bluuebunny/crossref_metadata_2025 huggingface dataset:
An evaluation of completed and averted school shootings
Authors: Winch Ashley T. , Alexander Kristi , Bowers Clint , Straub Frank , Beidel Deborah C.
Published: January 2024

Introduction

For over two decades school shootings have become a significant concern, especially in the United States. Following a rampage school shooting, extensive resources are devoted to gathering all of the information surrounding the event. To date, few studies have compared completed to averted, or near-miss, school shootings. This study utilized the largest known sample of cases based in the United States in an effort to identify potential targets for prevention.

Method

Data were derived from the Averted School Violence database of incidents occurring between 1999 and 2020. Statistical analyses were conducted to determine how age, co-conspirator involvement, engagement in leakage warning behavior, and motives – in isolation and in combination – varied between groups.

Results

In insolation, age, co-conspirator involvement, engagement in leakage warning behaviors, and motives were significantly different between groups. However, when these variables were combined into a logistic regression, co-conspirator involvement, engagement in leakage warning behaviors, and motives involving suicidal intent emerged as statistically significant predictors of group membership. Age no longer differentiated the two types of events.

Conclusion

This study demonstrates that regardless of suspect age, threats of school violence must be taken seriously and investigated fully. Further, students reporting their peers’ engagement in shooting-related behaviors (e.g., bringing a gun to school, mapping school, etc.) was one of the most significant predictors that a plot will be thwarted. While perpetrators who planned with others had increased odds of their plot being identified, those acting alone still demonstrated leakage behaviors. If individuals in the school environment are educated regarding warning behaviors, lone perpetrators can still be identified and reported to authorities. The perpetrator’s emotional distress, in particular depressive or suicidal thoughts were also a significant predictor of a completed school shooting. Future research efforts should focus on the development and evaluation of peer training programs to assist in the detection of school shooting warning behaviors.
Opportunities for synthetic data in nature and climate finance
Authors: Tkachenko Nataliya
Published: January 2024

This paper delves into the intricacies of synthetic data, emphasizing its growing significance in the realm of finance and more notably, sustainable finance. Synthetic data, artificially generated to simulate real-world data, is being recognized for its potential to address risk management, regulatory compliance, and the innovation of financial products. Especially in sustainable finance, synthetic data offers insights into modeling environmental uncertainties, assessing volatile social and governance scenarios, enhancing data availability, and protecting data confidentiality. This critical review attempts first ever classification of synthetic data production methods, when applied to sustainable finance data gaps, elucidates the methodologies behind its creation, and examines its assurance and controls. Further, it identifies the unique data needs of green finance going forward and breaks down potential risks tied to synthetic data utilization, including challenges from generative AI, input quality, and critical ethical considerations like bias and discrimination.
Did Algae Eat All the Silica in the World’s Oceans?
Authors: Pickering Rebecca A. , Doering Kristin
Published: January 2024

Silicon is a crucial nutrient that can join with the element oxygen to form a substance commonly called silica. Silica, commonly known as glass, is found in rocks in the Earth’s crust and dissolves into the oceans, where organisms like algae and sponges use it to build their glassy skeletons. This process, called biosilicification, is extremely important in the silica cycle. Over time, organisms have changed the silica cycle. Today, because of these organisms, the oceans no longer contain much silica. However, when the Earth was younger and these organisms had not evolved yet, no biological processes affected silica in the oceans. The evolution of these oceanic organisms across time has removed silica from the oceans. In this article, we discuss how the evolution of silicon-using sponges, as well as tiny organisms called zooplankton and algae, have changed the amount of silica in the world’s oceans through geologic time.
Barley and wheat beta-glucan content influenced by weather, fertilization, and genotype
Authors: Khaleghdoust Banafsheh , Esmaeilzadeh-Salestani Keyvan , Korge Mailiis , Alaru Maarika , Möll Kaidi , Värnik Rando , Koppel Reine , Tamm Ülle , Kurg Max , Altosaar Illimar , Loit Evelin
Published: January 2024

Introduction

Beta-glucan is one of the most important dietary fibers in cereal grains, having a great impact on human health and food quality. Wheat and barley are strategic crops and their fibers contribute toward their nutraceutical impact. However, the health of the environment is also important to ensure sustainable crop production. Hence the European decision makers have agreed to move toward organic cropping and to reduce the use of mineral fertilizers. Environment and agricultural management have a vast impact on the content of β-glucan in cereal grains. To exploit the valuable properties of β-glucan, the knowledge of factors increasing the amount of this component is crucial. We hypothesized that annual weather conditions, nitrogen fertilization, and cropping system affect the β-glucan amount in wheat and barley.

Methods

For this purpose, spring barley and winter wheat flour samples from an 8-year-long period (2014-2021) were characterized. The experiment included conventional and organic cropping systems with different nitrogen rates between 0 and 150 kg ha−1. In addition, the variation of β-glucan content in different varieties was analyzed.

