bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-preventable cases of breast, prostate, lung, and colorectal cancer in 2050 in an elimination scenario of modifiable risk factors
Authors: Palshof Frederik Knude , Mørch Lina Steinrud , Køster Brian , Engholm Gerda , Storm Hans Henrik , Andersson Therese M.-L. , Kroman Niels 
Published: April 2024

AbstractMost Western countries have increasing number of new cancer cases per year. Cancer incidence is primarily influenced by basically avoidable risk factors and an aging population. Through hypothetical elimination scenarios of multiple major risk factors for cancer, we estimated the number of new cancer cases that are non-preventable in 2050. We compare numbers of new postmenopausal breast, prostate, lung, and colorectal cancer cases in 2021 to projected numbers of new cases in 2050 under prevention scenarios regarding smoking, overweight and obesity, and alcohol consumption: no intervention, 50%, and 100% instant reduction. Cancer incidence data were derived from NORDCAN, and risk factor prevalence data from the Danish National Health Survey. Cancer projections were calculated with the Prevent program. Hypothetical 100% instant elimination of major risk factors for cancer in Denmark in 2022 will result in unchanged numbers of new breast and colorectal cancers in 2050. The number of new prostate cancers will increase by 25% compared to 2021. Unchanged risk factor levels will result in noticeable increase in cancer burden. Increase in life expectancy and age will entail an increase in cancer incidence, despite maximum effect of preventive actions in the population. Our results are important when planning future health care.
Effects of green tea and roasted green tea on human responses
Authors: Kurosaka Chie , Tagata Chika , Nakagawa Sae , Kobayashi Makoto , Miyake Shinji 
Published: April 2024

AbstractOur objective was to elucidate the effects of tea consumption on refreshment and stress reduction/recovery through examining the multiple associations among factors such as various physiological responses and task performance. Participants included 20 healthy young men who performed a mental arithmetic task while 11 physiological responses were measured. The experiments were conducted twice under different beverage consumption conditions on separate days. The mental arithmetic task was executed six times in 1 day; participants ingested hot water, green tea, or roasted green tea (hojicha) before each task. Several subjective assessments: subjective fatigue, stress, mental workload, and flow were evaluated after each task. The R–R intervals, heart rate variability spectral components, the Poincaré plot indices (SD1 and SD2) and plethysmogram amplitude tended to decrease during task periods compared to resting periods. Tissue blood volume/flow (TBV, TBF) and near-infrared spectroscopy responses (NIRS) were lower in the tea condition than in the hot water condition. By scrutinizing various indicators, we found that aromatic stimulation of Japanese tea beverages has the potential to induce positive effects, enhance mental task performance, promote refreshment, and alleviate feelings of fatigue. These positive effects were observed even in small quantities and within a short duration, mirroring responses observed in daily consumption.
Climate warming contributes to the record-shattering 2022 Pakistan rainfall
Authors: You Yujia , Ting Mingfang , Biasutti Michela 
Published: April 2024

AbstractA sequence of torrential rainstorms pounded Pakistan in the summer of 2022, shattering records by massive margins (7 sigma). The severe socioeconomic damages underscore the urgency of identifying its dynamic drivers and relationship with human-induced climate change. Here, we find that the downpours were primarily initiated by the synoptic low-pressure systems, whose intensity and longevity far exceeded their counterparts in history as fueled by a historically-high cross-equatorial moisture transport over the Arabian Sea. The moisture transport has been trending upward since the 1960s and, in 2022, along with the anomalous easterly moisture influx caused by the combination of La Niña and negative Indian Ocean Dipole events, created a corridor of heavy rainfall extending from central India toward southern Pakistan. While it is not yet established whether the observed trend of the cross-equatorial moisture transport has exceeded natural variability, model-based analysis confirms that it is consistent with the fingerprint of anthropogenic climate warming and will raise the likelihood of such rare events substantially in the coming decades.
Significant contribution of fractal morphology to aerosol light absorption in polluted environments dominated by black carbon (BC)
Authors: Romshoo Baseerat , Müller Thomas , Ahlawat Ajit , Wiedensohler Alfred , Haneef M. V. , Imran Mohd. , Warsi Aisha Baig , Mandariya Anil Kumar , Habib Gazala , Pöhlker Mira L. 
Published: April 2024

