bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing Comparative Yield and Yield Contributing Traits of Hybrid Rice Varieties Transplanted as Over-Aged Seedlings in the South-Central Coastal Ecosystem of Bangladesh
Authors: Khalil Md. Ibrahim , Ahamed Md. Subbir , Somaddar Uzzal , Dey Hridoy Chandra , Mamun Sameera , Sarker Uttam Kumer , Uddin Md. Romij , Mostofa Mohammad Golam , Saha Gopal
Published: January 2025

The transplanting of over-aged seedlings is an important adaptive strategy for rice cultivation during the monsoon season in the coastal areas of Bangladesh. This study aimed to identify promising hybrid rice varieties that can be transplanted as over-aged seedlings in the south-central tidal coastal ecosystem of Bangladesh. Three elite hybrid varieties, namely, AZ7006, Dhani Gold, and INH-16019, along with a popular local cultivar, Moulata (as the control), were used in this field study, and seedlings at two different ages (e.g., 50- and 60-day-old) were transplanted. The results showed that Dhani Gold had the highest grain yield (5.47 t/ha) with the maximum number of tillers (17) and effective tillers (15) per hill for the 50-day-old seedlings as compared with the 60-day-old seedlings. In addition, the hybrid variety INH-16019 also yielded 5.20 t/ha grain with the highest number of spikelets/panicle (191), filled grains/panicle (174), and unfilled grains/panicle (17) for 50-day-old seedlings. However, the local cultivar, Moulata, had the highest plant height (149 cm), required the maximum time for 50% flowering (75 days after transplanting, DAT) and harvesting (104 DAT), and also produced the lowest number of spikelets/panicle (114) and filled grains/panicle (108), leading to the lowest grain yield (3.57 t/ha) considering both ages of the seedlings. Our data further revealed the significant influence of seedling age (50- and 60-day-old seedlings) on the straw yield. Interestingly, this study revealed that the hybrid variety Dhani Gold showed a remarkable decrease in straw yield (by 36%) but obtained the highest (56.3%) harvest index when transplanted as very over-aged (e.g., 60-day-old) seedlings. Moreover, we recorded a higher grain nitrogen content in the hybrid varieties than in the local cultivar, Moulata. Among the hybrids, AZ7006 showed the highest nitrogen content (1.72%), which was statistically similar to INH-16019 (1.67%) and Dhani Gold (1.57%) for 50-day-old seedlings. Importantly, the grain yield of Dhani Gold was 50% higher than Moulata for 50-day-old seedlings, and the benefit–cost ratio of Dhani Gold was also the highest (1.34) for the same seedling age. Hence, the over-aged Dhani Gold hybrid rice seedlings would be a good choice for increasing Aman rice productivity during the monsoon season in the south-central coastal ecosystem of Bangladesh.
Perspectives of Women with Breast Cancer and Healthcare Providers Participating in an Adherence-Enhancing Program for Adjuvant Endocrine Therapy: A Qualitative Study
Authors: Turcotte Véronique , Guillaumie Laurence , Lemay Martine , Dionne Anne , Lemieux Julie , Labbé Angéline , Gotay Carolyn , Guénette Line , Lauzier Sophie
Published: January 2025

Background: Adjuvant endocrine therapy (AET) is prescribed for 5–10 years to women with hormone-sensitive breast cancer to prevent recurrence. However, a significant proportion of women do not adhere to AET. We developed SOIE, a one-year program designed to enhance the AET experience and adherence. SOIE was pilot-tested in a mixed-methods randomized controlled trial. This report presents the experience of women and healthcare providers (HCPs) with SOIE. Methods: A descriptive qualitative study using semi-structured interviews and thematic analysis was conducted with 20 women and 7 HCPs who participated in the program. Results: Most women and HCPs reported high satisfaction with the program. Women felt it addressed their need for information and strategies to manage side effects. They felt supported and developed a more positive attitude toward AET, which contributed to their intention to pursue AET. They perceived that the program helped them navigate the AET experience and reduced their stress or fear regarding AET. HCPs corroborated these benefits. Conclusions: Findings suggest that SOIE can enhance the experience and motivation to pursue the AET treatment by meeting important needs for information, side-effects management, and psycho-emotional support. Programs like SOIE can have benefits beyond adherence by improving patients’ well-being during this crucial long-term treatment.
Resolving Rapid Radiation of Voles and Lemmings (Arvicolinae: Cricetinae, Rodentia) with QuaddRAD Sequencing and Transcriptome Analysis
Authors: Abramson Natalia , Skalon Elizaveta , Bondareva Olga , Bodrov Semen , Petrova Tatyana , Dvoyashov Ivan
Published: January 2025

