bluuebunny/crossref_metadata_2025 huggingface dataset:
Functional Brain Network Disruptions in Parkinson’s Disease: Insights from Information Theory and Machine Learning
Authors: Akgüller Ömer , Balcı Mehmet Ali , Cioca Gabriela
Published: December 2024

Objectives: This study investigates disruptions in functional brain networks in Parkinson’s Disease (PD), using advanced modeling and machine learning. Functional networks were constructed using the Nonlinear Autoregressive Distributed Lag (NARDL) model, which captures nonlinear and asymmetric dependencies between regions of interest (ROIs). Key network metrics and information-theoretic measures were extracted to classify PD patients and healthy controls (HC), using deep learning models, with explainability methods employed to identify influential features. Methods: Resting-state fMRI data from the Parkinson’s Progression Markers Initiative (PPMI) dataset were used to construct NARDL-based networks. Metrics, such as Degree, Closeness, Betweenness, and Eigenvector Centrality, along with Network Entropy and Complexity, were analyzed. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) models, classified PD and HC groups. Explainability techniques, including SHAP and LIME, identified significant features driving the classifications. Results: PD patients showed reduced Closeness (22%) and Betweenness Centrality (18%). CNN achieved 91% accuracy, with Network Entropy and Eigenvector Centrality identified as key features. Increased Network Entropy indicated heightened randomness in PD brain networks. Conclusions: NARDL-based analysis with interpretable deep learning effectively distinguishes PD from HC, offering insights into neural disruptions and potential personalized treatments for PD.
Cascades Towards Noise-Induced Transitions on Networks Revealed Using Information Flows
Authors: van Elteren Casper , Quax Rick , Sloot Peter M. A.
Published: December 2024

Complex networks, from neuronal assemblies to social systems, can exhibit abrupt, system-wide transitions without external forcing. These endogenously generated “noise-induced transitions” emerge from the intricate interplay between network structure and local dynamics, yet their underlying mechanisms remain elusive. Our study unveils two critical roles that nodes play in catalyzing these transitions within dynamical networks governed by the Boltzmann–Gibbs distribution. We introduce the concept of “initiator nodes”, which absorb and propagate short-lived fluctuations, temporarily destabilizing their neighbors. This process initiates a domino effect, where the stability of a node inversely correlates with the number of destabilized neighbors required to tip it. As the system approaches a tipping point, we identify “stabilizer nodes” that encode the system’s long-term memory, ultimately reversing the domino effect and settling the network into a new stable attractor. Through targeted interventions, we demonstrate how these roles can be manipulated to either promote or inhibit systemic transitions. Our findings provide a novel framework for understanding and potentially controlling endogenously generated metastable behavior in complex networks. This approach opens new avenues for predicting and managing critical transitions in diverse fields, from neuroscience to social dynamics and beyond.
Enhancing Academic Writing in a Linguistics Course with Generative AI: An Empirical Study in a Higher Education Institution in Hong Kong
Authors: Wang Lixun , Ren Boyuan
Published: December 2024

The advent of generative artificial intelligence (AI) has transformed information acquisition and utilization in higher education academic writing. This study fills the empirical research gap by examining how university students use generative AI tools for digital academic writing. The study investigates the experiences of 140 undergraduates enrolled in an ‘Introduction to Linguistics’ course at a university in Hong Kong employing generative AI applications, such as ChatGPT3.5 and the AI Image Generation feature in Padlet, for a collaborative multimedia Wikibook project spanning 13 weeks. The mixed-method approach included analyzing AI-generated content integration within 29 multimedia-enhanced Wikibook chapters, development logs with ChatGPT interactions, the assimilation of AI-generated content into drafts, and collaborative composition dynamics. Additionally, a survey with 98 student responses assessed perceptions regarding AI efficacy in academic writing support. Key findings reveal the multifaceted role of ChatGPT in shaping discourse, offering linguistic tools, and structuring information. The students recognized the advantages of leveraging AI tools, rapidly acclimating to their strengths and limitations through practical engagement. The outcomes of this research offer insights into students’ effective utilization of AI-generated content and suggest potential directions for incorporating generative AI tools into academic writing pedagogy.
Pre-Service CLIL Teachers’ Conceptions on Bilingual Education: Impact of Initial Training on the Development of Their Teaching Skills
Authors: Zayas-Martínez Francisco , Estrada-Chichón José Luis , Segura-Caballero Natalia
Published: December 2024

Content and Language Integrated Learning (CLIL) has been adopted by schools to equip students with the competencies and language skills needed in today’s multilingual and multicultural society. However, teaching content courses through a foreign language requires specific teacher training, both initial and ongoing. This qualitative descriptive study analyses the development of bilingual teaching knowledge in terms of teaching skills among pre-service CLIL teachers (n = 15) at the University of Cádiz in Spain. Two reflective reports written by each pre-service teacher before and after participating in the course CLIL I: Foundations and Curricular Proposals for the Primary School Classroom (2022/23) were analyzed. The responses were categorized using Qualitative Content Analysis. The reflective reports included questions about their conception of bilingual teaching; methods for teaching bilingual courses; and justifications for their teaching methods. In addition, a focus group was conducted with a representative sample of participants. The results reveal a significant development in pre-service teachers’ knowledge of the teaching skills required for bilingual teaching. Particularly, there is an increase in contributions related to justifying their teaching methods. Moreover, the findings highlight pre-service teachers’ recognition of the differences between foreign language and CLIL teachers, as well as their ability to plan CLIL teaching effectively.
Comparative Analysis of Graph Neural Networks and Transformers for Robust Fake News Detection: A Verification and Reimplementation Study
Authors: Kuntur Soveatin , Krzywda Maciej , Wróblewska Anna , Paprzycki Marcin , Ganzha Maria
Published: December 2024