Results and discussion

The results showed that the content of β-glucan in barley and wheat grains was affected mainly by weather conditions not by fertilization. The latter finding means that the regulation of crop nutrition is not going to impact dietary fiber content in our everyday food. Lower temperatures during tillering and higher 1,000 kernel weight and test weight showed a positive correlation with β-glucan content in barley grains, while precipitation during the grain filling period had a negative correlation. Our findings suggest that β-glucan can be obtained from low-input and organic systems as efficiently as from fertilized treatments. However, there is a need for adaptation strategies in industry, as β-glucan content can vary from year to year.
Flunixin meglumine tissue residues after intravenous administration in goats
Authors: Giles Claire B. , Ferdous Farha , Halleran Jennifer L. , Yeatts Jim L. , Baynes Ronald E. , Mzyk Danielle A.
Published: January 2024

Background

Flunixin is commonly used in goats in an extra-label manner, indicating a significant need to determine withdrawal intervals for edible tissues.

Objective

The objectives of the present study were to investigate the depletion of flunixin meglumine in various goat tissues, including the liver, kidney, fat, and muscle.

Methods

Twenty Boer goats were enrolled and administered an intravenous dose (2.2 mg/kg) of flunixin meglumine. Five animals were randomly euthanized at 24, 48, 72, or 96 h following dosing. All samples were analyzed via ultra-performance liquid chromatography coupled with mass spectrometry.

Results

The concentration of flunixin in all tissues declined rapidly, with the highest mean concentrations quantified in the kidney (0.137 ± 0.062 μg/g) and liver (0.077 ± 0.029 μg/g) tissues at 24 h.

Conclusion

Since any detection of flunixin residues at slaughter found in goat tissues is considered a violative residue, a conservative withdrawal interval of 17 days was calculated to ensure levels of flunixin fell below the regulatory limits of detection in liver, kidney, and muscle tissues.
Design and Machining of a Spherical Shell Rotor for a Magnetically Levitated Momentum Ball
Authors: Ma Limei , Zhang Yongheng , Niu Yuli , Zhao Yong , Guan Shaoya , Wang Zijing , Wu Tuoda
Published: January 2024

Ball-shell rotors with non-standard shapes, non-uniform conductive coatings, and eccentric masses machined by conventional processes constrain the improvement of levitation and torque accuracy of magnetically levitated momentum balls. This paper focuses on the machining method of multilayer ball-shell rotors to develop a ball-shell rotor with a standard shape and uniform conductive coating, which can improve the levitation and torque accuracy of magnetically levitated momentum balls. In this paper, a machining method for multi-coated ball-shell rotors is proposed. The machining process combining hemispherical surface and workpiece is adopted, and the whole sphere is assembled by threading, which effectively reduces the machining error. The influence of the cutting depth and feed rate of the tool on the cutting force of the ball shell was analyzed through the cutting force model. The effect of cutting force on the deformation of the ball shell was analyzed by the finite element method. The superiority of the machining method was verified by measuring the dimensions of the ball shell with a coordinate measuring machine. Compared with the traditional machining process and assembly method, the proposed ball-shell rotor machining method effectively improves the dimensional accuracy, reduces the center of mass to center of mass deviation, and ensures the levitation accuracy and output torque accuracy of the magnetically levitated momentum ball. Measurement results show that the diameter values of the pure iron ball shell are between 98.694 and 98.707 mm with a machining error of ±0.007 mm, and the diameter values of the spray-painted ball shell are between 99.490 and 99.510 mm with a machining error of ±0.01 mm. The machining static equilibrium of the pure iron ball shell and the spray-painted ball shell is good by the static equilibrium test method.
Integrating Satellite and UAV Technologies for Maize Plant Height Estimation Using Advanced Machine Learning
Authors: Ferraz Marcelo Araújo Junqueira , Barboza Thiago Orlando Costa , Arantes Pablo de Sousa , Von Pinho Renzo Garcia , Santos Adão Felipe dos
Published: January 2024

The integration of aerial monitoring, utilizing both unmanned aerial vehicles (UAVs) and satellites, alongside sophisticated machine learning algorithms, has witnessed a burgeoning prevalence within contemporary agricultural frameworks. This study endeavors to systematically explore the inherent potential encapsulated in high-resolution satellite imagery, concomitantly accompanied by an RGB camera seamlessly integrated into an UAV. The overarching objective is to elucidate the viability of this technological amalgamation for accurate maize plant height estimation, facilitated by the application of advanced machine learning algorithms. The research involves the computation of key vegetation indices—NDVI, NDRE, and GNDVI—extracted from PlanetScope satellite images. Concurrently, UAV-based plant height estimation is executed using digital elevation models (DEMs). Data acquisition encompasses images captured on days 20, 29, 37, 44, 50, 61, and 71 post-sowing. The study yields compelling results: (1) Maize plant height, derived from DEMs, demonstrates a robust correlation with manual field measurements (r = 0.96) and establishes noteworthy associations with NDVI (r = 0.80), NDRE (r = 0.78), and GNDVI (r = 0.81). (2) The random forest (RF) model emerges as the frontrunner, displaying the most pronounced correlations between observed and estimated height values (r = 0.99). Additionally, the RF model’s superiority extends to performance metrics when fueled by input parameters, NDVI, NDRE, and GNDVI. This research underscores the transformative potential of combining satellite imagery, UAV technology, and machine learning for precision agriculture and maize plant height estimation.
Determination of Dry-Matter Content of Kiwifruit before Harvest Based on Hyperspectral Imaging
Authors: Yang Han , Chen Qian , Qian Jianping , Li Jiali , Lin Xintao , Liu Zihan , Fan Nana , Ma Wei
Published: January 2024