Voles and lemmings (subfamily Arvicolinae) remain some of the most difficult groups for disentangling phylogenetic relations owing to recent and very fast explosive radiation. The rapid radiation events are challenging for phylogenetic analysis and produce bushes of various shapes that are impossible to resolve with a straightforward approach using individual loci. Here using the quaddRAD-seq technique and transcriptomes, we tested whether data from the nuclear genome are consistent with trees inferred earlier from individual loci and from mitogenomes in topology and divergence dating. Both the transcriptome and quaddRAD-seq data convincingly place Arvicola as the earliest derivative within Arvicolini. This result is in agreement with the fossil record and conventional taxonomy. For the first time, whole-genome sequencing data resolved relations within the third radiation wave of the subfamily’s taxa, namely the tribes Arvicolini, Lagurini, and Ellobiusini, which have formed polytomy in mitochondrial trees in earlier articles. This study indicates that divergence dating is highly dependent on the number and position of calibration points in a tree and on taxa sampling. In terms of phylogenetic inference, the position of nodes with insufficient taxa representation is the most susceptible to errors.
Development of a Modular Virtual Calibration Platform for Power Machinery
Authors: Cui Wei , Liu Hongyu , Li He , Zhang Shutao , Wang Qingxin , Cui Wenrui , Zhao Feiyang , Yu Wenbin
Published: January 2025

The hardware-in-the-loop (HIL)-based virtual calibration technology for power machinery significantly enhances the efficiency and flexibility of electronic controller calibration, reducing dependency on physical experiments and lowering development costs. This paper presents an independently developed modular virtual calibration platform, designed based on the ISO/IEC 42010 architecture approach and the V-Model development process. The platform adopts a modular layered architecture comprising the physical layer, communication layer, model layer, software layer, and application layer. Each layer is independently developed and seamlessly integrated, ensuring excellent scalability and maintainability. Through performance validation conducted under steady-state and transient operating conditions of the diesel/natural gas dual-fuel engine electronic control unit (ECU), experimental results demonstrated that the platform possesses high computational accuracy, rapid response capability, and stable operation across different scenarios. The experiments validated that the platform can effectively replicate the real behavior of the controlled units in a virtual environment, confirming its adaptability and reliability. The platform offers an efficient and reliable solution for power machinery controller calibration.
Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Comprehensive Review
Authors: Elnaggar Kholoud , El-Gayar Mostafa , Elmogy Mohammed
Published: January 2025

Background: Mental disorders are disturbances of brain functions that cause cognitive, affective, volitional, and behavioral functions to be disrupted to varying degrees. One of these disorders is depression, a significant factor contributing to the increase in suicide cases worldwide. Consequently, depression has become a significant public health issue globally. Electroencephalogram (EEG) data can be utilized to diagnose mild depression disorder (MDD), offering valuable insights into the pathophysiological mechanisms underlying mental disorders and enhancing the understanding of MDD. Methods: This survey emphasizes the critical role of EEG in advancing artificial intelligence (AI)-driven approaches for depression diagnosis. By focusing on studies that integrate EEG with machine learning (ML) and deep learning (DL) techniques, we systematically analyze methods utilizing EEG signals to identify depression biomarkers. The survey highlights advancements in EEG preprocessing, feature extraction, and model development, showcasing how these approaches enhance the diagnostic precision, scalability, and automation of depression detection. Results: This survey is distinguished from prior reviews by addressing their limitations and providing researchers with valuable insights for future studies. It offers a comprehensive comparison of ML and DL approaches utilizing EEG and an overview of the five key steps in depression detection. The survey also presents existing datasets for depression diagnosis and critically analyzes their limitations. Furthermore, it explores future directions and challenges, such as enhancing diagnostic robustness with data augmentation techniques and optimizing EEG channel selection for improved accuracy. The potential of transfer learning and encoder-decoder architectures to leverage pre-trained models and enhance diagnostic performance is also discussed. Advancements in feature extraction methods for automated depression diagnosis are highlighted as avenues for improving ML and DL model performance. Additionally, integrating Internet of Things (IoT) devices with EEG for continuous mental health monitoring and distinguishing between different types of depression are identified as critical research areas. Finally, the review emphasizes improving the reliability and predictability of computational intelligence-based models to advance depression diagnosis. Conclusions: This study will serve as a well-organized and helpful reference for researchers working on detecting depression using EEG signals and provide insights into the future directions outlined above, guiding further advancements in the field.
Disturbance-Observer-Based Fixed-Time Backstepping Control for Quadrotors with Input Saturation and Actuator Failure
Authors: Huang Tao , Liu Kang , Yang Yefeng , Wen Chih-Yung , Huang Xianlin
Published: January 2025

This paper investigates the fixed-time tracking control problem of an unmanned aerial vehicle (UAV) considering the disturbance, input saturation, and actuator failure. According to the hierarchical control principle, the UAV dynamics are decomposed into a translational and rotational loop to accommodate the controller design. A novel nonsingular fixed-time backstepping controller based on switching variables is proposed to achieve fast convergence of system tracking errors within a fixed time. To overcome the effect of the disturbance and the actuator failure, two fixed-time disturbance observers are designed in two loops, respectively. By integrating the fixed-time auxiliary variables into the dynamic controllers, the problem of input saturation can be addressed. In addition, the tracking errors of the closed-loop system converge to the neighborhood of the origin in a fixed time. Finally, sufficient simulation results verify the validity of the proposed control framework for the UAV.
Anomalous Behavior of the Non-Hermitian Topological System with an Asymmetric Coupling Impurity
Authors: Wang Junjie , Li Fude , Cheng Weijun
Published: January 2025