This study compares Transformer-based models and Graph Neural Networks (GNNs) for fake news detection across three datasets: FakeNewsNet, ISOT, and WELFake. Transformer models (BERT, RoBERTa, GPT-2) demonstrated superior performance, achieving mean accuracies above 85% on FakeNewsNet and exceeding 98% on ISOT and WELFake. Specifically, RoBERTa achieved 86.16% accuracy on FakeNewsNet and 99.99% on ISOT, while GPT-2 reached 99.72% on WELFake. In contrast, GNNs (GCN, GraphSAGE, GIN, GAT) exhibited lower performance. GCN achieved 71% accuracy on FakeNewsNet but dropped to 53.30% on ISOT and 50.28% on WELFake, with F1 scores reflecting similar trends. Other GNNs, like GraphSAGE, showed even lower results, particularly on ISOT and WELFake, where performance hovered around 50%. Our findings indicate that while Transformers provide exceptional accuracy and reliability, GNNs offer potential efficiency benefits for resource-constrained scenarios despite their lower predictive performance. This study informs model selection for fake news detection tasks and encourages the exploration of hybrid approaches to balance accuracy and computational efficiency.
Optimized Edge-Cloud System for Activity Monitoring Using Knowledge Distillation
Authors: Deniz Daniel , Ros Eduardo , Ortigosa Eva M. , Barranco Francisco
Published: December 2024

Driven by the increasing care needs of residents in long-term care facilities, Ambient Assisted Living paradigms have become very popular, offering new solutions to alleviate this burden. This work proposes an efficient edge-cloud system for indoor activity monitoring in long-term care institutions. Action recognition from video streams is implemented via Deep Learning networks running at edge nodes. Edge Computing stands out for its power efficiency, reduction in data transmission bandwidth, and inherent protection of residents’ sensitive data. To implement Artificial Intelligence models on these resource-limited edge nodes, complex Deep Learning networks are first distilled. Knowledge distillation allows for more accurate and efficient neural networks, boosting recognition performance of the solution by up to 8% without impacting resource usage. Finally, the central server runs a Quality and Resource Management (QRM) tool that monitors hardware qualities and recognition performance. This QRM tool performs runtime resource load balancing among the local processing devices ensuring real-time operation and optimized energy consumption. Also, the QRM module conducts runtime reconfiguration switching the running neural network to optimize the use of resources at the node and to improve the overall recognition, especially for critical situations such as falls. As part of our contributions, we also release the manually curated Indoor Action Dataset.
AI-Powered VR for Enhanced Learning Compared to Traditional Methods
Authors: Cinar Omer Emin , Rafferty Karen , Cutting David , Wang Hui
Published: December 2024

This paper evaluates a VR (Virtual Reality) application aimed at enhancing the learning of Python collection data types and structures for electrical and electronic engineering students. By incorporating gamification and personalisation features, the application provides an immersive environment where students can interact with virtual representations of complex programming concepts. To further enhance interactivity and engagement, the application integrates a virtual assistant and example generator, developed using Meta Voice SDK (Software Development Kit) and wit.ai. These AI (Artificial Intelligence)-NLP (Natural Language Processing) tools create personalised learning paths and generate dynamic examples based on individual learning progress. A user study was conducted with a total of 48 participants. During the user study, participants were divided into two equal groups of 24, both wearing EEG (Electroencephalography) headsets: one group engaged with the VR application, while the other read the traditional booklet, allowing for the recording and analysis of attention and engagement levels. These measures of engagement and attention were then compared to those extracted from a benchmark cohort of students whose learning experience was through more traditional booklets. The results indicated a statistically significant improvement in understanding Python collections among VR users compared to their baseline scores, highlighting the benefits of interactive and tailored learning environments. Additionally, EEG data analysis showed that VR users exhibited higher average levels of attention and engagement compared to those using the paper-based method, demonstrating the effectiveness of immersive technologies in sustaining learner interest and focus, particularly in enhancing learning for software development.
DEFT: A Dynamic Environmental Filtering and Thresholding Algorithm for Adaptive Headlamp Control Using Ride Height Sensors
Authors: Jung Minwoo , Kim Dae-Young
Published: December 2024

