bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis, Crystal Structure, and Photophysical Properties of Bromothiophene‐Functionalized BF2‐Curcuminoid as a Versatile Building Block
Authors: Kang Haixia , Wang Qilian , Gao Xingrui , Feng Yongliang , Xu Hemin , Song Jinzhao , Han Jingfang , Gao Liujing , Li Ziyong
Published: June 2024

A novel bromothiophene‐functionalized BF2‐curcuminoid (BTC‐BF2) is synthesized by Knoevenagel condensation reaction. The structure of BTC‐BF2 is determined by 1H‐nuclear magnetic resonance (1H NMR), 13C‐nuclear magnetic resonance (13C NMR), and high‐resolution mass spectrometry (HRMS). Moreover, a nearly coplanar single crystal structure is successfully obtained and form a mesh structure through intermolecular multiple C─H···F hydrogen bond interactions. As expected, as‐prepared BTC‐BF2 exhibits solvent‐dependent photophysical properties in solvents with different polarity and an intense red solid‐state fluorescence. Density functional theory calculations further verify the relationships between its intrinsic electronic features and the photophysical properties. For its potential application aspect, BTC‐BF2 shows a certain ability to generate singlet oxygen under irradiation with 530 nm green light. Moreover, BTC‐BF2 can be utilized as versatile building block to construct novel far‐red or NIR BF2‐curcuminoid complexes for widely biological applications.
Recreational gymnastics exercise of moderate intensity enhances executive function in Chinese preschoolers: A randomized controlled trial
Authors: Zhang Jianhua , Lu Jiacheng , Sun Youbin , Li Ji
Published: June 2024

The current study aimed to investigate the impact of recreational gymnastics on executive function in Chinese preschoolers, with a focus on gymnastics potential to enhance core components of executive function. A total of 63 preschool children who received full‐time education were randomly assigned to either an intervention group (N = 31, mean age = 66.27 months, SD = 3.12 months) or a control group (N = 32, mean age = 66.79 months, SD = 3.34 months). The intervention group engaged in recreational gymnastics for 60 min, three times a week for 12 weeks. Meanwhile, the control group continued with their typical outdoor activities at kindergarten and did not participate in any organized sports. The intervention program was primarily conducted through group play and was facilitated by teachers who underwent standardized training. Various simple and complex tasks were utilized to evaluate delay gratification (Snack delay and Wrapped gift), inhibitory control (Stop signal task and Circle drawing task), working memory (Letter memory task and Keep track task), and cognitive flexibility (Go/No‐Go task and Dots task). The analysis of covariance revealed that the children who participated in the intervention outperformed the control group on most simple and complex executive function tasks. Specifically, these children demonstrated an enhanced ability to regulate persistent responses, process and update information, and manage high cognitive conflict. The findings of this investigation lend support to the hypothesis that moderate‐intensity recreational gymnastics is an efficacious means of enhancing executive function in early childhood. Future research should employ a larger sample size, incorporate a long‐term follow‐up design, and utilize a multi‐method approach to further substantiate the impact of moderate‐intensity gymnastics on the executive function of young children, as well as to investigate its underlying mechanism and generalizability.
Cover Image
Authors: Kavyani Sajjad , Soares João B. P. , Choi Phillip
Published: June 2024

The cover image is based on the Research Article Effect of hexyl‐branched backbone size on the size distribution and coalescence of free volume around an amphiphilic molecule by Sajjad Kavyani et al., https://doi.org/10.1002/pen.26705.

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3D Printing of Thermo‐Mechano‐Responsive Photoluminescent Noncovalent Cross‐Linked Ionogels with High‐Stretchability and Ultralow‐Hysteresis for Wearable Ionotronics and Anti‐Counterfeiting
Authors: Li Xin , Yang Xuemeng , Li Shuaijie , Lv Hongying , Wang Zhuoer , Gao Zhuoyou , Song Hongzan
Published: June 2024

Ionogel has recently emerged as a promising ionotronic material due to its good ionic conductivity and flexibility. However, low stretchability and significant hysteresis under long‐term loading limit their mechanical stability and repeatability. Developing ultralow hysteresis ionogels with high stretchability is of great significance. Here, a simple and effective strategy is developed to fabricate highly stretchable and ultralow‐hysteresis noncovalent cross‐linked ionogels based on phase separation by 3D printing of 2‐hydroxypropyl acrylate (HPA) in 1‐butyl‐3‐methylimidazolium tetrafluoroborate (BMIMBF4). Ingeniously, the sea‐island structure of the physically cross‐linked network constructed by the smaller nanodomains and larger nanodomain clusters significantly minimizes the energy dissipation, endowing these ionogels with remarkable stretchability (>1000%), ultra‐low hysteresis (as low as 0.2%), excellent temperature tolerance (−33–317 °C), extraordinary ionic conductivity (up to 1.7 mS cm−1), and outstanding durability (5000 cycles). Moreover, due to the formation of nanophase separation and cross‐linking structure, the as‐prepared ionogels exhibit unique thermochromic and multiple photoluminescent properties, which can synergistically be applied for anti‐counterfeiting and encrypting. Importantly, flexible thermo‐mechano‐multimodal visual ionotronic sensors for strain and temperature sensing with highly stable and reproducible electrical response over 20 000 cycles are fabricated, showing synergistically optical and electrical output performances.
A Green Liquid Chromatography Method for Simultaneous Quantification of Caffeine and its Three Major Metabolites in Urine, Drinks, and Herbal Products
Authors: Goldner Débora M. B. , do Nascimento Fernando H. , Masini Jorge C.
Published: June 2024