A notable feature of systems with non-Hermitian skin effects is the sensitivity to boundary conditions. In this work, we introduce one type of boundary condition provided by a coupling impurity. We consider a system where a two-level system as an impurity couples to a nonreciprocal Su–Schrieffer–Heeger chain under periodic boundary conditions at two points with asymmetric couplings. We first study the spectrum of the system and find that asymmetric couplings lead to topological phase transitions. Meanwhile, a striking feature is that the coupling impurity can act as an effective boundary, and asymmetric couplings can also induce a flexibly adjusted zero mode. It is localized at one of the two effective boundaries or both of them by tuning coupling strengths. Moreover, we uncover three types of localization behaviors of eigenstates for this non-Hermitian impurity system with on-site disorder. These results corroborate the potential for control of a class of non-Hermitian systems with coupling impurities.
Clamping Force Estimation for Electro-Mechanical Brake Based on Friction Torque Fusion Approach
Authors: Park Giseo , Kim Sooyoung , Kim Jungwoo
Published: January 2025

Electromechanical brakes (EMB) are anticipated to become the standard brake system in the future, gaining favor among researchers and automobile manufacturers due to their numerous advantages. However, to achieve cost competitiveness, expensive load cells used to measure the clamping force on the disc must be replaced with a clamping force estimation algorithm. To do this, an algorithm is first developed to estimate the pad contact point, which represents the point of contact between the pad and the disc, to determine where the clamping force occurs. Subsequently, this paper proposes a novel Kalman filter approach utilizing friction torque fusion for clamping force estimation. Specifically, the estimation performance is enhanced by incorporating both dynamic and static friction torque models. The proposed estimation algorithm is validated by comparing its results with the actual clamping force measured using a load cell sensor. Furthermore, experimental tests are conducted to confirm whether the proposed estimation algorithm maintains its performance under various control reference conditions, and overall, the estimation error was within about 5% in this paper.
Smart Distillation for Land Use Monitoring: A Lightweight Remote Sensing Segmentation Strategy
Authors: Wu Yaowei , Liu Jiao , Chen Yuangong , Gong Jianzhou
Published: January 2025

As the demand for land use monitoring continues to grow, high-precision remote sensing products have become increasingly important. Compared to traditional methods, deep learning networks demonstrate significant advantages in automatic feature extraction, handling complex scenes, and improving classification accuracy. However, as the complexity of these networks increases, so does the computational cost. To address this challenge, we propose an innovative knowledge distillation model, integrating two key modules—spatial-global attention feature distillation (SGAFD) and channel attention-based relational distillation (CARD). This model enables a lightweight “student” network to be guided by a large “teacher” network, enhancing classification performance while maintaining a compact model size. We validated our approach on the large-scale public remote sensing datasets GID15 and LoveDA, and the results show that these modules effectively improve classification performance, overcoming the limitations of lightweight models and advancing the practical applications of land use monitoring.
Package Positioning Based on Point Registration Network DCDNet-Att
Authors: Zhu Juan , Yang Chunrui , Zhu Guolyu , Yue Xiaofeng , Zhao Qingming
Published: January 2025

The application of robot technology in the automatic transportation process of packaging bags is becoming increasingly common. Point cloud registration is the key to applying industrial robots to automatic transportation systems. However, current point cloud registration models cannot effectively solve the registration of deformed targets like packaging bags. In this study, a new point cloud registration network, DCDNet-Att, is proposed, which uses a variable weight dynamic graph convolution module to extract point cloud features. A feature interaction module is used to extract common features between the source point cloud and the template point cloud. The same geometric features between the two pairs of point clouds are strengthened through a bottleneck module. A channel attention model is used to obtain the channel attention weights. The attention weight of each spatial position is calculated, and a rotation translation structure is used to sequentially obtain quaternions and translation vectors. A feature fitting loss function is used to constrain the parameters of the neural network model to have a larger receptive field. Compared with seven methods, including the ICP algorithm, GO-ICP algorithm, and FGR algorithm, the proposed method had rotation errors (MAE, RMSE, and Error of 1.458, 2.541, and 1.024 in the ModelNet40 dataset, respectively) and translation errors (MAE, RMSE, and Error of 0.0048, 0.0114, and 0.0174, respectively). When registering the ModelNet40 dataset with Gaussian noise, the rotation errors (MAE, RMSE, and Error) were 2.028, 3.437, and 2.478, respectively, and the translation errors (MAE, RMSE, and Error) were 0.0107, 0.0327, and 0.0285, respectively. The experimental results were superior to those of the other methods, and the model was effective at registering packaging bag point clouds.
Generalized Non-Convex Non-Smooth Group-Sparse Residual Prior for Image Denoising
Authors: Wang Shaohe , Han Rui , Qian Ping , Li Chen
Published: January 2025