DEFT (Dynamic Environmental Filtering and Thresholding) algorithm is proposed to optimize vehicle height control and improve the performance of Adaptive Headlamp Systems. The DEFT algorithm is designed to enhance the reliability and stability of headlamp control systems by dynamically adapting to various driving conditions and environmental changes in real time. To detect and stabilize irregular fluctuations in vehicle height due to load variations, this study integratesreal-time data acquisition based on Hall sensors, dynamic boundary setting using linear interpolation, and signal stabilization by applying Kalman and Median filters with hysteresis. These components work together to suppress control signal instability caused by noise and abnormal signals, thereby reinforcing the reliability and consistency of vehicle height control. In particular, the hysteresis function reduces unnecessary signal fluctuations near threshold values, which not only extends the control system’s lifespan but also ensures stable operation. Experimental results demonstrate that the DEFT algorithm overcomes the limitations of conventional variable-resistance sensors and significantly enhances adaptive headlamp control performance under various driving conditions. This study presents a high-reliability solution for real-time vehicle height adjustment within ADASs (Advanced Driver Assistance Systems) and demonstrates the potential for application in diverse vehicle control systems.
Effect of Cosmic Rays on the Failure Rate of Flexible Direct Current Converter Valves in High-Altitude Environment
Authors: Yang Liu , Zhang Zezhao , Zhou Yuebin , Wang Daming , Peng Chao , Zhang Hong , Lei Zhifeng , Zhang Zhangang , Fu Weili , Ma Teng
Published: December 2024

Aiming at the significant needs of flexible DC converter valve applications in high-altitude areas, we investigate the effect of atmospheric neutrons on the failure rate of key core power devices in three kinds of converter valves, namely IGBTs, thyristors, and diodes. The safe working voltage boundary of the devices is obtained, and the failure rate caused by atmospheric neutrons in the real working environment of the power devices is calculated according to the results of the ground-accelerated irradiation test of atmospheric neutrons and the atmospheric neutron environment under the actual working conditions. The test results show that the failure rate of IGBTs, thyristors, and diodes caused by atmospheric neutrons is greatly affected by the blocking voltage, and the larger the blocking voltage is, the higher the failure rate of the device is. The research results can provide a basis for the design of the operating voltage of key core power devices of flexible DC converter valves and guide the evaluation of the failure rate and engineering design of the electronic system of DC ultra-high-voltage power transmission and transformation converter stations in the high-altitude environment of the Qinghai-Tibet Plateau.
Application of Mixed-Mode Ventilation to Enhance Indoor Air Quality and Energy Efficiency in School Buildings
Authors: Otoo Christopher , Lu Tao , Lü Xiaoshu
Published: December 2024

Indoor air quality and energy efficiency are instrumental aspects of school facility design and construction, as they directly affect the physical well-being, comfort, and academic output of both pupils and staff. The challenge of balancing the need for adequate ventilation to enhance indoor air quality with the goal of reducing energy consumption has long been a topic of debate. The implementation of mixed-mode ventilation systems with automated controls presents a promising solution to address this issue. However, a comprehensive literature review on this subject is still missing. To address this gap, this review examines the potential application of mixed-mode ventilation systems as a solution to attaining improved energy savings without compromising indoor air quality and thermal comfort in educational environments. Mixed-mode ventilation systems, which combine natural ventilation and mechanical ventilation, provide the versatility to alternate between or merge both methods based on real-time indoor and outdoor environmental conditions. By analyzing empirical studies, case studies, and theoretical models, this review investigates the efficacy of mixed-mode ventilation systems in minimizing energy use and enhancing indoor air quality. Essential elements such as operable windows, sensors, and sophisticated control technologies are evaluated to illustrate how mixed-mode ventilation systems dynamically optimize ventilation to sustain comfortable and healthy indoor climates. This paper further addresses the challenges linked to the design and implementation of mixed-mode ventilation systems, including complexities in control and the necessity for climate-adaptive strategies. The findings suggest that mixed-mode ventilation systems can considerably lower heating, ventilation, and air conditioning energy usage, with energy savings ranging from 20% to 60% across various climate zones, while also enhancing indoor air quality with advanced control systems and data-driven control strategies. In conclusion, mixed-mode ventilation systems offer a promising approach for school buildings to achieve energy efficiency and effective ventilation without sacrificing indoor environment quality.
A Feed-Forward Back-Propagation Neural Network Approach for Integration of Electric Vehicles into Vehicle-to-Grid (V2G) to Predict State of Charge for Lithium-Ion Batteries
Authors: Cervellieri Alice
Published: December 2024

The accurate prediction and efficient management of the State of Charge (SoC) of electric vehicle (EV) batteries are critical challenges in the integration of vehicle-to-grid (V2G) systems within multi-energy microgrid (MMO) models. Inaccurate SoC estimation can lead to inefficiencies, increased costs, and potential disruptions in power generation. This paper addresses the problem of optimizing SoC estimation to enhance the reliability and efficiency of V2G scheduling and MMO coordination. In this work, we develop a Feed-Forward Back-Propagation Network (FFBPN) using MATLAB 2024 software, employing the Levenberg–Marquardt algorithm and varying the number of hidden neurons to achieve better performance; performance was measured by the maximum coefficient of determination (R2) and the minimum mean squared error (MSE). Utilizing the NASA Prognostics Center of Excellence (PCoE) dataset, we validate the model’s capability to accurately predict the life cycle of EV batteries. Our proposed FFBPN model demonstrates superior performance compared to existing methods from the literature, offering significant implications for future V2G system developments. The comparison between training, validation, and testing phases underscores the model’s validity and precisely identifies the characteristic curves of FFBPN, showcasing its potential to enhance profitability, efficiency, production, energy savings, and minimize environmental impact.
Nutritional Studies Evaluating Ketogenic Diets as a Treatment for Obesity and Obesity-Associated Morbidities: Underlying Mechanisms and Potential for Clinical Implementation
Authors: Pirola Luciano , Górecka Karolina , Gois Leandro Carol , Balcerczyk Aneta
Published: December 2024

