bluuebunny/crossref_metadata_2025 huggingface dataset:
An Empirical Study of Self-Supervised Learning with Wasserstein Distance
Authors: Yamada Makoto , Takezawa Yuki , Houry Guillaume , Düsterwald Kira Michaela , Sulem Deborah , Zhao Han , Tsai Yao-Hung
Published: November 2024

In this study, we consider the problem of self-supervised learning (SSL) utilizing the 1-Wasserstein distance on a tree structure (a.k.a., Tree-Wasserstein distance (TWD)), where TWD is defined as the L1 distance between two tree-embedded vectors. In SSL methods, the cosine similarity is often utilized as an objective function; however, it has not been well studied when utilizing the Wasserstein distance. Training the Wasserstein distance is numerically challenging. Thus, this study empirically investigates a strategy for optimizing the SSL with the Wasserstein distance and finds a stable training procedure. More specifically, we evaluate the combination of two types of TWD (total variation and ClusterTree) and several probability models, including the softmax function, the ArcFace probability model, and simplicial embedding. We propose a simple yet effective Jeffrey divergence-based regularization method to stabilize optimization. Through empirical experiments on STL10, CIFAR10, CIFAR100, and SVHN, we find that a simple combination of the softmax function and TWD can obtain significantly lower results than the standard SimCLR. Moreover, a simple combination of TWD and SimSiam fails to train the model. We find that the model performance depends on the combination of TWD and probability model, and that the Jeffrey divergence regularization helps in model training. Finally, we show that the appropriate combination of the TWD and probability model outperforms cosine similarity-based representation learning.
Hybrid Window Decoding for Joint Source Channel Anytime Coding System
Authors: Deng Li , Shi Zhiping , Wang Yixin , Yu Xiaoxi , Guan Yong Liang , Xu Zhiping
Published: November 2024

Joint source channel anytime coding (JSCAC) is a kind of joint source channel coding (JSCC) systems based on the causal spatially coupled coding and joint expanding window decoding (JEWD) techniques. JSCAC demonstrates greatly improved error correction performance, as well as higher decoding complexity. This work proposes a joint hybrid window decoding (JHWD) algorithm for JSCAC systems, aiming to reduce the decoding complexity while maintaining comparable error correction performance with the state of the art. Unlike the traditional JEWD technique and its variants, the proposed JHWD algorithm utilizes a hybrid window structure. It achieves this by implementing adaptive local expanding window decoding in the sliding window region, guided by syndrome-check-based detection. The hybrid window decoding characteristics of the proposed JHWD algorithm can both effectively reduce the decoding complexity caused by window expanding, and also mitigate the error propagation caused by window sliding. An improved density evolution algorithm is proposed for the asymptotic performance analysis of the proposed JHWD algorithm. Both the analytical and simulation results indicate that the proposed JHWD decoding scheme is a good low-complexity implementation option for JSCAC systems, which is also suitable for other JSCC systems with the spatially coupled coding structure.
Analyzing the Dimensionality of O*NET Cognitive Ability Ratings to Inform Assessment Design
Authors: Sireci Stephen G. , Longe Brendan , Suárez-Álvarez Javier , Oliveri Maria Elena
Published: November 2024

The O*NET database is an online repository of detailed information on the knowledge and skill requirements of thousands of jobs across the United States. Thus, it is a valuable resource for test developers who want to target cognitive and other abilities relevant to the contemporary workforce. In this study, we used multidimensional scaling (MDS) to analyze the mean importance ratings of the cognitive abilities and selected skills included in the O*NET database to identify the dimensionality of the data regarding importance and their consistency across job zones. Using the criteria of fit and interpretability, a two-dimensional MDS solution was selected as the best representation of the data. These dimensions reflected Social Interaction/Reasoning and Verbal/Non-Verbal skills and abilities. Interestingly, the dimensionality was not consistent across job zones. Job zones relative to lower education and training requirements were sufficiently represented by the Social Interaction/Reasoning dimension, and the Verbal/Non-Verbal dimension was most relevant to job zones requiring more education and experience. The implications of the results for developing assessments for adult learners are discussed, as is the utility of using MDS for understanding the dimensionality of O*NET data.
Negative Self-Concept: Cross-Country Evidence of Its Importance for Understanding Motivation and Academic Achievement
Authors: Gao Yu , Ali Farhan
Published: November 2024

Academic self-concept, the belief in one’s ability, is a key motivational construct in educational psychology and large-scale assessments. The construct is typically measured by instruments with positively (“I usually do well in science”) and negatively worded items (“I am just not good in science”). A single latent factor is often assumed. Here, we investigated this assumption using international large-scale assessment data across two age groups of children in fourth grade and adolescents in eighth grade (N = 296,320 students, 23 educational systems). We, instead, found strong evidence of the substantiveness of a negative self-concept factor derived from negatively worded items. Exploratory and confirmatory factor analyses uncovered negative self-concept as being distinct from positive self-concept. Furthermore, theory-driven modeling supported the internal/external (I/E) frame of reference model effect on negative self-concept: achievement has a stronger effect on eighth graders’ negative self-concept relative to fourth-grade children across many countries, especially for mathematics. Overall, understanding students’ negative appraisals and negative beliefs of their ability is an important theoretical and policy imperative.
Effects of Observing Urban and Natural Scenes on Working Memory Depletion and Restoration: An EEG Study
Authors: Consalvi Lorenzo , Ouwehand Kim , Paas Fred
Published: November 2024