Determining pre-harvest fruit maturity is vital to ensure the quality of kiwifruit, and dry-matter content is an important indicator of kiwifruit ripeness. To predict the pre-harvest dry-matter content of kiwifruit continuously in real-time with high accuracy, this study uses hyperspectral data of pre-harvest Jintao kiwifruit obtained by using a hyperspectral image acquisition device. The raw data underwent whiteboard correction, spectral data extraction, spectral pre-processing, and feature-band extraction, following which the dry-matter content of the fruit was predicted by using partial least squares (PLS) regression. The feature bands extracted by the random frog method were 538.93, 671.14, 693.41, 770.61, 796.98, 813.24, 841.21, 843.29, and 856.80 nm, which improve the accuracy of the PLS method for predicting dry-matter content, with R2 = 0.92 and a root mean square error (RMSE) of 0.41% for the training set, and R2 = 0.85 and a RMSE of 0.50% for the test set. These results show that the proposed method reduces the number of required bands while maintaining the prediction accuracy, thereby demonstrating the reliability of using hyperspectral data to predict the pre-harvest dry-matter content of kiwifruit. This method can effectively guide the management of kiwifruit harvesting period, establishing a theoretical foundation for precise unmanned harvesting.
A Novel Approach to Mapping the Spatial Distribution of Fruit Trees Using Phenological Characteristics
Authors: Han Liusheng , Wang Xiangyu , Li Dan , Yu Wenjie , Feng Zhaohui , Lu Xingqiang , Wang Shengshuai , Zhang Zhiyi , Gao Xin , Fan Junfu
Published: January 2024

The lack of high-spectral and high-resolution remote sensing data is impeding the differentiation of various fruit tree species that share comparable spectral and spatial features, especially for evergreen broadleaf trees in tropical and subtropical areas. Here, we propose a novel decision tree approach to map the spatial distribution of fruit trees at a 10 m spatial resolution based on the growth stage features extracted from Sentinel-1A (S-1A) time-series synthetic aperture radar (SAR) data. This novel method was applied to map the spatial distribution of fruit trees in Maoming City, which is known for its vast cultivation of fruit trees, such as litchi, citrus, and longan. The results showed that the key to extracting information on the distribution of fruit trees lies in the fact that the fruit ripening and expansion period attenuates the information on the vegetation of fruit trees, a characteristic of the reproductive period. Under VH polarization, different fruit tree growth stage traits were more separable and easier to distinguish. The optimal features, such as Hv (high valley value of the 14 May, 26 May, and 7 June SAR data), Tb (difference between the 7 June and 14 January SAR data), Cr (high valley value of the 13 July, 25 July, and 6 August SAR data), and Lo (high valley value of the 23 September, 17 October, and 11 November SAR data), were constructed based on the optimal window. The thresholds for these features were set to 1, 1, 1.5, and 1, respectively. The classification model can effectively distinguish different fruit trees and extract distribution information with overall accuracy (OA) of 90.34% and a Kappa coefficient of 0.84. The proposed method extracts the spatial distribution information of different fruit trees more accurately and provides a reference for the extraction of more tropical and subtropical species.
Establishment of a Practical Sperm Cryopreservation Pathway for the Axolotl (Ambystoma mexicanum): A Community-Level Approach to Germplasm Repository Development
Authors: Coxe Nicholas , Liu Yue , Arregui Lucía , Upton Rose , Bodenstein Sarah , Voss Steven Randal , Gutierrez-Wing Maria T. , Tiersch Terrence R.
Published: January 2024

The axolotl (Ambystoma mexicanum) draws great attention around the world for its importance as a biomedical research model, but housing and maintaining live animals is increasingly expensive and risky as new transgenic lines are developed. The goal of this work was to develop an initial practical pathway for sperm cryopreservation to support germplasm repository development. The present study assembled a pathway through the investigation of axolotl sperm collection by stripping, refrigerated storage in various osmotic pressures, cryopreservation in various cryoprotectants, and in vitro fertilization using thawed sperm. By the stripping of males, 25–800 µL of sperm fluid was collected at concentrations of 1.6 × 106 to 8.9 × 107 sperm/mL. Sperm remained motile for 5 d in Hanks’ Balanced Salt Solution (HBSS) at osmolalities of 100–600 mOsm/kg. Sperm cryopreserved in 0.25 mL French straws at 20 °C/min in a final concentration of 5% DMFA plus 200 mM trehalose and thawed at 25 °C for 15 s resulted in 52 ± 12% total post-thaw motility. In six in vitro fertilization trials, 20% of eggs tested with thawed sperm continued to develop to stage 7–8 after 24 h, and a third of those embryos (58) hatched. This work is the first report of successful production of axolotl offspring with cryopreserved sperm, providing a general framework for pathway development to establish Ambystoma germplasm repositories for future research and applications.
Genetic Structure and Population History of the Zaisan Toad-Headed Agama (Phrynocephalus melanurus) Inferred from Mitochondrial DNA
Authors: Ualiyeva Daniya , Liu Jinlong , Dujsebayeva Tatjana , Li Jun , Tian Lili , Cai Bo , Zeng Xiaomao , Guo Xianguang
Published: January 2024