Background: The ketogenic diet (KD), characterized by high-fat content, virtually no carbohydrates, and adequate protein intake, induces a metabolic state resembling fasting, as the absence of carbohydrates forces the body to rely on the energetic supply from hepatically produced ketone bodies using free fatty acids as substrate. While the KD is clinically used in pharmacologically refractory epilepsy and specific genetic conditions such as GLUT1 deficiency, recent research suggests that, due to its “fasting mimicking” properties, the KD may also beneficially affect obesity and obesity-associated metabolic diseases. Results: Here, we present a narrative review discussing completed and ongoing nutritional studies in human volunteers specifically addressing the potential of the ketogenic diet as an anti-obesity approach and, from a larger perspective, as an intervention to ameliorate the metabolic state in conditions such as type 1 and 2 diabetes and polycystic ovary syndrome (PCOS). Published studies as well as ongoing clinical trials will be discussed. Efficacy and safety considerations will be discussed, as well as the potential physiological mechanisms mediating the effects of the KD in humans in the context of the (i) energy balance model (EBM) and (ii) carbohydrate–insulin model (CIM) of body weight control. Conclusion: Ketogenic diets may be beneficial to attenuate obesity and improve obesity-related metabolic disease, and here, we try, based on current evidence, to define the boundaries of the KD’s nutritional and clinical usefulness.
Response of Topsoil Organic Carbon in the Forests of Northeast China Under Future Climate Scenarios
Authors: Wang Shuai , Guan Yuxi , Wang Zicheng , Yang Zijiao , Li Chen , Zhang Xingyu , Shi Di , Zhang Min
Published: December 2024

Soil organic carbon (SOC) serves as a highly sensitive indicator of climate change and plays a crucial role in terrestrial carbon cycles. Evaluating the impact of regional land use changes on SOC stocks is essential for assessing ecological and environmental effects. In this study, we utilized 157 soil samples and 11 environmental variables—including soil properties, topographic factors, and climatic conditions—to develop boosted regression tree (BRT) and random forest (RF) models to estimate topsoil SOC stocks for the year 2015. We used a 10-fold cross-validation approach, along with four validation metrics, to assess model performance. The BRT model demonstrated superior accuracy, with a higher R2 and Lin’s consistency correlation coefficient and a lower mean absolute error and root mean square error compared to the RF model. The key environmental factors influencing SOC stock variability in the BRT model included mean annual temperature, elevation, mean annual precipitation, the topographic wetness index (TWI), and catchment area. Based on this, we employed the space-for-time substitution approach and BRT model to forecast the spatial distribution of soil organic carbon (SOC) stocks in Northeast China’s forested regions under future climate scenarios for the 2050s and 2090s. Our findings indicate that, compared to the 2015 levels, the forecast indicates that SOC stocks will decrease by 122 Tg carbon and 123 Tg carbon under two different future scenarios, SSP245 and SSP585, respectively, by the 2050s. By the 2090s, these figures are expected to decrease further by 127 Tg C and 126 Tg C, respectively. Throughout both future periods, SOC stocks will predominantly be concentrated in the northwest region. This research highlights the necessity of thoroughly considering climatic factors in future studies of regional SOC stock dynamics. Moreover, the high-resolution maps produced in this study offer a scientific foundation for enhancing the implementation of ecological management practices in the forested regions of Northeast China, fostering environmental improvement and bolstering SOC and soil management strategies in response to future climate change.
A Novel Workflow for Mapping Forest Canopy Height by Synergizing ICESat-2 and Multi-Sensor Data
Authors: Guo Linghui , Zhang Yang , Xu Muchao , Yan Jingjing , Zhang Hebing , Zou Youfeng , Gao Jiangbo
Published: December 2024

Precise information on forest canopy height (FCH) is critical for forest carbon stocks estimation and management, but mapping continuous FCH with satellite data at regional scale is still a challenge. By fusing ICESat-2, Sentinel-1/2 images and ancillary data, this study aimed to develop a workflow to obtain an FCH map using a machine learning algorithm over large areas. The vegetation-type map was initially produced by a phenology-based spectral feature selection method. A forest characteristic-based model was then proposed to map spatially continuous FCH after a multivariate quality control. Our results show that the overall accuracy (OA) and average F1 Score (F1) for eight main vegetation types were more than 90% and 89%, respectively, and the vegetation-type map agreed well with the census areas. The forest characteristic-based model demonstrated a greater potential in FCH prediction, with an R-value 60.47% greater than the traditional single model, suggesting that the addition of the multivariate quality control and forest structure characteristics could positively contribute to the prediction of FCH. We generated a 30 m continuous FCH map by the forest characteristic-based model and evaluated the product with about 35 km2 of airborne laser scanning (ALS) validation data (R = 0.73, RMSE = 2.99 m), which were 45.34% more precise than the China FCH, 2019. These findings demonstrate the potential of our proposed workflow for monitoring regional continuous FCH, and will greatly benefit accurate forest resources assessment.
Stable Diversity but Distinct Metabolic Activity of Microbiome of Roots from Adult and Young Chinese Fir Trees
Authors: Wang Qingao , Wang Zhanling , Du Wenjun , Liu Yuxin , Hong Liang , Wu Pengfei , Ma Xiangqing , Wang Kai
Published: December 2024