Cognitive load theory focuses on the limited capacity of working memory (WM) to encapsulate information. While the original theory postulated a fixed capacity of working memory, research in the last decade has provided evidence for the depletion hypothesis. This hypothesis holds that WM becomes depleted after effortful cognitive operations that reduce its capacity, providing a framework for the restorative effects of rest periods. Rest periods during which natural scenery is observed have been found to replenish working memory after it has been subjected to depletion. In the present study, participants observed pictures depicting either a natural or an urban environment, after completing a cognitively depleting task. For this study, we obtained EEG measures of working memory by analyzing alpha and theta wave amplitudes. The motivation behind this choice was to derive a continuous index of WM capacity and address the lack of electrophysiological data regarding the depletion hypothesis. Previous research identified a decrease in alpha amplitude, and a simultaneous increase in theta activity with increasing WM load. Our findings partially replicated these results, as we observed a decrease in alpha amplitude with increasing cognitive load but no significant difference in theta power. Moreover, average signal amplitudes did not differ between the natural and the urban conditions, contrary to our hypothesis. These results suggest an absence of the expected environmental effect, opposing the outcome of existing research on the topic. The absence of this effect could also be attributed to similarities between the two conditions in certain factors thought to elicit differential physiological responses.
A Novel Ground Slot-Based Dual-Band Massive Multiple-Input Multiple-Output (MIMO) Antenna for n47 and n48 Smartphone Applications
Authors: Zahid Muhammad , Akbar Muhammad Uzair , Bhowmike Nirman , Bolla Devi Prasanth , Talib Asad Ullah , Shoaib Sultan , Amin Yasar , Shahid Saleem
Published: November 2024

A novel ground slot-based massive multiple-input multiple-output (MIMO) system with 14 elements for dual-band application of next-generation smartphones is developed. Every single antenna in the array is placed on the borders of a smartphone’s PCB, operating within the 3.55 GHz to 3.7 GHz (n48) and 5.855 GHz to 5.925 GHz (n47) frequency bands. This study also further assesses the performance of this system with analytical results, in which the self-s-parameters are less than −6 dB for each antenna, whereas isolation between components is less than −10 dB. At the same time, the overall efficiency reaches 65.55% and higher in the whole band. The evaluation of MIMO performance parameters such as envelope correlation coefficient (ECC), and DG of the MIMO system is well performed. A proposed MIMO antenna system is deployed and analyzed, which is a massive MIMO antenna, and the simulation results are compared to the measurement. The results validate the fact that the design of this particular MIMO system suits it to be incorporated in the n47 and n48 frequency bands, and therefore the presented MIMO system is suitable to be an option for integration into the upcoming smartphone architecture.
Dynamic Black-Box Model Watermarking for Heterogeneous Federated Learning
Authors: Liao Yuying , Jiang Rong , Zhou Bin
Published: November 2024

Heterogeneous federated learning, as an innovative variant of federated learning, aims to break through the constraints of vanilla federated learning on the consistency of model architectures to better accommodate the heterogeneity in mobile computing scenarios. It introduces heterogeneous and personalized local models, which effectively accommodates the heterogeneous data distributions and hardware resource constraints of individual clients, and thus improves computation and communication efficiency. However, it poses a challenge to model ownership protection, as watermarks embedded in the global model are corrupted to varying degrees when they are migrated to a user’s heterogeneous model and cannot continue to provide complete ownership protection in the local models. To tackle these issues, we propose a dynamic black-box model watermarking method for heterogeneous federated learning, PWFed. Specifically, we design an innovative dynamic watermark generation method which is based on generative adversarial network technology and is capable of generating watermark samples that are virtually indistinguishable from the original carriers. This approach effectively solves the limitation of the traditional black-box watermarking technique, which only considers static watermarks, and makes the generated watermarks significantly improved in terms of stealthiness and difficult to detect by potential model thieves, thus enhancing the robustness of the watermarks. In addition, we design two watermark embedding strategies with different granularities in the heterogeneous federated learning environment. During the watermark extraction and validation phase, PWFed accesses watermark samples claiming ownership of the model through an API interface and analyzes the differences between their output and the expected labels. Our experimental results show that PWFed achieves a 99.9% watermark verification rate with only a 0.1–4.8% sacrifice of main task accuracy on the CIFAR10 dataset.
Study of the Technical and Operational Parameters of Injectors Using Biogas Fuel
Authors: Bembenek Michał , Melnyk Vasyl , Karwat Bolesław , Rokita Tomasz , Hnyp Mariia , Mosora Yurii , Warguła Łukasz
Published: October 2024