Image denoising is a classic yet challenging problem in low-level image processing. Traditional image denoising approaches using convex regularized prior (e.g., L1-norm) often bring bias problems. To address this issue, a novel prior model based on a family of non-convex functions and group sparsity residual (GSC) prior constraint for image denoising is studied. We propose a generalized non-convex GSC prior model for the image denoising problem. We first utilize the group-sparse representation (GSR) before exploiting image prior information. Specifically, to further improve the image denoising performance of the GSC prior model, we employ several typical non-convex surrogate functions for the sparsity constraint. Then, a fast and efficient thresholding algorithm is proposed to minimize the resulting optimization problem. The experimental results have demonstrated that our proposed method can achieve the best reconstruction results compared with other image denoising approaches.
Single-Phase Transformerless Three-Level PV Inverter in CHB Configuration
Authors: Kołodziejski Wojciech , Jasielski Jacek , Machowski Witold , Godek Juliusz , Szerszeń Grzegorz
Published: January 2025

The paper proposes an original single-phase transformerless three-level (S-PT) photovoltaic (PV) inverter in the cascade H bridge (CHB) configuration. The DC-link voltage of the inverter is created by two serial voltage sources with a voltage twice as low as the DC-link voltage. An appropriate VCC DC-link voltage is generated by a two-phase DC-DC boost converter, fed from the string panel output at a level determined by the maximum power point tracking (MPPT) algorithm. Two symmetrical sources with VCC/2 are formed by a divider of two series-connected capacitors of large and the same capacitance. The common mode (CM) voltage of the proposed inverter is constant, and the voltage stresses across all switches, diodes and gate drive circuits are half of the DC-link voltage. The principles of operation of the S-PT inverter, an implementation of a complete gate control system with galvanic isolation for all IGBTs, are also presented. The proposed inverter topologies have been implemented using high-speed IGBTs and simulated in PSPICE, as well as being experimentally validated.
Optimal Allocation and Sizing of Battery Energy Storage System in Distribution Network Using Mountain Gazelle Optimization Algorithm
Authors: Mumtahina Umme , Alahakoon Sanath , Wolfs Peter
Published: January 2025

This paper addresses the problem of finding the optimal position and sizing of battery energy storage (BES) devices using a two-stage optimization technique. The primary stage uses mixed integer linear programming (MILP) to find the optimal positions along with their sizes. In the secondary stage, a relatively new algorithm called mountain gazelle optimizer (MGO) is implemented to find the technical feasibility of the solution, such as voltage regulation, energy loss reduction, etc., provided by the primary stage. The main objective of the proposed bi-level optimization technique is to improve the voltage profile and minimize the power loss. During the daily operation of the distribution grid, the charging and discharging behaviour is controlled by minimizing the voltage at each bus. The energy storage dispatch curve along with the locations and sizes are given as inputs to MGO to improve the voltage profile and reduce the line loss. Simulations are carried out in the MATLAB programming environment using an Australian radial distribution feeder, with results showing a reduction in system losses by 8.473%, which outperforms Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Cuckoo Search Algorithm (CSA) by 1.059%, 1.144%, and 1.056%, respectively. During the peak solar generation period, MGO manages to contain the voltages within the upper boundary, effectively reducing reverse power flow and enhancing voltage regulation. The voltage profile is also improved, with MGO achieving a 0.348% improvement in voltage during peak load periods, compared to improvements of 0.221%, 0.105%, and 0.253% by GWO, WOA, and CSA, respectively. Furthermore, MGO’s optimization achieves a reduction in the fitness value to 47.260 after 47 iterations, demonstrating faster and more consistent convergence compared to GWO (47.302 after 60 iterations), WOA (47.322 after 20 iterations), and CSA (47.352 after 79 iterations). This comparative analysis highlights the effectiveness of the proposed two-stage optimization approach in enhancing voltage stability, reducing power loss, and ensuring better performance over existing methods.
Enhance Oil Recovery in Fracture-Cave Carbonate Reservoirs Using Zwitterion-Anionic Composite Surfactant System
Authors: Peng Wei , You Qing , Liu Xiaoqiang , Zhou Bojie , Ding Xingxing , Du Yuechun , Xiao Liangfei
Published: January 2025

The carbonate fracture-cave reservoir in the Tahe oilfield, China, encounters development challenges because of its substantial burial depth (exceeding 5000 m). Its characteristics are low permeability, pronounced heterogeneity, extensive karst cavern systems, diverse connection configurations, and intricate spatial distribution. Prolonged conventional water flooding leads to predominant water channels, resulting in water channeling and limited sweep efficiency. Surfactant flooding is usually adopted in these conditions because it can mitigate water channeling and enhance sweep efficiency by lowering the interfacial tension (it refers to the force that is generated due to the unbalanced molecular attraction on the liquid surface layer and causes the liquid surface to contract) between oil and water. Nonetheless, the Tahe oilfield is a carbonate reservoir where surfactant is prone to loss near the well, thereby limiting its application. High-pressure injection flooding technology is an innovative method that utilizes injection pressure higher than the formation rupture pressure to alter reservoir permeability, specifically in low-permeability oil fields. Because of the high fluid flow rate, the contact time with the interface is decreased, enabling the ability for surfactants to reach the deep reservoir. In this article, based on the mixed adsorption mechanism of two surfactants and the hydrophilic and lipophilic equilibrium mechanisms, a set of high-temperature and high-salinity resistance surfactant systems appropriate for the Tahe oilfield is developed and its associated performance is evaluated. An oil displacement experiment is carried out to examine the effect of surfactant flooding by high-pressure injection. The results demonstrate that the ideal surfactant system can lower the interfacial tension to 10−2 mN/m and its capacity to reduce the interfacial tension to 10−2 mN/m after different aging periods. Besides, the surfactant system possesses excellent wettability (wetting angle changed from 135° to 42°) and certain emulsifying abilities. The oil displacement experiment shows that the oil recovery rate of surfactant flooding by high pressure reaches 26%. The effect of surfactant flooding by high-pressure injection is better than that of high-pressure injection flooding.
Advanced Control Strategies for Enhancing the Performance of Phase-Shifted Full-Bridge Series Resonant DC–DC Converters in Photovoltaic Micro-Inverters
Authors: Qin Geng , Ma Hui , Lei Jianhua , Hao Chuantong
Published: January 2025