A green reversed‐phase liquid chromatography method was developed to determine caffeine (CF), theobromine (TB), theophylline (TP), and paraxanthine (PX). The stationary phase was a silica‐based C18 monolithic column (100 × 3.0 mm2 i.d.), eluted with a gradient from 6% to 10% (v/v) of ethanol in water, at a flow rate of 0.75 mL/min (35°C) and UV detection at 272 nm. The compounds were separated in less than 6 min, with a total time of 10 min for column reconditioning. The effect of gradient composition, flow rate, injection volume, and column temperature was evaluated in the separation efficiency, especially regarding the critical pair PX/TP, for which the best resolution was 1.37. The linear range was between 0.5 and 100 µg/mL. The LODs were 0.065, 0.057, 0.040, and 0.044 mg/mL for TP, TB, PX, and CF, respectively. The method was applied to green tea and yerba mate infusions, soft drinks, energy drinks, and soluble coffee. Urine samples were analyzed before and after solid‐phase extraction. The accuracy was evaluated by the spiking/recovery strategy standing in the 79.6% to 116.4% range. The separation performance and the mobile phase consumption were compared to those of another method using a gradient of methanol in water.
Image Analysis Method of Substation Equipment Status Based on Cross‐Modal Learning
Authors: Li Zhuyun , Yoshie Osamu , Wu Hao , Mai Xiaoming , Yang Yingyi , Qu Xian
Published: June 2024

In response to increasing power supply needs, maintaining stable substations is vital for reliable electricity. Traditional manual equipment inspections in these substations are inefficient and risky, often leading to hazards and delayed detection of faults. Therefore, there's a growing shift towards using intelligent image recognition technology in video surveillance systems for safer and more efficient inspections. This paper focuses on enhancing the level of intelligent inspection in substations using artificial intelligence‐based visual recognition technology. It introduces a novel small‐sample classification algorithm based on the CLIP architecture. This method uses cross‐modal equipment status information as additional training samples, optimizing the loss function together with image samples, and devises hand‐crafted strategies for text sample inputs to distinguish between equipment and states. The experimental results show that with only 16 training samples per category for 21 types of electrical equipment states, our method achieved a maximum accuracy of 93.38%. This represents a 2.98% higher accuracy than the PPLCNet trained on the full dataset and an 8.63% higher accuracy than the PPLCNet trained with an equal number of samples, with significantly reduced training time. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
Sequence Analysis in Education: Principles, Technique, and Tutorial with R
Authors: Saqr Mohammed , López-Pernas Sonsoles , Helske Satu , Durand Marion , Murphy Keefe , Studer Matthias , Ritschard Gilbert
Published: June 2024

Sequence analysis is a data mining technique that is increasingly gaining ground in learning analytics. Sequence analysis enables researchers to extract meaningful insights from sequential data, i.e., to summarize the sequential patterns of learning data and classify those patterns into homogeneous groups. In this chapter, readers will become familiar with sequence analysis techniques and tools through real-life step-by-step examples of sequential trace log data of students’ online activities. Readers will be guided on how to visualize the common sequence plots and interpret such visualizations. An essential part of sequence analysis is the discovery of patterns within sequences through clustering techniques. Therefore, this chapter will demonstrate the various sequence clustering methods, calculator of cluster indices, and evaluation of clustering results.
Factor Analysis in Education Research Using R
Authors: Vogelsmeier Leonie V. D. E. , Saqr Mohammed , López-Pernas Sonsoles , Jongerling Joran
Published: June 2024

Factor analysis is a method commonly employed to reduce a large number of variables into fewer numbers of factors. The method is often used to identify which observable indicators are representative of latent, not directly-observed constructs. This is a key step in developing valid instruments to assess latent constructs in educational research (e.g., student engagement or motivation). The chapter describes the two main approaches for conducting factor analysis (and how to combine them in an integrated factor analysis strategy) and provides a tutorial on implementing both techniques in the R programming language. The first is confirmatory factor analysis (CFA), a more theory-driven approach, in which a researcher actively specifies the number of underlying constructs as well as the pattern of relations between these dimensions and observed variables. The second is exploratory factor analysis (EFA), a more data-driven approach, in which the number of underlying constructs is inferred from the data, and all underlying constructs are assumed to influence all observed variables (at least to some degree).
Cellphones Beyond the Workshop: Youth Researchers Owning Gender Transformative Change Through Participatory Visual Research in Rural India During COVID-19
Authors: MacEntee Katie , Panda Rukmini , Lowthers Megan , Mitchell Claudia
Published: June 2024