Using biogas fuel in a modern internal combustion engine equipped with gas equipment of the fourth and fifth generations can create several difficulties. This is due to the low heat of combustion of untreated biogas, the presence of moisture, and the specifics of the injectors. The main problem of the studies we considered is that there are no data on the operating parameters of biogas fuel injectors. Studies on the parameters of the Matrix, Barracuda, Valtek, Hana, and Keihin injectors in relation to biogas fuel were carried out according to performance indicators, the linearity of operation, the resistance of the injectors, the ability to maintain factory parameters, and service life. According to the indicators of performance and linearity of work, Valtek injectors have the highest deviation in productivity and linearity of work, with an average of 38.8%, and the lowest deviation of Barracuda injectors is 7.5%. Keihin (15.3%) and Hana injectors (19.1%) also showed good performance indicators, and therefore can be used effectively for biogas fuel systems. As a result of research on the response time of the injectors, it was established that the best indicators were found for Hana (1.75 ms) and Keihin (1.99 ms) injectors. Valtek injectors showed good response rates (2.07 ms), as did Barracuda injectors (2.19 ms), but the highest response time was found in Matrix injectors, with 2.44 ms. Keihin injectors had the lowest average resistance value of 1.25 ohms, and Valtek injectors had the highest resistance value of 3 ohms. According to the research results, Keihin, Matrix, and Barracuda injectors provide the best ability to maintain factory performance when using biogas fuel at 2 to 5%, and Valtek had the worst performance up to 20%. Keihin, Barracuda, and Hana experimental injectors had the highest service life, which is from 200 to 250 thousand km of car mileage. The lowest indicators were found for Valtek and Matrix injectors, the service life of which varies from 70 to 100 thousand km of mileage.
Characterization of Stages of CO2-Enhanced Oil Recovery Process in Low-Permeability Oil Reservoirs Based on Core Flooding Experiments
Authors: Zhu Yutong , Wang Xinwen , Kang Yulong , Guo Chaobin , He Qingcheng , Li Cai
Published: November 2024

Understanding the CO2 displacement mechanism in ultra-low-permeability reservoirs is essential for improving oil recovery. In this research, a series of displacement experiments were conducted on sandstone core samples from the Chang 6 reservoir in the Huaziping area using a multifunctional core displacement apparatus and Nuclear Magnetic Resonance (NMR) technology. The experiments were designed under conditions of constant pressure, variable pressure, and constant effective confining stress to simulate various reservoir scenarios. The results indicated that the distribution characteristics of the pore structure in the rock samples significantly influenced the CO2 displacement efficiency. Specifically, under identical conditions, rock cores with a higher macropore ratio exhibited a significantly enhanced recovery rate, reaching 68.21%, which represents a maximum increase of 31.97% compared to cores with a lower macropore ratio. Though fractures can facilitate CO2 flowing through pores, the confining pressure applied during displacement caused a partial closure of fractures, resulting in reduced rock permeability. Based on the oil-to-gas ratio and oil recovery in the outlet section of the fractured rock samples, the CO2 displacement process exhibited five stages of no gas, a small amount of gas, gas breakthrough, large gas channeling, and gas fluctuation. Although the displacement stage of different cores varies, the breakthrough stage consistently occurs within the range of 2 PV. These insights not only enhance our understanding of CO2 displacement mechanisms in low-permeability reservoirs but also provide actionable data to inform the development of more effective CO2-EOR strategies, significantly impacting industrial practices.
Test Methodology for Short-Circuit Assessment and Safe Operation Identification for Power SiC MOSFETs
Authors: Oliveira Joao , Reynes Jean-Michel , Morel Hervé , Frey Pascal , Perrotin Olivier , Allirand Laurence , Azzopardi Stéphane , Piton Michel , Coccetti Fabio
Published: November 2024

The short-circuit (SC) immunity of power silicon carbide (SiC) MOSFETs is critical for high-reliability applications, where robust monitoring and protection strategies are essential to ensure system safety. Despite their superior voltage blocking capabilities and high energy efficiency, SiC MOSFETs exhibit greater sensitivity to SC-induced degradation compared to their silicon counterparts. This increased vulnerability necessitates the precise assessment of the key SC performance metrics, such as short-circuit withstand time (TSCWT), as well as a deeper understanding of the failure mechanisms. In this study, a comprehensive experimental methodology for evaluating the SC behavior of SiC MOSFETs is presented and validated using industrial references. The investigation further explores the concept of a Safe Operating Area (SOA) under SC conditions, highlighting the significant impact of quasi-simultaneous SC events on device lifetime. Additionally, an application case study demonstrates how these events can drastically reduce the device’s lifespan.
Boosting the Development and Management of Wind Energy: Self-Organizing Map Neural Networks for Clustering Wind Power Outputs
Authors: Li Yanqian , Zhou Yanlai , Luo Yuxuan , Ning Zhihao , Xu Chong-Yu
Published: November 2024

Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. By identifying the appropriate representativeness and topological structure of the competition layer, cluster analysis of the wind power output process in four seasons is realized. The output characteristics are evaluated through multiple evaluation indicators. Taking the wind power output of the Hunan power grid as a case study, the results underscore that the 1 × 3-dimensional competition layer structure had the highest representativeness (72.9%), and the wind power output processes of each season were divided into three categories, with a robust and stable topology structure. Summer and winter were the most representative seasons. Summer had strong volatility and small wind power outputs, which required the utilization of other power sources to balance power supply and load demand. Winter featured low volatility and large wind power outputs, necessitating cooperation with peak-shaving power sources to enhance the power grid’s absorbability to wind power. The seasonal clustering analysis of wind power outputs will be helpful to analyze the seasonality of wind power outputs and can provide scientific and technical support for guiding the power grid’s operation and management.
Species Differentiation of Prunus serrulata and Prunus xueluoensis Based on Combined Analysis of SSR and cpDNA Markers
Authors: Gao Shucheng , Chen Xiangzhen , Peng Zhiqi , Zeng Xinglin , Yun Yingke , Wang Xianrong , Yi Xiangui
Published: November 2024

