After over a decade of consistent efforts in both decreasing the density of practical schemes and increasing the lower bound on the best possible density, there is still a large gap between the two.

Results

We prove a near-tight lower bound on the density of forward sampling schemes, a class of schemes that generalizes minimizer schemes. For small w and k, we observe that our bound is tight when k≡1(mod w). For large w and k, the bound can be approximated by 1w+k⌈w+kw⌉. Importantly, our lower bound implies that existing schemes are much closer to achieving optimal density than previously known. For example, with the current default minimap2 HiFi settings w = 19 and k = 19, we show that the best known scheme for these parameters, the double decycling-set-based minimizer of Pellow et al. is at most 3% denser than optimal, compared to the previous gap of at most 50%. Furthermore, when k≡1(mod w) and the alphabet size σ goes to ∞, we show that mod-minimizers introduced by Groot Koerkamp and Pibiri achieve optimal density matching our lower bound.

Availability and implementation

Minimizer implementations: github.com/RagnarGrootKoerkamp/minimizers ILP and analysis: github.com/treangenlab/sampling-scheme-analysis.
Inflammation during oocyte maturation reduces developmental competence and increases apoptosis in blastocysts
Authors: Tariq Arslan , Seekford Zachary K , Bromfield John J
Published: December 2024

Uterine infections cause ovarian dysfunction and infertility. The bacterial endotoxin, lipopolysaccharide, accumulates in the follicular fluid of dominant follicles of cows with uterine infections. Granulosa cells produce an innate inflammatory response to lipopolysaccharide, altering the follicular microenvironment of the oocyte. We hypothesized that developmental competence and embryo quality would be reduced when oocytes are matured in an inflammatory environment. Bovine mural granulosa cells were exposed to either 1 μg/mL of lipopolysaccharide or medium alone for 24 h to produce a conditioned medium. Inflammatory responses of mural granulosa cells were confirmed by increased expression of CXCL8, IL1B, IL6, and TNF. Bovine cumulus–oocyte complexes were matured for 22 ± 1 h in a medium supplemented with either 1 μg/mL of lipopolysaccharide, 10% v/v conditioned medium of granulosa cells treated with either lipopolysaccharide (LCM) or medium alone, or no supplementation. In addition, polymyxin B (20 μg/mL) was added to the maturation medium to sequester LPS. Following maturation, cumulus–oocyte complexes were fertilized and cultured for 7.5 days with no further treatment. Oocyte maturation using lipopolysaccharide or LCM impaired development to the blastocysts stage, reduced the number of total and CDX2-negative blastomeres, and increased TUNEL-positive cells in blastocysts. Polymyxin B could rescue these effects in the lipopolysaccharide group but not in the LCM group, indicating factors produced by granulosa cells and not lipopolysaccharide alone compromised oocyte development. These findings suggest that the inflammatory milieu produced by granulosa cells in response to lipopolysaccharide impairs oocyte competence and the quality of resultant blastocyst-stage embryos.
Generating training events for building cyber-physical security skills
Authors: Vineetha Harish Avanthika , Tam Kimberly , Jones Kevin
Published: December 2024

As the threat of cyber attacks increases, the cyber security sector continues to train professionals as one form of mitigation. Gamification and infrastructure like cyber ranges (CRs) have been proven useful in training cyber security specialists to detect and react to cyber-attacks in information technology. However, a subset of all digital threats target cyber-physical systems (CPS), not just information systems, and these threats have been growing rapidly in recent years. While those threats are increasing, training specific to cyber-physical threats is not growing as quickly as information technology training solutions. While CRs are useful for training and supporting events like cyber defence exercises and capture the flags events, limitations to CRs make it more difficult to make an immersive event for cyber-physical training. This paper demonstrates how to devise a cyber-physical training environment using a combination of CRs and CPS testbeds,and how to devise an accompanying immersive scenario to improve cyber-physical security skills through cooperative learning. To do this, an example training was created and implemented. This paper concludes with the results of running this event four times.
Enhancing security of medical images using code-based intermittent encryption and convolutional neural network
Authors: Aruchamy Prasanth , Sundarrajan M , Choudhry Mani Deepak , Jothi Akshya
Published: December 2024