The tree-associated microbiome is vital for both individual trees and the forest ecosystem. The microbiome is dynamic; however, it is influenced by the developmental stages and environmental stresses experienced by host trees. Chinese fir (Cunninghamia lanceolata) is an economically important tree species in the subtropical regions of China. This study investigated the diversity of microbial communities, including bacteria and fungi, in the roots and bulk soil of young (2 years old) and old (46 years old) Chinese fir. It specifically examined the functional characteristics of these microbial communities. Through a non-metric multidimensional scaling (NMDS) analysis, we examined differences in microbial community structures among root and soil samples of Chinese fir. Evaluations using α-diversity metrics (Chao1, Shannon, Pielou, etc.) confirmed significant differences in diversity and structure between soil and root samples but high similarity between young and old tree samples. A network analysis identified key bacterial and fungal genera, such as Burkholderia and Russula, which play pivotal roles in the microbiome structure. We also demonstrated significant variations in microbial metabolic functions, such as dioxin and benzoic acid degradation metabolic pathways, which might relate to stress alleviation for tree fitness. Additionally, for the detection of endophytic microorganisms in Chinese fir seeds, only small amounts (less than 10%) of fungal endophytes and bare bacterial endophytes were identified. In summary, this study revealed that the stable structure of the rhizosphere microbiome was established in the early stage of tree life in Chinese fir, which mostly originated from surrounding soil rather than seed endophytes. The associated microbial metabolic activity naturally decreased with tree aging, implicating the tree microbial dynamics and the need for the addition of an actively functional synthetic community for tree fitness.
Use of Chlorella vulgaris Lipidic Extracts in the Development of Healthier Pastry Products with Reduced Fat Contents
Authors: Pereira Tatiana , Barroso Sónia , Teixeira Paula , Domingues M. Rosário , Maurício Tatiana , Mendes Susana , Pinto Filipa R. , Freire Catarina D. , Matos Gabriela , Saraiva Jorge A. , Gil Maria M.
Published: December 2024

Pastry products constitute a significant segment of the food market. However, the high amount of fat used in their production poses a challenge when competing for the attention of modern consumers, who are more conscious of the health problems associated with the consumption of high-fat products. With this in mind, the main objective of this study is the reduction of the total fat and saturated fat contents of two bakery products, brioche-type bread and rice cake, by partial substitution of the main fat source with Chlorella vulgaris lipid extracts obtained through non-thermal high-pressure extraction (HPE). A reduction of 3% in the fat content of the brioche and a reduction of 11.4% in the total fat content of the rice cake were observed when the microalgae extracts were used to replace 10% of the margarine used in the brioche and 20% of the sunflower oil used in the rice cake. This substitution resulted in fat-reduced bakery products with similar physicochemical and nutritional properties to the full-fat controls. A triangle test demonstrated that no differences were perceived for the fat-reduced brioche, while in the rice cake, only slightly perceptible differences were detected. Moreover, brioche and rice cake containing the extract presented values of 1.22 ± 0.27 and 1.29 ± 0.39 mg GAE/g of total phenolic compounds, respectively. DPPH and FRAP activities were also quantified in 0.95 ± 0.38 and 1.83 ± 0.27 µmol AAE/g for brioche with extract and 1.10 ± 0.61 and 1.39 ± 0.39 µmol AAE/g for the rice cake with extract, respectively. The products were microbially stable for at least four days at room temperature. This study demonstrates the potential of using HPE microalgal lipid extracts as fat substitutes in bakery products.
Effects of Sodium Selenite on Accumulations of Selenium and GABA, Phenolic Profiles, and Antioxidant Activity of Foxtail Millet During Germination
Authors: Sun Shuaiduo , Zhang Jingjing , Li Yongji , Xu Yunfeng , Yang Runqiang , Luo Lei , Xiang Jinle
Published: December 2024

This study investigated the influence of soaking and spraying with a sodium selenite (Na2SeO3) solution on selenium accumulation, γ-aminobutyric acid (GABA) content, phenolic compositions, and the antioxidant activity of foxtail millet sprouts. The screening results showed that foxtail millet seeds soaked with 60 mg/L of Na2SeO3 solution and sprayed with 2 mg/L of Na2SeO3 solution were the appropriate concentrations for the germination process. Compared with the spraying method, a presoaking treatment presented far higher selenium content and significantly higher (p < 0.05) selenium enrichment rates in foxtail millet sprouts. The content of free and bound phenolics, as well as GABA, were significantly (p < 0.05) increased in foxtail millet sprouts through both soaking and spraying treatments. Correspondingly, most of the individual phenolic compounds were significantly (p < 0.05) increased, especially after germination for 3 days. Trans-ferulic acid and trans-p-coumaric acid were the predominate bound phenolic acids, feruloylquinic acid and 4-p-coumaroylquinic acid were the major free-form phenolic compounds, and N-feruloyl serotonin and N-(p-coumaroyl) serotonin were the new arising phenolic derivatives caused by germination. Both the soaking and spraying treatments induced the enrichment of these individual phenolic compositions, thus increasing the total phenolic content and in vitro antioxidant activity of foxtail millet sprouts. It was indicated that selenium-enriched germination treatment should be an effective method to produce functional selenium-enriched foxtail millet sprouts with more abundant GABA and polyphenols, thus enhancing the health benefits and added value of foxtail millet.
Characterizations of Pectin from Choerospondias axillaris Fruit Pulp: Comparison of Different Extraction Methods
Authors: Luyang Zian , Bu Zhibin , Wu Jijun , Yu Yuanshan , Cheng Lina , Peng Jian , Xu Yujuan
Published: December 2024