Prunus xueluoensis C. H. Nan & X. R. Wang is a new species of the Subg. Cerasus Mill., described by C. H. Nan and X. R. Wang in 2013. Since the publication of P. xueluoensis, its taxonomic status has been the subject of ongoing debate. This study focuses on wild populations of P. xueluoensis and Prunus serrulata (Lindley) London, utilizing 18 pairs of SSR molecular markers and variations in chloroplast DNA sequences (matK, trnD-E, and trnS-G) to delineate the relationship between the two species. The results showed that P. serrulate (N = 12.400, Na = 5.144, H = 0.578, I = 1.129, Ho = 0.493) and P. xueluoensis (N = 13.625, Na = 6.264, H = 0.614, I = 1.342, Ho = 0.495) populations exhibit rich genetic diversity, which may be related to their wide geographical distribution. The CpDNA genetic diversities of P. serrulata (Hd = 0.553, Pi = 0.00136) and P. xueluoensis (Hd = 0.496, Pi = 0.00180) are at a high level within the Subg. Cerasus Mill. The UPGMA clustering, along with MP and ML phylogenetic trees, show that the unique haplotypes of P. xueluoensis cluster separately as a terminal branch in the evolutionary tree with high support. The shared haplotypes and unique haplotypes of P. serrulata are predominantly located at the base of the phylogenetic tree, suggesting that the two species have diverged. In the TCS haplotype network, the central and key node haplotypes are primarily unique to P. serrulata and shared haplotypes, while the unique haplotypes of P. xueluoensis are all distributed along the network’s periphery. Both P. serrulata (Nst = 0.254, Gst = 0.103, Nst/Gst = 2.466, p < 0.05) and P. xueluoensis (Nst = 0.366, Gst = 0.268, Nst/Gst = 1.366, p < 0.05) exhibit phylogeographic structures. However, when considered as a whole, the combined entity of P. serrulata and P. xueluoensis does not show a significant phylogeographic structure (Nst = 0.317, Gst = 0.400, Nst/Gst = 0.793, p < 0.05), which supports the classification of these as two distinct species. The estimate of the average age of the latest common ancestor of P. serrulata and P. xueluoensis is 3.22 mya (PP = 1; 95% HPD: 3.07~3.46 mya). Due to environmental differences in altitude, the group of P. serrulata differentiated in the expansion into the cold and humid alpine environment and gradually formed P. xueluoensis. The findings support the classification of P. serrulata and P. xueluoensis as two distinct species.
The Spectral Response Characteristics of Potassium in Camellia oleifera Leaves at Different Growth Stages
Authors: Liu Deqing , Yan Lipeng , Zhang Chao , Xue Yongji , Chen Mengyu , Li Rui , Tang Xuehai
Published: November 2024

Camellia oleifera (Camellia oleifera Abel.) is a key woody oilseed tree. In recent years, China’s Camellia oleifera industry has shifted from extensive to refined management, with an action plan launched to boost productivity and efficiency. This study utilized remote sensing technology to diagnose crop nutrient levels. Focusing on 240 Camellia oleifera trees from four varieties at the Dechang Cooperative in Shucheng County, Anhui Province, the study collected full-spectrum canopy reflectance data (350–2500 nm) across five growing stages: spring shoot, summer shoot, fruit expanding, fruit ripening, and full blooming. First-order derivative (FD) and second-order derivative (SD) transformations were used to preprocess the spectral data and analyze the relationships between leaf potassium concentration (LKC) and the raw spectra (R), FD, and SD. The VCPA-IRIV strategy was then applied to identify sensitive wavelengths and artificial neural network algorithms were used to construct LKC estimation models. The main conclusions are as follows. (1) In the spring shoot stage, LKC ranged from 1.93 to 8.06 g/kg, with an average of 3.70 g/kg; in the summer shoot stage, LKC ranged from 2.01 to 8.82 g/kg, with an average of 4.96 g/kg; in the fruit expanding stage, LKC ranged from 1.40 to 18.27 g/kg, with an average of 4.90 g/kg; in the fruit ripening stage, LKC ranged from 1.45 to 8.90 g/kg, with an average of 3.71 g/kg.; and in the full blooming stage, LKC ranged from 2.38 to 9.57 g/kg, with an average of 5.79 g/kg. Across the five growth stages, the LKC content of Camellia oleifera showed a pattern of initially increasing, then decreasing, and subsequently increasing again. (2) The optimal LKC model for the spring shoot stage was FD-[7,6,2], with Rc2 = 0.6559, RMSEC = 0.1906 in the calibration set, RT2 = 0.4531, RMSET = 0.2009 in the test set. The optimal LKC model for the summer shoot stage was FD-[6,5,4], with Rc2 = 0.7419, RMSEC = 0.2489 in the calibration set, and RT2 = 0.7536, RMSET = 0.2259 in the test set; the optimal LKC model for the fruit expanding stage was SD-[7,6,2], with Rc2 = 0.3036, RMSEC = 0.2113 in the calibration set, and RT2 = 0.3314, RMSET = 0.1800 in the test set; the optimal LKC model for the fruit ripening stage was FD-[10,3,2], with Rc2 = 0.4197, RMSEC = 0.2375 in the calibration set, and RT2 = 0.5649, RMSET = 0.1772 in the test set, and the optimal LKC model for the full blooming stage was SD-[10,3,2], with Rc2 = 0.7013, RMSEC = 0.2322 in the calibration set, and RT2 = 0.5621, RMSET = 0.2507 in the test set.
Degradation of Anti-Nutritional Factors in Maize Gluten Feed by Fermentation with Bacillus subtilis: A Focused Study on Optimizing Fermentation Conditions
Authors: Sun Xiaohong , Ma Lei , Xuan Yaoquan , Liang Jianfen
Published: November 2024