Participatory visual methods are a means for marginalized communities to engage in research for social change. However, the technology gap, especially for economically disadvantaged youth in the Global South, can exclude groups from sustained participation in project activities. This article explores the significance of providing 20 Youth Researchers (YRs) with cellphones that they could keep and the impact of this cellphone ownership on research activities during COVID-19. The YRs learned how to use cellphones for cellphilm and photovoice methods to research gender-based violence (GBV) and sexual and reproductive health and rights (SRHR) in Odisha, India. Beyond the workshop, when YRs returned to their rural communities, they navigated multiple waves of COVID-19. Being sensitized to issues of gender equality and social justice, they used their cellphones to draw attention to issues of GBV and SRHR, public health, social justice, and equality. The ‘beyond the workshop’ outputs were indicative of the intersectional impacts of youth in their rural contexts during this unprecedented time and demonstrate how acquiring cellphones can promote youth ownership of project activities, personal transformation for YRs and youth-led advocacy.
From Samuelson’s multiplier-accelerator to bifurcations and chaos in economic dynamics
Authors: Bischi Gian Italo
Published: June 2024

This piece in the series of Milestones starts from the short, clear and highly cited paper by Samuelson (Rev Econ Stat 21(2):75–78, 1939) entitled “Interactions between the Multiplier Analysis and the Principle of Acceleration”, in which the author proposes a linear dynamic model obtained by combining the Keynesian multiplier and the principle of acceleration, together with a proper structure of time lags. Although very simple, it generated a rich and interesting literature as it provides some answers to the question of endogenous business cycles. Moreover, it raises the question of sensitivity with respect to small variations of the model’s parameters, thus sparking the attention of economists and social scientists toward studies on the qualitative theory of non-linear dynamical systems, and the related work on deterministic chaos and bifurcations which appeared between the 1960 s and 1970 s, such as the paper by Lorenz (J Atmos Sci 20:130–141, 1963) in the Journal of Atmospheric Sciences or the one by May (Nature 26:459–467, 1976) in Nature, thereby stimulating an interdisciplinary mathematical approach to dynamic complexity in physics, biology and social sciences.
Accuracy of intra ocular lens calculation formulae in patients with pseudoexfoliation syndrome
Authors: Gazit Inbal , Gershevich Anna , Einan-Lifshitz Adi , Pras Eran , Barrett Graham D. , Or Lior
Published: June 2024

Background

The purpose of this study was to investigate the visual and refractive outcomes in patients with pseudoexfoliation (PXF) undergoing routine cataract surgery and to compare the accuracy of intraocular lens (IOL) power calculation formulae.

Methods

Retrospective case-series study from Shamir medical center, a public hospital, Israel. Medical records of patients who underwent routine cataract surgery between January 2019 and August 2021 were investigated. Postoperative visual acuity and manifest refraction were examined. The error in predicted refraction and IOL power calculation accuracy within a range of ± 0.50 to ± 1.00 diopters were compared between different IOL calculating formulae.

Results

151 eyes of 151 patients ages 73.9 ± 7.1 years were included in this study- 58 eyes in the PXF group and 93 eyes in the control group. The mean absolute error (MAE) for the BUII formula was 0.63D ± 0.87 for the PXF group and 0.36D ± 0.48 for the control group (p < 0.05). The MAE for the Hill-RBF 3.0 formula was 0.61D ± 0.84 for the PXF group and 0.42D ± 0.55 for the control group (p = 0.05). There were significant differences in MAE and MedAE between PXF group and control group measures (p < 0.05). In the PXF group there were no significant differences between the different formulae.

Conclusions

There were significant differences in accuracy of IOL power calculations in all formulae between PXF group and control group measures. PXF patients show hyperopic shift from predicted refraction. Barret universal II formula had the highest proportion of eyes with absolute error in prediction below or equal to 0.50 D in both PXF and control groups.
Revealing accuracy in climate dynamics: enhancing evapotranspiration
estimation using advanced quantile regression and machine learning models
Authors: Sharafi Saeed , Mohammadi Ghaleni Mehdi
Published: June 2024

This study examines the effectiveness of various quantile regression (QR) and machine learning (ML) methodologies developed for analyzing the relationship between meteorological parameters and daily reference evapotranspiration (ETref) across diverse climates in Iran spanning from 1987 to 2022. The analyzed models include D-vine copula-based quantile regression (DVQR), multivariate linear quantile regression (MLQR), Bayesian model averaging quantile regression (BMAQR), as well as machine learning algorithms such as extreme learning machine (ELM), random forest (RF), M5 model Tree (M5Tree), least squares support vector regression algorithm (LSSVR), and extreme gradient boosting (XGBoost). Additionally, empirical equations (EEs) such as Baier and Robertson (BARO), Jensen and Haise (JEHA), and Penman (PENM) models were considered. While the EEs demonstrated acceptable performance, the QR and ML models exhibited superior accuracy. Among these, the MLQR model displayed the highest accuracy compared to DVQR and BMAQR models. Moreover, LSSVR, XGBoost, and M5Tree models outperformed ELM and RF models. Notably, LSSVR, XGBoost, and MLQR models exhibited comparable performance (R2 and NSE > 0.92, MBE and RMSE < 0.5, and SI > 0.05) to M5Tree and BMAQR models across all climates. Importantly, these models significantly outperformed EEs, DVQR, ELM, and RF models in all climates. In conclusion, high-dimensional QR and ML models are recommended as promising alternatives for accurately estimating daily ETref in diverse global climate conditions.
Towards a Precision Model for Environmental Public Health: Wastewater-based Epidemiology to Assess Population-level Exposures and Related Diseases
Authors: Bowes Devin A.
Published: June 2024

