bluuebunny/crossref_metadata_2025_split huggingface dataset:
Principles and prospects of Bose-Einstein correlation study at BESIII*
Authors: Hu 胡 Hai-Ming 海明 , Huang 黄 Guang-Shun 光顺 
Published: October 2024

Abstract
One method for determining the characteristic parameters of a hadron production source is to measure the Bose-Einstein correlation functions. In this study, we present fundamental concepts and formulas related to the Bose-Einstein correlations, focusing on the measurement principles and the Lund model from an experimental perspective. We perform Monte Carlo simulations using the Lund model generator in the 2–3 GeV energy range. Through these feasibility studies, we identify key features of the Bose-Einstein correlations that offer valuable insights for experimental measurements. Utilizing data samples collected at BESIII, we perform measurements of the Bose-Einstein correlation functions, with an expected experimental precision of a few percent for the hadron source radius and incoherence parameter.
Optimized BP-LSTM neural network based on IGA for heat exchanger outlet temperature prediction
Authors: Zhang Yu , Xu Bing 
Published: October 2024

Abstract
In this paper, an improved Genetic Algorithm (IGA) is proposed to optimize the BP-LSTM neural network for predicting heat exchanger temperature. The study introduces an enhanced genetic algorithm designed to optimize both short- and long-term memory networks (LSTM) in conjunction with the BP neural network temperature prediction model and the BP-LSTM neural network model. Simulation and comparison results demonstrate that the proposed prediction method can effectively forecast heat exchanger temperatures.
Application of improved deep learning algorithm in obstacle avoidance of UAV
Authors: Lu Qi , Guo Lejiang , Sun Xu 
Published: October 2024

Abstract
To enhance the intelligent obstacle avoidance capabilities of unmanned aerial vehicles (UAVs), we introduce a refined version of the You Only Look Once (YOLO) algorithm, termed Improved YOLO (I-YOLO). This novel algorithm not only maintains the swift detection speed inherent in the original YOLO but also augments its feature enhancement network. Specifically, we propose a three-branch parallel feature pyramid network (TBPFP) to capture richer semantic information pertaining to the context of small obstacles. Furthermore, we incorporate a generative adversarial network (GAN) in a cascaded manner to synthesize more realistic super-resolution images, thereby bolstering detection accuracy. Experimental evaluations demonstrate that, in comparison to the baseline YOLO algorithm, our I-YOLO variant exhibits superior detection precision and feature extraction capabilities in the perception of intricate environmental targets. While there is a modest decrement in detection speed, it remains adequate to fulfill real-time requirements, particularly in the context of video stream processing.
Metagenomic analysis of sedimentary archives reveals ‘historical’ antibiotic resistance genes diversity increased over recent decades in the environment
Authors: Yan Dongna , Han Yongming , Liu Jinzhao , Zan Sifan , Lu Yanfeng , An Zhisheng , Capo Eric 
Published: October 2024