Generally, the extraction method has a great influence on the quality of pectin. However, there is little study on the effect of extraction method on the properties of Choerospondias axillaris fruit pulp pectin (CAPP). Accordingly, the physicochemical, structural, and functional properties of CAPP extracted by hot water (HWE), hydrochloric acid (HAE), ultrasound (UAE), and ultrahigh pressure (UPE) were investigated. Among these four CAPPs, UPE had the highest yield (15.79%) and GalA content (60.44%). UAE showed the most abundant side chains and RG-I region (55.12%). The lowest molecular weight (233.13 kDa) and yield (8.64%) were found in HAE. Though HWE exhibited better yield than HAE, its Mw was the highest. Different from physicochemical characteristics, the extraction method had a small effect on the structure of CAPP. The crystalline structure and functional group composition of different CAPPs were similar, while the surface structure of UAE and UPE had irregular circular holes in comparison with HWE and HAE. Furthermore, the extraction method also showed a great impact on the function. Compared with HWE and HAE, UAE and UPE presented better thermal stability and emulsifying properties. Meanwhile, HAE and UAE showed better antioxidant ability and prebiotic properties among these four CAPPs. The above results indicated that UAE showed better yield and functional properties. Hence, ultrasound extraction could be used as an effective method to extract CAPP.
Dusty Common Envelope Evolution
Authors: Siess Lionel , Bermúdez-Bustamante Luis C. , De Marco Orsola , Price Daniel J. , González-Bolívar Miguel , Mu Chunliang , Lau Mike Y. M. , Hirai Ryosuke , Danilovich Taïssa
Published: December 2024

We present the first hydrodynamical simulations of common envelope evolution that include the formation of dust and the effect of radiation pressure on dust grains. We performed smoothed particle hydrodynamics simulations of the CE evolution for two systems made of a 1.7 M⊙ and 3.7 M⊙ AGB star primary with a 0.6 M⊙ binary companion. The results of our calculations indicate that dust formation has a negligible impact on the gas dynamics essentially because dust forms in the already unbound material. The expansion and cooling of the envelope yield very early and highly efficient production of dust. In our formalism, which does not consider dust destruction, almost 100% of the available carbon that is not locked in CO condensates in dust grains. This massive dust production, thus, strongly depends on the envelope mass and composition, in particular, its C/O ratio, and has a considerable impact on the observational aspect of the object, resulting in a photospheric radius that is approximatively one order of magnitude larger than that of a non-dusty system.
On the Ionization Tolerance of C20 Fullerene in Ground and Excited Electronic States in Planetary Nebulae
Authors: Sadjadi SeyedAbdolreza , Parker Quentin Andrew
Published: December 2024

As the smallest member of the fullerene family, C20 is yet to be discovered in planetary nebulae. In this work, we present a quantum chemical study via density functional theory (DFT) and partially by the MP2 on the ionization tolerance of this molecule in the space environment. Considering that the ionization and excitation phenomena play key roles in demonstrating the lifetime of a molecule, we examined both ground and excited electronic-state potential energy surfaces (PES) of C20 and its cations C20q+. Our theoretical results indicate that the C20 cage tolerates a positive charge as high as 13+ by characterizing local minimum geometries on both the abovementioned electronic states. The results are backed by characterizing both C2012+ and C2013+ as local minimum geometries at the MP2 level of computations. We also explored, theoretically and systematically, scenarios in which the electronic structure of neutral C20 is excited to very high spin multiplicity (beyond triplet state), and local minimum molecular geometries with cage structures are well characterized. We anticipate that such structural resistance to excitation and ionization delivers a prolonged lifetime necessary for the spectroscopic detection of this interesting molecule and its cations in space and potentially in planetary nebulae (PN).
Insight on AGB Mass-Loss and Dust Production from PNe
Authors: Tosi Silvia
Published: December 2024

The asymptotic giant branch (AGB) phase, experienced by low- and intermediate-mass stars (LIMSs), plays a crucial role in galaxies due to its significant dust production. Planetary nebulae (PNe) offer a novel perspective, providing valuable insights into the dust production mechanisms and the evolutionary history of LIMSs. We selected a sample of nine PNe from the Large Magellanic Cloud (LMC), likely originating from single stars. By modeling their spectral energy distributions (SEDs) with photoionization techniques, we successfully reproduced the observed photometric data, spectra, and chemical abundances. This approach enabled us to constrain key characteristics of the central stars (CSs), dust, and gaseous nebulae, which were then compared with predictions from stellar evolution models. By integrating observational data across ultraviolet (UV) to infrared (IR) wavelengths, we achieved a comprehensive understanding of the structure of the PNe in our sample. The results of the SED analysis are consistent with evolutionary models and previous studies that focus on individual components of the PN, such as dust or the gaseous nebula. Our analysis enabled us to determine the metallicity, the progenitor mass of the CSs, and the amount of dust and gas surrounding the CSs, linking these properties to the previous AGB phase. The PN phase provides critical insights into the physical processes active during earlier evolutionary stages. Additionally, we found that higher progenitor masses are associated with greater amounts of dust in the surrounding nebulae but lower amounts of gaseous material compared to sources with lower progenitor masses.
A Review of Algae-Based Carbon Capture, Utilization, and Storage (Algae-Based CCUS)
Authors: Li Guihe , Yao Jia
Published: December 2024