Purpose of Review

Impacts from climate change and use of toxic chemicals that contaminate our environment continue to pose a threat to the health of human populations. The field of wastewater-based epidemiology (WBE) has evolved significantly in recent years due to the COVID-19 global pandemic, however, investigating the utility of this application to fit within a broader environmental public health framework remains relatively unexplored. This review offers a comprehensive summary of the historical progression of WBE and highlights recent notable advancements to support its use for assessing environmental exposures in human populations.

Recent Findings

Early pioneering studies confirmed feasibility of this application, including measuring pesticides, plasticizers, and flame retardants in influent wastewater, that offered foundational knowledge to support successful expansion in recent work, including exposure to heavy metals and mycotoxins. Collectively, it was identified that evaluating biomarker suitability (e.g., in-sewer degradation, specificity) and pharmacokinetic data of excreted metabolites are crucial for accurate interpretation of results. Additionally, measurements of contaminants differed between catchment areas, indicating disproportionate exposures across populations.

The use of WBE offers a near real-time approach to address public health priorities, with strong evidence suggesting it can be applied to generate population-level environmental exposure assessments. Research gaps such as biomarker selection, near real-time intervention efficacy assessment, and data analysis approaches are identified in this review and encouraged to be addressed in future work, informing key areas to support the use of WBE towards a precision-based model for environmental public health.
Allergy skin prick tests with COVID-19 vaccines and their contribution to improve vaccination readiness and reduce anxiety
Authors: Pindel Tobias , Brandstetter Susanne , Sieber Wolfgang , Kabesch Michael
Published: June 2024

Background

When coronavirus disease 2019 (COVID-19) vaccines were introduced, they were suspected of triggering severe allergic reactions disproportionately often. This contributed to the fear of vaccination, particularly among allergy patients.

Methods

In an allergy center in eastern Bavaria, we used a skin prick test to investigate how often sensitization to COVID-19 vaccines can be detected and whether appropriate testing could significantly reduce the fear of vaccination.

Results

Comirnaty® (n = 245 tested/6.93% clearly positive reaction; Biontec/Pfizer, Mainz, Germany/New York City, NY, USA), Spikevax® (56/14.28%; Moderna, Cambridge, MA, USA), Vaxzevria® (208/4.32%; Astra Zeneca, Cambridge, England) and Jcovden® (48/4.16%; Johnson & Johnson, New Brunswick, NJ, USA) were tested by skin prick test. Most participants tested were female (83.6%) and had a history of allergies (94.8%). Depending on the result of the skin prick test, the test subjects were advised on vaccination. In a questionnaire survey approximately 1 year after testing, 75.7% of the N = 70 respondents stated that their fear of vaccination had been greatly or very greatly reduced as a result of the testing and counseling. In the follow-up survey, 88.5% of all respondents had been vaccinated at least once. No notable allergic problems occurred during the COVID-19 vaccination in study participants.

Conclusion

The study shows that simple skin prick testing could reduce fears and concerns about allergic reactions to COVID-19 vaccines, and thus significantly increase the willingness to vaccinate in the population, especially among allergy patients.
Multi-UAV pursuit-evasion gaming based on PSO-M3DDPG schemes
Authors: Zhang Yaozhong , Ding Meiyan , Zhang Jiandong , Yang Qiming , Shi Guoqing , Lu Meiqu , Jiang Frank
Published: June 2024

The sample data for reinforcement learning algorithms often exhibit sparsity and instability, making the training results susceptible to falling into local optima. Mini-Max-Multi-agent Deep Deterministic Policy Gradient (M3DDPG) algorithm is a multi-agent reinforcement learning algorithm, which introduces the minimax theorem into Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. It also has unstable convergence caused by sparse sample data and randomization. However, the Particle Swarm Optimisation (PSO) algorithm, unlike traditional reinforcement learning methods, involves the construction of independent populations of policy networks to generate sample data, followed by training the reinforcement learning algorithm. PSO optimizes and updates the policy population based on a fitness function, aiming to enhance the efficiency and convergence speed of the algorithm in learning from the sample data. In order to address the multi-agent pursuit-evasion problem, we propose the PSO-M3DDPG algorithm, which combines the PSO algorithm with the M3DDPG algorithm. Through experimental simulations, the improved algorithm demonstrates superior training results and faster convergence speeds, thus validating its effectiveness.
Evaluation of mechanical properties characterization of additively manufactured components
Authors: Frölich Felix , Bechtloff Lennart , Scheuring Benedikt M. , Heuer Anselm L. , Wittemann Florian , Kärger Luise , Liebig Wilfried V.
Published: June 2024