AbstractIn recent years, researchers have emphasized the use of fractal aggregate morphology instead of the core-shell morphology in global climate models for estimating black carbon (BC) forcing. This study confirms that fractal morphology plays an important role in reducing the overestimation of aerosol light absorption calculations in the case of an urban polluted environment. During periods of high anthropogenic BC emissions at Delhi, the particle light absorption is overestimated by 50 to 200% by assumptions of both external mixing and internal core-shell mixing. While incorporating the aggregate morphology model into light absorption simulations is beneficial in such cases, it comes with a high computational burden. To address this, we propose a metric known as morphology index (MI). This index distributes the weightage between the two extreme cases of core-shell and fractal aggregate to obtain accurate particle light absorption. Long-range transported aerosols were estimated to have an MI of 0.78, and fresh local emissions had an MI of 0.48. A BC-based aerosol classification approach was developed to determine the most relevant particle size mode for light absorption. The method is based on patterns found between the correlations of the BC mass concentrations and aerosol number concentrations at the different particles sizes (BC-size correlation spectra). BC-size correlation spectra are introduced as a concept that may be used (i) independently to understand the size-dependent heterogeneous distribution of aerosol light absorption and (ii) in conjunction with MI to accurately model the optical properties of aerosols in different BC regimes.
Deep learning evaluation of echocardiograms to identify occult atrial fibrillation
Authors: Yuan Neal , Stein Nathan R. , Duffy Grant , Sandhu Roopinder K. , Chugh Sumeet S. , Chen Peng-Sheng , Rosenberg Carine , Albert Christine M. , Cheng Susan , Siegel Robert J. , Ouyang David 
Published: April 2024

AbstractAtrial fibrillation (AF) often escapes detection, given its frequent paroxysmal and asymptomatic presentation. Deep learning of transthoracic echocardiograms (TTEs), which have structural information, could help identify occult AF. We created a two-stage deep learning algorithm using a video-based convolutional neural network model that (1) distinguished whether TTEs were in sinus rhythm or AF and then (2) predicted which of the TTEs in sinus rhythm were in patients who had experienced AF within 90 days. Our model, trained on 111,319 TTE videos, distinguished TTEs in AF from those in sinus rhythm with high accuracy in a held-out test cohort (AUC 0.96 (0.95–0.96), AUPRC 0.91 (0.90–0.92)). Among TTEs in sinus rhythm, the model predicted the presence of concurrent paroxysmal AF (AUC 0.74 (0.71–0.77), AUPRC 0.19 (0.16–0.23)). Model discrimination remained similar in an external cohort of 10,203 TTEs (AUC of 0.69 (0.67–0.70), AUPRC 0.34 (0.31–0.36)). Performance held across patients who were women (AUC 0.76 (0.72–0.81)), older than 65 years (0.73 (0.69–0.76)), or had a CHA2DS2VASc ≥2 (0.73 (0.79–0.77)). The model performed better than using clinical risk factors (AUC 0.64 (0.62–0.67)), TTE measurements (0.64 (0.62–0.67)), left atrial size (0.63 (0.62–0.64)), or CHA2DS2VASc (0.61 (0.60–0.62)). An ensemble model in a cohort subset combining the TTE model with an electrocardiogram (ECGs) deep learning model performed better than using the ECG model alone (AUC 0.81 vs. 0.79, p = 0.01). Deep learning using TTEs can predict patients with active or occult AF and could be used for opportunistic AF screening that could lead to earlier treatment.
Towards a common European ethical and legal framework for conducting clinical research: the GATEKEEPER experience
Authors: Maccaro Alessia , Tsiompanidou Vasiliki , Piaggio Davide , Gallego Montejo Alba M. , Cea Sánchez Gloria , de Batlle Jordi , Quesada Rodriguez Adrian , Fico Giuseppe , Pecchia Leandro 
Published: April 2024