Maize gluten feed is rich in micronutrients and serves as a good source of protein and dietary fiber, but also contains anti-nutritional factors. In this study, fermentation conditions for the degradation of phytic acid and water-unextractable arabinoxylans in maize gluten feed using Bacillus subtilis were optimized. Key variables influencing the fermentation process were identified from seven potential parameters using the Plackett–Burman design. Three statistically significant factors, i.e., fermentation time, inoculum dose, and material-to-liquid ratio were further optimized through a central composite design and the efficiency of fermentation conditions was predicted. The accuracy of the predicted model was validated by subsequent experimentation. The optimum fermentation conditions were determined to be a fermentation time of 84.5 h, inoculum dose of 17.1%, and material-to-liquid ratio of 1:3.4. Under these conditions, 48% of phytic acid and 32% water-unextractable arabinoxylans were degraded. Following fermentation, the activities of protease, xylanase, phytase, and cellulase in maize gluten feed were significantly increased (p < 0.001), contributing to the breakdown of phytic acid and water-unextractable arabinoxylans, which improved the protein dispersibility index, in vitro protein digestibility, and mineral bioavailability. These findings suggest that fermenting maize gluten feed with Bacillus subtilis is a practical and effective approach to reducing anti-nutrients and enhancing its nutritional quality.
Advancements and Future Prospects in Hypocrellins Production and Modification for Photodynamic Therapy
Authors: Zhang Xiang , Wei Qiulin , Tian Liwen , Huang Zhixian , Tang Yanbo , Wen Yongdi , Yu Fuqiang , Yan Xiaoxiao , Zhao Yunchun , Wu Zhenqiang , Tian Xiaofei
Published: November 2024

Hypocrellins (HYPs), naturally occurring 3,10-xylene-4,9-anthracene derivatives sourced from Shiraia bambusicola and Hypocrella bambusae, exhibit significant photobiological activities. Despite their capability for generating a high yield of reactive oxygen species, including singlet oxygen and superoxide anion radical, their application in photodynamic therapy (PDT) is constrained. This limitation is due to their low dark phototoxicity, weak absorption within the therapeutic window of PDT (600–900 nm), and inherent hydrophobicity, which hinder their immediate use in amphipathic PDT applications. This review comprehensively discusses the research advancements in the bioactivities and biosynthesis of HYPs, alongside the reported chemical and physical modifications that enhance their water solubility and extend their therapeutic window. Additionally, it explores potential strategies for developing pharmaceuticals, photocatalytic agents, and photosensitive pesticides based on HYPs.
A Brief Review of Hemp Fiber Length Measurement Techniques
Authors: Green Joia , Liu Xiaorui , Yin Rong
Published: November 2024

Accurate fiber length measurement is essential for the processing and quality management of textile products. This article reviews the current methods used to measure fiber length, including manual, photoelectric, capacitive, and optical techniques. Existing sample preparation processes for natural fiber characterization have been primarily developed for cotton and wool fibers. However, hemp fibers present unique challenges due to their greater length variability, high strength, and low elongation, making some traditional sample preparation methods less effective. Image processing offers a promising approach for scalable and precise measurement of hemp fiber length. Nevertheless, current image processing techniques are limited by the inability to effectively handle overlapping fibers, which increases both the time and cost of testing. Continued research into developing more advanced segmentation algorithms could lead to more widely adopted commercial methods for fiber measurement.
A Thermal Characteristics Study of Typical Industrial Oil Based on Thermogravimetric-Differential Scanning Calorimetry (TG-DSC)
Authors: Zhao Yaohong , Qian Yihua , Zhong Guobin , Wu Siyuan , Pan Siwei
Published: November 2024

Recent incidents of fire accidents attributed to oil combustion have emerged as a significant threat to both industrial safety and environmental conservation. In this study, the thermal oxidation and thermal analysis kinetics parameters of transformer oil, engine oil, and hydraulic oil in the air atmosphere were explored based on thermogravimetric-differential scanning calorimetry (TG-DSC). Industrial oils showed the same decomposition process in the thermal decomposition process. The peak temperature of the DSC curve was higher than that of the DTG curve, and the peak values of DTG and DSC curves increased with the increase of heating rate. The industrial oils underwent a main mass loss process, with respective ranges of approximately 80–84% for transformer oil, 73–79% for engine oil, and 86–89% for hydraulic oil. Notably, engine oil demonstrated the highest average apparent activation energy, amounting to 110.50 kJ/mol, significantly surpassing hydraulic oil (105.13 kJ/mol) and transformer oil (60.95 kJ/mol). The optimal kinetic model for the evaporative oxidation reaction of the industrial oils in air was identified as the reaction order model (Fn), with the corresponding kinetic mechanism function expressed as f(α) = (1 − α)n. The use of TG-DSC offers novel perspectives on the thermal stability and safety evaluation of oil products. Meanwhile, the optimal kinetic model and thermal oxidation stability of typical industrial oil evaporation and oxidation reaction in air was determined, possessing a good reference for the safety and the application of industrial oil.
Experimental Investigation of the Spread and Burning Behaviors of Diesel Spill Fires on a Water Surface
Authors: Zhao Jinlong , He Yijia , Xiao Jingwen , Su Zilong , Ma Hanchao , Zhai Xu
Published: November 2024