Additive manufacturing by material extrusion offers innovative potential for component design and is driving advances in many industries. However, fully harnessing these advancements necessitates a thorough comprehension of the process-specific anisotropic structural properties. The complex interactions between process parameters and their direct influence on structural properties often lead to discrepancies between the mechanical properties of tested specimens at the coupon level and the inherent properties of additively manufactured components. In addition, there is no standardized method for preparing specimens that represent the mechanical properties within a given component. This further complicates the comparison of measured properties of different series of measurements and the investigation of manufacturing effects that may occur during the production of a component. Given these challenges, the present work addresses the fundamental question of what aspects need to be considered to ensure that the test specimens reflect the process conditions being tested. The studies look at the requirements for producing representative specimens and for the test methodology to characterize the mechanical properties of additively manufactured structures. The tests are carried out on specimens that were produced directly using the material extrusion process and on specimens that were cut from additively manufactured plates. Water jet cutting, milling, and laser cutting are investigated and compared as cutting methods. The influence of the specimen geometry and the size of the additively manufactured plate is considered. The orientation-dependent mechanical properties, the significance of the individual tests, the measurement scatter, and scanning electron micrographs of the cut edges and fracture surfaces are analyzed. Finally, guidelines for performing representative tests to characterize the mechanical properties of additively manufactured components are proposed.
Multi-view Heterogeneous Graph Neural Networks for Node Classification
Authors: Zeng Xi , Lei Fang-Yuan , Wang Chang-Dong , Dai Qing-Yun
Published: June 2024

Recently, with graph neural networks (GNNs) becoming a powerful technique for graph representation, many excellent GNN-based models have been proposed for processing heterogeneous graphs, which are termed Heterogeneous graph neural networks (HGNNs). However, existing HGNNs tend to aggregate information from either direct neighbors or those connected by short metapaths, thereby neglecting the higher-order information and global feature similarity information in heterogeneous graphs. In this paper, we propose a Multi-View Heterogeneous graph neural network (MV-HGNN) to aggregate these information. Firstly, two auxiliary views, specifically a global feature similarity view and a graph diffusion view, are generated from the original heterogeneous graph. Secondly, MV-HGNN performs two message-passing strategies to get the representation of different views. Subsequently, a transformer-based aggregator is used to get the semantic information. Subsequently, the representations of the three views are fused into a final composite representation. We evaluate our method on the node classification task over three commonly used heterogeneous graph datasets, and the results demonstrate that our proposed MV-HGNN significantly outperforms state-of-the-art baselines.
Preliminary profile of the gut microbiota from amerindians in the Brazilian amazon experiencing a process of transition to urbanization
Authors: Alencar Rodrigo M. , Martínez José G. , Machado Valéria N. , Alzate Juan F. , Ortiz-Ojeda Cinthya P. , Matias Rosiane R. , Benzaquem Denise C. , Santos Maria C.F. , Assunção Enedina N. , Lira Evelyn C. , Astolfi-Filho Spartaco , Hrbek Tomas , Farias Izeni P. , Fantin Cleiton
Published: June 2024

The Yanomami are one of the oldest indigenous tribes in the Amazon and are direct descendants of the first people to colonize South America 12,000 years ago. They are located on the border between Venezuela and Brazil, with the Venezuelan side remaining uncontacted. While they maintain a hunter-gatherer society, they are currently experiencing contact with urbanized populations in Brazil. The human gut microbiota of traditional communities has become the subject of recent studies due to the Westernization of their diet and the introduction of antibiotics and other chemicals, which have affected microbial diversity in indigenous populations, thereby threatening their existence. In this study, we preliminarily characterized the diversity of the gut microbiota of the Yanomami, a hunter-gatherer society from the Amazon, experiencing contact with urbanized populations. Similarly, we compared their diversity with the population in Manaus, Amazonas. A metabarcoding approach of the 16 S rRNA gene was carried out on fecal samples. Differences were found between the two populations, particularly regarding the abundance of genera (e.g., Prevotella and Bacteroides) and the higher values of the phyla Bacteroidetes over Firmicutes, which were significant only in the Yanomami. Some bacteria were found exclusively in the Yanomami (Treponema and Succinivibrio). However, diversity was statistically equal between them. In conclusion, the composition of the Yanomami gut microbiota still maintains the profile characteristic of a community with a traditional lifestyle. However, our results suggest an underlying Westernization process of the Yanomami microbiota when compared with that of Manaus, which must be carefully monitored by authorities, as the loss of diversity can be a sign of growing danger to the health of the Yanomami.
Climatic and biogeographic factors show contrasted effects on continental and volcanic ISARs
Authors: Cáceres-Polgrossi L. , Buldrini F. , Bruzzaniti V. , Fernández-Palacios J. M. , Testolin R. , Zannini P. , Chiarucci A.
Published: June 2024

Aim

The study of islands biotas is fundamental to understand biodiversity patterns and process, both on evolutionary and ecological time scales. This study explores the influence of biogeographic and climatic factors on island species–area relationships (ISARs) in volcanic and continental islands, aiming to detect differences in slopes and intercepts between these island origins.