AbstractThis paper examines the ethical and legal challenges encountered during the GATEKEEPER Project and how these challenges informed the development of a comprehensive framework for future Large-Scale Pilot (LSP) projects. GATEKEEPER is a LSP Project with 48 partners conducting 30 implementation studies across Europe with 50,000 target participants grouped into 9 Reference Use Cases. The project underscored the complexity of obtaining ethical approval across various jurisdictions with divergent regulations and procedures. Through a detailed analysis of the issues faced and the strategies employed to navigate these challenges, this study proposes an ethical and legal framework. This framework, derived from a comparative analysis of ethical application forms and regulations, aims to streamline the ethical approval process for future LSP research projects. By addressing the hurdles encountered in GATEKEEPER, the proposed framework offers a roadmap for more efficient and effective project management, ensuring smoother implementation of similar projects in the future.
Neural circuits regulating visceral pain
Authors: Chang Xiaoli , Zhang Haiyan , Chen Shaozong 
Published: April 2024

AbstractVisceral hypersensitivity, a common clinical manifestation of irritable bowel syndrome, may contribute to the development of chronic visceral pain, which is a major challenge for both patients and health providers. Neural circuits in the brain encode, store, and transfer pain information across brain regions. In this review, we focus on the anterior cingulate cortex and paraventricular nucleus of the hypothalamus to highlight the progress in identifying the neural circuits involved in visceral pain. We also discuss several neural circuit mechanisms and emphasize the importance of cross-species, multiangle approaches and the identification of specific neurons in determining the neural circuits that control visceral pain.
Three-dimensional spontaneous flow transition in a homeotropic active nematic
Authors: Pratley Vincenzo J. , Caf Enej , Ravnik Miha , Alexander Gareth P. 
Published: April 2024

AbstractActive nematics are driven, non-equilibrium systems relevant to biological processes including tissue mechanics and morphogenesis, and to active metamaterials in general. We study the three-dimensional spontaneous flow transition of an active nematic in an infinite slab geometry using a combination of numerics and analytics. We show that it is determined by the interplay of two eigenmodes – called S- and D-mode – that are unstable at the same activity threshold and spontaneously breaks both rotational symmetry and chiral symmetry. The onset of the unstable modes is described by a non-Hermitian integro-differential operator, which we determine their exponential growth rates from using perturbation theory. The S-mode is the fastest growing. After it reaches a finite amplitude, the growth of the D-mode is anisotropic, being promoted perpendicular to the S-mode and suppressed parallel to it, forming a steady state with a full three-dimensional director field and a well-defined chirality. Lastly, we derive a model of the leading-order time evolution of the system close to the activity threshold.
Recent increase in the surface mass balance in central East Antarctica is unprecedented for the last 2000 years
Authors: Ekaykin Alexey A. , Veres Arina N. , Wang Yetang 
Published: April 2024

AbstractThe Antarctic Ice Sheet’s reaction on the continuing global warming is crucial to project the future sea level rise. The increasing air temperature causes both accelerating mass loss on the Antarctica’s margins and increased surface mass balance in its interior. Here, we use data from firn cores drilled near Vostok station (central East Antarctica) to analyze the surface mass balance variability over the past 2200 years. We show a 24% increase of snow accumulation rate since early 19 century confirmed by the instrumental data for the last 52 years. The mean surface mass balance value in 1970–2021 (2.25 ± 0.064 g cm−2 yr−1) is very likely unprecedented in the pre-industrial era. The surface mass balance growth is accompanied by the air temperature increase with the sensitivity of 11 ± 2% per 1 °C. These results will help to constrain the potential magnitude of Antarctic snowfall mitigation of future sea level rise.
Large transboundary health impact of Arctic wildfire smoke
Authors: Silver Ben , Arnold Steve R. , Reddington Carly L. , Emmons Louisa K. , Conibear Luke 
Published: April 2024

