bluuebunny/crossref_metadata_2025_split huggingface dataset:
IRDNet: An image-defogging algorithm based on dark channel prior
Authors: Guo Yanjun , Jia Guimin , Lu Pengyu 
Published: October 2024

Abstract
Image-defogging, as an important part of computer vision, has been widely used in intelligent driving, target recognition, satellite detection, underwater exploration and so on. Improving the performance of the defogging algorithm based on deep learning has practical significance for the completion of high-level vision tasks. This paper proposes an IRDNet algorithm by improving the image deep learning algorithm based on the RefineDNet framework and designing a new deep learning network structure. The proposed algorithm combines dark channel prior knowledge and atmospheric degradation model to decompose the input fogged images into dark channel images and degraded images, and then carries out feature extraction and detail enhancement for the two images by convolutional neural network. IRDNet introduces a fully connected convolutional structure, attention mechanism, and a pyramid structure to improve the performance of the overall defogging network. By testing on OTS datasets, we compare the IRDNet algorithm with traditional defogging methods, end-to-end deep learning defogging methods, and prior knowledge-guided deep learning defogging methods. Test results show that the performance of IRDNet is better than other methods, and the defogging network shows richer details of texture and more realistic colors.
Disentangled latent factors for muti-cause treatment effect estimation
Authors: Zhao Zhuoyue , Wang Haotian , Xu Liyang , Wang Qian , Tang Yuhua 
Published: October 2024

Abstract
Existing methods estimate treatment effects from observational data and assume that covariates are all confounders. However, observed covariates may not directly represent confounding variables that influence both treatment and outcome. They always include variables that only affect the treatment or the outcome. In addition, for multi-dimensional binary treatments, disentangled methods are mainly designed for binary variables and ignore the impact of multi-cause treatment variables on the inference of latent factors. To address these two issues, based on variable decomposition and proxy inference, we propose the Disentangled Latent Factors for Multi-cause Treatment Estimation (DEMTE) algorithm. It utilizes an identifiable autoencoder to infer and disentangle latent factors based on the joint distribution of variables in observational data. DEMTE evaluates the treatment effect on the disentangled factors. Synthetic experiments and semi-synthetic experiments demonstrate the effectiveness of the inference and disentanglement techniques and our method achieves more accurate treatment effect estimation.
Leukocyte detection based on convolutional neural network fusion with color Fourier ptychographic microscopy
Authors: Lyu Zhitong , Li Jie , Wang Haoming , Wang Hao 
Published: October 2024

Abstract
In the field of optical microscopic imaging, color Fourier Ptychographic Microscopy (FPM) technology has attracted much attention due to its advantages of large field of view, high resolution, and quantitative phase imaging. In this paper, a color FPM fusion algorithm based on deep learning is proposed in combination with Convolutional Neural Networks (CNN) and applied to leukocyte detection. Firstly, this paper introduces a fusion model of a convolutional neural network based on the traditional color FPM imaging method and fuses low-resolution color images and high-resolution grayscale images through a multilayer convolutional network. This method improves the quality of reconstructed images while reducing the reconstruction time. Secondly, this paper constructs a leukocyte detection dataset by using an improved color FPM reconstruction algorithm and builds a leukocyte detection system based on the YOLOv7 architecture. This paper shows that combining convolutional neural networks with color FPM technology can provide higher-quality reconstructed images in medical imaging and cell detection, which provides strong technical support for digital pathology and medical diagnosis.
Research on extraction method based on mark point projection
Authors: Diao Zhaoheng , Gao Mingliang , Shi Weili , Xing Shuo , Li Fei 
Published: October 2024

Abstract
In order to achieve high-precision positioning of surgical instruments, the accurate position of the mark point must be obtained in real time. The three-dimensional positioning process of mark points is generally divided into four parts: projection extraction of mark points, distortion correction, stereo matching, and three-dimensional reconstruction. Among them, the accuracy of mark point projection extraction directly determines the positioning accuracy of mark points, while the extraction of mark point projections can be divided into three parts: mark point edge detection, mark point contour screening, and mark point center extraction. In view of this, a mark point projection extraction method based on edge features is proposed to achieve high-precision extraction of the mark point center.
Importance of FinTech and green finance to achieve the carbon neutrality targets: a study of australian perspective
Authors: Anwar Humaira , Waheed Rida , Aziz Ghazala 
Published: October 2024

