bluuebunny/crossref_metadata_2025_split huggingface dataset:
The reliability and validity of the Affinity Altitude hypoxic generators in acute and chronic conditions
Authors: Willmott Ashley G. B. , Chung Henry C. , Roberts Justin D. , Maxwell Neil S. 
Published: April 2024

AbstractThis study investigated the reliability of the Affinity Altitude hypoxic generator within-day, between-day and between-generator under acute and chronic hypoxic conditions. An additional objective was to assess the validity of the fraction of inspired oxygen (FiO2) values in relation to the claimed manufacture’s reference values to ensure the accuracy and safety of the product. Three altitude generators (Affinity Altitude Ltd., Sussex, UK) were assessed across all available settings during a test–retest design for equivalent FiO2 and output volume. This consisted of two phases: 1) acute exposure (10 min per setting) and 2) chronic exposure (8 h per setting). FiO2 and volume data were calculated from 1 min collection samples using the Douglas bag method for acute and chronic exposures. There were low variations in FiO2 data across all settings within the acute exposure for within-day (coefficient of variation [CV] range: 0.0–2.6%), between-day (0.2–1.3%), and between-generator analysis (0.7–1.4%). This was similarly found for volume data within-day (0.1–3.7%), between-day (0.7–5.4%), and between-generator (1.2–3.0%). Equally, for chronic exposure trials, CV for FiO2 (< 4.0%) and volume (< 5.0%) across each of the generators presented low variations. The FiO2 values were similar to reference values, however, significant differences were found for settings 4 (−0.3% [17.6% vs. 17.9% reference value]) and 5 (−0.1% [15.8% vs. 15.9% reference value], both p < 0.05). A ‘good’ level of reliability (CV < 5%) and validity were found within and between the Affinity Altitude’s generators. However, a review of the reference values is warranted, and long-term experimental studies are required to determine the efficacy of this device for the purpose of physiological adaptations.
User gait biometrics in smart ambient applications through wearable accelerometer signals: an analysis of the influence of training setup on recognition accuracy
Authors: De Marsico Maria , Palermo Andrea 
Published: April 2024

AbstractGait recognition can exploit the signals from wearables, e.g., the accelerometers embedded in smart devices. At present, this kind of recognition mostly underlies subject verification: the incoming probe is compared only with the templates in the system gallery that belong to the claimed identity. For instance, several proposals tackle the continuous recognition of the device owner to detect possible theft or loss. In this case, assuming a short time between the gallery template acquisition and the probe is reasonable. This work rather investigates the viability of a wider range of applications including identification (comparison with a whole system gallery) in the medium-long term. The first contribution is a procedure for extraction and two-phase selection of the most relevant aggregate features from a gait signal. A model is trained for each identity using Logistic Regression. The second contribution is the experiments investigating the effect of the variability of the gait pattern in time. In particular, the recognition performance is influenced by the benchmark partition into training and testing sets when more acquisition sessions are available, like in the exploited ZJU-gaitacc dataset. When close-in-time acquisition data is only available, the results seem to suggest re-identification (short time among captures) as the most promising application for this kind of recognition. The exclusive use of different dataset sessions for training and testing can rather better highlight the dramatic effect of trait variability on the measured performance. This suggests acquiring enrollment data in more sessions when the intended use is in medium-long term applications of smart ambient intelligence.
Artificial Slags with Modulated Properties for Controlled Nickel Dissolution in Smelting Process
Authors: Urtnasan Erdenebold , Kumar Avneesh , Wang Jei-Pil 
Published: April 2024