AbstractRapid warming at high latitudes, particularly in Siberia, has led to large wildfires in recent years that cause widespread smoke plumes. These fires lead to substantial deterioration in summer air quality in the region, with a factor 4 increase in summer fine particulate matter (PM2.5) concentrations in parts of Siberia during 1998–2020. Exposure to PM2.5 is associated with increased risk of mortality due to cardiovascular and respiratory disease, and the atmospheric lifetime of PM2.5 means that it can be efficiently transported between regions and nations. We used the Community Earth System Model to quantify the fraction of PM2.5 attributed to high latitude wildfires that occur in the Arctic Council member states and estimated the attributable health impact locally and in neighbouring countries. During 2001–2020 we attribute ~21,000 excess deaths to Arctic Council wildfires on average each year, of which ~8000 occur in countries outside the Arctic Council. Our analysis shows that the health impact of Arctic wildfires decreased during 2001–2020, despite the increase of wildfire-sourced PM2.5, which we suggest is due to a northwards shift in the average latitude of Siberian wildfires, reducing their impact on more densely populated regions.
Balancing resistor-based online electrochemical impedance spectroscopy in battery systems: opportunities and limitations
Authors: Blömeke Alexander , Zappen Hendrik , Ringbeck Florian , Frie Fabian , Wasylowski David , Sauer Dirk Uwe 
Published: April 2024

AbstractActive dissipative balancing systems are essential in battery systems, particularly for compensating the leakage current differences in battery cells. This study focuses on using balancing resistors to stimulate battery cells for impedance measurement. The value of impedance spectroscopy for in-depth battery cell diagnostics, such as temperature or aging, is currently being demonstrated and recognized by vehicle manufacturers, chip producers, and academia. Our research systematically explores the feasibility of using existing balancing resistors in battery management systems and identifies potential limitations. Here we propose a formula to minimize hardware requirements through signal processing techniques. A quadrupling of the sampling rate, number of averaging values, or the size of the fast Fourier transform is equivalent, concerning the signal-to-noise ratio, to increasing the analog resolution by one bit or reducing the input filter bandwidth by a quarter.
Optimizing transmission line parameter estimation with hybrid evolutionary techniques
Authors: Shaikh Muhammad Suhail , Raj Saurav , Abdul Latif Shah , Mbasso Wulfran Fendzi , Kamel Salah 
Published: April 2024

AbstractPower flow, planning, economics, dispatch, and stability analysis rely on accurate transmission line parameters (TLPE). Standard optimization methods are employed to develop such analyses and obtain TLPE. Additionally, these methods have limitations, including precision, accuracy, and time complexity. It is challenging to find improved solutions using standard optimization methods due to slow convergence and limitations in identifying local optima. Concerned with these challenges, the study suggest a new application for an effective hybrid optimization method capable of addressing such limitations. The hybrid algorithm, named the Salp Swarm Algorithm with Sine Cosine Algorithm (HSSASCA), that aims to tackle the issues of slow convergence and local optima. The Sine Cosine Algorithm (SCA) is employed after the Salp Swarm Algorithm (SSA), and Salp integration is utilized to successfully explore and analyze the search space. To enhance the performance of HSSASCA, the hybrid technique aims to provide expanded exploration capabilities, effective exploitation of the search space, and a better convergence rate. These key features position the HSSASCA algorithm as an effective solution to complex optimization problems. To assess the efficiency of the HSSASCA algorithm, six different test systems are employed. Initially, the evaluation of exploration, exploitation, and minimized local optima is conducted using the CEC 2019 benchmark functions. Secondly, efficiency monitoring and verification of HSSASCA across different scenarios occur by comparing it with established optimization algorithms such as SSA, SCA, firefly optimization algorithm (FFO), Grey Wolf Optimization (GWO), student psychology‐based optimization (SPBO), and Symbiotic Organisms Search (SOS). Finally, statistical analysis is performed, revealing that the HSSASCA outperforms SSA, SCA, FFO, GWO, SPBO, and SOS. In terms of statistical results and convergence curves, the HSSASCA demonstrates superior performance in searching efficiency, convergence accuracy, and local optimum avoidance ability.
A state‐space average model of a three‐level PV inverter for transient short‐circuit currents analysis
Authors: Yuan Shuai , Wen Bing , Zhang Jian‐Ying , Li Zhimin 
Published: April 2024

AbstractThis paper presents a state‐space average model of a three‐level photovoltaic (PV) inverter to understand short‐circuit currents transient characteristics. Analytical solution of the model is also derived. The proposed model is validated with PV system controller hardware in‐loop (HIL) test method. The errors between simulation and HIL tests results under various conditions are evaluated, testifying the validity of the proposed model. With the model and analytical solution, the decaying time constants of sub‐transient and transient processes are derived, which shows that the time constants of a three‐level PV inverter are not fixed and they vary with voltage dips. The analytical expression of short‐circuit currents is also formulated. The findings from this study are essential to short‐circuit current calculations of a power grid with large‐scale PV plants, coordination of power system protections, and grid planning.
VGA‐Net: Vessel graph based attentional U‐Net for retinal vessel segmentation
Authors: Jalali Yeganeh , Fateh Mansoor , Rezvani Mohsen 
Published: April 2024