The agamid lizard Phrynocephalus melanurus is restricted to Northwest China (Dzungar Basin) and the adjacent Eastern Kazakhstan (Zaisan and Alakol basins). To elucidate the phylogeography of P. melanurus, we obtained the mitochondrial DNA COI segments of 175 sampled lizards from 44 localities across the whole distribution. Phylogenetic analyses revealed two main Clades comprising five geographically structured lineages (I, IIa, IIb1, IIb2, and IIb3) that fit an isolation-by-distance (IBD) model. The divergence from the most recent common ancestor was dated to ~1.87 million years ago (Ma). Demographic analyses demonstrated lineage-specific response to past climate change: stable population for Clade I, Subclade IIb1; past population expansion for IIb3 since 0.18 Ma, respectively. Bayesian phylogeographic diffusion analyses detected initial spreading at the Saur Mount vicinity, approximately 1.8 Ma. Historical species distribution model (SDM) projected expansion of the suitable habitat in the last interglacial and shift and contraction in the last glacial maximum and Holocene epochs. The SDM predicted a drastic reduction in suitable area throughout the range as a response to future climate change. Our findings suggest that the evolution of P. melanurus followed a parapatric divergence with subsequent dispersal and adaptation to cold and dry environments during the Quaternary. Overall, this work improves our understanding of the lineage diversification and population dynamics of P. melanurus, providing further insights into the evolutionary processes that occurred in Northwest China and adjacent Eastern Kazakhstan.
Comparison of Intestinal Microbiota of Blue Fox before and after Weaning
Authors: Su Hang , Jiang Xinshuai , Liu Hanyu , Bai Huixin , Bai Xiujuan , Xu Yuan , Du Zhiheng
Published: January 2024

Intestinal flora plays an important role in maintaining the internal stability and health of the intestine. Currently, intestinal microbes are considered an important “organ” but are mostly ignored by people. This study evaluated the flora structure of each intestinal segment of blue foxes pre-weaning and explored the differences between the fecal flora and intestinal flora structure of each segment after weaning. Samples of intestinal contents from three blue foxes at 45 days of age (before weaning) and intestinal contents and feces samples from at 80 days (after weaning) were collected for 16s rRNA flora analysis. The species and distribution characteristics of microorganisms in different intestinal segments of blue foxes before and after weaning were different. Except for the rectum, the dominant flora of each intestinal segment of blue fox changed significantly after experiencing weaning, and the fecal flora structure of young fox at the weaning stage did not represent the whole intestinal flora structure but was highly similar to that of the colon and rectum. To sum up, the intestinal flora of blue foxes changed systematically before and after weaning. When performing non-invasive experiments, the microflora structure of the colon and rectum of blue foxes can be predicted by collecting fecal samples.
Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
Authors: Wang Kai , Gao Bo , Shan Shijie , Wang Rong , Wang Xueyang
Published: January 2024

Most fault diagnosis models use a single input and have weak generalization performance. In order to obtain more fault information, a fault diagnosis method based on a Multi-channel Residual Attention Network with Efficient Channel Attention (ECA-MRANet) is proposed in this paper. In this method, the original time domain signal is first processed by a multi-domain transform, the result of which is input to the MRANet for feature extraction. Finally, the extracted features are fused by ECA to realize fault identification. The experimental results show that the proposed method can enhance the ability of the network to discriminate key features, and shows good generalization performance under different working conditions and with small-sample transfer between data sets.
The Effects of Fly Ash, Blast Furnace Slag, and Limestone Powder on the Physical and Mechanical Properties of Geopolymer Mortar
Authors: Aslan Salih , Erkan İbrahim Hakkı
Published: January 2024

This study investigates the alterations in the ratios of components such as class C fly ash (FA), blast furnace slag (BFS), and waste stone powder (WSP) types of limestone powder (LP) used in the production of geopolymer concrete. These components are meticulously examined concerning the physical and mechanical attributes of geopolymer concrete. Using the mixture-design method, 10 different mixing ratios were determined using FA, BFS, and LP, and experimental research on the mechanical attributes and workability of geopolymer mortar is presented. A series of experimental tests, including tests for compressive strength, impact strength, setting time, flow table, flexural strength, and water absorption, were carried out on the geopolymer mortars that were made using FA, BFS, and LP, to investigate and enhance their overall performance. The experimental study aimed to ascertain the extent to which variations in the materials used in the formation of geopolymer mortar affected its mechanical and physical properties. To achieve this objective, certain parameters for geopolymer mortar formulation were fixed, according to the literature (molarity: 10; aggregate/binder ratio: 2.5; plasticizer ratio: 2%; sodium silicate (SS)/sodium hydroxide (SH): 1.5; additional water content: 14.5%; alkali activators/binder: 0.5). Subsequently, mortars were produced according to the 10 different mixing ratios determined by the mixture-design method, and the experiments were completed. The samples of the 10 different mixes were subjected to air curing at an ambient temperature (23 °C ± 2 °C) for 28 days. Following the curing period, the tests revealed that mix No. 9 exhibited the best compressive, flexural, and impact strengths, while mix No. 10 demonstrated superior workability of geopolymer mortar. It was shown that impact, compressive, and flexural strength values decreased as the ratios of FA and LP increased. In contrast, the increases in the ratios of FA and LP positively influenced the workability of geopolymer mortar.
Let’s Talk about Sex Hormone Receptors and Their Physical Interaction with Sonic Hedgehog Protein: A Computational Study with Emphasis on Progesterone Receptor
Authors: Tomić Antonija , Čonkaš Josipa , Ozretić Petar
Published: January 2024