Abstract
Antibiotic Resistance Genes (ARGs) are widespread in freshwater environments and represent a concealed threat to public health and aquatic eco-system safety. To date, only a limited number of studies have investigated the historical distribution of ARGs and their hosts through the analysis of freshwater sedimentary archives. This research gap constrains our comprehensive of the mechanisms underlying natural bacterial resistance formation during pre-antibiotic era (prior to the 1940s) and the development of human-induced bacterial resistance in post-antibiotic era (since the 1940s). In this study, we examined the vertical distribution patterns of ARGs and their associated hosts within a sedimentary core from a eutrophic lake, employing shotgun sequencing methodology. The findings revealed a marked increase in ARG diversity during post-antibiotic era, and the predominant ARG types identified included those conferring resistance to multidrug, bacitracin, macrolide–lincosamide–streptogramin, beta-lactam, tetracycline, fluoroquinolone, glycopeptide and aminoglycoside, collectively accounting for 78.3%–85.6% of total ARG abundance. A total of 127 ARG subtypes were identified in samples, and 48 ARG subtypes shared across vertical sediment resistome profile with two of them, bacA and bcrA, occurring only in post-antibiotic era. Further, 137 metagenome-assembled genomes (83 species belonging to 12 phyla) were identified as ARG hosts, mainly belonging to the phyla Proteobacteria, Nitrospirota, Chloroflexota, Bacteroidota, Actinobacteriota, Cyanobacteria, and Firmicutes. Significant correlation was found between the diversity of ARG and the concentrations of organic matter and heavy metals, suggesting a common source of contamination. Aside the fact that human-induced eutrophication is a forcing factor acting in parallel to increase ARGs releases in water systems, both being indicators of increased urbanization in the catchment, eutrophication may significantly increase bacterial activity, thereby facilitating the spread of antibiotic-resistant bacteria in environment. This study reveals the marked increased in ARG diversity with the onset of antibiotic use by human societies with potential impact of aquatic ecosystem.
The level of preparedness of the local community for sustainable community tourism development in Mai Hich commune, Mai Chau district, Hoa Binh province, Vietnam
Authors: Thao N P , Trang D H , Huong P L , Tien N V , Thao P T 
Published: October 2024

Abstract
This study aims to assess the community’s readiness for implementing sustainable community tourism development in Mai Hich commune, Mai Chau district, Hoa Binh province. Evaluating the community’s readiness for sustainable tourism development is crucial to mitigate potential negative impacts from tourism activities. The research utilizes a mixed-methods approach, combining qualitative and quantitative research to determine the local community’s level of readiness for sustainable tourism development. The community’s readiness is found to be high in social and cultural aspects, while economic aspects reveal certain challenges. Additionally, the study analyzes various forms of community service provision. Survey results indicate that the local community’s involvement in tourism activities is rather limited, sporadic, and primarily characterized by small-scale, self-initiated endeavors. There is a lack of investment in products and services catering to tourists. These findings pose challenges for the development of sustainable community tourism in Mai Hich commune, Mai Chau district, Hoa Binh province.
Dual-mode 1 × 2 optical switch with simultaneous modulation based on inverse design devices
Authors: Wang Junqing , Jia Hao , Yang Shanglin 
Published: October 2024

Abstract
Mode division multiplexing (MDM) technology, based on the parallelism inherent in mode dimensions, provides an advancement in enhancing on-chip optical communication channel capacity. In MDM communication systems, the routing and switching of optical signals are of essential importance. However, conventional multimode optical switches typically follow the demultiplexing-processing-multiplexing technological route, leading to an unavoidable increase in device size. In scenarios where multiple modes need to be routed synchronously, the implementation of simultaneous modulation optical switches offers a more efficient and feasible solution. Here, we propose two 1 × 2 dual-mode optical switches with simultaneous modulation on a silicon-on-insulator platform in the 1525–1565 nm wavelength range, utilizing two optical phase modulation techniques: mode transformation and waveguide widening. Simultaneously, we employ inverse design methodologies based on the adjoint variable method and level-set method to create the compact single-connected devices, which are compatible with complementary metal-oxide semiconductor fabrication processes. The experimental results show that the insertion losses for both TE0 and TE1 modes are less than 1.6 dB (2.5 dB), with the worst modal crosstalk at most −13.5 dB (−12.7 dB) for the switch based on mode transformation (waveguide widening) strategy at the wavelength of 1550 nm. The extinction ratio of the two proposed optical switches exceeds 25 dB at the same wavelength. Furthermore, the switches exhibit a 10%–90% rise time of 15.2 μs and a 90%–10% fall time of 19.5 μs at 1550 nm, indicating the switching speed can be up to kilohertz. Our proposed 1 × 2 optical switches hold potential as a fundamental unit for optical signal switching in high-integration multimode optical communication systems.
Multi task deep learning phase unwrapping method based on semantic segmentation
Authors: Wang Linlin , Liang Wenjie , Guo Wanyi , Wang Zhujun , Wang Chuanyun , Gao Qian 
Published: October 2024