Excessive emissions of greenhouse gases, primarily carbon dioxide (CO2), have garnered worldwide attention due to their significant environmental impacts. Carbon capture, utilization, and storage (CCUS) techniques have emerged as effective solutions to address CO2 emissions. Recently, direct air capture (DAC) and bioenergy with carbon capture and storage (BECCS) have been advanced within the CCUS framework as negative emission technologies. BECCS, which involves cultivating biomass for energy production, then capturing and storing the resultant CO2 emissions, offers cost advantages over DAC. Algae-based CCUS is integral to the BECCS framework, leveraging algae’s biological processes to capture and sequester CO2 while simultaneously contributing to energy production and potentially achieving net negative carbon emissions. Algae’s high photosynthetic efficiency, rapid growth rates, and ability to grow in non-arable environments provide significant advantages over other BECCS methods. This comprehensive review explores recent innovations in algae-based CCUS technologies, focusing on the mechanisms of carbon capture, utilization, and storage through algae. It highlights advancements in algae cultivation for efficient carbon capture, algae-based biofuel production, and algae-based dual carbon storage materials, as well as key challenges that need to be addressed for further optimization. This review provides valuable insights into the potential of algae-based CCUS as a key component of global carbon reduction strategies.
Gamma Radiation-Induced Synthesis of Carboxymethyl Cellulose-Acrylic Acid Hydrogels for Methylene Blue Dye Removal
Authors: Sutradhar Sabuj Chandra , Banik Nipa , Islam Mobinul , Rahman Khan Mohammad Mizanur , Jeong Jae-Ho
Published: December 2024

This study aims to develop efficient and sustainable hydrogels for dye adsorption, addressing the critical need for improved wastewater treatment methods. Carboxymethyl cellulose (CMC)-based hydrogels grafted with AAc were synthesized using gamma radiation polymerization. Various AAc to CMC ratios (5:5, 5:7.5, 5:10, 5:15) were treated with 37% NaOH and exposed to 1–15 kGy radiation, with the optimal hydrogel obtained at 5 kGy. Swelling studies showed an increase in swelling with 5–7.5% AAc content, with the 5:7.5 hydrogel achieving the highest swelling at 18,774.60 (g/g). FTIR spectroscopy confirmed the interaction between AAc and CMC, indicating the successful formation of the hydrogel. DSC analysis revealed that higher AAc content led to increased glass transition and decomposition temperatures, thereby enhancing thermal stability. The swelling kinetics were linked to a reduction in pore size and improved AAc grafting. The 5:7.5 hydrogel demonstrated the highest adsorption capacity (681 mg/g) for methylene blue at 80 mg/L, achieving a desorption efficiency of 95% in 2M HCl. Kinetic analysis revealed non-uniform physisorption on a heterogeneous surface, which followed Schott’s pseudo-second-order model. This study advances the development of efficient hydrogels for water purification, providing a cost-effective and environmentally friendly solution for large-scale applications.
Synthesis and Application of SAPO-11 Molecular Sieves Prepared from Reaction Gels with Various Templates in the Hydroisomerization of Hexadecane
Authors: Serebrennikov Dmitry V. , Zabirov Arthur R. , Saliev Alexey N. , Yakovenko Roman E. , Prosochkina Tatyana R. , Fayzullina Zulfiya R. , Guskov Vladimir Yu. , Kutepov Boris I. , Agliullin Marat R.
Published: December 2024

Among the most selective catalytic systems for the hydroisomerization of C16+ n-paraffins, catalytic systems based on SAPO-11 are quite promising. In order to increase the activity and selectivity of these bifunctional catalysts, it is necessary to reduce the diffusion restrictions for the reacting molecules and their products in the microporous structure of SAPO-11 by reducing the crystal size. To solve this problem, we have studied the influence of different templates (diethylamine, dipropylamine, diisopropylamine, and dibutylamine) on the physicochemical properties of reaction gels and SAPO-11 silicoaluminophosphates during their crystallization. Using XRD, SEM, and NMR techniques, we found that regardless of the template used, the reaction gel after the aging process at 90 °C is an AlPO4·2H2O hydroaluminophosphate. At the same time, the nature of the template affects the morphology and crystal sizes of the intermediate alumophosphate, AlPO4·2H2O, and the molecular sieves, SAPO-11. The acidic properties and the porous structure characteristics of SAPO-11 are also affected by the template. A template was proposed to enable the synthesis of nanoscale SAPO-11 crystals. The influence of the morphology and crystal size of SAPO-11 on the catalytic properties of a bifunctional catalyst based on SAPO-11 in the hydroisomerization of hexadecane was investigated.
Characterization of the Complete Mitogenome of the Ring-Necked Pheasant Phasianus colchicus (Galliformes: Phasianidae) and Systematic Implications for Phasianinae Phylogenetics
Authors: Mei Qinggang , Deng Yiming , Zhao Dongmei , Jiang Daoyu , Liao Yaqing , Yu Xiangmei , Liu Peng , Jiang Lichun
Published: December 2024