Abstract
Based on Australian data from 2000 to 2022, this study examines how FinTech will influence the nation’s environmental future. The research shows that by directly intervening, FinTech-supported green finance is a chief driver of carbon reductions. This study contributes to existing literature by examine the role of Fintech and green finance in carbon neutrality targets of Australia. The complex web of interrelationships between technological and financial innovations is revealed as a factor that impacts the environment. When applied to Australia, which has its own FinTech industry and commitment to sustainable development, these results become even more eye-catching. This synergy takes global dimensions, with the research demonstrating a need to harmonize technological development and workable financial plans if environmental breakthroughs are to be made. However, the study points out to policymakers that they must foster a nurturing environment for digital financial innovations, develop stronger regulatory mechanisms, and encourage sustainable financial products. In terms of methodology, the research uses regression and mediation analysis to reach its conclusions. This research provides empirical evidence on the critical role of Fintech in advancing environmental sustainability in Australia, highlighting its direct and mediated effects on carbon emission reductions. By demonstrating the synergistic impact of technological and financial innovations, the study offers valuable insights for policymakers to foster a supportive ecosystem for sustainable digital finance.
Impact of urbanization, economic growth, FDI, and trade openness on energy demand in Ireland: an ARDL approach
Authors: Tahrim Farian , Hasan Md Atik , Akter Salma , Das Mihir Kumar , Pattak Dulal Chandra 
Published: October 2024

Abstract
This research study uses an autoregressive distributed lag (ARDL) paradigm to examine the effects of trade openness, urbanization (URBA), economic growth, and foreign direct investment (FDI) on energy demand in Ireland. This thorough theoretical framework anticipates a relationship between energy demand and trade, FDI, and URBA. This study demonstrates both short- and long-run cointegration among the variables employing an ARDL bounds-testing method for the data set period from 1972 to 2021. Additionally, fully modified ordinary least square (OLS), dynamic OLS, and canonical cointegrating regression were utilized in the study to verify the results’ robustness. Nonetheless, this analysis discovers that trade openness and rising gross domestic product per capita increase energy consumption (EC). The results show that for every 1% increase in GDP, EC increases by 0.967%. On the flip side, EC in Ireland is declining due to increased URBA and FDI. The results show that for every 1% increase in URBA, energy usage decreases by 1.738%. The investigation shows that Ireland’s energy usage is strongly impacted by trade liberalization, FDI, URBA, and economic expansion. Findings like these can help policymakers and stakeholders manage and optimize energy usage in the context of URBA, economic expansion, FDI, and global trade. The report recommends sustainable energy planning and policy initiatives to strike a healthy balance between meeting the increasing demand for energy and protecting the environment.
Metric flows with neural networks
Authors: Halverson James , Ruehle Fabian 
Published: October 2024

Abstract
We develop a general theory of flows in the space of Riemannian metrics induced by neural network (NN) gradient descent. This is motivated in part by recent advances in approximating Calabi–Yau metrics with NNs and is enabled by recent advances in understanding flows in the space of NNs. We derive the corresponding metric flow equations, which are governed by a metric neural tangent kernel (NTK), a complicated, non-local object that evolves in time. However, many architectures admit an infinite-width limit in which the kernel becomes fixed and the dynamics simplify. Additional assumptions can induce locality in the flow, which allows for the realization of Perelman’s formulation of Ricci flow that was used to resolve the 3d Poincaré conjecture. We demonstrate that such fixed kernel regimes lead to poor learning of numerical Calabi–Yau metrics, as is expected since the associated NNs do not learn features. Conversely, we demonstrate that well-learned numerical metrics at finite-width exhibit an evolving metric-NTK, associated with feature learning. Our theory of NN metric flows therefore explains why NNs are better at learning Calabi–Yau metrics than fixed kernel methods, such as the Ricci flow.
Humanity’s Debt to Science
Authors: Powell James Lawrence 
Published: October 2024