The mature form of the sonic hedgehog protein (SHH-N) is the main canonical activator of the Hedgehog-GLI signaling pathway whose aberrant activity can lead to the development of hormone-dependent cancers like breast or prostate cancer. In this study, we employed computational methods to explore the potential binding of SHH-N with the progesterone receptor (PR), the sole member of the nuclear sex hormone receptor (SHRs) subfamily not previously linked to SHH-N. Through a combination of molecular docking, robust molecular dynamics (MD) simulations, and free energy calculations, we predicted a stable binding between SHH-N-cholesterol and PR. To validate our findings, we extended our in silico investigation to encompass the complexes between SHH-N-cholesterol and estrogen receptor alpha (ERα) and androgen receptor (AR)—complexes that have been experimentally confirmed in our prior studies. The calculations not only confirmed the stable binding of SHH-N-cholesterol with both ERα and AR but also revealed the strongest binding occurred with ERα, followed by AR and PR, suggesting a non-canonical interaction with potential biological significance. Microsecond-long MD simulations unveiled tight cholesterol binding in the SHRs’ binding sites, and we gained insights into sub-molecular interactions contributing to protein-protein stabilization in complexes involving PR and ERα for the first time. The MM/PBSA calculations indicated comparable binding affinities of PR for progesterone and SHH-N-cholesterol, with ERα exhibiting a more favorable enthalpy of binding with SHH-N-cholesterol than with estradiol.
Enhancing Metal Forging Tools and Moulds: Advanced Repairs and Optimisation Using Directed Energy Deposition Hybrid Manufacturing
Authors: Petruse Radu Emanuil , Langa Mihai-Ciprian
Published: January 2024

This article investigates the efficacy of directed energy deposition (DED) processes in repairing forging tools and moulds, comparing mechanical properties between specimens fabricated from conventional sheet metal and those manufactured by DED techniques. A comparative analysis reveals significant mechanical differences between subtractive and DED-manufactured specimens, emphasising the nuanced balance between tensile strength and ductility in DED-produced components influenced by layering. Notable insights from scatter plot analyses highlight distinct material behaviours, particularly layer-dependent tendencies in DED-manufactured specimens. Regression-based predictive models aid in understanding material behaviours, aiding in informed material selection for manufacturing processes. Additionally, this article underlines the advantages of DED-based repair processes, highlighting precision, material efficiency, reduced lead times, and cost-effectiveness. The article studies die and mould repair, tool restoration, and critical considerations like material compatibility and quality assurance. The study concludes by emphasising the role of hybrid manufacturing in extending product lifecycles, in conformity with specific mechanical requirements, and fabricating complex geometries, despite potential higher costs in materials and technologies. Overall, this research demonstrates the efficacy of DED processes in enhancing component reliability and lifespan in metalworking industries.
Acute Effects of Combined Hypoxia and Fatigue on Balance in Young Men
Authors: Bujas Przemysław , Maciejczyk Marcin , Pałka Tomasz , Czerwińska-Ledwig Olga , Piotrowska Anna , Jaworski Janusz , Tchórzewski Dariusz , Spieszny Michał
Published: January 2024

The aim of this study was to determine the effects of maximal exercise and maximal exercise under hypoxic conditions on balance, the strategies of the balance-maintenance process and its sensory organization. A total of 60 men were randomly allocated to three experimental groups and a control group. All participants completed the sensory organization test for assessing balance. Participants in the experimental groups performed the same test after an hour of normobaric hypoxia (the first group), after supramaximal exercise (the second group) and after supramaximal exercise combined with 60 min of hypoxia exposure (the third group). The control group performed the test after 60 min of passive rest. Normobaric hypoxia conditions corresponded to an altitude of 2950 m (FIO2 15%). Physical effort in normoxia and hypoxia significantly impaired the participants’ stability on a stable platform with eyes open (η2 = 0.711, p = 0.001; η2 = 0.583, p = 0.001, respectively). On an unstable platform, a significant improvement in stability indices was observed in the group undertaking the exercise in hypoxia (p = 0.04, η2 = 0.249). The experimental conditions increased the role of hip strategies in maintaining balance in the experimental groups during trials requiring somatosensory information. An analysis of sensory organization shows that maximal effort in hypoxia increases the role of somatosensory (p = 0.002, η2 = 0.69) and vestibular (p = 0.02, η2 = 0.34) information, whereas hypoxia alone increases reliance on visual (p = 0.03, η2 = 0.38) and vestibular (p = 0.02, η2 = 0.36) information. This study indicates that individuals have poorer stability after maximal exercise, which may cause difficulties in engaging in some dynamic forms of activity, especially those with a large number of visual stimuli.
Novel Study for the Early Identification of Injury Risks in Athletes Using Machine Learning Techniques
Authors: Ayala Rocío Elizabeth Duarte , Granados David Pérez , Gutiérrez Carlos Alberto González , Ruíz Mauricio Alberto Ortega , Espinosa Natalia Rojas , Heredia Emanuel Canto
Published: January 2024