Methods

Data from 5049 vegetation plots on 58 Central Mediterranean Sea islands were collected from various sources. Islands were categorized as volcanic or continental based on their geological origin. area, isolation, maximum elevation, eccentricity, mean annual temperature and annual precipitation were calculated for each island. By using a moving window, we created groups of islands, and ISARs were fitted to each group using the Arrhenius power functions. Linear models and a permutation test were employed to examine how ISAR model parameters varied with the above-mentioned variables.

Results

While ISAR intercept values did not differ between island origins, volcanic islands showed higher ISAR slopes than continental islands. Whereas increasing island isolation and mean annual temperature increased ISAR intercepts on continental islands groups, it decreased on volcanic ones. Increasing annual precipitation decreased ISAR intercept on continental islands groups and increased on volcanic islands groups. Increasing island isolation, eccentricity and mean annual temperature increased the ISAR slope on volcanic islands groups while increasing annual precipitation decreased it. Increasing maximum elevation increased ISAR slope on continental islands groups.

Main conclusions

Our study provides evidence about the importance of island geological origin in determining the observed ISAR patterns. Biogeographic and climatic factors are pivotal in shaping species richness patterns on islands, exerting varying influences on both volcanic and continental islands.
Assessing the impact of ESG scores on market performance in polluting companies: a post-COVID-19 analysis
Authors: Pasupuleti Akhil , Ayyagari Lakshmana Rao , Akuthota Shankara Rao
Published: June 2024

The study aims to unravel the impact of Environmental Social Governance (ESG) scores on the firm’s market performance of polluting companies. Moreover, the study also finds out moderating effect of green initiatives. The study’s population consisted of 67 companies that were chosen from the list of polluting companies given by the Central Pollution Control Board of India for the post-COVID-19 timeframe of 2020–2023. Regression analysis was conducted to analyze the relationship between the variables. The results indicate that the performance of ESG will improve the financial performance of the company. In most cases, the results suggest that firms with heightened ESG performances have better market performance. Furthermore, the results show that there is no moderating effect of green initiatives. This study’s findings have important implications for stakeholders. The examination’s findings should help managers understand the state of ESG and financial market performance after the coronavirus crisis, as well as the relationship between FP and ESG responsibility fulfilment. The study has significant practical implications that may help managers create plans and guidelines for implementing and improving ESG scores to maximize performance. Population was the major limitation of the study.
Integration of the Non-linear Time Series GARCH Model with Fuzzy Model Optimized with Water Cycle Algorithm for River Streamflow Forecasting
Authors: Karami Mohammad , Shabanlou Saeid , Mazaheri Hosein , Mokhtari Shahroo , Najarchi Mohsen
Published: June 2024

For managing water resources and operating reservoirs in dynamic contexts, accurate hydrological forecasting is essential. However, it is difficult to track complex hydrological time series with highly non-linear and non-stationary characteristics. The intricacy of the issue is further increased by the risk and uncertainty that are brought about by the dependence of several factors on the hydrological system’s output. To hydrologically model river outflows, a hybrid GARCH time series model technique has been applied in this study. To improve the precision of the proposed model estimation, this hybrid model employs a controllable fuzzy logic system to explore the impact of various input variables and an Archimedean detail function to account for the uncertainty in the dependence of the variables. The prediction error in this model is minimized by utilizing weighting factors and problem analysis parameters that are calculated using the water cycle algorithm. It was found that the minimum root-mean-square error values for the training and testing modeling stages are RMSE = 1.89 m and 1.92 m, respectively, by looking at the hydrological modeling results for a watershed of the Karaj dam. For extended lead (i.e., a 6-month rainfall lag), the weakest forecasting capacity was found. The modeling of the copula function using a higher percentage of answers in the confidence band and a smaller bandwidth resulted in less uncertainty for the estimation of the suggested model, according to the uncertainty analysis.
Sustaining Hydrothermal Circulation With Gravity Relevant to Ocean Worlds
Authors: Fisher A. T. , Dickerson K. L. , Blackman D. K. , Randolph‐Flagg N. G. , German C. R. , Sotin C.
Published: June 2024