Abstract
Since the Enlightenment, science has brought about a dramatic improvement in both the length and the quality of human life. Yet individuals, and entire nations, reject science whenever it violates their ideology. Examples from history include Lysenkoism in the USSR, Mao’s failed agricultural policies, Nazi eugenics, so-called German physics, Mbeki’s AIDS denial, COVID-19, and manmade global warming. With global warming, the ultimate cost of state science denial has become frighteningly clear. Nations and their leaders today have a choice: either learn from history, trust scientists and act on their advice, or cripple the lives of coming generations.
Lysenko and the Origins of Soviet Pseudoscience
Authors: Powell James Lawrence 
Published: October 2024

Abstract
As the twentieth century began, Russian scientists were among the world’s best. By the late 1920s, however, the denial of genetics under Stalin and his agent, the pseudoscientist Trofim Lysenko, was on its way to becoming state policy. The leading Soviet geneticist, Nikolai Vavilov, tried to conciliate Lysenko, but that proved to be impossible. With Stalin’s approval, the USSR began to adopt Lysenko’s method of vernalization, in which plants were supposed to easily be changed to a different species, something that genetics showed was impossible. Those who rejected Lysenko and his pseudoscience began to be fired, persecuted, and even executed.
AIDS
Authors: Powell James Lawrence 
Published: October 2024

Abstract
No sooner was the virus that causes AIDS identified in 1984 than a denialist movement took root. It found its champion in a Nobel prize-winning biologist named Peter Duesberg, who, for example, published an article titled “HIV Is Not the Cause of AIDS.” One who was persuaded by Duesberg and the other denialists was President Thabo Mbeki of South Africa. He did his own internet research and concluded that HIV did not cause AIDS and that retroviral drugs did more harm than good. He appointed a denialist as his health minister, who recommended folk remedies such as garlic, beetroot, and potatoes. One study attributed 330,000 unnecessary deaths in South Africa to Mbeki’s AIDS denial.
Global Warming: The Ultimate Triumph of Science Denial?
Authors: Powell James Lawrence 
Published: October 2024

Abstract
Over the second half of the twentieth century and continuing today, the theory of manmade global warming has been rejected by growing numbers of the public and right-wing politicians. In 2008, Republican presidential candidate John McCain could still openly endorse the theory, but soon nearly every elected Republican had signed a pledge promising to do nothing about it. President Trump was on record as saying that global warming was a “Chinese hoax” and early in his presidency withdrew the United States from the Paris Agreement on climate change. An exposé showed that Exxon’s own scientists had told the company in the 1970s that global warming was caused by fossil fuels and was dangerous. But the company hid this finding and funded denialist organizations. In spite of the Paris Agreement and many noble sentiments, fossil fuel emissions, atmospheric carbon dioxide, and global temperatures have continued to rise. On this trajectory, science denial will achieve its ultimate triumph, at the cost of many if not most of the benefits science has brought humanity, possibly even human civilization itself.
Roadmap to Doomsday
Authors: Powell James Lawrence 
Published: October 2024

Abstract
Science is fundamentally a search for the truth about the natural world. Therefore, science is profoundly incompatible with science denial, which is often rooted in misconceptions or even lies. Truth is essential in all human affairs at all levels, from personal relations to international ones. To let lies replace truth is to abandon reason and common sense and, facing the existential threat of global warming, is apt to prove fatal. The Republican Party has planned to double-down on global warming denial and inaction if former President Trump wins a second term. For the sake of our children and grandchildren, Americans must elect politicians who trust in science.
Analyzing structural racism and its health impacts: it’s about time
Authors: Krieger Nancy 
Published: October 2024