This innovative study addresses the prevalent issue of sports injuries, particularly focusing on ankle injuries, utilizing advanced analytical tools such as artificial intelligence (AI) and machine learning (ML). Employing a logistic regression model, the research achieves a remarkable accuracy of 90.0%, providing a robust predictive tool for identifying and classifying athletes with injuries. The comprehensive evaluation of performance metrics, including recall, precision, and F1-Score, emphasizes the model’s reliability. Key determinants like practicing sports with injury risk and kinesiophobia reveal significant associations, offering vital insights for early risk detection and personalized preventive strategies. The study’s contribution extends beyond predictive modeling, incorporating a predictive factors analysis that sheds light on the nuanced relationships between various predictors and the occurrence of injuries. In essence, this research not only advances our understanding of sports injuries but also presents a potent tool with practical implications for injury prevention in athletes, bridging the gap between data-driven insights and actionable strategies.
Influence of Deep Foundation Pit Excavation on Adjacent Pipelines: A Case Study in Nanjing, China
Authors: Wu Bin , Ge Chenhe , Li Pengfei , Yang Meng , Li Liulian
Published: January 2024

To investigate the pipeline deformation pattern caused by the excavation of deep foundation pits in composite soil–rock strata, a comprehensive study integrating on-site monitoring and numerical simulation was conducted. This study centered on a deep foundation excavation project in the soft soil in Nanjing’s floodplain region. The analyses of pipeline settlement and deformation were performed based on field-measured data. This study investigated the impact of excavation on the mechanical properties of the surrounding soil that resulted in the progressive deformation of adjacent pipelines. Furthermore, numerical simulations were conducted using Plaxis 3D CONNECT Edition v22 finite element analysis software. This study elucidated the influence of factors such as pipeline–pit distance and burial depth on pipeline deformation, conducting a quantitative analysis of their effects. The results indicated that deformation primarily occurs unevenly near pit corners and is less pronounced in soil–rock strata than in single-type soil layers. This study established correlations between pipeline displacements and various factors, offering valuable insights for future excavation projects conducted under similar conditions.
Quantifying the Role of Model Internal Year-to-Year Variability in Estimating Anthropogenic Aerosol Radiative Effects
Authors: Shi Xiangjun , Zeng Yuxi
Published: January 2024

The model internal year-to-year variability (hereafter, internal variability) is a significant source of uncertainty when estimating anthropogenic aerosol effective radiative forcing (ERF). In this study, we investigate the impact of internal variability using large ensemble simulations (600 years in total) with the same climate model under prescribed anthropogenic aerosol forcings. A comparison of the magnitudes (i.e., standard deviation, Std) of these influences confirms that internal variability has negligible impacts on the instantaneous radiative forcing (RF) diagnosed by double radiation calls but has considerable impacts on estimating ERF through rapid adjustments (ADJ). Approximately half of the model grids exhibit a strong internal variability influence on ERF (Std > 5 W m−2). These strong internal variabilities lead to a 50% probability that the 30-year linear change can reach 2 W m−2 and the 10-year linear change can reach 4 W m−2. A 50-year simulation can provide a relatively stable annual mean map of ERF (ERF = ADJ + RF), but it fails for ADJ. The statistically significant areas in the annual mean maps of both ERF and ADJ from a 10-year simulation exhibit instability with evident chaotic features. The insights derived from our analysis aid in assessing the stability of modeled ERF and contribute to the design of comparative experiments.
Signs of Slowing Recovery of Antarctic Ozone Hole in Recent Late Winter–Early Spring Seasons (2020–2023)
Authors: Krzyścin Janusz , Czerwińska Agnieszka
Published: January 2024

Every year since the early 1980s, the ozone hole has appeared in late winter/spring over Antarctica. The ozone hole is expected to disappear due to the observed decrease in the concentration of ozone-depleting substances in the stratosphere, which is enforced by the 1987 Montreal Protocol and its subsequent amendments. However, large ozone holes have appeared four years in a row (2020–2023), which may be a signal that Antarctic ozone repair has stopped. Statistical analyses of ozone hole metrics (hole area, minimum total column ozone, ozone mass deficit, and ozone mass deficit per unit area of the hole) are presented to determine how adding the data from these years changes the ozone recovery pattern. Statistically significant trends in the hole metrics were revealed for the short period (2000–2019) but not for the longer period (2000–2023). The modeled time series of metrics from multiple regressions with standard chemical and dynamic explanatory variables indicate that the recovery has slowed since around 2010. Moreover, a sequence of extreme events (wildfires in Australia in the summer of 2019/2020 and the eruption of the Hunga Tonga volcano in January 2022) may have caused additional ozone losses in Antarctica that masked the repair of polar ozone for a while.
Multi-Time-Scale Analysis of Chaos and Predictability in vTEC
Authors: Materassi Massimo , Migoya-Orué Yenca , Radicella Sandro Maria , Alberti Tommaso , Consolini Giuseppe
Published: January 2024