Abstract
Phase unwrapping is a key step to obtain continuous phase distribution in optical phase measurement. When the wrapped phase obtained from the interference pattern is unclear and noisy, estimating the unwrapped phase becomes more challenging. As deep learning advances in optical image processing, it will enhance processing efficiency and accuracy, bringing broader possibilities for various applications. This paper introduces an innovative phase unwrapping method based on multi-task learning, aiming to simultaneously enhancing denoised images and predicting wrap count. The proposed network, named ICER-Net, comprises an encoder and two decoders, transforming the input low-luminance, noisy wrapped phase into two intermediate outputs: enhanced wrapped phase and wrap count. Finally, these two intermediate results are fused to obtain the unwrapped phase. Experimental results demonstrate that ICER-Net not only enhances the accuracy of phase unwrapping, particularly when facing challenges of various noise levels and luminance sizes but also exhibits outstanding performance in actual collected speckle phase images. This indicates that ICER-Net holds significant superiority in addressing complex issues in optical image processing.
Gate voltage dependence on the density of state of 30° twisted bilayer graphene using Trotter–Suzuki tight-binding time propagation method
Authors: Susanto Jaysen Brian , Widianto Eri , Subama Emmistasega , Nurwantoro Pekik , Santoso Iman 
Published: October 2024

Abstract
Twisted multilayer structures are considered a helpful framework for exploring strongly correlated many-particle systems, where phenomena of physics correlation emerge. Here, we present the Trotter–Suzuki tight-binding time propagation method for computing the density of states (DOS) in a 30° twisted bilayer graphene (TBG). This method solves the time-dependent Schrödinger equation by decomposing Hamiltonian matrices to derive the correlation function. The Fourier transform of this correlation function yields the DOS of the system up to ≈ 1000 000 atoms. Our calculation proves that geometry makes an outstanding contribution to our final results. Additionally, applying additional gate voltage induces shifts in Van Hove singularities, potentially leading to the emergence of new states at the Fermi energy level. The results demonstrate that TBG systems can be easily adjusted and modified for further investigation of optoelectronic features.
Systematic review of experimental paradigms and deep neural networks for electroencephalography-based cognitive workload detection
Authors: K N Vishnu , Gupta Cota Navin 
Published: October 2024

Abstract
This article summarizes a systematic literature review of deep neural network-based cognitive workload (CWL) estimation from electroencephalographic (EEG) signals. The focus of this article can be delineated into two main elements: first is the identification of experimental paradigms prevalently employed for CWL induction, and second, is an inquiry about the data structure and input formulations commonly utilized in deep neural networks (DNN)—based CWL detection. The survey revealed several experimental paradigms that can reliably induce either graded levels of CWL or a desired cognitive state due to sustained induction of CWL. This article has characterized them with respect to the number of distinct CWL levels, cognitive states, experimental environment, and agents in focus. Further, this literature analysis found that DNNs can successfully detect distinct levels of CWL despite the inter-subject and inter-session variability typically observed in EEG signals. Several methodologies were found using EEG signals in its native representation of a two-dimensional matrix as input to the classification algorithm, bypassing traditional feature selection steps. More often than not, researchers used DNNs as black-box type models, and only a few studies employed interpretable or explainable DNNs for CWL detection. However, these algorithms were mostly post hoc data analysis and classification schemes, and only a few studies adopted real-time CWL estimation methodologies. Further, it has been suggested that using interpretable deep learning methodologies may shed light on EEG correlates of CWL, but this remains mostly an unexplored area. This systematic review suggests using networks sensitive to temporal dependencies and appropriate input formulations for each type of DNN architecture to achieve robust classification performance. An additional suggestion is to utilize transfer learning methods to achieve high generalizability across tasks (task-independent classifiers), while simple cross-subject data pooling may achieve the same for subject-independent classifiers.
Regulating composition and structure of coal-based graphene and its electrochemical characteristics
Authors: Liu Wenfeng , Li Jun , Zhang Yating , Li Keke , Liu Zhen , Guo Cliff Yi , Wang Jingjing , Wei Linlin 
Published: October 2024