Some ocean worlds may sustain active, seafloor hydrothermal systems, but the characteristics and controls on fluid‐heat transport in these systems are not well understood. We developed three‐dimensional numerical simulations, using a ridge‐flank hydrothermal system on Earth as a reference, to test the influence of ocean world gravity on fluid and heat transport. Simulations represented the upper ∼4–5 km below the seafloor and explored ranges of: heat input at the base, aquifer thickness, depth, and permeability, and gravity values appropriate for Earth, Europa, and Enceladus. We tested when a hydrothermal siphon could be sustained and quantified consequent circulation temperatures, flow rates, and advective heat output. Calculations illustrate a trade‐off in energy between the reduction of buoyancy at lower gravity, which tends to reduce the primary forces driving fluid circulation, and the concomitant reduction in secondary convection, which consumes available energy. When a siphon was sustained under lower gravity, circulation temperatures tended to increase modestly (which should lead to more extensive geochemical reactions), whereas mass flow rates and advective heat output tended to be reduced. Deeper subseafloor circulation resulted in higher temperatures and flow rates, with a deeper, thin aquifer being more efficient in removing heat from the rocky interior. Water‐rock ratios were lower when gravity was lower, as was the efficiency of heat extraction, whereas the time required to circulate the volume of an ocean‐world's ocean through the seafloor increased. This may help to explain how small ocean worlds could sustain hydrothermal circulation for a long time despite limited heat sources.
An innovative fractional-order evolutionary game theoretical study of personal protection, quarantine, and isolation policies for combating epidemic diseases
Authors: Akter Masuda , Nurunnahar  , Ullah Mohammad Sharif , Meetei Mutum Zico , Zaagan Abdullah A. , Mahnashi Ali M.
Published: June 2024

This study uses imposed control techniques and vaccination game theory to study disease dynamics with transitory or diminishing immunity. Our model uses the ABC fractional-order derivative mechanism to show the effect of non-pharmaceutical interventions such as personal protection or awareness, quarantine, and isolation to simulate the essential control strategies against an infectious disease spread in an infinite and uniformly distributed population. A comprehensive evolutionary game theory study quantified the significant influence of people’s vaccination choices, with government forces participating in vaccination programs to improve obligatory control measures to reduce epidemic spread. This model uses the intervention options described above as a control strategy to reduce disease prevalence in human societies. Again, our simulated results show that a combined control strategy works exquisitely when the disease spreads even faster. A sluggish dissemination rate slows an epidemic outbreak, but modest control techniques can reestablish a disease-free equilibrium. Preventive vaccination regulates the border between the three phases, while personal protection, quarantine, and isolation methods reduce disease transmission in existing places. Thus, successfully combining these three intervention measures reduces epidemic or pandemic size, as represented by line graphs and 3D surface diagrams. For the first time, we use a fractional-order derivate to display the phase-portrayed trajectory graph to show the model’s dynamics if immunity wanes at a specific pace, considering various vaccination cost and effectiveness settings.
Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis
Authors: Sun Meng , Sun Jikui , Li Meng
Published: June 2024

Medulloblastoma is a malignant neuroepithelial tumor of the central nervous system. Accurate prediction of prognosis is essential for therapeutic decisions in medulloblastoma patients. We analyzed data from 2,322 medulloblastoma patients using the SEER database and randomly divided the dataset into training and testing datasets in a 7:3 ratio. We chose three models to build, one based on neural networks (DeepSurv), one based on ensemble learning that Random Survival Forest (RSF), and a typical Cox Proportional-hazards (CoxPH) model. The DeepSurv model outperformed the RSF and classic CoxPH models with C-indexes of 0.751 and 0.763 for the training and test datasets. Additionally, the DeepSurv model showed better accuracy in predicting 1-, 3-, and 5-year survival rates (AUC: 0.767–0.793). Therefore, our prediction model based on deep learning algorithms can more accurately predict the survival rate and survival period of medulloblastoma compared to other models.
Microalgal-bacterial granular sludge can remove complex organics from municipal wastewater with algae-bacteria interactions
Authors: Shi Yuting , Xu Chengxiang , Ji Bin , Li Anjie , Zhang Xiaoyuan , Liu Yu
Published: June 2024

Interactions between algae and bacteria are pivotal in transforming complex organics for microalgal-bacterial granular sludge process, but the intrinsic removal mechanisms have not been well understood. Here, we investigate the mechanisms by which complex organics are removed from municipal wastewater. Complex organics can be disposed during day-night cycles, significantly impacted by the carbon-to-nitrogen ratio in the influent. Upregulated gap2 and gpmA genes enhanced the conversion of complex organics into CO2, mediated by the interactions of Chlorophyceae with Acidobacteriae/Sumerlaeia/Fimbriimonadia, and the upregulated petH gene in Cyanobacteria strengthened the fixation of CO2 into biomass. The breakdown of starch, glycerol, and fatty acid were depended on Actinobacteriota, Chlorophyceae with Chloroflexia/Verrucomicrobiae, and Cyanobacteria with Desulfobacterota I, respectively. These findings provide new insights into the removal mechanisms of complex organics through microalgal-bacterial symbiosis and contribute to our understanding of the carbon cycle by microalgal-bacterial symbiosis in natural aquatic ecosystems.
Functional oligo- and polypeptide assemblies for photochemical, optical and electronic applications
Authors: Yamamoto Yohei , Heah Wey Yih , Tashiro Kentaro
Published: June 2024