AbstractIn pyrometallurgy process involving drying, calcination and reductive smelting steps, Saprolite ore is subjected in an electric arc furnace to produce Fe–Ni alloy and Ni matte. To optimize the nickel smelting step, the slag consisted of MgO–SiO2–FeO and having a lower viscosity and high fluidity is required. In this study, a number of new slag samples with varied degree of compositions are manufactured and used to examine the effect of their viscosity on nickel dissolution. Further correlation between the slag basicity and FeO addition on the smelting was also tested and simulated using Fe–Ni alloy from saprolite ore. As per the observations, the nickel dissolution increases with the increased content of FeO at a certain degree of activity which in turn can cause oxidation of nickel during the process. The slags with the ratio of MgO/SiO2 = 0.4–0.6, 30% fraction of FeO, and a viscosity of 1.8–1.06 poise in the olivine zone are found effective for the lowest dissolution rate of nickel with 0.021, 0.022, and 0.018 wt.%. The lowest dissolution rate is recorded for the slag with MgO/SiO2 ratio about 0.6. These slags with controlled amount of constituents could be used to recover precious metal from nickel smelter at a lower dissolution rate and thereby allowing to reduce the waste of the metal during smelting process.
Evaluation of NGS DNA barcoding for biosecurity diagnostic applications: case study from banana freckle incursion in Australia
Authors: Galaihalage Kalpani , Patel Shreya , Yadav Sonu 
Published: April 2024

AbstractMolecular diagnostics in combination with morphological identification is the method of choice for several cryptic microbial plant pathogens. For some diagnostic applications, traditional sequencing techniques can be time consuming, making them ill-suited for biosecurity incursion responses, where accurate results are needed in real time. More rapid next generation sequencing tools must be tested and compared with traditional methods to assess their utility in biosecurity applications. Here utilizing 95 samples infected with fungal pathogen Phyllosticta cavendishii, from a recent incursion in Australia, we compare species identification success using Internal Transcribed Spacer (ITS) gene barcode on conventional Sanger and Oxford Nanopore MinION sequencing platforms. For Sanger sequencing, the average pairwise identity percentage score between generated consensus sequences and P. cavendishii sequence from holotype material on NCBI database was 99.9% ± SE 0.0 whereas for MinION sequencing the average pairwise identity percentage was 99.1% ± SE 0.1. Relatively larger consensus sequences (mean 486 bp ± SE 2.4) were generated by Sanger sequencing compared to MinION sequencing (mean 435 bp ± SE 4.6). Our results confirm that both sequencing methods can reliably identify P. cavendishii. MinION sequencing, provided quicker results compared to Sanger sequencing and demonstrated diagnostic competence, with the added advantage of being portable, for front-line “point of incursion” biosecurity applications.
The emerging role of CARM1 in cancer
Authors: Xie Zizhuo , Tian Yuan , Guo Xiaohan , Xie Na 
Published: April 2024

AbstractCoactivator-associated arginine methyltransferase 1 (CARM1), pivotal for catalyzing arginine methylation of histone and non-histone proteins, plays a crucial role in developing various cancers. CARM1 was initially recognized as a transcriptional coregulator by orchestrating chromatin remodeling, transcription regulation, mRNA splicing and stability. This diverse functionality contributes to the recruitment of transcription factors that foster malignancies. Going beyond its established involvement in transcriptional control, CARM1-mediated methylation influences a spectrum of biological processes, including the cell cycle, metabolism, autophagy, redox homeostasis, and inflammation. By manipulating these physiological functions, CARM1 becomes essential in critical processes such as tumorigenesis, metastasis, and therapeutic resistance. Consequently, it emerges as a viable target for therapeutic intervention and a possible biomarker for medication response in specific cancer types. This review provides a comprehensive exploration of the various physiological functions of CARM1 in the context of cancer. Furthermore, we discuss potential CARM1-targeting pharmaceutical interventions for cancer therapy.
Ballast Settlement Accumulation in Zones with Unsupported Sleepers
Authors: Nabochenko Olga , Sysyn Mykola , Fischer Szabolcs 
Published: April 2024