Theoretical modelling of the local ionospheric medium (LIM) is made difficult by the occurrence of irregular ionospheric behaviours at many space and time scales, making prior hypotheses uncertain. Investigating the LIM from scratch with the tools of dynamical system theory may be an option, using the vertical total electron content (vTEC) as an appropriate tracer of the system variability. An embedding procedure is applied to vTEC time series to obtain the finite dimension (m∈N) of the phase space of an LIM-equivalent dynamical system, as well as its correlation dimension (D2) and Kolmogorov entropy rate (K2). In this paper, the dynamical features (m,D2,K2) are studied for the vTEC on the top of three GNSS stations depending on the time scale (τ) at which the vTEC is observed. First, the vTEC undergoes empirical mode decomposition; then (m,D2,K2) are calculated as functions of τ. This captures the multi-scale structure of the Earth’s ionospheric dynamics, demonstrating a net distinction between the behaviour at τ≤24h and τ≥24h. In particular, sub-diurnal-scale modes are assimilated to much more chaotic systems than over-diurnal-scale modes.
Long-Term Evolution in Noctilucent Clouds’ Response to the Solar Cycle: A Model-Based Study
Authors: Vellalassery Ashique , Baumgarten Gerd , Grygalashvyly Mykhaylo , Lübken Franz-Josef
Published: January 2024

Noctilucent clouds (NLC) are sensitive indicators in the upper mesosphere, reflecting changes in the background atmosphere. Studying NLC responses to the solar cycle is important for understanding solar-induced changes and assessing long-term climate trends in the upper mesosphere. Additionally, it enhances our understanding of how increases in greenhouse gas concentration in the atmosphere impact the Earth’s upper mesosphere and climate. This study presents long-term trends in the response of NLC and the background atmosphere to the 11-year solar cycle variations. We utilised model simulations from the Leibniz Institute Middle Atmosphere (LIMA) and the Mesospheric Ice Microphysics and Transport (MIMAS) over 170 years (1849 to 2019), covering 15 solar cycles. Background temperature and water vapour (H2O) exhibit an apparent response to the solar cycle, with an enhancement post-1960, followed by an acceleration of greenhouse gas concentrations. NLC properties, such as maximum brightness (βmax), calculated as the maximum backscatter coefficient, altitude of βmax (referred to as NLC altitude) and ice water content (IWC), show responses to solar cycle variations that increase over time. This increase is primarily due to an increase in background water vapour concentration caused by an increase in methane (CH4). The NLC altitude positively responds to the solar cycle mainly due to solar cycle-induced temperature changes. The response of NLC properties to the solar cycle varies with latitude, with most NLC properties showing larger and similar responses at higher latitudes (69° N and 78° N) than mid-latitudes (58° N).
Limitations of Protein Structure Prediction Algorithms in Therapeutic Protein Development
Authors: Niazi Sarfaraz K. , Mariam Zamara , Paracha Rehan Z.
Published: January 2024

The three-dimensional protein structure is pivotal in comprehending biological phenomena. It directly governs protein function and hence aids in drug discovery. The development of protein prediction algorithms, such as AlphaFold2, ESMFold, and trRosetta, has given much hope in expediting protein-based therapeutic discovery. Though no study has reported a conclusive application of these algorithms, the efforts continue with much optimism. We intended to test the application of these algorithms in rank-ordering therapeutic proteins for their instability during the pre-translational modification stages, as may be predicted according to the confidence of the structure predicted by these algorithms. The selected molecules were based on a harmonized category of licensed therapeutic proteins; out of the 204 licensed products, 188 that were not conjugated were chosen for analysis, resulting in a lack of correlation between the confidence scores and structural or protein properties. It is crucial to note here that the predictive accuracy of these algorithms is contingent upon the presence of the known structure of the protein in the accessible database. Consequently, our conclusion emphasizes that these algorithms primarily replicate information derived from existing structures. While our findings caution against relying on these algorithms for drug discovery purposes, we acknowledge the need for a nuanced interpretation. Considering their limitations and recognizing that their utility may be constrained to scenarios where known structures are available is important. Hence, caution is advised when applying these algorithms to characterize various attributes of therapeutic proteins without the support of adequate structural information. It is worth noting that the two main algorithms, AlfphaFold2 and ESMFold, also showed a 72% correlation in their scores, pointing to similar limitations. While much progress has been made in computational sciences, the Levinthal paradox remains unsolved.
Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning
Authors: Tian Tao , Liang Zhiwei , Wei Yuanfei , Luo Qifang , Zhou Yongquan
Published: January 2024