This article addresses the challenges of the reduced efficiency in phase-shifted full-bridge series resonant converters (PSFB-SRCs) used within micro-inverters (MIs), especially under light load and high input voltage conditions. To enhance performance, first-order and second-order time-domain equivalent models that accurately predict the output gain across a wide range of operating conditions are developed. A novel control strategy is proposed, featuring turn-on time as a feedback variable, with phase shift angle and dead time as feedforward variables, enabling precise computation of frequency, duty cycle, and phase shift time for digital controllers. This ensures optimal efficiency, stability, and dynamic response, regardless of the load conditions. Experimental results from the prototype confirmed zero-voltage switching under heavy loads and efficient frequency limiting under light loads, achieving a peak efficiency of 97.8% at a 25 V input. Notably, the light load efficiency remained above 90% even at a 50 V input. These contributions significantly advance PSFB-SRC technology, providing robust solutions for high-efficiency MI applications in photovoltaic systems.
Hierarchical Multi-Communities Energy Sharing Management with Electric Vehicle Integration
Authors: Khwanrit Ruengwit , Javaid Saher , Lim Yuto , Charoenlarpnopparut Chalie , Tan Yasuo
Published: January 2025

The widespread adoption of Electric Vehicles (EVs) in the smart grid is transforming the traditional grid into a more complex system. EVs have the ability to both charge and discharge, acting as loads that draw high power and sources that inject energy back into the grid. Consequently, energy sharing and management within smart grid communities integrated with EVs have become interesting aspects to study in order to efficiently utilize this energy. However, most existing research focuses solely on energy sharing within single communities, utilizing homogeneous energy profiles and neglecting the potential of heterogeneous energy across multiple communities. EVs also possess the capability to travel to different places and communities, where they can engage in energy sharing with areas that have varying load profiles and prices. In this work, a novel three-level energy sharing management approach is proposed for a multiple community system integrating movable energy storage such as EVs. This model involves three main entities: the Utility Company (UC), Community Energy Aggregator (CEA), and EVs. The energy sharing problem is formulated as a Stackelberg game, with all entities striving to maximize their utility through optimal strategies, including pricing, energy demand, or supply. The proposed model is validated through comparison with typical human charging behavior, as well as single- and multiple-community two-level game models. The findings reveal that the proposed model successfully optimizes pricing and energy strategies, significantly lowering the peak-to-average ratio and smoothing the overall energy profile.
A Named Entity Recognition Model for Chinese Electricity Violation Descriptions Based on Word-Character Fusion and Multi-Head Attention Mechanisms
Authors: Meng Lingwen , Wang Yulin , Huang Yuanjun , Ma Dingli , Zhu Xinshan , Zhang Shumei
Published: January 2025

Due to the complexity and technicality of named entity recognition (NER) in the power grid field, existing methods are ineffective at identifying specialized terms in power grid operation record texts. Therefore, this paper proposes a Chinese power violation description entity recognition model based on word-character fusion and multi-head attention mechanisms. The model first utilizes a collected power grid domain corpus to train a Word2Vec model, which produces static word vector representations. These static word vectors are then integrated with the dynamic character vector features of the input text generated by the BERT model, thereby mitigating the impact of segmentation errors on the NER model and enhancing the model’s ability to identify entity boundaries. The combined vectors are subsequently input into a BiGRU model for learning contextual features. The output from the BiGRU layer is then passed to an attention mechanism layer to obtain enhanced semantic features, which highlight key semantics and improve the model’s contextual understanding ability. Finally, the CRF layer decodes the output to generate the globally optimal label sequence with the highest probability. Experimental results on the constructed power grid field operation violation description dataset demonstrate that the proposed NER model outperforms the traditional BERT-BiLSTM-CRF model, with an average improvement of 1.58% in precision, recall, and F1-score. This demonstrates the effectiveness of the model design and further enhances the accuracy of entity recognition in the power grid domain.
Experimental Study on Chemiluminescence Properties of Ammonia-Methane Non-Premixed Laminar Flames
Authors: Su Haihang , Wu Yuxuan , Yan Jinzhi , Jiang Liqiao
Published: January 2025