Background: Phasianidae mitogenomes exhibit significant structural variations critical for understanding evolution and subspecies divergence. However, annotations of these features in some pheasant species remain limited. This study aimed to enhance understanding of Phasianidae mitogenomes and their evolutionary patterns. Methods: A comparative analysis of complete mitogenomes from Phasianus colchicus, Phasianus versicolor, and 22 other accipitrids was conducted, examining codon usage, rRNA structures, selective pressures, phylogenetics, and structural variations. Results: The mitogenome of P. colchicus is 16,696 bp, comprising 13 protein-coding genes, 2 rRNA genes, 22 tRNA genes, and a control region, with a base composition of A: 30.61%, T: 25.26%, C: 30.85%, and G: 13.28%. Phylogenetic analysis revealed P. colchicus and P. versicolor are closely related, with the following relationship: ((Phasianus + Chrysolophus) + (Crossoptilon + Lophura)). Divergence timing aligns with the Tibetan Plateau uplift during the Tertiary Pliocene. Ka/Ks analysis suggests the CO I, CO II, CO III, ND1, ND4L, and ND6 genes in Phasianus underwent strong selective pressure for plateau adaptation. Conclusions: The study confirms Phasianus monophyly and its close relationship with Chrysolophus. Adaptation-related selective pressures on the CO I, CO II, CO III, ND1, ND4L, and ND6 genes highlight its role in plateau environments, offering valuable insights into pheasant phylogeny.
Climate Fluctuations and Growing Sensitivity of Grape Production in Abruzzo (Central Italy) over the Past Sixty Years
Authors: Guerriero Vincenzo , Scorzini Anna Rita , Di Lena Bruno , Di Bacco Mario , Tallini Marco
Published: December 2024

The sensitivity of the agricultural production system to short- and long-term climate variations significantly affects the availability and prices of food resources, raising relevant issues of sustainability and food security. Globally, productive systems have adapted to climate change, leading to increased yields over the past century. However, the extent to which these adaptations mitigate the impacts of short-term climate fluctuations, both extreme and ordinary, remains poorly studied. To evaluate the vulnerability of crop yield to short-term climate fluctuations and to determine whether it changes over time, we conducted a statistical analysis focusing on one of the main crops in the Abruzzo region (central Italy) as a case study: grape. The study involves correlation analysis between opportune climatic indices (SPI and SPEI) and grape yield data over the sixty-year period from 1952 to 2014, aimed at evaluating the impact of short-term climatic fluctuations—both extreme and ordinary—on crop yield. Our findings reveal an increasing correlation, mainly in the summer–autumn season, which suggests a rising sensitivity of the productive system over time. The observed increase is indicative of the Abruzzo grape production system’s adaptation to climate change, resulting in higher overall yields but not enhancing the response to short-term climatic fluctuations.
Advancements in Ocean Mapping and Nautical Cartography
Authors: Masetti Giuseppe , Church Ian , Hiroji Anand , Andersen Ove
Published: December 2024

Ocean mapping and nautical cartography are foundational to understanding and managing marine environments [...]
Flood Hazard and Risk in Urban Areas
Authors: Santos Pedro Pinto
Published: December 2024

Floodplains and valleys have always been preferential locations for human presence [...]
Effects of Postural Changes Using a Standing Desk on the Craniovertebral Angle, Muscle Fatigue, Work Performance, and Discomfort in Individuals with a Forward Head Posture
Authors: Lee Hyunju , Lee Yongwoo
Published: December 2024

Forward head posture is common among office workers who sit for long periods of time and is one of the main causes of neck pain. This study aimed to assess the effects of utilizing a standing desk on the craniovertebral angle, muscle fatigue, work performance, and discomfort in individuals with forward head posture. Twenty-four participants meeting the inclusion criteria were randomly assigned to either a traditional desk group (n = 12) or a standing desk group (n = 12). Each group engaged in 30 min of computer typing. To evaluate the impact of posture change using a standing desk, pre- and post-experiment discomfort surveys were conducted, and the craniovertebral angle and muscle fatigue were measured throughout the experiment. Work performance was assessed post-experiment based on the work completed by the participants. Intra-group analysis of the craniovertebral angle showed a significant decrease in the traditional desk group (p < 0.05) and a significant increase in the standing desk group (p < 0.05). When comparing the craniovertebral angle between the two groups, a significant difference was observed (p < 0.05). Additionally, significant differences in muscle fatigue, particularly in the levator scapulae muscles, were observed between the two groups (p < 0.05). The traditional desk group reported increased discomfort in the neck and shoulders (p < 0.05). These findings suggest that utilizing a standing desk can positively impact posture, reduce muscle fatigue, and alleviate discomfort in individuals with forward head posture, potentially serving as an intervention to prevent musculoskeletal disorders.