AbstractThe high influence of impact and vibration on the behavior of crushed stone and ballast materials has been known for a long time. The zones with unsupported sleepers, which are always present in transition zones, crossings, welds, etc., are typically characterized by impact interaction, ballast full unloading, and additional preloading. However, no studies on ballast layer settlements consider impact vibration loading. Moreover, the influence of the cyclic loading on the ballast settlement intensity is considered ambiguously, with both decelerating and accelerating trends. The comprehensive literature review presents the influence of factors on settlement intensity. The present study aims to estimate the long-term processes of sleeper settlement accumulation depending on the loading factors: impact, cyclic loading, and preloading. The typical for a void zone ballast loading pattern was determined for various void sizes and the position along the track by using a model of vehicle-track interaction that was validated by experimental measurements. The loading patterns were parametrized with four parameters: maxima of the cyclic loading, impact loading, sleeper acceleration, and minimal preloading. A specially prepared DEM simulation model was used to estimate the ballast settlement intensity after initial settlement stabilization for more than 100 loading patterns of the void zone cases. The settlement simulation results clearly show that even a low-impact loading pattern causes many times increased settlement intensity than ordinary cyclic loading. Moreover, the initial preloading in the neighbor-to-void zones can cause even a decrease in the settlement intensity compared to the full ordinary or partial unloading. A statistical analysis using a machine learning approach and an analytic one was used to create the model for the intensity prediction regarding the loading patterns. The analytic approach demonstrates somewhat lower prediction quality, but it allows to receive plausible and simple analytic equations of the settlement intensity. The results show that the maximal cyclic loading has a nonlinear influence on the settlement intensity that corresponds to the 3–4 power function, and the impact loading is expressed by the linear to parabolic function. The ballast’s minimal preloading contributes to the reduction of the settlement intensity, especially for high cyclic loadings that are typical for neighbor-to-void zones. The results of the present study could be used for the complementing of the present phenomenological equations with the new factors and further application in the algorithms of the settlements accumulation prediction.
Combining transformer global and local feature extraction for object detection
Authors: Li Tianping , Zhang Zhenyi , Zhu Mengdi , Cui Zhaotong , Wei Dongmei 
Published: April 2024

AbstractConvolutional neural network (CNN)-based object detectors perform excellently but lack global feature extraction and cannot establish global dependencies between object pixels. Although the Transformer is able to compensate for this, it does not incorporate the advantages of convolution, which results in insufficient information being obtained about the details of local features, as well as slow speed and large computational parameters. In addition, Feature Pyramid Network (FPN) lacks information interaction across layers, which can reduce the acquisition of feature context information. To solve the above problems, this paper proposes a CNN-based anchor-free object detector that combines transformer global and local feature extraction (GLFT) to enhance the extraction of semantic information from images. First, the segmented channel extraction feature attention (SCEFA) module was designed to improve the extraction of local multiscale channel features from the model and enhance the discrimination of pixels in the object region. Second, the aggregated feature hybrid transformer (AFHTrans) module combined with convolution is designed to enhance the extraction of global and local feature information from the model and to establish the dependency of the pixels of distant objects. This approach compensates for the shortcomings of the FPN by means of multilayer information aggregation transmission. Compared with a transformer, these methods have obvious advantages. Finally, the feature extraction head (FE-Head) was designed to extract full-text information based on the features of different tasks. An accuracy of 47.0% and 82.76% was achieved on the COCO2017 and PASCAL VOC2007 + 2012 datasets, respectively, and the experimental results validate the effectiveness of our method.
NLKFill: high-resolution image inpainting with a novel large kernel attention
Authors: Wang Ting , Xiang Dong , Yang Chuan , Liang Jiaying , Shi Canghong 
Published: April 2024

AbstractThe integration of convolutional neural network (CNN) and transformer enhances the network’s capacity for concurrent modeling of texture details and global structures. However, training challenges with transformer limit their effectiveness to low-resolution images, leading to increased artifacts in slightly larger images. In this paper, we propose a single-stage network utilizing large kernel attention (LKA) to address high-resolution damaged images. LKA enables the capture of both global and local details, akin to transformer and CNN networks, resulting in high-quality inpainting. Our method excels in: (1) reducing parameters, improving inference speed, and enabling direct training on 1024$$\times $$
×
1024 resolution images; (2) utilizing LKA for enhanced extraction of global high-frequency and local details; (3) demonstrating excellent generalization on irregular mask models and common datasets such as Places2, Celeba-HQ, FFHQ, and the random irregular mask dataset Pconv from NVIDIA.
Deep matrix factorization via feature subspace transfer for recommendation system
Authors: Wang Weichen , Wang Jing 
Published: April 2024