The fundamental investigation on the chemiluminescence characteristics of NH3-based flames is essential for the development of low-cost, real-time optical diagnostic sensor technologies. In this study, we have experimentally examined the chemiluminescence properties of non-premixed ammonia-methane laminar jet flames under various initial NH3 blending ratios (XNH3 from 0.2 to 1.0 in volume) by conducting the emission spectrum analysis within the 200–800 nm band and capturing the distribution images of key excited radicals. The results revealed that the emission spectra of OH*, CH*, CN*, NH*, and NH2* were clearly identifiable. As anticipated, the chemiluminescence characteristics of NH3-CH4 non-premixed flames were significantly influenced by XNH3; i.e., the overall signal intensity decreased monotonically within the 200–400 nm band but increased within the 400–800 nm band as XNH3 increased. The signal intensity characteristics of OH*, CH*, NH*, and NH2*, indicated by radical images, were consistent with the spectrometer measurements. Particularly, it was found that the intensity ratio of CH*/NH2* was an ideal marker of initial XNH3 in present flames, given their sensitivity with XNH3 and relative ease of measurement with the cost-effective sensors designed for invisible wavelengths. Moreover, in the flame front, CH* was located in the oxidant side, while NH2* was in the fuel side with a broader distribution zone. An increase of XNH3 led to greater flame thickness and shifted the peak position of excited radicals far away from the fuel side.
Mining and Expression Pattern Analysis of Genes Related to the Regulation of Flowering in Korean Pine (Pinus koraiensis)
Authors: Du Junshuai , Hou Dan , Hao Junfei , Du Junping , Zhang Hanguo , Zhang Lei
Published: January 2025

Korean pine (Pinus koraiensis Siebold & Zucc.) is an important timber and economic tree species in northeast China. Its seeds serve as both a primary means of propagation and a significant economic product. In this study, we identified 12 full-length MADS-box genes based on the Korean pine flower-induced transcriptome data available in our laboratory. These genes were identified through multiple sequence alignment and screening for conserved structural domains. We analyzed the genetic relationships of these genes and predicted their physicochemical properties. Additionally, we examined the expression patterns of three SHORT VEGETATIVE PHASE (SVP) genes across different tissues and developmental stages of Korean pine. The results indicate that the amino acid composition, molecular weight, isoelectric point, and other physicochemical properties of the MADS-box gene family in Pinus koraiensis are generally similar, though some individual variations are observed. A total of 12 MADS-box family genes were identified from the Korean pine transcriptome, distributed across five subfamilies. Conserved motif analysis revealed that these genes share similar conserved sequences. Structural and physicochemical analyses showed that genes with similar sequences exhibited comparable characteristics. Expression levels of the SVP genes varied significantly across different developmental stages and tissues, with the expression of the three SVP genes in leaves being markedly higher than in buds (approximately 200-fold). The expression levels of these genes in leaves were not only higher than in buds but also exceeded those in other tissues. Based on these findings, we conclude that these three SVP genes primarily play a suppressive role in the process of flower bud formation, helping Korean pine maintain a juvenile state under certain conditions, and are also involved in the growth and development of its leaves. This research provides a basis for future studies on the flowering induction mechanism in Korean pine.
Exploration and Empirical Study on Spatial Distribution of SOC at the Core Area in Coastal Tamarix Forests’ Inland Side of Changyi National Marine Ecological Area
Authors: Liu Ruiting , Han Ping , Wang Jin , Zong Huiqian , Zhang Xuewan , Chen Qianxun , Chen Feiyong , Du Yufeng , Li Zhao , Liu Yaohui , Fu Pingjie , Cheng Xiaoxiang , Xu Jingtao
Published: January 2025

The forest soil carbon pool plays a vital role in terrestrial ecosystems, being of great significance for maintaining global balance, regulating the global carbon cycle, and facilitating ecological restoration. Shandong Changyi Marine Ecological Special Protection Area is the only state-level marine special protection area in China with tamarisk as the main object of protection, and it is the largest continuous and the best preserved natural tamarisk forest distribution area on the mainland coast of China. Compared to other forested areas, research on the spatial distribution of SOC at the core area in coastal Tamarix forests’ inland side appears to be relatively scarce. Based on this, this paper takes the core area of the Changyi National Marine Ecological Special Protection Zone, located on the southern coast of Laizhou Bay, as the research subject, based on the potassium dichromate oxidation-external heating, one-way ANOVA, and Bonferroni methods, analyzing the spatial distribution of the SOC content inland of coastal Tamarix forests. The research yielded the following conclusions: (1) The surface layer (0–10 cm) contributes significantly to the total SOC content within a 0–60 cm depth, accounting for at least 31% and shows notable surface accumulation. (2) The combined SOC content in the surface and subsurface layers (10–20 cm) accounts for at least 50% of the total SOC content within a 0–60 cm depth, indicating the dominance of these two soil layers in carbon storage. (3) The SOC content decreases with the soil depth at all six sampling points within the 0–60 cm range, with a marked drop from 0–10 cm to 10–20 cm. (4) One-way ANOVA and multiple comparisons reveal that the soil depth significantly affects the SOC distribution, particularly between the surface and 20–30 cm layers (p < 0.001), indicating high robustness and statistical significance. (5) Horizontally, the total SOC at 0 m is 45% lower than at 2 m in the 0–60 cm layer. The SOC in the 0–20 cm layer fluctuates significantly with distance from the shrub trunk, while the SOC in the 30–60 cm layers is low and stable, with minimal variations with depth. In addition, this study found that the SOC content in the core area of the protected area is lower than that in the common forest ecosystem. In the future, scientific ecological restoration projects and management protection methods should be used to improve soil’s carbon storage and carbon sink capacity. These findings not only validate the patterns of SOC’s spatial distribution in coastal Tamarix forest wetlands but also provide a scientific basis for carbon assessment and the formulation of ecological protection measures in coastal wetlands.
Assessing Relationships Between Deer (Cervidae) Damage and Stand Structure of Scots Pine (Pinus sylvestris) Stands in Hemiboreal Latvia
Authors: Done Gundega , Ķēniņa Laura , Elferts Didzis , Ozoliņš Jānis , Jansons Āris
Published: January 2025