Abstract
It’s about time. Since 2020, new work on structural racism and health has erupted in the United States, with 93% of the 1655 PubMed articles indexed by “structural racism” as of April 4, 2024 having been published during or since 2020. Among the 310 review articles included, most have focused either on improving conceptual, substantive, and methodologic clarity and precision about measuring and modeling structural racism, or reviewing evidence about its impacts on specific exposures or outcomes. However, only 2.5% of these 1655 articles are also indexed by the term “lifecourse” and its variants, and among the reviews considered here, none explicitly discuss issues involving etiologic period. Informed by ecosocial theory’s temporal theorizing about pathways of embodiment, and also Latin American social medicine-collective health framings of “health-illness-disease processes,” lifecourse models, and the construct of the “cancer control continuum,” in this brief commentary I consider how time matters in relation to concrete examples involving structural racism and cancer, and also inconsistent results reported by several studies using latent measures of structural racism. When it comes to structural racism and health, it truly is about time – and it is time for this work to tackle issues of time.
Blood Pressure Cuff Sizes for Pregnant Women in the United States: Findings From the National Health and Nutrition Examination Survey
Authors: Shahi Shamim , Streeter Taylor E , Wall Hilary K , Zhou Wen , Kuklina Elena V , Jackson Sandra L 
Published: October 2024

Abstract

BACKGROUND
The use of correctly sized blood pressure (BP) cuffs is important to ensure accurate measurement and effective management of hypertension. The goals of this study were to determine the proportions of pregnant women that would require small, adult, large, and extra-large (XL) cuff sizes, and to examine the demographic characteristics associated with need for a large or XL cuff.

METHODS
This cross-sectional study analyzed 1,176 pregnant women (≥18 years) included in the National Health and Nutrition Examination Survey (NHANES) 1999–2006 cycles. Recommended BP cuff sizes, based on American Heart Association recommendations, were categorized by mid-arm circumference: small adult (≤26 cm), adult (&gt;26 to ≤34 cm), large (&gt;34 to ≤44 cm), and extra-large (XL) (&gt;44 cm).

RESULTS
Among US pregnant women, recommended cuff sizes were: 17.9% small adult, 57.0% adult, and 25.1% for large or XL. About 38.5% of non-Hispanic Black, 21.6% of Mexican American and 21.0% of non-Hispanic White pregnant women required a large or XL cuff. About 81.8% of women in the highest quartile for BMI required large or XL cuffs, which was significantly higher than women in other quartiles.

CONCLUSION
Roughly one out of every four pregnant women required large or XL BP cuffs. The requirement for large or XL cuffs was highest among non-Hispanic Black women and women with the highest BMI. For pregnant women, measuring the arm circumference and selecting an appropriately sized cuff is important to facilitate accurate blood pressure monitoring and hypertension management.
Responses of oak seedlings to increased herbivory and drought: a possible trade-off?
Authors: Peláez Marta , López-Sánchez Aida , Fernandes Geraldo Wilson , Dirzo Rodolfo , Rodríguez-Calcerrada Jesús , Perea Ramón 
Published: October 2024

Abstract

Background and Aims
Anthropogenic disturbances are causing a co-occurring increase in biotic (ungulate herbivory) and abiotic (drought) stressors, threatening plant reproduction in oak-dominated ecosystems. However, could herbivory compensate for the adverse impact of drought by reducing evapotranspiration? Thus, we investigated the isolated and joint effects of herbivory and drought on oak seedlings of two contrasting Mediterranean species that differ in leaf habit and drought resistance.

Methods
California oak seedlings from the evergreen, and more drought-resistant, Quercus agrifolia and the deciduous Q. lobata (n = 387) were assigned to a fully crossed factorial design with herbivory and drought as stress factors. Seedlings were assigned in a glasshouse to three to four clipping levels simulating herbivory and three to four watering levels, depending on the species. We measured survival, growth and leaf attributes (chlorophyll, secondary metabolites, leaf area and weight) once a month (May–September) and harvested above- and below-ground biomass at the end of the growing season.

Key Results
For both oak species, simulated herbivory enhanced seedling survival during severe drought or delayed its adverse effects, probably due to reduced transpiration resulting from herbivory-induced leaf area reduction and compensatory root growth. Seedlings from the deciduous, and less drought-resistant species benefited from herbivory at lower levels of water stress, suggesting different response across species. We also found complex interactions between herbivory and drought on their impact on leaf attributes. In contrast to chlorophyll content which was not affected by herbivory, anthocyanins increased with herbivory – although water stress reduced differences in anthocyanins due to herbivory.