AbstractThe sparsity problem remains a significant bottleneck for recommendation systems. In recent years, deep matrix factorization has shown promising results in mitigating this issue. Furthermore, many works have improved the prediction accuracy of deep matrix factorization by incorporating the user’s and/or items’ auxiliary information. However, there are still two remaining drawbacks that need to be addressed. First, the initialization of latent feature representations has a substantial impact on the performance of deep matrix factorization, and most current models utilize a uniform approach to this initialization, constraining the model’s optimization potential. Secondly, many existing recommendation models lack versatility and efficiency in transferring auxiliary information from users or items to expand the feature space. This paper proposes a novel model to address the issues mentioned above. By using a semi-autoencoder, the pre-trained initialization of the latent feature representation is realized in this paper. Simultaneously, this model assimilates auxiliary information, like item attributes or rating matrices from diverse domains, to generate their latent feature representations. These representations are then transferred to the target task through subspace projection distance. With this, this model can utilize auxiliary information from various sources more efficiently and this model has better versatility. This is called deep matrix factorization via feature subspace transfer. Numerical experiments on several real-world data show the improvement of this method compared with state-of-the-art methods of introducing auxiliary information about items. Compared with the deep matrix factorization model, the proposed model can achieve 6.5% improvement at most in the mean absolute error and root mean square error.
Advances in All-Solid-State Lithium–Sulfur Batteries for Commercialization
Authors: Gicha Birhanu Bayissa , Tufa Lemma Teshome , Nwaji Njemuwa , Hu Xiaojun , Lee Jaebeom 
Published: April 2024

AbstractSolid-state batteries are commonly acknowledged as the forthcoming evolution in energy storage technologies. Recent development progress for these rechargeable batteries has notably accelerated their trajectory toward achieving commercial feasibility. In particular, all-solid-state lithium–sulfur batteries (ASSLSBs) that rely on lithium–sulfur reversible redox processes exhibit immense potential as an energy storage system, surpassing conventional lithium-ion batteries. This can be attributed predominantly to their exceptional energy density, extended operational lifespan, and heightened safety attributes. Despite these advantages, the adoption of ASSLSBs in the commercial sector has been sluggish. To expedite research and development in this particular area, this article provides a thorough review of the current state of ASSLSBs. We delve into an in-depth analysis of the rationale behind transitioning to ASSLSBs, explore the fundamental scientific principles involved, and provide a comprehensive evaluation of the main challenges faced by ASSLSBs. We suggest that future research in this field should prioritize plummeting the presence of inactive substances, adopting electrodes with optimum performance, minimizing interfacial resistance, and designing a scalable fabrication approach to facilitate the commercialization of ASSLSBs.
Structural Engineering of Hierarchical Magnetic/Carbon Nanocomposites via In Situ Growth for High-Efficient Electromagnetic Wave Absorption
Authors: Liu Xianyuan , Zhou Jinman , Xue Ying , Lu Xianyong 
Published: April 2024