Abstract
Coal, a carbon-rich mineral with plentiful reserves, serves not only as a fuel but also as a raw material, presenting lower pollution emissions in the latter use. From a materials chemistry standpoint, coal is a viable raw material for graphene production. This study develops a promising and sustainable method to convert coal into graphene, leveraging its unique macromolecular aromatic structure and high carbon content. The investigation includes an analysis of the lateral size, morphology, and chemical composition of coal-derived graphene using techniques such as X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy, and optical microscopy. Results confirm that coal can effectively replace natural graphite flakes in graphene production, with the derived graphene featuring three to six exfoliated layers and an oxygen content below 5.5%. While the graphene from coal shares a similar morphology to that derived from graphite, it exhibits more structural defects. Interestingly, the macroscopic size of the coal does not influence the microscopic composition and structure of the graphene. However, the thermal reduction method for oxidized graphene proves more effective at repairing structural defects than chemical reduction. Employing coal-derived graphene as a supercapacitor electrode demonstrates excellent cycling stability and ultra-high capacitance storage capacity. The H-CG-325 shows the highest discharge area-specific capacitance across various current densities. At an increased current density of 10 A/g, the H-CG-325 maintains 80.6% of its initial capacitance of 79 F/g observed at 1 A/g. Electrochemical tests reveal that coal-based graphene holds significant potential as a supercapacitor material, indicating promising applications in energy storage and conversion.
Advanced electrocardiography heart age: a prognostic, explainable machine learning approach applicable to sinus and non-sinus rhythms
Authors: Al-Falahi Zaidon S , Schlegel Todd T , Palencia-Lamela Israel , Li Annie , Schelbert Erik B , Niklasson Louise , Maanja Maren , Lindow Thomas , Ugander Martin 
Published: October 2024

Abstract

Aims
An explainable advanced electrocardiography (A-ECG) Heart Age gap is the difference between A-ECG Heart Age and chronological age. This gap is an estimate of accelerated cardiovascular aging expressed in years of healthy human aging, and can intuitively communicate cardiovascular risk to the general population. However, existing A-ECG Heart Age requires sinus rhythm. We aim to develop and prognostically validate a revised, explainable A-ECG Heart Age applicable to both sinus and non-sinus rhythms.

Methods and results
An A-ECG Heart Age excluding P-wave measures was derived from the 10-s 12-lead ECG in a derivation cohort using multivariable regression machine learning with Bayesian 5-min 12-lead A-ECG Heart Age as reference. The Heart Age was externally validated in a separate cohort of patients referred for cardiovascular magnetic resonance imaging by describing its association with heart failure hospitalization or death using Cox regression, and its association with comorbidities. In the derivation cohort (n = 2771), A-ECG Heart Age agreed with the 5-min Heart Age (R2 = 0.91, bias 0.0 ± 6.7 years), and increased with increasing comorbidity. In the validation cohort [n = 731, mean age 54 ± 15 years, 43% female, n = 139 events over 5.7 (4.8–6.7) years follow-up], increased A-ECG Heart Age gap (≥10 years) associated with events [hazard ratio, HR (95% confidence interval, CI) 2.04 (1.38–3.00), C-statistic 0.58 (0.54–0.62)], and the presence of hypertension, diabetes mellitus, hypercholesterolaemia, and heart failure (P ≤ 0.009 for all).