There has recently been a rise in the demand for telemedicine systems that securely and effectively transmit medical pictures. Cognitive radio (CR) significantly uses the unutilized spectrum by using the notion of spectrum sensing. Like certain other patient records, medical imaging data has strict requirements for security and anonymity. This makes sending healthcare picture information via an exposed system difficult because of the problems identified and the risks associated with massive data spillage. This study suggests a reliable CR technology with an image encryption technique to transmit medical images securely. In the proposed approach, the convolutional neural network method has been employed for complaisant spectrum sensing, where the Fusion Center trains the network for classification tasks using historical sensing data. Due to the proper training, the system runs in a time-slotted fashion. The proposed method provides an actor-critic transfer learning technique for a secondary user to select its processing method to raise confidence level while observing energy constraints. Finally, the numerical simulation results are examined to assess the suggested approaches under various configurations related to peak signal-to-noise ratio and structural similarity index which provide 90% more efficiency than the traditional simulated techniques.
Dual transcatheter edge-to-edge repair in a patient with cardiac amyloidosis and severe secondary mitral and tricuspid regurgitation: a case report
Authors: Vogel Julia , Luedike Peter , Mahabadi Amir Abbas , Rassaf Tienush , Michel Lars
Published: December 2024

Background

Mitral and tricuspid regurgitation in patients with cardiac amyloidosis (CA) pose significant diagnostic and therapeutic challenges due to its non-specific symptoms and limited treatment options. Transcatheter edge-to-edge repair (TEER) is complicated by altered cardiac geometry, advanced restriction, and potential amyloid valve deposits.

Case summary

We present the case of dual TEER in a 79-year-old male with advanced transthyretin cardiac amyloidosis (ATTR-CA) and severe symptomatic mitral and tricuspid regurgitation. In a staged approach, TEER for both the mitral and tricuspid valves was successfully conducted, resulting in improved valvular function and symptom relief. Transvalvular gradients were 5 mmHg for mitral valve and 2 mmHg for tricuspid valve, each with mild residual regurgitation, improved clinical status, and regressive natriuretic peptides.

Discussion

This case underscores the feasibility of dual TEER in CA patients with valvular involvement. Further research is necessary to optimize treatment strategies and address the multifaceted nature of this complex disease.
A perfect mimic: the role of multimodality imaging in right ventricular cardiomyopathy—a case report
Authors: Viskin Dana , Laufer-Perl Michal , Topilsky Yan , Banai Shmuel , Khoury Shafik
Published: December 2024

Background

Distinguishing right ventricular cardiac sarcoidosis (RVCS) from arrhythmogenic right ventricular cardiomyopathy (ARVC) is often a challenging task particularly when imaging findings are limited to the right ventricle and when electrocardiographic and arrhythmic findings are typical for ARVC. Here, we discuss the challenges of diagnosis and management in a patient whose initial work-up strongly suggested ARVC and was later diagnosed with cardiac sarcoidosis (CS), highlighting the role of multimodality imaging.

Case summary

On presentation, this patient displayed electrocardiographic abnormalities, arrhythmia morphology, and cardiac magnetic resonance imaging findings consistent with the criteria for a definite diagnosis of ARVC. However, through the use of multimodal imaging, a final diagnosis of CS was made. Subsequent immunosuppressive treatment effectively managed the inflammation, and as a primary prevention measure, an implantable cardioverter-defibrillator was implanted. Almost 2 years following the initial presentation, the patient received an appropriate shock from the device, attributed to rapid ventricular tachycardia arising from myocardial scarring.

Discussion

Right ventricular cardiac sarcoidosis can closely resemble ARVC, making it challenging to distinguish between the two. Multimodality imaging is pivotal for accurate diagnosis and risk assessment, as well as for adjusting immunosuppressive therapy and monitoring response to treatment in CS.