Functional peptide assemblies can be used for various applications, such as photo/electro catalysts, optical resonators, optical/electronic gates and sensors.
Identifying constitutive parameters for complex hyperelastic materials using physics-informed neural networks
Authors: Song Siyuan , Jin Hanxun
Published: June 2024

A robust PINN-based framework was developed to identify material parameters for hyperelastic soft materials, specifically those exhibiting complex constitutive behaviors, under large deformation in plane stress conditions.
Research of eddy current inspection system for condenser titanium tubes in nuclear power plant
Authors: Xu Junlong , Zhang Pengfei , Tang Bo
Published: June 2024

Condenser titanium tubes are easily to generate many defects in nuclear power plants, such as corrosion, damage, and wall thickness thinning. Eddy current technology is the main method of condenser titanium tubes in-service inspection, but manual operation has the problem of low efficiency. This article designed one new titanium tube eddy current inspection system based on automatic push-puller, and successfully applied in the nuclear power plant in China. The results show that this system can realize eddy current inspection in the condenser tubes with push-puller machine.
Mechanical evaluation of the dilation force of dilation devices during interventional endoscopic ultrasound
Authors: Ogura Takeshi , Ueno Saori , Hakoda Akitoshi , Okuda Atsushi , Nishioka Nobu , Sakamoto Jun , Yamamura Masahiro , Hattori Nobuhiro , Bessho Kimi , Nishikawa Hiroki , Kanaoka Rie , Kurose Youhei
Published: June 2024

Background and study aims To insert the metal stent delivery system (8.5F) during interventional endoscopic ultrasound (I-EUS), several dilation steps are needed, which may be related to increased bile leakage from a fistula. There have been no definitive studies of dilation force. The aim of the present study was to evaluate dilation force during I-EUS using several dilation devices.

Methods In the present study, seven dilation devices were evaluated including bougie dilators such as a straight-shaped dilator (the ES dilator, Soehendra dilator, a standard ERCP catheter) a screw-shaped dilator (Tornus ES, Soehendra stent retriever), and a 4-mm balloon catheter (REN biliary balloon catheter, Hurricane RX). The diameter of each dilator and dilation force were measured.

Results Of the bougie dilators, the dilation force of the ES dilator was the highest (0.908±0.035 kg). Of the balloon catheters, the dilation force of the Hurricane RX (3.261±0.024 kg) was slightly higher than that of the REN (3.159±0.072 kg). Of the bougie dilators, although the diameter of the ES dilator was not larger than that of the Tornus ES, the dilation force was stronger. Similarly, the diameter of the Soehendra stent retriever was greater than that of the ERCP catheter or Soehendra dilator and the dilation force was lower.

Conclusions Compared with bougie dilators, balloon catheters have
stronger dilation force according to our experimental study. The present results should be
evaluated in clinical trials.
Representations and generalization in artificial and brain neural networks
Authors: Li Qianyi , Sorscher Ben , Sompolinsky Haim
Published: June 2024

Humans and animals excel at generalizing from limited data, a capability yet to be fully replicated in artificial intelligence. This perspective investigates generalization in biological and artificial deep neural networks (DNNs), in both in-distribution and out-of-distribution contexts. We introduce two hypotheses: First, the geometric properties of the neural manifolds associated with discrete cognitive entities, such as objects, words, and concepts, are powerful order parameters. They link the neural substrate to the generalization capabilities and provide a unified methodology bridging gaps between neuroscience, machine learning, and cognitive science. We overview recent progress in studying the geometry of neural manifolds, particularly in visual object recognition, and discuss theories connecting manifold dimension and radius to generalization capacity. Second, we suggest that the theory of learning in wide DNNs, especially in the thermodynamic limit, provides mechanistic insights into the learning processes generating desired neural representational geometries and generalization. This includes the role of weight norm regularization, network architecture, and hyper-parameters. We will explore recent advances in this theory and ongoing challenges. We also discuss the dynamics of learning and its relevance to the issue of representational drift in the brain.
Pairing interacting protein sequences using masked language modeling
Authors: Lupo Umberto , Sgarbossa Damiano , Bitbol Anne-Florence
Published: June 2024

Predicting which proteins interact together from amino acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments (MSAs), such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate the problem of pairing interacting partners among the paralogs of two protein families in a differentiable way. We introduce a method called Differentiable Pairing using Alignment-based Language Models (DiffPALM) that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context. MSA Transformer encodes coevolution between functionally or structurally coupled amino acids within protein chains. It also captures inter-chain coevolution, despite being trained on single-chain data. Relying on MSA Transformer without fine-tuning, DiffPALM outperforms existing coevolution-based pairing methods on difficult benchmarks of shallow multiple sequence alignments extracted from ubiquitous prokaryotic protein datasets. It also outperforms an alternative method based on a state-of-the-art protein language model trained on single sequences. Paired alignments of interacting protein sequences are a crucial ingredient of supervised deep learning methods to predict the three-dimensional structure of protein complexes. Starting from sequences paired by DiffPALM substantially improves the structure prediction of some eukaryotic protein complexes by AlphaFold-Multimer. It also achieves competitive performance with using orthology-based pairing.