Intensive forest management has promoted an increase in deer (Cervidae) population density. Various silvicultural activities, such as pre-commercial thinning, can change the feeding conditions for deer species, therefore impacting browsing pressure on target tree species. In this study, we analyzed how several factors, including the density of the main tree species, admixture, undergrowth, and forest type, affect deer damage intensity in pine stands, considering deer densities and regional aspects in hemiboreal Latvia. GLMM analysis, based on data from 1238 sample plots, showed that the probability of browsing damage decreases with an increase in the density of undergrowth in young (<20 years) pine stands with a dominant height below 3 m. Also, the probability of pines being damaged by deer was significantly (p = 0.001) higher in stands with fresh pre-commercial thinning than in those with no thinning. However, differences in deer density between regions also determined browsing pressure. Results indicated that undergrowth density, pre-commercial thinning, and deer density may be important drivers of damage levels, especially in the winter browsing of young pine stands on wet mineral soils. Therefore, future research should continue to evaluate applied forest management strategies in hemiboreal forests that provide additional natural food base in the form of woody plants and shrubs in winter forage to ensure more deer-adapted practices.
From Young to Over-Mature: Long-Term Cultivation Effects on the Soil Nutrient Cycling Dynamics and Microbial Community Characteristics Across Age Chronosequence of Schima superba Plantations
Authors: Sun Yangyang , Zhang Yajing , Wang Liyan , Zhang Xinyu , Jiang Yuhui , Tigabu Mulualem , Wu Pengfei , Li Ming , Hu Xia
Published: January 2025

Optimizing forest management requires a comprehensive understanding of how soil properties and microbial communities evolve across different plantation ages. This study examines variations in soil nutrient dynamics, enzyme activities, and bacterial communities in Schima superba Gardn. & Champ plantations of 10, 15, 27, 55, and 64 years. By analyzing soil from depths of 0–20 cm, 20–40 cm, and 40–60 cm, we identified significant age-related trends in soil characteristics. Notably, nutrient contents, including total organic carbon (TOC), total phosphorus (TP), total carbon (TC), total nitrogen (TN), and nitrate nitrogen (NO3−-N), as well as soil water content (SWC), peaked in 55-year-old mature plantations and decreased in 64-year-old over-mature plantations. Enzyme activities, such as urease, sucrase, and acid phosphatase, decreased with soil depth and exhibited notable differences across stand ages. Microbial community analysis indicated the predominance of Acidobacteria, Chloroflexi, Proteobacteria, Actinobacteria, and Verrucomicrobiota in nutrient cycling, with their relative abundances varying significantly with age and depth. Mature and over-mature plantations exhibited higher absolute abundances of functional genes related to methane metabolism, nitrogen, phosphorus, and sulfur cycling. Reduced calcium ion levels were also linked to lower gene abundance in carbon degradation, carbon fixation, nitrogen, and phosphorus cycling, while increased TOC, NH4+-N, NO3−-N, and AP correlated with higher gene abundance in methane metabolism and phosphorus cycling. Our findings suggest that long-term cultivation of Schima superba enhances soil nutrient cycling. Calcium ion was identified as a significant factor in assessing soil properties and microbial dynamics across different stand ages, suggesting that extended plantation rotations can improve soil health and nutrient cycling.
NeRF-Accelerated Ecological Monitoring in Mixed-Evergreen Redwood Forest
Authors: Korycki Adam , Yeaton Cory , Gilbert Gregory S. , Josephson Colleen , McGuire Steve
Published: January 2025