AbstractMaterials exhibiting high-performance electromagnetic wave absorption have garnered considerable scientific and technological attention, yet encounter significant challenges. Developing new materials and innovative structural design concepts is crucial for expanding the application field of electromagnetic wave absorption. Particularly, hierarchical structure engineering has emerged as a promising approach to enhance the physical and chemical properties of materials, providing immense potential for creating versatile electromagnetic wave absorption materials. Herein, an exceptional multi-dimensional hierarchical structure was meticulously devised, unleashing the full microwave attenuation capabilities through in situ growth, self-reduction, and multi-heterogeneous interface integration. The hierarchical structure features a three-dimensional carbon framework, where magnetic nanoparticles grow in situ on the carbon skeleton, creating a necklace-like structure. Furthermore, magnetic nanosheets assemble within this framework. Enhanced impedance matching was achieved by precisely adjusting component proportions, and intelligent integration of diverse interfaces bolstered dielectric polarization. The obtain Fe3O4-Fe nanoparticles/carbon nanofibers/Al-Fe3O4-Fe nanosheets composites demonstrated outstanding performance with a minimum reflection loss (RLmin) value of − 59.3 dB and an effective absorption bandwidth (RL ≤  − 10 dB) extending up to 5.6 GHz at 2.2 mm. These notable accomplishments offer fresh insights into the precision design of high-efficient electromagnetic wave absorption materials.
Exoatmospheric Evasion Guidance Law with Total Energy Limit via Constrained Reinforcement Learning
Authors: Yan Mengda , Yang Rennong , Zhao Yu , Yue Longfei , Zhao Xiaoru 
Published: April 2024

AbstractDue to the lack of aerodynamic forces, the available propulsion for exoatmospheric pursuit-evasion problem is strictly limited, which has not been thoroughly investigated. This paper focuses on the evasion guidance in an exoatmospheric environment with total energy limit. A Constrained Reinforcement Learning (CRL) method is proposed to solve the problem. Firstly, the acceleration commands of the evader are defined as cost and an Actor-Critic-Cost (AC2) network structure is established to predict the accumulated cost of a trajectory. The learning objective of the agent becomes to maximize cumulative rewards while satisfying the cost constraint. Secondly, a Maximum-Minimum Entropy Learning (M2EL) method is proposed to minimize the randomness of acceleration commands while preserving the agent’s exploration capability. Our approaches address two challenges in the application of reinforcement learning: constraint specification and precise control. The well-trained agent is capable of generating accurate commands while satisfying the specified constraints. The simulation results indicate that the CRL and M2EL methods can effectively control the agent’s energy consumption within the specified constraints. The robustness of the agent under information error is also validated.
Raw milk cheeses from Beira Baixa, Portugal—A contributive study for the microbiological hygiene and safety assessment
Authors: Mendonça Rita , Furtado Rosália , Coelho Anabela , Correia Cristina Belo , Suyarko Elena , Borges Vítor , Gomes João Paulo , Pista Angela , Batista Rita 
Published: April 2024

AbstractDue to specific bacterial microbiota, raw milk cheeses have appreciated sensory properties. However, they may pose a threat to consumer safety due to potential pathogens presence. This study evaluated the microbiological contamination of 98 raw milk cheeses from Beira Baixa, Portugal. Presence and enumeration of Coagulase Positive Staphylococci (CPS), Listeria monocytogenes, Salmonella spp., pathogenic Escherichia coli, and indicator microorganisms (non-pathogenic E. coli and Listeria spp.) was attained. E. coli antimicrobial resistance (AMR) was also evaluated. PCR and/or Whole genome sequencing (WGS) was used to characterize E. coli, Salmonella spp. and L. monocytogenes isolates. Sixteen cheeses (16.3%) were classified as Satisfactory, 59 (60.2%) as Borderline and 23 (23.5%) as Unsatisfactory/Potential Injurious to Health. L. monocytogenes, CPS > 104 cfu g−1, Extraintestinal pathogenic E. coli (ExPEC) and Salmonella spp. were detected in 4.1%, 6.1%, 3.1% and 1.0% of the samples, respectively. Listeria innocua (4.1%) and E. coli > 104 cfu g−1 (16.3%) were also detected. AMR E. coli was detected in 23/98 (23.5%) of the cheese samples, of which two were multidrug resistant. WGS identified genotypes already associated to human disease and Listeria spp. cluster analysis indicated that cheese contamination might be related with noncompliance with Good Hygiene Practices during cheese production.