Large quantities of water are often used to extinguish fires when accidental fuel leakage occurs during storage and transportation. This may lead to spill fires on water. Boilover and splash of heavy oil spill fires in particular can pose a serious thermal threat to surrounding facilities and personnel. In this work, a series of diesel spill fire experiments were conducted on the surface of water. The results showed that, for the non-ignition cases, the fuel spread velocity was fast at first, then maintained a long period of steady spread, which can be successfully predicted by a developed spread model. During the ignition process, the burning of diesel fuel is divided into four phases. Following a brief quasi-steady burning phase, we observed an expansion of the burning area during the intermittent boilover phase, which was primarily driven by boilover. During the quasi-steady burning phase, the burning rate was lower than that of pool fires, which is attributed to the heat loss between the diesel and water layers. This heat loss also results in a lower flame height than the pool fire, and a dimensionless equation was proposed to eliminate discrepancies. During the intermittent boilover phase, the increase in the burning area was used to characterize the boilover intensity, which was found to be negatively correlated with the number of boilovers. Furthermore, the emergence of the boilover also caused flame radiation to rise rapidly; it was about 19% to 30% higher than that in the quasi-steady burning phase.
Depletion Estimation, Stock–Recruitment Relationships, and Interpretation of Biomass Reference Points
Authors: Maunder Mark N. , Piner Kevin R.
Published: November 2024

Stock depletion level is an important concept in the assessment and management of exploited fish stocks because it is often used in conjunction with reference points to infer stock status. Both the depletion level and reference points can be highly dependent on the stock–recruitment relationship. Here, we show how depletion level is estimated in stock assessment models, what data inform the depletion level, and how the stock–recruitment relationship influences the depletion level. There are a variety of data that provide information on abundance. In addition, to estimate the depletion level, unexploited absolute abundance needs to be determined. This often means extrapolating the abundance back in time to the start of the fishery, accounting for the removals and the productivity. Uncertainty in the depletion level arises because the model can account for the same removals by either estimating low productivity (e.g., low natural mortality) and high carrying capacity or high productivity and a low carrying capacity, and by estimating different relationships between productivity and depletion level, which are strongly controlled by the stock–recruitment relationship. Therefore, estimates of depletion are particularly sensitive to uncertainty in the biological processes related to natural mortality and the stock–recruitment relationship and to growth when length composition data are used. In addition, depletion-based reference points are highly dependent on the stock–recruitment relationship and need to account for recruitment variability, particularly autocorrelation, trends, and regime shifts. Future research needs to focus on estimating natural mortality, the stock–recruitment relationship, asymptotic length, shape of the selectivity curve, or management strategies that are robust to uncertainty in these parameters. Tagging studies, including close-kin mark-recapture, can address some of these issues. However, the stock–recruitment relationship will remain uncertain.
Soybean β-Conglycinin and Cowpea β-Vignin Peptides Inhibit Breast and Prostate Cancer Cell Growth: An In Silico and In Vitro Approach
Authors: Philadelpho Biane Oliveira , Santiago Victória Guimarães , Santos Johnnie Elton Machado dos , Silva Mariana Barros de Cerqueira e , De Grandis Rone Aparecido , Cilli Eduardo Maffud , Pavan Fernando Rogério , Castilho Marcelo Santos , Scarafoni Alessio , Souza Carolina Oliveira de , Ferreira Ederlan de Souza
Published: November 2024

B-cell lymphoma 2 protein (Bcl-2) is an important regulator of cell apoptosis. Inhibitors that mirror the structural domain 3 (BH3) of Bcl-2 can activate apoptosis in cancer cells, making them a promising target for anticancer treatment. Hence, the present study aimed to investigate potential BH3-mimetic peptides from two vicilin-derived legume proteins from soybean and cowpea bean. The proteins were isolated and sequentially hydrolyzed with pepsin/pancreatin. Peptides < 3 kDa from vicilin-derived proteins from soybean and cowpea beans experimentally inhibited the growth of cultivated breast and prostate cancer cells. In silico analysis allowed the identification of six potential candidates, all predicted to be able to interact with the BH3 domain. The VIPAAY peptide from the soybean β-conglycinin β subunit showed the highest potential to interact with Bcl-2, comparable to Venetoclax, a well-known anticancer drug. Further experiments are needed to confirm this study’s findings.
An Algorithm for the Solution of Integro-Fractional Differential Equations with a Generalized Symmetric Singular Kernel
Authors: Raad Sameeha A. , Abdou Mohammed A.
Published: November 2024