AbstractSegmentation is crucial in diagnosing retinal diseases by accurately identifiying retinal vessels. This paper addresses the complexity of segmenting retinal vessels, highlighting the need for precise analysis of blood vessel structures. Despite the progress made by convolutional neural networsks (CNNs) in image segmentation, their limitations in capturing the global structure of retinal vsessels and maintaining segmentation continuity present challenges. To tackle these issues, our proposed network integrates graph convolutional networks (GCNs) and attention mechansims. This allows the model to consider pixel relationships and learn vessel graphical structures, significantly improving segmentation accuracy. Additionally, the attentional feature fusion module, including pixel‐wise and channel‐wise attention mechansims within the U‐Net architecture, refines the model's focus on relevant features. This paper emphasizes the importance of continuty preservation, ensuring an accurate representation of pixel‐level information and structural details during sefmentation. Therefore, our method performs as an effective solution to overcome challenges in retinal vessel segmentation. The proposed method outperformed the state‐of‐the‐art approaches on DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (Structed Analysis of the Retina) datasets with accuracies of 0.12% and 0.14%, respecttively. Importantly, our proposed approach excelled in delineating slender and diminutive blood vessels, crucial for diagnosing vascular‐related diseases. Implementation is accessible on https://github.com/CVLab‐SHUT/VGA‐Net.
Cyber security analysis of connected vehicles
Authors: Mwanje Maria Drolence , Kaiwartya Omprakash , Aljaidi Mohammad , Cao Yue , Kumar Sushil , Jha Devki Nandan , Naser Abdallah , Lloret Jaime 
Published: April 2024

AbstractThe sensor‐enabled in‐vehicle communication and infrastructure‐centric vehicle‐to‐everything (V2X) communications have significantly contributed to the spark in the amount of data exchange in the connected and autonomous vehicles (CAV) environment. The growing vehicular communications pose a potential cyber security risk considering online vehicle hijacking. Therefore, there is a critical need to prioritize the cyber security issues in the CAV research theme. In this context, this paper presents a cyber security analysis of connected vehicle traffic environments (CyACV). Specifically, potential cyber security attacks in CAV are critically investigated and validated via experimental data sets. Trust in V2X communication for connected vehicles is explored in detail focusing on trust computation and trust management approaches and related challenges. A wide range of trust‐based cyber security solutions for CAV have been critically investigated considering their strengths and weaknesses. Open research directions have been highlighted as potential new research themes in CAV cyber security area.
Real‐time multi‐objective speed planning ATO considering assist driving for subway
Authors: Wang Xiaowen , Zhang Zipei , Wang Qingyuan , Sun Pengfei , Feng Xiaoyun 
Published: April 2024

AbstractSpeed curve planning is one of the most important functions of automatic train operation (ATO). To improve the real‐time optimization capability and driver‐friendliness of the existing ATO, an extended ATO framework considering both automatic driving and assisted driving is designed. A multi‐objective optimization model based on quadratic programming is established considering energy‐saving, punctuality, and comfort. However, due to the influence of the weight of multi‐objectives, this method cannot directly obtain the speed curve satisfying the trip time constraint. Further, based on the analysis about the weight of multi‐objects, a time‐constrained quadratic programming algorithm is proposed. With the proposed method, the speed curve can be calculated in real‐time both before operations and during operations. For the former, time‐varying train mass and trip time are considered to guarantee an optimal solution. For the latter, deviations, delays, and maloperations on the way are corrected. Simulation experiments verify the solvability and real‐time performance of the proposed method. In particular, compared with the dynamic programming and (mixed‐integer linear programming) MILP method, the proposed method is more energy‐efficient and easier to be followed by the driver. In addition, a prototype is developed for commercial tests on a Beijing subway line. The relevant performance is verified in commercial tests.