Conclusions
Herbivory seems to allow Mediterranean oak seedlings to withstand summer drought, potentially alleviating a key bottleneck in the oak recruitment process. Our study highlights the need to consider ontogenetic stages and species-specific traits in understanding complex relationships between herbivory and drought stressors for the persistence and restoration of multi-species oak savannas.
Social and seasonal variation in dwarf mongoose home-range size, daily movements, and burrow use
Authors: Arbon Josh J , Morris-Drake Amy , Kern Julie M , Giuggioli Luca , Radford Andrew N 
Published: October 2024

Abstract
When making decisions about resource use, social species must integrate not only environmental factors but also the influence of opportunities and costs associated with group living. Bigger groups are expected to move further and to need access to larger areas for adequate food acquisition, but the relationships with group size can vary seasonally and with reproductive stage. Shelters are often more consistent in availability than food, but their use relates to factors such as predator defense and parasite transmission that are themselves influenced by group size and seasonality. Here, we used long-term data to investigate resource use and associated movement in a wild population of dwarf mongooses (Helogale parvula). We found that bigger groups occupied larger home ranges, moved larger daily distances and covered more daily area than smaller ones, while environmental greenness (measured by normalized difference vegetation index [NDVI]) influenced daily movements in the breeding season but not the non-breeding season. Both assessed axes of seasonality also had pronounced effects on shelter use: mongoose groups used more unique sleeping burrows, and switched between burrows more often, in the breeding season, but also switched more when environmental greenness was higher. By investigating specific periods within the breeding season, we revealed the constraints that vulnerable, poorly mobile offspring impose on both group movements and burrow use, highlighting a potentially overlooked cost of reproduction. Our results show how both social and environmental factors can affect key resource-use decisions, demonstrating potential costs and benefits to group living within distinctly seasonal geographic areas.
Surgical complications after immediate implant-based breast reconstruction for breast cancer in women over 65 years
Authors: Liu Yihang , Johansson Anna L V , Oikonomou Ira , Frisell Axel , Adam Hannah C , Ansarei Dhirar , Halle Martin , Sackey Helena , de Boniface Jana 
Published: October 2024

Abstract

Background
While immediate breast reconstruction rates in breast cancer are increasing, they remain low in women over 65 years old. The aim was to investigate surgical outcomes in women older than 65 years receiving implant-based immediate breast reconstruction.

Method
The population-based Stockholm Breast Reconstruction Database includes all adult women with breast cancer receiving an implant-based immediate breast reconstruction in Stockholm, Sweden, 2005–2015. Primary outcomes within 30 days from immediate breast reconstruction were: infection requiring antibiotics and reoperation on. Implant removal was a secondary outcome. Women more than 65 years were compared with younger age groups. Chi-square tests and multivariable logistic regression were applied for the primary outcomes, and Kaplan–Meier analysis for the secondary outcome.

Results
Among 1749 cases of immediate breast reconstruction, 140 (8.0%) were in women more than 65 years. Median follow-up was 74 months (1–198). Postoperative infection was not more common in women older than 65 years old (22 of 140, 15.7%) than in women under 65 years old (303 of 1609, 18.8%; P = 0.221). Reoperation on was more frequent in women older than 65 years than in other age groups (more than 65: 8.6%; 50–64: 6.5%; 40–49: 3.5%; less than 40: 1.6%; P &lt; 0.001), however, age older than 65 years was not an independent risk factor in the multivariable analysis (OR 1.00, 95% c.i. 0.44 to 2.28). Overall, 6-year probability of implant removal was 11.4%, (8.1% due to complications and 3.3% due to patient preference). There was no statistically significant difference between age groups for either reason (P = 0.085 and P = 0.794 respectively).

Conclusion
Older age alone was not associated with worse surgical outcomes after implant-based immediate breast reconstruction in highly selected patients older than 65 years when compared with their younger counterparts.