Forest mapping provides critical observational data needed to understand the dynamics of forest environments. Notably, tree diameter at breast height (DBH) is a metric used to estimate forest biomass and carbon dioxide (CO2) sequestration. Manual methods of forest mapping are labor intensive and time consuming, a bottleneck for large-scale mapping efforts. Automated mapping relies on acquiring dense forest reconstructions, typically in the form of point clouds. Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) generate point clouds using expensive LiDAR sensing and have been used successfully to estimate tree diameter. Neural radiance fields (NeRFs) are an emergent technology enabling photorealistic, vision-based reconstruction by training a neural network on a sparse set of input views. In this paper, we present a comparison of MLS and NeRF forest reconstructions for the purpose of trunk diameter estimation in a mixed-evergreen Redwood forest. In addition, we propose an improved DBH-estimation method using convex-hull modeling. Using this approach, we achieved 1.68 cm RMSE (2.81%), which consistently outperformed standard cylinder modeling approaches.
Changes in the Timing of Autumn Leaf Senescence of Maple and Ginkgo Trees in South Korea over the Past 30 Years: A Comparative Assessment of Process-Based, Linear Regression, and Machine-Learning Models
Authors: Kim Sukyung , Moon Minkyu , Kim Hyun Seok
Published: January 2025

Changes in vegetation activities driven by climate change serve as both a sensitive indicator and a key driver of climate impacts, underscoring the need for accurate phenological predictions. Delays in leaf senescence due to rising air temperatures increase the risk of damage from early frost, potentially affecting growth and survival in subsequent years. This study aimed to quantify long-term changes in leaf senescence timing for palmate maple and ginkgo trees, explore their associations with environmental factors, and compare the performance of multiple modeling approaches to identify their strengths and limitations for phenological predictions. Using data from 48 sites across South Korea (1989–2020), this study analyzed trends in the timing of leaf senescence for maple and ginkgo trees and compared the performance of process-based models (CDD_T, CDD_P, TP_T, TP_P), a linear regression model, and machine-learning models (random forest, RF; gradient-boosting decision tree, GBTD). Leaf senescence timing for both species has progressively been delayed, with ginkgo trees showing a faster rate of change (0.20 vs. 0.17 days per year, p < 0.05). Delayed senescence was observed in most regions (81% for maple and 75% for ginkgo), with statistically significant delays (p < 0.05) at half of the sites. Machine-learning models demonstrated the highest training accuracy (RMSE < 4.0 days, r > 0.90). Evaluation with independent datasets revealed that the RF and process-based TP_P (including minimum temperature and photoperiod) using a site-specific approach performed best (RMSE < 5.5 days, r > 0.75). Key environmental factors identified by RF included autumn minimum or mean temperatures and a summer photoperiod. By conducting this comparative assessment, the study provides insights into the applicability of different modeling approaches for phenology research and highlights their implications for vegetation management and climate change adaptation.
Review of Fire Tests on Seats for Passenger Coaches and the Materials Used in Them
Authors: Hohenwarter Dieter
Published: January 2025

This study shows how the fire regulations for railway seats used in international traffic have changed over the last 30 years. In the past, a paper cushion was used as a flame source, and today, a 15 kW burner is used; consequently, the requirements have increased. In the paper cushion test, a foam with a density of between 60 and 95 kg/m3, a flame-retardant fleece, and a cover fabric was usually sufficient in terms of fire safety. Today, a high-quality flame-retardant foam is necessary to meet the requirements for flaming with the 15 kW burner. Two comparable seat structures show very different heat release and smoke formation in the paper cushion test due to different foam additives. If high-quality flame-retardant foams with a cover fabric are used for the 15 kW flame treatment, the results of the two test institutes show good agreement. If the seats that meet the requirements of the paper cushion test are flamed using the 15 kW treatment, they can catch fire and thus exhibit very different heat release rates, as the CERTIFER interlaboratory test with 12 participating test institutes shows. The heat release of old and new leather was examined, and it was found that the flame retardant applied to the leather surface appeared to have aged over the years and that the flame retardant was therefore no longer effective. The heat release of flame-retardant foams with a cover fabric was measured using irradiation with a cone calorimeter and flame treatment. Very different curves were observed, which means that it is not possible to draw simple conclusions about the heat release during flame treatment from the cone measurement.
The Effects of Polyphenols on Texture and Flavor of Egg Yolk: A Molecular Docking Study
Authors: Tang Tingting , Zhang Ruyi , Chang Cuihua , Li Junhua , Gu Luping , Yang Yanjun , Su Yujie
Published: January 2025

The effect of polyphenols on the texture and flavor of egg yolk hot gel (EY) was studied. Tea polyphenols (TP), rosmarinic acid (RE), and curcumin (CC) showed significant antioxidant properties during egg yolk processing and could effectively reduce lipid oxidation products (decreased by 68.9%, 76.4%, and 58.61%, respectively) and protein oxidation products (decreased by 47.49%, 37.47%, and 52.51%, respectively) and volatile odor components (styrene, nonanal and 1-octene-3-ol). In addition, these polyphenols enhanced the hot gel properties of the yolk, but did not significantly change the taste of the yolk. This improvement could be attributed to hydrophobic interactions, hydrogen bonds and ionic bonds formed between polyphenols and egg yolk proteins. These interactions produced a more stable structure that was less likely to unfold during heat treatment. As a result, exposure to free sulfhydryl groups, free amino acids and free fatty acids was minimized, thus reducing oxidation reactions.