This work studies an integro-fractional differential equation (I-FrDE) with a generalized symmetric singular kernel. The scientific approach in this study was to transform the integro-differential equation (I-DE) into a mixed integral equation (MIE) with an Able kernel in fractional time and a generalized symmetric singular kernel in position. Additionally, the authors first set conditions on the singular kernels, whether related to time or position, and then transform the integral equation into an integral operator. Secondly, the solution is unique, which is proven by means of fixed-point theorems. In combination with the solution rules, the convergence of the solution is studied, and the error equation resulting from the solution is a stable error-integral influencer equation. Next, to solve this MIE, the authors apply a special technique to separate the variables and produce an integral equation in position with coefficients, in the form of an integral operator in time. As the most effective technique for resolving singular integral equations, the Toeplitz matrix method (TMM) is utilized to convert the integral equation into an algebraic system for the purpose of solving the position problem. The existence of a solution to the linear algebraic system in Banach space is then demonstrated. Lastly, certain applications where the functions of the generalized symmetric kernel are cubic or exponential and it assumes the logarithmic, Cauchy, or Carleman form are discussed. In each case, Maple 18 is also used to compute the error estimate.
Estimation of Fractal Dimension and Detection of Fake Finger-Vein Images for Finger-Vein Recognition
Authors: Kim Seung Gu , Hong Jin Seong , Kim Jung Soo , Park Kang Ryoung
Published: November 2024

With recent advancements in deep learning, spoofing techniques have developed and generative adversarial networks (GANs) have become an emerging threat to finger-vein recognition systems. Therefore, previous research has been performed to generate finger-vein images for training spoof detectors. However, these are limited and researchers still cannot generate elaborate fake finger-vein images. Therefore, we develop a new densely updated contrastive learning-based self-attention generative adversarial network (DCS-GAN) to create elaborate fake finger-vein images, enabling the training of corresponding spoof detectors. Additionally, we propose an enhanced convolutional network for a next-dimension (ConvNeXt)-Small model with a large kernel attention module as a new spoof detector capable of distinguishing the generated fake finger-vein images. To improve the spoof detection performance of the proposed method, we introduce fractal dimension estimation to analyze the complexity and irregularity of class activation maps from real and fake finger-vein images, enabling the generation of more realistic and sophisticated fake finger-vein images. Experimental results obtained using two open databases showed that the fake images by the DCS-GAN exhibited Frechet inception distances (FID) of 7.601 and 23.351, with Wasserstein distances (WD) of 18.158 and 10.123, respectively, confirming the possibility of spoof attacks when using existing state-of-the-art (SOTA) frameworks of spoof detection. Furthermore, experiments conducted with the proposed spoof detector yielded average classification error rates of 0.4% and 0.12% on the two aforementioned open databases, respectively, outperforming existing SOTA methods for spoof detection.
A “Wonderful” Reference Dataset of Mira Variables
Authors: Baylis-Aguirre Dana K. , Creech-Eakman Michelle J. , van Belle Gerard T.
Published: November 2024

The conditions in Mira variable atmospheres make them wonderful laboratories to study a variety of stellar physics such as molecule–grain formation, dust production, shock chemistry, stellar winds, mass loss, opacity-driven pulsation, and shocks. We were awarded an NSF grant to analyze over a decade of synoptic observations from the Palomar Testbed Interferometer (PTI) of 106 Miras to curate a Mira Reference Dataset. The Miras included in this dataset include M-types, S-types, and C-types, and span a wide range of pulsation periods. PTI measured K-band angular sizes that when combined with a distance allow us to directly determine fundamental stellar parameters such as effective temperature, radial size, and bolometric flux. Supplementing observations with interferometric measurements of the stars opens the Mira laboratory to a wealth of different experiments. We provide two case studies to serve as examples of the power of the Mira Reference Dataset. The first case study describes combining PTI measurements with Spitzer IRS spectra of M-type Miras, which allowed us to fully characterize CO2 gas in their atmospheres. The second case study examines how PTI narrow-band data can be used to study phase-dependent pulsation effects on the stellar atmosphere. We provide a list of all the Miras (with coordinates) included in the set for anyone who would like to add them to their observing programs. All the data we produce and collate for this Mira Reference Dataset will be hosted and curated on a website open to the public so that other researchers and citizen scientists can participate in expanding the utility and body of knowledge on this set of “wonderful” stars.
Hyaluronic Acid-Based Dynamic Hydrogels for Cartilage Repair and Regeneration
Authors: Zhang Mingshuo , Ye Qianwen , Zhu Zebo , Shi Shuanglian , Xu Chunming , Xie Renjian , Li Yumei
Published: November 2024

Hyaluronic acid (HA), an important natural polysaccharide and meanwhile, an essential component of extracellular matrix (ECM), has been widely used in tissue repair and regeneration due to its high biocompatibility, biodegradation, and bioactivity, and the versatile chemical groups for modification. Specially, HA-based dynamic hydrogels, compared with the conventional hydrogels, offer an adaptable network and biomimetic microenvironment to optimize tissue repair and the regeneration process with a striking resemblance to ECM. Herein, this review comprehensively summarizes the recent advances of HA-based dynamic hydrogels and focuses on their applications in articular cartilage repair. First, the fabrication methods and advantages of HA dynamic hydrogels are presented. Then, the applications of HA dynamic hydrogels in cartilage repair are illustrated from the perspective of cell-free and cell-encapsulated and/or bioactive molecules (drugs, factors, and ions). Finally, the current challenges and prospective directions are outlined.
Boosting the Capacitance of Covalent Organic Framework Supercapacitors by Hydroquinone Redox Electrolyte Addition
Authors: Sierra Laura , Martín-Illán Jesús Á. , Zamora Félix , Ocón Pilar
Published: November 2024