Conclusion
An explainable A-ECG Heart Age gap applicable to both sinus and non-sinus rhythm associates with cardiovascular risk, cardiovascular morbidity, and survival.
Sex- and age-specific differences in the use of antiarrhythmic therapies among atrial fibrillation patients: a nationwide cohort study
Authors: Salmela Birgitta , Jaakkola Jussi , Kalatsova Ksenia , Inkovaara Jaakko , Aro Aapo L , Teppo Konsta , Penttilä Tero , Halminen Olli , Haukka Jari , Putaala Jukka , Linna Miika , Mustonen Pirjo , Hartikainen Juha , Airaksinen K E Juhani , Lehto Mika 
Published: October 2024

Abstract

Aims
Atrial fibrillation (AF) patients frequently require active rhythm control therapy to maintain sinus rhythm and reduce symptom burden. Our study assessed whether antiarrhythmic therapies (AATs) are used disproportionately between men and women after new-onset AF.

Methods and results
The nationwide Finnish anticoagulation in AF registry-based linkage study covers all patients with new-onset AF in Finland during 2007–2018. Study outcomes included initiation of AATs in the form of antiarrhythmic drugs (AADs), cardioversion, or catheter ablation. The study population constituted of 229 565 patients (50% females). Women were older than men (76.6 ± 11.8 vs. 68.9 ± 13.4 years) and had higher prevalence of hypertension or hyperthyroidism, but lower prevalence of vascular disease, diabetes, renal disease, and cardiomyopathies than men. Overall, 17.6% of women and 25.1% of men were treated with any AAT. Women were treated with AADs more often than men in all age groups [adjusted subdistribution hazard ratio (aSHR) 1.223, 95% confidence interval (CI) 1.187–1.261]. Cardioversions were also performed less often on women than on men aged &lt;65 years (aSHR 0.722, 95% CI 0.695–0.749), more often in patients ≥ 75 years (aSHR 1.166, 95% CI 1.108–1.227), while no difference between the sexes existed in patients aged 65–74 years. Ablations were performed less often in women aged &lt;65 years (aSHR 0.908, 95% CI 0.826–0.998) and ≥75 years (aSHR 0.521, 95% CI 0.354–0.766), whereas there was no difference in patients aged 65–74 years.

Conclusion
Women used more AAD than men in all age groups but underwent fewer cardioversion and ablation procedures when aged &lt;65 years.
Explaining extreme differences in species richness among co-occurring palm clades in Madagascar
Authors: Eiserhardt Wolf L , Hansen Lars Emil S F , Couvreur Thomas L P , Dransfield John , Ferreira Paola de Lima , Rakotoarinivo Mijoro , Bellot Sidonie , Baker William J 
Published: October 2024

Abstract
Imbalance in species richness among related clades is a pervasive, yet incompletely understood feature of biodiversity. Comparison of species-poor and species-rich clades that have evolved within the same region can shed light on the mechanisms underlying this phenomenon. The long-isolated island of Madagascar is an ideal place for doing this. Madagascar harbours at least ten clades of palms (Arecaceae) that have colonized the island independently and diversified to widely differing degrees, ranging from one to 180 known species. We estimated colonization times and diversification rates for these clades based on an extensive phylogenomic dataset and tested the degree to which clades that arrived in Madagascar earlier have more species (time-for-speciation effect), finding a moderate effect. For context, we tested for time-for-speciation effects in other plant and animal lineages, finding variable but qualitatively similar results. Our findings suggest that variation in diversification rate (i.e. speciation and/or extinction rate) is a major driver of species richness imbalance among Malagasy clades, both in palms and elsewhere. We demonstrate that in palms, differences in diversification rates originated long before colonization of the island, suggesting a minor role of classical ‘island radiation’ and a stronger role of heritable traits driving diversification rate. Ability to colonize new climates also appears to play a role. Future work should address the interplay between the dynamic environment of Madagascar and the inherited traits of colonizing lineages to fully explain the island’s intriguing mix of species-poor and species-rich clades.