With the wide application of mobile robots, mobile robot path planning (MRPP) has attracted the attention of scholars, and many metaheuristic algorithms have been used to solve MRPP. Swarm-based algorithms are suitable for solving MRPP due to their population-based computational approach. Hence, this paper utilizes the Whale Optimization Algorithm (WOA) to address the problem, aiming to improve the solution accuracy. Whale optimization algorithm (WOA) is an algorithm that imitates whale foraging behavior, and the firefly algorithm (FA) is an algorithm that imitates firefly behavior. This paper proposes a hybrid firefly-whale optimization algorithm (FWOA) based on multi-population and opposite-based learning using the above algorithms. This algorithm can quickly find the optimal path in the complex mobile robot working environment and can balance exploitation and exploration. In order to verify the FWOA’s performance, 23 benchmark functions have been used to test the FWOA, and they are used to optimize the MRPP. The FWOA is compared with ten other classical metaheuristic algorithms. The results clearly highlight the remarkable performance of the Whale Optimization Algorithm (WOA) in terms of convergence speed and exploration capability, surpassing other algorithms. Consequently, when compared to the most advanced metaheuristic algorithm, FWOA proves to be a strong competitor.
Remaining Useful Life Prediction of Rolling Bearings Based on ECA-CAE and Autoformer
Authors: Zhong Jianhua , Li Huying , Chen Yuquan , Huang Cong , Zhong Shuncong , Geng Haibin
Published: January 2024

In response to the need for multiple complete bearing degradation datasets in traditional deep learning networks to predict the impact on individual bearings, a novel deep learning-based rolling bearing remaining life prediction method is proposed in the absence of fully degraded bearng data. This method involves processing the raw vibration data through Channel-wise Attention Encoder (CAE) from the Encoder-Channel Attention (ECA), extracting features related to mutual correlation and relevance, selecting the desired characteristics, and incorporating the selected features into the constructed Autoformer-based time prediction model to forecast the degradation trend of bearings’ remaining time. The feature extraction method proposed in this approach outperforms CAE and multilayer perceptual-Attention Encoder in terms of feature extraction capabilities, resulting in reductions of 0.0059 and 0.0402 in mean square error, respectively. Additionally, the indirect prediction approach for the degradation trend of the target bearing demonstrates higher accuracy compared to Informer and Transformer models, with mean square error reductions of 0.3352 and 0.1174, respectively. This suggests that the combined deep learning model proposed in this paper for predicting rolling bearing life may be a more effective life prediction method deserving further research and application.
Shielded Cone Coil Array for Non-Invasive Deep Brain Magnetic Stimulation
Authors: Abu Yosef Rawan , Sultan Kamel , Mobashsher Ahmed Toaha , Zare Firuz , Mills Paul C. , Abbosh Amin
Published: January 2024

Non-invasive deep brain stimulation using transcranial magnetic stimulation is a promising technique for treating several neurological disorders, such as Alzheimer’s and Parkinson’s diseases. However, the currently used coils do not demonstrate the required stimulation performance in deep regions of the brain, such as the hippocampus, due to the rapid decay of the field inside the head. This study proposes an array that uses the cone coil method for deep stimulation. This study investigates the impact of magnetic core and shielding on field strength, focality, decay rate, and safety. The coil’s size and shape effects on the electric field distribution in deep brain areas are also examined. The finite element method is used to calculate the induced electric field in a realistic human head model. The simulation results indicate that the magnetic core and shielding increase the electric field intensity and enhance focality but do not improve the field decay rate. However, the decay rate can be reduced by increasing the coil size at the expense of focality. By adopting an optimum cone structure, the proposed five-coil array reduces the electric field attenuation rate to reach the stimulation threshold in deep regions while keeping all other regions within safety limits. In vitro and in vivo experimental results using a head phantom and a dead pig’s head validate the simulated results and confirm that the proposed design is a reliable and efficient candidate for non-invasive deep brain magnetic stimulation.
MoCA Domain-Specific Pattern of Cognitive Impairment in Stroke Patients Attending Intensive Inpatient Rehabilitation: A Prospective Study
Authors: Basagni Benedetta , Malloggi Serena , Polito Cristina , Pellicciari Leonardo , Campagnini Silvia , Pancani Silvia , Mannini Andrea , Gemignani Paola , Salvadori Emilia , Marignani Sara , Giovannelli Fabio , Viggiano Maria Pia , Hakiki Bahia , Grippo Antonello , Macchi Claudio , Cecchi Francesca
Published: January 2024

A domain-specific perspective to cognitive functioning in stroke patients may predict their cognitive recovery over time and target stroke rehabilitation intervention. However, data about domain-specific cognitive impairment after stroke are still scarce. This study prospectively investigated the domain-specific pattern of cognitive impairments, using the classification proposed by the Montreal Cognitive Assessment (MoCA), in a cohort of 49 stroke patients at admission (T0), discharge (T1), and six-month follow-up (T2) from subacute intensive rehabilitation. The predictive value of T0 cognitive domains cognitive impairment at T1 and T2 was also investigated. Patients’ cognitive functioning at T0, T1, and T2 was assessed through the MoCA domains for executive functioning, attention, language, visuospatial, orientation, and memory. Different evolutionary trends of cognitive domain impairments emerged across time-points. Patients’ impairments in all domains decreased from T0 to T1. Attention and executive impairments decreased from T0 to T2 (42.9% and 26.5% to 10.2% and 18.4%, respectively). Conversely, altered visuospatial, language, and orientation increased between T1 and T2 (16.3%, 36.7%, and 40.8%, respectively). Additionally, patients’ global cognitive functioning at T1 was predicted by the language and executive domains in a subacute phase (p = 0.031 and p = 0.001, respectively), while in the long term, only attention (p = 0.043) and executive (p = 0.019) domains intervened. Overall, these results confirm the importance of a domain-specific approach to target cognitive recovery across time in stroke patients.