Rapidly escalating energy demands have spurred a relentless quest for innovative materials and methodologies in energy storage technologies. Covalent organic frameworks (COFs) have emerged as promising candidates for energy storage applications owing to their customizable structure and inherent properties, including enduring porosity and expansive surface area. In this study, we introduce imine-based COF aerogels fashioned into flexible COF electrodes, employing redox electrolytes based on hydroquinone (HQ) dissolved in H2SO4 aqueous solution and 0.25 M TBAPF6 at concentration in acetonitrile. This strategic selection of electrolytes aims to augment capacitance and energy density when compared to non-redox electrolytes. Remarkably, our COF electrodes exhibit an outstanding areal capacitance of 843 mF cm−2 when utilizing HQ with 0.10 M H2SO4, operating at 1.3 mA cm−2, while maintaining approximately 100% capacity retention after 10,000 cycles. Notably, the capacitance of the 0.38 M HQ + 0.10 M H2SO4 is eight times greater than that achieved with organic electrolytes (111 mF cm−2).
Lithium-Ion-Sieve Hydrogel Based on Aluminum Doping with High Stretchability, Strong Adsorption Capacity and Low Dissolution Loss
Authors: Zhang Yujie , Wang Yang , Guo Le , Yan Chenzhengzhe , Li Long , Cui Shuyun , Wang Yujie
Published: November 2024

In recent years, with the development of the new energy industry, lithium resources need to be supplied in large quantities. The lithium-ion sieve (LIS) is regarded as an ideal adsorbent for recovering lithium resources from brine because of its excellent lithium adsorption capacity and structural stability. However, because it is powdery after molding, and there will be problems such as dissolution loss of manganese, which limits its industrial development. In this study, in the process of preparing hydrogels of acrylic acid (AA), acrylamide (AM) and chitosan (CS), an LIS hydrogel with high mechanical properties, strong adsorption capacity and low dissolution loss was prepared by doping LIS and Al ions. Among them, the stress of the prepared chitosan–acrylic acid–acrylamide hydrogel (PASA-1) with an Al doping content of 1% reached 603 KPa, and the maximum strain reached 189%, which showed excellent damage resistance. In addition, the adsorption performance of PASA-1 reached 43.2 mg/g, which was excellent, which was attributed to the addition of Al ions, which inhibited the dissolution loss of manganese ions. This idea has great potential in the direction of lithium resource recovery and provides a new method for the use of hydrogel in the direction of lithium-ion sieves.
Understanding American Indian/Alaska Native Students’ Barriers and Facilitators in the Pursuit of Health Professions Careers in Nebraska
Authors: King Keyonna M. , Idoate Regina , Allick Cole C. , Shope Ron , Haakenstad Magdalena , Leon Melissa A. , Rookwood Aislinn , Butler Robbins Hannah , De Alba Armando , Tutsch-Bryant Sonja F. , Abney Mariah , Hamilton Vanessa , Johansson Patrik L.
Published: November 2024

The U.S. health care system presents American Indian/Alaska Native populations with inequitable challenges that result in some of the worst health outcomes in the country. The literature indicates that increasing the proportion of American Indian/Alaska Native health professionals can improve these health disparities. This study aimed to explore the severe under-representation of American Indians and Alaska Natives in Nebraska’s health professions workforce by examining barriers and facilitators in this population’s pursuit of health professions careers. We conducted demographic questionnaires and three talking circles with students in reservation and urban settings to better understand their lived experiences of pursuing health professions careers. We analyzed these qualitative data through content analysis and identified eight emergent themes—four barriers and four facilitators. These findings can inform the development of strategies to improve Indigenous education, research, and pathways that promote increased American Indian/Alaska Native representation in health care.
Naming and Family Trees as Inter-Generational Epic Narratives in Bette-Obudu Culture, Cross River State
Authors: Betiang Liwhu , Apejoye-Okezie Esther Frank
Published: November 2024

This study articulates how naming and family trees can become epic texts upon which intended or unintended meanings, identities and narratives can be decoded, including mutations in families, as basic units of society. Many studies in African anthroponym have articulated names and naming from differing perspectives, but have tended to ignore the diachronic and synchronic significance of looking at family trees which are woven in time and space through naming. Within the framework of Darwinian Theory of Evolution, we used in-depth interviews with a purposive sample of respondents from the Bette people of the Obudu local government area, to enable us to build family trees which were subtextually analyzed for meaning and mutations through six generations. Our findings enabled us to develop deeper insights into how a longitudinal articulation of naming and family trees can enhance our understanding of the synchronic realities, increased cultural aliteracy, dislocation of homesteads due to occupational shifts, changing ideas of kinship, patriarchal attitudes towards women and challenge of new technologies like DNA testing and new media within the Bette traditional kinship tradition. Significantly, naming and family trees, beyond dynastic delineations for identity, inclusivity and otherness, can become signifiers of a people’s epic progression and mutation, and, as it were, a tapestry of significant narratives of micro and macro family history.